Abstract
This study evaluates the ability of 19 models of CMIP phase 6 (CMIP6) to simulate Paraguay’s climate features. Historical multi-member simulations of single models and their multi-model ensembles are bias-corrected and evaluated with statistical metrics. Future projections of precipitation and temperature are generated with the ensembles for three integrated scenarios of socio-economic development and greenhouse gas emissions (SSP1–2.6, SSP2–4.5, and SSP5–8.5).
The 19 models simulate well the observed mean temperature. The bias-corrected multi-model ensemble reaches the highest skill scores and accurately reproduces the mean spatial field and annual cycle. The bias-corrected multi-model ensemble of precipitation represents the annual cycle weakly, missing the sharp onset and decay of the South American Monsoon. Some individual models and the multi-model ensemble correctly reproduce the west-east gradient, although they underestimate its pronounced spatial variability.
Ensembles of future simulations project that by 2100, the annual mean temperature will increase for the three scenarios. On average, the increases are almost 1.7 °C in the sustainable development and low emissions scenario (SSP1–2.6), 3 °C in the middle-of-the-road development and medium emissions scenario (SSP2–4.5), and 5.5 °C in the fossil-fueled development and high emissions scenario (SSP5–8.5). Models project a slight decrease in annual precipitation towards the northwest (less than 50 mm) and an increase towards the southeast (more than 200 mm). Paraguay’s humid eastern part is projected to have a small growth in temperature and an increase in precipitation. In contrast, the western arid Chaco region would experience a substantial increase in temperature, while rainfall would slightly decrease.
This is a preview of subscription content,
to check access.










Similar content being viewed by others
Data availability
CRU TS 4.03 dataset is available at https://doi.org/10.5285/10d3e3640f004c578403419aac167d82 (University of East Anglia Climatic Research Unit 2020). CMIP6 multi-model dataset (Eyring et al. 2016) is available at https://esgf-node.llnl.gov/projects/cmip6/.
References
Allen MR, de Coninck H, Dube OP et al (2018) Technical Summary. In: Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D et al. (eds) Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty
Aparicio-Effen M, Arana I, Aparicio J, Cortez P, Coronel G, Pastén M, Nagy J, Galeano Rojas A, Flores L, Bidegain M (2016) Introducing hydro-climatic extremes and human impacts in Bolivia, Paraguay and Uruguay. In: Leal Filho W, Azeiteiro U, Alves F (eds) Climate change and health. Climate Change Management. Springer, Cham. https://doi.org/10.1007/978-3-319-24660-4_26
Barros V, Doyle M, Camilloni I (2008) Precipitation trends in southeastern South America: relationship with ENSO phases and with low-level circulation. Theor Appl Climatol 93:19–33. https://doi.org/10.1007/s00704-007-0329-x
Beck H, Zimmermann N, McVicar T et al (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5:180214. https://doi.org/10.1038/sdata.2018.214
Boada M, Saurí D (2002) The global change (in Spanish). Rubes, Barcelona, 143 pp
Caldas M, Goodin D, Sherwood S, Campos Krauer JM, Wisely SM (2015) Land-cover change in the Paraguayan Chaco: 2000–2011. J Land Use Sci 10(1):1–18. https://doi.org/10.1080/1747423X.2013.807314
Carril AF, Cavalcanti IFA, Menéndez CG et al (2016) Extreme events in the La Plata basin: a retrospective analysis of what we have learned during CLARIS-LPB project. Clim Res 68:95–116. https://doi.org/10.3354/cr01374
Carvalho LMV, Cavalcanti IFA (2016) The South American Monsoon System (SAMS). In: de Carvalho L, Jones C (eds) The monsoons and climate change. Springer Climate. Springer, Cham. https://doi.org/10.1007/978-3-319-21650-8_6
Cavalcanti IFA, Carril AF, Penalba OC et al (2015) Precipitation extremes over La Plata Basin – review and new results from observations and climate simulations. J Hydrol 523:211–230. https://doi.org/10.1016/j.jhydrol.2015.01.028
Coronel G, Pastén M, Báez J, Monte Domecq R, Bidegain M, Nagy GJ (2015) Improving capacities and communication on climate threats for water resources adaptation in Paraguay. In: Leal W (ed) Handbook of climate change adaptation. Springer, Berlin. https://doi.org/10.1007/978-3-642-38670-1_113
de Souza Custodio M, da Rocha RP, Ambrizzi T et al (2017) Impact of increased horizontal resolution in coupled and atmosphere-only models of the HadGEM1 family upon the climate patterns of South America. Clim Dyn 48:3341–3364. https://doi.org/10.1007/s00382-016-3271-8
Déqué M (2012) Continuous variables. In: Jolliffe IT, Stephenson DV (eds) Forecast verification – a practitioner’s guide in atmospheric science, 2nd edn. John Wiley and Sons, Chichester, pp 97–120
Deser C, Lehner F, Rodgers KB et al (2020) Insights from earth system model initial-condition large ensembles and future prospects. Nat Clim Chang 10:277–286. https://doi.org/10.1038/s41558-020-0731-2
Dirección General de Estadística, Encuestas y Censos (DGEEC) de la Secretaría Técnica de Planificación de la Presidencia de la República (2015) Paraguay: Proyección de la Población Nacional, Áreas Urbana y Rural, por Sexo y Edad, 2000-2025 (In Spanish). https://www.dgeec.gov.py/Publicaciones/Biblioteca/proyeccion%20nacional/Estimacion%20y%20proyeccion%20Nacional.pdf. Accessed 18 May 2020
Diaz L, Saurral R, Vera C (2020) Assessment of South America summer rainfall climatology and trends in a set of global climate models large ensembles. Int J Climatol. https://doi.org/10.1002/joc.6643
Donat MG, Sillmann J, Fischer EM (2020) Changes in climate extremes in observations and climate model simulations. From the past to the future. In: Sillmann J, Sippel S, Russo S (eds) Climate extremes and their implications for impact and risk assessment. Elsevier, Amsterdam, pp 31–57. https://doi.org/10.1016/b978-0-12-814895-2.00003-3
Economic Commission for Latin America and the Caribbean (ECLAC) (2014). La economía del Cambio Climático en el Paraguay (In Spanish). Santiago de Chile: United Nation. LC/W.617, 108 pp. Available at: https://www.cepal.org/es/publicaciones/37101-la-economia-cambio-climatico-paraguay. Accessed 19 May 2020
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Food and Agriculture Organization of the United Nations (FAO) (2020). FAO crop statistics. http://www.fao.org/faostat/en/#data/QC/visualize. Accessed 15 May 2020
Fischer EM, Beyerle U, Knutti R (2013) Robust spatially aggregated projections of climate extremes. Nat Clim Chang 3:1033–1038. https://doi.org/10.1038/nclimate2051
Flato G, Marotzke J, Abiodun B et al (2013) Evaluation of climate models. In Climate Change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker TF, Qin D, Plattner G-K et al (eds). Cambridge University Press: Cambridge, UK/New York, NY
Grimm AM (2019) South American Monsoon and its extremes. In: Sukhatme J, Murtugudde R, Roca R (eds) Venugopal V. Tropical Extremes, Natural Variability and Trends, Elsevier Inc, pp 51–93. https://doi.org/10.1016/B978-0-12-809248-4.00003-0
Grose MR, Narsey S, Delage FP et al (2020) Insights from CMIP6 for Australia's future climate. Earth's Future 8:e2019EF001469. https://doi.org/10.1029/2019EF001469
Gulizia C, Camilloni I (2015) Comparative analysis of the ability of a set of CMIP3 and CMIP5 global climate models to represent precipitation in South America. Int J Climatol 35:583–595. https://doi.org/10.1002/joc.4005
Gusain A, Ghosh S, Karmakar S (2019) Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmos Res 232:104680. https://doi.org/10.1016/j.atmosres.2019.104680
Harris I, Osborn TJ, Jones P, Lister D (2020) Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7:109. https://doi.org/10.1038/s41597-020-0453-3
Investigación para el Desarrollo (ID) - Centro para el Desarrollo de la Investigación Científica (CEDIC) (2016) Evaluación de la vulnerabilidad y la capacidad para enfrentar a los desafíos y oportunidades del Cambio Climático en Paraguay (In Spanish). Conacyt, Programa Paraguayo para el Desarrollo de la Ciencia y Tecnología. Asunción, Paraguay. https://www.conacyt.gov.py/sites/default/files/upload_editores/u294/evaluacion-vulnerabilidad-desafios-oportunidades-cambio-climatico-Paraguay.pdf. Accessed 5 May 2020
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 et al (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 pp.
IPCC (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change [Core writing team, Pachauri RK, Meyer LA (eds.)] IPCC, Geneva, Switzerland, 151 pp.
IPCC (2018) Summary for Policymakers. In: Masson-Delmotte V, Zhai P, Pörtner H-O et al (eds) Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty
Kumar D, Kodra E, Ganguly A (2014) Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation. Clim Dyn 43(9–10):2491–2518. https://doi.org/10.1007/s00382-014-2070-3
Lehner F, Deser C, Maher N, Marotzke J, Fischer EM, Brunner L, Knutti R, Hawkins E (2020) Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst Dynam 11:491–508. https://doi.org/10.5194/esd-11-491-2020
Lovino MA, Müller O, Berbery EH, Müller G (2018a) How have daily climate extremes changed in the recent past over northeastern Argentina? Glob Planet Chang 168:78–97. https://doi.org/10.1016/j.gloplacha.2018.06.008
Lovino MA, Müller OV, Müller GV, Sgroi LC, Baethgen WE (2018b) Interannual-to-multidecadal hydroclimate variability and its sectoral impacts in northeastern Argentina. Hydrol Earth Syst Sci 22:3155–3174. https://doi.org/10.5194/hess-22-3155-2018
Lovino MA, Müller OV, Berbery EH, Müller GV (2018c) Evaluation of CMIP5 retrospective simulations of temperature and precipitation in northeastern Argentina. Int J Climatol 38:e1158–e1175. https://doi.org/10.1002/joc.5441
Maenza RA, Agosta EA, Bettolli ML (2017) Climate change and precipitation variability over the western ‘pampas’ in Argentina. Int J Climatol 37:445–463. https://doi.org/10.1002/joc.5014
Magrin GO, Marengo JA, Boulanger J-P et al (2014) Central and South America. In: Barros VR, Field CB, Dokken DJ et al (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part B: regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp. 1499–1566
Maraun D, Wetterhall F, Ireson AM et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003. https://doi.org/10.1029/2009RG000314
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans Am Soc Agric Biol Eng 50(3):885–900. https://doi.org/10.13031/2013.23153
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part I A discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6
O'Neill BC, Tebaldi C, van Vuuren DP et al (2016) The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci Model Dev 9:3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
O'Neill BC, Kriegler E, Ebi KL et al (2017) The roads ahead: narratives for shared socio-economic pathways describing world futures in the 21st century. Glob Environ Chang 42:169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004
Penalba OC, Rivera JA (2016a) Precipitation response to El Niño/La Niña events in Southern South America – emphasis in regional drought occurrences. Adv Geosci 42:1–14. https://doi.org/10.5194/adgeo-42-1-2016
Penalba OC, Rivera JA (2016b) Regional aspects of future precipitation and meteorological drought characteristics over southern South America projected by a CMIP5 multimodel ensemble. Int J Climatol 36(2):974–986. https://doi.org/10.1002/joc.4398
Riahi K, van Vuuren DP, Kriegler E et al (2016) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environ Chang 42:153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
Rivera JA, Arnould G (2020) Evaluation of the ability of CMIP6 models to simulate precipitation over Southwestern South America: climatic features and long-term trends (1901–2014). Atmos Res 241:104953. https://doi.org/10.1016/j.atmosres.2020.104953
Seager R, Naik N, Baethgen W, Robertson A, Kushnir Y, Nakamura J, Jurburg S (2010) Tropical oceanic causes of interannual to multidecadal precipitation variability in Southeast South America over the past century. J Clim 23:5517–5539. https://doi.org/10.1175/2010JCLI3578.1
Sgroi L, Lovino M, Berbery EH, Müller GV (2020) Characteristics of droughts in Argentina's Core crop region. Hydrol Earth Syst Sci Discuss, https://doi.org/10.5194/hess-2020-236 (In review)
Sibson R (1981) A brief description of natural neighbor interpolation. In: Barnett V (ed) Interpreting Multivariate Data. Wiley, New York, pp 21–36
Skansi MM, Brunet M, Sigró J et al (2013) Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America. Glob Planet Chang 100:295–307. https://doi.org/10.1016/j.gloplacha.2012.11.004
Taylor K (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192. https://doi.org/10.1029/2000jd900719
Taylor K, Stouffer RJ, Meehl G (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. https://doi.org/10.1175/bams-d-11-00094.1
Tebaldi C, O'Neill BC (2020) Climate scenarios and their relevance and implications for impact studies. In: Sippel S, Russo S (eds) Sillmann J. Elsevier, Climate Extremes and Their Implications for Impact and Risk Assessment, pp 11–29. https://doi.org/10.1016/b978-0-12-814895-2.00002-1
University of East Anglia Climatic Research Unit, Harris, I.C., Jones, P.D., 2020. CRU TS4.03: Climatic research unit (CRU) time-series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018). Centre for environmental data analysis, 22 January 2020. https://doi.org/10.5285/10d3e3640f004c578403419aac167d82
Uribe Botero E (2015) El cambio climático y sus efectos en la biodiversidad en América Latina (In Spanish). LC/W.693, Naciones Unidas, Santiago, Chile. https://repositorio.cepal.org/bitstream/handle/11362/39855/S1501295_en.pdf?sequence=1. Accessed 15 May 2020
United States Department of Agriculture Foreign Agricultural Service (USDA) (2020) Livestock and Poultry: World Markets and Trade. https://apps.fas.usda.gov/psdonline/circulars/livestock_poultry.pdf. Accessed 25 May 2020
van Vuuren DP, Edmonds J, Kainuma M et al (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31. https://doi.org/10.1007/s10584-011-0148-z
Vannière B, Demory M, Vidale PL et al (2019) Multi-model evaluation of the sensitivity of the global energy budget and hydrological cycle to resolution. Clim Dyn 52:6817–6846. https://doi.org/10.1007/s00382-018-4547-y
Wang B, Biasutti M, Byrne M et al (2020) Monsoons climate change assessment. Bull Amer Meteor Soc. https://doi.org/10.1175/BAMS-D-19-0335.1
World Bank (2015) Análisis de riesgo del sector Agropecuario en Paraguay, Identificación, priorización, estrategia y plan de acción (In Spanish). World Bank Document. http://documentos.bancomundial.org/curated/es/105821468332711721/pdf/928660WP0SPANI00Box385339B00PUBLIC0.pdf. Accessed 26 April 2020
Zazulie N, Rusticucci M, Raga GB (2017) Regional climate of the subtropical Central Andes using high-resolution CMIP5 models. Part I: past performance (1980-2005). Clim Dyn 49(11−12):3937–3957. https://doi.org/10.1007/s00382-017-3560-x
Acknowledgments
We are grateful to the anonymous reviewers whose constructive comments and recommendations helped improve the manuscript. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.
Code availability
Not applicable.
Funding
This research was carried out with the support of Projects IO-2017-00254 and IO-2017-00 of the Ministry of Science, Technology, and Productive Innovation of Santa Fe (Argentina). Dr. Berbery was supported by NOAA grant NA19NES4320002 (Cooperative Institute for Satellite Earth System Studies—CISESS) at the University of Maryland/ESSIC.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
ESM 1
(DOCX 47 kb)
Rights and permissions
About this article
Cite this article
Lovino, M.A., Pierrestegui, M.J., Müller, O.V. et al. Evaluation of historical CMIP6 model simulations and future projections of temperature and precipitation in Paraguay. Climatic Change 164, 46 (2021). https://doi.org/10.1007/s10584-021-03012-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10584-021-03012-4