The MMD models suggest a general decrease in precipitation over most of Central America, consistent with Neelin et al. (2006), where the median annual change by the end of the 21st century is –9% under the A1B scenario, and half of the models project area mean changes from –16 to –5%, although the full range of the projections extends from –48 to 9%. Median changes in area mean precipitation in Amazonia and southern South America are small and the variation between the models is also more modest than in Central America, but the area means hide marked regional differences (Table 11.1, Figure 11.15).
Area mean precipitation in Central America decreases in most models in all seasons. It is only in some parts of north-eastern Mexico and over the eastern Pacific, where the ITCZ forms during JJA, that increases in summer precipitation are projected (Figure 11.15). However, since tropical storms can contribute a significant fraction of the rainfall in the hurricane season in this region, these conclusions might be modified by the possibility of increased rainfall in storms not well captured by these global models. In particular, if the number of storms does not change, Knutson and Tuleya (2004) estimate nearly a 20% increase in average precipitation rate within 100 km of the storm centre at the time of atmospheric carbon dioxide (CO2) doubling.
For South America, the multi-model mean precipitation response (Figure 11.15) indicates marked regional variations. The annual mean precipitation is projected to decrease over northern South America near the Caribbean coasts, as well as over large parts of northern Brazil, Chile and Patagonia, while it is projected to increase in Colombia, Ecuador and Peru, around the equator and in south-eastern South America. The seasonal cycle modulates this mean change, especially over the Amazon Basin where monsoon precipitation increases in DJF and decreases in JJA. In other regions (e.g., Pacific coasts of northern South America, a region centered over Uruguay, Patagonia) the sign of the response is preserved throughout the seasonal cycle.
As seen in the bottom panels of Figure 11.15, most models project a wetter climate near the Rio de la Plata and drier conditions along much of the southern Andes, especially in DJF. However, when estimating the likelihood of this response, the qualitative consensus within this set of models should be weighed against the fact that most models show considerable biases in regional precipitation patterns in their control simulations.
The poleward shift of the South Pacific and South Atlantic subtropical anticyclones is a robust response across the models. Parts of Chile and Patagonia are influenced by the polar boundary of the subtropical anticyclone in the South Pacific and experience particularly strong drying because of the combination of the poleward shift in circulation and increase in moisture divergence. The strength and position of the subtropical anticyclone in the South Atlantic is known to influence the climate of south-eastern South America and the South Atlantic Convergence Zone (Robertson et al., 2003; Liebmann et al., 2004). The increase in rainfall in south-eastern South America is related to a corresponding poleward shift in the Atlantic storm track (Yin, 2005).
Some projected changes in precipitation (such as the drying over east-central Amazonia and northeast Brazil and the wetter conditions over south-eastern South America) could be a partial consequence of the El-Niño like response projected by the models (Section 10.3). The accompanying shift and alterations in the Walker Circulation would directly affect tropical South America (Cazes Boezio et al., 2003) and affect southern South America through extratropical teleconnections (Mo and Nogués-Paegle, 2001).
Although feedbacks from carbon cycle and dynamic vegetation are not included in MMD models, a number of coupled carbon cycle-climate projections have been performed since the TAR (see Sections 7.2 and 10.4.1). The initial carbon-climate simulations suggest that drying of the Amazon potentially contributes to acceleration of the rate of anthropogenic global warming by increasing atmospheric CO2 (Cox et al., 2000; Friedlingstein et al., 2001; Dufresne et al., 2002; Jones et al., 2003). These models display large uncertainty in climate projections and differ in the timing and sharpness of the changes (Friedlingstein et al., 2003). Changes in CO2 are related to precipitation changes in regions such as the northern Amazon (Zeng et al., 2004). In a version of the HadCM3 model with dynamic vegetation and an interactive global carbon cycle (Betts et al., 2004), a tendency to a more El Niño-like state contributes to reduced rainfall and vegetation dieback in the Amazon (Cox et al., 2004). But the version of HadCM3 participating in the MMD projects by far the largest reduction in annual rainfall over AMZ (–21% for the A1B scenario). This stresses the necessity of being very cautious in interpreting carbon cycle impacts on the regional climate and ecosystem change until there is more convergence among models on rainfall projections for the Amazon with fixed vegetation. Box 11.4 summarises some of the major issues related to regional land use/land changes in the context of climate change.
Box 11.4: Land Use and Land Cover Change Experiments Related to Climate Change
Land use and land cover change significantly affect climate at the regional and local scales (e.g., Hansen et al., 1998; Bonan, 2001; Kabat et al., 2002; Foley et al., 2005). Recent modelling studies also show that in some instances these effects can extend beyond the areas where the land cover changes occur, through climate teleconnection processes (e.g., Gaertner et al., 2001; Pielke et al., 2002; Marland et al., 2003). Changes in vegetation result in alteration of surface properties, such as albedo and roughness length, and alter the efficiency of ecosystem exchange of water, energy and CO2 with the atmosphere (for more details see Section 7.2). The effects differ widely based on the type and location of the altered ecosystem. The effects of land use and land cover change on climate can also be divided into biogeochemical and biophysical effects (Brovkin et al., 1999; see Sections 7.2 and 2.5 for discussion of these effects).
The net effect of human land cover activities increases the concentration of greenhouse gases in the atmosphere, thus increasing warming (see Sections 7.2 and 10.4 for further discussion); it has been suggested that these land cover emissions have been underestimated in the future climate projections used in the SRES scenarios (Sitch, 2005). Climate models assessed in this report incorporate various aspects of the effects of land cover change including representation of the biogeochemical flux, inclusion of dynamic land use where natural vegetation shifts as climate changes, and explicit human land cover forcing. In all cases, these efforts should be considered at early stages of development (see Chapters 2 and 7, and Table 10.1 for more details on many of these aspects).
One important impact of land cover conversion, generally not simulated in GCMs, is urbanisation. Although small in aerial extent, conversion to urban land cover creates urban heat islands associated with considerable warming (Arnfield, 2003). Since much of the world population lives in urban environments (and this proportion may increase, thus expanding urban areas), many people will be exposed to climates that combine expanded urban heat island effects and increased temperature from greenhouse gas forcing (see Box 7.2 for more details on urban land use effects).
One major shift in land use, relevant historically and in the future, is conversion of forest to agriculture and agriculture back to forest. Most areas well suited to large-scale agriculture have already been converted to this land use/cover type. Yet land cover conversion to agriculture may continue in the future, especially in parts of western North America, tropical areas of south and central America and arable regions in Africa and south and central Asia (IPCC, 2001; RIVM, 2002). In the future, mid-latitude agricultural areal expansion (especially into forested areas) could possibly result in cooling that would offset a portion of the expected warming due to greenhouse gas effects alone. In contrast, reforestation may occur in eastern North America and the eastern portion of Europe. In these areas, climate effects may include local warming associated with reforestation due to decreased albedo values (Feddema et al., 2005).
Tropical land cover change results in a very different climate response compared to mid-latitude areas. Changes in plant cover and the reduced ability of the vegetation to transpire water to the atmosphere lead to temperatures that are warmer by as much as 2°C in regions of deforestation (Costa and Foley, 2000; Gedney and Valdes, 2000; De Fries et al., 2002). The decrease in transpiration acts to reduce precipitation, but this effect may be modified by changes in atmospheric moisture convergence. Most model simulations of Amazonian deforestation suggest reduced moisture convergence, which would amplify the decrease in precipitation (e.g., McGuffie et al., 1995; Costa and Foley, 2000; Avissar and Worth, 2005). However, increased precipitation and moisture convergence in Amazonia during the last few decades contrast with this expectation, suggesting that deforestation has not been the dominant driver of the observed changes (see Section 11.6).
Tropical regions also have the potential to affect climates beyond their immediate areal extent (Chase et al., 2000; Delire et al., 2001; Voldoire and Royer, 2004; Avissar and Werth, 2005; Feddema et al., 2005; Snyder, 2006). For example, changes in convection patterns can affect the Hadley Circulation and thus propagate climate perturbations into the mid-latitudes. In addition, tropical deforestation in the Amazon has been found to affect SSTs in nearby ocean locations, further amplifying teleconnections (Avissar and Werth, 2005;Feddema et al., 2005; Neelin and Su, 2005; Voldoire and Royer, 2005). However, studies also indicate that there are significantly different responses to similar land use changes in other tropical regions and that responses are typically linked to dry season conditions (Voldoire and Royer, 2004a; Feddema et al., 2005). However, tropical land cover change in Africa and southeast Asia appears to have weaker local impacts largely due to influences of the Asian and African monsoon circulation systems (Mabuchi et al., 2005a,b; Voldoire and Royer, 2005).
Several land cover change studies have explicitly assessed the potential impacts (limited to biophysical effects) associated with specific future SRES land cover change scenarios, and the interaction between land cover change and greenhouse gas forcings (De Fries et al., 2002; Maynard and Royer, 2004a; Feddema et al., 2005; Sitch et al., 2005; Voldoire, 2006). In the A2 scenario, large-scale Amazon deforestation could double the expected warming in the region (De Fries et al., 2002; Feddema et al., 2005). Lesser local impacts are expected in tropical Africa and south Asia, in part because of the difference in regional circulation patterns (Delire et al., 2001; Maynard and Royer, 2004a,b; Feddema et al., 2005; Mabuchi et al., 2005a,b). In mid-latitude regions, land-cover induced cooling could offset some of the greenhouse-gas induced warming. Feddema et al. (2005) suggest that in the B1 scenario (where reforestation occurs in many areas and there are other low-impact tropical land cover changes) there are few local tropical climate or teleconnection effects. However, in this scenario, mid-latitude reforestation could lead to additional local warming compared to greenhouse-gas forcing scenarios alone. (continued)
These simulations suggest that the effects of future land cover change will be a complex interaction of local land cover change impacts combined with teleconnection effects due to land cover change elsewhere, in particular the Amazon, and areas surrounding the Indian Ocean. However, projecting the potential outcomes of future climate effects due to land cover change is difficult for two reasons. First, there is considerable uncertainty regarding how land cover will change in the future. In this context, the past may not be a good indicator of the types of land transformation that may occur in the future. For example, if land cover change becomes a part of climate change mitigation (e.g., carbon trading) then a number of additional factors that include carbon sequestration in soils and additional land cover change processes will need to be incorporated in scenario development schemes. Second, current land process models cannot simulate all the potential impacts of human land cover transformation. Such processes as adequate simulation of urban systems, agricultural systems, ecosystem disturbance regimes (e.g., fire) and soil impacts are not yet well represented.