Special Report: Special Report on the Ocean and Cryosphere in a Changing Climate
Ch 05

Changing Ocean, Marine Ecosystems, and Dependent Communities

Coordinating Lead Authors:

  • Nathaniel L. Bindoff (Australia)
  • William W. L. Cheung (Canada)
  • James G. Kairo (Kenya)

Lead Authors:

  • Javier Arístegui (Spain)
  • Valeria A. Guinder (Argentina)
  • Robert Hallberg (United States)
  • Nathalie Hilmi (Monaco, France)
  • Nianzhi Jiao (China)
  • Md saiful Karim (Australia)
  • Lisa Levin (United States)
  • Sean O’Donoghue (South Africa)
  • Sara R. Purca Cuicapusa (Peru)
  • Baruch Rinkevich (Israel)
  • Toshio Suga (Japan)
  • Alessandro Tagliabue (United Kingdom)
  • Phillip Williamson (United Kingdom)

Contributing Authors:

  • Sevil Acar (Turkey)
  • Juan Jose Alava (Ecuador, Canada)
  • Eddie Allison (United Kingdom)
  • Brian Arbic (United States)
  • Tamatoa Bambridge (French Polynesia)
  • Inka Bartsch (Germany)
  • Laurent Bopp (France)
  • Philip W. Boyd (Australia, United Kingdom)
  • Thomas Browning (Germany, United Kingdom)
  • Jorn Bruggeman (Netherlands)
  • Momme Butenschön (Germany)
  • Francisco P. Chávez (United States)
  • Lijing Cheng (China)
  • Mine Cinar (United States)
  • Daniel Costa (United States)
  • Omar Defeo (Uruguay)
  • Salpie Djoundourian (Lebanon)
  • Catia Domingues (Australia)
  • Tyler Eddy (Canada)
  • Sonja Endres (Germany)
  • Alan Fox (United Kingdom)
  • Christopher Free (United States)
  • Thomas Frölicher (Switzerland)
  • Jean-Pierre Gattuso (France)
  • Gemma Gerber (South Africa)
  • Charles Greene (United States)
  • Nicolas Gruber (Switzerland)
  • Gustaav Hallegraef (Australia)
  • Matthew Harrison (United States)
  • Sebastian Hennige (United Kingdom)
  • Mark Hindell (Australia)
  • Andrew Hogg (Australia)
  • Taka Ito (United States)
  • Tiff-Annie Kenny ()
  • Kristy Kroeker (United States)
  • Lester Kwiatkowski (France, United Kingdom)
  • Vicky Lam (China, Canada)
  • Charlotte Laüfkotter (Switzerland, Germany)
  • Philippe LeBillon (Canada)
  • Nadine Le Bris (France)
  • Heike Lotze (Canada)
  • Jennifer MacKinnon (United States)
  • Annick de Marffy-Mantuano ()
  • Patrick Martel (South Africa)
  • Nadine Marshall (Australia)
  • Kathleen McInnes (Australia)
  • Jorge García Molinos (Japan, Spain)
  • Serena Moseman-Valtierra (United States)
  • Andries Motau (South Africa)
  • Sandor Mulsow (Brazil)
  • Kana Mutombo (South Africa)
  • Andreas Oschlies (Germany)
  • Muhammed Oyinlola (Nigeria)
  • Elvira S. Poloczanska (Australia)
  • Nicolas Pascal (France)
  • Maxime Philip (France)
  • Sarah Purkey (United States)
  • Saurabh Rathore (India)
  • Xavier Rebelo (South Africa)
  • Gabriel Reygondeau (France)
  • Jake Rice (Canada)
  • Anthony Richardson (Australia)
  • Ulf Riebesell (Germany)
  • Christopher Roach (France, Australia)
  • Joacim Rocklöv (Sweden)
  • Murray Roberts (United Kingdom)
  • Alain Safa (France)
  • Sunke Schmidtko (Germany)
  • Gerald Singh (Canada)
  • Bernadette Sloyan (Australia)
  • Karinna von Schuckmann (France)
  • Manal Shehabi (United Kingdom)
  • Matthew Smith (United States)
  • Amy Shurety (South Africa)
  • Fernando Tuya ()
  • Cristian Vargas (Chile)
  • Colette Wabnitz (France)
  • Caitlin Whalen (United States)

Review Editors:

  • Manuel Barange (South Africa)
  • Brad Seibel (United States)

Chapter Scientist:

  • Axel Durand (Australia)

FAQ5.1: How is life in the sea affected by climate change?

Climate change poses a serious threat to life in our seas, including coral reefs and fisheries, with impacts on marine ecosystems, economies and societies, especially those most dependent upon natural resources. The risk posed by climate change can be reduced by limiting global warming to no more than 1.5°C.

Life in most of the global ocean, from pole to pole and from sea surface to the abyssal depths, is already experiencing higher temperatures due to human-driven climate change. In many places, that increase may be barely measurable. In others, particularly in near-surface waters, warming has already had dramatic impacts on marine animals, plants and microbes. Due to closely linked changes in seawater chemistry, less oxygen remains available (in a process called ocean deoxygenation). Seawater contains more dissolved carbon dioxide, causing ocean acidification. Non-climatic effects of human activities are also ubiquitous, including over-fishing and pollution. Whilst these stressors and their combined effects are likely to be harmful to almost all marine organisms, food-webs and ecosystems, some are at greater risk (FAQ5.1, Figure 1). The consequences for human society can be serious unless sufficient action is taken to constrain future climate change.

Warm water coral reefs host a wide variety of marine life and are very important for tropical fisheries and other marine and human systems. They are particularly vulnerable, since they can suffer high mortalities when water temperatures persist above a threshold of between 1°C–2°C above the normal range. Such conditions occurred in many tropical seas between 2015 and 2017 and resulted in extensive coral bleaching, when the coral animal hosts ejected the algal partners upon which they depend. After mass coral mortalities due to bleaching, reef recovery typically takes at least 10–15 years. Other impacts of climate change include SLR, acidification and reef erosion. Whilst some coral species are more resilient than others, and impacts vary between regions, further reef degradation due to future climate change now seems inevitable, with serious consequences for other marine and coastal ecosystems, like loss of coastal protection for many islands and low-lying areas and loss of the high biodiversity these reefs host. Coral habitats can also occur in deeper waters and cooler seas, and more research is needed to understand impacts in these reefs. Although these cold water corals are not at risk from bleaching, due to their cooler environment, they may weaken or dissolve under ocean acidification, and other ocean changes. 

Mobile species, such as fish, may respond to climate change by moving to more favorable regions, with populations shifting poleward or to deeper water, to find their preferred range of water temperatures or oxygen levels. As a result, projections of total future fishery yields under different climate change scenarios only show a moderate decrease of around 4% (~3.4 million tonnes) per degree Celsius warming. However, there are dramatic regional variations. With high levels of climate change, fisheries in tropical regions could lose up to half of their current catch levels by the end of this century. Polar catch levels may increase slightly, although the extent of such gains is uncertain, because fish populations that are currently depleted by overfishing and subject to other stressors may not be capable of migrating to polar regions, as assumed in models.

In polar seas, species adapted to life on or under sea ice are directly threatened by habitat loss due to climate change. The Arctic and Southern Oceans are home to a rich diversity of life, from tiny plankton to fish, krill and seafloor invertebrates to whales, seals, polar bears or penguins. Their complex interactions may be altered if new warmer-water species extend their ranges as sea temperatures rise. The effects of acidification on shelled organisms, as well as increased human activities (e.g., shipping) in ice-free waters, can amplify these disruptions. 

Whilst some climate change impacts (like possible increased catch levels in polar regions) may benefit humans, most will be disruptive for ecosystems, economies and societies, especially those that are highly dependent upon natural resources. However, the impacts of climate change can be much reduced if the world as a whole, through inter-governmental interventions, manages to limit global warming to no more than 1.5°C. 

Figure 5.3
View details
Figure 5.9
View details
Figure 5.10
View details
Figure 5.11
View details
Figure 5.12
View details
Box 5.1, Figure 1
View details Changes on the Deep Seafloor
View details
Figure 5.15
View details
Figure 5.16
View details
Figure 5.17
View details
Figure 5.18
View details
Figure 5.19
View details
Figure 5.22
View details Context for Blue Carbon and Overview Assessment
View details
Box 5.5, Figure 1
View details
Figure 5.24
View details

Executive Summary

The ocean is essential for all aspects of human well-being and livelihood. It provides key services like climate regulation, through the energy budget, carbon cycle and nutrient cycle. The ocean is the home of biodiversity ranging from microbes to marine mammals that form a wide variety of ecosystems in open pelagic and coastal ocean.

Observations: Climate-related trends, impacts, adaptation

Carbon emissions from human activities are causing ocean warming, acidification and oxygen loss with some evidence of changes in nutrient cycling and primary production. The warming ocean is affecting marine organisms at multiple trophic levels, impacting fisheries with implications for food production and human communities. Concerns regarding the effectiveness of existing ocean and fisheries governance have already been reported, highlighting the need for timely mitigation and adaptation responses.

The ocean has warmed unabated since 2005, continuing the clear multi-decadal ocean warming trends documented in the IPCC Fifth Assessment Report (AR5). The warming trend is further confirmed by the improved ocean temperature measurements over the last decade. The 0−700 m and 700–2000 m layers of the ocean have warmed at rates of 5.31 ± 0.48 and 4.02 ± 0.97 ZJ yr-1 from 2005 to 2017. The long-term trend for 0–700 m and 700–2000 m layers have warmed 4.35 ± 0.8 and 2.25 ± 0.64 ZJ yr-1 from between the averages of 1971-1990 and 1998-2017 and is attributed to anthropogenic influences. It is likely 1 the ocean warming has continued in the abyssal and deep ocean below 2000 m (southern hemisphere and Southern Ocean). {1.8.1, 1.2, 5.2.2}

It is likely that the rate of ocean warming has increased since 1993. The 0−700 m and 700–2000 m layers of the ocean have warmed by 3.22 ± 1.61 ZJ and 0.97 ± 0.64 ZJ from 1969to 1993, and 6.28 ± 0.48 ZJ and 3.86 ± 2.09 ZJ from 1993 to 2017. This represents at least a two-fold increase in heat uptake. {Table 5.1, 5.2.2}

The upper ocean is very likely to have been stratifying since 1970. Observed warming and high-latitude freshening are making the surface ocean less dense over time relative to the deeper ocean (high confidence 2) and inhibiting the exchange between surface and deep waters. The upper 200 m stratification increase is in the very likely range of between 2.182.42% from 1970 to 2017. {5.2.2}

Multiple datasets and models show that the rate of ocean uptake of atmospheric CO2 has continued to strengthen in the recent two decades in response to the increasing concentration of CO2 in the atmosphere. The very likely range for ocean uptake is between 20–30% of total anthropogenic emissions in the recent two decades. Evidence is growing that the ocean carbon sink is dynamic on decadal timescales, especially in the Southern Ocean, which has affected the total global ocean carbon sink (medium confidence). {}

The ocean is continuing to acidify in response to ongoing ocean carbon uptake. The open ocean surface water pH is observed to be declining (virtually certain) by a very likely range of 0.0170.027 pH units per decade since the late 1980s across individual time series observations longer than 15 years. The anthropogenic pH signal is very likely to have emerged for three-quarters of the near-surface open ocean prior to 1950 and it is very likely that over 95% of the near surface open ocean has already been affected. These changes in pH have reduced the stability of mineral forms of calcium carbonate due to a lowering of carbonate ion concentrations, most notably in the upwelling and high-latitude regions of the ocean. {, Box 5.1} 

There is a growing consensus that the open ocean is losing oxygen overall with a very likely loss of 0.53.3% between 1970–2010 from the ocean surface to 1000 m (medium confidence). Globally, the oxygen loss due to warming is reinforced by other processes associated with ocean physics and biogeochemistry, which cause the majority of the observed oxygen decline (high confidence). The oxygen minimum zones (OMZs) are expanding by a very likely range of 3–8%, most notably in the tropical oceans, but there is substantial decadal variability that affects the attribution of the overall oxygen declines to human activity in tropical regions (high confidence). {}

In response to ocean warming and increased stratification, open ocean nutrient cycles are being perturbed and there is high confidence that this is having a regionally variable impact on primary producers. There is currently low confidence in appraising past open ocean productivity trends, including those determined by satellites, due to newly identified region-specific drivers of microbial growth and the lack of corroborating in situ time series datasets. {,} 

Ocean warming has contributed to observed changes in biogeography of organisms ranging from phytoplankton to marine mammals (high confidence), consequently changing community composition (high confidence), and in some cases, altering interactions between organisms (medium confidence). Observed rate of range shifts since the 1950s and its very likely range are estimated to be 51.5 ± 33.3 km per decade and 29.0 ± 15.5 km per decade for organisms in the epipelagic and seafloor ecosystems, respectively. The direction of the majority of the shifts of epipelagic organisms are consistent with a response to warming (high confidence). {5.2.3, 5.3}

Warming-induced range expansion of tropical species to higher latitudes has led to increased grazing on some coral reefs, rocky reefs, seagrass meadows and epipelagic ecosystems, leading to altered ecosystem structure (medium confidence). Warming, sea level rise (SLR) and enhanced loads of nutrients and sediments in deltas have contributed to salinisation and deoxygenation in estuaries (high confidence), and have caused upstream redistribution of benthic and pelagic species according to their tolerance limits (medium confidence). {5.3.4, 5.3.5, 5.3.6, 5.2.3} 

Fisheries catches and their composition in many regions are already impacted by the effects of warming and changing primary production on growth, reproduction and survival of fish stocks (high confidence). Ocean warming and changes in primary production since the 20th century are related to changes in productivity of many fish stocks (high confidence), with an average decrease of approximately 3% per decade in population replenishment and 4.1% (very likely range of 9.0% decline to 0.3% increase) in maximum catch potential (robust evidence, low agreement between fish stocks, medium confidence). Species composition of fisheries catches since the 1970s in many shelf seas ecosystems of the world is increasingly dominated by warm water species (medium confidence). {5.2.3, 5.4.1}

Warming-induced changes in spatial distribution and abundance of fish stocks have already challenged the management of some important fisheries and their economic benefits (high confidence). For existing international and national ocean and fisheries governance, there are concerns about the reduced effectiveness to achieve mandated ecological, economic, and social objectives because of observed climate impacts on fisheries resources (high confidence). {5.4.2, 5.5.2}

Coastal ecosystems are observed to be under stress from ocean warming and SLR that are exacerbated by non-climatic pressures from human activities on ocean and land (high confidence). Global wetland area has declined by nearly 50% relative to pre-industrial level as a result of warming, SLR, extreme climate events and other human impacts (medium confidence). Warming related mangrove encroachment into subtropical salt marshes has been observed in the past 50 years (high confidence). Distributions of seagrass meadows and kelp forests are contracting at low-latitudes that is attributable to warming (high confidence), and in some areas a loss of 36–43% following heat waves (medium confidence). Inundation, coastline erosion and salinisation are causing inland shifts in plant species distributions, which has been accelerating in the last decades (medium confidence). Warming has increased the frequency of large-scale coral bleaching events, causing worldwide reef degradation since 1997–1998 with cases of shifts to algal-dominated reefs (high confidence). Sessile calcified organisms (e.g., barnacles and mussels) in intertidal rocky shores are highly sensitive to extreme temperature events and acidification (high confidence), a reduction in their biodiversity and abundance have been observed in naturally-acidified rocky reef ecosystems (medium confidence). Increased nutrient and organic matter loads in estuaries since the 1970s have exacerbated the effects of warming on bacterial respiration and eutrophication, leading to expansion of hypoxic areas (high confidence). {5.3.1, 5.3.2, 5.3.4, 5.3.6}

Coastal and near-shore ecosystems including salt marshes, mangrove and vegetated dunes in sandy beaches have a varying capacity to build vertically and expand laterally in response to SLR. These ecosystems provide important services including coastal protection, carbon sequestration and habitat for diverse biota (high confidence). The carbon emission associated with the loss of vegetated coastal ecosystems is estimated to be 0.04–1.46 Gt C yr-1 (high confidence). The natural capacity of ecosystems to adapt to climate impacts may be limited by human activities that fragment wetland habitats and restrict landward migration (high confidence). {5.3.2, 5.3.3, 5.4.1, 5.5.1}

Three out of the four major Eastern Boundary Upwelling Systems (EBUS) have shown large-scale wind intensification in the past 60 years (high confidence). However, the interaction of coastal warming and local winds may have affected upwelling strength, with the direction of changes varies between and within EBUS (low confidence). Increasing trends in ocean acidification in the California Current EBUS and deoxygenation in California Current and Humboldt Current EBUS are observed in the last few decades (high confidence), although there is low confidence to distinguish anthropogenic forcing from internal climate variability. The expanding California EBUS OMZ has altered ecosystem structure and fisheries catches (medium confidence). {Box 5.3} 

Since the early 1980s, the occurrence of harmful algal blooms (HABs) and pathogenic organisms (e.g., Vibrio) has increased in coastal areas in response to warming, deoxygenation and eutrophication, with negative impacts on food provisioning, tourism, the economy and human health (high confidence). These impacts depend on species-specific responses to the interactive effects of climate change and other human drivers (e.g., pollution). Human communities in poorly monitored areas are among the most vulnerable to these biological hazards (medium confidence). {Box 5.4, 5.4.2}

Many frameworks for climate resilient coastal adaptation have been developed since AR5, with substantial variations in approach between and within countries, and across development status (high confidence). Few studies have assessed the success of implementing these frameworks due to the time-lag between implementation, monitoring, evaluation and reporting (medium confidence). {5.5.2}

Projections: scenarios and time horizons

Climate models project significant changes in the ocean state over the coming century. Under the high emissions scenario (Representative Concentration Pathway (RCP)8.5) the impacts by 2090 are substantially larger and more widespread than for the low emissions scenario (RCP2.6) throughout the surface and deep ocean, including: warming (virtually certain); ocean acidification (virtually certain); decreased stability of mineral forms of calcite (virtually certain); oxygen loss (very likely); reduced near-surface nutrients (likely as not); decreased net primary productivity (high confidence); reduced fish production (likely) and loss of key ecosystems services (medium confidence) that are important for human well-being and sustainable development. {5.2.2, Box 5.1, 5.2.3, 5.2.4, 5.4}

By 2100 the ocean is very likely to warm by 2 to 4 times as much for low emissions (RCP2.6) and 5 to 7 times as much for the high emissions scenario (RCP8.5) compared with the observed changes since 1970. The 0–2000 m layer of the ocean is projected to warm by a further 2150 ZJ (very likely range 1710–2790 ZJ) between 2017 and 2100 for the RCP8.5 scenario. The 0–2000 m layer is projected to warm by 900 ZJ (very likely range 650–1340 ZJ) by 2100 for the RCP2.6 scenario, and the overall warming of the ocean will continue this century even after radiative forcing and mean surface temperatures stabilise (high confidence). {}

The upper ocean will continue to stratify. By the end of the century the annual mean stratification of the top 200 m (averaged between 60°S–60°N relative to the 1986–2005 period) is projected to increase in the very likely range of 1–9% and 12–30% for RCP2.6 and RCP8.5 respectively. {}

It is very likely that the majority of coastal regions will experience statistically significant changes in tidal amplitudes over the course of the 21st century. The sign and amplitude of local changes to tides are very likely to be impacted by both human coastal adaptation measures and climate drivers. {}

It is virtually certain that surface ocean pH will decline, by 0.036–0.042 or 0.287–0.29 pH units by 2081–2100, relative to 2006–2015, for the RCP2.6 or RCP8.5 scenarios, respectively. These pH changes are very likely to cause the Arctic and Southern Oceans, as well as the North Pacific and Northwestern Atlantic Oceans to become corrosive for the major mineral forms of calcium carbonate under RCP8.5, but these changes are virtually certain to be avoided under the RCP2.6 scenario. There is increasing evidence of an increase in the seasonal exposure to acidified conditions in the future (high confidence), with a very likely increase in the amplitude of seasonal cycle of hydrogen iron concentrations of 71–90% by 2100, relative to 2000 for the RCP8.5 scenario, especially at high latitudes. {}

Oxygen is projected to decline further. Globally, the oxygen content of the ocean is very likely to decline by 3.2–3.7% by 2081–2100, relative to 2006–2015, for the RCP8.5 scenario or by 1.6–2.0% for the RCP2.6 scenario. The volume of the oceans OMZ is projected to grow by a very likely range of 7.0 ± 5.6% by 2100 during the RCP8.5 scenario, relative to 1850–1900. The climate signal of oxygen loss will very likely emerge from the historical climate by 2050 with a very likely range of 59–80% of ocean area being affected by 2031–2050 and rising with a very likely range of 79–91% by 2081–2100 (RCP8.5). The emergence of oxygen loss is very likely smaller in area for the RCP2.6 scenario in the 21st century and by 2090 the emerged area is declining. {, Box 5.1 Figure 1}

Overall, nitrate concentrations in the upper 100 m are very likely to decline by 9–14% across CMIP5 models by 2081–2100, relative to 2006–2015, in response to increased stratification for RCP8.5, with medium confidence in these projections due to the limited evidence of past changes that can be robustly understood and reproduced by models. There is low confidence regarding projected increases in surface ocean iron levels due to systemic uncertainties in these models. {}

Climate models project that net primary productivity will very likely decline by 4–11% for RCP8.5 by 2081–2100, relative to 2006–2015. The decline is due to the combined effects of warming, stratification, light, nutrients and predation and will show regional variations between low and high latitudes (low confidence). The tropical ocean NPP will very likely decline by 7–16% for RCP8.5, with medium confidence as there are improved constraints from historical variability in this region. Globally, the sinking flux of organic matter from the upper ocean into the ocean interior is very likely to decrease by 9–16% for RCP8.5 in response to increased stratification and reduced nutrient supply, especially in tropical regions (medium confidence), which will reduce organic carbon supply to deep sea ecosystems (high confidence). The reduction in food supply to the deep sea is projected to lead to a 5–6% reduction in biomass of benthic biota over more than 97% of the abyssal seafloor by 2100 (medium confidence). {,}

New ocean states for a broad suite of climate indices will progressively emerge over a substantial fractions of the ocean in the coming century (relative to past internal ocean variability), with Earth System Models (ESMs) showing an ordered emergence of first pH, followed by sea surface temperature (SST), interior oxygen, upper ocean nutrient levels and finally net primary production (NPP). The anthropogenic pH signal has very likely emerged for three quarters of the ocean prior to 1950, with little difference between scenarios. Oxygen changes will very likely emerge over 59–80% of the ocean area by 2031–2050 and rises to 79–91% by 2081–2100 (RCP8.5). The projected time of emergence for five primary drivers of marine ecosystem change (surface warming and acidification, oxygen loss, nitrate content and net primary production change) are all prior to 2100 for over 60% of the ocean area under RCP8.5 and over 30% under RCP2.6 (very likely). {Box 5.1, Box 5.1 Figure 1} 

Simulated ocean warming and changes in NPP during the 21st century are projected to alter community structure of marine organisms (high confidence), reduce global marine animal biomass (medium confidence) and the maximum potential catches of fish stocks (medium confidence) with regional differences in the direction and magnitude of changes (high confidence). The global biomass of marine animals, including those that contribute to fisheries, is projected to decrease with a very likely range under RCP2.6 and RCP8.5 of 4.3 ± 2.0% and 15.0 ± 5.9%, respectively, by 2080–2099 relative to 1986–2005. The maximum catch potential is projected to decrease by 3.4% to 6.4% (RCP2.6) and 20.5% to 24.1% (RCP8.5) in the 21st century. {5.4.1}

Projected decreases in global marine animal biomass and fish catch potential could elevate the risk of impacts on income, livelihood and food security of the dependent human communities (medium confidence). Projected climate change impacts on fisheries also increase the risk of potential conflicts among fishery area users and authorities or among two different communities within the same country (medium confidence), exacerbated through competing resource exploitation from international actors and mal-adapted policies (low confidence). {5.2.3, 5.4, 5.5.3}

Projected decrease in upper ocean export of organic carbon to the deep seafloor is expected to result in a loss of animal biomass on the deep seafloor by 5.217.6% by 2090–2100 compared to the present (2006–2015) under RCP8.5 with regional variations (medium confidence). Some increases are projected in the polar regions, due to enhanced stratification in the surface ocean, reduced primary production and shifts towards small phytoplankton (medium confidence). The projected impacts on biomass in the abyssal seafloor are larger under RCP8.5 than RCP4.5 (very likely). The increase in climatic hazards beyond thresholds of tolerance of deep sea organisms will increase the risk of loss of biodiversity and impacts on functioning of deep water column and seafloor that is important to support ecosystem services, such as carbon sequestration (medium confidence). {5.2.4}

Structure and functions of all types of coastal ecosystems will continue to be at moderate to high risk under the RCP2.6 scenario (medium confidence) and will face high to very high risk under the RCP8.5 scenario (high confidence) by 2100. Seagrass meadows (high confidence) and kelp forests (high confidence) will face moderate to high risk at temperature above 1.5oC global sea surface warming. Coral reefs will face very high risk at temperatures 1.5°C of global sea surface warming (very high confidence). Intertidal rocky shores are also expected to be at very high risk (transition above 3°C) under the RCP8.5 scenario (medium confidence). These ecosystems have low to moderate adaptive capacity, as they are highly sensitive to ocean temperatures and acidification. The ecosystems with moderate to high risk (transition above 1.8°C) under future emissions scenarios are mangrove forests, sandy beaches, estuaries and salt marshes (medium confidence). Estuaries and sandy beaches are subject to highly dynamic hydrological and geomorphological processes, giving them more natural adaptive capacity to climate hazards. In these systems, sediment relocation, soil accretion and landward expansion of vegetation may initially mitigate against flooding and habitat loss, but salt marshes in particular will be at very high risk in the context of SLR and extreme climate-driven erosion under RCP8.5. {5.3, Figure 5.16}

Expected coastal ecosystem responses over the 21st century are habitat contraction, migration and loss of biodiversity and functionality. Pervasive human coastal disturbances will limit natural ecosystem adaptation to climate hazards (high confidence). Global coastal wetlands will lose between 20–90% of their area depending on emissions scenario with impacts on their contributions to carbon sequestration and coastal protection (high confidence). Kelp forests at low-latitudes and temperate seagrass meadows will continue to retreat as a result of intensified extreme temperatures, and their low dispersal ability will elevate the risk of local extinction under RCP8.5 (high confidence). Intertidal rocky shores will continue to be affected by ocean acidification, warming, and extreme heat exposure during low tide emersion, causing reduction of calcareous species and loss of ecosystem biodiversity and complexity shifting towards algae dominated habitats (high confidence). Salinisation and expansion of hypoxic conditions will intensify in eutrophic estuaries, especially in mid and high latitudes with microtidal regimes (high confidence). Sandy beach ecosystems will increasingly be at risk of eroding, reducing the habitable area for dependent organisms (high confidence). {5.3, 5.4.1}

Almost all coral reefs will degrade from their current state, even if global warming remains below 2ᴼC (very high confidence), and the remaining shallow coral reef communities will differ in species composition and diversity from present reefs (very high confidence). These declines in coral reef health will greatly diminish the services they provide to society, such as food provision (high confidence), coastal protection (high confidence) and tourism (medium confidence). {5.3.4, 5.4.1}

Multiple hazards of warming, deoxygenation, aragonite under-saturation and decrease in flux of organic carbon from the surface ocean will decrease calcification and exacerbate the bioerosion and dissolution of the non-living component of cold water coral. Habitat-forming, cold water corals will be vulnerable where temperature and oxygen exceed the species’ thresholds (medium confidence). Reduced particulate food supply is projected to be experienced by 95% of cold water coral ecosystems by 2100 under RCP8.5 relative to the present, leading to a very likely range of 8.6 ± 2% biomass loss (medium confidence). {5.2.4, Box 5.2}

Anthropogenic changes in EBUS will emerge primarily in the second half of the 21st century (medium confidence). EBUS will be impacted by climate change in different ways, with strong regional variability with consequences for fisheries, recreation and climate regulation (medium confidence). The Pacific EBUS are projected to have calcium carbonate undersaturation in surface waters within a few decades under RCP8.5 (high confidence); combined with warming and decreasing oxygen levels, this will increase the impacts on shellfish larvae, benthic invertebrates and demersal fishes (high confidence) and related fisheries and aquaculture (medium confidence). The inherent natural variability of EBUS, together with uncertainties in present and future trends in the intensity and seasonality of upwelling, coastal warming and stratification, primary production and biogeochemistry of source waters poses large challenges in projecting the response of EBUS to climate change and to the adaptation of governance of biodiversity conservation and living marine resources in EBUS (high confidence). {Box 5.3}

Climate change impacts on ecosystems and their goods and services threatens key cultural dimensions of lives and livelihoods. These threats include erosion of Indigenous and non-indigenous culture, their knowledge about the ocean and knowledge transmission, reduced access to traditional food, loss of opportunities for aesthetic and spiritual appreciation of the ecosystems, and marine recreational activities (medium confidence). Ultimately, these can lead to the loss of part of people’s cultural identity and values beyond the rate at which identify and values can be adjusted or substituted (medium confidence). {5.4.2} 

Climate change increases the exposure and bioaccumulation of contaminants such as persistent organic pollutants and mercury (medium confidence), and their risk of impacts on marine ecosystems and seafood safety (high agreement, medium evidence, medium confidence). Such risks are particularly large for top predators and for human communities that have high consumption on these organisms, including coastal Indigenous communities (medium confidence). {5.4.2} 

Shifting distributions of fish stocks between governance jurisdictions will increase the risk of potential conflicts among fishery area users and authorities or different communities within the same country (medium confidence). These fishery governance related risks are widespread under high emissions scenarios with regional hotspots (medium confidence), and highlight the limits of existing natural resource management frameworks for addressing ecosystem change (high confidence). {5.2.5,, 5.5, 5.5.2} 

Response options to enhance resilience

There is clear evidence for observed climate change impacts throughout the ocean with consequences for human communities and require options to reduce risks and impacts. Coastal blue carbon can contribute to mitigation for many nations but its global scope is modest (offset of <2% of current emissions) (likely). Some ocean indices are expected to emerge earlier than others (e.g., warming, acidification and effects on fish stocks) and could therefore be used to prioritise planning and building resilience. The survival of some keystone ecosystems (e.g., coral reefs) are at risk, while governance structures are not well-matched to the spatial and temporal scale of climate change impacts on ocean systems. Ecosystem restoration may be able to locally reduce climate risks (medium confidence) but at relatively high cost and effectiveness limited to low emissions scenarios and to less sensitive systems (high confidence). {5.2, 5.3, 5.4, 5.5} 

Coastal blue carbon ecosystems, such as mangroves, salt marshes and seagrasses, can help reduce the risks and impacts of climate change, with multiple co-benefits. Some 151 countries around the world contain at least one of these coastal blue carbon ecosystems and 71 countries contain all three. Below-ground carbon storage in vegetated marine habitats can be up to 1000 tC ha-1, much higher than most terrestrial ecosystems (high confidence). Successful implementation of measures to maintain and promote carbon storage in such coastal ecosystems could assist several countries in achieving a balance between emissions and removals of greenhouse gases (medium confidence). Conservation of these habitats would also sustain the wide range of ecosystem services they provide and assist with climate adaptation through improving critical habitats for biodiversity, enhancing local fisheries production, and protecting coastal communities from SLR and storm events (high confidence). The climate mitigation effectiveness of other natural carbon removal processes in coastal waters, such as seaweed ecosystems and proposed non-biological marine CO2 removal methods, are smaller or currently have higher associated uncertainties. Seaweed aquaculture warrants further research attention. {,, 5.5.1, 5.5.2,,}

The potential climatic benefits of blue carbon ecosystems can only be a very modest addition to, and not a replacement for, the very rapid reduction of greenhouse gas emissions. The maximum global mitigation benefits of cost-effective coastal wetland restoration is unlikely to be more than 2% of current total emissions from all sources. Nevertheless, the protection and enhancement of coastal blue carbon can be an important contribution to both mitigation and adaptation at the national scale. The feasibility of climate mitigation by open ocean fertilisation of productivity is limited to negligible, due to the likely decadal-scale return to the atmosphere of nearly all the extra carbon removed, associated difficulties in carbon accounting, risks of unintended side effects and low acceptability. Other human interventions to enhance marine carbon uptake, for example, ocean alkalinisation (enhanced weathering), would also have governance challenges, with the increased risk of undesirable ecological consequences (high confidence). {}

Socioinstitutional adaptation responses are more frequently reported in the literature than ecosystem-based and built infrastructure approaches. Hard engineering responses are more effective when supported by ecosystem-based adaptation approaches (high agreement), and both approaches are enhanced by combining with socioinstitutional approaches for adaptation (high confidence). Stakeholder engagement is necessary (robust evidence, high agreement). {5.5.2} 

Ecosystem-based adaptation is a cost-effective coastal protection tool that can have many co-benefits, including supporting livelihoods, contributing to carbon sequestration and the provision of a range of other valuable ecosystem services (high confidence). Such adaptation does, however, assume that the climate can be stabilised. Under changing climatic conditions there are limits to the effectiveness of ecosystem-based adaptation, and these limits are currently difficult to determine. {}

Socioinstitutional adaptation responses, including community-based adaptation, capacity-building, participatory processes, institutional support for adaptation planning and support mechanisms for communities are important tools to address climate change impacts (high confidence). For fisheries management, improving coordination of integrated coastal management and marine protected areas (MPAs) have emerged in the literature as important adaptation governance responses (robust evidence, medium agreement).{,}

Observed widespread decline in warm water corals has led to the consideration of alternative restoration approaches to enhance climate resilience. Approaches, such as ‘coral reef gardening’ have been tested, and ecological engineering and other approaches such as assisted evolution, colonisation and chimerism are being researched for reef restoration. However, the effectiveness of these approaches to increase resilience to climate stressors and their large-scale implementation for reef restoration will be limited unless warming and ocean acidification are rapidly controlled (high confidence). {Box 5.5, 5.5.2}

Existing ocean governance structures are already facing multi-dimensional, scale-related challenges because of climate change. This trend of increasing complexity will continue (high confidence). The mechanisms for the governance of marine Areas Beyond National Jurisdiction (ABNJ), such as ocean acidification, would benefit from further development (high confidence). There is also scope to increase the overall effectiveness of international and national ocean governance regimes by increasing cooperation, integration and widening participation (medium confidence). Diverse adaptations of ocean related governance are being tried, and some are producing promising results. However, rigorous evaluation is needed of the effectiveness of these adaptations in achieving their goals. {5.5.3}

There are a broad range of identified barriers and limits for adaptation to climate change in ecosystems and human systems (high confidence). Limitations include the space that ecosystems require, non-climatic drivers and human impacts that need to be addressed as part of the adaptation response, the lowering of adaptive capacity of ecosystems because of climate change, and the slower ecosystem recovery rates relative to the recurrence of climate impacts, availability of technology, knowledge and financial support and existing governance structures (medium confidence). {5.5.2}



The ocean is a key component of the Earth system (Chapter 1) as it provides essential life supporting services (Inniss et al., 2017). For example, it stores heat trapped in the atmosphere caused by increasing concentrations of greenhouse gases, it masks and slows surface warming, it stores excess carbon dioxide and is an important component of global biogeochemical cycles. The ocean is the home to the largest continuous ecosystem, provides habitats for rich marine biodiversity, is an essential source of food and contributes to human health, livelihood and security. The ocean also supports other services to humans, for example, transport and trade, tourism, renewable energy, and cultural services such as aesthetic appeal, local and traditional knowledge and religious practices. Governance of the ocean has a unique set of challenges and opportunities compared with land systems and requiring different treatment under a changing climate. 

AR5 from Working Group I (WGI) showed that there are ongoing changes to the physical and chemical state of the ocean. The AR5 WGI report (IPCC, 2013) concluded that (1) ‘ocean warming dominates the increased energy stored in the climate system with more than 90% of the energy accumulated since 1971’; (2) ‘the ocean has absorbed about 30% of the emitted anthropogenic carbon causing ocean acidification’ since pre-industrial times; and (3) it is ‘extremely likely that human influence has been the dominant cause of warming since mid 20th century’. 

The IPCC AR5 Working Group II (WGII) concluded that changes in the ocean such as warming, acidification and deoxygenation are affecting marine life from molecular processes to organisms and ecosystems, with major impacts on the use of marine systems by human societies (Pörtner, 2012). IPCC Special Report on the Impacts of Global Warming of 1.5oC above pre-industrial levels and related global greenhouse gas emission pathways (SR15) also concluded that reducing these risks by ‘limiting warming to 1.5°C above pre-industrial levels would require transformative systemic change, integrated with sustainable development’ and that ‘adaptation needs will be lower in a 1.5°C world compared to a 2°C world.’ (de Coninck et al., 2018; Hoegh-Guldberg et al., 2018).

This report updates earlier assessments, evaluating new research and knowledge regarding changing ocean climate and ecosystems, risks to ecosystem services, and vulnerability of the dependent communities including governance. It also delves into changes the ocean that were beyond the scope of the previous reports. Radiation management techniques (also known as sunlight reflection methods) are excluded here. Such geo-engineering approaches are addressed in the SR15. However, natural carbon uptake and stores in the marine environment are included (Section 5.5.1). 

The chapter is structured around three guiding questions: 

  • What are the key changes in the physical and biogeochemical properties of the ocean? (Section 5.2.2) 
  • How have these changes impacted key ecosystems, risks to ecosystems services and human well-being? (Section 5.2.3, 5.2.4, 5.3, 5.4)
  • Are there effective pathways for adaptation and nature-based solutions to risk reduction for marine dependent communities? (Section 5.5)

This chapter covers both regional and global scales and across natural and human systems. Chapter 3 covers the polar regions, including their oceans, Chapter 4 covers sea level and its implications, and Chapter 6 covers extremes and abrupt events. This chapter uses IPCC calibrated language around scientific uncertainty, as described in Section 1.8.3. Two emissions scenarios, RCP2.6 and RCP8.5, are used for projections of climate change (see Cross-Chapter Box 1 in Chapter 1).


Changing Ocean and Biodiversity



This section assesses changes in the ocean. It includes the physical and chemical properties (Section 5.2.2), their impacts on the pelagic ecosystem (Section 5.2.3) and deep seafloor system (Section 5.2.4). In this assessment, the open ocean and deep seafloor includes areas where the water column is deeper than 200 m; it is the main subject of Section 5.2. Coastal and shelf seas are primarily discussed in Section 5.3. 


Changes in Physical and Biogeochemical Properties

Introduction to Changing Open Ocean

The ocean is getting progressively warmer, with parallel changes in ocean chemistry such as acidification and oxygen loss, as documented in the AR5 (Rhein et al., 2013). The global scale warming and acidification trends are readily detectable in oceanic observations, well understood scientifically, and consistently projected by ESMs. Each of these has been directly attributed to anthropogenic forcing from changing concentrations of greenhouse gases and aerosols (Bindoff et al., 2013). These trends in the global average ocean temperature will continue for centuries after the anthropogenic forcing is stabilised (Collins et al., 2013). 

The impacts on ocean ecosystems and human societies are primarily driven by regional trends and by the local manifestation of the global-scale changes. At these smaller scales, the temperature, acidification, salinity, nutrient and oxygen concentrations in the ocean are also expected to exhibit basin and local-scale changes. However, the ocean also has significant natural variability at basin and local-scales with time scales from minutes to decades and longer (Rhein et al., 2013), which can mask the underlying observed and projected trends (see Box 5.1). The impact of multiple stressors on marine ecosystems is one of the main subjects of this chapter (Section 5.2.3, 5.2.4, 5.3), including new evidence and understanding since the last assessment report (e.g., Gunderson et al., 2016). The most severe impacts of a changing climate will typically be experienced when conditions are driven outside the range of previous experience at rates that are faster than human or ecological systems can adapt (Pörtner et al., 2014; Box 5.1).

This section summarises our emerging understanding of the primary changes to the ocean, along with an assessment of several key areas of scientific uncertainty about these changes. Because many of these long-term trends have already been extensively discussed in previous assessments (IPCC, 2013), much of this summary of the physical changes is brief except where there are significant new findings.

Changing Temperature, Salinity, Circulation

Historically, scientific research expeditions starting in the 19th century have provided occasional sections measuring deep ocean properties (Roemmich et al., 2012). Greater spatial and temporal coverage of temperatures down to about 700 m was obtained using expendable bathythermographs along commercial shipping tracks starting in the 1970s (Abraham et al., 2013). Since the early 2000s, thousands of autonomous profiling floats (Argo floats) have provided high-quality temperature and salinity profiles of the upper 2000 m in ice-free regions of the ocean (Abraham et al., 2013; Riser et al., 2016). Further advances in autonomous floats have been developed that now allow these floats to operate in seasonally ice covered oceans (Wong and Riser, 2011; Wong and Riser, 2013), and more recently to profile the entire depth of the water column down to 4000 or 6000 m (Johnson et al., 2015; Zilberman, 2017) and to include biogeochemical properties (Johnson et al., 2017). Autonomous floats have revolutionised our sampling and accuracy of the global ocean temperature and salinity records and increased certainty and confidence in global estimates of the earth heat (temperature) budget, particularly since 2004 (Von Schuckmann et al., 2014; Roemmich et al., 2015; Riser et al., 2016), as demonstrated by the convergence of observational estimates of the changes in the heat budget of the upper 2000 m (Figure 5.1). New findings using data collected from such observing platforms mark significant progress since AR5.

To understand the recent and future climate, we use ensembles of coupled ocean-atmosphere-cryosphere-ecosystem models (ESMs) with the full-time history of atmospheric forcing (greenhouse gases, aerosols, solar radiation and volcanic eruptions) for the historical period and projections of the concentrations or emissions of these forcings to 2100. For these projections the RCPs of atmospheric emissions scenarios are used as specified by the Coupled Model Intercomparison Project, Phase 5 (CMIP5) (see Section, Cross-Chapter Box 1, and also IPCC AR5) 3. This chapter focuses on the low and high emissions scenarios RCP2.6 and RCP8.5, respectively. When these scenarios are used to drive ESMs, it is possible to simulate the recent and future patterns of changes in the ocean temperature, salinity and circulation (and other oceanic properties such as ocean oxygen concentration and acidification, Section and Finally, the projections of ocean changes also informs the detection, attribution and projection of risk and impacts on ecosystems (Sections 5.2.3, 5.2.4 and 5.3), ecosystem services (Section 5.4.1) and human well-being (Section 5.4.2) under climate change.
Observed and projected global ocean heat uptake

As AR5 concluded, the ocean is warming as a direct result of anthropogenic changes to the radiative properties of the atmosphere and the heat budget of the Earth (very likely) (Bindoff et al., 2013). Over the past few decades our ocean observing system has measured an increase in ocean temperature (Figure 5.1). This temperature increase corresponds to an uptake of over 90% of the excess heat accumulated in the Earth system over this period (Bindoff et al., 2013; Rhein et al., 2013). This heat in the ocean also causes it to expand and has contributed about 43% of the observed global mean SLR from 1970–2015 (Section 

Since AR5, there have been further improvements in our ability to understand and correct instrumental errors and new estimates also attempt to minimise biases in estimating temperature changes arising from traditional data-void filling strategies (Abraham et al., 2013; Durack, 2015; Cheng and Chen, 2017; Cheng et al., 2017). New estimates from ocean observations of ocean heat uptake in the top 2000 m between 1993 and 2017 very likely range from 9.2 ± 2.3 ZJ yr-1 to 12.1 ± 3.1 ZJ yr-1 (Johnson et al., 2018) 4. Three recent independent estimates do a better job of accounting for instrumental biases and the sparseness of historical ocean temperature measurements than the older studies assessed in AR5, and provide larger and more consistent estimates of heat uptake rates for the 0-2000 m layer of 5.8 ± 1.0 ZJ yr-1 (Cheng and Chen, 2017; Cheng et al., 2017; Ishii et al., 2017), 6.0 ± 0.8 ZJ yr-1 (updated from Domingues et al. (2008)) and 6.3 ± 1.8 ZJ yr-1 (Cheng and Chen, 2017; Cheng et al., 2017; Ishii et al., 2017) for the 1971–2010 period assessed by AR5. 

Based on these new published methods and revised atlases we update the estimates for ocean heat uptake (Table 5.1, and SM5.1). For all of the periods assessed in Table 5.1, it is virtually certain that the upper ocean (0–700 m) has warmed. These results are consistent with earlier research into the duration of record needed to detect a significant signal in global ocean heat content (Gleckler et al., 2012). Critically, the high confidence and high agreement in the ocean temperature data means we can detect discernable rates of increase in ocean heat uptake (Gleckler et al., 2012; Cheng et al., 2019). The rate of heat uptake in the upper ocean (0–700 m) is very likely higher in the 1993–2017 (or 2005–2017) period compared with the 1969–1993 period (see Table 5.1). The deeper layer (700–2000 m) heat uptake rate is likely to be higher in the 1993–2017 period compared with the 1969–1993 period.

Table 5.1

The assessed rate of increase in ocean heat content in the two depth layers 0–700 m and 700–2000 m and their very likely ranges. Fluxes in Wm-2 are averaged over the Earth’s entire surface area. The four periods cover earlier and more recent trends; the 2005–2017 period has the most complete interior ocean data coverage and the greatest consistency between estimates, while longer trends are better for distinguishing between forced changes and internal variability. These observationally-estimated rates come from an assessment of the recent research (see SM5.1), while the Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models (ESM) estimates are based on a combined 28-member ensemble of historical, Representative Concentration Pathway (RCP)2.6 and RCP8.5 simulations.


Ocean Heat Uptake Rate, ZJ yr-1 Ocean Heat Uptake as Average Fluxes, W m-2
Period 1969–1993 1993–2017 1970–2017 2005–2017 1969–1993 1993–2017 1970–2017 2005–2017
Observationally Based Ocean Heat Uptake Estimates:
0–700 m 3.22 ± 1.61 6.28 ± 0.48 4.35 ± 0.80 5.31 ± 0.48 0.20 ± 0.1 0.39 ± 0.03 0.27 ± 0.05 0.33 ± 0.03
700–2000 m  0.97 ± 0.64 3.86 ± 2.09 2.25 ± 0.64 4.02 ± 0.97 0.06 ± 0.04 0.24 ± 0.13 0.14 ± 0.04 0.25 ± 0.06
CMIP5 ESM Ensemble-mean Ocean Heat Uptake with 90% Certainty Range from Ensemble Spread:
0–700 m 3.60 ± 1.92 7.37 ± 2.09 5.64 ± 1.90 7.85 ± 2.71 0.22 ± 0.12 0.46 ± 0.13 0.35 ± 0.12 0.49 ± 0.17
700–2000 m  1.32 ± 1.49 2.72 ± 1.41 1.99 ± 1.51 3.33 ± 1.75 0.08 ± 0.09 0.17 ± 0.09 0.12 ± 0.09 0.21 ± 0.11

The direct comparison of the observed changes in ocean heat content and the simulated historical changes is undertaken to detect climate change, to attribute the causes of climate change to the forcings in the system, and to evaluate the performance of ESMs. Attribution studies also reject competing hypotheses to explain the global ocean changes such as natural forcing from solar variability or volcanic eruptions (see Section 1.3) (Bindoff et al., 2013). Detection and attribution studies have since been used to detect changes in the rate of ocean heat uptake and to attribute these changes to human activity (Gleckler et al., 2016). 

Updated observationally-based estimates of ocean heat uptake are consistent with simulations of equivalent time-periods from an ensemble of CMIP5 ESMs (Table 5.1 and the inset panel in Figure 5.1) (high confidence), once the limitations of the historical ocean observing network and the internally generated variability with a single realisation of the real world are taken into account (see Section Following the CMIP5 protocol, the ESMs are radiatively forced with observationally derived estimates of greenhouse gas concentrations and aerosols, including natural forcing variations from volcanic eruptions and solar forcing, through 2005; after 2006 each of the ESMs uses either the RCP2.6 or RCP8.5 emissions scenarios. The very likely ranges of the observed trends of heat uptake for the four periods and two layers all fall within the very likely range of simulated heat uptake from the ESM ensemble (Table 5.1). The difference between observations and average of the simulations in the upper ocean is an overestimate of heat uptake by about 20% and for the deeper layer there an underestimate by a similar amount, but this difference is still well within the very likely range from the ensemble of simulations. The overall consistency between observationally-based estimates and ESM simulations of the historical period gives greater confidence in the projections; it is very likely that historical simulations agree with observations of the global ocean heat uptake (Table 5.1).

While the collection of the worlds’ ESMs have been criticised for having an ensemble mean that does not exhibit the observed ‘hiatus’ or ‘slowdown’ of global mean surface temperature increase in the early 21st century (Meehl et al., 2011; Trenberth et al., 2016) , it is increasingly clear that this is at least in part due to the redistribution of heat within the climate system from the surface into the interior ocean and between ocean basins. Individual realisations of ESMs do show decades with slow increases in mean surface temperature change comparable to what was observed, even though these cases exhibit continued interior ocean heat uptake, and every ensemble member exhibits surface warming closer to the ensemble-mean over multi-decadal timescales (Meehl et al., 2011; England et al., 2015; Knutson et al., 2016)

Figure 5.1

Figure 5.1 | Time series of globally integrated upper 2000 m ocean heat content changes in ZJ, relative to the 2000–2010 period average, as inferred from observations (magenta) and as simulated for historical (tan), Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) forcing by a 25-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) […]

Figure 5.1 | Time series of globally integrated upper 2000 m ocean heat content changes in ZJ, relative to the 2000–2010 period average, as inferred from observations (magenta) and as simulated for historical (tan), Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) forcing by a 25-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models (ESMs) (Cheng et al. 2019). The shaded magenta in the outer panel is the very likely range determined by combining data from 4 long-term estimates (Palmer et al. 2007; Levitus et al. 2012; Lyman and Johnson, 2014; Cheng and Chen, 2017; Cheng et al. 2017; Ishii et al. 2017) processed as in Johnson et al. (2018). The tan, blue and red lines are the ESM ensemble means, while shading shows each ensemble’s 5th to 95th percentile range. In the inset subpanel, the four different shaded magenta areas are the reported very likely range of heat content changes as inferred from observations by four independent groups (Magenta shading; Palmer et al. 2007; Lyman and Johnson, 2014; Cheng and Chen, 2017; Cheng et al. 2017; Ishii et al. 2017) processed as in Johnson et al. (2018). In the inset subpanel the RCP2.6 and RCP8.5 projections after 2005 are combined into a single ensemble with the historical simulations.

Figure 5.2

Figure 5.2 | Heat uptake by the top 700 m of the ocean, as determined by differences between the averages over two 5- or 20-year intervals converted to a heat flux into the ocean (W m–2), either from observationally-based analyses or a 38-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models […]

Figure 5.2 | Heat uptake by the top 700 m of the ocean, as determined by differences between the averages over two 5- or 20-year intervals converted to a heat flux into the ocean (W m–2), either from observationally-based analyses or a 38-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models (ESMs). (a) Change between (1971–1990) and (1998–2017) as inferred from observations (Good et al. 2013); (b) The ensemble mean change in CMIP5 ESMs for the same time periods as in (a); (c) Projected ensemble mean change in CMIP5 ESMs between (1986–2005) and (2081–2100) for the RCP8.5 forcing scenario. In panels (b) and (c), stippling indicates regions where the ensemble mean change is not significantly different from 0 at the 95% confidence level based on the models’ temporal variability. (d) Change between (2004–2008) and (2013–2017) as inferred from observations by the SODA 3.4.2 reanalysis product (Carton et al. 2018); (e) and (f) Estimates of change in heat uptake as in (d) but from two individual realisations of the CCSM ESM (Table SM5.2). These two realisations are identical apart from their initial conditions, which leads to different timing in their internal modes of variability; they were selected from the full CMIP5 ensemble as examples where one is reminiscent of the recent observed changes while the other has regional changes that have dissimilar timing.

The ocean will continue to take up heat in the coming decades for all plausible scenarios. As depicted in Figure 5.1, the ensemble of CMIP5 ESMs used by Cheng et al. (2019) project that under RCP2.6, the top 2000 m of the ocean will take up 935 ZJ of heat between 2015 and 2100 (with a very likely range of 650–1340 ZJ based on the 5th and 95th percentiles of the 25 ESMs used here that have available data from the historical, scenario and control runs for RCP2.6). Under RCP8.5 this ensemble projects heat uptake of 2180 ZJ (with a very likely range of 1710–2790 ZJ, based on 35 ESMs) between 2015 and 2100. By 2100 the ocean is very likely to warm by 2 to 4 times as much for low emissions (RCP2.6) and 5 to 7 times as much for the high emissions scenario (RCP8.5) compared with the observed changes since 1970. With the RCP8.5 scenario, the ocean is very likely to take up about twice as much heat as RCP2.6 (Figure. 5.1). Even under RCP2.6 the ocean will continue to warm for several centuries to come (Collins et al., 2013). It is virtually certain that the ocean will continue to take up heat throughout the 21st century, and the rate of uptake will depend upon on the emissions scenario we collectively choose to follow.

Figure 5.3

Figure 5.3 | Side-view basin-averaged zonal-mean trends (change per century) in water-mass properties in the top 2000 m by basin (a) as inferred from observations (average of 2013–2017 minus average of 2005–2009) and (b) Coupled Model Intercomparison Project Phase 5 (CMIP5) model projections with Representative Concentration Pathway (RCP)8.5 forcing (average of 2081–2100 minus average of […]

Figure 5.3 | Side-view basin-averaged zonal-mean trends (change per century) in water-mass properties in the top 2000 m by basin (a) as inferred from observations (average of 2013–2017 minus average of 2005–2009) and (b) Coupled Model Intercomparison Project Phase 5 (CMIP5) model projections with Representative Concentration Pathway (RCP)8.5 forcing (average of 2081–2100 minus average of 1981–2000) trends in water-mass changes forcing. Subpanels within each group: top-to-bottom (Atlantic, combined Pacific and Indian, Global); left-to-right (Temperature, in situ Density, Salinity). Shaded areas show where the projected changes are not statistically significant at the 95% level. This figure uses the same observationally-derived reanalysis datasets and ensemble of Earth System Models (ESMs) as in Figure 5.2c and 5.2d. Solid lines show present contours of these fields; the notable structure in the northern hemisphere of the global-zonal mean contours of density and salinity are due to the relatively salty Mediterranean and fresh Black seas.
Structure of anthropogenic climate changes in the ocean

The ensemble average of the CMIP5 ESMs projects widespread ocean warming over the coming century, concentrated in the upper ocean (Figures 5.2c and 5.3) (Kuhlbrodt and Gregory, 2012). The anthropogenic heat will penetrate into the ocean following well-established circulation pathways (Jones et al., 2016a). The greatest vertically integrated heat uptake occurs where there is already the formation of interior waters, such as Antarctic Intermediate Water along the Antarctic Circumpolar Current (Frölicher et al., 2015) or NADW precursors in the Nordic Seas (Figure 5.2c), but all water-masses 5 that are subducted over decades are 

expected to experience significant warming (see Figure 5.3). The warming in the subtropical gyres penetrates deeper into the ocean than other gyres (roughly 15°N–45°N and 15°S–45°S in Figure 5.3), following the wind-driven bowing down of the density surfaces (the solid lines in Figure 5.3) in these gyres (Terada and Minobe, 2018). The greater warming at 700-2000 m in the Atlantic than the Pacific or Indian Oceans (Figure 5.3) reflects the strong southward transport of recently formed NADW at these depths by the AMOC. Two areas that commonly exhibit substantially reduced near-surface warming over the course of the 21st century are the northern north Atlantic, where a slowing AMOC (see Section reduces the northward heat transport and brings the surface temperatures closer to what is found in other ocean basins at these latitudes (Collins et al., 2013), and the southern side of the Southern Ocean, where water upwells that has been submerged for so long that it has not yet experienced significant anthropogenic climate change (Armour et al., 2016). Most of these projected warming patterns are broadly consistent across the current and previous generations of climate models (Mitchell et al., 1995; Collins et al., 2014) as well as observations and theoretical understanding. These multiple lines of evidence give high confidence that the projections describe the changes in the real world (high agreement, robust evidence).

Attachment field is blank. Please add an attachment or remove this block.

The near surface salinity of the ocean is both observed and projected to evolve in ways that reflect the increased intensity of the Earth’s hydrologic cycle (Durack, 2015) and the increasing near-surface ocean stratification (Zika et al., 2018). As described in WGI AR5, the ocean surface in areas that currently have net evaporation are expected to become saltier, while areas with net precipitation are expected to get fresher (Rhein et al., 2013), as the patterns of precipitation and evaporation are generally expected to be amplified (Held and Soden, 2006). At longer time-scales of decades, the larger scale changes in the ocean circulation and basin integrated freshwater imbalances emerge in the near-surface salinity changes, as shown in Figure 5.3b, with an increasingly salty tropical and subtropical Atlantic and Mediterranean contrasting with a freshening Pacific and polar Arctic emerging as robust signals across the suite of ESMs (Collins et al., 2013). The freshening of the high latitudes in the north Atlantic and Arctic basin is consistent with the widely expected weakening of the AMOC (also discussed in Section 6.7), hydrological cycle changes and a decline in the volume of sea ice (discussed in Section 3.2.2).

Projected salinity changes in the subsurface ocean reflect changes in the rates of formation of water masses or their newly formed properties (Purich et al., 2018). Thus, projected freshening of the Southern Ocean surface leads to a freshening of the Antarctic Intermediate Water that is subducted there, flowing northward from the Southern Ocean as a relatively fresh water-mass at depths of 500–1500 m (Figure 5.3b). Increased surface salinity in the Atlantic subtropical gyres are pumped into the interior by the winds, leading to an increased salinity of the interior subtropical gyres, along with contributions from increasingly salty Mediterranean water (Jordà et al., 2017). Conversely, freshwater capping of the northwestern north Atlantic is projected to inhibit deep convection in the Labrador Sea and the consequent production of Labrador Sea Water in some models (Collins et al., 2013), and contributes to the increased salinity of the north Atlantic between 1000–2000 m depths (Figure 5.3b). 

Identifying the specific patterns of anthropogenic climate changes in oceanic observations is complicated by the presence of basin-scale natural variability with timescales ranging from tidal to multi-decadal, and due to the difficulties associated with maintaining high-precision observing systems spanning the ocean basins and limited observational coverage of the extratropical Southern Hemisphere before 2006 (Rhein et al., 2013). Inferences based on oceanographic observations from the 1970s onward show wide-spread warming of the upper 700 m (Figure 5.2a), in broad agreement with the ensemble of historical CMIP5 ESM simulations (Figure 5.2b). These ESMs indicate that anthropogenic regional warming over the past half-century should be discernable at the 95% confidence level in much of the upper oceans (un-stippled areas in Figure 5.2b). Most of the areas where observational analyses (Figure 5.2a) exhibit long-term cooling are either regions where the internally generated variability is large enough to mask the trends (e.g., the Eastern Tropical Pacific, Northwest Atlantic, and Kurushio extension east of Japan, which are stippled in Figure 5.2b), or where the observational coverage early in the record is limited and different analyses can disagree about trends (e.g., the Southern Ocean and extratropical South Pacific). When internal variability is taken into account, the broad consistency in the magnitude and regional distribution of observed and simulated 50-year trends gives confidence to the ESM projections of longer-term oceanic changes described previously.

Detailed regional patterns of trends in temperature and heat content at depths of 0–2000 m during the early 21st century are consistent in various analysis, owing to the improved observing network (Roemmich et al., 2015; Desbruyères et al., 2016a) (Figure 5.2d). At depths of 700–2000 m, observations in all of the ocean basins show broadly warming trends in the well-observed Argo era (2006 to present), with particularly significant warming patterns in the Southern Hemisphere extratropics around 40oS and the subpolar north Atlantic (Figure 5.3a). These observed changes support the notion that deep ocean heat content has been continuously increasing. As a result, regional climate change signatures emerge from confounding natural variability sooner in the 700–2000 m depth range than in upper 700 m of the ocean, where interannual modes of variability have a larger influence on the circulation (for a more complete discussion see Johnson et al. (2018). Despite regional patches of cooling water in the upper 700 m (Figure 5.2d), every one of the world’s ocean basins volume averaged over depths of 0–2000 m has experienced significant warming over the last decade (Figure 5.3, and also Desbruyères et al. (2016a)). The greatest warming of the top 2000 m has been in the Southern Ocean (Roemmich et al., 2015; Trenberth et al., 2016), the tropical and subtropical Pacific Ocean (Roemmich et al., 2015), and the tropical and subtropical Atlantic Ocean (Cheng and Chen, 2017). The Southern Hemisphere extratropical oceans accounted for 67–98% of the total ocean heat increase in the uppermost 2000 m for the period of 2006–2013 (Roemmich et al., 2015). Shi et al. (2018) suggest that the dominant ocean heat uptake by the Southern Hemisphere in the early 21st century is expected to become more balanced between the hemispheres as the asymmetric cooling by aerosols decreases.

Large-scale patterns of natural variability at interannual to decadal time scales can mask the long-term warming trend in the upper 700 m, particularly in the tropical Pacific and Indian Oceans (England et al., 2014; Liu et al., 2016) and in the north Atlantic (Buckley and Marshall, 2015). The most significant upper 700 m warming between five-year averages centered on 2007–2015 occurred in a large extratropical band of the Southern Hemisphere between 30ºS–60ºS, and in the tropical Indian Ocean, the eastern North Pacific and western subtropical north Atlantic (Figure 5.2d). Warming of the southern hemisphere subtropical gyres is driven, in part, by an intensification of Southern Ocean winds in recent decades, facilitating the penetration of heat to deeper depths (Gao et al., 2018). Marginal seas, such as the Mediterranean and Red seas have also exhibited notable warming. Conversely, over this timeframe there were also regions of cooling in the upper 700 m, notably in the north Atlantic around 40oN–60oN and in the western tropical Pacific (Figure 5.2d). Recent relatively cold and fresh surface and subsurface conditions in the north Atlantic have been attributed to anomalous atmospheric forcing (Josey et al., 2018) or weakened transport by the north Atlantic Current and AMOC (Smeed et al., 2018), and in turn may have contributed to an intensification of deep convection in the Labrador Sea since 2012 (Yashayaev and Loder, 2017). All these observed decadal changes can be related to internal decadal variability (Robson et al., 2014; Yeager et al., 2015) even though they resemble expected longer-term anthropogenically forced trends. Substantial decadal-scale warming and cooling trends in the tropical Pacific and Indian oceans can arise from natural El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole variability (Han et al., 2014). Large ensembles of freely running CMIP5 ESM simulations also show that internal variability can dominate the regional manifestation of the anthropogenic climate signal on decadal timescales (Kay et al., 2014). This is illustrated by the differing warming trends in Figure 5.2e and 5.2f from two identical ESMs that differ only in the weather in their 1850 initial conditions, averaged over the whole 21st century, by contrast, the ensemble of CMIP5 models project statistically significant anthropogenic regional upper 700 m heat content trends almost everywhere (Figure 5.2c).

There are well documented changes in observed ocean temperatures and salinities (Abraham et al., 2013; Ishii et al., 2017). However, attributing these changes in the state of the ocean to anthropogenic causes can be challenging due to the presence of internally generated variability, which can swamp the underlying climate change signal in short records and on regional scales. As can be seen in Figure 5.2, the observed long-term trends (Figure 5.2a) exhibit a striking similarity to the CMIP5 ensemble mean in areas where the models suggest that anthropogenic changes should be statistically significant (Figure 5.2b). However, the trends in the shorter well-observed period covering 2005–2017 (Figure 5.2d) exhibits strong trends from internal variability, as illustrated by the differences of two ensemble members of the same ESM with the same forcing but initialised with different weather (Figure 5.2e and 5.2f). Detection and Attribution studies take the internal variability into account and separate the underlying climate signals with the same spatio-temporal sampling as the observations, and apply a range of statistical tests to determine the coherence of the observations with the co-sampled observations (Bindoff et al., 2013; AR5 WG1 Box 10.1). 

Since AR5, the use of different and updated oceanographic data sets and increase in the number of ensembles of the CMIP5 simulations (Kay et al., 2014) has improved the overall detection and attribution of human influence. Together these measures increase the coherence of the simulations and reduce noise. For example, an isotherm approach used to reduce the noise from the displacement of isotherms in the upper water column allowing detection in each of the mid-latitude ocean basins was achieved on 60-year time series (Weller et al., 2016). Using all the available ocean temperature and salinity profiles from the Southern Ocean, Swart et al. (2018) show that the warming and freshening patterns were consistent primarily with increased human induced greenhouse gases and secondarily from ozone depletion in the stratosphere, but inconsistent with internal variability. Together the evidence from the AR5, and the discussion above with the new evidence on regional scales across the global oceans, we conclude that the observed long-term upper ocean temperature changes are very likely to have a substantial contribution from anthropogenic forcing.

The wind-driven ocean circulation at the end of the 21st century is expected to be qualitatively similar to that in the present day, even as important buoyancy-loss driven overturning circulations are expected to weaken. ESM projections suggest that some major ocean current transports will exhibit a modest increase (such as the Kuroshio Extension (Terada and Minobe, 2018) or a small decrease such as for the Indonesian Throughflow (Sen Gupta et al., 2016); many predominantly wind-driven current-system transports are expected to exhibit smaller than 20% changes by 2100 with RCP8.5 forcing. Climate-change induced changes of the circulation in other mid-latitude basins may be difficult to detect or reliably project because of significant natural variability at inter-annual (e.g., El Niño) to decadal (e.g., the Pacific Decadal Oscillation) timescales. The Antarctic Circumpolar Current is projected to be subject to strengthening westerly winds and substantially reduced rates of Antarctic Bottom Water (AABW) formation, as assessed in the Cross-Chapter Box 7 in Chapter 3. The heat transported by the buoyancy-loss driven AMOC, in particular, contributes to the relatively clement climate of northern Europe and the north Atlantic Basin as a whole, although the wind-driven ocean gyres also contribute to the meridional ocean heat transport (see the review by Buckley and Marshall (2015). As a result, there is a concern that significant changes in ocean circulation could lead to localised climate changes that are much larger than the global mean. Projected and observed changes in the AMOC and the rates of formation of deep water-masses in the north Atlantic are discussed in Chapter 6.7.1, along with the possibility of abrupt or enduring changes resulting from forcing by Greenlandic meltwater. A significant reduction in AMOC would, in turn, modestly weaken the Gulf Stream transport, which also has a substantial wind driven component (Frajka-Williams et al., 2016). Most aspects of the large-scale wind-driven ocean circulation are very likely to be qualitatively similar to the circulation in the present day, with only modest changes in transports and current location. 

The global ocean below 2000 m has warmed significantly between the 1980s and 2010s (Figure 5.4), contributing to ocean heat uptake and through thermal expansion to SLR (Purkey and Johnson, 2010; Desbruyères et al., 2016b). The observed deep warming rate varies regionally and by depth reflecting differences in the waters influencing particular regions. The deep and abyssal north Atlantic, fed by North Atlantic Deep Water (NADW), has reversed from warming to cooling over the past decade, possibly associated with the North Atlantic Oscillation (NAO) (e.g., Yashayaev, 2007; Desbruyères et al., 2014) or longer-term weakening in north Atlantic overturning circulation (Caesar et al., 2018; Thornalley et al., 2018). The strongest warming is observed in regions of the deep ocean AABW (Purkey et al., 2014). Regions of the ocean fed by AABW from the Weddell Sea have exhibited a possible slowdown in local AABW warming rates (Lyman and Johnson, 2014), while the Pacific, fed by AABW from the shelves along the Ross and Adelie Coast, has continued to warm at an accelerating rate between 1990 and 2018 (Desbruyères et al., 2016b). 

To date, assessment of deep ocean (below 2000 m) heat content has mostly been from ship-based data collected along decadal repeats of oceanographic transects (Figure 5.4b) (Talley et al., 2016). While relatively sparse in space and time compared to the upper ocean, these transects were positioned to optimise sampling of most deep ocean basins and provide the highest quality of salinity, temperature and pressure data. Argo floats capable of sampling to 6000 m have just started to populate select deep ocean basins; this Deep Argo data has just started providing regional deep ocean warming estimates (Johnson et al., 2019). Decadal monitoring by the full global Deep Argo array (Johnson et al., 2015), complemented by indirect estimates from space (Llovel et al., 2014; Von Schuckmann et al., 2014), will strongly reduce the currently large uncertainties of deep ocean heat content change estimates in the future.

The spatial and temporal sparseness of observations below 4000 m, along with significant differences between various ESMs, limits our understanding of the exact mechanisms driving the abyssal ocean variability. However, ESMs consistently predict an anthropogenic climate-change induced long-term abyssal warming trend originating in the Southern Ocean due to a reduction in the formation rates of cold AABW (Heuzé et al., 2015). Although the abyssal modes of natural variability are not as pronounced as closer to the surface, deep ocean heat content can vary on relatively short time scales through the communication of topographic and planetary waves driven by changes in the rate of deep water formation at high latitudes (Kawase, 1987; Masuda et al., 2010; Spence et al., 2017). AABW has shown variability in properties and production rates over the past half century (Purkey and Johnson, 2013; Menezes et al., 2017). A slowdown in AABW formation rates may arise from freshening of shelf waters, changes in local winds driving cross shelf mixing, or larger scale dynamics controlling the spin up or down of Southern Ocean gyres influencing the density of outflowing waters over deep sills. Large-scale circulation changes can also alter the properties of the ambient water that is entrained as dense water descends along the Antarctic continental slopes (Spence et al., 2017). Evolving AABW properties may also reflect changes in deep Southern Ocean convection. The Weddell Polynya is a large opening in the wintertime ice of the Weddell Sea that is kept ice-free despite intense heat loss to the atmosphere by convective mixing bringing up warm and salty water from the deep ocean. (See Box 3.2 for a more extensive discussion of polynyas and the Weddell Polynya in particular). The Weddell Polynya was present in three of the first years of infrared satellite observations of wintertime sea ice concentrations in the mid-1970s, but it has been closed since 1976, only to reopen in 2016 and 2017. The prominent Weddell Polynya in the mid-1970s greatly increased the volume of the coldest waters in the deep Weddell Sea. Weddell Polynyas are documented to drive abyssal cold and salty signals and can spread thermal signals as waves further and faster than could be explained by slow advective signals (Martin et al., 2015; Zanowski and Hallberg, 2017); these waves do not directly heat individual water parcels, but instead warm the ocean where they cause the coldest deep layers to spread laterally and thin. However, recovery from the large Weddell polynya of the early 1970s can only explain about 20% of the observed abyssal warming trend (Zanowski et al., 2015).

Figure 5.4

Figure 5.4 | Observed rates of warming from 1981 to 2019 (a) as a function of depth globally (orange) and south of the Sub-Antarctic Front (the purple line in (b) at about 55°S) (purple) with 90% confidence intervals and (b) average warming rate (colours) in the abyss (below 4000 m) over various ocean basins (whose […]

Figure 5.4 | Observed rates of warming from 1981 to 2019 (a) as a function of depth globally (orange) and south of the Sub-Antarctic Front (the purple line in (b) at about 55°S) (purple) with 90% confidence intervals and (b) average warming rate (colours) in the abyss (below 4000 m) over various ocean basins (whose boundaries are shown in grey lines), with stippling indicating basins with no significant changes. The black lines show the repeat hydrographic sections used to make these estimates. These figures use updated GoShip data and the techniques of Purkey and Johnson (2010).

Figure 5.5

Figure 5.5 | Zonal and 20-year mean stratification averaged over the top 200 m of the ocean for the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble of simulations at the end of the historical runs (green), and for the end of the 21st century for Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) scenarios. […]

Figure 5.5 | Zonal and 20-year mean stratification averaged over the top 200 m of the ocean for the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble of simulations at the end of the historical runs (green), and for the end of the 21st century for Representative Concentration Pathway (RCP)2.6 (blue) and RCP8.5 (red) scenarios. The values between the 5th and 95th percentiles of the ensembles are shaded, while the lines are the ensemble mean. These model results are not adjusted by the control-run, so the spread in the various estimates primarily reflect model formulation differences. The average squared buoyancy frequency shown here is nearly linearly proportional to the density difference between the surface and 200 m, and is a measure of the density stratification of the upper ocean.

The ocean’s properties are changing most rapidly in the near surface waters that are more immediately exposed to atmospheric forcing. As a result of the surface-intensified warming, the upper few hundred meters of the ocean are becoming more stably stratified (Helm et al., 2011; Talley et al., 2016). The combination of surface intensified warming and near-surface freshening at high latitudes leading to a projection of more intense near-surface stratification (the downward-increasing vertical gradient of density) across all ocean basins (Figures 5.3 and 5.5) is a robust result with a high agreement across successive generations of coupled climate models (Capotondi et al., 2012; Bopp et al., 2013). Based on the projected changes from individual models between 1986–2005 and 2081–2100, the mean stratification of the upper 200 m averaged between 60°S–60°N, normalised by the ensemble mean value from 1986–2005 will very likely increase by between 1.0–9.3% (with 95% confidence and a CMIP5 median change of 2.6%) for RCP2.6, and by between 12.2–30.0% (median value 21.2%) for RCP8.5. Inferences from oceanic observations (Good et al., 2013) suggest that the 20-year mean stratification averaged between 60°S–60°N and over the top 200 m very likely increased by between 2.18–2.42% from 1971–1990 to 1998–2017. By contrast, the bottom intensified warming in the abyss (see Figure 5.4) which is consistent with a slowing in the rate of AABW formation, is also associated with a reduction in the abyssal stratification of the ocean (Lyman and Johnson, 2014; Desbruyères et al., 2016b). Both of these changes have consequences for the evolving turbulence and ocean water-mass structure. Based on observational evidence, theoretical understanding and robust ESM projections, it is very likely that stratification in the upper few hundred meters of the ocean below the mixed layer will increase significantly in the 21st century over most ocean basins as a result of climate change, and abyssal stratification will likely decrease.

Many dynamical consequences of increased stratification are understood with very high confidence (see, for instance, Gill (1982) and Vallis (2017)). For the same turbulent kinetic energy dissipation, locally increased stratification reduces the turbulent vertical diffusivity of heat, salinity, oxygen and nutrients (see Section Increased stratification in the tropics and subtropical gyres will likely lead to a net reduction in the vertical diffusivities of nutrients and other gases within the main thermocline, reducing the flux of nutrients into the euphotic zone and increasing the gradient in oxygen concentrations between the near surface ocean and the interior. Increasing upper ocean stratification (Figure 5.5) acts to restrict the depth of the ocean’s surface mixed layer. Increasing stratification increases the buoyancy frequency and the lateral propagation speed of internal gravity waves and boundary waves by about half the percentage change of the stratification itself. Increasing stratification increases both the length of the internal deformation radius (a typical length scale in baroclinic eddy dynamics) and the horizontal scales of internal tides (see Section proportionately with the changes in the internal gravity wave speeds. An increase in stratification will increase the lateral propagation of internal Rossby waves (which set up the basin-scale ocean density structure) proportionately. For the same forcing, increasing stratification reduces the geostrophically balanced slope of density surfaces, and hence the vertical extent of basin-scale wind-driven gyres or coastal upwelling circulations. The flattening of density surfaces by increased stratification inhibits advective exchange between the surface and interior ocean (Wang et al., 2015a), with consequences for the uptake of anthropogenic carbon (Section, the evolving oxygen distribution (Section and the supply of nutrients to support primary production (Section
Tides and coastal physical changes in a changing climate

Coastal systems are subject to the same large-scale warming trends as the open ocean, but the local response may be dominated by a complex of localised changes in factors such as circulation, mixing, river plumes or the seasonal upwelling of cold water. Using ESMs to project how these factors will interact often requires much finer resolution than is currently affordable in global models, however regional high-resolution models can be effective, especially in marginal seas like the Mediterranean with restricted interactions with the open ocean and that respond primarily to local forcing (Adloff et al., 2015). High resolution regional models have also been used to project robust localised ocean climate changes in wide shelf seas with more extensive interactions with the open ocean, like those in northwestern Europe (Tinker et al., 2016). The technical difficulties of using nested regional models are much greater in coasts adjacent to energetic large-scale currents like the Gulf Stream, Kuroshio, and Agulhas, and projecting detailed coastal climate change such places may require the use of expensive high resolution global models (Saba et al., 2016). These physical coastal changes have consequences that cascade through ecosystems to people, as is illustrated in detail for eastern boundary upwelling systems in Box 5.2.

Both human structures and ecological systems in the coastal zone are directly impacted by tidal amplitudes, which contribute to high-water levels and the tidal flushing rates of estuaries, embayments, marshes and mangroves. The tides are the response of a forced-damped-resonance system (Arbic et al., 2009). The M2 tide is the dominant tidal constituent in most places, with a period of half a lunar day, or 12 hours, 25 minutes; the M2 tides are created by the differential motion of the solid Earth and oceans in response to the gravitational attraction of the moon (Newton, 1687; Laplace, 1799). The astronomical forcing evolves only slowly, however the tidal damping and basin resonance at tidal frequencies can change in response to changes in sea level, stratification and coastal conditions (Müller, 2012; Schindelegger et al., 2018). Several recent studies have analysed historical coastal tide gauge data and found amplitude trends of order 14% per century (Ray, 2009; Woodworth, 2010; Müller et al., 2011). In some locations, the changes in the tides have been of comparable importance to changes in mean sea level for explaining changes in high water levels (Jay, 2009). For many individual tide gauges, the trends in tidal amplitude are strongly positively or negatively correlated with local time-mean sea level trends (Devlin et al., 2017). Another source of secular tidal changes, changes in oceanic stratification, modifies the rate of energy conversion from the barotropic tides to the internal tides (Jayne and St. Laurent, 2001), the vertical profile of turbulent viscosity on shelves (Müller, 2012), and the propagation speed of the internal tides (Zhao, 2016). For example, Colosi and Munk (2006) found an increase in the amplitude of the principal lunar semidiurnal tide M2 in Honolulu of about 1 cm over the past 100 years, which they attributed primarily to changes in oceanic stratification bringing about local changes in relative phases of the internal and external M2 tides, increasing constructive interference. Both sea level and stratification are expected to exhibit robust secular positive trends in the coming century due to climate change, at rates that are significantly larger than historical trends, and people may choose to replace natural beaches and marshes with sea-walls in response to rising sea levels. As a result, it is very likely that the majority of coastal regions will experience statistically significant changes in tidal amplitudes over the course of the 21st century.

Because coastal tides are near resonance in many locations, small changes in sea level and bay shape can change the local tides significantly. For example, the insertion of tidal power plants can have a significant impact on the local tides (Ward et al., 2012). Various observational and modeling studies demonstrate that SLR has spatial heterogeneous impacts on the tides, with some locations experiencing decreased tidal amplitudes and others experiencing increased tidal amplitudes (Pickering et al., 2012; Devlin et al., 2017; Pickering et al., 2017). Projections of tidal changes indicate that the patterns and even the sign of changes in tidal amplitudes depend on whether the coastlines are allowed to recede with rising sea levels or are held in place (Pickering et al., 2017; Schindelegger et al., 2018). Pelling et al. (2013) and Hwang et al. (2014) demonstrate that the rapid coastline changes in China’s Bohai Sea have already altered the tides in that region and throughout the Yellow Sea (Hwang et al., 2014). Pelling and Green (2014) examine the impact of flood defenses as well as SLR on tides on the European Shelf. Such tidal changes have implications for designing flood defenses, for tidal renewable energy, for tidal flushing timescales of estuaries and embayments, and for navigational dredging requirements (Pickering et al., 2012) (Section 5.4.2). The sign and amplitude of local changes to tides are very likely to be impacted by both human coastal adaptation measures and climate drivers (listed above). Systematic sources of uncertainty in projections of ocean physical changes

ESMs are able to capture the dynamics of the climate system, but all numerical models have approximations and biases. The most commonly used type of ocean component in ESMs is known to exhibit numerically induced vertical mixing that can be a significant fraction of the physical mixing (Ilıcak et al., 2012; Megann, 2018). Because so many ocean models exhibit the same sign of bias, there is a systematic warming of the lower-main thermocline that is not cancelled out when taking the average over the ensemble of all the models in CMIP5. These biases are widely known within the ocean modelling community, and various groups are working to reduce these biases in future ESMs with better ocean model numerics and parameterisations. To correct for model biases, ESM projections are always taken as the difference from a control run without the anomalous forcing. However, some aspects of the ocean response to climate change are nonlinear, and model biases can introduce uncertainties into climate projections. In the case of heat uptake, this is of the order of 10% uncertainty, while for the rate of steric SLR (which depends on the nonlinear equation of state of seawater) the uncertainty in CMIP5 models is of the order of 20% (Hallberg et al., 2012). 

Mesoscale eddies (geostrophic rotating vortices with spatial scales of 10–100 km that penetrate deeply into the water column, and are often described as the ocean’s weather) play an important role in regulating the changes to the larger scale ocean circulation, especially in the Antarctic Circumpolar current, as is discussed in Cross Chapter Box 7. In addition, sub-mesoscale eddies (rotationally influenced motions with smaller horizontal scales of hundreds of metres to about 10 km and intrinsic timescales of a few days that especially arise in association with fronts in the ocean’s surface properties) are known to be particularly important in the dynamics of the near-surface ocean boundary layer (see the review by Mahadevan (2016)). Sub-mesoscale instabilities are associated with re-stratifying overturning circulations that can limit the thickness of the well-mixed ocean surface boundary layer near fronts (Bachman et al., 2017). Moreover, sub-mesoscale motions generate strong vertical velocities that drive fluxes of nutrients from the interior ocean into the euphotic zone or create pockets of reduced mixing with increased phytoplankton residency time within the euphotic zone (Lévy et al., 2012). Intense mesoscale eddies are known to create favourable conditions for sub-mesoscale instabilities as shown in both observational (Bachman et al., 2017) and numerical studies (Brannigan et al., 2017). Intensifying Southern Ocean eddy fields will have a significant local impact on biological productivity, ecosystem structure, and carbon uptake, both directly and via sub-mesoscale processes. At typical CMIP5 ESM resolutions, it is only in the tropics that mesoscale eddies are adequately resolved to explicitly model their effects (Hallberg, 2013), while sub-mesoscale eddies are not resolved anywhere, so eddy effects need to be parameterised in ESMs. Despite great progress over the past 30 years in parameterising eddy effects, uncertainties in these parameterisations and how eddies will respond to novel conditions continue to contribute to uncertainties in projections of oceanic climate change (medium confidence).

Ocean turbulent mixing is a key process regulating the ocean circulation and climate. Turbulent mixing is important for the uptake and redistribution of heat, carbon, nutrients, oxygen and other tracers (properties that are carried along with the flow of water) in the ocean (Schmittner et al., 2009; MacKinnon et al., 2017). Both observations and theory indicate that turbulent mixing in the ocean is not constant in space or time. Global estimates of both the turbulent kinetic energy dissipation rate and the vertical diffusivity, two measures of ocean turbulence, vary over several orders of magnitude throughout the ocean (Figure 5.6) (Polzin et al., 1997; Waterman et al., 2012; Whalen et al., 2012; Alford et al., 2013; Hummels et al., 2013; Sheen et al., 2013; Waterhouse et al., 2014; Kunze, 2017). For a given energy dissipation rate, the turbulent diffusivities of heat, salinity, nutrients and other tracers tend to be smaller with stronger stratification. This dependency on stratification helps explain why the observationally inferred diffusivity in the heavily stratified main thermocline (250–1000 m depth) is of similar magnitude to those deeper in the water column, while the turbulent energy density and dissipation rate are much stronger at the shallower depths (Whalen et al., 2012). Oceanic turbulence also fluctuates in time, is modulated by tidal cycles (Klymak et al., 2008), the mesoscale eddy field and seasonal changes (Whalen et al., 2018). In the mixed layer and directly below, turbulence changes according to local conditions, such as the winds, heating rates and local stratification (Sloyan et al., 2010; Moum et al., 2013; D’Asaro, 2014; Tanaka et al., 2015) at diurnal to seasonal and longer timescales. These variations in near-surface turbulence need to be taken into account for ESMs to reproduce more accurately the observed seasonal cycle of surface properties and spatial structure of the depth of the thermally well-mixed near surface layer of the ocean. The spatial and temporal patterns of ocean turbulence help shape ocean tracer distributions (heat, dissolved greenhouse gases and nutrients) and how they will evolve in a changing climate (high confidence). 

Figure 5.6

Figure 5.6 | Estimate of the average vertical turbulent diffusivity between 250–1000 m calculated by applying fine structure techniques to Argo float data from below the well-mixed near-surface boundary layer. Only bins with at least three estimates are plotted and regions with insufficient data are coloured grey. This figure was created using updated data through […]

Figure 5.6 | Estimate of the average vertical turbulent diffusivity between 250–1000 m calculated by applying fine structure techniques to Argo float data from below the well-mixed near-surface boundary layer. Only bins with at least three estimates are plotted and regions with insufficient data are coloured grey. This figure was created using updated data through April, 2018 with the techniques from Whalen et al. (2012).

Ocean turbulent mixing requires energy sources, many of which are expected to change with a changing climate. Surface wind and buoyancy forcing, the mean and eddying larger-scale ocean circulation itself, and the barotropic tides are all thought to be significant sources of the energy that drives mixing (Wunsch and Ferrari, 2004). Often this energy first passes through the ocean’s pervasive field of internal gravity waves that propagate and refract through the varying ocean circulation, often breaking into turbulent mixing far from their sources (Eden and Olbers, 2014; Alford et al., 2016; Melet et al., 2016; Meyer et al., 2016; Zhao et al., 2016b). The energy contributing to the internal waves from the winds and the subsequent turbulence will be altered by changes in tropical storm activity or sea ice coverage. For example, the increasing extent of ice-free Arctic Ocean has already been observed to lead to increased wind-driven internal waves (Dosser and Rainville, 2016). The Southern Annular Mode is expected to intensify as a result of climate change (Young et al., 2011; Jones et al., 2016b), bringing with it stronger winds, and more wind-energy input over most of the Southern Ocean and a more intense mesoscale eddy field (Hogg et al., 2015). Changes in the near-bottom stratification will alter the rate that the barotropic tides generate internal waves, thereby altering the strength and distribution of the tidally generated mixing. Some of the parameterisations of interior ocean mixing used in CMIP5 ESMs take some changing turbulent energy sources into account (Jayne and St. Laurent, 2001), and more comprehensive mixing treatments are being developed for use in future generations of ESMs (Eden and Olbers, 2014). However, not all of the physical processes leading to the rich structure of mixing shown in Figure 5.6 are well understood or included in ESMs; the prospect of significant changes in the patterns and intensity of ocean turbulent mixing is a potential source of uncertainty (probably at the 10% level) in projections of physical and ecological changes in the ocean, including heat uptake, stratification changes, steric SLR, deoxygenisation and nutrient fluxes (medium confidence).

Changes in Ocean Carbon

Since AR5, new global-scale data synthesis products, novel methods for their analyses, as well as progress in modeling have substantially increased our quantitative understanding of the role of the ocean in absorbing and storing CO2 from the atmosphere. The most important progress concerns the data-based quantification of the temporal variability of the ocean carbon sink. While AR5 assessed primarily the climatological mean processes governing the ocean carbon cycle, the most recent work now permits us to assess how these processes have changed in recent decades in response to climate variability and change. Here we focus specifically on the open ocean carbon cycle.
Ocean carbon fluxes and inventories

The analyses of the steadily growing number of surface ocean CO2 observations (now more than 20 million observations, SOCATv6 (www.socat.info/index.php/2018/06/19/v6-release) demonstrate that the net ocean uptake of CO2 from the atmosphere has increased from around 1.2 ± 0.5 Pg C yr-1 in the early 1980s to 2.0 ± 0.5 Pg C yr-1 in the years 2010–2015 (Rödenbeck et al., 2014; Landschützer et al., 2016). Once new estimates of the outgassing flux stemming from river derived carbon of 0.8 Pg C yr-1 (Resplandy et al. 2018) are accounted for, these new observations imply that the rate of global ocean uptake of anthropogenic CO2 increased from 2.0 ± 0.5 Pg C yr-1 to 2.8 ± 0.5 Pg C yr-1 between the early 1980s and 2010–2015 (Rödenbeck et al., 2014; Landschützer et al., 2016; Le Quéré et al., 2018). This increase is supported by the current generation of ocean carbon cycle models (Le Quéré et al., 2018), and commensurate with the increase in atmospheric CO2

The continuing efforts to re-measure dissolved inorganic carbon (DIC) along many of the repeat hydrographic lines that were occupied during the 1980s and 1990 (Talley et al., 2016), alongside the preparation of a global quality controlled database of ocean interior observations (Olsen et al., 2016a), have led to progress since AR5 regarding to the oceanic interior storage of anthropogenic CO2. Several studies analysed the changes in the amount of anthropogenic CO2 that have accumulated between different occupations in the different ocean basins (Wanninkhof et al., 2010; Pérez et al., 2013; Woosley et al., 2016; Carter et al., 2017), confirming that the anthropogenic CO2 taken up from the atmosphere is transported to depth, where most of it is stored. Using a newly developed reconstruction method, Gruber et al. (2019) extended these results to the globe. They find that between 1994 and 2007, across two standard deviations, that the global ocean has accumulated an additional 30-38 Pg C of anthropogenic CO2, which is equivalent to an air-sea CO2 flux of between 2.3–2.9 Pg C yr-1 (coherent with surface ocean CO2 observations), bringing the total inventory for the year 2007 to 150 ± 20 Pg C. Extrapolating this estimate to the year 2010 gives an inventory of 158 ± 18 Pg C, which is statistically indistinguishable from the ‘best’ estimate provided by Khatiwala et al. (2013) of 155 ± 31 Pg C and more recently also found from a steady-state ocean model (DeVries, 2014) for this reference year. If the inventory-based estimates are adjusted for the loss of natural carbon, a very likely total increase in storage between 1994 and 2007 of 24–34 Pg C, or around 25% of total emissions, is found (Gruber, 2019).

Thus, there is very high confidence from surface ocean and ocean interior carbon data that the strength of the ocean sink for anthropogenic carbon has increased in the last two decades in response to the growth of atmospheric CO2. Multiple lines of evidence indicate that it is very likely that the ocean has taken up 20–30% of the global emissions of CO2 from the burning of fossil fuels, cement production, and land-use change since the mid 1980s. The consistency between independent surface ocean observations and the ocean interior data-based reconstructions supports the assessment of very high confidence and provides robust evidence that fraction of emissions taken up by the ocean has not changed in a statistically significant manner in the last few decades and remains consistent with AR5.

Alongside a globally integrated perspective, these new surface ocean observations also reveal a substantial degree of variability at interannual and decadal scales (Rödenbeck et al., 2015; Landschützer et al., 2016; Le Quéré et al., 2018). Most notable are the air-sea CO2 flux variations in the tropics linked to ENSO variations (Rödenbeck et al., 2015; Landschützer et al., 2016), as well as the strong decadal variations in the high latitudes, especially the Southern Ocean (Landschützer et al., 2015; Munro et al., 2015; Ritter et al., 2017), discussed further in Chapter 3 (Section Fluctuations in the Southern Ocean CO2 flux are important as they impart a substantial imprint also on the global uptake fluxes. For instance, reduced Southern Ocean uptake in the 1990–2000 period coincided with an exceptionally weak global net uptake of only about 0.8 ± 0.5 Pg C yr −1.

Thus, there is growing evidence from multiple datasets that the ocean carbon sink exhibits decadal variability at regional scales that significantly alter the globally integrated sink (medium confidence).

Detailed analyses of the spatial structure of the change in storage of anthropogenic CO2 confirm the variable nature of the ocean carbon sink suggested by the surface observations (Pérez et al., 2013), which are most likely a consequence of changes in ocean circulation (DeVries and Weber, 2017). The increase in anthropogenic CO2 between 1994 and 2007 occurs throughout the upper 1000 m, but with very different penetration depths, reflecting largely differences in the efficiency, with which the anthropogenic CO2 is transported from the surface to depth (Gruber et al., 2019) (Figure 5.7). This spatial distribution of how the amount of anthropogenic CO2 has changed between 1994 and 2007 is similar to the distribution of anthropogenic CO2 reconstructed for 1994 (Sabine et al., 2004), although the imprint of regional variations in ocean circulation and transport are discernible (Gruber, 2019).

Figure 5.7

Figure 5.7 | Vertical sections of the change in anthropogenic CO2 from 1994 to 2007 represented by the zonal mean sections in each ocean basin, organised around the Southern Ocean in the centre. The upper 500 m are expanded. Contour intervals of anthropogenic CO2 are 2 μmol kg–1 (Gruber, 2019).

Figure 5.7 | Vertical sections of the change in anthropogenic CO2 from 1994 to 2007 represented by the zonal mean sections in each ocean basin, organised around the Southern Ocean in the centre. The upper 500 m are expanded. Contour intervals of anthropogenic CO2 are 2 μmol kg–1 (Gruber, 2019).
Ocean carbon chemistry

Analyses of direct measurements of ocean chemistry from time series stations and merged shipboard studies show consistent decreases in surface-ocean pH over the past few decades. Reductions range between 0.013–0.03 pH units decade-1 over records that span up to 25 years (Table SM5.3). Focusing on the individual time series locations with records longer than 15 years, there is an overall decline of 0.017–0.027 (across 99% confidence intervals). Trends calculated from repeat measurements on ocean surveys show a consistent value of around –0.02 pH units decade-1 for diverse oceanic regions (Table SM5.3), with greater subsurface than surface trends reported in the subtropical oceans (Dore et al., 2009). At larger spatial scales, surface-ocean pH trends are assessed using shipboard observations of the fugacity of CO2 and estimates of ocean alkalinity (Takahashi et al., 2014; Lauvset et al., 2015). Between 1991–2011, mean surface-ocean pH has declined by 0.018 ± 0.004 units decade–1 in 70% of ocean biomes, with the largest declines in the Indian Ocean (–0.027 units decade–1), eastern Equatorial Pacific (–0.026 units decade–1) and the South Pacific subtropical (–0.022 units decade–1) biomes (Lauvset et al., 2015). Due to the close link between carbonate ion concentrations and pH, mean trends in the stability of mineral forms of aragonite and calcite (known as the ‘saturation state’) that are important for organisms such as coccolithophorids, pteropods and corals follow those of pH, with high-latitude regions most vulnerable to under-saturation due to naturally lower mean values.

It is virtually certain that ocean pH is declining, and the very likely range of this decline is 0.017–0.027 pH units per decade for the 8 locations where individual time series observations longer than 15 years exist. This trend is lowering the chemical stability of mineral forms of calcium carbonate and can be attributed to rising atmospheric CO2 levels. 

CMIP5 models are in good agreement with historical observations of declining surface-ocean pH (Figure 5.8a). Models project global surface-ocean declines between 2006–2015 and 2081–2100 of 0.287–0.291 and 0.036–0.042 pH units (both across 99% confidence intervals) for the RCP2.6 and RCP8.5 scenarios, respectively, with higher reductions in the subsurface of subtropical oceans (Bopp et al., 2013; Gattuso et al., 2015). These changes in pH will be greatest in the Arctic Ocean and the high latitudes of the Atlantic and Pacific Oceans due to their lower buffer capacity and are lowest in contemporary upwelling systems (Figure 5.8b) and will also reduce the stability of calcite minerals (Bopp et al., 2013; Gattuso et al., 2015). The area of the surface ocean (0–10 m) characterised by undersaturated conditions in CMIP5 models by 2081–2100 reduces from a very likely range of 6.4–9.5 x 1012 m2 or 5.5–7.3 x 1013 m2 under RCP8.5 (as much as 16–20% of ocean surface area for aragonite), to just 0.01–0.2  x 1012 m2 or 0.01–0.13 x 1013 m2 under RCP2.6 for either calcite or aragonite minerals, respectively. Under RCP8.5, hotspots for undersaturated waters for calcite remain restricted to the Arctic Ocean, while for aragonite, much of the Southern Ocean and the North Pacific and Northwestern Atlantic Oceans are also projected to become undersaturated (Orr et al., 2005; Hauri et al., 2015; Sasse et al., 2015). These results arise from the very well understood reductions in carbonate ion concentrations at lower pH, the vulnerability of regions with naturally low mean values, and the greater overall sensitivity of aragonite solubility. Regional models, with higher resolution that ESMs, also project year-round corrosive conditions for aragonite in some eastern boundary upwelling systems (Franco et al., 2018a). In the ocean interior, the decline in pH and calcium carbonate saturation state is more uncertain across models (Steiner et al., 2014) as it is modulated by changes to ocean overturning and water mass subduction (Resplandy et al., 2013; Chen et al., 2017). Projected benthic changes in pH over the next century are highly localised and are linked to transport of surface anomalies to depth, with over 20% of the north Atlantic sea floor deeper than 500 m projected to experience pH reductions greater than 0.2 units by 2100 under the RCP8.5 scenario (Gehlen et al., 2014). Changes in pH in the abyssal ocean (>3000 m deep) are greatest in the Atlantic and Arctic Oceans, with lesser impact in the Southern and Pacific Oceans by 2100, mainly due to the circulation timescales (Sweetman et al., 2017). 

Overall, it is virtually certain that the future surface open ocean will experience pH drops of either 0.036–0.042 (RCP2.6) or 0.287–0.291 (RCP8.5) pH units by 2081–2100, relative to 2006–2105. These pH changes are very likely to cause 16–20% of the surface ocean, specifically the Arctic and Southern Oceans, as well as the northern Pacific and northwestern Atlantic Oceans, to experience year-round corrosive conditions for aragonite by 2081–2100. It is virtually certain these impacts will be avoided under the RCP2.6 scenario. There is medium confidence, due to the potential for parallel changes in ocean circulation, that the Arctic and north Atlantic seafloors will experience the largest pH changes over the next century.

Although ocean acidification results in long-term trends in mean ocean chemistry, it can also influence seasonal cycles. Observation-based products indicate that the seasonal cycle of global surface-ocean pCO2 increased in amplitude by 2.2 ± 0.4 μatm between 1982 and 2014 (Landschützer et al., 2018). CMIP5 models and data-based products similarly project consistent future increases in the seasonal cycle of surface-ocean pCO2 under the RCP8.5 emissions scenario, with enhanced amplification in high-latitude waters (McNeil and Sasse, 2016). The amplitude of the seasonal cycle of global surface-ocean free acidity ([H+]) is projected to increase by 71–91% (across 90% confidence intervals) over the 21st century under RCP8.5, also with greater amplification in the high-latitudes (Kwiatkowski and Orr, 2018). Conversely, models project a 12–20% reduction (across 90% confidence intervals) in the seasonal amplitude of surface-ocean pH, as changes in pH represent relative changes in [H+] due to their logarithmic relationship, and there are typically greater projected increases in annual mean state [H+] than the seasonal amplitude of [H+]. Models also project a 4–14% (across 90% confidence intervals) reduction in the seasonal amplitude of global mean surface-ocean aragonite saturation state under RCP8.5, with a slight amplification in the subtropics being outweighed by dampening elsewhere. The contrasting changes in the seasonal amplitudes of ocean carbonate chemistry variables derive from different sensitivities to atmospheric CO2 and climate change and to diverging trends in the seasonal cycles of DIC, alkalinity and temperature. Model skill at simulating the seasonal cycles of carbonate chemistry is moderate, with persistent biases in the Southern Ocean, particularly for pCO2, [H+] and pH (Kwiatkowski and Orr, 2018; Mongwe et al., 2018).

Overall, we assess that alongside the strong mean state changes, it is very likely that the amplitude of the seasonal cycle in free acidity will increase by 71–91%, while it is very likely that the seasonal cycles of pH and aragonite saturation will decrease by 12–20% and 4–14%, respectively. 

Figure 5.8

Figure 5.8 | Panels a, d, g and j display simulated global changes over the period of 1900–2100 (with solid lines representing the multi-model mean and the envelope representing 90% confidence intervals for RCP8.5 and RCP2.6), for surface pH, O2 concentration averaged over 100–600 m depth, upper 100 m nitrate concentrations and NPP integrated over […]

Figure 5.8 | Panels a, d, g and j display simulated global changes over the period of 1900–2100 (with solid lines representing the multi-model mean and the envelope representing 90% confidence intervals for RCP8.5 and RCP2.6), for surface pH, O2 concentration averaged over 100–600 m depth, upper 100 m nitrate concentrations and NPP integrated over the top 100 m. Differences are calculated relative to the 1850–1900 period. Panels b, e, h and k show spatial patterns of simulated change in surface pH, upper 100 m nitrate concentrations, O2 concentration averaged over 100 to 600 m depth, and NPP integrated over the top 100 m averaged over 2081–2100, relative to 1850–1900 for RCP8.5. Panels c, f, i and l display time series of the percentage of total uncertainty ascribed to internal variability uncertainty, model uncertainty, and scenario uncertainty in projections of global annual mean changes. Figure adapted after (Frölicher et al. 2016). Please note that confidence intervals can be affected by the different number of models available for the RCP8.5 and RCP2.6 scenarios and for different variables. See also Table SM5.4.

Changing Ocean Oxygen

Ocean oxygen (O2) levels at the surface are controlled by the balance between oxygen production during photosynthesis, temperature-controlled solubility and air-sea exchange. Deeper in the water column, consumption of oxygen during respiration and redistribution by ocean circulation and mixing are dominant processes. In theory, a warmer more stratified ocean would have a reduced oxygen content, due to the combined influence of lowered gas solubility and a greater interior respiration of organic matter due to enhanced physical isolation of subsurface waters. In accord, global changes in ocean oxygen assessed from three different analyses of compiled global oxygen datasets going back to the 1960s agree that there is a net loss of oxygen from the ocean over all depths (see Table 5.2). For the 0–1000 m depth stratum that contains the most data and is common to all three analyses, oxygen is assessed to have declined by a very likely range of 0.5–3.3% between 1970 and 2010. For the surface ocean (0–100 m) and the thermocline later of 100–600 m the very likely range of oxygen declines are 0.2–2.1% and 0.7–3.5%, respectively (Table 5.2). Across two studies, global oxygen is assessed to have declined by a very likely range of 0.3–2.0%, with a similar range of decline for waters deeper than 600 m (Table 5.2). The regions of lowest oxygen, known as OMZs, with oxygen levels lower than 80 μmol L-1), are observed to be expanding by a very likely range of 3.0–8.3% across the three studies.

Regionally, all studies agree that the north Pacific and Southern Oceans have shown the largest overall oxygen declines (Figure 5.9), but there is some disagreement regarding the magnitude of the oxygen change in the tropical ocean, with some studies suggesting significant declines (Schmidtko et al., 2017) and other reporting more modest reductions (Helm et al., 2011; Ito et al., 2017) and data coverage is still limited for some regions and deeper than 1000 m. Based on the available data, the strongest declines in deep ocean oxygen have occurred in the Equatorial Pacific, North Pacific, Southern Ocean and South Atlantic, with intermediate declines in the Arctic, South Pacific and Equatorial Atlantic, while the north Atlantic has experienced a moderate oxygen increase below 1200 m (Figure 5.9). A particular difference between parallel oxygen analyses concerns the means of integrating and mapping sparse data across the ocean, both horizontally and vertically, with different studies making specific decisions about averaging grids and integration methods. Moreover, data remains sparse for some ocean regions, depths and periods. Taken together, the challenges of data sparsity, regional differences and the relatively large uncertainties on the oxygen changes across different studies, but also recognising that oxygen declines are significantly different to zero, leads to medium confidence in the observed oxygen decline. 

Syntheses of datasets from local time series tend to document stronger trends, with oxygen declines of over 20% at sites in the northeastern Pacific between 1956–2006 (Whitney et al., 2007), the Northwestern Pacific between 1954–2014 (Sasano et al., 2015) and the California Current between 1984–2011 (Bograd et al., 2015). Despite holding the highest inventory of oxygen in the ocean, oxygen levels in Southern Ocean contributed 25% to the global decline between 1970–1992 (Helm et al., 2011) and have fallen by over 150 Tmol per decade from the 1960s to present (Schmidtko et al., 2017). Observations along ocean cruises as part of the CLIVAR programme have also documented broad thermocline oxygen declines in the northern hemisphere oceans, accompanied by well understood oxygen increases in subtropical and southern hemispheres (Talley et al., 2016). 

Overall there is medium confidence that the oxygen content of the upper 1000 m has declined with a very likely loss of 0.5–3.3% between 1970-2010. OMZ are expanding in volume, by a very likely range of 3.0–8.3%. There is medium confidence that the largest regional changes have occurred in the Southern Ocean, equatorial regions, North Pacific and South Atlantic due to medium agreement among studies. 

The role of ocean warming alone in driving the oxygen changes can be appraised using solubility estimates, which vary between around 15–50% for the upper 1000 m oxygen trend between studies (Helm et al., 2011; Ito et al., 2017; Schmidtko et al., 2017). The role of other processes, linked to changing ocean ventilation and respiration are challenging to appraise directly, but tend to reinforce the impacts from warming and are probably predominant overall (Oschlies et al., 2018). Indeed, that the observed oxygen decline is negatively correlated with ocean heat content changes (Ito et al., 2017) reflects the overriding role of changing ocean ventilation and associated processes (see also Section 5.2.2). That the ratio of the associated oxygen to heat changes is larger than would be expected from thermal processes alone also highlights the role played by other processes (Oschlies et al., 2018). Local oxygen trends have emphasised the role of changes to ocean physics in western Northern Pacific (Whitney et al., 2013); Sasano et al. (2015), the southern California Current region (Goericke et al., 2015), and the Santa Barbara Basin (Goericke et al., 2015). In regions of high mesoscale activity, such as the tropical north Atlantic, low oxygen eddies can have a significant impact on oxygen dynamics (Karstensen et al., 2015; Grundle et al., 2017). Oxygen fluctuations in the deep ocean have been linked to changes in large scale ocean circulation (Watanabe et al., 2003; Stendardo and Gruber, 2012) and at the global scale, the observed oxygen decline is negatively correlated with ocean heat content changes (Ito et al., 2017). Changes to respiration rates, either due to temperature enhancement or in the amount/quality of organic material can also be important and the enhanced respiratory demand associated with an intensified monsoon has been invoked as a driver of the expansion of the Arabian Sea OMZ (Lachkar et al., 2018).

Ocean oxygen changes are also affected by climate variability on interannual and decadal timescales, especially for the tropical ocean OMZs (Deutsch et al., 2011). ENSO variability in particular affects the thermocline structure, which then alongside changes in circulation modulates oxygen solubility and respiratory demand in this region (Ito and Deutsch, 2013; Eddebbar et al., 2017). These drivers may then be combined with modifications to overturning and ventilation of OMZs by lateral jets and equatorial current intensity (Duteil et al., 2014). Centennial scale studies based on isotope proxies for low oxygen regions have demonstrated fluctuations in OMZ extent linked to decadal changes in tropical trade winds that affects interior ocean respiratory oxygen demand, which implies that it will be difficult to attribute recent changes in the Pacific OMZ to anthropogenic forcing alone (Deutsch et al., 2015). Parallel work based on oxygen observations (Llanillo et al., 2013), as well as modelling (Duteil et al., 2018) supports the importance of decadal scale variability in the eastern tropical Pacific OMZ. There is some evidence for the potential of a modulating impact on tropical Pacific oxygen at interannual timescales from atmospheric deposition of nitrogen and iron (Ito et al., 2016; Yang and Gruber, 2016).

Table 5.2

Observed oxygen changes for the period 1970–2010 for 6 different layers within the ocean. The changes are shown as percentage change of global averages. The layers are depths 0–100, 100–600, 0–1000, and 600–bottom are in metres. The oxygen minimum zone (OMZ) is defined as the ocean volume change that is less than 80 μmol L-1. The estimates and confidence intervals are based published papers (Schmidtko et al. 2018, Ito et al. 2017 and Helm et al. 2011). The assessed change is the average of the available estimates and the 90% Confidence Interval (CI) combines the confidence as their standard deviation with two degrees of freedom.


Schmidtko Ito Helm Assessed Change
Layer Period Change 90 CI Change 90 CI Change 90 CI Change 90 CI
0–100 1970–2010 –0.38% ±1.06% –1.65% ±0.63% –1.30% ±0.54% –1.11% ±0.95%
100–600 1970–2010 –1.06% ±1.36% –3.17% ±1.34% –2.04% ±0.60% –2.09% ±1.42%
0–1000 1970–2010 –1.35% ±1.38% –2.70% ±1.30% –1.74% ±0.54% –1.93% ±1.39%
600–bottom 1970–2010 –1.51% ±0.62% n.a. n.a. –0.81% ±0.57% –1.16% ±0.84%
OMZ 1970–2010 6.33% ±2.52% 6.10% 1.2% 4.49% ±2.25% 5.64% ±2.66%
Global 1970–2010 –1.43% ±0.70% n.a. n.a. –0.87% ±0.53% –1.15% ±0.88%
Figure 5.9

Figure 5.9 | Absolute change in dissolved oxygen (umol kg–1 per decade) between water depths of (a) 0 and 1200 m, and (b) 1200 m and the sea floor over the period 1960–2010. Lines indicate boundaries of OMZs with less than 80 μ mol kg–1 oxygen anywhere within the water column (dashed/dotted), less than 40 […]

Figure 5.9 | Absolute change in dissolved oxygen (umol kg–1 per decade) between water depths of (a) 0 and 1200 m, and (b) 1200 m and the sea floor over the period 1960–2010. Lines indicate boundaries of OMZs with less than 80 μ mol kg–1 oxygen anywhere within the water column (dashed/dotted), less than 40 μ mol kg–1 (dashed) and less than 20 μ mol kg–1 (solid). Redrawn from Oschlies et al. (2018).

Future changes in oxygen can be appraised from ESMs that account for the combined effects of ocean physics and biogeochemistry. Globally, these models project that it is very likely oxygen will decline by 3.2–3.7% or 1.6–2.0% (both across 90% confidence limits) for RCP8.5 or RCP2.6, respectively, relative to 2000 (Bopp et al., 2013). Focussing on the 100–600 m depth stratum, O2 changes by –4 to –3.1% for the RCP8.5 or by –0.5–0.1% for the RCP2.6 scenario (relative to 2006–2015, Figure 5.8d). It should be noted that ESMs appear to be underestimating the rate of oxygen change from available datasets from the historical period (Oschlies et al., 2018). Increased tropical ocean stratification reduces interior ocean oxygen by diminishing pathways of ventilation in the subtropical gyres and by inhibiting turbulent mixing with the oxygen-rich surface ocean (see Section This relatively robust global modelled trend (Figure 5.8d) however masks important uncertainties in the projection of regional trends (Figure 5.8e), particularly in the tropical ocean OMZs (Bopp et al., 2013; Cocco et al., 2013; Cabré et al., 2015). The uncertainty in the trends in tropical ocean OMZs arises due to the fact that oxygen depletion due to warming induced reductions in oxygen saturation are opposed by oxygen enrichment due to reduced oxygen consumption during respiration in response to predicted declines in marine export production, as well as biases due to model resolution in the tropics and the length of the model spin up (Bopp et al., 2017). The 80 μmol L-1 threshold that may be used to define the volume of the oxygen minimum is projected to grow by a very likely range of 7.0 ± 5.6% by 2100 during the RCP8.5 scenario or show virtually no change during the RCP2.6 scenario, relative to a 1850–1900 reference period (Figure 5.10). At the seafloor, between 200–3000 m depth strata, the north Pacific, north Atlantic, Arctic and Southern Oceans may see oxygen declines by 0.3–3.7% by 2100 (relative to 2005), with abyssal ocean changes being lower and more localised around regions in the north Atlantic and Southern Ocean (Sweetman et al., 2017), but will be modulated by any future changes in overturning strength. There is high confidence that the largest changes in deep sea systems will occur after 2100 (Battaglia and Joos, 2018).

Figure 5.10

Figure 5.10 | The evolution of the volume of the 100–600 m layer of the ocean with oxygen concentrations less than 80 mmol L–1 for the RCP8.5 (red line) and the RCP2.6 (blue line), normalised to the volume in 1850–1900. Dashed lines indicated the very likely range (90% confidence intervals) across the CMIP5 models (CNRM-CM5, […]

Figure 5.10 | The evolution of the volume of the 100–600 m layer of the ocean with oxygen concentrations less than 80 mmol L–1 for the RCP8.5 (red line) and the RCP2.6 (blue line), normalised to the volume in 1850–1900. Dashed lines indicated the very likely range (90% confidence intervals) across the CMIP5 models (CNRM-CM5, GFDL-ESM2M, GFDL-ESM2G, IPSL-CM5A-LR, IPSL-CM5A-MR, MPI-ESM-LR, MPI-ESM-MR and the NCAR-CESM1 models). Models are corrected for drift in O2 using their control simulations.

Simulations extended to 2300 suggest that by 2150 the trend of declining tropical ocean oxygen (both in terms of concentrations and volume of low oxygen waters) may reverse itself, mainly due to the effect of strong declines in primary production and organic matter fluxes to the ocean interior (Fu et al., 2018) or due to enhanced Antarctic ventilation (Yamamoto et al., 2015), but with low confidence due to limited evidence. At the global scale, 10,000 year intermediate complexity model simulations find that overall ocean oxygen loss shows near linear relationships to equilibrium temperature, itself linearly related to cumulative emissions, and any climate mitigation scenario will reduce peak oxygen loss by 4.4% per degree Celsius of avoided warming (Battaglia and Joos, 2018). 

In summary, the total oxygen content of the ocean is very likely to decline by 3.2–3.7% by 2100, relative to 2000, for RCP8.5 or by between 1.6–2.0% for RCP2.6 with medium confidence. There is medium confidence that sea floor changes will be more localised in the north Atlantic and Southern Oceans by 2100, but high confidence that the largest deep sea floor changes in oxygen will occur after 2100.

Changing Ocean Nutrients

Changes to ocean nutrient cycling are driven by modifications to ocean mixing and transport (Section, internal biogeochemical cycling and fluctuations in external supply, particularly from rivers and the atmosphere. This assessment will focus on the main nutrients important for driving microbial growth (Section, namely nitrogen, phosphorus and iron. 

Diverse studies (including shipboard experiments and use of protein biomarkers) have highlighted nitrogen and phosphorus limitation in the stratified tropical ocean regions accompanied by widespread iron limitation at high latitudes and in upwelling regions that typically have elevated levels of productivity (Figure 5.11) (Moore et al., 2013; Saito et al., 2014; Browning et al., 2017; Tagliabue et al., 2017). Moreover, more extensive experimental work has demonstrated overlapping nitrogen-iron co-limitation at the boundaries between gyre and upwelling regimes (Browning et al., 2017). There is high confidence arising from robust evidence and high agreement across different types of studies that the main limiting nutrient is either iron (in most major upwelling regions and the Southern, north Atlantic and sub-Arctic Pacific Oceans) or nitrogen and phosphorus (in the low productivity tropical ocean gyres).

Figure 5.11

Figure 5.11 | Map of the dominant limiting resource (Moore et al. 2013), updated to include new experiments from the north Pacific, tropical Atlantic and south east Atlantic (Browning et al. 2017; Shilova et al. 2017). The background is depth integrated primary productivity using the Vertically Generalized Production Model algorithm. Colouring of the circles indicates […]

Figure 5.11 | Map of the dominant limiting resource (Moore et al. 2013), updated to include new experiments from the north Pacific, tropical Atlantic and south east Atlantic (Browning et al. 2017; Shilova et al. 2017). The background is depth integrated primary productivity using the Vertically Generalized Production Model algorithm. Colouring of the circles indicates the primary limiting nutrients inferred from chlorophyll and/or primary productivity increases following artificial amendment of: N (blue), P (black), Fe (red), Co (yellow) and Zn (cyan). Divided circles indicate potentially co-limiting nutrients, for example, a red-blue divided circle indicates Fe-N co-limitation.

There is limited evidence on contemporary trends in nutrient levels, either from time series sites or broader meta-analyses. Increasing inputs of anthropogenic nitrogen from the atmosphere are perturbing ocean nutrient levels (Jickells et al., 2017). In the North Pacific in particular, additional atmospheric nitrogen input has raised the nitrogen to phosphorus ratio between 1988–2011 and induced a progressive shift towards phosphorus limitation in this region (Kim et al., 2011; Kim et al., 2014; Ren et al., 2017). This tendency is supported by modelling experiments that find enhanced atmospheric nitrogen input only has a small influence on productivity due to expanded phosphorus limitation (Yang and Gruber, 2016) and other nitrogen cycle feedbacks (Somes et al., 2016; Landolfi et al., 2017).

In general, future increases in stratification (Dave and Lozier, 2013; Talley et al., 2016; Kwiatkowski et al., 2017; and see also Section will trap nutrients in the ocean interior and reduce upper ocean nutrient levels, alongside an additional local impact from changes to atmospheric delivery. However, no CMIP5 models accounted for changes in nutrient delivery from dust and anthropogenic aerosols during their experiments, which could be an important component of regional change (Wang et al., 2015b; Somes et al., 2016; Yang and Gruber, 2016). ESMs project a decline in the nitrate content of the upper 100 m of 9–14% or 1.5–6% (across 90% confidence intervals) for the RCP8.5 or RCP2.6 scenario, respectively, by 2081–2100 relative to 2006–2015 (Figure 5.8g). The largest absolute declines in nitrate content is projected in the present day upwelling zones (Figure 5.8h). Projected changes to upper 100 m nitrate concentrations are significantly different to zero for both RCP8.5 and RCP2.6 at the 90% confidence level, but are overall lower for the RCP2.6. Scenario, internal variability and inter-model variability contribute roughly equally to the overall projection uncertainty in 2100 (Figure 5.8i) and there is no clear separation of nitrate trends between RCP8.5 and RCP2.6 outside the model uncertainty (Figure 5.8h).

Iron concentrations are projected to increase in the future from ESM simulations, due to enhanced lateral transport into high-latitude oceans and reduced biological consumption in regions of declining nitrate (Misumi et al., 2013). Other modelling efforts also suggest greater levels of the more biologically available Fe(II) species in a warmer and more acidic ocean (Tagliabue and Völker, 2011). These modelling studies tend to indicate greater ocean iron availability in the future overall, but the very limited skill of contemporary global ocean iron models in reproducing observations available from the new basin scale datasets from the international GEOTRACES program and neglect for parallel dust supply changes lower the confidence in the models’ projected changes (Tagliabue et al., 2016). 

Overall, nitrate concentrations in the upper 100 m are very likely to decline by 9–14% by 2081–2100, relative to 2006–2015 for RCP8.5 or 1.5–6% for RCP2.6, in response to increased stratification, with medium confidence in these projections due to the limited evidence of past changes that can be robustly understood and reproduced by models. Surface ocean iron levels is projected to increase in the 21st century with low confidence due to systemic uncertainties in these models.

Changing Ocean Primary and Export Production

Ocean primary productivity is a key process in the ocean carbon cycle (see Section, as well as for supporting pelagic ocean ecosystems (see Section 5.2.3). NPP is the product of phytoplankton growth rate and standing stock. Phytoplankton growth is controlled by the combination of temperature, light and nutrients, while the phytoplankton standing stock is modified by both gains from growth and losses due to grazing by zooplankton (Figure 5.12). Export production is here defined as the sinking flux of particulate organic carbon (produced by NPP) across a specified depth horizon. Otherwise known as the biological pump, export production is also a key component of the global carbon cycle (see Section and an essential food supply to benthic organisms (see Section Export production is regulated by the level of primary production and the transfer efficiency with depth, itself controlled by the type of sinking organic carbon, which is affected by the upper ocean food web structure (Boyd et al., 2019). 

Satellite datasets that use mathematical algorithms to convert ocean colour, often alongside other remotely sensed information, into chlorophyll or other indexes of phytoplankton biomass and NPP provide the potential to deliver a global meta-analysis of changes in NPP. Since AR5, a variety of studies have reported relatively insignificant changes in overall open ocean chlorophyll levels of <±1% yr–1 for individual time periods (Boyce et al., 2014; Gregg and Rousseaux, 2014; Boyce and Worm, 2015; Hammond et al., 2017). Regionally, trends of ±4% between 2002–2015 for different regions are found when different satellite products are merged, with increases at high latitudes and moderate decreases at low latitudes (Mélin et al., 2017). While some studies report good comparability of merged products (Mélin et al., 2017), others highlight significant mismatches regarding absolute values and decadal trends in NPP between NPP algorithms (Gómez-Letona et al., 2017). Satellite derived NPP shows significant mismatches when compared to in situ data and reducing uncertainties in derived NPP is a high priority for the community (Lee et al., 2015), although there is a reasonable correlation in higher biomass coastal regions (Kahru et al., 2009). Importantly, satellite records are not yet long enough to unambiguously isolate long term climate related trends from natural variability (Beaulieu et al., 2013). Overall, there is low confidence in satellite-based trends in global ocean NPP due to the time series length and lack of corroborating in situ measurements or other validation time series. This is especially true at regional scales where distinct sets of poorly understood processes dominate. 

Future changes in NPP will result from the changing influence from temperature, light, nutrients and grazing (Figure 5.12). Across CMIP models, NPP is predicted to broadly decline or remain constant by 2081–2100, with mean changes by 2100 of –3.8 to –10.6% and –1.1–0.8% across 90% confidence intervals for the RCP85 and RCP26 scenario, respectively (all relative to 2006–2015), with a strong degree of regional symmetry (Figure 5.8k). As seen for nitrate, changes are most marked in low-latitude upwelling regions, which are projected to show the largest absolute declines. As for nitrate, projected NPP changes are lower for the RCP26 scenario (Figure 5.8j), but the overall uncertainty is dominated by internal and inter-model variability in 2100 (Figure 5.8l) which results in no clear separation of NPP trends between the RCP85 and RCP26 (Figure 5.8j). Tropical ocean NPP is projected to show a large decline, but is underpinned by substantial intermodal uncertainty, with mean changes of 11 ± 24% across the suite of CMIP5 models by 2100, relative to 2000 under RCP8.5 (Laufkötter et al., 2015). However, if emergent constraints from the historical record that link the variability of tropical productivity to temperature anomalies then a four-fold decline in inter-model uncertainty results. This leads to a projected tropical ocean decline of 11 ± 6%, or from 6.8–16.2% across 90% confidence limits, depending on which historical constraint is used (Kwiatkowski et al., 2017). NPP is projected to increases for higher latitude regions, such as the Arctic and Southern Oceans.

Detailed analyses of the interplay between different drivers of NPP, including temperature, light, nutrient levels and grazing from a subset of CMIP5 models, reveals a complex interplay with a strong latitudinal dependence (Laufkötter et al., 2015) summarised in Figure 5.12. Warming acts to enhance growth, most notably at lower latitudes, while light conditions are also predicted to improve, mostly at the poles. Nutrient limitation shows a much more complex response across models, but tends to increase in the tropics and northern high latitudes, with little change in the Southern Ocean. Taken together there is a tendency for reduced growth rates across the entire ocean, but there is a large amount of inter-model variability. The changes in growth are allied to a consistent increase in the grazing loss of biomass to upper trophic levels. Since AR5, we have an increasing body of literature concerning role of biological feedbacks, especially due to interactions between organisms, specific physiological responses and from upper trophic levels on nutrient concentrations, linked to variable food quality (Kwiatkowski et al., 2018), resource recycling (Boyd et al., 2015a; Tagliabue et al., 2017) and interactions between organisms (Lima-Mendez et al., 2015), but their role in shaping the response of NPP to climate change remains a major unknown. Lastly, modelling work suggests that the increasing deposition of anthropogenic aerosols (supplying N and Fe) stimulates biological activity (Wang et al., 2015b) and may compensate for warming driven reductions in primary productivity (Wang et al., 2015b), but these effects do not form part of the CMIP5 projections assessed here.

CMIP5 models show a strong negative relationship between changes in stratification that reduces net nutrient supply and integrated export production (Fu et al., 2016). Export production is projected to decline by 8.9–15.8% or 1.6–4.9% (across 90% confidence intervals) by 2100, relative to 2000 for the RCP8.5 or RCP2.6 scenario, respectively (Bopp et al., 2013; Fu et al., 2016; Laufkötter et al., 2016). The projected changes in export production can be larger than global primary production because they are affected by both the NPP changes, but also how shifts in food web structure modulates the ‘transfer efficiency’ of particulate organic material (Guidi et al., 2016; Tréguer et al., 2018), which then affects the sinking speed and lability of exported particles through the ocean interior to the sea floor (Bopp et al., 2013; Fu et al., 2016; Laufkötter et al., 2016). Declines in export production over much of the ocean mean that the flux arriving at the sea floor is also predicted to decline, while increases in export production are projected in the polar regions that see enhanced NPP (Sweetman et al., 2017). 

The realism in model projections can be appraised via their ability to accurately simulate the limiting nutrient in specific ocean regions (Figure 5.11), with high model skill in reproducing surface distributions of nitrate and phosphate (Laufkötter et al., 2015), raising confidence in projections in nitrogen and phosphorus limited systems, but poor skill in reproducing iron distributions (Tagliabue et al., 2016) lowering confidence in iron limited regions (Figure 5.11). In addition to concentrations of specific nutrients, the response of NPP to environmental change is strongly controlled by accurate representation of the ratio of resources (Moreno et al., 2017). Overall CMIP5 models skill in reproducing patterns of NPP and export production from limited satellite derived estimates range from poor to average (correlation coefficients of 0.1–0.6 across different models (Laufkötter et al., 2016; Moreno et al., 2017)), but it should be noted that complete comprehensive observational datasets do not exist for these metrics with very few in situ observations. As export production is a much better understood net integral of changing net nutrient supply (Sarmiento and Gruber, 2002) and can be constrained by interior ocean nutrient and oxygen levels, there is medium confidence in these projections for global changes. Improving the ability of models to reproduce historical NPP is crucial for more accurate projections as model biases in simulating contemporary ocean biogeochemistry play a key role in driving future projections (Fu et al., 2016). 

Overall, these assessments balance the range of projections across models alongside the strength of different kinds of observational constraints available, as well as our theoretical or experimental understanding of the impact of a warmer, more stratified ocean on NPP and export production. As for AR5, net primary productivity is very likely to decline by 4–11% by 2081–2100, relative to 1850–1900, across CMIP5 models for RCP8.5, but there is low confidence for this estimate due to the medium agreement among models and the limited evidence from observations. It is very likely that tropical NPP will decline by 7–16% by 2100 for RCP8.5with medium confidence, as there are improved constraints from historical variability in this region. Globally, the increased stratification in the future is very likely to reduce export production by 9–16% in response to reduced nutrient supply, especially in tropical regions (medium confidence).

Figure 5.12

Figure 5.12 | A schematic diagram to illustrate how net primary production (NPP) is a combination of microbial growth and biomass. In this context, growth is controlled by three limiting factors (nutrients, light and temperature), while biomass is affected by grazing. The grey lines in the plots represent results from different Coupled Model Intercomparison Project […]

Figure 5.12 | A schematic diagram to illustrate how net primary production (NPP) is a combination of microbial growth and biomass. In this context, growth is controlled by three limiting factors (nutrients, light and temperature), while biomass is affected by grazing. The grey lines in the plots represent results from different Coupled Model Intercomparison Project Phase 5 (CMIP5) models as reported by Laufkötter et al. (2015). Poorly understood feedbacks from upper trophic levels on autotroph biomass and nutrients are represented by dashed arrows.


Impacts on Pelagic Ecosystems

Marine pelagic ecosystems (the water column extending from the surface ocean down to the deep sea floor) face increasing climate related hazards from the changing environmental conditions (see Section 5.2.2). WGII AR5 (Pörtner et al., 2014) concluded, as also confirmed in Section 5.2.2, that long time series of more than three or four decades in length are necessary for determining biological trends in the ocean. However, long-term biological observations of pelagic ecosystems are rare and biased toward mid to high-latitude systems in the Northern Hemisphere (Edwards et al., 2013; Poloczanska et al., 2013; Poloczanska et al., 2016). This assessment, therefore, combines multiple lines of evidence ranging from experiments, field observations to model simulations to detect and attribute drivers of biological changes in the past, project future climate impacts and risks of pelagic ecosystems. In this section the pelagic ecosystem is subdivided into the surface, epipelagic ocean (<200 m, the uppermost part of the ocean that receives enough sunlight to allow photosynthesis) (Section and the deep pelagic ocean, comprising the twilight, mesopelagic zone (200–1000 m) and the dark, bathypelagic zone (>1000 m deep) (Section Although the WGII AR5 Chapter 30 defined the deep sea as below 1000 m (Hoegh-Guldberg et al., 2014), the absence of photosynthetically useful light and ensuing critical ecological, biogeochemical transformations, and altered human interactions that occur on much of the sea floor below 200 m have led both pelagic and benthic biologists to include the ocean waters and seafloor below 200 m within the definition of the deep sea (Herring and Dixon, 1998; Gage, 2003).

The Epipelagic Ocean

This section synthesises new evidence since AR5 to assess observed changes in relation to the effects of and the interactions between multiple climate and non-climate hazards, and to project future risks of impacts from these hazards on the epipelagic organisms, communities and food web interactions, and their scope and limitation to adapt.
Detection and attribution of biological changes in the epipelagic ocean Temperature-driven shifts in distribution and phenology

WGII AR5 concluded that the vulnerability of most organisms to warming is set by their physiology, which defines their limited temperature ranges and thermal sensitivity (Pörtner et al., 2014). Although different hypotheses have been proposed since AR5 to explain the mechanism linking temperature sensitivity of marine organisms and their physiological tolerances (Schulte, 2015; Pörtner et al., 2017; Somero et al., 2017), evidence from physiological experiments and observations from paleo- and contemporary periods continue to support the conclusion from AR5 on the impacts of temperature change beyond thermal tolerance ranges on biological functions such as metabolism, growth and reproduction (Payne et al., 2016; Pörtner and Gutt, 2016; Gunderson et al., 2017), contributing to changes in biogeography and community structure (Beaugrand et al., 2015; Stuart-Smith et al., 2015) (high agreement, high confidence). Comparison of biota across land and ocean suggests that marine species are generally inhabiting environment that is closer to their upper temperature limits, explaining the substantially higher rate of local extirpation related to warming relative to those on land (Pinsky et al., 2019). Hypoxia and acidification can also limit the temperature ranges of organisms and exacerbate their sensitivity to warming (Mackenzie et al., 2014; Rosas-Navarro et al., 2016; Pörtner et al., 2017), although interactions vary strongly between species and biological processes (Gobler and Baumann, 2016; Lefevre, 2016).

Shifts in distribution of marine species from phytoplankton to marine mammals continued to be observed since AR5 across all ocean regions (Poloczanska et al., 2016). Recent evidence continues to support that a large proportion of records of observed range shifts in the epipelagic ecosystem (Poloczanska et al., 2016) are correlated with ocean temperature, with an estimated average shift in distribution (including range centroids, northward and southward boundaries) from these records of 51.5 ± 33.3 km per decade since the 1950s (Figure 5.13). Such rate of shift is significantly faster than those records for organisms in the seafloor; the latter has an average rate of distribution shift of 29.0 ± 15.5 km per decade (44% of the records for seafloor species with range shifts that are consistent with expectation from the observed temperature changes)(very likely) (Figure 5.13). Comparison of global seafloor-derived planktonic foraminifera from pre-industrial age with recent (from year 1978) communities show that the recent assemblages differ from their pre-industrial with increasing dominance of warmer or cooler species that are mostly consistent with temperature changes (Jonkers et al., 2019). Rate of observed responses also varies between and within animal groups among ocean regions, with zooplankton and fishes having faster recorded range shifts (Pinsky et al., 2013; Asch, 2015; Jones and Cheung, 2015; Poloczanska et al., 2016). For example, analysis of the Continuous Plankton Recorder (CPR) data-series from the north Atlantic in the last decades shows that the range of dinoflagellates tended to closely track the velocity of climate change (the rate of isotherm movement). In contrast, the distribution range of diatoms shifted much more slowly (Chivers et al., 2017) and its distribution seems to be primary influenced by multi-decadal variability rather than from secular temperature trends. The CPR surveys have also provided evidence that some calanoid copepods are expanding poleward in the Northeast Atlantic, at a rate up to 232 km per decade (Beaugrand, 2009; Chivers et al., 2017), although different calanoid species respond differently in the rate and direction of shifts (Philippart et al., 2003; Edwards and Richardson, 2004; Asch, 2015; Crespo et al., 2017). Overall, the observed changes in biogeography are consistent with expected responses to changes in ocean temperature for the majority of marine biota (high confidence). This is also consistent with theories and experimental evidence that scale from individual organisms’ physiological responses to community level effects (high confidence). Sensitivity of organisms’ biogeography varies between taxonomic groups (high confidence). 

Figure 5.13

Figure 5.13 | Evidence of climate change responses of marine organisms to changes in ocean conditions under climate change. (a) evidence of interactive effects (including synergistic and antagonistic) of multiple climatic hazards (based on Przeslawski et al. (2015); Lefevre (2016); Section 5.2.2, 5.2.3, 5.2.4, 5.3). ‘Others’ mainly include mammals, seabirds and marine reptiles). The lighter-coloured […]

Figure 5.13 | Evidence of climate change responses of marine organisms to changes in ocean conditions under climate change. (a) evidence of interactive effects (including synergistic and antagonistic) of multiple climatic hazards (based on Przeslawski et al. (2015); Lefevre (2016); Section 5.2.2, 5.2.3, 5.2.4, 5.3). ‘Others’ mainly include mammals, seabirds and marine reptiles). The lighter-coloured cell represents insufficient information to draw conclusion; (b–d) observations on changes in latitudinal range and (e–h) phenology (based on Poloczanska et al. 2013). For b–h, each bar represents one record.

The rate and direction of observed range shifts are shaped by the interaction between climatic and non-climatic factors (Poloczanska et al., 2013; Sydeman et al., 2015; Poloczanska et al., 2016), such as local temperature and oxygen gradients in the habitat across depth (Cheung et al., 2013; Deutsch et al., 2015), latitude and longitude (Burrows et al., 2014; Barton et al., 2016), ocean currents (Sunday et al., 2015; Barton et al., 2016; García Molinos et al., 2017), bathymetry in all or part of their life stages (for organisms living on or close to the seafloor) (Pinsky et al., 2013; Kleisner et al., 2015), geographical barriers (Pinsky et al., 2013; Burrows et al., 2014), availability of food and critical habitat (Sydeman et al., 2015), fishing and other non-climatic human impacts (Engelhard et al., 2014; Hoegh-Guldberg et al., 2014). Moreover, observed range shifts in respond to climate change in some regions such as the north Atlantic are strongly influenced by warming due to multi-decadal variability (Edwards et al., 2013; Harris et al., 2014), suggesting that there is a longer time-of-emergence of range shifts from natural variability and a need for longer biological time series for robust attribution. The rate of shifts in biogeography of organism is influenced by multiple climatic and non-climatic factors (high confidence) that can result in non-synchronous shifts in community composition (high confidence). There is general under-representation of biogeographical records in low latitudes (Dornelas et al., 2018), rendering detection and attribution of shifts in biogeography in these regions having medium confidence. The variation in responses of marine biota to range shifts can cause spatial restructuring of the pelagic ecosystem with consequences for organisms at higher trophic levels (Chivers et al., 2017; Pecl et al., 2017) (high confidence). Marine ectotherms have demonstrated some capacity for physiological adjustment and evolutionary adaptation that lowers their sensitivity to warming and decrease in oxygen (Pörtner et al., 2014; Cavallo et al., 2015) (low confidence). However, historical responses in abundance and ranges of marine species to ocean warming suggest that adaptation not always suffices to mitigate projected impacts (WGII AR5 Chapter 6) (high confidence). 

Marine reptiles, seabirds and mammals breathe air, instead of obtaining oxygen from water, and many of them spend some of their life cycle on land, being their abundance and distribution still affected by temperature (Pörtner et al., 2014). Long term population changes and shifts in distribution associated with climate change have been observed for temperate species of seabirds and marine mammals (Henderson et al., 2014; Hiscock and Chilvers, 2014; Ramp et al., 2015) (high confidence). For example, Laysan, Phoebastria immutabilis, and Wandering, Diomedea exulans, albatross have responded positively to climate change as they have been able to take advantage of the increased intensity of winds. This has allowed them to forage farther and faster, making their foraging trips shorter, increasing their foraging efficiency and breeding success (Descamps et al., 2015; Thorne et al., 2016). For reptiles, like sea turtles and snakes, temperature directly affects important life history traits including hatchling size, sex, viability and performance (high confidence) (Hays et al., 2003; Pike, 2014; Dudley et al., 2016; Santos et al., 2017). This is particularly important for marine turtles as changing temperatures will affect the hatchling sex ratio because sex is determined by nest site temperature (high confidence) (Hatfield et al., 2012; Santidrián Tomillo et al., 2014; Patricio et al., 2017). Loss of breeding substrate, including mostly coastal habitats such as sandy beaches (Section 5.3.3), can reduce the available nesting or pupping habitat for land breeding marine turtles, lizards, seabirds and pinnipeds (Fish et al., 2005; Fuentes et al., 2010; Funayama et al., 2013; Reece et al., 2013; Katselidis et al., 2014; Patino-Martinez et al., 2014; Pike et al., 2015; Reynolds et al., 2015; Marshall et al., 2017) (high confidence). Climatic hazards such as SLR contributes to the loss of these coastal habitats (see Section 5.3 and Chapter 3). Changes in ocean temperature will also indirectly impact marine mammals, seabirds and reptiles by changing the abundance and distribution of their prey (Polovina, 2005; Polovina et al., 2011; Doney et al., 2012; Sydeman et al., 2015; Briscoe et al., 2017; Woodworth-Jefcoats et al., 2017) (high confidence). The distributions of some of these large animals is determined by the occurrence and persistence of oceanic bridges and barriers that are related to climate driven processes (Ascani et al., 2016; McKeon et al., 2016). For example, the decline of Arctic sea ice is affecting the range and migration patterns of some species and is allowing the exchange of species previously restricted to either the Pacific or Atlantic oceans (Alter et al., 2015; George et al., 2015; Laidre et al., 2015; MacIntyre et al., 2015; McKeon et al., 2016; Breed et al., 2017; Hauser et al., 2017) (Chapter 3). Also, the range expansion of some of these predatory megafauna can affect species endemic to the habitat; for example, while the decrease in summer sea ice in the Arctic may favour the expansion of killer whales (Orcinus orca), their occurrence can result in narwhale (Monodon monoceros) to avoid the use of key habitats to reduce the risk of killer whales’ predation (Bost et al., 2009; Sydeman et al., 2015; Breed et al., 2017) (see Chapter 3; section In addition, marine mammals, seabirds and sea turtles present habitat requirements associated with bathymetric and mesoscale features that facilitate the aggregation of their prey (Bost et al., 2015; Kavanaugh et al., 2015; Hindell et al., 2016; Hunt et al., 2016; Santora et al., 2017). The persistence and location of these features are linked to variations in climate (Crocker et al., 2006; Baez et al., 2011; Dugger et al., 2014; Abrahms et al., 2017; Youngflesh et al., 2017) and to foraging success, juvenile recruitment, breeding phenology, growth rates and population stability (Costa et al., 2010; Ancona and Drummond, 2013; Ducklow et al., 2013; Chambers et al., 2014; Descamps et al., 2015; Abadi et al., 2017; Bjorndal et al., 2017; Fluhr et al., 2017; Youngflesh et al., 2017) (high confidence). Overall, recent evidence further support that impacts of climate change on some marine reptiles, mammals and birds have been observed in recent decades (high confidence) and that the direction of impacts vary between species, population and geographic locations (Trivelpiece et al., 2011; Hazen et al., 2013; Clucas et al., 2014; Constable et al., 2014; George et al., 2015) (high confidence). 

Warming has contributed also to observed changes in phenology (timing of repeated seasonal activities) of marine organisms (Gittings et al., 2018), although observations are biased towards the northeast Atlantic (Poloczanska et al., 2016; Thackeray et al., 2016). Shifts in the timing of interacting species have occurred in the last decades, eventually leading to uncoupling between prey and predators, with cascading community and ecosystem consequences (Kharouba et al., 2018; Neuheimer et al., 2018). Timing of spring phenology of marine organisms is shifting to earlier in the year under warming, at an average rate of 4.4 ± 1.1 days per decade (Poloczanska et al., 2013), although it is variable among taxonomic groups and among ocean regions (Lindley and Kirby, 2010). This is consistent with the expectations based on the close relationship between temperature and these biological events, supporting evidence from AR5 (Bruge et al., 2016; Poloczanska et al., 2016). Thus, the growing amount of literature and new studies since AR5 WGII and SR15 further support that phenology of marine ectotherms in the epipelagic systems are related to ocean warming (high confidence) and that the timing of biological events has shifted earlier (high confidence).

Observed impacts of multiple climatic hazards

WGII AR5 concludes that multiple climatic hazards from ocean acidification, hypoxia and decrease in nutrient and food supplies pose risks to marine ecosystems, and the risk can be elevated when combined with warming (Riebesell and Gattuso, 2014; Gattuso et al., 2015). In a recent meta-analysis of 632 published experiments, primary production by temperate non-calcifying plankton increases with elevated temperature and CO2, whereas tropical plankton decreases productivity because of acidification (Nagelkerken and Connell, 2015). Also, temperature increases consumption and metabolic rates of herbivores but not secondary production; the latter decreases with acidification in calcifying and non-calcifying species. These effects together create a mismatch with carnivores whose metabolic and foraging costs increase with temperature (Nagelkerken and Connell, 2015). Warming may also exacerbate the effects of ocean acidification on the rate of photosynthesis in phytoplankton (Lefevre, 2016). There is some, but limited, reports of observed impacts on calcified pelagic organisms that are attributed to secular trend in ocean acidification and warming (Harvey et al., 2013; Kroeker et al., 2013; Nagelkerken et al., 2015; Boyd et al., 2016). For example, Rivero-Calle et al. (2015) reported, using CPR archives, that stocks of coccolithophores (a group of phytoplankton that forms calcium carbonate plateles) have increased by 2% to over 20% in the north Atlantic over the last five decades, and that this increase is linked to synergistic effects of increasing anthropogenic CO2 and rising temperatures, as supported by their statistical analysis and a number of experimental studies. Most of the available evidence supports that ocean acidification and hypoxia can act additively or synergistically between each other and with temperature across different groups of biota (Figure 5.13). Limitation of nutrient and food availability and predation pressures can further increase the sensitivity of organismal groups to climate change in specific ecosystems (Riebesell et al., 2017). Climate change also affects organisms indirectly through the impacts on competitiveness between organisms that favour those that are more adaptive to the changing environmental conditions (Alguero-Muniz et al., 2017) and changes in trophic interactions (Seebacher et al., 2014). Overall, direct in situ observations and laboratory experiments show that there are significant responses to the multiple stressors of warming, ocean acidification and low oxygen on phytoplankton, zooplankton and fishes and that these responses can be additive or synergistic (high confidence, Figure 5.13).
Future changes in the epipelagic ocean

WGII AR5 and SR15 conclude that projected ocean warming will continue to cause poleward shifts in the distribution and biomass of pelagic species, paralleled by altered seasonal timing of their activities, species abundance, migration pattern and reduction in body size in the 21st century under scenarios of increasing greenhouse gas emission (Pörtner et al., 2014; Hoegh-Guldberg et al., 2018). Simultaneously, projected expansion of OMZ and ocean acidification could lead to shifts in community composition toward hypoxia-tolerant and non-calcified organisms, respectively. However, these projected biological changes in the ocean raise questions about how individuals, communities and food webs will respond to the multiple impacts from climatic and non-climatic stressors in the future, and the feedbacks of the effects of their ecological impacts on modifying the physical and biogeochemical conditions of the ocean (Schaum et al., 2013; Boyd et al., 2016; O’Brien et al., 2016; Moore, 2018). This section focuses on addressing these questions in order to assess the future risk of impacts of climate change on the epipelagic ecosystem.

Future projections on phytoplankton distribution, community structure and biomass

While analysis of outputs from CMIP5 ESMs project that global average NPP and biomass of phytoplankton community will decrease in the 21st century under RCP2.6 and RCP8.5 (see Section However, the future risk of impacts of epipelagic ecosystem can also depend on changes in community structure of phytoplankton species. Barton et al. (2016) projected the biogeography of 87 taxa of phytoplankton (diatoms and dinoflagellates) in the north Atlantic to 2051–2100 relative to the past (1951–2000) with scenarios of changes in temperature and other ocean conditions such as salinity, density and nutrients under RCP8.5. The study found that 74% of the studied taxa exhibit a poleward shift at a median rate of 12.9 km per decade, but 90% of the taxa shift eastward at a median rate of 42.7 km per decade. Such changes may affect food webs and biogeochemical cycles, and with consequence to the productivity of living marine resources (Stock et al., 2014; Barton et al., 2016).

Outputs from CMIP5 ESMs suggest that projected warming and reduction in nutrient availability in low latitudes, as a result of increasing stratification of the ocean under climate change, will increase the dominance of small-sized phytoplankton, growing more efficiently than larger taxa at low nutrient levels (Dutkiewicz et al., 2013b). Dominant groups in subtropical oceans, like the picoplanktonic cyanobacteria Synechococcus and Prochlorococcus, are projected to expand their range of distribution towards higher latitudes and increase their abundances by 14–29%, respectively, under a future warmer ocean (Flombaum et al., 2013), although synergistic effects of warming and CO2 on photosynthetic rates could lead to a dominance of Synechococcus over Prochlorococcus (Fu et al., 2007) (low confidence). Similarly, temperature-driven range shifts towards higher latitudes are also likely for tropical diazotrophic (N2-fixing) cyanobacteria, although they could disappear from parts of their current tropical ranges where future warming may exceed their maximum thermal tolerance limits (Hutchins and Fu, 2017) (low confidence). Modelling experiments show that the effects of warming on phytoplankton community will be exacerbated by ocean acidification at levels expected in the 21st century for RCP8.5, leading to increasing growth rate responses of some phytoplankton groups, such as diazotrophs and Synechococcus, with predicted increases in biomass up to 10% in tropical and subtropical waters (Dutkiewicz et al., 2015) (low confidence). Furthermore, warming is projected to interact with decreasing oxygen levels and increases in iron in the nutrient-impoverished subtropical waters, favoring the dominance of the diazotrophic colonial cyanobacteria Trichodesmium (Sohm et al., 2011; Boyd et al., 2013; Ward et al., 2013; Hutchins and Fu, 2017) (medium confidence). 

Regional differences in the changes in phytoplankton community and their impacts on epipelagic ecosystem are however complex and depends on multiple interactions of co-varying climate change stressors at regional level (Boyd and Hutchins, 2012). Based on global ocean model simulations, Boyd et al. (2015b) show that the interaction between warming, increased CO2 and a decline in phosphate and silicate would benefit coccolithophores against diatoms in the northern north Atlantic, despite decreasing rates of calcification. Evidence, based on long-term experiments of acclimation or adaptation to increasing temperatures in combination with elevated CO2, show that individual growth and carbon fixation rates of coccolithophores at high CO2 are modulated by temperature, light, nutrients and UV radiation, and could increase calcification while the responses are also species-specific (Lohbeck et al., 2012; Khanna et al., 2013). Calcification of planktonic foraminifera will be however negatively affected by acidification (Roy et al., 2015), and their populations are predicted to experience the greatest decrease in diversity and abundance in sub-polar and tropical areas, under RCP8.5 (Brussaard et al., 2013), however environmental controls of calcite production by foraminifera are still poorly understood (low confidence). Boyd et al. (2015b) analysis indicate also that diatoms would benefit from the synergistic effects of increased warming and iron supply in the northern Southern Ocean, as supported by laboratory experiments and field studies with polar diatoms (Rose et al., 2009) (low confidence). At low-latitude provinces, projected concurrent increases of CO2 and iron, and decreases in both nitrate and phosphate supply, may favour nitrogen fixers, but with ocean regional variability, since iron is thought to limit N2 fixation in the eastern Pacific and phosphorus in the Atlantic Ocean (Gruber, 2019; Wang et al., 2019). However, recent experimental work with the diazotrophic colonial Trichodesmium and the unicellular Crocosphaera have shown a broad range of responses from rising CO2, with either increases or decreases in N2 fixation rates, and with mixed evidence on co-limiting processes (Eichner et al., 2014; Garcia et al., 2014; Gradoville et al., 2014; Walworth et al., 2016; Hong et al., 2017; Luo et al., 2019) (low confidence). 

Overall, the response of phytoplankton to the interactive effects of multiple drivers is complex, and presently ESMs do not resolve the full complexity of their physiological responses (Breitberg et al., 2015; Hutchins and Boyd, 2016; O’Brien et al., 2016), precluding a clear assessment of the effects of these regional distinctive multi-stressor patterns (high confidence).

Future projections on zooplankton distribution and biomass

An ensemble of 12 CMIP5 ESMs project average declines of 6.4 ± 0.79% (95% confident limits) and 13.6 ± 1.70%) in zooplankton biomass in the 21st century relative to 19901999 historical values under RCP2.6 and RCP8.5 (Kwiatkowski et al., 2019). Also, production of mesozooplankton is projected from a single ESM to decrease by 7.9% between 19512000 and 20512100 under RCP8.5 (Stock et al., 2014). Such projected decreases in zooplankton biomass and production are partly contributed by climate-induced reduction in phytoplankton production and trophic transfer efficiency particularly in low-latitude ecosystems (Stock et al., 2014) ( The impacts may be larger than these projections if changes in the relative abundance of carbon, nitrogen and phosphorus are considered by the models (Kwiatkowski et al., 2019). The overall projected decrease in zooplankton biomass is characterised by a strong latitudinal differences, with the largest decrease in tropical regions and increase in the polar regions, particularly the Arctic Ocean (Chust et al., 2014; Stock et al., 2014; Kwiatkowski et al., 2019) (Chapter 3) (high agreement). However, the projected increase in zooplankton biomass in the polar region may be affected by the seasonality of light cycle at high latitudes that may limit the bloom season at high latitude (Sundby et al., 2016). The projected decrease in zooplankton abundance, particularly in tropical regions, can impact marine organisms higher in the foodweb, including fish populations that are important to fisheries (Woodworth-Jefcoats et al., 2017). Therefore, there is high agreement in model projections that global zooplankton biomass will very likely reduce in the 21st century, with projected decline under RCP8.5 almost doubled that of RCP2.6 (very likely). However, the strong dependence of the projected declines on phytoplankton production (low confidence, and simplification in representation of the zooplankton communities and foodweb render their projections having low confidence. 

Future responses of zooplankton species and communities to climate change are however affected by interactions between multiple climatic drivers. Experiments in laboratory show that acidification could partly counteract some observed effects of increased temperature on zooplankton, although the level and direction of the biological responses vary largely between species (Mayor et al., 2015; Garzke et al., 2016), with results ranging from no effects (Weydmann et al., 2012; McConville et al., 2013; Cripps et al., 2014; Alguero-Muniz et al., 2016; Bailey et al., 2016), to negative effects (Lischka et al., 2011; Cripps et al., 2014; Alguero-Muniz et al., 2017) or positive effects (Alguero-Muniz et al., 2017; Taucher et al., 2017). These differences in response can affect trophic interactions between zooplankton species; for example, some predatory non-calcifying zooplankton may perform better under warmer and lower pH conditions, leading to increased predation on other zooplankton species (Caron and Hutchins, 2012; Winder et al., 2017). Therefore, the large variation in sensitivity between zooplankton to future conditions of warming and ocean acidification suggests elevated risk on community structure and inter-specific interactions of zooplankton in the 21st century (medium confidence). Consideration of these species-specific responses may further modify the projected changes in zooplankton biomass by ESMs (Boyd et al., 2015a).

 Future projections on fish distribution, size and biomass

Recent model projections since AR5 and SR15 continue to support global-scale range shifts of marine fishes at rates of tens to hundreds of km per decade in the 21st century, with rate of shifts being substantially higher under RCP8.5 than RCP2.6 (Jones and Cheung, 2015; Robinson et al., 2015; Morley et al., 2018). Globally, the general direction of range shifts of epipelagic fishes is poleward (Jones and Cheung, 2015; Robinson et al., 2015), while the projected directions of regional and local range shifts generally follow temperature gradients (Morley et al., 2018). Polewards range shifts are projected to result in decreases in species richness in tropical oceans, and increases in mid to high-latitude regions leading to global-scale species turnover (sum of species local extinction and expansion) (Ben Rais Lasram et al., 2010; Jones and Cheung, 2015; Cheung and Pauly, 2016; Molinos et al., 2016) (medium confidence on trends, low confidence on magnitude because of model uncertainties and limited number of published model simulations). For example, species turnover relative to their present day richness in the tropical oceans (30oN–30oS) is projected to be 14–21% and 37–39% by 2031–2050 and 2081–2100 under RCP8.5 (ranges of mean projections from two sets of simulation for marine fish distributions) (Jones and Cheung, 2015; Molinos et al., 2016). In contrast, high-latitude regions (>60oN–60oS) is projected to have higher rate of species turnover than the tropics (an average of 48% between the two data sets for region >60oN). The high species turnover in the Arctic is explained by species’ range expansion from lower-latitude and the relatively lower present day fish species richness in the Arctic. The projected intensity of species turnover is lower under lower emission scenarios (Jones and Cheung, 2015; Molinos et al., 2016) (see also Section 5.4.1) (high confidence). Projections from multiple fish species distribution models show hotspots of decrease in species richness in the Indo-Pacific region, and semi-enclosed seas such as the Red Sea and Persian Gulf (Cheung et al., 2013; Burrows et al., 2014; García Molinos et al., 2015; Jones and Cheung, 2015; Wabnitz et al., 2018) (medium evidence, high agreement). In addition, geographic barriers such as land boundaries in the poleward species range edge in semi-enclosed seas or lower oxygen water in deeper waters are projected to limit range shifts, resulting in larger relative decrease in species richness (medium confidence) (Cheung et al., 2013; Burrows et al., 2014; García Molinos et al., 2015; Jones and Cheung, 2015; Rutterford et al., 2015). 

Warming and decrease in oxygen content is projected to impact growth of fishes, leading to reduction in body size and contraction of suitable environmental conditions (Deutsch et al., 2015; Pauly and Cheung, 2017), with the intensity of impacts being directly related to the level of climate change. The projected reduction in abundance of larger-bodied fishes could reduce predation and exacerbate the increase in dominance of smaller-bodied fishes in the epipelagic ecosystem (Lefort et al., 2015). Fishes exposed to ocean acidification level expected under RCP8.5 showed impairments of sensory ability and alteration of behaviour including olfaction, hearing, vision, homing and predator avoidance (Kroeker et al., 2013; Heuer and Grosell, 2014; Nagelkerken et al., 2015). The combined effects of warming, ocean deoxygenation and acidification in the 21st century are projected to exacerbate the impacts on the body size, growth, reproduction and mortality of fishes, and consequently increases their risk of population decline (medium evidence, high agreement, high confidence). 

Table 5.3

Projected changes in total animal biomass by the mid- and end- of the 21st century under Representative Concentration Pathway (RCP)2.6 and RCP8.5. Total animal biomass is based on 10 sets of projections for each RCP under the Fisheries and Marine Ecosystems Impact Model Intercomparison Project (FISMIP) (Lotze et al. 2018). The very likely ranges of the projections (95% confidence intervals) are provided. Reference period is 1986–2005.

Total animal biomass (%)
RCP2.6 RCP8.5
Region 20312050 20812100 20312050 20812100
>60oN 8.4 ± 9.3 8.5 ± 13.7 7 ± 9.2 –1.1 ± 20.2
30oN50oN –8.1 ± 4 –4.5 ± 3.6 –10.1 ± 4.7 –21.3 ± 9.4
30oN30oS –7.2 ± 2.7 –7.3 ± 3.1 –9 ± 3.6 –23.2 ± 9.5
30oS50oS –3.3 ± 2.1 –3.5 ± 2.5 –4.2 ± 2.9 –9 ± 9.8
<60oS 1.7 ± 4.5 –0.9 ± 2.9 0.7 ± 3.9 12.4 ±11.9


An ensemble of global-scale marine ecosystem and fisheries models that are part of the Fisheries and Marine Ecosystems Impact Models Intercomparison Project (FISHMIP) undertook coordinated simulation experiments and projected future changes in marine animals (mainly invertebrate and fish) globally under climate change (Lotze et al., 2018). These models represent marine biota and ecosystems differently, ranging from population-based to functional traits- and size-based structure and their responses are driven primarily by temperature and NPP, although oxygen, salinity and ocean advection are considered in a subset of models and play a secondary role in affecting the projected changes in biomass (Blanchard et al., 2012; Fernandes et al., 2013; Carozza et al., 2016; Cheung et al., 2016a). Overall, potential total marine animal biomass is projected to decrease by 4.3 ± 2.0% (95% confident intervals) and 15.0 ± 5.9% under RCP2.6 and RCP8.5, respectively, by 2080–2099 relative to 1986–2005, while the decrease is around 4.9% by 2031-2050 across all RCP2.6 and RCP8.5 (very likely) (Figure 5.14). Accounting for the removal of biomass by fishing exacerbates the decrease in biomass for large-bodied animals which are particularly sensitive to fishing (likely for the direction of changes). Regionally, total animal biomass decreases largely in tropical and mid-latitude oceans (very likely) (Table 5.3, Figure 5.14) (Bryndum-Buchholz et al., 2019). The high uncertainty and the low confidence in the projection in the Arctic Ocean (Chapter 3) is because of the large variations in simulation results for this region between the ESMs and between the FISHMIP models, as well as the insufficient understanding of the oceanographic changes and their biological implications in the Arctic Ocean. In the Southern Ocean, the decrease in consumer biomass is mainly in the southern Indian Ocean while other parts of the Southern Ocean are projected to have an increase in animal biomass by 2100 under RCP8.5, reflecting mainly the projected pattern of changes in NPP from the ESMs (see Section

Figure 5.14

Figure 5.14 | Projected changes in total animal biomass (including fishes and invertebrates) based on outputs from 10 sets of projections for each Representative Concentration Pathway (RCP) from the Fisheries and Marine Ecosystems Impact Model Intercomparison Project (FISMIP, www.isimip.org/gettingstarted/marine- ecosystems-fisheries) (Lotze et al. 2018); (a, b) multi-model mean change (%) in un-fished total marine animal […]

Figure 5.14 | Projected changes in total animal biomass (including fishes and invertebrates) based on outputs from 10 sets of projections for each Representative Concentration Pathway (RCP) from the Fisheries and Marine Ecosystems Impact Model Intercomparison Project (FISMIP, www.isimip.org/gettingstarted/marine- ecosystems-fisheries) (Lotze et al. 2018); (a, b) multi-model mean change (%) in un-fished total marine animal biomass in 2085–2099 relative to 1986–2005 under RCP2.6 and RCP8.5, respectively. Dotted area represents 8 out of 10 sets of model projections agree in the direction of change (c) projected change in global total animal biomass from 1970 to 2099 under RCP2.6 (red) and RCP8.5 (blue). Variability among different ecosystem and Earth-system model combinations (n=10) expressed as the very likely range (95% confidence interval).

Future projections on epipelagic components of the biological pump

A wide range of studies, from laboratory experiments, mesocosm enclosures, synthesis of observations to modeling experiments, provide insights into how the multi-faceted components of the ‘biological pump’ (the physical and biologically mediated processes responsible for transporting organic carbon from the upper ocean to depth) are projected to be altered in the coming decades. A synthesis of the individual components reported to both influence the performance of the biological pump, and which are sensitive to changing ocean conditions, is presented in Table 5.4. The table lists the putative controlling of each environmental factor, such as warming, that influences the biological pump, and the reported modification (where available) of each individual factor by changing ocean conditions for both the epipelagic ocean and the deep ocean. Analyses of long-term trends in primary production and particle export production, as well as model simulations, reveal that increasing temperatures, leading to enhanced stratification and nutrient limitation, will have the greatest influence on decreasing the flux of particulate organic carbon (POC) to the deep ocean (Bopp et al., 2013; Boyd et al., 2015a; Fu et al., 2016; Laufkötter et al., 2016). However, different lines of evidence (including observation, modeling and experimental studies) provide low confidence on the mechanistic understanding of how climatic drivers affect different components of the biological pump in the epipelagic ocean, as well as changes in the efficiency and magnitude of carbon export in the deep ocean (see section below and Table 5.4); this renders the projection of future contribution of the biological carbon pump to the export of POC to the deep ocean having low confidence

Table 5.4

Projected future changes to the ocean biological pump (adapted from Boyd et al. (2015a)). Environmental controls on individual factors that influence downward POC flux are based on published reports from experiments (denoted by E), modelling simulations (M) and observations (O). In some cases, due to the paucity, and regional specificity, of published reports it has been indicated the sign of the projected change on export (in italics), as opposed to the magnitude. NPP: Net Primary Production; POC: Particulate Organic Carbon; DOC: Dissolved Organic Carbon; TEP: Transparent Exopolymer Particles; OA: Ocean Acidification. Climate change denotes multiple controls such as nutrients, temperature and irradiance, as parameterised in coupled ocean atmosphere models. *denotes observation for low latitudes only. ** represents major uncertainty over environmental modulation of this component of the biological pump. ***denotes joint influence of temperature and acidification.


Pump component Oceanic driver Projected change (by year 2100) Confidence References & Lines of evidence
Epipelagic Ocean
Phytoplankton growth Temperature (warming) ~10% Faster (nutrient-replete) no change (nutrient-deplete) High (Boyd et al., 2013) E; (Maranon et al., 2014) O*
NPP Climate change (temperature, nutrients, CO2) 1020% decrease (low latitudes); 

1020% increase (high latitudes)

Medium (Bopp et al., 2013) M
Partitioning of NPP (POC, TEP, DOC) OA ~20% increase in TEP production  Medium (Engel et al., 2014) E; (Riebesell et al., 2007) E; (Seebah et al., 2014) E 
Food web retention of NPP OA Enhanced transfer of organic matter to higher trophic levels, reduced N and P sedimentation by 10% Low (Boxhammer et al., 2018) E
Floristic shifts Climate change (warming, salinity, OA, iron) Shift to smaller or larger cells

(less export vs more export; inconclusive

Low (Moràn et al., 2010) O; (Li et al., 2009) O; (Dutkiewicz et al., 2013a) M; (Tréguer et al., 2018) O; (Sett et al., 2014) E
Differential susceptibility Temperature (warming) Growth-rate of grazers more temperature dependent than prey 

(less export)

Low (Rose and Caron, 2007) O 
Bacterial hydrolytic effects Warming, OA Increase under warming and low pH (variable response in different plankton communities) Low (Burrell et al., 2017) E
Grazer physiological responses Warming  Copepods had faster respiration and ingestion rates, but higher mortality 


Low (Isla et al., 2008) E
Faunistic shifts Temperate and subpolar zooplankton species shifts  Temperature 


Low (Edwards et al., 2013) O
Food web amplification Warming Zooplankton negatively amplify the climate change signal that propagates up from phytoplankton in tropical regions, and positively amplify in polar regions Low (Chust et al., 2014) M; (Stock et al., 2014) M
Deep Ocean
Bacterial hydrolytic enzyme activity Temperature  20% increase (resource-replete) to no change (resource-deplete) Low (Wohlers-Zöllner et al., 2011) E; (Endres et al., 2014) E; (Bendtsen et al., 2015) E; (Piontek et al., 2015) E***
Particle sinking rates (viscosity) Warming 5% faster sinking/°C warming Low (Taucher et al., 2014) M
Mesozooplankton community composition Temperature** Shifts which increase/decrease

particle transformations

(less/more export, respectively)

Low (Burd and Jackson, 2002) M; (Ikeda et al., 2001) O
Vertical migrators Climate change (irradiance, temperature) (more export) Low (Almén et al., 2014) O; (Berge et al., 2014) O
Deoxygenation Climate change (more export) Low (Rykaczewski and Dunne, 2010) M; (Cocco et al., 2013) O; (Hofmann and Schellnhuber, 2009) M

The Deep Pelagic Ocean
Detection and attribution of biological changes in the deep ocean

The pelagic realm of the deep ocean represents a key site for remineralisation of organic matter and long-term biological carbon storage and burial in the biosphere (Arístegui et al., 2009), but the observed effects of climate change on deep sea organisms, communities and biological processes are largely unknown (high confidence). Observational and model-based methods provide limited evidence that the transfer efficiency of organic carbon to the sea floor is partly controlled by temperature and oxygen in the mesopelagic zone, affecting microbial metabolism and zooplankton community structure, with highest efficiencies for high-latitude and OMZ) (see Section for more detail on OMZs), while below 1000 m organic carbon transfer is controlled by particle sinking speed (Boyd et al., 2015a; Marsay et al., 2015; DeVries and Weber, 2017). However, there are contrasting results and low confidence on whether transfer efficiencies are highest at low or high latitudes (Boyd et al., 2015a; Marsay et al., 2015; Guidi et al., 2016; DeVries and Weber, 2017; Sweetman et al., 2017). There is also low confidence on the effects of increasing temperatures on POC remineralisation to CO2 versus POC solubilisation to dissolved organic carbon (DOC) by microbial communities and its storage as refractory DOC (i.e., with life times of >16,000 years) (Legendre et al., 2015).
Future changes in the deep ocean

The global magnitude of the biological pump and how this will be affected by climate change is also uncertain. Model-based studies agree in projecting a global decline in particle gravitational flux to the deep sea floor, but with regional variability in both the total particle export flux and transfer efficiency (DeVries and Weber, 2017; Sweetman et al., 2017) (see Sections 5.2.2 and 5.2.4). However, recent evidence suggest that other physical and biological processes may contribute nearly as much as the gravitational flux to the carbon transport from the surface to the deep ocean (Boyd et al., 2019), with low confidence on the future rate of change in magnitude and direction of these processes. In particular, the ‘active flux’ of organic carbon due to vertical migration of zooplankton and fishes has been reported to account from 10 to 40% of the gravitational sinking flux (Bianchi et al., 2013; Davison et al., 2013; Hudson et al., 2014; Jónasdóttir et al., 2015; Aumont et al., 2018; Gorgues et al., 2019). Predictions based on model studies suggest that mesopelagic zooplankton and fish communities living at deep scattering layers (DSLs) will increase their biomass by 2100, enhancing their trophic efficiency, because of deep-ocean warming (Section; Figures 5.2 and 5.3) and shallowing of DSL (Proud et al., 2017) (low confidence). Expansion of OMZs (see Section will also widen the DSL and increase the exposure of mesopelagic organisms to shallower depths (Gilly et al., 2013; Netburn and Anthony Koslow, 2015). In the California Current, the abundance of mesopelagic fishes is closely tied to variations in the OMZ, whose dynamic is linked to the Pacific Decadal Oscillation and ENSO cycles (Koslow et al., 2015). Some large predators, like the Humboldt squid, could indirectly benefit from expanding OMZs due to the aggregation of their primary food source, myctophid fishes (Stewart et al., 2014). However, many non-adapted fish and invertebrates (like diurnal vertical migrators) will have their depth distributions compressed, affecting the carbon transport and trophic efficiency of food webs in the mesopelagic (Stramma et al., 2011; Brown and Thatje, 2014; Rogers, 2015) (low confidence). In OMZ waters, where zooplankton is almost absent, like in the Eastern Tropical North Pacific, the microbial remineralisation efficiency of sinking particles would be reduced, eventually increasing the transfer efficiency of organic matter to the deep ocean and thus biological carbon storage (Cavan et al., 2017) (low confidence; Table 1). However, increases in ocean temperature may also lead to shallower remineralisation of POC in warm tropical regions, counteracting the storage of carbon in the dark ocean (Marsay et al., 2015). Overall, the direct impacts of climate change on the biological pump are not well understood for the deep pelagic organisms and ecosystems (Pörtner et al., 2014), and there is low confidence on the effect of climate change drivers on biological processes in the deep ocean (Table 5.1).


Impacts on Deep Seafloor Systems

Changes on the Deep Seafloor

The deep seafloor is assessed here as the vast area of the ocean bottom >200 m deep, beyond most continental shelves (Levin and Sibuet, 2012; Boyd et al., 2019) (Figure 5.15). Below 200 m changes in light, food supply and the physical environment lead to altered benthic (seafloor) animal taxonomic composition, morphologies, lifestyles and body sizes collectively understood to represent the deep sea (Tyler, 2003). 


Attachment field is blank. Please add an attachment or remove this block.

Most deep seafloor ecosystems globally are experiencing rising temperatures, declining oxygen levels, and elevated CO2, leading to lower pH and carbonate undersaturation (WGII AR5 30.5.7; Section Small changes in exposure to these hazards by deep seafloor ecosystem have been confirmed by observation over the past 50 years. However, analysis using direct seafloor observations of these hazards over the past 15-29 years suggest that the environmental conditions are highly variable over time because of the strong and variable influences by ocean conditions from the sea surface (Frigstad et al., 2015; Thomsen et al., 2017). Such high environmental variability makes it difficult to attribute observed trends to anthropogenic drivers using existing datasets (Smith et al., 2013; Hartman et al., 2015; Soltwedel et al., 2016; Thomsen et al., 2017) (high confidence). Projections from global ESMs suggest large changes for temperature by 2100 and beyond under RCP8.5 (relative to present day variation) (Mora et al., 2013; Sweetman et al., 2017; FAO, 2019). The magnitude of the projected changes is lower under RCP2.6, and in some cases the direction of projected change to 2100 varies regionally under either scenario (FAO, 2019) (high confidence).

Open Ocean Seafloor - Abyssal Plains (3000-6000 m)

Abyssal communities (3000–6000 m) cover over 50% of the ocean’s surface and are considered to be extremely food limited (Gage and Tyler, 1992; Smith et al., 2018). There is a strong positive relationship between surface primary production, export flux, and organic matter supply to the abyssal seafloor (Smith et al., 2008), with pulses of surface production reflected as carbon input on the deep seafloor in days to months (Thomsen et al., 2017). Both vertical and horizontal transport contribute organic matter to the sea floor (Frischknecht et al., 2018). Food supply to the seafloor regulates faunal biomass, explaining the strong positive relationships documented between surface production and seafloor faunal biomass in the Pacific Ocean (Smith et al., 2013), Gulf of Mexico (Wei et al., 2011) and north Atlantic Ocean (Hartman et al., 2015). Extended time series and broad spatial coverage reveal strong positive relationship between annual POC flux and abyssal sediment community oxygen consumption (Rowe et al., 2008; Smith et al., 2016a). Observed reduction in in POC flux at the abyssal seafloor enhances the relative importance of the microbial loop and reduces the importance of benthic invertebrates in carbon transfer (Dunlop et al., 2016) (single study, limited evidence). However, changes in the overlying mesopelagic and bathypelagic communities (see Section will also affect food flux to the deep seafloor, as nekton and zooplankton transfer energy to depth through diel (daily day-night) vertical migrations, ontogenetic (life staged-based) migrations and falls of dead carcasses (Gage, 2003). Therefore, climate change impacts on organic carbon export from the epipelagic (Section and deeper pelagic systems (Section can affect the energy available to support the abyssal seafloor ecosystems (medium confidence). However, because observations on historical changes in POC flux in abyssal seafloor ecosystems are limited to a few locations, long-term records show high variability, and mechanistic understanding of factors affecting the biological carbon pump is incomplete, there is limited evidence that the abyssal seafloor ecosystem has already been affected by changes in POC flux as a result of climate change. The metabolic rate of deep seafloor ectotherms, and consequently their demand for food, increases with temperature. Thus, observed warming in deep sea ecosystems (Hoegh-Guldberg et al., 2014) (Section is expected to increase the sensitivity of deep seafloor biota to decrease in food supplies associated with a change in POC flux (high confidence). However, there is limited evidence of observed changes in abyssal biota. Small deep sea biota demonstrate increased efficiency (effective use of food energy for growth and metabolism with minimal loss) at low food inputs (due to small size and dominance by prokaryotic taxa) (Gambi et al., 2017). Adaptation to low food availability in abyssal ecosystems may confer higher capacity to adjust to reduced food availability than for shallow biota (limited evidence). Overall, the risk of impacts of climate change on abyssal ecosystems through reduction in food supplies from declining POC flux in the present day is low with low confidence

The globally integrated export flux of carbon is projected to decrease in the open ocean in the 21st century under RCP2.6 (by 1.6–4.9%) and RCP8.5 (by 8.9–15.8%) relative to 2000 (medium confidence) (Section This change in export flux of carbon is projected to yield declines in POC flux at the abyssal seafloor (representing food supply to benthos) of up to –27% in the Atlantic and up to –31 to –40% in the Pacific and Indian Oceans, with some increases in polar regions (Sweetman et al. 2017). In some models, additional dissolution of calcium carbonate due to ocean acidification further lowers POC flux, causing the projected export production declines to be up to 38% at the northeast Atlantic seafloor (Jones et al., 2014). Lower POC fluxes to the abyss reduce food supply and have been projected to cause a size-shift towards smaller organisms (Jones et al., 2014), resulting in rising respiration rates, lower biomass production efficiency, and lesser energy transfer to higher trophic levels (Brown et al., 2004) (medium confidence). Changes are projected to be largest for macrofauna and lesser and similar for megafauna and meiofauna (Jones et al., 2014) (limited evidence, low confidence). Projections using outputs from seven CMIP5 models suggest that 97.8 ± 0.6% (95% CI) of the abyssal seafloor area will experience a biomass decline by 2091–2100 relative to 2006–2015 under RCP8.5. The projected decreases in overall POC flux to the abyssal seafloor are projected to cause a 5.2–17.6% reduction in seafloor biomass in 2090–2100, relative to 2006–2015 under RCP8.5 (Jones et al., 2014). The projected impacts on abyssal seafloor biomass are significantly larger under RCP8.5 than RCP4.5 (Jones et al., 2014). However, existing estimates are based on total POC flux changes and do not account for changes in the type or quality of the sinking material, to which macrofaunal and meiofaunal invertebrates are highly sensitive (Smith et al., 2008; Smith et al., 2009; Tittensor et al., 2011). The projections also do not account for direct faunal responses to changes in temperature, oxygen or the carbonate system, all of which will influence benthic responses to changing food availability (AR5 Chapter 30.5.7), reducing to medium confidence the risk assessment that is based on these projections (Figure 5.16). 

Regionally, while reductions in POC flux are projected at low and mid latitudes in the Pacific, Indian and Atlantic Oceans, increases are projected at high latitudes associated in part with reduction in sea ice cover (Yool et al., 2013; Rogers, 2015; Sweetman et al., 2017; Yool et al. 2017; FAO 2019) (see Chapter 3) (medium confidence). Notably, Arctic and Southern Ocean POC fluxes at the abyssal seafloor are projected to increase by up to 38% and 21%, respectively by 2100 under RCP8.5 (Sweetman et al., 2017). While an increase in food supply may yield higher benthic biomass at high latitudes, warmer temperatures and reduced pH projected for the polar regions (Chapter 3) would elevate faunal metabolic demands, likely diminishing the benefit of elevated food supply to an unknown extent (Sweetman et al., 2017). Overall, given the limited food availability for fauna in the abyssal plains and the projected warming (Section that increases the demand for food to support the elevated metabolic rates, the projected decrease in influx of organic matter and seafloor biomass will result in high risks of impacts to abyssal ecosystems by the end of the 21st century under RCP8.5 (medium confidence) (Figure 5.16). The risk of impacts is projected to be substantially lower under RCP4.5 or RCP2.6 (high confidence). The impacts on abyssal seafloor ecosystems affect functions that are important to support ecosystem services (see Section 5.4.1). For example, smaller-sized organisms exhibit reduced bioturbation intensity and depth of mixing causing reduced carbon sequestration (Smith et al., 2008) (Figure 5.15). 

Figure 5.15

Figure 5.15 | A conceptual diagram illustrating how climate drivers are projected to modify deep sea ecosystems as discussed in Section 5.2.4.

Figure 5.15 | A conceptual diagram illustrating how climate drivers are projected to modify deep sea ecosystems as discussed in Section 5.2.4.

Bathyal Ecosystems (200–3000 m)

Bathyal ecosystems consist of numerous geomorphic features with steep topography (Figure 5.15). These include continental slopes covering 5.2% of the seafloor, over 9400 steep-sided canyons, and >9000 conical seamounts (submarine volcanos which are mainly inactive), as well as guyots and ridges which together cover ~6% of the seafloor (Harris et al., 2014). Seamounts and canyons support high animal densities and biomass including cold water coral, sponge and bryozoan reefs, exhibit high secondary production supported by locally enhanced primary production and intensified water flow, function as diversity hotspots and serve as stepping stones for larval dispersal (Rowden et al., 2010). Canyons transport particulate organic matter, migrating plankton and coarse material from the shelf, and are sites where intensified mixing and advection of water masses occurs (De Leo et al., 2010; Levin and Sibuet, 2012; Fernandez-Arcaya et al., 2017).  Slopes, canyons and seamounts exhibit strong vertical temperature, oxygen and pH gradients generating sharp ecological zonation (Levin and Sibuet, 2012), thus changes in exposures are expected to alter the distributions of their communities (Figure 5.15, 5.16) (medium confidence).  

In some regions, observational records document changing conditions in bathyal ecosystems (Levin, 2018; Section In the Northeast Pacific continental slopes associated with the California Current ecosystem, observations over the past 25 years show high variability but an overall trend of decreasing ocean oxygen and pH levels with oxygen declines of up to 40% and pH declines of 0.08 units in California . (Goericke et al., 2015) (high agreement, robust evidence, high confidence). Large oxygen declines are linked to past warming events on continental margins, over multiple time scales from 1–100 ky (Dickson et al., 2012; Moffitt et al., 2015). Studies across modern oxygen gradients on slopes reveal that suboxic (5–10 µMol kg-1 O2) values lead to loss of biodiversity of fish (Gallo and Levin, 2016), invertebrates (Levin, 2003; Gallo and Levin, 2016; Sperling et al., 2016), and protozoans (Bernhard and Reimers, 1991; Gooday et al., 2000; Moffitt et al., 2014) (high confidence). Shoaling oxyclines on continental slopes have altered depth distributions of multiple co-occurring echinoid species over the past 25 years (Sato et al., 2017) and can reduce the growth rate, and change the skeletal structure and biochemical composition of a common sea urchin (Sato et al., 2018). In central Pacific oceanic canyons, fish abundance and diversity are reduced at 4 to 5 times higher oxygen concentrations than on continental slopes (<31 µMol kg-1 O2) (De Leo et al., 2012). Low oxygen on continental slopes causes reductions in faunal body size and bioturbation (Diaz and Rosenberg, 1995; Levin, 2003; Middelburg and Levin, 2009; Sturdivant et al., 2012), simplification of trophic structure reducing energy flow to upper trophic levels (Sperling et al., 2013), shifts in carbon processing pathways from metazoans to protozoans (Woulds et al., 2009), and reduced colonisation potential (Levin et al., 2013). These changes are expected to lead to altered ecosystem structure and function, with lower carbon burial (Smith et al., 2000; Levin and Dayton, 2009) (medium confidence). Both carbon sequestration and nitrogen recycling are highly sensitive to small changes in oxygenation within the suboxic zone (Deutsch et al., 2011).

Bathyal species adapted to OMZs where CO2 levels are characteristically high, appear less vulnerable to the negative impacts of ocean acidification (Taylor et al., 2014). Benthic foraminifera, which are often the numerically dominant deep sea taxon, show no significant effect of short-term exposure to ocean acidification on survival of multiple species (Dissard et al., 2010; Haynert et al., 2011; Keul et al., 2013; McIntyre-Wressnig et al., 2014; Wit et al., 2016) and in fact hypoxia in combination with elevated pCO2 favors survival of some foraminifera (Wit et al., 2016). However, lower pH exacerbates shallow foraminiferal sensitivity to warming (Webster et al., 2016). Limited evidence suggests that combined declines in pH and oxygen may lead to increase in some agglutinating taxa and a decrease in carbonate-producing foraminifera, including those using carbonate cement (van Dijk et al., 2017). Exposure to acidification (0.4 unit pH decrease) reduces fecundity and embryo development rate in a bathyal polychaete. Where both oxygen and CO2 stress occur together on bathyal slopes, oxygen can be the primary driver of change (Taylor et al. 2014; Sato et al. 2018). Nematodes are sensitive to changes in temperature (Danovaro et al., 2001; Danovaro et al., 2004; Yodnarasri et al., 2008) and elevated CO2 (Barry et al., 2004; Fleeger et al., 2006; Fleeger et al., 2010). There is low agreement about the direction of meiofaunal responses among studies, reflecting opposing responses in different regions. However, there is high agreement that meiofauna are sensitive to change in environment and food supply (medium confidence). Additional research is needed across all taxa on how hypoxia and pH interact (Gobler and Baumann, 2016). 

Continental slopes, seamounts and canyons (200–2500 m) are projected to experience significant warming, pH decline, oxygen loss and decline in POC flux by 2081–2100 (compared to 1951–2000) under RCP8.5 (Table 5.5). In contrast, the average changes are projected to be 30–50% less under RCP2.6 (Table 5.5) by 2081-2100. Most ocean regions at bathyal depths (200–2500 m) except the Southern and Arctic Oceans are predicted to experience on average declining export POC flux under RCP8.5 by 2081–2100 (Yool et al., 2017; FAO, 2019) with the largest declines of 0.7–8.1 mg C m-2 d-1 in the Northeast Atlantic (FAO, 2019). There is a strong macroecological relationship between depth, export POC flux, biomass and zonation of macrobenthos on continental slopes (Wei et al., 2011), such that lower POC fluxes will alter seafloor community biomass and structure (medium confidence) (See also Section This is modified on the local scale by near-bottom currents, which alter sediment grain size, food availability, and larval dispersal (Wei et al., 2011). 

Declines in faunal biomass (6.1 ± 1.6% 95% C.I) are predicted for 96.6 ± 1.2% of seamounts under RCP8.5 by 2091–2100 relative to 2006–2015, driven by a projected 13.8 ± 3.3% drop in POC flux (Jones et al., 2014). The majority (85%) of mapped canyons are projected to experience comparable benthic biomass declines (Jones et al., 2014). By 2100 under RCP8.5, pH reductions exceeding -0.2 pH units are projected in ~ 23% of north Atlantic deep sea canyons and 8% of seamounts (Gehlen et al., 2014), with potential negative consequences for their cold water coral habitats (See Box 5.2).

Mean temperature (warming) signals are projected to emerge from background variability before 2040 in canyons of the Antarctic, northwest Atlantic, and South Pacific (FAO, 2019). Enhanced stratification and change in the intensity and frequency of downwelling processes under atmospheric forcing (including storms and density-driven cascading events would alter organic matter transported through canyons (Allen and Durrieu de Madron, 2009) (low confidence). Changes in the quantity and quality of transferred particulate organic matter, as well as physical disturbance during extreme events cause a complex combination of positive and negative impacts at different depths along the canyon floor (Canals et al., 2006; Pusceddu et al., 2010). Canyons and slopes are recognised as hosting many methane seeps and other chemosynthetic habitats (e.g., whale and wood falls) supported by massive transport of terrestrial organic matter (Pruski et al., 2017); their climate vulnerabilities are discussed below. 

Seamounts have been proposed to serve as refugia for cold water corals facing shoaling aragonite saturation horizons (Tittensor et al., 2011), but could become too warm for deep-water corals in some regions (e.g., projections off Australia) (Thresher et al., 2015) (one study, low confidence). Seamounts are major spawning grounds for fishes; reproduction on seamounts may be disrupted by warming (Henry et al., 2016) (one study, low confidence). In the north Atlantic, models suggest seamounts are an important source of cold water coral larvae that maintain resilience under shifting NAO conditions (Fox et al., 2016), thus loss of suitable seamount habitat may have far-reaching consequences (Gehlen et al., 2014) (low confidence) (also see Box 5.2).

Table 5.5

Projected climate changes from the present to 2081–2100 given as mean (min, max) at the deep seafloor for continental slopes, canyons, seamounts and cold water corals mapped from 200–2500 m under RCP8.5 and RCP2.6 Projections are based on three 3D, fully coupled earth system models (ESMs) (as part of CMIP5): the Geophysical Fluid Dynamics Laboratory’s ESM 2G (GFDL-ESM-2G); the Institut Pierre Simon Laplace’s CM6-MR (IPSL-CM5A-MR); and (iii) the Max Planck Institute’s ESM-MR (MPI-ESM-MR). Export flux at 100 m was converted to export POC flux at the seafloor (epc) using the Martin curve following the equation: epc = epc100 (depth/ export depth)-0.858. Projections were made onto the (i) slope from a global ocean basin mask from World Ocean Atlas 2013 V2 (NOAA, 2013), (ii) global distribution of submarine canyons with canyon heads shallower than 1500 m (Harris and Whiteway, 2011); (iii) global distribution of seamounts with summits between 200–2500 m (Kim et al. 2011); and (iv) global occurrence of cold water corals between 200–2500 m (Freiwald et al. 2017).



      pH  DO 

(µMol kg-1)

POC flux

(mgC m-2 d-1)

  RCP2.6 RCP2.6 RCP2.6  RCP2.6
Continental slopes +0.30 (–0.44, + 2.30) –0.06 (–0.19, –0.02) –3.1 (–49.3, +61.7) –0.39 (–16.0, +3.9)
Canyons +0.31 (–0.27, +1.76) -0.05 (-0.13, +0.01) –3.5 (–44.7, +29.3) –0.33 (–10.53, +3.53)
Seamounts +0.13 (+0.01, +0.67) -0.02 (-0.11, +0.005) –3.46 (–18.9, +4.1) –0.15 (–2.20, +1.33)
Cold water corals +4.3 (–0.29, +1.85) -0.07 (-0.13, 0.0) –3.5 (–25.6, +24.7) –0.7 (–10.5, +3.4)
RCP8.5 RCP8.5 RCP8.5 RCP8.5
Continental slopes +0.75 (–8.4, +4.4) –0.14 (–0.44, –0.02) –10.2 (–67.8, +53.8)  –0.66 (–33.33, +10.3)
Canyons +0.19 (–0.03, +1.14) -0.11 (-0.35, +0.02) –0.8 (–28.8, +10.1) –0.80 (–28.76, +10.07)
Seamounts +0.66 (–0.75, +3.19) -0.03 (-0.19, +0.001) –0.50 (–7.2, +3.0) –0.50 (–7.18, +2.98)
Cold water corals +0.96 (–0.42, +3.84) -0.15 (-0.39, +0.001) –10.6 (–59.2, +11.1) –1.69 (–20.1, +4.6)

Chemosynthetic Ecosystems

Despite having nutrition derived largely from chemosynthetic sources fueled by fluids from the earth’s interior, hydrothermal vent and methane seep ecosystems are linked to surface ocean environments and water-column processes in many ways that can expose them to aspects of climate change (medium confidence). The reliance of vent and seep mussels on surface-derived photosynthetic production to supplement chemosynthetic food sources (Riou et al., 2010; Riekenberg et al., 2016; Demopoulos et al., 2019), and in some cases as a cue for synchronised gametogenesis (sperm and egg production) (Dixon et al., 2006; Tyler et al., 2007) can make them vulnerable to changing amounts or timing of POC flux to the deep seabed in most areas except high latitudes, or to changes in timing of surface production (see Section (limited evidence) Most of the large, habitat-forming (foundation) species at vents and seeps such as mussels, tubeworms, and clams require oxygen to serve as electron acceptor for aerobic hydrogen-, sulfide- and methane oxidation (Dubilier et al., 2008) and appear unable to grow under dysoxic conditions (<5–10 µmol kg–1 O2) (Sweetman et al., 2017) (medium confidence). The distributions of these taxa at seeps could be constrained by climate-driven expansion of midwater oxygen minima (Stramma et al., 2008; Schmidtko et al., 2017), which is occurring at water depths where seep ecosystems typically occur on continental margins (200–1000 m). Rising bottom temperatures or shifting of warm currents on continental margins could increase dissociation of buried gas hydrates on margins (Phrampus and Hornbach, 2012) (low confidence) potentially intensifying anaerobic methane oxidation (which produces hydrogen sulfide) (Boetius and Wenzhoefer, 2013) and expanding cover of methane seep communities (limited evidence). Larvae of vent species such as bathymodiolin mussels, alvinocarid shrimp, and some limpets that develop in or near surface waters (Herring and Dixon, 1998; Arellano et al., 2014), are likely to be exposed to warming waters, decreasing pH and carbonate saturation states, and in some places, reduced phytoplankton availability (Section 5.2.2), causing reduced calcification and growth rates (as in shallow water mussel larvae, Frieder et al. (2014)) (limited evidence, low confidence). Larvae originating at vents or seeps beneath upwelling regions may also be impaired by effects of hypoxia associated with expanding OMZ (Stramma et al., 2008) during migration to the surface (limited evidence). Warming and its effects on climate cycles have the potential to alter patterns of larval transport and population connectivity through changes in circulation (Fox et al., 2016) or surface generated mesoscale eddies (Adams et al., 2011) (limited evidence; low confidence). Climate-induced changes in the distribution and cover of vent and seep foundation species may involve alteration of attachment substrate, food and refuge for the many habitat-endemic species that rely on them (Cordes et al., 2010) and for the surrounding deep sea ecosystems which interact through transport of nutrients and microbes, movement of vagrant predators and scavengers, and plankton interactions (Levin et al., 2016) (limited evidence; low confidence). There is, however, insufficient analysis of faunal symbiont and nutritional requirements, life histories, larval transport and cross-system interaction to quantify the extent of the consequences described above under future climate conditions. 


Risk Assessment of Open Ocean Ecosystems

This section synthesises the assessment of climate impacts on open ocean and deep seafloor ecosystem structure and functioning and the levels of risk under future conditions of global warming (see SM5.2). The format for Figure 5.16 matches that of Figure 19.4 of AR5 (Pörtner et al., 2014) and Figure 3.20 of SR15 (Hoegh-Guldberg et al., 2018), indicating the levels of additional risk as colours (white, yellow, red and purple). Each column in Figure 5.16 indicates how risks increase with ocean warming, acidification (OA), deoxygenation, and POC flux with a focus on present day conditions (2000s) and future conditions by the year 2100 under low (RCP2.6) and high (RCP8.5) CO2 emission scenarios. The transition between the levels of risk to each type of ecosystem is estimated from key evidence assessed in earlier parts of this chapter (Sections 5.2.2, 5.2.3, 5.2.4). SST is chosen to provide an indication of the changes in all these variables because it is closely related to cumulative carbon emission (Gattuso et al., 2015) which is the main climatic driver of the hazards. SST scales with Global Mean Surface Temperature (GMST) by a factor of 1.44 according to changes in an ensemble of RCP8.5 simulations; with an uncertainty of about 4 % in this scaling factor based on differences between the RCP2.6 and RCP8.5 scenarios. The transition values may have an error of ±0.3°C depending on the consensus of expert judgment. The deep seafloor embers are generated based on earth system model projection of climate variables to the seafloor under RCP2.6 and RCP8.5 scenarios, and then translated to RCP associated change in SST. The assessed confidence in assigning the levels of risk at present day and future scenarios are low, medium, high and very high levels of confidence. A detailed account of the procedures involved in the ember for each type of ecosystem, such as their exposure to climate hazards, sensitivity of key biotic and abiotic components, natural adaptive capacity, observed impacts and projected risks, and regional hotspots of vulnerability is provided in the SM5.2 and Table 5.5. The risk assessment for cold water corals is in agreement with the conclusions in AR5 Chapter, although more recent literature is assessed in Box 5.2 and Table SM5.5. 

Overall, the upper ocean (0−700 m) and 700−2000 m layers have both warmed from 2004 to 2016 (virtually certain) and the abyssal ocean continues to warm in the Southern Hemisphere (high confidence). The ocean is stratifying; observed warming and high-latitude freshening are both surface intensified trends making the surface ocean lighter at a faster rate than deeper in the ocean (high confidence) (Section It is very likely that stratification in the upper few hundred meters of the ocean will increase significantly in the 21st century. It is virtually certain that ocean pH is declining by ~0.02 pH units per decade where time series observations exist (Section The anthropogenic pH signal has already emerged over the entire surface ocean (high confidence) and emission scenarios are the most important control of surface ocean pH relative to internal variability for most of the 21st century at both global and local scale (virtually certain). The oxygen content of the global ocean has declined by about 0.5−3.3% in 0−1000 m layer (Section Over the next century oxygen declines of 3.5% by 2100 are predicted by CMIP5 models globally (medium confidence), with low confidence at regional scales, especially in the tropics. The largest changes in the deep sea will occur after 2100 (Section CMIP5 models project a decrease in global NPP (medium confidence) with increases in high-latitude (low confidence) and decreases in low latitude (medium confidence) (Section in response to changes in ocean nutrient supply (Section These models also project reductions by 8.9−15.8% in the globally integrated POC flux for RCP8.5, with decreases in tropical regions and increases at higher latitudes (medium confidence), affecting the organic carbon supply to the deep sea floor ecosystems (high confidence) (Section However, there is low confidence on the mechanistic understanding of how climatic drivers will affect the different components of the biological pump in the epipelagic ocean (Table 5.4). Therefore, the exposure to hazard for epipelagic ecosystems ranges from moderate (RCP2.6) to high (RCP8.5), with uncertain effects and tolerance of planktonic organisms, fishes and large vertebrates to interactive climate stressors. Major risks are predicted for declining productivity and fish biomass in tropical and subtropical waters (RCP8.5) (SM5.2). 

The climatic hazards for pelagic organisms from plankton to mammals are driving changes in eco-physiology, biogeography and ecology and biodiversity (high confidence) (Section Observed and projected population declines in the equator-ward range boundary (medium confidence), expansion in the poleward boundary (high confidence), earlier timing of biological events (high confidence), overall shift species composition (high confidence) and decreases in animal biomass (medium confidence), are consistent with expected responses to climate change (Section 5.2.3; Figure 5.13). It is likely that increased OA has not yet caused sufficient reduction in fitness to decrease abundances of calcifying phytoplankton and zooplankton, but is very likely (high confidence) that calcifying planktonic organisms will experience great decreases in abundance and diversity under high emission scenarios by the end of the century. Therefore, impacts to the epipelagic ecosystems are already observed in the present day (Figure 5.16). Based on simulation modelling and experimental findings, the combined effects of warming, ocean deoxygenation, OA and changes in NPP in the 21st century are projected to exacerbate the impacts on the growth, reproduction and mortality of fishes, and consequently increase the risk of population decline (high confidence) (Section There may be some capacity for adjustment and evolutionary adaptation that lowers their sensitivity to warming and decrease in oxygen (low confidence). However, historical responses in abundance and ranges of marine fishes to ocean warming and decrease in oxygen in the past suggest that adaptation is not always sufficient to mitigate the observed impacts (medium confidence) (Section 5.2.3) (SM5.2). 

Despite its remoteness, most of the deep seafloor ecosystems already have or are projected to experience rising temperatures and declining oxygen, pH and POC flux beyond natural variability within the next half century (See Section 5.2.4). On slopes, seamounts and canyons these changes are projected to be much larger under RCP8.5 than under RCP2.6 (high confidence), with greatest effects on seafloor community diversity and function from expansion of low oxygen zones and aragonite undersaturation (medium confidence). As critical thresholds of temperature, oxygen and CO2 are exceeded, coral species will alter their depth distributions, non-living carbonate will experience dissolution and bioerosion, and stress will be exacerbated by lower food supply. These changes are projected to cause loss of cold water coral habitat with highest climate hazard in the Arctic and north Atlantic Ocean (medium confidence), while sponges may be more tolerant (Box 5.2) (low confidence). Projected changes in food supply to the seafloor at abyssal depths combined with warmer temperatures are anticipated to cause reductions in biomass and body size (medium confidence) that could affect the carbon cycle in this century under RCP8.5 (low confidence). Even at hydrothermal vents and methane seeps, some dominant species such as mussels may be vulnerable to reduced photosynthetically-based food supply or have planktonic larvae or oxidising symbionts that are negatively affected by warming, acidification and oxygen loss (low confidence). 

Widespread attributes of deep seafloor fauna (e.g., great longevity, high levels of habitat specialisation including well-defined physiological tolerances and thresholds, dependence on environmental triggers for reproduction, and highly developed mutualistic interactions) can increase the vulnerability of selected taxa to changing conditions (FAO, 2019) (medium confidence). However, some deep sea taxa (e.g., foraminifera and nematodes) may be more resilient to environmental change than their shallow-water counterparts (low confidence). Observations, experiments and model projections indicate that impacts of climate change have or are expected to take place in this century, indicating a transition from undetectable risk to moderate risk at <1.5oC warming of sea surface temperature for continental slope, canyon and seamount habitats, and for cold water corals (Figure 5.16). Emergence of risk is expected to occur later at around the mid-21st century under RCP8.5 for abyssal plain and chemosynthetic ecosystems (vents and seeps) (Figure 5.16). All deep seafloor ecosystems are expected to be subject to at least moderate risk under RCP8.5 by the end of the 21st century, with cold water corals experiencing a transition from moderate to high risk below 3oC (SM5.2).


Changing Coastal Ecosystems and Biodiversity

The world’s shelf seas and coastal waters (hereafter ‘coastal seas’) extend from the coastline to the 200 m water depth contour. They encompass diverse ecosystems, including estuaries, sandy beaches, kelp forests, mangroves and coral reefs. Although they occupy a small part of the global ocean (7.6%), coastal seas provide up to 30% of global marine primary production and about 50% of the organic carbon supplied to the deep ocean (Chen, 2003; Bauer et al., 2013) (Sections and Coastal seas include several frontal and upwelling areas (Box 5.3) that support high fisheries yields (Scales et al., 2014), and productive coastal ecosystems, such as wetlands (McLeod et al., 2011). Mangrove forests, seagrass meadows and kelp forests form important habitats supporting high biodiversity while offering opportunities for climate change mitigation and adaptation (Section (Duarte et al., 2013), with mangrove forests providing physical protection against extreme events such as storms and floods (Kelleway et al., 2017a) (Sections and The regional characteristics and habitat heterogeneity of many coastal seas support endemic fauna and flora (e.g., seagrass meadows in the Mediterranean), which makes them particularly vulnerable to climate change impacts with high risk of diversity loss and alterations in ecosystem structure and functioning (Rilov, 2016; Chefaoui et al., 2018).

Near-shore coastal ecosystems are classified by their geomorphological structure (e.g., estuaries, sandy beaches and rocky shores) or foundation species (e.g., salt marshes, seagrass meadows, mangrove forests, coral reefs and kelp forests). All these coastal ecosystems are threatened to a varying degree by SLR (SLR), warming, acidification, deoxygenation and extreme weather events (Sections 5.3.1 to 5.3.7). Unlike the open ocean where detection and attribution of climate driven-physical and chemical changes are robust (Section 5.2.2), coastal ecosystems display regional complexity that can render the conclusive detection and attribution of climate effects uncertain. The hydrological complexity of coastal ecosystems that affects their biota is driven by the interactions between the land (e.g., river and groundwater discharges), the sea (e.g., circulation, tides) (Section and seabed structures and substrates (Sharples et al., 2017; Chen et al., 2018; Laurent et al., 2018; Zahid et al., 2018).

Additionally, the high density of human populations on coastal land causes most of the adjacent marine ecosystems to be impacted by local anthropogenic disturbances such as eutrophication, coastline modifications, pollution and overfishing (Levin et al., 2015; Diop and Scheren, 2016; Maavara et al., 2017; Dunn et al., 2018) (Section, Cross-Chapter Box 9). Climate driven impacts interact with such human disturbances and pose a serious risk to ecosystems structure and functioning (Gattuso et al., 2015). Projections of the ecological impacts of climate change in coastal ecosystems must therefore deal with many emerging complexities such as the differentiation between the long-term climate trends (e.g., progressive ocean acidification) and the short-term natural fluctuations (Boyd et al., 2018), ranging from the seasons to interannual climate oscillations like El Niño. The ‘time of emergence’ for specific climate drivers to exceed background variability varies between ecosystems and is strongly sensitive to projected emission scenarios (Hammond et al., 2017; Reusch et al., 2018) (Box 5.1).

This section summarises our updated understanding of ecological and functional changes that coastal ecosystems are experiencing due to multiple climate and non-climatic human drivers, and their synergies. Additional experimental and long-term observational evidence since AR5 WGII (Wong et al., 2014a) and SR15 (Hoegh-Guldberg et al., 2018) improves the attribution of impacts on all the types of coastal ecosystems assessed here to climate trends (Sections 5.3.1 to 5.3.6). Moreover, the emergent impacts detected in the present strengthen the projection of risk of each ecosystem under future emission scenarios by 2100, depending on their exposure to different climate hazards (Section 5.3.7).



Estuarine ecosystems are defined by the river-sea interface that provides high habitat heterogeneity and supports high biodiversity across freshwater and subtidal zones (Basset et al., 2013). AR5 WGII (Wong et al., 2014a) and SR15 (Hoegh-Guldberg et al., 2018) concluded that estuarine ecosystems have been impacted by SLR and human influences that drive salinisation, resulting in increased flooding, land degradation and erosion of coastal areas around estuaries.

Observations since AR5 provide further evidence that SLR increases seawater intrusions and raises salinity in estuaries. Salinisation of estuaries can be exacerbated by droughts and modifications of drainage area by human activities (Ross et al., 2015; Cardoso-Mohedano et al., 2018; Hallett et al., 2018; Zahid et al., 2018). The changing salinity gradients in estuaries have been linked to the observed upstream expansion of brackish and marine benthic and pelagic communities, and a reduction in the diversity and richness of freshwater fauna (Robins et al., 2016; Raimonet and Cloern, 2017; Hallett et al., 2018; Addino et al., 2019) (medium confidence). However, because the distribution of benthic species in estuaries is strongly determined by sediment properties like grain size, the gradient of sediment types in estuaries can be a barrier to upstream shifts of brackish and marine benthic biota, leading to a reduction in species richness in mid- to upper- estuarine areas and altering food webs (Little et al., 2017; Hudson et al., 2018; Addino et al., 2019). Similarly, estuarine wetlands (Section 5.3.2) and tidal flats (Murray et al. 2019) have reduced their extent and productivity in response to increased salinity, inundation and wave exposure, especially in areas with limited capacity for soil accretion or inland migration due to coastal squeezing (Sections, 5.3.2) (high confidence). Poleward migration of tropical and sub-tropical biota between estuaries has been observed in response to warming (Hallett et al., 2018) (medium confidence), in agreement with the global trend of biogeographic shifts of marine organisms (Sections;

Intensive human activities around estuaries and river deltas worldwide has substantially increased nutrient and organic matter inputs into such systems since the 1970s (Maavara et al., 2017). Increased organic matter accumulation has been shown to interact with warming, resulting in intensification of bacterial degradation and eutrophication (Maavara et al., 2017; Chen et al., 2018; Fennel and Testa, 2019), contributing to an increase in the frequency and extent of hypoxic zones (Breitberg et al., 2015; Gobler and Baumann, 2016). The interaction between warming, increased nutrient loading, and hypoxia has shown to be related to the increased occurrences of HABs (Anderson et al., 2015; Paerl et al., 2018) (Box 5.4) (high confidence), pathogenic bacteria such as Vibrio species (Baker-Austin et al., 2017; Kopprio et al., 2017) (Section 5.4.2) (low confidence), and mortalities of invertebrates and fish communities (Jeppesen et al., 2018; Warwick et al., 2018) (medium confidence).

Fluctuations in estuarine salinity, turbidity and nutrient gradients are influenced by changes in precipitation and wind-stress caused by large-scale climatic variations such as the ENSO, the NAO and the South Atlantic Meridional Overturning Circulation (SAMOC) which have shown persistent anomalies associated with climate change since the 1970s (Wang and Cai, 2013; Delworth and Zeng, 2016; García-Moreiras et al., 2018). Similarly, storm surges and heat waves have increased nutrients and sediment loads in estuaries (Tweedley et al., 2016; Arias-Ortiz et al., 2018; Chen et al., 2018). Sustained long-term observations (1540 years) provide evidence that large-scale climatic variations and extreme events affect plankton phenology and composition in estuaries worldwide with regional differences in the characteristics of the responses (Thompson et al., 2015; Abreu et al., 2017; Marques et al., 2017; Arias-Ortiz et al., 2018; López-Abbate et al., 2019) (high confidence). Although these changes in ecosystem components may be attributed to climate variability (Box 5.1), they demonstrate the sensitivity of estuarine ecosystems to climate change. Also, these large-scale climate events are likely to be intensified in the 21st century (Stocker, 2014) (Section 6.5.1).

Salinisation in estuaries is projected to continue in response to SLR, warming and droughts under global warming greater than 1.5°C (high confidence), and will pose further risks to ecosystems biodiversity and functioning (Zhou et al., 2017; Hallett et al., 2018; Zahid et al., 2018; Elliott et al., 2019) (Section, Cross-Chapter Box 7) (medium confidence). Estuarine wetlands are resilient to modest rates of SLR due to their sediment relocation capacity, but such adaptation is not expected to keep pace with projected rates of SLR under the RCP8.5 climate scenario (Section 5.3.2) (high confidence). Moreover, human activities that inhibit sediment movement and deposition in coastal deltas increase the likelihood of their shrinking as a result of SLR (Brown et al., 2018b; Schuerch et al., 2018) (medium confidence).

Oxygen-depleted dead zones in coastal areas are already a problem; they are projected to increase under the co-occurrence and intensification of climate threats and eutrophication (Breitburg et al., 2018; Laurent et al., 2018) (Section While warming is the primary climate driver of deoxygenation in the open ocean, eutrophication is projected to increase in estuaries due to human activities and intensified precipitation increasing riverine nitrogen loads under both RCP2.6 and RCP8.5 scenarios, both mid-century (2031–2060) and later (2071–2100) (Sinha et al., 2017). Moreover, enhanced stratification in estuaries in response to warming is also expected to increase the risk of hypoxia through reduced vertical mixing (Du et al., 2018; Hallett et al., 2018; Warwick et al., 2018).The effects of warming will be more pronounced on high-latitude and temperate shallow estuaries with limited exchange with the open ocean (e.g., Río de La Plata Estuary, Baltic Sea and Chesapeake Bay) and seasonality that already leads to dead zone development when summertime temperatures reach critical values (e.g., Black Sea) (Altieri and Gedan, 2015) (medium confidence). The coastal acidification related to this expansion of hypoxic zones (Zhang and Gao, 2016; Cai et al., 2017; Laurent et al., 2017) imposes risk for sensitive organisms (Beck et al., 2011; Duarte et al., 2013; Feely et al., 2016; Carstensen et al., 2018).

The interaction of SLR and changes in precipitation will have a more severe impact on shallow estuaries (<10 m) than on deep basin estuaries (>10 m) (Hallett et al., 2018; Elliott et al., 2019) (medium confidence). For a projected SLR of 1 m, climate-related risks for shallow estuaries ecosystems are estimated to increase through increased tidal current amplitudes (by 5% on average), energy dissipation, vertical mixing and salinity intrusion (Prandle and Lane, 2015). Estuaries with high tidal exchanges and associated well-developed sediment areas are more resilient to global climate changes than estuaries with low tidal exchanges and sediment supply, since the latter are more vulnerable to SLR and changes in river flow (Brown et al., 2018b; Warwick et al., 2018) (medium confidence). 

Overall, this assessment concludes that there is evidence of upstream redistribution of marine biotic communities in estuaries driven by increased sea water intrusion (medium confidence). Such distribution shifts are limited by physical barriers such as the availability of benthic substrates leading to reduction of suitable habitats for estuarine communities (medium confidence). Warming has led to poleward range shifts of biota between estuaries (medium confidence). Increased nutrient inputs from intensive human development in deltas increases bacterial respiration, which in turn is exacerbated by warming, leading to an expansion of suboxic and anoxic areas (high confidence). These changes reduce the survival of estuarine animals (medium confidence), and increase the occurrence of HABs and pathogenic microbes (medium confidence). Projected warming, SLR and tidal changes in the 21st century will continue to expand salinisation and hypoxia in estuaries (medium confidence). These impacts will be more pronounced under higher emission scenarios, and in temperate and high-latitude estuaries that are eutrophic, shallow and that naturally have low sediment supply.


Coastal Wetlands (Salt Marshes, Seagrass Meadows and Mangrove Forests)

Coastal vegetated wetlands include salt marshes, mangrove forests and subtidal seagrass meadows ecosystems, considered to be the main ‘blue carbon’ habitats (Sections 5.4.1 and (McLeod et al., 2011). AR5 WGII and SR15 concluded that wetland salinisation is occurring at a large geographic scale (high confidence); that rising water temperatures has led to shifts in plant species distribution (medium confidence)(Wong et al., 2014b); and that SLR and storms are causing wetland erosion and habitat loss, enhanced by human disturbances (high confidence) (Section (Wong et al., 2014b). This section assesses new evidence since AR5 and SR15 of observed climate impacts and future risks of these vegetated wetlands in terms of their role in supporting biodiversity and key ecosystem functions. The recent literature confirms and strengthens the SR15 conclusions (Section 5.3.7 and Figure 5.16).

Nearly 50% of the pre-industrial, natural extent of global coastal wetlands have been lost since the 19th century (Li et al., 2018a). Such a reduction in wetlands is primarily caused by non-climatic drivers such as alteration of drainage, agriculture development, coastal settlement, hydrological alterations and reductions in sediment supply (Adam, 2002; Wang et al., 2014; Kroeger et al., 2017; Thomas et al., 2017; Li et al., 2018a). However, large-scale mortality events of mangroves from ‘natural causes’ has also occurred globally since the 1960s; ~70% of this loss has resulted from low frequency, high intensity weather events, such as tropical cyclones (45%) and climatic extremes such as droughts, SLR variations and heat waves (Sippo et al., 2018) (high confidence). In Australia, the mangrove loss due to heat waves accounted for 22% of global mangrove forests (Sippo et al., 2018), with negative impacts on ecosystem biodiversity and the provisioning of services (Carugati et al., 2018; Saintilan et al., 2018) (Section 5.4). In coastal areas with sufficient sediment supply across the Indo-Pacific region, inland expansion of mangroves is occurring as a result of vertical accretion and root growth, allowing them to keep pace with current SLR (Lovelock et al., 2015). In seagrass meadows, temperature is the main limiting range factor, and over the past decades there have been several global die-off events (Hoegh-Guldberg et al., 2018). The vulnerability of seagrasses to warming varies locally depending on soil accretion and herbivory (El-Hacen et al., 2018; Marbà et al., 2018; Vergés et al., 2018) and on the population assemblages (e.g., expansion at high latitudes) (Beca-Carretero et al., 2018; Duarte et al., 2018). The compounding effects of heat waves, hypersaline conditions and increased turbidity and nutrient levels associated with floods have been shown to cause negative changes in the composition and biomass of co-occurring seagrass species (Nowicki et al., 2017; Arias-Ortiz et al., 2018; Lin et al., 2018) (high confidence). For example, in Shark Bay, Western Australia, a marine heat wave in austral summer 2010/2011 caused widespread losses (36% of area) of seagrass meadows, with negative implications for carbon storage (Arias-Ortiz et al., 2018). The poleward expansion of tropical mangroves into subtropical salt marshes as a result of increase in temperature has been also observed over the past half century on five continents (Saintilan et al., 2014; Saintilan et al., 2018) (high confidence); for example, in the Texas Gulf Coast (Armitage et al., 2015). The loss of open areas with herbaceous plants (salt marshes) reduces food and habitat availability for resident and migratory animals (Kelleway et al., 2017a; Lin et al., 2018) (Section 

The ability of salt marshes to increase their elevation and withstand erosion under SLR depends on the development of new soil by the external supply of mineral sediments and organic accretion by local biota (Section 5.4.1, Figure 5.19) (Bouma et al., 2016). In some places, critical organic accretion rates are declining due to reduced plant productivity from stress by more frequent inundation, and increased plant and microbial respiration rates as a result of warming; consequently, the elevation of marshes from soil accretion is slower than the rate of rising sea level, resulting in reduction of salt marsh area (Carey et al., 2017; Watson et al., 2017b). Vegetation loss rates were significantly negatively correlated with marsh elevation, suggesting inundation due to SLR since 1970 as the main driver, enhanced by storms and increased tidal range in back barrier marshes (Watson et al., 2017b). Plant species that are more sensitive to higher temperatures and increases in saltwater intrusion were found to be less abundant and in some cases replaced by salinity-tolerant species (Janousek et al., 2017; Piovan et al., 2019). Plant community restructuring has resulted in biodiversity loss (Pratolongo et al., 2013; Raposa et al., 2017) and reduced above- and below-ground productivity (McLeod et al., 2011; Watson et al., 2017b). As a result of tidal flooding, salt marsh soils do not dry out and high levels of carbon can accumulate under anaerobic conditions. This is coupled with generally low rates of methane emission which is strongly limited in saline marshes (Poffenbarger et al., 2011; Martin and Moseman-Valtierra, 2015; Kroeger et al., 2017; Tong et al., 2018) (high confidence).

Non-climatic human pressures on wetland ecosystems, including overfishing (Crotty et al., 2017), eutrophication (Legault II et al., 2018), and invasive species (Zhang et al., 2016), interact with climate change drivers and affect wetlands composition and structure, with the impacts varying between regions and species (Tomas et al., 2015; O’Brien et al., 2017; Pagès et al., 2017; York et al., 2017). The intensity of herbivory on seagrasses is expected to increase with global warming, particularly in temperate areas, because of the migration of tropical herbivores into temperate seagrass meadows (Hyndes et al., 2016; Vergés et al., 2018) (medium confidence, Section Warming also reduces the fitness of seedlings by increasing necrosis and susceptibility to consumers and pathogenic pressure while reducing establishment potential and nutritional (Olsen et al., 2016b; Hernán et al., 2017). Because herbivores play a key role in modulating the biomass of plant communities, their more intense activity affects the provision of services in these ecosystems (Scott et al., 2018) (Section 5.4). 

Globally, between 20−90% of existing coastal wetland area is projected to be lost by 2100 (Blankespoor et al., 2014; Crosby et al., 2016; Spencer et al., 2016), depending on different SLR projections under future emission scenarios. These projected changes vary regionally and between different types of wetlands. Gaining area may be possible, at least locally, if vertical sediment accretion occurs together with lateral re-accommodation (Brown et al., 2018b; Schuerch et al., 2018) (Section Local losses may also be higher; for example, in New England, where regional rates of SLR have been as much as 50% greater than the global average (from 1−5.83 mm yr-1; 1979-2015) (Watson et al., 2017a) and where projections suggest that 40–95% of salt marshes will be submerged by the end of this century (Valiela et al., 2018). In some species of seagrasses, enhanced temperature-driven flowering (Ruiz-Frau et al., 2017) and greater biomass production in response to elevated CO2 (Campbell and Fourqurean, 2018) may increase resilience to warming. Nevertheless, severe habitat loss (70%) of endemic species such as Posidonia oceanica is projected by 2050 with the potential for functional extinction by 2100 under RCP8.5 climate scenario. For Cymodosea nodosa, the species with the highest thermal optima (Savva et al., 2018), warming is expected to lead to significant reduction of meadows (46% under RCP8.5) in the Mediterranean, although potentially compensated in part by future expansion into the Atlantic (Chefaoui et al., 2018). 

The mangrove habitats of small islands, with lack of rivers, steep topography, sediment-starved areas, groundwater extraction and coastal development, are particularly vulnerable to SLR. Although mangrove ecosystems may survive the increased storm intensity and sea levels projected until 2100 under RCP2.6 (Ward et al., 2016), for RCP8.5 they are only resilient up to 2050 conditions (Sasmito et al., 2016). Negative climate impacts will be exacerbated in cases where anthropogenic barriers cause further ‘coastal squeeze’ that prevents inland movement of plants and limits relocation of sediment (medium confidence) (Enwright et al., 2016; Borchert et al., 2018).

In conclusion, substantial evidence supports with high confidence that warming and salinisation of wetlands caused by SLR are causing shifts in the distribution of plant species inland and poleward, such as mangrove encroachment into subtropical salt marshes (high confidence) or seagrass meadows contraction at low latitudes (high confidence). Plants with low tolerance to flooding and extreme temperatures are particularly vulnerable and may be locally extirpated (medium confidence). The flooded area of salt marshes can become a mudflat or be colonised by more tolerant, invasive species, whose expansion is favoured by combined effects of warming, rising CO2 and nutrient enrichment (medium confidence). The loss of vegetated coastal ecosystems causes a reduction in carbon storage with positive feedbacks to the climate system (high confidence) (Section SLR and warming are expected to continue to reduce the area of coastal wetlands, with a projected global loss of 20–90% by the end of the century depending on emission scenarios. High risk of total local loss is projected under the RCP8.5 emission scenario by 2100 (medium confidence), especially if landward migration and sediment supply is constrained by human modification of shorelines and river flows (medium confidence).


Sandy Beaches

Sandy beaches represent 31% of the world’s ice-free shoreline (Luijendijk et al., 2018). They provide habitat for dune vegetation, benthic fauna and sea birds, nesting areas for marine turtles (Defeo et al., 2009), and several key ecosystem services (Drius et al., 2019) (Section Sandy beach ecosystems are physically dynamic, where sediment movement is a key driver of benthic flora and fauna zonation (Schlacher and Thompson, 2013; van Puijenbroek et al., 2017). In AR5 WGII (Wong et al., 2014b) and SR15 (Hoegh-Guldberg et al., 2018), climate impacts on sandy beach ecosystems were not assessed individually but together with other coastal systems that included beaches, barriers, sand dunes, rocky coasts, aquifers and lagoons. Those assessments concluded with high confidence that SLR, storminess, wave energy and weathering regimes will continue to erode coastal shorelines and affect the soil accretion and land-based ecosystems, with highly site-specific effects (high confidence). Infrastructure and geological constraints reduce shoreline movement and cause coastal squeeze (high confidence). Assessment in Section supports the conclusions in AR5 and SR15 regarding the erosion of sandy coastlines. This section specifically assesses the combined climate and non-climatic impacts on sandy beach biodiversity, ecosystem structure and functioning.

Worldwide, sandy beaches show vegetation transformations caused by erosion following locally severe wave events (Castelle et al., 2017; Delgado-Fernandez et al., 2019; Zinnert et al., 2019) (Table SM5.7). The original dense vegetation is replaced by sparser vegetation (Zinnert et al., 2019) and has a generally slow recovery (multiple years to decades) (Castelle et al., 2017). In some instances, the changes persist over decades, resulting in a regime shift in the beach morphology (Kuriyama and Yanagishima, 2018). Such changes in vegetation and beach morphology in response to local disturbances were also related to shifts in the associated fauna composition (Carcedo et al., 2017; Delgado-Fernandez et al., 2019). Direct attribution of these observed events to climate change is not available despite early evidence (since the 1970s) and an emerging literature (Section, Table SM5.7).

Sandy beaches show similar patterns of biogeographical shifts following warming, with increased dominance of species more tolerant to higher temperatures, as observed in other ocean ecosystems (Section, Table SM5.7). Examples of these observed shifts in abundance and distribution of benthic fauna in sandy beaches are found in the Pacific and Atlantic coasts of North and South America, and in Australia, including increased mortality of clam populations close to their upper temperature limits with low population recovery (Orlando et al., 2019), and poleward expansion of crabs since the 1980s that were related to warming (Schoeman et al., 2015) (Table SM5.7). Also, mass mortalities of beach clams have occurred during warm phases of El Niño events (Orlando et al., 2019)(Table SM5.7), parasite infestations on dense populations (Vázquez et al., 2016) and high wave exposure (Turra et al., 2016).

Human disturbances have caused coastal squeeze and morphological changes in sandy beaches (Martínez et al., 2017; Rêgo et al., 2018; Delgado-Fernandez et al., 2019). Along with SLR and climate-driven intensification of waves and offshore winds, these hazards have increased erosion rates suggesting a reduced resilience due to insufficient sediment supply and accretion capacity (Castelle et al., 2017; Houser et al., 2018; Kuriyama and Yanagishima, 2018). Narrow sandy beaches such as those in south California (Vitousek et al., 2017) or central Chile (Martínez et al., 2017) are particularly vulnerable to climate hazards when combined with human disturbances and where landward retreat of beach profile and benthic organisms is constrained due to increasing urbanisation (Hubbard et al., 2014) (Section

Notwithstanding the uncertainty in projecting future interactions of SLR with other natural and human impacts on sandy shorelines (Le Cozannet et al., 2019; Orlando et al., 2019), they are expected to continue to reduce their area and change their topography due to SLR and increased extreme climatic erosive events. This will be especially important in low-lying coastal areas with high population and building densities (medium confidence, SM 4.2). Megafauna that use sandy beaches during vulnerable parts of their life cycles could be particularly impacted (Laloë et al., 2017). For example, the modelled incubation temperatures of green turtles have increased by 1°C since the mid-1970s, resulting in an average 20% increase in the proportion of female hatchlings over this period (Patrício et al., 2019). By 2100, global temperatures will approach lethal levels for incubation in existing nesting sites, and hatchling success is expected to drop to 32% under RCP8.5 scenario, with 93% of the hatchlings expected to be female (76% under RCP4.5). A possible microhabitat adaptation such as shadowed vegetated areas, however, could allow for continued male production throughout the 21st century (Patrício et al., 2019). In addition, a projected global mean SLR of ~1.2 m under the upper likely range of RCP8.5 by 2100 implies a loss of 59% and 67% in the present nesting area of the green turtle and the loggerhead respectively in the Mediterranean (Varela et al., 2019), and a loss of 43% in the nesting area of green turtles in West Africa (Patrício et al., 2019). Moreover, benthic crustaceans of sandy beaches, including isopods, crabs and amphipods, generally follow the temperature-body size gradient in which body size decreases towards warmer lower-latitude regions (Jaramillo et al., 2017). Assuming that the physiological underpinning of the relationship between body size and temperature can be applied to warming (see Section 5.2.2, medium confidence), the body size of sandy beach crustaceans is expected to decrease under warming (low evidence, medium agreement).

Overall, changes in sandy beach morphology have been observed from climate related events, such as storm surges, intensified offshore winds, and from coastal degradation caused by humans (high confidence), with impacts on beach habitats (e.g., benthic megafauna) (medium confidence). The direct influence of contemporary SLR on shoreline behaviour is emerging, but attribution of such changes to SLR remains difficult (Section Projected changes in mean and extreme sea levels (Section 4.2.3) and warming (Section 5.2.1) under RCP8.5 are expected to result in high risk of impacts on sandy beach ecosystems by the end of the 21st century (medium confidence, Figure 5.16), taking account of the slow recovery rate of sandy beach vegetation, the direct loss of habitats and the high climatic sensitivity of some fauna. Under RCP2.6, the risk of impacts on sandy beaches is expected to be only slightly higher than the present day level (low confidence, Figure 5.16). However, pervasive coastal urbanisation lowers the buffering capacity and recovery potential of sandy beach ecosystems to impacts from SLR and warming and thus is expected to limit their resilience to climate change (high confidence).


Coral Reefs

Human activities and warming have already led to major impacts on shallow water tropical coral reefs caused by species replacement, bleaching and decreased coral cover while warming, ocean acidification and climate hazards will put warm water corals at very high risk even if global warming can be limited to 1.5°C above pre-industrial level (Hoegh-Guldberg et al., 2018; Kubicek et al., 2019; Sully et al., 2019). While providing new evidence to support these previous assessments (Kleypas, 2019), this assessment focuses on evaluating the variations in sensitivities and responses of coral reefs and their associated biota to highlight comparative risks and resiliences. 

New evidence since AR5 and SR15 confirms the impacts of ocean warming (Kao et al., 2018; Jury and Toonen, 2019) and acidification (Jiang et al., 2018; Mollica et al., 2018; Bove et al., 2019) on coral reefs (high confidence), enhancing reef dissolution and bioerosion (high confidence), affecting coral species distribution, and leading to community changes (Agostini et al., 2018) (high confidence). The rate of SLR (primarily noticed in small reef islands) may outpace the growth of reefs to keep up although there is low agreement in the literature (Brown et al., 2011; Perry et al., 2018) (low confidence). Reefs are further exposed to other increased impacts, such as enhanced storm intensity (Lavender et al., 2018), turbidity and increased runoff from the land (Kleypas, 2019) (high confidence). Recovery of coral reefs resulting from repeated disturbance events is slow (Hughes et al., 2019a; Ingeman et al., 2019) (high confidence). Only few coral reef areas show some resilience to global change drivers (Fine et al., 2019) (low confidence). 

Globally, coral reefs and their associated communities are projected to change their species composition and biodiversity as a result of future interactions of multiple climatic and non-climatic hazards (Kleypas, 2019; Kubicek et al., 2019; Rinkevich, 2019) (high evidence, very high agreement, very high confidence). Multiple stressors act together to increase the risk of population declines or local extinction of reef-associated species through impacts of warming and ocean acidification on physiology and behaviours (Gunderson et al., 2017) (high confidence). Alteration of composition of coral reef-associated biota is exacerbated by changes in habitat conditions through increased sedimentation and nutrient concentrations from human coastal activities (Fabricius, 2005) (high confidence). Coral ecosystems in tropical small islands are also at high risk of being affected by extreme events, including storms, with their impacts exacerbated by SLR (Duvat et al., 2017; Harborne et al., 2017) (high confidence). Such risks on coral reef associated communities are substantially elevated when the level of these climatic and non-climatic hazards are above thresholds that may cause phase shifts in reef communities (McCook, 1999; Hughes et al., 2010; Graham et al., 2013; Hughes et al., 2018) (high confidence). A phase shift is characterised by an abrupt decrease in coral abundance or cover, with concurrent increase in the dominance of non-reef building organisms, such as algae and soft corals (Kleypas, 2019). Such phase shifts have already been observed in many coral reefs worldwide (Wernberg et al., 2016; Kleypas, 2019).

Notwithstanding the conclusion that coral reefs globally are projected to greatly decline at 2°C warming relative to pre-industrial level (Cacciapaglia and van Woesik, 2018; Dietz et al., 2018; Hoegh-Guldberg et al., 2018), climate impacts can be affected by variations in the sensitivity and adaptive capacity across coral species and coral reef ecosystems. Laboratory experiments show that some warm water corals possess the cellular, physiological or molecular machineries that could help them acclimatise or adapt to the effects of global change (medium confidence) (DeBiasse and Kelly, 2016; Gibbin et al., 2017; Wall et al., 2017; Camp et al., 2018; Donelson et al., 2018; Drake et al., 2018; Veilleux and Donelson, 2018; Hughes et al., 2019b). For example, there are species or genotypes that show less impacts by either ocean acidification or increased temperatures (Cornwall et al., 2018; Gintert et al., 2018). Some corals and their symbionts might be able to use epigenetic (heritable phenotype changes that do not involve alterations in the DNA sequences) mechanisms to reduce their sensitivity to temperature changes in their environment and to pass such traits to their offspring (Liew et al., 2017; Torda et al., 2017; Li et al., 2018b; Liew et al., 2018). The variations in sensitivity and adaptive capacity of coral species to warming and ocean acidification contribute to changes in species composition of coral reefs as they are exposed to climatic and non-climatic hazards (Ingeman et al., 2019; Kleypas, 2019; Kubicek et al., 2019) (high confidence). However, it has not yet been established whether coral and coral associated biota adaptation may hold beyond 1.5°C warming. The onset of coral bleaching in the last decade has occurred at higher SSTs (0.5°C) than in the previous decade, suggesting that coral populations that remain after preceding bleaching events may have a higher thermal threshold (Sully et al., 2019) (medium confidence), potentially as a result of the increased dominance of species with lower sensitivity or higher adaptive capacity (Schulz et al., 2013; McClanahan et al., 2014; Mumby and van Woesik, 2014; Pandolfi, 2015; Folkersen, 2018) (medium confidence). 

Coral reefs in deeper or mesophotic waters (found in tropical/subtropical regions at 30–150 m depth) may serve as refuges and sources for larval supply to those reefs exposed to disturbances (e.g., bleaching, storms, floods from land, sedimentation and tourism impacts) (Bridge et al., 2013; Thomas et al., 2015; Lindfield et al., 2016; Smith et al., 2016b; Bongaerts et al., 2017). Reefs exposed to local oceanographic characteristics that reduce warming, such as upwelling, may similarly provide refuges and larval sources (Tkachenko and Soong, 2017). However, recent evidence suggests that mesophotic coral reefs are at higher risk than

previously indicated (Rocha et al., 2018). Monitoring of coral reefs worldwide shows that some areas in the eastern tropical Pacific Ocean (Smith et al., 2017), the Caribbean (Chollett and Mumby, 2013), the Red Sea (Fine et al., 2013; Osman et al., 2017), the Persian Gulf (Coles and Riegl, 2013) and the Great Barrier Reef, Australia (Hughes et al., 2010; Morgan et al., 2017) have recovered more rapidly after bleaching than the larger-scale average (medium confidence). There are regional differences in reef vulnerability when considering scales larger than 100 km or over latitudinal gradients (van Hooidonk et al., 2013; Heron et al., 2016; Langlais et al., 2017; McClenachan et al., 2017) (high confidence).

Based on findings from simulation modelling, SR15 concluded that “coral reefs are projected to decline by a further 70–90% at 1.5°C (very high confidence) with larger losses (>99%) at 2°C (very high confidence)”. The variations in exposure, sensitivity and adaptive capacity between coral populations and regions are further projected to cause large changes in the composition and structure of the remaining coral reefs, with large regional differences (van Hooidonk et al., 2016; Hoegh-Guldberg et al., 2018; Kleypas, 2019; Kubicek et al., 2019; Sully et al., 2019). 


Rocky Shores

Rocky shore ecosystems span the intertidal and shallow subtidal zones of the world’s temperate coasts and are typically dominated by calcareous mussels or seaweeds (macroalgae). Other organisms that inhabit rocky shores are coralline algae (i.e., maerl beds), polychaetes, molluscs, bryozoans and sponges. Intertidal habitats are characterised by strong environmental gradients, and are exposed to marine and atmospheric climate regimes (Hawkins et al., 2016). IPCC AR5 (Wong et al., 2014a) concluded that rocky shores are among the better-understood coastal ecosystems in terms of potential impacts of climate variability and change. The high sensitivity of sessile organisms (e.g., barnacles, mussels) to extreme temperature events (e.g., mass mortality and drastic biodiversity loss of mussels beds), and to acidification (widely observed in manipulative experiments) gives high confidence that rocky shore species are at high risk of changes in distribution and abundance from these two drivers. SR15 (Hoegh-Guldberg et al., 2018) concluded that rocky coasts are already experiencing large-scale changes, and critical thresholds are expected to be reached at warming of 1.5°C and above (high confidence).

More observational and empirical evidence since AR5 and SR15 confirms that climate change poses high risk to rocky shore ecosystems’ biodiversity, structure and functioning through warming, acidification, SLR and extreme events (Agostini et al., 2018; Duarte and Krause-Jensen, 2018; Ullah et al., 2018; Milazzo et al., 2019). Immobile intertidal organisms are especially vulnerable to warming, due to the potential for extreme heat exposure during low tide emersion and prolonged desiccation events (Hawkins et al., 2016; Zamir et al., 2018) (high confidence). This effect is expected to lower the upper vertical limit of intertidal communities (Hawkins et al., 2016), reducing their suitable habitat (Harley, 2011), and accompanied by temperature-induced increases in predation by consumers (Sanford, 1999). While previous studies have documented a poleward shift in species distributions of rocky intertidal and reef algae (Duarte et al., 2013; Nicastro et al., 2013) and faunal species (Barry et al., 1995; Mieszkowska et al., 2006; Lima et al., 2007), local extinctions at the equatorial or warm edge of species ranges are increasingly being attributed to climate change (Yeruham et al., 2015; Sorte et al., 2017) (high confidence). Extreme heat waves are expected to cause mortality among rocky shore species (Gazeau et al., 2014; Jurgens et al., 2015) and subsequent declines or losses in important species can have cascading effects on the whole intertidal community and the services it provides (Gatti et al., 2017; Sorte et al., 2017; Sunday et al., 2017). Coralline fauna adapted to narrow environmental conditions seem especially vulnerable to heat waves, with observed mass mortalities in the Adriatic Sea in response to extreme summer temperatures (Kružić et al., 2016). The loss of thermal refugia associated with continued warming could exacerbate the impacts of heat stress on rocky intertidal communities (Lima et al., 2016). Nevertheless, experimental data indicate that some coralline algae that are well adapted to highly variable transitional environments can tolerate the warming projected for 2100 under RCP8.5; for these species, ocean acidification will constitute the main hazard (Nannini et al., 2015). 

Ocean acidification is expected to decrease the net calcification (high confidence) and abundance (medium confidence) of rocky intertidal and reef-associated species (Kroeker et al., 2013), and the dissolution of calcareous species has already been documented in tide-pool communities (Kwiatkowski et al., 2016; Duarte and Krause-Jensen, 2018). Recent experimental and field studies, however, have demonstrated the importance of food resources in mediating the effects of ocean acidification on vulnerable rocky shores species (Ciais et al., 2013; Ramajo et al., 2016), suggesting that species’ vulnerability to ocean acidification may be most pronounced in areas of high heat stress and low food availability (medium confidence) (Kroeker et al., 2017). There is increasing evidence that the interactions between multiple climate drivers will determine species vulnerability and the ecosystem impacts of climate change (Hewitt et al., 2016).

Studies on naturally acidified rocky reef ecosystems suggest ocean acidification will simplify rocky shore ecosystems, due to an overgrowth by macroalgae, a reduction in biodiversity and a reduction in the abundance of calcareous species (medium confidence) (Kroeker et al., 2013; Linares et al., 2015). These shifts in community structure and function have been observed in CO2 seep communities (Hall-Spencer et al., 2008), already exposed to levels of pCO2 expected to generally occur by the end of the century (Agostini et al., 2018). Reductions in the abundance of calcareous herbivores that can create space for rarer species by grazing the dominant algae, are expected to contribute to the overgrowth of fleshy macroalgae on rocky shores (Baggini et al., 2015). This shift towards macroalgae is associated with a simplification of the food web at lower trophic levels (Kroeker et al., 2011). 

At the local scale, warming and ocean acidification are expected to change energy flows within rocky shores ecosystems (medium confidence). Experiments indicate that both climate drivers may boost primary productivity in some cases (Goldenberg et al., 2017); however, increased metabolic demands and greater consumption by predators under warmer temperature increase the strength of top-down control (predation mortalities of herbivores) and thus counteracts the effects of increased bottom-up productivity (Goldenberg et al., 2017; Kordas et al., 2017). Ocean acidification could also increase species energetic costs and the grazing rate of herbivores, affecting ecosystem responses to increased primary productivity (Ghedini et al., 2015). Although these increasingly complex experiments have highlighted the potential for species interactions to mediate the effects of climate change, our understanding of the effects on intact, functioning ecosystems is limited. Despite predictions for increased production and herbivory with warming and acidification, an experimental study of a more complex food web revealed an overall reduction in the energy flow to higher trophic levels and a shift towards detritus-based food webs (Ullah et al., 2018).

Overall, intertidal rocky shores ecosystems are highly sensitive to ocean warming, acidification and extreme heat exposure during low tide emersion (high confidence). More field and experimental evidence shows that these ecosystems are at a moderate risk at present and this level is expected to rise to very high under the RCP8.5 scenario by the end of the century (see Section 5.3.7). Benthic species will continue to relocate in the intertidal zones and experience mass mortality events due to warming (high confidence). Interactive effects between acidification and warming will exacerbate the negative impacts on rocky shore communities, causing a shift towards a less diverse ecosystem in terms of species richness and complexity, increasingly dominated by macroalgae (high confidence).


Kelp Forests

Kelp forests are three-dimensional, highly productive coastal ecosystems with a reported global NPP between 1.02‒1.96 GtC yr–1 (Krause-Jensen and Duarte, 2016). They cover about 25% of the world’s coastline (Filbee-Dexter et al., 2016), mostly temperate and polar (Steneck et al., 2003). Canopy-forming macroalgae provide habitat for many associated invertebrates and fish communities (Pessarrodona et al., 2019). This assessment synthesises new evidence since SR15 on climate risks and impacts, and their interactions with non-climatic drivers on ecosystem biodiversity, structure and functioning.

Observational and experimental evidence since SR15 (Hoegh-Guldberg et al. 2018) supports that report’s conclusions that kelp forests are already experiencing large-scale changes, and that critical thresholds occur for some forests at 1.5°C of global warming (high confidence). Due to their low capacity to relocate and high sensitivity to warming, kelp forests are projected to experience higher frequency of mass mortality events as the exposure to extreme temperature rises (very high confidence). Moreover, changes in ocean currents have facilitated the entry of tropical herbivorous fish into temperate kelp forests decreasing their distribution and abundance (medium confidence). More evidence from model projections in the 21st century supports this observed range contraction of kelp forests at the warm end of their distributional margins and expansion at the poleward end with the rate being faster for high emission scenarios (high confidence).

New global estimates show that the abundance of kelp forests has decreased at a rate of ~2% per year over the past half century (Wernberg et al., 2019), mainly due to ocean warming and marine heat waves (e.g., in western Australia a mean loss of 43% in area followed a marine heat weave in summer 2010–2011 (Wernberg et al., 2016), Section, as well as from other human stressors (high confidence) (Filbee-Dexter and Wernberg, 2018). At some localities, human-driven environmental changes such as coastal eutrophication and pollution is causing severe deterioration of kelp forests adding to the loss of these ecosystems from warming, storms and heat weaves (Andersen et al., 2013; Filbee-Dexter and Wernberg, 2018). 

Two global datasets and one dataset covering European coastlines (Araujo et al., 2016; Krumhans et al., 2016; Poloczanska et al., 2016) identify large local and regional variations in kelp abundance over the past half century with 38% of these ecoregions showing a decline, 27% an increase and 35% no change (Krumhans et al., 2016). These data reflect the high spatio-temporal variability and resilience of kelp forests (Reed et al., 2016; Wernberg et al., 2018). For example, a 34 year dataset of kelp canopy biomass along the California coastline does not yet show a significant response to global warming because this ecosystem responds to low frequency marine climate oscillations (Bell et al., 2018c). However, between 1950‒2010 regional warming caused consistent negative responses in abundance, phenology, demography and calcification of macroalgae for the northeast Atlantic and southeast Indian Ocean (Poloczanska et al., 2016). Declines in kelp forest abundance attributed to climate change and not related to sea urchin overgrazing (which is a major driver of decline and regime shift; Ling et al. (2014)) have been documented since the 1970s and evidence has increased within the last two decades (Filbee-Dexter and Wernberg, 2018). Despite a lack of data from some regions such as South America (Pérez-Matus et al., 2017), observational evidence since SR15 supports with very high confidence that warming is driving a contraction of kelp forests at low latitudes (Franco et al., 2018b; Casado-Amezúa et al., 2019; Pessarrodona et al., 2019) and expansion in polar regions (medium confidence) (Section (Bartsch et al., 2016; Paar et al., 2016). 

In many areas worldwide where the distribution range of kelp has contracted due to climatic and non-climatic drivers, it has been replaced by a less diverse and less complex turf-dominated ecosystem (Filbee-Dexter and Wernberg, 2018) (high confidence). Kelp supports other ecosystem components by providing food, substrate for spawning and habitat that mediate trophic interactions (O’Brien et al., 2018); its degradation therefore reduces species richness, biomass production and dependent flora and fauna species (Teagle and Smale, 2018; Pessarrodona et al., 2019). In the northeast Atlantic, the warm water species Laminaria ochroleuca is expanding poleward into regions previously dominated by the cold water species L. hyperborea which is retreating at its southern edge. These two kelp species are similar in morphology, but the cold water L. hyperborea hosts sessile communities of algae and invertebrates 12 times more diverse and richer in biomass than the warm water kelp species (Teagle and Smale, 2018). Climate-driven shifts in the species composition also affect carbon cycling, because warm-temperate kelps produce larger pools of organic matter than cold-temperate species, and their detritus is degraded faster (Pessarrodona et al., 2019).

New empirical eco-physiological studies in combination with field surveys support the evidence for climate change causing kelp forest degradation and range shifts (Franco et al., 2018b; Wernberg et al., 2018). For example, interactive effects of ocean warming and acidification cause kelp degradation and disease-like symptoms, with detrimental effects on photosynthetic efficiency (Qiu et al., 2019). Enhanced herbivory due to warming and the establishment of herbivorous fish species in temperate kelp forest has been observed to enhance ecosystem degradation (Vergés et al., 2016). However, invader seaweed species driven by warming can create more complex trophic interactions, reducing the consumption by herbivorous gastropods (Miranda et al., 2019). Increased physical stress by storm events also alters the kelps community, affecting the recruitment time of kelp species. The resulting dominance of younger stages favors species with a year-round spore production or an opportunistic life strategy, reducing the kelp canopy (Pereira et al., 2017).

Projections of future distribution of kelp species based on their physiological thresholds show major species-specific range shifts under different emission scenarios. For example, under RCP2.6, laminaria and other canopy-forming seaweed species in the Northwest Atlantic are projected to show northward range shifts at their southern (warm) edge of ≤40 km, with some equatorial range expansion from 2050 to 2100. That northward range shift increases to 406 km under RCP8.5 (at 13–19 km per decade, including contractions of their warmer edges) (Wilson et al., 2019). Whilst no changes in species richness are projected under RCP2.6, more than 50% richness loss is projected under RCP8.5 in some areas (Wilson et al., 2019). Overall, model projections show that worldwide range contractions of kelps can be expected to continue at the warm end of distributional margins and range expansions at their poleward end (high confidence) (Raybaud et al., 2013; Assis et al., 2016; Assis et al., 2018; Wilson et al., 2019).

In summary, kelp forests have experienced large-scale habitat loss and degradation of ecosystem structure and functioning over the past half century, implying a moderate to high level of risk at present conditions of global warming (high confidence) (Section 5.3.7). The loss of kelp forests is followed by the colonisation of turfs, which contributes to the reduction in habitat complexity, carbon storage and diversity (high confidence). Kelp ecosystems are expected to continue to decline in temperate regions driven by ocean warming and intensification of extreme climate events (high confidence). The level of risk for the ecosystem is projected to rise to very high under RCP8.5 scenario by 2100 (high confidence)


Risk Assessment for Coastal Ecosystems

This section synthesises the assessment of climate impacts on coastal ecosystems’ biodiversity, structure and functioning and the levels of risk under contrasting future conditions of global warming. As described in Section 5.2.5, the format for Figure 5.16 matches that of Figure 19.4 of AR5 (Oppenheimer et al., 2015) and Figure 3.20 of SR15 (Hoegh-Guldberg et al., 2018), indicating the levels of additional risk as colours (white, yellow, red and purple). The elements or burning embers for coastal ecosystems (Figure 5.16) indicate how risks increase with ocean warming, acidification, deoxygenation, SLR and extreme events with a comparison between present day conditions (2000s) and future conditions by the year 2100 under low (RCP2.6) and high (RCP8.5) CO2 emission scenarios. The transition between the levels of risk for each type of coastal ecosystem is estimated from key evidence assessed in Sections 5.3.1 to 5.3.6. The embers are based on SST  and the transition-values may have an error of ±0.3°C depending on the consensus of expert judgment. The assessed confidence in assigning the levels of risk at present day and future scenarios are low, medium, high and very high levels of confidence. A detailed account of the procedures involved in developing the ember for each type of coastal ecosystem is given in the supplementary material (SM5.3). This includes the description of climate hazards, sensitivity of key biotic and abiotic components, natural adaptive capacity, and observed impacts and projected risks. The burning embers for seagrass meadows, warm water corals and mangrove forests are in agreement with the conclusions in SR15 (Hoegh-Guldberg et al., 2018). The more recent literature assessed here strengthens the overall confidence in the assignment of transition and the level of risk for each ecosystem. 

Detection and attribution studies show that climate change impacts began over the past 50 years in coastal ecosystems, indicating a transition from undetectable risk (white areas in Figure 5.16) to moderate risk below recent sea surface temperatures for some ecosystems (high confidence). This transition occurs at lower global levels of sea surface warming for coral reefs (0.2°C‒0.4°C) (high confidence), seagrass meadows (0.5°C‒0.8°C) (very high confidence) and kelp forests (0.6°C‒1.0°C) (high confidence), with coral reefs already at high risk (0.4°C‒0.6°C) for the present day (very high confidence). Global common responses include large-scale coral bleaching events (Section 5.3.4) and contraction of seagrass meadows (Section 5.3.2) and kelp forests (Section 5.3.6) at low-latitudes (high confidence), in response to warming and marine heat waves. Degraded coral reefs and kelp forests have shifted to algal and turf-dominated ecosystem at several regions worldwide, causing loss of habitat complexity and biodiversity.

The transition from undetectable to moderate risk in salt marshes (Section 5.3.2) and rocky shores (Section 5.3.5) takes place between 0.7°C‒1.2°C of global sea surface warming (medium/high confidence), and between 0.9°C‒1.8°C (medium confidence) in sandy beaches (Section 5.3.3), estuaries (Section 5.3.1) and mangrove forests (Section 5.3.2) (Figure 5.16). In all these coastal ecosystems, the detection and attribution of changes in biodiversity, structure and functioning are not as robust as in coral, seagrass and kelp ecosystems that have been extensively studied over the past decades and are highly sensitive to extreme climate events. Estuaries and sandy beaches are highly dynamic in terms of hydrological and geomorphological processes, giving them more natural adaptive capacity to climate impacts. In these systems, sediment relocation, soil accretion and landward expansion of vegetation may mitigate against flooding and habitat loss in the context of SLR and extreme climate-driven erosion. Common global responses observed since 1970 include poleward expansion of mangrove forests due to warming; transformation of salt marshes into mudflats; shifts in species composition in response to flooding and salinisation; upstream migration of estuarine biota; and redistribution of macrobenthic communities in sandy beaches. Calcified organisms in intertidal rocky shores are highly sensitive to ocean warming and acidification, marine heat waves and heat exposure during low tide, with observed mass mortality events and reduced calcification.

In all coastal ecosystems, multiple climate hazards will emerge from historical variability in the 21st century under RCP8.5 (Box 5.1), while the time of emergence will be later and with less climate hazard under RCP2.6. Non-climatic human impacts such as eutrophication add to, and in some cases, exacerbate these large-scale slow climate drivers beyond biological thresholds at local scale (e.g., deoxygenation). 

All coastal ecosystems will experience high to very high risk under RCP8.5 by the end of the 21st century. The ecosystems expected to be at very high risk under the high emission scenario are coral reefs (transition from high to very high risk 0.6°C‒1.2°C) (very high confidence), seagrasses meadows (2.2°C‒3.0°C) (high confidence), kelp forests (2.2°C‒2.8°C) (high confidence) and rocky shores (2.9°C‒3.4°C) (medium confidence). These ecosystems have low to moderate adaptive capacity, as they are highly sensitive to ocean warming, marine heat waves and acidification. For example, kelp forests at low-latitudes and temperate seagrass meadows with endemic species will continue to retreat with more frequent extreme temperatures, and their low dispersal ability will elevate the risk of local extinction. Biogenic shallow reefs with calcified organisms (e.g., corals, mussels, calcified algae) are particularly sensitive to ocean acidification and compound effects with rising temperatures, deoxygenation, SLR and increasing extreme events, making these ecosystems highly vulnerable (with low resilience) to future emission scenarios. Furthermore, almost all coral reefs will greatly decline from their current levels, even if global warming remains below 2°C (very high confidence). Any coral reefs that do survive to the end of the century will not be the same because of irreversible changes in habitat structure and functioning, including species extinctions and food web disruptions; these changes are already taking place (e.g., the Caribbean reefs). The transition to new ecosystem states driven by unpredictable pulses of disturbance and progressive climate hazards will have negative impacts on ecosystem services (Section 5.4).

The ecosystems at moderate to high risk under future emission scenarios (Figure 5.16) are mangrove forests (transition from moderate to high risk at 2.5°C‒2.7°C of global sea surface warming), estuaries and sandy beaches (2.3°C‒3.0°C) and salt marshes (transition from moderate to high risk at 1.8°C‒2.7°C and from high to very high risk at 3.0°C‒3.4°C) (medium confidence). Mangrove forests and salt marshes can initially cope with SLR by plant biomass accumulation, soil accretion and sediment relocation, but the evidence shows they are unlikely to withstand the SLR projected under RCP8.5. Moreover, pervasive coastal squeeze and human-driven habitat deterioration will reduce the natural capacity of these ecosystems to adapt to climate impacts (high confidence). Projected warming and SLR by the end of the century will continue to expand salinisation and hypoxia in estuaries with high risk of impacts for benthic and pelagic biota. These impacts will be more pronounced under RCP8.5 in more vulnerable eutrophic, shallow and microtidal estuaries in temperate and high latitudes. Erosion in sandy beach ecosystems will continue with global warming, rising sea level and more intense and frequent storm surges and marine heat waves. The risk of losing habitats for flora and fauna is expected to rise to high level under the high emission scenario by the end of the 21st century (medium confidence, Figure 5.16). By contrast, the risk of impacts is expected to be only slightly higher than present for a low emission scenario than today (medium confidence, Figure 5.16).

All types of ecosystems that have been assessed in the open ocean (Sections 5.2.3 and 5.2.4) and coastal areas (Sections 5.3.1 to 5.3.6) show increased risk under both the low and the high emission scenarios (RCP2.6 and RCP8.5) compared with the present level of change (Figure 5.16). In all assessed cases with all of the factors considered (climate drivers and physiological understanding), RCP2.6 has a lower level of risk than RCP8.5 (very high confidence). 

Figure 5.16

Figure 5.16 | Risk scenarios for open ocean (upper panel) and coastal (lower panel) ecosystems based on observed and projected climate impacts. ‘Present day’ corresponds to the 2000s, whereas the different greenhouse emissions scenarios: Representative Concentration Pathway (RCP)2.6 and RCP8.5 correspond to year 2100. Multiple climatic hazards are considered, including ocean warming, deoxygenation, acidification, changes […]

Figure 5.16 | Risk scenarios for open ocean (upper panel) and coastal (lower panel) ecosystems based on observed and projected climate impacts. ‘Present day’ corresponds to the 2000s, whereas the different greenhouse emissions scenarios: Representative Concentration Pathway (RCP)2.6 and RCP8.5 correspond to year 2100. Multiple climatic hazards are considered, including ocean warming, deoxygenation, acidification, changes in nutrients, particulate organic carbon flux and sea level rise (SLR) (see sections 5.2 and 5.3). The projected changes in sea surface temperature (SST) from an ensemble of general circulation models (left panels) indicate the level of ocean changes under RCP2.6 and RCP8.5 (see Cross Chapter Box 1 Table CB1 for the projected global average changes in average air temperature, SST and other selected ocean variables). Global average impacts/risks are represented. Regional variations of risks/impacts are described in Section 5.2.5, 5.3.7, SM5.2 and SM5.5. Impact/risk levels do not consider human risk reduction strategies such as societal adaptation, or future changes in non-climatic hazards. The grey vertical bars indicate the transition between the levels of risks, with their confidence level based on expert judgment. Note: The figure depicts climate change impacts and risks on warm water corals taken from SR15, based on global models. Observed impacts on coral reefs ecosystems outlined in Section 5.3.4 and Box 5.5 reveal a more complex situation that may result in regional differences in confidence levels.


Changing Marine Ecosystem Services and Human Well-being

Ecosystem services are the environmental processes and functions that have monetary or intrinsic value to human society; they render benefits to people and support human well-being (Tallis et al., 2010; Costanza et al., 2014). Marine ecosystem services are generated throughout the ocean, from shallow water to the deep sea (Armstrong et al., 2012; Thurber et al., 2014). Although all ecosystem services are interconnected (Leadley et al., 2014), they can be broadly divided into provisioning services, regulating services, supporting services and cultural services (Sandifer and Sutton-Grier, 2014), as considered below (Section 5.4.1) together with their implications for human well-being (Section 5.4.2). Ecosystem services have also been described as ‘nature’s contribution to people’ (Díaz et al., 2018).


Changes in Key Ecosystem Services

AR5 WGII concluded that climate change increases the risk of impacts on the goods and services derived from marine biodiversity and ecosystems (Pörtner et al., 2014). SR15 concluded that current ecosystem services from the ocean are expected to be reduced at 1.5°C of global warming, with losses being even greater at 2°C of global warming. These reductions in services are driven by decreasing ocean productivity, biogeographic shifts, damage to ecosystems, loss of fisheries productivity and changes to ocean chemistry (high confidence) (Hoegh-Guldberg et al., 2018). Building on these previous assessments, this section assesses new evidence on observed impacts and future risk of climate change on ecosystem goods and services from the open ocean (Section 5.2) and coastal ecosystems (Section 5.3). Chapter 3 assesses ecosystem services in polar oceans.

Provisioning Services

Fisheries are an important provisioning service from marine ecosystems, providing food, nutrition, income and livelihoods for many millions of people around the world (FAO, 2018). Globally, total fish catches amount to 80‒105 Mt annually in the 2000s (FAO, 2016; FAO 2018; Pauly and Zeller, 2016), directly generating over 80 billion USD of revenue (Sumaila et al., 2015). Most global fisheries are considered to be fully- to over-exploited (FAO, 2018). Over 80% of the global fish catch is estimated to be from coastal and shelf seas with less than 20% from the high seas (Sumaila et al., 2015) (Figure 5.17).

Figure 5.17

Figure 5.17 | Global distribution of fish catches (average 2010–2014, based on (Pauly and Zeller, 2016)), coastal habitats including seagrasses (UNEP-WCMC and FT, 2017) salt marshes (Mcowen et al. 2017), mangrove forests (Spalding, 2010), coral reefs (UNEP-WCMC and WRI, 2010) and an index (called Marine Focus Factor) for the inclusion of the ocean in the […]

Figure 5.17 | Global distribution of fish catches (average 2010–2014, based on (Pauly and Zeller, 2016)), coastal habitats including seagrasses (UNEP-WCMC and FT, 2017) salt marshes (Mcowen et al. 2017), mangrove forests (Spalding, 2010), coral reefs (UNEP-WCMC and WRI, 2010) and an index (called Marine Focus Factor) for the inclusion of the ocean in the Nationally Determined Contributions (NDCs) published by each country (Gallo et al. 2017). The higher the Marine Focus Factor, the more frequent use of ocean in the country’s NDCs.

Figure 5.18

Figure 5.18 | Historical and projected maximum sustainable yield (MSY) and maximum fish catch potential by region. Historical trends in MSY is based on time series of fish stock assessment data (Free et al. 2019) represented as circles in panels (a) and (b). The size of the circle represents the number of assessed fish stocks […]

Figure 5.18 | Historical and projected maximum sustainable yield (MSY) and maximum fish catch potential by region. Historical trends in MSY is based on time series of fish stock assessment data (Free et al. 2019) represented as circles in panels (a) and (b). The size of the circle represents the number of assessed fish stocks while the number in the circle represents the estimated percent change in MSY since the 1930s. Projected changes in maximum catch potential by 2050 (average between 2041–2060) relative to 2000 (1991–2010) under (a) RCP2.6 and (b) RCP8.5 scenarios from two models: Dynamic Bioclimate Envelope Model and dynamic size-spectrum foodweb model with the colour in each ocean region representing the projected level of change and the shading representing where both models agree in the direction of change (Cheung et al. 2018a). Also presented is the scaling between projected global atmospheric warming (relative to 1950–1961) and (c) changes in maximum fish catch potential and (d) species turnover using the Dynamic Bioclimate Envelope Model and outputs from three Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models (ESMs) (Cheung and Pauly, 2016). Model projections (a,b) are provided by Plymouth Marine Laboratory, Euro-Meditteranean Centre for Climate Change and Fisheries and Marine Ecosystem Impact Model Intercomparison Project (FISHMIP) of coastal ecosystems.

Observed fish catches have been related to NPP and water temperature, with the direction and magnitude of the relationship varying between regions and fish stocks (Cheung et al., 2008; McOwen et al., 2015; Britten et al., 2016; Stock et al., 2017). The maximum catch potential of large marine ecosystems generally increases with their NPP and energy transfer efficiency, but the relative importance of total NPP to fisheries production is lower in nutrient-poor systems with microbially-dominated foodwebs (Section 5.2) and empirical relationships between NPP and fisheries production over-estimate potential catches in polar regions (Stock et al., 2017) (Chapter 3). Here, potential fish catch or maximum catch potential refers to the potential of the fish stocks to provide long-term fish catches; it is considered a proxy of maximum sustainable yield (MSY). However, the actual catches realised by fisheries will depend strongly on past and present fishing effort and the exploitation status of the resources (Cheung et al., 2018a; Barange, 2019). Observed variations between regions suggest that changes in temperature and NPP in the past (Section 5.2.2, 5.2.3) may have also affected maximum catch potential (medium evidence, high agreement, medium confidence).

Changes in fish catches from 1998 to 2006 in 47 large marine ecosystems around the world were found to be significantly related to: changes in estimated cholorophyll a (a proxy for phytoplankton biomass) in 18 of these ecosystems (mostly tropical and eastern boundary upwelling systems); changes in SST in 12 of these ecosystems (mostly mid-latitude); and changes in fishing intensity in 16 of these ecosystems (widely spread) (McOwen et al., 2015). Analysis of population data since the 1950s for 262 fish stocks across 39 large marine ecosystems and the high seas suggest that average recruitment to the stocks has declined by around 3% of the historical maximum per decade with variations between regions and stocks (Britten et al., 2016). The declines (69% of the studied stocks, 31 of the 39 assessed large marine ecosystems) are significantly related to estimated chlorophyll a concentration and the intensity of fishing, with the North Atlantic showing the steepest declines (Britten et al., 2016). In addition, recent meta-analysis of population data from 235 fish stocks worldwide from 1930 to 2010 suggest that the maximum catch potential from these populations decreased by 4.1% (95% confidence span 9.0% decline to 0.3% increase) during this period with variations between fish stocks and regions (Free et al., 2019). Specifically, temperature is a significant factor explaining changes in catch potential of 12% of the fish stocks, with East Asian regions having the largest stock declines related to warming. In intermediate latitudes across the Atlantic, Indian and Pacific Oceans, catches of tropical tunas, including skipjack and yellowfin tuna, are significantly and positively related to increases in SST, although the overall catches across latitudinal zones do not show significant change (Monllor-Hurtado et al., 2017). Observational evidence from spatial and temporal linkages between catches and oceanographic variables therefore supports the conclusions from AR5 WGII and SR15 that potential fisheries catches have already been impacted by the effects of warming and changing primary production on growth, reproduction and survival of fish stocks (robust evidence, high agreement, high confidence).

There are substantial variations in the direction of changes and the attribution of climatic drivers between regions and fish stocks, and the availability of datasets is biased towards mid-latitude areas and epipelagic and coastal ecosystems. As a result, quantitative attribution of climate impacts on the productivity of specific fish stocks has low confidence. Changes in catch potential for fish stocks and regions worldwide that were considered overfished were most sensitive to warming (Essington et al., 2015; Britten et al., 2016; Free et al., 2019). This suggest that climatic drivers and overfishing have interacted synergistically in impacting some fish stocks and their catches (high confidence). In addition, analysis of historical catch records since AR5 show further warming related changes in species composition, with an increased dominance of warm water species in coastal and shelf seas since the 1970s (Cheung et al., 2013; Keskin and Pauly, 2014; Tsikliras et al., 2014; Maharaj et al., 2018). Many marine ecosystems worldwide have shown an increased dominance of warm water species following increases in sea water temperature (Section 5.2.3, 5.3), with parallel changes in the species composition of fish catches since the 1970s in many of the studied shelf seas (high confidence).

Based on CMIP5 ESM projections of changes in temperature, net primary production, oxygen, salinity and sea ice extent, two marine ecosystem and fisheries models project a decrease in maximum catch potential under RCP 2.6 of 3.9–8.5% by 2041-2060 and 3.4–6.4% by 2081-2100 relative to 1986-2005 (based on model projections described in Barange et al. 2018). Under RCP 8.5, the projected decrease was larger: 8.6–14.2% and 20.5–24.1% by the mid- and end- of the 21st century (Figure 5.18). The trends agree with the projected changes in total marine animal biomass for the 21st century (Blanchard et al., 2017; Lotze et al., 2018) (Section 5.2.3). A single fisheries model with atmospheric warming projected a potential catch loss of 3.4 million tonnes and decreases of 6.4% of catch potential of the exploited species per degree Celsius atmospheric warming relative to 1951–1960 level (Cheung et al., 2016b) (Figure 5.18). Interactions between temperature, net primary production and transfer efficiency of energy across the foodweb are projected to amplify these trends, with projected decreases greater than 50% in some regions by 2100 under high emissions scenarios (Stock et al., 2017). Thus, there is high model agreement that ocean warming and changes in NPP in the 21st century will reduce the global maximum catch potential, particularly in tropical oceans (high confidence) and alter the distribution and composition of exploited species (high confidence). The projected risk of these fisheries impacts increases with increasing greenhouse gas emissions (high confidence). However, given the uncertainties of projected changes in ocean conditions from ESMs (Section 5.2.2), and that most global scale fisheries models are largely driven by changes in temperature and primary production while other changes in ocean biogeochemical changes are not explicitly considered (Tittensor et al., 2018), the quantitative magnitude of the projected changes in maximum catch potential is considered to have medium and low confidence at global and regional scales, respectively. Given the significant interactions between catch potential and level of fisheries exploitation, the realised catches in the 21st century would depend on future scenarios of fishing and fisheries governance (Section 5.4.2, 5.5). As a result, projections of realised catches have low confidence.

Tropical oceans are projected to experience much larger impacts (three times or more decrease in catch potential) than the global average, particularly the western central Pacific Ocean, eastern central Atlantic Ocean and the western Indian Ocean, by the end of the 21st century under RCP8.5 (Blanchard et al., 2017). For example, around the exclusive economic zones of the Pacific Islands states, more than 50% of exploited fishes and invertebrates are projected to become locally extinct in many regions by 2100 relative to the recent past under RCP8.5 (Asch et al., 2018). These factors cause 74% of the area to experience a projected loss in catch potential of more than 50%. Under RCP2.6, the area of large projected catch loss is projected to be halved (Asch et al., 2018). However, while temperate commercially-important tunas species such as albacore, Atlantic and southern bluefin) are projected to shift poleward and decrease in abundance in the tropics, some tropical species such as skipjack tuna are projected to remain abundant, but with changes in distribution patterns in low-latitude regions by the mid-21st century, with some models projecting subsequent decrease under RCP8.5 (Lehodey et al., 2013; Dueri et al., 2014; Erauskin-Extramiana et al., 2019). Recent evidence therefore supports the conclusion from previous assessments (Pörtner et al., 2014; Hoegh-Guldberg et al., 2018) that low-latitude fish catch potential are projected to have a high risk of climate impacts, which will be exacerbated by higher greenhouse gas emissions (medium evidence, high agreement, high confidence). Tropical fish catch potential of some species resilient to the changing environment may have lower climate risk in the near-term although their risk increases substantially further into the 21st century under RCP8.5 (medium confidence). In contrast, the catch potential in the Arctic is projected to increase, although with high inter-model variability (medium evidence, low agreement, low confidence) (Cheung and Pauly, 2016; Blanchard et al., 2017) (Chapter 3). 

Although demersal fisheries in the deep ocean represent a small proportion of global fisheries catches, they are economically valuable for some countries, and there is increasing commercial interest in mesopelagic (deep pelagic ocean) fisheries (St. John et al., 2016). Commercially-exploited fish and shellfish from deep sea ecosystems will be exposed to climate risks from physical and chemical changes in ocean conditions including warming, decreased oxygen, reduced aragonite saturation state, and decreased supply of particulate organic matter from the upper ocean (Section 5.2.3, 5.2.4) (FAO, 2019). These biogeochemical changes may reduce the growth, reproduction and survivorship of deep-ocean fish stocks, which will alter their distributions, in similar ways to those in the surface ocean, impacting their fish catch potential (FAO, 2019). For example, in the eastern Pacific near-bottom oxygen concentration is positively correlated with biomass of commercially harvested species (Keller et al., 2010) and catch per unit effort (Banse, 1968; Rosenberg et al., 1983; Keller et al., 2015); some commercially harvested species only appear during oxygenation events associated with El Niño (Arntz et al., 2006). In the mesopelagic zone, expansion of the OMZ results in habitat compression that can increase catchability of fish stocks such as tunas (Prince et al., 2010; Stramma et al., 2011). Also, as OMZ expands, the potential may exist for increased availability and harvest of hypoxia-tolerant species such as Humboldt squid (Dosidicus gigas), thornyheads (Sebastolobus spp.) or dover sole (Microstomus pacificus) (Gilly et al., 2013; Gallo and Levin, 2016). However, any expansion of the OMZ will interact with other climatic hazards such as warming, which then adds to the overall risk of impacts on fish stocks and their catches (Breitburg et al., 2018). Overall, the abundance of fisheries resources and potential catches from the deep sea will be at high risk of impacts in the 21st century under RCP8.5 (low confidence), with reduced risk under RCP2.6 (medium confidence). 

In addition to capture fisheries, mariculture (marine aquaculture) is also an important marine ecosystem provisioning service, contributing about 27.7 million tonnes of seafood in 2016 (FAO, 2018). Recent projections of climate change impacts on mariculture, based on thermal tolerance and the effects of changing temperature, primary production and ocean acidification, suggest an overall decline in mariculture potential by 2100 under RCP8.5 with large regional variations (Froehlich et al., 2018). Modelling analyses for farmed Atlantic salmon, cobia and seabream also suggest that climate change would reduce their growth potential in ocean areas where temperature is projected to increase to levels outside the thermal tolerance ranges of these species (Klinger et al., 2017). This decrease in growth could therefore translate into a decrease in the general productivity of the sector (limited evidence, low confidence); however, new potential areas and the use of more climate resilient strains or species for mariculture may emerge that could reduce the risk of impacts on potential mariculture production (limited evidence, low confidence).

Regulating Services

Regulating services are those ecosystem functions, like climate regulation, that allow the environment to be in conditions conducive to human well-being and development (Costanza et al., 2017). AR5 WGII concluded that climate change will alter biological, chemical and physical processes in the ocean that provide feedback on the climate system through their effects on atmospheric composition (high confidence) (Pörtner et al., 2014). Sections 5.2 and 5.3 consider new evidence since AR5 regarding climate impacts on marine ecosystems and associated risks; their implications for regulating services are examined here.

A major regulating service provided by marine ecosystems is carbon sequestration. The observed net carbon uptake from the atmosphere to the global ocean varied between 1.0–2.5 GtC yr–1 between 2000 and 2012, with a very likely uptake of 30–38 Gt of anthropogenic C over the period 1994–2007 (Section, Gruber et al., 2019). Estimates of carbon sequestered in the deep ocean range from 0.4 GtC yr–1 (Rogers, 2015) to 1.6 GtC yr–1 (Armstrong et al., 2010) with the annual burial rate (permanent removal to sediment) around 0.2 GtC yr–1 (Armstrong et al., 2010). 

Deep sea ecosystems also contribute to the removal of methane released from the beneath the seabed through microbial anaerobic oxidation and the sequestration of methane-derived carbon in carbonate (Marlow et al., 2014; Thurber et al., 2014). In coastal ecosystems, carbon is biologically sequestered in coastal sediments, commonly known as ‘blue carbon’ (Section 5.5.1). Tidal wetlands play disproportionately important roles in coastal carbon budgets, forming critical linkages between rivers, estuaries, and oceans (Najjar et al., 2018). Mean carbon storage in the top meter of soil is estimated at 280 MgC ha–1 for mangroves, 250 MgC ha–1 for salt marshes, and 140 MgC ha–1 for seagrass meadows, with long-term rates of carbon accumulation in sediments of salt marshes, mangroves, and seagrasses ranging from 18–1713 gC m–2 yr–1 (Pendleton et al., 2012). These values are, however, highly variable (Section The large space and time scales mean that there is a long time-lag between seafloor change and detectable changes in carbon sequestration. These large lags, in turn render assessment of climate impacts on regulatory services in the deep ocean having low confidence

Under RCP2.6, CMIP5 ESMs project a reduced net ocean carbon uptake by 2080, to around 1.0 GtC yr–1. Under RCP8.5, net ocean carbon uptake increases to a net sink of around 5.5 GtC yr–1, but with variability between models (Lovenduski et al., 2016). Although the open ocean biological pump contributes only part of current carbon uptake (Boyd et al. 2019), the downward carbon flux at 1000 m is projected to decrease by 9–16% globally under RCP8.5 by 2100. A projected decrease in carbon sequestration in the North Atlantic by 27–41% has been estimated to represent a loss of 170‒3000 billion USD in abatement (mitigation) costs and 23–401 billion USD in social costs (Barange et al., 2017). Others have highlighted the declining value of open ocean carbon sequestration in the eastern tropical Pacific (Martin et al., 2016b) and the Mediterranean (Melaku Canu et al., 2015). The open ocean therefore seems very likely to reduce its carbon uptake by the end of the 21st century, with the reduction very likely being greater under RCP8.5 than for RCP2.6; however, specific projections only have medium confidence due to uncertainties associated with the structure of the models and with the future behaviour of the biological carbon pump (Section, 5.2.3). 

Coastal blue carbon ecosystems provide climate regulatory services through their carbon removal and storage (Section 5.3.3). The current rates of loss of blue carbon ecosystems, partly due to climate change (Section 5.3) results in release of their stored CO2 to the atmosphere (Section However, increases in carbon sequestration are also possible; for example, temperature-driven displacement of salt marsh plants by mangrove trees may increase carbon uptake in coastal wetlands (Megonigal et al., 2016). Different rates of SLR may have opposite effects, with potential increases in net carbon uptake for slowly rising sea levels (assuming inland habitat migration is possible), but net carbon release for more rapid SLR (Figure 5.19). Such contrasting feedbacks between scenarios arise from the different responses of plant biomass, sediment accretion and inundation that control the overall response of vegetated coastal ecosystems to rising sea level (Gonneea et al., 2019). Thus, under high emission scenarios, SLR and warming are expected to reduce carbon sequestration by vegetated coastal ecosystems (medium confidence); however, under conditions of slow SLR, there may be net increase in carbon uptake by some coastal wetlands (medium confidence)

Figure 5.19

Figure 5.19 | Biogeomorphic climate feedbacks involving plant biomass, sediment accretion and inundation that control the response of vegetated coastal ecosystems to rising sea levels. (A) Under high rates of soil formation plants are able to offset gradual sea level rise (SLR) and may produce a negative feedback by increasing the uptake of atmospheric CO2. […]

Figure 5.19 | Biogeomorphic climate feedbacks involving plant biomass, sediment accretion and inundation that control the response of vegetated coastal ecosystems to rising sea levels. (A) Under high rates of soil formation plants are able to offset gradual sea level rise (SLR) and may produce a negative feedback by increasing the uptake of atmospheric CO2. In addition, below ground root production contributes to the formation of new soils and consolidates the seabed substrates. (B) Under low rate of soil formation, and when SLRs exceed critical thresholds, plants become severely stressed by inundation leading to less organic accretion and below ground subsidence and decay, producing a positive feedback by net CO2 outgassing. This figure does not consider landward movements, controlled by topography and human land-use.

Coastal vegetation-rich ecosystems such as mangrove, coral reefs and salt marshes reduce storm impacts, protect the coastline from erosion, and help buffer the impacts of SLR, wave action and even moderate-sized tsunamis (Orth et al., 2006; Ferrario et al., 2014; Rao et al., 2015) (Section Their loss or degradation under climate change (Sections 5.3) would therefore reduce the benefits of these regulatory services to coastal human communities (Perry et al., 2018), increasing the risk of damage and mortality from natural disasters (Rao et al., 2015) (high confidence). In some locations where climate-induced range expansion of coastal wetlands occurs, regulatory services such as storm protection and nutrient storage may be enhanced; however, the replacement of an existing ecosystem by others (e.g., salt marshes replaced by mangroves) may reduce habitat availability for fauna requiring specific vegetation structure and consequently other types of ecosystem services (Kelleway et al., 2017b; Sheng and Zou, 2017).

Supporting Services

Supporting ecosystem services are structures and processes, such as habitats, biodiversity and productivity, that maintain the ecosystem functions that deliver other services (Costanza et al., 2017). Marine supporting services include: primary and secondary production; habitat provision for feeding, spawning or nursery grounds, and refugia; and biodiversity. All these provide essential support for provisioning, regulating or cultural services (Haines-Young and Potschin, 2013; Bopp et al., 2017). Therefore, climate change impacts on supporting services provided by marine ecosystems are directly dependent on the risks and impacts on their biodiversity and ecosystem functions, which are assessed in Sections 5.2.3, 5.2.4 and 5.3. Previously, AR5 highlighted the importance of the potential loss or degradation of habitat forming calcifying algae and corals, and the projected changes in waterways for Arctic shipping (Pörtner et al., 2014). The latter topic is considered in Chapter 3 and Section 

Publications since AR5 provide further evidence that coastal habitats are at risk from SLR, warming and other climate-related hazards (see Section 5.3). All these changes to supporting services have implications for other ecosystem services (Costanza et al., 2014), such as altering fish catches and their composition (Pratchett et al., 2014; Carrasquilla-Henao and Juanes, 2017; Maharaj et al., 2018) (Section and carbon sequestration (Section In the epipelagic ocean, climate change affects the pattern and magnitude of global NPP (Section and the export of organic matter; both these processes support ecosystem services in the deep ocean (Section 5.2.4) and elsewhere. Projected ocean acidification and oxygen loss will also affect deep ocean biodiversity and habitats that are linked to provisioning services in the deep ocean (Section, 5.2.4). Overall, there is high confidence that marine habitat loss and degradation have already impacted supporting services from many marine ecosystems worldwide. The confidence on the attribution of those impacts to climate change depends on the assessment of the ocean and coastal ecosystems (Section 5.2.3, 5.2.4, 5.3). Projected climate-driven alterations of marine habitats will increase the future risks of impacts on supporting services (high confidence).

Cultural Services

Cultural ecosystem services include recreation, tourism, aesthetic, cultural identity and spiritual experiences. These services are a product of humans experiencing nature and the availability of nature to provide the experiences (Chan et al., 2012). There is increasing evidence to support the conclusion in WGII AR5 that the intrinsic values and cultural importance of marine ecosystems, such as indigenous culture, recreational fishing and tourism, that are dependent on biodiversity and other ecosystem functions, are at risk from climate change. Since marine cultural services are inherently integrated with human well-being, their assessment is provided in Section 5.4.2. 


Climate Risk, Vulnerability and Exposure of Human Communities and their Well-being

Human communities heavily depend on the ocean through the goods and services provided by marine ecosystems (Section 5.4.1) (Hilmi et al., 2015). The values of ocean-based economic activities are estimated to be trillions of USD, generating hundreds of millions of jobs (Hoegh-Guldberg, 2015; Spalding, 2016). As climate change is impacting marine biodiversity and ecosystem services (Section 5.3.1), human communities and their well-being will also be affected. This section is based on diverse types of information, from quantitative modelling to qualitative studies, using expert opinion, local knowledge and Indigenous knowledge (Cross-Chapter Box 4 in Chapter 1). Projection and assessment of risk and vulnerabilities not only depend on climate change scenarios but are also strongly dependent on scenarios of future social-economic development (Cross-Chapter Box 1 in Chapter 1). 

This assessment divides the linkages between ecosystem services and human communities and their well-being into the three pillars of sustainable development, as used by the World Commission on Environment and Development. The three pillars are social and cultural, economic and environmental. Table 5.6 lists the specific dimensions under these pillars that are assessed in this section. Synthesis of risks and opportunities of climate change on human communities and well-being is at the end of this section through the lens of ocean economy and the United Nations’ Sustainable Development Goals (SDGs). 

Table 5.6

The social, cultural and economic dimensions assessed in Section 5.4.2.


Dimensions Sections under 5.4.2 
Human and environmental health  Water-borne diseases (

Harmful algal blooms (HABs) (Box 5.4)

Interactions with contaminants (

Food security (

Culture and other social dimensions Cultural and aesthetic values (

Potential conflicts in resource utilisation (

Monetary and material wealth Fisheries (

Coastal and marine tourism (

Property values and coastal infrastructure (

Human Health and Environmental Health
Water-borne diseases

SR15 concluded that climate change will result in an aerial expansion and increased risk of water-borne disease with regional differences (high to very high confidence)(Hoegh-Guldberg et al., 2018). AR5 concluded that warming, excessive nutrient and seawater inundation due to SLR are projected to exacerbate the expansion and threat of cholera (Pörtner et al., 2014) (medium confidence). This assessment focuses on health risks caused by Vibrio bacteria and HABs. Vibrio cholerae (causing cholera) is estimated to be responsible for around 760,000 and 650,000 cases of human illness and death respectively in the world in 2010 (Kirk et al., 2015). An assessment of HABs is given in Box 5.4. 

Vibrio species naturally occur in warm, nutrient-rich and low salinity coastal waters. Since AR5, analysis of the the Continuous Plankton Recorder dataset (Section 5.2.3) has shown a significant increase in Vibrio abundance in the North Sea over the period 1958‒2011 related to sea surface warming (Vezzulli et al., 2016). Other time series data have confirmed a poleward expansion of Vibrio pathogens in mid- to high-latitude regions, ascribed at least partly to climate change (Baker-Austin et al., 2013; Baker-Austin et al., 2017). Extreme weather events such as flooding and tropical cyclones are also linked to increased incidences of Vibrio-related disease, suggested to be caused by the increased exposure of human populations to the pathogens during these extreme events (Baker-Austin et al., 2017). New evidence since AR5 therefore increases support for the linkages between warming, extreme weather events and increased risk of diseases caused by Vibrio bacteria (very high confidence).

Extrapolating from the observed relationship between environmental conditions and current Vibrio distributions, coastal areas that experience future warming, changes in precipitation and increases in nutrient inputs can be expected to see an increase in prevalence of Vibrio pathogens. These effects have been simulated in a global-scale model that relates occurrences of Vibrio with SST, pH, dissolved oxygen and chlorophyll a concentration under the SRES B1 scenario (Escobar et al., 2015). In the Baltic Sea, a nearly two-fold increase in the area suitable for Vibrio is projected between 2015 and 2050 for both RCP4.5 and RCP8.5 scenarios (relating to projected SST increase of 4°C‒5°C), resulting in an elevated risk of Vibrio infections (Semenza et al., 2017). Projected conditions of increased coastal flooding from storm surges and SLR (Section 5.2.2) will also increase exposure to waterborne disease (Ashbolt, 2019), such as Vibrio (medium confidence). However, uncertainty in the socioeconomic factors affecting the future vulnerabilities of human populations render quantitative projections of the magnitude of health impacts uncertain (Lloyd et al., 2016).
Interactions between climate change and contaminants

Climate change–contaminant interactions can alter the bioaccumulation and amplify biomagnification of several contaminant classes (Boxall et al., 2009; Alava et al., 2018). This section assesses two types of contaminants that are of concern to environmental and human health as examples of other contaminants with similar properties (Alava et al., 2017). These two types of contaminants are the toxic and fat-soluble persistent organic pollutants (POPs), such as polychlorinated biphenyls (PCBs), as well as the neurotoxic and protein-binding organic form of mercury, methylmercury (MeHg) (Alava et al., 2017). POPs and MeHg are bioaccumulated by marine organisms and biomagnified in food webs, reaching exposure concentrations that become harmful and toxic to populations of apex predators such as marine mammals (Desforges et al., 2017; Desforges et al., 2018) (Figure 5.20). Human exposure to POPs and MeHg can lead to serious health effects (Ishikawa and Ikegaki, 1980; UNEP, 2013; Fort et al., 2015; Scheuhammer et al., 2015).

Figure 5.20

Figure 5.20 | The pathways through which scenario of climatic and pollutant hazards (orange boxes) and their interactions can lead to increases in exposure to hazards by the biota, ecosystems and people, their sensitivity (blue box) and the risk of impacts to ecosystem and human health and societies (red box). Such risks will interact with […]

Figure 5.20 | The pathways through which scenario of climatic and pollutant hazards (orange boxes) and their interactions can lead to increases in exposure to hazards by the biota, ecosystems and people, their sensitivity (blue box) and the risk of impacts to ecosystem and human health and societies (red box). Such risks will interact with climate-pollutant risk management and policy. The synthesis is based on literature review presented in Alava et al. (2017). Figure adapted from Alava et al. (2017).

Inorganic forms of mercury are more soluble in low pH water, while higher temperature increases mercury uptake and the metabolic activity of bacteria, thereby increasing mercury methylation, uptake by organisms and bioaccumulation rates (Scheuhammer, 1991; Celo et al., 2006; López et al., 2010; Macdonald and Loseto, 2010; Riget et al., 2010; Corbitt et al., 2011; Krabbenhoft and Sunderland, 2013; Roberts et al., 2013; de Orte et al., 2014; McKinney et al., 2015), although there is limited evidence on the extent of exacerbation by ocean acidification expected in the 21st century. Increased melting of snow and ice from alpine ecosystems and mountains (Chapter 2) can also increase the release of POPs and MeHg from land-based sources into coastal ecosystems (Morrissey et al., 2005). Modelling projections for the Faroe Islands region suggest increased bioaccumulation of methyl mercury under climate change, with an average increases in MeHg concentrations in marine species of 1.6‒1.8% and 4.1‒4.7% under ocean warming scenarios of 0.8°C and 2.0°C, respectively, with an associated increase in potential human intake of mercury beyond levels recommended by the World Health Organization (Booth and Zeller, 2005). Foodweb modeling for the northeastern Pacific projects that concentrations of MeHg and PCBs in top predators could increase by 8% and 3%, respectively, by 2100 under RCP8.5 relative to current levels (Alava et al., 2018). Climate-related pollution risks are of particular concern in Arctic ecosystems and their associated indigenous communities because of the bioaccumulation of POPs and MeHg, causing long-term contamination of traditional seafoods (Marques et al., 2010; Tirado et al., 2010; Alava et al., 2017) of high dietary importance (Cisneros-Montemayor et al., 2016). 

Overall, climate change can increase the exposure and bioaccumulation of contaminants and thus the risk of impacts of POPs and MeHg on marine ecosystems and their dependent human communities as suggested by indirect evidence and model simulations (Marques et al., 2010; Tirado et al., 2010; Alava et al., 2017) (high agreement). However, there is limited evidence on observed increase in POPs and MeHg due to climate change. Apex predators and human communities that consume them, including Arctic communities and other coastal indigenous populations, are thus vulnerable to increase in exposure to these contaminants and the resulting health effects (medium evidence, medium agreement). 

The risk of microplastics has become a major concern for the ocean as they are highly persistent and have accumulated in many different marine environments, including the deep sea (Woodall et al., 2014; GESAMP, 2015; van Sebille et al., 2015; Waller et al., 2017; de Sá et al., 2018; Everaert et al., 2018; Botterell et al., 2019). There is limited evidence at present to assess their risk to marine ecosystems, wildlife and potentially humans through human consumption of seafood under climate change.
Food security

Seafood provides protein, fatty acids, vitamins and other micronutrients essential for human health such as iodine and selenium (Golden et al., 2016). Over 4.5 billion people in the world obtain more than 15% of their protein intake from seafood, including algae and marine mammals as well as fish and shellfish (Béné et al., 2015; FAO, 2017)). Around 1.39 billion people obtain at least 20% of their supply of essential micronutrients from fish (Golden et al., 2016). SR15 concluded that global warming poses large risks to food security globally and regionally, especially in low-latitude areas, including fisheries (medium confidence) (Hoegh-Guldberg et al., 2018). This section builds on the assessment on observed and projected climate impacts on fish catches (Section and further assess how such impacts interact with other climatic and non-climatic drivers in affecting food security through fisheries.

Many populations that are already facing challenges in food insecurity reside in low-latitude regions such as in the Pacific Islands and West Africa where maximum fisheries catch potential is projected to decrease under climate change security (Golden et al., 2016; Hilmi et al., 2017) (Section 5.4.1; Figure 5.21) and where land-based food production is also at risk (Blanchard et al., 2017) (medium confidence). Populations in these regions are also estimated to have the highest proportion of their micronutrient intake relative to the total animal sourced food (Golden et al., 2016) (ASF; Figure 5.21). This highlights their strong dependence on seafood as a source of nutrition that further elevates their vulnerability to food security from climate change impacts on seafood supply (high confidence). Modeling of seafood trade networks suggests that Central and West African nations are particularly vulnerable to shocks from decrease in seafood supply from international imports; thus their climate risks of seafood insecurity could be exacerbated by climate impacts on catches and seafood supply elsewhere (Gephart et al., 2016). In addition, experimental studies suggest that warming and ocean acidification reduce the nutritional quality of some seafood by reducing levels of protein, lipid and omega-3 fatty acids (Tate et al., 2017; Ab Lah et al., 2018; Lemasson et al., 2019). 

Non-climatic factors may exacerbate climate effects on seafood security. Over-exploitation of fish stocks reduces fish catches (Section (Golden et al., 2016), whilst strong cultural dependence on seafood in many coastal communities may pose constraints in their adaptive capacity to changing fish availability (Marushka et al., 2019). The shift from traditional nutritious wild caught seafood-based diets of coastal indigenous communities, towards increased consumption of processed energy dense foods high in fat, refined sugar and sodium, due to social and economic changes (Kuhnlein and Receveur, 1996; Shannon, 2002; Charlton et al., 2016; Batal et al., 2017), has important consequences on diet quality and nutritional status (Thaman, 1982; Quinn et al., 2012; Luick et al., 2014). This has led to an increased prevalence of obesity, diabetes, and other diet-related chronic diseases (Gracey, 2007; Sheikh et al., 2011) as well as the related decrease in access to culturally or religiously significant food items. The risk of climate change on coastal communities through the ocean could therefore be increased by non-climatic factors such as economic development, trade, effectiveness of resource governance and cultural changes (high confidence). 

In summary, the food security of many coastal communities, particularly in low-latitude developing regions, is vulnerable to decreases in seafood supply (medium confidence) because of their strong dependence on seafood to meet their basic nutritional requirements (medium confidence), limited alternative sources of some of the essential nutrients obtained from seafood (medium confidence), and exposure to multiple hazards on their food security (high confidence). Although direct evidence from attribution analysis is not available, climate change may have already contributed to malnutrition by decreasing seafood supply in these vulnerable communities (low confidence) and reduce coastal Indigenous communities’ reliance on seafood-based diets (low confidence). Projected decreases in potential fish catches in tropical areas (high confidence) and a possible decrease in the nutritional content of seafood (low confidence) will further increase the risk of impacts on food security in low-latitude developing regions, with that risk being greater under high emission scenarios (medium confidence).

Figure 5.21

Figure 5.21 | Over the ocean the projected changes in catch potential (Section, and on land, each countries current proportion of fish micronutrient intake relative to the total animal sourced food (ASF) (Golden et al. 2016). The colour scale on land is the proportion of fish micronutrient intake relative to the total ASF; the […]

Figure 5.21 | Over the ocean the projected changes in catch potential (Section, and on land, each countries current proportion of fish micronutrient intake relative to the total animal sourced food (ASF) (Golden et al. 2016). The colour scale on land is the proportion of fish micronutrient intake relative to the total ASF; the scale on the ocean is projected change in maximum catch potential under Representative Concentration Pathway (RCP)8.5 by 2100 relative to the 2000s.

Cultural and Other Social Dimensions
Cultural and aesthetic values

Climate change threatens key cultural dimensions of lives and livelihoods (Adger et al., 2012), because people develop strong cultural ties and associate distinctive meanings with many natural places and biota in the form of traditions, customs and ways of life (Marshall et al., 2018). These impacts have been felt both by indigenous and non-indigenous peoples. Recent estimates suggest that there are more than 1900 indigenous groups along the coastline with around 27 million people across 87 countries (Cisneros-Montemayor et al., 2016). AR5 concluded that climate change will affect the harvests of marine species with spiritual and aesthetic importance to indigenous cultures (Pörtner et al., 2014). This section further assesses the effects of climate change on Indigenous knowledge and local knowledge and their transmission and the implication for well-being of people, complementing the assessment for Arctic indigenous people in Chapter 3. 

Indigenous knowledge is passed and appreciated over timeframes ranging from several generations to a few centuries (Cross Chapter Box 4 in Chapter 1). The adjustment of the transmission and the network of Indigenous knowledge on the ocean and coasts, and related perceptions and practice, implies a reworking of these knowledge systems where the individuals and the groups are actors in a narrative and historical construction (Roué, 2012; Alderson-Day et al., 2015). SLR is already transforming the seascape, such as the shape of shores in many low-lying islands in the Pacific, leading to modification or disappearance of geomorphological features that represent gods and mythological ancestors (Camus, 2017; Kench et al., 2018). These changing seascape also affects the mobility of people and residence patterns, and consequently, the structure and transmission of Indigenous knowledge (Camus, 2017). The fear of SLR and climate change encourage security measures and the grouping of local people to the safest places, contributing to the erosion of indigenous culture and their knowledge about the ocean (Bambridge and Le Meur, 2018), and impairment of opportunity for social elevation for some Pacific indigenous communities (Borthwick, 2016). 

Climate change is also projected to shift the biogeography and potential catches of fishes and invertebrates (, 5.3, that form an integral part of the culture, economy and diet of many indigenous communities, such as those situated along the Pacific Coast of North America (Lynn et al., 2013). Indigenous fishing communities that depend on traditional marine resources for food and economic security are particularly vulnerable to climate change through reduced capacity to conduct traditional harvests because of reduced access to, or availability of, resources (Larsen et al., 2014; Weatherdon et al., 2016). Overall, the transmission of indigenous culture and knowledge is at risk because of SLR affecting sea- and land-scapes, the availability and access to culturally important marine species, and communities’ reliance on the ocean for their livelihood and their cultural beliefs (medium confidence). Strong attachment to traditional marine-based livelihoods has also been reported for non-indigenous communities in Canada (Davis, 2015), the USA (Paolisso et al., 2012), Spain (Ruiz et al., 2012) and Australia (Metcalf et al., 2015). Reduction in populations of fish species that have supported livelihoods for generations, and deteriorations of iconic elements of seascapes are putting the well-being of these communities at risk (high confidence).

Other cultural values supported by the ocean are diverse. They include education, based on knowledge of marine environments. Such education can increase knowledge and awareness of climate change impacts and the efficacy of their mitigation (Meadows, 2011); it can also influence the extent to which stewardship activities are adopted (von Heland et al., 2014; Wynveen and Sutton, 2015; Bennett et al., 2018), and can help develop new networks between coastal people and environmental managers for the purposes of planning and implementing new adaptation strategies (Wynveen and Sutton, 2015). A critical element in reducing vulnerability to climate change is to educate people that they are an integral part of the Earth system and have a huge influence on the balance of the system. An important marine ecosystem service is to support such education (Malone, 2016). Thus, education can play a pivotal role in how climate change is perceived and experienced, and marine biodiversity and ecosystems play an important role in this. At the same time, climate change impacts on marine ecosystems (Sections 5.2.3, 5.2.4) can affect the role of the ocean in supporting such public education (medium evidence, high agreement, medium confidence).

The aesthetic appreciation of natural places is one of the fundamental ways in which people relate to their environment. AR5 noted that climate change may impact marine species with aesthetic importance that affect local and indigenous cultures, local economies and challenge cultural preservation (Pörtner et al., 2014). Evidence since AR5 confirms that aesthetically appreciated aspects of marine ecosystems are important for supporting local and international economies (especially through tourism), human well-being, and stewardship. For example, Marshall et al. (2018) found that aesthetic values are a critically important cultural value for all cultural groups, and are important for maintaining sense of place, pride, identity and opportunities for inspiration, spirituality, recreation and well-being. However, climate change induced degradation and loss of biodiversity and habitats (Section 5.2.3, 5.2.4, 5.3) can also negatively impact the ecosystem features that are currently appreciated by human communities, such as coral reefs, mangroves, charismatic species (such as some marine mammals and seabirds) and geomorphological features (e.g., sandy beaches). There are also aesthetic and inspirational values of marine biodiversity and ecosystems that are important to the psychological and spiritual well-being of people, including film, literature, art and recreation (Pescaroli and Magni, 2015). Other cultural dimensions that are becoming more widely acknowledged as potentially disturbed by climate change include the appreciation of scientific, artistic, spiritual, and health opportunities, as well as appreciation of biodiversity, lifestyle and aesthetics (Marshall et al., 2018). Thus, climate change may also affect the way in which marine ecosystems support human well-being through cultural dimensions. However, the difficulties in evaluating the importance of aesthetic aspects of marine ecosystems, and in detecting and attributing of climate change impacts, result in such assessment having low confidence

Climate change affects human cultures and well-being differently. For example, Marshall et al. (2018) assessed the importance of identity, pride, place, aesthetics, biodiversity, lifestyle, scientific value and well-being within the Great Barrier Reef region by 8,300 people across multiple cultural groups. These groups included indigenous and non-indigenous local residents, Australians (non-local), international and domestic tourists, tourism operators, and commercial fishers. They found that all groups highly rated all (listed) cultural values, suggesting that these values are critically associated with iconic ecosystems. Climate change impacts upon the Great Barrier Reef, through increased temperatures, cyclones and SLR that cumulatively degrade the quality of the Reef, are therefore liable to result in cultural impacts for all groups. However, survey that assess the emotional responses to degradation of the Great Barrier Reef by similar stakeholder groups reported different levels of impacts among these groups (Marshall et al., 2019). Therefore, many ocean and coastal dependent communities value marine ecosystems highly and climate impacts can affect their well-being, although the sensitivity to such impacts can vary among stakeholder groups (Marshall et al., 2019) (low confidence).

Climate change may alter the environment too rapidly for cultural adaptation to keep pace. This is because the culture that forms around a natural environment can be so integral to people’s lives that disassociation from that environment can induce a sense of disorientation and disempowerment (Fisher and Brown, 2015). The adaptive capacity of people to moderate or influence cultural impacts, and thereby reduce vulnerability to such impacts, is also culturally determined (Cinner et al., 2018). For example, when a resource user such as a fisher, farmer, or forester is suddenly faced with the prospect that their resource-based occupation is no longer viable, they lose not only a means of earning an income but also an important part of their identity (Marshall et al., 2012; Tidball, 2012). Loss of identity can, in turn, have severe economic, psychological and cultural impacts (Turner et al., 2008). Climate change can quickly alter the quality of, or access to, a natural resource through degradation or coastal inundation, so that livelihoods and lifestyles are no longer able to be supported by that resource. When people are displaced from places that they value, there is strong evidence that their cultures are diminished, and in many cases endangered. There are no effective substitutions for, or adequate compensation for, lost sites of significance (Adger et al., 2012). As sensitive marine ecosystems such as coral reefs and kelp forest are impacted by climate change at rapid rate (Section 5.3), these can lead to the loss of part of people’s cultural identity and values beyond the rate at which identify and values can be adjusted or substituted (medium confidence).
Potential conflicts in resource utilisation

Redistribution of marine species in response to direct and indirect effects of climate change may also disrupt existing marine resource sharing and associated governance (Miller and Russ, 2014; Pinsky et al., 2018). These effects have contributed to disputes in international fisheries management for North Atlantic mackerel (Spijkers and Boonstra, 2017) and Pacific salmon (Miller and Russ, 2014). These disagreements have stressed diplomatic relations in some cases (Pinsky et al., 2018). Decreases and fluctuations in fish stock abundance and fish catches have also contributed to past disputes (Belhabib et al., 2016; Pomeroy et al., 2016; Blasiak et al., 2017). Under climate change, shifts in abundance and distribution of fish stocks are projected to intensify in the 21st century (Sections 5.2.3, 5.3, Stocks may locally increase and decrease elsewhere. New or increased fishing opportunities may be created when exploited fish stocks shift their distribution into a country’s waters where their abundance was previously too low to support viable fisheries (Pinsky et al., 2018). The number of new transboundary stocks occurring in exclusive economic zones worldwide was projected to be around 46 and 60 under RCP2.6 and RCP8.5, respectively, by 2060 relative to 1950‒2014 (Pinsky et al., 2018). However, such alteration of the sharing of resources between countries would challenge existing international fisheries governance regimes and, without sufficient adaptation responses, increase the potential for disputes in resource allocation and management (Belhabib et al., 2018; Pinsky et al., 2018). Overall, projected climate change impacts on fisheries in the 21st century increase the risk of potential conflicts among fishery area users and authorities or between two different communities within the same country (Ndhlovu et al., 2017; Shaffril et al., 2017; Spijkers and Boonstra, 2017) (medium confidence), exacerbated through competing resource exploitation from international actors and mal-adapted policies (low confidence). Such risks can be reduced by appropriate fisheries governance responses that are discussed in Sections 5.5.2 and 5.5.3.

Monetary and Material Wealth
Wealth generated from fisheries

Global gross revenues from marine fisheries were around 150 billion in 2010 USD (Swartz et al., 2013; Tai et al., 2017). Capture fisheries provide full-time and part-time jobs for an estimated 260 ± 6 million people in the 2000s period, of whom 22 ± 0.45 million are smallscale fishers (Teh and Sumaila, 2013). Small-scale fisheries are important for the livelihood and viability of coastal communities worldwide (Chuenpagdee, 2011). AR5 concluded with low confidence that climate change will lead to a global decrease in revenue with regional differences that are driven by spatial variations of climate impacts on and the flexibility and capacities of food production systems (Pörtner et al., 2014). AR5 also highlighted the high vulnerability of mollusc aquaculture to ocean acidification. For example, the oyster industry in the Pacific has lost nearly 110 million USD in annual revenue due to ocean acidification (Ekstrom et al., 2015). This section examines the rapidly growing literature assessing the risks of climate change on fisheries and aquaculture sectors, and the potential interaction between climatic and non-climatic drivers on the economics of fisheries. However, new evidence on observed economic impacts of climate change on fisheries since AR5 is limited. 

Since AR5, projections on climate change impacts on the economics of marine fisheries have incorporated a broader range of social-economic considerations. Driven by shifts in species distributions and maximum catch potential of fish stocks (Section 5.4.1), if the ex-vessel price of catches remains the same, marine fisheries maximum revenue potential are projected to be negatively impacted in 89% of the world’s fishing countries under the RCP8.5 scenario by the 2050s relative to the current status, with projected global decreases of 10.4 ± 4.2% and 7.1 ± 3.5% under RCP8.5 and RCP2.6, respectively, by 2050 relative to 2000 (Lam et al., 2016). While the projected changes in revenues are sensitive to price scenarios (Lam et al., 2016), future maximum revenue potential is reduced under high emission scenarios (Sumaila et al., 2019). For example, when the elasticity of seafood price in relation to their supply was modelled explicitly, fisheries maximum revenue potential under a 1.5°C atmospheric warming scenario was projected to be higher than for 3.5°C warming by 7.4% (13.1 billion USD) ± 2.3%, across projections from three CMIP5 models (Sumaila et al., 2019). Accounting for the subsequent impacts on the dependent communities and relative to the 1.5°C warming scenario, that study also projected a decrease in seafood workers’ incomes of 7.8% (3.7 billion USD) ± 2.3% and an increase in households’ seafood expenditure by the global population of 3.2% (6.3 billion USD) ± 3.9% annually under a 3.5°C warming scenario (Sumaila et al., 2019). 

Fisheries management strategies and fishing effort affect the realised catch and economic benefits of fishing (Barange, 2019). Modelling analysis of fish stocks with available data worldwide showed that for RCP6.0, adaptation of fisheries by accommodating shifts in species distribution and abundance, as well as rebuilding existing overexploited or depleted fish stocks, is projected to lead to substantially higher global profits (154%), harvest (34%), and biomass (60%) in the future, relative to a no adaptation scenario. However, the total profit, harvest and biomass are negatively affected even with the full adaptation scenario under RCP8.5 (Gaines et al., 2018). Overall, climate change impacts on the abundance, distribution and potential catches of fish stocks (see Section 5.3.1) are expected to reduce the maximum potential revenues of global fisheries (high agreement, medium evidence, medium confidence). These impacts on fisheries will increase the risk of impacts on the income and livelihoods of people working in these economic sectors by 2050 under high greenhouse gas emission scenarios relative to low emission scenario (high confidence). Rebuilding overexploited or depleted fisheries can help improve economic efficiency and reduce climate risk, provided that emissions are greatly reduced (medium confidence).

The economic implications of climate change on fisheries vary between regions and countries because of the differences in exposure to revenue changes and the sensitivity and adaptive capacity of the fishing communities to these changes (Hilmi et al., 2015). Regions where the maximum potential revenue is projected to decrease coincide with areas where indicators such as human development index suggest high economic vulnerability to climate change (Barbier, 2015; Lam et al., 2016). Many coastal communities in these regions rely heavily on fish and fisheries as a major source of animal proteins, nutritional needs, income and job opportunities (FAO, 2019). Negative impacts on the catch and total fisheries revenues for these countries are expected to have greater implications for jobs, economies, food and nutritional security than the impacts on regions with high Human Development Index (Allison et al., 2009; Srinivasan et al., 2010; Golden et al., 2016; Blasiak et al., 2017). Climate change impacts to coral reefs and other fish habitats, as well as to targeted fish and invertebrate species themselves are expected to reduce harvests from small-scale, coastal fisheries by up to 20% by 2050, and by up to 50% by 2100, under RCP8.5 (Bell et al., 2018a). Therefore, climate risk to communities that are strongly dependent on fisheries associated with ecosystems that are particularly sensitive to climate change such as coral reefs will have be particularly high (Cinner et al., 2016) (high confidence). 

Climate change may also worsen non-climate related socioeconomic shocks and stresses, and hence is an obstacle to economic developments (Hallegatte et al., 2015). Climate risk on the economics of fishing is projected to be higher for tropical developing countries where existing adaptive capacity to the risk is lower, thereby challenging their sustainable economic development (high confidence). However, observed impacts are not yet well documented (Lacoue-Labarthe et al., 2016) , and there are many uncertainties relating to how climate change would affect the dynamics of fishing costs, with consequent adjustment of fishing effort that might intensify or lessen the overcapacity issue. Studies have attempted to project how fishers may respond to changes in fish distribution and abundance by incorporating different management systems (Haynie and Pfeiffer, 2012; Galbraith et al., 2017). However, the impacts of climate change on management effectiveness and trade practices is still inadequately understood (Galbraith et al., 2017).
Wealth generated from coastal and marine tourism sector

Tourism is one of the largest sectors in the global economy. Between 1995‒1998 and 2011‒2014, the average total contribution of tourism to global GDP increased from 69 billion USD (6.8%) to 166 billion USD (8.5%) respectively, and generated more than 21 million jobs between 2011‒2014 (UNCTAD, 2018). Coastal tourism and other marine-related recreational activities contributes substantially to the tourism sector (Cisneros-Montemayor et al., 2013; O’Malley et al., 2013; Spalding et al., 2017; Giorgio et al., 2018; UNWTO, 2018). For example, it is estimated that around 121 million people a year participated in marine-based recreational activities, generating  47 billion in 2003 USD in expenditures and supporting one million jobs (Cisneros-Montemayor and Sumaila, 2010). Tourism is one of the main industries that provides opportunities for social and economic development (Jiang and DeLacy, 2014), and marine tourism is particularly important for many coastal developing countries and Small Island Developing States (SIDS). AR5 identified the tourism sector in the Caribbean region as particularly vulnerable to climate change effects, due to hurricanes, whilst SR15 concluded that warming will directly affect climate-dependent tourism markets on a worldwide basis (medium confidence) (Hoegh-Guldberg et al., 2018). This assessment provides updates since AR5 and SR15. 

Empirical modelling of future risks to tourism is based on projected climate impacts (Section 5.3) for relevant coastal ecosystems, including degradation or loss of beach and coral reef assets (Weatherdon et al., 2016) (Section These projections are developed from the relationship between the economic benefits generated from coral reef related tourism with observed characteristics of coral reefs, the characteristics of tourism activities. Based on scenarios of projected future warming and decreases in coral reef coverage, a global loss of tourism and recreation value in the near-future (2031‒2050) of 2.57–2.95 billion yr-1 in 2000 USD is projected under RCP2.6, and of 3.88‒5.80 billion yr-1 in 2000 USD under RCP8.5 (Chen et al., 2015). Opinion surveys in four countries suggest that if severe coral bleaching persists in the Great Barrier Reef, tourism in adjacent areas could greatly decline, from 2.8 million to around 1.7 million visitors per year, equivalent to more than 1 billion AUS (~0.69 billion USD using exchange rate in 2019), that is, in tourism expenditure and with potential loss of around 10,000 jobs (Swann and Campbell, 2016). 

Many coastal tourism destinations are exposed to risks of flooding, SLR and coastal squeeze on coastal ecosystems (Lithgow et al., 2019) (Section 5.3); there are also other climate related-risks. Droughts, which are projected to be more frequent, will also impact the tourism industry (and local food security) through water and food shortages (Pearce et al., 2018). If climate change and ocean acidification reduce the seafood supply, the attractiveness of coastal regions for tourists will also decrease (Wabnitz et al., 2017). North Atlantic hurricanes and tropical storms have increased in intensity over the last 30 years, with climate projections indicating an increasing trend in hurricane intensity (Chapter 6). Three major Caribbean storms, Harvey, Irma and Maria, occurred in 2017, with loss and damage to the tourism industries of Dominica, the British Virgin Islands, and Antigua and Barbuda estimated at 2.2 billion USD, and environmental recovery costs estimated at 6.8 million USD (UNDP, 2017). Pacific tourist destinations, which tend to focus on nature-based and marine activities, are also at high risk of extreme events and other climate change impacts (Klint et al., 2015). However, global tourism has a high carbon footprint (flights, cruises, etc.) (Lenzen et al., 2018), so any reduction in the intensity of this sector would help mitigate climate change. 

Evidence from recent studies on projected climate risks on recreational fishing is equivocal, with the direction of impacts depending on the location, species targeted and societal context. For example: poleward range shifts of marine fish (Section 5.2.3) could yield new opportunities for recreational fishing in mid- to high-latitude regions (DiSegni and Shechter, 2013); projected increases in air temperature may enable longer fishing days in some area (Dundas and von Haefen, 2015); and extreme events may alter the composition of recreational fishing catches (Santos et al., 2016). Since climate risks to recreational fishing vary largely depending on the responses of the targeted species to climate-related pressures, there is low confidence in the overall risk to the activity. 

Overall, evidence since AR5 and SR15 confirms that climate impacts to coastal ecosystems would increase risks to coastal tourism, particularly under high emission scenarios (medium confidence). Economic impacts will be greatest for those developing countries where tourism is the main source of foreign revenue (medium to high evidence).
Property values

The integrity of ecosystems and their services can affect the value of human assets, particularly coastal properties and infrastructure (Hoegh-Guldberg et al., 2018). Climate change is expected to have negative impacts on coastal properties and their value through the loss and damage caused by SLR, increased storm intensity (hurricanes and cyclones), heat waves, floods, droughts and other extreme events, particularly in tropical SIDS (Chapter 4). Natural disasters already cost Pacific Island Countries and Territories between 0.5‒6.6% of GDP yr-1 (World Bank, 2017), with localised damages and losses from individual storms far exceeding these estimates (e.g., 64% of Vanuatu’s GDP for Cyclone Pam in 2015). The impacts of natural disasters on Jamaica’s coastal transport infrastructure are currently estimated to be a significant proportion of their GDP, and such costs are projected to increase substantially in the next few decades under climate change (UNCTAD, 2017; Monioudi et al., 2018). In 2015, tropical storm Erika devastated Dominica causing 483 million USD in damages and losses (mostly related to transport, housing and agriculture), equivalent to 90% of Dominica’s GDP (World Bank, 2017). For the USA, Ackerman and Stanton (2007) forecast that annual real estate losses due to climate change could increase from 0.17% of GDP in 2025 to 0.36% in 2100, with Atlantic and Gulf Coast states being the most vulnerable. Other North American studies have shown that informed coastal property owners are willing to initially invest in infrastructure to counter climate change impacts (McNamara and Keeler, 2013); however, they would avoid further investment if adaptation costs increase substantially and there are greater risks of long-term impacts (Putra et al., 2015).

The impacts of changing marine ecosystems and ecosystem services on the value of human assets need to consider the risk perception, future development and adaptation responses of human communities (Section 5.5.2, Chapter 4) (Bunten and Kahn, 2014). For example, the potential for climate impacts on the value of coastal real estate will depend on the changing insurance market or the cost of adaptation measures, which in turn depend on the willingness to pay by asset holders and wider society, including local and national governments. Further research is needed to discount valuations for potential losses that may occur in the future but with uncertain occurrence, and to improve real estate loss estimates over local to regional scales. 

Marine ecosystem services contribute to climate moderation and coastal defenses (Section However, while the above studies in this section acknowledge the contribution of many climate impacts on real estate and infrastructure through ecosystem losses and degradation, often they are not accounted for in quantitative economic impact assessments. Overall, there is high confidence that SLR, increases in storm intensity and other extreme events will impact the values of coastal real estates and infrastructure, particularly in tropical SIDS, through the risk and impacts of direct physical damages. However, there is low confidence that impacts due to underlying loss and damage of ecosystems and their services are being similarly accounted for.

Risk and Opportunities for Ocean Economy

The ‘ocean economy’ refers to the sustainable use of ocean resources for economic growth, improved livelihoods and jobs, and ocean ecosystem health (World Bank, 2017). In SR15 (Hoegh-Guldberg et al., 2018) and elsewhere here (Chapters 3 and 5), the risks and opportunities of specific sectors that contribute to the ocean economy under climate change are assessed. The fishing industry is particularly important in this context. As previously noted, warming has already directly impacted coastal and open ocean fishing activities in some regions (Section,; the risk of fishery impacts is exacerbated by the observed climate-driven changes to coral reefs and other coastal ecosystems that contribute to the productivity of exploited fish species (Section,; and there are challenges to sustainable management of transboundary fisheries resources caused by species’ range shifts and associated governance challenges (Section

Fisheries-related national and local economies of many tropical developing countries are exposed high climate risks (Section (Blasiak et al., 2017), as a result of the projected large decrease in maximum catch and revenue potential under RCP8.5 in the 21st century (Section Historical examples from fishery over-exploitation indicate that a large decrease in catches for specific fish stocks have had substantial negative effects for dependent economies and communities (Brierley and Kingsford, 2009; Davis, 2015). Moreover, coastal economies that are dependent on marine tourism and recreational activities are also exposed to elevated risks from impacts on biota that are important for these sectors (Section Nevertheless, new opportunities for coastal tourism may occur in future for some regions as a result of species’ biogeographic shifts (Section and increased accessibility, such as in the Arctic (Chapter 3). 

Decrease in sea ice in the Arctic is opening up economic opportunities for the oil and gas exploration, mining industries and shipping that are currently important economic sectors in the ocean (Pelletier and Guy, 2012; George, 2013) (Section 3.4.3; 3.5.3). Although the Arctic region has oil and gas reserves estimated to account for one-tenth of world oil and a quarter of global gas (U.S. Geological Survey released on 24 July 2008), offshore oil and gas exploration with poor regulation or as a result of accidents poses additional risk of impacts on species, populations, assemblages, to ecosystems by modifying a variety of ecological parameters (e.g., biodiversity, biomass, and productivity) (Cordes et al., 2016) threatening the sensitive Arctic ecosystems and the livelihood of dependent communities (Section 

Similarly, global warming and changing weather patterns may have a substantial impact on global trade and transport pathways (Koetse and Rietveld, 2009); for example, the reduction in sea ice in the Arctic Ocean during summer opens up the possibility for sea transport on the Northwest or Northeast Passage for several months per year (Ng et al., 2018) (Section Both routes may provide opportunities for more efficient transport between North America, Europe, Russia and China for fleets with established Arctic equipment, and may open up access to known natural resources which have so far been covered by ice (Guy and Lasserre, 2016). However, whether the Arctic shipping routes will be a realistic alternative depends not only on regulatory frameworks and economic aspects (such as infrastructure and reliability of the routes) but also on societal trends and values, demographics and tourism demand (Prowse et al., 2009; Wassmann et al., 2010; Pelletier and Guy, 2012; George, 2013; Hodgson et al., 2016; Pizzolato et al., 2016; Dawson, 2017) (Section, Simultaneously, shipping routes through the Arctic pose additional risk from human impact such as pollution, introduction of invasive species and collision with marine mammals, and emission of short-lived climate forcers that can amplify warming in the region and accelerate localised warming (Wan et al., 2016) (Section 

Existing governance may not be sufficient to limit the elevated risk on Arctic ecosystems and their dependent economies from increased shipping activities (Section 3.4.3, 3.5.3). Climate change may bring new economic opportunities, particularly for polar oil and gas development (medium confidence), shipping (medium confidence) and tourism (low confidence) although realisation of these opportunities will pose uncertain ecological risks to sensitive ecosystems and biota, and the dependent human communities in the region (high confidence). 

Ocean renewable energy provides an emerging alternative to fossil fuels and comprises energy extraction from offshore winds, tides, waves, ocean thermal gradients, currents and salinity gradients (Harrison and Wallace, 2005; Koetse and Rietveld, 2009; Bae et al., 2010; Jaroszweski et al., 2010; O Rourke et al., 2010; Hooper and Austen, 2013; Kempener and Neumann, 2014b; Kempener and Neumann, 2014a; Abanades et al., 2015; Astariz et al., 2015; Borthwick, 2016; Foteinis and Tsoutsos, 2017; Manasseh et al., 2017; Becker et al., 2018; Gattuso et al., 2018; Hemer et al., 2018; Dinh and McKeogh, 2019b; Dinh and McKeogh, 2019a). Other potential sources of marine renewable energy include algal biofuels (Greene et al., 2010; Greene et al., 2016). While such approaches offers a way to mitigate climate change, changes in climatic conditions (such as waves and winds) may impact marine renewable energy installations and their effectiveness (Harrison and Wallace, 2005). A more comprehensive assessment of these issues is expected to be provided by IPCC WGIII in the AR6 full report. 

Overall, some major existing ocean economy sectors such as fishing, coastal tourism and recreation are already at risk by climate change (medium confidence), and all sectors are expected to have elevated risks with high future emission scenarios (high confidence). The emerging demand for alternative energy sources is expected to generate economic opportunities for the ocean renewable energy section (high confidence), although their potential may also be affected by climate change (low confidence).

Impacts of Changing Ocean on Sustainable Development Goals

Climate change impacts will have consequences for the ability of human society to achieve sustainable development. SR15 concludes that “Limiting global warming to 1.5°C rather than 2°C would make it markedly easier to achieve many aspects of sustainable development, with greater potential to eradicate poverty and reduce inequalities (medium evidence, high agreement)”. This assessment focuses on how climate change impacts on marine ecosystems would challenge sustainable development, using the United Nations SDGs as a framework to discuss the linkages between those issues. 

Climate impacts on marine ecosystems affect their ability to provide seafood and raw materials, and to support biodiversity, habitats and other regulating processes (Section 5.4.1), and these impacts on the ocean affect people directly and indirectly (Sections,, SDG 14 is the goal that is most directly relevant: “Life below water: including indicators for marine pollution, habitat restoration and protected areas, ocean acidification, fisheries, and coastal development.” 

Climate impacts in the ocean to other SDGs are mediated through social and economic factors when the SDG targets are affected (Singh et al. 2019). For example, climate impacts on marine ecosystem services related to primary industries that provide food, income and livelihood to people have direct implications for a range of SDGs. These SDGs include ‘no poverty’ (SDG 1), ‘zero hunger’ (SDG 2), ‘decent work and economic growth’ (SDG 8), ‘reduced inequalities’ (SDG 10) and ‘responsible consumption and production’ (SGD 12) (Singh et al. 2019, Figure 5.22). These impacts relate to changing ocean under climate change that affect the pathways to build sustainable economies and eliminate poverty (Sections, eliminate hunger and achieve food security (Section, reduce inequalities (Sections and achieve responsible consumption and production (Sections (Carvalho et al., 2017; Castells-Quintana et al., 2017). Climate change is also creating living conditions in coastal areas that are less suitable to human settlement and changing distributions of marine disease vectors (Section,, reducing our chances of achieving the goal for good health and well-being (SDG 3) (Pearse, 2017; Wouters et al., 2017). Women are often engaged in jobs and livelihood sources that are more exposed to climate change impacts from the ocean such as impacts on fisheries (Section and impacts of SLR on coastal regions (Chapter 4). For example, in Senegal, women disproportionately engage in rice crop cultivation in coastal flood plain (Linares, 2009), and are thus exposed to the risks on their livelihood from rising sea levels and resulting salinisation (Dennis et al., 1995). Flooding in Bangladesh has increased the vulnerability of women to harassment and abuse as the flooding upends normal life and increases crime rates (Azad et al., 2013). As such, climate change may negatively affect our ability to achieve “gender equality” (SDG 5) (Salehyan, 2008). Impacts on living conditions as well as changing recreational, aesthetic, and spiritual experiences also affect our ability to achieve ‘sustainable cities and communities’ (SDG 11) (Section The consequences of climate change in the ocean to achieving the remaining SDGs are less clear. However, the SDGs are interlinked, and achieving SDG 14, and especially the targets of increasing economic benefits to SIDS and Least Developed Countries, as well as eliminating illegal fishing and overfishing, will benefits all other SDGs (Singh et al., 2017). The interlinkages among SDGS mean climate change impact on the ocean will affect all other SDGs beside SDG14 in various ways, some possible direct and many indirect (low confidence). 

Overall, climate change impacts on the ocean will negatively affect the chance of achieving the SDGs and sustaining their benefits (medium confidence). 

Figure 5.22

Figure 5.22 | Summary of the types of relationships (negative, neutral and positive) between impacted marine ecosystem services (Provisioning, Regulating, Supporting and Cultural) and the Sustainable Development Goals (SDGs) based on literature review and expert-based analysis (Singh et al. 2019). Pie charts represent the proportion of targets within SDGs that a particular ocean SDG target […]

Figure 5.22 | Summary of the types of relationships (negative, neutral and positive) between impacted marine ecosystem services (Provisioning, Regulating, Supporting and Cultural) and the Sustainable Development Goals (SDGs) based on literature review and expert-based analysis (Singh et al. 2019). Pie charts represent the proportion of targets within SDGs that a particular ocean SDG target contributes to according to the literature reviewed and expert-based analysis presented in Singh et al. (2019).


Risk-reduction Responses and their Governance


Ocean-based Mitigation

Context for Blue Carbon and Overview Assessment

There is political and scientific agreement on the need for a wide range of mitigation actions to avoid dangerous climate change (UNEP, 2017; IPCC, 2018). Opportunities to reduce emissions by the greater use of ocean renewable energy are identified in Section Here, in accordance with the approved scoping of this report, the assessment of mitigation options is limited to the management of natural ocean processes, that is, requiring policy intervention, with a focus on ‘blue carbon’. Natural processes per se, although important to the climate system and the global carbon cycle, are not a mitigation response. Two management approaches are possible: first, actions to maintain the integrity of natural carbon stores, thereby decreasing their potential release of greenhouse gases, whether caused by human or climate-drivers; and second, through actions that enhance the longterm (century-scale) removal of greenhouse gases from the atmosphere by marine systems, primarily by biological means. 

These mitigation approaches match those proposed using terrestrial natural processes (Griscom et al., 2017), with extensive afforestation and reforestation included in all climate models that limit future warming to 1.5⁰C (de Coninck et al., 2018). As on land, reliable carbon accounting is a critical consideration (Grassi et al., 2017), together with confidence in the longterm security of carbon storage. The feasibility of climatically-significant (and societally acceptable) mitigation using marine natural processes therefore depends on a robust quantitative understanding of how human actions can affect the uptake and release of greenhouse gases from different marine environments, interacting with natural biological, physical and chemical processes. Whilst CO2 is the most important greenhouse gas, marine fluxes of methane and nitrous oxide can also be important, for both coastal regions and the open ocean (Arévalo-Martínez et al., 2015; Borges et al., 2016; Hamdan and Wickland, 2016).

The term ‘blue carbon’ was originally used to cover biological carbon in all marine ecosystems (Nellemann et al., 2009). Subsequent use of the term has focused on carbon-accumulating coastal habitats structured by rooted plants, such as mangroves, tidal salt marshes and seagrass meadows, that are relatively amenable to management (McLeod et al., 2011; Pendleton et al., 2012; Thomas, 2014; Macreadie et al., 2017a; Alongi, 2018; Windham-Myers et al., 2019; Lovelock and Duarte, 2019). Comparisons across the full range of freshwater and saline wetland types are assisted by standardised approaches (Nahlik and Fennessy, 2016; Vázquez-González et al., 2017). Seaweeds (macroalgae) can also be considered as coastal blue carbon (Krause-Jensen and Duarte, 2016; Krause-Jensen et al., 2018; Raven, 2018), however, because of differences in their carbon processing, their climate mitigation potential is assessed separately within Section below.

In the open ocean, the biological carbon pump is driven by the combination of photosynthesis by phytoplankton and downward transfer of particulate carbon by a variety of processes (Henson et al., 2010; DeVries et al., 2017); it results in large-scale transfer of around 10 GtC yr-1 carbon from near-surface waters to the ocean interior (Boyd et al., 2019). Most of this carbon is respired in the mesopelagic and contributes to the 37,000 GtC inventory of DIC, with around ~0.1 GtC yr-1 eventually being permanently removed in deep sea sediments (Cartapanis et al., 2018). In addition, the microbial carbon pump (Jiao et al., 2010) produces refractory dissolved organic molecules throughout the water column at a rate of around 0.4 GtC yr-1 (Jiao et al., 2014b), which due to their residence time of hundreds to thousands of years maintain the 700 GtC inventory of dissolved organic carbon in the ocean (Jiao et al., 2010; Jiao et al., 2014a; Legendre et al., 2015; Jiao et al., 2018a). The natural removal of carbon by the various carbon pumps is closely balanced by upwelling and outgassing, with the ocean a moderate source of CO2 under pre-industrial conditions (Ciais et al., 2013). The mitigation potential of managing natural processes in the open ocean is only briefly assessed here (Section

Gattuso et al. (2018) provide an overview assessment of the environmental, technical and societal feasibilities of using a range of ocean management actions to reduce climate change and its impacts. Their results for nine actions based on natural processes are summarised in Figure 5.23, also including marine renewable energy (wind, wave and tidal) for comparison. Eight semi-quantitative criteria were used to assess each action: maximum potential effectiveness by 2100 in reducing climatic drivers (ocean warming, ocean acidification and SLR), assuming full theoretical implementation; technological readiness and lead time to full potential effectiveness (subsequently combined as technical feasibility); duration of benefits; co-benefits; trade-offs (originally described as dis-benefits); cost-effectiveness; and governability (capability of implementation, and management of any associated conflicts). Here, governability is considered as a constraint (governability challenges) reversing the scoring scale used by Gattuso et al. (2018)

Global measures (circles in Figure 5.23) can be regarded as mitigation, reducing drivers; local measures (rectangles), are primarily EbA, reducing impacts (Section 5.5.2), although they may also contribute to mitigation; two actions were considered at both scales. Gattuso et al. (2018) did not consider the effects of actions on ocean oxygenation, notwithstanding the importance of deoxygenation as a component of climate change. Additional detail is given in SM5.4.

Attachment field is blank. Please add an attachment or remove this block.

Climate Mitigation in the Coastal Ocean
Opportunities and challenges relating to coastal carbon

Estuaries, shelf seas and a wide range of other intertidal and shallow-water habitats (Section 5.3) play an important role in the global carbon cycle through their primary production by rooted plants, seaweeds (macroalgae) and phytoplankton, and also by processing riverine organic carbon. However, the natural carbon dynamics of these systems have been greatly changed by human activities (Regnier et al., 2013; Cloern et al., 2016; Day and Rybczyk, 2019) (high confidence). Direct anthropogenic impacts include coastal land-use change (Ramesh et al., 2015; Li et al., 2018a); indirect effects include increased nutrient delivery and other changes in river catchments (Jiao et al., 2011; Regnier et al., 2013), and marine resource exploitation in shelf seas (Bauer et al., 2013). There is high confidence that these human-driven changes will continue, reflecting coastal settlement trends and global population growth (Barragán and de Andrés, 2015)

Policy recognition of the mitigation benefits of coastal ecosystems requires quantitative information on their actual and potential carbon uptake and storage at the local and national scale, within an international framework for carbon accounting (Crooks et al., 2011; Hejnowicz et al., 2015). Such methods are being developed for coastal habitats structured by rooted plants (Needelman et al., 2018; Troxler et al., 2018; Needelman et al., 2019), considered here as ‘coastal vegetation’, linked to protocols for verification of longterm carbon removal and financial incentives (Crooks et al., 2011; Hejnowicz et al., 2015) and building on techniques used for managing terrestrial carbon sinks (Ahmed and Glaser, 2016b; Aziz et al., 2016). Proposals to apply carbon accounting to seaweeds, the water column and shelf sea sediments (Krause-Jensen and Duarte, 2016; Zhang et al., 2017) are less well-developed.
Coastal vegetation: mangrove, salt marsh and seagrass ecosystems

Mangrove, salt marsh and seagrass habitats are widely recognised as blue carbon ecosystems with mitigation potential (Chmura et al., 2003; Duarte et al., 2005; Kennedy et al., 2010; McLeod et al., 2011). Although covering only ~0.1% of the Earth’s surface, these three ecosystems together have been estimated to support 1–10% of global marine primary production (Duarte et al., 2017). More than 150 countries contain at least one of these ecosystems; 71 countries contain all three (Herr and Landis, 2016), and 74 countries mention such coastal wetlands (five specifically as blue carbon) in their Nationally Determined Contributions (NDCs) to the Paris Agreement (Martin et al., 2016a; Gallo et al., 2017).

These three vegetated coastal habitats are characterised by high, yet variable, organic carbon storage in their soils and sediments on a per unit area basis (high confidence). In the humid tropics, mangrove below-ground organic carbon is typically 500–1000 tC ha–1 (Donato et al., 2011; Alongi and Mukhopadhyay, 2015; Howard et al., 2017)), although only ~50 tC ha–1 in arid regions (Almahasheer et al., 2017). Australian salt marshes show particularly wide variation in organic carbon storage, ranging from 15–1000 tC ha–1 (top 1 m) with mean of 165 tC ha–1 (Kelleway et al., 2016; Macreadie et al., 2017b). For seagrass meadows, storage values are typically 400–1600 tC ha–1 but can exceed 2000 tC ha–1 (Serrano et al., 2014). These accumulations have occurred over decadal to millennial time scales (McKee et al., 2007; Lo Iacono et al., 2008). Such blue carbon stock values are similar to freshwater wetlands and peat, but higher than for most forest soils (Laffoley and Grimsditch, 2009; Pan et al., 2011) (high confidence). 

When vegetated coastal ecosystems are disturbed, a proportion of their stored carbon is released back to the atmosphere, along with other greenhouse gases (Marba and Duarte, 2009; Duarte et al., 2010; Pendleton et al., 2012; Lovelock et al., 2017). Globally, around 25–50% of vegetated coastal habitats have already been lost or degraded due to coastal agricultural developments, urbanisation and other human disturbance during the past 100 years (McLeod et al., 2011). The highest historical losses (60–90%) have occurred in Europe and China (Jickells et al., 2015; Gu et al., 2018; Li et al., 2018a). Current losses are estimated at 0.2–3.0% yr-1, depending on vegetation type and location (FAO et al., 2014; Alongi and Mukhopadhyay, 2015; Atwood et al., 2017) (medium confidence). Associated global carbon emissions are estimated at 0.04–0.28 GtC yr–1  (Pendleton et al., 2012); 0.06–0.61 GtC yr–1 (Howard et al., 2017); 0.10–1.46 GtC yr–1 (Lovelock et al., 2017); and 0.007 GtC yr–1 (mangroves only) (Taillardat et al., 2018). This range of values reflects uncertainties regarding the global rate of habitat loss, and the proportion of carbon remineralised to CO2.

Mitigation through emission reduction can therefore be achieved by habitat protection, to greatly reduce or end the human-driven loss of mangrove, salt marsh and seagrass ecosystems. Such action could potentially produce nationally-significant mitigation (>1% of fossil fuel emissions) for several countries (Taillardat et al., 2018). However, there are still many uncertainties in quantifying carbon release due to habitat degradation and loss (Lovelock et al., 2017), and hence in determining emission reductions. Furthermore, this mitigation option is not available to those countries where habitat loss is not currently occurring, for example, in Bangladesh (Taillardat et al., 2018). Since legal structures already exist in many countries to protect coastal wetlands, the main policy need may be the enforcement of national regulation and site-specific MPAs (Miteva et al., 2015; Herr et al., 2017; Howard et al., 2017).

The alternative mitigation approach using coastal blue carbon ecosystems is to enhance the natural carbon uptake of such habitats, not only by increasing their spatial coverage through habitat restoration and new habitat creation, but also by taking management measures to maximise the carbon uptake and storage for existing coastal ecosystems. Such measures include reducing anthropogenic nutrient inputs and other pollutants; restoring hydrology, by removing barriers to tidal flow and sediment delivery; and reinstating predators (to reduce carbon loss caused by some bioturbators) (Macreadie et al., 2017a). Per unit area of habitat created, restored or rehabilitated, such actions may offer high rates of carbon removal: widely-quoted values are 226 ± 39 gC m-1 yr-1 for mangroves, 218 ± 24 gC m-1 yr-1 for salt marsh and 138 ± 38 gC m-1 yr-1 for seagrass ecosystems (McLeod et al., 2011; Isensee et al., 2019). 

Around 90 restoration and rehabilitation projects for mangroves have been documented (López-Portillo et al., 2017), with associated development of a range of restoration evaluation methods (Zhao et al., 2016a). Salt marsh restoration is reviewed by Adam (2019) and seagrass restoration by van Katwijk et al. (2016). Consistent conclusions, supported by other studies (Bayraktarov et al., 2016; Wylie et al., 2016) are that: natural regeneration increases the likelihood of longterm survival; higher success rates are achieved with strong stakeholder engagement; and it is critical that the (human) factors causing original loss and degradation have been properly addressed (high confidence).

Quantification of the climatic benefits of such actions is, however, not straightforward. Measurements of carbon burial rates show high site-specific variability, being strongly affected by a wide range of environmental factors for mangroves (Adame et al., 2017; Schile et al., 2017), seagrasses (Lavery et al., 2013) and salt marshes (Kelleway et al., 2017b). The reliable determination of sediment accumulation rates is a key consideration, with associated uncertainties not fully reflected in the McLeod et al. (2011) estimates given above. In particular, geochemical-based studies have indicated that seagrass carbon burial may have been greatly overestimated (Johannessen and Macdonald, 2016). These issues are contentious (Johannessen and Macdonald, 2018a; Johannessen and Macdonald, 2018b; Macreadie et al., 2018; Oreska et al., 2018); their scientific resolution is highly desirable. Additional complexities relating to the mitigation role of coastal blue carbon ecosystems include the following:

  • Emissions of other greenhouse gases also need to be taken into account (Keller, 2019b). Methane release from mangrove habitats can reduce the scale of their climatic benefits by 18–22% (Adams et al., 2012; Chen and Ganapin, 2016; Chmura et al., 2016; Rosentreter et al., 2018; Cameron et al., 2019) and nitrous oxide and methane together may offset salt marsh CO2 uptake by 24–31% (Adams et al., 2012). Nitrous oxide emissions are strongly affected by nutrient loading (Chmura et al., 2016); under pristine conditions, mangroves can provide a sink rather than a source (Maher et al., 2016). Note that values of the ‘offset’ depend on the metrics used for determining CO2 equivalents.
  • Carbonate formation, releasing CO2, may also reduce the benefits of carbon storage by similar proportions (Howard et al., 2017; Macreadie et al., 2017a; Kennedy et al., 2018; Saderne et al., 2019).
  • Lateral transfers are not well-quantified. Whilst some of the carbon stored in coastal marine sediments may be recalcitrant carbon from terrestrial or atmospheric sources (and should therefore be excluded) (Chew and Gallagher, 2018), export of dissolved organic carbon, inorganic carbon and alkalinity may be considered as additional sequestration (Maher et al., 2018; Santos et al., 2019). 
  • The permanence of vegetated coastal systems, even if well-protected, cannot be assumed under future temperature regimes (Ward et al., 2016; Duke et al., 2017; Jennerjahn et al., 2017; Nowicki et al., 2017)
  • Responses to future SLR are also uncertain and complex (Kirwan and Megonigal, 2013; Spencer et al., 2016) . However, impacts are not necessarily negative: carbon sequestration capacity may increase where totally new habitats are created (Barnes, 2017), or if mangroves replace salt marshes (Kelleway et al., 2016). 

In summary, a combination of both conservation and restoration of mangrove, salt marsh and seagrass habitats can contribute to national mitigation effort for those countries with relatively large coastlines where such ecosystems naturally occur (Murdiyarso et al., 2015; Atwood et al., 2017). However, the associated current uncertainties in quantifying relevant carbon storage and flows are expected to be problematic for reliable measurement, reporting and verification (high confidence).

At the global scale, synthesis studies have estimated the potential additional sequestration achieved by cost effective coastal blue carbon restoration as ~0.05 GtC yr–1 (Griscom et al., 2017) and 0.04 GtC yr–1 (National Academies of Sciences, Engineering, and Medicine, 2019), assuming that a relatively high proportion of vegetated ecosystems can be re-instated to their 1980–1990 extents. These values compare to current net anthropogenic emissions from all sources of 10.0 GtC yr–1 (Le Quéré et al., 2018), and are consistent with the ‘very low’ scores by (Gattuso et al., 2018) for the climate mitigation benefits of conserving and restoring coastal vegetation (Figure 5.23). Coastal ecosystem restoration could theoretically achieve higher sequestration, around ~0.2 GtC yr-1 (Griscom et al., 2017), but would be challenging, because of the semi-permanent and on-going nature of most coastal land-use change, such as human settlement, conversion to agriculture and aquaculture, shoreline hardening and port development (Gittman et al., 2015; Li et al., 2018a). 

Restoration costs could also be an important constraint for large-scale application. Based on published data from 246 observations, Bayraktarov et al. (2016) estimated median total costs for restoration of one hectare of mangrove, salt marsh and seagrass habitat to be ~2,508, 151,129 and 383,672 respectively, in 2010 USD. For each ecosystem, there was high variability in costs according to the economy of the country where the restoration projects were carried out, and the restoration technique applied. Assessment of coastal conservation and restoration costs is also given in Section, in Box 5.5 (in the context of coral reef restoration costs) and Section 

Measures to protect and restore coastal blue carbon habitats provide many other societal benefits in addition to climate regulation (Section 5.4.1). In particular, there is high confidence that coastal wetlands benefit local fisheries, enhance biodiversity, give storm protection, reduce coastal erosion, improve water quality and support local livelihoods (Costanza et al., 2008; Spalding et al., 2014). Coastal ecosystems may keep pace with sufficiently gradual SLR, and may be more cost-effective in flood protection than hard infrastructure like seawalls (Temmerman et al., 2013; Möller, 2019). Coastal blue carbon can therefore be considered as a ‘no regrets’ mitigation option at the national level in many countries, in addition to (not a replacement for) more effective mitigation measures. Additional research is needed over the full range of environmental conditions to improve knowledge and understanding of the complex carbon dynamics of coastal vegetation and associated systems, to enable well-quantified and cost-effective carbon sequestration enhancement (Vázquez-González et al., 2017; Windham-Myers et al., 2019).
Seaweeds (macroalgae)

Seaweeds do not directly transfer carbon to marine sediments, unlike the rooted coastal vegetation considered above (Howard et al., 2017). Nevertheless, seaweed detritus can deliver carbon to sedimentary sites (Hill et al., 2015) and may provide a source of refractory dissolved organic (Krause-Jensen and Duarte, 2016). Recent studies indicate that globally important amounts of carbon may be involved in these processes (Krause-Jensen and Duarte, 2016; Krause-Jensen et al., 2018; Smale et al., 2018). There is, however, currently low confidence that enhancement of natural seaweed production can provide a significant mitigation response, due to large uncertainties relating to sequestration duration and effectiveness. Such considerations relate to transport pathways, the fate of material transported to deeper water, and the timescales of its subsequent return to the atmosphere over decadal to century timescales. 

Seaweed aquaculture is inherently more manageable as a mitigation response (N‘Yeurt et al., 2012; Chung et al., 2013; Chung et al., 2017; Duarte et al., 2017). If linked to biofuel or biogas production (N‘Yeurt and Iese, 2014; Moreira and Pires, 2016; Sondak et al., 2017), there would be potential to reduce emissions (as an alternative to fossil fuels); if also linked to carbon capture and storage (Hughes et al., 2012), it may be possible to achieve negative emissions (net CO2 removal from the atmosphere). Full life cycle analyses are needed to assess the energy efficiency of such approaches, and the viability of scaling them up to climatically-important levels, taking account of associated environmental and socioeconomic implications.

A different mitigation option using seaweeds relates to their use as a dietary supplement for ruminants to suppress methane production. In vitro studies have given promising results (Dubois et al., 2013; Machado et al., 2016; Machado et al., 2018). However, because the potential scale of real-world benefits have yet to be quantified, there is low confidence in this approach as a mitigation option.
Land-sea integrated eco-engineering

Land-based nutrient management could, in theory, be used to enhance carbon storage in coastal seas and deeper waters, by increasing the amount of refractory dissolved organic carbon (Jiao et al., 2011; Jiao et al., 2014b; Jiao et al., 2018b). This idea is supported by a statistical analysis of the relationship between organic carbon and nitrate in various natural environments (Taylor and Townsend, 2010) as well as by experimental results in estuarine and offshore waters (Yuan et al., 2010; Jiao et al., 2011; Jiao et al., 2014b). Delivery of nutrients from agricultural fertilisers and sewage discharge to coastal waters may currently promote the microbial breakdown of river-derived terrestrial dissolved organic carbon, reducing carbon storage (Liu et al., 2014). Thus reducing nutrient inputs in the future may expand carbon storage by favouring the microbial carbon pump, in addition to the multiple co-benefits of reduced nutrient loads related to HABs, oxygenation and ocean acidification (Miranda et al., 2013; Jiao et al., 2018a; Zhang et al., 2018). Although there is some evidence for the impact of dissolved organic carbon variations on global scale climate (Rothman et al., 2003) the benefits of this approach have yet to be determined quantitatively and uncertainties remain regarding the longevity of removal and associated carbon accounting (measurement, reporting and verification). Until such issues are better resolved, there is low confidence that stimulation of refractory dissolved organic carbon production could provide an operational long-term mitigation measure.
Control of sediment disturbance, enhanced weathering and other geochemical approaches

Anthropogenic sediment disturbance, through fishing, dredging and the installation of offshore structures, affects the security of carbon storage in shelf sea sediments (Hale et al., 2017). Management of such activities might therefore increase carbon retention, over relatively large areas of shelf seas (Avelar et al., 2017; Luisetti et al., 2019). However, there is a lack of data and understanding of the complex processes that affect carbon storage in the potentially mobile fraction of marine sediments (van de Velde et al., 2018); exceptions are provided by Hu et al. (2016) and Diesing et al. (2017). Due to these uncertainties, there is currently low confidence that control of sediment disturbance can be used for climate mitigation.

There is theoretically greater potential for carbon removal by ‘enhanced weathering’ using mineral additions to coastal waters (and the open ocean) (Rau, 2011; Renforth and Henderson, 2017). These approaches are based on increasing the naturally-occurring uptake of CO2 by carbonates (e.g., calcite and dolomite) or silicate minerals (such as olivine). Such rock-weathering currently sequesters ~0.25 GtC yr–1, on land and at sea (Taylor et al., 2015) and provides the longterm control of atmospheric CO2 concentrations. It could be enhanced by adding ground minerals to beaches (Montserrat et al., 2017) or the sea surface. Other geochemical approaches for adding alkalinity that are less directly based on natural processes (Rau et al., 2012; GESAMP, 2019) are not considered here.

Enhanced weathering methods might be used to reduce local impacts, for example, for coral reefs (Albright et al., 2016b; Feng et al., 2016), as well as contributing to wider mitigation of climate change. However, their climatic benefits would be difficult to quantify, with other constraints on their development and deployment relating to the governance, cost and uncertain environmental impacts of large-scale application (Gattuso et al., 2018). The combination of these factors results in low confidence that enhanced weathering can provide a viable and acceptable climate mitigation approach.

Climate Mitigation in the Open Ocean

Recent reviews of the scope for using natural processes in the open ocean for climate mitigation are provided by Keller (2019a) and GESAMP (2019). The summary assessment given here is limited to direct and indirect biologically-based approaches, consistent with the scoping of this report and the major governance constraints on the large-scale application of open ocean interventions. 

Current NPP by marine phytoplankton is estimated to be 58 ± 7 GtC yr–1 (Legendre et al., 2015), similar to terrestrial primary production and around 6 times greater than anthropogenic emissions (Le Quere et al. (2016). However, over 99% of the biologically-fixed carbon returns to the atmosphere over a range of timescales (Cartapanis et al., 2018).

The direct method of increasing marine productivity involves adding land-derived nutrients that may currently limit primary production, particularly iron. This approach has been investigated experimentally, by modelling and by observations of natural system behaviour (Keller et al., 2014a; Bowie et al., 2015; Tagliabue et al., 2017). The 13 experimental studies to date (seven in the Southern Ocean, five in the Pacific, and one in the sub-tropical Atlantic) have shown that primary production can be, but is not always, enhanced by the addition of iron (Boyd et al., 2007; Yoon et al., 2016; GESAMP, 2019). 

The difficulties arise in demonstrating the time-scale of additional carbon removal, and in obtaining information on the consequences of the fertilisation for other marine ecosystem components, including ocean acidification and other potential side-effects (Williamson and Turley, 2012). Modelling studies (Aumont and Bopp, 2006) indicate that the climatic benefits could be relatively short-lived. Furthermore, public and political acceptability for ocean fertilisation is low (Williamson et al., 2012; Boyd and Bressac, 2016; Williamson and Bodle, 2016; Fuentes-George, 2017; McGee et al., 2018). Ocean iron fertilisation is regulated by the London Protocol, with amendments prohibiting such action unless constituting legitimate scientific research authorised under permit (see Section There are additional governance constraints for the Southern Ocean where ocean iron fertilisation is theoretically considered to be most effective (Robinson et al., 2014).

Open ocean fertilisation by macro-nutrients (e.g., nitrate) has also been proposed, with modelled potential for gigaton-scale carbon removal (Harrison, 2017). Similar technical and governance considerations apply with regard to the quantification of mitigation benefits, the monitoring of potential adverse impacts, and the political acceptability of large-scale deployment. This approach would also involve higher costs, because of the much greater quantities of nutrients required (Williamson and Turley, 2012).

The indirect method of enhancing marine productivity uses physical devices to increase upwelling, thereby increasing the supply of a wide range of naturally-occurring nutrients from deeper water. This technique risks releasing additional CO2 to the atmosphere, reducing its potential for climate mitigation (Bauman et al., 2014). There may also be other undesirable climatic consequences, including disruption of regional weather patterns and long-term warming rather than cooling, if enhanced upwelling is deployed at large scale (Kwiatkowski et al., 2015).

Because of the many technical, environmental and governance issues relating to marine productivity enhancement, by either direct fertilisation or upwelling, there is low confidence that such open ocean manipulations provide a viable mitigation measure.


Ocean-based Adaptation

The AR5 concluded, with high agreement but limited evidence, that climate change impacts on coastal human settlements and communities could be reduced through coastal adaptation activities (Wong et al., 2014a). The limited evidence of the context-specific application of adaptation principles to support the assessment was highlighted as a knowledge gap for future research. This assessment reports progress made with developing such evidence and assesses human adaptation response to climate change in ecosystems, coastal communities and marine environments. 

Components of human adaptation responses include risk assessment, risk reduction, and pathways towards resilience (Cross-Chapter Box 2; Chapter 1.6). Residual risk remains where hazard, vulnerability and exposure intersect, subsequent to an adaptation pathway response. Here we focus on adaptation responses within ecosystems and in human systems, as framed in Chapter 1, and defined by: 


  • Nature-based or ecosystem-based adaptation ( The use of biodiversity and ecosystem services as part of an overall adaptation strategy to help people to adapt to the adverse effects of climate change. EbA uses the range of opportunities for the sustainable management, conservation, and restoration of ecosystems to provide services that enable people to adapt to the impacts of climate change (Narayan et al., 2016; Moosavi, 2017).
  • Human systems – Built environment adaptation ( Adaptation solutions pertaining to coastal built infrastructure and the systems that support such infrastructure (Mutombo and Ölçer, 2016; Forzieri et al., 2018).
  • Human systems – Socioinstitutional adaptation ( Adaptation responses within human social, governance and economic systems and sectors (Oswald Beiler et al., 2016; Thorne et al., 2017). This includes, but is not limited to community-based adaptation by coastal communities ( based on empowering and promoting the adaptive capacity of communities, through appropriate use of context, culture, knowledge, agency, and community preferences (Archer et al., 2014; Shaffiril et al., 2017)

To avoid duplication, detailed consideration of adaptation responses to SLR and extreme events (including heat waves, and compound and cascading events) are avoided here, as they are covered by Chapter 4 and Chapter 6, respectively. Tables 5.7 and 5.8 provide a summary assessment of climate change impacts, human adaptation response and benefits in ecosystems and human systems respectively. Details of the assessed literature are in SM Table 5.7. Climate drivers and impacts reported in the adaptation literature are consistent with those reported in Sections 5.2 and 5.3. Physical impacts include the disruption of physical coastal processes, like sediment dynamics, leading to, for example, erosion, flooding and coastal infrastructure damage (see Tables 5.7 and 5.8). Ecological impacts include the loss of ecosystems and biodiversity (Sections 5.2.3, 5.2.4, 5.3), which affected provision of ecosystem services, like coastal protection or food provision. The most commonly reported non-climate human drivers are growing human coastal populations (Elliff and Silva, 2017; van Oppen et al., 2017a; Gattuso et al., 2018) with poorly planned or managed urban development (Barbier, 2015; Wigand et al., 2017), land use change (Robins et al., 2016), loss of ecosystems (Runting et al., 2017), socioeconomic vulnerability (Broto et al., 2015; Bennett et al., 2016) of many coastal communities, ineffective governance and knowledge gaps for implementation.

Ecosystem-based Adaptation

This section assesses adaptation response in coastal ecosystems, beginning with biological adaptation in species, and followed by a summary assessment of EbA as a response to climate change.
Biological adaptation

There are many studies on biological climate change adaptation responses (Crozier and Hutchings, 2014; Miller et al., 2017; Diamond, 2018). Sections 5.2.3 and 5.3.3 discuss three main types of biological adaptation, broadly defined: evolutionary (genetic) adaptation through natural selection; phenotypic plasticity (acclimatisation), within an organism’s lifetime; and individual or population mobility towards more favourable conditions. There are, however, expected to be limits to such natural adaptation, and large variations between species and populations (Gienapp and Merilä, 2018). 

An accurate understanding of climate change impacts upon species, their sensitivity and adaptive capacity and consequent ecological effects (considering both indirect as well as direct impacts) is used to estimate extinction risk, so that an appropriate management response can be developed (Butt et al., 2016). EbA takes these complex interactions into account (Hobday et al., 2015), including the disruptive impacts of alien invasive species (Ondiviela et al., 2014; Wigand et al., 2017) . Effective adaptation action, therefore, contains a broader consideration than historical conservation practices (medium evidence, high agreement), including the development of international collaborations and databases to improve ocean-scale understanding of climate change impacts (Okey et al., 2014; Young et al., 2015). A key knowledge gap relates to the critical thresholds for irreversible change for species (Powell et al., 2017).

Table 5.7

Summary of reported Adaptation responses (A), the Impacts (I) they aimed to address, and the expected Benefits (B) in coastal ecosystems within Physical, Ecological, Social, Governance, Economic and Knowledge categories. For further details of impacts on ecosystems see Section 5.3. Legend: a + sign indicates robust evidence, a triangle indicates medium evidence and an underline indicates limited evidence. Dark blue cells indicate high agreement, blue indicates medium agreement and light blue indicates either low agreement (denoted by presence of a sign) if sufficient papers were reviewed for an assessment or no assessment (if less than three papers were assessed per cell). The papers used for this assessment can be found in SM5.5.
Adaptation in coral reefs

Coral reefs are currently threatened by the continuous global degradation of warm water coral reef ecosystems and the failure of traditional conservation actions to revive most of the degrading reefs (Rinkevich, 2008; Miller and Russ, 2014). Interventions to rehabilitate degraded coral reef ecosystems can be categorised as preventive (‘passive’ restoration) or adaptive (‘active’ restoration) (Miller and Russ, 2014; Linden and Rinkevich, 2017) (see Box 5.5). 

Inspired by silviculture (forestation) approaches to terrestrial ecosystem restoration, studies (Rinkevich, 1995; Rinkevich, 2005; Rinkevich, 2006; Rinkevich, 2008; Bongiorni et al., 2011) have proposed a two step restoration strategy for warm water coral reefs termed gardening of denuded coral reefs. In the first step, a large pool of coral colonies (derived from coral nubbins and fragments, and from sexually derived spat) are farmed in underwater nurseries, preferably on mid-water floating devices installed in sheltered zones, in which coral material can be cultured for up to several years. In the second step, nursery-grown coral colonies, together with recruited associated biota, are transplanted to degraded reef sites (Shafir and Rinkevich, 2008; Mbije et al., 2010; Shaish et al., 2010b; Shaish et al., 2010a; Bongiorni et al., 2011; Horoszowski-Fridman et al., 2011; Linden and Rinkevich, 2011; Mbije et al., 2013; Cruz et al., 2014; Chavanich et al., 2015; Horoszowski-Fridman et al., 2015; Lirman and Schopmeyer, 2016; Montoya Maya et al., 2016; Ng et al., 2016; Lohr and Patterson, 2017; Rachmilovitz and Rinkevich, 2017). Active restoration of coral reefs, while still in its infancy and facing a variety of challenges (Rinkevich, 2015b; Hein et al., 2017), has been suggested to potentially improve the ecological status of degraded coral reefs and the socioeconomic benefits that the reefs provide (Rinkevich, 2014; Rinkevich, 2015b; Linden and Rinkevich, 2017). 

Ecological engineering approaches may promote coral reef adaptation (Rinkevich, 2014; Forsman et al., 2015; Coelho et al., 2017; Horoszowski-Fridman and Rinkevich, 2017; Linden and Rinkevich, 2017; Rachmilovitz and Rinkevich, 2017). They also include: augmenting functional diversity, including that of the microbiome (Casey et al., 2015; Horoszowski-Fridman and Rinkevich, 2017; Shaver and Silliman, 2017); transplantating whole habitats (Shaish et al., 2010b; Gómez et al., 2014); and enhancing genetic diversity (Iwao et al., 2014; Drury et al., 2016; Horoszowski-Fridman and Rinkevich, 2017). Active restoration can contribute to reef rehabilitation in all major reef regions (Rinkevich, 2014; Rinkevich, 2015b). However, there is limited evidence on how resistant these manipulated corals are to global change drivers (Shaish et al., 2010b; Shaish et al., 2010a) or how the nursery time affects biological traits like reproduction in coral transplants (Horoszowski-Fridman et al., 2011). Coral epigenetics may also be used as an adaptive management tool for reef rehabilitation (low confidence), as suggested by studies on coral adaptation (Brown et al., 2002; Horoszowski-Fridman et al., 2011; Palumbi et al., 2014; Putnam and Gates, 2015; Putnam et al., 2016). 

Research on active coral reef restoration (Box 5.5) suggests the potential to help rehabilitate degraded coral reefs, provided that the underlying drivers of the impacts are mitigated (high confidence). Ongoing and new research in active coral reef restoration may further improve active reef restoration outcomes (Box 5.5) (low confidence). However, these coral reef restoration options may be ineffectual if global warming exceeds 1.5oC relative to pre-industrial levels (Hoegh-Guldberg et al., 2018; IPCC, 2018).
Adaptation in mangroves and other coastal ecosystems

Mangroves provide significant ecosystem services, including localised coastal protection from extreme storm events (Section 5.4.1), supporting services through increased sedimentation rates (Hayden and Granek, 2015) and provisioning services for local communities, for example, habitats for nurseries to support fisheries. Mangroves provide limited carbon mitigation, in terms of global emissions reduction, and substantial job creation (Table 5.7) co-benefits (for example through Reducing Emissions from Deforestation and Forest Degradation programmes) when managed properly (Section 5.4.1,, and there is evidence of their value in supporting aquaculture and fishery initiatives (Huxham et al., 2015; Ahmed and Glaser, 2016a). 

Mangrove EbA responses most commonly reported included ecosystem restoration (Sierra-Correa and Cantera Kintz, 2015; Romañach et al., 2018) and management such as mangroves re-planting through community participation programmes (Nanlohy et al., 2015; Nguyen et al., 2017; Triyanti et al., 2017). Mangrove EbA has been reported to provide multiple co-benefits in terms of improvement in support for coastal physical processes, including: shoreline stabilisation (Hayden and Granek, 2015; Nanlohy et al., 2015); ecological functioning (Sierra-Correa and Cantera Kintz, 2015; Miller et al., 2017) with improved ecosystem services (Alongi, 2015; Nanlohy et al., 2015; Palacios and Cantera, 2017); carbon mitigation (; supporting livelihoods (Nanlohy et al., 2015; Nguyen et al., 2017); and reductions in coastal infrastructure damage and community vulnerability to climate change impacts. Managed retreat to counter coastal squeeze (Section 5.3) through improved governance, creation of finance and land use planning can allow mangroves to move up the shoreline contour or down the latitudinal gradient (Sierra-Correa and Cantera Kintz, 2015; Ward et al., 2016; Romañach et al., 2018). Therefore, mangrove EbA responses can strengthen coastal ecosystem services through shoreline stabilisation and provide multiple co-benefits for coastal communities, like job creation and improved access to ecosystem services (high confidence).

There are, however, examples where community mangrove restoration projects have resulted in maladaptive outcomes, in which the resulting ecosystem degradation could not provide the ecosystem services required (Nguyen et al., 2017; Romañach et al., 2018). Such maladaptation can be a result of poor governance processes or a lack of community compliance with restoration plans. These examples emphasise the value of designing effective governance to implement adaptation responses with broad community participation to improve the climate risk reduction outcomes and co-benefits (Sierra-Correa and Cantera Kintz, 2015; Nguyen et al., 2017) (medium evidence, high agreement).

Mangrove and other coastal ecosystems restoration and management can be applied through reducing non-climatic hazards (Gilman et al., 2008; Ataur Rahman and Rahman, 2015; Sierra-Correa and Cantera Kintz, 2015; Ahmed and Glaser, 2016a; Nguyen et al., 2017; Romañach et al., 2018). Coastal and catchment development, including wetland transformation and degradation (Miloshis and Fairfield, 2015; Schaeffer-Novelli et al., 2016; Watson et al., 2017a; Schuerch et al., 2018), the disruption of physical processes impacting sedimentation rates (Watson et al., 2017a) and coastal squeeze compound coastal climate change impacts like erosion, flooding and saltwater intrusion (Ondiviela et al., 2014; Miloshis and Fairfield, 2015; Schaeffer-Novelli et al., 2016; Wigand et al., 2017) (Section 5.3). This reduces the ability of these ecosystems to provide protection from wave and storm impacts, whilst positive feedbacks may occur that cause a net release of carbon into the atmosphere, for example, in salt marshes (Wong et al., 2014a) (Section 5.4.1). In some cases, effective interventions requires management at a broad spatial scale that includes a variety of ecosystems, for example, including ecosystems like mussel beds on the seaward side of seagrass beds to reduce wave energy and erosion (Ondiviela et al., 2014). Where sediment accretion matches the SLR rate, wetlands and salt marshes provide effective coastal protection and other important ecosystem services (high confidence).

Coastal dune systems are widely transformed globally. Human disturbance and the limited stabilising ability of dune vegetation are key causes of degradation (Onaka et al., 2015; Ranasinghe, 2016; MacDonald et al., 2017; Pranzini, 2017; Salgado and Martinez, 2017; Vikolainen et al., 2017; Gracia et al., 2018), while restoration efforts can be supported by both hard (Sutton-Grier et al., 2015; Pranzini, 2017) and soft (Sutton-Grier et al., 2015; Vikolainen et al., 2017) engineering responses. Reduced coastal erosion (Sánchez-Arcilla et al., 2016; Goreau and Prong, 2017; Vikolainen et al., 2017; Carro, 2018; Gracia et al., 2018) and flood risk (Onaka et al., 2015; MacDonald et al., 2017; Nehren et al., 2017) through maintaining dunes as natural buffers against wave energy (Nehren et al., 2017) can increase resilience to climate change impacts (Sutton-Grier et al., 2015; Magnan and Duvat, 2018). Engineered responses and sand replenishment are considered complementary approaches (Onaka et al., 2015; Martínez et al., 2017). Section provides an overview of sediment-based adaptation response measures, including cost estimates for beach nourishment and dune maintenance, a discussion of co-benefits and drawbacks of combining hard and soft infrastructure measures, and challenges with sourcing sediment for beach replenishment. In some cases dune restoration and sand replenishment projects have not been successful, due to fire damage (Shumack and Hesse, 2017) or the rapid loss of sand within replenishment schemes due to coastal processes and stakeholder rejection of adaptation activities (Pranzini, 2017). Coastal dune restoration and beach replenishment are effective responses against coastal erosion and flooding, where sufficient materials and space to implement are available (medium confidence).
Ecosystem-based adaptation

There is a growing body of literature regarding the effectiveness and economics of EbA. In addition to building resilience to climate change, EbA is expected to bring a wide range of co-benefits that include increasing ecological complexity, with multiple ecosystem services, and other economic co-benefits (Perkins et al., 2015; Perry, 2015; Moosavi, 2017; Scarano, 2017). The cost-effectiveness of EbA approaches varies between marine ecosystem types; for example, coral reefs (Perkins et al., 2015; Beetham et al., 2017; Elliff and Silva, 2017; Beck et al., 2018; Comte and Pendleton, 2018) and salt-marshes (Ondiviela et al., 2014; Miloshis and Fairfield, 2015; Schaeffer-Novelli et al., 2016; Wigand et al., 2017) performed best at reducing wave heights, whilst salt marshes and mangroves were two to five times cheaper than submerged breakwaters for wave heights of less than half a meter. Although low regrets, win-win approaches like EbA are supported in the literature (Watkiss et al., 2014; Barange et al. 2018), syntheses of experience from context-specific practical implementation of EbA and assessment of their cost-effectiveness are limited (Narayan et al., 2016). Therefore, EbA can be a cost-effective approach for securing climate change-related ecosystem services with multiple co-benefits (medium evidence, high agreement).

The application of EbA approaches can be more effective when incorporating local knowledge and Indigenous knowledge and cultural practices into adaptation responses (Ataur Rahman and Rahman, 2015; Perkins et al., 2015; Sutton-Grier et al., 2015; Sánchez-Arcilla et al., 2016; van der Nat et al., 2016). The application of synergistic combinations of adaptation responses in multiple ecosystems can provide a range of co-benefits, and this approach is strengthened when combined with socioinstitutional approaches (Kochnower et al., 2015; MacDonald et al., 2017). Research to improve and refine EbA approaches and increase their specificity to local context is important for their effectiveness in reducing climate risks and generating co-benefits (Sutton-Grier et al., 2015). Conversely, a lack of inclusion of local communities and economic undervaluation of specific coastal and marine ecosystems, compounded by gaps in scientific data, can undermine the potential effectiveness of EbA approaches (Perkins et al., 2015; Hernández-González et al.; Narayan et al., 2016; Roberts et al., 2017).

Despite the abundance of EbA examples in the literature, knowledge gaps pertaining to their implementation and limitations remain. Developing this literature could help with understanding context specific application of EbA and improve their effectiveness (medium confidence).

Human Systems

Many of the world’s great cities lie within the coastal region, and climate change impacts put these cities, their inhabitants and their economic activities at risk. Section assesses the impacts of climate change, adaptation response and benefits upon human systems, including coastal communities, built infrastructure, fisheries and aquaculture, coastal tourism, government and health systems. Table 5.8 provides a summary of the assessment, with citations provided in the Supplementary Material Table 5.7.

Poorly planned (Ataur Rahman and Rahman, 2015), located (Abedin et al., 2014; Betzold and Mohamed, 2017; Linkon, 2018) and managed urban settlements or human systems, driven by growing human coastal populations (Perkins et al., 2015; Moosavi, 2017; Carter, 2018) and compounded by the disruption of coastal and catchment physical processes (Nagy et al., 2014; Broto et al., 2015; Marfai et al., 2015; Kabisch et al., 2017) and pollution (Zikra et al., 2015; Peng et al., 2017) are major human drivers of change compounding the impacts of climate change.

Coastal communities, built infrastructure and fisheries and aquaculture (Table 5.8) are likely to be significantly affected through the disruption of coastal physical processes (DasGupta and Shaw, 2015; Betzold and Mohamed, 2017; Hagedoorn et al., 2019) leading to coastal erosion, flooding, salt water intrusion and built infrastructure damage (Dhar and Khirfan, 2016; Hobday et al., 2016a; Jurjonas and Seekamp, 2018) (robust evidence, high agreement). Ecosystem degradation and biodiversity loss will further compound impacts in coastal communities and fisheries and aquaculture (Ataur Rahman and Rahman, 2015; Petzold and Ratter, 2015; Dhar and Khirfan, 2016), with sub-lethal species impacts like changes in the productivity and distribution of fisheries target species reported for the latter (Gourlie et al., 2018; Nursey-Bray et al., 2018; Pinsky et al., 2018) (high confidence). This is likely to result in decreased access to ecosystem services (Asch et al., 2018; Cheung et al., 2018b; Finkbeiner et al., 2018) (medium evidence, high agreement), local declines in agriculture and fisheries (Cvitanovic et al., 2016; Faraco et al., 2016) (high confidence) and livelihood impacts (Harkes et al., 2015; Busch et al., 2016; Valmonte-Santos et al., 2016) (high confidence) in coastal communities and fisheries and aquaculture, particularly increased food insecurity and health risk in the latter (high confidence). These livelihood impacts are likely to increase social vulnerability (high confidence). Businesses within coastal communities are likely to experience disruptions and losses (robust evidence, high agreement).
Coastal communities

This section describes a range of adaptation responses reported at the level of the individual or community. Hard engineering responses included small scale hard infrastructure coastal defenses (Betzold and Mohamed, 2017; Jamero et al., 2018), design responses at the household level (Ataur Rahman and Rahman, 2015; Linkon, 2018) and retreat (Marfai et al., 2015). Ecosystem restoration and protection, particularly in mangroves (Ataur Rahman and Rahman, 2015; Bennett et al., 2016; Jamero et al., 2018; Hagedoorn et al., 2019) through community participation programmes (Barbier, 2015; Petzold and Ratter, 2015; Bennett et al., 2016; Dhar and Khirfan, 2016; Jamero et al., 2018) was strongly supported in the literature as a means to improve access to or storage of natural resources (medium evidence, high agreement). 

Table 5.8

Summary of reported Adaptation responses (A), the Impacts (I) they aimed to address, and the expected Benefits (B) in human systems within Physical, Ecological, Social, Governance, Economic and Knowledge categories. Legend: a + sign indicates robust evidence, a triangle indicates medium evidence and an underline indicates limited evidence. Dark blue cells indicate high agreement, blue indicates medium agreement and light blue indicates either low agreement (denoted by presence of a sign) if sufficient papers were reviewed for an assessment or no assessment (if less than three papers were assessed per cell). Papers used for this assessment can be found in SM5.6.

Social responses include increasing climate change awareness, improving participatory decision making through bottom-up approaches, community organisation for action and engagements with local management authorities (Dutra et al., 2015; Tapsuwan and Rongrongmuang, 2015; Galappaththi et al., 2017; Ray et al., 2017; Cinner et al., 2018; Hagedoorn et al., 2019). In coastal communities, and indeed in most other sectors, despite consensus on the importance of cooperation in tackling climate change (Elrick-Barr et al., 2016), adaptation progress may be hampered by competing economic interests and worldviews (Hamilton and Safford, 2015), which can be compounded by limited climate change knowledge (Nanlohy et al., 2015). Factors like home ownership and a general future planning ability support resilience (Elrick-Barr et al., 2016). Climate change adaptation capacity is shaped by historical path dependencies, local context and international linkages, while action is shaped by science, research partnerships and citizen participation (Hernández-Delgado, 2015; Sheller and León, 2016). Locally context-specific data to guide appropriate adaptation response remains a knowledge gap (Abedin and Shaw, 2015; Hobday et al., 2015; Lirman and Schopmeyer, 2016; Williams et al., 2016)

Coastal and oceanic adaptation responses are greatly complicated by the presence of competing interests (either between user groups, communities or nations), where considerations other than climate change need to be incorporated into cooperation agreements and policy (Wong et al., 2014a). The deployment of either built or natural protection systems, or adopting a ‘wait and see’ approach, is subject to the social acceptance of these approaches in communities (Poumadère et al., 2015; Sherren et al., 2016; Torabi et al., 2018). Similarly, the willingness to move away from climate change impacted zones is dependent upon a range of other socioeconomic factors like age, access to resources and crime (Bukvic et al.; Rulleau and Rey-Valette, 2017). Adaptation to climate change includes a range of non-climatic and social variables that complicate implementation of adaptation plans (robust evidence, high agreement).

Improving community participation and integrating knowledge systems (local, traditional and scientific) supports coastal community adaptation responses (high confidence), providing improved co-production of knowledge (medium evidence, high agreement), improved community awareness (medium evidence, medium agreement) and better-informed, more cohesive coastal communities (limited evidence, medium agreement).
Built infrastructure

Built infrastructure impacts are most frequently addressed through hard engineering approaches including: construction of groins, seawalls, revetments, gabions and breakwaters (Friedrich and Kretzinger, 2012; Vikolainen et al., 2017); improving drainage and raising the height of roadways and other fixed-location infrastructure (Perkins et al., 2015; Becker et al., 2016; Colin et al., 2016; Asadabadi and Miller-Hooks, 2017; Brown et al., 2018a); erosion control systems (Jeong et al., 2014); and the relocation of infrastructure (Friedrich and Kretzinger, 2012; Colin et al., 2016). Nature-based responses are increasingly being reported as complementary and supporting tools (van der Nat et al., 2016; Kabisch et al., 2017; Gracia et al., 2018) using ecological engineering (Perkins et al., 2015; van der Nat et al., 2016; Moosavi, 2017) combined with innovative construction strategies (Moosavi, 2017). 

When implemented together, hard and soft engineering responses provide social (Gracia et al., 2018, Martínez et al., 2018; Woodruff, 2018) and ecological (Perkins et al., 2015; van der Nat et al., 2016; Gracia et al., 2018) co-benefits with reduced damage costs (Jeong et al., 2014). Constraints on implementation include the space and extra cost required by ecological infrastructure, sub-optimal performance when impacted by natural physical processes that are disrupted (Gracia et al., 2018) or restrictions associated with governance (Vikolainen et al., 2017). Adaptation planning including local communities can improve implementation and help fill knowledge gaps (Kaja and Mellic, 2017; Moosavi, 2017; Martínez et al., 2018; Mikellidou et al., 2018). Benefits include increased resilience in coastal infrastructure and better informed decision making tools (medium confidence),
Adaptation in fisheries and aquaculture

Sixty percent of assessed species are projected to be at high risk from both overfishing and climate change by 2050 (RCP8.5), particularly tropical and subtropical species (Cheung et al., 2018b). Overfishing is one of the most important non-climatic drivers affecting the sustainability of fisheries (Islam et al., 2013; Heenan et al., 2015; Faraco et al., 2016; Dasgupta et al., 2017; Cheung et al., 2018b; Harvey et al., 2018). Pursuing sustainable fisheries practices under a low emissions scenario would decrease risk by 63%. This highlights the importance of effective fisheries management (Gaines et al., 2018). Eliminating overfishing would, however, require reducing current levels of fishing effort, with a potential short-term reduction in catches impacting livelihoods and the food security of coastal communities (Hobday et al., 2015; Dey et al., 2016; Rosegrant et al., 2016; Campbell, 2017; Finkbeiner et al., 2018). Despite consensus on the effectiveness of eliminating overfishing in supporting climate change adaptation in fisheries (robust evidence, high agreement), successful adaptation outcomes remain aspirational. 

Range shifts under ocean warming (Section 5.2.3) will alter the distribution of fish stocks across political boundaries, thus demand for transboundary fisheries management will increase. Redistribution of transboundary fish stocks between countries (Ho et al., 2016; Gourlie et al., 2017; Asch et al., 2018) could destabilise existing international fisheries agreements and increase the risk of international conflicts (Section 5.4.2). Adaptation to reduce risks in international fisheries management could involve improving planning for cooperative management between countries informed by reliable predictions (Payne et al., 2017) and projections (Pinsky et al., 2018) of species shifts and associated uncertainties. Cooperative international fisheries arrangements, such as flexible fishing effort allocation and adaptive frameworks (Colburn et al., 2016; Cvitanovic et al., 2016; Faraco et al., 2016) may also improve the robustness of fisheries management (Miller et al., 2013). Thus, although range shifts pose significant challenges to transboundary fisheries management, proactive planning and adjustment of fisheries management arrangements, informed by scientific projections, could help improve adaptive capacity (medium confidence). The effectiveness of incorporating MPAs as an adaptation strategy to climate change can be improved by considering climate impacts in the design of MPAs (medium, high agreement). 

Improving integrated coastal management and better planning for MPAs by incorporating projected shifting biological communities, abundance and life history changes (Álvarez-Romero et al., 2018) due to climate change could contribute towards improved fisheries adaptive management by, for example, increasing resilience of habitats, providing refugia for species with shifting distributions and by conserving biodiversity (Faraco et al., 2016; Valmonte-Santos et al., 2016; Dasgupta et al., 2017; Le Cornu et al., 2017; Roberts et al., 2017; Asch et al., 2018; Cheung et al., 2018b; Harvey et al., 2018; Jones et al., 2018; O’Leary and Roberts, 2018) (Sections 5.2.3, 5.3, 5.4.1), but MPAs may also reduce access to subsistence fishers, increasing their vulnerability to food insecurity (Bennett et al., 2016; Faraco et al., 2016). The global area of MPAs is rapidly increasing towards the United Nations’ target of 10% of the global ocean. While this is encouraging, it is estimated that only 2% of the ocean is well enough managed, as described in (Edgar et al., 2014), to meet conservation goals (Sala et al., 2018). Improving the implementation and coordination of policies, and improving integrated coastal management and MPAs have emerged in the literature as important adaptation governance responses (robust evidence, medium agreement).

Governance responses to support adaptation in fisheries communities include conducting vulnerability assessments, improving monitoring of ecosystem indicators and evaluating management strategies (Himes-Cornell and Kasperski, 2015b; Busch et al., 2016). Socioeconomic factors like access to alternative income, mobility, gender and religion collectively shape a community’s adaptation response (Arroyo Mina et al., 2016). In West Africa, the industrial fishery response to climate change induced reductions in landings was the expansion of fishing grounds, which increased operational costs (Belhabib et al., 2016). This response is not available to artisanal and local fishing communities, who are considered highly vulnerable (Kais and Islam, 2017). Access to finance to support these communities or their governments could help them reach novel fishing grounds, and, therefore, potentially reduce their vulnerability. Food security linked to fisheries depends on stock recovery, but also on access to and distribution of the harvest, as well as gender considerations (Béné et al., 2015). Hence, granting preferential access to dependent coastal communities should be considered in examining policy options. Other adaptation responses include improved fishing gear and technology, use of fish aggregating devices and uptake of insurance products (Zougmoré et al., 2016) [see Barange et al. (2018) for a summary of possible adaptation responses]. Community response as a part of climate change adaptation for local fisheries is an important element in assessing adaptive capacity (medium evidence, good agreement), 

Fisheries management strategies depend heavily upon data collection and monitoring systems. These include the accuracy of data collected in respect of predicting environmental conditions, over time scales from months to decades (Dunstan et al., 2018), effective monitoring and evaluative mechanisms (Le Cornu et al., 2017; Gourlie et al., 2018), controlling for aspects of fish population dynamics like recruitment success and fish movement (Mace, 2001). Seasonal to decadal climate prediction systems allow for skillful predictions of climate variables relevant to fisheries management strategies (Hobday et al., 2016b; Payne et al., 2017). Effective fisheries adaptation responses will require knowledge development including better monitoring, modelling and improving decision support frameworks (medium evidence, high agreement) and improving forecasting and early warning systems (medium evidence, medium agreement).

In considering a participatory decision making approach for fisheries management that responds to climate change, Heenan et al. (2015){Heenan,  #274;Heenan, 2015 #274} provided a number of key elements that contribute towards a successful outcome. These include expert knowledge of climate change threats to fish habitats, stocks and landings, the necessity of transdisciplinary collaboration and stakeholder participation, broadening the range and scope of fisheries systems and increased commitment of resources and capacity. This was considered in the context of the ability of developing countries to sustainably exploit fisheries resources and related ecosystems. More research is required on socioecological responses to climate change impacts on fishery communities, including such aspect as like risk reduction, adaptive capacity through knowledge attainment and social networks, developing alternative skills and participatory approaches to decision making (Dubey et al., 2017; Shaffril et al., 2017; Finkbeiner et al., 2018). Important fisheries adaptation responses in relation to knowledge management include improving participatory processes (robust evidence, high agreement), integrating knowledge systems (medium evidence, high agreement), and stakeholder identification, outreach and education (medium evidence, medium agreement). Ecosystem-based adaptation, community participatory programmes, and improving agricultural and fisheries practices are very strongly supported in the literature (high confidence).

Less still is known about how climate change will affect the deep oceans and its fisheries (Section 5.2.3 and 5.2.4), the vulnerability of its habitats to fishing disturbance and future effects on resources not currently harvested (FAO, 2019). Johnson et al. (2019) concluded that in a 20- to 50-year timeframe, the effectiveness of virtually all north Atlantic deep water and open ocean area-based management tools can be expected to be affected. They concluded that more precise and detailed oceanographic data are needed to determine possible refugia, and more research on adaptation and resilience in the deep sea is needed to predict ecosystem response times. 

As with fisheries, community- and ecosystem-based adaptation responses, an integrated coastal management framework is considered useful for planning for anticipated challenges for aquaculture (Ahmed and Diana, 2015b; Barange et al. 2018). Where in situ adaptation is not possible, translocation and polyculture (Ahmed and Diana, 2015a; Bunting et al., 2017) have been suggested as appropriate responses, but this would suit commercial rather than subsistence interests. Policy, economic, knowledge and other types of support are required to build socioecological resilience of vulnerable coastal aquaculture communities (Harkes et al., 2015; Bunting et al., 2017; Rodríguez-Rodríguez and Bande Ramudo, 2017), which requires a deep understanding of the nature of stressors and a commitment for collective action (Galappaththi et al., 2017). Climate resilient pathway development (see Cross-Chapter Box 2) is considered a useful framework for Sri Lankan shrimp aquaculture (Harkes et al., 2015). Another example of successful aquaculture adaptation is the employment of near real time monitoring technology to track the carbonate chemistry in water to reduce bio-erosion in shellfish from acidification (Barton et al., 2015; Cooley et al., 2016). Numerous adaptation responses are available for aquaculture, but some options, like translocation and technological responses may not be available to subsistence-based communities (medium evidence). 

An example of eco-engineering-based adaptation option in seaweed aquaculture under climate change is 

artificial upwelling, as shown by experiments and observations. Artificial upwelling powered by green energy (solar, wind, wave or tidal energy) to seaweeds (Jiao et al., 2014b; Zhang et al., 2015; Pan and Schimel, 2016) can moderate the amount of deep water upwelled to the euphotic zone to just meet the demands of nutrients and DIC by the seaweed for photosynthesis, while avoiding the acidification and hypoxia that often occur in natural upwelling systems (Jiao et al., 2018a; Jiao et al., 2018b) (high confidence). Such artificial upwelling based eco-engineering may also gradually release the ‘bomb’ of rich nutrients and hypoxia in the bottom water, which could otherwise breakout following storms (Daneri et al., 2012) (high confidence).
Coastal tourism

The coastal tourism economic sector is highly sensitive to climate change. Tourism response, in terms of mitigating carbon emissions and adapting to climate change impacts, are assessed here. Coastal tourism is likely to be impacted by ecosystem degradation and loss (limited evidence, medium agreement), which underscores the importance of nature-based tourism. An example of coastal erosion in Latin America illustrates this, whereby SLR interacting with non-climate change impacts including sand mining, inappropriate development and habitat destruction (e.g., mangroves), resulted in declines in tourism (Rangel-Buitrago et al., 2015). The management recommendation was appropriate legislation with a marine spatial planning emphasis, enforcement, sustainable funding mechanisms and support networks for decision making.

Climate change impacts upon tourism are nuanced and not restricted to just physical impacts on tourism establishments (Biggs et al., 2015). Understanding the drivers of tourist choices could help support adaptation in the industry through marine spatial planning processes (Papageorgiou, 2016). For example, in an survey ranking mitigation and adaptation responses in Greece, tourists prioritised rational energy use, energy efficiency and water saving measures (Michailidou et al., 2016b). Location specific information of tourist choices could help shape local industries. In one example from the Thailand dive industry, climate change adaptation responses of participants were reported to be based on misconceptions about climate change and personal observations (Tapsuwan and Rongrongmuang, 2015). To improve community-based adaptation, efforts aimed at broadening the level of awareness about climate change could improve decision making processes (Tapsuwan and Rongrongmuang, 2015). Tourist behaviour is shaped by changing ocean physical processes and degrading ecosystems at tourist destinations, which drive destination changes, economic flows and market share adjustments. (Bujosa et al., 2015; De Urioste-Stone et al., 2016). 

It is very likely that climate change will have direct and nuanced impacts upon coastal tourism. Improving decision support frameworks (low evidence, medium agreement) for better-informed decision making tools could contribute towards increasing resilience in coastal tourism (low evidence, limited agreement).
Government responses

Government responses included adopting and mainstreaming sustainability policies, including investments and policies for climate change (Aylett, 2015; Buurman and Babovic, 2016) and applying the precautionary principle in the absence of precise scientific guidance (Johnson et al., 2018). Developing adequate governance and management systems (Johnson et al., 2018), strengthening capacity (Gallo et al., 2017; Paterson et al., 2017), increasing cooperation (Nunn et al., 2014; Gormley et al., 2015) and aligning policies of local authorities (Porter et al., 2015; Gallo et al., 2017; Rosendo et al., 2018) could help to improve implementation (Sano et al., 2015; Elsharouny, 2016). This includes planning for MPAs and improving integrated coastal management (Abelshausen et al., 2015; Roberts et al., 2017; Rosendo et al., 2018) by incorporating climate science (Hopkins et al., 2016; Johnson et al., 2018) to optimise priority marine habitats (Gormley et al., 2015; Jones et al., 2018). An advantage of integrated coastal management is that it helps manage the interactions between multiple climate and non-climatic drivers of coastal ecosystems and sectors. Incorporating stakeholder participation with local knowledge and Indigenous knowledge could help to reduce the risk of maladaptation, and increase buy-in for implementation (Serrao-Neumann et al., 2013). Improving participatory processes strengthens governance decision making and flexible risk management processes (Gerkensmeier and Ratter, 2018; Rosendo et al., 2018), while stimulating bi-directional knowledge flow and improving social learning (Abelshausen et al., 2015).

Technology for environmental monitoring, for example using drones (Clark, 2017), web-based coastal information systems (Mayerle et al., 2016; Newell and Canessa, 2017), the Internet of Things and machine learning solutions promise to improve the local scale knowledge base, which should improve climate adaptation planning and resilience effort and environmental management decisions (Conde et al., 2015). Where such knowledge gaps persist, the implementation of climate change adaptation measures could proceed on the basis of a set of general principals of best practice (Sheaves et al., 2016; Thorne et al., 2017).

Benefits of effective government adaptation response includes the promotion of sustainable use, development and protection of coastal ecosystems (Rosendo et al., 2018) and the protection of biodiversity through setting appropriate conservation priorities (Gormley et al., 2015). Improved governance includes consideration of social processes in risk management (Gerkensmeier and Ratter, 2018; Rosendo et al., 2018) and improved systematic conservation planning (Johnson et al., 2018). At a local level, this translates into sustained service delivery (Aylett, 2015), improved rationality and effective policy making (Serrao-Neumann et al., 2013; Rosendo et al., 2018).

Improving the implementation and coordination of policies and improving integrated coastal management are both considered important climate change adaptation governance responses (robust evidence, high agreement), as are developing partnerships and building capacity (medium evidence, high agreement) and adopting or mainstreaming sustainability policies (limited evidence, medium agreement). Benefits include improved ecosystem resilience, better planning processes, implementation and policies (all limited evidence, medium agreement).

Ocean-based Climate Change Adaptation Frameworks

Adaptation action in pursuit of a climate resilient development pathway is likely to have a deeper transformative outcome than stepwise or ad hoc responses (Cross-Chapter Box 2 in Chapter 1). Recent literature highlighting the effectiveness of components of adaptation planning includes quantitative assessments of vulnerability in ecosystems (Kuhfuss et al., 2016), species (Cheung et al., 2015; Cushing et al., 2018), and communities (Islam et al., 2013; Himes-Cornell and Kasperski, 2015b), and integrated assessments of all of the above (Peirson et al., 2015; Kaplan-Hallam et al., 2017; McNeeley et al., 2017; Ramm et al., 2017; Mavromatidi et al., 2018). Seasonal and decadal forecasting tools have improved rapidly since AR5, especially in supporting management of living marine resources (Payne et al., 2017) and modelling to support decision making processes (Čerkasova et al., 2016; Chapman and Darby, 2016; Jiang et al., 2016; Justic et al., 2016; Joyce et al., 2017; Mitchell et al., 2017). Decision making processes are supported by economic evaluations (Bujosa et al., 2015; Jones et al., 2015), evaluations of ecosystem services (MacDonald et al., 2017; Micallef et al., 2018), participatory processes (Byrne et al., 2015) and social learning outcomes, the development of adaptation pathways, frameworks and decision making (Buurman and Babovic, 2016; Dittrich et al., 2016; Michailidou et al., 2016a; Osorio-Cano et al., 2017; Cumiskey et al., 2018), and indicators to support evaluation of adaptation actions (Carapuço et al., 2016; Nguyen et al., 2016) through monitoring frameworks (Huxham et al., 2015). Climate change adaptation responses are more effective when developed within institutional frameworks that include effective planning and cross-sector integration.

Evidence-based decision making for climate adaptation is strongly supported in the literature (Endo et al., 2017; Thorne et al., 2017) through better understanding of coastal ecosystems and human adaptation responses (Dutra et al., 2015; Cvitanovic et al., 2016), as well as consideration of non-climate change related factors. Relevant research includes the topics of: multiple-stakeholder participatory planning (Archer et al., 2014; Abedin and Shaw, 2015); trans-boundary ocean management (Gormley et al., 2015; Williams et al., 2016); ecosystem-based adaptation (Hobday et al., 2015; Dalyander et al., 2016; McNeeley et al., 2017; Osorio-Cano et al., 2017); and community-based adaptation with socioeconomic outcomes (Merkens et al., 2016). Research on applying ‘big data’ and high end computational capabilities could also help develop a comprehensive understanding of climate and non-climate variables in planning for coastal adaptation (Rumson et al., 2017). New knowledge from these research areas could substantially improve planning, implementation and monitoring of climate adaptation responses for marine systems, if research processes are participatory and inclusive (medium confidence).

Despite such interest, evaluations of the planning, implementation and monitoring of adaptation actions remain scarce (Miller et al., 2017). In a global analysis of 401 local governments, only 15% reported on adaptation actions (mostly large cities in high income countries), and 18% reported on planning towards adaptation policy (Araos et al., 2016). Thus, integrated adaptation planning with non-climate change related impacts remains an under-achieved ambition, especially in developing countries (Finkbeiner et al., 2018). Challenges reported for adaptation planning include uncoordinated, top-down approaches, a lack of political will, insufficient resources (Elias and Omojola, 2015; Porter et al., 2015), and access to information (Thorne et al., 2017).

Characteristics of successful adaptation frameworks include: a robust but flexible approach, accounting for deep uncertainty through well-coordinated participatory processes (Dutra et al., 2015; Jiao et al., 2015; Buurman and Babovic, 2016; Dittrich et al., 2016); well-developed monitoring systems (Barrett et al., 2015; Bell et al., 2018b); and taking a whole systems approach (Sheaves et al., 2016), with the identification of co-benefits for human development and the environment (Wise et al., 2016). The coastal adaptation framework literature is dominated by Australian, North American and European cities, with fewer studies from African and Caribbean sites, least developed countries and SIDS (Kuruppu and Willie, 2015; Torresan et al., 2016). 

In contrast with the many examples of proposed frameworks for climate resilient coastal adaptation, few studies have assessed their success, possibly due to the time-lag between implementation, monitoring, evaluation and reporting. Nevertheless, there is substantial support for ‘no regrets’ approaches addressing both proximate and systematic underlying drivers of vulnerability (Sánchez-Arcilla et al., 2016; Pentz and Klenk, 2017; Zandvoort et al., 2017) with leadership, adaptive management, capacity and the monitoring and evaluation of actions considered useful in governance responses (Dutra et al., 2015; Doherty et al., 2016). More extensive learning processes could help build decision makers’ capacity to tackle systemic drivers, guide pursuance climate change appropriate policies (Barange et al. 2018) and to scrutinise potentially maladaptive infrastructural investments (Wise et al., 2016). More effective coordination across a range of stakeholders, within and between organisations, especially in developing countries, would strengthen the global coastal adaptation response (medium confidence).

The Role of Education and Local Knowledge in Adapting to Climate Change.

Education can help improve understanding of issues related to climate change and increase adaptive capacity (Fauville et al., 2011; Marshall et al., 2013; von Heland et al., 2014; Pescaroli and Magni, 2015; Tapsuwan and Rongrongmuang, 2015; Wynveen and Sutton, 2015). Participatory processes can facilitate the development of networks between coastal communities and environmental managers for the purposes of developing and implementing adaptation strategies (Wynveen and Sutton, 2015). Education, combined with other forms of institutional support empowers fisheries and aquaculture communities (Table 5.8) to make informed adaptation decisions and take action (medium evidence, medium agreement).

Local knowledge and Indigenous knowledge systems can complement scientific knowledge by, for example, improving community ability to understand their local environment (Andrachuk and Armitage, 2015), forecast extreme events (Audefroy and Sánchez, 2017) and help to increase community resilience (Leon et al., 2015; Sakakibara, 2017; Cinner et al., 2018; Panikkar et al., 2018). Committing resources could strengthen local level adaptation planning (Alam et al., 2016; Novak Colwell et al., 2017) through the inclusion of cultural practices (Audefroy and Sánchez, 2017; Fatorić and Seekamp, 2017) and Indigenous knowledge systems (Kuruppu and Willie, 2015; von Storch et al., 2015). Local knowledge can, however, act as a barrier to adaptation where there is a strong dependency upon such knowledge for immediate survival, to the detriment of long-term adaptation planning (Marshall et al., 2013; Metcalf et al., 2015). There is evidence, however, to suggest that vulnerability in fisheries communities and coastal tourism operators with high levels of local knowledge is reduced where they have a correspondingly high level of adaptive capacity (Marshall et al., 2013). Resource users with high levels of local knowledge may also be able to identify signals of change within their environment, and recognise the need to adapt. In these instances, fishers with higher local knowledge are expected to demonstrate a higher adaptive capacity than fishers with lower local knowledge, and can be expected to progress towards developing new strategies to combat the impacts of climate change (Kittinger et al., 2012). In these instances, local knowledge acts to promote adaptation (medium confidence). 

Localised, individual-scale behaviors can aggregate rapidly and contribute to the global adaptation response. This can be supported by clear messaging that clarifies the role of individuals, households and local businesses in addressing climate change. Coastal communities can improve the co-production of climate change knowledge (medium evidence, good agreement) through the integration of knowledge systems (Table 5.8). In fisheries and aquaculture, better-informed decision making tools (medium evidence, medium agreement) are supported by improved participatory processes (high confidence), integrating knowledge systems (medium evidence, good agreement) and improving decision support frameworks (medium evidence, medium agreement).

Costs and Limits for Coastal Climate Change Adaptation

Challenges persist in conducting economic assessments for built infrastructure adaptation due to complicated uncertainties such as the accuracy of climate projections and limited information regarding paths for future economic growth and adaptation technologies. Annual investment and maintenance costs of protecting coasts were projected to be 12–71 billion USD (Hinkel et al., 2014), which was considered significantly less than damage costs in the absence of such action. In an analysis of twelve Pacific island countries, 57% of assessed built infrastructure was located within 500 m of coastlines, requiring a replacement value of 21.9 billion USD. Substantial coastal adaptation costs (and international financing) are likely to be required in these countries (medium confidence).

In West African fisheries, loss of coastal ecosystems and productivity are estimated to require 5–10% of countries’ GDP in adaptation costs (Zougmoré et al., 2016). Similarly, for Pacific Islands and Coastal Territories, fisheries adaptation will require significant investment from local governments and the private sector (Rosegrant et al., 2016), with adaptation costs considered beyond the means of most of these countries (Campbell, 2017). In SIDS, tourism could provide the funding for climate change adaptation, but concerns with creating investment barriers, assumptions around cost-effectiveness and consumer driven demand remain barriers (Hess and Kelman, 2017). MPAs with multiple co-benefits, are considered a cost-effective strategy (Byrne et al., 2015). In 2004, the annual cost of managing 20–30% of global seas as MPAs was estimated at between 5–19 billion USD, with the creation of approximately one million jobs (Balmford et al., 2004).

Estimating adaptation costs is challenging because of wide ranging regional responses and uncertainty (Dittrich et al., 2016). Despite these challenges, the protection from flooding and frequent storms that coral reefs provide has been quantified by (Beck et al., 2018), who estimated that without reefs, damage from flooding and costs from frequent storms would double and triple respectively, while countries from Southeast Asia, East Asia and Central America could each save in excess of 400 million USD through good reef management. Although quantifying global adaptation costs remains challenging because of a wide range of regional responses and contexts, it is likely that managing ecosystems will contribute towards reducing costs associated with climate change associated coastal storms (medium confidence). Further research evaluating natural infrastructure is required (Roberts et al., 2017) to better understand costs and benefits of EBA.

There is a broad range of reported barriers and limits to climate change adaptation for both ecosystems and human systems. Coastal ecosystem-based adaptation can be physically constrained by space requirements and coastal squeeze (Sutton-Grier et al., 2015; Robins et al., 2016; Sánchez-Arcilla et al., 2016; Ahmed et al., 2017; Peña-Alonso et al., 2017; Salgado and Martinez, 2017; Triyanti et al., 2017; Schuerch et al., 2018), while the pace of climate change may exceed the adaptive capacity of ecosystems, for example, SLR may outpace the vertical reef accretion rate (Beetham et al., 2017; Elliff and Silva, 2017; Joyce et al., 2017). One technical limit for coral reef adaptation is that tools have not yet been developed for large-scale implementation (van Oppen et al., 2017a). Ecosystems may also have physiological and ecological constraints which are exceeded by climate change impacts (Miller et al., 2017; Wigand et al., 2017), and the recovery periods of natural systems (Gracia et al., 2018) and for ecological succession (Salgado and Martinez, 2017) may be outpaced by climate change impacts. The performance of ecosystems in EBA projects may be inhibited by the poor condition of the ecosystem (Nehren et al., 2017), highlighting the importance of effective implementation (Salgado and Martinez, 2017).

Social and cultural norms with conflicting and competing values (Miller et al., 2017), public lack of knowledge on climate change and distrust of information sources (Wynveen and Sutton, 2015), as well as populations increasingly distanced from, and unconcerned about nature (Romañach et al., 2018), may constrain ecosystem-based adaptation response. Examples of governance adaptation constraints include: inadequate policy, governance and institutional structures (Sánchez-Arcilla et al., 2016; Miller et al., 2017; Wigand et al., 2017), limited capacity (Sutton-Grier et al., 2015; Thorne et al., 2017), ineffective implementation (Nguyen et al., 2017; Comte and Pendleton, 2018), and poor enforcement (Nguyen et al., 2017). Governance constraints are compounded by lack of finances (Miller et al., 2017), financial costs of design and implementation (Gallagher et al., 2015) and the high cost of coastal land (Gracia et al., 2018), although ecosystem-based adaptation is considered cheaper than human-made structures (Nehren et al., 2017; Salgado and Martinez, 2017; Vikolainen et al., 2017; Gracia et al., 2018).

Knowledge limitations can include a lack of data (Sutton-Grier et al., 2015; Wigand et al., 2017; Romañach et al., 2018), for example, when an absence of baseline data may undermine coastline management (Perkins et al., 2015). Scale-relevant information may be required for local decision making (Robins et al., 2016; Thorne et al., 2017) and to comply with localised design requirements (Vikolainen et al., 2017). Other knowledge barriers include inherent uncertainties in models (Schaeffer-Novelli et al., 2016) and complexity of coastal systems (Wigand et al., 2017). A more nuanced knowledge barrier is the disconnect between scientific, community and decision making processes (Romañach et al., 2018). 

Substantial knowledge gaps are reported for ecosystem-based adaptation, including restoration of coral reef systems as an adaptation tool (Comte and Pendleton, 2018), managing mangrove and human response to climate change (Ward et al., 2016), advancing coastal EBA science by quantifying ecosystem services (Hernández-González et al.), and evaluating natural infrastructure (Roberts et al., 2017). Few syntheses of the context-specific application and cost-effectiveness of EBA approaches are to be found in the literature (Narayan et al., 2016).

Human systems have similar limitations. Improved understanding of limitations in built infrastructure, beach nourishment and nature-based adaptation responses, especially with respect to cost effectiveness and resilience, would substantially aid shoreline stabilisation attempts (Mackey and Ware, 2018). For artisanal fisheries, a range of physical and socioinstitutional limits and barriers to adaptation have been reported, including increasing occurrence and severity of storms limiting fishing time, technologically poor boats and fishing equipment and lack of access to credit and markets, among others (Islam et al., 2013). Conflicting interests and values of stakeholders (Evans et al., 2016), the path-dependent nature of organisations and resistance to change (Evans et al., 2016) and inadequate collaboration and public awareness (Oulahen et al., 2018) have been reported as socioinstitutional barriers. A knowledge gap persists in understanding how such limits and barriers interact to suppress adaptation response.

In some communities, climate change may not be prioritised in the face of chronic, daily challenges to secure livelihoods (Esteban et al., 2017; Fischer, 2018) or risk severity may be underestimated due to a high frequency of exposure in the recent past (Esteban et al., 2017). In a world with competing risks and urgent priorities, some local inhabitants appear to be unable to avoid, or are willing to carry, the risk associated with a climate impact in order to meet other, more pressing needs. This example reflects the reality of many poor, informal settlement dwellers in coastal areas around the world (medium confidence). Other human system barriers to effective adaptation action include insufficient climate change knowledge, inappropriate coping strategies, high dependency upon natural resources, level of exposure to hazards and weak community networks (Islam et al., 2013; Nanlohy et al., 2015; Lohmann, 2016; Koya et al., 2017; Senapati and Gupta, 2017; Cumiskey et al., 2018).

In summary, it is concluded that the broad range of reported barriers and limits to climate change adaptation for ecosystem and human system adaptation responses (high confidence). Limitations include the space that ecosystems require, non-climatic drivers and human impacts that need to be addressed as part of the adaptation response, the lowering of adaptive capacity of ecosystems because of climate change, and slower ecosystem recovery rates relative to the recurrence of climate impacts, availability of technology, knowledge and financial support and existing governance structures (medium confidence). (


There has been a substantial amount of literature focused on coastal and oceanic adaptation since AR5. Socio-institutional adaptation responses are the more numerous of the three types of adaptation responses assessed in this chapter. There is broad agreement that hard engineering responses are optimally supported by ecosystem-based adaptation approaches, and both approaches should be augmented by socioinstitutional approaches for adaptation (high confidence) (Nicholls et al., 2015; Peirson et al., 2015; Sánchez-Arcilla et al., 2016; van der Nat et al., 2016; Francesch-Huidobro et al., 2017; Khamis et al., 2017). In planning adaptation responses, awareness-raising and stakeholder engagement processes are important for buy-in and ownership of responses (robust evidence, high agreement) as is institutional capacity within local government organisations, whose importance in coastal adaptation initiatives has been emphasised in the recent literature (robust evidence, high agreement). With all three types of adaptation, basic good governance and effective implementation of service delivery processes are prerequisites for successful adaptation planning and response.


Governance Across All Scales

There are many global, regional, national and local governance structures with interests in climate-driven ocean warming, acidification, deoxygenation and SLR, and their impacts on marine ecosystems and dependent communities (Galland et al., 2012; Stephens, 2015; Fennel and VanderZwaag, 2016; Diamond, 2018). The legal, policy and institutional response is therefore shared by many institutions developed for a number of distinct but inter-related fields, including governance regimes for ocean systems, climate change, marine environment, fisheries and the environment generally. A changing ocean poses several scale-related challenges for these governance institutions and processes, arising from:  

  • The global and transboundary scales of the major changes to ocean properties (temperature, circulation, oxygen loss, acidification, etc.), with variability in their local expression;
  • The regional scales of changes in ecosystem services following from the changes in ocean properties (including services provided to humans living far from the coasts); 
  • The global scales of land-based drivers of those changes (both greenhouse gas emissions and changes in ecosystems services), which often motivate policy responses (primarily at the national level) and behavioural responses (primarily at the community level); 
  • The scale dependent need for coordinated responses by the different governance structures, to ensure their overall effectiveness (see also Chapter 1)

For all of these challenges, the scales of the climate-related issues may be poorly matched to the scales of most governance institutions and processes, making effective responses or proactive initiatives difficult. Sections 5.2 to 5.4 provide evidence, through case histories and thematic overviews, that illustrates these four types of challenges. In some cases, more than one type of challenge is illustrated in a single example, such as when a change in an amount or availability of an ecosystem service is discussed in the context of factors influencing the vulnerability of socio-ecological systems to climate change (Sections 5.2., 5.3 and 5.4).  

Existing ocean governance structures for the ocean already face multi-dimensional challenges because of climate change, and this trend of increasing complexity will continue (Galaz et al., 2012). Current international governance regimes and structures for fisheries and the ocean environment do not yet adequately address the issues of ocean warming, acidification and deoxygenation (Oral, 2018); Box 5.6). At the time of the initial development and adoption of these legal and governance regimes, minimal attention was given to climate change and the effects of carbon dioxide emissions on the ocean, with associated impacts on the interacting physical, chemical, biological properties of the ecosystems, and the resulting risks and vulnerabilities of dependent communities and economic sectors. In particular, the governance of ocean ABNJ is a major challenge (Levin and Le Bris, 2015); the collaborative structures and mechanisms for environmental assessment in ABNJ need further development (Warner, 2018) (high confidence). Negotiations are currently ongoing regarding a new international agreement for marine biodiversity of ABNJ (UNEP, 2016).  


Table 5.9: Ocean Governance and Climate Change: Major Issues 


Area of Governance  Major Legal Instruments  Major Issues and Actions 
Marine Environment Generally  UNCLOS, CBD, CITES, WHC, MARPOL and other IMO legal instruments, regional seas conventions and other legal instruments  UNCLOS imposes obligations on state parties to take action to combat the main sources of ocean pollution. Tools and techniques in UNCLOS may need adjustment in response to the emerging challenges created by ocean climate change (Redgwell, 2012). However, success of the umbrella regulatory framework of UNCLOS depends heavily on the further development, modification and implementation of detailed regulations by relevant international, regional and national institutions (Karim, 2015). 

The London Protocol to the London Convention was amended in 2006 to address the issue of carbon dioxide storage processes for sequestration. Two subsequent amendments concern sharing transboundary sub-seabed geological formations for sequestration projects, and ocean fertilisation and other marine geoengineering. One of these new amendments prohibits ocean fertilisation except for research purposes (Dixon et al., 2014). 

The issue of ocean acidification has been considered within the framework of the OSPAR Convention, the CCAMLR Convention (Herr et al., 2014), and the CBD (CBD,2014) ; this issue is discussed further in Box 5.6. 

The CBD has also considered regulatory issues relating to ocean fertilisation and other (marine) geoengineering (Williamson and Bodle, 2016). In 2018, the CBD adopted Voluntary Guidelines for the Design and Effective Implementation of Ecosystem-Based Approaches to Climate Change Adaptation and Disaster Risk Reduction. However, even if Parties to the Convention choose to adopt the voluntary guidelines, there is no mechanism to implement them beyond their exclusive economic zones in the water column and their extended continental shelves (if recognised) in the seabed

Most of the 29 world heritage listed coral reefs are facing severe heat stress (Heron, 2017) and the WHC may play a role for coral reef protection. 

Climate Change  UNFCCC, Paris Agreement, MARPOL Convention and other legal instruments  Existing international legal instruments do not adequately address climate change challenges for the open ocean and coastal seas (Galland et al., 2012; Redgwell, 2012; Herr et al., 2014; Magnan et al., 2016; Gallo et al., 2017; Heron, 2017). Nevertheless, ocean and coastal areas will benefit from the overall UNFCCC goal for preventing dangerous interference with the climate system. A study of the 161 national pledges for climate change mitigation and adaptation (NDCs) identified ‘gaps between scientific [understanding] and government attention, including on ocean deoxygenation, which is barely mentioned’ (Gallo et al., 2017). 

In 2011, the MARPOL convention was amended to include technical and operational measures for the reduction of greenhouse gas emissions from ships. However, the effectiveness of these provisions depends on the national implementation by flag, port and coastal states, with no international enforcement authority (Karim, 2015).

Fisheries UNCLOS, UN Fish Stocks Agreement, FAO Compliance Agreement, 

FAO PSMA, Regional Fisheries Agreements and other legal instruments

The impact of climate change on marine fisheries is expected to be very significant (Sections 5.3, 5.4) (Barange et al. 2018; FAO, 2019), with adverse impacts on food security, livelihood and national development in many coastal countries; least developed countries seem particularly vulnerable (Blasiak et al., 2017). Regional fisheries management systems need to address these emerging challenges (Brooks et al., 2013). The ecological and socioecological criteria and standards for performance can be set at regional levels where Regional Fisheries Management Organizations have been established, but their effectiveness is variable depending on the characteristics of regulatory instruments and other factors (Ojea et al., 2017). The current international regulatory framework for fisheries management has a responsiveness gap, since it does not fully incorporate issues related to the fluctuating and changing distribution of fisheries (Pentz and Klenk, 2017; Pinsky et al., 2018). 

However, some regional fisheries management organisations (RFMOs) have initiated processes to improve the equity of sharing fishery resources affected by climate change (Aqorau et al., 2018). A climate-informed ecosystem-based fisheries governance approach has been suggested for enhancing climate change resilience of marine fisheries in the developing world (Heenan et al., 2015), but robust and effective management, policy, legislation and planning based on flexibility and scientific understanding will be required for coastal fisheries (Gourlie et al., 2017). The existing failing condition of many stocks, coupled with maladaptive responses to climate change, may create serious challenges for the sustainability of global fisheries; improved fisheries governance can offset some of these challenges (Gaines et al., 2018). 

The fisheries agreements and the provisions in UNCLOS have helped RFMOs to increase the sustainability of fisheries on stocks in or migrating through international waters, and equity of access to them. Because the distribution of many stocks changes with changes in physical oceanic conditions (particularly temperature and current regimes), many of the measures and access arrangements negotiated and adopted by the RFMOs have reduced effectiveness in a changing climate. New arrangements have been difficult to negotiate, in part because of concerns that the distributions and productivities will continue to change as climate change continues to drive changes on ocean conditions (Blasiak et al., 2017; Ojea et al., 2017; Pentz and Klenk, 2017; Aqorau et al., 2018; Pinsky et al., 2018). 

Acronyms and organisations: CBD, Convention on Biological Diversity; CCAMLR, Convention on the Conservation of Antarctic Marine Living Resources; CITES, Convention on International Trade in Endangered Species of Wild Fauna and Flora; IMO: International Maritime Organization; London Convention: Convention on the Prevention of Marine Pollution by Dumping of Wastes and Other Matter; London Protocol: 1996 Protocol to the Convention on the Prevention of Marine Pollution by Dumping of Wastes and Other Matter; MARPOL Convention: International Convention for the Prevention of Pollution from Ships; NDCs: Nationally Determined Contributions; OSPAR Convention: Convention for the Protection of the Marine Environment of the North-East Atlantic; UN Fish Stocks Agreement: The Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea of 10 December 1982 relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks; UNCLOS: United Nations Convention on the Law of the Sea; UNFCCC: United Nations Framework Convention on Climate Change; WHC, World Heritage Convention: Convention Concerning the Protection of the World Cultural and Natural Heritage; FAO Compliance Agreement: The Agreement to Promote Compliance with International Conservation and Management Measures by Fishing Vessels on the High Seas; FAO PSMA: The Agreement on Port State Measures 

The following changes in governance may improve the ability of governance institutions and processes to address the challenges identified above:

  • Cooperation on regional and global scales through various types of agreements of varying degrees of formality for States and other participants in governance 
  • Increasing the voice and role in decision making for non-governmental participants such as Indigenous peoples, social and labour organisations 
  • Increasing the horizontal integration of decision making across industry and societal sectors, under processes such as ‘integrated management’ and ‘marine spatial planning’ 
  • Increasing resource mobilisation at the community scale to enable communities to experiment and innovate to address the challenges, and then to share their experiences with other communities and build cooperative approaches to promote strategies with successful outcomes 

These governance innovation strategies have the potential to increase the ability of the governance institutions and processes to successfully respond to all four types of scale-related challenges listed earlier. However, any of them also have the potential to fail to address their intended concerns effectively if implemented inappropriately, or to create new challenges as the initial priorities are addressed. In some countries, lack of capacity of the existing governance institutions, lack of access to basic facilities, insufficient income diversification and illiteracy are major hindrance for ocean governance in a changing climate (Bennett et al., 2014; Salik et al., 2015; Weng et al., 2015; Karim and Uddin, 2019; Sarkodie and Strezov, 2019) (high confidence

Additional considerations identified by recent studies of ocean related mitigation and adaptation include the need for: early warning and precautionary management; multi-level and multi-sectoral governance responses; holistic, integrated and flexible management systems; integration of scientific and local knowledge as well as natural, social and economic investigation; identification and incorporation of a set of social indicators and checklists; adaptive governance; and incorporation of climate change effects in marine spatial planning (Hiwasaki et al., 2014; Kettle et al., 2014; Hernández-Delgado, 2015; Himes-Cornell and Kasperski, 2015a; Pittman et al., 2015; Colburn et al., 2016; Creighton et al., 2016; Hobday et al., 2016a; Audefroy and Sánchez, 2017; Gissi et al., 2019; Tuda et al., 2019). Diverse adaptations of governance are being tried, and some are producing promising results (Sections 5.2, 5.3 and 5.4). However, rigorous further evaluation is needed regarding the effectiveness of these adaptations in achieving their goals in addressing specific governance challenges. Robust conclusions on the effectiveness of specific types of governance adaptations in various socioecological contexts would require a targeted assessment of ocean (and terrestrial) governance in a changing climate, possible as a key part of AR6. 



This chapter has documented an extraordinary array of observed changes in the open ocean, deep sea and coasts. It draws on evidence from thousands of references from the literature, millions of observations and hundreds of simulations of the past and future scenarios. The ocean climate and its state, ecosystems and human systems have changed (Section, 5.2, 5.3, 5.4, Figure 5.24) and are projected to change further. The ocean is a highly connected environment allowing water and living organisms to move freely. Change is observed across physical conditions that pose hazards to ecosystems in all regions from the surface to the deepest parts (Figure 5.24). All types of human and managed systems that have been covered in this chapter have evidence of mostly negative impacts but also some positive, some very significantly, some less so (Figure 5.24). Overall the multiple lines of evidence from the literature and the assessment in this chapter’s Executive Summary point to profound and pervasive changes on regional and global scales (Figure 5.24).

The level of knowledge and confidence of the changes in the marine environment that are particularly relevant to ecosystems and human systems ranges from virtually certain to low confidence (see Figure 5.24). Many of the observed changes in some variables can be directly attributed to human influence from rising greenhouse gases and other anthropogenic forcings (Section 5.2.2 and 5.2.3). For other variables and in some systems the evidence is less direct, but the cascading of risks from changing ocean, marine ecosystems and dependent communities remains robust when considered as a whole. The observed and projected changes in the ocean systems that are covered in this chapter are consistent with our understanding of ocean chemistry and circulation, and our knowledge of the ecosystems responses. In many cases, the assessments of risk level of ecosystems for the recent past and long-term future are based on multiple lines of evidence, combining ecological and physiological knowledge (from experiments, direct observations and model projections) with the major climate drivers (e.g., Sections 5.2.5 and 5.3.4). Globally, all the marine ecosystems assessed here have elevated risk for biodiversity, ecosystem function, structure and services with increasing greenhouse gas emissions (Figure 5.16) (high confidence). These risks result from ocean warming, stratification, acidification, deoxygenation, SLR and associated changes as well as interactions with non-climatic human drivers. Most importantly, all the coastal ecosystems that were assessed, where linkages between natural systems and human communities are the strongest, had increased risk, and none saw a risk reduction from a warming climate (high confidence).

Figure 5.24

Figure SPM.2 | Synthesis of observed regional hazards and impacts in ocean (top) and high mountain and polar land regions (bottom) assessed in SROCC. For each region, physical changes, impacts on key ecosystems, and impacts on human systems and ecosystem function and services are shown. For physical changes, yellow/green refers to an increase/decrease, respectively, in […]

Figure SPM.2 | Synthesis of observed regional hazards and impacts in ocean24 (top) and high mountain and polar land regions (bottom) assessed in SROCC. For each region, physical changes, impacts on key ecosystems, and impacts on human systems and ecosystem function and services are shown. For physical changes, yellow/green refers to an increase/decrease, respectively, in amount or frequency of the measured variable. For impacts on ecosystems, human systems and ecosystems services blue or red depicts whether an observed impact is positive (beneficial) or negative (adverse), respectively, to the given system or service. Cells assigned ‘increase and decrease’ indicate that within that region, both increase and decrease of physical changes are found, but are not necessarily equal; the same holds for cells showing ‘positive and negative’ attributable impacts. For ocean regions, the confidence level refers to the confidence in attributing observed changes to changes in greenhouse gas forcing for physical changes and to climate change for ecosystem, human systems, and ecosystem services. For high mountain and polar land regions, the level of confidence in attributing physical changes and impacts at least partly to a change in the cryosphere is shown. No assessment means: not applicable, not assessed at regional scale, or the evidence is insufficient for assessment. The physical changes in the ocean are defined as: Temperature change in 0–700 m layer of the ocean except for Southern Ocean (0–2000 m) and Arctic Ocean (upper mixed layer and major inflowing branches); Oxygen in the 0–1200 m layer or oxygen minimum layer; Ocean pH as surface pH (decreasing pH corresponds to increasing ocean acidification). Ecosystems in the ocean: Coral refers to warm-water coral reefs and cold-water corals. The ‘upper water column’ category refers to epipelagic zone for all ocean regions except Polar Regions, where the impacts on some pelagic organisms in open water deeper than the upper 200 m were included. Coastal wetland includes salt marshes, mangroves and seagrasses. Kelp forests are habitats of a specific group of macroalgae. Rocky shores are coastal habitats dominated by immobile calcified organisms such as mussels and barnacles. Deep sea is seafloor ecosystems that are 3000–6000 m deep. Sea-ice associated includes ecosystems in, on and below sea ice. Habitat services refer to supporting structures and services (e.g., habitat, biodiversity, primary production). Coastal Carbon Sequestration refers to the uptake and storage of carbon by coastal blue carbon ecosystems. Ecosystems on Land: Tundra refers to tundra and alpine meadows, and includes terrestrial Antarctic ecosystems. Migration refers to an increase or decrease in net migration, not to beneficial/adverse value. Impacts on tourism refer to the operating conditions for the tourism sector. Cultural services include cultural identity, sense of home, and spiritual, intrinsic and aesthetic values, as well as contributions from glacier archaeology. The underlying information is given for land regions in tables SM2.6, SM2.7, SM2.8, SM3.8, SM3.9, and SM3.10, and for ocean regions in tables SM5.10, SM5.11, SM3.8, SM3.9, and SM3.10. {2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.3.5, 2.3.6, 2.3.7, Figure 2.1, 3.2.1, 3.2.3, 3.2.4, 3.3.3, 3.4.1, 3.4.3, 3.5.2, Box 3.4, 4.2.2, 5.2.2, 5.2.3, 5.3.3, 5.4, 5.6, Figure 5.24, Box 5.3}

The observed and projected changes in the open ocean and coastal seas have consequences on human communities and affect all aspects of well-being and have social, economic and environmental costs (Section 5.4, high confidence). The range and diversity of impacts is striking, with varying consequences for the wider community when analysed across the key marine ecosystems services. These consequences clearly affect the capacity for human society to achieve the SDGs (e.g., Figure 5.22). The evidence of climate change in the ocean is a pervasive thread through all types of coupled human-natural systems and projections amplify these observed impacts with the least impact from lower emission scenarios. 

Risk-reduction responses and their governance through adaptation at the local scale are the most common responses to climate change from ocean systems (Section 5.5.2). It is clear that there are many choices for reducing risk of climate change. Many of the actions have benefits and relatively few dis-benefits, while others have large dis-benefits and marginal effectiveness (Section 5.5.1, Figure 5.23, Table 5.7 and Table 5.8). Many of the risk reduction approaches are limited in their capacity to reduce the risks of climate change, or are at best temporary solutions, which is a significant challenge to adapting to climate change (high confidence). In particular, the effectiveness of the assessed risk reduction measures are minimal under high greenhouse gas emission scenarios, highlighting the critical importance of mitigation. The assessment points to the increased effectiveness and importance of a portfolio of different types of mitigation and adaptation options. Governance is also a critical element in the portfolio of options and occurs at local, national and international scales. Such responses can be more effective with the support of scientific information, local knowledge and Indigenous knowledge, and the consideration of local context and the inclusion of stakeholders.


Key Uncertainties and Gaps

This chapter was designed around three guiding questions (Section 5.1). These guiding questions mean that the report covers both regional and global scales of the ocean and many aspects of human systems, including governance and institutions, and adaptation pathways for dependent communities. This assessment is new linking together a broad and complex set of ocean disciplines and therefore also provides a unique perspective on key uncertainties and gaps in these systems. These gaps limit the extent of the assessments that were possible in this report. Notable outstanding uncertainties and gaps from this assessment include the following:

Physical and biogeochemical processes: While the Earth system is better monitored and the relevant data are more accessible than the other areas of assessment there is considerable room to improve these capabilities. For example, gaps remain in predictive modelling of climate change in coastal areas, deep ocean temperature and salinity measurements for sea level and closure of the energy budget, and oxygen and carbon measurements dense enough to measure deoxygenation of the world ocean and track the mechanisms driving the ocean carbon cycle. Our capacity to understand and model net primary productivity and the rates of carbon burial in coastal sediments are also significant weaknesses. Projections of future changes in the Earth system depend on the use of ESMs, in which there are uncertainties arising from physical or ecological processes that are either omitted or incompletely understood. Most ESMs still rely on relatively simple representations of ocean biogeochemical cycling and the linkages to ocean ecosystem structure and function (Section 5.2.3). Other examples of under-assessed biogeochemical process in the ocean that may have implications for the Earth system under climate change include the fate of methane in the deep ocean (Section 5.2.4). Open ocean primary productivity and its projections requires critical corroborating measurements and improved understanding of its drivers to project changes in ocean productivity with higher confidence (Sections 5.2.2 and 5.2.3).


Biological processes and monitoring: There are a number of marine environments (e.g., on the deep sea floor) and ecosystem components (e.g., viruses and protists) where insufficient scientific understanding limits the assessments of risks to low confidence or no assessment. Examples of gaps include the narrow range of climate and non-climatic hazards and their interactions in simulation models, the linkages between single organisms to communities of organisms, knowledge of climate feedbacks in biological systems (Section 5.3.4, Section 5.2.4), and the capacity and limits of biological adaptation for many ecosystems (Section 5.2.2, 5.2.3, 5.2.4, 5.3). Increasing observational capacity can help provide the data to improve understanding and modelling of these important biophysical responses to climate change.

Variance in human systems and effectiveness of responses: The wide range of contributing factors (physical, social and economic) that interact with localised climate projections make projecting site-specific costs of impacts and benefits of adaptation difficult. There were few examples in the literature evaluating implemented adaptation actions, and there was low confidence in their reliability and provenance, thus largely precluding any assessments of their cost effectiveness. This lack of evidence on costs and benefits particularly affected assessments in Section 5.4 and 5.5. Adaptation responses to climate change have been undertaken by communities, industry and governments. However, their effectiveness for mitigating the risks of climate change (e.g., different types of adaptation response on the coasts, Section 5.5.2) is largely unassessed here, and consequently precludes a global understanding of the capacity in the world to address the risks of climate change in coastal seas, open ocean and the deep sea. A partial solution would be establishing an appropriate ocean and coasts database, including costs-benefits, for these types of studies.




Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.


  1. In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, and Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.9.2 and Figure 1.4 for more details). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome lies within the 17-83% probability range.
  2. In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.9.2 and Figure 1.4 for more details).
  3. The 30 CMIP5 ESMs used in here in various contexts were selected based on the availability of ocean data from the historical period, RCP2.6 and RCP8.5 projections, and corresponding control runs to correct for model drift. The models used include: ACCESS1.0, ACCESS1.3, BNU-ESM, BCC-CSM1-1, CCSM4, CESM1, CMCC-CESM, CMCC-CMS, CNRM-CM5, CSIRO-Mk3, CanESM2, FGOALS-S2.0, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H, GISS-E2-R, HadGEM2-AO, HadGEM2-CC, HadGEM2-ES, INM-CM4, IPSL-CM5A-LR, IPSL-CM5A-MR, IPSL-CM5B-LR, MIROC-ESM, MIROC5, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, and NorESM1-M. Up to 3 ensemble members or variants were included per model, and all changes are relative to a control run with an identical initial condition but with preindustrial forcing. A table with a description and citations for each of these models, along with more detailed discussion of the use of ESM output, can be found in Flato et al. (2013).
  4. ZJ is Zettajoule and is equal to 1021 Joules. Warming the entire ocean by 1℃ requires about 5500 ZJ; 144 ZJ would warm the top 100 m by about 1℃.
  5. Following common oceanographic practice dating back to Helland-Hansen (1916) and discussed in detail by Sverdrup et al. (1942), an ocean water-mass is defined as a large volume of seawater with a characteristic range of temperature and salinity properties, typically falling along a line in temperature-salinity space, often with common formation processes and locations.


  1. Ab Lah, R., B.P. Kelaher, D. Bucher and K. Benkendorff, 2018: Ocean warming and acidification affect the nutritional quality of the commercially-harvested turbinid snail Turbo militaris. Mar. Environ. Res., 141, 100–108, doi:10.1016/j.marenvres.2018.08.009.
  2. Abadi, F., C. Barbraud and O. Gimenez, 2017: Integrated population modeling reveals the impact of climate on the survival of juvenile emperor penguins. Global Change Biol., 23(3), 1353–1359, doi:10.1111/gcb.13538.
  3. Abanades, J., D. Greaves and G. Iglesias, 2015: Coastal defence using wave farms: The role of farm-to-coast distance. Renew. Energ., 75, 572–582, doi:10.1016/j.renene.2014.10.048.
  4. Abedin, M.A., U. Habiba and R. Shaw, 2014: Community Perception and Adaptation to Safe Drinking Water Scarcity: Salinity, Arsenic, and Drought Risks in Coastal Bangladesh. Int. J. Disast. Risk Sci., 5(2), 110–124, doi:10.1007/s13753-014-0021-6.
  5. Abedin, M.A. and R. Shaw, 2015: The role of university networks in disaster risk reduction: Perspective from coastal Bangladesh. Int. J. Disast. Risk Reduc., 13, 381–389, doi:10.1016/j.ijdrr.2015.08.001.
  6. Abelshausen, B., T. Vanwing and W. Jacquet, 2015: Participatory integrated coastal zone management in Vietnam: Theory versus practice case study: Thua Thien Hue province. Journal of Marine and Island Cultures, 4(1), 42–53, doi:10.1016/j.imic.2015.06.004.
  7. Abraham, J.P. et al., 2013: A review of global ocean temperature observations: Implications for ocean heat content estimates and climate change. Rev. Geophys., 51(3), 450–483, doi:10.1002/rog.20022.
  8. Abrahms, B. et al., 2017: Climate mediates the success of migration strategies in a marine predator. Ecol. Lett., 14, 21: 63-71, doi:10.1111/ele.12871.
  9. Abreu, P.C., J. Marangoni and C. Odebrecht, 2017: So close, so far: differences in long-term chlorophyll a variability in three nearby estuarine-coastal stations. Mar. Biol. Res., 13(1), 9–21, doi:10.1080/17451000.2016.1189081.
  10. Ackerman, F. and E.A. Stanton, 2007: The cost of climate change: what we’ll pay if global warming continues unchecked., Natural Resources Defense Council, New York. 33 pp. https://www.nrdc.org/sites/default/files/cost.pdf
  11. Adam, P., 2002: Saltmarshes in a time of change. Environ. Conserv., 29(1), 39–61.
  12. Adam, P., 2019: Salt marsh restoration. In: Coastal Wetlands. [G.M.E. Perillo, E. Wolanski, D.R. Cahoon, C.S. Hopkinson, eds.]Elsevier, Amsterdam, Netherlands, pp. 817–861. ISBN: 978-0-444-63893-9.
  13. Adame, M.F., S. Cherian, R. Reef and B. Stewart-Koster, 2017: Mangrove root biomass and the uncertainty of belowground carbon estimations. Forest Ecol. Manag., 403, 52–60, doi:10.1016/j.foreco.2017.08.016.
  14. Adams, C.A., J.E. Andrews and T. Jickells, 2012: Nitrous oxide and methane fluxes vs. carbon, nitrogen and phosphorous burial in new intertidal and saltmarsh sediments. Sci. Total Environ., 434, 240–251, doi:10.1016/j.scitotenv.2011.11.058.
  15. Adams, D.K. et al., 2011: Surface-generated mesoscale eddies transport deep sea products from hydrothermal vents. Science, 332(6029), 580–583, doi:10.1126/science.1201066.
  16. Addamo, A.M. et al., 2016: Merging scleractinian genera: the overwhelming genetic similarity between solitary Desmophyllum and colonial Lophelia. BMC Evol. Biol., 16(1), 108.
  17. Addino, M.S. et al., 2019: Growth changes of the stout razor clam Tagelus plebeius (Lightfoot, 1786) under different salinities in SW Atlantic estuaries. J. Sea Res., 146, 14–23, doi:10.1016/j.seares.2019.01.005.
  18. Adger, W.N. et al., 2012: Cultural dimensions of climate change impacts and adaptation. Nat. Clim. Change, 3, 112, doi:10.1038/nclimate1666.
  19. Adloff, F. et al., 2015: Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios. Clim. Dyn., 45(9), 2775–2802, doi:10.1007/s00382-015-2507-3.
  20. Agostini, S. et al., 2018: Ocean acidification drives community shifts towards simplified non-calcified habitats in a subtropical−temperate transition zone. Sci. Rep., 8(1), 11354, doi:10.1038/s41598-018-29251-7.
  21. Ahmed, N., W.W.L. Cheung, S. Thompson and M. Glaser, 2017: Solutions to blue carbon emissions: Shrimp cultivation, mangrove deforestation and climate change in coastal Bangladesh. Mar. Policy, 82, 68–75, doi:10.1016/j.marpol.2017.05.007.
  22. Ahmed, N. and J.S. Diana, 2015a: Coastal to inland: Expansion of prawn farming for adaptation to climate change in Bangladesh. Aquacult. Rep., 2, 67–76, doi:10.1016/j.aqrep.2015.08.001.
  23. Ahmed, N. and J.S. Diana, 2015b: Threatening “white gold”: Impacts of climate change on shrimp farming in coastal Bangladesh. Ocean Coast. Manage., 114, 42–52, doi:10.1016/j.ocecoaman.2015.06.008.
  24. Ahmed, N. and M. Glaser, 2016a: Can “Integrated Multi-Trophic Aquaculture (IMTA)” adapt to climate change in coastal Bangladesh? Ocean Coast. Manage., 132, 120–131, doi:10.1016/j.ocecoaman.2016.08.017.
  25. Ahmed, N. and M. Glaser, 2016b: Coastal aquaculture, mangrove deforestation and blue carbon emissions: Is REDD+ a solution? Mar. Policy, 66, 58–66, doi:10.1016/j.marpol.2016.01.011.
  26. Akselman, R. et al., 2015: Protoceratium reticulatum (Dinophyceae) in the austral Southwestern Atlantic and the first report on YTX-production in shelf waters of Argentina. Harmful Algae, 45, 40–52.
  27. Alam, G.M.M., K. Alam and S. Mushtaq, 2016: Influence of institutional access and social capital on adaptation decision: Empirical evidence from hazard-prone rural households in Bangladesh. Ecol. Econ., 130, 243–251, doi:10.1016/j.ecolecon.2016.07.012.
  28. Alava, J.J., W.W.L. Cheung, P.S. Ross and U.R. Sumaila, 2017: Climate change-contaminant interactions in marine food webs: Toward a conceptual framework. Global Change Biol., 23(10), 3984–4001, doi:10.1111/gcb.13667.
  29. Alava, J.J., A.M. Cisneros-Montemayor, U.R. Sumaila and W.W.L. Cheung, 2018: Projected amplification of food web bioaccumulation of MeHg and PCBs under climate change in the Northeastern Pacific. Sci. Rep., 8(1), 13460, doi:10.1038/s41598-018-31824-5.
  30. Albright, R. et al., 2016a: Ocean acidification: Linking science to management solutions using the Great Barrier Reef as a case study. J. Environ. Manage., 182, 641–650, doi:10.1016/j.jenvman.2016.07.038.
  31. Albright, R. et al., 2016b: Reversal of ocean acidification enhances net coral reef calcification. Nature, 531, 362, doi:10.1038/nature17155.
  32. Alderson-Day, B., S. McCarthy-Jones and C. Fernyhough, 2015: Hearing voices in the resting brain: A review of intrinsic functional connectivity research on auditory verbal hallucinations. Neurosci. Biobehav. Rev., 55(Supplement C), 78–87, doi:10.1016/j.neubiorev.2015.04.016.
  33. Alford, M.H. et al., 2013: Turbulent mixing and hydraulic control of abyssal water in the Samoan Passage. Geophys. Res. Lett., 40(17), 4668–4674, doi:10.1002/grl.50684.
  34. Alford, M.H., J.A. MacKinnon, H.L. Simmons and J.D. Nash, 2016: Near-Inertial Internal Gravity Waves in the Ocean. Annu. Rev. Mar. Sci., 8(1), 95–123, doi:10.1146/annurev-marine-010814-015746.
  35. Alguero-Muniz, M. et al., 2017: Ocean acidification effects on mesozooplankton community development: Results from a long-term mesocosm experiment. PLoS One, 12(4), doi:10.1371/journal.pone.0175851.
  36. Alguero-Muniz, M. et al., 2016: Withstanding multiple stressors: ephyrae of the moon jellyfish (Aurelia aurita, Scyphozoa) in a high-temperature, high-CO2 and low-oxygen environment. Mar. Biol., 163(9), doi:10.1007/s00227-016-2958-z.
  37. Alin, S.R. et al., 2012: Robust empirical relationships for estimating the carbonate system in the southern California Current System and application to CalCOFI hydrographic cruise data (2005–2011). J. Geophys. Res-Oceans, 117(C5), doi:10.1029/2011JC007511.
  38. Allen, S.E. and X. Durrieu de Madron, 2009: A review of the role of submarine canyons in deep-ocean exchange with the shelf. Ocean Sci., 5(4), 607–620, doi:10.5194/os-5-607-2009.
  39. Allison, E.H. et al., 2009: Vulnerability of national economies to the impacts of climate change on fisheries. Fish Fish., 10(2), 173–196, doi:10.1111/j.1467-2979.2008.00310.x.
  40. Almahasheer, H. et al., 2017: Low Carbon sink capacity of Red Sea mangroves. Sci. Rep., 7(1), 9700, doi:10.1038/s41598-017-10424-9.
  41. Almén, A.-K., A. Vehmaa, A. Brutemark and J. Engström-Öst, 2014: Coping with climate change? Copepods experience drastic variations in their physicochemical environment on a diurnal basis. J. Exp. Mar. Biol. Ecol., 460, 120–128, doi:10.1016/j.jembe.2014.07.001.
  42. Alongi, D.M., 2015: The impact of climate change on mangrove forests. Curr. Clim. Change Rep., 1(1), 30–39.
  43. Alongi, D.M., 2018: Blue Carbon: Coastal Sequestration for Climate Change Mitigation. Springer, Cham, Switzerland. ISBN: 978-3-319-91697-2 
  44. Alongi, D.M. and S.K. Mukhopadhyay, 2015: Contribution of mangroves to coastal carbon cycling in low latitude seas. Agric. For. Meteorol., 213, 266–272, doi:10.1016/j.agrformet.2014.10.005.
  45. Alter, S.E. et al., 2015: Climate impacts on transocean dispersal and habitat in gray whales from the Pleistocene to 2100. Mol. Ecol., 24(7), 1510–1522, doi:10.1111/mec.13121.
  46. Altieri, A.H. and K.B. Gedan, 2015: Climate change and dead zones. Global Change Biol., 21(4), 1395–1406.
  47. Álvarez, G. et al., 2019: Paralytic Shellfish Toxins in Surf Clams Mesodesma donacium during a Large Bloom of Alexandrium catenella Dinoflagellates Associated to an Intense Shellfish Mass Mortality. Toxins, 11(4), 188. doi:10.3390/toxins11040188.
  48. Álvarez-Romero, J.G. et al., 2018: Designing connected marine reserves in the face of global warming. Global Change Biol., 24(2), e671–e691, doi:10.1111/gcb.13989.
  49. Amaya, O. et al., 2018: Large-Scale sea turtle mortality events in El Salvador attributed to paralytic shellfish toxin-producing algae blooms. Front. Mar. Sci., 5(411), doi:10.3389/fmars.2018.00411.
  50. Ancona, S. and H. Drummond, 2013: Life History Plasticity of a Tropical Seabird in Response to El Nino Anomalies during Early Life. PLoS One, 8(9), doi:10.1371/journal.pone.0072665.
  51. Andersen, G.S., M.F. Pedersen and S.L. Nielsen, 2013: Temperature Acclimation and Heat Tolerance of Photosynthesis in Norwegian Saccharina Latissima ( Laminariales, Phaeophyceae). J. Phycol., 49(4), 689–700, doi:10.1111/jpy.12077.
  52. Anderson, C.R. et al., 2015: Living with harmful algal blooms in a changing world: strategies for modeling and mitigating their effects in coastal marine ecosystems. In Castal and Marine Hazards, Risks, and Disasters [J. F. Shroder, J.T. Ellis, D.J. Sherman eds.] Elsevier BV, Amsterdam, pp. 495–561. ISBN: 978-0-12-396483-0.
  53. Andrachuk, M. and D. Armitage, 2015: Understanding social-ecological change and transformation through community perceptions of system identity. Ecol. Soc., 20(4). 26. http://dx.doi.org/10.5751/ES-07759-200426
  54. Aqorau, T., J. Bell and J.N. Kittinger, 2018: Good governance for migratory species. Science, 361(6408), 1208, doi:10.1126/science.aav2051.
  55. Araos, M. et al., 2016: Climate change adaptation planning in large cities: A systematic global assessment. Environ. Sci. Policy, 66, 375–382, doi:10.1016/j.envsci.2016.06.009.
  56. Araujo, R.M. et al., 2016: Status, trends and drivers of kelp forests in Europe: an expert assessment. Biodivers. Conserv., 25(7), 1319–1348, doi:10.1007/s10531-016-1141-7.
  57. Arbic, B.K., R.H. Karsten and C. Garrett, 2009: On tidal resonance in the global ocean and the back‐effect of coastal tides upon open‐ocean tides. Atmos. Ocean, 47(4), 239–266, doi:10.3137/OC311.2009.
  58. Archer, D. et al., 2014: Moving towards inclusive urban adaptation: approaches to integrating community-based adaptation to climate change at city and national scale. Clim. Dev., 6(4), 345–356, doi:10.1080/17565529.2014.918868.
  59. Arellano, S.M. et al., 2014: Larvae from deep sea methane seeps disperse in surface waters. Proc. Roy. Soc. B. Biol., 281(1786), 20133276, doi:10.1098/rspb.2013.3276.
  60. Arévalo-Martínez, D.L. et al., 2015: Massive nitrous oxide emissions from the tropical South Pacific Ocean. Nat. Geosci., 8, 530, doi:10.1038/ngeo2469.
  61. Arias-Ortiz, A. et al., 2018: A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Change, 8, 338–344
  62. Arístegui, J., M. Gasol Josep, M. Duarte Carlos and J. Herndld Gerhard, 2009: Microbial oceanography of the dark ocean’s pelagic realm. Limnol. Oceanogr., 54(5), 1501–1529, doi:10.4319/lo.2009.54.5.1501.
  63. Armitage, A.R., W.E. Highfield, S.D. Brody and P. Louchouarn, 2015: The Contribution of Mangrove Expansion to Salt Marsh Loss on the Texas Gulf Coast. PLoS One, 10(5), e0125404, doi:10.1371/journal.pone.0125404.
  64. Armour, K.C. et al., 2016: Southern Ocean warming delayed by circumpolar upwelling and equatorward transport. Nat. Geosci., 9(7), 549.
  65. Armstrong, C.W., N. Foley, R. Tinch and S. van den Hove, 2010: Ecosystem goods and services of the deep sea. Deliverable D6, Universititet i Tromsø, Tromsø, 68 pp. https://www.pik-potsdam.de/news/public-events/archiv/alter-net/former-ss/2010/13.09.2010/van_den_hove/d6-2-final.pdf
  66. Armstrong, C.W., N.S. Foley, R. Tinch and S. van den Hove, 2012: Services from the deep: Steps towards valuation of deep sea goods and services. Ecosyst. Serv., 2, 2–13, doi:10.1016/j.ecoser.2012.07.001.
  67. Arntz, W.E. et al., 2006: El Niño and similar perturbation effects on the benthos of the Humboldt, California, and Benguela Current upwelling ecosystems. Adv. Geosci., 6, 243–265, doi:10.5194/adgeo-6-243-2006.
  68. Arroyo Mina, J.S., D.A. Revollo Fernandez, A. Aguilar Ibarra and N. Georgantzis, 2016: Economic behavior of fishers under climate-related uncertainty: Results from field experiments in Mexico and Colombia. Fish. Res., 183, 304–317, doi:10.1016/j.fishres.2016.05.020.
  69. Asadabadi, A. and E. Miller-Hooks, 2017: Assessing strategies for protecting transportation infrastructure from an uncertain climate future. Transport. Res. A-Pol., 105, 27–41.
  70. Ascani, F. et al., 2016: Juvenile recruitment in loggerhead sea turtles linked to decadal changes in ocean circulation. Global Change Biol., 22(11), 3529–3538, doi:10.1111/gcb.13331.
  71. Asch, R.G., 2015: Climate change and decadal shifts in the phenology of larval fishes in the California Current ecosystem. PNAS, 112(30), E4065–E4074, doi:10.1073/pnas.1421946112.
  72. Asch, R.G., W.W.L. Cheung and G. Reygondeau, 2018: Future marine ecosystem drivers, biodiversity, and fisheries maximum catch potential in Pacific Island countries and territories under climate change. Mar. Policy, 88, 285–294, doi:10.1016/j.marpol.2017.08.015.
  73. Ashbolt, N.J., 2019: Flood and Infectious Disease Risk Assessment. In: Health in Ecological Perspectives in the Anthropocene [t. Watanabe, C. Watanabe eds].. Springer, Singapore. pp. 145–159. ISBN: 978-981-13-2525-0
  74. Assis, J., M.B. Araújo and E.A. Serrão, 2018: Projected climate changes threaten ancient refugia of kelp forests in the North Atlantic. Global Change Biol., 24(1), e55–e66, doi:10.1111/gcb.13818.
  75. Assis, J., A.V. Lucas, I. Barbara and E.A. Serrao, 2016: Future climate change is predicted to shift long-term persistence zones in the cold-temperate kelp Laminaria hyperborea. Mar. Environ. Res., 113, 174–182, doi:10.1016/j.marenvres.2015.11.005.
  76. Astariz, S., C. Perez-Collazo, J. Abanades and G. Iglesias, 2015: Towards the optimal design of a co-located wind-wave farm. Energy, 84, 15–24, doi:10.1016/j.energy.2015.01.114.
  77. Ataur Rahman, M. and S. Rahman, 2015: Natural and traditional defense mechanisms to reduce climate risks in coastal zones of Bangladesh. Weather and Climate Extremes, 7, 84–95, doi:10.1016/j.wace.2014.12.004.
  78. Atwood, T.B. et al., 2017: Global patterns in mangrove soil carbon stocks and losses. Nat. Clim. Change, 7, 523, doi:10.1038/nclimate3326.
  79. Audefroy, J.F. and B.N.C. Sánchez, 2017: Integrating local knowledge for climate change adaptation in Yucatán, Mexico. Int. J. Sustain. Built Environ., 6(1), 228–237, doi:10.1016/j.ijsbe.2017.03.007.
  80. Aumont, O. and L. Bopp, 2006: Globalizing results from ocean in situ iron fertilization studies. Global Biogeochem. Cy., 20(2), doi:10.1029/2005GB002591.
  81. Aumont, O., O. Maury, S. Lefort and L. Bopp, 2018: Evaluating the Potential Impacts of the Diurnal Vertical Migration by Marine Organisms on Marine Biogeochemistry. Global Biogeochem. Cy., 32(11), 1622–1643, doi:10.1029/2018GB005886.
  82. Avelar, S., T.S. van der Voort and T.I. Eglinton, 2017: Relevance of carbon stocks of marine sediments for national greenhouse gas inventories of maritime nations. Carbon Bal. Manage., 12(1), 10, doi:10.1186/s13021-017-0077-x.
  83. Aylett, A., 2015: Institutionalizing the urban governance of climate change adaptation: Results of an international survey. Urban Clim., 14, 4–16, doi:10.1016/j.uclim.2015.06.005.
  84. Azad, A.K., K.M. Hossain and M. Nasreen, 2013: Flood-induced vulnerabilities and problems encountered by women in northern Bangladesh. Int. J. Disast. Risk Sci., 4(4), 190–199, doi:10.1007/s13753-013-0020-z.
  85. Aziz, A.A., S. Thomas, P. Dargusch and S. Phinn, 2016: Assessing the potential of REDD+ in a production mangrove forest in Malaysia using stakeholder analysis and ecosystem services mapping. Mar. Policy, 74, 6–17, doi:10.1016/j.marpol.2016.09.013.
  86. Bachman, S.D., J.R. Taylor, K.A. Adams and P.J. Hosegood, 2017: Mesoscale and Submesoscale Effects on Mixed Layer Depth in the Southern Ocean. J. Phys. Oceanogr., 47(9), 2173–2188, doi:10.1175/JPO-D-17-0034.1.
  87. Baco, A. R. et al., 2017: Defying dissolution: discovery of deep sea scleractinian coral reefs in the North Pacific. Sci. Rep., 7(1), 5436.
  88. Bae, Y.H., K.O. Kim and B.H. Choi, 2010: Lake Sihwa tidal power plant project. Ocean Eng., 37(5), 454–463, doi:10.1016/j.oceaneng.2010.01.015.
  89. Baez, J.C. et al., 2011: The North Atlantic Oscillation and sea surface temperature affect loggerhead abundance around the Strait of Gibraltar. Sci. Mar., 75(3), 571–575, doi:10.3989/scimar.2011.75n3571.
  90. Baggini, C., Y. Issaris, M. Salomidi and J. Hall-Spencer, 2015: Herbivore diversity improves benthic community resilience to ocean acidification. J. Exp. Mar. Biol. Ecol., 469, 98–104, doi:10.1016/j.jembe.2015.04.019.
  91. Bailey, A. et al., 2016: Early life stages of the Arctic copepod Calanus glacialisare unaffected by increased seawater pCO2. ICES J. Mar. Sci., 74(4), 996-1004 doi:10.1093/icesjms/fsw066.
  92. Baker-Austin, C., J. Trinanes, N. Gonzalez-Escalona and J. Martinez-Urtaza, 2017: Non-Cholera Vibrios: The Microbial Barometer of Climate Change. Trends Microbiol., 25(1), 76–84, doi:10.1016/j.tim.2016.09.008.
  93. Baker-Austin, C. et al., 2013: Emerging Vibrio risk at high latitudes in response to ocean warming. Nat. Clim. Change, 3(1), 73–77, doi:10.1038/NCLIMATE1628.
  94. Balmford, A. et al., 2004: The worldwide costs of marine protected areas. PNAS, 101(26), 9694–9697, doi:10.1073/pnas.0403239101.
  95. Bambridge, T. and P.Y. Le Meur, 2018: Savoirs locaux et biodiversité aux îles Marquises: don, pouvoir et perte. Revue d’anthropologie et des connaissances, 12(1), 29-55. 
  96. Ban, S.S., N.A.J. Graham and S.R. Connolly, 2014: Evidence for multiple stressor interactions and effects on coral reefs. Global Change Biol., 20(3), 681–697, doi:10.1111/gcb.12453.
  97. Banse, K., 1968: Hydrography of the Arabian Sea Shelf of India and Pakistan and effects on demersal fishes. Deep Sea Res. Pt. I, 15(1), 45–79, doi:10.1016/0011-7471(68)90028-4.
  98. Barange, M., 2019: Avoiding misinterpretation of climate change projections of fish catches. ICES J. Mar. Sci., doi:10.1093/icesjms/fsz061.
  99. Barange, M. et al., 2017: The Cost of Reducing the North Atlantic Ocean Biological Carbon Pump. Front. Mar. Sci., 3, 290.
  100. Barbier, E.B., 2015: Climate change impacts on rural poverty in low-elevation coastal zones. Estuar. Coast. Shelf Sci., 165, A1–A13, doi:10.1016/j.ecss.2015.05.035.
  101. Barnes, R.S.K., 2017: Are seaward pneumatophore fringes transitional between mangrove and lower-shore system compartments? Mar. Environ. Res., 125, 99–109, doi:10.1016/j.marenvres.2017.01.008.
  102. Barragán, J.M. and M. de Andrés, 2015: Analysis and trends of the world’s coastal cities and agglomerations. Ocean Coast. Manage., 114, 11–20, doi:10.1016/j.ocecoaman.2015.06.004.
  103. Barrett, J. et al., 2015: Development of an estuarine climate change monitoring program. Ecol. Indic., 53, 182–186, doi:10.1016/j.ecolind.2015.01.039.
  104. Barry, J.P., C.H. Baxter, R.D. Sagarin and S.E. Gilman, 1995: Climate-related, long-term faunal changes in a california rocky intertidal community. Science, 267(5198), 672–675, doi:10.1126/science.267.5198.672.
  105. Barry, J.P. et al., 2004: Effects of Direct Ocean CO2 Injection on Deep sea Meiofauna. J. Oceanogr., 60(4), 759–766, doi:10.1007/s10872-004-5768-8.
  106. Barton, A. et al., 2015: Impacts of coastal acidification on the Pacific Northwest shellfish industry and adaptation strategies implemented in response. Oceanography, 28(2), 146–159.
  107. Barton, A.D., A.J. Irwin, Z.V. Finkel and C.A. Stock, 2016: Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. PNAS, 113(11), 2964–2969, doi:10.1073/pnas.1519080113.
  108. Barton, J.A., B.L. Willis and K.S. Hutson, 2017: Coral propagation: a review of techniques for ornamental trade and reef restoration. Rev. Aquacult., 9(3), 238–256, doi:10.1111/raq.12135.
  109. Bartsch, I. et al., 2016: Changes in kelp forest biomass and depth distribution in Kongsfjorden, Svalbard, between 1996–1998 and 2012–2014 reflect Arctic warming. Polar Biol., 39(11), 2021–2036, doi:10.1007/s00300-015-1870-1.
  110. Basset, A., M. Elliott, R.J. West and J.G. Wilson, 2013: Estuarine and lagoon biodiversity and their natural goods and services. Estuar. Coast. Shelf Sci., 132, 1–4, doi:10.1016/j.ecss.2013.05.018.
  111. Batal, M. et al., 2017: Quantifying associations of the dietary share of ultra-processed foods with overall diet quality in First Nations peoples in the Canadian provinces of British Columbia, Alberta, Manitoba and Ontario. Public Health Nutr., 21(1), 103-113. doi:10.1017/S1368980017001677.
  112. Battaglia, G. and F. Joos, 2018: Hazards of decreasing marine oxygen: the near-term and millennial-scale benefits of meeting the Paris climate targets. Earth Syst. Dyn., 9(2), 797.
  113. Bauer, J.E. et al., 2013: The changing carbon cycle of the coastal ocean. Nature, 504(7478), 61–70, doi:10.1038/nature12857.
  114. Bauman, S.J. et al., 2014: Augmenting the biological pump: The shortcomings of geoengineered upwelling. Oceanography, 27(3), 17–23.
  115. Bayraktarov, E. et al., 2016: The cost and feasibility of marine coastal restoration. Ecol. Appl., 26(4), 1055–1074, doi:10.1890/15-1077.
  116. Beaugrand, G., 2009: Decadal changes in climate and ecosystems in the North Atlantic Ocean and adjacent seas. Deep Sea Res. Pt. II, 56(8–10), 656–673, doi:10.1016/j.dsr2.2008.12.022.
  117. Beaugrand, G. et al., 2015: Future vulnerability of marine biodiversity compared with contemporary and past changes. Nat. Clim. Change, 5(7), 695–701, doi:10.1038/nclimate2650.
  118. Beaulieu, C. et al., 2013: Factors challenging our ability to detect long-term trends in ocean chlorophyll. Biogeosciences, 10(4), 2711–2724, doi:10.5194/bg-10-2711-2013.
  119. Beca-Carretero, P., B. Olesen, N. Marbà and D. Krause-Jensen, 2018: Response to experimental warming in northern eelgrass populations: comparison across a range of temperature adaptations. Mar. Ecol. Prog. Ser., 589, 59–72.
  120. Beck, M.W. et al., 2011: Oyster Reefs at Risk and Recommendations for Conservation, Restoration, and Management. BioScience, 61(2), 107–116, doi:10.1525/bio.2011.61.2.5.
  121. Beck, M.W. et al., 2018: The global flood protection savings provided by coral reefs. Nat. Commun., 9(1), 2186, doi:10.1038/s41467-018-04568-z.
  122. Becker, A., K.Y. Ng Adolf, D. McEvoy and J. Mullett, 2018: Implications of climate change for shipping: Ports and supply chains. WiRes. Clim. Change, 9(2), e508, doi:10.1002/wcc.508.
  123. Becker, A.H. et al., 2016: A method to estimate climate-critical construction materials applied to seaport protection. Global Environ. Change, 40, 125–136.
  124. Bednaršek, N., R. A. Feely, J. C. P. Reum, B. Peterson, J. Menkel, S. R. Alin, and B. Hales. “Limacina helicina shell dissolution as an indicator of declining habitat suitability owing to ocean acidification in the California Current Ecosystem.” Proceedings of the Royal Society B: Biological Sciences 281, no. 1785 (2014): 20140123.
  125. Beetham, E., P.S. Kench and S. Popinet, 2017: Future Reef Growth Can Mitigate Physical Impacts of Sea level Rise on Atoll Islands. Earth’s Future, 5(10), 1002–1014, doi:10.1002/2017ef000589.
  126. Belhabib, D. et al., 2018: Impacts of anthropogenic and natural “extreme events” on global fisheries. Fish Fish., doi:10.1111/faf.12314.
  127. Belhabib, D., V.W.Y. Lam and W.W.L. Cheung, 2016: Overview of West African fisheries under climate change: Impacts, vulnerabilities and adaptive responses of the artisanal and industrial sectors. Mar. Policy, 71(Supplement C), 15–28, doi:10.1016/j.marpol.2016.05.009.
  128. Bell, J. et al., 2018a: Climate change impacts, vulnerabilities and adaptations: Western and Central Pacific Ocean marine fisheries. In: Impacts of climate change on fisheries and aquaculture [Barange, M., Bahri, T., Beveridge, M.C.M., Cochrane, K.L., Funge-Smith, S. & Poulain, F. (eds.)]. FAO Fisheries and Aquaculture Technical Paper T, FAO, Rome, Italy. 305-324. ISBN: 978-92-5-130607-9
  129. Bell, J.D. et al., 2018b: Adaptations to maintain the contributions of small-scale fisheries to food security in the Pacific Islands. Mar. Policy, 88, 303–314, doi:10.1016/j.marpol.2017.05.019.
  130. Bell, T.W., J.G. Allen, K.C. Cavanaugh and D.A. Siegel, 2018c: Three decades of variability in California’s giant kelp forests from the Landsat satellites. Remote Sens. Environ., doi:10.1016/j.rse.2018.06.039.
  131. Belmadani, A. et al., 2014: What dynamics drive future wind scenarios for coastal upwelling off Peru and Chile? Clim. Dyn., 43(7), 1893–1914, doi:10.1007/s00382-013-2015-2.
  132. Ben Rais Lasram, F. et al., 2010: The Mediterranean Sea as a ‘cul‐de‐sac’ for endemic fishes facing climate change. Global Change Biol., 16(12), 3233–3245, doi:10.1111/j.1365-2486.2010.02224.x.
  133. Bendtsen, J., J. Mortensen, K. Lennert and S. Rysgaard, 2015: Heat sources for glacial ice melt in a west Greenland tidewater outlet glacier fjord: The role of subglacial freshwater discharge. Geophys. Res. Lett., 42(10), 4089–4095, doi:10.1002/2015GL063846.
  134. Béné, C. et al., 2015: Feeding 9 billion by 2050 – Putting fish back on the menu. Food Secur., 7(2), 261–274, doi:10.1007/s12571-015-0427-z.
  135. Bennett, N.J., P. Dearden, G. Murray and A. Kadfak, 2014: The capacity to adapt?: communities in a changing climate, environment, and economy on the northern Andaman coast of Thailand. Ecol. Soc., 19(2), doi:10.5751/ES-06315-190205.
  136. Bennett, N.J., A. Kadfak and P. Dearden, 2016: Community-based scenario planning: a process for vulnerability analysis and adaptation planning to social–ecological change in coastal communities. Environ. Dev. Sustain., 18(6), 1771–1799, doi:10.1007/s10668-015-9707-1.
  137. Bennett, N.J. et al., 2018: Environmental Stewardship: A Conceptual Review and Analytical Framework. Environ. Manage., 61(4), 597–614, doi:10.1007/s00267-017-0993-2.
  138. Berdalet, E. et al., 2017: GlobalHAB: a new program to promote international research, observations, and modeling of harmful algal blooms in aquatic systems. Oceanography, 30(1), 70–81.
  139. Berge, J. et al., 2014: Arctic complexity: a case study on diel vertical migration of zooplankton. J. Plankton Res., 36(5), 1279–1297, doi:10.1093/plankt/fbu059.
  140. Bernhard, J.M. and C.E. Reimers, 1991: Benthic foraminiferal population fluctuations related to anoxia: Santa Barbara Basin. Biogeochemistry, 15(2), 127–149, doi:10.1007/BF00003221.
  141. Betzold, C. and I. Mohamed, 2017: Seawalls as a response to coastal erosion and flooding: a case study from Grande Comore, Comoros (West Indian Ocean). Reg. Environ. Change, 17(4), 1077–1087, doi:10.1007/s10113-016-1044-x.
  142. Bianchi, D., C. Stock, E.D. Galbraith and J.L. Sarmiento, 2013: Diel vertical migration: Ecological controls and impacts on the biological pump in a one-dimensional ocean model. Global Biogeochem. Cy., 27(2), 478–491, doi:10.1002/gbc.20031.
  143. Biggs, D., C.C. Hicks, J.E. Cinner and C.M. Hall, 2015: Marine tourism in the face of global change: The resilience of enterprises to crises in Thailand and Australia. Ocean Coast. Manage., 105, 65–74, doi:10.1016/j.ocecoaman.2014.12.019.
  144. Billé, R. et al., 2013: Taking Action Against Ocean Acidification: A Review of Management and Policy Options. Environ. Manage., 52(4), 761–779, doi:10.1007/s00267-013-0132-7.
  145. Bindoff, N.L. et al., 2013: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 867–952.
  146. Bjorndal, K.A. et al., 2017: Ecological regime shift drives declining growth rates of sea turtles throughout the West Atlantic. Global Change Biol., 23(11), 4556–4568, doi:10.1111/gcb.13712.
  147. Black, B.A. et al., 2014: Six centuries of variability and extremes in a coupled marine-terrestrial ecosystem. Science, 345(6203), 1498.
  148. Blackford, J. et al., 2014: Detection and impacts of leakage from sub-seafloor deep geological carbon dioxide storage. Nat. Clim. Change, 4, 1011, doi:10.1038/nclimate2381.
  149. Blanchard, J.L. et al., 2012: Potential consequences of climate change for primary production and fish production in large marine ecosystems. Philos. Trans. Roy. Soc. B., 367(1605), 2979–2989.
  150. Blanchard, J.L. et al., 2017: Linked sustainability challenges and trade-offs among fisheries, aquaculture and agriculture. Nat. Ecol. Evol., 1(9), 1240–1249, doi:10.1038/s41559-017-0258-8.
  151. Blankespoor, B., S. Dasgupta and B. Laplante, 2014: Sea level rise and coastal wetlands. Ambio, 43(8), 996–1005, doi:10.1007/s13280-014-0500-4.
  152. Blasiak, R. et al., 2017: Climate change and marine fisheries: Least developed countries top global index of vulnerability. PLoS One, 12(6), e0179632, doi:10.1371/journal.pone.0179632.
  153. Boetius, A. and F. Wenzhoefer, 2013: Seafloor oxygen consumption fuelled by methane from cold seeps. Nat. Geosci., 6(9), 725–734, doi:10.1038/NGEO1926.
  154. Bograd, S.J. et al., 2015: Changes in source waters to the Southern California Bight. Deep Sea Res. Pt. II, 112, 42–52, doi:10.1016/j.dsr2.2014.04.009.
  155. Bongaerts, P. et al., 2017: Deep reefs are not universal refuges: Reseeding potential varies among coral species. Sci. Adv., 3(2), e1602373, doi:10.1126/sciadv.1602373.
  156. Bongiorni, L. et al., 2011: First step in the restoration of a highly degraded coral reef (Singapore) by in situ coral intensive farming. Aquaculture, 322–323(Supplement C), 191–200.
  157. Booth, S. and D. Zeller, 2005: Mercury, food webs, and marine mammals: Implications of diet and climate change for human health. Environ. Health. Perspect., 113(5), 521–526, doi:10.1289/ehp.7603.
  158. Bopp, L. et al., 2013: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences, 10(10), 6225–6245, doi:10.5194/bg-10-6225-2013.
  159. Bopp, L. et al., 2017: Ocean (de)oxygenation from the Last Glacial Maximum to the twenty-first century: insights from Earth System models. Philos. Trans. Roy. Soc. A., 375(2102), 20160323, doi:10.1098/rsta.2016.0323.
  160. Borbor-Córdova, M.J. et al., 2018: Risk Perception of Coastal Communities and Authorities on Harmful Algal Blooms in Ecuador. Front. Mar. Sci., 5(365), doi:10.3389/fmars.2018.00365.
  161. Borchert, S.M., M.J. Osland, N.M. Enwright and K. Griffith, 2018: Coastal wetland adaptation to sea level rise: Quantifying potential for landward migration and coastal squeeze. J. Appl. Ecol., 55(6), 2876–2887, doi:10.1111/1365-2664.13169.
  162. Borges, A.V. et al., 2016: Massive marine methane emissions from near-shore shallow coastal areas. Sci. Rep., 6, 27908, doi:10.1038/srep27908.
  163. Borthwick, A.G.L., 2016: Marine Renewable Energy Seascape. Engineering, 2(1), 69–78, doi:10.1016/J.ENG.2016.01.011.
  164. Bost, C.A. et al., 2009: The importance of oceanographic fronts to marine birds and mammals of the southern oceans. J. Mar. Syst., 78(3), 363–376, doi:10.1016/j.jmarsys.2008.11.022.
  165. Bost, C.A. et al., 2015: Large-scale climatic anomalies affect marine predator foraging behaviour and demography. Nat. Commun., 6, 8220, doi:10.1038/ncomms9220.
  166. Botterell, Z.L.R. et al., 2019: Bioavailability and effects of microplastics on marine zooplankton: A review. Environ. Pollut., 245, 98–110, doi:10.1016/j.envpol.2018.10.065.
  167. Bouma, T.J. et al., 2016: Short-term mudflat dynamics drive long-term cyclic salt marsh dynamics. Limnol. Oceanogr., 61(6), 2261–2275, doi:10.1002/lno.10374.
  168. Bourne, D.G., K.M. Morrow and N.S. Webster, 2016: Insights into the Coral Microbiome: Underpinning the Health and Resilience of Reef Ecosystems. Annu. Rev. Microbiol., 70(1), 317–340, doi:10.1146/annurev-micro-102215-095440.
  169. Bove, C.B. et al., 2019: Common Caribbean corals exhibit highly variable responses to future acidification and warming. Proc. Roy. Soc. B., 286(1900), 20182840.
  170. Bowie, A.R. et al., 2015: Iron budgets for three distinct biogeochemical sites around the Kerguelen Archipelago (Southern Ocean) during the natural fertilisation study, KEOPS-2. Biogeosciences, 12(14), 4421–4445.
  171. Boxall, A.B.A. et al., 2009: Impacts of climate change on indirect human exposure to pathogens and chemicals from agriculture. Environ. Health. Perspect., 117(4), 508–514, doi:10.1289/ehp.0800084.
  172. Boxhammer, T. et al., 2018: Enhanced transfer of organic matter to higher trophic levels caused by ocean acidification and its implications for export production: A mass balance approach. PLoS One, 13(5), e0197502, doi:10.1371/journal.pone.0197502.
  173. Boyce, D.G., M. Dowd, M.R. Lewis and B. Worm, 2014: Estimating global chlorophyll changes over the past century. Progr. Oceanogr., 122, 163–173, doi:10.1016/j.pocean.2014.01.004.
  174. Boyce, D. G. and B. Worm, 2015: Patterns and ecological implications of historical marine phytoplankton change. Mar. Ecol. Prog. Ser., 534, 251–272, doi:10.3354/meps11411.
  175. Boyd, P.W. and M. Bressac, 2016: Developing a test-bed for robust research governance of geoengineering: the contribution of ocean iron biogeochemistry. Philos. Trans. Roy. Soc. A., 374(2081).
  176. Boyd, P.W. et al., 2019: Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature, 568(7752), 327–335, doi:10.1038/s41586-019-1098-2.
  177. Boyd, P.W. et al., 2018: Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—A review. Global Change Biol., 24(6), 2239–2261, doi:10.1111/gcb.14102.
  178. Boyd, P.W. et al., 2016: Biological responses to environmental heterogeneity under future ocean conditions. Global Change Biol., 22(8), 2633–2650, doi:10.1111/gcb.13287.
  179. Boyd, P.W. et al., 2015a: Physiological responses of a Southern Ocean diatom to complex future ocean conditions. Nat. Clim. Change, 6(2), 207–213, doi:10.1038/nclimate2811.
  180. Boyd, P.W. and D.A. Hutchins, 2012: Understanding the responses of ocean biota to a complex matrix of cumulative anthropogenic change. Mar. Ecol. Prog. Ser., 470, 125–135.
  181. Boyd, P.W. et al., 2007: Mesoscale Iron Enrichment Experiments 1993-2005: Synthesis and Future Directions. Science, 315(5812), 612, doi:10.1126/science.1131669.
  182. Boyd, P.W. et al., 2013: Marine Phytoplankton Temperature versus Growth Responses from Polar to Tropical Waters – Outcome of a Scientific Community-Wide Study. PLoS One, 8(5), e63091, doi:10.1371/journal.pone.0063091.
  183. Boyd, P.W. et al., 2015b: Why are biotic iron pools uniform across high- and low-iron pelagic ecosystems? Global Biogeochem. Cy., 29(7), 1028–1043, doi:10.1002/2014GB005014.
  184. Brady, R.X., M.A. Alexander, N.S. Lovenduski and R.R. Rykaczewski, 2017: Emergent anthropogenic trends in California Current upwelling. Geophys. Res. Lett., 44(10), 5044–5052, doi:10.1002/2017GL072945.
  185. Brannigan, L. et al., 2017: Submesoscale Instabilities in Mesoscale Eddies. J. Phys. Oceanogr., 47(12), 3061–3085, doi:10.1175/JPO-D-16-0178.1.
  186. Bravo, J., F. Suárez, A. Ramírez and F. Acosta, 2015: Ciguatera, an emerging human poisoning in Europe. J. Aquac. Mar. Biol, 3, 00053.
  187. Breed, G.A. et al., 2017: Sustained disruption of narwhal habitat use and behavior in the presence of Arctic killer whales. PNAS, 114(10), 2628–2633, doi:10.1073/pnas.1611707114.
  188. Breitberg, D. et al., 2015: And on Top of All That… Coping with Ocean Acidification in the Midst of Many Stressors. Oceanography, 25(2), 48–61, doi:10.5670/oceanog.2015.31.
  189. Breitburg, D. et al., 2018: Declining oxygen in the global ocean and coastal waters. Science, 359(6371).
  190. Bridge, T.C.L. et al., 2013: Depth-dependent mortality of reef corals following a severe bleaching event: implications for thermal refuges and population recovery. F1000Research, 2, 187, doi:10.12688/f1000research.2-187.v3.
  191. Brierley, A.S. and M.J. Kingsford, 2009: Impacts of Climate Change on Marine Organisms and Ecosystems. Curr. Biol., 19(14), R602–R614, doi:10.1016/j.cub.2009.05.046.
  192. Briscoe, D.K. et al., 2017: Ecological bridges and barriers in pelagic ecosystems. Deep sea Res. Pt. II, 140, 182–192, doi:10.1016/j.dsr2.2016.11.004.
  193. Britten, G.L., M. Dowd and B. Worm, 2016: Changing recruitment capacity in global fish stocks. PNAS, 113(1), 134–139, doi:10.1073/pnas.1504709112.
  194. Brooke, S. et al., 2013: Temperature tolerance of the deep sea coral Lophelia pertusa from the southeastern United States. Deep sea Res. Pt. II, 92, 240–248.
  195. Brooks, C.M., J.B. Weller, K. Gjerde and U.R. Sumaila, 2013: Challenging the right to fish in a fast-changing ocean. Stan. Envtl. LJ, 33, 289.
  196. Broto, V.C., E. Boyd and J. Ensor, 2015: Participatory urban planning for climate change adaptation in coastal cities: lessons from a pilot experience in Maputo, Mozambique. Curr. Opin. Environ. Sustain., 13, 11–18, doi:10.1016/j.cosust.2014.12.005.
  197. Brown, A. and S. Thatje, 2014: The effects of changing climate on faunal depth distributions determine winners and losers. Global Change Biol., 21(1), 173–180, doi:10.1111/gcb.12680.
  198. Brown, B., R. Dunne, M. Goodson and A. Douglas, 2002: Experience shapes the susceptibility of a reef coral to bleaching. Coral Reefs, 21(2), 119–126.
  199. Brown, B.E., R.P. Dunne, N. Phongsuwan and P.J. Somerfield, 2011: Increased sea level promotes coral cover on shallow reef flats in the Andaman Sea, eastern Indian Ocean. Coral Reefs, 30(4), 867, doi:10.1007/s00338-011-0804-9.
  200. Brown, J.H. et al., 2004: Toward a Metabolic Theory of Ecology. Ecology, 85(7), 1771–1789, doi:10.1890/03-9000.
  201. Brown, J.M. et al., 2018a: A coastal vulnerability assessment for planning climate resilient infrastructure. Ocean Coast. Manage., 163, 101–112.
  202. Brown, S. et al., 2018b: What are the implications of sea level rise for a 1.5, 2 and 3°C rise in global mean temperatures in the Ganges-Brahmaputra-Meghna and other vulnerable deltas? Reg. Environ. Change, 18(6), 1829–1842, doi:10.1007/s10113-018-1311-0.
  203. Browning, T.J. et al., 2017: Nutrient co-limitation at the boundary of an oceanic gyre. Nature, 551(7679), 242–246, doi:10.1038/nature24063.
  204. Bruge, A. et al., 2016: Thermal Niche Tracking and Future Distribution of Atlantic Mackerel Spawning in Response to Ocean Warming. Front. Mar. Sci., 3(86), doi:10.3389/fmars.2016.00086.
  205. Brunson, J.K. et al., 2018: Biosynthesis of the neurotoxin domoic acid in a bloom-forming diatom. Science, 361(6409), 1356–1358.
  206. Brussaard, C. et al., 2013: Arctic microbial community dynamics influenced by elevated CO2 levels. Biogeosciences, 10(2), 719–731.
  207. Bryndum-Buchholz, A. et al., 2019: Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Global Change Biol., 25(2), 459–472, doi:10.1111/gcb.14512.
  208. Buckley, M.W. and J. Marshall, 2015: Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review. Rev. Geophys., 54(1), 5–63, doi:10.1002/2015RG000493.
  209. Buhl-Mortensen, L. et al., 2010: Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins. Mar. Ecol-Evol. Persp., 31(1), 21–50, doi:10.1111/j.1439-0485.2010.00359.x.
  210. Bujosa, A., A. Riera and C.M. Torres, 2015: Valuing tourism demand attributes to guide climate change adaptation measures efficiently: The case of the Spanish domestic travel market. Tourism Manage., 47, 233–239, doi:10.1016/j.tourman.2014.09.023.
  211. Bukvic, A., A. Smith and A. Zhang, 2015: Evaluating drivers of coastal relocation in Hurricane Sandy affected communities. Int. J. Disast. Risk Reduc., 13, 215–228, doi:10.1016/j.ijdrr.2015.06.008.
  212. Bunten, D. and M. Kahn, 2014: The Impact of Emerging Climate Risks on Urban Real Estate Price Dynamics. National Bureau of Economic Research, Cambridge, MA [Available at: http://www.nber.org/papers/w20018.pdf%5D. Accessed: 2019/09/30.
  213. Bunting, S.W., N. Kundu and N. Ahmed, 2017: Evaluating the contribution of diversified shrimp-rice agroecosystems in Bangladesh and West Bengal, India to social-ecological resilience. Ocean Coast. Manage., 148, 63–74, doi:10.1016/j.ocecoaman.2017.07.010.
  214. Burd, A.B. and G.A. Jackson, 2002: Modeling steady-state particle size spectra. Environ. Sci. Technol., 36(3), 323–327.
  215. Burrell, T.J., E.W. Maas, D.A. Hulston and C.S. Law, 2017: Variable response to warming and ocean acidification by bacterial processes in different plankton communities. Aqut. Microb. Ecol., 79(1), 49–62.
  216. Burrows, M.T. et al., 2014: Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507(7493), 492–495, doi:10.1038/nature12976.
  217. Busch, D.S. et al., 2016: Climate science strategy of the US National Marine Fisheries Service. Mar. Policy, 74, 58–67, doi:10.1016/j.marpol.2016.09.001.
  218. Büscher, J.V., A.U. Form and U. Riebesell, 2017: Interactive Effects of Ocean Acidification and Warming on Growth, Fitness and Survival of the Cold water Coral Lophelia pertusa under Different Food Availabilities. Front. Mar. Sci., 4, 119, doi:10.3389/fmars.2017.00101.
  219. Butt, N. et al., 2016: Challenges in assessing the vulnerability of species to climate change to inform conservation actions. Biol. Conserv., 199, 10–15, doi:10.1016/j.biocon.2016.04.020.
  220. Buurman, J. and V. Babovic, 2016: Adaptation Pathways and Real Options Analysis: An approach to deep uncertainty in climate change adaptation policies. Policy Soc., 35(2), 137–150, doi:10.1016/j.polsoc.2016.05.002.
  221. Byrne, J.A., A.Y. Lo and Y. Jianjun, 2015: Residents’ understanding of the role of green infrastructure for climate change adaptation in Hangzhou, China. Landscape Urban Plan., 138, 132–143, doi:10.1016/j.landurbplan.2015.02.013.
  222. Cabré, A., I. Marinov, R. Bernardello and D. Bianchi, 2015: Oxygen minimum zones in the tropical Pacific across CMIP5 models: mean state differences and climate change trends. Biogeosciences, 12(18), 5429–5454, doi:10.5194/bg-12-5429-2015.
  223. Cacciapaglia, C. and R. van Woesik, 2018: Marine species distribution modelling and the effects of genetic isolation under climate change. J. Biogeogr., 45(1), 154–163, doi:10.1111/jbi.13115.
  224. Caesar, L. et al., 2018: Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), 191–196, doi:10.1038/s41586-018-0006-5.
  225. Cai, W.-J. et al., 2017: Redox reactions and weak buffering capacity lead to acidification in the Chesapeake Bay. Nat. Commun., 8(1), 369, doi:10.1038/s41467-017-00417-7.
  226. Cameron, C., L.B. Hutley and D.A. Friess, 2019: Estimating the full greenhouse gas emissions offset potential and profile between rehabilitating and established mangroves. Sci. Total Environ., 665, 419–431, doi:10.1016/j.scitotenv.2019.02.104.
  227. Camp, E.F. et al., 2018: The Future of Coral Reefs Subject to Rapid Climate Change: Lessons from Natural Extreme Environments. Front. Mar. Sci., 5, 4.
  228. Campbell, J.E. and J.W. Fourqurean, 2018: Does Nutrient Availability Regulate Seagrass Response to Elevated CO2? Ecosystems, 21(7), 1269–1282.
  229. Campbell, J.R., 2017: Climate Change Impacts on Atolls and Island Nations in the South Pacific. Encyclopedia of the Anthropocene: Volume 2. [Dellasala, D.A., Goldstein, M.I. (eds.)] Elsevier, New York. p. 227-232. ISBN: 978-0-12-813576-1.
  230. Camus, V.G., 2017: Le cas de l’atoll de Tabiteuea, république de Kiribati. In: Les atolls du Pacifique face au changement climatique. Une comparaison Tuamotu-Kiribati, Karthala [T. Bambridge and J.-P. Latouche (eds.)], Karthala, Chavannes de Bogis, Switzerland,122 pp. ISBN: 978-2811117399
  231. Canals, M. et al., 2006: Flushing submarine canyons. Nature, 444(7117), 354–357, doi:10.1038/nature05271.
  232. Capotondi, A. et al., 2012: Enhanced upper ocean stratification with climate change in the CMIP3 models. J. Geophys. Res-Oceans, 117(C4), doi:10.1029/2011JC007409.
  233. Carapuço, M.M. et al., 2016: Coastal geoindicators: Towards the establishment of a common framework for sandy coastal environments. Earth-Sci. Rev., 154, 183–190, doi:10.1016/j.earscirev.2016.01.002.
  234. Carcedo, M.C., S.M. Fiori and C.S. Bremec, 2017: Zonation of macrobenthos across a mesotidal sandy beach: Variability based on physical factors. J. Sea Res., 121, 1–10.
  235. Cardoso-Mohedano, J.-G. et al., 2018: Sub-tropical coastal lagoon salinization associated to shrimp ponds effluents. Estuar. Coast. Shelf Sci., 203, 72–79, doi:10.1016/j.ecss.2018.01.022.
  236. Carey, J. et al., 2017: The declining role of organic matter in New England salt marshes. Estuar. Coast., 40(3), 626–639.
  237. Carlson, A.K., W.W. Taylor, J. Liu and I. Orlic, 2017: The telecoupling framework: an integrative tool for enhancing fisheries management. Fisheries, 42(8), 395–397.
  238. Caron, D.A. and D.A. Hutchins, 2012: The effects of changing climate on microzooplankton grazing and community structure: drivers, predictions and knowledge gaps. J. Plankton Res., 35(2), 235–252, doi:10.1093/plankt/fbs091.
  239. Carozza, D.A., D. Bianchi and E.D. Galbraith, 2016: The ecological module of BOATS-1.0: a bioenergetically constrained model of marine upper trophic levels suitable for studies of fisheries and ocean biogeochemistry. Geosci. Model Dev., 9(4), 1545–1565, doi:10.5194/gmd-9-1545-2016.
  240. Carrasquilla-Henao, M. and F. Juanes, 2017: Mangroves enhance local fisheries catches: a global meta-analysis. Fish Fish., 18(1), 79–93, doi:10.1111/faf.12168.
  241. Carro, I., Seijo, L., Nagy, G.J., Lagos, X. and Gutiérrez, O., 2018: Building capacity on ecosystem-based adaption strategy to cope with extreme events and sea level rise on the Uruguayan coast. Int. J. Clim. Change Strategies Manage., 10(4), 504–522, doi:10.1108/IJCCSM-07-2017-0149.
  242. Carstensen, J., M. Chierici, B. G. Gustafsson and E. Gustafsson, 2018: Long-Term and Seasonal Trends in Estuarine and Coastal Carbonate Systems. Global Biogeochem. Cy., 32(3), 497–513, doi:10.1002/2017GB005781.
  243. Cartapanis, O., E.D. Galbraith, D. Bianchi and S. L. Jaccard, 2018: Carbon burial in deep sea sediment and implications for oceanic inventories of carbon and alkalinity over the last glacial cycle. Clim. Past, 14(11), 1819–1850, doi:10.5194/cp-14-1819-2018.
  244. Carter, B. et al., 2017: Two decades of Pacific anthropogenic carbon storage and ocean acidification along Global Ocean Ship‐based Hydrographic Investigations Program sections P16 and P02. Global Biogeochem. Cy., 31(2), 306–327.
  245. Carter, J.G., 2018: Urban climate change adaptation: Exploring the implications of future land cover scenarios. Cities, 77, 73–80.
  246. Carton, J.A., G.A. Chepurin and L. Chen, 2018: SODA3: A New Ocean Climate Reanalysis. J. Clim., 31(17), 6967–6983, doi:10.1175/JCLI-D-18-0149.1.
  247. Carugati, L. et al., 2018: Impact of mangrove forests degradation on biodiversity and ecosystem functioning. Sci. Rep., 8(1), 13298, doi:10.1038/s41598-018-31683-0.
  248. Carvalho, B., E. Rangel and M. Vale, 2017: Evaluation of the impacts of climate change on disease vectors through ecological niche modelling. Bulletin of entomological research, 107(4), 419–430.
  249. Casado-Amezúa, P. et al., 2019: Distributional shifts of canopy-forming seaweeds from the Atlantic coast of Southern Europe. Biodivers. Conserv., 28(5), 1151–1172, doi:10.1007/s10531-019-01716-9.
  250. Casey, J.M., S.R. Connolly and T.D. Ainsworth, 2015: Coral transplantation triggers shift in microbiome and promotion of coral disease associated potential pathogens. Sci. Rep., 5(1), 833, doi:10.1038/srep11903.
  251. Castelle, B., S. Bujan, S. Ferreira and G. Dodet, 2017: Foredune morphological changes and beach recovery from the extreme 2013/2014 winter at a high-energy sandy coast. Mar. Geol., 385, 41–55, doi:10.1016/j.margeo.2016.12.006.
  252. Castells-Quintana, D., M.d.P. Lopez-Uribe and T.K. McDermott, 2017: Geography, institutions and development: a review of the long-run impacts of climate change. Clim. Dev., 9(5), 452–470.
  253. Cavallo, C. et al., 2015: Predicting climate warming effects on green turtle hatchling viability and dispersal performance. Funct. Ecol., 29(6), 768–778, doi:10.1111/1365-2435.12389.
  254. Cavan, E.L., M. Trimmer, F. Shelley and R. Sanders, 2017: Remineralization of particulate organic carbon in an ocean oxygen minimum zone. Nat. Commun., 8, 14847, doi:10.1038/ncomms14847.
  255. Celo, V., D.R.S. Lean and S.L. Scott, 2006: Abiotic methylation of mercury in the aquatic environment. Sci. Total Environ., 368(1), 126–137, doi:10.1016/j.scitotenv.2005.09.043.
  256. Čerkasova, N. et al., 2016: Curonian Lagoon drainage basin modelling and assessment of climate change impact. Oceanologia, 58(2), 90–102, doi:10.1016/j.oceano.2016.01.003.
  257. Chambers, L.E., P. Dann, B. Cannell and E.J. Woehler, 2014: Climate as a driver of phenological change in southern seabirds. International Journal of Biometeorology, 58(4), 603–612, doi:10.1007/s00484-013-0711-6.
  258. Chan, K.M., T. Satterfield and J. Goldstein, 2012: Rethinking ecosystem services to better address and navigate cultural values. Ecol. Econ., 74, 8–18.
  259. Chapman, A. and S. Darby, 2016: Evaluating sustainable adaptation strategies for vulnerable mega-deltas using system dynamics modelling: Rice agriculture in the Mekong Delta’s An Giang Province, Vietnam. Sci. Total Environ., 559, 326–338, doi:10.1016/j.scitotenv.2016.02.162.
  260. Charlton, K.E. et al., 2016: Fish, food security and health in Pacific Island countries and territories: a systematic literature review. BMC Public Health, 16(1), 285, doi:10.1186/s12889-016-2953-9.
  261. Chauvenet, A.L.M. et al., 2013: Maximizing the success of assisted colonizations. Animal Conserv., 16(2), 161–169, doi:10.1111/j.1469-1795.2012.00589.x.
  262. Chavanich, S. et al., 2015: Conservation, management, and restoration of coral reefs. Animal evolution: early emerging animals matter, 118(2), 132–134.
  263. Chefaoui, R. M., C. M. Duarte and E. A. Serrão, 2018: Dramatic loss of seagrass habitat under projected climate change in the Mediterranean Sea. Global Change Biol., 24(10), 4919-4928, doi:10.1111/gcb.14401.
  264. Chen, C.-T. A., 2003: New vs. export production on the continental shelf. Deep Sea Res. Pt. II, 50(6), 1327–1333, doi:10.1016/S0967-0645(03)00026-2.
  265. Chen, C.-T. A. et al., 2017: Deep oceans may acidify faster than anticipated due to global warming. Nat. Clim. Change, 7(12), 890–894, doi:10.1038/s41558-017-0003-y.
  266. Chen, N. et al., 2018: Storm induced estuarine turbidity maxima and controls on nutrient fluxes across river-estuary-coast continuum. Sci. Total Environ., 628, 1108–1120.
  267. Chen, P.-Y., C.-C. Chen, L. Chu and B. McCarl, 2015: Evaluating the economic damage of climate change on global coral reefs. Global Environ. Change, 30(Supplement C), 12–20, doi:10.1016/j.gloenvcha.2014.10.011.
  268. Chen, S. and D. Ganapin, 2016: Polycentric coastal and ocean management in the Caribbean Sea Large Marine Ecosystem: harnessing community-based actions to implement regional frameworks. Environ. Dev., 17, 264–276, doi:10.1016/j.envdev.2015.07.010.
  269. Cheng, H.-Q. and J.-Y. Chen, 2017: Adapting cities to sea level rise: A perspective from Chinese deltas. Advances in Climate Change Research, 8(2), 130–136, doi:10.1016/j.accre.2017.05.006.
  270. Cheng, L., J. Abraham, Z. Hausfather and K.E. Trenberth, 2019: How fast are the oceans warming? Science, 363(6423), 128, doi:10.1126/science.aav7619.
  271. Cheng, L. et al., 2017: Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv., 3(3), e1601545, doi:10.1126/sciadv.1601545.
  272. Cheung, W.W.L., R.D. Brodeur, T.A. Okey and D. Pauly, 2015: Projecting future changes in distributions of pelagic fish species of Northeast Pacific shelf seas. Progr. Oceanogr., 130, 19–31, doi:10.1016/j.pocean.2014.09.003.
  273. Cheung, W.W.L., J. Bruggeman and M. Butenschön, 2018a: Projected changes in global and national potential marine fisheries catch under climate change scenarios in the twenty-first century. In: Impacts of climate change on fisheries and aquaculture [Barange, M., Bahri, T., Beveridge, M.C.M., Cochrane, K.L., Funge-Smith, S. & Poulain, F. (eds.)]. FAO Fisheries and Aquaculture Technical Paper T, FAO, Rome, Italy. 63-86. ISBN: 978-92-5-130607-9
  274. Cheung, W.W.L. et al., 2008: Application of macroecological theory to predict effects of climate change on global fisheries potential. Mar. Ecol. Prog. Ser., 365, 187–197.
  275. Cheung, W.W.L. et al., 2016a: Transform high seas management to build climate resilience in marine seafood supply. Fish Fish., 18(2), 254–263, doi:10.1111/faf.12177.
  276. Cheung, W.W.L., M.C. Jones, G. Reygondeau and T.L. Frölicher, 2018b: Opportunities for climate-risk reduction through effective fisheries management. Global Change Biol., 2108, 1–15, doi:doi:10.1111/gcb.14390.
  277. Cheung, W.W.L. and D. Pauly, 2016: Impacts and Effects of Ocean Warming on Marine Fishes. 239–253.
  278. Cheung, W.W.L., G. Reygondeau and T.L. Frölicher, 2016b: Large benefits to marine fisheries of meeting the 1.5°C global warming target. Science, 354(6319), 1591–1594, doi:10.1126/science.aag2331.
  279. Cheung, W.W.L., R. Watson and D. Pauly, 2013: Signature of ocean warming in global fisheries catch. Nature, 497, 365, doi:10.1038/nature121.
  280. Chew, S.T. and J.B. Gallagher, 2018: Accounting for black carbon lowers estimates of blue carbon storage services. Sci. Rep., 8(1), 2553, doi:10.1038/s41598-018-20644-2.
  281. Chivers, W.J., A.W. Walne and G.C. Hays, 2017: Mismatch between marine plankton range movements and the velocity of climate change. Nat. Commun., 8, doi:10.1038/ncomms14434.
  282. Chmura, G.L., S.C. Anisfeld, D.R. Cahoon and J.C. Lynch, 2003: Global carbon sequestration in tidal, saline wetland soils. Global Biogeochem. Cy., 17(4), doi:10.1029/2002GB001917.
  283. Chmura, G.L., L. Kellman, L. van Ardenne and G.R. Guntenspergen, 2016: Greenhouse Gas Fluxes from Salt Marshes Exposed to Chronic Nutrient Enrichment. PLoS One, 11(2), e0149937, doi:10.1371/journal.pone.0149937.
  284. Chollett, I. and P.J. Mumby, 2013: Reefs of last resort: Locating and assessing thermal refugia in the wider Caribbean. Biol. Conserv., 167, 179–186, doi:10.1016/j.biocon.2013.08.010.
  285. Chuenpagdee, R., 2011: World small-scale fisheries: contemporary visions. EburonAcademic Publishers, Delft, The Netherlands. 400 pp. ISBN: 978-90-5972-539-3
  286. Chung, I.K. et al., 2013: Installing kelp forests/seaweed beds for mitigation and adaptation against global warming: Korean Project Overview. ICES J. Mar. Sci., 70(5), 1038–1044, doi:10.1093/icesjms/fss206.
  287. Chung, I.K., C.F.A. Sondak and J. Beardall, 2017: The future of seaweed aquaculture in a rapidly changing world. European J. Phycol., 52(4), 495–505, doi:10.1080/09670262.2017.1359678.
  288. Chust, G. et al., 2014: Biomass changes and trophic amplification of plankton in a warmer ocean. Global Change Biol., 20(7), 2124–2139, doi:10.1111/gcb.12562.
  289. Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 465–570.
  290. Cinner, J.E. et al., 2018: Building adaptive capacity to climate change in tropical coastal communities. Nat. Clim. Change, 8(2), 117–123, doi:10.1038/s41558-017-0065-x.
  291. Cinner, J.E. et al., 2016: A framework for understanding climate change impacts on coral reef social–ecological systems. Reg. Environ. Change, 16(4), 1133–1146, doi:10.1007/s10113-015-0832-z.
  292. Cisneros-Montemayor, A.M. et al., 2013: Global economic value of shark ecotourism: implications for conservation. Oryx, 47(3), 381–388.
  293. Cisneros-Montemayor, A.M., D. Pauly, L.V. Weatherdon and Y. Ota, 2016: A Global Estimate of Seafood Consumption by Coastal Indigenous Peoples. PLoS One, 11(12), doi:10.1371/journal.pone.0166681.
  294. Cisneros-Montemayor, A.M. and U.R. Sumaila, 2010: A global estimate of benefits from ecosystem-based marine recreation: potential impacts and implications for management. Journal of Bioeconomics, 12(3), 245–268.
  295. Clark, A., 2017: Small unmanned aerial systems comparative analysis for the application to coastal erosion monitoring. Geo. Res. J., 13, 175–185, doi:10.1016/j.grj.2017.05.001.
  296. Cloern, J.E. et al., 2016: Human activities and climate variability drive fast‐paced change across the world’s estuarine–coastal ecosystems. Global Change Biol., 22(2), 513–529.
  297. Clucas, G.V. et al., 2014: A reversal of fortunes: climate change &apos;winners&apos; and &apos;losers&apos; in Antarctic Peninsula penguins. Sci. Rep., 4, 5024, doi:10.1038/srep05024.
  298. Cocco, V. et al., 2013: Oxygen and indicators of stress for marine life in multi-model global warming projections. Biogeosciences, 10(3), 1849–1868, doi:10.5194/bg-10-1849-2013.
  299. Coelho, V.R. et al., 2017: Shading as a mitigation tool for coral bleaching in three common Indo-Pacific species. J. Exp. Mar. Biol. Ecol., 497(Supplement C), 152–163.
  300. Colburn, L.L. et al., 2016: Indicators of climate change and social vulnerability in fishing dependent communities along the Eastern and Gulf Coasts of the United States. Mar. Policy, 74, 323–333, doi:10.1016/j.marpol.2016.04.030.
  301. Coles, S.L. and B.M. Riegl, 2013: Thermal tolerances of reef corals in the Gulf: A review of the potential for increasing coral survival and adaptation to climate change through assisted translocation. Mar. Pollut. Bull., 72(2), 323–332, doi:10.1016/j.marpolbul.2012.09.006.
  302. Colin, M., F. Palhol and A. Leuxe, 2016: Adaptation of transport infrastructures and networks to climate change. Transp. Res. Proc., 14, 86–95.
  303. Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1029–1136.
  304. Collins, S., B. Rost and T.A. Rynearson, 2014: Evolutionary potential of marine phytoplankton under ocean acidification. Evol. Appl., 7(1), 140–155, doi:doi:10.1111/eva.12120.
  305. Colosi, J.A. and W. Munk, 2006: Tales of the Venerable Honolulu Tide Gauge. J. Phys. Oceanogr., 36(6), 967–996, doi:10.1175/JPO2876.1.
  306. Comte, A. and L.H. Pendleton, 2018: Management strategies for coral reefs and people under Global Environ. Change: 25 years of scientific research. J. Environ. Manage., 209, 462–474, doi:10.1016/j.jenvman.2017.12.051.
  307. Conde, D. et al., 2015: Solutions for Sustainable Coastal Lagoon Management. In: Coastal Zones – Solutions for the 21st Century. [Baztan, J., Chouinard, O., Jorgensen, B., Tett, P., Vanderlinden, J-P., Vasseur, L. (eds)]. Elsevier, New York. pp. 217–250. ISBN: 978-0-12-802748-6
  308. Constable, A.J. et al., 2014: Climate change and Southern Ocean ecosystems I: how changes in physical habitats directly affect marine biota. Global Change Biol., 20(10), 3004–3025, doi:10.1111/gcb.12623.
  309. Cooley, S.R., C.R. Ono, S. Melcer and J. Roberson, 2016: Community-Level Actions that Can Address Ocean Acidification. . Front. Mar. Sci.,, 2(128), 1–12.
  310. Corbitt, E.S. et al., 2011: Global Source–Receptor Relationships for Mercury Deposition Under Present-Day and 2050 Emissions Scenarios. Environ. Sci. Technol., 45(24), 10477–10484, doi:10.1021/es202496y.
  311. Cordes, E.E. et al., 2010: The influence of geological, geochemical, and biogenic habitat heterogeneity on seep biodiversity. Mar. Ecol-Evol. Persp., 31(1), 51–65, doi:10.1111/j.1439-0485.2009.00334.x.
  312. Cordes, E.E. et al., 2016: Environmental Impacts of the Deep-Water Oil and Gas Industry: A Review to Guide Management Strategies. Front. Environ. Sci., 4, 58.
  313. Cornwall, C.E. et al., 2018: Resistance of corals and coralline algae to ocean acidification: physiological control of calcification under natural pH variability. Proc. Roy. Soc. B. Biol., 285(1884). https://doi.org/10.1098/rspb.2018.1168
  314. Costa, D.P. et al., 2010: Approaches to studying climatic change and its role on the habitat selection of antarctic pinnipeds. Integr. Comp. Biol., 50(6), 1018–1030, doi:10.1093/icb/icq054.
  315. Costanza, R. et al., 2017: Twenty years of ecosystem services: How far have we come and how far do we still need to go? Ecosyst. Serv., 28, 1–16.
  316. Costanza, R. et al., 2014: Changes in the global value of ecosystem services. Global Environ. Change, 26, 152–158, doi:10.1016/j.gloenvcha.2014.04.002.
  317. Costanza, R. et al., 2008: The value of coastal wetlands for hurricane protection. Ambio, 37(4), 241–248.
  318. Creighton, C., A.J. Hobday, M. Lockwood and G.T. Pecl, 2016: Adapting Management of Marine Environments to a Changing Climate: A Checklist to Guide Reform and Assess Progress. Ecosystems, 19(2), 187–219, doi:10.1007/s10021-015-9925-2.
  319. Crespo, O., D. Guillermo and C. Daniel, 2017: A review of the impacts of fisheries on open-ocean ecosystems. ICES J. Mar. Sci., 74(9), 2283–2297, doi:10.1093/icesjms/fsx084.
  320. Cripps, G., P. Lindeque and K.J. Flynn, 2014: Have we been underestimating the effects of ocean acidification in zooplankton? Global Change Biol., 20(11), 3377–3385, doi:10.1111/gcb.12582.
  321. Crocker, D.E. et al., 2006: Impact of El Niño on the foraging behavior of female northern elephant seals. Mar. Ecol. Prog. Ser., 309(1), 1–10, doi:10.3354/meps309001.
  322. Crooks, S. et al., 2011: Mitigating climate change through restoration and management of coastal wetlands and near-shore marine ecosystems: challenges and opportunities. Environment Department Paper 121, World Bank, Washington, D.C. 59 p.
  323. Crosby, S.C. et al., 2016: Salt marsh persistence is threatened by predicted sea level rise. Estuar. Coast. Shelf Sci., 181, 93–99, doi:10.1016/j.ecss.2016.08.018.
  324. Crotty, S.M., C. Angelini and M.D. Bertness, 2017: Multiple stressors and the potential for synergistic loss of New England salt marshes. PLOS ONE, 12(8), e0183058.
  325. Crozier, L.G. and J.A. Hutchings, 2014: Plastic and evolutionary responses to climate change in fish. Evol. Appl., 7(1), 68–87, doi:doi:10.1111/eva.12135.
  326. Cruz, D.W.d., R.D. Villanueva and M.V.B. Baria, 2014: Community-based, low-tech method of restoring a lost thicket of Acropora corals. ICES J. Mar. Sci., 71(7), 1866–1875, doi:10.1093/icesjms/fst228.
  327. Cuellar-Martinez, T. et al., 2018: Addressing the Problem of Harmful Algal Blooms in Latin America and the Caribbean- A Regional Network for Early Warning and Response. Front. Mar. Sci., 5(409), doi:10.3389/fmars.2018.00409.
  328. Cumiskey, L. et al., 2018: A framework to include the (inter)dependencies of Disaster Risk Reduction measures in coastal risk assessment. Coast. Eng., 134, 81–92, doi:10.1016/j.coastaleng.2017.08.009.
  329. Cushing, D. A., D. D. Roby and D. B. Irons, 2018: Patterns of distribution, abundance, and change over time in a subarctic marine bird community. Deep Sea Res. Pt. II, 147, 148–163, doi:10.1016/j.dsr2.2017.07.012.
  330. Cvitanovic, C. et al., 2016: Linking adaptation science to action to build food secure Pacific Island communities. Clim. Risk Manage., 11, 53–62, doi:10.1016/j.crm.2016.01.003.
  331. D’Asaro, E.A., 2014: Turbulence in the Upper-Ocean Mixed Layer. Annu. Rev. Mar. Sci., 6(1), 101–115, doi:10.1146/annurev-marine-010213-135138.
  332. Dalyander, P.S. et al., 2016: Use of structured decision-making to explicitly incorporate environmental process understanding in management of coastal restoration projects: Case study on barrier islands of the northern Gulf of Mexico. J. Environ. Manage., 183(3), 497–509, doi:10.1016/j.jenvman.2016.08.078.
  333. Daneri, G. et al., 2012: Wind forcing and short-term variability of phytoplankton and heterotrophic bacterioplankton in the coastal zone of the Concepción upwelling system (Central Chile). Progr. Oceanogr., 92–95(Supplement C), 92–96.
  334. Danovaro, R., A. Dell’Anno and A. Pusceddu, 2004: Biodiversity response to climate change in a warm deep sea. Ecol. Lett., 7(9), 821–828, doi:10.1111/j.1461-0248.2004.00634.x.
  335. Danovaro, R. et al., 2001: Deep sea ecosystem response to climate changes: the eastern Mediterranean case study. Trends Ecol. Evol., 16(9), 505–510, doi:10.1016/S0169-5347(01)02215-7.
  336. DasGupta, R. and R. Shaw, 2015: An indicator based approach to assess coastal communities’ resilience against climate related disasters in Indian Sundarbans. J. Coast. Conserv., 19(1), 85–101, doi:10.1007/s11852-014-0369-1.
  337. Dasgupta, S. et al., 2017: The Impact of Aquatic Salinization on Fish Habitats and Poor Communities in a Changing Climate: Evidence from Southwest Coastal Bangladesh. Ecol. Econ., 139, 128–139, doi:10.1016/j.ecolecon.2017.04.009.
  338. Dave, A.C. and M.S. Lozier, 2013: Examining the global record of interannual variability in stratification and marine productivity in the low-latitude and mid-latitude ocean. J. Geophys. Res-Oceans, 118(6), 3114–3127, doi:10.1002/jgrc.20224.
  339. Davis, R., 2015: ‘All in’: Snow crab, capitalization, and the future of small-scale fisheries in Newfoundland. Mar. Policy, 61, 323–330, doi:10.1016/j.marpol.2015.04.008.
  340. Davison, P.C., D.M. Checkley, J.A. Koslow and J. Barlow, 2013: Carbon export mediated by mesopelagic fishes in the northeast Pacific Ocean. Progr. Oceanogr., 116, 14–30, doi:10.1016/j.pocean.2013.05.013.
  341. Dawson, J., 2017: Climate Change Adaptation Strategies and Policy Options for Arctic Shipping. Transport Canada. Ottawa, Canada. 154 pp. http://hdl.handle.net/10393/36016
  342. Day, J.W. and J.M. Rybczyk, 2019: Global Change Impacts on the Future of Coastal Systems: Perverse Interactions Among Climate Change, Ecosystem Degradation, Energy Scarcity, and Population. In: Coasts and Estuaries. Elsevier, 621–639.
  343. de Coninck, H. et al., 2018: Chapter 4: Strengthening and implementing the global response. In: An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V.P., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla, P.R., Pirani, A., Moufouma-Okla, W., Péan, C., Pidcock, R., Connors, S., Matthews, J.B.R., Chen, Y., Zhou, X., Gomis, M.I., Lonnoy, E., Maycock, T., Tignor, M., Waterfield, T. (eds.)]. In Press.
  344. De Leo, F.C. et al., 2012: The effects of submarine canyons and the oxygen minimum zone on deep sea fish assemblages off Hawai’i. Deep sea Res. Pt. I, 64, 54–70, doi:10.1016/j.dsr.2012.01.014.
  345. De Leo, F.C. et al., 2010: Submarine canyons: hotspots of benthic biomass and productivity in the deep sea. Proc. Biol. Sci., 277(1695), 2783–2792, doi:10.1098/rspb.2010.0462.
  346. de Orte, M.R. et al., 2014: Effects on the mobility of metals from acidification caused by possible CO2 leakage from sub-seabed geological formations. Sci. Total Environ., 470–471(Supplement C), 356–363, doi:10.1016/j.scitotenv.2013.09.095.
  347. de Sá, L.C. et al., 2018: Studies of the effects of microplastics on aquatic organisms: What do we know and where should we focus our efforts in the future? Sci. Total Environ., 645, 1029–1039, doi:10.1016/j.scitotenv.2018.07.207.
  348. De Urioste-Stone, S.M., L. Le, M.D. Scaccia and E. Wilkins, 2016: Nature-based tourism and climate change risk: Visitors’ perceptions in mount desert island, Maine. J. Outdoor Recreat. Tour., 13, 57–65, doi:10.1016/j.jort.2016.01.003.
  349. DeBiasse, M.B. and M.W. Kelly, 2016: Plastic and Evolved Responses to Global Change: What Can We Learn from Comparative Transcriptomics? J. Hered., 107(1), 71–81, doi:10.1093/jhered/esv073.
  350. Defeo, O. et al., 2009: Threats to sandy beach ecosystems: A review. Estuar. Coast. Shelf Sci., 81(1), 1–12, doi:10.1016/j.ecss.2008.09.022.
  351. Delgado-Fernandez, I., N. O’Keeffe and R.G.D. Davidson-Arnott, 2019: Natural and human controls on dune vegetation cover and disturbance. Sci. Total Environ., 672, 643–656, doi:10.1016/j.scitotenv.2019.03.494.
  352. Delworth, T.L. and F. Zeng, 2016: The impact of the North Atlantic Oscillation on climate through its influence on the Atlantic Meridional Overturning Circulation. J. Clim., 29(3), 941–962, doi:10.1175/JCLI-D-15-0396.1.
  353. Demopoulos, A.W.J. et al., 2019: Examination of Bathymodiolus childressi nutritional sources, isotopic niches, and food-web linkages at two seeps in the US Atlantic margin using stable isotope analysis and mixing models. Deep sea Res. Pt. I, doi:10.1016/j.dsr.2019.04.002.
  354. Dennis, K.C., I. Niang-Diop and R.J. Nicholls, 1995: Sea level Rise and Senegal: Potential Impacts and Consequences. J. Coast. Res., 243–261.
  355. Desbruyères, D., E. L. McDonagh, B.A. King and V. Thierry, 2016a: Global and Full-Depth Ocean Temperature Trends during the Early Twenty-First Century from Argo and Repeat Hydrography. J. Clim., 30(6), 1985–1997, doi:10.1175/JCLI-D-16-0396.1.
  356. Desbruyères, D.G. et al., 2014: Full-depth temperature trends in the northeastern Atlantic through the early 21st century. Geophys. Res. Lett., 41(22), 7971–7979, doi:10.1002/2014GL061844.
  357. Desbruyères, D.G. et al., 2016b: Deep and abyssal ocean warming from 35 years of repeat hydrography. Geophys. Res. Lett., 43(19), 10,356–10,365, doi:10.1002/2016gl070413.
  358. Descamps, S. et al., 2015: Demographic effects of extreme weather events: snow storms, breeding success, and population growth rate in a long-lived Antarctic seabird. Ecol. Evol., 5(2), 314–325, doi:10.1002/ece3.1357.
  359. Desforges, J.-P. et al., 2018: Predicting global killer whale population collapse from PCB pollution. Science, 361(6409), 1373, doi:10.1126/science.aat1953.
  360. Desforges, J.-P. et al., 2017: Effects of Polar Bear and Killer Whale Derived Contaminant Cocktails on Marine Mammal Immunity. Environ. Sci. Technol., 51(19), 11431–11439, doi:10.1021/acs.est.7b03532.
  361. Deutsch, C. et al., 2011: Climate-Forced Variability of Ocean Hypoxia. Science, 333(6040), 336.
  362. Deutsch, C. et al., 2015: Climate change tightens a metabolic constraint on marine habitats. Science, 348(6239), 1132.
  363. Devlin, A.T. et al., 2017: Tidal Variability Related to Sea Level Variability in the Pacific Ocean. J. Geophys. Res-Ocean, 122(11), 8445-8463 doi:10.1002/2017JC013165.
  364. DeVries, T., 2014: The oceanic anthropogenic CO2 sink: Storage, air‐sea fluxes, and transports over the industrial era. Global Biogeochem. Cy., 28(7), 631–647, doi:10.1002/2013GB004739.
  365. DeVries, T., M. Holzer and F. Primeau, 2017: Recent increase in oceanic carbon uptake driven by weaker upper-ocean overturning. Nature, 542(7640), 215.
  366. DeVries, T. and T. Weber, 2017: The export and fate of organic matter in the ocean: New constraints from combining satellite and oceanographic tracer observations. Global Biogeochem. Cy., 31(3), 535–555, doi:10.1002/2016GB005551.
  367. Dey, M.M. et al., 2016: Analysis of the economic impact of climate change and climate change adaptation strategies for fisheries sector in Pacific coral triangle countries: Model, estimation strategy, and baseline results. Mar. Policy, 67, 156–163, doi:10.1016/j.marpol.2015.12.011.
  368. Dhar, T.K. and L. Khirfan, 2016: Community-based adaptation through ecological design: lessons from Negril, Jamaica. J. Urban Des., 21(2), 234–255, doi:10.1080/13574809.2015.1133224.
  369. Diamond, S.E., 2018: Contemporary climate-driven range shifts: Putting evolution back on the table. Funct. Ecol., 32(7), 1652–1665, doi:10.1111/1365-2435.13095.
  370. Díaz, P.A. et al., 2019: Impacts of harmful algal blooms on the aquaculture industry: Chile as a case study. Perspect. Phycol., 6, 1-2. doi: 10.1127/pip/2019/0081
  371. Diaz, R.J. and R. Rosenberg, 1995: Marine benthic hypoxia: a review of its ecological effects and the behavioural responses of benthic macrofauna. Oceanogr. Mar. Biol., 33, 245–03.
  372. Díaz, S. et al., 2018: Assessing nature’s contributions to people. Science, 359(6373), 270.
  373. Dickson, A.J., A.S. Cohen and A.L. Coe, 2012: Seawater oxygenation during the Paleocene-Eocene Thermal Maximum. Geology, 40(7), 639–642, doi:10.1130/G32977.1.
  374. Diesing, M. et al., 2017: Predicting the standing stock of organic carbon in surface sediments of the North–West European continental shelf. Biogeochemistry, 135(1), 183–200, doi:10.1007/s10533-017-0310-4.
  375. Dietz, S. et al., 2018: The Economics of 1.5°C Climate Change. Annu. Rev. Environ. Resourc., 43(1), 455–480, doi:10.1146/annurev-environ-102017-025817.
  376. Dinh, V.N. and E. McKeogh, 2019a: Offshore Wind Energy: Technology Opportunities and Challenges. In: Proceedings of the 1st Vietnam Symposium on Advances in Offshore Engineering, [Randolph, M.F., D.H. Doan, A.M. Tang, M. Bui and V.N. Dinh (eds.)], Springer Singapore, pp. 3–22.
  377. Dinh, V.N. and E. McKeogh, 2019b: Offshore Wind Energy: Technology Opportunities and Challenges. In: Proceedings of the 1st Vietnam Symposium on Advances in Offshore Engineering. Energy and Geotechnics [Marco di Prisco, S.-H. C., Giovanni Solari, Ioannis Vayas (ed.)][Randolph, M.F., D.H. Doan, A.M. Tang, M. Bui and V.N. Dinh (eds.)]. Springer, Singapore, 3–22.
  378. Diop, S. and P. Scheren, 2016: Sustainable oceans and coasts: Lessons learnt from Eastern and Western Africa. Estuar. Coast. Shelf Sci., 183, 327–339.
  379. DiSegni, D.M. and M. Shechter, 2013: Socioeconomic Aspects: Human Migrations, Tourism and Fisheries. In: The Mediterranean Sea. Springer Netherlands, Dordrecht, pp. 571–575.
  380. Dissard, D., G. Nehrke, G. J. Reichart and J. Bijma, 2010: Impact of seawater CO2 on calcification and Mg/Ca and Sr/Ca ratios in benthic foraminifera calcite: results from culturing experiments with Ammonia tepida. Biogeosciences, 7(1), 81–93, doi:10.5194/bg-7-81-2010.
  381. Dittrich, R., A. Wreford and D. Moran, 2016: A survey of decision-making approaches for climate change adaptation: Are robust methods the way forward? Ecol. Econ., 122, 79–89, doi:10.1016/j.ecolecon.2015.12.006.
  382. Dixon, D.R. et al., 2006: Evidence of seasonal reproduction in the Atlantic vent mussel Bathymodiolus azoricus, and an apparent link with the timing of photosynthetic primary production. J. Mar. Biol. Assoc. U.K. , 86(6), 1363–1371, doi:10.1017/S0025315406014391.
  383. Dixon, T., J. Garrett and E. Kleverlaan, 2014: Update on the London Protocol – Developments on Transboundary CCS and on Geoengineering. 12th International Conference on Greenhouse Gas Control Technologies, GHGT-12, 63(Supplement C), 6623–6628, doi:10.1016/j.egypro.2014.11.698.
  384. Doherty, M., K. Klima and J.J. Hellmann, 2016: Climate change in the urban environment: Advancing, measuring and achieving resiliency. Environ. Sci. Policy, 66, 310–313, doi:10.1016/j.envsci.2016.09.001.
  385. Domingues, C.M. et al., 2008: Improved estimates of upper-ocean warming and multi-decadal sea level rise. Nature, 453, 1090, doi:10.1038/nature07080.
  386. Donato, D.C. et al., 2011: Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci., 4, 293, doi:10.1038/ngeo1123.
  387. Donelson, J.M., S. Salinas, P.L. Munday and L.N.S. Shama, 2018: Transgenerational plasticity and climate change experiments: Where do we go from here? Glob Chang Biol, 24(1), 13–34, doi:10.1111/gcb.13903.
  388. Doney, S.C. et al., 2012: Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci., 4(1), 11–37, doi:10.1146/annurev-marine-041911-111611.
  389. Dore, J.E. et al., 2009: Physical and biogeochemical modulation of ocean acidification in the central North Pacific. PNAS, 106(30), 12235, doi:10.1073/pnas.0906044106.
  390. Dornelas, M. et al., 2018: BioTIME: A database of biodiversity time series for the Anthropocene. Global Ecol. Biogeogr., 27(7), 760–786, doi:10.1111/geb.12729.
  391. Dosser, H.V. and L. Rainville, 2016: Dynamics of the Changing Near-Inertial Internal Wave Field in the Arctic Ocean. J. Phys. Oceanogr., 46(2), 395–415, doi:10.1175/jpo-d-15-0056.1.
  392. Drake, J.L. et al., 2018: Molecular and geochemical perspectives on the influence of CO2 on calcification in coral cell cultures. Limnol. Oceanogr., 63(1), 107–121, doi:10.1002/lno.10617.
  393. Drius, M. et al., 2019: Not just a sandy beach. The multi-service value of Mediterranean coastal dunes. Sci. Total Environ., 668, 1139–1155, doi:10.1016/j.scitotenv.2019.02.364.
  394. Drury, C. et al., 2016: Genomic variation among populations of threatened coral: Acropora cervicornis. BMC Genomics, 17(1), 958, doi:10.1186/s12864-016-2583-8.
  395. Du, J. et al., 2018: Worsened physical condition due to climate change contributes to the increasing hypoxia in Chesapeake Bay. Sci. Total Environ., 630, 707–717, doi:10.1016/j.scitotenv.2018.02.265.
  396. Duarte, B. et al., 2018: Climate Change Impacts on Seagrass Meadows and Macroalgal Forests: An Integrative Perspective on Acclimation and Adaptation Potential. Front. Mar. Sci., 5, 190.
  397. Duarte, C.M. et al., 2013: Is ocean acidification an open-ocean syndrome? Understanding anthropogenic impacts on seawater pH. Estuar. Coast., 36(2), 221–236.
  398. Duarte, C.M. and D. Krause-Jensen, 2018: Greenland Tidal Pools as Hot Spots for Ecosystem Metabolism and Calcification. Estuar. Coast., 41(5), 1314–1321, doi:10.1007/s12237-018-0368-9.
  399. Duarte, C.M. et al., 2010: Seagrass community metabolism: Assessing the carbon sink capacity of seagrass meadows. Global Biogeochem. Cy., 24(4), doi:10.1029/2010GB003793.
  400. Duarte, C.M., J.J. Middelburg and N. Caraco, 2005: Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences, 2(1), 1–8, doi:10.5194/bg-2-1-2005.
  401. Duarte, C.M. et al., 2017: Can Seaweed Farming Play a Role in Climate Change Mitigation and Adaptation? Front. Mar. Sci., 4, 100.
  402. Dubey, S.K. et al., 2017: Farmers’ perceptions of climate change, impacts on freshwater aquaculture and adaptation strategies in climatic change hotspots: A case of the Indian Sundarban delta. Environ. Dev., 21, 38–51, doi:10.1016/j.envdev.2016.12.002.
  403. Dubilier, N., C. Bergin and C. Lott, 2008: Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat. Rev. Microbiol., 6, 725, doi:10.1038/nrmicro1992.
  404. Dubois, B. et al., 2013: Effect of Tropical Algae as Additives on Rumen in Vitro Gas Production and Fermentation Characteristics. Am. J. Plant Sci., 04(12), 34–43, doi:10.4236/ajps.2013.412A2005.
  405. Ducklow, H.W. et al., 2013: West Antarctic Peninsula: An Ice-Dependent Coastal Marine Ecosystem in Transition. Oceanography, 26(3), 190–203, doi:10.5670/oceanog.2013.62.
  406. Dudley, P.N., R. Bonazza and W P. Porter, 2016: Climate change impacts on nesting and internesting leatherback sea turtles using 3D animated computational fluid dynamics and finite volume heat transfer. Ecol. Modell., 320(Supplement C), 231–240, doi:10.1016/j.ecolmodel.2015.10.012.
  407. Dueri, S., L. Bopp and O. Maury, 2014: Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution. Global Change Biol., 20(3), 742–753, doi:10.1111/gcb.12460.
  408. Dugger, K.M. et al., 2014: Adelie penguins coping with environmental change: results from a natural experiment at the edge of their breeding range. Frontiers in Ecol. Evol., 2, doi:10.3389/fevo.2014.00068.
  409. Duke, N.C. et al., 2017: Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: a severe ecosystem response, coincidental with an unusually extreme weather event. Mar. Freshw. Res., 68(10), 1816–1829.
  410. Dundas, S.J. and R.H. von Haefen, 2015: Weather effects on the demand for coastal recreational fishing: Implications for a changing climate. CEnREP Working Paper No. 15-015, 63 pp. doi: 10.22004/ag.econ.264980
  411. Dunlop, K.M. et al., 2016: Carbon cycling in the deep eastern North Pacific benthic food web: Investigating the effect of organic carbon input. Limnol. Oceanogr., 61(6), 1956–1968, doi:10.1002/lno.10345.
  412. Dunn, F.E. et al., 2018: Projections of historical and 21st century fluvial sediment delivery to the Ganges-Brahmaputra-Meghna, Mahanadi, and Volta deltas. Sci. Total Environ., 642, 105–116.
  413. Dunstan, P.K. et al., 2018: How can climate predictions improve sustainability of coastal fisheries in Pacific Small-Island Developing States? Mar. Policy, 88, 295-302. doi:10.1016/j.marpol.2017.09.033.
  414. Durack, P.J., 2015: Ocean Salinity and the Global Water Cycle. Oceanography, 28(1), 20–31.
  415. Duteil, O., A. Oschlies and C.W. Böning, 2018: Pacific Decadal Oscillation and recent oxygen decline in the eastern tropical Pacific Ocean. Biogeosciences, 15, 7111-7126.
  416. Duteil, O., F.U. Schwarzkopf, C.W. Böning and A. Oschlies, 2014: Major role of the equatorial current system in setting oxygen levels in the eastern tropical Atlantic Ocean: A high-resolution model study. Geophys. Res. Lett., 41(6), 2033–2040, doi:10.1002/2013GL058888.
  417. Dutkiewicz, S. et al., 2015: Impact of ocean acidification on the structure of future phytoplankton communities. Nat. Clim. Change, 5, 1002, doi:10.1038/nclimate2722.
  418. Dutkiewicz, S., J.R. Scott and M. Follows, 2013a: Winners and losers: Ecological and biogeochemical changes in a warming ocean. Global Biogeochem. Cy., 27(2), 463–477.
  419. Dutkiewicz, S., J.R. Scott and M.J. Follows, 2013b: Winners and losers: Ecological and biogeochemical changes in a warming ocean. Global Biogeochem. Cy., 27(2), 463–477, doi:10.1002/gbc.20042.
  420. Dutra, L.X.C. et al., 2015: Organizational drivers that strengthen adaptive capacity in the coastal zone of Australia. Ocean Coast. Manage., 109, 64–76, doi:10.1016/j.landusepol.2015.09.003.
  421. Duvat, V.K.E. et al., 2017: Trajectories of exposure and vulnerability of small islands to climate change. WiRes. Clim. Change, 8(6), e478 doi:10.1002/wcc.478.
  422. Eddebbar, Y.A. et al., 2017: Impacts of ENSO on air-sea oxygen exchange: Observations and mechanisms. Global Biogeochem. Cy., 31(5), 901–921, doi:doi:10.1002/2017GB005630.
  423. Eden, C. and D. Olbers, 2014: An Energy Compartment Model for Propagation, Nonlinear Interaction, and Dissipation of Internal Gravity Waves. J. Phys. Oceanogr., 44(8), 2093–2106, doi:10.1175/JPO-D-13-0224.1.
  424. Edgar, G.J. et al., 2014: Global conservation outcomes depend on marine protected areas with five key features. Nature, 506, 216, doi:10.1038/nature13022.
  425. Edwards, M. et al., 2013: Marine Ecosystem Response to the Atlantic Multidecadal Oscillation. PLoS One, 8(2), doi:10.1371/journal.pone.0057212.
  426. Edwards, M. and A.J. Richardson, 2004: Impact of climate change on marine pelagic phenology and trophic mismatch. Nature, 430(7002), nature02808-884, doi:10.1038/nature02808.
  427. Eichner, M., B. Rost and S.A. Kranz, 2014: Diversity of ocean acidification effects on marine N2 fixers. J. Exp. Mar. Biol. Ecol., 457, 199–207, doi:10.1016/j.jembe.2014.04.015.
  428. Ekstrom, J.A. et al., 2015: Vulnerability and adaptation of US shellfisheries to ocean acidification. Nat. Clim. Change, 5, 207, doi:10.1038/nclimate2508.
  429. El-Hacen, E.-H. M. et al., 2018: Evidence for ‘critical slowing down’ in seagrass: a stress gradient experiment at the southern limit of its range. Sci. Rep., 8(1), 17263, doi:10.1038/s41598-018-34977-5.
  430. Elias, P. and A. Omojola, 2015: Case study: The challenges of climate change for Lagos, Nigeria. Curr. Opin. Environ. Sustain., 13, 74–78, doi:10.1016/j.cosust.2015.02.008.
  431. Elliff, C.I. and I.R. Silva, 2017: Coral reefs as the first line of defense: Shoreline protection in face of climate change. Mar. Environ. Res., 127, 148–154, doi:10.1016/j.marenvres.2017.03.007.
  432. Elliott, M., J.W. Day, R. Ramachandran and E. Wolanski, 2019: Chapter 1 – A Synthesis: What Is the Future for Coasts, Estuaries, Deltas and Other Transitional Habitats in 2050 and Beyond? In: Coasts and Estuaries [Wolanski, E., J.W. Day, M. Elliott and R. Ramachandran (eds.)]. Elsevier, pp. 1–28. ISBN: 9780128140031
  433. Elrick-Barr, C.E. et al., 2016: How are coastal households responding to climate change? Environ. Sci. Policy, 63, 177–186, doi:10.1016/j.envsci.2016.05.013.
  434. Elsharouny, M.R.M.M., 2016: Planning Coastal Areas and Waterfronts for Adaptation to Climate Change in Developing Countries. Procedia Environ. Sci., 34, 348–359, doi:10.1016/j.proenv.2016.04.031.
  435. Endo, H., K. Suehiro, X. Gao and Y. Agatsuma, 2017: Interactive effects of elevated summer temperature, nutrient availability, and irradiance on growth and chemical compositions of juvenile kelp, Eisenia bicyclis. Phycol. Res., 65(2), 118–126, doi:10.1111/pre.12170.
  436. Endres, S. et al., 2014: Stimulated Bacterial Growth under Elevated pCO2: Results from an Off-Shore Mesocosm Study. PLoS One, 9(6), e99228, doi:10.1371/journal.pone.0099228.
  437. Engel, J. et al., 2014: Towards the Disease Biomarker in an Individual Patient Using Statistical Health Monitoring. PLoS One, 9(4), e92452, doi:10.1371/journal.pone.0092452.
  438. Engelhard, G.H., D.A. Righton and J.K. Pinnegar, 2014: Climate change and fishing: a century of shifting distribution in North Sea cod. Global Change Biol., 20(8), 2473–2483, doi:10.1111/gcb.12513.
  439. England, M.H., J.B. Kajtar and N. Maher, 2015: Robust warming projections despite the recent hiatus. Nat. Clim. Change, 5, 394, doi:10.1038/nclimate2575.
  440. England, M.H. et al., 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Clim. Change, 4, 222, doi:10.1038/nclimate2106.
  441. Enwright, N.M., K.T. Griffith and M.J. Osland, 2016: Barriers to and opportunities for landward migration of coastal wetlands with sea level rise. Front. Ecol. Environ., 14(6), 307–316, doi:10.1002/fee.1282.
  442. Erauskin-Extramiana, M. et al., 2019: Large-scale distribution of tuna species in a warming ocean. Global Change Biol., 25(6), 2043–2060, doi:10.1111/gcb.14630.
  443. Escobar, L.E. et al., 2015: A global map of suitability for coastal Vibrio cholerae under current and future climate conditions. Acta Trop., 149(Supplement C), 202–211, doi:10.1016/j.actatropica.2015.05.028.
  444. Essington, T.E. et al., 2015: Fishing amplifies forage fish population collapses. PNAS, 112(21), 6648.
  445. Esteban, M. et al., 2017: Awareness of coastal floods in impoverished subsiding coastal communities in Jakarta: Tsunamis, typhoon storm surges and dyke-induced tsunamis. Int. J. Disast. Risk Reduc., 23, 70–79, doi:10.1016/j.ijdrr.2017.04.007.
  446. Evans, L.S. et al., 2016: Structural and Psycho-Social Limits to Climate Change Adaptation in the Great Barrier Reef Region. PLoS One, 11(3), e0150575.
  447. Everaert, G. et al., 2018: Risk assessment of microplastics in the ocean: Modelling approach and first conclusions. Environ. Pollut., 242, 1930–1938, doi:10.1016/j.envpol.2018.07.069.
  448. Eyre, B.D., A.J. Andersson and T. Cyronak, 2014: Benthic coral reef calcium carbonate dissolution in an acidifying ocean. Nat. Clim. Change, 4, 969 EP -, doi:10.1038/nclimate2380.
  449. Fabricius, K.E., 2005: Effects of terrestrial runoff on the ecology of corals and coral reefs: review and synthesis. Mar. Pollut. Bull., 50(2), 125–146, doi:10.1016/j.marpolbul.2004.11.028.
  450. FAO, 2016: The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all, FAO, Rome, 200 pp. ISBN 978-92-5-109185-2
  451. FAO, IFAD, UNICEF, WFP, and WHO, 2017: The State of Food Security and Nutrition In The World, Building Resilience for Peace and Food Security.FAO, Rome, 132 pp. ISBN: 978-92-5-109888-2
  452. FAO, 2018: The State of World Fisheries and Aquaculture 2018 – Meeting the sustainable development goals. FAO, Rome, pp 1-227. ISBN: 978-92-5-1305562-1
  453. FAO, 2019: Deep-ocean climate change impacts on habitat, fish and fisheries [Levin, L.A., M. Baker and A. Thompson (eds.)]. 638, FAO, Rome, 186 pp.
  454. Faraco, L.F.D. et al., 2016: Vulnerability Among Fishers in Southern Brazil and its Relation to Marine Protected Areas in a Scenario of Declining Fisheries. Desenvolvimento e Meio Ambiente, 38(1), 51–76, doi:10.5380/dma.v38i0.45850.
  455. Fatorić, S. and E. Seekamp, 2017: Securing the Future of Cultural Heritage by Identifying Barriers to and Strategizing Solutions for Preservation under Changing Climate Conditions. Sustainability, 9(11), 1-20, doi:10.3390/su9112143.
  456. Fauville, G. et al., 2011: Virtual Ocean Acidification Laboratory as an Efficient Educational Tool to Address Climate Change Issues in The Economic, Social and Political Elements of Climate Change [W. Filho Leal ed.]. Springer Berlin Heidelberg, pp. 825–836. ISBN: 978-3-642-14776-0
  457. Feely, R.A. et al., 2016: Chemical and biological impacts of ocean acidification along the west coast of North America. Estuar. Coast. Shelf Sci., 183, 260–270, doi:10.1016/j.ecss.2016.08.043.
  458. Feng, E.Y., P.K. David, K. Wolfgang and O. Andreas, 2016: Could artificial ocean alkalinization protect tropical coral ecosystems from ocean acidification? Environ. Res. Lett., 11(7), 074008.
  459. Fennel, K. and J.M. Testa, 2019: Biogeochemical Controls on Coastal Hypoxia. Annu. Rev. Mar. Sci., 11(1), 105–130, doi:10.1146/annurev-marine-010318-095138.
  460. Fennel, K. and D.L. VanderZwaag, 2016: Ocean acidification: Scientific surges, lagging law and policy responses in Routledge handbook of maritime regulation and enforcement [R. Warner and S. Kaye eds]. Routledge 1st eddition, pp. 342-362. London, UK.
  461. Fernandes, J.A. et al., 2013: Modelling the effects of climate change on the distribution and production of marine fishes: accounting for trophic interactions in a dynamic bioclimate envelope model. Global Change Biol., 19(8), 2596–2607, doi:10.1111/gcb.12231.
  462. Fernandez-Arcaya, U. et al., 2017: Ecological Role of Submarine Canyons and Need for Canyon Conservation: A Review. Front. Mar. Sci., 4, 69, doi:10.3389/fmars.2017.00005.
  463. Ferrario, F. et al., 2014: The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nat. Commun., 5, 3794, doi:10.1038/ncomms4794.
  464. Filbee-Dexter, K., C.J. Feehan and R.E. Scheibling, 2016: Large-scale degradation of a kelp ecosystem in an ocean warming hotspot. Mar. Ecol. Prog. Ser., 543, 141–152, doi:10.3354/meps11554.
  465. Filbee-Dexter, K. and T. Wernberg, 2018: Rise of turfs: A new battlefront for globally declining kelp forests. BioScience, 68(2), 64–76.
  466. Fillinger, L. and C. Richter, 2013: Vertical and horizontal distribution of Desmophyllum dianthus in Comau Fjord, Chile: a cold water coral thriving at low pH. Peerj, 1, e194.
  467. Fine, M. et al., 2019: Coral reefs of the Red Sea — Challenges and potential solutions. Reg. Stud. Mar. Sci., 25, 100498, doi:10.1016/j.rsma.2018.100498.
  468. Fine, M., H. Gildor and A. Genin, 2013: A coral reef refuge in the Red Sea. Global Change Biol., 19(12), 3640–3647, doi:10.1111/gcb.12356.
  469. Finkbeiner, E.M. et al., 2018: Exploring trade-offs in climate change response in the context of Pacific Island fisheries. Mar. Policy, 88, 359–364, doi:10.1016/j.marpol.2017.09.032.
  470. Fischer, A.P., 2018: Pathways of adaptation to external stressors in coastal natural-resource-dependent communities: Implications for climate change. World Dev., 108, 235–248, doi:10.1016/j.worlddev.2017.12.007.
  471. Fish, M.R. et al., 2005: Predicting the impact of sea level rise on Caribbean sea turtle nesting habitat. Conserv. Biol., 19(2), 482–491, doi:10.1111/j.1523-1739.2005.00146.x.
  472. Fisher, J. A. and K. Brown, 2015: Reprint of” Ecosystem services concepts and approaches in conservation: Just a rhetorical tool?”. Ecol. Econ., 117, 261–269.
  473. Flato, G. et al., 2013: Evaluation of Climate Models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 741–866.
  474. Fleeger, J.W. et al., 2006: Simulated sequestration of anthropogenic carbon dioxide at a deep sea site: Effects on nematode abundance and biovolume. Deep sea Res. Pt. I, 53(7), 1135–1147, doi:10.1016/j.dsr.2006.05.007.
  475. Fleeger, J.W. et al., 2010: The response of nematodes to deep sea CO2 sequestration: A quantile regression approach. Deep sea Res. Pt. I, 57(5), 696–707, doi:10.1016/j.dsr.2010.03.003.
  476. Flombaum, P. et al., 2013: Present and future global distributions of the marine Cyanobacteria, Prochlorococcus and Synechococcus. PNAS, 110(24), 9824, doi:10.1073/pnas.1307701110.
  477. Flores, R. . et al., 2017: Application of Transplantation Technology to Improve Coral Reef Resources for Sustainable Fisheries and Underwater Tourism. Int. J. Environ. Sci. Dev., 8(1), 44.
  478. Fluhr, J. et al., 2017: Weakening of the subpolar gyre as a key driver of North Atlantic seabird demography: a case study with Brunnich’s guillemots in Svalbard. Mar. Ecol. Prog. Ser., 563, 1–11, doi:10.3354/meps11982.
  479. Folkersen, M.V., 2018: Ecosystem valuation: Changing discourse in a time of climate change. Ecosyst. Serv., 29, 1–12, doi:10.1016/j.ecoser.2017.11.008.
  480. Form, A.U. and U. Riebesell, 2011: Acclimation to ocean acidification during long-term CO2 exposure in the cold water coral Lophelia pertusa. Global Change Biol., 18(3), 843–853, doi:10.1111/j.1365-2486.2011.02583.x.
  481. Forsman, Z.H., C.A. Page, R.J. Toonen and D. Vaughan, 2015: Growing coral larger and faster: micro-colony-fusion as a strategy for accelerating coral cover. Peerj, 3(1), e1313, doi:10.7717/peerj.1313.
  482. Fort, J. et al., 2015: Mercury in wintering seabirds, an aggravating factor to winter wrecks? Sci. Total Environ., 527–528(Supplement C), 448–454, doi:10.1016/j.scitotenv.2015.05.018.
  483. Forzieri, G. et al., 2018: Escalating impacts of climate extremes on critical infrastructures in Europe. Global Environ. Change, 48, 97–107, doi:10.1016/j.gloenvcha.2017.11.007.
  484. Foteinis, S. and T. Tsoutsos, 2017: Strategies to improve sustainability and offset the initial high capital expenditure of wave energy converters (WECs). Renew. Sustain. Energ. Rev., 70, 775–785, doi:10.1016/j.rser.2016.11.258.
  485. Fox, A.D., L.-A. Henry, D.W. Corne and J.M. Roberts, 2016: Sensitivity of marine protected area network connectivity to atmospheric variability. R. Soc. Open Sci., 3(11), 160494.
  486. Frajka-Williams, E. et al., 2016: Compensation between meridional flow components of the Atlantic MOC at 26N. Ocean Sci., 12(2), 481–493.
  487. Francesch-Huidobro, M. et al., 2017: Governance challenges of flood-prone delta cities: Integrating flood risk management and climate change in spatial planning. Progr. Plan., 114, 1–27, doi:10.1016/j.progress.2015.11.001.
  488. Franco, A.C., N. Gruber, T.L. Frölicher and L. Kropuenske Artman, 2018a: Contrasting Impact of Future CO2 Emission Scenarios on the Extent of CaCO3 Mineral Undersaturation in the Humboldt Current System. J. Geophys. Res-Oceans, 123(3), 2018–2036, doi:10.1002/2018JC013857.
  489. Franco, J.N. et al., 2018b: The ‘golden kelp’ Laminaria ochroleuca under global change: Integrating multiple eco-physiological responses with species distribution models. J. Ecol., 106(1), 47–58, doi:10.1111/1365-2745.12810.
  490. Free, C.M. et al., 2019: Impacts of historical warming on marine fisheries production. Science, 363(6430), 979, doi:10.1126/science.aau1758.
  491. Freiwald A., Rogers A., Hall-Spencer J., Guinotte J.M., Davies A.J., Yesson C., Martin C.S. & Weatherdon L.V. 2017. Global distribution of cold-water corals (version 3.0) [online[. Second update to the dataset in Freiwald et al. (2004) by UNEP-WCMC, in collaboration with Andre Freiwald and John Guinotte. Cambridge (UK). UNEP World Conservation Monitoring Centre. http://data.unep-wcmc.org/datasets/3.
  492. Frias-Torres, S. and C. van de Geer, 2015: Testing animal-assisted cleaning prior to transplantation in coral reef restoration. Peerj, 3, e1287, doi:10.7717/peerj.1287.
  493. Frieder, C.A. et al., 2014: Evaluating ocean acidification consequences under natural oxygen and periodicity regimes: Mussel development on upwelling margins. Global Change Biol., (20), 754–764.
  494. Friedrich, E. and D. Kretzinger, 2012: Vulnerability of wastewater infrastructure of coastal cities to sea level rise: A South African case study. Water SA, 38(5), 755–764.
  495. Friedrich, T. et al., 2012: Detecting regional anthropogenic trends in ocean acidification against natural variability. Nat. Clim. Change, 2(3), 167–171, doi:10.1038/nclimate1372.
  496. Frigstad, H. et al., 2015: Links between surface productivity and deep ocean particle flux at the Porcupine Abyssal Plain sustained observatory. Biogeosciences, 12(19), 5885–5897, doi:10.5194/bg-12-5885-2015.
  497. Frischknecht, M., M. Münnich and N. Gruber, 2018: Origin, Transformation, and Fate: The Three-Dimensional Biological Pump in the California Current System. J. Geophys. Res-Oceans, 123(11), 7939–7962, doi:10.1029/2018JC013934.
  498. Froehlich, H.E. et al., 2018: Comparative terrestrial feed and land use of an aquaculture-dominant world. PNAS, 115(20), 5295, doi:10.1073/pnas.1801692115.
  499. Frolicher, T.L., K.B. Rodgers, C.A. Stock and W.W.L. Cheung, 2016: Sources of uncertainties in 21st century projections of potential ocean ecosystem stressors. Global Biogeochem. Cy., 30(8), 1224–1243, doi:10.1002/2015gb005338.
  500. Frölicher, T.L. et al., 2015: Dominance of the Southern Ocean in Anthropogenic Carbon and Heat Uptake in CMIP5 Models. J. Clim., 28(2), 862–886, doi:10.1175/jcli-d-14-00117.1.
  501. Fu, F.-X. et al., 2007: Effects of increased temperature and CO2 on photosynthesis, growth and elemental ratios in marine Synechococcus and Prochlorococcus (Cyanobacteria). J. Phycol., 43(3), 485–496, doi:10.1111/j.1529-8817.2007.00355.x.
  502. Fu, W. et al., 2018: Reversal of Increasing Tropical Ocean Hypoxia Trends with Sustained Climate Warming. Global Biogeochem. Cy.,32(4), 551-564, doi:10.1002/2017gb005788.
  503. Fu, W., J.T. Randerson and J.K. Moore, 2016: Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models. Biogeosciences, 13(18), 5151–5170, doi:10.5194/bg-13-5151-2016.
  504. Fuentes, M., C.J. Limpus, M. Hamann and J. Dawson, 2010: Potential impacts of projected sea level rise on sea turtle rookeries. Aquat. Conserv. Mar. Frewshw. Ecosyst., 20(2), 132–139, doi:10.1002/aqc.1088.
  505. Fuentes-George, K., 2017: Consensus, Certainty, and Catastrophe: Discourse, Governance, and Ocean Iron Fertilization. Global Environmental Politics, 17(2), 125–143, doi:10.1162/GLEP_a_00404.
  506. Funayama, K., E. Hines, J. Davis and S. Allen, 2013: Effects of sea level rise on northern elephant seal breeding habitat at Point Reyes Peninsula, California. Aquat. Conserv. Mar. Frewshw. Ecosyst., 23(2), 233–245, doi:10.1002/aqc.2318.
  507. Gage, J.D., 2003: Food inputs, utilization, carbon flow and energetics. In: Ecosystems of the Deep Sea [Tyler, P.A. (ed.)]. Elsevier, Amsterdam, Volume 28, 1st eddition, pp. 313–380. ISBN: 9780080494654
  508. Gage, J.D. and P.A. Tyler, 1993: Deep sea biology: a natural history of organisms at the deep sea floor [Gage, J.D. and P.A. Tyler eds.]. Cambridge University Press, Paperbackk eddition, 504 pp, Journal of the Marine Biological Association of the United Kingdom 73, no. 1, doi: 10.1017/S0025315400070156
  509. Gaines, S.D. et al., 2018: Improved fisheries management could offset many negative effects of climate change. Sci. Adv., 4(8), eaao1378, doi:10.1126/sciadv.aao1378.
  510. Galappaththi, I.M., E.K. Galappaththi and S.S. Kodithuwakku, 2017: Can start-up motives influence social-ecological resilience in community-based entrepreneurship setting? Case of coastal shrimp farmers in Sri Lanka. Mar. Policy, 86, 156–163, doi:10.1016/j.marpol.2017.09.024.
  511. Galaz, V. et al., 2012: Polycentric systems and interacting planetary boundaries – Emerging governance of climate change-ocean acidification-marine biodiversity. Ecol. Econ., 81, 21–32, doi:10.1016/j.ecolecon.2011.11.012.
  512. Galbraith, E.D., D.A. Carozza and D. Bianchi, 2017: A coupled human-Earth model perspective on long-term trends in the global marine fishery. Nat. Commun., 8, 14884 EP -, doi:10.1038/ncomms14884.
  513. Gallagher, R.V., R.O. Makinson, P.M. Hogbin and N. Hancock, 2015: Assisted colonization as a climate change adaptation tool. Austral Ecol.., 40(1), 12–20, doi:10.1111/aec.12163.
  514. Galland, G., E. Harrould-Kolieb and D. Herr, 2012: The ocean and climate change policy. Clim. Policy, 12(6), 764–771, doi:10.1080/14693062.2012.692207.
  515. Gallo, N.D. and L.A. Levin, 2016: Fish Ecol. Evol. in the World&apos;s Oxygen Minimum Zones and Implications of Ocean Deoxygenation. Adv. Mar. Biol., Vol, 74, 117–198, doi:10.1016/bs.amb.2016.04.001.
  516. Gallo, N.D., D.G. Victor and L.A. Levin, 2017: Ocean commitments under the Paris Agreement. Nat. Clim. Change, 7(11), 833-838, doi:10.1038/NCLIMATE3422.
  517. Gambi, C. et al., 2017: Functional response to food limitation can reduce the impact of global change in the deep‐sea benthos. Global Ecol. Biogeogr., 26(9), 1008–1021, doi:10.1111/geb.12608.
  518. Gao, L., S.R. Rintoul and W. Yu, 2018: Recent wind-driven change in Subantarctic Mode Water and its impact on ocean heat storage. Nat. Clim. Change, 8(1), 58–63, doi:10.1038/s41558-017-0022-8.
  519. García Molinos, J., M.T. Burrows and E.S. Poloczanska, 2017: Ocean currents modify the coupling between climate change and biogeographical shifts. Sci. Rep., 7(1), 1332, doi:10.1038/s41598-017-01309-y.
  520. García Molinos, J. et al., 2015: Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change, 6, 83, doi:10.1038/nclimate2769.
  521. Garcia, N.S., F. Fu, P.N. Sedwick and D.A. Hutchins, 2014: Iron deficiency increases growth and nitrogen-fixation rates of phosphorus-deficient marine cyanobacteria. The Isme Journal, 9, 238, doi:10.1038/ismej.2014.104.
  522. García-Mendoza, E. et al., 2018: Mass Mortality of Cultivated Northern Bluefin Tuna Thunnus thynnus orientalis Associated With Chattonella Species in Baja California, Mexico. Front. Mar. Sci., 5(454), doi:10.3389/fmars.2018.00454.
  523. García-Moreiras, I., V. Pospelova, S. García-Gil and C. Muñoz Sobrino, 2018: Climatic and anthropogenic impacts on the Ría de Vigo (NW Iberia) over the last two centuries: A high-resolution dinoflagellate cyst sedimentary record. Palaeogeogr. Palaeoclim. Palaeoecol., 504, 201–218, doi:10.1016/j.palaeo.2018.05.032.
  524. García-Reyes, M. et al., 2015: Under Pressure: Climate Change, Upwelling, and Eastern Boundary Upwelling Ecosystems. Front. Mar. Sci., 2, 109.
  525. Garzke, J., T. Hansen, S.M.H. Ismar and U. Sommer, 2016: Combined Effects of Ocean Warming and Acidification on Copepod Abundance, Body Size and Fatty Acid Content. PLoS One, 11(5), e0155952, doi:10.1371/journal.pone.0155952.
  526. Gatti, G. et al., 2017: Observational information on a temperate reef community helps understanding the marine climate and ecosystem shift of the 1980–90s. Mar. Pollut. Bull., 114(1), 528–538.
  527. Gattuso, J.-P. et al., 2015: OCEANOGRAPHY. Contrasting futures for ocean and society from different anthropogenic CO₂ emissions scenarios. Science, 349(6243), 1 -10, doi:10.1126/science.aac4722.
  528. Gattuso, J.-P. et al., 2018: Ocean Solutions to Address Climate Change and Its Effects on Marine Ecosystems. Front. Mar. Sci., 5(337), doi:10.3389/fmars.2018.00337.
  529. Gazeau, F. et al., 2014: Impact of ocean acidification and warming on the Mediterranean mussel (Mytilus galloprovincialis). Front. Mar. Sci., 1, 62, doi:10.3389/fmars.2014.00062.
  530. Gehlen, M. et al., 2014: Projected pH reductions by 2100 might put deep North Atlantic biodiversity at risk. Biogeosciences, 11(23), 6955–6967, doi:10.5194/bg-11-6955-2014.
  531. George, J.C. et al., 2015: Bowhead whale body condition and links to summer sea ice and upwelling in the Beaufort Sea. Progr. Oceanogr., 136, 250–262, doi:10.1016/j.pocean.2015.05.001.
  532. George, R., 2013: Ninety percent of everything: inside shipping, the invisible industry that puts clothes on your back, gas in your car, and food on your plate. Macmillan-Picador, 1st eddition,  304 pp, USA, ISBN: 9781250058294.
  533. Georgian, S.E. et al., 2016: Biogeographic variability in the physiological response of the cold water coralLophelia pertusato ocean acidification. Mar. Ecol., 37(6), 1345–1359, doi:10.1111/maec.12373.
  534. Gephart, J.A. et al., 2016: Vulnerability to shocks in the global seafood trade network. Environ. Res. Lett., 11(3), 035008.
  535. Gerkensmeier, B. and B.M.W. Ratter, 2018: Governing coastal risks as a social process—Facilitating integrative risk management by enhanced multi-stakeholder collaboration. Environ. Sci. Policy, 80, 144–151, doi:10.1016/j.envsci.2017.11.011.
  536. GESAMP, 2015: Sources, fate and effects of microplastics in the marine environment: a global assessment. [Kershaw, P.J. (ed.)]. International Maritime Organization, 96 pp., London, UK, ISSN: 1020-4873
  537. GESAMP, 2019: High Level Review of a Wide Range of Proposed Marine Geoengineering Techniques [Boyd, P.W. and C.M.G. Vivian (eds.)]. IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UN Environment/UNDP/ISA Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection, GESAMP, International Maritime Organization, No. 98, 144 pp. London, UK, ISSN: 1020-4973.
  538. Ghedini, G., B.D. Russell and S.D. Connell, 2015: Trophic compensation reinforces resistance: herbivory absorbs the increasing effects of multiple disturbances. Ecol. Lett., 18(2), 182–187.
  539. Gibbin, E.M. et al., 2017: The evolution of phenotypic plasticity under global change. Sci. Rep., 7(1), 17253, doi:10.1038/s41598-017-17554-0.
  540. Gienapp, P. and J. Merilä, 2018: Evolutionary Responses to Climate Change. In: Encyclopedia of the Anthropocene [Dellasala, D.A. and M.I. Goldstein (eds.)]. Elsevier, Oxford, pp. 51–59., ISBN: 9780128096659
  541. Gill, A.E., 1982: Atmosphere—ocean dynamics [W.L Donn ed.] International Geophysics Series, 30, 662 pp., Academic Press, San Diego, Claifornia, USA, ISBN: 0-12-283520-4
  542. Gilly, W.F., J.M. Beman, S.Y. Litvin and B.H. Robison, 2013: Oceanographic and Biological Effects of Shoaling of the Oxygen Minimum Zone. Annu. Rev. Mar. Sci., 5(1), 393–420, doi:10.1146/annurev-marine-120710-100849.
  543. Gilman, E.L., J. Ellison, N.C. Duke and C. Field, 2008: Threats to mangroves from climate change and adaptation options: a review. Aquat. Bot., 89(2), 237–250.
  544. Gintert, B.E. et al., 2018: Marked annual coral bleaching resilience of an inshore patch reef in the Florida Keys: A nugget of hope, aberrance, or last man standing? Coral Reefs, 37(2), 533–547, doi:10.1007/s00338-018-1678-x.
  545. Giorgio, A. et al., 2018: Coastal Tourism Importance and Beach Users’ Preferences: The “Big Fives” Criterions and Related Management Aspects. J. Tourism Hospit., 7(347), 2167–0269.1000347.
  546. Gissi, E., S. Fraschetti and F. Micheli, 2019: Incorporating change in marine spatial planning: A review. Environ. Sci. Policy, 92, 191–200, doi:10.1016/j.envsci.2018.12.002.
  547. Gittings, J.A., D.E. Raitsos, G. Krokos and I. Hoteit, 2018: Impacts of warming on phytoplankton abundance and phenology in a typical tropical marine ecosystem. Sci. Rep., 8(1), 2240, doi:10.1038/s41598-018-20560-5.
  548. Gittman, R.K. et al., 2015: Engineering away our natural defenses: an analysis of shoreline hardening in the US. Front. Ecol. Environ., 13(6), 301–307, doi:10.1890/150065.
  549. Gleckler, P.J. et al., 2016: Industrial-era global ocean heat uptake doubles in recent decades. Nat. Clim. Change, 6, 394, doi:10.1038/nclimate2915.
  550. Gleckler, P.J. et al., 2012: Human-induced global ocean warming on multidecadal timescales. Nat. Clim. Change, 2, 524, doi:10.1038/nclimate1553.
  551. Glibert, P.M. et al., 2018: Key Questions and Recent Research Advances on Harmful Algal Blooms in Relation to Nutrients and Eutrophication. In: Global Ecology and Oceanography of Harmful Algal Blooms [Glibert, P.M., E. Berdalet, M.A. Burford, G.C. Pitcher and M. Zhou (eds.)]. Springer International Publishing, Cham, pp. 229–259, ISBN: 978-3-319-70069-4.
  552. Glibert, P.M. et al., 2014: Vulnerability of coastal ecosystems to changes in harmful algal bloom distribution in response to climate change: projections based on model analysis. Global Change Biol., 20(12), 3845–3858.
  553. Gobler, C.J. and H. Baumann, 2016: Hypoxia and acidification in ocean ecosystems: coupled dynamics and effects on marine life. Biol. Lett., 12(5), 20150976, doi:10.1098/rsbl.2015.0976.
  554. Gobler, C.J. et al., 2017: Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacific oceans. PNAS, 114(19): 4975-4980, doi: 10.1073/pnas.1619575114.
  555. Goericke, R., S.J. Bograd and D.S. Grundle, 2015: Denitrification and flushing of the Santa Barbara Basin bottom waters. Deep Sea Res. Pt. II, 112, 53–60, doi:10.1016/j.dsr2.2014.07.012.
  556. Golden, C.D. et al., 2016: Nutrition: Fall in fish catch threatens human health. Nature, 534(7607), 317–320, doi:10.1038/534317a.
  557. Goldenberg, S.U. et al., 2017: Boosted food web productivity through ocean acidification collapses under warming. Global Change Biol., 23(10), 4177–4184.
  558. Gómez, C.E.G. et al., 2014: Responses of the tropical gorgonian coral Eunicea fusca to ocean acidification conditions. Coral Reefs, 34, 451–460.
  559. Gómez-Letona, M., A.G. Ramos, J. Coca and J. Arístegui, 2017: Trends in Primary Production in the Canary Current Upwelling System—A Regional Perspective Comparing Remote Sensing Models. Front. Mar. Sci., 4, 1–18, doi:10.3389/fmars.2017.00370.
  560. Gonneea, M.E. et al., 2019: Salt marsh ecosystem restructuring enhances elevation resilience and carbon storage during accelerating relative sea level rise. Estuar. Coast. Shelf Sci., 217, 56–68, doi:10.1016/j.ecss.2018.11.003.
  561. Good, P. et al., 2018: Recent progress in understanding climate thresholds: Ice sheets, the Atlantic meridional overturning circulation, tropical forests and responses to ocean acidification. Prog. Phys. Geog., 42(1), 24–60, doi:10.1177/0309133317751843.
  562. Good, S.A., M.J. Martin and N.A. Rayner, 2013: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res-Oceans, 118(12), 6704–6716, doi:10.1002/2013JC009067.
  563. Gooday, A.J., J.M. Bernhard, L.A. Levin and S.B. Suhr, 2000: Foraminifera in the Arabian Sea oxygen minimum zone and other oxygen-deficient settings: taxonomic composition, diversity, and relation to metazoan faunas. Deep Sea Res. Pt. II, 47(1), 25–54, doi:10.1016/S0967-0645(99)00099-5.
  564. Goreau, T.J.F. and P. Prong, 2017: Biorock Electric Reefs Grow Back Severely Eroded Beaches in Months. J. Mar. Sci. Eng., 5(4), 48.
  565. Gorgues, T., O. Aumont and L. Memery, 2019: Simulated Changes in the Particulate Carbon Export Efficiency due to Diel Vertical Migration of Zooplankton in the North Atlantic. Geophys. Res. Lett., 46(10); 5387-5395, doi:10.1029/2018GL081748.
  566. Gori, A. et al., 2016: Physiological response of the cold water coral Desmophyllum dianthusto thermal stress and ocean acidification. Peerj, 4, e1606, doi:10.7717/peerj.1606.
  567. Gormley, K.S.G. et al., 2015: Adaptive management, international co-operation and planning for marine conservation hotspots in a changing climate. Mar. Policy, 53, 54–66, doi:10.1016/j.marpol.2014.11.017.
  568. Gourlie, D. et al., 2017: Performing “A New Song”: Suggested Considerations for Drafting Effective Coastal Fisheries Legislation Under Climate Change. Mar. Policy, 88; 342-349, doi:10.1016/j.marpol.2017.06.012.
  569. Gourlie, D. et al., 2018: Performing “A New Song”: Suggested Considerations for Drafting Effective Coastal Fisheries Legislation Under Climate Change. Mar. Policy, 88, 342–349, doi:10.1016/j.marpol.2017.06.012.
  570. Gracey, M.S., 2007: Nutrition-related disorders in Indigenous Australians: how things have changed. Med, J. Aust., 186(1), 15.
  571. Gracia, A., N. Rangel-Buitrago, J.A. Oakley and A.T. Williams, 2018: Use of ecosystems in coastal erosion management. Ocean Coast. Manage., 156, 277–289, doi:10.1016/j.ocecoaman.2017.07.009.
  572. Gradoville, M.R. et al., 2014: Diversity trumps acidification: Lack of evidence for carbon dioxide enhancement of Trichodesmium community nitrogen or carbon fixation at Station ALOHA. Limnol. Oceanogr., 59(3), 645–659, doi:10.4319/lo.2014.59.3.0645.
  573. Graham, N.A.J. et al., 2013: Managing resilience to reverse phase shifts in coral reefs. Front. Ecol. Environ., 11(10), 541–548, doi:10.1890/120305.
  574. Graham, N.A.J., J.E. Cinner, A.V. Norstrom and M. Nystrom, 2014: Coral reefs as novel ecosystems: embracing new futures. Curr. Opin. Environ. Sustain., 7, 9–14, doi:10.1016/j.cosust.2013.11.023.
  575. Grassi, G. et al., 2017: The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Change, 7, 220, doi:10.1038/nclimate3227.
  576. Greene, C., B. Monger and M. Huntley, 2010: Geoengineering: The inescapable truth of getting to 350. Solutions, 1(5), 57–66.
  577. Greene, C.H. et al., 2016: Marine microalgae: Climate, energy, and food security from the sea. Oceanography, 29(4), 10–15.
  578. Gregg, W.W. and C.S. Rousseaux, 2014: Decadal trends in global pelagic ocean chlorophyll: A new assessment integrating multiple satellites, in situ data, and models. J. Geophys. Res-Oceans, 119(9), 5921–5933, doi:10.1002/2014JC010158.
  579. Griscom, B.W. et al., 2017: Natural climate solutions. PNAS, 114(44), 11645.
  580. Gruber, N., 2019: A diagnosis for marine nitrogen fixation. Nature, 566(7743), 191–193.
  581. Gruber, N. et al., 2019: The oceanic sink for anthropogenic CO2 from 1994 to 2007. Science, 363(6432), 1193, doi:10.1126/science.aau5153.
  582. Gruber, N. et al., 2012: Rapid Progression of Ocean Acidification in the California Current System. Science, 337(6091), 220.
  583. Grundle, D.S. et al., 2017: Low oxygen eddies in the eastern tropical North Atlantic: Implications for N2O cycling. Sci. Rep., 7(1), 4806, doi:10.1038/s41598-017-04745-y.
  584. Gu, J. et al., 2018: Losses of salt marsh in China: Trends, threats and management. Estuar. Coast. Shelf Sci., 214, 98–109, doi:10.1016/j.ecss.2018.09.015.
  585. Guevara-Carrasco, R. and J. Lleonart, 2008: Dynamics and fishery of the Peruvian hake: Between nature and man. J. Mar. Syst., 71(3), 249–259, doi:10.1016/j.jmarsys.2007.02.030.
  586. Guidi, L. et al., 2016: Plankton networks driving carbon export in the oligotrophic ocean. Nature, 532, 465, doi:10.1038/nature16942.
  587. Guinder, V.A. et al., 2018: Plankton multiproxy analyses in the Northern Patagonian Shelf, Argentina: community structure, phycotoxins and characterization of Alexandrium strains. Front. Mar. Sci., 5, 394.
  588. Guinotte, J.M. et al., 2006: Will human-induced changes in seawater chemistry alter the distribution of deep sea scleractinian corals? Front. Ecol. Environ., 4(3), 141–146, doi:10.1890/1540-9295(2006)004[0141:WHCISC]2.0.CO;2.
  589. Gunderson, A.R., E.J. Armstrong and J.H. Stillman, 2016: Multiple Stressors in a Changing World: The Need for an Improved Perspective on Physiological Responses to the Dynamic Marine Environment. Annu. Rev. Mar. Sci., 8(1), 357–378, doi:10.1146/annurev-marine-122414-033953.
  590. Gunderson, A.R., B. Tsukimura and J.H. Stillman, 2017: Indirect Effects of Global Change: From Physiological and Behavioral Mechanisms to Ecological Consequences. Integr. Comp. Biol., 57(1), 48–54, doi:10.1093/icb/icx056.
  591. Gutiérrez, M.T., P. Jorge Castillo, B. Laura Naranjo and M.J. Akester, 2017: Current state of goods, services and governance of the Humboldt Current Large Marine Ecosystem in the context of climate change. Environ. Dev., 22, 175–190, doi:10.1016/j.envdev.2017.02.006.
  592. Guy, E. and F. Lasserre, 2016: Commercial shipping in the Arctic: new perspectives, challenges and regulations. Polar Record, 52(3), 294–304, doi:10.1017/S0032247415001011.
  593. Hagedoorn, L.C. et al., 2019: Community-based adaptation to climate change in small island developing states: an analysis of the role of social capital. Clim. Dev., 1–12, doi:10.1080/17565529.2018.1562869.
  594. Haines-Young, R. and M. Potschin, 2013: Common International Classification of Eco- system Services (CICES): Consultation on Version 4, August-December 2012, pp 34.
  595. Hale, R. et al., 2017: Mediation of macronutrients and carbon by post-disturbance shelf sea sediment communities. Biogeochemistry, 135(1), 121–133, doi:10.1007/s10533-017-0350-9.
  596. Hall-Spencer, J.M. et al., 2008: Volcanic carbon dioxide vents show ecosystem effects of ocean acidification. Nature, 454, 96, doi:10.1038/nature07051.
  597. Hallberg, R., 2013: Using a resolution function to regulate parameterizations of oceanic mesoscale eddy effects. Ocean Model.., 72, 92–103, doi:10.1016/j.ocemod.2013.08.007.
  598. Hallberg, R. et al., 2012: Sensitivity of Twenty-First-Century Global-Mean Steric Sea Level Rise to Ocean Model Formulation. J. Clim., 26(9), 2947–2956, doi:10.1175/JCLI-D-12-00506.1.
  599. Hallegatte, S. et al., 2015: Shock waves: managing the impacts of climate change on poverty. The World Bank.
  600. Hallegraeff, G.M., 2010: Ocean climate change, phytoplankton community responses, and harmful algal blooms: a formidable predictive challenge1. J. Phycol., 46(2), 220–235.
  601. Hallegraeff, G.M., 2016: Impacts and effects of ocean warming on marine phytoplankton and harmful algal blooms. in Explaining ocean warming: Causes, scale, effects and consequences. Full Report. [D. Laffoley and J.M. Baxter eds.], 456 pp, IUCN, Gland, Switzerland, ISBN: 978-8317-1806-4
  602. Hallett, C.S. et al., 2018: Observed and predicted impacts of climate change on the estuaries of south-western Australia, a Mediterranean climate region. Reg. Environ. Change, 18(5), 1357–1373, doi:10.1007/s10113-017-1264-8.
  603. Hamdan, L.J. and K.P. Wickland, 2016: Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate. Limnol. Oceanogr., 61(S1), S3–S12, doi:10.1002/lno.10449.
  604. Hameau, A., J. Mignot and F. Joos, 2019: Assessment of time of emergence of anthropogenic deoxygenation and warming: insights from a CESM simulation from 850 to 2100 CE. Biogeosciences, 16(8), 1755–1780, doi:10.5194/bg-16-1755-2019.
  605. Hamilton, L.C. and T.G. Safford, 2015: Environmental Views from the Coast: Public Concern about Local to Global Marine Issues. Society & Natural Resources, 28(1), 57–74, doi:10.1080/08941920.2014.933926.
  606. Hammond, M.L., C. Beaulieu, S.K. Sahu and S.A. Henson, 2017: Assessing trends and uncertainties in satellite-era ocean chlorophyll using space-time modeling. Global Biogeochem. Cy., 31(7), 1103–1117, doi:10.1002/2016gb005600.
  607. Han, W. et al., 2014: Indian Ocean Decadal Variability: A Review. Bull. Am. Meteorol. Soc., 95(11), 1679–1703, doi:10.1175/BAMS-D-13-00028.1.
  608. Hanz, U. et al., 2019: Environmental factors influencing cold water coral ecosystems in the oxygen minimum zones on the Angolan and Namibian margins. Biogeosciences, (In review) 1–37.
  609. Harborne, A.R. et al., 2017: Multiple Stressors and the Functioning of Coral Reefs. Annu. Rev. Mar. Sci., Vol 8, 9(1), 445–468, doi:10.1146/annurev-marine-010816-060551.
  610. Harkes, I.H.T. et al., 2015: Shrimp aquaculture as a vehicle for Climate Compatible Development in Sri Lanka. The case of Puttalam Lagoon. Mar. Policy, 61, 273–283, doi:10.1016/j.marpol.2015.08.003.
  611. Harley, C.D.G., 2011: Climate change, keystone predation, and biodiversity loss. Science, 334(6059), 1124–1127, doi:10.1126/science.1210199.
  612. Harris, P.T., M. Macmillan-Lawler, J. Rupp and E.K. Baker, 2014: Geomorphology of the oceans. Mar. Geol., 352(Supplement C), 4–24.
  613. Harris, P.T. and T. Whiteway, 2011: Global distribution of large submarine canyons: Geomorphic differences between active and passive continental margins. Mar. Geol., 285(1), 69–86, doi:10.1016/j.margeo.2011.05.008.
  614. Harrison, D.P., 2017: Global negative emissions capacity of ocean macronutrient fertilization. Environ. Res. Lett., 12(3), 035001.
  615. Harrison, G.P. and A.R. Wallace, 2005: Climate sensitivity of marine energy. Renew. Energ., 30(12), 1801–1817, doi:10.1016/j.renene.2004.12.006.
  616. Harrould-Kolieb, E.R. and D. Herr, 2012: Ocean acidification and climate change: synergies and challenges of addressing both under the UNFCCC. Clim. Policy, 12(3), 378–389, doi:10.1080/14693062.2012.620788.
  617. Harrould-Kolieb, E.R. and O. Hoegh-Guldberg, 2019: A governing framework for international ocean acidification policy. Mar. Policy, 102, 10–20, doi:10.1016/j.marpol.2019.02.004.
  618. Hartman, S.E. et al., 2015: Biogeochemical variations at the Porcupine Abyssal Plain sustained Observatory in the northeast Atlantic Ocean, from weekly to inter-annual timescales. Biogeosciences, 12(3), 845–853, doi:10.5194/bg-12-845-2015.
  619. Harvey, B.J., K.L. Nash, J.L. Blanchard and D.P. Edwards, 2018: Ecosystem-based management of coral reefs under climate change. Ecol. Evol., 8(12), 6354–6368, doi:10.1002/ece3.4146.
  620. Harvey, B.P., D. Gwynn-Jones and P.J. Moore, 2013: Meta-analysis reveals complex marine biological responses to the interactive effects of ocean acidification and warming. Ecol. Evol., 3(4), 1016–1030, doi:10.1002/ece3.516.
  621. Hatfield, J.S., M.H. Reynolds, N.E. Seavy and C.M. Krause, 2012: Population dynamics of Hawaiian seabird colonies vulnerable to sea level rise. Conserv. Biol., 26(4), 667–78, doi:10.1111/j.1523-1739.2012.01853.x.
  622. Hauri, C., T. Friedrich and A. Timmermann, 2015: Abrupt onset and prolongation of aragonite undersaturation events in the Southern Ocean. Nat. Clim. Change, 6, 172, doi:10.1038/nclimate2844.
  623. Hauser, D.D.W. et al., 2017: Decadal shifts in autumn migration timing by Pacific Arctic beluga whales are related to delayed annual sea ice formation. Global Change Biol., 23(6), 2206–2217, doi:10.1111/gcb.13564.
  624. Hawkins, E. and R. Sutton, 2012: Time of emergence of climate signals. Geophys. Res. Lett., 39(1); 1-6, doi:10.1029/2011gl050087.
  625. Hawkins, S. et al., 2016: Impacts and effects of ocean warming on intertidal rocky habitats in Explaining ocean warming: Cause, scale, effects and consequences. Full report. [D. Laffoley and J.M. Baxeter eds.] IUCN, 147-176, Gland, CH, ISBN: 978-2-8317-1806-4  
  626. Hayden, H.L. and E.F. Granek, 2015: Coastal sediment elevation change following anthropogenic mangrove clearing. Estuar. Coast. Shelf Sci., 165, 70–74, doi:10.1016/j.ecss.2015.09.004.
  627. Haynert, K. et al., 2011: Biometry and dissolution features of the benthic foraminifer Ammonia aomoriensis at high pCO2. Mar. Ecol. Prog. Ser., 432, 53–67.
  628. Haynie, A.C. and L. Pfeiffer, 2012: Why economics matters for understanding the effects of climate change on fisheries. ICES J. Mar. Sci., 69(7), 1160–1167, doi:10.1093/icesjms/fss021.
  629. Hays, G.C., A.C. Broderick, F. Glen and B.J. Godley, 2003: Climate change and sea turtles: a 150-year reconstruction of incubation temperatures at a major marine turtle rookery. Global Change Biol., 9(4), 642–646, doi:10.1046/j.1365-2486.2003.00606.x.
  630. Hazen, E.L. et al., 2013: Predicted habitat shifts of Pacific top predators in a changing climate. Nat. Clim. Change, 3(3), 234–238, doi:10.1038/nclimate1686.
  631. Heenan, A. et al., 2015: A climate-informed, ecosystem approach to fisheries management. Mar. Policy, 57, 182–192, doi:10.1016/j.marpol.2015.03.018.
  632. Hein, M.Y., B.L. Willis, R. Beeden and A. Birtles, 2017: The need for broader ecological and socioeconomic tools to evaluate the effectiveness of coral restoration programs. Restor. Ecol., 25(6), 873–883, doi:10.1111/rec.12580.
  633. Hejnowicz, A.P., H. Kennedy, M.A. Rudd and M.R. Huxham, 2015: Harnessing the climate mitigation, conservation and poverty alleviation potential of seagrasses: prospects for developing blue carbon initiatives and payment for ecosystem service programmes. Front. Mar. Sci., 2, 32.
  634. Held, I.M. and B.J. Soden, 2006: Robust Responses of the Hydrological Cycle to Global Warming. J. Clim., 19(21), 5686–5699, doi:10.1175/JCLI3990.1.
  635. Helland-Hansen, B., 1916: Nogen hydrografiske metoder. Scand. Naturforsker Mote, Kristiana, Oslo.
  636. Helm, K.P., N.L. Bindoff and J.A. Church, 2011: Observed decreases in oxygen content of the global ocean. Geophys. Res. Lett., 38(23), doi:10.1029/2011GL049513.
  637. Hemer, M.A. et al., 2018: Perspectives on a way forward for ocean renewable energy in Australia. Renew. Energ., 127, 733–745, doi:10.1016/j.renene.2018.05.036.
  638. Henderson, E.E. et al., 2014: Effects of fluctuations in sea-surface temperature on the occurrence of small cetaceans off Southern California. Fish-B NOAA, 112(2–3), 159–177, doi:10.7755/fb.112.2-3.5.
  639. Hennige, S.J. et al., 2015: Hidden impacts of ocean acidification to live and dead coral framework. Proc. Biol. Sci., 282(1813), doi:10.1098/rspb.2015.0990.
  640. Henry, L.A. et al., 2016: Seamount egg‐laying grounds of the deep‐water skate Bathyraja richardsoni. J. Fish Biol., 89(2), 1473–1481, doi:10.1111/jfb.13041.
  641. Henson, S.A. et al., 2017: Rapid emergence of climate change in environmental drivers of marine ecosystems. Nat. Commun., 8, 14682, doi:10.1038/ncomms14682.
  642. Henson, S.A., C. Beaulieu and R. Lampitt, 2016: Observing climate change trends in ocean biogeochemistry: when and where. Global Change Biol., 22(4), 1561–1571, doi:10.1111/gcb.13152.
  643. Henson, S.A. et al., 2010: Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity. Biogeosciences, 7(2), 621–640, doi:10.5194/bg-7-621-2010.
  644. Hernán, G. et al., 2017: Future warmer seas: increased stress and susceptibility to grazing in seedlings of a marine habitat‐forming species. Global Change Biol., 23(11), 4530–4543, doi:10.1111/gcb.13768.
  645. Hernández-Delgado, E.A., 2015: The emerging threats of climate change on tropical coastal ecosystem services, public health, local economies and livelihood sustainability of small islands: Cumulative impacts and synergies. Mar. Pollut. Bull., 101(1), 5–28, doi:10.1016/j.marpolbul.2015.09.018.
  646. Hernández-Delgado, E.A. et al., 2014: Community-Based Coral Reef Rehabilitation in a Changing Climate: Lessons Learned from Hurricanes, Extreme Rainfall, and Changing Land Use Impacts. Open Ecol. J., 04(14), 918–944, doi:10.4236/oje.2014.414077.
  647. Hernández-González, Y. et al., 2016: Perspectives on contentions about climate change adaptation in the Canary Islands: A case study for Tenerife. Publication Office of the European Union, Luxembourg, 74 pp, ISBN: 978-92-79-64595-2
  648. Heron, S.F., 2017: Impacts of Climate Change on World Heritage Coral Reefs : A First Global Scientic Assessment. UNESCO World Heritage Centre, Paris, 16 pp.
  649. Heron, S.F., J.A. Maynard, R. van Hooidonk and C.M. Eakin, 2016: Warming Trends and Bleaching Stress of the World&apos;s Coral Reefs 1985–2012. Sci. Rep., 6(1), doi:10.1038/srep38402.
  650. Herr, D., K. Isensee, E. Harrould-Kolieb and C. Turley, 2014: Ocean Acidification, iv. IUCN, Gland, Switzerland, 52 pp.
  651. Herr, D. and E. Landis, 2016: Coastal blue carbon ecosystems. Opportunities for nationally determined contributions. Policy Brief. Gland, Switzerland: IUCN and Washington, DC, USA: TNC.
  652. Herr, D., M. Unger, D. Laffoley and A. McGivern, 2017: Pathways for implementation of blue carbon initiatives. Aquat. Conserv. Mar. Freshw. Ecosyst., 27(S1), 116–129, doi:10.1002/aqc.2793.
  653. Herring, P.J. and D.R. Dixon, 1998: Extensive deep sea dispersal of postlarval shrimp from a hydrothermal vent. Deep sea Res. Pt. I, 45(12), 2105–2118, doi:10.1016/S0967-0637(98)00050-8.
  654. Hess, J. and I. Kelman, 2017: Tourism Industry Financing of Climate Change Adaptation: Exploring the Potential in Small Island Developing States. Clim. Disast. Dev. J., 2(2), 33–45.
  655. Heuer, R.M. and M. Grosell, 2014: Physiological impacts of elevated carbon dioxide and ocean acidification on fish. Am. J. Physiol. Regul. Integr. Comp. Physiol., 307(9), R1061–R1084, doi:10.1152/ajpregu.00064.2014.
  656. Heuzé, C., K.J. Heywood, D.P. Stevens and J.K. Ridley, 2015: Changes in global ocean bottom properties and volume transports in CMIP5 models under climate change scenarios. J. Clim., 28(8), 2917–2944.
  657. Hewitt, J.E., J.I. Ellis and S.F. Thrush, 2016: Multiple stressors, nonlinear effects and the implications of climate change impacts on marine coastal ecosystems. Global Change Biol., 22(8), 2665–2675.
  658. Hill, R. et al., 2015: Can macroalgae contribute to blue carbon? An Australian perspective. Limnol. Oceanogr., 60(5), 1689–1706, doi:10.1002/lno.10128.
  659. Hilmi, N. et al., 2017: Ocean acidification in the Middle East and North African region. Region et Developpement, 46, 43–57 pp, LEADm Universite du Sud – Toulon Var.
  660. Hilmi, N. et al., 2015: Bridging the gap between ocean acidification impacts and economic valuation: regional impacts of ocean acidification on fisheries and aquaculture. Brochure of The Third International Monaco Workshop on Economics of Ocean Acidification, Monaco.
  661. Himes-Cornell, A. and S. Kasperski, 2015a: Assessing climate change vulnerability in Alaska’s fishing communities. Fish. Res., 162, 1–11, doi:10.1016/j.fishres.2014.09.010.
  662. Himes-Cornell, A. and S. Kasperski, 2015b: Assessing climate change vulnerability in Alaska&apos;s fishing communities. Fish. Res., 162, 1–11, doi:10.1016/j.fishres.2014.09.010.
  663. Hindell, M.A. et al., 2016: Circumpolar habitat use in the southern elephant seal: implications for foraging success and population trajectories. Ecosphere, 7(5), e01213, doi:10.1002/ecs2.1213.
  664. Hinkel, J. et al., 2014: Coastal flood damage and adaptation costs under 21st century sea level rise. PNAS, (9) 3292-3297, doi:10.1073/pnas.1222469111
  665. Hiscock, J.A. and B.L. Chilvers, 2014: Declining eastern rockhopper (Eudyptes filholi) and erect-crested (E-sclateri) penguins on the Antipodes Islands, New Zealand. New Zeal. J. Ecol., 38(1), 124–131.
  666. Hiwasaki, L., E. Luna, Syamsidik and R. Shaw, 2014: Process for integrating local and indigenous knowledge with science for hydro-meteorological disaster risk reduction and climate change adaptation in coastal and small island communities. Int. J. Disast. Risk Reduc., 10, 15–27, doi:10.1016/j.ijdrr.2014.07.007.
  667. Ho, C.-H. et al., 2016: Mitigating uncertainty and enhancing resilience to climate change in the fisheries sector in Taiwan: Policy implications for food security. Ocean Coast. Manage., 130, 355–372, doi:10.1016/j.ocecoaman.2016.06.020.
  668. Hobday, A.J. et al., 2015: Reconciling conflicts in pelagic fisheries under climate change. Deep Sea Res. Pt. II, 113, 291–300, doi:10.1016/j.dsr2.2014.10.024.
  669. Hobday, A.J. et al., 2016a: Planning adaptation to climate change in fast-warming marine regions with seafood-dependent coastal communities. Rev. Fish Biol. Fisher., 26(2), 249–264, doi:10.1007/s11160-016-9419-0.
  670. Hobday, A.J., C.M. Spillman, J. Paige Eveson and J.R. Hartog, 2016b: Seasonal forecasting for decision support in marine fisheries and aquaculture. Fish. Oceanogr., 25(S1), 45–56, doi:doi:10.1111/fog.12083.
  671. Hodgson, E.E. et al., 2018: Consequences of spatially variable ocean acidification in the California Current: Lower pH drives strongest declines in benthic species in southern regions while greatest economic impacts occur in northern regions. Ecol. Modell., 383, 106–117, doi:10.1016/j.ecolmodel.2018.05.018.
  672. Hodgson, J., W. Russell and M. Megannety, 2016: Exploring plausible futures for marine transportation in the Canadian arctic, a scenarios based approach. Prepared for Transport Canada. Hodgson and Associates, Vancouver, Canada, 120 pp.
  673. Hoegh-Guldberg, O., 2015: Reviving the Ocean Economy: the case for action-2015. WWF International. Gland, Switzerland, Geneva.
  674. Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1655–1731 pp., ISBN: 978-1-107-05807-1
  675. Hoegh-Guldberg, O. et al., 2008: Assisted Colonization and Rapid Climate Change. Science, 321(5887), 345.
  676. Hoegh-Guldberg, O. et al., 2018: Impacts of 1.5ºC Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. 630 pp., In Press.
  677. Höfer, J. et al., 2018: All you can eat: the functional response of the cold water coral Desmophyllum dianthus feeding on krill and copepods. Peerj, 6, e5872.
  678. Hofmann, M. and H.-J. Schellnhuber, 2009: Oceanic acidification affects marine carbon pump and triggers extended marine oxygen holes. PNAS, 106(9), 3017.
  679. Hogg, A.M. et al., 2015: Recent trends in the Southern Ocean eddy field. J. Geophys. Res-Oceans, 120(1), 257–267, doi:10.1002/2014JC010470.
  680. Hong, H. et al., 2017: The complex effects of ocean acidification on the prominent N&lt;sub&gt;2&lt;/sub&gt;-fixing cyanobacterium Trichodesmium. Science, 356(6337), 527, doi:10.1126/science.aal2981.
  681. Hooper, T. and M. Austen, 2013: Tidal barrages in the UK: Ecological and social impacts, potential mitigation, and tools to support barrage planning. Renew. Sustain. Energ. Rev., 23, 289–298, doi:10.1016/j.rser.2013.03.001.
  682. Hopkins, C.R., D.M. Bailey and T. Potts, 2016: Perceptions of practitioners: Managing marine protected areas for climate change resilience. Ocean Coast. Manage., 128, 18–28, doi:10.1016/j.ocecoaman.2016.04.014.
  683. Horoszowski-Fridman, Y.B., J.-C. Brêthes, N. Rahmani and B. Rinkevich, 2015: Marine silviculture: Incorporating ecosystem engineering properties into reef restoration acts. Ecol. Eng., 82(Supplement C), 201–213.
  684. Horoszowski-Fridman, Y.B., I. Izhaki and B. Rinkevich, 2011: Engineering of coral reef larval supply through transplantation of nursery-farmed gravid colonies. J. Exp. Mar. Biol. Ecol., 399(2), 162–166, doi:10.1016/j.jembe.2011.01.005.
  685. Horoszowski-Fridman, Y.B. and B. Rinkevich, 2017: Restoration of the Animal Forests: Harnessing Silviculture Biodiversity Concepts for Coral Transplantation in Marine Animal Forests.[Rossi, S., L. Bramanti, A. Gori and C. Orejas (eds.)]. Springer International Publishing, Cham, pp. 1–2, ISBN: 978-3-319-21011-7 .
  686. Houser, C., P. Wernette and B.A. Weymer, 2018: Scale-dependent behavior of the foredune: Implications for barrier island response to storms and sea level rise. Geomorphology, 303, 362–374, doi:10.1016/j.geomorph.2017.12.011.
  687. Howard, J. et al., 2017: Clarifying the role of coastal and marine systems in climate mitigation. Front. Ecol. Environ., 15(1), 42–50, doi:10.1002/fee.1451.
  688. Hu, L. et al., 2016: Recent organic carbon sequestration in the shelf sediments of the Bohai Sea and Yellow Sea, China. J. Mar. Syst., 155, 50–58, doi:10.1016/j.jmarsys.2015.10.018.
  689. Hubbard, D., J. Dugan, N. Schooler and S. Viola, 2014: Local extirpations and regional declines of endemic upper beach invertebrates in southern California. Estuar. Coast. Shelf Sci., 150, 67–75.
  690. Hudson, D.M. et al., 2018: Physiological and behavioral response of the Asian shore crab, Hemigrapsus sanguineus, to salinity: implications for estuarine distribution and invasion. Peerj, 6, e5446, doi:10.7717/peerj.5446.
  691. Hudson, J.M. et al., 2014: Myctophid feeding ecology and carbon transport along the northern Mid-Atlantic Ridge. Deep sea Res. Pt. I, 93, 104–116, doi:10.1016/j.dsr.2014.07.002.
  692. Hughes, A.D. et al., 2012: Does seaweed offer a solution for bioenergy with biological carbon capture and storage? Greenh, Gases:, 2(6), 402–407, doi:10.1002/ghg.1319.
  693. Hughes, T.P. et al., 2018: Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science, 359(6371), 80, doi:10.1126/science.aan8048.
  694. Hughes, T.P. et al., 2010: Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol., 25(11), 633–642, doi:10.1016/j.tree.2010.07.011.
  695. Hughes, T.P. et al., 2019a: Global warming impairs stock–recruitment dynamics of corals. Nature, 568(7752), 387–390, doi:10.1038/s41586-019-1081-y.
  696. Hughes, T.P. et al., 2019b: Ecological memory modifies the cumulative impact of recurrent climate extremes. Nat. Clim. Change, 9(1), 40–43, doi:10.1038/s41558-018-0351-2.
  697. Hughes, T.P. et al., 2013: Living dangerously on borrowed time during slow, unrecognized regime shifts. Trends Ecol. Evol., 28(3), 149–155, doi:10.1016/j.tree.2012.08.022.
  698. Hummels, R., M. Dengler and B. Bourlès, 2013: Seasonal and regional variability of upper ocean diapycnal heat flux in the Atlantic cold tongue. Progr. Oceanogr., 111, 52–74, doi:10.1016/j.pocean.2012.11.001.
  699. Hunt, G.L. et al., 2016: Advection in polar and sub-polar environments: Impacts on high latitude marine ecosystems. Progr. Oceanogr., 149(40), 40–81, doi:10.1016/j.pocean.2016.10.004.
  700. Hutchins, D.A. and P.W. Boyd, 2016: Marine phytoplankton and the changing ocean iron cycle. Nat. Clim. Change, 6(12), 1072–1079, doi:10.1038/NCLIMATE3147.
  701. Hutchins, D.A. and F. Fu, 2017: Microorganisms and ocean global change. Nature Microbiol., 2, 17058, doi:10.1038/nmicrobiol.2017.58.
  702. Huxham, M. et al., 2015: Applying Climate Compatible Development and economic valuation to coastal management: A case study of Kenya&apos;s mangrove forests. J. Environ. Manage., 157, 168–181, doi:10.1016/j.jenvman.2015.04.018.
  703. Hwang, J.H. et al., 2014: The physical processes in the Yellow Sea. Ocean Coast. Manage., 102, 449–457.
  704. Hyndes, G.A. et al., 2016: Accelerating Tropicalization and the Transformation of Temperate Seagrass Meadows. BioScience, 66(11), 938–948, doi:10.1093/biosci/biw111.
  705. Ikeda, T., Y. Kanno, K. Ozaki and A. Shinada, 2001: Metabolic rates of epipelagic marine copepods as a function of body mass and temperature. Mar. Biol., 139(3), 587–596, doi:10.1007/s002270100608.
  706. Ilıcak, M., A.J. Adcroft, S.M. Griffies and R.W. Hallberg, 2012: Spurious dianeutral mixing and the role of momentum closure. Ocean Model.., 45–46(Supplement C), 37–58, doi:10.1016/j.ocemod.2011.10.003.
  707. Ilyina, T., R.E. Zeebe, E. Maier-Reimer and C. Heinze, 2009: Early detection of ocean acidification effects on marine calcification. Global Biogeochem. Cy., 23(1); 1-11, doi:10.1029/2008gb003278.
  708. Ingeman, K.E., J.F. Samhouri and A.C. Stier, 2019: Ocean recoveries for tomorrow’s Earth: Hitting a moving target. Science, 363(6425), eaav1004, doi:10.1126/science.aav1004.
  709. Inniss, L. et al., 2017: The First Global Integrated Marine Assessment: World Ocean Assessment I. United Nations, New York, 1752 pp.
  710. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
  711. IPCC, 2014: 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands [Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N., Baasansuren, J., Fukuda, M. and Troxler, T.G. eds.].IPCC, Switzerland, 354 pp., ISBN: 978-92-9169-139-5
  712. IPCC, 2018: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA., 630
  713. Isensee, K., J. Howard, E. Pidgeon and J. Ramos, 2019: Coastal blue carbon. In: WMO Statement on the State of the Global Climate in 2018. WMO, Geneva, pp. 10–11, ISBN: 978-92-63-11233-0
  714. Ishii, M. et al., 2017: Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets. SOLA, 13, 163–167.
  715. Ishikawa, T. and Y. Ikegaki, 1980: Control of Mercury Pollution in Japan and the Minamata Bay Cleanup. J.Water Pollut. Contro Fed., 52(5), 1013–1018.
  716. Isla, J.A., K. Lengfellner and U. Sommer, 2008: Physiological response of the copepod Pseudocalanus sp in the Baltic Sea at different thermal scenarios. Global Change Biol., 14(4), 895–906, doi:10.1111/j.1365-2486.2008.01531.x.
  717. Islam, M.M., S. Sallu, K. Hubacek and J. Paavola, 2013: Vulnerability of fishery-based livelihoods to the impacts of climate variability and change: insights from coastal Bangladesh. Reg. Environ. Change, 14(1), 281–294, doi:10.1007/s10113-013-0487-6.
  718. Ito, T. and C. Deutsch, 2013: Variability of the oxygen minimum zone in the tropical North Pacific during the late twentieth century. Global Biogeochem. Cy., 27(4), 1119–1128, doi:10.1002/2013gb004567.
  719. Ito, T., S. Minobe, M.C. Long and C. Deutsch, 2017: Upper ocean O2 trends: 1958–2015. Geophys. Res. Lett., 44(9), 4214–4223, doi:10.1002/2017GL073613.
  720. Ito, T. et al., 2016: Acceleration of oxygen decline in the tropical Pacific over the past decades by aerosol pollutants. Nat. Geosci., 9, 443, doi:10.1038/ngeo2717.
  721. Iwao, K., N. Wada, A. Ohdera and M. Omori, 2014: How many donor colonies should be cross-fertilized for nursery farming of sexually propagated corals? Natural Resources, 05(10), 521–526, doi:10.4236/nr.2014.510047.
  722. Jacob, C., A. Buffard, S. Pioch and S. Thorin, 2017: Marine ecosystem restoration and biodiversity offset. Ecol. Eng., doi:10.1016/j.ecoleng.2017.09.007.
  723. Jagers, S.C. et al., 2018: Societal causes of, and responses to, ocean acidification. Ambio, (48)8, 816–830, doi:10.1007/s13280-018-1103-2
  724. Jamero, M.L., M. Onuki, M. Esteban and N. Tan, 2018: Community-based adaptation in low-lying islands in the Philippines: challenges and lessons learned. Reg. Environ. Change, 18(8), 2249–2260, doi:10.1007/s10113-018-1332-8.
  725. Janousek, C.N. et al., 2017: Inundation, vegetation, and sediment effects on litter decomposition in Pacific Coast tidal marshes. Ecosystems, 20(7), 1296–1310.
  726. Jaramillo, E. et al., 2017: Macroscale patterns in body size of intertidal crustaceans provide insights on climate change effects. PLoS One, 12(5), e0177116, doi:10.1371/journal.pone.0177116.
  727. Jaroszweski, D., L. Chapman and J. Petts, 2010: Assessing the potential impact of climate change on transportation: the need for an interdisciplinary approach. J. Transport. Geogr., 18(2), 331–335, doi:10.1016/j.jtrangeo.2009.07.005.
  728. Jay, D.A., 2009: Evolution of tidal amplitudes in the eastern Pacific Ocean. Geophys. Res. Lett., 36(4), n/a–n/a, doi:10.1029/2008GL036185.
  729. Jayne, S.R. and L.C. St. Laurent, 2001: Parameterizing tidal dissipation over rough topography. Geophys. Res. Lett., 28(5), 811–814.
  730. Jennerjahn, T.C. et al., 2017: Mangrove Ecosystems under Climate Change. In: Mangrove Ecosystems: A Global Biogeographic Perspective [V.H. Rivera-Monroy, S.Y. Lee, E. Kristensen, and R.R. Twilley eds.]. Springer, pp. 211–244, ISBN: 978-3-319-62206-4
  731. Jeong, H., H. Lee, H. Kim and H. Kim, 2014: Algorithm for economic assessment of infrastructure adaptation to climate change. In ISARC Proceedings of the International Symposium on Automation and Robotics in Construction. IAARC Publications, Australia, 31, 1.ISBN: 978-0-64-659711-9  
  732. Jeppesen, R. et al., 2018: Effects of Hypoxia on Fish Survival and Oyster Growth in a Highly Eutrophic Estuary. Estuar. Coast., 41(1), 89–98, doi:10.1007/s12237-016-0169-y.
  733. Jiang, J. et al., 2016: Defining the next generation modeling of coastal ecotone dynamics in response to global change. Ecol. Model., 326, 168–176, doi:10.1016/j.ecolmodel.2015.04.013.
  734. Jiang, L. et al., 2018: Increased temperature mitigates the effects of ocean acidification on the calcification of juvenile Pocillopora damicornis, but at a cost. Coral Reefs, 37(1), 71–79.
  735. Jiang, M. and T. DeLacy, 2014: 14 A climate change adaptation framework for Pacific Island tourism. In T. DeLacy, M. Jiang, G. Lipman and S. Vorster (Eds), Green Growth and Travelism: Concept, Policy and Practice for Sustainable Tourism, Routledge, 225. 
  736. Jiao, N. et al., 2018a: Unveiling the enigma of refractory carbon in the ocean. Natl. Sci. Rev., 5(4), 459-463. , doi:10.1093/nsr/nwy020.
  737. Jiao, N. et al., 2010: Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat. Rev. Microbiol., 8(8), 593–599, doi:10.1038/Nrmicro2386.
  738. Jiao, N. et al., 2014a: Presence of Prochlorococcus in the aphotic waters of the western Pacific Ocean. Biogeosciences, 11(8), 2391–2400, doi:10.5194/bg-11-2391-2014.
  739. Jiao, N. et al., 2014b: Mechanisms of microbial carbon sequestration in the ocean &ndash; future research directions. Biogeosciences, 11(19), 5285–5306, doi:10.5194/bg-11-5285-2014.
  740. Jiao, N., K. Tang, H. Cai and Y. Mao, 2011: Increasing the microbial carbon sink in the sea by reducing chemical fertilization on the land. Nat. Rev. Microbiol., 9(1), doi:10.1038/nrmicro2386-c2.
  741. Jiao, N., H. Wang, G. Xu and S. Aricò, 2018b: Blue Carbon on the Rise:Challenges and Opportunities. Natl. Sci. Rev., 5(4), 464-468  doi:10.1093/nsr/nwy030.
  742. Jiao, N.-Z. et al., 2015: Climate change and anthropogenic impacts on marine ecosystems and countermeasures in China. Advances in Climate Change Research, 6(2), 118–125, doi:10.1016/j.accre.2015.09.010.
  743. Jickells, T.D., J.E. Andrews and D.J. Parkes, 2015: Direct and Indirect Effects of Estuarine Reclamation on Nutrient and Metal Fluxes in the Global Coastal Zone. Aquat. Geochem., 22(4), 337–348, doi:10.1007/s10498-015-9278-7.
  744. Jickells, T.D. et al., 2017: A reevaluation of the magnitude and impacts of anthropogenic atmospheric nitrogen inputs on the ocean. Global Biogeochem. Cy., 31(2), 289–305, doi:10.1002/2016gb005586.
  745. Johannessen, S.C. and R.W. Macdonald, 2018a: Reply to Oreska et al ‘Comment on Geoengineering with seagrasses: is credit due where credit is given?’. Environ. Res. Lett., 13(3), 038002.
  746. Johannessen, S.C. and R.W. Macdonald, 2016: Geoengineering with seagrasses: is credit due where credit is given? Environ. Res. Lett., 11(11), 113001.
  747. Johannessen, S.C. and R.W. Macdonald, 2018b: Reply to Macreadie et al Comment on ‘Geoengineering with seagrasses: is credit due where credit is given?’. Environ. Res. Lett., 13(2), 028001.
  748. Johnson, D., M. Adelaide Ferreira and E. Kenchington, 2018: Climate change is likely to severely limit the effectiveness of deep sea ABMTs in the North Atlantic. Mar. Policy, 87, 111–122, doi:10.1016/j.marpol.2017.09.034.
  749. Johnson, G.C., J.M. Lyman and S.G. Purkey, 2015: Informing Deep Argo Array Design Using Argo and Full-Depth Hydrographic Section Data. J. Atmos. Ocean. Tech., 32(11), 2187–2198, doi:10.1175/JTECH-D-15-0139.1.
  750. Johnson, G.C., S.G. Purkey, N.V. Zilberman and D. Roemmich, 2019: Deep Argo Quantifies Bottom Water Warming Rates in the Southwest Pacific Basin. Geophys. Res. Lett., 46(5), 2662–2669, doi:10.1029/2018GL081685.
  751. Johnson, K.S. et al., 2017: Biogeochemical sensor performance in the SOCCOM profiling float array. J. Geophys. Res-Oceans, 122(8), 6416–6436, doi:10.1002/2017JC012838.
  752. Jónasdóttir, S.H., A.W. Visser, K. Richardson and M.R. Heath, 2015: Seasonal copepod lipid pump promotes carbon sequestration in the deep North Atlantic. PNAS, 112(39), 12122.
  753. Jones, D.C. et al., 2016a: How does subantarctic mode water ventilate the Southern Hemisphere subtropics? J. Geophys. Res-Oceans, 121(9), 6558–6582.
  754. Jones, D.O. et al., 2014: Global reductions in seafloor biomass in response to climate change. Glob Chang Biol, 20(6), 1861–72, doi:10.1111/gcb.12480.
  755. Jones, J.M. et al., 2016b: Assessing recent trends in high-latitude Southern Hemisphere surface climate. Nat. Clim. Change, 6, 917, doi:10.1038/nclimate3103.
  756. Jones, K.R. et al., 2018: The Location and Protection Status of Earth’s Diminishing Marine Wilderness. Curr. Biol., 28(15), 2506–2512.e3, doi:10.1016/j.cub.2018.06.010.
  757. Jones, M.C. and W.W.L. Cheung, 2015: Multi-model ensemble projections of climate change effects on global marine biodiversity. ICES J. Mar. Sci., 72(3), 741–752, doi:10.1093/icesjms/fsu172.
  758. Jones, M.C. and W.W.L. Cheung, 2018: Using fuzzy logic to determine the vulnerability of marine species to climate change. Glob Chang Biol, 24(2), e719–e731, doi:10.1111/gcb.13869.
  759. Jones, N., J.R.A. Clark and C. Malesios, 2015: Social capital and willingness-to-pay for coastal defences in south-east England. Ecol. Econ., 119, 74–82, doi:10.1016/j.ecolecon.2015.07.023.
  760. Jonkers, L., H. Hillebrand and M. Kucera, 2019: Global change drives modern plankton communities away from the pre-industrial state. Nature, 570, 372-375, doi:10.1038/s41586-019-1230-3.
  761. Jordà, G. et al., 2017: The Mediterranean Sea heat and mass budgets: Estimates, uncertainties and perspectives. Progr. Oceanogr., 156, 174–208, doi:10.1016/j.pocean.2017.07.001.
  762. Josey, S.A. et al., 2018: The Recent Atlantic Cold Anomaly: Causes, Consequences, and Related Phenomena. Annu. Rev. Mar. Sci., 10(1), 475–501, doi:10.1146/annurev-marine-121916-063102.
  763. Joyce, J. et al., 2017: Developing a multi-scale modeling system for resilience assessment of green-grey drainage infrastructures under climate change and sea level rise impact. Environ. Modell. Softw., 90, 1–26, doi:10.1016/j.envsoft.2016.11.026.
  764. Jurgens, L.J. et al., 2015: Patterns of mass mortality among rocky shore invertebrates across 100 km of northeastern Pacific coastline. PLoS One, 10(6), e0126280.
  765. Jurjonas, M. and E. Seekamp, 2018: Rural coastal community resilience: Assessing a framework in eastern North Carolina. Ocean Coast. Manage., 162, 137–150, doi:10.1016/j.ocecoaman.2017.10.010.
  766. Jury, C.P. and R.J. Toonen, 2019: Adaptive responses and local stressor mitigation drive coral resilience in warmer, more acidic oceans. Proc. Roy. Soc. B., 286(1902), 20190614.
  767. Justic, D. et al., 2016: Chapter 11 – Coastal Ecosystem Modeling in the Context of Climate Change: An Overview With Case Studies. In: Developments in Environmental Modelling, Volume 28 [Sven Erik, J. (ed.)]. Elsevier, Netherlands, pp. 227–260. ISSN: 0167-8892.
  768. Kabisch, N., H. Korn, J. Stadler and A. Bonn, 2017: Naturebased Solutions to Climate Change Adaptation in Urban Areas. Linkages between Science, Policy and Practice. Theory and Practice of Urban Sustainability
  769. Transitions, Springer Open, 337 pp.
  770. Kahru, M., R. Kudela, M. Manzano‐Sarabia and B.G. Mitchell, 2009: Trends in primary production in the California Current detected with satellite data. J. Geophys. Res-Oceans, 114(C2).
  771. Kais, S.M. and M.S. Islam, 2017: Impacts of and resilience to climate change at the bottom of the shrimp commodity chain in Bangladesh: A preliminary investigation. Aquaculture, doi:10.1016/j.aquaculture.2017.05.024.
  772. Kaja, N. and M. Mellic, 2017: Climate change: Issues of Built Heritage Structures in the coastal region. Journal of Scientific Research, 13, 54–60.
  773. Kämpf, J. and P. Chapman, 2016: Upwelling Systems of the World.Springer International Publishing Switzerland. ISBN 978-3-319-42522-1
  774. Kao, K.-W. et al., 2018: Repeated and Prolonged Temperature Anomalies Negate Symbiodiniaceae Genera Shuffling in the Coral Platygyra verweyi (Scleractinia; Merulinidae). Zool. Stud., 57(55).
  775. Kaplan-Hallam, M., N.J. Bennett and T. Satterfield, 2017: Catching sea cucumber fever in coastal communities: Conceptualizing the impacts of shocks versus trends on social-ecological systems. Global Environ. Change, 45, 89–98, doi:10.1016/j.gloenvcha.2017.05.003.
  776. Karim, M.S., 2015: Prevention of Pollution of the Marine Environment from Vessels. Springer International Publishing, Cham.ISBN 978-3-319-10608-3
  777. Karim, M.S. and M.M. Uddin, 2019: Swatch-of-no-ground marine protected area for sharks, dolphins, porpoises and whales: Legal and institutional challenges. Mar. Pollut. Bull., 139, 275–281, doi:10.1016/j.marpolbul.2018.12.037.
  778. Karl, T.R. et al., 2015: Possible artifacts of data biases in the recent global surface warming hiatus. Science, 348(6242), 1469, doi:10.1126/science.aaa5632.
  779. Karstensen, J. et al., 2015: Open ocean dead zones in the tropical North Atlantic Ocean. Biogeosciences, 12(8), 2597–2605, doi:10.5194/bg-12-2597-2015.
  780. Katselidis, K.A. et al., 2014: Employing sea level rise scenarios to strategically select sea turtle nesting habitat important for long-term management at a temperate breeding area. J. Exp. Mar. Biol. Ecol., 450, 47–54, doi:10.1016/j.jembe.2013.10.017.
  781. Kavanaugh, M.T. et al., 2015: Effect of continental shelf canyons on phytoplankton biomass and community composition along the western Antarctic Peninsula. Mar. Ecol. Prog. Ser., 524, 11–26, doi:10.3354/meps11189.
  782. Kawase, M., 1987: Establishment of Deep Ocean Circulation Driven by Deep-Water Production. J. Phys. Oceanogr., 17(12), 2294–2317, doi:10.1175/1520-0485(1987)017<2294:EODOCD>2.0.CO;2.
  783. Kay, J.E. et al., 2014: The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability. Bull. Am. Meteorol. Soc., 96(8), 1333–1349, doi:10.1175/BAMS-D-13-00255.1.
  784. Keller, A.A. et al., 2015: Occurrence of demersal fishes in relation to near-bottom oxygen levels within the California Current large marine ecosystem. Fish. Oceanogr., 24(2), 162–176, doi:10.1111/fog.12100.
  785. Keller, A.A. et al., 2010: Demersal fish and invertebrate biomass in relation to an offshore hypoxic zone along the US West Coast. Fish. Oceanogr., 19(1), 76–87, doi:10.1111/j.1365-2419.2009.00529.x.
  786. Keller, D.P., 2019a: Marine climate engineering. In: Handbook on Marine Environment Protection: Science, Impacts and Sustainable Management [Salomon, M. and T. Markus (eds.)]. Springer, Switzerland. ISBN: 978-3-319-60154-0
  787. Keller, D.P., E.Y. Feng and A. Oschlies, 2014a: Potential climate engineering effectiveness and side effects during a high carbon dioxide-emission scenario. Nat. Commun., 5, 3304, doi:10.1038/ncomms4304.
  788. Keller, J.K., 2019b: Greenhouse Gases. In: A Blue Carbon Primer, The State of Coastal Wetland Carbon Science, Practice and Policy [Windham-Myers, L., S. Crooks and T.G. Troxler (eds.)]. Taylor and Francis Group, United States. ISBN: 978-1-4987-6909-9.
  789. Keller, K.M., F. Joos and C.C. Raible, 2014b: Time of emergence of trends in ocean biogeochemistry. Biogeosciences, 11(13), 3647–3659, doi:10.5194/bg-11-3647-2014.
  790. Kelleway, J.J. et al., 2017a: Review of the ecosystem service implications of mangrove encroachment into salt marshes. Global Change Biol. 23(10), 3967-3983.
  791. Kelleway, J.J. et al., 2017b: Geochemical analyses reveal the importance of environmental history for blue carbon sequestration. J. Geophys. Res-Biogeo., 122(7), 1789–1805, doi:10.1002/2017JG003775.
  792. Kelleway, J.J. et al., 2016: Seventy years of continuous encroachment substantially increases ‘blue carbon’ capacity as mangroves replace intertidal salt marshes. Glob Chang Biol, 22(3), 1097–109, doi:10.1111/gcb.13158.
  793. Kelly, R.P. et al., 2011: Mitigating Local Causes of Ocean Acidification with Existing Laws. Science, 332(6033), 1036.
  794. Kempener, R. and F. Neumann, 2014a: Salinity gradient energy—technology brief. IRENA Ocean Energy Technology, 4.
  795. Kempener, R. and F. Neumann, 2014b: Tidal Energy: Technology Brief. International Renewable Energy Agency (IRENA). Abu Dhabi.
  796. Kench, P.S., M.R. Ford and S.D. Owen, 2018: Patterns of island change and persistence offer alternate adaptation pathways for atoll nations. Nat. Commun., 9(1), 605, doi:10.1038/s41467-018-02954-1.
  797. Kennedy, H. et al., 2010: Seagrass sediments as a global carbon sink: Isotopic constraints. Global Biogeochem. Cy., 24(4), n/a–n/a, doi:10.1029/2010GB003848.
  798. Kennedy, H., J.W. Fourqurean and S. Papadimitriou, 2018: The Calcium Carbonate Cycle in Seagrass Ecosystems. In: A Blue Carbon Primer. CRC Press, pp. 107–119.
  799. Keskin, C. and D. Pauly, 2014: Changes in the ‘Mean Temperature of the Catch’: application of a new concept to the North-eastern Aegean Sea. Acta Adriatica: international journal of Marine Sciences, 55(2), 213–218.
  800. Kettle, N.P. et al., 2014: Integrating scientific and local knowledge to inform risk-based management approaches for climate adaptation. Clim. Risk Manage., 4–5, 17–31, doi:10.1016/j.crm.2014.07.001.
  801. Keul, N., G. Langer, L.J. de Nooijer and J. Bijma, 2013: Effect of ocean acidification on the benthic foraminifera Ammonia sp. is caused by a decrease in carbonate ion concentration. Biogeosciences, 10(10), 6185–6198, doi:10.5194/bg-10-6185-2013.
  802. Khamis, Z.A., R. Kalliola and N. Käyhkö, 2017: Geographical characterization of the Zanzibar coastal zone and its management perspectives. Ocean Coast. Manage., 149, 116–134, doi:10.1016/j.ocecoaman.2017.10.003.
  803. Khanna, N., J.A. Godbold, W.E.N. Austin and D.M. Paterson, 2013: The Impact of Ocean Acidification on the Functional Morphology of Foraminifera. PLoS One, 8(12), e83118, doi:10.1371/journal.pone.0083118.
  804. Kharouba, H.M. et al., 2018: Global shifts in the phenological synchrony of species interactions over recent decades. PNAS, 115(20), 5211, doi:10.1073/pnas.1714511115.
  805. Khatiwala, S. et al., 2013: Global ocean storage of anthropogenic carbon. Biogeosciences, 10(4), 2169–2191.
  806. Kim, I.-N. et al., 2014: Increasing anthropogenic nitrogen in the North Pacific Ocean. Science, 346(6213), 1102, doi:10.1126/science.1258396.
  807. Kim, R.E., 2012: Is a New Multilateral Environmental Agreement on Ocean Acidification Necessary? Review of European Community & International Environmental Law, 21(3), 243–258, doi:10.1111/reel.12000.x.
  808. Kim, T.-W. et al., 2011: Increasing N Abundance in the Northwestern Pacific Ocean Due to Atmospheric Nitrogen Deposition. Science, 334(6055), 505, doi:10.1126/science.1206583.
  809. Kirk, M.D. et al., 2015: World Health Organization estimates of the global and regional disease burden of 22 foodborne bacterial, protozoal, and viral diseases, 2010: a data synthesis. PLoS medicine, 12(12), e1001921.