Surrogate Climate Change Scenario and Projections with a Regional Climate Model: Impact on the Aridity in South America
ABSTRACT
The impact of global warming on the aridity in South America (SA) is investigated. For this purpose, the methodology for generating surrogate climate-change scenarios with a RCM is employed. For the present climate (CTRL) the RCM is initialized with and driven by ECMWF/ERA-Interim reanalysis data. Two aridity indices are used: the Budyko and the UNEP indices. The results for the CTR are in agreement with other model studies which indicate future warming; rainfall increases in southeastern South America, Ecuador and Peru and decreases in the central and eastern Amazon. In general the model reproduces the aridity in the continent compared with the observed data for both indices. The distribution of aridity over SA in surrogate climate-change scenario shows an increase of the dryness in the continent. Over Amazonia the aridity increases 23.9% (for the UNEP index) and 3.1% (for the Budyko index), suggesting that portions of the Amazonia forest are replaced by dry land area. The semi-arid zone over northeast Brazil expands westward, attaining the interior of north Brazil. In this region the aridity increases 20% (for the UNEP index) and 0.6% (for the Budyko index) indicating that areas of humid regime may be occupied by areas with dry land regime. The RCM was also integrated driven by the AOGCM ECHAM5/MPI-OM for the reference climate (CTRL2) and under A1B SRES scenario. The results for the present-day climate are similar in CTRL2 and CTRL, and are in agreement with CRU data. The distribution of the aridity for the present climate seems to be better represented in CTRL using both Budyko and UNEP indices. The changes in aridity (future climate minus control) are higher in the run forced by the A1B SRES scenario. Although the UNEP and Budyko indices show potentialities and limitations to represent the aridity distribution over SA, the changes in aridity due to a pseudo-scenario of global warming are higher using the UNEP index.
The impact of global warming on the aridity in South America (SA) is investigated. For this purpose, the methodology for generating surrogate climate-change scenarios with a RCM is employed. For the present climate (CTRL) the RCM is initialized with and driven by ECMWF/ERA-Interim reanalysis data. Two aridity indices are used: the Budyko and the UNEP indices. The results for the CTR are in agreement with other model studies which indicate future warming; rainfall increases in southeastern South America, Ecuador and Peru and decreases in the central and eastern Amazon. In general the model reproduces the aridity in the continent compared with the observed data for both indices. The distribution of aridity over SA in surrogate climate-change scenario shows an increase of the dryness in the continent. Over Amazonia the aridity increases 23.9% (for the UNEP index) and 3.1% (for the Budyko index), suggesting that portions of the Amazonia forest are replaced by dry land area. The semi-arid zone over northeast Brazil expands westward, attaining the interior of north Brazil. In this region the aridity increases 20% (for the UNEP index) and 0.6% (for the Budyko index) indicating that areas of humid regime may be occupied by areas with dry land regime. The RCM was also integrated driven by the AOGCM ECHAM5/MPI-OM for the reference climate (CTRL2) and under A1B SRES scenario. The results for the present-day climate are similar in CTRL2 and CTRL, and are in agreement with CRU data. The distribution of the aridity for the present climate seems to be better represented in CTRL using both Budyko and UNEP indices. The changes in aridity (future climate minus control) are higher in the run forced by the A1B SRES scenario. Although the UNEP and Budyko indices show potentialities and limitations to represent the aridity distribution over SA, the changes in aridity due to a pseudo-scenario of global warming are higher using the UNEP index.
Cite this paper
Franchito, S. , Fernandez, J. and Pareja, D. (2014) Surrogate Climate Change Scenario and Projections with a Regional Climate Model: Impact on the Aridity in South America. American Journal of Climate Change, 3, 474-489. doi: 10.4236/ajcc.2014.35041.
Franchito, S. , Fernandez, J. and Pareja, D. (2014) Surrogate Climate Change Scenario and Projections with a Regional Climate Model: Impact on the Aridity in South America. American Journal of Climate Change, 3, 474-489. doi: 10.4236/ajcc.2014.35041.
References
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http://dx.doi.org/10.1029/2009GL041801 [13] Winter, J.M. and Eltahir, E.A.B. (2012) Modeling the Hydroclimatology of the Midwestern United States. Part 2: Future Climate. Climate Dynamics, 38, 595-611.
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http://dx.doi.org/10.1175/BAMS-88-9-1395 [20] Grell, G.A., Dudhia, J. and Stauffer, D.R. (1994) A Description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5). National Center for Atmospheric Research Tech Note NCAR/TN-398 +STR, NCAR, Boulder. [21] daRocha, R.P., Cuadra, S.V., Reboita, M.S., Kruger, L.F., Ambrizzi, T. and Krusche, N. (2012) Effects of RegCM3 Parameterizations on Simulated Rainy Season over South America. Climate Research, 52, 253-265.
http://dx.doi.org/10.3354/cr01065 [22] Giorgi, F., Jones, C. and Asrar, G. (2009) Addressing Climate Information Needs at the Regional Level: The CORDEX Framework. WMO Bulletin, 58, 175-183. [23] Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011) The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System. Quarterly Journal of the Royal Meteorological Society, 137, 553-597.
http://dx.doi.org/10.1002/qj.828 [24] Reynolds, R.W., Rayner, N.A., Smith, T.M., Stokes, D.C., Diane, C. and Wang, W. (2002) An Improved in Situ and Satellite SST Analysis for Climate. Journal of Climate, 15, 1609-1625.
http://dx.doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2 [25] Randall, D.A., Wood, R.A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., et al. (2007) Climate Models and Their Evaluation. In: Climate Change 2007: The Physical Sciences Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge. [26] Arora, V.K. (2002) The Use of the Aridity Index to Assess Climate Change Effect on Annual Runoff. Journal of Hydrology, 265, 164-177.
http://dx.doi.org/10.1016/S0022-1694(02)00101-4 [27] Sun, Y.I., Yan, X.D. and Xie, D.T. (2006) Analyzing Vegetation-Climate Interactions in China Based on Budyko’s Indices. Resources Science, 28, 23-29. (In Chinese with English Abstract) [28] Gao, X.J. and Giorgi, F. (2008) Increased Aridity in the Mediterranean Region under Greenhouse Gas Forcing Estimated from High Resolution Simulations with a Regional Climate Model. Global and Planetary Change, 62, 195-209.
http://dx.doi.org/10.1016/j.gloplacha.2008.02.002 [29] Thornthwaite, C.W. (1948) An Approach toward a Rational Classification of Climate. Geographical Review, 38, 55-94.
http://dx.doi.org/10.2307/210739 [30] Gupta, S.K., Stackhouse, P.W., Cox, S.J., Mikovitz, J.C. and Zhang, T. (2006) 22-Year Surface Radiation Budget Data Set. GEWEX News, 16, 12-13. [31] New, M., Hulme, M. and Jones, P. (1999) Representing Twentieth Century Space-Time Climate Variability. Part 1: Development of a 1961-90 Mean Monthly Terrestrial Climatology. Journal of Climate, 12, 829-856.
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http://dx.doi.org/10.1002/joc.1800 [33] Urrutia, R. and Vuille, M. (2009) Climate Change Projections for the Tropical Andes Using a Regional Climate Model: Temperature and Precipitation Simulations for the End of the 21st Century. Journal of Geophysical Research, 114, Article ID: D02108.
http://dx.doi.org/10.1029/2008JD011021 [34] Marengo, J.A., Ambrizzi, T., da Rocha, R.P., Alves, M.L., Cuadra, S.V., Valverde, M.C., Torres, R.R., Santos, D.C. and Ferraz, S.E.T. (2010) Future Change of Climate in South America in the Late Twenty-First Century: Intercomparison of Scenarios from Three Regional Climate Models. Climate Dynamics, 35, 1073-1097.
http://dx.doi.org/10.1007/s00382-009-0721-6 [35] Meehl, G.A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.F.B., Stouffer, R.J. and Taylor, K.E. (2007) The WCRP CMIP3 Multimodel Data Set: A New Era in Climate Change Research. Bulletin of the American Meteorological Society, 88, 1383-1394.
http://dx.doi.org/10.1175/BAMS-88-9-1383 [36] Jungclaus, J.H., Botzet, M., Haak, H., Keenlyside, N., Luo, J.J., Latif, M., Marotzke, J., Mikolajewicz, U. and Roeckner, E. (2006) Ocean Circulation and Tropical Variability in the Coupled Model ECHAM5/MPI-OM. Journal of Climate, 19, 3952-3972.
http://dx.doi.org/10.1175/JCLI3827.1 [37] van Oldenborgh, G.J., Philip, S.Y. and Collins, M. (2005) El Nino in a Changing Climate: A Multi-Model Study. Ocean Science, 1, 81-95.
http://dx.doi.org/10.5194/os-1-81-2005 [38] Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., Gaffin, S., et al. (2000) Special Report on Emissions Scenarios. Intergovernmental Panel on Climate Change Tech. Rep., 570.
[1] Meehl, G.A., Stocker, T.F., Collins, W.D., Friedlingstein, A.T., Gaye, A.T., Gregory, J.M., Kitoh, A., Knutti, R., Murphy, J.M., Noda, A., Raper, S.C.B., Watterson, I.G., Weaver, A.J. and Zhao, Z. (2007) Global Climate Projections. In: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 747-845. [2] Franchito, S.H., Rao, V.B. and Moraes, E.C. (2011) Impact of Global Warming on the Geobotanic Zones: An Experiment with a Statistical-Dynamical Climate Model. Climate Dynamics, 37, 2011-2034.
http://dx.doi.org/10.1007/s00382-010-0952-6 [3] Cox, P.M., Betts, R.A., Collins, M., Harris, P.P., Huntingford, C. and Jones, C.D. (2004) Amazonian Forest Dieback under Climate-Carbon Cycle Projections for the 21st Century. Theoretical and Applied Climatology, 78, 137-156.
http://dx.doi.org/10.1007/s00704-004-0049-4 [4] Betts, R.A., Cox, P.M., Collins, M., Harris, P.P., Huntingford, C. and Jones, C.D. (2004) The Role of Ecosystem-Atmosphere Interactions in Simulated Amazonian Precipitation Decrease and Forest Dieback under Global Climate Warming. Theoretical and Applied Climatology, 78, 157-175.
http://dx.doi.org/10.1007/s00704-004-0050-y [5] Cook, K.H. and Vizy, K.H. (2008) Effects of Twenty-First-Century Climate Change on the Amazon Rain Forest. Journal of Climate, 21, 542-560.
http://dx.doi.org/10.1175/2007JCLI1838.1 [6] Vergara, W. and Scholz, S.M. (2010) Assessment of the Risk of Amazon Dieback. The World Bank, Washington, D.C., 96. [7] da Silva, R.R., Werth, D. and Avissar, R. (2008) Regional Impacts of Future Land-Cover on the Amazon Basin Wet-Season Climate. Journal of Climate, 21, 1153-1170.
http://dx.doi.org/10.1175/2007JCLI1304.1 [8] Oyama, M.D. and Nobre, C.A. (2003) A New Climate-Vegetation Equilibrium State for the Tropical South America. Geophysical Research Letters, 30, 2199.
http://dx.doi.org/10.1029/2003GL018600 [9] Salazar, L.F., Nobre, C.A. and Oyama, M.D. (2007) Climate Change Consequences on the Biome Distribution in Tropical South America. Geophysical Research Letters, 34, L09708.
http://dx.doi.org/10.1029/2007GL029695 [10] Schar, C., Christoph, F., Lutthi, D. and Davies, H.C. (1996) Surrogate Climate-Change Scenarios for Regional Climate Models. Geophysical Research Letters, 23, 669-672.
http://dx.doi.org/10.1029/96GL00265 [11] Seneviratne, S., Pal, J., Eltahir, E. and Schar, C. (2002) Summer Dryness in a Warmer Climate: A Process Study with a Regional Climate Model. Climate Dynamics, 20, 69-85.
http://dx.doi.org/10.1007/s00382-002-0258-4 [12] Im, E., Coppola, E., Giorgi, F. and Bi, X. (2010) Local Effects of Climate Change over the Alpine Region: A Study with a High Resolution Regional Climate Model with a Surrogate Climate Change Scenario. Geophysical Research Letters, 37, Article ID: L05704.
http://dx.doi.org/10.1029/2009GL041801 [13] Winter, J.M. and Eltahir, E.A.B. (2012) Modeling the Hydroclimatology of the Midwestern United States. Part 2: Future Climate. Climate Dynamics, 38, 595-611.
http://dx.doi.org/10.1007/s00382-011-1183-1 [14] Budyko, M.I. (1958) The Heat Balance of the Earth’s Surface. Translated by N. A. Stepanova, U.S. Department of Commerce, Washington DC, 259 p. [15] UNEP (1992) World Atlas of Desertification. Edward Arnold, London. [16] Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M.B., Bi, X., et al. (2012) RegCM4: Model Description and Preliminary Tests over Multiple CORDEX Domains. Climate Research, 52, 7-29.
http://dx.doi.org/10.3354/cr01018 [17] Giorgi, F., Marinucci, M.R. and Bates, G.T. (1993) Development of a Second-Generation Regional Climate Model (RegCM2). Part I: Boundary-Layer and Radiative Transfer Processes. Monthly Weather Review, 121, 2794-2813.
http://dx.doi.org/10.1175/1520-0493(1993)121<2794:DOASGR>2.0.CO;2 [18] Giorgi, F., Marinucci, M.R., Bates, G.T. and De Canio, G. (1993) Development of a Second-Generation Regional Climate Model (RegCM2). Part II: Convective Processes and Assimilation of Lateral Boundary Conditions. Monthly Weather Review, 121, 2814-2832.
http://dx.doi.org/10.1175/1520-0493(1993)121<2814:DOASGR>2.0.CO;2 [19] Pal, J.S., Giorgi, F., Bi, X.Q., Elguindi, N., Solmon, F., Rauscher, S.A., et al. (2007) Regional Climate Modeling for the Developing World: The ITCP RegCM3 and RegNET. Bulletin of the American Meteorological Society, 88, 1395-1409.
http://dx.doi.org/10.1175/BAMS-88-9-1395 [20] Grell, G.A., Dudhia, J. and Stauffer, D.R. (1994) A Description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5). National Center for Atmospheric Research Tech Note NCAR/TN-398 +STR, NCAR, Boulder. [21] daRocha, R.P., Cuadra, S.V., Reboita, M.S., Kruger, L.F., Ambrizzi, T. and Krusche, N. (2012) Effects of RegCM3 Parameterizations on Simulated Rainy Season over South America. Climate Research, 52, 253-265.
http://dx.doi.org/10.3354/cr01065 [22] Giorgi, F., Jones, C. and Asrar, G. (2009) Addressing Climate Information Needs at the Regional Level: The CORDEX Framework. WMO Bulletin, 58, 175-183. [23] Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011) The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System. Quarterly Journal of the Royal Meteorological Society, 137, 553-597.
http://dx.doi.org/10.1002/qj.828 [24] Reynolds, R.W., Rayner, N.A., Smith, T.M., Stokes, D.C., Diane, C. and Wang, W. (2002) An Improved in Situ and Satellite SST Analysis for Climate. Journal of Climate, 15, 1609-1625.
http://dx.doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2 [25] Randall, D.A., Wood, R.A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., et al. (2007) Climate Models and Their Evaluation. In: Climate Change 2007: The Physical Sciences Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge. [26] Arora, V.K. (2002) The Use of the Aridity Index to Assess Climate Change Effect on Annual Runoff. Journal of Hydrology, 265, 164-177.
http://dx.doi.org/10.1016/S0022-1694(02)00101-4 [27] Sun, Y.I., Yan, X.D. and Xie, D.T. (2006) Analyzing Vegetation-Climate Interactions in China Based on Budyko’s Indices. Resources Science, 28, 23-29. (In Chinese with English Abstract) [28] Gao, X.J. and Giorgi, F. (2008) Increased Aridity in the Mediterranean Region under Greenhouse Gas Forcing Estimated from High Resolution Simulations with a Regional Climate Model. Global and Planetary Change, 62, 195-209.
http://dx.doi.org/10.1016/j.gloplacha.2008.02.002 [29] Thornthwaite, C.W. (1948) An Approach toward a Rational Classification of Climate. Geographical Review, 38, 55-94.
http://dx.doi.org/10.2307/210739 [30] Gupta, S.K., Stackhouse, P.W., Cox, S.J., Mikovitz, J.C. and Zhang, T. (2006) 22-Year Surface Radiation Budget Data Set. GEWEX News, 16, 12-13. [31] New, M., Hulme, M. and Jones, P. (1999) Representing Twentieth Century Space-Time Climate Variability. Part 1: Development of a 1961-90 Mean Monthly Terrestrial Climatology. Journal of Climate, 12, 829-856.
http://dx.doi.org/10.1175/1520-0442(1999)012<0829:RTCSTC>2.0.CO;2 [32] Soares, W. and Marengo, J.A. (2009) Assessments of Moisture Fluxes East of the Andes in South America in a Global Warming Scenario. International Journal of Climatology, 29, 1395-1414.
http://dx.doi.org/10.1002/joc.1800 [33] Urrutia, R. and Vuille, M. (2009) Climate Change Projections for the Tropical Andes Using a Regional Climate Model: Temperature and Precipitation Simulations for the End of the 21st Century. Journal of Geophysical Research, 114, Article ID: D02108.
http://dx.doi.org/10.1029/2008JD011021 [34] Marengo, J.A., Ambrizzi, T., da Rocha, R.P., Alves, M.L., Cuadra, S.V., Valverde, M.C., Torres, R.R., Santos, D.C. and Ferraz, S.E.T. (2010) Future Change of Climate in South America in the Late Twenty-First Century: Intercomparison of Scenarios from Three Regional Climate Models. Climate Dynamics, 35, 1073-1097.
http://dx.doi.org/10.1007/s00382-009-0721-6 [35] Meehl, G.A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.F.B., Stouffer, R.J. and Taylor, K.E. (2007) The WCRP CMIP3 Multimodel Data Set: A New Era in Climate Change Research. Bulletin of the American Meteorological Society, 88, 1383-1394.
http://dx.doi.org/10.1175/BAMS-88-9-1383 [36] Jungclaus, J.H., Botzet, M., Haak, H., Keenlyside, N., Luo, J.J., Latif, M., Marotzke, J., Mikolajewicz, U. and Roeckner, E. (2006) Ocean Circulation and Tropical Variability in the Coupled Model ECHAM5/MPI-OM. Journal of Climate, 19, 3952-3972.
http://dx.doi.org/10.1175/JCLI3827.1 [37] van Oldenborgh, G.J., Philip, S.Y. and Collins, M. (2005) El Nino in a Changing Climate: A Multi-Model Study. Ocean Science, 1, 81-95.
http://dx.doi.org/10.5194/os-1-81-2005 [38] Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., Gaffin, S., et al. (2000) Special Report on Emissions Scenarios. Intergovernmental Panel on Climate Change Tech. Rep., 570.