IPCC global coupled model simulations of the South America monsoon system
This study examines the variability of the South America monsoon system (SAMS) over tropical South America (SA). The onset, end, and total rainfall during the summer monsoon are investigated using precipitation pentad estimates from the global precipitation climatology project (GPCP) 1979–2006. Likewise, the variability of SAMS characteristics is examined in ten Intergovernmental Panel on Climate Change (IPCC) global coupled climate models in the twentieth century (1981–2000) and in a future scenario of global change (A1B) (2081–2100). It is shown that most IPCC models misrepresent the inter-tropical convergence zone and therefore do not capture the actual annual cycle of precipitation over the Amazon and northwest SA. Most models can correctly represent the spatiotemporal variability of the annual cycle of precipitation in central and eastern Brazil such as the correct phase of dry and wet seasons, onset dates, duration of rainy season and total accumulated precipitation during the summer monsoon for the twentieth century runs. Nevertheless, poor representation of the total monsoonal precipitation over the Amazon and northeast Brazil is observed in a large majority of the models. Overall, MIROC3.2-hires, MIROC3.2-medres and MRI-CGCM3.2.3 show the most realistic representation of SAMS’s characteristics such as onset, duration, total monsoonal precipitation, and its interannual variability. On the other hand, ECHAM5, GFDL-CM2.0 and GFDL-CM2.1 have the least realistic representation of the same characteristics. For the A1B scenario the most coherent feature observed in the IPCC models is a reduction in precipitation over central-eastern Brazil during the summer monsoon, comparatively with the present climate. The IPCC models do not indicate statistically significant changes in SAMS onset and demise dates for the same scenario.
KeywordsSouth America monsoon system Climate change Global change IPCC global coupled climate models A1B scenario
Tropical South America (SA) is under the influence of a monsoon regime. Unlike other monsoon systems, easterly winds dominate during the entire year over northern SA and tropical Atlantic. Zhou and Lau (1998) demonstrated that when the annual mean is removed from winter and summer composites, a clear reverse in the low-level circulation monthly anomalies becomes evident, which supports the existence of the South America monsoon system (SAMS). The beginning of the summer monsoon in SA is characterized by the increase in convective activity over northwest Amazon that progressively intensifies over southeast SA (Kousky 1988; Marengo et al. 2001; Liebmann and Marengo 2001; Gan et al. 2004; Vera et al. 2006a). Over central and southeast Brazil the onset of the rainy season is observed between September and November whereas the demise of the rainy season is observed between March and April (Gan et al. 2004; Silva and Carvalho 2007).
Another prominent feature during the rainy season in tropical SA is the presence of a northwest–southeast oriented band of clouds and precipitation that originates in the Amazon and runs toward the subtropical Atlantic, which is known as the South Atlantic convergence zone (SACZ) (Kodama 1992). The SACZ is, therefore, an important component of SAMS and plays a significant role in the rainfall variability during the rainy season over central and southeast Brazil (Liebmann et al. 2001; Carvalho et al. 2002a, 2004). Moreover, the monsoon regime in SA is characterized by large spatial (Carvalho et al. 2002a, 2004) and temporal variability from intraseasonal to interannual timescales (Kayano and Kousky 1992; Lenters and Cook 1999; Grimm et al. 1998; Jones and Carvalho 2002; Carvalho et al. 2002b; Vera et al. 2006a; Silva and Carvalho 2007).
The Earth’s average surface temperature has increased by 0.6 ± 0.2°C since the late nineteenth century with global impacts. The response of the climate system to the rapid increase of greenhouse gases remains uncertain Intergovernmental Panel on Climate Change (IPCC 2007). Nevertheless, recent studies have indicated that climate changes resulting from the increase of CO2 may affect the intensity and frequency of extremes temperature and precipitation in several regions over the globe with large socio-economical implications (Kharin et al. 2007). Over SA, modifications in the probability of extremes will have significant impacts on water resources, endangered ecosystems, agriculture, and human health (IPCC 2001). In recent years, the implications of global warming to the spatiotemporal variability of precipitation in monsoon regimes have received further attention. Ashrit et al. (2003), for instance, examined transient climate change simulations of the CNRM ocean–atmosphere coupled climate model (CCM) with increase in greenhouse gases. Their focus was on the Indian summer monsoon and El Niño southern oscillation (ENSO) teleconnections. They found no clear strengthening of the monsoon circulation but an increase in the monsoon precipitation likely linked to large increase in precipitable water over India due to global warming.
Labraga and Lopez (1997) and Carril et al. (1997) are some of the earliest studies that have investigated the impacts on SA precipitation in a future scenario with double the present CO2 concentration. Labraga and Lopez (1997) examined simulations of five general circulation models coupled to an oceanic model with a single mixing layer, whereas Carril et al. (1997) evaluated four simulations of early versions of the IPCC coupled models. Both studies indicate an increase in precipitation over west Pacific inter-tropical convergence zone (ITCZ) and west coast of SA as a response to the increase in CO2. Giorgi and Francisco (2000) examined models from the third assessment report (IPCC 2001), which are an earlier generation of models. They evaluated five IPCC coupled models in four distinct future scenarios and verified an increase of about 10% in precipitation over tropical SA during December–February season. According to Meehl et al. (2005), a warmer climate implies a larger availability of water vapor in the atmosphere and a larger capacity of the air to retain humidity. With more humidity in the atmosphere, relatively more intense rainfall and/or potentially strong snowstorms can occur.
Although most IPCC models have improved since the third assessment report (IPCC 2001), there are still many uncertainties and discrepancies among models in some regions. For instance, a large majority of the IPCC models underestimate precipitation over tropical SA including the Amazon (Sun et al. 2005; Dai 2006; Vera et al. 2006b). On the other hand, Vera et al. (2006b) investigated seasonal precipitation over SA using seven global coupled IPCC models and observed that most models reproduce the mean basic characteristics of the annual cycle of precipitation, such as the seasonal migration of convection over tropical SA and the maximum precipitation observed over southern Andes. Nevertheless, models diverge in the location and intensity of that maximum. Other remarkable discrepancies discussed in Vera et al. (2006b) are associated with the SACZ. Some models (GFDL, MIROC ad MRI) represent the SACZ intensity and location similar to observations, whereas for a few others the SACZ is displaced northeastward of its climatological position or is even absent.
Most previous studies discussed here have focused mainly on the skill of the global climate models in representing the mean seasonal intensity, frequency and large-scale patterns of precipitation in the present climate and future scenarios of global change due to increase in greenhouse gases. However, some characteristics of the monsoon regime such as the onset and duration of the rainy season and total precipitation, which are essential for water resource management and agriculture, have not been properly examined yet. Large variations in these characteristics due to global warming will imply in a significant socio-economic impacts for all SA counties. In the present study we examine the ability of ten IPCC global coupled models in realistically simulating SAMS onset and demise dates, as well as the total summer monsoon precipitation in the present climate (1981–2000). Likewise, we investigate future projections of these models for the A1B scenario with twice the present CO2 concentration (2081–2100). The identification of the performance of individual IPCC models in simulating SAMS characteristics will be useful to detect regions of large and poor reliability for the interpretation of projections in future scenarios of climate change.
This study is organized as follows: data and models are presented in Sect. 2. Section 3 discusses the method applied to define the monsoon onset, demise and total precipitation. Sections 4–7 discuss the IPCC simulations of the monsoon characteristics for the present climate. Section 8 shows the IPCC projections for the A1B scenario. The main conclusions are presented in Sect. 9.
Precipitation data used in this study are 5-day mean (pentad) rainfall from the global precipitation climatology project (GPCP) from 1979 to 2006. The GPCP pentad is based on station gauges and satellite estimates with spatial resolution 2.5° × 2.5° lat/lon (Xie et al. 2003). The advantage of using GPCP is its global coverage, including oceanic areas. In addition, Muza and Carvalho (2006) have shown that GPCP shows a good correspondence with gridded precipitation from stations (Liebmann and Allured 2005) in areas over tropical and subtropical Brazil. The domain examined in the present study extends from 5.0°N–35.0°S to 30.0°W–80°W.
Model description: name, country, spatial resolution, number of simulations for both scenarios twentieth century (20CM) and A1B, and key reference for each model
Resolution lat × lon
~2.8 × 2.8
Flato et al. (2000)
~1.9 × 1.9
Gordon et al. (2002)
~2.8 × 2.8
Salas-Mélia et al. (2005)
~1.9 × 1.9
Roeckner et al. (2003)
~2.8 × 2.8
Yu et al. (2004)
2.0 × 2.5
Delworth et al. (2006)
2.0 × 2.5
Delworth et al. (2006)
~1.125 × 1.125
Hasumi and Emori (2004)
~2.8 × 2.8
Hasumi and Emori (2004)
~2.8 × 2.8
Yukimoto et al. (2006)
All IPCC models discussed here are coupled models with precipitation on daily resolution and with integrations available in the two distinct periods indicated above. In addition, all models have at least 2.8° spatial resolution. In order to compare observations and simulations in the present climate, pentad precipitation was calculated for all models. There is more than one simulation available for FGOALS-g1.0, MIROC3.2-medres, and MRI-CGCM2.3.2 models in both scenarios (Table 1). Therefore, all simulations available for these three models are considered in this work.
All IPCC models examined here have atmospheric and oceanic components. However, no model has dynamical vegetation. Only CNRM-CM3 has ozone transport and simplified atmospheric chemistry reactions (Cariolle and Déqué 1986; Cariolle et al. 1990). With the exception of GFDL-CM2.0 and GFDL-CM2.1 that do not include aerosols, all other models include some type of aerosol, in general sulfates. MIROC3.2-hires and MIROC3.2-medres, and MPI-ECHAM5 include indirect effects of the aerosols. CGCM3.1(T63) and MRI-CGCM2.3.2 have global flux adjustment for heat and water and the MRI-CGCM2.3.2 has momentum adjustment between 12°N and 12°S. The only difference between MIROC3.2-hires and MIROC3.2-medres is the resolution, whereas GFDL-CM2.0 and GFDL-CM2.1 differ in the numeric scheme for atmospheric advection (Dai 2006).
3 Method to estimate SAMS onset, end and total precipitation
4 Summer daily precipitation
The IPCC models (Fig. 2b–k), in general, capture the main spatial patterns of the summer (DJF) mean daily precipitation, such as the precipitation maxima over the continent and in association with the ITCZ and SACZ. Nevertheless, most IPCC models simulate the continental maximum displaced southeastward of its actual position, approximately co-located with the Brazilian highland. These results are consistent with the patterns of seasonal mean precipitation shown in Lambert and Boer (2001) and Vera et al. (2006b). The displacement is particularly noticeable for ECHAM5 (Fig. 2e) that misplaces the maximum precipitation toward west tropical Atlantic, as an extension of the Atlantic ITCZ. In addition, ECHAM5 (Fig. 2e) does not reproduce an NW–SE oriented band of precipitation observed in all other IPCC models examined here. These characteristics are particular of this version of the model in contrast with previous versions (e.g. ECHAM4.5) that show a much more realistic representation of SAMS (Liebmann et al. 2007). Another issue in the representation of the seasonal precipitation in the IPCC models is the unrealistic double ITCZ pattern observed for GFDL-CM2.0 (Fig. 2g), GFDL-CM2.a (Fig. 2h) and MIROC3.2-hires (Fig. 2i) (Dai 2006). MIROC3.2-medres (Fig. 2j) and MRI-CGCM2.3.2 (Fig. 2k) (both with 2.8° resolution) show an unrealistic wide ITCZ. The other feature simulated in all models (Fig. 2b–k) that is not observed in GPCP (Fig. 2a) is the maximum precipitation along the Andes.
5 Precipitation annual cycle
Realistic simulations of SAMS characteristics depend on the ability of models to reproduce the observed precipitation annual cycle. Figure 3 shows the climatological annual cycle of rainfall for observation and models in distinct areas over tropical SA and over the Atlantic Ocean as indicated by boxes in Fig. 3a. Areas were selected with the objective to verify the ability of the models in simulating distinct precipitation regimes in the following regions: northwest SA (2.5°N, 72.5°W), Amazon mouth (0.0°S, 50.0°W), central Amazon (5.0°S, 60.0°W), western Amazon (70.0°W, 5.0°S), southern Amazon (10.0°S, 55.0°W), central Brazil (17.5°S, 50.0°W) and the oceanic portion of the SACZ (30.0°S, 37.5°W). The latter, indicated by the box over subtropical Atlantic (Fig. 3a), corresponds approximately to the region where high variance in daily outgoing long-wave radiation (OLR) is observed in association with the SACZ (e.g. Carvalho et al. 2004). For this discussion, the average annual cycle in each box preserved the original spatial resolution of the models such that we can identify the role of increasing resolution in different versions of the models (as in the case of MIROC3.2-hires and MIROC3.2-medres).
The observed (GPCP) mean annual cycle of precipitation over northwest Amazon (Fig. 3b) shows maximum precipitation in June (~pentad 31) and minimum in January (~pentad 1). Figure 3b indicates a large dispersion among IPCC models for the simulation of the mean annual cycle in that region. GFDL-CM2.1 and GFDL-CM2.0, for instance, simulate an annual cycle about 6 months out-of-phase with respect to observations, with minimum precipitation in June and maximum in January. An unrealistic spring and fall peak in the annual cycle is also observed for many models and is more pronounced for MRI-CGCM2.3.2. MIROC3.2-hires and MIROC3.2-medres capture the correct phase of the annual cycle, although MIROC3.2-hires overestimates (underestimates) precipitation in the rainy (dry) season comparatively to observations. With the exception of MIROC3.2-hires all other models underestimate precipitation in the rainy season. The large dispersion in the results and also the double peak in the annual cycle are likely related to the misrepresentation of the Pacific and Atlantic ITCZ and their seasonal variability in most models investigated here (see Fig. 2).
Near the Amazon mouth (Fig. 3c), western (Fig. 3d) and central (Fig. 3f) Amazon a large dispersion among models for the simulations of the annual cycle of precipitation is also observed. The dispersion decreases and the simulated phase of the annual cycle approaches observation over southern Amazon (Fig. 3g), perhaps as the result of a weaker influence of the ITCZ. Over the Amazon mouth (Fig. 3c) all other models show an out-of-phase maximum of precipitation and underestimate the observed precipitation during the peak of the rainy season.
The dispersion among model simulations of the precipitation annual cycle decreases dramatically over central Brazil (Fig. 3g) and SACZ (Fig. 3h). All models are capable of correctly identifying the wet and dry seasons over central Brazil (Fig. 3g) where SAMS has its large signal (Silva and Carvalho 2007). However, all models maintain a negative bias during the dry season. During the wet season, different models present either positive or negative bias with respect to observations. Over the oceanic SACZ (Fig. 3h), observations show a low amplitude annual cycle, with minimum precipitation during SH winter. All models correctly simulate the low seasonal variability, but with amplitudes that are lower than observed. Some models such as GFDL-CM2.0 and GFDL-CM2.1 show no seasonal variation in the annual cycle in that region.
6 SAMS onset, end, duration and total precipitation
The onset, end and duration of SAMS were estimated for every grid point and for every season by applying the Liebmann and Marengo (2001) method (Eq. 1). The total summer monsoon precipitation was also computed for every season as the total rainfall accumulated between the onset and demise of SAMS. Medians were used to describe the features of the 20CM and A1B experiments to avoid assumptions regarding the actual distributions of the variables and provide information about the central value that is less influenced by extreme values (e.g. Wilks 2006). The onset, demise, duration and total precipitation were computed separately for each simulation of FGOALS-g1.0, MIROC3.2-medres and MRI-CGCM2.3.2, whereas the statistical analyses were performed considering all model simulations together.
6.1 Total summer monsoon precipitation
For the 20CM simulations, large discrepancies are observed over the Amazon (Fig. 4b–k) as diagnosed in previous studies (Sun et al. 2005; Dai 2006; Vera et al. 2006b). For instance, CGCMT63 (Fig. 4b) underestimates in about 400 mm and CSIRO-Mk3.0 (Fig. 4d) in more than 600 mm the total median precipitation over the entire Amazon. In addition CSIRO-Mk3.0 displaces the maximum precipitation (~1,200 mm) toward central Brazil. CNMR (Fig. 4c) and ECHAM5 (Fig. 4e) are not capable of correctly identifying the precipitation regime over northeast Brazil (known as “nordeste”) and distinguish it from SAMS and ITCZ, which results in unrealistic large summer precipitation in that region. Moreover, CNRM-CM3 (Fig. 4c) overestimates precipitation over central Brazil by about 600 mm. Both GFDL-CM2.0 (Fig. 4g) and GFDL-CM2.1 (Fig. 4h) show a very poor representation of the total summer monsoon precipitation. MIROC3.2-hires (Fig. 4i) simulates the maximum precipitation (>1,600 mm) over western Amazon around its actual position (Fig. 4a) and shows a secondary maximum over northern Brazil, displaced eastward from the maximum observed with GPCP over the Amazon mouth. MIROC3.2-medres (Fig. 4j) shows a good representation of the actual pattern of the summer monsoon precipitation. MRI-CGCM2.3.2 (Fig. 4k) realistically represents the total precipitation over northeast Brazil and captures the main spatial features of SAMS.
6.2 Variability of the summer monsoon precipitation
CNRM-CM3 (Fig. 5c) is the only model that simulates high interannual variability of total precipitation (>200 mm) in association with the model’s SACZ (compare with Fig. 2c). All other models show relatively low variability of precipitation in association with the SACZ and over the SAMS core in center Brazil (Silva and Carvalho 2007). MIROC3.2-hires (Fig. 5i) shows the most realistic pattern of MAD over SA. MIROC3.2-medres (Fig. 5j) shows less interannual variability (<200 mm) in basically all SA, with the exception of a small area over the Amazon and central east Brazil. All models tend to increase the interannual variability of precipitation over northern SA and ITCZ likely as a response to the model’s ENSO. Nevertheless, only ECHAM5 (Fig. 5e) and MIROC3.2-hires (Fig. 5i) show a large MAD over southern Brazil in agreement with observations (Fig. 5a).
6.3 SAMS onset
A good performance of some models in simulating the actual median precipitation does not imply a good representation of SAMS onset. For instance, MIROC3.2-hires shows one of the most realistic representations of SAMS median precipitation features (Fig. 4i). However, this model shows early onsets (between pentad 56 and 58) from south Amazon toward southeast Brazil, in addition to a fairly unrealistic representation of onsets over northern Amazon (Fig. 6i). On the other hand, ECHAM5 does not correctly simulate the SACZ and underestimates SAMS precipitation (Fig. 4e), but captures the onset of the rainy season over central Brazil (Fig. 6e). FGOALS-g1.0 (Fig. 6f), and MRI-CGCM2.3.2 (Fig. 6k) show the earliest onsets for the rainy season over central Amazon and southeastern Brazil (between pentad 56 and 58).
A remarkable unrealistic representation of the onset of the rainy season over north Amazon by all IPCC models is clearly evident (compare Fig. 6a with Fig. 6b–k). For instance, the out-of-phase onsets of the rainy season over north Amazon (Fig. 3b) and over the Amazon mouth (Fig. 3c) in models such as GFDL-CM2.0 and GFDL-CM2.1 appears as a large feature over northern SA (Fig. 6g, h).
6.4 SAMS demise and duration
The median end of the rainy season (Eq. 1) over central and southeast Brazil occurs between pentad 18–21 (end March to mid-April) as intense precipitation gradually migrates from south Amazon and central Brazil toward the equator (Kousky 1988; Marengo et al. 2001; Gan et al. 2004; Vera et al. 2006a; Silva and Carvalho 2007) (not shown). During SAMS demise, convection associated with the Atlantic ITCZ weakens (Vera et al. 2006a). Due to the simulation of a stronger than observed and/or double ITCZ, the IPCC models tend to simulate early demises of the rainy season over northeast Brazil (between 4 and 6 pentads, not shown). CSIRO-Mk3.0, for instance, indicates the demise of the rainy season as early as four pentads over central Brazil. GFDL-CM2.0 and FGOALS-g1.0 show relatively less difference, between −1 and 1 pentad over the same region, although both GFDL-CM2.0 and GFDL-CM2.1 do not realistically simulate the pattern of demise dates over SA, showing basically the same dates for the north and central Brazil regions.
7 Summary of SAMS Simulation in the 20CM scenario
Spatial correlation (top) and spatial RMSE (bottom) between IPCC models simulation and GPCP for median (right) and MAD (left) of onset, demise, duration, and total precipitation
Spatial correlation (median)
Spatial correlation (MAD)
Total precipitation (mm)
Total precipitation (mm)
Spatial RMSE (median)
Spatial RMSE (MAD)
Total precipitation (mm)
Total precipitation (mm)
High SC (i.e. SC above 0.7) is observed for the large majority of models for the median onset and demise. However, lower SC is observed for duration, with only two models (MRI-CGCM2.3.2 and ECHAM5) indicating values above 0.7 (SC ~ 0.71 and 0.74, respectively). High SC (>0.7) is observed for total precipitation for most models, with the worst performance observed for ECHAM5 (SC ~ 0.15). MIROC3.2-hires shows the best skill in reproducing the spatial patterns of total precipitation (SC ~ 0.92) followed by MIROC3.2-medres (SC ~ 0.90) and MRI-CGCM2.3.2 (SC ~ 0.83). Nevertheless, large discrepancies are observed among models with respect to the skill in reproducing the spatial pattern of MAD. This is particularly remarkable for MAD of total precipitation. The best performance in this case is observed for MIROC3.2-hires (SC ~ 0.591). Among all IPCC models investigated here, MIROC3.2-hires, MIROC3.2-medres and MRI-CGCM2.3.2 are the most skilful in reproducing the overall spatial variation of median and MAD characteristics of SAMS.
The RMSE observed between median and MAD simulations and observations is, in all cases, very high for all models (Table 2). This is not surprising given that the RMSE averages the square of the difference of the magnitudes between observations and simulations grid point to grid point. Therefore, the analysis of RMSE should be considered along with SC as an indication of the skill of a model in reproducing SAMS spatial characteristics. Thus, taking these two parameters into account, MIROC3.2-hires, MIROC3.2-medres and MRI-CGCM2.3.2 are the models that best characterize the spatial patterns of the median and MAD of onset, demise, duration and total precipitation. They show relatively high SC and low RMSE for most medians and MAD. Using the same rationale, the worst performance is observed for ECHAM5, GFDL-CM2.0 and GFDL-CM2.1.
8 Projections for the A1B scenario
The same methodology applied for the 20CM simulations was used for the IPCC models in the A1B scenario. Median SAMS onset, demise, duration and total precipitation simulated for the A1B scenario were compared with the respective simulation for the 20CM run. The comparison was carried out by examining differences between medians obtained for the 20CM and A1B scenario.
As discussed in the introduction and according to Meehl et al. (2005) a warmer planet could result in the enhancement of the potential for intense precipitation events and more snowstorms. Consistently with the present study, these authors also observed a decrease in the mean daily precipitation for central-eastern Brazil. A possible explanation for this apparent paradox may be found in Tebaldi et al. (2007). They verified that the IPCC models show a consistent signal of longer consecutive dry days and an increase in the rainfall intensity for the same region in a scenario of global change. Our results indicate that for most models and for central-eastern Brazil no statistically significant differences in the median onset, demise and duration are observed for the A1B scenario with respect to the present climate. These combined projections suggest that although rainfall intensity might increase in a future scenario of global change, the total monsoonal precipitation could decrease due to the increase in the number of dry days.
Another remarkable feature indicated by MIROC3.2-hires is the increase in the monsoonal precipitation over North Brazil with maximum of more than 400 mm at the coast of Maranhão and Pará states in Brazil. These features are observed in a region where MIROC3.2-hires shows a maximum in total precipitation for the 20CM run that is misplaced eastward of the actual observations (Fig. 4j). The increase in total precipitation over southern Brazil, Uruguay and Argentina in most models that show a decrease of total precipitation over eastern Brazil is consistent with observations of a seesaw in precipitation on several timescales reported in many previous studies (e.g. Vera et al. 2006a).
The difference between MAD for the A1B and 20CM scenarios was computed for the total monsoon precipitation. There is no statistically significant difference (at 5% level) observed for MAD, suggesting that the dispersion around the median is similar in both scenarios (not shown). This result does not exclude differences at the very end of the tails of the distributions that characterize extreme events, an issue not investigated in the present study.
This study examined simulations of SAMS characteristics by ten coupled IPCC global climate models with distinct physics and resolutions. The skill in the simulations was evaluated with GPCP precipitation during 28 rainy seasons (1979–2006). The focus of this analysis was on the correct simulations of the spatial patterns and variability of daily and total monsoonal precipitation, onset and demise dates, and duration of the monsoon. In this analysis every model was examined individually, in order to identify those that have poor skill against those with good skill in representing SAMS characteristics and how they can potentially influence the ensemble in the present climate and future scenarios of global change.
The SACZ is one of the most important features of SAMS. With the exception of ECHAM5, all models tend to represent an elongated band of precipitation emanating from the Amazon with an orientation NW–SE similar to the SACZ. MIROC3.2hires is the model that simulates more realistically precipitation daily average and standard deviation during the peak of the summer (DJF) in association with the SACZ. FGOALS-g1.0 and ECHAM5, on the other hand, show the poorest representation of the SACZ daily precipitation characteristics.
The mean annual cycle of precipitation was examined in some regions with distinct regimes. We show that over north SA, the annual cycle is poorly represented by most models. With the exception of MIROC3.2-hires, most models tend to underestimate precipitation during the peak of the rainy season. The misrepresentation of the ITCZ and its seasonal cycle seems to be one of the main reasons for the unrealistic out-of-phase annual cycles simulated near the equator by many GCMs. As a consequence, simulations of the total seasonal precipitation, onset and end of the rainy season diverge among models and are notoriously unrealistic over north and northwest Amazon for most models.
On the other hand, the good perspective in using IPCC models to understand and predict future climate changes in SAMS is that the large majority of the IPCC models realistically simulate the median characteristics and dispersion, and phase of the precipitation annual cycle over central SA for the 20CM runs. Previous studies have shown that this region is the core of the monsoon, where precipitation, low and high level winds, and humidity show the largest seasonal amplitude. In this region, the median precipitation during the rainy season is between 1,000 and 1,400 mm. The median onset is observed between the beginning and the end of October and the demise occurs between end of March and mid-April, with duration of the rainy season between 32 and 36 pentads. The best performance in simulating onset, end, duration and total monsoonal precipitation and its interannual variability is observed with the following models: MIROC3.2-hires, MIROC3.2-medres and MRI-CGCM2.3.2. The worst performance in simulating the same characteristics for central SA is observed with ECHAM5, GFDL-CM2.0 and GFDL-CM2.1. CSIRO-Mk3.0 model shows the best simulation of the evolution of the onset and end of the rainy season over the Amazon.
For the A1B scenario, the most coherent feature shown in six out of ten models is the decrease of total monsoonal precipitation over central and eastern SA, which coincides with a region where models show high skill in simulating SAMS characteristics in the 20CM runs. MIROC3.2-hires, which shows the best performance in simulating the characteristics of the total monsoonal precipitation and daily precipitation in the peak of the rainy season, indicates a deficit in precipitation between −100 and −200 mm in the A1B scenario comparatively to the 20CM, extending approximately from southern Amazon toward eastern Brazil. Today, this region covers an area of intensive land-use change, mostly due to expansion of agriculture and pasture activities. Moreover, the northwestern boundary of this region makes a frontier with the Amazon forest, a region where large rate of deforestation, soil degradation and human conflicts have been observed in the last decade (IPCC 2007). Regional analyses are therefore necessary to understand further impact of the projected decrease in precipitation in this region.
We thank Dr. Charles Jones and Dr. Humberto R. Rocha and the two anonymous reviewers for their valuable comments and suggestions for this manuscript. We also thank the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modeling (WGCM) for making available the WCRP CMIP3 multi-model dataset. GPCP data were provided by NOAA. The authors greatly acknowledge the financial support of the following agencies: FAPESP (Proc: 02/09289-9); R. J. Bombardi FAPESP (06/53769-6); L. M. V. Carvalho CNPq (Proc: 482447/2007-9 and 474033/2004-0) and NOAA Office of Global Programs (NOAA NA07OAR4310211).
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