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Keywords:

  • GCM;
  • global warming;
  • high-resolution modeling;
  • South America

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[1] Two 25 year time-slice experiments were conducted using a 20 km mesh global atmospheric model, one for the present (1979–2003) and the other for the future (2075–2099). To assess the uncertainty of climate change projections, we performed ensemble simulations with the 60 km mesh model combining 4 different sea surface temperatures and 3 atmospheric initial conditions. Horizontal resolution of these global models is higher than or comparable to that of regional climate models applied to South American climate change projections. Both the 20 km mesh model and 60 km mesh model reproduce sufficiently well the observed seasonal precipitation patterns. These models project an increase in wet-season precipitation and a decrease in dry-season precipitation over most of South America. In the future, almost all over South America, precipitation intensity will increase. In particular, precipitation intensity is largest over the southeast South America in the present-day simulation, where future change is also large, implying an increasing risk of flooding in this region including the Parana River. At the same time a large increase of consecutive dry days is projected over the western part of the Amazon, where the amplitude of the seasonal hydrograph is projected to increase in the Amazon River, implying more floods in wet season and droughts in dry season.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[2] In the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), data set of more than 20 global coupled atmosphere-ocean general circulation model (AOGCM) is fully utilized to project future climate changes by various scenarios. These AOGCM simulations were performed under the third phase of the Coupled Model Intercomparison Project (CMIP3) of the World Climate Research Programme (WCRP). The CMIP3 multimodel mean precipitation changes projected for South America are assessed in IPCC [2007], which shows a decrease of annual mean precipitation over northern South America near the Caribbean coasts, as well as over Chile and Patagonia, and an increase in Colombia, Ecuador and Peru, and in southeastern South America. These features are almost similar in austral summer and winter, except in central to eastern Brazil where winter precipitation is projected to decrease. These multimodel mean results mitigate the error between models through averaging the results of numerous models, however, it cannot be denied that the low resolution of the models has an undesirable impact on the results particularly on extreme weather events, because the horizontal resolution of these model is of about 100 km to 400 km. Though these models express large-scale mountains, they cannot represent small-scale mountain ranges and detailed land-sea distributions and there is also a limit on the expression of mountainous precipitation. The effect of not well–resolved Andes Mountains was affecting the assessment over much of South America [IPCC, 2007]. Therefore, a high-spatial-resolution model is anticipated for use to study extreme weather events and to project their modification by climate changes for adaptation studies and measures.

[3] As high-resolution AOGCM simulations are very difficult to be used for regional climate scenario experiments because of their high computational burden, dynamical downscaling such as regional climate model (RCM) or high-resolution atmospheric general circulation model (AGCM) is a plausible solution. The results from RCM simulations for global warming projections over South America are, however, few. Marengo et al. [2009] used the PRECIS (Providing Regional Climates for Impacts Studies) regional climate modeling system with HadRM3P for South America, and Nuñez et al. [2009] utilized the Fifth generation Pennsylvania State University-NCAR nonhydrostatic Mesoscale Model (MM5) for southern South America. Their projection is performed with the 50 km spatial resolution, and reasonably resolves many climate features that are triggered by regional forcing, and also better fits in assessing future changes in weather extremes such as heavy precipitation. However, regional characteristics of future climate change with RCM depend on the reproducibility of large-scale circulation of GCM, and thus other scenarios based on different models are needed to get robust features of regional climate change information.

[4] Recently, a high-resolution AGCM with the horizontal grid size of about 20 km has been developed for use in climate change study [Mizuta et al., 2006], and has been used for climate change projections under increasing atmospheric concentrations of greenhouse gases [e.g., Kusunoki et al., 2006; Oouchi et al., 2006]. The grid size of this model is several times higher than that previously used in climate model simulations, and is still higher than or comparable to that used in many RCMs. Actually, this grid size is the same as the current extended-range (i.e., a week to 10 days) numerical weather forecasting model of some operational centers. Kitoh and Kusunoki [2008] evaluated the East Asian summer climate in its present-day climate simulation in comparison with observations as well as lower-resolution versions of the same model. Rajendran and Kitoh [2008] and Kitoh et al. [2008] investigated future climate projections at the end of the 21st century with this 20 km mesh AGCM over India and Middle East, respectively. Kamiguchi et al. [2006] investigated changes in precipitation-based extremes indices due to global warming, and found that both the heavy precipitation and the dry spell will increase at the end of the 21st century over Amazon.

[5] In the above experiments, the present-day climate simulation was performed with the climatological sea surface temperature (SST), while the future climate is forced with SST anomalies of one particular model projection. In the time-slice experiments, simulated climate changes are greatly affected by how to prescribe the future SST. We have developed a technique that incorporates effects of the future climate change with correcting climatic biases of each model and realistic interannual variability that is smoothed out by the multimodel mean [Mizuta et al., 2008; Kitoh et al., 2009]. Although the global 20 km model is a unique one in terms of its horizontal resolution for global change studies with integration period up to 25 years, the computer power is still unable to make ensemble simulation experiments and limits its application to a single member experiment. To cover this caveat, parallel experiments with lower-resolution versions of the same model (60 and 180 km mesh) are performed. In particular, ensemble simulations with the 60 km resolution have been performed and compared with the 20 km version. In this paper, results of 25 year time-slice experiments using both the 20 and 60 km mesh AGCMs are analyzed for the South American region. By doing so, the robust climate change projections at the highest available horizontal resolution can be achieved.

2. Model and Experiment

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

2.1. Model

[6] The model used is a global hydrostatic AGCM developed by the Meteorological Research Institute (MRI) and Japan Meteorological Agency (JMA). Some of the physical parameterizations of then-operational short-term numerical weather prediction model (60 km mesh) of JMA were modified for the long-term climate simulation at MRI. By incorporating a semi-Lagrangian scheme, the simulations were made possible at a triangular truncation 959 with linear Gaussian grid (TL959) in the horizontal, in which the transform grid uses 1920 × 960 grid cells, corresponding to a grid size of about 20 km. The model has 60 layers in the vertical with the model top at 0.1 hPa. For the cumulus parameterization, the Arakawa-Schubert scheme with prognostic closure is used. Detailed description of the model is given in the work of Mizuta et al. [2006].

2.2. Experiment

[7] For the present-day climate simulation, we have used the observed monthly SST and sea-ice concentration during 1979–2003 (HadISST; see Rayner et al. [2003]). We made three sets of simulations. One is an original 20 km mesh version of the model, and the others are a reduced resolution experiment. Here we adopted TL319 and TL95, which have a grid size of 60 and 180 km, respectively. All three experiments are run for 25 years during 1979–2003. This simulation corresponds to an Atmospheric Model Intercomparison Project (AMIP) run. For the future climate, we have performed the time-slice 25 year simulation corresponding to the end of the 21st century (2075–2099). The boundary SST data were prepared by superposing: (1) the trend in the multimodel ensemble (MME) of SST projected by CMIP3 multimodel data set, (2) future change in MME of SST and (3) the detrended observed SST anomalies for the period 1979–2003. Future change in MME of SST was evaluated by the difference between the 20th century simulations and future simulation under the Special Report on Emission Scenario (SRES) A1B emission scenario. It is noted that this MME SST future change pattern is El Niño–like in the tropical Pacific [IPCC, 2007]. Figure 1 describes a schematic diagram of the boundary SST setup for the time-slice experiment. The design retains observed year-to-year variability and El Niño–Southern Oscillation (ENSO) events in future climate, but with a higher mean and clear increasing trend in SST. Future sea-ice distribution is obtained in a similar fashion. Details of the method are described in the work of Mizuta et al. [2008]. The change in global annual mean 25 year averaged SST prescribed is 2.16°C.

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Figure 1. Schematic diagram of the estimation method for the future sea surface temperatures (SST). CGCM, coupled general circulation model.

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[8] To assess the uncertainty of climate change projections, we perform ensemble simulations with the 60 km resolution model. The experimental design is summarized in Table 1. Four different SSTs are used for future climate simulations by the 60 km mesh model. One experiment uses the CMIP3 model ensemble SST and sea-ice distributions as in the 20 km mesh model experiment. Second, third and fourth experiments use the SST anomalies of CSIRO-Mk3.0, MRI-CGCM2.3.2 and MIROC3.2 (hires) models. The change in global annual mean 25 year averaged SST prescribed is 1.43°C, 1.73°C and 3.49°C, respectively. The annual mean tropical SST anomaly pattern of the CMIP3 model ensemble SST, CSIRO SST and MRI SST is an El Niño–like pattern with the eastern equatorial SST rising more than the western equatorial SST, while the MIROC SST is a La Niña–like pattern. For each of the standard set of AMIP run and the CMIP3 SST anomaly run, three member simulations are performed with different initial conditions.

Table 1. Experimental Design
Date RangeGrid Size (km)Run NameaSea Surface TemperatureEnsemble Size
  • a

    First character of run name denotes horizontal resolution: S, superhigh (20 km); H, high (60 km); and L, low (180 km). Second character of run name denotes target period: P, present day; and F, future.

  • b

    Observational data by the Hadley Center of Met Office, UK [Rayner et al., 2003].

  • c

    Coupled Model Intercomparison Project, third phase.

  • d

    Commonwealth Scientific and Industrial Research Organisation, Atmospheric Research, Australia.

  • e

    Center for Climate System Research (University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change of Japan Agency for Marine-Earth Science and Technology, Japan.

  • f

    Meteorological Research Institute, Japan.

Present Day
1979–200320SPobservation HadISST1b1
1979–200360HPobservation HadISST13
1979–2003180LPobservation HadISST11
 
Future
2075–209920SFCMIP3c multimodel ensemble1
2075–209960HFCMIP3 multimodel ensemble3
2075–209960HF_CSIROCSIRO-MK3.0d3
2075–209960HF_MIROCMIROC3.2 (hires)e3
2075–209960HF_MRIMRI-CGCM2.3.2f3

2.3. Observed Data for Verification

[9] To verify the precipitation climatology in the present-day climate simulation (1979 to 2003, 25 years), four different observational data are used. The precipitation climatology of the first three observed data is used to verify the modeled geographical distribution of the seasonal mean precipitation, considering the available space of the paper. The first one is the Climate Prediction Center Merged Analysis of Precipitation (CMAP) compiled by Xie and Arkin [1997]. The monthly mean data without model-generated precipitation in the process of data assimilation (V0809_std) is used. The horizontal resolution is 2.5 degree in longitude and latitude. The data cover the whole period of the present-day climate simulation. This data set is widely used to verify the climate models. The second is Climate Research Unit 0.5 degree monthly climate time series known as CRU TS 2.1 [Mitchell and Jones, 2005]. This high-resolution version of the data set has 0.5 degree latitude and longitude grid covering the global land surface for the period 1901–2002. The 24 year (1979–2002) mean is constructed. The third one is the Tropical Rainfall Measuring Mission (TRMM) PR3A25 V6 data set for 11 years (1998–2008) on a 0.5 degree grid from 37.5°S to 37.5°N (TRMM 3A25; see Iguchi et al. [2000]). This is also the monthly mean data. These two high-resolution data are more appropriate to evaluate the small-scale structure of the 20 and 60 km mesh models than conventional 2.5 degree resolution data. In addition, we use one more data for the verification of the regional mean precipitation: the One-Degree Daily (1DD) data of Global Precipitation Climatology Project (GPCP) V1.1 compiled by Huffman et al. [2001]. Horizontal resolution is one degree in longitude and latitude. This data covers only 12 years from 1997 to 2008. Although the averaged period differs among the observation data used and some do not cover the entire period of the present-day simulation, these data is sufficient for model-data comparison in precipitation climatology.

3. Present-Day Simulation

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[10] The climatological seasonal mean precipitation reproduced in the present-day simulation is evaluated against available observed data. Figure 2 shows the geographical distribution of December–January–February (DJF) averaged 25 year (1979–2003) mean precipitation for the 180, 60 and 20 km mesh models. Three observed climatology, CMAP, CRU and TRMM 3A25, are also shown. Observations show large seasonal mean precipitation in austral summer over Amazon [Vera et al., 2006a]. The Intertropical Convergence Zone (ITCZ) over the tropical Atlantic, and the South Atlantic Convergence Zone (SACZ) to the southeastward of the Brazilian Plateau are also seen. Location of precipitation maximum over Amazon in TRMM 3A25 tends to locate northwest relative to CMAP and CRU data.

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Figure 2. Geographical distributions of December–January–February mean precipitation climatology (mm/d): (a) Climate Prediction Center Merged Analysis of Precipitation (CMAP), (b) Climate Research Unit (CRU), (c) Tropical Rainfall Measuring Mission (TRMM) 3A25, (d) 180 km model, (e) 60 km model, and (f) 20 km model. LP, low horizontal resolution, present day; HP, high horizontal resolution, present day; SP, superhigh horizontal resolution, present day.

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[11] The MRI AGCM reproduces general features of large precipitation over Amazon, ITCZ and SACZ. The model tends to have maximum precipitation located more northwestward compared to CMAP and CRU, but the mismatch of the location is smaller with the TRMM data. The model underestimates precipitation over the Brazilian Plateau compared to CMAP and CRU, but quantitative resemblance becomes better when compared to TRMM. There are no distinct differences in large-scale patterns of precipitation among three realizations with different horizontal resolutions. However, orographic precipitation is naturally well reproduced by higher-resolution model. The 180 km mesh model exaggerates the Amazon precipitation. Also the 20 km mesh model shows more precipitation over the Brazilian Plateau than the 60 and 180 km mesh models. The 20 and 60 km mesh models show large precipitations along the northern Andes, which is consistent with high-resolution TRMM data. Vera et al. [2006b] investigated the January–February–March mean precipitation fields for the present period of the 7 CMIP3 models. When compared to those 7 models, the high-resolution MRI AGCM better reproduced the SACZ and a maximum over the narrow southern Andes.

[12] Figure 3 shows the June–July–August (JJA) mean precipitation climatology of the three observed data and three model realizations. In this season, a major rain area moves northward, and large precipitation is found over the northern South America. It is very dry over northeast Brazil and southern Amazonia. Southeastern South America is covered with rainy area extending from the South Atlantic. According to Vera et al. [2006b], CMIP3 models are unable to reproduce this precipitation maximum over southeastern South America. The MRI AGCM reproduces this feature well, with most quantitative agreement in the 20 km mesh model. Another rainy area is located over southern Chile where westerly winds bring moisture. The higher-resolution model shows the large Andean precipitation, though this is not well resolved by coarse resolution observed data set.

image

Figure 3. Geographical distributions of June–July–August mean precipitation climatology (mm/d): (a) CMAP, (b) CRU, (c) TRMM 3A25, (d) 180 km model, (e) 60 km model, and (f) 20 km model. Abbreviations are the same as in Figure 2.

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4. Climate Change Projections

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

4.1. Seasonal Mean Geographical Patterns of Precipitation Changes

[13] In this section, projected changes at the end of the 21st century (2075–2099) compared to the present (1979–2003) are discussed. Figure 4 shows the seasonal mean precipitation changes between the present and the end of the 21st century for the 60 km mesh model ensembles and the 20 km mesh model. For the 60 km mesh model, results from the four different SST experiments are averaged. For each SST experiment, there are three realizations with different initial conditions (Table 1), so that a total of 12 members exist for 60 km mesh model data. The statistical significance is calculated by 3 (present) plus 12 (future) members. Each year is assumed independent. The degree of freedom is thus 373. The areas with significance greater than 95% are shaded in color. As another measure of robustness of the results, grid points where all four SST experiments (each is three member ensemble mean) agree in the sign of the change are marked with diagonal lines. For the 20 km mesh model, the differences with significance greater than 95% are shaded in color. The degree of freedom is 48. As will be shown later, the three member simulations with the 60 km mesh model yield mostly consistent responses, thus making more significant results in the 60 km mesh model than in the 20 km mesh model.

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Figure 4. Seasonal mean precipitation changes (mm/d) between the present and the end of the 21st century for 60 and 20 km mesh atmospheric general circulation models. (a) December–January–February (DJF) 60 km, (b) DJF 20 km, (c) March–April–May (MAM) 60 km, (d) MAM 20 km, (e) June–July–August (JJA) 60 km, (f) JJA 20 km, (g) September–October–November (SON) 60 km, and (h) SON 20 km. For the 60 km model, areas statistically significant at 95% level are colored, and areas where all four different sea surface temperature experiments show consistent changes in sign are hatched. For the 20 km model, areas statistically significant at 95% level are colored. Contour interval is 1 mm/d. ENS denotes ensemble mean.

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[14] In general, 20 km mesh model results are consistent with 60 km mesh model results. In austral summer (DJF), the both models show an increase in precipitation over all tropical and subtropical South America and a decrease over the Andes south of 20°S. In March–April–May (MAM), changes in precipitation are concentrated over the ITCZ and northwest Amazonia near the equator. Increased precipitation over Amazonia is largest in this season both for the 60 and 20 km mesh models. A decreased precipitation in southern Chile is much enhanced from DJF. In JJA, an area of increasing precipitation moves further to the north of the equator compared with the MAM case, and precipitation is projected to decrease over most of Amazon and southern South America. Southern Brazil may be a region of increased precipitation. In September–October–November (SON), an increase of precipitation over southeastern South America becomes more significant both in the 60 and 20 km mesh models, but changes over Amazon are not so systematic. The CMIP3 multimodel mean projections show that over the Amazon Basin, monsoon precipitation increases in DJF and decreases in JJA [IPCC, 2007], although model agreement is not high [Vera et al. 2006b]. This seasonal contrast over the Amazon Basin is also seen in our model projections. Most CMIP3 models project a wetter climate over the Parana River Basin [IPCC, 2007]. The 60 and 20 km mesh models show the increase in precipitation over the Parana River Basin in DJF and MAM, while the wetter region shifts northeast toward the upper Parana River Basin in JJA and SON. While the CMIP3 multimodel mean projections [IPCC, 2007] show a wide area of decreased precipitation over southern South America, the decrease over Chile and Patagonia is much concentrated windward side of the Andes in our model. This difference may be attributable to high-resolution nature of the current model such as sharp and high mountain ranges.

[15] In order to investigate possible reasons of regional changes in precipitation, atmospheric circulation changes are studied. As in other model studies, the Atlantic and Pacific subtropical anticyclones are intensified and a poleward shift of midlatitude westerlies are simulated in future climate both in the 20 and 60 km mesh models (not shown). Over the Amazon, westerly wind anomalies (i.e., weakened easterly winds) are simulated near the surface and in the lower troposphere from the equatorial Atlantic to the Amazon Basin (not shown). However, atmospheric moisture buildup due to rising temperature could result in larger moisture flux as found in the Indian monsoon region [Kitoh et al., 1997].

[16] Figure 5a shows the DJF mean vertically integrated moisture flux and its divergence for the 20 km mesh model present-day climate, while Figure 5b shows the difference between the present and the future. The vertically integrated zonal and meridional moisture flux is calculated at every time step, and then monthly mean value is stored. This vertically integrated moisture flux is dominated by the lower troposphere owing to abundance of moisture near the surface, and thus is representative of the lower tropospheric moisture fields. Large easterly moisture flux from the tropical Atlantic Ocean through ITCZ region into the Amazon is simulated in the present-day climate. This moisture flux hits the Andes and deviates to the south associated with the South American Low-Level Jet (SALLJ) with its maximum around 60°W, 20°S. This corresponds well to the observed analysis. As shown in Figure 5b, there is an increase in moisture flux convergence over Amazon. There are significant moisture flux changes into the Brazilian Plateau. Northerly moisture flux along the SALLJ significantly increased. Intensification of the SALLJ with the global warming is also reported by Soares and Marengo [2009], who found an intensified and more frequent SALLJ in their HadRM3P RCM results under the IPCC A2 and B2 scenarios in the future, transporting more moisture from the Amazonia to the Parana River Basin to result in more frequent and intense heavy rainfall events [Marengo et al., 2009].

image

Figure 5. (a) December–January–February (DJF) mean vertically integrated moisture flux vectors and its divergence in millimeters per day by the 20 km model. (b) Same as Figure 5a but for the difference between the future and the present. (c) Same as Figure 5a but for June–July–August (JJA). (d) Same as Figure 5b but for JJA.

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[17] The vertically integrated moisture flux and its divergence in JJA are shown in Figures 5c and 5d. In the present-day simulation, the southeasterly flux dominates in the tropical South America near the Atlantic coast, which transport moisture from south of the equator to north of the equator, with moisture flux divergence to the south and convergence to the north of the equator. This mainly contributes to the dry season in northeast Brazil and the southeastern Amazon. There are northerly moisture flux in Bolivia and Paraguay, with the SALLJ maximum around 60°W, 20°S, bringing about moisture into the Parana River Basin and southeastern South America. In the future, the difference pattern is almost similar to the present-day moisture flux and its convergence, thus enforcing the present-day characteristics. This suggests that the contribution from the moisture buildup dominates that from the circulation changes, thus enhancing the wet-dry contrast; that is, the wet region becomes wetter and the dry region becomes drier.

[18] Figures 68 show seasonal mean changes in evaporation, soil wetness at the uppermost layer of the model, and surface runoff between the present and the future for the 60 km mesh model ensembles and the 20 km mesh model. Owing to an overall temperature increase, the evaporation increases throughout a year almost all over South America (Figure 6). An exception is found in northeastern Brazil where evaporation is projected to decrease in the dry season (JJA and SON). This is associated with drier soil over that region (Figure 7). Drier soil is not restricted to northeastern Brazil, but is projected to occur over most of the continent except for northern and central Argentina. Soil dry-up is particularly large in the dry season. Even in the wet season (DJF), Amazon is expected to be much drier in the future at the uppermost layer of the soil. Eastern Brazil is expected to have wetter soil in DJF by the 60 km mesh model results. The soil wetness change in DJF by the 20 km mesh model over eastern Brazil is, however, not significant. Significant changes in the runoff by the 20 km mesh model are limited to some regions (Figure 8), but in those regions the 60 km mesh model also shows significant changes with the same sign with the 20 km mesh model. In SON and DJF, runoff in western Amazon will decrease while it increases in eastern and southern Brazil. In MAM, northwestern Amazonia experiences more runoff. Further analyses for regional hydrological changes are done in section 4.3.

image

Figure 6. Same as Figure 4, but for evaporation change (mm/d).

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Figure 7. Same as Figure 4, but for soil wetness change at the uppermost layer (%).

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Figure 8. Same as Figure 4, but for runoff change (mm/d).

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4.2. Precipitation Extremes

[19] Global warming would result in not only changes in mean precipitation but also increases in the amplitude and frequency of extreme precipitation events. Changes in extremes are more important for our adaptation to climate changes. Here two extreme indices for precipitation are used to illustrate changes in precipitation extremes over South America, one for heavy precipitation and one for dryness. The extremes indices are calculated for each year on the annual basis. Statistical significance is calculated using these annual values. Statistical significance is only plotted for the 60 km mesh model. Significance level for the 20 km mesh model is actually very low, and barely surpasses the 95% level over the South America land area on the grid point basis.

[20] Figure 9 shows the changes in maximum 5 day precipitation total (RX5D) for the 60 and 20 km mesh models. Almost all over South America except for Chile and some spotty areas, RX5D is projected to increase in the future warming world both in 20 and 60 km mesh models. Large RX5D increase is found over Amazonia, southern Brazil and northern Argentina. This is consistent with Kamiguchi et al. [2006], but is different from Marengo et al. [2009] where increased intensity of precipitation is found over southeastern South America and western Amazonia while a slight decrease or no change is projected in northeast Brazil and eastern Amazonia. The reason for the difference is not clear.

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Figure 9. Changes in maximum 5 day precipitation total (mm) between the present and the end of the 21st century for the (a) 60 km (b) 20 km mesh atmospheric general circulation models. For the 60 km model, areas where all 4 different sea surface temperature experiments show consistent changes in sign are hatched. For the 20 km model, statistical significance level is not shown. Zero lines are contoured.

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[21] It is noted that the 20 km mesh model projects larger increase in RX5D than the 60 km mesh model. However, this is only true in grid point scale. When averaged over the whole land area of Figure 9, RX5D increases from 99.0 to 112.9 mm/d in the 60 km model, while it is from 101.8 to 116.1 mm/d. Therefore the area average change itself is almost similar to each other (13.9 versus 14.3 mm/d), but the higher-resolution model shows the larger increase in concentrated areas. This implies the resolution dependency on extremely large precipitation and also the difficulty in quantitative assessment of those changes at the local climate level.

[22] Figure 10 shows the changes in maximum number of consecutive dry days (CDD) for the 60 and 20 km mesh models. Here “dry day” is defined as a day with precipitation less than 1 mm/d. Large CDD change is projected over the Brazilian Plateau, and northern Chile and Altiplano. Kamiguchi et al. [2006] analyzed future increases in CDD over the Brazilian Plateau using a former experiment data set with the 20 km mesh model. In the present-day simulation, some intermittent rain occurs in dry season (JJA). This intermittent rain in the dry season is verified by station data. In the present-day simulation, when the rain takes place in the dry season, equatorial easterly low-level winds from ITCZ brings about scattered clouds to the Brazilian Plateau. In the future climate, a weak Walker circulation associated with the El Niño–like SST changes and a weak Atlantic anticyclone contribute to the weakening of the equatorial easterly wind and the suppression of rainfall over land, and thus, this weak rainfall in the dry season vanishes, and thus longer CDD occurs.

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Figure 10. Same as Figure 9, but for consecutive dry days (day).

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4.3. Regional Average: Precipitation and Seasonal Cycle

[23] We have selected five domains for the regional analyses (Figure 11). Rather than defining the small local area, we keep a broad region as much as possible (most have 15 degree longitude by 15 degree latitude area). So some regions such as Patagonia include both the upstream and downstream sides of the westerlies across the Andes mountains with different climate regimes. This caveat can be remedied by looking into geographical figures in other sections.

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Figure 11. Definition of target regions for regional analyses of land-only precipitation. Amazon (AMZ), 72.5°W–57.5°W, 10°S–5°N; Nordeste (NOR), 50°W–35°W, 15°S–0°S; Parana (PAR), 63°W–48°W, 40°S–25°S; Andes (AND), 75°W–67°W, 40°S–15°S; Patagonia (PAT), 75°W–60°W, 55°S–40°S. The locations of the six gauging stations used in section 4.4 are also shown.

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[24] Figures 12, 14, 16, 18, and 20 show the DJF and JJA mean land-only precipitation averaged for these domains. In each figure, three observations (CMAP data, CRU 2.1 data and GPCP 1.0 degree data) are compared with the present-day simulations by the 20 and 60 km mesh models. For the future, the 20 and 60 km mesh model projections are plotted for the CMIP3 SST experiment, and 3 member 60 km mesh model data are plotted for three different SSTs used. A horizontal line denotes the ensemble mean of three individual SST experiments. Note that vertical scales are not the same for each figure. Generally speaking, the domain averaged precipitation of the three member ensemble simulations with different initial conditions by the 60 km mesh model does not show much difference as three crosses and a circle have almost same values. Second, the ensemble mean of three individual future SST experiments is close to the CMIP3 SST experiment results.

[25] Figures 13, 15, 17, 19, and 21 show the seasonal cycle of the monthly mean precipitation, evaporation and runoff averaged over these five selected domains. For the difference between the future and the present, both the 20 and 60 m mesh model results are shown. When the change is statistically significance at the 95% level in each month, solid circles and open circles are marked for the 20 and 60 km mesh models, respectively.

[26] In the Amazonia region (Figure 12), the models overestimate the observed DJF and JJA precipitation. It is noted that the 20 km mesh model more matches the observations than the 60 km mesh model. In the future, the DJF and JJA precipitation is projected to increase. The results are similar between the 20 and 60 km mesh models. Here in Amazon, scatter among four 60 km mesh realizations with different SSTs is largest compared with other four domains. The 60 km mesh model with the MRI SST shows the little change. The annual mean precipitation change shows an increase in northern Amazon, and a decrease in southern Amazon. As pointed out by Li et al. [2006], the SST distribution, such as El Niño–like or La Niña–like, affects atmospheric circulation and thus precipitation changes. Figure 13 shows that the future precipitation is projected to increase in the wet season and changes are smaller in the relatively dry season (July–September), thus increasing the seasonality in precipitation. There is a larger increase in April–May when there are precipitation maxima in the present. In this region, evaporation takes the maximum in the relatively dry season of August–October. In the future, evaporation will increase throughout the year owing to surface temperature increase. In Amazonia, precipitation exceeds evaporation in every month, and thus there is considerable runoff with its maximum in April–May and minimum in September–October. Future climate change will exaggerate this seasonal cycle in runoff. An increase in runoff is projected in this region during a first half-year (March–July), and a decrease during September–November.

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Figure 12. Amazon (AMZ, 72.5–57.5°W, 10°S–5°N) region average land-only precipitation during (a) December–January–February (DJF) and (b) June–July–August (JJA). Units are in millimeters per day. The left sides of Figures 12a and 12b show the present-day climate, and the right sides show future climate. Observational data are CMAP (2.5 degree, 1979–2003, 25 years), CRU 2.1 (0.5 degree, 1979–2002, 24 years), and GPCP 1DD (1.0 degree, 1997–2008, 12 years). Red diamonds show the 20 km model simulations. Black crosses show the 60 km model simulations. Green circles show the ensemble average of three 60 km model simulations with different initial condition. Horizontal lines at the top right show the average of all 9 ensemble simulations by the 60 km model forced with sea surface temperatures (SSTs) of CSIRO, MRI, and MIROC atmosphere-ocean general circulation models.

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Figure 13. (top) Seasonal cycle in (a) precipitation, (b) evaporation, and (c) runoff averaged for Amazonia (AMZ, 72.5°W–57.5°W, 10°S–5°N). Units are in millimeters per day. The horizontal axis shows month. Only the land grid points are used. Dashed lines show the present-day 20 km model. Solid lines show the future 20 km model. (bottom) Future minus present day for the 20 km (thick solid lines) and 60 km models (thin solid lines). Note the different ranges between Figures 13a, 13b, and 13c. Significance at the 95% level in each month is denoted by solid and open circles at the upper or lower end of the box for the 20 and the 60 km models, respectively.

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[27] In the Nordeste region (Figures 14 and 15), the models overestimated the precipitation in DJF, but well reproduced the JJA precipitation. In the future, the DJF precipitation will increase while the JJA precipitation will not change much. The results are similar between the 20 and 60 km mesh models. The model projects an increase in precipitation during the pre–wet season (January–February). Evaporation will decrease in a dry season (August–October) even with higher temperature due to decreased precipitation and drier soil conditions. The runoff will increase during the wet season.

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Figure 14. Same as Figure 12, but for Nordeste (NOR, 50°W–35°W, 15°S–0°S).

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Figure 15. Same as Figure 13, but for Nordeste (NOR, 50°W–35°W, 15°S–0°S).

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[28] In the Parana region (Figures 16 and 17), the models overestimated the precipitation in DJF and JJA compared to CMAP and GPCP, but in close agreement with the CRU data. In the future, the DJF precipitation will increase while the JJA precipitation will not change much. The results are similar between the 20 and 60 km mesh models. Here, all the precipitation, evaporation and runoff will increase in the future almost throughout the year, except for winter precipitation that does not show significant changes. An increase in precipitation and runoff during March–April and October–December is notable, thus contributing a prolonged wet season.

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Figure 16. Same as Figure 12, but for Parana (PAR, 63°W–48°W, 40°S–25°S).

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Figure 17. Same as Figure 13, but for Parana (PAR, 63°W–48°W, 40°S–25°S).

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[29] In the Andes region (Figures 18 and 19), the models overestimated the precipitation in DJF and JJA. In the future, the DJF precipitation will not change while the JJA precipitation will slightly decrease. The results are similar between the 20 and 60 km mesh models. The seasonal cycle in the present-day climate shows that there is a double peak in precipitation, one in January–February and the other in June–July. The model projects a notable decrease in this second peak (a decrease in May–June is significant). As the evaporation has a single peak in summer season (December–January–February), the runoff has a double peak of comparable magnitude. A winter runoff peak will decrease in future. As the change in evaporation is small, circulation changes mostly explain the precipitation changes.

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Figure 18. Same as Figure 12, but for Andes (AND, 75°W–67°W, 40°S–15°S).

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Figure 19. Same as Figure 13, but for Andes (AND, 75°W–67°W, 40°S–15°S).

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[30] In Patagonia (Figures 20 and 21), the models overestimated the precipitation in DJF and JJA. The MM5 simulations [Solman et al., 2008] also overestimate the austral winter maximum over this region. It is argued that this difference may be partly plausible due to insufficient observation network in this mountainous area. It is also noted that a steep Andes makes different climate regimes between the western side and the eastern side of the Andes, making these broad area averages obscure. This is also the case for the future projections. There is a clear annual cycle in precipitation with a maximum in winter. In this region, the 20 and 60 km mesh models contradict each other in precipitation changes in future in austral winter. The 20 km mesh model projects large increase in winter precipitation west of the Andes (Figure 4f), while the 60 km mesh model shows an overall decrease in this region (Figure 4e). The 60 km mesh model projects a decrease in precipitation during February–June, while the 20 km mesh model shows a decrease only in February–March. The MAM mean precipitation changes are mostly limited to the west of the Andes in the 20 km mesh model (Figure 4d), while the 60 km mesh model projects a decrease for the entire area (Figure 4c). This clearly illustrates the effect of horizontal resolution to the future climate projections.

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Figure 20. Same as Figure 12, but for Patagonia (PAT, 75°W–60°W, 55°S–40°S).

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Figure 21. Same as Figure 13, but for Patagonia (PAT, 75°W–60°W, 55°S–40°S).

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4.4. Streamflow

[31] We select 6 rivers to assess the future projection of climatological mean streamflow. Table 2 lists the characteristic features of the 6 rivers including gauging stations with basin areas.

Table 2. Selected Rivers and the Gauging Stations With Areas and Reproducibility of Annual Streamflow in the 20 km Mesh Present-Day Climate Simulation
RiverTotal Area (km2)StationLocation (Latitude, Longitude)Subarea (km2)Annual Streamflow Ratio (Simulated/Observed)
Orinoco945,000Puente Angostura8.15°N, 63.6°W836,0001.03
Amazon7,050,000Obidos1.90°S, 55.5°W4,640,3000.82
Tocantins757,000Tucuruí3.76°S, 49.67°W742,3000.80
Sao Francisco630,000Juazeiro9.42°S, 40.52°W510,8000.87
Parana3,100,000Posadas27.37°S, 55.88°W975,0000.61
Negro116,000Primera Angostura40.43°S, 63.67°W95,0000.17

[32] The annual streamflow is well reproduced in the 6 rivers except for the Parana and Negro Rivers (see Table 2). Both the river basins receive precipitation similar between the present-day climate simulation and in the observation (figure not shown). Evaporation, however, may be more overestimated than precipitation; therefore, streamflow as the residual is underestimated in the Parana and Negro Rivers.

[33] Figure 22b shows the percentage change in climatological annual streamflow between the present-day and future climates in the 20 km mesh simulations. The percentage change ratio in annual streamflow in the future climate projection shows some regional characteristics. It is projected that annual streamflow increases in the major stream region and some part of northwest Amazonia. There will be large decrease in annual streamflow in the southwestern Amazonia (75°W–60°W, 15°S–5°S). Increasing annual streamflow is projected in the central western part of the Parana River basin. Annual streamflow will decrease over southern Argentina including the Negro River basin. Indistinctive annual streamflow change at each gauging station is projected in the Orinoco, Tocantins, and Sao Francisco River; however some areas of these basins show statistically significant changes: increase in the upper basin areas of the Tocantins and Sao Francisco River, and decrease in the middle basin area of the Orinoco River. An overall spatial pattern of projected change in annual streamflow by the 20 km mesh model is similar to the CMIP3 multimodel analysis by Nohara et al. [2006], except for the western Amazonia where the 20 km mesh model shows a distinct difference south of 5°S and north of that latitude, while the CMIP3 multimodel analysis shows general increase in annual streamflow there.

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Figure 22. Percentage changes in streamflow between the present and the end of the 21st century for the (a) 60 and (b) 20 km mesh atmospheric general circulation models. For the 60 km model, areas where all 4 different sea surface temperature experiments show consistent changes in sign are hatched. Zero lines are contoured.

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[34] Most of the 60 km mesh model experiments agree on the change direction of the 20 km mesh model experiment in the future climate as shown in Figure 22. Poor agreement areas overlap the areas with no statistical significance or transition zone from one sign to another in Figure 22a. The contrast of annual streamflow change between southwest and northeast Amazonia is robust. So are increasing annual streamflow in the central western part of the Parana River basin and decreasing one in the Negro River basin. Nakaegawa and Vergara [2010] projected annual streamflow in the Magdalena River, Colombia, bordered by the west of the Orinoco River using the 20 km mesh model without quantitative robustness, and Figure 22b can add information that the signs of the changes in the basin are robust. Milly et al. [2005] presented similar results but with 24 CMIP3 multimodel analysis. An overall spatial pattern of high intermodel agreements on the change direction in the multimodel analysis is similar to that of Figure 22 except for the northeastern South America and small-scale features appearing in only our results. It is plausible that the horizontal resolution of the CMIP3 models is too coarse to resolve complex terrain in their simulation of streamflow and/or the multimodel ensemble mean process has diluted regional-scale characteristics in annual streamflow distribution. The high-spatial-resolution results presented here are crucial, since countermeasures to climatic hydrological changes highly depend on locations.

[35] Figure 23 shows the seasonal hydrographs for the six rivers in the present day and the future climates and the percent change in monthly streamflow. Seasonal hydrograph for each river is very well reproduced. Seasonal peak streamflow occurs in March–April in the Tocantins River, in May–June in the Amazon River, and in July–August in the Orinoco River. The seasonal variations of hydrographs for both the Parana and Negro Rivers are not so well captured, although this reproducibility is better than the reproducibility of annual streamflow. The amplitudes of hydrographs in the Tocantins, Sao Francisco and Parana Rivers are larger in the present-day climate simulation than in the observations. Juazeiro in the Sao Francisco River is located just after the Sobradinho Reservoir, the capacity of which corresponds to 2.5 year annual streamflow. A large reservoir, the Tucuruí Reservoir exists in the Tocantins River as well. The low (high) flow is controlled to increase (decrease) in the real world and therefore the amplitude of hydrograph is larger in the present-day climate simulation in Figures 23c and 23d and the peak of hydrograph is shifted. The Parana River has two massive reservoirs, Yacyretá and Itaipu Reservoirs, which reduce the amplitude of hydrograph artificially as well. Since the model simulation does not include any effects of artificial intervention by human such as dams, these differences are reasonable. In addition, the overestimation of precipitation or runoff in the present-day climate simulation may be a possible reason for these overestimations.

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Figure 23. Climatological monthly streamflow (m3/s) for the six rivers in the present-day (black) and future climates (red) and the percentage change in climatological monthly streamflow (blue). Long-term mean values (dotted line) with the range of interannual standard deviations (shadings) of streamflow at the gauging stations listed in Table 2 by the Global River Discharge Center are shown together. A blue circle denotes a statistically significant change at the 95% level. A black line with square shows consistent changes in sign for the four different sea surface temperature experiments.

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[36] Climatological monthly streamflow at Puente Angostura in the Orinoco River for July and August is projected to significantly increase, although the annual one is not so. The amplitude of the seasonal hydrograph is projected to increase at Obidos in the Amazon River, since the future streamflow increases in high-flow season and decreases in low-flow season, implying more floods in wet season and droughts in dry season. The peak month of the high-flow season is also seen to delay and high-flow season becomes longer. Increasing climatological monthly streamflow at Posadas in the Parana River for November and December is projected. No significant change in climatological monthly streamflow is projected in the Tocantins and Sao Francisco Rivers where no significant change in annual streamflow is done as well. These features for each river resemble those of runoff depicted in Figures 13, 15, 17, and 19 except for Figure 21 where the difference in runoff change between the 20 and 60 km mesh AGCM experiments is large.

[37] The robustness indicated in Figure 23 is consistent with the statistical significance. Statistically significant change in climatological monthly streamflow for the 20 km mesh AGCM experiment mostly corresponds to consistent changes in sign for all the four different SST experiments not vice versa. Therefore, the statistically significant changes for the 20 km mesh AGCM experiment are robust as well. The changes in climatological monthly streamflow in the Tocantins and Sao Francisco Rivers are not statistically significant, but the increase in high-flow season seems robust. Such information may be useful for impact assessment and decision making.

5. Summary and Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[38] We have used the 20 and 60 km mesh global atmospheric models, whose horizontal resolution is higher than or comparable to that of regional climate models applied to South American climate change projections. One of uncertainties in the climate change projections is the future change in SST. In order to tackle this problem, we used the CMIP3 multimodel ensemble of SST in the 20 km mesh model experiment. Although the global 20 km mesh model is a unique one in terms of its horizontal resolution for global change studies with multidecades integrations, the computer power is still unable to make ensemble simulations with such a super-high-resolution model. To cover this caveat, ensemble simulations with the 60 km resolution version of the same model have also been performed. In addition to initial condition ensembles, we performed the SST ensemble experiments with the 60 km mesh model. By investigating whether the geographical changes are consistent or not among four different SST ensemble experiments, we assessed the uncertainty of the projected results.

[39] Future climate change projections by the 20 and 60 km mesh models are mostly consistent each other in terms of the spatial pattern and seasonal changes of mean precipitation and its extremes. Both the 20 and 60 km mesh models project an increase in wet-season precipitation and a decrease in dry-season precipitation over most of the targeted regions. As evaporation increases due to temperature rise, runoff and streamflow may decrease in some region even precipitation increases. Heavy precipitation increases over Amazon, while consecutive dry days become longer over Brazilian highlands. Differences are found in the magnitude of extremely heavy precipitation index, with the 20 km mesh model projects much larger changes. However, an overall agreement is found between the 20 and 60 km mesh model results. The 60 km model results with different SSTs also gave us robust agreement on the future changes of the precipitation extremes. Annual mean runoff increases over equatorial Amazon and decreases over upper Amazon. Even where annual mean streamflow increases, dry-season streamflow may decreases over Amazon.

[40] It is well known that the interannual variability of the tropical circulation system is strongly influenced by ENSO, and thus future changes in ENSO will affect climate variability in this region. As the future change in ENSO is highly variable among the models and is very uncertain [IPCC, 2007], we assumed the same interannual variability of the SST in the future to that in the present in our experimental setup [Mizuta et al., 2008]. We believe that this approach is better until the reliable assessment on future ENSO changes become available. The CMIP3 models' ensemble mean SST in the future is an El Niño–like change in the tropical Pacific. In order to quantify the uncertainties of regional climate changes coming from these SST changes, the SST ensemble experiments were performed with the 60 km mesh model in this experiment.

[41] The multimodel approach has being widely used in global climate projections studies. For the regional climate modeling, a multi-GCMs/multi-RCMs approach is becoming feasible. Time-slice experiment approach with a high-resolution atmospheric GCM such as this study cannot adopt a multimodel approach, but at least a multiphysics approach can be pursued. We plan to do so with the 60 km mesh model with different model physics.

[42] In this experiment, we have specified the same vegetation types for the present and the future. There will a potential in land use and land cover changes particularly over Amazon by climate-vegetation feedback [Cox et al., 2004; Salazar et al., 2007]. Climate change will affect the distribution of vegetation into the future, and the changes in the distribution and characteristics of vegetation may influence climate. Assessment of such a possible climate-vegetation feedback was beyond the scope of this study.

[43] Last, in this study, we focused on the future projections in precipitation and its extremes, but future changes in temperature-related extremes are also important to our life, particularly for health. Previous studies suggest an increase of the extreme maximum temperature in the Amazon [Uchiyama et al., 2006] associated with a more severe dry season there. The high-resolution modeling demonstrated in this study paved a way not only to climate change projections but also to various impact assessment and adaptation works in all over the world.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

[44] The authors thank the reviewers for constructive comments. This work was conducted under the framework of the Projection of the Change in Future Weather Extremes Using Super-High-Resolution Atmospheric Models supported by the KAKUSHIN Program of the Ministry of Education, Culture, Sports, Science and Technology of Japan. The calculations were performed on the Earth Simulator of the Japan Agency for Marine-Earth Science and Technology.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model and Experiment
  5. 3. Present-Day Simulation
  6. 4. Climate Change Projections
  7. 5. Summary and Discussion
  8. Acknowledgments
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
jgrd16814-sup-0001-t01.txtplain text document1KTab-delimited Table 1.
jgrd16814-sup-0002-t02.txtplain text document1KTab-delimited Table 2.

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