@article{Aalto2016,
abstract = {Abstract Long-term time series of key climate variables with a relevant spatiotemporal resolution are essential for environmental science. Moreover, such spatially continuous data, based on weather observations, are commonly used in, e.g., downscaling and bias correcting of climate model simulations. Here we conducted a comprehensive spatial interpolation scheme where seven climate variables (daily mean, maximum, and minimum surface air temperatures, daily precipitation sum, relative humidity, sea level air pressure, and snow depth) were interpolated over Finland at the spatial resolution of 10???10?km2. More precisely, (1) we produced daily gridded time series (FMI{\_}ClimGrid) of the variables covering the period of 1961?2010, with a special focus on evaluation and permutation-based uncertainty estimates, and (2) we investigated temporal trends in the climate variables based on the gridded data. National climate station observations were supplemented by records from the surrounding countries, and kriging interpolation was applied to account for topography and water bodies. For daily precipitation sum and snow depth, a two-stage interpolation with a binary classifier was deployed for an accurate delineation of areas with no precipitation or snow. A robust cross-validation indicated a good agreement between the observed and interpolated values especially for the temperature variables and air pressure, although the effect of seasons was evident. Permutation-based analysis suggested increased uncertainty toward northern areas, thus identifying regions with suboptimal station density. Finally, several variables had a statistically significant trend indicating a clear but locally varying signal of climate change during the last five decades.},
author = {Aalto, Juha and Pirinen, Pentti and Jylh{\"{a}}, Kirsti},
doi = {10.1002/2015JD024651},
journal = {Journal of Geophysical Research: Atmospheres},
month = {apr},
number = {8},
pages = {3807--3823},
publisher = {Wiley-Blackwell},
title = {{New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate}},
volume = {121},
year = {2016}
}
@article{Ablain2019,
author = {Ablain, Micha{\"{e}}l and Meyssignac, Beno{\^{i}}t and Zawadzki, Lionel and Jugier, R{\'{e}}mi and Ribes, Aur{\'{e}}lien and Spada, Giorgio and Benveniste, Jer{\^{o}}me and Cazenave, Anny and Picot, Nicolas},
doi = {10.5194/essd-11-1189-2019},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {aug},
number = {3},
pages = {1189--1202},
title = {{Uncertainty in satellite estimates of global mean sea-level changes, trend and acceleration}},
volume = {11},
year = {2019}
}
@article{Adler2018,
abstract = {The new Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly analysis is described in terms of changes made to improve the homogeneity of the product, especially after 2002. These changes include corrections to cross-calibration of satellite data inputs and updates to the gauge analysis. Over-ocean changes starting in 2003 resulted in an overall precipitation increase of 1.8{\%} after 2009. Updating the gauge analysis to its final, high-quality version increases the global land total by 1.8{\%} for the post-2002 period. These changes correct a small, incorrect dip in the estimated global precipitation over the last decade given by the earlier Version 2.2. The GPCP analysis is also used to describe global precipitation in 2017. The general La Ni{\~{n}}a pattern for 2017 is noted and the evolution from the early 2016 El Ni{\~{n}}o pattern is described. The 2017 global value is one of the highest for the 1979–2017 period, exceeded only by 2016 and 1998 (both El Ni{\~{n}}o years), and reinforces the small positive trend. Results for 2017 also reinforce significant trends in precipitation intensity (on a monthly scale) in the tropics. These results for 2017 indicate the value of the GPCP analysis, in addition to research, for climate monitoring.},
author = {Adler, Robert F. and Sapiano, Mathew R.P. and Huffman, George J. and Wang, Jian Jian and Gu, Guojun and Bolvin, David and Chiu, Long and Schneider, Udo and Becker, Andreas and Nelkin, Eric and Xie, Pingping and Ferraro, Ralph and Shin, Dong Bin},
doi = {10.3390/atmos9040138},
issn = {20734433},
journal = {Atmosphere},
number = {4},
pages = {138},
pmid = {30013797},
title = {{The Global Precipitation Climatology Project (GPCP) monthly analysis (New Version 2.3) and a review of 2017 global precipitation}},
volume = {9},
year = {2018}
}
@article{Allan2006,
abstract = {An upgraded version of the Hadley Centre's monthly historical mean sea level pressure (MSLP) dataset (HadSLP2) is presented. HadSLP2 covers the period from 1850 to date, and is based on numerous terres- trial and marine data compilations. Each terrestrial pressure series used in HadSLP2 underwent a series of quality control tests, and erroneous or suspect values were either corrected, where possible, or removed. Marine observations from the International Comprehensive Ocean Atmosphere Data Set were quality controlled (assessed against climatology and near neighbors) and then gridded. The final gridded form of HadSLP2 was created by blending together the processed terrestrial and gridded marine MSLP data. MSLP fields were made spatially complete using reduced-space optimal interpolation. Gridpoint error estimates were also produced. HadSLP2 was found to have generally stronger subtropical anticyclones and higher-latitude features across the Northern Hemisphere than an earlier product (HadSLP1). During the austral winter, however, it appears that the pressures in the southern Atlantic and Indian Ocean midlatitude regions are too high; this is seen in comparisons with both HadSLP1 and the 40-yrECMWFRe-Analysis (ERA-40). Over regions of high altitude, HadSLP2 and ERA-40 showed consistent differences suggestive of potential biases in the reanalysis model, though the region over the Himalayas in HadSLP2 is biased compared with HadSLP1 and improvements are required in this region. Consistent differences were also observed in regions of sparse data, particularly over the higher latitudes of the Southern Ocean and in the southeastern Pacific. Unlike the earlier HadSLP1 product, error estimates are available with HadSLP2 to guide the user in these regions of low confidence. An evaluation of major phenomena in the climate system using HadSLP2 provided further validation of the dataset. Important climatic features/indices such as the North Atlantic Oscillation, Arctic Oscillation, North Pacific index, Southern Oscillation index, Trans-Polar index, Antarctic Oscillation, Antarctic Cir- cumpolar Wave, East Asian Summer Monsoon index, and the Siberian High index have all been resolved in HadSLP2, with extensions back to the mid-nineteenth century.},
author = {Allan, Rob and Ansell, Tara},
doi = {10.1175/JCLI3937.1},
issn = {08948755},
journal = {Journal of Climate},
number = {22},
pages = {5816--5842},
title = {{A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850–2004}},
volume = {19},
year = {2006}
}
@article{Allan2014,
abstract = {Combining satellite data, atmospheric reanalyses, and climate model simulations, variability in the net downward radiative flux imbalance at the top of Earth's atmosphere (N) is reconstructed and linked to recent climate change. Over the 1985-1999 period mean N (0.34 ± 0.67 Wm(-2)) is lower than for the 2000-2012 period (0.62 ± 0.43 Wm(-2), uncertainties at 90{\%} confidence level) despite the slower rate of surface temperature rise since 2000. While the precise magnitude of N remains uncertain, the reconstruction captures interannual variability which is dominated by the eruption of Mount Pinatubo in 1991 and the El Ni{\~{n}}o Southern Oscillation. Monthly deseasonalized interannual variability in N generated by an ensemble of nine climate model simulations using prescribed sea surface temperature and radiative forcings and from the satellite-based reconstruction is significantly correlated (r∼0.6) over the 1985-2012 period.},
author = {Allan, Richard P. and Liu, Chunlei and Loeb, Norman G. and Palmer, Matthew D. and Roberts, Malcolm and Smith, Doug and Vidale, Pier Luigi},
doi = {10.1002/2014GL060962},
isbn = {00948276},
issn = {19448007},
journal = {Geophysical Research Letters},
keywords = {climate model,climate variability,energy balance,radiative flux,satellite data,temperature},
month = {aug},
number = {15},
pages = {5588--5597},
pmid = {25821270},
publisher = {Wiley-Blackwell},
title = {{Changes in global net radiative imbalance 1985–2012}},
url = {http://doi.wiley.com/10.1002/2014GL060962},
volume = {41},
year = {2014}
}
@misc{Andersson2017,
author = {Andersson, A. and Graw, K. and Schr{\"{o}}der, M. and Fennig, K. and Liman, J. and Bakan, S. and Hollmann, R. and Klepp, C.},
doi = {10.5676/EUM_SAF_CM/HOAPS/V002},
publisher = {Satellite Application Facility on Climate Monitoring},
title = {{Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data - HOAPS 4.0}},
year = {2017}
}
@article{essd-2-215-2010,
author = {Andersson, A and Fennig, K and Klepp, C and Bakan, S and Gra{\ss}l, H and Schulz, J},
doi = {10.5194/essd-2-215-2010},
journal = {Earth System Science Data},
number = {2},
pages = {215--234},
title = {{The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3}},
url = {https://www.earth-syst-sci-data.net/2/215/2010/},
volume = {2},
year = {2010}
}
@article{Angerer2017,
author = {Angerer, B and Ladst{\"{a}}dter, F and Scherllin-Pirscher, B and Schw{\"{a}}rz, M and Steiner, A K and Foelsche, U and Kirchengast, G},
doi = {10.5194/amt-10-4845-2017},
journal = {Atmospheric Measurement Techniques},
number = {12},
pages = {4845--4863},
title = {{Quality aspects of the Wegener Center multi-satellite GPS radio occultation record OPSv5.6}},
volume = {10},
year = {2017}
}
@article{Aono2010,
abstract = {We investigated documents and diaries from the ninth to the fourteenth centuries to supplement the phenological data series of the flowering of Japanese cherry (Prunus jamasakura) in Kyoto, Japan, to improve and fill gaps in temperature estimates based on previously reported phenological data. We then reconstructed a nearly continuous series of March mean temperatures based on 224 years of cherry flowering data, including 51 years of previously unused data, to clarify springtime climate changes. We also attempted to estimate cherry full-flowering dates from phenological records of other deciduous species, adding further data for 6 years in the tenth and eleventh centuries by using the flowering phenology of Japanese wisteria (Wisteria floribunda). The reconstructed tenth century March mean temperatures were around 7°C, indicating warmer conditions than at present. Temperatures then fell until the 1180s, recovered gradually until the 1310s, and then declined again in the mid-fourteenth century. {\textcopyright} 2009 ISB.},
author = {Aono, Yasuyuki and Saito, Shizuka},
doi = {10.1007/s00484-009-0272-x},
issn = {00207128},
journal = {International Journal of Biometeorology},
keywords = {Cherry blossom,Climatic reconstruction,Kyoto,Phenology,Wisteria flower},
number = {2},
pages = {211--219},
pmid = {19851790},
title = {{Clarifying springtime temperature reconstructions of the medieval period by gap-filling the cherry blossom phenological data series at Kyoto, Japan}},
volume = {54},
year = {2010}
}
@article{Aryee2018,
abstract = {ABSTRACT Various sectors of the country's economy ? agriculture, health, energy, among others ? largely depend on climate information, hence availability of quality climate data is very essential for climate-impact studies in these sectors. In this paper, a monthly rainfall database (GMet v1.0) has been developed at a 0.5°???0.5° spatial resolution, from 113 Ghana Meteorological Agency (GMet) gauge network distributed across the four agro-ecological zones of Ghana, and spanning a 23-year period (1990?2012). The datasets were first homogenized with quantile-matching adjustments and thereafter, gridded at a spatial resolution of 0.5°???0.5° using Minimum Surface Curvature with tensioning parameter, allowing for comprehensive spatial fields assessment on the developed dataset. Afterwards, point-pixel validation was performed using GMet v1.0 against gauge data from stations that were earlier excluded due to large datagaps. This proved the reliability of GMet v1.0, with high and statistically significant correlations at 99{\%} confidence level, and relatively low biases and rmse. Furthermore, GMet v1.0 was compared with GPCC and TRMM rainfall estimates, with both products found to adequately mimick GMet v1.0, with high correlations which are significant at 99{\%} confidence level, low biases and rmse. In addition, the ratio of 90th ? percentile provided fairly similar capture of extremes by both TRMM and GPCC, in relation to GMet v1.0. Finally, based on annual rainfall totals and monthly variability, k-means cluster analysis was performed on GMet v1.0, which delineated the country into four distinct climatic zones. The developed rainfall data, when officially released, will be a useful product for climate impact and further rainfall validation studies in Ghana.},
author = {Aryee, J. N. A. and Amekudzi, L. K. and Quansah, E and Klutse, N. A. B. and Atiah, W. A. and Yorke, C},
doi = {10.1002/joc.5238},
journal = {International Journal of Climatology},
month = {aug},
number = {3},
pages = {1201--1215},
publisher = {Wiley-Blackwell},
title = {{Development of high spatial resolution rainfall data for Ghana}},
volume = {38},
year = {2018}
}
@article{Ashouri2015,
abstract = {A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales.},
author = {Ashouri, Hamed and Hsu, Kuo-Lin and Sorooshian, Soroosh and Braithwaite, Dan K. and Knapp, Kenneth R and Cecil, L Dewayne and Nelson, Brian R and Prat, Olivier P},
doi = {10.1175/BAMS-D-13-00068.1},
issn = {0003-0007},
journal = {Bulletin of the American Meteorological Society},
month = {jan},
number = {1},
pages = {69--83},
title = {{PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies}},
url = {https://journals.ametsoc.org/doi/10.1175/BAMS-D-13-00068.1},
volume = {96},
year = {2015}
}
@article{Atlas2011,
author = {Atlas, Robert and Hoffman, Ross N. and Ardizzone, Joseph and Leidner, S. Mark and Jusem, Juan Carlos and Smith, Deborah K. and Gombos, Daniel},
doi = {10.1175/2010BAMS2946.1},
issn = {0003-0007},
journal = {Bulletin of the American Meteorological Society},
month = {feb},
number = {2},
pages = {157--174},
title = {{A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications}},
url = {http://journals.ametsoc.org/doi/10.1175/2010BAMS2946.1},
volume = {92},
year = {2011}
}
@article{Bakker2016,
author = {Bakker, D C E and Pfeil, B and Landa, C S and Metzl, N and O'Brien, K M and Olsen, A and Smith, K and Cosca, C and Harasawa, S and Jones, S D and Nakaoka, S.-I. and Nojiri, Y and Schuster, U and Steinhoff, T and Sweeney, C and Takahashi, T and Tilbrook, B and Wada, C and Wanninkhof, R and Alin, S R and Balestrini, C F and Barbero, L and Bates, N R and Bianchi, A A and Bonou, F and Boutin, J and Bozec, Y and Burger, E F and Cai, W.-J. and Castle, R D and Chen, L and Chierici, M and Currie, K and Evans, W and Featherstone, C and Feely, R A and Fransson, A and Goyet, C and Greenwood, N and Gregor, L and Hankin, S and Hardman-Mountford, N J and Harlay, J and Hauck, J and Hoppema, M and Humphreys, M P and Hunt, C W and Huss, B and Ib{\'{a}}nhez, J S P and Johannessen, T and Keeling, R and Kitidis, V and K{\"{o}}rtzinger, A and Kozyr, A and Krasakopoulou, E and Kuwata, A and Landsch{\"{u}}tzer, P and Lauvset, S K and Lef{\`{e}}vre, N and {Lo Monaco}, C and Manke, A and Mathis, J T and Merlivat, L and Millero, F J and Monteiro, P M S and Munro, D R and Murata, A and Newberger, T and Omar, A M and Ono, T and Paterson, K and Pearce, D and Pierrot, D and Robbins, L L and Saito, S and Salisbury, J and Schlitzer, R and Schneider, B and Schweitzer, R and Sieger, R and Skjelvan, I and Sullivan, K F and Sutherland, S C and Sutton, A J and Tadokoro, K and Telszewski, M and Tuma, M and van Heuven, S M A C and Vandemark, D and Ward, B and Watson, A J and Xu, S},
doi = {10.5194/essd-8-383-2016},
journal = {Earth System Science Data},
number = {2},
pages = {383--413},
title = {{A multi-decade record of high-quality CO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)}},
volume = {8},
year = {2016}
}
@article{Balsamo2015,
abstract = {{\textless}p{\textgreater}ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models.{\textless}/p{\textgreater}},
author = {Balsamo, G and Albergel, C and Beljaars, A and Boussetta, S and Brun, E and Cloke, H and Dee, D and Dutra, E and Mun{\~{o}}z-Sabater, J and Pappenberger, F and {De Rosnay}, P and Stockdale, T and Vitart, F},
doi = {10.5194/hess-19-389-2015},
isbn = {1607-7938},
issn = {16077938},
journal = {Hydrology and Earth System Sciences},
number = {1},
pages = {389--407},
title = {{ERA-Interim/Land: A global land surface reanalysis data set}},
volume = {19},
year = {2015}
}
@article{Bamber_2018,
abstract = {Since 1992, there has been a revolution in our ability to quantify the land ice contribution to sea level rise using a variety of satellite missions and technologies. Each mission has provided unique, but sometimes conflicting, insights into the mass trends of land ice. Over the last decade, over fifty estimates of land ice trends have been published, providing a confusing and often inconsistent picture. The IPCC Fifth Assessment Report (AR5) attempted to synthesise estimates published up to early 2013. Since then, considerable advances have been made in understanding the origin of the inconsistencies, reducing uncertainties in estimates and extending time series. We assess and synthesise results published, primarily, since the AR5, to produce a consistent estimate of land ice mass trends during the satellite era (1992–2016). We combine observations from multiple missions and approaches including sea level budget analyses. Our resulting synthesis is both consistent and rigorous, drawing on (i) the published literature, (ii) expert assessment of that literature, and (iii) a new analysis of Arctic glacier and ice cap trends combined with statistical modelling.
We present annual and pentad (five-year mean) time series for the East, West Antarctic and Greenland Ice Sheets and glaciers separately and combined. When averaged over pentads, covering the entire period considered, we obtain a monotonic trend in mass contribution to the oceans, increasing from 0.31 ± 0.35 mm of sea level equivalent for 1992–1996 to 1.85 ± 0.13 for 2012–2016. Our integrated land ice trend is lower than many estimates of GRACE-derived ocean mass change for the same periods. This is due, in part, to a smaller estimate for glacier and ice cap mass trends compared to previous assessments. We discuss this, and other likely reasons, for the difference between GRACE ocean mass and land ice trends.},
author = {Bamber, Jonathan L and Westaway, Richard M and Marzeion, Ben and Wouters, Bert},
doi = {10.1088/1748-9326/aac2f0},
journal = {Environmental Research Letters},
month = {jun},
number = {6},
pages = {63008},
publisher = {{\{}IOP{\}} Publishing},
title = {{The land ice contribution to sea level during the satellite era}},
url = {https://doi.org/10.1088{\%}2F1748-9326{\%}2Faac2f0},
volume = {13},
year = {2018}
}
@article{Banzon2016,
author = {Banzon, V and Smith, T M and Chin, T M and Liu, C and Hankins, W},
doi = {10.5194/essd-8-165-2016},
journal = {Earth System Science Data},
number = {1},
pages = {165--176},
title = {{A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies}},
volume = {8},
year = {2016}
}
@article{Barbarossa2018,
abstract = {FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015},
author = {Barbarossa, Valerio and Huijbregts, Mark A.J. and Beusen, Arthur H.W. and Beck, Hylke E. and King, Henry and Schipper, Aafke M.},
doi = {10.1038/sdata.2018.52},
issn = {20524463},
journal = {Scientific Data},
number = {February},
pages = {1--11},
title = {{Data Descriptor: FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015}},
volume = {5},
year = {2018}
}
@article{Bates2020,
abstract = {Ocean chemical and physical conditions are changing. Here we show decadal variability and recent acceleration of surface warming, salinification, deoxygenation, carbon dioxide (CO2) and acidification in the subtropical North Atlantic Ocean (Bermuda Atlantic Time-series Study site; 1980s to present). Surface temperatures and salinity exhibited interdecadal variability, increased by {\~{}}0.85 °C (with recent warming of 1.2 °C) and 0.12, respectively, while dissolved oxygen levels decreased by {\~{}}8{\%} ({\~{}}2{\%} per decade). Concurrently, seawater DIC, fCO2 (fugacity of CO2) and anthropogenic CO2 increased by {\~{}}8{\%}, 22{\%}, and 72{\%} respectively. The winter versus summer fCO2 difference increased by 4 to 8 µatm decade−1 due to seasonally divergent thermal and alkalinity changes. Ocean pH declined by 0.07 ({\~{}}17{\%} increase in acidity) and other acidification indicators by {\~{}}10{\%}. Over the past nearly forty years, the highest increase in ocean CO2 and ocean acidification occurred during decades of weakest atmospheric CO2 growth and vice versa.},
author = {Bates, Nicholas Robert and Johnson, Rodney J},
doi = {10.1038/s43247-020-00030-5},
issn = {2662-4435},
journal = {Communications Earth {\&} Environment},
number = {1},
pages = {33},
title = {{Acceleration of ocean warming, salinification, deoxygenation and acidification in the surface subtropical North Atlantic Ocean}},
volume = {1},
year = {2020}
}
@article{article,
annote = {Sustained observations provide critically needed data and understanding not only about ocean warming and water cycle reorganization (e.g., salinity changes), ocean eutrophication, and ocean deoxygenation, but also about changes in ocean chemistry. As an example of changes in the global ocean carbon cycle, consistent changes in surface seawater CO2-carbonate chemistry are documented by seven independent CO2 time series that provide sustained ocean observations collected for periods from 15 to 30 years: (1) Iceland Sea, (2) Irminger Sea, (3) Bermuda Atlantic Time-series Study (BATS), (4) European Station for Time series in the Ocean at the Canary Islands (ESTOC), (5) CArbon Retention In A Colored Ocean sites in the North Atlantic (CARIACO), (6) Hawaii Ocean Time-series (HOT), and (7) Munida in the Pacific Ocean. These ocean time-series sites exhibit very consistent changes in surface ocean chemistry that reflect the impact of uptake of anthropogenic CO2 and ocean acidification. The article discusses the long-term changes in dissolved inorganic carbon (DIC), salinity-normalized DIC, and surface seawater pCO2 (partial pressure of CO2) due to the uptake of anthropogenic CO2 and its impact on the ocean�s buffering capacity. In addition, we evaluate changes in seawater chemistry that are due to ocean acidification and its impact on pH and saturation states for biogenic calcium carbonate minerals.},
author = {Bates, Nicholas R. and Astor, Yrene M. and Church, Matthew J. and Currie, Kim and Dore, John E. and Gonz{\'{a}}lez-D{\'{a}}vila, Melchor and Lorenzoni, Laura and Muller-Karger, Frank and Olafsson, Jon and Santana-Casiano, J. Magdalena},
doi = {10.5670/oceanog.2014.16},
issn = {10428275},
journal = {Oceanography},
month = {mar},
number = {1},
pages = {126--141},
title = {{A Time-Series View of Changing Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO2 and Ocean Acidification}},
url = {https://doi.org/10.5670/oceanog.2014.16 https://tos.org/oceanography/article/a-time-series-view-of-changing-ocean-chemistry-due-to-ocean-uptake-ofanthro},
volume = {27},
year = {2014}
}
@article{hess-21-589-2017,
author = {Beck, H E and van Dijk, A I J M and Levizzani, V and Schellekens, J and Miralles, D G and Martens, B and de Roo, A},
doi = {10.5194/hess-21-589-2017},
journal = {Hydrology and Earth System Sciences},
number = {1},
pages = {589--615},
title = {{MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data}},
url = {https://www.hydrol-earth-syst-sci.net/21/589/2017/},
volume = {21},
year = {2017}
}
@article{Becker2013,
abstract = {The availability of highly accessible and reliable monthly gridded data sets of global land-surface precipitation is a need that was already identified in the mid-1980s when there was a complete lack of globally homogeneous gauge-based precipitation analyses. Since 1989, the Global Precipitation Climatology Centre (GPCC) has built up its unique capacity to assemble, quality assure, and analyse rain gauge data gathered from all over theworld. The resulting database has exceeded 200 yr in temporal coverage and has acquired data from more than 85 000 stations worldwide. Based on this database, this paper provides the reference publication for the four globally gridded monthly precipitation products of the GPCC, covering a 111-yr analysis period from 1901–present. As required for a reference publication, the content of the product portfolio, as well as the underlying methodologies to process and interpolate are detailed. Moreover, we provide information on the systematic and statistical errors associated with the data products. Finally, sample applications provide poten- tial users of GPCC data products with suitable advice on capabilities and constraints of the gridded data sets. In doing so, the capabilities to access El Ni˜ no–Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) sensitive precipitation regions and to perform trend analyses across the past 110 yr are demonstrated. The four gridded products, i.e. the Climatology (CLIM) V2011, the Full Data Reanalysis (FD) V6, the Monitoring Product (MP) V4, and the First Guess Product (FG), are publicly available on easily accessible latitude/longitude grids encoded in zipped clear text ASCII files for subsequent visualization and download through the GPCC download gate hosted on ftp://ftp.dwd.de/pub/data/gpcc/html/download gate.html by the Deutscher Wetterdienst (DWD), Offenbach, Germany. Depending on the product, four (0.25◦, 0.5◦, 1.0◦, 2.5◦ for CLIM), three (0.5◦, 1.0◦, 2.5◦, for FD), two (1.0◦, 2.5◦ for MP) or one (1.0◦ for FG) resolution is provided, and for each product a DOI reference is provided allowing for public user access to the products. A preliminary description of the scope of a fifth product – the Homogenized Precipitation Analysis (HOMPRA) – is also provided. Its comprehensive description will be submitted later in an extra paper upon completion of this data product},
author = {Becker, A. and Finger, P. and Meyer-Christoffer, A. and Rudolf, B. and Schamm, K. and Schneider, U. and Ziese, M.},
doi = {10.5194/essd-5-71-2013},
isbn = {1866-3516},
issn = {18663508},
journal = {Earth System Science Data},
number = {1},
pages = {71--99},
title = {{A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present}},
volume = {5},
year = {2013}
}
@misc{Beckley2016,
address = {CA, USA},
author = {Beckley, B.; and Zelensky, N.P.; and Holmes, S.A.; and Lemoine, F.G.; and Ray, R.D.; and Mitchum, G.T.; and Desai, S.; and Brown, S.T.},
doi = {10.5067/GMSLM-TJ142},
publisher = {PO.DAAC},
title = {{Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon Jason-1 and OSTM/Jason-2 Version 4.2}},
url = {https://doi.org/10.5067/GMSLM-TJ142},
year = {2016}
}
@article{BENTAMY2017196,
abstract = {For over a decade, several research groups have been developing air-sea heat flux information over the global ocean, including latent (LHF) and sensible (SHF) heat fluxes over the global ocean. This paper aims to provide new insight into the quality and error characteristics of turbulent heat flux estimates at various spatial and temporal scales (from daily upwards). The study is performed within the European Space Agency (ESA) Ocean Heat Flux (OHF) project. One of the main objectives of the OHF project is to meet the recommendations and requirements expressed by various international programs such as the World Research Climate Program (WCRP) and Climate and Ocean Variability, Predictability, and Change (CLIVAR), recognizing the need for better characterization of existing flux errors with respect to the input bulk variables (e.g. surface wind, air and sea surface temperatures, air and surface specific humidities), and to the atmospheric and oceanic conditions (e.g. wind conditions and sea state). The analysis is based on the use of daily averaged LHF and SHF and the associated bulk variables derived from major satellite-based and atmospheric reanalysis products. Inter-comparisons of heat flux products indicate that all of them exhibit similar space and time patterns. However, they also reveal significant differences in magnitude in some specific regions such as the western ocean boundaries during the Northern Hemisphere winter season, and the high southern latitudes. The differences tend to be closely related to large differences in surface wind speed and/or specific air humidity (for LHF) and to air and sea temperature differences (for SHF). Further quality investigations are performed through comprehensive comparisons with daily-averaged LHF and SHF estimated from moorings. The resulting statistics are used to assess the error of each OHF product. Consideration of error correlation between products and observations (e.g., by their assimilation) is also given. This reveals generally high noise variance in all products and a weak signal in common with in situ observations, with some products only slightly better than others. The OHF LHF and SHF products, and their associated error characteristics, are used to compute daily OHF multiproduct-ensemble (OHF/MPE) estimates of LHF and SHF over the ice-free global ocean on a 0.25°×0.25° grid. The accuracy of this heat multiproduct, determined from comparisons with mooring data, is greater than for any individual product. It is used as a reference for the anomaly characterization of each individual OHF product.},
author = {Bentamy, A and Pioll{\'{e}}, J F and Grouazel, A and Danielson, R and Gulev, S and Paul, F and Azelmat, H and Mathieu, P P and von Schuckmann, K and Sathyendranath, S and Evers-King, H and Esau, I and Johannessen, J A and Clayson, C A and Pinker, R T and Grodsky, S A and Bourassa, M and Smith, S R and Haines, K and Valdivieso, M and Merchant, C J and Chapron, B and Anderson, A and Hollmann, R and Josey, S A},
doi = {https://doi.org/10.1016/j.rse.2017.08.016},
issn = {0034-4257},
journal = {Remote Sensing of Environment},
keywords = {Latent heat flux,Ocean Heat Flux,Ocean heat content,OceanSites,Remotely sensed data,Scatterometer,Sensible heat flux,Specfic air humidity,Surface wind},
pages = {196--218},
title = {{Review and assessment of latent and sensible heat flux accuracy over the global oceans}},
url = {http://www.sciencedirect.com/science/article/pii/S0034425717303826},
volume = {201},
year = {2017}
}
@article{Berry2011,
author = {Berry, David I and Kent, Elizabeth C},
doi = {10.1002/joc.2059},
journal = {International Journal of Climatology},
pages = {987--1001},
title = {{Air–Sea fluxes from ICOADS : the construction of a new gridded dataset with uncertainty estimates}},
volume = {31},
year = {2011}
}
@article{Blazquez2018,
abstract = {Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission provide quantitative estimates of the globalwater budget components. However, these estimates are uncertain as they showdiscrepancieswhen different parameters are used in the processing of the GRACE data.We examine trends in ocean mass, ice loss from Antarctica, Greenland, arctic islands and trends in water storage over land and glaciers from GRACE data (2005-2015) and explore the associated uncertainty. We consider variations in six different GRACE processing parameters, namely the processing centre of the raw GRACE solutions, the geocentre motion, the Earth oblateness, the filtering, the leakage correction and the glacial isostatic adjustment (GIA). Considering all possible combinations of the different processing parameters leads to an ensemble of 1500 post-processed GRACE solutions, which is assumed to cover a significant part of the uncertainty range of GRACE estimates. The ensemble-mean trend in all global water budget components agree within uncertaintieswith previous estimates based on different sources of observations. The uncertainty in the globalwater budget is±0.27mmyr-1 [at the 90 per cent confidence level (CL)] over 2005-2015. We find that the uncertainty in the geocentre motion and GIA corrections dominate the uncertainty in GRACE estimate of the global water budget. Their contribution to the uncertainty in GRACE estimate is respectively ±0.21 and ±0.12 mm yr-1 (90 per cent CL). This uncertainty in GRACE estimate implies an uncertainty in the net warming of the ocean and the Earth energy budget of ±0.25Wm-2 (90 per cent CL) when inferred using the sea level budget approach.},
author = {Blazquez, A and Meyssignac, B and Lemoine, J M and Berthier, E and Ribes, A and Cazenave, A},
doi = {10.1093/gji/ggy293},
issn = {1365246X},
journal = {Geophysical Journal International},
number = {1},
pages = {415--430},
publisher = {Oxford University Press},
title = {{Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface: Implications for the global water and sea level budgets}},
volume = {215},
year = {2018}
}
@article{Bliznak2018,
abstract = {Mean and absolute seasonal precipitation maxima of short-term totals (up to 1?hour and 6?hours, respectively) are detected in altitudes between 300 and 600?m a. s. l. Maximum frequency of mean warm-season precipitation occurs two hours earlier in the mountains, whereas mean totals remain at the same level until 2100 UTC. The mean time during which precipitation maxima occur generally does not change with altitude.},
author = {Bli{\v{z}}ň{\'{a}}k, Vojt{\v{e}}ch and Ka{\v{s}}par, Marek and M{\"{u}}ller, Miloslav},
doi = {10.1002/joc.5202},
journal = {International Journal of Climatology},
month = {jul},
number = {2},
pages = {677--691},
publisher = {Wiley-Blackwell},
title = {{Radar-based summer precipitation climatology of the Czech Republic}},
volume = {38},
year = {2018}
}
@incollection{Braesicke2018,
address = {Geneva, Switzerland},
author = {Braesicke, A P. and {Neu (Lead Authors)}, J. and Fioletov, V. and Godin-Beekman, S. and Hubert, D. and Petropavlovskikh, I. and Shiotani, M. and {B.-M. Sinnhuber}},
booktitle = {Scientific Assessment of Ozone Depletion: 2018},
pages = {3.1--3.74},
publisher = {World Meteorological Organization (WMO)},
series = {Global Ozone Research and Monitoring Project – Report No. 58},
title = {{Update on Global Ozone: Past, Present and Future}},
url = {https://csl.noaa.gov/assessments/ozone/2018/downloads/},
year = {2018}
}
@misc{Brown2002,
address = {Boulder, CO, USA},
author = {Brown, R. D.},
doi = {10.7265/N5V985Z6},
publisher = {National Snow {\&} Ice Data Center (NSIDC)},
title = {{Reconstructed North American, Eurasian, and Northern Hemisphere Snow Cover Extent, 1915–1997, Version 1}},
url = {https://doi.org/10.7265/N5V985Z6},
year = {2002}
}
@article{Brown2011,
author = {Brown, R. D. and Robinson, D. A.},
doi = {10.5194/tc-5-219-2011},
issn = {1994-0424},
journal = {The Cryosphere},
month = {mar},
number = {1},
pages = {219--229},
title = {{Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty}},
url = {http://www.the-cryosphere.net/5/219/2011/},
volume = {5},
year = {2011}
}
@article{Bulygina2014,
abstract = {The principles of information support for climate research in Russia are considered using the example of creating specialized data arrays. It describes the basic requirements and principles for creating climate databases implemented in Roshydromet in accordance with the Climate Doctrine. A brief description is given of specialized data arrays of the main climatic parameters of various time resolutions posted on the VNIIGMI-WDC website for free access.},
author = {Bulygina, O. N. and Korshunova, N. N. and Razuvaev, V. N.},
journal = {Trudy of VNIIGMI-WDC},
title = {{Specialized datasets for climate research [in Russian]}},
url = {http://meteo.ru/publications/125-trudy-vniigmi/trudy-vniigmi-mtsd-vypusk-177-2014-g/518-spetsializirovannye-massivy-dannykh-dlya-klimaticheskikh-issledovanij},
volume = {177},
year = {2014}
}
@article{Cabanes2013,
abstract = {The French program Coriolis, as part of the French operational oceanographic system, produces the COriolis dataset for Re-Analysis (CORA) on a yearly basis. This dataset contains in-situ temperature and salinity profiles from different data types. The latest release CORA3 covers the period 1990 to 2010. Several tests have been developed to ensure a homogeneous quality control of the dataset and to meet the requirements of the physical ocean reanalysis activities (assimilation and validation). Improved tests include some simple tests based on comparison with climatology and a model background check based on a global ocean reanalysis. Visual quality control is performed on all suspicious temperature and salinity profiles identified by the tests, and quality flags are modified in the dataset if necessary. In addition, improved diagnostic tools have been developed - including global ocean indicators - which give information on the quality of the CORA3 dataset and its potential applications. CORA3 is available on request through the MyOcean Service Desk (http://www.myocean.eu/). {\textcopyright} Author(s) 2013. CC Attribution 3.0 License.},
author = {Cabanes, C. and Grouazel, A. and {Von Schuckmann}, K. and Hamon, M. and Turpin, V. and Coatanoan, C. and Paris, F. and Guinehut, S. and Boone, C. and Ferry, N. and {De Boyer Mont{\'{e}}gut}, C. and Carval, T. and Reverdin, G. and Pouliquen, S. and {Le Traon}, P. Y.},
doi = {10.5194/os-9-1-2013},
issn = {18120784},
journal = {Ocean Science},
number = {1},
pages = {1--18},
title = {{The CORA dataset: Validation and diagnostics of in-situ ocean temperature and salinity measurements}},
volume = {9},
year = {2013}
}
@article{Caesar2006,
author = {Caesar, John and Alexander, Lisa and Vose, Russell},
doi = {10.1029/2005JD006280},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Atmospheres},
month = {mar},
number = {D5},
pages = {D05101},
publisher = {John Wiley {\&} Sons, Ltd},
title = {{Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set}},
volume = {111},
year = {2006}
}
@article{Callendar1938,
abstract = {Abstract By fuel combustion man has added about 150,000 million tons of carbon dioxide to the air during the past half century. The author estimates from the best available data that approximately three quarters of this has remained in the atmosphere. The radiation absorption coefficients of carbon dioxide and water vapour are used to show the effect of carbon dioxide on “sky radiation.” From this the increase in mean temperature, due to the artificial production of carbon dioxide, is estimated to be at the rate of 0.003°C. per year at the present time. The temperature observations a t zoo meteorological stations are used to show that world temperatures have actually increased at an average rate of 0.005°C. per year during the past half century.},
author = {Callendar, G S},
doi = {https://doi.org/10.1002/qj.49706427503},
journal = {Quarterly Journal of the Royal Meteorological Society},
number = {275},
pages = {223--240},
title = {{The artificial production of carbon dioxide and its influence on temperature}},
volume = {64},
year = {1938}
}
@article{Caluwaerts2020,
abstract = {As urban environments have a specific climate that poses extra challenges (e.g. increased heat stress during heat waves), gaining detailed insight into the urban climate is important. This paper presents the high-accuracy MOCCA (MOnitoring the City's Climate and Atmosphere) network, which is monitoring the urban climate of the city of Ghent since July 2016. The study illustrates the complementarity between modelling and observing the urban climate. Two different modelling approaches are used: 1 km resolution runs of the SURFEX land surface model and 100 m resolution runs of the computationally cheaper UrbClim boundary layer model. On the one hand, urban models are able to simulate the spatial variability of the urban climate. As such, these models serve as a tool to help deciding on the locations of the measurement stations. On the other hand, the MOCCA observations are used to validate the high-resolution urban model experiments for the summer (July-August-September) of 2016. Our results demonstrate that the models capture the nighttime intra-urban temperature differences, but they are not able to reproduce the observed daytime temperature differences which are determined by the micro-scale environment.},
author = {Caluwaerts, Steven and Hamdi, Rafiq and Top, Sara and Lauwaet, Dirk and Berckmans, Julie and Degrauwe, Daan and Dejonghe, Herwig and {De Ridder}, Koen and {De Troch}, Rozemien and Duch{\^{e}}ne, Francois and Maiheu, Bino and {Van Ginderachter}, Michiel and Verdonck, Marie-Leen and Vergauwen, Thomas and Wauters, Guy and Termonia, Piet},
doi = {https://doi.org/10.1016/j.uclim.2019.100565},
issn = {2212-0955},
journal = {Urban Climate},
pages = {100565},
title = {{The urban climate of Ghent, Belgium: A case study combining a high-accuracy monitoring network with numerical simulations}},
volume = {31},
year = {2020}
}
@article{Camera2014,
abstract = {Abstract High-resolution gridded daily data sets are essential for natural resource management and the analyses of climate changes and their effects. This study aims to evaluate the performance of 15 simple or complex interpolation techniques in reproducing daily precipitation at a resolution of 1?km2 over topographically complex areas. Methods are tested considering two different sets of observation densities and different rainfall amounts. We used rainfall data that were recorded at 74 and 145 observational stations, respectively, spread over the 5760?km2 of the Republic of Cyprus, in the Eastern Mediterranean. Regression analyses utilizing geographical copredictors and neighboring interpolation techniques were evaluated both in isolation and combined. Linear multiple regression (LMR) and geographically weighted regression methods (GWR) were tested. These included a step-wise selection of covariables, as well as inverse distance weighting (IDW), kriging, and 3D-thin plate splines (TPS). The relative rank of the different techniques changes with different station density and rainfall amounts. Our results indicate that TPS performs well for low station density and large-scale events and also when coupled with regression models. It performs poorly for high station density. The opposite is observed when using IDW. Simple IDW performs best for local events, while a combination of step-wise GWR and IDW proves to be the best method for large-scale events and high station density. This study indicates that the use of step-wise regression with a variable set of geographic parameters can improve the interpolation of large-scale events because it facilitates the representation of local climate dynamics.},
author = {Camera, Corrado and Bruggeman, Adriana and Hadjinicolaou, Panos and Pashiardis, Stelios and Lange, Manfred A},
doi = {10.1002/2013JD020611},
journal = {Journal of Geophysical Research: Atmospheres},
month = {dec},
number = {2},
pages = {693--712},
publisher = {Wiley-Blackwell},
title = {{Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980–2010}},
volume = {119},
year = {2014}
}
@misc{Cavalieri1996,
address = {Boulder, CO, USA},
author = {Cavalieri, D. J. and Parkinson, C. L. and Gloersen, P. and Zwally, H J},
doi = {10.5067/8GQ8LZQVL0VL},
publisher = {Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center},
title = {{Sea ice concentrations form Nimbus-7 SMMR and DMSP SSM/I passive microwave data, Version 1}},
url = {https://nsidc.org/data/NSIDC-0051/versions/1},
year = {1996}
}
@article{Chaney2014,
abstract = {AbstractAssessing changes in the frequency and intensity of extreme meteorological events and their impact on water resources, agriculture, and infrastructure is necessary to adequately prepare and adapt to future change. This is a challenge in data-sparse regions such as sub-Saharan Africa, where a lack of high-density and temporally consistent long-term in situ measurements complicates the analysis. To address this, a temporally homogenous and high-temporal- and high-spatial-resolution meteorological dataset is developed over sub-Saharan Africa (5°S?25°N), covering the time period between 1979 and 2005. It is developed by spatially downscaling the National Centers for Environmental Prediction?National Center for Atmospheric Research (NCEP?NCAR) reanalysis to a 0.1° spatial resolution, detecting and correcting for temporal inhomogeneities, and by removing random errors and biases by assimilating quality-controlled and gap-filled Global Summary of the Day (GSOD) in situ measurements. The dataset is then used to determine the statistical significance and magnitude of changes in climate extremes between 1979 and 2005. The results suggest a shift in the distribution of daily maximum and minimum temperatures toward a warmer mean with a faster increase in warm than cold events. Changes in the mean annual precipitation and heavy rainfall events are significant only in regions affected by the Sahel droughts of the 1970s and 1980s.},
author = {Chaney, Nathaniel W and Sheffield, Justin and Villarini, Gabriele and Wood, Eric F},
doi = {10.1175/JCLI-D-13-00423.1},
journal = {Journal of Climate},
month = {may},
number = {15},
pages = {5815--5835},
publisher = {American Meteorological Society},
title = {{Development of a High-Resolution Gridded Daily Meteorological Dataset over Sub-Saharan Africa: Spatial Analysis of Trends in Climate Extremes}},
volume = {27},
year = {2014}
}
@article{Chang2010,
abstract = {This project analyzed the effectiveness of a city-wide wireless weather sensor network, the Taipei Weather Science Learning Network (TWIN), in facilitating elementary and junior high students' study of weather science. The network, composed of sixty school-based weather sensor nodes and a centralized weather data archive server, provides students with current weather data at specific locations in the city. In 2006-2008, annual weather science tournaments were held to encourage students to use this resource, and up to now 171 registered teams, including 447 grade 4-9 students and 220 teachers, have participated in competitions. This study of the tournament data makes clear the over-all efficacy and usability of the network. An analysis of the students' weather science ability demonstrated that they could perform well in the questioning phase, the planning phase and the analyzing phase but not as well in the interpreting phase of their specific weather-science inquires.},
author = {Chang, Ben and Wang, Hsue Yie and Peng, Tian Yin and Hsu, Ying Shao},
doi = {10.1172/JCI37539.as},
journal = {Journal of Educational Technology {\&} Society},
number = {3},
pages = {270--280},
title = {{Development and evaluation of a city-wide wireless weather sensor network}},
volume = {13},
year = {2010}
}
@article{Chapman2015,
abstract = {AbstractThe Birmingham Urban Climate Laboratory (BUCL) is a near-real-time, high-resolution urban meteorological network (UMN) of automatic weather stations and inexpensive, nonstandard air temperature sensors. The network has recently been implemented with an initial focus on monitoring urban heat, infrastructure, and health applications. A number of UMNs exist worldwide; however, BUCL is novel in its density, the low-cost nature of the sensors, and the use of proprietary Wi-Fi networks. This paper provides an overview of the logistical aspects of implementing a UMN test bed at such a density, including selecting appropriate urban sites; testing and calibrating low-cost, nonstandard equipment; implementing strict quality-assurance/quality-control mechanisms (including metadata); and utilizing preexisting Wi-Fi networks to transmit data. Also included are visualizations of data collected by the network, including data from the July 2013 U.K. heatwave as well as highlighting potential applications. The paper is an open invitation to use the facility as a test bed for evaluating models and/or other nonstandard observation techniques such as those generated via crowdsourcing techniques.},
author = {Chapman, Lee and Muller, Catherine L and Young, Duick T and Warren, Elliott L and Grimmond, C S B and Cai, Xiao-Ming and Ferranti, Emma J S},
doi = {10.1175/BAMS-D-13-00193.1},
journal = {Bulletin of the American Meteorological Society},
month = {dec},
number = {9},
pages = {1545--1560},
publisher = {American Meteorological Society},
title = {{The Birmingham Urban Climate Laboratory: An Open Meteorological Test Bed and Challenges of the Smart City}},
volume = {96},
year = {2015}
}
@article{Chen2008,
author = {Chen, Mingyue and Shi, Wei and Xie, Pingping and Silva, Viviane B. S. and Kousky, Vernon E. and {Wayne Higgins}, R. and Janowiak, John E.},
doi = {10.1029/2007JD009132},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Atmospheres},
month = {feb},
number = {D4},
pages = {D04110},
publisher = {Wiley-Blackwell},
title = {{Assessing objective techniques for gauge-based analyses of global daily precipitation}},
volume = {113},
year = {2008}
}
@article{Chenge1601545,
abstract = {Earth{\{}$\backslash$textquoteright{\}}s energy imbalance (EEI) drives the ongoing global warming and can best be assessed across the historical record (that is, since 1960) from ocean heat content (OHC) changes. An accurate assessment of OHC is a challenge, mainly because of insufficient and irregular data coverage. We provide updated OHC estimates with the goal of minimizing associated sampling error. We performed a subsample test, in which subsets of data during the data-rich Argo era are colocated with locations of earlier ocean observations, to quantify this error. Our results provide a new OHC estimate with an unbiased mean sampling error and with variability on decadal and multidecadal time scales (signal) that can be reliably distinguished from sampling error (noise) with signal-to-noise ratios higher than 3. The inferred integrated EEI is greater than that reported in previous assessments and is consistent with a reconstruction of the radiative imbalance at the top of atmosphere starting in 1985. We found that changes in OHC are relatively small before about 1980; since then, OHC has increased fairly steadily and, since 1990, has increasingly involved deeper layers of the ocean. In addition, OHC changes in six major oceans are reliable on decadal time scales. All ocean basins examined have experienced significant warming since 1998, with the greatest warming in the southern oceans, the tropical/subtropical Pacific Ocean, and the tropical/subtropical Atlantic Ocean. This new look at OHC and EEI changes over time provides greater confidence than previously possible, and the data sets produced are a valuable resource for further study.},
author = {Cheng, Lijing and Trenberth, Kevin E and Fasullo, John and Boyer, Tim and Abraham, John and Zhu, Jiang},
doi = {10.1126/sciadv.1601545},
journal = {Science Advances},
number = {3},
pages = {e1601545},
publisher = {American Association for the Advancement of Science},
title = {{Improved estimates of ocean heat content from 1960 to 2015}},
url = {http://advances.sciencemag.org/content/3/3/e1601545},
volume = {3},
year = {2017}
}
@article{Chipperfield2018,
abstract = {Abstract We use height-resolved and total column satellite observations and 3-D chemical transport model simulations to study stratospheric ozone variations during 1998?2017 as ozone-depleting substances decline. In 2017 extrapolar lower stratospheric ozone displayed a strong positive anomaly following much lower values in 2016. This points to large interannual variability rather than an ongoing downward trend, as reported recently by Ball et al. (2018, https://doi.org/10.5194/acp-18-1379-2018). The observed ozone variations are well captured by the chemical transport model throughout the stratosphere and are largely driven by meteorology. Model sensitivity experiments show that the contribution of past trends in short-lived chlorine species to the ozone changes is small. Similarly, the potential impact of modest trends in natural brominated short-lived species is small. These results confirm the important role that atmospheric dynamics plays in controlling ozone in the extrapolar lower stratosphere on multiannual time scales and the continued importance of monitoring ozone profiles as the stratosphere changes.},
author = {Chipperfield, Martyn P and Dhomse, Sandip and Hossaini, Ryan and Feng, Wuhu and Santee, Michelle L and Weber, Mark and Burrows, John P and Wild, Jeanette D and Loyola, Diego and Coldewey-Egbers, Melanie},
doi = {10.1029/2018GL078071},
issn = {0094-8276},
journal = {Geophysical Research Letters},
month = {jun},
number = {11},
pages = {5718--5726},
publisher = {John Wiley {\&} Sons, Ltd},
title = {{On the Cause of Recent Variations in Lower Stratospheric Ozone}},
volume = {45},
year = {2018}
}
@article{Church2011,
author = {Church, John A. and White, Neil J.},
doi = {10.1007/s10712-011-9119-1.},
journal = {Surveys in Geophysics},
pages = {585},
title = {{Sea-level rise from the late 19th to the early 21st Century}},
volume = {32},
year = {2011}
}
@article{acp-18-5415-2018,
abstract = {Abstract. In situ measurements in the upper troposphere–lower stratosphere (UTLS) have been performed in the framework of the European research infrastructure IAGOS (In-service Aircraft for a Global Observing System) for ozone since 1994 and for carbon monoxide (CO) since 2002. The flight tracks cover a wide range of longitudes in the northern extratropics, extending from the North American western coast (125°W) to the eastern Asian coast (135°E) and more recently over the northern Pacific Ocean. Several tropical regions are also sampled frequently, such as the Brazilian coast, central and southern Africa, southeastern Asia, and the western half of the Maritime Continent. As a result, a new set of climatologies for O3 (August 1994–December 2013) and CO (December 2001–December 2013) in the upper troposphere (UT), tropopause layer, and lower stratosphere (LS) are made available, including gridded horizontal distributions on a semi-global scale and seasonal cycles over eight well-sampled regions of interest in the northern extratropics. The seasonal cycles generally show a summertime maximum in O3 and a springtime maximum in CO in the UT, in contrast to the systematic springtime maximum in O3 and the quasi-absence of a seasonal cycle of CO in the LS. This study highlights some regional variabilities in the UT, notably (i) a west–east difference of O3 in boreal summer with up to 15ppb more O3 over central Russia compared with northeast America, (ii) a systematic west–east gradient of CO from 60 to 140°E, especially noticeable in spring and summer with about 5ppb by 10 degrees longitude, (iii) a broad spring/summer maximum of CO over northeast Asia, and (iv) a spring maximum of O3 over western North America. Thanks to almost 20 years of O3 and 12 years of CO measurements, the IAGOS database is a unique data set to derive trends in the UTLS at northern midlatitudes. Trends in O3 in the UT are positive and statistically significant in most regions, ranging from +0.25 to +0.45ppbyr−1, characterized by the significant increase in the lowest values of the distribution. No significant trends of O3 are detected in the LS.{\ldots}},
author = {Cohen, Yann and Petetin, Herv{\'{e}} and Thouret, Val{\'{e}}rie and Mar{\'{e}}cal, Virginie and Josse, B{\'{e}}atrice and Clark, Hannah and Sauvage, Bastien and Fontaine, Alain and Athier, Gilles and Blot, Romain and Boulanger, Damien and Cousin, Jean-Marc J.-M. and N{\'{e}}d{\'{e}}lec, Philippe},
doi = {10.5194/acp-18-5415-2018},
issn = {1680-7324},
journal = {Atmospheric Chemistry and Physics},
month = {apr},
number = {8},
pages = {5415--5453},
title = {{Climatology and long-term evolution of ozone and carbon monoxide in the upper troposphere–lower stratosphere (UTLS) at northern midlatitudes, as seen by IAGOS from 1995 to 2013}},
url = {https://www.atmos-chem-phys.net/18/5415/2018/},
volume = {18},
year = {2018}
}
@article{Coldewey-Egbers2015,
author = {Coldewey-Egbers, M and Loyola, D G and Koukouli, M and Balis, D and Lambert, J.-C. and Verhoelst, T and Granville, J and van Roozendael, M and Lerot, C and Spurr, R and Frith, S M and Zehner, C},
doi = {10.5194/amt-8-3923-2015},
journal = {Atmospheric Measurement Techniques},
number = {9},
pages = {3923--3940},
title = {{The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record from the ESA Climate Change Initiative}},
volume = {8},
year = {2015}
}
@article{Colgan2019,
abstract = {{\textless}p{\textgreater}The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has measured ice-sheet elevation and thickness via repeat airborne surveys circumscribing the ice sheet at an average elevation of 1708 ± 5 m (S{\o}rensen{\textless}em{\textgreater} et al.{\textless}/em{\textgreater} 2018). We refer to this 5415 km survey as the ‘PROMICE perimeter'. Here, we assess ice-sheet mass balance following the input-output approach of Andersen{\textless}em{\textgreater} et al{\textless}/em{\textgreater}. (2015). We estimate ice-sheet output, or the ice discharge across the ice-sheet grounding line, by applying downstream corrections to the ice flux across the PROMICE perimeter. We subtract this ice discharge from ice-sheet input, or the area-integrated, ice sheet surface mass balance, estimated by a regional climate model. While Andersen{\textless}em{\textgreater} et al{\textless}/em{\textgreater}. (2015) assessed ice-sheet mass balance in 2007 and 2011, this updated input-output assessment now estimates the annual sea-level rise contribution from eighteen sub-sectors of the Greenland ice sheet over the 1995–2015 period.{\textless}/p{\textgreater}},
author = {Colgan, William and Mankoff, Kenneth D and Kjeldsen, Kristian K and Bj{\o}rk, Anders A and Box, Jason E and Simonsen, Sebastian B and S{\o}rensen, Louise S and Khan, S Abbas and Solgaard, Anne M and Forsberg, Rene and Skourup, Henriette and Stenseng, Lars and Kristensen, Steen S and Hvidegaard, Sine M and Citterio, Michele and Karlsson, Nanna and Fettweis, Xavier and Ahlstr{\o}m, Andreas P and Andersen, Signe B and van As, Dirk and Fausto, Robert S},
doi = {10.34194/GEUSB-201943-02-01},
journal = {GEUS Bulletin},
month = {jul},
title = {{Greenland ice sheet mass balance assessed by PROMICE (1995–2015)}},
url = {https://geusbulletin.org/index.php/geusb/article/view/4276},
volume = {43},
year = {2019}
}
@misc{Comiso2017,
address = {Boulder, CO, USA},
author = {Comiso, J. C.},
doi = {10.5067/7Q8HCCWS4I0R},
publisher = {NASA National Snow and Ice Data Center Distributed Active Archive Center},
title = {{Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS, Version 3}},
url = {https://nsidc.org/data/nsidc-0079},
year = {2017}
}
@article{Compo2011,
author = {Compo, Gilbert P and Whitaker, Jeffrey S and Sardeshmukh, Prashant D and Matsui, Nobuki and Allan, Robert J and Yin, Xungang and Gleason, Byron E and Vose, Russell S and Rutledge, Glenn and Bessemoulin, Pierre and Br{\"{o}}nnimann, S. and Brunet, M. and Crouthamel, R. I. and Grant, A. N. and Groisman, P. Y. and Jones, P. D. and Kruk, M. C. and Kruger, A. C. and Marshall, G. J. and Maugeri, M. and Mok, H. Y. and Nordli, {\O}. and Ross, T. F. and Trigo, R. M. and Wang, X. L. and Woodruff, S. D. and Worley, S. J.},
doi = {10.1002/qj.776},
issn = {00359009},
journal = {Quarterly Journal of the Royal Meteorological Society},
month = {jan},
number = {654},
pages = {1--28},
publisher = {Wiley Online Library},
title = {{The Twentieth Century Reanalysis Project}},
url = {https://onlinelibrary.wiley.com/doi/10.1002/qj.776},
volume = {137},
year = {2011}
}
@article{Contractor2020,
author = {Contractor, S and Donat, M G and Alexander, L V and Ziese, M and Meyer-Christoffer, A and Schneider, U and Rustemeier, E and Becker, A and Durre, I and Vose, R S},
doi = {10.5194/hess-24-919-2020},
journal = {Hydrology and Earth System Sciences},
number = {2},
pages = {919--943},
title = {{Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016}},
volume = {24},
year = {2020}
}
@article{Cooper2020,
abstract = {Extracting globally representative trend information from lower tropospheric ozone observations is extremely difficult due to the highly variable distribution and interannual variability of ozone, and the ongoing shift of ozone precursor emissions from high latitudes to low latitudes. Here we report surface ozone trends at 27 globally distributed remote locations (20 in the Northern Hemisphere, 7 in the Southern Hemisphere), focusing on continuous time series that extend from the present back to at least 1995. While these sites are only representative of less than 25{\%} of the global surface area, this analysis provides a range of regional long-term ozone trends for the evaluation of global chemistry-climate models. Trends are based on monthly mean ozone anomalies, and all sites have at least 20 years of data, which improves the likelihood that a robust trend value is due to changes in ozone precursor emissions and/or forced climate change rather than naturally occurring climate variability. Since 1995, the Northern Hemisphere sites are nearly evenly split between positive and negative ozone trends, while 5 of 7 Southern Hemisphere sites have positive trends. Positive trends are in the range of 0.5–2 ppbv decade–1, with ozone increasing at Mauna Loa by roughly 50{\%} since the late 1950s. Two high elevation Alpine sites, discussed by previous assessments, exhibit decreasing ozone trends in contrast to the positive trend observed by IAGOS commercial aircraft in the European lower free-troposphere. The Alpine sites frequently sample polluted European boundary layer air, especially in summer, and can only be representative of lower free tropospheric ozone if the data are carefully filtered to avoid boundary layer air. The highly variable ozone trends at these 27 surface sites are not necessarily indicative of free tropospheric trends, which have been overwhelmingly positive since the mid-1990s, as shown by recent studies of ozonesonde and aircraft observations.},
author = {Cooper, Owen R. and Schultz, Martin G. and Schr{\"{o}}der, Sabine and Chang, Kai-Lan and Gaudel, Audrey and Ben{\'{i}}tez, Gerardo Carbajal and Cuevas, Emilio and Fr{\"{o}}hlich, Marina and Galbally, Ian E. and Molloy, Suzie and Kubistin, Dagmar and Lu, Xiao and McClure-Begley, Audra and N{\'{e}}d{\'{e}}lec, Philippe and O'Brien, Jason and Oltmans, Samuel J. and Petropavlovskikh, Irina and Ries, Ludwig and Senik, Irina and Sj{\"{o}}berg, Karin and Solberg, Sverre and Spain, Gerard T. and Spangl, Wolfgang and Steinbacher, Martin and Tarasick, David and Thouret, Valerie and Xu, Xiaobin},
doi = {10.1525/elementa.420},
editor = {Helmig, Detlev and Faloona, Ian},
issn = {2325-1026},
journal = {Elementa: Science of the Anthropocene},
month = {jan},
number = {1},
pages = {23},
title = {{Multi-decadal surface ozone trends at globally distributed remote locations}},
url = {https://online.ucpress.edu/elementa/article/doi/10.1525/elementa.420/112772/Multidecadal-surface-ozone-trends-at-globally},
volume = {8},
year = {2020}
}
@article{doi:10.1029/2017JD028200,
abstract = {Abstract We describe the construction of a new version of the Europe-wide E-OBS temperature (daily minimum, mean, and maximum values) and precipitation data set. This version provides an improved estimation of interpolation uncertainty through the calculation of a 100-member ensemble of realizations of each daily field. The data set covers the period back to 1950 and provides gridded fields at a spacing of 0.25∘ × 0.25∘ in regular latitude/longitude coordinates. As with the original E-OBS data set, the ensemble version is based on the station series collated as part of the ECA{\&}D initiative. Station density varies significantly over the domain, and over time, and a reliable estimation of interpolation uncertainty in the gridded fields is therefore important for users of the data set. The uncertainty quantified by the ensemble data set is more realistic than the uncertainty estimates in the original version, although uncertainty is still underestimated in data-sparse regions. The new data set is compared against the earlier version of E-OBS and against regional gridded data sets produced by a selection of National Meteorological Services. In terms of both climatological averages and extreme values, the new version of E-OBS is broadly comparable to the earlier version. Nonetheless, users will notice differences between the two E-OBS versions, especially for precipitation, which arises from the different gridding method used.},
author = {Cornes, Richard C and van der Schrier, Gerard and van den Besselaar, Else J M and Jones, Philip D},
doi = {10.1029/2017JD028200},
journal = {Journal of Geophysical Research: Atmospheres},
number = {17},
pages = {9391--9409},
title = {{An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2017JD028200},
volume = {123},
year = {2018}
}
@article{Cowtan2014,
abstract = {Abstract Incomplete global coverage is a potential source of bias in global temperature reconstructions if the unsampled regions are not uniformly distributed over the planet's surface. The widely used Hadley Centre–Climatic Reseach Unit Version 4 (HadCRUT4) dataset covers on average about 84{\%} of the globe over recent decades, with the unsampled regions being concentrated at the poles and over Africa. Three existing reconstructions with near-global coverage are examined, each suggesting that HadCRUT4 is subject to bias due to its treatment of unobserved regions. Two alternative approaches for reconstructing global temperatures are explored, one based on an optimal interpolation algorithm and the other a hybrid method incorporating additional information from the satellite temperature record. The methods are validated on the basis of their skill at reconstructing omitted sets of observations. Both methods provide results superior to excluding the unsampled regions, with the hybrid method showing particular skill around the regions where no observations are available. Temperature trends are compared for the hybrid global temperature reconstruction and the raw HadCRUT4 data. The widely quoted trend since 1997 in the hybrid global reconstruction is two and a half times greater than the corresponding trend in the coverage-biased HadCRUT4 data. Coverage bias causes a cool bias in recent temperatures relative to the late 1990s, which increases from around 1998 to the present. Trends starting in 1997 or 1998 are particularly biased with respect to the global trend. The issue is exacerbated by the strong El Ni{\~{n}}o event of 1997–1998, which also tends to suppress trends starting during those years.},
author = {Cowtan, Kevin and Way, Robert G},
doi = {10.1002/qj.2297},
journal = {Quarterly Journal of the Royal Meteorological Society},
number = {683},
pages = {1935--1944},
title = {{Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends}},
volume = {140},
year = {2014}
}
@article{Cucchi2020,
abstract = {Abstract. The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5∘ spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data – allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020).},
author = {Cucchi, Marco and Weedon, Graham P. and Amici, Alessandro and Bellouin, Nicolas and Lange, Stefan and {M{\"{u}}ller Schmied}, Hannes and Hersbach, Hans and Buontempo, Carlo},
doi = {10.5194/essd-12-2097-2020},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {sep},
number = {3},
pages = {2097--2120},
title = {{WFDE5: bias-adjusted ERA5 reanalysis data for impact studies}},
url = {https://essd.copernicus.org/articles/12/2097/2020/},
volume = {12},
year = {2020}
}
@article{Cuervo-Robayo2014,
abstract = {ABSTRACT Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple applications in research planning, experimental design, and technology transfer. Although high-resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high-resolution (30?arc sec) climatic surfaces for Mexico for the average monthly climate period 1910?2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin-plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26?1.12?°C and 24.67{\%} for monthly precipitation, and a RTMSE of 0.48?0.56?°C and 11.11{\%}. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high-resolution climatology for the country.},
author = {Cuervo-Robayo, Angela P and T{\'{e}}llez-Vald{\'{e}}s, Oswaldo and G{\'{o}}mez-Albores, Miguel A and Venegas-Barrera, Crystian S and Manjarrez, Javier and Mart{\'{i}}nez-Meyer, Enrique},
doi = {10.1002/joc.3848},
journal = {International Journal of Climatology},
month = {oct},
number = {7},
pages = {2427--2437},
publisher = {Wiley-Blackwell},
title = {{An update of high-resolution monthly climate surfaces for Mexico}},
volume = {34},
year = {2014}
}
@article{Dahlgren2016,
abstract = {A regional reanalysis covering the years 1989?2010 has been produced with the HIgh Resolution Limited-Area Model (HIRLAM) forecast model and data assimilation system. Surface and upper-air variables were analysed at 0000, 0600, 1200 and 1800 UTC on a three-dimensional grid-mesh with 22 km spacing covering Europe using conventional insitu observations. Information from the global reanalysis ERA-Interim has been used as a large-scale constraint in the data assimilation and as lateral boundaries in the forecast model integrations. Comparison to the global forcing reanalysis shows good agreement in the large-scale structures, as expected given the forcing from the boundaries and in the analysis. Comparison to the observed climatological distribution and a skill score evaluation showed that the HIRLAM reanalysis is better than ERA-Interim at describing extreme values of 2 m temperature and 24 h accumulated precipitation. However, no added value in the HIRLAM reanalysis could be quantified for the wind speed at 10 m over land. The first production run covered the years 1989?2010 and the statistics presented in this paper are based on that dataset. This reanalysis has also been used as input to a two-dimensional surface analysis using the MESoscale ANalysis (MESAN) system which is presented in Part 2. Since then both the two- and three dimensional reanalyses have been extended to cover the years 1979?2014.},
author = {Dahlgren, P and Landelius, T and K{\aa}llberg, P and Gollvik, S},
doi = {10.1002/qj.2807},
journal = {Quarterly Journal of the Royal Meteorological Society},
month = {apr},
number = {698},
pages = {2119--2131},
publisher = {Wiley-Blackwell},
title = {{A high-resolution regional reanalysis for Europe. Part 1: Three-dimensional reanalysis with the regional HIgh-Resolution Limited-Area Model (HIRLAM)}},
volume = {142},
year = {2016}
}
@article{Dangendorf2019,
abstract = {Previous studies reconstructed twentieth-century global mean sea level (GMSL) from sparse tide-gauge records to understand whether the recent high rates obtained from satellite altimetry are part of a longer-term acceleration. However, these analyses used techniques that can only accurately capture either the trend or the variability in GMSL, but not both. Here we present an improved hybrid sea-level reconstruction during 1900–2015 that combines previous techniques at time scales where they perform best. We find a persistent acceleration in GMSL since the 1960s and demonstrate that this is largely ({\~{}}76{\%}) associated with sea-level changes in the Indo-Pacific and South Atlantic. We show that the initiation of the acceleration in the 1960s is tightly linked to an intensification and a basin-scale equatorward shift of Southern Hemispheric westerlies, leading to increased ocean heat uptake, and hence greater rates of GMSL rise, through changes in the circulation of the Southern Ocean.},
author = {Dangendorf, S{\"{o}}nke and Hay, Carling and Calafat, Francisco M and Marcos, Marta and Piecuch, Christopher G and Berk, Kevin and Jensen, J{\"{u}}rgen},
doi = {10.1038/s41558-019-0531-8},
issn = {1758-6798},
journal = {Nature Climate Change},
number = {9},
pages = {705--710},
title = {{Persistent acceleration in global sea-level rise since the 1960s}},
volume = {9},
year = {2019}
}
@article{Dangendorf2017,
abstract = {Estimates of global mean sea level (GMSL) before the advent of satellite altimetry vary widely, mainly because of the uneven coverage and limited temporal sampling of tide gauge records, which track local sea level rather than the global mean. Here we introduce an approach that combines recent advances in solid Earth and geoid corrections for individual tide gauges with improved knowledge about their geographical representation of ocean internal variability. Our assessment yields smaller trends before 1990 than previously reported, leading to a larger overall acceleration; identifies three major explanations for differences with previous estimates; and reconciles observational GMSL estimates with the sum of individually modeled contributions from the Coupled Model Intercomparison Project 5 database for the entire 20th century.The rate at which global mean sea level (GMSL) rose during the 20th century is uncertain, with little consensus between various reconstructions that indicate rates of rise ranging from 1.3 to 2 mm.y-1. Here we present a 20th-century GMSL reconstruction computed using an area-weighting technique for averaging tide gauge records that both incorporates up-to-date observations of vertical land motion (VLM) and corrections for local geoid changes resulting from ice melting and terrestrial freshwater storage and allows for the identification of possible differences compared with earlier attempts. Our reconstructed GMSL trend of 1.1 {\{}$\backslash$textpm{\}} 0.3 mm.y-1 (1$\sigma$) before 1990 falls below previous estimates, whereas our estimate of 3.1 {\{}$\backslash$textpm{\}} 1.4 mm.y-1 from 1993 to 2012 is consistent with independent estimates from satellite altimetry, leading to overall acceleration larger than previously suggested. This feature is geographically dominated by the Indian Ocean{\{}$\backslash$textendash{\}}Southern Pacific region, marking a transition from lower-than-average rates before 1990 toward unprecedented high rates in recent decades. We demonstrate that VLM corrections, area weighting, and our use of a common reference datum for tide gauges may explain the lower rates compared with earlier GMSL estimates in approximately equal proportion. The trends and multidecadal variability of our GMSL curve also compare well to the sum of individual contributions obtained from historical outputs of the Coupled Model Intercomparison Project Phase 5. This, in turn, increases our confidence in process-based projections presented in the Fifth Assessment Report of the Intergovernment{\ldots}},
author = {Dangendorf, S{\"{o}}nke and Marcos, Marta and W{\"{o}}ppelmann, Guy and Conrad, Clinton P and Frederikse, Thomas and Riva, Riccardo},
doi = {10.1073/pnas.1616007114},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
number = {23},
pages = {5946--5951},
publisher = {National Academy of Sciences},
title = {{Reassessment of 20th century global mean sea level rise}},
volume = {114},
year = {2017}
}
@article{Davis2016,
author = {Davis, S M and Rosenlof, K H and Hassler, B and Hurst, D F and Read, W G and V{\"{o}}mel, H and Selkirk, H and Fujiwara, M and Damadeo, R},
doi = {10.5194/essd-8-461-2016},
journal = {Earth System Science Data},
number = {2},
pages = {461--490},
title = {{The Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database: a long-term database for climate studies}},
volume = {8},
year = {2016}
}
@article{DeBoyerMontegut2004,
author = {{de Boyer Mont{\'{e}}gut}, Cl{\'{e}}ment and Madec, G. and Fischer, A. S and Lazar, A. and Iduicone, D.},
doi = {10.1029/2004JC002378},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Oceans},
number = {C12},
pages = {C12003},
title = {{Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology}},
url = {http://doi.wiley.com/10.1029/2004JC002378},
volume = {109},
year = {2004}
}
@article{Dee2011,
abstract = {Abstract ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright {\textcopyright} 2011 Royal Meteorological Society},
author = {Dee, D P and Uppala, S M and Simmons, A J and Berrisford, P and Poli, P and Kobayashi, S and Andrae, U and Balmaseda, M A and Balsamo, G and Bauer, P and Bechtold, P and Beljaars, A C M and van de Berg, L and Bidlot, J and Bormann, N and Delsol, C and Dragani, R and Fuentes, M and Geer, A J and Haimberger, L and Healy, S B and Hersbach, H and H{\'{o}}lm, E V and Isaksen, L and K{\aa}llberg, P and K{\"{o}}hler, M and Matricardi, M and McNally, A P and Monge-Sanz, B M and Morcrette, J.-J. and Park, B.-K. and Peubey, C and de Rosnay, P and Tavolato, C and Th{\'{e}}paut, J.-N. and Vitart, F},
doi = {10.1002/qj.828},
journal = {Quarterly Journal of the Royal Meteorological Society},
number = {656},
pages = {553--597},
title = {{The ERA-Interim reanalysis: configuration and performance of the data assimilation system}},
volume = {137},
year = {2011}
}
@article{Dinku2014,
abstract = {ABSTRACT Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30-years (1983-todate) of rainfall estimates over Ethiopia at a spatial resolution of 10?km and a ten-daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared-only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded-gauge and combined gauge-satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.},
author = {Dinku, Tufa and Hailemariam, Kinfe and Maidment, Ross and Tarnavsky, Elena and Connor, Stephen},
doi = {10.1002/joc.3855},
journal = {International Journal of Climatology},
month = {nov},
number = {7},
pages = {2489--2504},
publisher = {Wiley-Blackwell},
title = {{Combined use of satellite estimates and rain gauge observations to generate high-quality historical rainfall time series over Ethiopia}},
volume = {34},
year = {2014}
}
@misc{DlugokenckyEd;Tans,
author = {Dlugokencky, Ed and Tans, Pieter;},
booktitle = {Earth System Research Laboratory (NOAA/ESRL)},
publisher = {National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML)},
title = {{Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration}},
url = {http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html},
urldate = {2021-01-18},
year = {2019}
}
@article{Do2018,
abstract = {Abstract. This is the first part of a two-paper series presenting the Global Streamflow Indices and Metadata archive (GSIM), a worldwide collection of metadata and indices derived from more than 35 000 daily streamflow time series. This paper focuses on the compilation of the daily streamflow time series based on 12 free-to-access streamflow databases (seven national databases and five international collections). It also describes the development of three metadata products (freely available at https://doi.pangaea.de/10.1594/PANGAEA.887477): (1) a GSIM catalogue collating basic metadata associated with each time series, (2) catchment boundaries for the contributing area of each gauge, and (3) catchment metadata extracted from 12 gridded global data products representing essential properties such as land cover type, soil type, and climate and topographic characteristics. The quality of the delineated catchment boundary is also made available and should be consulted in GSIM application. The second paper in the series then explores production and analysis of streamflow indices. Having collated an unprecedented number of stations and associated metadata, GSIM can be used to advance large-scale hydrological research and improve understanding of the global water cycle.},
author = {Do, Hong Xuan and Gudmundsson, Lukas and Leonard, Michael and Westra, Seth},
doi = {10.5194/essd-10-765-2018},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {apr},
number = {2},
pages = {765--785},
title = {{The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata}},
url = {https://essd.copernicus.org/articles/10/765/2018/},
volume = {10},
year = {2018}
}
@misc{DoerrJakob;NotzDirk;Kern2021,
author = {Doerr, Jakob and Notz, Dirk and Kern, Stefan;},
title = {{UHH Sea Ice Area Product (Version 2019{\_}fv0.01)}},
url = {http://doi.org/10.25592/uhhfdm.8559},
year = {2021}
}
@article{Domingues2008,
abstract = {Changes in the climate system's energy budget are predominantly revealed in ocean temperatures1, 2 and the associated thermal expansion contribution to sea-level rise2. Climate models, however, do not reproduce the large decadal variability in globally averaged ocean heat content inferred from the sparse observational database3, 4, even when volcanic and other variable climate forcings are included. The sum of the observed contributions has also not adequately explained the overall multi-decadal rise2. Here we report improved estimates of near-global ocean heat content and thermal expansion for the upper 300 m and 700 m of the ocean for 1950–2003, using statistical techniques that allow for sparse data coverage5, 6, 7 and applying recent corrections8 to reduce systematic biases in the most common ocean temperature observations9. Our ocean warming and thermal expansion trends for 1961–2003 are about 50 per cent larger than earlier estimates but about 40 per cent smaller for 1993–2003, which is consistent with the recognition that previously estimated rates for the 1990s had a positive bias as a result of instrumental errors8, 9, 10. On average, the decadal variability of the climate models with volcanic forcing now agrees approximately with the observations, but the modelled multi-decadal trends are smaller than observed. We add our observational estimate of upper-ocean thermal expansion to other contributions to sea-level rise and find that the sum of contributions from 1961 to 2003 is about 1.5 plusminus 0.4 mm yr-1, in good agreement with our updated estimate of near-global mean sea-level rise (using techniques established in earlier studies6, 7) of 1.6 plusminus 0.2 mm yr-1},
author = {Domingues, Catia M. and Church, John A. and White, Neil J. and Gleckler, Peter J. and Wijffels, Susan E. and Barker, Paul M. and Dunn, Jeff R.},
doi = {10.1038/nature07080},
isbn = {0028-0836$\backslash$r1476-4687},
issn = {14764687},
journal = {Nature},
month = {jun},
number = {7198},
pages = {1090--1093},
pmid = {18563162},
publisher = {Nature Publishing Group},
title = {{Improved estimates of upper-ocean warming and multi-decadal sea-level rise}},
url = {http://www.nature.com/articles/nature07080},
volume = {453},
year = {2008}
}
@article{Donat2013,
abstract = {In this study, we present the collation and analysis of the gridded land-based dataset of indices of temperature and precipitation extremes: HadEX2. Indices were calculated based on station data using a consistent approach recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices, resulting in the production of 17 temperature and 12 precipitation indices derived from daily maximum and minimum temperature and precipitation observations. High-quality in situ observations from over 7000 temperature and 11,000 precipitation meteorological stations across the globe were obtained to calculate the indices over the period of record available for each station. Monthly and annual indices were then interpolated onto a 3.75° × 2.5° longitude-latitude grid over the period 1901–2010. Linear trends in the gridded fields were computed and tested for statistical significance. Overall there was very good agreement with the previous HadEX dataset during the overlapping data period. Results showed widespread significant changes in temperature extremes consistent with warming, especially for those indices derived from daily minimum temperature over the whole 110 years of record but with stronger trends in more recent decades. Seasonal results showed significant warming in all seasons but more so in the colder months. Precipitation indices also showed widespread and significant trends, but the changes were much more spatially heterogeneous compared with temperature changes. However, results indicated more areas with significant increasing trends in extreme precipitation amounts, intensity, and frequency than areas with decreasing trends.},
author = {Donat, M. G. and Alexander, L. V. and Yang, H. and Durre, I. and Vose, R. and Dunn, R. J. H. and Willett, K. M. and Aguilar, E. and Brunet, M. and Caesar, J. and Hewitson, B. and Jack, C. and {Klein Tank}, A. M. G. and Kruger, A. C. and Marengo, J. and Peterson, T. C. and Renom, M. and {Oria Rojas}, C. and Rusticucci, M. and Salinger, J. and Elrayah, A. S. and Sekele, S. S. and Srivastava, A. K. and Trewin, B. and Villarroel, C. and Vincent, L. A. and Zhai, P. and Zhang, X. and Kitching, S.},
doi = {10.1002/jgrd.50150},
issn = {2169897X},
journal = {Journal of Geophysical Research: Atmospheres},
month = {mar},
number = {5},
pages = {2098--2118},
title = {{Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset}},
url = {http://doi.wiley.com/10.1002/jgrd.50150},
volume = {118},
year = {2013}
}
@article{Donat2013a,
abstract = {F or more than a decade, the World Meteorological Organization (WMO) Commission for Climatology (CCl)/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) has been facilitating the international coordination of a suite of indices that primarily represent the more extreme aspects of climate. The main aim of this team has been to fill in data gaps using a consistent and traceable approach in order to provide a clear global picture of the long-term variability of extremes, to provide the necessary data to perform appropriate “detection and attribution” studies, and to be able to evaluate climate models and assess their efficacy in simulating and projecting the future of climate extremes. To this end, the ETCCDI held a number of regional workshops over many years, the data from which were used to help create HadEX, the first global land-based, gridded dataset of temperature and precipitation extremes covering the second half of the twentieth century. While HadEX facilitated the analysis of trends in extremes, its relatively short record (1951–2003) and static nature (i.e., it is not updated) presents critical gaps in our ability to adequately assess and monitor changes in extremes. Furthermore, much of the data from the regional workshops is not publically available, making it difficult to independently reproduce the results of HadEX. For these reasons, the authors set out to develop a new dataset to address these issues using the world's largest repository of daily in situ observations of temperature and precipitation—the National Climatic Data Center (NCDC)'s Global Historical Climatology Network (GHCN)-Daily dataset. This article describes the resulting dataset, termed GHCNDEX—an operationally updated, global land gridded dataset of climate extremes. We also demonstrate the application of the dataset for climate change and climate monitoring purposes in addition to assessing some issues regarding uncertainty by comparing the results with existing datasets.},
author = {Donat, M.G. and Alexander, L.V. and Yang, H. and Durre, I. and Vose, R. and Caesar, J.},
doi = {10.1175/bams-d-12-00109.1},
isbn = {i1520-0477-94-7-997},
issn = {0003-0007},
journal = {Bulletin of the American Meteorological Society},
number = {7},
pages = {997--1006},
title = {{Global Land-Based Datasets for Monitoring Climatic Extremes}},
volume = {94},
year = {2013}
}
@article{Dore12235,
abstract = {Atmospheric carbon dioxide (CO2) is increasing at an accelerating rate, primarily due to fossil fuel combustion and land use change. A substantial fraction of anthropogenic CO2 emissions is absorbed by the oceans, resulting in a reduction of seawater pH. Continued acidification may over time have profound effects on marine biota and biogeochemical cycles. Although the physical and chemical basis for ocean acidification is well understood, there exist few field data of sufficient duration, resolution, and accuracy to document the acidification rate and to elucidate the factors governing its variability. Here we report the results of nearly 20 years of time-series measurements of seawater pH and associated parameters at Station ALOHA in the central North Pacific Ocean near Hawaii. We document a significant long-term decreasing trend of -0.0019 {\{}$\backslash$textpm{\}} 0.0002 y-1 in surface pH, which is indistinguishable from the rate of acidification expected from equilibration with the atmosphere. Superimposed upon this trend is a strong seasonal pH cycle driven by temperature, mixing, and net photosynthetic CO2 assimilation. We also observe substantial interannual variability in surface pH, influenced by climate-induced fluctuations in upper ocean stability. Below the mixed layer, we find that the change in acidification is enhanced within distinct subsurface strata. These zones are influenced by remote water mass formation and intrusion, biological carbon remineralization, or both. We suggest that physical and biogeochemical processes alter the acidification rate with depth and time and must therefore be given due consideration when designing and interpreting ocean pH monitoring efforts and predictive models.},
author = {Dore, John E and Lukas, Roger and Sadler, Daniel W and Church, Matthew J and Karl, David M},
doi = {10.1073/pnas.0906044106},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
number = {30},
pages = {12235--12240},
publisher = {National Academy of Sciences},
title = {{Physical and biogeochemical modulation of ocean acidification in the central North Pacific}},
url = {https://www.pnas.org/content/106/30/12235},
volume = {106},
year = {2009}
}
@article{Dorigo2017,
abstract = {Climate Data Records of soil moisture are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. To respond to this need, in 2012 the European Space Agency (ESA) released the first multi-decadal, global satellite-observed soil moisture (SM) dataset as part of its Climate Change Initiative (CCI) program. This product, named ESA CCI SM, combines various single-sensor active and passive microwave soil moisture products into three harmonised products: a merged ACTIVE, a merged PASSIVE, and a COMBINED active + passive microwave product. Compared to the first product release, the latest version of ESA CCI SM includes a large number of enhancements, incorporates various new satellite sensors, and extends its temporal coverage to the period 1978–2015. In this study, we first provide a comprehensive overview of the characteristics, evolution, and performance of the ESA CCI SM products. Based on original research and a review of existing literature we show that the product quality has steadily increased with each successive release and that the merged products generally outperform the single-sensor input products. Although ESA CCI SM generally agrees well with the spatial and temporal patterns estimated by land surface models and observed in-situ, we identify surface conditions (e.g., dense vegetation, organic soils) for which it still has large uncertainties. Second, capitalising on the results of {\textgreater}100 research studies that made use of the ESA CCI SM data we provide a synopsis of how it has contributed to improved process understanding in the following Earth system domains: climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology. While in some disciplines the use of ESA CCI SM is already widespread (e.g. in the evaluation of model soil moisture states) in others (e.g. in numerical weather prediction or flood forecasting) it is still in its infancy. The latter is partly related to current shortcomings of the product, e.g., the lack of near-real-time availability and data gaps in time and space. This study discloses the discrepancies between current ESA CCI SM product characteristics and the preferred characteristics of long-term satellite soil moisture products as outlined by the Global Climate Observing System (GCOS), and provides important directions for future ESA CCI SM product improvements to bridge these gaps.},
author = {Dorigo, Wouter and Wagner, Wolfgang and Albergel, Clement and Albrecht, Franziska and Balsamo, Gianpaolo and Brocca, Luca and Chung, Daniel and Ertl, Martin and Forkel, Matthias and Gruber, Alexander and Haas, Eva and Hamer, Paul D and Hirschi, Martin and Ikonen, Jaakko and de Jeu, Richard and Kidd, Richard and Lahoz, William and Liu, Yi Y and Miralles, Diego and Mistelbauer, Thomas and Nicolai-Shaw, Nadine and Parinussa, Robert and Pratola, Chiara and Reimer, Christoph and van der Schalie, Robin and Seneviratne, Sonia I and Smolander, Tuomo and Lecomte, Pascal},
doi = {10.1016/j.rse.2017.07.001},
isbn = {1588011224},
issn = {00344257},
journal = {Remote Sensing of Environment},
keywords = {Biogeochemistry,Climate Data Record,Climate change,Earth observation,Earth system modelling,Essential Climate Variable,Microwave remote sensing,Soil moisture},
pages = {185--215},
publisher = {Elsevier Inc.},
title = {{ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions}},
url = {https://doi.org/10.1016/j.rse.2017.07.001},
volume = {203},
year = {2017}
}
@article{Dumitrescu2016,
abstract = {ABSTRACT A two-step interpolation framework is proposed for the first time for computing sub-daily (6 h) precipitation maps over Romania, over 36 years (1975?2010), using meteorological and ancillary data. In the first step, the monthly climatology maps were constructed. Here, the auxiliary predictors were quantified with the help of multivariate geostatistical model (Regression Kriging). In the second step ? the interpolation of the 6-h anomaly maps of precipitation ? three methods were compared: Multiquadratic, Ordinary Kriging and 3d Kriging. Owing to the good results in interpolating precipitation anomalies (and to the fewer steps required for producing the maps), the Multiquadratic method was chosen to construct the 6-h precipitation ratios maps for the period 1975?2010. By comparing the new gridded data set to similar sets of data, and by using precipitation data from five independent stations, it was proven that the proposed methodology was suitable for accurately estimating in space and time the 6-h precipitation values. The final outcome of this work is a gridded precipitation data set, at a 6-h time step, available in high spatial resolution (1 km ? 1 km) together with the estimated accuracy.},
author = {Dumitrescu, Alexandru and Birsan, Marius-Victor and Manea, Ancuta},
doi = {10.1002/joc.4427},
journal = {International Journal of Climatology},
month = {jul},
number = {3},
pages = {1331--1343},
publisher = {Wiley-Blackwell},
title = {{Spatio-temporal interpolation of sub-daily (6 h) precipitation over Romania for the period 1975–2010}},
volume = {36},
year = {2016}
}
@article{Dunn2016,
abstract = {Abstract. HadISD is a sub-daily, station-based, quality-controlled dataset designed to study past extremes of temperature, pressure and humidity and allow comparisons to future projections. Herein we describe the first major update to the HadISD dataset. The temporal coverage of the dataset has been extended to 1931 to present, doubling the time range over which data are provided. Improvements made to the station selection and merging procedures result in 7677 stations being provided in version 2.0.0.2015p of this dataset. The selection of stations to merge together making composites has also been improved and made more robust. The underlying structure of the quality control procedure is the same as for HadISD.1.0.x, but a number of improvements have been implemented in individual tests. Also, more detailed quality control tests for wind speed and direction have been added. The data will be made available as NetCDF files at http://www.metoffice.gov.uk/hadobs/hadisd and updated annually.},
author = {Dunn, Robert J H and Willett, Kate M and Parker, David E and Mitchell, Lorna},
doi = {10.5194/gi-5-473-2016},
issn = {2193-0864},
journal = {Geoscientific Instrumentation, Methods and Data Systems},
month = {sep},
number = {2},
pages = {473--491},
title = {{Expanding HadISD: quality-controlled, sub-daily station data from 1931}},
url = {https://gi.copernicus.org/articles/5/473/2016/},
volume = {5},
year = {2016}
}
@article{Dunn2020,
abstract = {Abstract We present the second update to a data set of gridded land-based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°?1.25° longitude-latitude grid, covering 1901?2018. We show changes in these indices by examining ?global?-average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global-scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950?2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961?1990 and 1981?2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.},
annote = {https://doi.org/10.1029/2019JD032263},
author = {Dunn, Robert J H and Alexander, Lisa V and Donat, Markus G and Zhang, Xuebin and Bador, Margot and Herold, Nicholas and Lippmann, Tanya and Allan, Rob and Aguilar, Enric and Barry, Abdoul Aziz and Brunet, Manola and Caesar, John and Chagnaud, Guillaume and Cheng, Vincent and Cinco, Thelma and Durre, Imke and de Guzman, Rosaline and Htay, Tin Mar and {Wan Ibadullah}, Wan Maisarah and {Bin Ibrahim}, Muhammad Khairul Izzat and Khoshkam, Mahbobeh and Kruger, Andries and Kubota, Hisayuki and Leng, Tan Wee and Lim, Gerald and Li-Sha, Lim and Marengo, Jose and Mbatha, Sifiso and McGree, Simon and Menne, Matthew and {de los Milagros Skansi}, Maria and Ngwenya, Sandile and Nkrumah, Francis and Oonariya, Chalump and Pabon-Caicedo, Jose Daniel and Panthou, G{\'{e}}r{\'{e}}my and Pham, Cham and Rahimzadeh, Fatemeh and Ramos, Andrea and Salgado, Ernesto and Salinger, Jim and San{\'{e}}, Youssouph and Sopaheluwakan, Ardhasena and Srivastava, Arvind and Sun, Ying and Timbal, Bertrand and Trachow, Nichanun and Trewin, Blair and van der Schrier, Gerard and Vazquez-Aguirre, Jorge and Vasquez, Ricardo and Villarroel, Claudia and Vincent, Lucie and Vischel, Theo and Vose, Russ and {Bin Hj Yussof}, Mohd Noor'Arifin},
doi = {https://doi.org/10.1029/2019JD032263},
issn = {2169-897X},
journal = {Journal of Geophysical Research: Atmospheres},
keywords = {climate extremes,global-gridded data set,observations,precipitation,temperature},
month = {aug},
number = {16},
pages = {e2019JD032263},
publisher = {John Wiley {\&} Sons, Ltd},
title = {{Development of an Updated Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes: HadEX3}},
url = {https://doi.org/10.1029/2019JD032263},
volume = {125},
year = {2020}
}
@article{Dunn2012,
abstract = {This paper describes the creation of HadISD: an automatically quality-controlled synoptic resolution dataset of temperature, dewpoint temperature, sea-level pressure, wind speed, wind direction and cloud cover from global weather stations for 1973–2011. The full dataset consists of over 6000 stations, with 3427 long-term stations deemed to have sufficient sampling and quality for climate applications requiring sub-daily resolution. As with other surface datasets, coverage is heavily skewed towards Northern Hemisphere mid-latitudes. The dataset is constructed from a large pre-existing ASCII flatfile data bank that represents over a decade of substantial effort at data retrieval, reformatting and provision. These raw data have had varying levels of quality control applied to them by individual data providers. The work proceeded in several steps: merging stations with multiple reporting identifiers; reformatting to netCDF; quality control; and then filtering to form a final dataset. Particular attention has been paid to maintaining true extreme values where possible within an automated, objective process. Detailed validation has been performed on a subset of global stations and also on UK data using known extreme events to help finalise the QC tests. Further validation was performed on a selection of extreme events world-wide (Hurricane Katrina in 2005, the cold snap in Alaska in 1989 and heat waves in SE Australia in 2009). Some very initial analyses are performed to illustrate some of the types of problems to which the final data could be applied. Although the filtering has removed the poorest station records, no attempt has been made to homogenise the data thus far, due to the complexity of retaining the true distribution of high-resolution data when applying adjustments. Hence non-climatic, time-varying errors may still exist in many of the individual station records and care is needed in inferring long-term trends from these data. This dataset will allow the study of high frequency variations of temperature, pressure and humidity on a global basis over the last four decades. Both individual extremes and the overall population of extreme events could be investigated in detail to allow for comparison with past and projected climate. A version-control system has been constructed for this dataset to allow for the clear documentation of any updates and corrections in the future.},
author = {Dunn, R J H and Willett, K M and Thorne, P W and Woolley, E V and Durre, I and Dai, A and Parker, D E and Vose, R S},
doi = {10.5194/cp-8-1649-2012},
issn = {1814-9332},
journal = {Climate of the Past},
month = {oct},
number = {5},
pages = {1649--1679},
title = {{HadISD: a quality-controlled global synoptic report database for selected variables at long-term stations from 1973–2011}},
url = {https://cp.copernicus.org/articles/8/1649/2012/},
volume = {8},
year = {2012}
}
@article{doi:10.1175/JCLI3594.1,
abstract = { Abstract This paper provides a general description of the Integrated Global Radiosonde Archive (IGRA), a new radiosonde dataset from the National Climatic Data Center (NCDC). IGRA consists of radiosonde and pilot balloon observations at more than 1500 globally distributed stations with varying periods of record, many of which extend from the 1960s to present. Observations include pressure, temperature, geopotential height, dewpoint depression, wind direction, and wind speed at standard, surface, tropopause, and significant levels. IGRA contains quality-assured data from 11 different sources. Rigorous procedures are employed to ensure proper station identification, eliminate duplicate levels within soundings, and select one sounding for every station, date, and time. The quality assurance algorithms check for format problems, physically implausible values, internal inconsistencies among variables, runs of values across soundings and levels, climatological outliers, and temporal and vertical inconsistencies in temperature. The performance of the various checks was evaluated by careful inspection of selected soundings and time series. In its final form, IGRA is the largest and most comprehensive dataset of quality-assured radiosonde observations freely available. Its temporal and spatial coverage is most complete over the United States, western Europe, Russia, and Australia. The vertical resolution and extent of soundings improve significantly over time, with nearly three-quarters of all soundings reaching up to at least 100 hPa by 2003. IGRA data are updated on a daily basis and are available online from NCDC as both individual soundings and monthly means. },
author = {Durre, Imke and Vose, Russell S and Wuertz, David B},
doi = {10.1175/JCLI3594.1},
journal = {Journal of Climate},
number = {1},
pages = {53--68},
title = {{Overview of the Integrated Global Radiosonde Archive}},
url = {https://doi.org/10.1175/JCLI3594.1},
volume = {19},
year = {2006}
}
@article{Estilow2015,
abstract = {This paper describes the long-term, satellite-based visible snow cover extent National Oceanic and Atmospheric Administration (NOAA) climate data record (CDR) currently available for climate studies, monitoring, and model validation. This environmental data product is developed from weekly Northern Hemisphere snow cover extent data that have been digitized from snow cover maps onto a Cartesian grid draped over a polar stereographic projection. The data have a spatial resolution of 190.6 km at 60° latitude, are updated monthly, and span the period from 4 October 1966 to the present. The data comprise the longest satellite-based CDR of any environmental variable. Access to the data is provided in Network Common Data Form (netCDF) and archived by NOAA's National Climatic Data Center (NCDC) under the satellite Climate Data Record Program (doi:10.7289/V5N014G9). The basic characteristics, history, and evolution of the data set are presented herein. In general, the CDR provides similar spatial and temporal variability to its widely used predecessor product. Key refinements included in the CDR improve the product's grid accuracy and documentation and bring metadata into compliance with current standards for climate data records.},
author = {Estilow, T. W. and Young, A. H. and Robinson, D. A.},
doi = {10.5194/essd-7-137-2015},
isbn = {1866-3516},
issn = {18663516},
journal = {Earth System Science Data},
number = {1},
pages = {137--142},
title = {{A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring}},
volume = {7},
year = {2015}
}
@techreport{Evans2020,
address = {Australia},
author = {Evans, A. and Jones, D. A and Smalley, Robert and Lellyett, S.},
isbn = {978-1-925738-12-4},
publisher = {Bureau of Meteorology (BOM)},
series = {Bureau Research Report – 41},
title = {{An enhanced gridded rainfall analysis scheme for Australia}},
url = {http://www.bom.gov.au/research/publications/researchreports/BRR-041.pdf},
year = {2020}
}
@misc{Fetterer2017,
address = {Boulder, CO, USA},
author = {Fetterer, F. and Knowles, K and Meier, W. N. and Savoie, M. H. and Windnagel, A. K.},
doi = {10.7265/N5K072F8},
publisher = {National Snow {\&} Ice Data Center (NSIDC)},
title = {{Sea Ice Index, Version 3}},
url = {https://dx.doi.org/10.7265/N5K072F8},
year = {2017}
}
@article{Fioletov2002,
abstract = {Six data sets of monthly average zonal total ozone were intercompared and then used to estimate latitudinal and global total ozone temporal variations and trends. The data sets were prepared by different groups and are based on TOMS, SBUV-SBUV/2, GOME, and ground-based measurements. Different approaches have been used to homogenize the records over the period 1979–2000. Systematic differences of up to 3{\%} were found between different data sets for zonal and global total ozone area weighted average values. However, when these systematic differences were removed by deseasonalizing the data, the residuals agreed to within ±0.5{\%} of the long-term mean ozone values. All data sets show changes in the rate of the total ozone decline in recent years. While global ozone was fairly constant during the 1990s, the average values of the 1990s are about 2–3{\%} lower than those of the late 1970s. About 38{\%} of the global ozone is located between 25°S and 25°N where the data show no decline. The strongest decline and the largest variability occur over the 35°N–60°N zone during the winter-spring season with the largest negative deviations occurring in 1993 and 1995. The decline in autumn is much smaller at these latitudes. Over the 35°S–60°S zone the ozone decline shows less seasonal dependence, and the largest deviations there were observed in 1985 and 1997. Sliding 11-year trends were calculated to estimate ozone changes over different time intervals. The first interval was from 1964 to 1974, and the last interval was from 1990 to 2000. The steepest year-round trends, of up to −5{\%} per decade, occurred in the 11-year periods ending between 1992 and 1997 over the 35°–60°N zone and between 1985 and 1993 over the 35°–55°S zone. More recent 11-year trends have smaller declines.},
author = {Fioletov, V E and Bodeker, G E and Miller, A J and McPeters, R D and Stolarski, R},
doi = {10.1029/2001JD001350},
journal = {Journal of Geophysical Research: Atmospheres},
number = {D22},
pages = {ACH 21--1--ACH 21--14},
title = {{Global and zonal total ozone variations estimated from ground-based and satellite measurements: 1964–2000}},
volume = {107},
year = {2002}
}
@article{Fogt2009,
abstract = {This second paper examines the Southern Hemisphere annular mode (SAM) variability from reconstructions, observed indices, and simulations from 17 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) models from 1865 to 2005. Comparisons reveal the models do not fully simulate the duration of strong natural variability within the reconstructions during the 1930s and 1960s. Seasonal indices are examined to understand the relative roles of forced and natural fluctuations. The models capture the recent (1957-2005) positive SAM trends in austral summer, which reconstructions indicate is the strongest trend during the last 150 yr; ozone depletion is the dominant mechanism driving these trends. In autumn, negative trends after 1930 in the reconstructions are stronger than the recent positive trend. Furthermore, model trends in autumn during 1957-2005 are the most different from observations. Both of these conditions suggest the recent autumn trend is most likely natural climate variability, with external forcing playing a secondary role. Many models also produce significant spring trends during this period not seen in observations. Although insignificant, these differences arise because of vastly different spatial structures in the Southern Hemisphere pressure trends. As the trend differences between models and observations in austral spring have been increasing over the last 30 yr, care must be exercised when examining the future SAM projections and their impacts in this season. {\textcopyright} 2009 American Meteorological Society.},
author = {Fogt, Ryan L. and Perlwitz, Judith and Monaghan, Andrew J. and Bromwich, David H. and Jones, Julie M. and Marshall, Gareth J.},
doi = {10.1175/2009JCLI2786.1},
issn = {08948755},
journal = {Journal of Climate},
number = {20},
pages = {5346--5365},
title = {{Historical SAM variability. Part II: Twentieth-century variability and trends from reconstructions, Observations, and the IPCC AR4 models}},
volume = {22},
year = {2009}
}
@incollection{Francey2003,
address = {Australia},
author = {Francey, R J and Steele, L. P. and Spencer, D A and Langenfelds, R. L. and Law, R M and Krummel, P.B. and Fraser, P.J. and Etheridge, D. M. and Derek, N. and Coram, S. A and Cooper, L. N. and Allison, C. E. and Porter, L and Baly, S.},
booktitle = {Baseline Atmospheric Program Australia 1999–2000},
editor = {Tindale, N W and Derek, N and Fraser, P J},
pages = {42--53},
publisher = {Bureau of Meteorology (BOM) and CSIRO Atmospheric Research},
title = {{The CSIRO (Australia) measurement of greenhouse gases in the global atmosphere}},
url = {http://www.cmar.csiro.au/e-print/open/baseline{\_}1999-2000.pdf},
year = {2003}
}
@article{Frederikse2020,
author = {Frederikse, Thomas and Landerer, Felix and Caron, Lambert and Adhikari, Surendra and Parkes, David and Humphrey, Vincent W and Dangendorf, S{\"{o}}nke and Hogarth, Peter and Zanna, Laure and Cheng, Lijing and Wu, Yun Hao},
doi = {10.1038/s41586-020-2591-3},
isbn = {4158602025},
issn = {14764687},
journal = {Nature},
number = {7821},
pages = {393--397},
publisher = {Springer US},
title = {{The causes of sea-level rise since 1900}},
volume = {584},
year = {2020}
}
@article{Frederikse2018,
author = {Frederikse, Thomas and Jevrejeva, Svetlana and Riva, Riccardo E M and Dangendorf, S{\"{o}}nke},
doi = {10.1175/JCLI-D-17-0502.1},
issn = {08948755},
journal = {Journal of Climate},
number = {3},
pages = {1267--1280},
title = {{A consistent sea-level reconstruction and its budget on basin and global scales over 1958–2014}},
volume = {31},
year = {2018}
}
@article{Freeman2017,
abstract = {ABSTRACT We highlight improvements to the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) in the latest Release 3.0 (R3.0; covering 1662–2014). ICOADS is the most widely used freely available collection of surface marine observations, providing data for the construction of gridded analyses of sea surface temperature, estimates of air–sea interaction and other meteorological variables. ICOADS observations are assimilated into all major atmospheric, oceanic and coupled reanalyses, further widening its impact. R3.0 therefore includes changes designed to enable effective exchange of information describing data quality between ICOADS, reanalysis centres, data set developers, scientists and the public. These user-driven innovations include the assignment of a unique identifier (UID) to each marine report – to enable tracing of observations, linking with reports and improved data sharing. Other revisions and extensions of the ICOADS' International Maritime Meteorological Archive common data format incorporate new near-surface oceanographic data elements and cloud parameters. Many new input data sources have been assembled, and updates and improvements to existing data sources, or removal of erroneous data, made. Coupled with enhanced ‘preliminary' monthly data and product extensions past 2014, R3.0 provides improved support of climate assessment and monitoring, reanalyses and near-real-time applications.},
author = {Freeman, Eric and Woodruff, Scott D and Worley, Steven J and Lubker, Sandra J and Kent, Elizabeth C and Angel, William E and Berry, David I and Brohan, Philip and Eastman, Ryan and Gates, Lydia and Gloeden, Wolfgang and Ji, Zaihua and Lawrimore, Jay and Rayner, Nick A and Rosenhagen, Gudrun and Smith, Shawn R},
doi = {10.1002/joc.4775},
journal = {International Journal of Climatology},
number = {5},
pages = {2211--2232},
title = {{ICOADS Release 3.0: a major update to the historical marine climate record}},
volume = {37},
year = {2017}
}
@article{essd-12-3269-2020,
author = {Friedlingstein, P and O'Sullivan, M and Jones, M W and Andrew, R M and Hauck, J and Olsen, A and Peters, G P and Peters, W and Pongratz, J and Sitch, S and {Le Qu{\'{e}}r{\'{e}}}, C and Canadell, J G and Ciais, P and Jackson, R B and Alin, S and Arag{\~{a}}o, L E O C and Arneth, A and Arora, V and Bates, N R and Becker, M and Benoit-Cattin, A and Bittig, H C and Bopp, L and Bultan, S and Chandra, N and Chevallier, F and Chini, L P and Evans, W and Florentie, L and Forster, P M and Gasser, T and Gehlen, M and Gilfillan, D and Gkritzalis, T and Gregor, L and Gruber, N and Harris, I and Hartung, K and Haverd, V and Houghton, R A and Ilyina, T and Jain, A K and Joetzjer, E and Kadono, K and Kato, E and Kitidis, V and Korsbakken, J I and Landsch{\"{u}}tzer, P and Lef{\`{e}}vre, N and Lenton, A and Lienert, S and Liu, Z and Lombardozzi, D and Marland, G and Metzl, N and Munro, D R and Nabel, J E M S and Nakaoka, S.-I. and Niwa, Y and O'Brien, K and Ono, T and Palmer, P I and Pierrot, D and Poulter, B and Resplandy, L and Robertson, E and R{\"{o}}denbeck, C and Schwinger, J and S{\'{e}}f{\'{e}}rian, R and Skjelvan, I and Smith, A J P and Sutton, A J and Tanhua, T and Tans, P P and Tian, H and Tilbrook, B and van der Werf, G and Vuichard, N and Walker, A P and Wanninkhof, R and Watson, A J and Willis, D and Wiltshire, A J and Yuan, W and Yue, X and Zaehle, S},
doi = {10.5194/essd-12-3269-2020},
journal = {Earth System Science Data},
number = {4},
pages = {3269--3340},
title = {{Global Carbon Budget 2020}},
url = {https://essd.copernicus.org/articles/12/3269/2020/},
volume = {12},
year = {2020}
}
@article{Frith2017,
author = {Frith, S M and Stolarski, R S and Kramarova, N A and McPeters, R D},
doi = {10.5194/acp-17-14695-2017},
journal = {Atmospheric Chemistry and Physics},
number = {23},
pages = {14695--14707},
title = {{Estimating uncertainties in the SBUV Version 8.6 merged profile ozone data set}},
volume = {17},
year = {2017}
}
@article{Funk2015,
abstract = {The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to 'smart' interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.},
author = {Funk, Chris and Peterson, Pete and Landsfeld, Martin and Pedreros, Diego and Verdin, James and Shukla, Shraddhanand and Husak, Gregory and Rowland, James and Harrison, Laura and Hoell, Andrew and Michaelsen, Joel},
doi = {10.1038/sdata.2015.66},
isbn = {0034-4257},
issn = {2052-4463},
journal = {Scientific Data},
month = {dec},
number = {1},
pages = {150066},
pmid = {26646728},
title = {{The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes}},
url = {http://www.nature.com/articles/sdata201566},
volume = {2},
year = {2015}
}
@article{Gaillard2015,
abstract = {The In Situ Analysis System (ISAS) was developed to produce gridded fields of temperature and salinity that preserve as much as possible the time and space sampling capabilities of the Argo network of profiling floats. Since the first global reanalysis performed in 2009, the system has evolved, and a careful delayed-mode processing of the 2002–12 dataset has been carried out using version 6 of ISAS and updating the statistics to produce the ISAS13 analysis. This last version is now implemented as the operational analysis tool at the Coriolis data center. The robustness of the results with respect to the system evolution is explored through global quantities of climatological interest: the ocean heat content and the steric height. Estimates of errors consistent with the methodology are computed. This study shows that building reliable statistics on the fields is fundamental to improve the monthly estimates and to determine the absolute error bars. The new mean fields and variances deduced from the ISAS13 reanalysis and dataset show significant changes relative to the previous ISAS estimates, in particular in the Southern Ocean, justifying the iterative procedure. During the decade covered by Argo, the intermediate waters appear warmer and saltier in the North Atlantic and fresher in the Southern Ocean than in World Ocean Atlas 2005 long-term mean. At interannual scale, the impact of ENSO on the ocean heat content and steric height is observed during the 2006/07 and 2009/10 events captured by the network.},
annote = {doi: 10.1175/JCLI-D-15-0028.1},
author = {Gaillard, Fabienne and Reynaud, Thierry and Thierry, Virginie and Kolodziejczyk, Nicolas and von Schuckmann, Karina},
doi = {10.1175/JCLI-D-15-0028.1},
issn = {0894-8755},
journal = {Journal of Climate},
month = {feb},
number = {4},
pages = {1305--1323},
publisher = {American Meteorological Society},
title = {{In Situ–Based Reanalysis of the Global Ocean Temperature and Salinity with ISAS: Variability of the Heat Content and Steric Height}},
url = {https://doi.org/10.1175/JCLI-D-15-0028.1 http://journals.ametsoc.org/doi/10.1175/JCLI-D-15-0028.1},
volume = {29},
year = {2016}
}
@article{Garay2017,
author = {Garay, M J and Kalashnikova, O V and Bull, M A},
doi = {10.5194/acp-17-5095-2017},
journal = {Atmospheric Chemistry and Physics},
number = {8},
pages = {5095--5106},
title = {{Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product using AERONET-DRAGON data}},
volume = {17},
year = {2017}
}
@article{Gaudel2018,
author = {Gaudel, A. and Cooper, O. R. and Ancellet, G. and Barret, B. and Boynard, A. and Burrows, J. P. and Clerbaux, C. and Coheur, P. -F. and Cuesta, J. and Cuevas, E. and Doniki, S. and Dufour, G. and Ebojie, F. and Foret, G. and Garcia, O. and Mu{\~{n}}os, M. J. Granados and Hannigan, J. W. and Hase, F. and Huang, G. and Hassler, B. and Hurtmans, D. and Jaffe, D. and Jones, N. and Kalabokas, P. and Kerridge, B. and Kulawik, S. S. and Latter, B. and Leblanc, T. and Flochmo{\"{e}}n, E. Le and Lin, W. and Liu, J. and Liu, X. and Mahieu, E. and McClure-Begley, A. and Neu, J. L. and Osman, M. and Palm, M. and Petetin, H. and Petropavlovskikh, I. and Querel, R. and Rahpoe, N. and Rozanov, A. and Schultz, M. G. and Schwab, J. and Siddans, R. and Smale, D. and Steinbacher, M. and Tanimoto, H. and Tarasick, D. W. and Thouret, V. and Thompson, A. M. and Trickl, T. and Weatherhead, E. and Wespes, C. and Worden, H. M. and Vigouroux, C. and Xu, X. and Zeng, G. and Ziemke, J.},
doi = {10.1525/elementa.291},
journal = {Elementa: Science of the Anthropocene},
number = {39},
title = {{Tropospheric Ozone Assessment Report: Present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation}},
volume = {6},
year = {2018}
}
@article{Gaudel2020,
author = {Gaudel, Audrey and Cooper, Owen R. and Chang, Kai-Lan and Bourgeois, Ilann and Ziemke, Jerry R. and Strode, Sarah A. and Oman, Luke D. and Sellitto, Pasquale and N{\'{e}}d{\'{e}}lec, Philippe and Blot, Romain and Thouret, Val{\'{e}}rie and Granier, Claire},
doi = {10.1126/sciadv.aba8272},
issn = {2375-2548},
journal = {Science Advances},
month = {aug},
number = {34},
pages = {eaba8272},
title = {{Aircraft observations since the 1990s reveal increases of tropospheric ozone at multiple locations across the Northern Hemisphere}},
url = {https://www.science.org/doi/10.1126/sciadv.aba8272},
volume = {6},
year = {2020}
}
@article{Ge2014,
abstract = {Extending phenological records into the past is essential for the understanding of past ecological change and evaluating the effects of climate change on ecosystems. A growing body of historical phenological information is now available for Europe, North America, and Asia. In East Asia, long-term phenological series are still relatively scarce. This study extracted plant phenological observations from old diaries in the period 1834-1962. A spring phenology index (SPI) for the modern period (1963-2009) was defined as the mean flowering time of three shrubs (first flowering of Amygdalus davidiana and Cercis chinensis, 50{\%} of full flowering of Paeonia suffruticosa) according to the data availability. Applying calibrated transfer functions from the modern period to the historical data, we reconstructed a continuous SPI time series across eastern China from 1834 to 2009. In the recent 30years, the SPI is 2.1-6.3days earlier than during any other consecutive 30year period before 1970. A moving linear trend analysis shows that the advancing trend of SPI over the past three decades reaches upward of 4.1d/decade, which exceeds all previously observed trends in the past 30year period. In addition, the SPI series correlates significantly with spring (February to April) temperatures in the study area, with an increase in spring temperature of 1C inducing an earlier SPI by 3.1days. These shifts of SPI provide important information regarding regional vegetation-climate relationships, and they are helpful to assess long term of climate change impacts on biophysical systems and biodiversity. Key {\textcopyright}2014. American Geophysical Union. All Rights Reserved.},
author = {Ge, Quansheng and Wang, Huanjiong and Zheng, Jingyun and This, Rutishauser and Dai, Junhu},
doi = {10.1002/2013JG002565},
issn = {21698961},
journal = {Journal of Geophysical Research: Biogeosciences},
number = {3},
pages = {301--311},
title = {{A 170 year spring phenology index of plants in eastern China}},
volume = {119},
year = {2014}
}
@article{DOI: 10.1080/1755876X.2020.1785097,
author = {Gehlen, M. and Chau, T. and Conchon, A. and Denvil-Sommer, A. and Chevallier, F. and Vrac, M. and Mejia, C},
doi = {10.1080/1755876X.2020.1785097},
journal = {Journal of Operational Oceanography},
number = {sup1},
pages = {s64--s67},
title = {{Ocean acidification [in “The Copernicus Marine Service Ocean State Report, Issue 4”]}},
volume = {13},
year = {2020}
}
@article{Gelaro2017,
abstract = { AbstractThe Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA's terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams and converged to a single near-real-time stream in mid-2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC). },
author = {Gelaro, Ronald and McCarty, Will and Su{\'{a}}rez, Max J and Todling, Ricardo and Molod, Andrea and Takacs, Lawrence and Randles, Cynthia A and Darmenov, Anton and Bosilovich, Michael G and Reichle, Rolf and Wargan, Krzysztof and Coy, Lawrence and Cullather, Richard and Draper, Clara and Akella, Santha and Buchard, Virginie and Conaty, Austin and da Silva, Arlindo M and Gu, Wei and Kim, Gi-Kong and Koster, Randal and Lucchesi, Robert and Merkova, Dagmar and Nielsen, Jon Eric and Partyka, Gary and Pawson, Steven and Putman, William and Rienecker, Michele and Schubert, Siegfried D and Sienkiewicz, Meta and Zhao, Bin},
doi = {10.1175/JCLI-D-16-0758.1},
journal = {Journal of Climate},
number = {14},
pages = {5419--5454},
title = {{The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)}},
volume = {30},
year = {2017}
}
@article{acp-19-6269-2019,
author = {Georgoulias, A K and van der A, R J and Stammes, P and Boersma, K F and Eskes, H J},
doi = {10.5194/acp-19-6269-2019},
journal = {Atmospheric Chemistry and Physics},
number = {9},
pages = {6269--6294},
title = {{Trends and trend reversal detection in 2 decades of tropospheric NO2 satellite observations}},
url = {https://acp.copernicus.org/articles/19/6269/2019/},
volume = {19},
year = {2019}
}
@article{Ghimire2014,
abstract = {Widespread anthropogenic land-cover change over the last five centuries has influenced the global climate system through both biogeochemical and biophysical processes. Models indicate that warming from carbon emissions associated with land-cover conversion have been partially offset by cooling from elevated albedo, but considerable uncertainty remains partly because of uncertainty in model treatments of albedo. This study incorporates a new spatially and temporally explicit, land-cover specific albedo product derived from MODIS with a historical land-use dataset (Land Use Harmonization product) to provide more precise, observationally derived estimates of albedo impacts from anthropogenic land-cover change with a complete range of dataset specific uncertainty. The mean annual global albedo increase due to land-cover change during 1700–2005 was estimated as 0.00106 ± 0.00008 (mean ± standard deviation), mainly driven by snow exposure due to land-cover transitions from natural vegetation to agriculture. This translates to a top-of-atmosphere (TOA) radiative cooling of −0.15 ± 0.1 W m−2 (mean ± standard deviation). Our estimate was in the middle of the IPCC AR5 range of −0.05 to −0.25 W m−2, and incorporates variability in albedo within land-cover classes.},
author = {Ghimire, Bardan and Williams, Christopher A. and Masek, Jeffrey and Gao, Feng and Wang, Zhuosen and Schaaf, Crystal and He, Tao},
doi = {10.1002/2014GL061671},
issn = {19448007},
journal = {Geophysical Research Letters},
keywords = {MODIS,albedo,global climate system,land cover change,radiative forcing},
number = {24},
pages = {9087--9096},
title = {{Global albedo change and radiative cooling from anthropogenic land cover change, 1700 to 2005 based on MODIS, land use harmonization, radiative kernels, and reanalysis}},
volume = {41},
year = {2014}
}
@article{Giles2019,
abstract = {Abstract. The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases. ]]{\textgreater}},
author = {Giles, David M. and Sinyuk, Alexander and Sorokin, Mikhail G. and Schafer, Joel S. and Smirnov, Alexander and Slutsker, Ilya and Eck, Thomas F. and Holben, Brent N. and Lewis, Jasper R. and Campbell, James R. and Welton, Ellsworth J. and Korkin, Sergey V. and Lyapustin, Alexei I.},
doi = {10.5194/amt-12-169-2019},
issn = {1867-8548},
journal = {Atmospheric Measurement Techniques},
month = {jan},
number = {1},
pages = {169--209},
title = {{Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements}},
url = {https://www.atmos-meas-tech.net/12/169/2019/},
volume = {12},
year = {2019}
}
@book{GlaThiDaConsortium2019,
address = {Zurich, Switzerland},
author = {{GlaThiDa Consortium}},
doi = {10.5904/wgms-glathida-2019-03},
publisher = {World Glacier Monitoring Service},
title = {{Glacier Thickness Database 3.0.1}},
url = {https://dx.doi.org/10.5904/wgms-glathida-2019-03},
year = {2019}
}
@article{amt-13-3081-2020,
author = {Gleisner, H and Lauritsen, K B and Nielsen, J K and Syndergaard, S},
doi = {10.5194/amt-13-3081-2020},
journal = {Atmospheric Measurement Techniques},
number = {6},
pages = {3081--3098},
title = {{Evaluation of the 15-year ROM SAF monthly mean GPS radio occultation climate data record}},
url = {https://amt.copernicus.org/articles/13/3081/2020/},
volume = {13},
year = {2020}
}
@article{Gobron2018,
author = {Gobron, N.},
doi = {10.1175/2018BAMSStateoftheClimate.1},
journal = {Bulletin of the American Meteorological Society},
pages = {S62--S63},
title = {{Terrestrial Vegetation Activity [in “State of the Climate in 2017”]}},
volume = {99},
year = {2018}
}
@article{bg-7-3067-2010,
author = {Gonz{\'{a}}lez-D{\'{a}}vila, M and Santana-Casiano, J M and Rueda, M J and Llin{\'{a}}s, O},
doi = {10.5194/bg-7-3067-2010},
journal = {Biogeosciences},
number = {10},
pages = {3067--3081},
title = {{The water column distribution of carbonate system variables at the ESTOC site from 1995 to 2004}},
url = {https://www.biogeosciences.net/7/3067/2010/},
volume = {7},
year = {2010}
}
@article{doi:10.1002/2013JC009067,
abstract = {We present version 4 of the Met Office Hadley Centre “EN” series of data sets of global quality controlled ocean temperature and salinity profiles and monthly objective analyses, which covers the period 1900 to present. We briefly describe the EN4 data sources, processing, quality control procedures, and the method of generating the analyses. In particular, we highlight improvements relative to previous versions, which include a new duplicate profile removal procedure and the inclusion of three new quality control checks. We discuss in detail a novel method for providing uncertainty estimates for the objective analyses and improving the background error variance estimates used by the analysis system. These were calculated using an iterative method that is relatively robust to initial misspecification of background error variances. We also show how the method can be used to identify issues with the analyses such as those caused by misspecification of error variances and demonstrate the impact of changes in the observing system on the uncertainty in the analyses.},
author = {Good, Simon A and Martin, Matthew J and Rayner, Nick A},
doi = {10.1002/2013JC009067},
journal = {Journal of Geophysical Research: Oceans},
keywords = {objective analysis,ocean observations,quality control,uncertainty estimation},
number = {12},
pages = {6704--6716},
title = {{EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2013JC009067},
volume = {118},
year = {2013}
}
@article{Gregor2021,
author = {Gregor, L and Gruber, N},
doi = {10.5194/essd-13-777-2021},
journal = {Earth System Science Data},
number = {2},
pages = {777--808},
title = {{OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification}},
volume = {13},
year = {2021}
}
@misc{Gregor2019,
author = {Gregor, Luke},
doi = {10.6084/m9.figshare.7894976.v1},
publisher = {figshare},
title = {{Global surface ocean pCO2 from CSIR-ML6 (version 2019a)}},
url = {https://figshare.com/articles/Global{\_}surface{\_}ocean{\_}pCO2{\_}from{\_}CSIR-ML6{\_}version{\_}2019a{\_}/7894976},
year = {2019}
}
@article{Gruber2017,
abstract = {IEEE We propose a method for merging soil moisture retrievals from spaceborne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from using triple collocation analysis (TCA). In regions where TCA is deemed unreliable, we use correlation significance levels (p-values) as indicator for retrieval quality to decide whether to use active data only, passive data only, or an unweighted average. We apply the proposed merging scheme to active retrievals from advanced scatterometer and passive retrievals from the Advanced Microwave Scanning Radiometer--Earth Observing System using Global Land Data Assimilation System-Noah to complement the triplet required for TCA. The merged time series is evaluated against soil moisture estimates from ERA-Interim/Land and in situ measurements from the International Soil Moisture Network using the European Space Agency's (ESA's) current Climate Change Initiative--Soil Moisture (ESA CCI SM) product version v02.3 as benchmark merging scheme. Results show that the p-value classification provides a robust basis for decisions regarding using either active or passive data alone, or an unweighted average in cases where relative weights cannot be estimated reliably, and that the weights estimated from TCA in almost all cases outperform the ternary decision upon which the ESA CCI SM v02.3 is based. The proposed method forms the basis for the new ESA CCI SM product version v03.x and higher.},
author = {Gruber, Alexander and Dorigo, Wouter Arnoud and Crow, Wade and Wagner, Wolfgang},
doi = {10.1109/TGRS.2017.2734070},
issn = {01962892},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
number = {12},
pages = {6780--6792},
title = {{Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals}},
volume = {55},
year = {2017}
}
@article{Gruber1193,
abstract = {The ocean is an important sink for anthropogenic CO2 and has absorbed roughly 30{\%} of our emissions between the beginning of the industrial revolution and the mid-1990s. This effect is an important moderator of climate change, but can we count on it to remain as strong in the future? Gruber et al. calculated the ocean uptake of anthropogenic CO2 for the interval from 1994 to 2007, which continued as expected. They also observed clear regional deviations from this pattern, suggesting that there is no guarantee that uptake will remain as robust with time.Science, this issue p. 1193We quantify the oceanic sink for anthropogenic carbon dioxide (CO2) over the period 1994 to 2007 by using observations from the global repeat hydrography program and contrasting them to observations from the 1990s. Using a linear regression{\{}$\backslash$textendash{\}}based method, we find a global increase in the anthropogenic CO2 inventory of 34 {\{}$\backslash$textpm{\}} 4 petagrams of carbon (Pg C) between 1994 and 2007. This is equivalent to an average uptake rate of 2.6 {\{}$\backslash$textpm{\}} 0.3 Pg C year-1 and represents 31 {\{}$\backslash$textpm{\}} 4{\%} of the global anthropogenic CO2 emissions over this period. Although this global ocean sink estimate is consistent with the expectation of the ocean uptake having increased in proportion to the rise in atmospheric CO2, substantial regional differences in storage rate are found, likely owing to climate variability{\{}$\backslash$textendash{\}}driven changes in ocean circulation.},
author = {Gruber, Nicolas and Clement, Dominic and Carter, Brendan R and Feely, Richard A and van Heuven, Steven and Hoppema, Mario and Ishii, Masao and Key, Robert M and Kozyr, Alex and Lauvset, Siv K and {Lo Monaco}, Claire and Mathis, Jeremy T and Murata, Akihiko and Olsen, Are and Perez, Fiz F and Sabine, Christopher L and Tanhua, Toste and Wanninkhof, Rik},
doi = {10.1126/science.aau5153},
issn = {0036-8075},
journal = {Science},
number = {6432},
pages = {1193--1199},
publisher = {American Association for the Advancement of Science},
title = {{The oceanic sink for anthropogenic CO2 from 1994 to 2007}},
url = {https://science.sciencemag.org/content/363/6432/1193},
volume = {363},
year = {2019}
}
@article{Gurney2003,
abstract = {Spatial and temporal variations of atmospheric CO2 concentrations contain information about surface sources and sinks, which can be quantitatively interpreted through tracer transport inversion. Previous CO2 inversion calculations obtained differing results due to different data, methods and transport models used. To isolate the sources of uncertainty, we have conducted a set of annual mean inversion experiments in which 17 different transport models or model variants were used to calculate regional carbon sources and sinks from the same data with a standardized method. Simulated transport is a significant source of uncertainty in these calculations, particularly in the response to prescribed "background" fluxes due to fossil fuel combustion, a balanced terrestrial biosphere, and air-sea gas exchange. Individual model-estimated fluxes are often a direct reflection of their response to these background fluxes. Models that generate strong surface maxima near background exchange locations tend to require larger uptake near those locations. Models with weak surface maxima tend to have less uptake in those same regions but may infer small sources downwind. In some cases, individual model flux estimates cannot be analyzed through simple relationships to background flux responses but are likely due to local transport differences or particular responses at individual CO2 observing locations. The response to the background biosphere exchange generates the greatest variation in the estimated fluxes, particularly over land in the Northern Hemisphere. More observational data in the tropical regions may help in both lowering the uncertain tropical land flux uncertainties and constraining the northern land estimates because of compensation between these two broad regions in the inversion. More optimistically, examination of the model-mean retrieved fluxes indicates a general insensitivity to the prior fluxes and the prior flux uncertainties. Less uptake in the Southern Ocean than implied by oceanographic observations, and an evenly distributed northern land sink, remain in spite of changes in this aspect of the inversion setup.},
author = {Gurney, Kevin Robert and Law, Rachel M. and Denning, A. Scott and Rayner, Peter J. and Baker, David and Bousquet, Philippe and Bruhwiler, Lori and Chen, Yu Han and Ciais, Philippe and Fan, Songmiao and Fung, Inez Y. and Gloor, Manuel and Heimann, Martin and Higuchi, Kaz and John, Jasmin and Kowalczyk, Eva and Maki, Takashi and Maksyutov, Shamil and Peylin, Philippe and Prather, Michael and Pak, Bernard C. and Sarmiento, Jorge and Taguchi, Shoichi and Takahashi, Taro and Yuen, Chiu Wai},
doi = {10.1034/j.1600-0889.2003.00049.x},
isbn = {0280-6509},
issn = {02806509},
journal = {Tellus, Series B: Chemical and Physical Meteorology},
month = {apr},
number = {2},
pages = {555--579},
title = {{TransCom 3 CO2 inversion intercomparison: 1. Annual mean control results and sensitivity to transport and prior flux information}},
volume = {55},
year = {2003}
}
@article{GUTMAN2013249,
abstract = {The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from four sensors: Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. In general, the three latest GLS datasets are of a better quality than the GLS-1990 and GLS-1975 datasets, with most of the imagery (85{\%}) having cloud cover of less than 10{\%}, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This paper provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.},
author = {Gutman, Garik and Huang, Chengquan and Chander, Gyanesh and Noojipady, Praveen and Masek, Jeffrey G},
doi = {https://doi.org/10.1016/j.rse.2013.02.026},
issn = {0034-4257},
journal = {Remote Sensing of Environment},
keywords = {Global Land Surveys,Landsat,Quality assessment},
pages = {249--265},
title = {{Assessment of the NASA–USGS Global Land Survey (GLS) datasets}},
url = {http://www.sciencedirect.com/science/article/pii/S0034425713000758},
volume = {134},
year = {2013}
}
@article{Haddad1997a,
author = {Haddad, Ziad S and Smith, Eric A and Kummerow, Christian D and Iguchi, Toshio and Farrar, Michael R and Durden, Stephen L and Alves, Marcos and Olson, William S},
doi = {10.2151/jmsj1965.75.4_799},
journal = {Journal of the Meteorological Society of Japan. Series II},
number = {4},
pages = {799--809},
title = {{The TRMM Day-1 Radar/Radiometer Combined Rain-Profiling Algorithm}},
volume = {75},
year = {1997}
}
@article{Haimberger2012,
abstract = { AbstractThis article describes progress in the homogenization of global radiosonde temperatures with updated versions of the Radiosonde Observation Correction Using Reanalyses (RAOBCORE) and Radiosonde Innovation Composite Homogenization (RICH) software packages. These are automated methods to homogenize the global radiosonde temperature dataset back to 1958. The break dates are determined from analysis of time series of differences between radiosonde temperatures (obs) and background forecasts (bg) of climate data assimilation systems used for the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and the ongoing interim ECMWF Re-Analysis (ERA-Interim).RAOBCORE uses the obs−bg time series also for estimating the break sizes. RICH determines the break sizes either by comparing the observations of a tested time series with observations of neighboring radiosonde time series (RICH-obs) or by comparing their background departures (RICH-$\tau$). Consequently RAOBCORE results may be influenced by inhomogeneities in the bg, whereas break size estimation with RICH-obs is independent of the bg. The adjustment quality of RICH-obs, on the other hand, may suffer from large interpolation errors at remote stations. RICH-$\tau$ is a compromise that substantially reduces interpolation errors at the cost of slight dependence on the bg.Adjustment uncertainty is estimated by comparing the three methods and also by varying parameters in RICH. The adjusted radiosonde time series are compared with recent temperature datasets based on (Advanced) Microwave Sounding Unit [(A)MSU] radiances. The overall spatiotemporal consistency of the homogenized dataset has improved compared to earlier versions, particularly in the presatellite era. Vertical profiles of temperature trends are more consistent with satellite data as well. },
author = {Haimberger, Leopold and Tavolato, Christina and Sperka, Stefan},
doi = {10.1175/JCLI-D-11-00668.1},
journal = {Journal of Climate},
number = {23},
pages = {8108--8131},
title = {{Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations}},
volume = {25},
year = {2012}
}
@article{Hall2011,
abstract = {Abstract. Sulfur hexafluoride (SF6) is a potent greenhouse gas and useful atmospheric tracer. Measurements of SF6 on global and regional scales are necessary to estimate emissions and to verify or examine the performance of atmospheric transport models. Typical precision for common gas chromatographic methods with electron capture detection (GC-ECD) is 1–2{\%}. We have modified a common GC-ECD method to achieve measurement precision of 0.5{\%} or better. Global mean SF6 measurements were used to examine changes in the growth rate of SF6 and corresponding SF6 emissions. Global emissions and mixing ratios from 2000–2008 are consistent with recently published work. More recent observations show a 10{\%} decline in SF6 emissions in 2008–2009, which seems to coincide with a decrease in world economic output. This decline was short-lived, as the global SF6 growth rate has recently increased to near its 2007–2008 maximum value of 0.30±0.03 pmol mol−1 (ppt) yr−1 (95{\%} C.L.).},
author = {Hall, B. D. and Dutton, G. S. and Mondeel, D. J. and Nance, J. D. and Rigby, M. and Butler, J. H. and Moore, F. L. and Hurst, D. F. and Elkins, J. W.},
doi = {10.5194/amt-4-2441-2011},
issn = {1867-8548},
journal = {Atmospheric Measurement Techniques},
month = {nov},
number = {11},
pages = {2441--2451},
title = {{Improving measurements of SF6 for the study of atmospheric transport and emissions}},
url = {https://amt.copernicus.org/articles/4/2441/2011/},
volume = {4},
year = {2011}
}
@article{Harada2016,
author = {Harada, Yayoi and Kamahori, Hirotaka and Kobayashi, Chiaki and Endo, Hirokazu and Kobayashi, Shinya and Ota, Yukinari and Onoda, Hirokatsu and Onogi, Kazutoshi and Miyaoka, Kengo and Takahashi, Kiyotoshi},
doi = {10.2151/jmsj.2016-015},
journal = {Journal of the Meteorological Society of Japan. Series II},
number = {3},
pages = {269--302},
title = {{The JRA-55 Reanalysis: Representation of atmospheric circulation and climate variability}},
volume = {94},
year = {2016}
}
@article{Harris2014,
abstract = {This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). {\textcopyright} 2013 Royal Meteorological Society},
author = {Harris, I. and Jones, P. D. and Osborn, T. J. and Lister, D. H.},
doi = {10.1002/joc.3711},
isbn = {1097-0088},
issn = {08998418},
journal = {International Journal of Climatology},
number = {3},
pages = {623--642},
title = {{Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset}},
volume = {34},
year = {2014}
}
@article{Harris2020,
abstract = {CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901–2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.},
author = {Harris, Ian and Osborn, Timothy J and Jones, Phil and Lister, David},
doi = {10.1038/s41597-020-0453-3},
issn = {2052-4463},
journal = {Scientific Data},
number = {1},
pages = {109},
title = {{Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset}},
volume = {7},
year = {2020}
}
@article{Hawkins2013,
abstract = {Abstract In 1938, Guy Stewart Callendar was the first to demonstrate that the Earth's land surface was warming. Callendar also suggested that the production of carbon dioxide by the combustion of fossil fuels was responsible for much of this modern change in climate. This short note marks the 75th anniversary of Callendar's landmark study and demonstrates that his global land temperature estimates agree remarkably well with more recent analyses.},
author = {Hawkins, Ed and Jones, Phil. D},
doi = {https://doi.org/10.1002/qj.2178},
journal = {Quarterly Journal of the Royal Meteorological Society},
number = {677},
pages = {1961--1963},
title = {{On increasing global temperatures: 75 years after Callendar}},
volume = {139},
year = {2013}
}
@article{Hay2015,
abstract = {Estimating and accounting for twentieth-century global mean sea-level (GMSL) rise is critical to characterizing current and future human-induced sea-level change. Several previous analyses of tide gauge records1, 2, 3, 4, 5, 6—employing different methods to accommodate the spatial sparsity and temporal incompleteness of the data and to constrain the geometry of long-term sea-level change—have concluded that GMSL rose over the twentieth century at a mean rate of 1.6 to 1.9 millimetres per year. Efforts to account for this rate by summing estimates of individual contributions from glacier and ice-sheet mass loss, ocean thermal expansion, and changes in land water storage fall significantly short in the period before 19907. The failure to close the budget of GMSL during this period has led to suggestions that several contributions may have been systematically underestimated8. However, the extent to which the limitations of tide gauge analyses have affected estimates of the GMSL rate of change is unclear. Here we revisit estimates of twentieth-century GMSL rise using probabilistic techniques9, 10 and find a rate of GMSL rise from 1901 to 1990 of 1.2 ± 0.2 millimetres per year (90{\%} confidence interval). Based on individual contributions tabulated in the Fifth Assessment Report7 of the Intergovernmental Panel on Climate Change, this estimate closes the twentieth-century sea-level budget. Our analysis, which combines tide gauge records with physics-based and model-derived geometries of the various contributing signals, also indicates that GMSL rose at a rate of 3.0 ± 0.7 millimetres per year between 1993 and 2010, consistent with prior estimates from tide gauge records4. The increase in rate relative to the 1901–90 trend is accordingly larger than previously thought; this revision may affect some projections11 of future sea-level rise.},
author = {Hay, Carling C. and Morrow, Eric and Kopp, Robert E. and Mitrovica, Jerry X.},
doi = {10.1038/nature14093},
isbn = {1476-4687},
issn = {14764687},
journal = {Nature},
number = {7535},
pages = {481--484},
pmid = {25629092},
title = {{Probabilistic reanalysis of twentieth-century sea-level rise}},
volume = {517},
year = {2015}
}
@article{Haylock2008,
author = {Haylock, M. R. and Hofstra, N. and {Klein Tank}, A. M. G. and Klok, E. J. and Jones, P. D. and New, M.},
doi = {10.1029/2008JD010201},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Atmospheres},
month = {oct},
number = {D20},
pages = {D20119},
publisher = {Wiley-Blackwell},
title = {{A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006}},
volume = {113},
year = {2008}
}
@article{Hegglin2014a,
author = {Hegglin, M I and Plummer, D A and Shepherd, T G and Scinocca, J F and Anderson, J and Froidevaux, L and Funke, B and Hurst, D and Rozanov, A and Urban, J and von Clarmann, T and Walker, K A and Wang, H J and Tegtmeier, S and Weigel, K},
doi = {10.1038/ngeo2236},
journal = {Nature Geoscience},
month = {aug},
pages = {768},
publisher = {Nature Publishing Group},
title = {{Vertical structure of stratospheric water vapour trends derived from merged satellite data}},
volume = {7},
year = {2014}
}
@article{Herrera2016,
abstract = {Observational gridded products are commonly used to evaluate the performance of regional climate models. To this aim, gridded datasets should be comparable to the output of these models and, thus, should represent grid-cell area-averaged values and, whenever possible, they should be defined on the same spatial domains as the models, in order to avoid re-gridding or re-projection. In this study, we present an update of the Spain02 gridded observational dataset for daily precipitation and mean temperature building on the grids defined for the EURO-CORDEX initiative. In order to assess and intercompare different interpolation approaches, we analysed (1) two standard methodologies (ordinary kriging and thin plate splines), (2) three horizontal resolutions: 0.11 ∘ , 0.22 ∘ and 0.44 ∘ (matching the rotated EURO-CORDEX and ENSEMBLES grids), (3) two different approaches to guarantee either area-averaged or point representativity of the resulting grid values and (4) including/excluding orography as a covariable in the interpolation procedure. Besides introducing the new gridded datasets, in this work we also present some preliminary results on the sensitivity of temperature and precipitation (both mean and extreme regimes) to all these factors.},
author = {Herrera, S. and Fern{\'{a}}ndez, J. and Guti{\'{e}}rrez, J. M.},
doi = {10.1002/joc.4391},
isbn = {5072842649},
issn = {08998418},
journal = {International Journal of Climatology},
month = {feb},
number = {2},
pages = {900--908},
title = {{Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: assessing the effect of the interpolation methodology}},
url = {https://onlinelibrary.wiley.com/doi/10.1002/joc.4391},
volume = {36},
year = {2016}
}
@article{Hersbach2020a,
abstract = {Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis which, once completed, will embody a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards. This new reanalysis replaces the ERA-Interim reanalysis (spanning 1979 onwards) which was started in 2006. ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA-Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3-hourly at half the horizontal resolution). This paper describes the general set-up of ERA5, as well as a basic evaluation of characteristics and performance, with a focus on the dataset from 1979 onwards which is currently publicly available. Re-forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA-Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global-mean correlation with monthly-mean GPCP data is increased from 67{\%} to 77{\%}. In general, low-frequency variability is found to be well represented and from 10 hPa downwards general patterns of anomalies in temperature match those from the ERA-Interim, MERRA-2 and JRA-55 reanalyses.},
author = {Hersbach, Hans and Bell, Bill and Berrisford, Paul and Hirahara, Shoji and Hor{\'{a}}nyi, Andr{\'{a}}s and Mu{\~{n}}oz-Sabater, Joaqu{\'{i}}n and Nicolas, Julien and Peubey, Carole and Radu, Raluca and Schepers, Dinand and Simmons, Adrian and Soci, Cornel and Abdalla, Saleh and Abellan, Xavier and Balsamo, Gianpaolo and Bechtold, Peter and Biavati, Gionata and Bidlot, Jean and Bonavita, Massimo and {De Chiara}, Giovanna and Dahlgren, Per and Dee, Dick and Diamantakis, Michail and Dragani, Rossana and Flemming, Johannes and Forbes, Richard and Fuentes, Manuel and Geer, Alan and Haimberger, Leo and Healy, Sean and Hogan, Robin J. and H{\'{o}}lm, El{\'{i}}as and Janiskov{\'{a}}, Marta and Keeley, Sarah and Laloyaux, Patrick and Lopez, Philippe and Lupu, Cristina and Radnoti, Gabor and de Rosnay, Patricia and Rozum, Iryna and Vamborg, Freja and Villaume, Sebastien and Th{\'{e}}paut, Jean No{\"{e}}l},
doi = {10.1002/qj.3803},
issn = {1477870X},
journal = {Quarterly Journal of the Royal Meteorological Society},
keywords = {Copernicus Climate Change Service,ERA5,climate reanalysis,data assimilation,historical observations},
pages = {1999--2049},
title = {{The ERA5 global reanalysis}},
volume = {146},
year = {2020}
}
@article{doi:10.1002/qj.2528,
abstract = {This article describes an ensemble of ten atmospheric model integrations for the years 1899–2010, performed at the European Centre for Medium-Range Weather Forecasts (ECMWF). Horizontal spectral resolution is T159 (about 125 km), using 91 levels in the vertical from the surface up to 1 Pa, and a time step of 1 h. This ensemble, denoted by ERA-20CM, formed the first step toward a twentieth-century reanalysis within ERA-CLIM, a three-year European funded project involving nine partners. Sea-surface temperature and sea-ice cover are prescribed by an ensemble of realizations (HadISST2), as recently produced by the Met Office Hadley Centre within ERA-CLIM. Variation in these realizations reflect uncertainties in the available observational sources on which this product is based. Forcing terms in the model radiation scheme follow CMIP5 recommendations. Any effect of their uncertainty is neglected. These terms include solar forcing, greenhouse gases, ozone and aerosols. Both the ocean surface and radiative forcing incorporate a proper long-term evolution of climate trends in the twentieth century, and the occurrence of major events, such as the El Ni{\~{n}}o–Southern Oscillations and volcanic eruptions. No atmospheric observations were assimilated. For this reason ERA-20CM is not able to reproduce actual synoptic situations. However, the ensemble is able to provide a statistical estimate of the climate. Overall, the temperature rise over land is in fair agreement with the CRUTEM4 observational product. Over the last two decades the warming over land exceeds the warming over sea, which is consistent with models participating in the CMIP5 project, as well as with the ECMWF ERA-Interim reanalysis. Some aspects of warming and of the hydrological cycle are discerned, and the model response to volcanic eruptions is qualitatively correct. The data from ERA-20CM are freely available, embracing monthly-mean fields for many atmospheric and ocean-wave quantities, and synoptic fields for a small, essential subset.},
author = {Hersbach, Hans and Peubey, Carole and Simmons, Adrian and Berrisford, Paul and Poli, Paul and Dee, Dick},
doi = {10.1002/qj.2528},
journal = {Quarterly Journal of the Royal Meteorological Society},
keywords = {CMIP5,ERA-CLIM,HadISST2,atmospheric model integration,twentieth-century climate},
number = {691},
pages = {2350--2375},
title = {{ERA-20CM: a twentieth-century atmospheric model ensemble}},
url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.2528},
volume = {141},
year = {2015}
}
@article{Heue2016,
abstract = {In preparation of the TROPOMI/S5P launch in autumn 2016 a tropospheric ozone retrieval based on the convective cloud differential method was developed. For intensive tests we applied the algorithm to the total ozone columns and cloud data of the satellites GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B. Thereby a time series of 20 years (1995{\&}ndash;2015) of tropospheric ozone columns was retrieved. To have a consistent total ozone data set for all sensors one common retrieval algorithm, namely GODFITv3, has been applied to all sensors and the L1 reflectances have also been soft calibrated. These data were input into the tropospheric ozone retrieval. However, the Tropical Tropospheric Ozone Columns (TTOC) for the individual instruments still showed small differences and therefore we harmonised the data set. For this purpose a multi-variant function was fitted to the averaged difference between SCIAMACHY's TTOC and those from the other sensors. The original TTOC was corrected by the fitted offset. GOME-2B data were corrected relative to the harmonised data from OMI and GOME-2A. The harmonisation leads to a better agreement between the different instruments. Also a direct comparison of the TTOCs in the overlapping periods proves that GOME-2A agrees much better with SCIAMACHY after the harmonisation. The improvements for OMI were small. The GOME and SCIAMACHY data overlap for one year for the complete tropics, this turned out to be insufficient to extrapolate back until 1995. {\textless}/br{\textgreater}{\textless}/br{\textgreater} Based on the harmonised observations, we created a merged data product, containing the TTOC from July 1995 to Dec. 2015. A first application of this 20 years record is a trend analysis. The global tropical trend is 0.75 {\&}pm; 0.12 DU decade{\textless}sup{\textgreater}{\&}minus;1{\textless}/sup{\textgreater}. Regionally the trends reaches up to 1.8 DU decade{\textless}sup{\textgreater}{\&}minus;1{\textless}/sup{\textgreater} like on the African Atlantic coast, over the Western Pacific the tropospheric ozone declined over the last 20 years with up to 0.8 DU decade{\textless}sup{\textgreater}{\&}minus;1{\textless}/sup{\textgreater}. The tropical tropospheric data record will be extended in the future with the TROPOMI/S5P data, where the TTOC is part of the operational products.},
author = {Heue, Klaus Peter and Coldewey-Egbers, Melanie and Delcloo, Andy and Lerot, Christophe and Loyola, Diego and Valks, Pieter and {Van Roozendael}, Michel},
doi = {10.5194/amt-9-5037-2016},
issn = {18678548},
journal = {Atmospheric Measurement Techniques},
number = {10},
pages = {5037--5051},
title = {{Trends of tropical tropospheric ozone from 20 years of European satellite measurements and perspectives for the Sentinel-5 Precursor}},
volume = {9},
year = {2016}
}
@article{Hicks2012,
abstract = {The utility of aggregating data from near-surface meteorological networks for initiating dispersion models is examined by using data from the “WeatherBug” network that is operated by Earth Networks, Inc. WeatherBug instruments are typically mounted 2–3 m above the eaves of buildings and thus are more representative of the immediate surroundings than of conditions over the broader area. This study focuses on subnetworks of WeatherBug sites that are within circles of varying radius about selected stations of the DCNet program. DCNet is a Washington, D.C., research program of the NOAA Air Resources Laboratory. The aggregation of data within varying-sized circles of 3–10-km radius yields average velocities and velocity-component standard deviations that are largely independent of the number of stations reporting—provided that number exceeds about 10. Given this finding, variances of wind components are aggregated from arrays of WeatherBug stations within a 5-km radius of selected central DCNet locations, with on average 11 WeatherBug stations per array. The total variance of wind components from the surface (WeatherBug) subnetworks is taken to be the sum of two parts: the temporal variance is the average of the conventional wind-component variances at each site and the spatial variance is based on the velocity-component averages of the individual sites. These two variances (and the standard deviations derived from them) are found to be similar. Moreover, the total wind-component variance is comparable to that observed at the DCNet reference stations. The near-surface rooftop wind velocities are about 35{\%} of the magnitudes of the DCNet measurements. Limited additional data indicate that these results can be extended to New York City.},
author = {Hicks, Bruce B. and Callahan, William J. and Pendergrass, William R. and Dobosy, Ronald J. and Novakovskaia, Elena},
doi = {10.1175/JAMC-D-11-015.1},
issn = {1558-8424},
journal = {Journal of Applied Meteorology and Climatology},
month = {feb},
number = {2},
pages = {205--218},
title = {{Urban Turbulence in Space and in Time}},
url = {https://journals.ametsoc.org/view/journals/apme/51/2/jamc-d-11-015.1.xml},
volume = {51},
year = {2012}
}
@techreport{Higgins2000,
abstract = {A principal goal of the GEWEX Continental-Scale International Project (GCIP) Program is to improve the analysis of precipitation over a range of space and time scales. Over the past several years the Climate Prediction Center (CPC) has developed a US Precipitation Quality Control (QC) System and Analysis that addresses two principal aspects of this goal: 1) Improved QC of raingauge data used in precipitation analyses for the United States and 2) Improved precipitation products and applications in support of climate monitoring, climate prediction, and applied research. Specific topics covered by this Atlas include: 1. Development of the U.S. precipitation QC system and analysis; 2. QC Initiatives involving radar and satellite data; 3. Daily, monthly and seasonal precipitation products / applications; 4. A Unified Raingauge Dataset (URD) for the U.S. (1948-present); 5. A daily precipitation reanalysis for the U.S. based on the URD. The variability of U.S. precipitation at time scales ranging from daily to interannual is examined using the precipitation reanalysis. Comparisons are made to an existing climatology in use at CPC.},
address = {Camp Springs, MD, USA},
author = {Higgins, R. and Shi, W. and Yarosh, E. and Joyce, R.},
publisher = {National Oceanic and Atmospheric Administration (NOAA)/ National Weather Service (NWS)},
series = {NCEP/Climate Prediction Center ATLAS No. 7},
title = {{Improved United States Precipitation Quality Control System and Analysis}},
url = {https://www.cpc.ncep.noaa.gov/research{\_}papers/ncep{\_}cpc{\_}atlas/7/index.html},
year = {2000}
}
@article{Hirahara2014,
abstract = { AbstractA new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice–SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period. },
author = {Hirahara, Shoji and Ishii, Masayoshi and Fukuda, Yoshikazu},
doi = {10.1175/JCLI-D-12-00837.1},
journal = {Journal of Climate},
number = {1},
pages = {57--75},
title = {{Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty}},
volume = {27},
year = {2014}
}
@article{gmd-11-369-2018,
author = {Hoesly, R M and Smith, S J and Feng, L and Klimont, Z and Janssens-Maenhout, G and Pitkanen, T and Seibert, J J and Vu, L and Andres, R J and Bolt, R M and Bond, T C and Dawidowski, L and Kholod, N and Kurokawa, J.-I. and Li, M and Liu, L and Lu, Z and Moura, M C P and O'Rourke, P R and Zhang, Q},
doi = {10.5194/gmd-11-369-2018},
journal = {Geoscientific Model Development},
number = {1},
pages = {369--408},
title = {{Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)}},
url = {https://gmd.copernicus.org/articles/11/369/2018/},
volume = {11},
year = {2018}
}
@article{Huang2017,
abstract = { AbstractThe monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Ni{\~{n}}o and La Ni{\~{n}}a events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4. },
author = {Huang, Boyin and Thorne, Peter W and Banzon, Viva F and Boyer, Tim and Chepurin, Gennady and Lawrimore, Jay H and Menne, Matthew J and Smith, Thomas M and Vose, Russell S and Zhang, Huai-Min},
doi = {10.1175/JCLI-D-16-0836.1},
journal = {Journal of Climate},
number = {20},
pages = {8179--8205},
title = {{Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons}},
volume = {30},
year = {2017}
}
@article{doi:10.1175/JCLI-D-19-0395.1,
abstract = {This analysis estimates uncertainty in the NOAA global surface temperature (GST) version 5 (NOAAGlobalTemp v5) product, which consists of sea surface temperature (SST) from the Extended Reconstructed SST version 5 (ERSSTv5) and land surface air temperature (LSAT) from the Global Historical Climatology Network monthly version 4 (GHCNm v4). Total uncertainty in SST and LSAT consists of parametric and reconstruction uncertainties. The parametric uncertainty represents the dependence of SST/LSAT reconstructions on selecting 28 (6) internal parameters of SST (LSAT), and is estimated by a 1000-member ensemble from 1854 to 2016. The reconstruction uncertainty represents the residual error of using a limited number of 140 (65) modes for SST (LSAT). Uncertainty is quantified at the global scale as well as the local grid scale. Uncertainties in SST and LSAT at the local grid scale are larger in the earlier period (1880s–1910s) and during the two world wars due to sparse observations, then decrease in the modern period (1950s–2010s) due to increased data coverage. Uncertainties in SST and LSAT at the global scale are much smaller than those at the local grid scale due to error cancellations by averaging. Uncertainties are smaller in SST than in LSAT due to smaller SST variabilities. Comparisons show that GST and its uncertainty in NOAAGlobalTemp v5 are comparable to those in other internationally recognized GST products. The differences between NOAAGlobalTemp v5 and other GST products are within their uncertainties at the 95{\%} confidence level.},
author = {Huang, Boyin and Menne, Matthew J and Boyer, Tim and Freeman, Eric and Gleason, Byron E and Lawrimore, Jay H and Liu, Chunying and Rennie, J Jared and Schreck, Carl J and Sun, Fengying and Vose, Russell and Williams, Claude N and Yin, Xungang and Zhang, Huai-Min},
doi = {10.1175/JCLI-D-19-0395.1},
issn = {0894-8755},
journal = {Journal of Climate},
month = {feb},
number = {4},
pages = {1351--1379},
title = {{Uncertainty Estimates for Sea Surface Temperature and Land Surface Air Temperature in NOAAGlobalTemp Version 5}},
url = {https://doi.org/10.1175/JCLI-D-19-0395.1 http://journals.ametsoc.org/doi/10.1175/JCLI-D-19-0395.1},
volume = {33},
year = {2020}
}
@article{Huffman2007,
abstract = {The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the surface observation–based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other finescale estimators. Examples are provided of a flood event and diurnal cycle determination.},
author = {Huffman, George J. and Bolvin, David T. and Nelkin, Eric J. and Wolff, David B and Adler, Robert F. and Gu, Guojun and Hong, Yang and Bowman, Kenneth P and Stocker, Erich F},
doi = {10.1175/JHM560.1},
issn = {1525-7541},
journal = {Journal of Hydrometeorology},
month = {feb},
number = {1},
pages = {38--55},
title = {{The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales}},
url = {http://journals.ametsoc.org/doi/10.1175/JHM560.1},
volume = {8},
year = {2007}
}
@article{Hugonnet2021,
author = {Hugonnet, Romain and McNabb, Robert and Berthier, Etienne and Menounos, Brian and Nuth, Christopher and Girod, Luc and Farinotti, Daniel and Huss, Matthias and Dussaillant, Ines and Brun, Fanny and K{\"{a}}{\"{a}}b, Andreas},
doi = {10.1038/s41586-021-03436-z},
issn = {0028-0836},
journal = {Nature},
month = {apr},
number = {7856},
pages = {726--731},
title = {{Accelerated global glacier mass loss in the early twenty-first century}},
url = {http://www.nature.com/articles/s41586-021-03436-z},
volume = {592},
year = {2021}
}
@article{Hung2012,
author = {Hung, Tam Kwong and Wo, Ong Chung},
doi = {10.1002/wea.1883},
journal = {Weather},
month = {jan},
number = {2},
pages = {48--50},
publisher = {Wiley-Blackwell},
title = {{Development of a Community Weather Information Network (Co-WIN) in Hong Kong}},
volume = {67},
year = {2012}
}
@article{Hurst2011,
abstract = {Trend analyses are presented for 30 years (1980?2010) of balloon-borne stratospheric water vapor measurements over Boulder, Colorado. The data record is broken into four multiple-year periods of water vapor trends, including two that span the well-examined but unattributed 1980?2000 period of stratospheric water vapor growth. Trends are determined for five 2 km stratospheric layers (16?26 km) utilizing weighted, piecewise regression analyses. Stratospheric water vapor abundance increased by an average of 1.0 ± 0.2 ppmv (27 ± 6{\%}) during 1980?2010 with significant shorter-term variations along the way. Growth during period 1 (1980?1989) was positive and weakened with altitude from 0.44 ± 0.13 ppmv at 16?18 km to 0.07 ± 0.07 ppmv at 24?26 km. Water vapor increased during period 2 (1990?2000) by an average 0.57 ± 0.25 ppmv, decreased during period 3 (2001?2005) by an average 0.35 ± 0.04 ppmv, then increased again during period 4 (2006?2010) by an average 0.49 ± 0.17 ppmv. The diminishing growth with altitude observed during period 1 is consistent with a water vapor increase in the tropical lower stratosphere that propagated to the midlatitudes. In contrast, growth during periods 2 and 4 is stronger at higher altitudes, revealing contributions from at least one mechanism that strengthens with altitude, such as methane oxidation. The amount of methane oxidized in the stratosphere increased considerably during 1980?2010, but this source can account for at most 28 ± 4{\%}, 14 ± 4{\%}, and 25 ± 5{\%} of the net stratospheric water vapor increases during 1980?2000, 1990?2000, and 1980?2010, respectively.},
author = {Hurst, Dale F and Oltmans, Samuel J and V{\"{o}}mel, Holger and Rosenlof, Karen H and Davis, Sean M and Ray, Eric A and Hall, Emrys G and Jordan, Allen F},
doi = {10.1029/2010JD015065},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Atmospheres},
month = {jan},
number = {D2},
pages = {D02306},
publisher = {Wiley-Blackwell},
title = {{Stratospheric water vapor trends over Boulder, Colorado: Analysis of the 30 year Boulder record}},
volume = {116},
year = {2011}
}
@article{Iguchi2000,
abstract = {Abstract This paper describes the Tropical Rainfall Measuring Mission (TRMM) standard algorithm that estimates the vertical profiles of attenuation-corrected radar reflectivity factor and rainfall rate. In particular, this paper focuses on the critical steps in the algorithm. These steps are attenuation correction, selection of the default drop size distribution model including vertical variations, and correction for the nonuniform beam-filling effect. The attenuation correction is based on a hybrid of the Hitschfeld?Bordan method and a surface reference method. A new algorithm to obtain an optimum weighting function is described. The nonuniform beam-filling problem is analyzed as a two-dimensional problem. The default drop size distribution model is selected according to the criterion that the attenuation estimates derived from the model and the independent estimates from the surface reference with the nonuniform beam-filling correction are consistent for rain over ocean. It is found that the drop size distribution models that are consistent for convective rain over ocean are not consistent over land, indicating a change in the size distributions associated with convective rain over land and ocean, respectively.},
annote = {doi: 10.1175/1520-0450(2001)0402.0.CO;2},
author = {Iguchi, Toshio and Kozu, Toshiaki and Meneghini, Robert and Awaka, Jun and Okamoto, Ken'ichi},
doi = {10.1175/1520-0450(2001)040<2038:RPAFTT>2.0.CO;2},
issn = {0894-8763},
journal = {Journal of Applied Meteorology},
month = {dec},
number = {12},
pages = {2038--2052},
publisher = {American Meteorological Society},
title = {{Rain-Profiling Algorithm for the TRMM Precipitation Radar}},
url = {https://doi.org/10.1175/1520-0450(2001)040{\%}3C2038:RPAFTT{\%}3E2.0.CO http://0.0.0.2},
volume = {39},
year = {2000}
}
@article{Inamdar2015,
abstract = {AbstractThe International Satellite Cloud Climatology Project (ISCCP) B1 data, which were recently rescued at the National Oceanic and Atmospheric Administration?s National Climatic Data Center (NOAA/NCDC), are a resource for the study of the earth?s climate. The ISCCP B1 data represent geostationary satellite imagery for all channels, including the infrared (IR), visible, and IR water vapor sensors. These are global 3-hourly snapshots from satellites around the world, covering the time period from 1979 to present at approximately 10-km spatial resolution. ISCCP B1 data will be used in the reprocessing of the cloud products, resulting in a higher-resolution ISCCP cloud climatology, surface radiation budget (SRB), etc. To realize the promise of a higher-resolution cloud climatology from the B1 data, an independent assessment of the calibration of the visible band was performed. The present study aims to accomplish this by cross-calibrating with the intercalibrated Advanced Very High Resolution Radiometer (AVHRR) reflectance data from the AVHRR Pathfinder Atmospheres?Extended (PATMOS-x) dataset. Since the reflectance calibration approach followed in the PATMOS-x dataset is radiometrically tied to the absolute calibration of the National Aeronautics and Space Administration?s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) imager instrument, the present intercalibration scheme yields calibration coefficients consistent with MODIS. Results from this study show that the two independent sets (this study and the ISCCP) of results agree to within their mutual uncertainties. An independent approach to calibration based on multiyear observations over spatially and temporally invariant desert sites has also been used for validation. Results reveal that for most of the geostationary satellites, the mean difference with ISCCP calibration is less than 3{\%} with the random errors under 2{\%}. Another result is that this extends the intercalibrated record to beyond what ISCCP provides (prior to 1983 and beyond 2009).},
annote = {doi: 10.1175/JTECH-D-14-00040.1},
author = {Inamdar, Anand K and Knapp, Kenneth R},
doi = {10.1175/JTECH-D-14-00040.1},
issn = {0739-0572},
journal = {Journal of Atmospheric and Oceanic Technology},
month = {apr},
number = {6},
pages = {1225--1240},
publisher = {American Meteorological Society},
title = {{Intercomparison of Independent Calibration Techniques Applied to the Visible Channel of the ISCCP B1 Data}},
url = {https://doi.org/10.1175/JTECH-D-14-00040.1},
volume = {32},
year = {2015}
}
@article{acp-19-3515-2019,
author = {Inness, A and Ades, M and Agust{\'{i}}-Panareda, A and Barr{\'{e}}, J and Benedictow, A and Blechschmidt, A.-M. and Dominguez, J J and Engelen, R and Eskes, H and Flemming, J and Huijnen, V and Jones, L and Kipling, Z and Massart, S and Parrington, M and Peuch, V.-H. and Razinger, M and Remy, S and Schulz, M and Suttie, M},
doi = {10.5194/acp-19-3515-2019},
journal = {Atmospheric Chemistry and Physics},
number = {6},
pages = {3515--3556},
title = {{The CAMS reanalysis of atmospheric composition}},
url = {https://www.atmos-chem-phys.net/19/3515/2019/},
volume = {19},
year = {2019}
}
@article{Ishii2017,
author = {Ishii, Masayoshi and Fukuda, Yoshikazu and Hirahara, Shoji and Yasui, Soichiro and Suzuki, Toru and Sato, Kanako},
doi = {10.2151/sola.2017-030},
journal = {SOLA},
pages = {163--167},
title = {{Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets}},
volume = {13},
year = {2017}
}
@article{Ishijima2007,
abstract = {Histories of atmospheric N2O concentration and its $\delta$15N and $\delta$18O were reconstructed for the period 1952–2001 on the basis of the analyses of firn air collected at the North Greenland Ice Core Project (NGRIP), Greenland, and Dome Fuji and H72, Antarctica. The N2O concentration increased from 290 ppbv in 1952 to 316 ppbv in 2001, which agrees well with the results from atmospheric observations and polar ice core analyses. The $\delta$15N and $\delta$18O showed a secular decrease, the respective values being 8.9 and 21.5‰ in 1952 and 7.0 and 20.5‰ in 2001. Their rates of change also varied, from about −0.02‰ yr−1 in the 1950s to about −0.04‰ yr−1 in 1960–2001 for $\delta$15N, and from about 0‰ yr−1 to −0.02‰ yr−1 for $\delta$18O. The isotopic budgetary calculations using a two‐box model indicated that anthropogenic N2O emission from soils played a main role in the atmospheric N2O increase after industrialization, as well as that the average isotopic ratio of anthropogenic N2O has potentially been changed temporally.},
author = {Ishijima, Kentaro and Sugawara, Satoshi and Kawamura, Kenji and Hashida, Gen and Morimoto, Shinji and Murayama, Shohei and Aoki, Shuji and Nakazawa, Takakiyo},
doi = {10.1029/2006JD007208},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Atmospheres},
month = {feb},
number = {D3},
pages = {D03305},
publisher = {Wiley-Blackwell},
title = {{Temporal variations of the atmospheric nitrous oxide concentration and its $\delta$15N and $\delta$18O for the latter half of the 20th century reconstructed from firn air analyses}},
volume = {112},
year = {2007}
}
@article{Isotta2014,
abstract = {In the region of the European Alps, national and regional meteorological services operate rain-gauge networks, which together, constitute one of the densest in situ observation systems in a large-scale high-mountain region. Data from these networks are consistently analyzed, in this study, to develop a pan-Alpine grid dataset and to describe the region's mesoscale precipitation climate, including the occurrence of heavy precipitation and long dry periods. The analyses are based on a collation of high-resolution rain-gauge data from seven Alpine countries, with 5500 measurements per day on average, spanning the period 1971–2008. The dataset is an update of an earlier version with improved data density and more thorough quality control. The grid dataset has a grid spacing of 5 km, daily time resolution, and was constructed with a distance-angular weighting scheme that integrates climatological precipitation–topography relationships. Scales effectively resolved in the dataset are coarser than the grid spacing and vary in time and space, depending on station density. We quantify the uncertainty of the dataset by cross-validation and in relation to topographic complexity, data density and season. Results indicate that grid point estimates are systematically underestimated (overestimated) at large (small) precipitation intensities, when they are interpreted as point estimates. Our climatological analyses highlight interesting variations in indicators of daily precipitation that deviate from the pattern and course of mean precipitation and illustrate the complex role of topography. The daily Alpine precipitation grid dataset was developed as part of the EU funded EURO4M project and is freely available for scientific use.},
author = {Isotta, Francesco A. and Frei, Christoph and Weilguni, Viktor and {Per{\v{c}}ec Tadi{\'{c}}}, Melita and Lass{\`{e}}gues, Pierre and Rudolf, Bruno and Pavan, Valentina and Cacciamani, Carlo and Antolini, Gabriele and Ratto, Sara M. and Munari, Michela and Micheletti, Stefano and Bonati, Veronica and Lussana, Cristian and Ronchi, Christian and Panettieri, Elvio and Marigo, Gianni and Verta{\v{c}}nik, Gregor},
doi = {10.1002/joc.3794},
issn = {08998418},
journal = {International Journal of Climatology},
month = {apr},
number = {5},
pages = {1657--1675},
title = {{The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data}},
url = {https://onlinelibrary.wiley.com/doi/10.1002/joc.3794},
volume = {34},
year = {2014}
}
@article{Janssens-Maenhout2019,
author = {Janssens-Maenhout, Greet and Crippa, Monica and Guizzardi, Diego and Muntean, Marilena and Schaaf, Edwin and Dentener, Frank and Bergamaschi, Peter and Pagliari, Valerio and Olivier, Jos G. J. and Peters, Jeroen A. H. W. and van Aardenne, John A. and Monni, Suvi and Doering, Ulrike and Petrescu, A. M. Roxana and Solazzo, Efisio and Oreggioni, Gabriel D.},
doi = {10.5194/essd-11-959-2019},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {jul},
number = {3},
pages = {959--1002},
title = {{EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012}},
url = {https://essd.copernicus.org/articles/11/959/2019/},
volume = {11},
year = {2019}
}
@article{JEVREJEVA201411,
abstract = {We use 1277 tide gauge records since 1807 to provide an improved global sea level reconstruction and analyse the evolution of sea level trend and acceleration. In particular we use new data from the polar regions and remote islands to improve data coverage and extend the reconstruction to 2009. There is a good agreement between the rate of sea level rise (3.2±0.4mm{\textperiodcentered}yr−1) calculated from satellite altimetry and the rate of 3.1±0.6mm{\textperiodcentered}yr−1 from tide gauge based reconstruction for the overlapping time period (1993–2009). The new reconstruction suggests a linear trend of 1.9±0.3mm{\textperiodcentered}yr−1 during the 20th century, with 1.8±0.5mm{\textperiodcentered}yr−1 since 1970. Regional linear trends for 14 ocean basins since 1970 show the fastest sea level rise for the Antarctica (4.1±0.8mm{\textperiodcentered}yr−1) and Arctic (3.6±0.3mm{\textperiodcentered}yr−1). Choice of GIA correction is critical in the trends for the local and regional sea levels, introducing up to 8mm{\textperiodcentered}yr−1 uncertainties for individual tide gauge records, up to 2mm{\textperiodcentered}yr−1 for regional curves and up to 0.3–0.6mm{\textperiodcentered}yr−1 in global sea level reconstruction. We calculate an acceleration of 0.02±0.01mm{\textperiodcentered}yr−2 in global sea level (1807–2009). In comparison the steric component of sea level shows an acceleration of 0.006mm{\textperiodcentered}yr−2 and mass loss of glaciers accelerates at 0.003mm{\textperiodcentered}yr−2 over 200year long time series.},
author = {Jevrejeva, S and Moore, J C and Grinsted, A and Matthews, A P and Spada, G},
doi = {https://doi.org/10.1016/j.gloplacha.2013.12.004},
issn = {0921-8181},
journal = {Global and Planetary Change},
keywords = {GIA corrections,sea level acceleration,sea level trends,tide gauge records},
pages = {11--22},
title = {{Trends and acceleration in global and regional sea levels since 1807}},
url = {http://www.sciencedirect.com/science/article/pii/S0921818113002750},
volume = {113},
year = {2014}
}
@article{Jones2009,
abstract = {In this paper, we describe a new high-quality set of historical and ongoing realtime climate analyses for Australia. These analyses have been developed for improving the definition of past climate variability and change over Australia and to improve on estimates of recent climate. The climate analyses cover the variables of rainfall, temperature (maximum and minimum) as well as vapour pressure at daily and monthly timescales and are complemented by remotely sensed and modelderived data described elsewhere. New robust topography-resolving analysis methods have been developed and applied to in situ observations of rainfall, temperature and vapour pressure to produce analyses at a resolution of 0.05° × 0.05° (approximately 5 km × 5 km). The new methodologies are similar to those applied internationally, but in applying them to Australia we found it necessary and desirable to introduce a number of innovations. The resulting analyses represent substantial improvements on operational analyses currently produced by the Australian Bureau of Meteorology, and have a number of advantages over other similar data-sets currently available. Careful attention has been paid to developing systems and data-sets which are robust and useful for the monitoring of both climate variability and climate change. These systems are now running in real time and are expected to form the basis for the ongoing monitoring of Australia's surface climate variability and change by the Australian Bureau of Meteorology. The underlying data and associated error surfaces (grids and station data) are updated in real time and are all available free of charge through the Bureau's climate website (www.bom.gov.au/climate).},
author = {Jones, David A and Wang, William and Fawcett, Robert},
doi = {10.22499/2.5804.003},
isbn = {1836-716X},
issn = {1836-716X},
journal = {Australian Meteorological and Oceanographic Journal},
pages = {233--248},
title = {{High-quality spatial climate data-sets for Australia}},
volume = {58},
year = {2009}
}
@article{doi:10.1029/2011JD017139,
abstract = {This study is an extensive revision of the Climatic Research Unit (CRU) land station temperature database that has been used to produce a grid-box data set of 5° latitude × 5° longitude temperature anomalies. The new database (CRUTEM4) comprises 5583 station records of which 4842 have enough data for the 1961–1990 period to calculate or estimate the average temperatures for this period. Many station records have had their data replaced by newly homogenized series that have been produced by a number of studies, particularly from National Meteorological Services (NMSs). Hemispheric temperature averages for land areas developed with the new CRUTEM4 data set differ slightly from their CRUTEM3 equivalent. The inclusion of much additional data from the Arctic (particularly the Russian Arctic) has led to estimates for the Northern Hemisphere (NH) being warmer by about 0.1°C for the years since 2001. The NH/Southern Hemisphere (SH) warms by 1.12°C/0.84°C over the period 1901–2010. The robustness of the hemispheric averages is assessed by producing five different analyses, each including a different subset of 20{\%} of the station time series and by omitting some large countries. CRUTEM4 is also compared with hemispheric averages produced by reanalyses undertaken by the European Centre for Medium-Range Weather Forecasts (ECMWF): ERA-40 (1958–2001) and ERA-Interim (1979–2010) data sets. For the NH, agreement is good back to 1958 and excellent from 1979 at monthly, annual, and decadal time scales. For the SH, agreement is poorer, but if the area is restricted to the SH north of 60°S, the agreement is dramatically improved from the mid-1970s.},
author = {Jones, P D and Lister, D H and Osborn, T J and Harpham, C and Salmon, M and Morice, C P},
doi = {10.1029/2011JD017139},
journal = {Journal of Geophysical Research: Atmospheres},
keywords = {global temperature,hemispheric temperature,land surface},
number = {D5},
pages = {D05127},
title = {{Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to 2010}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011JD017139},
volume = {117},
year = {2012}
}
@article{jones2003hemispheric,
author = {Jones, Philip D and Moberg, Anders},
doi = {10.1175/1520-0442(2003)016<0206:HALSSA>2.0.CO;2},
issn = {0894-8755},
journal = {Journal of Climate},
month = {jan},
number = {2},
pages = {206--223},
title = {{Hemispheric and Large-Scale Surface Air Temperature Variations: An Extensive Revision and an Update to 2001}},
url = {http://journals.ametsoc.org/doi/10.1175/1520-0442(2003)016{\%}3C0206:HALSSA{\%}3E2.0.CO;2},
volume = {16},
year = {2003}
}
@misc{jones2015daca,
abstract = {We have developed a statistical gap-filling method adapted to the specific coverage and properties of observed fugacity of surface ocean CO2 (fCO2). We have used this method to interpolate the Surface Ocean CO2 Atlas (SOCAT) v2 database on a 2.5{\{}$\backslash$textdegree{\}}{\{}$\backslash$texttimes{\}}2.5{\{}$\backslash$textdegree{\}} global grid (south of 70{\{}$\backslash$textdegree{\}}N) for 1985-2011 at monthly resolution. The method combines a spatial interpolation based on a {\{}$\backslash$textquotesingle{\}}radius of influence{\{}$\backslash$textquotesingle{\}} to determine nearby similar fCO2 values with temporal harmonic and cubic spline curve-fitting, and also fits long term trends and seasonal cycles. Interannual variability is established using deviations of observations from the fitted trends and seasonal cycles. An uncertainty is computed for all interpolated values based on the spatial and temporal range of the interpolation. Tests of the method using model data show that it performs as well as or better than previous regional interpolation methods, but in addition it provides a near-global and interannual coverage.},
annote = {Supplement to: Jones, SD et al. (2015): A statistical gap-filling method to interpolate global monthly surface ocean carbon dioxide data. Journal of Advances in Modeling Earth Systems, 7(4), 1554-1575, https://doi.org/10.1002/2014MS000416},
author = {Jones, Stephen D and {Le Qu{\'{e}}r{\'{e}}}, Corinne and R{\"{o}}denbeck, Christian and Manning, Andrew C and Olsen, Are},
doi = {10.1594/PANGAEA.849262},
publisher = {PANGAEA},
title = {{Data and Code archive for the interpolation of surface ocean carbon dioxide}},
type = {data set},
url = {https://doi.org/10.1594/PANGAEA.849262},
year = {2015}
}
@article{Journee2015,
author = {Journ{\'{e}}e, M and Delvaux, C and Bertrand, C},
doi = {10.5194/asr-12-73-2015},
journal = {Advances in Science and Research},
number = {1},
pages = {73--78},
title = {{Precipitation climate maps of Belgium}},
volume = {12},
year = {2015}
}
@article{Jung2011,
abstract = {We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5° × 0.5° spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 ± 7 J × 1018 yr−1), H (164 ± 15 J × 1018 yr−1), and GPP (119 ± 6 Pg C yr−1) were similar to independent estimates. Our global TER estimate (96 ± 6 Pg C yr−1) was likely underestimated by 5–10{\%}. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.},
author = {Jung, Martin and Reichstein, Markus and Margolis, Hank A and Cescatti, Alessandro and Richardson, Andrew D and Arain, M Altaf and Arneth, Almut and Bernhofer, Christian and Bonal, Damien and Chen, Jiquan and Gianelle, Damiano and Gobron, Nadine and Kiely, Gerald and Kutsch, Werner and Lasslop, Gitta and Law, Beverly E and Lindroth, Anders and Merbold, Lutz and Montagnani, Leonardo and Moors, Eddy J and Papale, Dario and Sottocornola, Matteo and Vaccari, Francesco and Williams, Christopher},
doi = {10.1029/2010JG001566},
isbn = {0148-0227},
issn = {01480227},
journal = {Journal of Geophysical Research: Biogeosciences},
number = {3},
pages = {1--16},
pmid = {16373352},
title = {{Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations}},
volume = {116},
year = {2011}
}
@article{Kadow2020,
abstract = {Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Ni{\~{n}}o from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.},
author = {Kadow, Christopher and Hall, David Matthew and Ulbrich, Uwe},
doi = {10.1038/s41561-020-0582-5},
issn = {1752-0908},
journal = {Nature Geoscience},
number = {6},
pages = {408--413},
title = {{Artificial intelligence reconstructs missing climate information}},
volume = {13},
year = {2020}
}
@article{Kalnay1996,
abstract = {The NCEP and NCAR are cooperating in a project (denoted ?reanalysis?) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957?96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided by different countries and organizations. The system has been designed with advanced quality control and monitoring components, and can produce 1 mon of reanalysis per day on a Cray YMP/8 supercomputer. Different types of output archives are being created to satisfy different user needs, including a ?quick look? CD-ROM (one per year) with six tropospheric and stratospheric fields available twice daily, as well as surface, top-of-the-atmosphere, and isentropic fields. Reanalysis information and selected output is also available on-line via the Internet (http//:nic.fb4.noaa.gov:8000). A special CDROM, containing 13 years of selected observed, daily, monthly, and climatological data from the NCEP/NCAR Reanalysis, is included with this issue. Output variables are classified into four classes, depending on the degree to which they are influenced by the observations and/or the model. For example, ?C? variables (such as precipitation and surface fluxes) are completely determined by the model during the data assimilation and should be used with caution. Nevertheless, a comparison of these variables with observations and with several climatologies shows that they generally contain considerable useful information. Eight-day forecasts, produced every 5 days, should be useful for predictability studies and for monitoring the quality of the observing systems. The 40 years of reanalysis (1957?96) should be completed in early 1997. A continuation into the future through an iden{\ldots}},
author = {Kalnay, E and Kanamitsu, M and Kistler, R and Collins, W and Deaven, D and Gandin, L and Iredell, M and Saha, S and White, G and Woollen, J and Zhu, Y and Chelliah, M and Ebisuzaki, W and Higgins, W and Janowiak, J and Mo, K C and Ropelewski, C and Wang, J and Leetmaa, A and Reynolds, R and Jenne, Roy and Joseph, Dennis},
doi = {10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2},
issn = {0003-0007},
journal = {Bulletin of the American Meteorological Society},
month = {mar},
number = {3},
pages = {437--472},
publisher = {American Meteorological Society},
title = {{The NCEP/NCAR 40-Year Reanalysis Project}},
volume = {77},
year = {1996}
}
@article{Kamiguchi2010,
abstract = {We constructed historical (1900-) high-resolution (0.05° × 0.05°) daily precipitation data over the Japanese land area as part of the product of the "Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources" (APHRODITE) project. This product APHRO{\_}JP is derived from rain gauge observations and is intended to accurately represent both mean and extreme values. Due to new interpolation techniques developed in APHRODITE, estimation accuracy for orographic precipitation is improved, and bias for long-term amount is reduced, even for the early 20th century in which the observation network was sparse in space. Moreover, the product can be used for statistical analysis of heavy precipitation up to about 150 mm/day, over a long term period (≥ 100 years). APHRO{\_}JP enables diverse research, including validation of meso-scale models and analysis of the longterm extreme precipitation trend in Japan.},
author = {Kamiguchi, Kenji and Arakawa, Osamu and Kitoh, Akio and Yatagai, Akiyo and Hamada, Atsushi and Yasutomi, Natsuko},
doi = {10.3178/hrl.4.60},
issn = {1882-3416},
journal = {Hydrological Research Letters},
pages = {60--64},
title = {{Development of APHRO{\_}JP, the first Japanese high-resolution daily precipitation product for more than 100 years}},
volume = {4},
year = {2010}
}
@article{Kaplan1998,
abstract = {Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kaiman filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the timemarching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) [Parker et al., 1994] of the Global Ocean Surface Temperature Atlas (GOSTA) [Bottomley et al., 1990]. These analyses are compared with each other, with GOSTA, and with an analysis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. [1996]). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and GOSTA. It is found that the first four give comparable results in the data-rich periods (1951–1991), but at times when data is sparse the first three differ significantly from P and GOSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous. The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the El Ni{\~{n}}o-Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.},
author = {Kaplan, Alexey and Cane, Mark A and Kushnir, Yochanan and Clement, Amy C and Blumenthal, M Benno and Rajagopalan, Balaji},
doi = {10.1029/97JC01736},
journal = {Journal of Geophysical Research: Oceans},
number = {C9},
pages = {18567--18589},
title = {{Analyses of global sea surface temperature 1856–1991}},
volume = {103},
year = {1998}
}
@article{Kawanishi2003,
author = {Kawanishi, T and Sezai, T and Ito, Y and Imaoka, K and Takeshima, T and Ishido, Y and Shibata, A and Miura, M and Inahata, H and Spencer, R W},
doi = {10.1109/TGRS.2002.808331},
issn = {0196-2892 VO  - 41},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
keywords = {6.9 to 89 GHz,AMSR,AMSR-E,Condition monitoring,Earth Observing System,Hardware,Instruments,Microwave imaging,Microwave radiometry,Ocean temperature,Satellite broadcasting,Space missions,Spatial resolution,atmosphere,atmospheric measuring apparatus,atmospheric techniques,geophysical equipment,geophysical techniques,hydrological equipment,hydrological techniques,hydrology,instrument,measurement technique,meteorology,microwave radiometer,microwave radiometry,ocean,oceanographic equipment,oceanographic techniques,radiometers,remote sensing,satellite remote sensing,sea surface},
number = {2},
pages = {184--194},
title = {{The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies}},
volume = {41},
year = {2003}
}
@techreport{Keeling2001,
address = {San Diego, CA, USA},
author = {Keeling, C D and Piper, S C and Bacastow, R B and Wahlen, M and Whorf, T P and Heimann, M and Meijer, H A},
pages = {28},
publisher = {Scripps Institution of Oceanography},
series = {SIO Reference No. 01-06},
title = {{Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and oceans from 1978 to 2000. I. Global Aspects}},
year = {2001}
}
@incollection{Keeling2005,
address = {New York, NY, USA},
author = {Keeling, C. D. and Piper, S. C. and Bacastow, R. B. and Wahlen, M. and Whorf, T. P. and Heimann, M. and Meijer, H. A.},
booktitle = {A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems},
doi = {10.1007/0-387-27048-5_5},
editor = {Ehleringer, James R. and Cerling, Thure and Dearing, M. Denise},
isbn = {978-0-387-27048-7},
pages = {83--113},
publisher = {Springer},
title = {{Atmospheric CO2 and 13CO2 Exchange with the Terrestrial Biosphere and Oceans from 1978 to 2000: Observations and Carbon Cycle Implications}},
year = {2005}
}
@article{Kennedy2019,
author = {Kennedy, J J and Rayner, N. A. and Atkinson, Christopher P and Killick, R. E.},
doi = {10.1029/2018JD029867},
journal = {Journal of Geophysical Research: Atmospheres},
pages = {7719--7763},
title = {{An ensemble data set of sea-surface temperature change from 1850: the Met Office 1 Hadley Centre HadSST.4.0.0.0 data set}},
volume = {124},
year = {2019}
}
@article{Kent2013,
abstract = {An updated version of the Met Office Hadley Centre's monthly night marine air temperature data set is presented. It is available on a 5° latitude-longitude grid from 1880 as anomalies relative to 1961–1990 calendar-monthly climatological average night marine air temperature (NMAT). Adjustments are made for changes in observation height; these depend on estimates of the stability of the near surface atmospheric boundary layer. In previous versions of the data set, ad hoc adjustments were also made for three periods and regions where poor observational practice was prevalent. These adjustments are re-examined. Estimates of uncertainty are calculated for every grid box and result from measurement errors, uncertainty in adjustments applied to the observations, uncertainty in the measurement height, and under-sampling. The new data set is a clear improvement over previous versions in terms of coverage because of the recent digitization of historical observations from ships' logbooks. However, the periods prior to about 1890 and around World War II remain particularly uncertain, and sampling is still sparse in some regions in other periods. A further improvement is the availability of uncertainty estimates for every grid box and every month. Previous versions required adjustments that were dependent on contemporary measurements of sea surface temperature (SST); to avoid these, the new data set starts in 1880 rather than 1856. Overall agreement with variations of SST is better for the updated data set than for previous versions, supporting existing estimates of global warming and increasing confidence in the global record of temperature variability and change.},
author = {Kent, Elizabeth C and Rayner, Nick A and Berry, David I and Saunby, Michael and Moat, Bengamin I and Kennedy, John J and Parker, David E},
doi = {10.1002/jgrd.50152},
journal = {Journal of Geophysical Research: Atmospheres},
number = {3},
pages = {1281--1298},
title = {{Global analysis of night marine air temperature and its uncertainty since 1880: The HadNMAT2 data set}},
volume = {118},
year = {2013}
}
@article{King2020a,
abstract = {The Greenland Ice Sheet is losing mass at accelerated rates in the 21st century, making it the largest single contributor to rising sea levels. Faster flow of outlet glaciers has substantially contributed to this loss, with the cause of speedup, and potential for future change, uncertain. Here we combine more than three decades of remotely sensed observational products of outlet glacier velocity, elevation, and front position changes over the full ice sheet. We compare decadal variability in discharge and calving front position and find that increased glacier discharge was due almost entirely to the retreat of glacier fronts, rather than inland ice sheet processes, with a remarkably consistent speedup of 4–5{\%} per km of retreat across the ice sheet. We show that widespread retreat between 2000 and 2005 resulted in a step-increase in discharge and a switch to a new dynamic state of sustained mass loss that would persist even under a decline in surface melt.},
author = {King, Michalea D and Howat, Ian M and Candela, Salvatore G and Noh, Myoung J and Jeong, Seongsu and No{\"{e}}l, Brice P Y and van den Broeke, Michiel R and Wouters, Bert and Negrete, Adelaide},
doi = {10.1038/s43247-020-0001-2},
issn = {2662-4435},
journal = {Communications Earth {\&} Environment},
number = {1},
pages = {1},
title = {{Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat}},
url = {https://doi.org/10.1038/s43247-020-0001-2},
volume = {1},
year = {2020}
}
@article{Kirschke2013,
abstract = {Methane is an important greenhouse gas, responsible for about 20{\%} of the warming induced by long-lived greenhouse gases since pre-industrial times. By reacting with hydroxyl radicals, methane reduces the oxidizing capacity of the atmosphere and generates ozone in the troposphere. Although most sources and sinks of methane have been identified, their relative contributions to atmospheric methane levels are highly uncertain. As such, the factors responsible for the observed stabilization of atmospheric methane levels in the early 2000s, and the renewed rise after 2006, remain unclear. Here, we construct decadal budgets for methane sources and sinks between 1980 and 2010, using a combination of atmospheric measurements and results from chemical transport models, ecosystem models, climate chemistry models and inventories of anthropogenic emissions. The resultant budgets suggest that data-driven approaches and ecosystem models overestimate total natural emissions. We build three contrasting emission scenarios — which differ in fossil fuel and microbial emissions — to explain the decadal variability in atmospheric methane levels detected, here and in previous studies, since 1985. Although uncertainties in emission trends do not allow definitive conclusions to be drawn, we show that the observed stabilization of methane levels between 1999 and 2006 can potentially be explained by decreasing-to-stable fossil fuel emissions, combined with stable-to-increasing microbial emissions. We show that a rise in natural wetland emissions and fossil fuel emissions probably accounts for the renewed increase in global methane levels after 2006, although the relative contribution of these two sources remains uncertain.},
author = {Kirschke, Stefanie and Bousquet, Philippe and Ciais, Philippe and Saunois, Marielle and Canadell, Josep G. and Dlugokencky, Edward J. and Bergamaschi, Peter and Bergmann, Daniel and Blake, Donald R. and Bruhwiler, Lori and Cameron-Smith, Philip and Castaldi, Simona and Chevallier, Fr{\'{e}}d{\'{e}}ric and Feng, Liang and Fraser, Annemarie and Heimann, Martin and Hodson, Elke L. and Houweling, Sander and Josse, B{\'{e}}atrice and Fraser, Paul J. and Krummel, Paul B. and Lamarque, Jean Fran{\c{c}}ois and Langenfelds, Ray L. and {Le Qu{\'{e}}r{\'{e}}}, Corinne and Naik, Vaishali and O'doherty, Simon and Palmer, Paul I. and Pison, Isabelle and Plummer, David and Poulter, Benjamin and Prinn, Ronald G. and Rigby, Matt and Ringeval, Bruno and Santini, Monia and Schmidt, Martina and Shindell, Drew T. and Simpson, Isobel J. and Spahni, Renato and Steele, L. Paul and Strode, Sarah A. and Sudo, Kengo and Szopa, Sophie and {Van Der Werf}, Guido R. and Voulgarakis, Apostolos and {Van Weele}, Michiel and Weiss, Ray F. and Williams, Jason E. and Zeng, Guang},
doi = {10.1038/ngeo1955},
isbn = {1752-0894},
issn = {17520894},
journal = {Nature Geoscience},
number = {10},
pages = {813--823},
pmid = {22994201},
title = {{Three decades of global methane sources and sinks}},
volume = {6},
year = {2013}
}
@article{KleinTank2002,
abstract = {We present a dataset of daily resolution climatic time series that has been compiled for the European Climate Assessment (ECA). As of December 2001, this ECA dataset comprises 199 series of minimum, maximum and/or daily mean temperature and 195 series of daily precipitation amount observed at meteorological stations in Europe and the Middle East. Almost all series cover the standard normal period 1961–90, and about 50{\%} extends back to at least 1925. Part of the dataset (90{\%}) is made available for climate research on CDROM and through the Internet (at http://www.knmi.nl/samenw/eca). A comparison of the ECA dataset with existing gridded datasets, having monthly resolution, shows that correlation coefficients between ECA stations and nearest land grid boxes between 1946 and 1999 are higher than 0.8 for 93{\%} of the temperature series and for 51{\%} of the precipitation series. The overall trends in the ECA dataset are of comparable magnitude to those in the gridded datasets. The potential of the ECA dataset for climate studies is demonstrated in two examples. In the first example, it is shown that the winter (October–March) warming in Europe in the 1976–99 period is accompanied by a positive trend in the number of warm-spell days at most stations, but not by a negative trend in the number of cold-spell days. Instead, the number of cold-spell days increases over Europe. In the second example, it is shown for winter precipitation between 1946 and 1999 that positive trends in the mean amount per wet day prevail in areas that are getting drier and wetter. Because of its daily resolution, the ECA dataset enables a variety of empirical climate studies, including detailed analyses of changes in the occurrence of extremes in relation to changes in mean temperature and total precipitation. Copyright},
author = {{Klein Tank}, A. M. G. and Wijngaard, J. B. and K{\"{o}}nnen, G. P. and B{\"{o}}hm, R. and Demar{\'{e}}e, G. and Gocheva, A. and Mileta, M. and Pashiardis, S. and Hejkrlik, L. and Kern-Hansen, C. and Heino, R. and Bessemoulin, P. and M{\"{u}}ller-Westermeier, G. and Tzanakou, M. and Szalai, S. and P{\'{a}}lsd{\'{o}}ttir, T. and Fitzgerald, D. and Rubin, S. and Capaldo, M. and Maugeri, M. and Leitass, A. and Bukantis, A. and Aberfeld, R. and van Engelen, A. F. V. and Forland, E. and Mietus, M. and Coelho, F. and Mares, C. and Razuvaev, V. and Nieplova, E. and Cegnar, T. and {Antonio L{\'{o}}pez}, J. and Dahlstr{\"{o}}m, B. and Moberg, A. and Kirchhofer, W. and Ceylan, A. and Pachaliuk, O. and Alexander, L. V. and Petrovic, P.},
doi = {10.1002/joc.773},
isbn = {0899-8418},
issn = {08998418},
journal = {International Journal of Climatology},
month = {oct},
number = {12},
pages = {1441--1453},
pmid = {20580674},
title = {{Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment}},
url = {https://onlinelibrary.wiley.com/doi/10.1002/joc.773},
volume = {22},
year = {2002}
}
@article{Kobayashi2015,
author = {Kobayashi, Shinya and Ota, Yukinari and Harada, Yayoi and Ebita, Ayataka and Moriya, Masami and Onoda, Hirokatsu and Onogi, Kazutoshi and Kamahori, Hirotaka and Kobayashi, Chiaki and Endo, Hirokazu and Miyaoka, Kengo and Takahashi, Kiyotoshi},
doi = {10.2151/jmsj.2015-001},
issn = {0026-1165},
journal = {Journal of the Meteorological Society of Japan. Series II},
number = {1},
pages = {5--48},
publisher = {Meteorological Society of Japan},
title = {{The JRA-55 Reanalysis: General Specifications and Basic Characteristics}},
url = {https://www.jstage.jst.go.jp/article/jmsj/93/1/93{\_}2015-001/{\_}article},
volume = {93},
year = {2015}
}
@misc{KolodziejczykN.A.PrigentMazella2017,
author = {Kolodziejczyk, Nicolas and Prigent-Mazella, Annaig and Gaillard, Fabienne},
doi = {10.17882/52367},
publisher = {SEANOE},
title = {{ISAS temperature and salinity gridded fields}},
url = {https://dx.doi.org/10.17882/52367},
year = {2017}
}
@incollection{Kubota2020,
abstract = {As the Japanese Global Precipitation Measurement (GPM) product, the Global Satellite Mapping of Precipitation (GSMaP) has been provided by the Japan Aerospace Exploration Agency (JAXA) to distribute hourly global precipitation map with 0.1{\{}$\backslash$textdegree{\}} {\{}$\backslash$texttimes{\}} 0.1{\{}$\backslash$textdegree{\}} lat/lon grid. Since JAXA started near-real-time processing of the GSMaP on November 2007, there have been various significant improvements to the GSMaP. This paper summarizes GSMaP products and related algorithms in the GPM era and shows validation results in Japan and the United States.},
address = {Cham, Switzerland},
author = {Kubota, Takuji and Aonashi, Kazumasa and Ushio, Tomoo and Shige, Shoichi and Takayabu, Yukari N and Kachi, Misako and Arai, Yoriko and Tashima, Tomoko and Masaki, Takeshi and Kawamoto, Nozomi and Mega, Tomoaki and Yamamoto, Munehisa K and Hamada, Atsushi and Yamaji, Moeka and Liu, Guosheng and Oki, Riko},
booktitle = {Satellite Precipitation Measurement: Volume 1},
doi = {10.1007/978-3-030-24568-9_20},
editor = {Levizzani, Vincenzo and Kidd, Christopher and Kirschbaum, Dalia B and Kummerow, Christian D and Nakamura, Kenji and Turk, F Joseph},
isbn = {978-3-030-24568-9},
pages = {355--373},
publisher = {Springer},
title = {{Global Satellite Mapping of Precipitation (GSMaP) Products in the GPM Era}},
url = {https://doi.org/10.1007/978-3-030-24568-9{\_}20},
year = {2020}
}
@misc{Kummerow2015,
address = {Huntsville, AL, USA},
author = {Kummerow, Christian and Ferraro, Ralph and Duncan, David},
doi = {10.5067/AMSR2/A2_RainOcn_NRT},
publisher = {NASA Global Hydrology Center DAAC},
title = {{NRT AMSR2 L2B Global Swath Goddard Profiling Algorithm 2010: Surface Precipitation, Wind Speed Over Ocean, Water Vapor over Ocean and Cloud Liquid Water over Ocean}},
url = {https://dx.doi.org/10.5067/AMSR2/A2{\_}RainOcn{\_}NRT},
year = {2015}
}
@article{Kwok2009,
abstract = {We present our best estimate of the thickness and volume of the Arctic Ocean ice cover from 10 Ice, Cloud, and land Elevation Satellite (ICESat) campaigns that span a 5-year period between 2003 and 2008. Derived ice drafts are consistently within 0.5 m of those from a submarine cruise in mid-November of 2005 and 4 years of ice draft profiles from moorings in the Chukchi and Beaufort seas. Along with a more than 42{\%} decrease in multiyear (MY) ice coverage since 2005, there was a remarkable thinning of ∼0.6 m in MY ice thickness over 4 years. In contrast, the average thickness of the seasonal ice in midwinter (∼2 m), which covered more than two-thirds of the Arctic Ocean in 2007, exhibited a negligible trend. Average winter sea ice volume over the period, weighted by a loss of ∼3000 km3 between 2007 and 2008, was ∼14,000 km3. The total MY ice volume in the winter has experienced a net loss of 6300 km3 ({\textgreater}40{\%}) in the 4 years since 2005, while the first-year ice cover gained volume owing to increased overall area coverage. The overall decline in volume and thickness are explained almost entirely by changes in the MY ice cover. Combined with a large decline in MY ice coverage over this short record, there is a reversal in the volumetric and areal contributions of the two ice types to the total volume and area of the Arctic Ocean ice cover. Seasonal ice, having surpassed that of MY ice in winter area coverage and volume, became the dominant ice type. It seems that the near-zero replenishment of the MY ice cover after the summers of 2005 and 2007, an imbalance in the cycle of replenishment and ice export, has played a significant role in the loss of Arctic sea ice volume over the ICESat record.},
author = {Kwok, R. and Cunningham, G. F. and Wensnahan, M. and Rigor, I. and Zwally, H. J. and Yi, D.},
doi = {10.1029/2009JC005312},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Oceans},
month = {jul},
number = {C7},
pages = {C07005},
title = {{Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008}},
url = {http://doi.wiley.com/10.1029/2009JC005312},
volume = {114},
year = {2009}
}
@article{Kwok2015,
abstract = {We present our estimates of the thickness and volume of the Arctic Ocean ice cover from CryoSat-2 data acquired between October 2010 and May 2014. Average ice thickness and draft differences are within 0.16 m of measurements from other sources (moorings, submarine, electromagnetic sensors, IceBridge). The choice of parameters that affect the conversion of ice freeboard to thickness is discussed. Estimates between 2011 and 2013 suggest moderate decreases in volume followed by a notable increase of more than 2500 km3 (or 0.34 m of thickness over the basin) in 2014, which could be attributed to not only a cooler summer in 2013 but also to large-scale ice convergence just west of the Canadian Arctic Archipelago due to wind-driven onshore drift. Variability of volume and thickness in the multiyear ice zone underscores the importance of dynamics in maintaining the thickness of the Arctic ice cover. Volume estimates are compared with those from ICESat as well as the trends in ice thickness derived from submarine ice draft between 1980 and 2004. The combined ICESat and CryoSat-2 record yields reduced trends in volume loss compared with the 5 year ICESat record, which was weighted by the record-setting ice extent after the summer of 2007.},
author = {Kwok, R. and Cunningham, G. F.},
doi = {10.1098/rsta.2014.0157},
isbn = {1471-2962},
issn = {1364503X},
journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
keywords = {Arctic ocean,Ice deformation,Ice drift,Ice thickness,Ice volume},
pages = {2045},
pmid = {26032317},
title = {{Variability of arctic sea ice thickness and volume from CryoSat-2}},
volume = {373},
year = {2015}
}
@article{LabbeT.C.PfisterS.BronnimannD.RousseauJ.Franke2019,
author = {Labb{\'{e}}, Thomas and Pfister, Christian and Br{\"{o}}nnimann, Stefan and Rousseau, Daniel and Franke, J{\"{o}}rg and Bois, Benjamin},
doi = {10.5194/cp-15-1485-2019},
issn = {1814-9332},
journal = {Climate of the Past},
month = {aug},
number = {4},
pages = {1485--1501},
title = {{The longest homogeneous series of grape harvest dates, Beaune 1354–2018, and its significance for the understanding of past and present climate}},
url = {https://cp.copernicus.org/articles/15/1485/2019/},
volume = {15},
year = {2019}
}
@article{Laloyaux2018a,
abstract = {Abstract CERA-20C is a coupled reanalysis of the twentieth century which aims to reconstruct the past weather and climate of the Earth system including the atmosphere, ocean, land, ocean waves, and sea ice. This reanalysis is based on the CERA coupled atmosphere-ocean assimilation system developed at ECMWF. CERA-20C provides a 10 member ensemble of reanalyses to account for errors in the observational record as well as model error. It benefited from the prior experience of the retrospective atmospheric analysis ERA-20C. The dynamical model and the data assimilation systems initially developed for NWP had been modified to take into account the evolution of the radiative forcing and the observing system. To limit the impact of changes in the observing system throughout the century, only conventional surface observations have been used in the atmosphere. CERA-20C improves the specification of the background and the observation errors, two key elements to ensure a consistent weighting of the uncertainties across geophysical variables, space, and time. The quality of CERA-20C has been evaluated against other centennial reanalyses and independent observations. Although CERA-20C inherits some limitations of ERA-20C to represent correctly the tropical cyclones in the first part of the century, it shows significant improvements in the troposphere, compared to ERA-20C and 20CRv2c (the twentieth century reanalysis produced by NOAA/CIRES). A preliminary study of the climate variability in CERA-20C has been carried out. CERA-20C improves on the representation of atmosphere-ocean heat fluxes and mean sea level pressure compared to previous uncoupled ocean and atmospheric historical reanalyses performed at ECMWF.},
annote = {https://doi.org/10.1029/2018MS001273},
author = {Laloyaux, Patrick and de Boisseson, Eric and Balmaseda, Magdalena and Bidlot, Jean-Raymond and Broennimann, Stefan and Buizza, Roberto and Dalhgren, Per and Dee, Dick and Haimberger, Leopold and Hersbach, Hans and Kosaka, Yuki and Martin, Matthew and Poli, Paul and Rayner, Nick and Rustemeier, Elke and Schepers, Dinand},
doi = {https://doi.org/10.1029/2018MS001273},
issn = {1942-2466},
journal = {Journal of Advances in Modeling Earth Systems},
keywords = {Climate reanalysis,Coupled assimilation,Earth system model},
month = {may},
number = {5},
pages = {1172--1195},
publisher = {John Wiley {\&} Sons, Ltd},
title = {{CERA-20C: A Coupled Reanalysis of the Twentieth Century}},
url = {https://doi.org/10.1029/2018MS001273},
volume = {10},
year = {2018}
}
@article{Landschutzer2016,
author = {Landsch{\"{u}}tzer, Peter and Gruber, Nicolas and Bakker, Dorothee C. E.},
doi = {10.1002/2015GB005359},
issn = {08866236},
journal = {Global Biogeochemical Cycles},
keywords = {carbon sink variability,climate change,global carbon budget,global carbon cycle,ocean biogeochemistry},
month = {oct},
number = {10},
pages = {1396--1417},
publisher = {Wiley-Blackwell},
title = {{Decadal variations and trends of the global ocean carbon sink}},
url = {http://doi.wiley.com/10.1002/2015GB005359},
volume = {30},
year = {2016}
}
@misc{Lange2019b,
author = {Lange, Stefan},
doi = {10.5880/pik.2019.023},
publisher = {GFZ Data Services},
title = {{WFDE5 over land merged with ERA5 over the ocean (W5E5). V. 1.0}},
url = {https://dx.doi.org/10.5880/pik.2019.023},
year = {2019}
}
@article{Langenfelds2002,
abstract = {High-precision, multispecies measurements of flask air samples since 1992 from CSIRO's global sampling network reveal strong correlation among interannual growth rate variations of CO2 and its d13C, H2,CH4, and CO. We show that a major fraction of the variability is consistent with two emission pulses coinciding with large biomass burning events in 1994/1995 and 1997/1998 in tropical and boreal regions, and observations of unusually high levels of combustion products in the overlying troposphere at these times. Implied pulse strengths and multispecies emission ratios are not consistent with any other single process, but do not exclude possible contributions from covarying processes that are linked through climatic forcing. Comparison ofCO2 with its d13C indicates that most of the CO2 variation is from terrestrial exchange, but does not distinguish forcing by biomass burning from imbalance in photosynthesis/respiration of terrestrial ecosystems. Partitioning of terrestrial CO2 fluxes is constrained by H2,CH4, and CO, all of which are products of biomass burning but which have no direct link to net respiration of CO2. While CO is a strong indicator of biomass burning, its short lifetime prevents it from usefully constraining the magnitude ofCO2 emissions. If theH2 andCH4 variations were dominated by biomass burning, they would imply associated carbon emissions in excess of mean annual levels of other years, of 0.6–3.5 and 0.8–3.7 Pg C for 1994/1995 and 1997/1998, respectively. The large range in emission estimates mainly reflects uncertainty in H2/CO2 and CH4/CO2 emission ratios of fires in these years.},
author = {Langenfelds, R L and Francey, R J and Pak, B C and Steele, L P and Lloyd, J and Trudinger, C M and Allison, C E},
doi = {10.1029/2001GB001466},
isbn = {1944-9224},
issn = {08866236},
journal = {Global Biogeochemical Cycles},
title = {{Interannual growth rate variations of atmospheric CO2 and its $\delta$13C, H2, CH4, and CO between 1992 and 1999 linked to biomass burning}},
year = {2002}
}
@article{Lavergne2019,
abstract = {Abstract. We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: first, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR {\&} SSM/I {\&} SSMIS or AMSR-E {\&} AMSR2), in the imaging frequency channels (37GHz and either 6 or 19GHz), in their horizontal resolution (25 or 50km), and in the time period they cover. We introduce the underlying algorithms and provide an evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover. ]]{\textgreater}},
author = {Lavergne, Thomas and S{\o}rensen, Atle Macdonald and Kern, Stefan and Tonboe, Rasmus and Notz, Dirk and Aaboe, Signe and Bell, Louisa and Dybkj{\ae}r, Gorm and Eastwood, Steinar and Gabarro, Carolina and Heygster, Georg and Killie, Mari Anne and {Brandt Kreiner}, Matilde and Lavelle, John and Saldo, Roberto and Sandven, Stein and Pedersen, Leif Toudal},
doi = {10.5194/tc-13-49-2019},
issn = {1994-0424},
journal = {The Cryosphere},
month = {jan},
number = {1},
pages = {49--78},
title = {{Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records}},
url = {https://www.the-cryosphere.net/13/49/2019/},
volume = {13},
year = {2019}
}
@article{Legeais2018,
abstract = {Abstract. Sea level is a very sensitive index of climate change since it integrates the impacts of ocean warming and ice mass loss from glaciers and the ice sheets. Sea level has been listed as an essential climate variable (ECV) by the Global Climate Observing System (GCOS). During the past 25 years, the sea level ECV has been measured from space by different altimetry missions that have provided global and regional observations of sea level variations. As part of the Climate Change Initiative (CCI) program of the European Space Agency (ESA) (established in 2010), the Sea Level project (SL{\_}cci) aimed to provide an accurate and homogeneous long-term satellite-based sea level record. At the end of the first phase of the project (2010–2013), an initial version (v1.1) of the sea level ECV was made available to users (Ablain et al., 2015). During the second phase of the project (2014–2017), improved altimeter standards were selected to produce new sea level products (called SL{\_}cci v2.0) based on nine altimeter missions for the period 1993–2015 (https://doi.org/10.5270/esa-sea{\_}level{\_}cci-1993{\_}2015-v{\_}2.0-201612; Legeais and the ESA SL{\_}cci team, 2016c). Corresponding orbit solutions, geophysical corrections and altimeter standards used in this v2.0 dataset are described in detail in Quartly et al. (2017). The present paper focuses on the description of the SL{\_}cci v2.0 ECV and associated uncertainty and discusses how it has been validated. Various approaches have been used for the quality assessment such as internal validation, comparisons with sea level records from other groups and with in situ measurements, sea level budget closure analyses and comparisons with model outputs. Compared with the previous version of the sea level ECV, we show that use of improved geophysical corrections, careful bias reduction between missions and inclusion of new altimeter missions lead to improved sea level products with reduced uncertainties on different spatial and temporal scales. However, there is still room for improvement since the uncertainties remain larger than the GCOS requirements (GCOS, 2011). Perspectives on subsequent evolution are also discussed.},
author = {Legeais, Jean-Fran{\c{c}}ois and Ablain, Micha{\"{e}}l and Zawadzki, Lionel and Zuo, Hao and Johannessen, Johnny A and Scharffenberg, Martin G and Fenoglio-Marc, Luciana and Fernandes, M Joana and Andersen, Ole Baltazar and Rudenko, Sergei and Cipollini, Paolo and Quartly, Graham D. and Passaro, Marcello and Cazenave, Anny and Benveniste, J{\'{e}}r{\^{o}}me},
doi = {10.5194/essd-10-281-2018},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {feb},
number = {1},
pages = {281--301},
publisher = {Copernicus Publications},
title = {{An improved and homogeneous altimeter sea level record from the ESA Climate Change Initiative}},
url = {https://essd.copernicus.org/articles/10/281/2018/},
volume = {10},
year = {2018}
}
@article{Lenssen2019,
abstract = {Abstract We outline a new and improved uncertainty analysis for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys, and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state-of-the-art reanalyses, and incorporate independently derived estimates for ocean data processing, station homogenization, and other structural biases. The resulting 95{\%} uncertainties are near 0.05 °C in the global annual mean for the last 50 years and increase going back further in time reaching 0.15 °C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date) and conclude with 86{\%} likelihood that 2016 was indeed the hottest year of the instrumental period (so far).},
author = {Lenssen, Nathan J L and Schmidt, Gavin A and Hansen, James E and Menne, Matthew J and Persin, Avraham and Ruedy, Reto and Zyss, Daniel},
doi = {10.1029/2018JD029522},
journal = {Journal of Geophysical Research: Atmospheres},
number = {12},
pages = {6307--6326},
title = {{Improvements in the GISTEMP Uncertainty Model}},
volume = {124},
year = {2019}
}
@article{Leventidou2018,
abstract = {Using the convective clouds differential (CCD) method on total ozone and cloud data from three European satellite instruments GOME/ERS-2 (1995{\&}ndash;2003), SCIAMACHY/Envisat (2002{\&}ndash;2012), and GOME-2/MetOp-A (2007{\&}ndash;2015) it is possible to retrieve tropical tropospheric columns of ozone (TTCO) which are in good agreement with in-situ measurements. Small differences in TTCO between the individual instruments are evident and therefore the individual datasets retrieved are harmonised into one consistent time-series starting from 1996 until 2015. Correction offsets (bias) between the instruments using SCIAMACHY as intermediate reference have been calculated and six different harmonisation scenarios have been tested. Finally, the datasets have been harmonised applying no correction to GOME data while GOME-2 has been corrected using for each grid-box the mean bias with respect SCIAMACHY for the years of common operation (2007{\&}ndash;2012). Depending on the choice of harmonisation, the magnitude, pattern, and uncertainty of the trend can strongly vary. The harmonisation represents an additional source of uncertainty in the merged dataset and derived trend estimates. For the preferred harmonised dataset, the trend ranges between {\&}minus;4 and 4{\&}thinsp;DU{\&}thinsp;decade{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater}. The trend of the tropically averaged tropospheric ozone is equal to 0{\&}thinsp;±{\&}thinsp;0.64{\&}thinsp;DU{\&}thinsp;decade{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater} (2$\sigma$). Regionally, tropospheric ozone has a statistically significant increase by {\~{}}{\&}thinsp;3{\&}thinsp;DU{\&}thinsp;decade{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater} over southern Africa ({\~{}}{\&}thinsp;1.5{\&}thinsp;{\%}{\&}thinsp;year{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater}), the southern tropical Atlantic ({\~{}}{\&}thinsp;1.5{\&}thinsp;{\%}{\&}thinsp;year{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater}), southeastern tropical Pacific Ocean ({\~{}}{\&}thinsp;1{\&}thinsp;{\%}{\&}thinsp;year{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater}), and central Oceania ({\~{}}{\&}thinsp;2{\&}thinsp;{\%}{\&}thinsp;year{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater}). Additionally, over central Africa (2{\&}ndash;2.5{\&}thinsp;{\%}{\&}thinsp;year{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater}) and south India ({\~{}}{\&}thinsp;1.5{\&}thinsp;{\%}{\&}thinsp;year{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater}), tropospheric ozone increases by {\~{}}{\&}thinsp;2{\&}thinsp;DU{\&}thinsp;decade{\textless}sup{\textgreater}-1{\textless}/sup{\textgreater}. These regional positive tropospheric ozone trends maybe linked to anthropogenic activities such as emissions in mega cities or biomass burning in combination with changes in meteorology or/and long range transport of precursor emissions. On the other hand, troposphe{\ldots}},
author = {Leventidou, Elpida and Weber, Mark and Eichmann, K.-U. Kai Uwe and Burrows, John P. and Heue, K.-P. Klaus Peter and Thompson, Anne M. and Johnson, Bryan J.},
doi = {10.5194/acp-18-9189-2018},
issn = {16807324},
journal = {Atmospheric Chemistry and Physics},
number = {13},
pages = {9189--9205},
title = {{Harmonisation and trends of 20-year tropical tropospheric ozone data}},
volume = {18},
year = {2018}
}
@article{Levitus2012,
abstract = {We provide updated estimates of the change of ocean heat content and the thermosteric component of sea level change of the 0-700 and 0-2000m layers of the World Ocean for 1955-2010. Our estimates are based on historical data not previously available, additional modern data, and bathythermograph data corrected for instrumental biases. We have also used Argo data corrected by the Argo DAC if available and used uncorrected Argo data if no corrections were available at the time we downloaded the Argo data. The heat content of the World Ocean for the 0-2000m layer increased by 24.01.9 ? 1022 J (2S.E.) corresponding to a rate of 0.39Wm-2 (per unit area of the World Ocean) and a volume mean warming of 0.09C. This warming corresponds to a rate of 0.27Wm-2 per unit area of earth's surface. The heat content of the World Ocean for the 0-700m layer increased by 16.71.6 ? 10 22 J corresponding to a rate of 0.27Wm-2 (per unit area of the World Ocean) and a volume mean warming of 0.18C. The World Ocean accounts for approximately 93{\%} of the warming of the earth system that has occurred since 1955. The 700-2000m ocean layer accounted for approximately one-third of the warming of the 0-2000m layer of the World Ocean. The thermosteric component of sea level trend was 0.54.05mmyr-1 for the 0-2000m layer and 0.41.04mmyr-1 for the 0-700m layer of the World Ocean for 1955-2010. ? Copyright 2012 by the American Geophysical Union.},
author = {Levitus, S. and Antonov, J. I. and Boyer, T. P. and Baranova, O. K. and Garcia, H. E. and Locarnini, R. A. and Mishonov, A. V. and Reagan, J. R. and Seidov, D. and Yarosh, E. S. and Zweng, M. M.},
doi = {10.1029/2012GL051106},
issn = {00948276},
journal = {Geophysical Research Letters},
month = {may},
number = {10},
pages = {L10603},
publisher = {Wiley Online Library},
title = {{World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010}},
url = {http://doi.wiley.com/10.1029/2012GL051106},
volume = {39},
year = {2012}
}
@article{Liu2012b,
abstract = {A series of satellite-based passive and active microwave instruments provide soil moisture retrievals spanning altogether more than three decades. This offers the opportunity to generate a combined product that incorporates the advantages of both microwave techniques and spans the observation period starting 1979. However, there are several challenges in developing such a dataset, e.g., differences in instrument specifications result in different absolute soil moisture values, the global passive and active microwave retrieval methods produce conceptually different quantities, and products vary in their relative performances depending on vegetation density. This paper presents an approach for combining four passive microwave products from the VU University Amsterdam/National Aeronautics and Space Administration and two active microwave products from the Vienna University of Technology. First, passive microwave soil moisture retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), and the Tropical Rainfall Measuring Mission microwave imager (TMI) instruments were scaled to the climatology of the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) derived product and then all four were combined into a single merged passive microwave product. Second, active microwave soil moisture estimates from the European Remote Sensing (ERS) Scatterometer instrument were scaled to the climatology of the Advanced Scatterometer (ASCAT) derived estimates. Both were combined into a merged active microwave product. Finally, the two merged products were rescaled to a common globally available reference soil moisture dataset provided by a land surface model (GLDAS-1-Noah) and then blended into a single passive/active product. Blending of the active and passive data sets was based on their respective sensitivity to vegetation density. While this three step approach imposes the absolute values of the land surface model dataset to the final product, it preserves the relative dynamics (e.g., seasonality and inter-annual variations) of the original satellite derived retrievals. More importantly, the long term changes evident in the original soil moisture products were also preserved. The method presented in this paper allows the long term product to be extended with data from other current and future operational satellites. The multi-decadal blended dataset is expected to enhance our basic understanding{\ldots}},
author = {Liu, Y Y and Dorigo, W A and Parinussa, R M and {De Jeu}, R A M and Wagner, W and McCabe, M F and Evans, J P and {Van Dijk}, A I J M},
doi = {10.1016/j.rse.2012.03.014},
isbn = {0034-4257},
issn = {00344257},
journal = {Remote Sensing of Environment},
number = {October 2006},
pages = {280--297},
publisher = {Elsevier Inc.},
title = {{Trend-preserving blending of passive and active microwave soil moisture retrievals}},
volume = {123},
year = {2012}
}
@article{acp-13-10659-2013,
author = {Liu, G and Liu, J and Tarasick, D W and Fioletov, V E and Jin, J J and Moeini, O and Liu, X and Sioris, C E and Osman, M},
doi = {10.5194/acp-13-10659-2013},
journal = {Atmospheric Chemistry and Physics},
number = {21},
pages = {10659--10675},
title = {{A global tropospheric ozone climatology from trajectory-mapped ozone soundings}},
url = {https://acp.copernicus.org/articles/13/10659/2013/},
volume = {13},
year = {2013}
}
@article{Liu2012a,
abstract = {Precipitation is a critical component of the Earth's hydrological cycle. Launched on 27 November 1997, TRMM is a joint U.S.?Japan satellite mission to provide the first detailed and comprehensive dataset of the four-dimensional distribution of rainfall and latent heating over vastly undersampled tropical and subtropical oceans and continents (40°S?40°N). Over the past 14 years, TRMM has been a major data source for meteorological, hydrological, and other research and application activities around the world. This short article describes how the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides TRMM archive and nearreal- time precipitation datasets and services for research and applications. TRMM data consist of orbital data from TRMM instruments at the sensor's resolution, gridded data at a range of spatial and temporal resolutions, subsets, ground-based instrument data, and ancillary data. Data analysis, display, and delivery are facilitated by the following services: (1) Mirador (data search and access); (2) TOVAS (TRMM Online Visualization and Analysis System); (3) OPeNDAP (Opensource Project for a Network Data Access Protocol); (4) GrADS Data Server (GDS); and (5) Open Geospatial Consortium (OGC) Web Map Service (WMS) for the GIS community. Precipitation data application services are available to support a wide variety of applications around the world. Future plans include enhanced and new services to address data-related issues from the user community. Meanwhile, the GES DISC is preparing for the Global Precipitation Measurement (GPM) mission, which is scheduled for launch in 2014.},
annote = {doi: 10.1175/BAMS-D-11-00152.1},
author = {Liu, Zhong and Ostrenga, Dana and Teng, William and Kempler, Steven},
doi = {10.1175/BAMS-D-11-00152.1},
issn = {0003-0007},
journal = {Bulletin of the American Meteorological Society},
month = {mar},
number = {9},
pages = {1317--1325},
publisher = {American Meteorological Society},
title = {{Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications}},
url = {https://doi.org/10.1175/BAMS-D-11-00152.1},
volume = {93},
year = {2012}
}
@techreport{Locarnini2019,
abstract = {This atlas consists of a description of data analysis procedures and horizontal maps of climatological distribution fields of dissolved inorganic nutrients (phosphate, nitrate and nitrate+nitrite, and silicate) at selected standard depth levels of the World Ocean on a one-degree latitude-longitude grid. The aim of the maps is to illustrate large-scale characteristics of the distribution of these nutrients. The oceanographic data fields used to generate these climatological maps were computed by objective analysis of all scientifically quality-controlled historical nutrient data in the World Ocean Database 2018. Maps are presented for climatological composite periods (annual, seasonal, monthly, seasonal and monthly difference fields from the annual mean field, and the number of observations) at 102 standard depths. We also provide estimates of the basin-scale uncertainty of the WOA18 nutrient objectively analyzed annual fields.},
address = {Silver Spring, MD, USA},
author = {Locarnini, R.A. and Mishonov, A.V. and Baranova, O.K. and Boyer, T.P. and Zweng, M.M. and Garcia, H.E. and Reagan, J.R. and Seidov, D. and Weathers, K.W. and Paver, C.R. and Smolyar, I.V.},
editor = {Mishonov, A.},
pages = {43},
publisher = {National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS)},
series = {NOAA Atlas NESDIS 81},
title = {{World Ocean Atlas 2018, Volume 1: Temperature}},
url = {https://www.ncei.noaa.gov/data/oceans/woa/WOA18/DOC/woa18{\_}vol1.pdf},
year = {2019}
}
@article{Loeb2012,
abstract = {Global climate change results from a small yet persistent imbalance between the amount of sunlight absorbed by Earth and the thermal radiation emitted back to space. An apparent inconsistency has been diagnosed between interannual variations in the net radiation imbalance inferred from satellite measurements and upper-ocean heating rate from in situ measurements, and this inconsistency has been interpreted as ‘missing energy' in the system. Here we present a revised analysis of net radiation at the top of the atmosphere from satellite data, and we estimate ocean heat content, based on three independent sources. We find that the difference between the heat balance at the top of the atmosphere and upper-ocean heat content change is not statistically significant when accounting for observational uncertainties in ocean measurements, given transitions in instrumentation and sampling. Furthermore, variability in Earth's energy imbalance relating to El Ni{\~{n}}o-Southern Oscillation is found to be consistent within observational uncertainties among the satellite measurements, a reanalysis model simulation and one of the ocean heat content records. We combine satellite data with ocean measurements to depths of 1,800 m, and show that between January 2001 and December 2010, Earth has been steadily accumulating energy at a rate of 0.50±0.43 Wm−2 (uncertainties at the 90{\%} confidence level). We conclude that energy storage is continuing to increase in the sub-surface ocean.},
author = {Loeb, Norman G. and Lyman, John M. and Johnson, Gregory C. and Allan, Richard P. and Doelling, David R. and Wong, Takmeng and Soden, Brian J. and Stephens, Graeme L.},
doi = {10.1038/ngeo1375},
isbn = {1752-0894},
issn = {17520894},
journal = {Nature Geoscience},
month = {jan},
number = {2},
pages = {110--113},
publisher = {Nature Publishing Group},
title = {{Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty}},
volume = {5},
year = {2012}
}
@article{Loeb2009,
abstract = {Despite recent improvements in satellite instrument calibration and the algorithms used to determine reflected solar (SW) and emitted thermal (LW) top-of-atmosphere (TOA) radiative fluxes, a sizeable im- balance persists in the average global net radiation at theTOAfrom satellite observations. This imbalance is problematic in applications that use earth radiation budget (ERB) data for climate model evaluation, esti- mate the earth's annual global mean energy budget, and in studies that infer meridional heat transports. This study provides a detailed error analysis of TOA fluxes based on the latest generation of Clouds and the Earth's Radiant Energy System (CERES) gridded monthly mean data products [the monthly TOA/surface averages geostationary (SRBAVG-GEO)] and uses an objective constrainment algorithm to adjust SW and LW TOA fluxes within their range of uncertainty to remove the inconsistency between average global net TOA flux and heat storage in the earth–atmosphere system. The 5-yr global mean CERES net flux from the standard CERES product is 6.5Wm22, much larger than the best estimate of 0.85Wm22 based on observed ocean heat content data and model simulations. The major sources of uncertainty in the CERESestimate are from instrument calibration (4.2Wm22) and the assumed value for total solar irradiance (1Wm22). After adjustment, the global mean CERES SW TOA flux is 99.5Wm22, corresponding to an albedo of 0.293, and the global mean LW TOA flux is 239.6 W m22. These values differ markedly from previously published adjusted global means based on the ERB Experiment in which the global mean SW TOA flux is 107Wm22 and the LW TOA flux is 234W m22. 1.},
author = {Loeb, Norman G. and Wielicki, Bruce A. and Doelling, David R. and Smith, G. Louis and Keyes, Dennis F. and Kato, Seiji and Manalo-Smith, Natividad and Wong, Takmeng},
doi = {10.1175/2008JCLI2637.1},
isbn = {0894-8755},
issn = {08948755},
journal = {Journal of Climate},
number = {3},
pages = {748--766},
title = {{Toward optimal closure of the Earth's top-of-atmosphere radiation budget}},
volume = {22},
year = {2009}
}
@article{Loeb2017,
abstract = {AbstractThe Clouds and the Earth?s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA), Edition 4.0 (Ed4.0), data product is described. EBAF Ed4.0 is an update to EBAF Ed2.8, incorporating all of the Ed4.0 suite of CERES data product algorithm improvements and consistent input datasets throughout the record. A one-time adjustment to shortwave (SW) and longwave (LW) TOA fluxes is made to ensure that global mean net TOA flux for July 2005?June 2015 is consistent with the in situ value of 0.71 W m?2. While global mean all-sky TOA flux differences between Ed4.0 and Ed2.8 are within 0.5 W m?2, appreciable SW regional differences occur over marine stratocumulus and snow/sea ice regions. Marked regional differences in SW clear-sky TOA flux occur in polar regions and dust areas over ocean. Clear-sky LW TOA fluxes in EBAF Ed4.0 exceed Ed2.8 in regions of persistent high cloud cover. Owing to substantial differences in global mean clear-sky TOA fluxes, the net cloud radiative effect in EBAF Ed4.0 is ?18 W m?2 compared to ?21 W m?2 in EBAF Ed2.8. The overall uncertainty in 1° ? 1° latitude?longitude regional monthly all-sky TOA flux is estimated to be 3 W m?2 [one standard deviation (1$\sigma$)] for the Terra-only period and 2.5 W m?2 for the Terra?Aqua period both for SW and LW fluxes. The SW clear-sky regional monthly flux uncertainty is estimated to be 6 W m?2 for the Terra-only period and 5 W m?2 for the Terra?Aqua period. The LW clear-sky regional monthly flux uncertainty is 5 W m?2 for Terra only and 4.5 W m?2 for Terra?Aqua.},
annote = {doi: 10.1175/JCLI-D-17-0208.1},
author = {Loeb, Norman G and Doelling, David R and Wang, Hailan and Su, Wenying and Nguyen, Cathy and Corbett, Joseph G and Liang, Lusheng and Mitrescu, Cristian and Rose, Fred G and Kato, Seiji},
doi = {10.1175/JCLI-D-17-0208.1},
issn = {0894-8755},
journal = {Journal of Climate},
month = {nov},
number = {2},
pages = {895--918},
publisher = {American Meteorological Society},
title = {{Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product}},
url = {https://doi.org/10.1175/JCLI-D-17-0208.1},
volume = {31},
year = {2017}
}
@article{Loeb2020,
abstract = {Abstract We compare top-of-atmosphere (TOA) radiative fluxes observed by the Clouds and the Earth's Radiant Energy System (CERES) and simulated by seven general circulation models forced with observed sea-surface temperature (SST) and sea-ice boundary conditions. In response to increased SSTs along the equator and over the eastern Pacific (EP) following the so-called global warming “hiatus” of the early 21st century, simulated TOA flux changes are remarkably similar to CERES. Both show outgoing shortwave and longwave TOA flux changes that largely cancel over the west and central tropical Pacific, and large reductions in shortwave flux for EP low-cloud regions. A model's ability to represent changes in the relationship between global mean net TOA flux and surface temperature depends upon how well it represents shortwave flux changes in low-cloud regions, with most showing too little sensitivity to EP SST changes, suggesting a “pattern effect” that may be too weak compared to observations.},
author = {Loeb, Norman G and Wang, Hailan and Allan, Richard P and Andrews, Timothy and Armour, Kyle and Cole, Jason N S and Dufresne, Jean-Louis and Forster, Piers and Gettelman, Andrew and Guo, Huan and Mauritsen, Thorsten and Ming, Yi and Paynter, David and Proistosescu, Cristian and Stuecker, Malte F and Will{\'{e}}n, Ulrika and Wyser, Klaus},
doi = {https://doi.org/10.1029/2019GL086705},
journal = {Geophysical Research Letters},
number = {5},
pages = {e2019GL086705},
title = {{New Generation of Climate Models Track Recent Unprecedented Changes in Earth's Radiation Budget Observed by CERES}},
volume = {47},
year = {2020}
}
@article{Loupian2015,
abstract = {The paper describes the architecture and capabilities of the center for collective use (CCU) “IKI-Monitoring” launched by the Space Research Institute of Russian Academy of Sciences (IKI RAS), designed to provide access to specialists conducting various research and development projects to the archives of satellite data and information obtained on their basis, as well as tools for processing and analysis. The urgency of creating this kind of center is primarily due to the fact that remote sensing methods are currently widely used for solving a variety of scientific and applied problems. Meanwhile, due to continuous expansion of the capabilities of satellite Earth observation systems, the traditional way of remote sensing data management, requiring creation of an own system for data collection, processing and analysis for every research project, sometimes experiencing severe resource limitations of individual research groups and organizations, won't be effective anymore. At the same time the development of modern technologies of distributed data processing enables the creation and development of new approaches that not only provide remote search and selection of the information needed to solve specific research problems, but also to create tools for distributed analysis of information that can be used to solve various scientific problems. The introduction of modern remote sensing data processing capabilities was the goal of launching of the “IKI- Monitoring” CCU by IKI in 2012. The paper describes the major tasks and capabilities of the center. It also describes the center's main construction principles and the architecture. It discusses the basic technical solutions which enabled the possibility of creation and development of the center. The paper also discusses issues related to the development prospects of the centre.},
author = {Loupian, Evgeny and Burtsev, Mikhail A and Bartalev, Sergey A and Kashnitskii, Alexandr},
journal = {Current Problems In Remote Sensing Of The Earth From Space},
number = {5},
pages = {263--284},
title = {{IKI center for collective use of satellite data archiving, processing and analysis systems aimed at solving the problems of environmental study and monitoring [in Russian]}},
url = {http://d33.infospace.ru/d33{\_}conf/sb2015t5/263–284.pdf},
volume = {12},
year = {2015}
}
@article{Loveland1997,
annote = {doi: 10.1080/014311697217099},
author = {Loveland, T R and Belward, A S},
doi = {10.1080/014311697217099},
issn = {0143-1161},
journal = {International Journal of Remote Sensing},
month = {oct},
number = {15},
pages = {3289--3295},
publisher = {Taylor {\&} Francis},
title = {{The IGBP-DIS global 1km land cover data set, DISCover: First results}},
url = {https://doi.org/10.1080/014311697217099},
volume = {18},
year = {1997}
}
@article{Lussana2018,
abstract = {The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall–runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957–2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional{\_}archive/PREC1d/gridded{\_}dataset/catalog.html.},
author = {Lussana, Cristian and Saloranta, Tuomo and Skaugen, Thomas and Magnusson, Jan and Tveito, Ole Einar and Andersen, Jess},
doi = {10.5194/essd-10-235-2018},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {feb},
number = {1},
pages = {235--249},
title = {{seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day}},
url = {https://essd.copernicus.org/articles/10/235/2018/},
volume = {10},
year = {2018}
}
@article{Lyman2013,
abstract = {Ocean heat content anomalies are analyzed from 1950 to 2011 in five distinct depth layers (0–100, 100–300, 300–700, 700–900, and 900–1800 m). These layers correspond to historic increases in common maximum sampling depths of ocean temperature measurements with time, as different instruments—mechanical bathythermograph (MBT), shallow expendable bathythermograph (XBT), deep XBT, early sometimes shallower Argo profiling floats, and recent Argo floats capable of worldwide sampling to 2000 m—have come into widespread use. This vertical separation of maps allows computation of annual ocean heat content anomalies and their sampling uncertainties back to 1950 while taking account of in situ sampling advances and changing sampling patterns. The 0–100-m layer is measured over 50{\%} of the globe annually starting in 1956, the 100–300-m layer starting in 1967, the 300–700-m layer starting in 1983, and the deepest two layers considered here starting in 2003 and 2004, during the implementation of Argo. Furthermore, global ocean heat uptake estimates since 1950 depend strongly on assumptions made concerning changes in undersampled or unsampled ocean regions. If unsampled areas are assumed to have zero anomalies and are included in the global integrals, the choice of climatological reference from which anomalies are estimated can strongly influence the global integral values and their trend: the sparser the sampling and the bigger the mean difference between climatological and actual values, the larger the influence.},
author = {Lyman, John M. and Johnson, Gregory C.},
doi = {10.1175/JCLI-D-12-00752.1},
issn = {0894-8755},
journal = {Journal of Climate},
keywords = {climatology,content,heat,ocean,upper},
month = {mar},
number = {5},
pages = {1945--1957},
publisher = {American Meteorological Society},
title = {{Estimating Global Ocean Heat Content Changes in the Upper 1800 m since 1950 and the Influence of Climatology Choice}},
url = {http://dx.doi.org/10.1175/jcli-d-12-00752.1 http://journals.ametsoc.org/doi/10.1175/JCLI-D-12-00752.1},
volume = {27},
year = {2014}
}
@article{Mahmood2018,
abstract = {A high resolution, long-term regional reanalysis over the Indian subcontinent has been developed and is currently in production. The regional reanalysis has been produced as part of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project and is the outcome of a collaboration between the Met Office (MO), the National Centre for Medium Range Weather Forecasting (NCMRWF) and the India Meteorological Department (IMD). The reanalysis will produce a consistent data set of high-resolution fields for a wide range of atmospheric variables available from 1979 to 2016. Production runs started in 2017, and computations for 10 years have been completed as of May 2017. The entire production will be completed in early 2018. This article introduces the IMDAA regional reanalysis, describes the forecast model, data assimilation method, and input data sets used to produce the reanalysis. The performance of the system from a pilot study run for 2008?2009 are presented indicating that the regional reanalysis is able to capture major monsoon features?a key phenomenon in the Indian subcontinent.},
author = {Mahmood, Sana and Davie, Jemma and Jermey, Peter and Renshaw, Richard and George, John P and Rajagopal, E N and Rani, S Indira},
doi = {10.1002/asl.808},
journal = {Atmospheric Science Letters},
month = {mar},
number = {3},
pages = {e808},
publisher = {Wiley-Blackwell},
title = {{Indian monsoon data assimilation and analysis regional reanalysis: Configuration and performance}},
volume = {19},
year = {2018}
}
@article{Maidment2014,
abstract = {African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30 year (1983?2012), temporally consistent rainfall data set for Africa known as TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series) using archived Meteosat thermal infrared imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10 day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation data sets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit and Global Precipitation Climatology Centre gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by ?0.37?mm?d?1 (21{\%}) compared to other data sets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.},
author = {Maidment, Ross I and Grimes, David and Allan, Richard P and Tarnavsky, Elena and Stringer, Marc and Hewison, Tim and Roebeling, Rob and Black, Emily},
doi = {10.1002/2014JD021927},
journal = {Journal of Geophysical Research: Atmospheres},
month = {jul},
number = {18},
pages = {10619--10644},
publisher = {Wiley-Blackwell},
title = {{The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set}},
volume = {119},
year = {2014}
}
@article{Mankoff2019,
author = {Mankoff, K D and Colgan, W and Solgaard, A and Karlsson, N B and Ahlstr{\o}m, A P and van As, D and Box, J E and Khan, S A and Kjeldsen, K K and Mouginot, J and Fausto, R S},
doi = {10.5194/essd-11-769-2019},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {jun},
number = {2},
pages = {769--786},
publisher = {Copernicus Publications},
title = {{Greenland Ice Sheet solid ice discharge from 1986 through 2017}},
url = {https://www.earth-syst-sci-data.net/11/769/2019/ https://www.earth-syst-sci-data.net/11/769/2019/essd-11-769-2019.pdf},
volume = {11},
year = {2019}
}
@article{Marshall2003,
author = {Marshall, Gareth J.},
doi = {10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2},
issn = {0894-8755},
journal = {Journal of Climate},
month = {dec},
number = {24},
pages = {4134--4143},
title = {{Trends in the Southern Annular Mode from Observations and Reanalyses}},
url = {http://journals.ametsoc.org/doi/10.1175/1520-0442(2003)016{\%}3C4134:TITSAM{\%}3E2.0.CO;2},
volume = {16},
year = {2003}
}
@article{Masarie2004,
abstract = {Atmospheric transport models are used to constrain sources and sinks of carbon dioxide by requiring that the modeled spatial and temporal concentration patterns are consistent with the observations. Serious obstacles to this approach are the sparsity of sampling sites and the lack of temporal continuity among observations at different locations. A procedure is presented that attempts to extend the knowledge gained during a limited period of measurements beyond the period itself resulting in records containing measurement data and extrapolated and interpolated values. From limited measurements we can define trace gas climatologies that describe average seasonal cycles, trends, and changes in trends at individual sampling sites. A comparison of the site climatologies with a reference defined over a much longer period of time constitutes the framework used in the development of the data extension procedure. Two extension methods are described. The benchmark trend method uses a deseasonalized long-term trend from a single site as a reference to individual site climatologies. The latitude reference method utilizes measurements from many sites in constructing a reference to the climatologies. Both methods are evaluated and the advantages and limitations of each are discussed. Data extension is not based on any atmospheric models but entirely on the data themselves. The methods described here are relatively straightforward and reproducible and result in extended records that are model independent. The cooperative air sampling network maintained by the National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory in Boulder, Colorado, provides a test bed for the development of the data extension method; we intend to integrate and extend CO2 measurement records from other laboratories providing a globally consistent atmospheric CO2 database to the modeling community.},
author = {Masarie, Kenneth A and Tans, Pieter P},
doi = {10.1029/95JD00859},
journal = {Journal of Geophysical Research: Atmospheres},
number = {D6},
pages = {11593--11610},
title = {{Extension and integration of atmospheric carbon dioxide data into a globally consistent measurement record}},
volume = {100},
year = {2004}
}
@article{Mears2017,
abstract = { AbstractTemperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S–80°N, 1979–2016) = 0.174°C decade−1], {\~{}}30{\%} larger than our previous version of the dataset [global trend (70°S–80°N, 1979–2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics. },
author = {Mears, Carl A and Wentz, Frank J},
doi = {10.1175/JCLI-D-16-0768.1},
journal = {Journal of Climate},
number = {19},
pages = {7695--7718},
title = {{A Satellite-Derived Lower-Tropospheric Atmospheric Temperature Dataset Using an Optimized Adjustment for Diurnal Effects}},
volume = {30},
year = {2017}
}
@article{Meinshausen2017,
abstract = {Atmospheric greenhouse gas (GHG) concentrations are at unprecedented, record-high levels compared to the last 800 000 years. Those elevated GHG concentrations warm the planet and – partially offset by net cooling effects by aerosols – are largely responsible for the observed warming over the past 150 years. An accurate representation of GHG concentrations is hence important to understand and model recent climate change. So far, community efforts to create composite datasets of GHG concentrations with seasonal and latitudinal information have focused on marine boundary layer conditions and recent trends since the 1980s. Here, we provide consolidated datasets of historical atmospheric concentrations (mole fractions) of 43 GHGs to be used in the Climate Model Intercomparison Project – Phase 6 (CMIP6) experiments. The presented datasets are based on AGAGE and NOAA networks, firn and ice core data, and archived air data, and a large set of published studies. In contrast to previous intercomparisons, the new datasets are latitudinally resolved and include seasonality. We focus on the period 1850–2014 for historical CMIP6 runs, but data are also provided for the last 2000 years. We provide consolidated datasets in various spatiotemporal resolutions for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as 40 other GHGs, namely 17 ozone-depleting substances, 11 hydrofluorocarbons (HFCs), 9 perfluorocarbons (PFCs), sulfur hexafluoride (SF6), nitrogen trifluoride (NF3) and sulfuryl fluoride (SO2F2). In addition, we provide three equivalence species that aggregate concentrations of GHGs other than CO2, CH4 and N2O, weighted by their radiative forcing efficiencies. For the year 1850, which is used for pre-industrial control runs, we estimate annual global-mean surface concentrations of CO2 at 284.3 ppm, CH4 at 808.2 ppb and N2O at 273.0 ppb. The data are available at https://esgf-node.llnl.gov/search/input4mips/ and http://www.climatecollege.unimelb.edu.au/cmip6. While the minimum CMIP6 recommendation is to use the global- and annual-mean time series, modelling groups can also choose our monthly and latitudinally resolved concentrations, which imply a stronger radiative forcing in the Northern Hemisphere winter (due to the latitudinal gradient and seasonality).},
author = {Meinshausen, Malte and Vogel, Elisabeth and Nauels, Alexander and Lorbacher, Katja and Meinshausen, Nicolai and Etheridge, David M and Fraser, Paul J and Montzka, Stephen A and Rayner, Peter J and Trudinger, Cathy M and Krummel, Paul B and Beyerle, Urs and Canadell, Josep G and Daniel, John S and Enting, Ian G and Law, Rachel M and Lunder, Chris R and O'Doherty, Simon and Prinn, Ron G and Reimann, Stefan and Rubino, Mauro and Velders, Guus J M and Vollmer, Martin K and Wang, Ray H J and Weiss, Ray},
doi = {10.5194/gmd-10-2057-2017},
issn = {1991-9603},
journal = {Geoscientific Model Development},
month = {may},
number = {5},
pages = {2057--2116},
publisher = {Copernicus Publications},
title = {{Historical greenhouse gas concentrations for climate modelling (CMIP6)}},
url = {https://gmd.copernicus.org/articles/10/2057/2017/},
volume = {10},
year = {2017}
}
@article{Menne2018,
abstract = {We describe a fourth version of the Global Historical Climatology Network (GHCN)-monthly (GHCNm) temperature dataset. Version 4 (v4) fulfills the goal of aligning GHCNm temperature values with the GHCN-daily dataset and makes use of data from previous versions of GHCNm as well as data collated under the auspices of the International Surface Temperature Initiative. GHCNm v4 has many thousands of additional stations compared to version 3 (v3) both historically and with short time-delay updates. The greater number of stations as well as the use of records with incomplete data during the base period provides for greater global coverage throughout the record compared to earlier versions. Like v3, the monthly averages are screened for random errors and homogenized to address systematic errors. New to v4, uncertainties are calculated for each station series, and regional uncertainties scale directly from the station uncertainties. Correlated errors in the station series are quantified by running the homogenization algorithm as an ensemble. Additional uncertainties associated with incomplete homogenization and use of anomalies are then incorporated into the station ensemble. Further uncertainties are quantified at the regional level, the most important of which is for incomplete spatial coverage. Overall, homogenization has a smaller impact on the v4 global trend compared to v3, though adjustments lead to much greater consistency than between the unadjusted versions. The adjusted v3 global mean therefore falls within the range of uncertainty for v4 adjusted data. Likewise, annual anomaly uncertainties for the other major independent land surface air temperature datasets overlap with GHCNm v4 uncertainties.},
author = {Menne, Matthew J and Williams, Claude N and Gleason, Byron E and Rennie, J Jared and Lawrimore, Jay H},
doi = {10.1175/JCLI-D-18-0094.1},
issn = {0894-8755},
journal = {Journal of Climate},
month = {dec},
number = {24},
pages = {9835--9854},
title = {{The Global Historical Climatology Network Monthly Temperature Dataset, Version 4}},
url = {https://journals.ametsoc.org/doi/10.1175/JCLI-D-18-0094.1},
volume = {31},
year = {2018}
}
@article{Merchant2014a,
abstract = {Sea surface temperature (SST) datasets have been generated from satellite observations for the period 1991–2010, intended for use in climate science applications. Attributes of the datasets specifically relevant to climate applications are: first, independence from in situ observations; second, effort to ensure homogeneity and stability through the time-series; third, context-specific uncertainty estimates attached to each SST value; and, fourth, provision of estimates of both skin SST (the fundamental measurement, relevant to air-sea fluxes) and SST at standard depth and local time (partly model mediated, enabling comparison with historical in situ datasets). These attributes in part reflect requirements solicited from climate data users prior to and during the project. Datasets consisting of SSTs on satellite swaths are derived from the Along-Track Scanning Radiometers (ATSRs) and Advanced Very High Resolution Radiometers (AVHRRs). These are then used as sole SST inputs to a daily, spatially complete, analysis SST product, with a latitude-longitude resolution of 0.05°C and good discrimination of ocean surface thermal features. A product user guide is available, linking to reports describing the datasets' algorithmic basis, validation results, format, uncertainty information and experimental use in trial climate applications. Future versions of the datasets will span at least 1982–2015, better addressing the need in many climate applications for stable records of global SST that are at least 30 years in length.},
author = {Merchant, C J and Embury, O and Roberts-Jones, J and Fiedler, E and Bulgin, C E and Corlett, G K and Good, S and McLaren, A and Rayner, N and Morak-Bozzo, S and Donlon, C},
doi = {10.1002/gdj3.20},
isbn = {2049-6060},
issn = {20496060},
journal = {Geoscience Data Journal},
number = {2},
pages = {179--191},
title = {{Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI)}},
volume = {1},
year = {2014}
}
@misc{Merchant2014,
author = {Merchant, C J and Embury, O and Roberts-Jones, J and Fiedler, E K and Bulgin, C E and Corlett, G K and Good, S and McLaren, A and Rayner, N A and Donlon, C},
doi = {10.5285/878bef44-d32a-40cd-a02d-49b6286f0ea4},
publisher = {NERC Earth Observation Data Centre, 24th February 2014},
title = {{ESA Sea Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long term product version 1.0}},
year = {2014}
}
@article{bg-15-5653-2018,
author = {Merlivat, L and Boutin, J and Antoine, D and Beaumont, L and Golbol, M and Vellucci, V},
doi = {10.5194/bg-15-5653-2018},
journal = {Biogeosciences},
number = {18},
pages = {5653--5662},
title = {{Increase of dissolved inorganic carbon and decrease in pH in near-surface waters in the Mediterranean Sea during the past two decades}},
url = {https://www.biogeosciences.net/15/5653/2018/},
volume = {15},
year = {2018}
}
@article{Montzka2009,
abstract = {Tropospheric accumulation rates of the three most abundant hydrochlorofluorocarbons (HCFCs) were up to two times faster during 2007 than measured in 2003{\&}{\#}8211;2004. Tropospheric chlorine from HCFCs increased at 10 pptCl/yr during 2006{\&}{\#}8211;2007, up from 6 pptCl/yr in 2003{\&}{\#}8211;2004, and offset declines in chlorine from other anthropogenic ozone-depleting substances in 2007 by approximately one-third. Derived global emissions for HCFCs increased by up to 60{\%} since 2004, and, for HCFC-142b, emissions during 2007 were two times larger than projected recently. Measured tropospheric distributions suggest a shift in HCFC emissions to lower latitudes of the Northern Hemisphere. These changes coincide with exponential increases in developing country production and consumption and decreases in other countries. When weighted by direct, 100-yr global warming potentials, HCFC emissions in 2007 amounted to 0.78 GtCO2-equivalents, or 30{\%} larger than the average during 2000{\&}{\#}8211;2004, and were approximately 2.6{\%} of fossil-fuel and cement related CO2 emissions.},
author = {Montzka, S. A. and Hall, B. D. and Elkins, J. W.},
doi = {10.1029/2008GL036475},
issn = {00948276},
journal = {Geophysical Research Letters},
month = {feb},
number = {3},
pages = {L03804},
title = {{Accelerated increases observed for hydrochlorofluorocarbons since 2004 in the global atmosphere}},
url = {http://doi.wiley.com/10.1029/2008GL036475},
volume = {36},
year = {2009}
}
@article{Montzka2015,
abstract = {Global-scale atmospheric measurements are used to investigate the effectiveness of recent adjustments to production and consumption controls on hydrochlorofluorocarbons (HCFCs) under the Montreal Protocol on Substances that Deplete the Ozone Layer (Montreal Protocol) and to assess recent projections of large increases in hydrofluorocarbon (HFC) production and emission. The results show that aggregate global HCFC emissions did not increase appreciably during 2007-2012 and suggest that the 2007 Adjustments to the Montreal Protocol played a role in limiting HCFC emissions well in advance of the 2013 cap on global production. HCFC emissions varied between 27 and 29 kt CFC-11-equivalent (eq)/y or 0.76 and 0.79 GtCO{\textless}inf{\textgreater}2{\textless}/inf{\textgreater}-eq/y during this period. Despite slower than projected increases in aggregate HCFC emissions since 2007, total emissions of HFCs used as substitutes for HCFCs and chlorofluorocarbons (CFCs) have not increased more rapidly than rates projected [Velders, G. J. M.; Fahey, D. W.; Daniel, J. S.; McFarland, M.; Andersen, S. O. The Large Contribution of Projected HFC Emissions to Future Climate Forcing. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 10949-10954] for 2007-2012. HFC global emission magnitudes related to this substitution totaled 0.51 (-0.03, +0.04) GtCO{\textless}inf{\textgreater}2{\textless}/inf{\textgreater}-eq/y in 2012, a magnitude about two times larger than emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC) for these HFCs. Assuming accurate reporting to the UNFCCC, the results imply that developing countries (non-Annex I Parties) not reporting to the UNFCCC now account for nearly 50{\%} of global HFC emissions used as substitutes for ozone-depleting substances (ODSs). Global HFC emissions (as CO{\textless}inf{\textgreater}2{\textless}/inf{\textgreater}-eq) from ODS substitution can be attributed approximately equally to mobile air conditioning, commercial refrigeration, and the sum of all other applications.},
author = {Montzka, S. A. and Mcfarland, M. and Andersen, S. O. and Miller, B. R. and Fahey, D. W. and Hall, B. D. and Hu, L. and Siso, C. and Elkins, J. W.},
doi = {10.1021/jp5097376},
issn = {15205215},
journal = {Journal of Physical Chemistry A},
number = {19},
pages = {4439--4449},
pmid = {25405363},
title = {{Recent trends in global emissions of hydrochlorofluorocarbons and hydrofluorocarbons: Reflecting on the 2007 Adjustments to the Montreal protocol}},
volume = {119},
year = {2015}
}
@article{Morice2012,
abstract = {Recent developments in observational near-surface air temperature and sea-surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea-surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea-surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near-surface temperature analyses. Fitted linear trends in temperature anomalies are approximately 0.07°C/decade from 1901 to 2010 and 0.17°C/decade from 1979 to 2010 globally. Northern/southern hemispheric trends are 0.08/0.07°C/decade over 1901 to 2010 and 0.24/0.10°C/decade over 1979 to 2010. Linear trends in other prominent near-surface temperature analyses agree well with the range of trends computed from the HadCRUT4 ensemble members.},
author = {Morice, Colin P and Kennedy, John J and Rayner, Nick A and Jones, Phil D},
doi = {10.1029/2011JD017187},
journal = {Journal of Geophysical Research: Atmospheres},
number = {D8},
pages = {D08101},
title = {{Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set}},
volume = {117},
year = {2012}
}
@article{Morice2019,
author = {Morice, Colin P and Kennedy, John J. and Rayner, Nick A. and Winn, J. P. and Hogan, E. and Killick, R. E. and Dunn, R. J. H. and Osborn, Timothy J. and Jones, P. D. and Simpson, I. R.},
doi = {10.1029/2019JD032361},
issn = {2169-897X},
journal = {Journal of Geophysical Research: Atmospheres},
month = {feb},
number = {3},
pages = {e2019JD032361},
title = {{An Updated Assessment of Near‐Surface Temperature Change From 1850: The HadCRUT5 Data Set}},
url = {https://onlinelibrary.wiley.com/doi/10.1029/2019JD032361},
volume = {126},
year = {2021}
}
@article{Mudryk2020,
abstract = {{\textless}p{\textgreater}{\textless}![CDATA[Abstract. This paper presents an analysis of observed and simulated historical snow cover extent and snow mass, along with future snow cover projections from models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 6 (CMIP6). Where appropriate, the CMIP6 output is compared to CMIP5 results in order to assess progress (or absence thereof) between successive model generations. An ensemble of six observation-based products is used to produce a new time series of historical Northern Hemisphere snow extent anomalies and trends; a subset of four of these products is used for snow mass. Trends in snow extent over 1981–2018 are negative in all months and exceed -50×103 km2 yr−1 during November, December, March, and May. Snow mass trends are approximately −5 Gt yr−1 or more for all months from December to May. Overall, the CMIP6 multi-model ensemble better represents the snow extent climatology over the 1981–2014 period for all months, correcting a low bias in CMIP5. Simulated snow extent and snow mass trends over the 1981–2014 period are stronger in CMIP6 than in CMIP5, although large inter-model spread remains in the simulated trends for both variables. There is a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all CMIP6 Shared Socioeconomic Pathways. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8 {\%} relative to the 1995–2014 level per degree Celsius of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast-response components of the cryosphere such as sea ice and near-surface permafrost extent.]]{\textgreater}{\textless}/p{\textgreater}},
author = {Mudryk, Lawrence and Santolaria-Ot{\'{i}}n, Mar{\'{i}}a and Krinner, Gerhard and M{\'{e}}n{\'{e}}goz, Martin and Derksen, Chris and Brutel-Vuilmet, Claire and Brady, Mike and Essery, Richard},
doi = {10.5194/tc-14-2495-2020},
issn = {1994-0424},
journal = {The Cryosphere},
month = {jul},
number = {7},
pages = {2495--2514},
title = {{Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble}},
url = {https://tc.copernicus.org/articles/14/2495/2020/},
volume = {14},
year = {2020}
}
@article{mueller2013benchmark,
abstract = {Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr−2 in our merged product) followed by a significant decrease in this trend (−0.18 mm yr−2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr−1 (1.35 mm d−1) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr−1 (34 406 km3 yr−1) for a total land area of 130 922 000 km2. Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.},
author = {Mueller, Brigitte and Hirschi, Martin and Jimenez, C and Ciais, P and Dirmeyer, P A and Dolman, A J and Fisher, J B and Jung, Martin and Ludwig, F and Maignan, F and Miralles, D. G. and McCabe, M. F. and Reichstein, M. and Sheffield, J. and Wang, K. and Wood, E. F. and Zhang, Y. and Seneviratne, S. I.},
doi = {10.5194/hess-17-3707-2013},
issn = {1607-7938},
journal = {Hydrology and Earth System Sciences},
month = {oct},
number = {10},
pages = {3707--3720},
publisher = {Copernicus GmbH},
title = {{Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis}},
url = {https://hess.copernicus.org/articles/17/3707/2013/},
volume = {17},
year = {2013}
}
@misc{Myneni2015,
author = {Myneni, R. and Kynazikhin, Y and Park, T},
doi = {10.5067/MODIS/MCD15A2H.006},
publisher = {NASA EOSDIS Land Processes DAAC},
title = {{MCD15A2H MODIS/Terra+Aqua Leaf Area Index/FPAR 8-day L4 Global 500m SIN Grid V006 [Data set]}},
url = {https://doi.org/10.5067/MODIS/MCD15A2H.006},
year = {2015}
}
@article{Nerem2022,
abstract = {Satellite altimetry has shown that global mean sea level has been rising at a rate of {\~{}}3 {\{}$\backslash$textpm{\}} 0.4 mm/y since 1993. Using the altimeter record coupled with careful consideration of interannual and decadal variability as well as potential instrument errors, we show that this rate is accelerating at 0.084 {\{}$\backslash$textpm{\}} 0.025 mm/y2, which agrees well with climate model projections. If sea level continues to change at this rate and acceleration, sea-level rise by 2100 ({\~{}}65 cm) will be more than double the amount if the rate was constant at 3 mm/y.Using a 25-y time series of precision satellite altimeter data from TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3, we estimate the climate-change{\{}$\backslash$textendash{\}}driven acceleration of global mean sea level over the last 25 y to be 0.084 {\{}$\backslash$textpm{\}} 0.025 mm/y2. Coupled with the average climate-change{\{}$\backslash$textendash{\}}driven rate of sea level rise over these same 25 y of 2.9 mm/y, simple extrapolation of the quadratic implies global mean sea level could rise 65 {\{}$\backslash$textpm{\}} 12 cm by 2100 compared with 2005, roughly in agreement with the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5) model projections.},
author = {Nerem, R S and Beckley, B D and Fasullo, J T and Hamlington, B D and Masters, D and Mitchum, G T},
doi = {10.1073/pnas.1717312115},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
number = {9},
pages = {2022--2025},
publisher = {National Academy of Sciences},
title = {{Climate-change–driven accelerated sea-level rise detected in the altimeter era}},
url = {https://www.pnas.org/content/115/9/2022},
volume = {115},
year = {2018}
}
@techreport{NIWA2020,
address = {Wellington, New Zealand},
author = {NIWA},
pages = {36},
publisher = {National Institute of Water {\&} Atmospheric Research (NIWA)},
title = {{Ministry for the Environment Atmosphere and Climate Report 2020: Updated Datasets supplied by NIWA}},
url = {https://www.mfe.govt.nz/publications/environmental-reporting/ministry-environment-atmosphere-and-climate-report-2020-updated},
year = {2020}
}
@article{Novella2013,
abstract = {This paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983–2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), wit...},
author = {Novella, Nicholas S. and Thiaw, Wassila M.},
doi = {10.1175/JAMC-D-11-0238.1},
journal = {Journal of Applied Meteorology and Climatology},
number = {3},
pages = {588--606},
title = {{African rainfall climatology version 2 for famine early warning systems}},
volume = {52},
year = {2013}
}
@article{essd-11-1437-2019,
author = {Olsen, A and Lange, N and Key, R M and Tanhua, T and {\'{A}}lvarez, M and Becker, S and Bittig, H C and Carter, B R and da Cunha, L and Feely, R A and van Heuven, S and Hoppema, M and Ishii, M and Jeansson, E and Jones, S D and Jutterstr{\"{o}}m, S and Karlsen, M K and Kozyr, A and Lauvset, S K and {Lo Monaco}, C and Murata, A and P{\'{e}}rez, F F and Pfeil, B and Schirnick, C and Steinfeldt, R and Suzuki, T and Telszewski, M and Tilbrook, B and Velo, A and Wanninkhof, R},
doi = {10.5194/essd-11-1437-2019},
journal = {Earth System Science Data},
number = {3},
pages = {1437--1461},
title = {{GLODAPv2.2019 – an update of GLODAPv2}},
url = {https://www.earth-syst-sci-data.net/11/1437/2019/},
volume = {11},
year = {2019}
}
@article{2007,
author = {Onogi, Kazutoshi and Tsutsui, Junichi and Koide, Hiroshi and Sakamoto, Masami and Kobayashi, Shinya and Hatsushika, Hiroaki and Matsumoto, Takanori and Yamazaki, Nobuo and Kamahori, Hirotaka and Takahashi, Kiyotoshi and Kadokura, Shinji and Wada, Koji and Kato, Koji and Oyama, Ryo and Ose, Tomoaki and Mannoji, Nobutaka and Taira, Ryusuke},
doi = {10.2151/jmsj.85.369},
journal = {Journal of the Meteorological Society of Japan. Series II},
number = {3},
pages = {369--432},
title = {{The JRA-25 Reanalysis}},
volume = {85},
year = {2007}
}
@article{Osborn2019,
author = {Osborn, T. J. and Jones, P. D. and Lister, D. H. and Morice, C P and Simpson, I. R. and Winn, J. P. and Hogan, E. and Harris, I C},
doi = {10.1029/2019JD032352},
issn = {2169-897X},
journal = {Journal of Geophysical Research: Atmospheres},
month = {jan},
number = {2},
pages = {e2019JD032352},
title = {{Land Surface Air Temperature Variations Across the Globe Updated to 2019: The CRUTEM5 Data Set}},
url = {https://onlinelibrary.wiley.com/doi/10.1029/2019JD032352},
volume = {126},
year = {2021}
}
@article{Oyler2015,
abstract = {ABSTRACT Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (?800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56?°C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets.},
author = {Oyler, Jared W and Ballantyne, Ashley and Jencso, Kelsey and Sweet, Michael and Running, Steven W},
doi = {10.1002/joc.4127},
journal = {International Journal of Climatology},
month = {aug},
number = {9},
pages = {2258--2279},
publisher = {Wiley-Blackwell},
title = {{Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature}},
volume = {35},
year = {2015}
}
@article{Palmer2021,
abstract = {We present an ensemble approach to quantify historical global mean sea-level (GMSL) rise based on tide gauge reconstructions. This approach combines the maximum internal uncertainty across the ensemble with an estimate of structural uncertainty to provide a conservative estimate of the total uncertainty. Comparisons of GMSL rise over the 20th century based on deltas and linear trends (and their respective uncertainties) are consistent with past Intergovernmental Panel on Climate Change (IPCC) assessments and show good agreement with satellite altimeter timeseries. Sensitivity tests show that our estimates of GMSL rise are robust to the choice of reference period and central estimate timeseries. The methods proposed in this study are generic and could be easily applied to other global or regional climate change indicators.},
author = {Palmer, Matthew D and Domingues, Catia M and Slangen, Aim{\'{e}}e B A and {Boeira Dias}, F},
doi = {10.1088/1748-9326/abdaec},
issn = {1748-9326},
journal = {Environmental Research Letters},
month = {apr},
number = {4},
pages = {044043},
title = {{An ensemble approach to quantify global mean sea-level rise over the 20th century from tide gauge reconstructions}},
volume = {16},
year = {2021}
}
@article{doi:10.1175/JCLI-D-11-00300.1,
abstract = { AbstractA systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984–2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems. },
author = {Pan, Ming and Sahoo, Alok K and Troy, Tara J and Vinukollu, Raghuveer K and Sheffield, Justin and Wood, Eric F},
doi = {10.1175/JCLI-D-11-00300.1},
journal = {Journal of Climate},
number = {9},
pages = {3191--3206},
title = {{Multisource Estimation of Long-Term Terrestrial Water Budget for Major Global River Basins}},
url = {https://doi.org/10.1175/JCLI-D-11-00300.1},
volume = {25},
year = {2012}
}
@article{Panchen2012,
abstract = {Premise of the study: The global climate is changing rapidly and is expected to continue changing in coming decades. Studying changes in plant flowering times during a historical period of warming temperatures gives us a way to examine the impacts of climate change and allows us to predict further changes in coming decades. The Greater Philadelphia region has a long and rich history of botanical study and documentation, with abundant herbarium specimens, field observations, and botanical photographs from the mid-1800s onward. These extensive records also provide an opportunity to validate methodologies employed by other climate change researchers at a different biogeographical area and with a different group of species. Methods: Data for 2539 flowering records from 1840 to 2010 were assessed to examine changes in flowering response over time and in relation to monthly minimum temperatures of 28 Piedmont species native to the Greater Philadelphia region. Key results: Regression analysis of the date of flowering with year or with temperature showed that, on average, the Greater Philadelphia species studied are flowering 16 d earlier over the 170-yr period and 2.7 d earlier per 1°C rise in monthly minimum temperature. Conclusions: Of the species studied, woody plants with short flowering duration are the best indicators of a warming climate. For monthly minimum temperatures, temperatures 1 or 2 mo prior to flowering are most significantly correlated with flowering time. Studies combining herbarium specimens, photographs, and field observations are an effective method for detecting the effects of climate change on flowering times. {\textcopyright} 2012 Botanical Society of America.},
author = {Panchen, Zoe A. and Primack, Richard B. and Ani{\'{s}}ko, Tomasz and Lyons, Robert E.},
doi = {10.3732/ajb.1100198},
issn = {00029122},
journal = {American Journal of Botany},
number = {4},
pages = {751--756},
title = {{Herbarium specimens, photographs, and field observations show Philadelphia area plants are responding to climate change}},
volume = {99},
year = {2012}
}
@article{Panziera2018,
abstract = {The characterization of the alpine extreme precipitation is the basis to study the projected changes in frequency and intensity of heavy rainfall and is needed to improve the resilience of communities to high-impact weather. Climatological features of extreme daily and sub-daily precipitation are documented here for the Swiss Alps and surrounding regions at a high spatial resolution (1?km2). The basis is 12?years of data from rain gauges and CombiPrecip, a rainfall field produced by locally adjusting the radar precipitation map to the values measured by rain gauges. The agreement between rain gauges and CombiPrecip concerning both the timing and the magnitude of the extreme events is quantified by cross-validation; overall, it increases with diminishing the severity of the extremes and increasing accumulation time. If the extremes represent on average the 10 most intense rainfall accumulations per year, in general 50?65{\%} of rain gauges extremes are extremes also for CombiPrecip, 40?50{\%} of CombiPrecip extremes are not extremes according to rain gauges, and CombiPrecip extremes are till 7{\%} lower than rain gauges extremes. The maps presented in this paper show that both daily and sub-daily extremes are more intense along the alpine slopes compared to the crest of the Alps in all seasons, with the Lago Maggiore region showing the largest values. The fraction of yearly rainfall due to extremes is generally smaller in the Alps than in flat terrain. Extreme 1-hr precipitation is more clustered in time in the inner Alps, but is less frequent, and exhibits a strong diurnal cycle in summer. The paper also shows that sub-daily and daily extremes occur essentially over the same 24-hr period.},
author = {Panziera, L and Gabella, M and Germann, U and Martius, O},
doi = {10.1002/joc.5528},
journal = {International Journal of Climatology},
month = {apr},
number = {10},
pages = {3749--3769},
publisher = {Wiley-Blackwell},
title = {{A 12-year radar-based climatology of daily and sub-daily extreme precipitation over the Swiss Alps}},
volume = {38},
year = {2018}
}
@article{Park2012,
abstract = {The atmospheric nitrous oxide concentration has increased by 20{\%} since 1750. Analyses of Antarctic firn and archived air samples reveal seasonal cycles in the isotopic signature of nitrous oxide, which can help to disentangle the contribution of surface sources.},
author = {Park, S. and Croteau, P. and Boering, K. A. and Etheridge, D. M. and Ferretti, D. and Fraser, P. J. and Kim, K-R. and Krummel, P. B. and Langenfelds, R. L. and van Ommen, T. D. and Steele, L. P. and Trudinger, C. M.},
doi = {10.1038/ngeo1421},
issn = {1752-0894},
journal = {Nature Geoscience},
month = {apr},
number = {4},
pages = {261--265},
publisher = {Nature Publishing Group},
title = {{Trends and seasonal cycles in the isotopic composition of nitrous oxide since 1940}},
volume = {5},
year = {2012}
}
@article{Parthasarathy1994,
abstract = {The Indian rainfall has often been used as a proxy data for the Asian monsoon as a whole for understanding the energy budget of the major circulation features and also used as an input parameter in estimating the other regional parameters. In view of this, a long homogeneous rainfall series of All-India (India taken as one unit) has been prepared based on a fixed and well distributed network of 306 raingauge stations over India by giving proper area-weightage. This paper contains a listing of All-India monthly, seasonal and annual homogeneous data series for the period 1871–1993. Some statistical details and long-term changes of the All-India monsoon rainfall have been discussed.},
author = {Parthasarathy, B and Munot, A A and Kothawale, D R},
doi = {10.1007/BF00867461},
issn = {1434-4483},
journal = {Theoretical and Applied Climatology},
number = {4},
pages = {217--224},
title = {{All-India monthly and seasonal rainfall series: 1871–1993}},
url = {https://doi.org/10.1007/BF00867461},
volume = {49},
year = {1994}
}
@article{Patra2016,
abstract = {Methane (CH4) plays important roles in atmospheric chemistry and short-term forcing of climate. A clear understanding of atmospheric CH4's budget of emissions and losses is required to aid sustainable management of Earth's future environment. We used an atmospheric chemistry-transport model (JAMSTEC's ACTM) for simulating atmospheric CH4. A global inverse modeling system has been developed for estimating CH4 emissions from 53 land regions for 2002–2012 using measurements at 39 sites. An ensemble of 7 inversions is performed by varying a priori emissions. Global net CH4emissions varied between 505–509 and 524–545 Tg yr–1during 2002–2006 and 2008–2012, respectively (ranges based on 7 inversion cases), with a step like increase in 2007 in agreement with atmospheric measurements. The inversion system did not account for interannual variations in OH radicals reacting with CH4in the atmosphere. Our results suggest that the recent update of the EDGAR inventory (version 4.2FT2010) overestimated the global total emissions by at least 25 Tg yr–1in 2010. The increase in CH4 emission since 2004 originated in the tropical and southern hemisphere regions, coinciding with an increase in non-dairy cattle stocks by {\~{}}10 {\%} from 2002 (with 1056 million heads) to 2012, leading to {\~{}}10 Tg yr–1 increase in emissions from enteric fermentation. All 7 ensemble cases robustly estimated the interannual variations in emissions, but poorly constrained the seasonal cycle amplitude or phase consistently for all regions due to the sparse observational network. Forward simulation results using both a priori and a posteriori emissions are compared with independent aircraft measurements for validation. Based on the results of the comparison, we reject the upper limit (545 Tg yr–1) of global total emissions as 14 Tg yr–1 too high during 2008–2012, which allows us to further conclude that the increase in CH4 emissions over the East Asia (mainly China) region was 7–8 Tg yr–1between the 2002–2006 and 2008–2012 periods, contrary to 1–17 Tg yr–1in the a priori emissions.},
author = {Patra, Prabir K. and Saeki, Tazu and Dlugokencky, Edward J. and Ishijima, Kentaro and Umezawa, Taku and Ito, Akihiko and Aoki, Shuji and Morimoto, Shinji and Kort, Eric A. and Crotwell, Andrew and {Ravi Kumar}, Kunchala and Nakazawa, Takakiyo},
doi = {10.2151/jmsj.2016-006},
issn = {0026-1165},
journal = {Journal of the Meteorological Society of Japan. Series II},
number = {1},
pages = {91--113},
title = {{Regional Methane Emission Estimation Based on Observed Atmospheric Concentrations (2002–2012)}},
url = {https://www.jstage.jst.go.jp/article/jmsj/94/1/94{\_}2016-006/{\_}article},
volume = {94},
year = {2016}
}
@article{Patra2018,
author = {Patra, Prabir K and Takigawa, Masayuki and Watanabe, Shingo and Chandra, Naveen and Ishijima, Kentaro and Yamashita, Yousuke},
doi = {10.2151/sola.2018-016},
journal = {SOLA},
pages = {91--96},
title = {{Improved Chemical Tracer Simulation by MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM)}},
volume = {14},
year = {2018}
}
@article{Paulat2008,
abstract = {A so-called disaggregation technique is used to combine daily rain gauge measurements and hourly radar composites in order to produce a dataset of hourly precipitation in Germany on a grid with a horizontal resolution of 7 km for the years 2001–2004. This state-of-the-art observation-based dataset of precipitation has a high temporal and spatial resolution and will be extended continuously during the upcoming years. Limitations of its quality, which are due to intrinsic problems with observing the highly variable field of precipitation, are discussed and quantified where possible. The dataset offers novel possibilities to investigate the climatology of precipitation and to verify precipitation forecasts from numerical weather prediction models. The frequency of hourly precipitation in Germany above the detection limit of 0.1 mm/h amounts to 10–30{\%}inwinter,with clearmaxima in themountainous regions, and to 6–20{\%}in summer,when the spatial variability is considerably reduced. The 95th percentile of the frequency distribution is significantly larger in summer than in winter, with local maxima in the mountainous regions in winter, and in the Alpine Foreland and upper Elbe catchment in summer. It is shown that the operational model COSMO-7 with a horizontal resolution of 7 kmcaptures the geographical distribution of the frequency and of the 95th percentile of hourly precipitation inGermany verywell. In contrast, themodel is not able to realistically simulate the diurnal cycle of precipitation in any region of Germany during summer.},
author = {Paulat, M. and Frei, C. and Hagen, M.n and Wernli, H.},
doi = {10.1127/0941-2948/2008/0332},
issn = {09412948},
journal = {Meteorologische Zeitschrift},
number = {6},
pages = {719--732},
title = {{A gridded dataset of hourly precipitation in Germany: Its construction, climatology and application}},
volume = {17},
year = {2008}
}
@article{Peng2013,
abstract = {Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1.},
author = {Peng, G. and Meier, W. N. and Scott, D. J. and Savoie, M. H.},
doi = {10.5194/essd-5-311-2013},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {oct},
number = {2},
pages = {311--318},
title = {{A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring}},
url = {https://www.earth-syst-sci-data.net/5/311/2013/},
volume = {5},
year = {2013}
}
@article{Platnick2003,
abstract = {The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began Earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.},
author = {Platnick, Steven and King, M.D. and Ackerman, S.A. and Menzel, W.P. and Baum, B.A. and Riedi, J.C. and Frey, R.A.},
doi = {10.1109/TGRS.2002.808301},
issn = {0196-2892},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
month = {feb},
number = {2},
pages = {459--473},
title = {{The MODIS cloud products: algorithms and examples from Terra}},
url = {http://ieeexplore.ieee.org/document/1196061/},
volume = {41},
year = {2003}
}
@article{Poli2016,
abstract = { AbstractThe ECMWF twentieth century reanalysis (ERA-20C; 1900–2010) assimilates surface pressure and marine wind observations. The reanalysis is single-member, and the background errors are spatiotemporally varying, derived from an ensemble. The atmospheric general circulation model uses the same configuration as the control member of the ERA-20CM ensemble, forced by observationally based analyses of sea surface temperature, sea ice cover, atmospheric composition changes, and solar forcing. The resulting climate trend estimations resemble ERA-20CM for temperature and the water cycle. The ERA-20C water cycle features stable precipitation minus evaporation global averages and no spurious jumps or trends. The assimilation of observations adds realism on synoptic time scales as compared to ERA-20CM in regions that are sufficiently well observed. Comparing to nighttime ship observations, ERA-20C air temperatures are 1 K colder. Generally, the synoptic quality of the product and the agreement in terms of climate indices with other products improve with the availability of observations. The MJO mean amplitude in ERA-20C is larger than in 20CR version 2c throughout the century, and in agreement with other reanalyses such as JRA-55. A novelty in ERA-20C is the availability of observation feedback information. As shown, this information can help assess the product's quality on selected time scales and regions. },
author = {Poli, Paul and Hersbach, Hans and Dee, Dick P and Berrisford, Paul and Simmons, Adrian J and Vitart, Fr{\'{e}}d{\'{e}}ric and Laloyaux, Patrick and Tan, David G H and Peubey, Carole and Th{\'{e}}paut, Jean-No{\"{e}}l and Tr{\'{e}}molet, Yannick and H{\'{o}}lm, El{\'{i}}as V and Bonavita, Massimo and Isaksen, Lars and Fisher, Michael},
doi = {10.1175/JCLI-D-15-0556.1},
journal = {Journal of Climate},
number = {11},
pages = {4083--4097},
title = {{ERA-20C: An Atmospheric Reanalysis of the Twentieth Century}},
volume = {29},
year = {2016}
}
@article{Prinn2018,
author = {Prinn, Ronald G. and Weiss, Ray F. and Arduini, Jgor and Arnold, Tim and DeWitt, H. Langley and Fraser, Paul J. and Ganesan, Anita L. and Gasore, Jimmy and Harth, Christina M. and Hermansen, Ove and Kim, Jooil and Krummel, Paul B. and Li, Shanlan and Loh, Zo{\"{e}} M. and Lunder, Chris R. and Maione, Michela and Manning, Alistair J. and Miller, Ben R. and Mitrevski, Blagoj and M{\"{u}}hle, Jens and O'Doherty, Simon and Park, Sunyoung and Reimann, Stefan and Rigby, Matt and Saito, Takuya and Salameh, Peter K. and Schmidt, Roland and Simmonds, Peter G. and Steele, L. Paul and Vollmer, Martin K. and Wang, Ray H. and Yao, Bo and Yokouchi, Yoko and Young, Dickon and Zhou, Lingxi},
doi = {10.5194/essd-10-985-2018},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {jun},
number = {2},
pages = {985--1018},
title = {{History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE)}},
url = {https://essd.copernicus.org/articles/10/985/2018/},
volume = {10},
year = {2018}
}
@article{Purkey2010,
abstract = { Abstract Abyssal global and deep Southern Ocean temperature trends are quantified between the 1990s and 2000s to assess the role of recent warming of these regions in global heat and sea level budgets. The authors 1) compute warming rates with uncertainties along 28 full-depth, high-quality hydrographic sections that have been occupied two or more times between 1980 and 2010; 2) divide the global ocean into 32 basins, defined by the topography and climatological ocean bottom temperatures; and then 3) estimate temperature trends in the 24 sampled basins. The three southernmost basins show a strong statistically significant abyssal warming trend, with that warming signal weakening to the north in the central Pacific, western Atlantic, and eastern Indian Oceans. Eastern Atlantic and western Indian Ocean basins show statistically insignificant abyssal cooling trends. Excepting the Arctic Ocean and Nordic seas, the rate of abyssal (below 4000 m) global ocean heat content change in the 1990s and 2000s is equivalent to a heat flux of 0.027 (±0.009) W m−2 applied over the entire surface of the earth. Deep (1000–4000 m) warming south of the Subantarctic Front of the Antarctic Circumpolar Current adds 0.068 (±0.062) W m−2. The abyssal warming produces a 0.053 (±0.017) mm yr−1 increase in global average sea level and the deep warming south of the Subantarctic Front adds another 0.093 (±0.081) mm yr−1. Thus, warming in these regions, ventilated primarily by Antarctic Bottom Water, accounts for a statistically significant fraction of the present global energy and sea level budgets. },
author = {Purkey, Sarah G and Johnson, Gregory C},
doi = {10.1175/2010JCLI3682.1},
journal = {Journal of Climate},
number = {23},
pages = {6336--6351},
title = {{Warming of Global Abyssal and Deep Southern Ocean Waters between the 1990s and 2000s: Contributions to Global Heat and Sea Level Rise Budgets}},
volume = {23},
year = {2010}
}
@article{os-9-193-2013,
author = {R{\"{o}}denbeck, C and Keeling, R F and Bakker, D C E and Metzl, N and Olsen, A and Sabine, C and Heimann, M},
doi = {10.5194/os-9-193-2013},
journal = {Ocean Science},
number = {2},
pages = {193--216},
title = {{Global surface-ocean pCO2 and sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme}},
url = {https://www.ocean-sci.net/9/193/2013/},
volume = {9},
year = {2013}
}
@article{bg-11-4599-2014,
author = {R{\"{o}}denbeck, C and Bakker, D C E and Metzl, N and Olsen, A and Sabine, C and Cassar, N and Reum, F and Keeling, R F and Heimann, M},
doi = {10.5194/bg-11-4599-2014},
journal = {Biogeosciences},
number = {17},
pages = {4599--4613},
title = {{Interannual sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme}},
url = {https://www.biogeosciences.net/11/4599/2014/},
volume = {11},
year = {2014}
}
@article{Rajeevan2006,
abstract = {Here, we report the development of a high resolution (1° × 1° lat./long.) gridded daily rainfall dataset for the Indian region. There are only 1803 stations with mini- mum 90{\%} data availability during the analysis period (1951–2003). For the analysis, we have followed the in- terpolation method proposed by Shepard. Standard quality-controls were performed before carrying out the interpolation analysis. Comparison with similar global gridded rainfall datasets revealed that the pre- sent rainfall analysis is better in accurate representation of spatial rainfall variation. Using this gridded rainfall dataset, an analysis was made to identify the break and active periods during the southwest monsoon season (June–September). Break (active) periods during the monsoon season were iden- tified as those in which the standardized daily rainfall anomaly averaged over Central India (21–27°N, 72– 85°E) is less than –1.0 (more than 1.0). The break peri- ods thus identified for the period 1951–2003 were comparable with those identified by earlier studies. Contrary to a recent study, no evidence was found for any statistically significant trends in the number of break or active days during the period 1951–2003. This gridded rainfall dataset is available for non- commercial applications.},
author = {Rajeevan, M. and Bhate, Jyoti and Kale, J. D. and Lal, B.},
journal = {Current Science},
number = {3},
pages = {296--306},
title = {{High resolution daily gridded rainfall data for the Indian region: Analysis of break and active monsoon spells}},
url = {http://www.jstor.org/stable/24094135},
volume = {91},
year = {2006}
}
@article{Ray2011,
abstract = {One approach to reconstructing historical sea level from the relatively sparse tide-gauge network is to employ Empirical Orthogonal Functions (EOFs) as interpolatory spatial basis functions. The EOFs are determined from independent global data, generally sea-surface heights from either satellite altimetry or a numerical ocean model. The problem is revisited here for sea level since 1900. A new approach to handling the tide-gauge datum problem by direct solution offers possible advantages over the method of integrating sea-level differences, with the potential of eventually adjusting datums into the global terrestrial reference frame. The resulting time series of global mean sea levels appears fairly insensitive to the adopted set of EOFs. In contrast, charts of regional sea level anomalies and trends are very sensitive to the adopted set of EOFs, especially for the sparser network of gauges in the early 20th century. The reconstructions appear especially suspect before 1950 in the tropical Pacific. While this limits some applications of the sea-level reconstructions, the sensitivity does appear adequately captured by formal uncertainties. All our solutions show regional trends over the past five decades to be fairly uniform throughout the global ocean, in contrast to trends observed over the shorter altimeter era. Consistent with several previous estimates, the global sea-level rise since 1900 is 1.70±0.26mmyr−1. The global trend since 1995 exceeds 3mmyr−1 which is consistent with altimeter measurements, but this large trend was possibly also reached between 1935 and 1950.},
author = {Ray, Richard D and Douglas, Bruce C},
doi = {https://doi.org/10.1016/j.pocean.2011.07.021},
issn = {0079-6611},
journal = {Progress in Oceanography},
number = {4},
pages = {496--515},
title = {{Experiments in reconstructing twentieth-century sea levels}},
volume = {91},
year = {2011}
}
@article{Rayner2003,
abstract = {We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1° latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5° latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reduced-space optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month-to-month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.},
author = {Rayner, N A and Parker, D E and Horton, E B and Folland, C K and Alexander, L V and Rowell, D P and Kent, E C and Kaplan, A},
doi = {10.1029/2002JD002670},
journal = {Journal of Geophysical Research: Atmospheres},
number = {D14},
pages = {4407},
title = {{Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century}},
volume = {108},
year = {2003}
}
@techreport{Reichle2012,
address = {Greenbelt, MD, USA},
author = {Reichle, R H},
pages = {38},
publisher = {Global Modeling and Assimilation Office (GMAO)},
series = {GMAO Office Note No. 3 (Version 1.2)},
title = {{The MERRA-Land Data Product}},
url = {http://gmao.gsfc.nasa.gov/pubs/office{\_}notes},
year = {2012}
}
@article{Reynolds2002,
abstract = {Abstract A weekly 1° spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present. The weekly product has been available since 1993 and is widely used for weather and climate monitoring and forecasting. Errors in the satellite bias correction and the sea ice to SST conversion algorithm are discussed, and then an improved version of the OI analysis is developed. The changes result in a modest reduction in the satellite bias that leaves small global residual biases of roughly ?0.03°C. The major improvement in the analysis occurs at high latitudes due to the new sea ice algorithm where local differences between the old and new analysis can exceed 1°C. Comparisons with other SST products are needed to determine the consistency of the OI. These comparisons show that the differences among products occur on large time- and space scales with monthly rms differences exceeding 0.5°C in some regions. These regions are primarily the mid- and high-latitude Southern Oceans and the Arctic where data are sparse, as well as high-gradient areas such as the Gulf Stream and Kuroshio where the gradients cannot be properly resolved on a 1° grid. In addition, globally averaged differences of roughly 0.05°C occur among the products on decadal scales. These differences primarily arise from the same regions where the rms differences are large. However, smaller unexplained differences also occur in other regions of the midlatitude Northern Hemisphere where in situ data should be adequate.},
annote = {doi: 10.1175/1520-0442(2002)0152.0.CO;2},
author = {Reynolds, Richard W and Rayner, Nick A and Smith, Thomas M and Stokes, Diane C and Wang, Wanqiu},
doi = {10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2},
issn = {0894-8755},
journal = {Journal of Climate},
month = {jul},
number = {13},
pages = {1609--1625},
publisher = {American Meteorological Society},
title = {{An Improved In Situ and Satellite SST Analysis for Climate}},
url = {https://doi.org/10.1175/1520-0442(2002)015{\%}3C1609:AIISAS{\%}3E2.0.CO 2},
volume = {15},
year = {2002}
}
@article{Rice2016,
abstract = {There is no scientific consensus on the drivers of the atmospheric methane growth rate over the past three decades. Here, we report carbon and hydrogen isotopic measurements of atmospheric methane in archived air samples collected 1977{\{}$\backslash$textendash{\}}1998, and modeling of these with more contemporary data to infer changes in methane sources over the period 1984{\{}$\backslash$textendash{\}}2009. We present strong evidence that methane emissions from fossil fuel sectors were approximately constant in the 1980s and 1990s but increased significantly between 2000 and 2009. This finding challenges recent conclusions based on atmospheric ethane that fugitive fossil fuel emissions fell during much of this period. Emissions from other anthropogenic sources also increased, but were partially offset by reductions in wetland and fire emissions.Observations of atmospheric methane (CH4) since the late 1970s and measurements of CH4 trapped in ice and snow reveal a meteoric rise in concentration during much of the twentieth century. Since 1750, levels of atmospheric CH4 have more than doubled to current globally averaged concentration near 1,800 ppb. During the late 1980s and 1990s, the CH4 growth rate slowed substantially and was near or at zero between 1999 and 2006. There is no scientific consensus on the drivers of this slowdown. Here, we report measurements of the stable isotopic composition of atmospheric CH4 (13C/12C and D/H) from a rare air archive dating from 1977 to 1998. Together with more modern records of isotopic atmospheric CH4, we performed a time-dependent retrieval of methane fluxes spanning 25 y (1984{\{}$\backslash$textendash{\}}2009) using a 3D chemical transport model. This inversion results in a 24 [18, 27] Tg y-1 CH4 increase in fugitive fossil fuel emissions since 1984 with most of this growth occurring after year 2000. This result is consistent with some bottom-up emissions inventories but not with recent estimates based on atmospheric ethane. In fact, when forced with decreasing emissions from fossil fuel sources our inversion estimates unreasonably high emissions in other sources. Further, the inversion estimates a decrease in biomass-burning emissions that could explain falling ethane abundance. A range of sensitivity tests suggests that these results are robust.},
author = {Rice, Andrew L and Butenhoff, Christopher L and Teama, Doaa G and R{\"{o}}ger, Florian H and Khalil, M Aslam K and Rasmussen, Reinhold A},
doi = {10.1073/pnas.1522923113},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
month = {sep},
number = {39},
pages = {10791--10796},
publisher = {National Academy of Sciences},
title = {{Atmospheric methane isotopic record favors fossil sources flat in 1980s and 1990s with recent increase}},
volume = {113},
year = {2016}
}
@article{Rienecker2011,
abstract = { AbstractThe Modern-Era Retrospective Analysis for Research and Applications (MERRA) was undertaken by NASA's Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA's Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses. Focusing on the satellite era, from 1979 to the present, MERRA has achieved its goals with significant improvements in precipitation and water vapor climatology. Here, a brief overview of the system and some aspects of its performance, including quality assessment diagnostics from innovation and residual statistics, is given.By comparing MERRA with other updated reanalyses [the interim version of the next ECMWF Re-Analysis (ERA-Interim) and the Climate Forecast System Reanalysis (CFSR)], advances made in this new generation of reanalyses, as well as remaining deficiencies, are identified. Although there is little difference between the new reanalyses in many aspects of climate variability, substantial differences remain in poorly constrained quantities such as precipitation and surface fluxes. These differences, due to variations both in the models and in the analysis techniques, are an important measure of the uncertainty in reanalysis products. It is also found that all reanalyses are still quite sensitive to observing system changes. Dealing with this sensitivity remains the most pressing challenge for the next generation of reanalyses.Production has now caught up to the current period and MERRA is being continued as a near-real-time climate analysis. The output is available online through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). },
author = {Rienecker, Michele M and Suarez, Max J and Gelaro, Ronald and Todling, Ricardo and Bacmeister, Julio and Liu, Emily and Bosilovich, Michael G and Schubert, Siegfried D and Takacs, Lawrence and Kim, Gi-Kong and Bloom, Stephen and Chen, Junye and Collins, Douglas and Conaty, Austin and da Silva, Arlindo and Gu, Wei and Joiner, Joanna and Koster, Randal D and Lucchesi, Robert and Molod, Andrea and Owens, Tommy and Pawson, Steven and Pegion, Philip and Redder, Christopher R and Reichle, Rolf and Robertson, Franklin R and Ruddick, Albert G and Sienkiewicz, Meta and Woollen, Jack},
doi = {10.1175/JCLI-D-11-00015.1},
journal = {Journal of Climate},
number = {14},
pages = {3624--3648},
title = {{MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications}},
volume = {24},
year = {2011}
}
@article{Rignot1095,
abstract = {We evaluate the state of the mass balance of the Antarctic Ice Sheet over the last four decades using a comprehensive, precise satellite record and output products from a regional atmospheric climate model to document its impact on sea-level rise. The mass loss is dominated by enhanced glacier flow in areas closest to warm, salty, subsurface circumpolar deep water, including East Antarctica, which has been a major contributor over the entire period. The same sectors are likely to dominate sea-level rise from Antarctica in decades to come as enhanced polar westerlies push more circumpolar deep water toward the glaciers.We use updated drainage inventory, ice thickness, and ice velocity data to calculate the grounding line ice discharge of 176 basins draining the Antarctic Ice Sheet from 1979 to 2017. We compare the results with a surface mass balance model to deduce the ice sheet mass balance. The total mass loss increased from 40 {\{}$\backslash$textpm{\}} 9 Gt/y in 1979{\{}$\backslash$textendash{\}}1990 to 50 {\{}$\backslash$textpm{\}} 14 Gt/y in 1989{\{}$\backslash$textendash{\}}2000, 166 {\{}$\backslash$textpm{\}} 18 Gt/y in 1999{\{}$\backslash$textendash{\}}2009, and 252 {\{}$\backslash$textpm{\}} 26 Gt/y in 2009{\{}$\backslash$textendash{\}}2017. In 2009{\{}$\backslash$textendash{\}}2017, the mass loss was dominated by the Amundsen/Bellingshausen Sea sectors, in West Antarctica (159 {\{}$\backslash$textpm{\}} 8 Gt/y), Wilkes Land, in East Antarctica (51 {\{}$\backslash$textpm{\}} 13 Gt/y), and West and Northeast Peninsula (42 {\{}$\backslash$textpm{\}} 5 Gt/y). The contribution to sea-level rise from Antarctica averaged 3.6 {\{}$\backslash$textpm{\}} 0.5 mm per decade with a cumulative 14.0 {\{}$\backslash$textpm{\}} 2.0 mm since 1979, including 6.9 {\{}$\backslash$textpm{\}} 0.6 mm from West Antarctica, 4.4 {\{}$\backslash$textpm{\}} 0.9 mm from East Antarctica, and 2.5 {\{}$\backslash$textpm{\}} 0.4 mm from the Peninsula (i.e., East Antarctica is a major participant in the mass loss). During the entire period, the mass loss concentrated in areas closest to warm, salty, subsurface, circumpolar deep water (CDW), that is, consistent with enhanced polar westerlies pushing CDW toward Antarctica to melt its floating ice shelves, destabilize the glaciers, and raise sea level.},
author = {Rignot, Eric and Mouginot, J{\'{e}}r{\'{e}}mie and Scheuchl, Bernd and van den Broeke, Michiel and van Wessem, Melchior J and Morlighem, Mathieu},
doi = {10.1073/pnas.1812883116},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
number = {4},
pages = {1095--1103},
publisher = {National Academy of Sciences},
title = {{Four decades of Antarctic Ice Sheet mass balance from 1979–2017}},
url = {https://www.pnas.org/content/116/4/1095},
volume = {116},
year = {2019}
}
@article{rodell2004global,
author = {Rodell, Matthew and Houser, P R and Jambor, U. and Gottschalck, J and Mitchell, K and Meng, C-J and Arsenault, K and Cosgrove, B and Radakovich, J and Bosilovich, M and Entin, J. K. and Walker, J. P. and Lohmann, D. and Toll, D.},
doi = {10.1175/BAMS-85-3-381},
issn = {0003-0007},
journal = {Bulletin of the American Meteorological Society},
month = {mar},
number = {3},
pages = {381--394},
publisher = {American Meteorological Society},
title = {{The Global Land Data Assimilation System}},
url = {https://journals.ametsoc.org/doi/10.1175/BAMS-85-3-381},
volume = {85},
year = {2004}
}
@article{Roebeling2009,
abstract = {This paper presents and validates a new algorithm to detect precipitating clouds and estimate rain rates from cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The precipitation properties (PP) algorithm uses information on cloud condensed water path (CWP), particle effective radius, and cloud thermodynamic phase to detect precipitating clouds, while information on CWP and cloud top height is used to estimate rain rates. An independent data set of weather radar data is used to determine the optimum settings of the PP algorithm and calibrated it. For a 2-month period, the ability of SEVIRI to discriminate precipitating from nonprecipitating clouds is evaluated using weather radar over the Netherlands. In addition, weather radar and rain gauge observations are used to validate the SEVIRI retrievals of rain rate and accumulated rainfall across the entire study area and period. During the observation period, the spatial extents of precipitation over the study area from SEVIRI and weather radar are highly correlated (correlation ≈ 0.90), while weaker correlations (correlation ≈ 0.63) are found between the spatially mean rain rate retrievals from these instruments. The combined use of information on CWP, cloud thermodynamic phase, and particle size for the detection of precipitation results in an increase in explained variance (∼10{\%}) and decrease in false alarms (∼15{\%}), as compared to detection methods that are solely based on a threshold CWP. At a pixel level, the SEVIRI retrievals have an acceptable accuracy (bias) of about 0.1 mm h−1 and a precision (standard error) of about 0.8 mm h−1. It is argued that parts of the differences are caused by collocation errors and parallax shifts in the SEVIRI data and by irregularities in the weather radar data. In future studies we intend to exploit the observations of the European weather radar network Operational Programme for the Exchange of Weather Radar Information (OPERA) and extend this study to the entirety of Europe.},
author = {Roebeling, R. A. and Holleman, I.},
doi = {10.1029/2009JD012102},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Atmospheres},
month = {nov},
number = {D21},
pages = {D21202},
title = {{SEVIRI rainfall retrieval and validation using weather radar observations}},
url = {http://doi.wiley.com/10.1029/2009JD012102},
volume = {114},
year = {2009}
}
@article{essd-12-3469-2020,
author = {Rohde, R A and Hausfather, Z},
doi = {10.5194/essd-12-3469-2020},
journal = {Earth System Science Data},
number = {4},
pages = {3469--3479},
title = {{The Berkeley Earth Land/Ocean Temperature Record}},
url = {https://essd.copernicus.org/articles/12/3469/2020/},
volume = {12},
year = {2020}
}
@article{RomanovskyVESmithSLIsaksenKNylandKEKholodovALShiklomanovNIStreletskiyDAFarquharsonLMDrozdovDSMalkovaGV2020,
author = {Romanovsky, VE and Smith, SL and Isaksen, K and Nyland, KE and Kholodov, AL and Shiklomanov, NI and Streletskiy, DA and Farquharson, LM and Drozdov, DS and Malkova, GV and Christiansen, HH},
doi = {10.1175/BAMS-D-20-0086.1},
journal = {Bulletin of the American Meteorological Society},
number = {8},
pages = {S265--S271},
title = {{Terrestrial Permafrost [in “State of the Climate in 2019”]}},
volume = {101},
year = {2020}
}
@article{Rostkier-Edelstein2014,
abstract = {ABSTRACT This study demonstrates the capability of the Weather Research and Forecasting (WRF) model with four-dimensional data assimilation (WRF-FDDA) to produce a high-resolution climatography of seasonal precipitation over Israel and the surrounding areas. The system was used to dynamically downscale global Climate Forecast System (CFS) reanalysis with continuous assimilation of conventional and unconventional observations. Precipitation seasons (December-January-February) in 7?years, including two extreme dry and wet seasons observed in the past decades, were generated at 2-km spatial resolution. Verification against rain-gauge observations shows that the WRF-FDDA system effectively reproduces the spatial and inter-annual variability, as well as the timing, intensity, and length of wet and dry spells. The best agreement between model and observations was obtained at areas dominated by complex terrain, illustrating the benefit of the high-resolution lower boundary forcing in the dynamical downscaling process. In contrast, some biases were observed over coastal-flat terrain. The model was able to reproduce some of the extreme events, but exhibited limitations in the case of rare events. This specific discrepancy between the model and observations suggests that further fine tuning and different model configurations may be needed to correctly simulate extreme events. The use of an objective weather-regimes verification procedure reveals the skill of the climatography for different types of extra-tropical cyclones: while biases are larger at coastal-flat areas under shallow-cyclonic conditions, deep-cyclonic conditions lead to more significant biases in complex terrain regions. The weather-regimes dependent information may be used for further calibration of the downscaled precipitation.},
author = {Rostkier-Edelstein, D and Liu, Y and Wu, W and Kunin, P and Givati, A and Ge, M},
doi = {10.1002/joc.3814},
journal = {International Journal of Climatology},
month = {sep},
number = {6},
pages = {1964--1979},
publisher = {Wiley-Blackwell},
title = {{Towards a high-resolution climatography of seasonal precipitation over Israel}},
volume = {34},
year = {2014}
}
@article{Rothrock2008,
abstract = {Naval submarines have collected operational data of sea-ice draft (93{\%} of thickness) in the Arctic Ocean since 1958. Data from 34 U.S. cruises are publicly archived. They span the years 1975 to 2000, are equally distributed in spring and autumn, and cover roughly half the Arctic Ocean. The data set is strong: we use 2203 values of mean draft, each value averaged over a nominal length of 50 km. These values range from 0 to 6 m with a standard deviation of 0.99 m. Multiple regression is used to separate the interannual change, the annual cycle, and the spatial field. The solution gives a climatology for ice draft as a function of space and time. The residuals of the regression have a standard deviation of 0.46 m, slightly more than the observational error standard deviation of 0.38 m. The overall mean of the solution is 2.97 m. Annual mean ice draft declined from a peak of 3.42 m in 1980 to a minimum of 2.29 m in 2000, a decrease of 1.13 m (1.25 m in thickness). The steepest rate of decrease is −0.08 meters per year (m/a) in 1990. The rate slows to −0.007 m/a at the end of the record. The annual cycle has a maximum on 30 April and a peak-to-trough amplitude of 1.06 m (1.12 m in thickness). The spatial contour map of the temporal mean draft varies from a minimum draft of 2.2 m near Alaska to a maximum just over 4 m at the edge of the data release area 200 miles north of Ellesmere Island.},
author = {Rothrock, D. A. and Percival, D. B. and Wensnahan, M.},
doi = {10.1029/2007JC004252},
issn = {0148-0227},
journal = {Journal of Geophysical Research},
month = {may},
number = {C5},
pages = {C05003},
title = {{The decline in arctic sea-ice thickness: Separating the spatial, annual, and interannual variability in a quarter century of submarine data}},
url = {http://doi.wiley.com/10.1029/2007JC004252},
volume = {113},
year = {2008}
}
@article{Saeki2017,
abstract = {Measurement and modelling of regional or country-level carbon dioxide (CO2) fluxes are becoming critical for verification of the greenhouse gases emission control. One of the commonly adopted approaches is inverse modelling, where CO2 fluxes (emission: positive flux, sink: negative flux) from the terrestrial ecosystems are estimated by combining atmospheric CO2 measurements with atmospheric transport models. The inverse models assume anthropogenic emissions are known, and thus the uncertainties in the emissions introduce systematic bias in estimation of the terrestrial (residual) fluxes by inverse modelling. Here we show that the CO2 sink increase, estimated by the inverse model, over East Asia (China, Japan, Korea and Mongolia), by about 0.26 PgC year−1 (1 Pg = 1012 g) during 2001–2010, is likely to be an artifact of the anthropogenic CO2 emissions increasing too quickly in China by 1.41 PgC year−1. Independent results from methane (CH4) inversion suggested about 41{\%} lower rate of East Asian CH4 emission increase during 2002–2012. We apply a scaling factor of 0.59, based on CH4 inversion, to the rate of anthropogenic CO2 emission increase since the anthropogenic emissions of both CO2 and CH4 increase linearly in the emission inventory. We find no systematic increase in land CO2 uptake over East Asia during 1993–2010 or 2000–2009 when scaled anthropogenic CO2 emissions are used, and that there is a need of higher emission increase rate for 2010–2012 compared to those calculated by the inventory methods. High bias in anthropogenic CO2 emissions leads to stronger land sinks in global land–ocean flux partitioning in our inverse model. The corrected anthropogenic CO2 emissions also produce measurable reductions in the rate of global land CO2 sink increase post-2002, leading to a better agreement with the terrestrial biospheric model simulations that include CO2-fertilization and climate effects.},
author = {Saeki, Tazu and Patra, Prabir K.},
doi = {10.1186/s40562-017-0074-7},
issn = {2196-4092},
journal = {Geoscience Letters},
month = {dec},
number = {1},
pages = {9},
publisher = {Springer International Publishing},
title = {{Implications of overestimated anthropogenic CO2 emissions on East Asian and global land CO2 flux inversion}},
volume = {4},
year = {2017}
}
@article{doi:10.1175/2010BAMS3001.1,
abstract = { The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is {\~{}}38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system. CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research. Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model. },
author = {Saha, Suranjana and Moorthi, Shrinivas and Pan, Hua-Lu and Wu, Xingren and Wang, Jiande and Nadiga, Sudhir and Tripp, Patrick and Kistler, Robert and Woollen, John and Behringer, David and Liu, Haixia and Stokes, Diane and Grumbine, Robert and Gayno, George and Wang, Jun and Hou, Yu-Tai and Chuang, Hui-ya and Juang, Hann-Ming H and Sela, Joe and Iredell, Mark and Treadon, Russ and Kleist, Daryl and {Van Delst}, Paul and Keyser, Dennis and Derber, John and Ek, Michael and Meng, Jesse and Wei, Helin and Yang, Rongqian and Lord, Stephen and van den Dool, Huug and Kumar, Arun and Wang, Wanqiu and Long, Craig and Chelliah, Muthuvel and Xue, Yan and Huang, Boyin and Schemm, Jae-Kyung and Ebisuzaki, Wesley and Lin, Roger and Xie, Pingping and Chen, Mingyue and Zhou, Shuntai and Higgins, Wayne and Zou, Cheng-Zhi and Liu, Quanhua and Chen, Yong and Han, Yong and Cucurull, Lidia and Reynolds, Richard W and Rutledge, Glenn and Goldberg, Mitch},
doi = {10.1175/2010BAMS3001.1},
journal = {Bulletin of the American Meteorological Society},
number = {8},
pages = {1015--1058},
title = {{The NCEP Climate Forecast System Reanalysis}},
url = {https://doi.org/10.1175/2010BAMS3001.1},
volume = {91},
year = {2010}
}
@article{s19194285,
abstract = {Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.},
author = {Sathyendranath, Shubha and Brewin, Robert J W and Brockmann, Carsten and Brotas, Vanda and Calton, Ben and Chuprin, Andrei and Cipollini, Paolo and Couto, Andr{\'{e}} B and Dingle, James and Doerffer, Roland and Donlon, Craig and Dowell, Mark and Farman, Alex and Grant, Mike and Groom, Steve and Horseman, Andrew and Jackson, Thomas and Krasemann, Hajo and Lavender, Samantha and Martinez-Vicente, Victor and Mazeran, Constant and M{\'{e}}lin, Fr{\'{e}}d{\'{e}}ric and Moore, Timothy S and M{\"{u}}ller, Dagmar and Regner, Peter and Roy, Shovonlal and Steele, Chris J and Steinmetz, Fran{\c{c}}ois and Swinton, John and Taberner, Malcolm and Thompson, Adam and Valente, Andr{\'{e}} and Z{\"{u}}hlke, Marco and Brando, Vittorio E and Feng, Hui and Feldman, Gene and Franz, Bryan A and Frouin, Robert and Gould, Richard W and Hooker, Stanford B and Kahru, Mati and Kratzer, Susanne and Mitchell, B Greg and Muller-Karger, Frank E and Sosik, Heidi M and Voss, Kenneth J and Werdell, Jeremy and Platt, Trevor},
doi = {10.3390/s19194285},
issn = {1424-8220},
journal = {Sensors},
number = {19},
pages = {4285},
title = {{An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)}},
url = {https://www.mdpi.com/1424-8220/19/19/4285},
volume = {19},
year = {2019}
}
@article{essd-12-1561-2020,
author = {Saunois, M and Stavert, A R and Poulter, B and Bousquet, P and Canadell, J G and Jackson, R B and Raymond, P A and Dlugokencky, E J and Houweling, S and Patra, P K and Ciais, P and Arora, V K and Bastviken, D and Bergamaschi, P and Blake, D R and Brailsford, G and Bruhwiler, L and Carlson, K M and Carrol, M and Castaldi, S and Chandra, N and Crevoisier, C and Crill, P M and Covey, K and Curry, C L and Etiope, G and Frankenberg, C and Gedney, N and Hegglin, M I and H{\"{o}}glund-Isaksson, L and Hugelius, G and Ishizawa, M and Ito, A and Janssens-Maenhout, G and Jensen, K M and Joos, F and Kleinen, T and Krummel, P B and Langenfelds, R L and Laruelle, G G and Liu, L and Machida, T and Maksyutov, S and McDonald, K C and McNorton, J and Miller, P A and Melton, J R and Morino, I and M{\"{u}}ller, J and Murguia-Flores, F and Naik, V and Niwa, Y and Noce, S and O'Doherty, S and Parker, R J and Peng, C and Peng, S and Peters, G P and Prigent, C and Prinn, R and Ramonet, M and Regnier, P and Riley, W J and Rosentreter, J A and Segers, A and Simpson, I J and Shi, H and Smith, S J and Steele, L P and Thornton, B F and Tian, H and Tohjima, Y and Tubiello, F N and Tsuruta, A and Viovy, N and Voulgarakis, A and Weber, T S and van Weele, M and van der Werf, G R and Weiss, R F and Worthy, D and Wunch, D and Yin, Y and Yoshida, Y and Zhang, W and Zhang, Z and Zhao, Y and Zheng, B and Zhu, Q and Zhu, Q and Zhuang, Q},
doi = {10.5194/essd-12-1561-2020},
journal = {Earth System Science Data},
number = {3},
pages = {1561--1623},
title = {{The Global Methane Budget 2000–2017}},
url = {https://essd.copernicus.org/articles/12/1561/2020/},
volume = {12},
year = {2020}
}
@article{essd-9-389-2017,
author = {Schellekens, J and Dutra, E and la Torre, A and Balsamo, G and van Dijk, A and {Sperna Weiland}, F and Minvielle, M and Calvet, J.-C. and Decharme, B and Eisner, S and Fink, G and Fl{\"{o}}rke, M and Pe{\ss}enteiner, S and van Beek, R and Polcher, J and Beck, H and Orth, R and Calton, B and Burke, S and Dorigo, W and Weedon, G P},
doi = {10.5194/essd-9-389-2017},
journal = {Earth System Science Data},
number = {2},
pages = {389--413},
title = {{A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset}},
url = {https://www.earth-syst-sci-data.net/9/389/2017/},
volume = {9},
year = {2017}
}
@article{Scherler2018,
author = {Scherler, Dirk and Wulf, Hendrik and Gorelick, Noel},
doi = {10.1029/2018GL080158},
issn = {00948276},
journal = {Geophysical Research Letters},
month = {nov},
number = {21},
pages = {11798--11805},
title = {{Global Assessment of Supraglacial Debris-Cover Extents}},
url = {http://doi.wiley.com/10.1029/2018GL080158},
volume = {45},
year = {2018}
}
@article{Schneider2017,
abstract = {The 2015 release of the precipitation climatology from the Global Precipitation Climatology Centre (GPCC) for 1951–2000, based on climatological normals of about 75,100 rain gauges, allows for quantification of mean land surface precipitation as part of the global water cycle. In GPCC's 2011-release, a bulk climatological correction was applied to compensate for gauge undercatch. In this paper we derive an improved correction approach based on the synoptic weather reports for the period 1982–2015. The compared results show that the climatological approach tends to overestimate the correction for Central and Eastern Europe, especially in the northern winter, and in other regions throughout the year. Applying the mean weather-dependent correction to the GPCC's uncorrected precipitation climatology for 1951–2000 gives a value of 854.7 mm of precipitation per year (excluding Antarctica) or 790 mm for the global land surface. The warming of nearly 1 K relative to pre-industrial temperatures is expected to be accompanied by a 2{\%}–3{\%} increase in global (land and ocean) precipitation. However, a comparison of climatology for 30-year reference periods from 1931–1960 up to 1981–2010 reveals no significant trend for land surface precipitation. This may be caused by the large variability of precipitation, the varying data coverage over time and other issues related to the sampling of rain-gauge networks. The GPCC continues to enlarge and further improve the quality of its database, and will generate precipitation analyses with homogeneous data coverage over time. Another way to reduce the sampling issues is the combination of rain gauge-based analyses with remote sensing (i.e., satellite or radar) datasets.},
author = {Schneider, Udo and Finger, Peter and Meyer-Christoffer, Anja and Rustemeier, Elke and Ziese, Markus and Becker, Andreas},
doi = {10.3390/atmos8030052},
issn = {20734433},
journal = {Atmosphere},
number = {3},
pages = {52},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the Global Precipitation Climatology Centre (GPCC)}},
volume = {8},
year = {2017}
}
@article{Schroder2018,
abstract = {Abstract. The Global Energy and Water cycle Exchanges (GEWEX) Data and Assessments Panel (GDAP) initiated the GEWEX Water Vapor Assessment (G-VAP), which has the main objectives to quantify the current state of the art in water vapour products being constructed for climate applications and to support the selection process of suitable water vapour products by GDAP for its production of globally consistent water and energy cycle products. During the construction of the G-VAP data archive, freely available and mature satellite and reanalysis data records with a minimum temporal coverage of 10 years were considered. The archive contains total column water vapour (TCWV) as well as specific humidity and temperature at four pressure levels (1000, 700, 500, 300hPa) from 22 different data records. All data records were remapped to a regular longitude–latitude grid of 2° × 2°. The archive consists of four different folders: 22 TCWV data records covering the period 2003–2008, 11 TCWV data records covering the period 1988–2008, as well as 7 specific humidity and 7 temperature data records covering the period 1988–2009. The G-VAP data archive is referenced under the following digital object identifier (doi): https://doi.org/10.5676/EUM{\_}SAF{\_}CM/GVAP/V001. Within G-VAP, the characterization of water vapour products is, among other ways, achieved through intercomparisons of the considered data records, as a whole and grouped into three classes of predominant retrieval condition: clear-sky, cloudy-sky and all-sky. Associated results are shown using the 22 TCWV data records. The standard deviations among the 22 TCWV data records have been analysed and exhibit distinct maxima over central Africa and the tropical warm pool (in absolute terms) as well as over the poles and mountain regions (in relative terms). The variability in TCWV within each class can be large and prohibits conclusions about systematic differences in TCWV between the classes.},
author = {Schr{\"{o}}der, Marc and Lockhoff, Maarit and Fell, Frank and Forsythe, John and Trent, Tim and Bennartz, Ralf and Borbas, Eva and Bosilovich, Michael G. and Castelli, Elisa and Hersbach, Hans and Kachi, Misako and Kobayashi, Shinya and {Robert Kursinski}, E. and Loyola, DIego and Mears, Carl and Preusker, Rene and Rossow, William B. and Saha, Suranjana},
doi = {10.5194/essd-10-1093-2018},
issn = {18663516},
journal = {Earth System Science Data},
number = {2},
pages = {1093--1117},
title = {{The GEWEX Water Vapor Assessment archive of water vapour products from satellite observations and reanalyses}},
volume = {10},
year = {2018}
}
@article{Schultz2017,
abstract = {In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world's largest collection of in-situ hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of a posteriori data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface observation network both in terms of regions without monitoring, and in terms of regions that have monitoring programs but no public access to the data archive. Therefore future improvements to the database will require not only improved data harmonization, but also expanded data sharing and increased monitoring in data-sparse regions.},
author = {Schultz, Martin G. and Schr{\"{o}}der, Sabine and Lyapina, Olga and Cooper, Owen and Galbally, Ian and Petropavlovskikh, Irina and {Von Schneidemesser}, Erika and Tanimoto, Hiroshi and Elshorbany, Yasin and Naja, Manish and Seguel, Rodrigo and Dauert, Ute and Eckhardt, Paul and Feigenspahn, Stefan and Fiebig, Markus and Hjellbrekke, Anne-Gunn and Hong, You-Deog and {Christian Kjeld}, Peter and Koide, Hiroshi and Lear, Gary and Tarasick, David and Ueno, Mikio and Wallasch, Markus and Baumgardner, Darrel and Chuang, Ming-Tung and Gillett, Robert and Lee, Meehye and Molloy, Suzie and Moolla, Raeesa and Wang, Tao and Sharps, Katrina and Adame, Jose A. and Ancellet, Gerard and Apadula, Francesco and Artaxo, Paulo and Barlasina, Maria and Bogucka, Magdalena and Bonasoni, Paolo and Chang, Limseok and Colomb, Aurelie and Cuevas, Emilio and Cupeiro, Manuel and Degorska, Anna and Ding, Aijun and Fr{\"{o}}hlich, Marina and Frolova, Marina and Gadhavi, Harish and Gheusi, Francois and Gilge, Stefan and Gonzalez, Margarita Y. and Gros, Valerie and Hamad, Samera H. and Helmig, Detlev and Henriques, Diamantino and Hermansen, Ove and Holla, Robert and Huber, Jacques and Im, Ulas and Jaffe, Daniel A. and Komala, Ninong and Kubistin, Dagmar and Lam, Ka-Se and Laurila, Tuomas and Lee, Haeyoung and Levy, Ilan and Mazzoleni, Claudio and Mazzoleni, Lynn and McClure-Begley, Audra and Mohamad, Maznorizan and Murovic, Marijana and Navarro-Comas, M. and Nicodim, Florin and Parrish, David and Read, Katie A. and Reid, Nick and Ries, Ludwig and Saxena, Pallavi and Schwab, James J. and Scorgie, Yvonne and Senik, Irina and Simmonds, Peter and Sinha, Vinayak and Skorokhod, Andrey and Spain, Gerard and Spangl, Wolfgang and Spoor, Ronald and Springston, Stephen R. and Steer, Kelvyn and Steinbacher, Martin and Suharguniyawan, Eka and Torre, Paul and Trickl, Thomas and Weili, Lin and Weller, Rolf and Xu, Xiaobin and Xue, Likun and Zhiqiang, Ma},
doi = {10.1525/elementa.244},
isbn = {1417692014},
issn = {2325-1026},
journal = {Elementa: Science of the Anthropocene},
pages = {58},
title = {{Tropospheric Ozone Assessment Report: Database and Metrics Data of Global Surface Ozone Observations}},
volume = {5},
year = {2017}
}
@article{https://doi.org/10.1029/2011JC007084,
abstract = {Uncertainty in the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) Arctic sea ice volume record is characterized. A range of observations and approaches, including in situ ice thickness measurements, ICESat retrieved ice thickness, and model sensitivity studies, yields a conservative estimate for October Arctic ice volume uncertainty of 1.35 × 103 km3 and an uncertainty of the ice volume trend over the 1979–2010 period of 1.0 × 103 km3 decade–1. A conservative estimate of the trend over this period is −2.8 × 103 km3 decade–1. PIOMAS ice thickness estimates agree well with ICESat ice thickness retrievals ({\textless}0.1 m mean difference) for the area for which submarine data are available, while difference outside this area are larger. PIOMAS spatial thickness patterns agree well with ICESat thickness estimates with pattern correlations of above 0.8. PIOMAS appears to overestimate thin ice thickness and underestimate thick ice, yielding a smaller downward trend than apparent in reconstructions from observations. PIOMAS ice volume uncertainties and trends are examined in the context of climate change attribution and the declaration of record minima. The distribution of 32 year trends in a preindustrial coupled model simulation shows no trends comparable to those seen in the PIOMAS retrospective, even when the trend uncertainty is accounted for. Attempts to label September minima as new record lows are sensitive to modeling error. However, the September 2010 ice volume anomaly did in fact exceed the previous 2007 minimum by a large enough margin to establish a statistically significant new record.},
author = {Schweiger, Axel and Lindsay, Ron and Zhang, Jinlun and Steele, Mike and Stern, Harry and Kwok, Ron},
doi = {https://doi.org/10.1029/2011JC007084},
journal = {Journal of Geophysical Research: Oceans},
keywords = {Arctic,climate change,ice volume,modelling,sea ice},
number = {C8},
pages = {C00D06},
title = {{Uncertainty in modeled Arctic sea ice volume}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011JC007084},
volume = {116},
year = {2011}
}
@article{Shen2018,
abstract = {Based on high-density gauge precipitation observations, high-resolution weather radar quantitative precipitation estimation (QPE) and seamless satellite-based precipitation estimates, a 1-km experimental gauge-radar-satellite merged precipitation dataset has been developed using the proposed local gauge correction (LGC) and optimal interpolation (OI) merging strategies. First, hourly precipitation analyses from approximately 40,000 automatic weather stations at 0.01° resolution were used to correct bias in the radar QPE Group System (QPEGS), developed by the China Meteorological Administration (CMA) and the Climate Prediction Center Morphing (CMORPH) precipitation products. As precipitation events tend to have a more localized distribution at the hourly and 0.01° resolutions, three core parameters were improved using the OI method. (a) The spatial dependence of the error variance for radar QPE was accounted for over six sub-regions in China and is shown as a non-linear function of the gauge precipitation analysis. (b) The spatial dependence of error correlation for the radar QPE decreased exponentially with distance. (c) The error of the hourly gauge-based precipitation analysis was quantified as a function of the precipitation amount and the gauge network density, using the Monte Carlo method to randomly sample the gauge observations over the dense gauge network. The performance of the 1-km experimental gauge-radar-satellite merged precipitation dataset (named as China Merged Precipitation Analysis: CMPA{\_}1km) was assessed at 6 h-temporal resolutions and 0.03° × 0.03° spatial resolution using precipitation observations from 208 independent hydrological stations as a reference. Compared with radar QPE and CMORPH, the CMPA-1km showed obviously better accuracy in all sub-regions and during all seasons. In contrast, gauge analysis and CMPA-1km shared similar accuracy, but the latter could estimate heavy precipitation more accurately than the former, as well as the latter has the advantage of seamless spatial coverage. However, the CMPA-1km exhibits larger uncertainty during the cold season compared to the warm season, which will need further improvement in future work. The downscaled bias-corrected 0.01° resolution CMORPH was employed to fill the gaps in regions, mainly in Western China and the Tibetan Plateau, where gauge and radar measurements are limited.},
author = {Shen, Yan and Hong, Zhen and Pan, Yang and Yu, Jingjing and Maguire, Lane},
doi = {10.3390/rs10020264},
issn = {2072-4292},
journal = {Remote Sensing},
month = {feb},
number = {2},
pages = {264},
title = {{China's 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset}},
url = {http://www.mdpi.com/2072-4292/10/2/264},
volume = {10},
year = {2018}
}
@article{Simpson2012,
abstract = {After methane, ethane is the most abundant hydrocarbon in the remote atmosphere. It is a precursor to tropospheric ozone and it influences the atmosphere/'s oxidative capacity through its reaction with the hydroxyl radical, ethane/'s primary atmospheric sink. Here we present the longest continuous record of global atmospheric ethane levels. We show that global ethane emission rates decreased from 14.3 to 11.3 teragrams per year, or by 21 per cent, from 1984 to 2010. We attribute this to decreasing fugitive emissions from ethane/'s fossil fuel source—most probably decreased venting and flaring of natural gas in oil fields—rather than a decline in its other major sources, biofuel use and biomass burning. Ethane/'s major emission sources are shared with methane, and recent studies have disagreed on whether reduced fossil fuel or microbial emissions have caused methane/'s atmospheric growth rate to slow. Our findings suggest that reduced fugitive fossil fuel emissions account for at least 10-21 teragrams per year (30-70 per cent) of the decrease in methane/'s global emissions, significantly contributing to methane/'s slowing atmospheric growth rate since the mid-1980s.},
author = {Simpson, Isobel J. and Andersen, Mads P.Sulbaek and Meinardi, Simone and Bruhwiler, Lori and Blake, Nicola J. and Helmig, Detlev and {Sherwood Rowland}, F. and Blake, Donald R.},
doi = {10.1038/nature11342},
issn = {00280836},
journal = {Nature},
month = {aug},
number = {7412},
pages = {490--494},
title = {{Long-term decline of global atmospheric ethane concentrations and implications for methane}},
volume = {488},
year = {2012}
}
@article{doi:10.1002/qj.3598,
abstract = {Historical reanalyses that span more than a century are needed for a wide range of studies, from understanding large-scale climate trends to diagnosing the impacts of individual historical extreme weather events. The Twentieth Century Reanalysis (20CR) Project is an effort to fill this need. It is supported by the National Oceanic and Atmospheric Administration (NOAA), the Cooperative Institute for Research in Environmental Sciences (CIRES), and the U.S. Department of Energy (DOE), and is facilitated by collaboration with the international Atmospheric Circulation Reconstructions over the Earth initiative. 20CR is the first ensemble of sub-daily global atmospheric conditions spanning over 100 years. This provides a best estimate of the weather at any given place and time as well as an estimate of its confidence and uncertainty. While extremely useful, version 2c of this dataset (20CRv2c) has several significant issues, including inaccurate estimates of confidence and a global sea level pressure bias in the mid-19th century. These and other issues can reduce its effectiveness for studies at many spatial and temporal scales. Therefore, the 20CR system underwent a series of developments to generate a significant new version of the reanalysis. The version 3 system (NOAA-CIRES-DOE 20CRv3) uses upgraded data assimilation methods including an adaptive inflation algorithm; has a newer, higher-resolution forecast model that specifies dry air mass; and assimilates a larger set of pressure observations. These changes have improved the ensemble-based estimates of confidence, removed spin-up effects in the precipitation fields, and diminished the sea-level pressure bias. Other improvements include more accurate representations of storm intensity, smaller errors, and large-scale reductions in model bias. The 20CRv3 system is comprehensively reviewed, focusing on the aspects that have ameliorated issues in 20CRv2c. Despite the many improvements, some challenges remain, including a systematic bias in tropical precipitation and time-varying biases in southern high-latitude pressure fields.},
author = {Slivinski, Laura C and Compo, Gilbert P and Whitaker, Jeffrey S and Sardeshmukh, Prashant D and Giese, Benjamin S and McColl, Chesley and Allan, Rob and Yin, Xungang and Vose, Russell and Titchner, Holly and Kennedy, John and Spencer, Lawrence J and Ashcroft, Linden and Br{\"{o}}nnimann, Stefan and Brunet, Manola and Camuffo, Dario and Cornes, Richard and Cram, Thomas A and Crouthamel, Richard and Dom{\'{i}}nguez-Castro, Fernando and Freeman, J Eric and Gergis, Jo{\"{e}}lle and Hawkins, Ed and Jones, Philip D and Jourdain, Sylvie and Kaplan, Alexey and Kubota, Hisayuki and Blancq, Frank Le and Lee, Tsz-Cheung and Lorrey, Andrew and Luterbacher, J{\"{u}}rg and Maugeri, Maurizio and Mock, Cary J and Moore, G W Kent and Przybylak, Rajmund and Pudmenzky, Christa and Reason, Chris and Slonosky, Victoria C and Smith, Catherine A and Tinz, Birger and Trewin, Blair and Valente, Maria Ant{\'{o}}nia and Wang, Xiaolan L and Wilkinson, Clive and Wood, Kevin and Wyszy{\'{n}}ski, Przemys{\l}aw},
doi = {10.1002/qj.3598},
journal = {Quarterly Journal of the Royal Meteorological Society},
keywords = {20CRv3,data assimilation,reanalysis,surface pressure},
number = {724},
pages = {2876--2908},
title = {{Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system}},
url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3598},
volume = {145},
year = {2019}
}
@article{Smeed2018,
author = {Smeed, D A and Josey, S A and Beaulieu, C and Johns, W E and Moat, B I and Frajka‐Williams, E. and Rayner, D and Meinen, C S and Baringer, M O and Bryden, H L and McCarthy, G. D.},
doi = {10.1002/2017GL076350},
issn = {0094-8276},
journal = {Geophysical Research Letters},
month = {feb},
number = {3},
pages = {1527--1533},
publisher = {Wiley Online Library},
title = {{The North Atlantic Ocean Is in a State of Reduced Overturning}},
url = {https://onlinelibrary.wiley.com/doi/10.1002/2017GL076350},
volume = {45},
year = {2018}
}
@article{Spencer2017,
author = {Spencer, Roy W and Christy, John R and Braswell, William D},
doi = {10.1007/s13143-017-0010-y},
journal = {Asia-Pacific Jouurnal of Atmospheric Science},
number = {1},
pages = {121--130},
title = {{UAH Version 6 Global Satellite Temperature Products: Methodology and Results}},
volume = {53},
year = {2017}
}
@article{Staehelin2018,
abstract = {In 1926 the stratospheric ozone measurements of the Light Climatic Observatory (LKO) of Arosa (Switzerland) started, marking the start of the world's longest total (or column) ozone measurements. These measurements were driven by the recognition of the importance of atmospheric ozone for human health as well as by scientific curiosity in this by then not well characterized atmospheric trace gas. Since the mid-1970s ground-based measurements of stratospheric ozone have also been justified to society by the need to document the effects of anthropogenic Ozone Depleting Substances (ODSs), which cause stratospheric ozone depletion. Levels of ODSs peaked around the mid-1990s as a result of a global environmental policy to protect the ozone layer implemented by the 1987 Montreal Protocol and its subsequent amendments and adjustments. Consequently, chemical ozone depletion caused by ODSs stopped worsening around the mid-1990s. This renders justification for continued ozone measurements more difficult, and is likely to do so even more in future, when stratospheric ozone recovery is expected. Tendencies of increased cost savings in ozone measurements seem perceptible worldwide, also in Arosa. However, the large natural variability in ozone on diurnal, seasonal and interannual scales complicates to demonstrate the success of the Montreal Protocol. Moreover, chemistry-climate models predict a “super-recovery” of the ozone layer in the second half of this century, i.e. an increase of ozone concentrations beyond pre-1970 levels, as a consequence of ongoing climate change. This paper presents the evolution of the ozone layer and the history of international ozone research and discusses the justification of these measurements for past, present and future.},
author = {Staehelin, Johannes and Viatte, Pierre and St{\"{u}}bi, Rene and Tummon, Fiona and Peter, Thomas},
doi = {10.5194/acp-18-6567-2018},
issn = {16807324},
journal = {Atmospheric Chemistry and Physics},
number = {9},
pages = {6567--6584},
title = {{Stratospheric ozone measurements at Arosa (Switzerland): History and scientific relevance}},
volume = {18},
year = {2018}
}
@article{amt-13-2547-2020,
author = {Steiner, A K and Ladst{\"{a}}dter, F and Ao, C O and Gleisner, H and Ho, S.-P. and Hunt, D and Schmidt, T and Foelsche, U and Kirchengast, G and Kuo, Y.-H. and Lauritsen, K B and Mannucci, A J and Nielsen, J K and Schreiner, W and Schw{\"{a}}rz, M and Sokolovskiy, S and Syndergaard, S and Wickert, J},
doi = {10.5194/amt-13-2547-2020},
journal = {Atmospheric Measurement Techniques},
number = {5},
pages = {2547--2575},
title = {{Consistency and structural uncertainty of multi-mission GPS radio occultation records}},
url = {https://amt.copernicus.org/articles/13/2547/2020/},
volume = {13},
year = {2020}
}
@article{doi:10.1175/JTECH-D-17-0166.1,
abstract = { AbstractThe National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F). The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent.A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products. },
author = {Stocker, E F and Alquaied, F and Bilanow, S and Ji, Y and Jones, L},
doi = {10.1175/JTECH-D-17-0166.1},
journal = {Journal of Atmospheric and Oceanic Technology},
number = {6},
pages = {1181--1199},
title = {{TRMM Version 8 Reprocessing Improvements and Incorporation into the GPM Data Suite}},
url = {https://doi.org/10.1175/JTECH-D-17-0166.1},
volume = {35},
year = {2018}
}
@article{Sun2021,
abstract = {Based on C-LSAT2.0, using high- and low-frequency components reconstruction methods, combined with observation constraint masking, a reconstructed C-LSAT2.0 with 756 ensemble members from the 1850s to 2018 has been developed. These ensemble versions have been merged with the ERSSTv5 ensemble dataset, and an upgraded version of the CMST-Interim dataset with 5° × 5° resolution has been developed. The CMST-Interim dataset has significantly improved the coverage rate of global surface temperature data. After reconstruction, the data coverage before 1950 increased from 78{\%}–81{\%} of the original CMST to 81{\%}–89{\%}. The total coverage after 1955 reached about 93{\%}, including more than 98{\%} in the Northern Hemisphere and 81{\%}–89{\%} in the Southern Hemisphere. Through the reconstruction ensemble experiments with different parameters, a good basis is provided for more systematic uncertainty assessment of C-LSAT2.0 and CMST-Interim. In comparison with the original CMST, the global mean surface temperatures are estimated to be cooler in the second half of 19th century and warmer during the 21st century, which shows that the global warming trend is further amplified. The global warming trends are updated from 0.085 ± 0.004°C (10 yr) −1 and 0.128 ± 0.006°C (10 yr) −1 to 0.089 ± 0.004°C (10 yr) −1 and 0.137 ± 0.007°C (10 yr) −1 , respectively, since the start and the second half of 20th century.},
author = {Sun, Wenbin and Li, Qingxiang and Huang, Boyin and Cheng, Jiayi and Song, Zhaoyang and Li, Haiyan and Dong, Wenjie and Zhai, Panmao and Jones, Phil},
doi = {10.1007/s00376-021-1012-3},
issn = {0256-1530},
journal = {Advances in Atmospheric Sciences},
month = {may},
number = {5},
pages = {875--888},
title = {{The Assessment of Global Surface Temperature Change from 1850s: The C-LSAT2.0 Ensemble and the CMST-Interim Datasets}},
url = {https://doi.org/10.1007/s00376-021-1012-3 https://link.springer.com/10.1007/s00376-021-1012-3},
volume = {38},
year = {2021}
}
@article{Susskind2006,
abstract = {AIRS was launched on EOS Aqua on 4 May 2002, together with AMSU A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The sounding goals of AIRS are to produce 1 km tropospheric layer mean temperatures with an RMS error of 1 K, and layer precipitable water with an RMS error of 20{\%}, in cases with up to 80{\%} effective cloud cover. The basic theory used to analyze AIRS/AMSU/HSB data in the presence of clouds, called the at-launch algorithm, was described previously. Prelaunch simulation studies using this algorithm indicated that these results should be achievable. Some modifications have been made to the at-launch retrieval algorithm as described in this paper. Sample fields of parameters retrieved from AIRS/AMSU/HSB data are presented and validated as a function of retrieved fractional cloud cover. As in simulation, the degradation of retrieval accuracy with increasing cloud cover is small and the RMS accuracy of lower-tropospheric temperature retrieved with 80{\%} cloud cover is about 0.5 K poorer than for clear cases. HSB failed in February 2003, and consequently, HSB channel radiances are not used in the results shown in this paper. The AIRS/AMSU retrieval algorithm described in this paper, called version 4, become operational at the Goddard DAAC (Distributed Active Archive Center) in April 2003 and is being used to analyze near-real time AIRS/AMSU data. Historical AIRS/AMSU data, going backward from March 2005 through September 2002, is also being analyzed by the DAAC using the version 4 algorithm.},
author = {Susskind, Joel and Barnet, Chris and Blaisdell, John and Iredell, Lena and Keita, Fricky and Kouvaris, Lou and Molnar, Gyula and Chahine, Moustafa},
doi = {10.1029/2005JD006272},
issn = {0148-0227},
journal = {Journal of Geophysical Research: Atmospheres},
number = {D9},
pages = {D09S17},
title = {{Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover}},
url = {http://doi.wiley.com/10.1029/2005JD006272},
volume = {111},
year = {2006}
}
@article{Susskind2014,
author = {Susskind, Joel and Blaisdell, John M and Iredell, Lena},
doi = {10.1117/1.JRS.8.084994},
journal = {Journal of Applied Remote Sensing},
number = {1},
pages = {1--34},
publisher = {SPIE},
title = {{Improved methodology for surface and atmospheric soundings, error estimates, and quality control procedures: the atmospheric infrared sounder science team version-6 retrieval algorithm}},
volume = {8},
year = {2014}
}
@article{TAKAHASHI2011,
abstract = {The aim of this study was to investigate the role of theory of mind competence in inference processing in adolescents with Aspergersyndrome (AS). We sought to pinpoint the level at which AS individuals experience difficulty drawing inferences and identifythe factors that account for their inference-drawing problems. We hypothesized that this difficulty could be related to a theory of mind(ToM) deficit. To test this hypothesis, we conducted an experiment investigating the processing of causal, predictive and pragmaticinferences. Participants also performed a second-order false-belief task. Ten adolescents with AS and ten controls matched for age, sexand verbal IQ took part in the study. Results indicated that the individuals with AS had greater difficulty processing inferences(both semantic and pragmatic) than the controls and that ToM could subtend inference-drawing. The findings are discussed in thelight of the two main psychological theories: theory of mind and weak central coherence.},
author = {Takahashi, Kazuyuki and Mikami, Takehiko and Takahashi, Hideo},
doi = {10.5026/jgeography.120.341},
issn = {0022-135X},
journal = {Journal of Geography (Chigaku Zasshi)},
number = {2},
pages = {341--358},
title = {{Influence of the Urban Heat Island Phenomenon in Tokyo on the Local Wind System at Nighttime in Summer}},
volume = {120},
year = {2011}
}
@article{TAKAHASHI201495,
abstract = {Climatological mean monthly distributions of pH in the total H+ scale, total CO2 concentration (TCO2), and the degree of CaCO3 saturation for the global surface ocean waters (excluding coastal areas) are calculated using a data set for pCO2, alkalinity and nutrient concentrations in surface waters (depths {\textless}50m), which is built upon the GLODAP, CARINA and LDEO databases. The mutual consistency among these measured parameters is demonstrated using the inorganic carbon chemistry model with the dissociation constants for carbonic acid by Lueker et al. (2000) and for boric acid by Dickson (1990). Linear potential alkalinity-salinity relationships are established for 24 regions of the global ocean. The mean monthly distributions of pH and carbon chemistry parameters for the reference year 2005 are computed using the climatological mean monthly pCO2 data adjusted to a reference year 2005 and the alkalinity estimated from the potential alkalinity-salinity relationships. The equatorial zone (4°N-4°S) of the Pacific is excluded from the analysis because of the large interannual changes associated with ENSO events. The pH thus calculated ranges from 7.9 to 8.2. Lower values are located in the upwelling regions in the tropical Pacific and in the Arabian and Bering Seas; higher values are found in the subpolar and polar waters during the spring-summer months of intense photosynthetic production. The vast areas of subtropical oceans have seasonally varying pH values ranging from 8.05 during warmer months to 8.15 during colder months. The warm tropical and subtropical waters are supersaturated by a factor of as much as 4.2 with respect to aragonite and 6.3 for calcite, whereas the cold subpolar and polar waters are supersaturated by 1.2 for aragonite and 2.0 for calcite because of the lower pH values resulting from greater TCO2 concentrations. In the western Arctic Ocean, aragonite undersaturation is observed. The time-series data from the Bermuda (BATS), Hawaii (HOT), Canary (ESTOC) and the Drake Passage show that pH has been declining at a mean rate of about -0.02 pH per decade, and that pCO2 has been increasing at about 19 $\mu$atm per decade tracking the atmospheric pCO2 increase rate. This suggests that the ocean acidification is caused primarily by the uptake of atmospheric CO2. The relative importance of the four environmental drivers (temperature, salinity, alkalinity and total CO2 concentration) controlling the seasonal variability of carbonate chemistry at these sites is quantitatively assessed. The ocean carbon chemistry is governed sensitively by the TA/TCO2 ratio, and the rate of change in TA is equally important for the future ocean environment as is the TCO2 in ocean waters increases in the future.},
author = {Takahashi, Taro and Sutherland, S C and Chipman, D W and Goddard, J G and Ho, Cheng and Newberger, Timothy and Sweeney, Colm and Munro, D R},
doi = {https://doi.org/10.1016/j.marchem.2014.06.004},
issn = {0304-4203},
journal = {Marine Chemistry},
keywords = {Carbonate chemistry,Climatology,Global ocean,Seasonal and decadal change,Surface water,pH},
pages = {95--125},
title = {{Climatological distributions of pH, pCO2, total CO2, alkalinity, and CaCO3 saturation in the global surface ocean, and temporal changes at selected locations}},
url = {http://www.sciencedirect.com/science/article/pii/S0304420314001042},
volume = {164},
year = {2014}
}
@article{Tanelli2008,
author = {Tanelli, S and Durden, S L and Im, E and Pak, K S and Reinke, D G and Partain, P and Haynes, J M and Marchand, R T},
doi = {10.1109/TGRS.2008.2002030},
issn = {0196-2892 VO  - 46},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
keywords = {A-train,Atmosphere,CPR,Calibration,Cloud Profiling Radar,CloudSat,CloudSat Mission,Clouds,Instruments,Laboratories,Power measurement,Propulsion,Radar antennas,Radar measurements,Spaceborne radar,atmospheric precipitation,backscatter,backscatter measurement,calibration,clouds,frequency 94 GHz,global time series,hydrometeors,meteorological radar,radar,radar clutter,remote sensing by radar,spaceborne radar,surface clutter rejection algorithm},
number = {11},
pages = {3560--3573},
title = {{CloudSat's Cloud Profiling Radar After Two Years in Orbit: Performance, Calibration, and Processing}},
volume = {46},
year = {2008}
}
@article{doi:10.1029/2004GL019920,
abstract = {The GRACE mission is designed to track changes in the Earth's gravity field for a period of five years. Launched in March 2002, the two GRACE satellites have collected nearly two years of data. A span of data available during the Commissioning Phase was used to obtain initial gravity models. The gravity models developed with this data are more than an order of magnitude better at the long and mid wavelengths than previous models. The error estimates indicate a 2-cm accuracy uniformly over the land and ocean regions, a consequence of the highly accurate, global and homogenous nature of the GRACE data. These early results are a strong affirmation of the GRACE mission concept.},
author = {Tapley, B D and Bettadpur, S and Watkins, M and Reigber, C},
doi = {10.1029/2004GL019920},
journal = {Geophysical Research Letters},
number = {9},
pages = {L09607},
title = {{The gravity recovery and climate experiment: Mission overview and early results}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2004GL019920},
volume = {31},
year = {2004}
}
@article{Tarasick2019,
abstract = {From the earliest observations of ozone in the lower atmosphere in the 19th century, both measurement methods and the portion of the globe observed have evolved and changed. These methods have different uncertainties and biases, and the data records differ with respect to coverage (space and time), information content, and representativeness. In this study, various ozone measurement methods and ozone datasets are reviewed and selected for inclusion in the historical record of background ozone levels, based on relationship of the measurement technique to the modern UV absorption standard, absence of interfering pollutants, representativeness of the well-mixed boundary layer and expert judgement of their credibility. There are significant uncertainties with the 19th and early 20th-century measurements related to interference of other gases. Spectroscopic methods applied before 1960 have likely underestimated ozone by as much as 11{\%} at the surface and by about 24{\%} in the free troposphere, due to the use of differing ozone absorption coefficients.},
author = {Tarasick, David and Galbally, Ian E. and Cooper, Owen R. and Schultz, Martin G. and Ancellet, Gerard and Leblanc, Thierry and Wallington, Timothy J. and Ziemke, Jerry and Liu, Xiong and Steinbacher, Martin and Staehelin, Johannes and Vigouroux, Corinne and Hannigan, James W. and Garc{\'{i}}a, Omaira and Foret, Gilles and Zanis, Prodromos and Weatherhead, Elizabeth and Petropavlovskikh, Irina and Worden, Helen and Osman, Mohammed and Liu, Jane and Chang, Kai-Lan and Gaudel, Audrey and Lin, Meiyun and Granados-Mu{\~{n}}oz, Maria and Thompson, Anne M. and Oltmans, Samuel J. and Cuesta, Juan and Dufour, Gaelle and Thouret, Valerie and Hassler, Birgit and Trickl, Thomas and Neu, Jessica L.},
doi = {10.1525/elementa.376},
editor = {Helmig, Detlev and Lewis, Alastair},
issn = {2325-1026},
journal = {Elementa: Science of the Anthropocene},
month = {jan},
pages = {39},
title = {{Tropospheric Ozone Assessment Report: Tropospheric ozone from 1877 to 2016, observed levels, trends and uncertainties}},
url = {https://online.ucpress.edu/elementa/article/doi/10.1525/elementa.376/112518/Tropospheric-Ozone-Assessment-Report-Tropospheric},
volume = {7},
year = {2019}
}
@article{https://doi.org/10.1029/2009JD012918,
abstract = {The IONS-04, IONS-06, and ARC-IONS ozone sounding campaigns over North America in 2004, 2006, and 2008 obtained approximately 1400 profiles, in five series of coordinated and closely spaced (typically daily) launches. Although this coverage is unprecedented, it is still somewhat sparse in its geographical spacing. Here we use forward and back trajectory calculations for each sounding to map ozone measurements to a number of other locations and so to fill in the spatial domain. This is possible because the lifetime of ozone in the troposphere is of the order of weeks. The trajectory-mapped ozone values show reasonable agreement, where they overlap, to the actual soundings, and the patterns produced separately by forward and backward trajectory calculations are similar. Comparisons with MOZAIC profiles and surface station data show generally good agreement. A variable-length smoothing algorithm is used to fill data gaps: for each point on the map, the smoothing radius is such that a minimum of 10 data points are included in the average. The total tropospheric ozone column maps calculated by integrating the smoothed fields agree well with similar maps derived from TOMS and OMI/MLS measurements. The resulting three-dimensional picture of the tropospheric ozone field for the INTEX and ARCTAS periods facilitates visualization and comparison of different years and seasons and will be useful to other researchers.},
author = {Tarasick, D W and Jin, J J and Fioletov, V E and Liu, G and Thompson, A M and Oltmans, S J and Liu, J and Sioris, C E and Liu, X and Cooper, O R and Dann, T and Thouret, V},
doi = {https://doi.org/10.1029/2009JD012918},
journal = {Journal of Geophysical Research: Atmospheres},
keywords = {IONS,ozone,troposphere},
number = {D20},
pages = {D20301},
title = {{High-resolution tropospheric ozone fields for INTEX and ARCTAS from IONS ozonesondes}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009JD012918},
volume = {115},
year = {2010}
}
@article{Shepherd2019b,
abstract = {In recent decades, the Greenland Ice Sheet has been a major contributor to global sea-level rise1,2, and it is expected to be so in the future3. Although increases in glacier flow4–6 and surface melting7–9 have been driven by oceanic10–12 and atmospheric13,14 warming, the degree and trajectory of today's imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet's volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. Although the ice sheet was close to a state of balance in the 1990s, annual losses have risen since then, peaking at 335 ± 62 billion tonnes per year in 2011. In all, Greenland lost 3,800 ± 339 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.6 ± 0.9 millimetres. Using three regional climate models, we show that reduced surface mass balance has driven 1,971 ± 555 billion tonnes (52{\%}) of the ice loss owing to increased meltwater runoff. The remaining 1,827 ± 538 billion tonnes (48{\%}) of ice loss was due to increased glacier discharge, which rose from 41 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. Between 2013 and 2017, the total rate of ice loss slowed to 217 ± 32 billion tonnes per year, on average, as atmospheric circulation favoured cooler conditions15 and as ocean temperatures fell at the terminus of Jakobshavn Isbr{\ae}16. Cumulative ice losses from Greenland as a whole have been close to the IPCC's predicted rates for their high-end climate warming scenario17, which forecast an additional 50 to 120 millimetres of global sea-level rise by 2100 when compared to their central estimate.},
author = {{The IMBIE Team}},
doi = {10.1038/s41586-019-1855-2},
issn = {0028-0836},
journal = {Nature},
month = {mar},
number = {7798},
pages = {233--239},
title = {{Mass balance of the Greenland Ice Sheet from 1992 to 2018}},
url = {https://doi.org/10.1038/s41586-019-1855-2 http://www.nature.com/articles/s41586-019-1855-2},
volume = {579},
year = {2020}
}
@article{Shepherd2018a,
abstract = {The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.},
author = {{The IMBIE Team}},
doi = {10.1038/s41586-018-0179-y},
issn = {0028-0836},
journal = {Nature},
month = {jun},
number = {7709},
pages = {219--222},
title = {{Mass balance of the Antarctic Ice Sheet from 1992 to 2017}},
url = {http://www.nature.com/articles/s41586-018-0179-y},
volume = {558},
year = {2018}
}
@article{Thomason2018,
abstract = {We describe the construction of a continuous 38-year record of stratospheric aerosol optical properties. The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, provided the input data to the construction of the Climate Model Intercomparison Project stratospheric aerosol forcing data set (1979–2014) and we have extended it through 2016 following an identical process. GloSSAC focuses on the Stratospheric Aerosol and Gas Experiment (SAGE) series of instruments through mid-2005, and on the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data thereafter. We also use data from other space instruments and from ground-based, air, and balloon borne instruments to fill in key gaps in the data set. The end result is a global and gap-free data set focused on aerosol extinction coefficient at 525 and 1020 nm and other parameters on an "as available" basis. For the primary data sets, we developed a new method for filling the post-Pinatubo eruption data gap for 1991–1993 based on data from the Cryogenic Limb Array Etalon Spectrometer. In addition, we developed a new method for populating wintertime high latitudes during the SAGE period employing a latitude-equivalent latitude conversion process that greatly improves the depiction of aerosol at high latitudes compared to earlier similar efforts. We report data in the troposphere only when and where it is available. This is primarily during the SAGE II period except for the most enhanced part of the Pinatubo period. It is likely that the upper troposphere during Pinatubo was greatly enhanced over non-volcanic periods and that domain remains substantially under-characterized. We note that aerosol levels during the OSIRIS/CALIPSO period in the lower stratosphere at mid- and high latitudes is routinely higher than what we observed during the SAGE II period. While this period had nearly continuous low-level volcanic activity, it is possible that the enhancement in part reflects deficiencies in the data set. We also expended substantial effort to quality assess the data set and the product is by far the best we have produced. GloSSAC version 1.0 is available in netCDF format at the NASA Atmospheric Data Center at https://eosweb.larc.nasa.gov/. GloSSAC users should cite this paper and the data set DOI (https://doi.org/10.5067/GloSSAC-L3-V1.0).},
author = {Thomason, Larry W. and Ernest, Nicholas and Mill{\'{a}}n, Luis and Rieger, Landon and Bourassa, Adam and Vernier, Jean-Paul and Manney, Gloria and Luo, Beiping and Arfeuille, Florian and Peter, Thomas},
doi = {10.5194/essd-10-469-2018},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {mar},
number = {1},
pages = {469--492},
title = {{A global space-based stratospheric aerosol climatology: 1979–2016}},
volume = {10},
year = {2018}
}
@article{Thompson,
author = {Thompson, Rona L. and Lassaletta, L and Patra, Prabir K. and Wilson, C and Wells, K. C. and Gressent, A and Koffi, E. N. and Chipperfield, M. P. and Winiwarter, Wilfried and Davidson, Eric A. and Tian, Hanqin and Canadell, J. G.},
doi = {10.1038/s41558-019-0613-7},
issn = {1758-678X},
journal = {Nature Climate Change},
month = {dec},
number = {12},
pages = {993--998},
title = {{Acceleration of global N2O emissions seen from two decades of atmospheric inversion}},
url = {http://www.nature.com/articles/s41558-019-0613-7},
volume = {9},
year = {2019}
}
@article{doi:10.1029/2004JD005753,
abstract = {HadAT is a new analysis of the global upper air temperature record from 1958 to 2002 based upon radiosonde data alone. This analysis makes use of a greater number of stations than previous radiosonde analyses, combining a number of digital data sources. Neighbor buddy checks are applied to ensure that both spatial and temporal consistency are maintained. A framework of previously quality controlled stations is used to define the initial station network to minimize the effects of any pervasive biases in the raw data upon the adjustments. The analysis is subsequently expanded to consider all remaining available long-term records. The final data set consists of 676 radiosonde stations, with a bias toward continental Northern Hemisphere midlatitudes. Temperature anomaly time series are provided on 9 mandatory reporting pressure levels from 850 to 30 hPa. The effects of sampling and adjustment uncertainty are calculated at all scales from the station series to the global mean and from seasonal to multidecadal. These estimates are solely parametric uncertainty, given our methodological choices, and not structural uncertainty which relates to sensitivity to choice of approach. An initial analysis of HadAT does not fundamentally alter our understanding of long-term changes in upper air temperature changes.},
author = {Thorne, Peter W and Parker, David E and Tett, Simon F B and Jones, Phil D and McCarthy, Mark and Coleman, Holly and Brohan, Philip},
doi = {10.1029/2004JD005753},
journal = {Journal of Geophysical Research: Atmospheres},
keywords = {climate,radiosonde,temperatures},
number = {D18},
pages = {D18105},
title = {{Revisiting radiosonde upper air temperatures from 1958 to 2002}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2004JD005753},
volume = {110},
year = {2005}
}
@article{Tian2013,
abstract = {This paper documents the climatological mean features of the Atmospheric Infrared Sounder (AIRS) monthly mean tropospheric air temperature (ta, K) and specific humidity (hus, kg/kg) products as part of the Obs4MIPs project and compares them to those from NASA's Modern Era Retrospective analysis for Research and Applications (MERRA) for validation and 16 models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) for CMIP5 model evaluation. MERRA is warmer than AIRS in the free troposphere but colder in the boundary layer with differences typically less than 1K. MERRA is also drier ({\~{}}10{\%}) than AIRS in the tropical boundary layer but wetter ({\~{}}30{\%}) in the tropical free troposphere and the extratropical troposphere. In particular, the large MERRA-AIRS specific humidity differences are mainly located in the deep convective cloudy regions indicating that the low sampling of AIRS in the cloudy regions may be the main reason for these differences. In comparison to AIRS and MERRA, the sixteen CMIP5 models can generally reproduce the climatological features of tropospheric air temperature and specific humidity well, but several noticeable biases exist. The models have a tropospheric cold bias (around 2 K), especially in the extratropical upper troposphere, and a double-ITCZ problem in the troposphere from 1000 hPa to 300 hPa, especially in the tropical Pacific. The upper-tropospheric cold bias exists in the most (13 of 16) models, and the double-ITCZ bias is found in all 16 CMIP5 models. Both biases are independent of the reference dataset used (AIRS or MERRA).},
author = {Tian, Baijun and Fetzer, Eric J. and Kahn, Brian H. and Teixeira, Joao and Manning, Evan and Hearty, Thomas},
doi = {10.1029/2012JD018607},
issn = {2169897X},
journal = {Journal of Geophysical Research: Atmospheres},
month = {jan},
number = {1},
pages = {114--134},
title = {{Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology}},
url = {http://doi.wiley.com/10.1029/2012JD018607},
volume = {118},
year = {2013}
}
@article{Tokinaga2011a,
abstract = {Ship-based measurements of sea surface wind speed display a spurious upward trend due to increases in anemometer height. To correct this bias, the authors constructed a new sea surface wind dataset from ship observations of wind speed and wind wave height archived in the International Comprehensive Ocean–Atmosphere Data Set (ICOADS). The Wave- and Anemometer-based Sea surface Wind (WASWind) dataset is available for wind velocity and scalar speed at monthly resolution on a 4° × 4° longitude–latitude grid from 1950 to 2008. It substantially reduces the upward trend in wind speed through height correction for anemometer-measured winds, rejection of spurious Beaufort winds, and use of estimated winds from wind wave height. The reduced global upward trend is smallest among the existing global datasets of in situ observations and comparable with those of reanalysis products. Despite the significant reduction of globally averaged wind speed trend, WASWind features rich spatial structures in trend pattern, making it a valuable dataset for studies of climate changes on regional scales. Not only does the combination of ship winds and wind wave height successfully reproduce major modes of seasonal-to-decadal variability; its trend patterns are also physically consistent with sea level pressure (SLP) measurements. WASWind is in close agreement with wind changes in satellite measurements by the Special Sensor Microwave Imagers (SSM/Is) for the recent two decades. The agreement in trend pattern with such independent observations illustrates the utility of WASWind for climate trend analysis. An application to the South Asian summer monsoon is presented.},
author = {Tokinaga, Hiroki and Xie, Shang-Ping},
doi = {10.1175/2010JCLI3789.1},
issn = {1520-0442},
journal = {Journal of Climate},
month = {jan},
number = {1},
pages = {267--285},
title = {{Wave- and Anemometer-Based Sea Surface Wind (WASWind) for Climate Change Analysis}},
url = {http://journals.ametsoc.org/doi/10.1175/2010JCLI3789.1},
volume = {24},
year = {2011}
}
@article{Tomita2017,
author = {Tomita, Hiroyuki and Hihara, Tsutomu and Kako, Shin'ichiro and Kubota, Masahisa and Kutsuwada, Kunio},
doi = {10.1007/s10872-018-0493-x},
issn = {0916-8370},
journal = {Journal of Oceanography},
month = {apr},
number = {2},
pages = {171--194},
title = {{An introduction to J-OFURO3, a third-generation Japanese ocean flux data set using remote-sensing observations}},
url = {http://link.springer.com/10.1007/s10872-018-0493-x},
volume = {75},
year = {2019}
}
@article{Trewin2020,
abstract = {Abstract A new version of the long-term Australian temperature data set, known as ACORN-SAT (Australian Climate Observations Reference Network—Surface Air Temperature), has been developed. ACORN-SAT includes homogenized daily maximum and minimum temperature data from 112 locations across Australia, encompassing the period from 1910 to the present, with 60 of the locations having data for the full 1910–2018 period. Homogenization is achieved using a percentile-matching methodology with a number of improvements beyond practices used in previous versions, including more effective detection and removal of potentially inhomogeneous reference stations and an enhanced breakpoint detection methodology. Explicit corrections have also been introduced for a change in instrument screen size, whilst an assessment has found that the transition from manual to automatic instruments and changes in effective response time of automatic instruments have had a negligible impact on the data. Adjustments associated with documented site moves from in-town to out-of-town locations are predominantly negative, particularly for minimum temperature, with other adjustments showing no strong bias towards either positive or negative values. The new data set shows slightly stronger warming (0.12°C per decade in mean temperature over the 1910–2016 period) than either the previous ACORN-SAT version (0.10°C) or the unhomogenized gridded data (0.08°C), primarily due to more effective treatment of systematic moves of sites out of towns and the removal of a rounding bias in the version 1 methodology.},
author = {Trewin, Blair and Braganza, Karl and Fawcett, Robert and Grainger, Simon and Jovanovic, Branislava and Jones, David and Martin, David and Smalley, Robert and Webb, Vanessa},
doi = {10.1002/gdj3.95},
isbn = {2049-6060},
issn = {2049-6060},
journal = {Geoscience Data Journal},
month = {nov},
number = {2},
pages = {149--169},
title = {{An updated long‐term homogenized daily temperature data set for Australia}},
url = {https://onlinelibrary.wiley.com/doi/10.1002/gdj3.95},
volume = {7},
year = {2020}
}
@misc{TRMM2011,
address = {Greenbelt, MD, USA},
author = {TRMM},
doi = {10.5067/TRMM/TMPA/3H/7},
publisher = {Goddard Earth Sciences Data and Information Services Center (GES DISC)},
title = {{TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25 degree x 0.25 degree V7 (TRMM{\_}3B42)}},
url = {https://dx.doi.org/10.5067/TRMM/TMPA/3H/7},
year = {2011}
}
@article{Troup1965,
abstract = {Abstract An attempt is made to obtain a coherent picture of the extent and mode of operation of the ‘southern oscillation.' This term is used here, following Sir Gilbert Walker, to describe a standing fluctuation of opposed pressure anomalies in both eastern and western hemispheres. The existence of this opposition has been verified, using more recent data, for stations in the Indian and Pacific Ocean regions; results show the oscillation was less marked in recent decades. The representativeness and physical meaning of the index devised by Walker to characterize the state of the oscillation are considered. The geographical extent of the phenomenon is examined using correlation and regression charts of pressures with Walker's index. The temperature and rainfall anomalies associated with it may be derived qualitatively from the pressure anomalies. Recent data are used to verify persistence and lag correlations between station pressures; while there has again been some decline, the lag correlations of elements with previous South America pressures still hold good. The decline in these various quantities is indicative of a minor secular change commencing in the 1920's, which is also evident in a decrease in the variability of pressure. What ‘periodicities' appear to exist in elements affected by the southern oscillation may well be an outcome of sampling fluctuations in (often persistent) random series. This is suggested by the variety of supposed ‘periods' reported, and their evanescence in space and time. An example of this evanescence in time is provided from the Darwin pressure record. A mechanism for the oscillation is proposed in terms of variations in a direct toroidal circulation between warmer eastern and cooler western hemispheres. These variations are attributed, (following a model by Palmer of the synoptic climatology of the tropical Pacific) to variations in the south-east trades in the South Pacific and the consequent variations in cyclonic vortex generation in the West Pacific. The persistence of anomalies is then due to the extent of ocean areas in the south-east Pacific where the sea temperature is lower than the air temperature. The lag correlations observed may be due to this persistence and to a transmission of anomalies along the trades through air-sea interaction.},
author = {Troup, A J},
doi = {10.1002/qj.49709139009},
journal = {Quarterly Journal of the Royal Meteorological Society},
number = {390},
pages = {490--506},
title = {{The ‘southern oscillation'}},
volume = {91},
year = {1965}
}
@techreport{Tsutsumi2009,
address = {Geneva, Switzerland},
author = {Tsutsumi, Y and Mori, K and Hirahara, T and Ikegami, M and Conway, Thomas J.},
pages = {23},
publisher = {World Meteorological Organization (WMO)},
series = {GAW Report No. 184},
title = {{Technical Report of Global Analysis Method for Major Greenhouse Gases by the World Data Center for Greenhouse Gases}},
url = {https://library.wmo.int/index.php?lvl=notice{\_}display{\&}id=12631{\#}.YbnYdWhKiUk},
year = {2009}
}
@article{Turnbull2017,
abstract = {Abstract. We present 60 years of $\Delta$14CO2 measurements from Wellington, New Zealand (41°S, 175°E). The record has been extended and fully revised. New measurements have been used to evaluate the existing record and to replace original measurements where warranted. This is the earliest direct atmospheric $\Delta$14CO2 record and records the rise of the 14C bomb spike and the subsequent decline in $\Delta$14CO2 as bomb 14C moved throughout the carbon cycle and increasing fossil fuel CO2 emissions further decreased atmospheric $\Delta$14CO2. The initially large seasonal cycle in the 1960s reduces in amplitude and eventually reverses in phase, resulting in a small seasonal cycle of about 2‰ in the 2000s. The seasonal cycle at Wellington is dominated by the seasonality of cross-tropopause transport and differs slightly from that at Cape Grim, Australia, which is influenced by anthropogenic sources in winter. $\Delta$14CO2 at Cape Grim and Wellington show very similar trends, with significant differences only during periods of known measurement uncertainty. In contrast, similar clean-air sites in the Northern Hemisphere show a higher and earlier bomb 14C peak, consistent with a 1.4-year interhemispheric exchange time. From the 1970s until the early 2000s, the Northern and Southern Hemisphere $\Delta$14CO2 were quite similar, apparently due to the balance of 14C-free fossil fuel CO2 emissions in the north and 14C-depleted ocean upwelling in the south. The Southern Hemisphere sites have shown a consistent and marked elevation above the Northern Hemisphere sites since the early 2000s, which is most likely due to reduced upwelling of 14C-depleted and carbon-rich deep waters in the Southern Ocean, although an underestimate of fossil fuel CO2 emissions or changes in biospheric exchange are also possible explanations. This developing $\Delta$14CO2 interhemispheric gradient is consistent with recent studies that indicate a reinvigorated Southern Ocean carbon sink since the mid-2000s and suggests that the upwelling of deep waters plays an important role in this change.},
author = {Turnbull, Jocelyn C. and {Mikaloff Fletcher}, Sara E. and Ansell, India and Brailsford, Gordon W. and Moss, Rowena C. and Norris, Margaret W. and Steinkamp, Kay},
doi = {10.5194/acp-17-14771-2017},
issn = {1680-7324},
journal = {Atmospheric Chemistry and Physics},
month = {dec},
number = {23},
pages = {14771--14784},
title = {{Sixty years of radiocarbon dioxide measurements at Wellington, New Zealand: 1954–2014}},
volume = {17},
year = {2017}
}
@article{ClimatefieldcompletionviaMarkovrandomfieldsApplicationtotheHadCRUT46temperaturedataset,
abstract = {Surface temperature is a vital metric of Earth's climate state but is incompletely observed in both space and time: over half of monthly values are missing from the widely used HadCRUT4.6 global surface temperature dataset. Here we apply the graphical expectation–maximization algorithm (GraphEM), a recently developed imputation method, to construct a spatially complete estimate of HadCRUT4.6 temperatures. GraphEM leverages Gaussian Markov random fields (also known as Gaussian graphical models) to better estimate covariance relationships within a climate field, detecting anisotropic features such as land–ocean contrasts, orography, ocean currents, and wave-propagation pathways. This detection leads to improved estimates of missing values compared to methods (such as kriging) that assume isotropic covariance relationships, as we show with real and synthetic data. This interpolated analysis of HadCRUT4.6 data is available as a 100-member ensemble, propagating information about sampling variability available from the original HadCRUT4.6 dataset. A comparison of Ni{\~{n}}o-3.4 and global mean monthly temperature series with published datasets reveals similarities and differences due in part to the spatial interpolation method. Notably, the GraphEM-completed HadCRUT4.6 global temperature displays a stronger early twenty-first-century warming trend than its uninterpolated counterpart, consistent with recent analyses using other datasets. Known events like the 1877/78 El Ni{\~{n}}o are recovered with greater fidelity than with kriging, and result in different assessments of changes in ENSO variability through time. Gaussian Markov random fields provide a more geophysically motivated way to impute missing values in climate fields, and the associated graph provides a powerful tool to analyze the structure of teleconnection patterns. We close with a discussion of wider applications of Markov random fields in climate science.},
address = {Boston MA, USA},
author = {Vaccaro, Adam and Emile-Geay, Julien and Guillot, Dominque and Verna, Resherle and Morice, Colin and Kennedy, John and Rajaratnam, Bala},
doi = {10.1175/JCLI-D-19-0814.1},
issn = {0894-8755},
journal = {Journal of Climate},
month = {may},
number = {10},
pages = {4169--4188},
publisher = {American Meteorological Society},
title = {{Climate Field Completion via Markov Random Fields: Application to the HadCRUT4.6 Temperature Dataset}},
url = {https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-19-0814.1/JCLI-D-19-0814.1.xml https://journals.ametsoc.org/view/journals/clim/34/10/JCLI-D-19-0814.1.xml},
volume = {34},
year = {2021}
}
@article{Vandemeulebroucke2019,
author = {Vandemeulebroucke, Isabeau and Calle, Klaas and Caluwaerts, Steven and {De Kock}, Tim and {Van Den Bossche}, Nathan},
doi = {10.1139/cjce-2018-0594},
issn = {0315-1468},
journal = {Canadian Journal of Civil Engineering},
month = {feb},
number = {11},
pages = {1032--1042},
publisher = {NRC Research Press},
title = {{Does historic construction suffer or benefit from the urban heat island effect in Ghent and global warming across Europe?}},
volume = {46},
year = {2019}
}
@article{Vidal2010,
abstract = {Abstract The assessment of regional climate change requires the development of reference long-term retrospective meteorological datasets. This article presents an 8-km-resolution atmospheric reanalysis over France performed with the the Safran-gauge-based analysis system for the period 1958?2008. Climatological features of the Safran 50-year analysis?long-term mean values, inter-annual and seasonal variability?are first presented for all computed variables: rainfall, snowfall, mean air temperature, specific humidity, wind speed and solar and infrared radiation. The spatial patterns of precipitation, minimum and maximum temperature are compared with another spatialization method, and the temporal consistency of the reanalysis is assessed through various validation experiments with both dependent and independent data. These experiments demonstrate the overall robustness of the Safran reanalysis and the improvement of its quality with time, in connection with the sharp increase in the observation network density that occurred in the 1990s. They also show the differentiated sensitivity of variables to the number of available ground observations, with precipitation and air temperature being the more robust ones. The comparison of trends from the reanalysis with those from homogenized series finally shows that if spatial patterns are globally consistent with both approaches, care must be taken when using literal values from the reanalysis and corresponding statistical significance in climate change detection studies. The Safran 50-year atmospheric reanalysis constitutes a long-term forcing datasets for land surface schemes and thus enables the simulation of the past 50 years of water resources over France. Copyright ? 2009 Royal Meteorological Society},
author = {Vidal, Jean-Philippe and Martin, Eric and Franchist{\'{e}}guy, Laurent and Baillon, Martine and Soubeyroux, Jean-Michel},
doi = {10.1002/joc.2003},
journal = {International Journal of Climatology},
month = {sep},
number = {11},
pages = {1627--1644},
publisher = {Wiley-Blackwell},
title = {{A 50-year high-resolution atmospheric reanalysis over France with the Safran system}},
volume = {30},
year = {2010}
}
@article{VonderHaar2012,
abstract = {The NASA Water Vapor Project (NVAP) dataset is a global (land and ocean) water vapor dataset created by merging multiple sources of atmospheric water vapor to form a global data base of total and layered precipitable water vapor. Under the NASA Making Earth Science Data Records for Research Environments (MEaSUREs) program, NVAP is being reprocessed and extended, increasing its 14-year coverage to include 22 years of data. The NVAP-MEaSUREs (NVAP-M) dataset is geared towards varied user needs, and biases in the original dataset caused by algorithm and input changes were removed. This is accomplished by relying on peer reviewed algorithms and producing the data in multiple "streams" to create products geared towards studies of both climate and weather. We briefly discuss the need for reprocessing and extension, steps taken to improve the product, and provide some early science results highlighting the improvements and potential scientific uses of NVAP-M. {\textcopyright} 2012. American Geophysical Union. All Rights Reserved.},
author = {{Vonder Haar}, Thomas H. and Bytheway, Janice L. and Forsythe, John M.},
doi = {10.1029/2012GL052094},
issn = {00948276},
journal = {Geophysical Research Letters},
month = {aug},
number = {15},
pages = {1--6},
title = {{Weather and climate analyses using improved global water vapor observations}},
volume = {39},
year = {2012}
}
@article{https://doi.org/10.1029/2020GL090873,
abstract = {Plain Language Summary NOAA provides a suite of climate services to government, business, academia and the public to support informed decision-making. Among these services is the State of the Climate report, which is a collection of monthly summaries recapping climate-related occurrences across the globe. This report relies heavily upon NOAA's Global Surface Temperature dataset to depict recent monthly conditions and long-term changes. Our research introduces a new edition of this flagship dataset that is based upon additional temperature data and improved scientific methods. The new dataset extends back to 1850 and has complete coverage of all land and ocean areas for the first time. These improvements are particularly important in the Arctic, which has warmed more rapidly than the rest of the planet in recent decades, and the new dataset likewise has larger trends than its predecessor in that part of the world. The introduction of this new dataset is consistent with the NOAA practice of periodically developing improved versions of its foundational datasets, the goal being to ensure the best possible representation of historical conditions across the globe. The results of this paper suggest that the new dataset can substantially contribute to future NOAA monitoring and assessment activities.},
annote = {e2020GL090873 2020GL090873},
author = {Vose, R S and Huang, B and Yin, X and Arndt, D and Easterling, D R and Lawrimore, J H and Menne, M J and Sanchez-Lugo, A and Zhang, H M},
doi = {10.1029/2020GL090873},
journal = {Geophysical Research Letters},
keywords = {arctic,global temperature,trends},
number = {4},
pages = {e2020GL090873},
title = {{Implementing Full Spatial Coverage in NOAA's Global Temperature Analysis}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL090873},
volume = {48},
year = {2021}
}
@article{WAGNER1999191,
abstract = {The potential of using ERS Scatterometer data for soil moisture monitoring over the Ukraine is investigated. The ERS Scatterometer is a C-band radar with a spatial resolution of 50 km and a high temporal sampling rate. An algorithm for estimating the surface soil moisture content is applied to 6 years of data. A qualitative comparison with meteorological observations and auxiliary information indicates that good-quality surface wetness values can be determined. A simple method is developed to relate the surface estimates with the profile soil moisture content. This model requires as input the remotely sensed radar data and soil data encompassing wilting level, field capacity, and porosity. The method was validated with an extensive data set of gravimetric soil moisture measurements in the 0–20 cm and 0–100 cm layers from the agrometeorological network in the Ukraine. It is found that the ERS Scatterometer data can be used to distinguish about five soil moisture levels with good confidence.},
author = {Wagner, Wolfgang and Lemoine, Guido and Rott, Helmut},
doi = {https://doi.org/10.1016/S0034-4257(99)00036-X},
issn = {0034-4257},
journal = {Remote Sensing of Environment},
number = {2},
pages = {191--207},
title = {{A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data}},
url = {http://www.sciencedirect.com/science/article/pii/S003442579900036X},
volume = {70},
year = {1999}
}
@article{Wakita2017,
abstract = {Abstract We used carbon dioxide (CO2) system data collected during 1999–2015 to investigate ocean acidification at time series sites in the western subarctic region of the North Pacific Ocean. The annual mean pH at station K2 decreased at a rate of 0.0025 ± 0.0010 year−1 mostly in response to oceanic uptake of anthropogenic CO2. The Revelle factor increased rapidly (0.046 ± 0.022 year−1), an indication that the buffering capacity of this region of the ocean has declined faster than at other time series sites. In the western subarctic region, the pH during the winter decline at a slower rate of 0.0008 ± 0.0004 year−1. This was attributed to a reduced rate of increase of dissolved inorganic carbon (DIC) and an increase of total alkalinity (TA). The reduction of DIC increase was caused by the decline of surface water density associated with the pycnocline depression and the reduction of vertical diffusion flux from the upper pycnocline. These physical changes were probably caused by northward shrinkage of the western subarctic gyre and global warming. Meanwhile, the contribution of the density decline to the TA increase is canceled out by that of the reduced vertical diffusive flux. We speculated that the winter TA increase is caused mainly by the accumulation of TA due to the weakened calcification by organisms during the winter.},
author = {Wakita, Masahide and Nagano, Akira and Fujiki, Tetsuichi and Watanabe, Shuichi},
doi = {10.1002/2017JC013002},
journal = {Journal of Geophysical Research: Oceans},
number = {8},
pages = {6923--6935},
title = {{Slow acidification of the winter mixed layer in the subarctic western North Pacific}},
volume = {122},
year = {2017}
}
@article{Walsh2017,
abstract = {Arctic sea ice data from a variety of historical sources have been synthesized into a database extending back to 1850 with monthly time-resolution. The synthesis procedure includes interpolation to a uniform grid and an analog-based estimation of ice concentrations in areas of no data. The consolidated database shows that there is no precedent as far back as 1850 for the 21st century's minimum ice extent of sea ice on the pan-Arctic scale. A regional-scale exception to this statement is the Bering Sea. The rate of retreat since the 1990s is also unprecedented and especially large in the Beaufort and Chukchi Seas. Decadal and multidecadal variations have occurred in some regions, but their magnitudes are smaller than that of the recent ice loss. Interannual variability is prominent in all regions and will pose a challenge to sea ice prediction efforts.},
author = {Walsh, John E. and Fetterer, Florence and Stewart, J. Scott and Chapman, William L.},
doi = {10.1111/j.1931-0846.2016.12195.x},
issn = {0016-7428},
journal = {Geographical Review},
month = {jan},
number = {1},
pages = {89--107},
title = {{A database for depicting Arctic sea ice variations back to 1850}},
url = {https://www.tandfonline.com/doi/full/10.1111/j.1931-0846.2016.12195.x},
volume = {107},
year = {2017}
}
@article{WCRPGlobalSeaLevelBudgetGroup2018,
abstract = {Abstract. Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g., acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled Regional Sea Level and Coastal Impacts, an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017–2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1±0.3mmyr−1 and acceleration of 0.1mmyr−2 over 1993–present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42{\%}, 21{\%}, 15{\%} and 8{\%} to the global mean sea level over the 1993–present period. We also study the sea-level budget over 2005–present, using GRACE-based ocean mass estimates instead of the sum of individual mass components. Our results demonstrate that the global mean sea level can be closed to within 0.3mmyr−1 (1$\sigma$). Substantial uncertainty remains for the land water storage component, as shown when examining individual mass contributions to sea level.},
author = {{WCRP Global Sea Level Budget Group}},
doi = {10.5194/essd-10-1551-2018},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {aug},
number = {3},
pages = {1551--1590},
publisher = {Copernicus Publications},
title = {{Global sea-level budget 1993–present}},
volume = {10},
year = {2018}
}
@article{Webb2011,
abstract = {An extensive assessment of historical trends in winegrape maturity dates from vineyards located in geographically diverse winegrape growing regions in Australia has been undertaken. Records from 44 vineyard blocks, representing a range of varieties of Vitis vinifera L., were accessed. These comprise 33 short-term datasets (average 17 years in length) and 11 long-term datasets, ranging from 25 to 115 years in length (average 50 years). Time series of the day of the year grapes attain maturity were assessed. A trend to earlier maturity of winegrapes was observed in 43 of the 44 vineyard blocks. This trend was significant for six out of the 11 long-term blocks for the complete time period for which records were available. For the period 1993-2009, 35 of the 44 vineyard blocks assessed displayed a statistically significant trend to earlier maturity. The average advance in the phenology was dependent on the time period of observation, with a more rapid advance over more recent decades. Over the more recent 1993-2009 period, the average advance was 1.7 daysyear, whereas for the period 1985-2009 the rate of advance was 0.8 daysyr-1 on average in the 10 long-term vineyard blocks assessed for cross-regional comparison. The trend to earlier maturity was associated with warming temperature trends for all of the blocks assessed in the study. {\textcopyright} 2011 Blackwell Publishing Ltd.},
author = {Webb, L. B. and Whetton, P. H. and Barlow, E. W.R.},
doi = {10.1111/j.1365-2486.2011.02434.x},
issn = {13541013},
journal = {Global Change Biology},
number = {8},
pages = {2707--2719},
title = {{Observed trends in winegrape maturity in Australia}},
volume = {17},
year = {2011}
}
@article{Weber2018,
author = {Weber, M. and Steinbrecht, W. and van der A, R. and Frith, S. M. and Anderson, J. and Coldewey-Egbers, M. and Davis, S. and Degenstein, D. and Fioletov, V. E. and Froidevaux, L. and Hubert, D. and de Laat, J. and Long, C. S. and Loyola, D. and Sofieva, V. and Tourpali, K. and Roth, C. and Wang, R. and Wild, J. D.},
doi = {10.1175/2018BAMSStateoftheClimate.1},
journal = {Bulletin of the American Meteorological Society},
number = {8},
pages = {S51--s54},
title = {{Stratospheric ozone [in “State of the Climate in 2017”]}},
volume = {99},
year = {2018}
}
@article{Weber2020,
author = {Weber, M. and Steinbrecht, W. and Arosio, C. and van der A, R. and Frith, S. M. and Anderson, M. and {Coldewey-Egbers, S. Davis}, D. and Degenstein and {V. E. Fioletov Froidevaux}, L and Hubert, D. and Long, C. S. and Loyola, D. and Rozanov, A. and Roth, C. and Sofieva, V. and Tourpali, K. and Wang, R. and Wild, J. D.},
doi = {10.1175/ BAMS-D-20-0104.1.},
journal = {Bulletin of the American Meteorological Society},
number = {8},
pages = {S81--S83},
title = {{Stratospheric ozone [in “State of the Climate in 2019”]}},
volume = {101},
year = {2020}
}
@article{Weber2018b,
abstract = {Abstract. We report on updated trends using different merged datasets from satellite and ground-based observations for the period from 1979 to 2016. Trends were determined by applying a multiple linear regression (MLR) to annual mean zonal mean data. Merged datasets used here include NASA MOD v8.6 and National Oceanic and Atmospheric Administration (NOAA) merge v8.6, both based on data from the series of Solar Backscatter UltraViolet (SBUV) and SBUV-2 satellite instruments (1978–present) as well as the Global Ozone Monitoring Experiment (GOME)-type Total Ozone (GTO) and GOME-SCIAMACHY-GOME-2 (GSG) merged datasets (1995–present), mainly comprising satellite data from GOME, the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and GOME-2A. The fifth dataset consists of the monthly mean zonal mean data from ground-based measurements collected at World Ozone and UV Data Center (WOUDC). The addition of four more years of data since the last World Meteorological Organization (WMO) ozone assessment (2013–2016) shows that for most datasets and regions the trends since the stratospheric halogen reached its maximum (∼ 1996 globally and ∼ 2000 in polar regions) are mostly not significantly different from zero. However, for some latitudes, in particular the Southern Hemisphere extratropics and Northern Hemisphere subtropics, several datasets show small positive trends of slightly below +1 {\%} decade−1 that are barely statistically significant at the 2$\sigma$ uncertainty level. In the tropics, only two datasets show significant trends of +0.5 to +0.8 {\%} decade−1, while the others show near-zero trends. Positive trends since 2000 have been observed over Antarctica in September, but near-zero trends are found in October as well as in March over the Arctic. Uncertainties due to possible drifts between the datasets, from the merging procedure used to combine satellite datasets and related to the low sampling of ground-based data, are not accounted for in the trend analysis. Consequently, the retrieved trends can be only considered to be at the brink of becoming significant, but there are indications that we are about to emerge into the expected recovery phase. However, the recent trends are still considerably masked by the observed large year-to-year dynamical variability in total ozone.},
author = {Weber, Mark and Coldewey-Egbers, Melanie and Fioletov, Vitali E. and Frith, Stacey M. and Wild, Jeannette D. and Burrows, John P. and Long, Craig S. and Loyola, Diego},
doi = {10.5194/acp-18-2097-2018},
issn = {1680-7324},
journal = {Atmospheric Chemistry and Physics},
month = {feb},
number = {3},
pages = {2097--2117},
title = {{Total ozone trends from 1979 to 2016 derived from five merged observational datasets – the emergence into ozone recovery}},
url = {https://acp.copernicus.org/articles/18/2097/2018/},
volume = {18},
year = {2018}
}
@techreport{Wentz2013,
address = {Santa Rosa, CA, USA},
author = {Wentz, F J},
pages = {44},
publisher = {Remote Sensing Systems (RSS)},
series = {RSS Technical Report 011012},
title = {{SSM/I Version-7 Calibration Report}},
url = {http://images.remss.com/papers/rsstech/2012{\_}011012{\_}Wentz{\_}Version-7{\_}SSMI{\_}Calibration.pdf},
year = {2013}
}
@article{Wentz2001,
author = {Wentz, F.J. and Ashcroft, P. and Gentemann, C.},
doi = {10.1109/36.905249},
issn = {01962892},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
number = {2},
pages = {415--422},
title = {{Post-launch calibration of the TRMM microwave imager}},
url = {http://ieeexplore.ieee.org/document/905249/},
volume = {39},
year = {2001}
}
@article{Wenzel2014,
abstract = {New and improved estimates of sea ice production in the Arctic Ocean are derived from AMSR- E satellite and atmospheric reanalysis data for the period 2002–2011, at a spatial resolution of 6.25 km and using a newly developed fast-ice mask. High ice production in the major coastal polynyas is well demon- strated. The total annual cumulative ice production in the major 10 polynya regions is about 1180±70km3. The interannual variability of the ice production for each polynya is presented during 2002–2011. No obvious relationship is noted between the ice production and the recent drastic reduction in the preceding summer Arctic sea ice extent. Most polynya regions exhibit maximum ice production in autumn (October – November), before areas offshore have been covered with consolidated pack ice. Sea ice production from October to November in the marginal ice zone of the Pacific Ocean sector is negatively correlated with summer ice extent there. The ice production from October to November of 2007 (a record minimum summer ice extent) was about twice as large as that in other years. The high ice production area shifted to higher latitudes i.e., toward the deep Canada Basin, due to the retreat of the summer ice edge. We speculate that the resultant increase in brine input could change the oceanic structure in the basin, specifically deep- ening the winter mixed layer.},
author = {Wenzel, Manfred and Schr{\"{o}}ter, Jens},
doi = {10.1002/2014JC009900},
issn = {21699275},
journal = {Journal of Geophysical Research: Oceans},
month = {nov},
number = {11},
pages = {7493--7508},
title = {{Global and regional sea level change during the 20th century}},
volume = {119},
year = {2014}
}
@article{Wijffels2016,
author = {Wijffels, Susan and Roemmich, Dean and Monselesan, Didier and Church, John and Gilson, John},
doi = {10.1038/nclimate2924},
issn = {1758-6798},
journal = {Nature Climate Change},
number = {2},
pages = {116--118},
title = {{Ocean temperatures chronicle the ongoing warming of Earth}},
url = {https://doi.org/10.1038/nclimate2924},
volume = {6},
year = {2016}
}
@inproceedings{Wild2016a,
author = {Wild, J. D. and Yang, S-K. and Long, C. S.},
booktitle = {Quadrennial Ozone Symposium 2016, Edinburgh, 2–9 September 2016},
title = {{Ozone Profile Trends: An SBUV/2 Perspective}},
url = {https://meetingorganizer.copernicus.org/QOS2016/QOS2016-133.pdf},
year = {2016}
}
@article{Willett2014,
author = {Willett, K M and Dunn, R J H and Thorne, P W and Bell, S and Podesta, M De and Parker, D E and Jones, P D and Jr, C N Williams},
doi = {10.5194/cp-10-1983-2014},
journal = {Climate of the Past},
pages = {1983--2006},
title = {{HadISDH land surface multi-variable humidity and temperature record for climate monitoring}},
volume = {10},
year = {2014}
}
@article{Willett2020,
author = {Willett, Kate M. and Dunn, Robert J. H. and Kennedy, John J. and Berry, David I.},
doi = {10.5194/essd-12-2853-2020},
issn = {1866-3516},
journal = {Earth System Science Data},
month = {nov},
number = {4},
pages = {2853--2880},
publisher = {Copernicus Publications},
title = {{Development of the HadISDH.marine humidity climate monitoring dataset}},
volume = {12},
year = {2020}
}
@article{WMO2019,
author = {WMO},
journal = {WMO Greenhouse Gas Bulletin},
pages = {1--8},
title = {{The State of Greenhouse Gases in the Atmosphere Based on Global Observations through 2018}},
url = {https://library.wmo.int/index.php?lvl=notice{\_}display{\&}id=21620{\#}.Ybiw12jMKUk},
volume = {15},
year = {2019}
}
@article{Wolter1998,
author = {Wolter, Klaus and Timlin, Michael S.},
doi = {10.1002/j.1477-8696.1998.tb06408.x},
issn = {00431656},
journal = {Weather},
month = {sep},
number = {9},
pages = {315--324},
title = {{Measuring the strength of ENSO events: How does 1997/98 rank?}},
volume = {53},
year = {1998}
}
@article{Wood2013,
abstract = {The Helsinki Urban Boundary-Layer Atmosphere Network (UrBAN: http://urban.fmi.fi) is a dedicated research-grade observational network where the physical processes in the atmosphere above the city are studied. Helsinki UrBAN is the most poleward intensive urban research observation network in the world and thus will allow studying some unique features such as strong seasonality. The network's key purpose is for the understanding of the physical processes in the urban boundary layer and associated fluxes of heat, momentum, moisture, and other gases. A further purpose is to secure a research-grade database, which can be used internationally to validate and develop numerical models of air quality and weather prediction. Scintillometers, a scanning Doppler lidar, ceilometers, a sodar, eddy-covariance stations, and radiometers are used. This equipment is supplemented by auxiliary measurements, which were primarily set up for general weather and/or air-quality mandatory purposes, such as vertical soundings and the operational Doppler radar network. Examples are presented as a testimony to the potential of the network for urban studies, such as (i) evidence of a stable boundary layer possibly coupled to an urban surface, (ii) the comparison of scintillometer data with sonic anemometry above an urban surface, (iii) the application of scanning lidar over a city, and (iv) combination of sodar and lidar to give a fuller range of sampling heights for boundary layer profiling.},
author = {Wood, C R and J{\"{a}}rvi, L and Kouznetsov, R D and Nordbo, A and Joffre, S and Drebs, A and Vihma, T and Hirsikko, A and Suomi, I and Fortelius, C and O'Connor, E and Moiseev, D and Haapanala, S and Moilanen, J and Kangas, M and Karppinen, A and Vesala, T and Kukkonen, J},
doi = {10.1175/BAMS-D-12-00146.1},
journal = {Bulletin of the American Meteorological Society},
month = {apr},
number = {11},
pages = {1675--1690},
publisher = {American Meteorological Society},
title = {{An Overview of the Urban Boundary Layer Atmosphere Network in Helsinki}},
volume = {94},
year = {2013}
}
@article{Wouters2019,
abstract = {Glaciers outside of the ice sheets are known to be important contributors to sea level rise. In this work, we provide an overview of changes in the mass of the world's glaciers, excluding those in Greenland and Antarctica, between 2002 and 2016, based on satellite gravimetry observations of the Gravity Recovery and Climate Experiment (GRACE). Glaciers lost mass at a rate of 199 ± 32 Gt yr−1 during this 14-yr period, equivalent to a cumulative sea level contribution of 8 mm. We present annual mass balances for 17 glacier regions, that show a qualitatively good agreement with published estimates from in situ observations. We find that annual mass balance varies considerably from year to year, which can in part be attributed to changes in the large-scale circulation of the atmosphere. These variations, combined with the relatively short observational record, hamper the detection of acceleration of glacier mass loss. Our study highlights the need for continued observations of the Earth's glacierized regions.},
author = {Wouters, Bert and Gardner, Alex S and Moholdt, Geir},
doi = {10.3389/feart.2019.00096},
issn = {2296-6463},
journal = {Frontiers in Earth Science},
pages = {96},
title = {{Global Glacier Mass Loss During the GRACE Satellite Mission (2002–2016)}},
volume = {7},
year = {2019}
}
@article{Wu2013,
abstract = {A new gridded daily dataset with the resolution of 0.25 degrees latitude by 0.25 degrees longitude, CN05.1, is constructed for the purpose of high resolution climate model validation over China region. The dataset is based on the interpolation from over 2400 observing stations in China, includes 4 variables: daily mean, minimum and maximum temperature, daily precipitation. The "anomaly approach" is applied in this interpolation. The climatology is first interpolated by thin-plate smoothing splines and then a gridded daily anomaly derived from angular distance weighting method is added to climatology to obtain the final dataset. Intercomparison of the dataset with other three daily datasets, CN05 for temperature, and EA05 and APHRO for precipitation is conducted. The analysis period is from 1961 to 2005. For multi-annual mean temperature variables, results show small differences over eastern China with dense observation stations, but larger differences (warmer) over western China with less stations between CN05.1 and CN05. The temperature extremes are measured by TX3D (mean of the 3 greatest maximum temperatures in a year) and TN3D (mean of the 3 lowest minimum temperatures). CN05. 1 in general shows a warmer TX3D over China, while a lower TN3D in the east and greater TN3D in the west are found compared to CN05. A greater value of annual mean precipitation compared to EA05 and APHRO, especially to the latter, is found in CN05.1. For precipitation extreme of R3D (mean of the 3 largest precipitations in a year), CN05.1 presents lover value of it in western China compared to EA05. A gridded daily observation dataset over China region and comparison with the... | Request PDF. Available from: https://www.researchgate.net/publication/278189384{\_}A{\_}gridded{\_}daily{\_}observation{\_}dataset{\_}over{\_}China{\_}region{\_}and{\_}comparison{\_}with{\_}the{\_}other{\_}datasets{\_}in{\_}Chinese [accessed Jan 09 2018].},
author = {Wu, Jia and Gao, Xue-Jie},
doi = {10.6038/cjg20130406},
issn = {00015733},
journal = {Chinese Journal of Geophysics},
title = {{A gridded daily observation dataset over China region and comparison with the other datasets}},
url = {http://en.igg-journals.cn/article/doi/10.6038/cjg20130406},
year = {2013}
}
@article{Xavier2016,
abstract = {ABSTRACT Basic meteorological data are essential for evaluating impacts of spatiotemporal variability in climate forcing on hydrology and agroecosystems. The objective of this work was to develop high-resolution grids (0.25? ? 0.25?) of daily precipitation, evapotranspiration, and the five climate variables generally required to estimate evapostranspiration for Brazil. These five variables are maximum and minimum temperature, solar radiation, relative humidity, and wind speed. We tested six different interpolation schemes to create the grids for these variables. The data were obtained from 3625 rain gauge and 735 weather stations for period of 1980?2013. We used a cross-validation approach that compares point observed data to point interpolated estimates to select the best interpolation scheme for each climate variable. We also present the performance of the best interpolation for each climate variable at daily timescales and for river basins. The inverse distance weighting and angular distance weighting methods produced the best results. Performance of all methods was poorer prior to 1995 because of fewer stations and available data. The performance of the interpolation varies for different seasons for almost all variables. Forecasting capability was tested for precipitation only and performed adequately for the system state (wet or dry). Variations in the interpolation schemes across river basins are primarily attributed to differences in gauge or station network density. This freely available gridded meteorological data set significantly advances the availability of climate data in Brazil.},
author = {Xavier, Alexandre C and King, Carey W and Scanlon, Bridget R},
doi = {10.1002/joc.4518},
journal = {International Journal of Climatology},
month = {oct},
number = {6},
pages = {2644--2659},
publisher = {Wiley-Blackwell},
title = {{Daily gridded meteorological variables in Brazil (1980–2013)}},
volume = {36},
year = {2016}
}
@article{Xie2007a,
abstract = {Abstract A new gauge-based analysis of daily precipitation has been constructed on a 0.5° latitude?longitude grid over East Asia (5°?60°N, 65°?155°E) for a 26-yr period from 1978 to 2003 using gauge observations at over 2200 stations collected from several individual sources. First, analyzed fields of daily climatology are computed by interpolating station climatology defined as the summation of the first six harmonics of the 365-calendar-day time series of the mean daily values averaged over a 20-yr period from 1978 to 1997. These fields of daily climatology are then adjusted by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) monthly precipitation climatology to correct the bias caused by orographic effects. Gridded fields of the ratio of daily precipitation to the daily climatology are created by interpolating the corresponding station values using the optimal interpolation method. Analyses of total daily precipitation are finally calculated by multiplying the daily climatology by the daily ratio. Cross-validation tests indicated that this gauge-based analysis has high quantitative quality with a negligible bias and a correlation coefficient of ?0.6 for comparisons between withdrawn station data and the analysis at a 0.05° latitude?longitude grid box. The quality of the analysis increases with the gauge network density. The mean distribution and annual cycle of this new gauge analysis present similar patterns but with more detailed structures and slightly larger magnitude compared to other published monthly gauge analyses over the region. The East Asia gauge analysis is applied to verify the performance of five satellite-based precipitation estimates. This examination reveals the regionally and seasonally dependent performance of the satellite products with the best statistics observed for relatively wet regions. Further improvements of the daily gauge analysis are underway to increase the gauge network density and to refine the algorithm to better deal with the orographic effects especially over South and Southeast Asia.},
author = {Xie, Pingping and Chen, Mingyue and Yang, Song and Yatagai, Akiyo and Hayasaka, Tadahiro and Fukushima, Yoshihiro and Liu, Changming},
doi = {10.1175/JHM583.1},
journal = {Journal of Hydrometeorology},
month = {jun},
number = {3},
pages = {607--626},
publisher = {American Meteorological Society},
title = {{A Gauge-Based Analysis of Daily Precipitation over East Asia}},
volume = {8},
year = {2007}
}
@incollection{Xie2007,
address = {Dordrecht},
author = {Xie, Pingping and Arkin, Phillip A. and Janowiak, John E.},
booktitle = {Advances in Global Change Research},
doi = {10.1007/978-1-4020-5835-6_25},
issn = {22151621},
pages = {319--328},
publisher = {Springer Netherlands},
title = {{CMAP: The CPC merged analysis of precipitation}},
volume = {28},
year = {2007}
}
@inproceedings{Xie2010,
author = {Xie, P and Chen, M and Shi, W},
booktitle = {24th Conference of Hydrology, Atlanta, 16-21 January 2010},
title = {{CPC unified gauge-based analysis of global daily precipitation}},
year = {2010}
}
@article{Xu2018,
abstract = {A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50{\%} of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50{\%} of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900--2014, 1979--2014 and 1998--2014. The best estimates of warming trends and there 95{\%} confidence ranges for 1900--2014 are approximately 0.102{\{}$\backslash$thinspace{\}}{\{}$\backslash$textpm{\}}{\{}$\backslash$thinspace{\}}0.006{\{}$\backslash$thinspace{\}}{\{}$\backslash$textdegree{\}}C/decade for the whole year, and 0.104{\{}$\backslash$thinspace{\}}{\{}$\backslash$textpm{\}}{\{}$\backslash$thinspace{\}}0.009, 0.112{\{}$\backslash$thinspace{\}}{\{}$\backslash$textpm{\}}{\{}$\backslash$thinspace{\}}0.007, 0.090{\{}$\backslash$thinspace{\}}{\{}$\backslash$textpm{\}}{\{}$\backslash$thinspace{\}}0.006, and 0.092{\{}$\backslash$thinspace{\}}{\{}$\backslash$textpm{\}}{\{}$\backslash$thinspace{\}}0.007{\{}$\backslash$thinspace{\}}{\{}$\backslash$textdegree{\}}C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900--2014 and 1979--2014. For an even shorter and more recent period (1998--2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes.},
author = {Xu, Wenhui and Li, Qingxiang and Jones, Phil and Wang, Xiaolan L and Trewin, Blair and Yang, Su and Zhu, Chen and Zhai, Panmao and Wang, Jinfeng and Vincent, Lucie and Dai, Aiguo and Gao, Yun and Ding, Yihui},
doi = {10.1007/s00382-017-3755-1},
issn = {1432-0894},
journal = {Climate Dynamics},
month = {apr},
number = {7},
pages = {2513--2536},
title = {{A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900}},
volume = {50},
year = {2018}
}
@article{Yang2017a,
abstract = {Abstract With high spatial-temporal resolution, Satellite-based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground-based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high-quality precipitation estimates. An inverse-root-mean-square-error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) estimates (daily, 0.04° ? 0.04°) over Chile, for the 6 year period of 2009?2014. Daily observations from about 90{\%} of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground ?truth? for validation. Evaluation results indicate high effectiveness of the model in producing high-resolution-precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root-mean-square errors, and absolute mean biases that were consistently improved from the original PERSIANN-CCS estimates. The cross-validation evidences that the framework is effective in providing high-quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates.},
author = {Yang, Zhongwen and Hsu, Kuolin and Sorooshian, Soroosh and Xu, Xinyi and Braithwaite, Dan and Zhang, Yuan and Verbist, Koen M J},
doi = {10.1002/2016JD026177},
issn = {2169897X},
journal = {Journal of Geophysical Research: Atmospheres},
month = {may},
number = {10},
pages = {5267--5284},
publisher = {Wiley-Blackwell},
title = {{Merging high-resolution satellite-based precipitation fields and point-scale rain gauge measurements – A case study in Chile}},
volume = {122},
year = {2017}
}
@article{Yang2017,
abstract = {Phenological responses of vegetation to climate, in particular to the ongoing warming trend, have received much attention. However, divergent results from the analyses of remote sensing data have been obtained for the Tibetan Plateau (TP), the world's largest highelevation region. This study provides a perspective on vegetation phenology shifts during 1960-2014, gained using an innovative approach based on a well-validated, process-based, tree-ring growth model that is independent of temporal changes in technical properties and image quality of remote sensing products. Twenty composite site chronologies were analyzed, comprising about 3,000 trees from forested areas across the TP. We found that the start of the growing season (SOS) has advanced, on average, by 0.28 d/y over the period 1960-2014. The end of the growing season (EOS) has been delayed, by an estimated 0.33 d/y during 1982-2014. No significant changes in SOS or EOS were observed during 1960-1981. April-June and August-September minimum temperatures are the main climatic drivers for SOS and EOS, respectively. An increase of 1 °C in April-June minimum temperature shifted the dates of xylem phenology by 6 to 7 d, lengthening the period of tree-ring formation. This study extends the chronology of TP phenology farther back in time and reconciles the disparate views on SOS derived from remote sensing data. Scaling up this analysis may improve understanding of climate change effects and related phenological and plant productivity on a global scale.},
author = {Yang, Bao and He, Minhui and Shishov, Vladimir and Tychkov, Ivan and Vaganov, Eugene and Rossi, Sergio and Ljungqvist, Fredrik Charpentier and Br{\"{a}}uning, Achim and Grie{\ss}inger, Jussi},
doi = {10.1073/pnas.1616608114},
issn = {10916490},
journal = {Proceedings of the National Academy of Sciences},
number = {27},
pages = {6966--6971},
title = {{New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data}},
volume = {114},
year = {2017}
}
@article{Yasutomi2011,
author = {Yasutomi, Natsuko and Hamada, Atsushi and Yatagai, Akiyo},
journal = {Global Environmental Research},
pages = {165--172},
title = {{Development of a Long-term Daily Gridded Temperature Dataset and Its Application to Rain/Snow Discrimination of Daily Precipitation}},
url = {https://www.chikyu.ac.jp/precip/data/Yasutomi2011GER.pdf},
volume = {15},
year = {2011}
}
@article{Yatagai2012,
author = {Yatagai, Akiyo and Kamiguchi, Kenji and Arakawa, Osamu and Hamada, Atsushi and Yasutomi, Natsuko and Kitoh, Akio},
doi = {10.1175/BAMS-D-11-00122.1},
isbn = {0003-0007},
issn = {0003-0007},
journal = {Bulletin of the American Meteorological Society},
month = {sep},
number = {9},
pages = {1401--1415},
publisher = {American Meteorological Society},
title = {{APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges}},
volume = {93},
year = {2012}
}
@article{amt-6-1533-2013,
author = {Yoshida, Y and Kikuchi, N and Morino, I and Uchino, O and Oshchepkov, S and Bril, A and Saeki, T and Schutgens, N and Toon, G C and Wunch, D and Roehl, C M and Wennberg, P O and Griffith, D W T and Deutscher, N M and Warneke, T and Notholt, J and Robinson, J and Sherlock, V and Connor, B and Rettinger, M and Sussmann, R and Ahonen, P and Heikkinen, P and Kyr{\"{o}}, E and Mendonca, J and Strong, K and Hase, F and Dohe, S and Yokota, T},
doi = {10.5194/amt-6-1533-2013},
journal = {Atmospheric Measurement Techniques},
number = {6},
pages = {1533--1547},
title = {{Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data}},
url = {https://amt.copernicus.org/articles/6/1533/2013/},
volume = {6},
year = {2013}
}
@techreport{Yu2008,
address = {Woods Hole, MA, USA},
author = {Yu, L and Jin, X and Weller, R A},
pages = {1--64},
publisher = {Woods Hole Oceanographic Institution (WHOI)},
series = {OAFlux Project Technical Report (OA-2008-01)},
title = {{Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables}},
year = {2008}
}
@article{Zanna2019,
abstract = {Since the 19th century, rising greenhouse gas concentrations have caused the ocean to absorb most of the Earth's excess heat and warm up. Before the 1990s, most ocean temperature measurements were above 700 m and therefore, insufficient for an accurate global estimate of ocean warming. We present a method to reconstruct ocean temperature changes with global, full-depth ocean coverage, revealing warming of 436 ×1021 J since 1871. Our reconstruction, which agrees with other estimates for the well-observed period, demonstrates that the ocean absorbed as much heat during 1921–1946 as during 1990–2015. Since the 1950s, up to one-half of excess heat in the Atlantic Ocean at midlatitudes has come from other regions via circulation-related changes in heat transport.Most of the excess energy stored in the climate system due to anthropogenic greenhouse gas emissions has been taken up by the oceans, leading to thermal expansion and sea-level rise. The oceans thus have an important role in the Earth's energy imbalance. Observational constraints on future anthropogenic warming critically depend on accurate estimates of past ocean heat content (OHC) change. We present a reconstruction of OHC since 1871, with global coverage of the full ocean depth. Our estimates combine timeseries of observed sea surface temperatures with much longer historical coverage than those in the ocean interior together with a representation (a Green's function) of time-independent ocean transport processes. For 1955–2017, our estimates are comparable with direct estimates made by infilling the available 3D time-dependent ocean temperature observations. We find that the global ocean absorbed heat during this period at a rate of 0.30 ± 0.06 W/m2 in the upper 2,000 m and 0.028 ± 0.026 W/m2 below 2,000 m, with large decadal fluctuations. The total OHC change since 1871 is estimated at 436 ± 91 ×1021 J, with an increase during 1921–1946 (145 ± 62 ×1021 J) that is as large as during 1990–2015. By comparing with direct estimates, we also infer that, during 1955–2017, up to one-half of the Atlantic Ocean warming and thermosteric sea-level rise at low latitudes to midlatitudes emerged due to heat convergence from changes in ocean transport.},
author = {Zanna, Laure and Khatiwala, Samar and Gregory, Jonathan M and Ison, Jonathan and Heimbach, Patrick},
doi = {10.1073/pnas.1808838115},
journal = {Proceedings of the National Academy of Sciences},
month = {jan},
number = {4},
pages = {1126--1131},
title = {{Global reconstruction of historical ocean heat storage and transport}},
volume = {116},
year = {2019}
}
@article{Zeng2014,
abstract = {The atmospheric carbon dioxide (CO2) record displays a prominent seasonal cycle that arises mainly from changes in vegetation growth and the corresponding CO2 uptake during the boreal spring and summer growing seasons and CO2 release during the autumn and winter seasons. The CO2 seasonal amplitude has increased over the past five decades, suggesting an increase in Northern Hemisphere biospheric activity. It has been proposed that vegetation growth may have been stimulated by higher concentrations of CO2 as well as by warming in recent decades, but such mechanisms have been unable to explain the full range and magnitude of the observed increase in CO2 seasonal amplitude. Here we suggest that the intensification of agriculture (the Green Revolution, in which much greater crop yield per unit area was achieved by hybridization, irrigation and fertilization) during the past five decades is a driver of changes in the seasonal characteristics of the global carbon cycle. Our analysis of CO2 data and atmospheric inversions shows a robust 15 per cent long-term increase in CO2 seasonal amplitude from 1961 to 2010, punctuated by large decadal and interannual variations. Using a terrestrial carbon cycle model that takes into account high-yield cultivars, fertilizer use and irrigation, we find that the long-term increase in CO2 seasonal amplitude arises from two major regions: the mid-latitude cropland between 25° N and 60° N and the high-latitude natural vegetation between 50° N and 70° N. The long-term trend of seasonal amplitude increase is 0.311 ± 0.027 per cent per year, of which sensitivity experiments attribute 45, 29 and 26 per cent to land-use change, climate variability and change, and increased productivity due to CO2 fertilization, respectively. Vegetation growth was earlier by one to two weeks, as measured by the mid-point of vegetation carbon uptake, and took up 0.5 petagrams more carbon in July, the height of the growing season, during 2001–2010 than in 1961–1970, suggesting that human land use and management contribute to seasonal changes in the CO2 exchange between the biosphere and the atmosphere.},
author = {Zeng, Ning and Zhao, Fang and Collatz, George J. and Kalnay, Eugenia and Salawitch, Ross J. and West, Tristram O. and Guanter, Luis},
doi = {10.1038/nature13893},
isbn = {0028-0836},
issn = {14764687},
journal = {Nature},
month = {nov},
number = {7527},
pages = {394--397},
pmid = {25409829},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude}},
volume = {515},
year = {2014}
}
@article{ModelingGlobalSeaIcewithaThicknessandEnthalpyDistributionModelinGeneralizedCurvilinearCoordinates,
address = {Boston MA, USA},
author = {Zhang, Jinlun and Rothrock, D A},
doi = {10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2},
journal = {Monthly Weather Review},
number = {5},
pages = {845--861},
publisher = {American Meteorological Society},
title = {{Modeling Global Sea Ice with a Thickness and Enthalpy Distribution Model in Generalized Curvilinear Coordinates}},
url = {https://journals.ametsoc.org/view/journals/mwre/131/5/1520-0493{\_}2003{\_}131{\_}0845{\_}mgsiwa{\_}2.0.co{\_}2.xml},
volume = {131},
year = {2003}
}
@article{ANewApproachtoHomogenizeGlobalSubdailyRadiosondeTemperatureDatafrom1958to2018,
address = {Boston MA, USA},
author = {Zhou, Chunl{\"{u}}e and Wang, Junhong and Dai, Aiguo and Thorne, Peter W},
doi = {10.1175/JCLI-D-20-0352.1},
journal = {Journal of Climate},
number = {3},
pages = {1163--1183},
publisher = {American Meteorological Society},
title = {{A New Approach to Homogenize Global Subdaily Radiosonde Temperature Data from 1958 to 2018}},
url = {https://journals.ametsoc.org/view/journals/clim/34/3/JCLI-D-20-0352.1.xml},
volume = {34},
year = {2021}
}
@article{Zhu2013,
abstract = {Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Ni{\~{n}}o-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.},
author = {Zhu, Zaichun and Bi, Jian and Pan, Yaozhong and Ganguly, Sangram and Anav, Alessandro and Xu, Liang and Samanta, Arindam and Piao, Shilong and Nemani, Ramakrishna R and Myneni, Ranga B},
doi = {10.3390/rs5020927},
isbn = {2072-4292},
issn = {20724292},
journal = {Remote Sensing},
number = {2},
pages = {927--948},
pmid = {449},
title = {{Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3G) for the period 1981 to 2}},
volume = {5},
year = {2013}
}
@article{Ziemke2019,
abstract = {Past studies have suggested that ozone in the troposphere has increased globally throughout much of the 20th century due to increases in anthropogenic emissions and transport. We show, by combining satellite measurements with a chemical transport model, that during the last four decades tropospheric ozone does indeed indicate increases that are global in nature, yet still highly regional. Satellite ozone measurements from Nimbus-7 and Earth Probe Total Ozone Mapping Spectrometer (TOMS) are merged with ozone measurements from the Aura Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) to determine trends in tropospheric ozone for 1979-2016. Both TOMS (1979-2005) and OMI/MLS (2005-2016) depict large increases in tropospheric ozone from the Near East to India and East Asia and further eastward over the Pacific Ocean. The 38-year merged satellite record shows total net change over this region of about C6 to C7 Dobson units (DU) (i.e., ∼ 15 {\%}-20{\%}of average background ozone), with the largest increase (∼ 4 DU) occurring during the 2005- 2016 Aura period. The Global Modeling Initiative (GMI) chemical transport model with time-varying emissions is used to aid in the interpretation of tropospheric ozone trends for 1980-2016. The GMI simulation for the combined record also depicts the greatest increases of C6 to C7DU over India and East Asia, very similar to the satellite measurements. In regions of significant increases in tropospheric column ozone (TCO) the trends are a factor of 2-2.5 larger for the Aura record when compared to the earlier TOMS record; for India and East Asia the trends in TCO for both GMI and satellite measurements are ∼ +3DUdecade-1 or greater during 2005-2016 compared to about C1:2 to C1:4DUdecade-1 for 1979-2005. The GMI simulation and satellite data also reveal a tropospheric ozone increases in ∼ +4 to C5DU for the 38-year record over central Africa and the tropical Atlantic Ocean. Both the GMI simulation and satellitemeasured tropospheric ozone during the latter Aura time period show increases of ∼ +3DUdecade-1 over the N Atlantic and NE Pacific.},
author = {Ziemke, Jerry R. and Oman, Luke D. and Strode, Sarah A. and Douglass, Anne R. and Olsen, Mark A. and McPeters, Richard D. and Bhartia, Pawan K. and Froidevaux, Lucien and Labow, Gordon J. and Witte, Jacquie C. and Thompson, Anne M. and Haffner, David P. and Kramarova, Natalya A. and Frith, Stacey M. and Huang, Liang Kang and Jaross, Glen R. and Seftor, Colin J. and Deland, Mathew T. and Taylor, Steven L.},
doi = {10.5194/acp-19-3257-2019},
issn = {16807324},
journal = {Atmospheric Chemistry and Physics},
number = {5},
pages = {3257--3269},
title = {{Trends in global tropospheric ozone inferred from a composite record of TOMS/OMI/MLS/OMPS satellite measurements and the MERRA-2 GMI simulation}},
volume = {19},
year = {2019}
}
@article{doi:10.1175/BAMS-D-12-00134.1,
abstract = { The STAMMEX (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe) project has developed a high-resolution gridded long-term precipitation dataset based on the daily-observing precipitation network of the German Weather Service DWD, which runs one of the world's densest rain gauge networks, comprising more than 7,500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931–onward (with 0.5° resolution), 1951–onward (0.5° and 0.25°), and 1971–2000 (0.5°, 0.25°, and 0.1°). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates, the STAMMEX datasets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/ dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS)—which include considerably less observations compared to those used in STAMMEX—demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability patterns and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models. },
author = {Zolina, Olga and Simmer, Clemens and Kapala, Alice and Shabanov, Pavel and Becker, Paul and M{\"{a}}chel, Hermann and Gulev, Sergey and Groisman, Pavel},
doi = {10.1175/BAMS-D-12-00134.1},
journal = {Bulletin of the American Meteorological Society},
number = {7},
pages = {995--1002},
title = {{Precipitation Variability and Extremes in Central Europe: New View from STAMMEX Results}},
url = {https://doi.org/10.1175/BAMS-D-12-00134.1},
volume = {95},
year = {2014}
}
@article{Zou2011,
abstract = {Long-term observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites NOAA 15, 16, 17, and 18 and European Meteorological Operational satellite program-A (MetOp-A) were intercalibrated using their overlap observations. Simultaneous nadir overpasses (SNOs) and global ocean mean differences between these satellites were used to characterize calibration errors and to obtain calibration coefficients. Calibration errors were found manifesting themselves as certain scatter or temporal patterns of intersatellite biases, such as well-defined seasonal cycles in the Arctic and Antarctic SNO difference time series or a unique pattern closely correlated to the instrument temperature variability induced by Solar Beta Angle (SBA) variations in global ocean mean difference time series. Analyses of these patterns revealed five different types of biases that need to be removed from existing prelaunch-calibrated AMSU-A observations, which include relatively stable intersatellite biases between most satellite pairs, bias drifts on NOAA 16 and channel 7 of MetOp-A, sun-heating-induced instrument temperature variability in radiances, scene temperature dependency in biases due to inaccurate calibration nonlinearity, and biases due to channel frequency shift from its prelaunch measurement in certain satellite channels. Level-1c time-dependent calibration offsets and nonlinear coefficients were introduced and determined from SNO and global ocean mean temperature regressions to remove or minimize the first four types of biases. Channel frequency shift in NOAA 15 channel 6 was obtained from the radiative transfer model simulation experiments. The new calibration coefficients and channel frequency values have significantly reduced the five different types of biases and resulted in more consistent multisatellite radiance observations for intercalibrated satellite channels. The intercalibrated AMSU-A observations have been merged with its precursor, the intercalibrated microwave sounding unit (MSU), to generate the NOAA/Center for Satellite Applications and Research (STAR) version 2.0 upper-air temperature climate data record (CDR) for climate trend and variability monitoring from 1979 to the present. The intercalibrated AMSU-A radiance data are expected to further improve accuracies of numerical weather prediction and consistencies in climate reanalysis and CDR developments.},
author = {Zou, Cheng-Zhi and Wang, Wenhui},
doi = {10.1029/2011JD016205},
journal = {Journal of Geophysical Research: Atmospheres},
number = {D23},
pages = {D23113},
title = {{Intersatellite calibration of AMSU-A observations for weather and climate applications}},
volume = {116},
year = {2011}
}
@techreport{Zweng2019,
abstract = {This atlas consists of a description of data analysis procedures and horizontal maps of climatological distribution fields of salinity at selected standard depth levels of the World Ocean on a one-degree and quarter-degree latitude-longitude grids. The aim of the maps is to illustrate large-scale characteristics of the distribution of ocean salinity. The fields used to generate these climatological maps were computed by objective analysis of all scientifically quality-controlled historical salinity data in the World Ocean Database 2018. Maps are presented for climatological composite periods (annual, seasonal, monthly, seasonal and monthly difference fields from the annual mean field, and the number of observations) at 102 standard depths.},
address = {Silver Spring, MD, USA},
author = {Zweng, M.M. and Reagan, J.R. and Seidov, D. and Boyer, T.P. and Locarnini, R.A. and Garcia, H.E. and Mishonov, A.V. and Baranova, O.K. and Weathers, K.W. and Paver, C.R. and Smolyar, I.V.},
editor = {Mishonov, A.},
pages = {50},
publisher = {National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS)},
series = {NOAA Atlas NESDIS 82},
title = {{World Ocean Atlas 2018, Volume 2: Salinity}},
url = {https://www.ncei.noaa.gov/data/oceans/woa/WOA18/DOC/woa18{\_}vol2.pdf},
year = {2019}
}