This study identifies possible hotspots of climate change in South America through an examination of the spatial pattern of the Regional Climate Change Index (RCCI) over the region by the end of the twenty-first century. The RCCI is a qualitative index that can synthesize a large number of climate model projections, and it is suitable for identifying those regions where climate change could be more pronounced in a warmer climate. The reliability and uncertainties of the results are evaluated by using numerous state-of-the-art general circulation models (GCMs) and forcing scenarios from the Coupled Model Intercomparison Project phases 3 and 5. The results show that southern Amazonia and the central-western region and western portion of Minas Gerais state in Brazil are persistent climate change hotspots through different forcing scenarios and GCM datasets. In general, as the scenarios vary from low- to high-level forcing, the area of high values of RCCI increase and the magnitude intensify from central-western and southeast Brazil to northwest South America. In general, the climatic hotspots identified in this study are characterized by an increase of mean surface air temperature, mainly in the austral winter; by an increase of interannual temperature variability, predominantly in the austral summer; and by a change in the mean and interannual variability of precipitation during the austral winter.
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The word “hotspots” is used in this study to indicate areas with large regional climate changes.
Baettig MB, Wild M, Imboden DM (2007) A climate change index: where climate change may be most prominent in the 21st century. Geophys Res Lett 34, L01705
Blázquez J, Nuñez MN (2013) Analysis of uncertainties in future climate projections for South America: comparison of WCRP-CMIP3 and WCRP-CMIP5 models. Clim Dyn 41:1039–1056
Bombardi RJ, Carvalho LMV (2009) IPCC global coupled climate model simulations of the South America monsoon system. Clim Dyn 33:893–916
Carvalho LMV, Jones C (2013) CMIP5 simulations of low-level tropospheric temperature and moisture over tropical Americas. J Clim 26:6257–6286
Diffenbaugh NS, Giorgi F, Raymond L, Bi X (2007) Indicators of 21st century socioclimatic exposure. Proc Natl Acad Sci U S A 104:20195–20198
Diffenbaugh NS, Giorgi F, Pal JS (2008) Climate change hotspots in the United States. Geophys Res Lett 35, L16709
Diffenbaugh NS, Giorgi F (2012) Climate change hotspots in the CMIP5 global climate model ensemble. Clim Chang 114:813–822
Donat MG, Alexander LV, Yang H, Durre I, Vose R, Dunn RJH, Willett KM, Aguilar E, Brunet M, Caesar J, Hewitson B, Jack C, Klein Tank AMG, Kruger AC, Marengo JA, Peterson TC, Renom M, Oria Rojas C, Rusticucci M, Salinger J, Elrayah AS, Sekele SS, Srivastava AK, Trewin B, Villarroel C, Vincent LA, Zhai P, Zhang X, Kitching S (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. J Geophys Res Atmos 118(5):2098–2118
Giorgi F (2005) Climate change prediction. Clim Chang 73:239–265
Giorgi F, Bi XQ (2005) Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations. Geophys Res Lett 32, L21715
Giorgi F (2006) Climate change hot-spots. Geophys Res Lett 33, L08707
Herzog SK, Martínez R, Jorgensen PM, Tiessen H (2011). Climate change and biodiversity in the tropical Andes. Inter-American Institute for Global Change Research (IAI) and Scientific Committee on Problems of the Environment (SCOPE), 348 pp. http://www.iai.int/index.php?option=com_content&view=article&id=118&Itemid=73. Accessed 15 June 2013
IPCC (2007) Summary for policymakers. In: Solomon S, Qin D, Mamming M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) 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
IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, 582 pp.
Jones PW (1999) First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon Weather Rev 127:2204–2210
Jones C, Carvalho LMV (2013) Climate change in the South American monsoon system: present climate and CMIP5 projections. J Clim 26(17):6660–6678
Knutti R, Allen MR, Friedlingstein P, Gregory JM, Hegerl GC, Meehl GA, Meinshausen M, Murphy JM, Plattner GK, Raper SCB, Stocker TF, Stott PA, Teng H, Wigley TLM (2008) A review of uncertainties in global temperature projections over the twenty first century. J Clim 21:2651–2663
Knutti R, Sedlácek J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Chang 3:369–373
Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199
Li J, Lin X, Chen A, Peterson T, Ma K, Bertzky M, Ciais P, Kapos V, Peng C, Poulter B (2013) Global priority conservation areas in the face of 21st century climate change. PLoS ONE 8(1):e54839. doi:10.1371/journal.pone.0054839
Liebmann B, Marengo JA (2001) Interannual variability of the rainy season and rainfall in the Brazilian Amazon Basin. J Clim 14(22):4308–4318
Rao VB, Lima MC, Franchito SH (1993) Seasonal and interannual variations of rainfall over eastern northeast Brazil. J Clim 6(9):1754–1763
May W (2012) Assessing the strength of regional changes in near-surface climate associated with a global warming of 2°C. Clim Chang 110(3–4):619–644
Marengo JA, Ambrizzi T, Rocha RP, Alves LM, Cuadra SV, Valverde M, Ferraz SET, Torres RR, Santos DC (2010a) Future change of climate in South America in the late XXI century: intercomparison of scenarios from three regional climate models. Clim Dyn 35:1073–1097
Marengo JA, Rusticucci M, Penalba O, Renom M (2010b) An intercomparison of observed and simulated extreme rainfall and temperature events during the last half of the twentieth century. Part 2: historical trends. Clim Chang 98:509–529
Marengo JA, Nobre CA, Chou SH, Tomasella J, Sampaio G, Alves LM, Obregón GO, Soares WR, Betts R, Kay G (2011) Dangerous climate change. A Brazil–UK analysis of climate change and deforestation impacts in the Amazon. 55 pp. Available at http://mudancasclimaticas.cptec.inpe.br/~rmclima/pdfs/destaques/relatorio_ingl.pdf Accessed 13 April 2013
Marengo JA, Chou SC, Kay G, Alves LM, Pesquero JF, Soares WR, Santos DC, Lyra AA, Sueiro G, Betts R, Chagas DJ, Gomes JL, Bustamante JF, Tavares P (2012) Development of regional future climate change scenarios in South America using the Eta CPTEC/HadCM3 climate change projections: climatology and regional analyses for the Amazon, São Francisco and the Parana River Basins. Clim Dyn 38(9–10):1829–1848
Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394
Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Willbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756
Nakicenovic N, Alcamo J, Davis G, De Vries B, Fenhann J, Gaffin S, Gregory K, Grubler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner HH, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, Van Rooijen S, Victor N, Dadi Z (2000) Special report on emissions scenarios. Cambridge University Press, Cambridge
Preston BL, Yuen EJ, Westaway RM (2011) Putting vulnerability to climate change on the map: a review of approaches, benefits, and risks. Sustain Sci 6:177–202
Räisänen J (2002) CO2-induced changes in interannual temperature and precipitation variability in 19 CMIP2 experiments. J Clim 15:2395–2411
Rusticucci M, Marengo JA, Penalba O, Renom M (2010) An intercomparison of observed and simulated extreme rainfall and temperature events during the last half of the twentieth century. Part 1: mean values and variability. Clim Chang 98:493–508
Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013a) Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res 118:1716–1733
Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013b) Climate extremes indices in the CMIP5 multimodel ensemble: part 2 Future climate projections. J Geophys Res 118:2473–2493
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498
Tebaldi C, Hayhoe K, Arblaster JM, Meehl G (2006) Going to the extremes. An intercomparison of model-simulated historical and future changes in extreme events. Clim Chang 79:185–211
Torres RR, Lapola DM, Marengo JA, Lombardo MA (2012) Socio-climatic hotspots in Brazil. Clim Chang 115:597–609
Torres RR, Marengo JA (2013) Uncertainty assessments of climate change projections over South America. Theor Appl Climatol 112:253–272
van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31
Xu Y, Xuejie G, Giorgi F (2009) Regional variability of climate change hot-spots in East Asia. Adv Atmos Sci 26(4):783–792
We are thankful to two anonymous reviewers that provided suggestions that considerably improved the quality of the manuscript. The first author was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and by Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). Additional support was provided by the Amazonia Security Agenda Project led by CIAT and GCP and funded by the Climate and Development Knowledge Network, by the projects Rede-CLIMA, the National Institute of Science and Technology for Climate Change (INCT-CC), from the FAPESP-Assessment of Impacts and Vulnerability to Climate Change in Brazil and Strategies for Adaptation Options project (Ref. 2008/581611). We also thank the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for making the CMIP3 and CMIP5 multi-model dataset available.
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Torres, R.R., Marengo, J.A. Climate change hotspots over South America: from CMIP3 to CMIP5 multi-model datasets. Theor Appl Climatol 117, 579–587 (2014). https://doi.org/10.1007/s00704-013-1030-x
- Austral Summer
- Representative Concentration Pathway
- Austral Winter
- Force Scenario
- Regional Climate Change Index