Theoretical and Applied Climatology

, Volume 132, Issue 3–4, pp 867–883 | Cite as

Climatic change on the Gulf of Fonseca (Central America) using two-step statistical downscaling of CMIP5 model outputs

  • Jaime RibalayguaEmail author
  • Emma Gaitán
  • Javier Pórtoles
  • Robert Monjo
Original Paper


A two-step statistical downscaling method has been reviewed and adapted to simulate twenty-first-century climate projections for the Gulf of Fonseca (Central America, Pacific Coast) using Coupled Model Intercomparison Project (CMIP5) climate models. The downscaling methodology is adjusted after looking for good predictor fields for this area (where the geostrophic approximation fails and the real wind fields are the most applicable). The method’s performance for daily precipitation and maximum and minimum temperature is analysed and revealed suitable results for all variables. For instance, the method is able to simulate the characteristic cycle of the wet season for this area, which includes a mid-summer drought between two peaks. Future projections show a gradual temperature increase throughout the twenty-first century and a change in the features of the wet season (the first peak and mid-summer rainfall being reduced relative to the second peak, earlier onset of the wet season and a broader second peak).



This study was supported by the European Commission (EuropeAid) through the DCI-ENV/2010/256-823 project. We thank the CIDEA Institute (Universidad Centroamericana (UCA), Managua, Nicaragua, and the Institute for Hunger Studies ( for their support. We thank the Institute of Territorial Studies (INETER) of Nicaragua and the Department of Water Resources (DGRH/SERNA) of Honduras for the availability of meteorological station data and the National Climatic Data Center (NCDC) of the USA for providing the Global Summary of the Day (GSOD). We would like to acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 1) for producing their model output and making it available. We additionally thank the National Centres for Environmental Prediction, NCEP/NCAR (NOAA/OAR/ESRL PSD, Boulder, CO, USA) ( for offering the NCEP/NCAR reanalysis data. And our gratitude also goes to the International Centre for Tropical Agriculture (CIAT) and the CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS) for providing the PRECIS simulation data used (


  1. Atkinson GD (2002) Forecasters’ guide to tropical meteorology. University Press of the Pacific, HonoluluGoogle Scholar
  2. Benestad R, Hanssen-Bauer I, Chen D (2008) Empirical-statistical downscaling. World Scientific Publishers, SingaporeCrossRefGoogle Scholar
  3. Bentsen M, Bethke I, Debernard JB, Iversen T, Kirkevåg A, Seland Ø, Drange H, Roelandt C, Seierstad IA, Hoose C, Kristjánsson JE (2012) The Norwegian Earth System Model, NorESM1-M—part 1: description and basic evaluation. Geosci Model Dev Discuss 5:2843–2931. doi: 10.5194/gmdd-5-2843-2012 CrossRefGoogle Scholar
  4. Chylek P, Li J, Dubey MK, Wang M, Lesins G (2011) Observed and model simulated 20th century arctic temperature variability: Canadian Earth System Model CanESM2. Atmos Chem Phys Discuss 11:22893–22907. doi: 10.5194/acpd-11-22893-2011 CrossRefGoogle Scholar
  5. Collins WJ, Bellouin N, Doutriaux-Boucher M, Gedney N, Hinton T, Jones CD, Liddicoat S, Martin G, O’Connor F, Rae J, Senior C, Totterdell I, Woodward S, Reichler T, Kim J, Halloran P (2008) Evaluation of the HadGEM2 model. Hadley Centre Technical Note HCTN, vol 74. Met Office Hadley Centre, Exeter, UKGoogle Scholar
  6. Cook J, Nuccitelli D, Green SA, Richardson M, Winkler B, Painting R, Way R, Jacobs P, Skuce A (2013) Quantifying the consensus on anthropogenic global warming in the scientific literature. Environ Res Lett 8:024024. doi: 10.1088/1748-9326/8/2/024024 CrossRefGoogle Scholar
  7. Diffenbaugh N, Giorgi F (2012) Climate change hotspots in the CMIP5 global climate model ensemble. Clim Chang 114:813–822. doi: 10.1007/s10584-012-0570-x CrossRefGoogle Scholar
  8. Dunne JP, John JG, Adcroft AJ, Griffies SM, Hallberg RW, Shevliakova E, Stouffer RJ, Cooke W, Dunne KA, Harrison MJ, Krasting JP, Malyshev SL, Milly PCD, Phillipps PJ, Sentman LT, Samuels BL, Spelman MJ, Winton M, Wittenberg AT, Zadeh N (2012) GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J Clim 25:6646–6665. doi: 10.1175/JCLI-D-11-00560.1 CrossRefGoogle Scholar
  9. Fuentes-Franco R, Giorgi F, Coppola E, Pavia EG, Diro GT, Graef F (2014) 21st century projections of summer precipitation over Mexico and Central America from the Phase I CORDEX RegCM hyper-matrix simulations. VAMOS/CORDEX Workshop on Latin-America and Caribbean, 7th–9th April 2014, Santo Domingo, Dominican Republic. Link: Accessed 1 Jan 2017
  10. Gao X, Schlosser CA, Xie P, Monier E, Entekhabi D (2014) An analogue approach to identify heavy precipitation events: evaluation and application to CMIP5 climate models in the United States. J Clim 27:5941–5963. doi: 10.1175/JCLI-D-13-00598.1 CrossRefGoogle Scholar
  11. Giorgi F (2006) Climate change hot-spots. Geophys Res Lett 33:L08707. doi: 10.1029/2006GL025734 CrossRefGoogle Scholar
  12. Giorgi F, Mearns L (1991) Approaches to the simulation of regional climate change—a review. Rev Geophys 29:191–216CrossRefGoogle Scholar
  13. Giorgi F, Coppola E, Solmon F, Mariotti L, Sylla MB, Bi X, Elguindi N, Diro GT, Nair V, Giuliani G, Turuncoglu U, Cozzini S, Güttler I, O’Brien TA, Tawfik AB, Shalaby A, Zakey AS, Steiner AL, Stordal F, Sloan LC, Brankovic C (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7–29CrossRefGoogle Scholar
  14. Goodess CM, Anagnostopoulou C, Bárdossy A, Frei C, Harpham C, Haylock MR, Hundecha Y, Maheras P, Ribalaygua J, Schmidli, J., Schmith T, Tolika K, Tomozeiu R, Wilby RL (2011) An intercomparison of statistical downscaling methods for Europe and European regions—assessing their performance with respect to extreme temperature and precipitation events. Climate Research Unit Research Publication, vol 11. University of East Anglia, UK. Link: Accessed 1 Jan 2017
  15. Heavens NG, Ward DS, Natalie MM (2013) Studying and projecting climate change with earth system models. Nat Educ Knowl 4:4Google Scholar
  16. Hidalgo HG, Amador JA, Alfaro EJ, Quesada B (2013) Hydrological climate change projections for Central America. J Hydrol 495:94–112. doi: 10.1016/j.jhydrol.2013.05.004 CrossRefGoogle Scholar
  17. Holton J (2004) An introduction to dynamic meteorology, 4th edn. Academic Press, New York 535 ppGoogle Scholar
  18. Imbert A, Benestad R (2005) An improvement of analog model strategy for more reliable local climate change scenarios. Theor Appl Climatol 82:245–255. doi: 10.1007/s00704-005-0133-4 CrossRefGoogle Scholar
  19. IPCC (2007) In: Solomon S, Qin D, Manning 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, CambridgeGoogle Scholar
  20. IPCC (2013) In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate Change 2013: the Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. doi: 10.1017/CBO9781107415324 Google Scholar
  21. Iversen T, Bentsen M, Bethke I, Debernard JB, Kirkevåg A, Seland Ø, Drange H, Kristjánsson JE, Medhaug I, Sand M, Seierstad IA (2012) The Norwegian Earth System Model, NorESM1-M—part 2: climate response and scenario projections. Geosci Model Dev Discuss 5:2933–2998. doi: 10.5194/gmdd-5-2933-2012 CrossRefGoogle Scholar
  22. Jones R, Noguer M, Hassell D, Hudson D, Wilson S, Jenkins G, Mitchell J (2004) Generating high resolution climate change scenarios using PRECIS. Met Office Hadley Centre, Exeter, UK. Data: Link: Accessed 1 Jan 2017
  23. Jones CD, Hughes JK, Bellouin N, Hardiman SC, Jones GS, Knight J, Liddicoat S, O’Connor FM, Andres RJ, Bell C, Boo K-O, Bozzo A, Butchart N, Cadule P, Corbin KD, Doutriaux-Boucher M, Friedlingstein P, Gornall J, Gray L, Halloran PR, Hurtt G, Ingram WJ, Lamarque J-F, Law RM, Meinshausen M, Osprey S, Palin EJ, Parsons Chini L, Raddatz T, Sanderson MG, Sellar AA, Schurer A, Valdes P, Wood N, Woodward S, Yoshioka M, Zerroukat M (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4:543–570. doi: 10.5194/gmd-4-543-2011 CrossRefGoogle Scholar
  24. Kallache M, Vrac M, Naveau P, Michelangeli P-A (2011) Nonstationary probabilistic downscaling of extreme precipitation. J Geophys Res 116:D05113. doi: 10.1029/2010JD014892 CrossRefGoogle Scholar
  25. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–470CrossRefGoogle Scholar
  26. Karnauskas KB, Seager R (2012) The American Midsummer Drought in CMIP5: multi-model evaluation and projections. AGU Fall Meeting 2012. Link: Accessed 1 Jan 2017
  27. Magaña V, Amador JA, Medina S (1999) The midsummer drought over Mexico and Central America. J Clim 12:1577–1588. doi: 10.1175/1520-0442(1999)012<1577:TMDOMA>2.0.CO;2 CrossRefGoogle Scholar
  28. Maloney ED, Camargo SJ, Chang E, Colle B, Fu R, Geil KL, Hu Q, Jiang X, Johnson N, Karnauskas KB, Kinter J, Kirtman B, Kumar S, Langenbrunner B, Lombardo K, Long LN, Mariotti A, Meyerson JE, Mo MC, Neelin JD, Pan Z, Seager R, Serra Y, Seth A, Sheffield J, Stroeve J, Thibeault J, Xie S-P, Wang C, Wyman B, Zhao M (2014) North American climate in CMIP5 experiments: part III: assessment of 21st century projections. J Clim 27:2230–2270. doi: 10.1175/JCLI-D-13-00273.1 CrossRefGoogle Scholar
  29. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003. doi: 10.1029/2009RG000314 CrossRefGoogle Scholar
  30. Marsaglia G, Tsang WW, Wang J (2003) Evaluating Kolmogorov’s distribution. J Stat Softw 8:18Google Scholar
  31. Marsland SJ, Haak H, Jungclaus JH, Latif M, Roeske F (2003) The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model 5:91–127CrossRefGoogle Scholar
  32. Monjo R, Pórtoles J, Ribalaygua J (2013) Detection of inhomogeneities in daily data: a test based in the Kolmogorov-Smirnov goodness-of-fit test. 9th Data Management Workshop of EUMETNET, El Escorial (Madrid), 6th–8th NovemberGoogle Scholar
  33. Monjo R, Gaitán E, Pórtoles J, Ribalaygua J, Torres L (2016) Changes in extreme precipitation over Spain using statistical downscaling of CMIP5 projections. Int J Climatol 36:757–769. doi: 10.1002/joc.4380 CrossRefGoogle Scholar
  34. Moss RH, Edmonds JA et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756. doi: 10.1038/nature08823 CrossRefGoogle Scholar
  35. Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12:2256–2284CrossRefGoogle Scholar
  36. Peralta-Hernández AR, Baba-Martínez LR, Magaña-Rueda VO, Matthias AD, Luna-Ruíz JJ (2008) Temporal and spatial behavior of temperature and precipitation during the canícula (midsummer drought) under El Niño conditions in central México. Atmósfera 21:265–280Google Scholar
  37. R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3–900051–07-0, URL Accessed 1 Jan 2017
  38. Raddatz TJ, Reick CH, Knorr W, Kattge J, Roeckner E, Schnur R, Schnitzler K-G, Wetzel P, Jungclaus J (2007) Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty first century? Clim Dyn 29:565–574. doi: 10.1007/s00382-007-0247-8 CrossRefGoogle Scholar
  39. Ramirez-Villegas J, Jarvis A (2010) Downscaling global circulation model outputs: the delta method decision and policy analysis working paper no.1. policy analysis. Agricultura Eco-Eficiente para Reducir la Pobreza. Institute Center for Tropical Agriculture (CIAT). Link: Accessed 1 Jan 2017
  40. Ribalaygua J, Torres L, Pórtoles J, Monjo R, Gaitán E, Pino MR (2013) Description and validation of a two-step analogue/regression downscaling method. Theor Appl Climatol 114:253–269. doi: 10.1007/s00704-013-0836-x CrossRefGoogle Scholar
  41. Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 2. Future climate projections. J Geophys Res, Atmosferes 118:2473–2493. doi: 10.1002/jgrd.50188 CrossRefGoogle Scholar
  42. Small RJO, De Szoeke SP, Xie SP (2007) The Central American midsummer drought: regional aspects and large-scale forcing. J Clim 20:4853–4873. doi: 10.1175/JCLI4261.1 CrossRefGoogle Scholar
  43. STARDEX (2005) STARDEX Final Report-Downscaling Climate Exremes, European Union. Accessed 1 Jan 2017
  44. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. doi: 10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  45. Valiela I, Bowen JL, York JK (2001) Mangrove forests: one of the world’s threatened major tropical environments. Bioscience 51(10):807–815. doi: 10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2 CrossRefGoogle Scholar
  46. van der Linden P, Mitchell JFB (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, UKGoogle Scholar
  47. Voldoire A, Sanchez-Gomez E, Salas y Mélia D, Decharme B, Cassou C, Sénési S, Valcke S, Beau I, Alias A, Chevallier M, Déqué M, Deshayes J, Douville H, Fernandez E, Madec G, Maisonnave E, Moine M-P, Planton S, Saint-Martin D, Szopa S, Tyteca S, Alkama R, Belamari S, Braun A, Coquart L, Chauvin F (2013) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40:2091–2121. doi: 10.1007/s00382-011-1259-y CrossRefGoogle Scholar
  48. Wang B, Zhou T, Yu Y (2009) A view of earth system model development. Acta Meteorologica Sinica 23:1–17Google Scholar
  49. Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H, Nozawa T, Kawase H, Abe M, Yokohata T, Ise T, Sato H, Kato E, Takata K, Emori S, Kawamiya M (2011) MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci Model Dev 4:845–872. doi: 10.5194/gmd-4-845-2011 CrossRefGoogle Scholar
  50. Wilby R, Charles S, Zorita E, Timbal B, Whetton P, Mearns L (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change, DDC TGCIA-IPCC, vol 27Google Scholar
  51. Wilks DS (2005) Statistical methods in the atmospheric sciences, International geophysics series, vol 91. Academic Press, San DiegoGoogle Scholar
  52. Xiao-Ge X, Tong-Wen W, Jie Z (2013) Introduction of CMIP5 experiments carried out with the climate system models of Beijing Climate Center. Adv Clim Chang Res 4:41–49. doi: 10.3724/SP.J.1248.2013.041 CrossRefGoogle Scholar
  53. Yukimoto S, Yoshimura H, Hosaka M, Sakami T, Tsujino H, Hirabara M, Tanaka TY, Deushi M, Obata A, Nakano H, Adachi Y, Shindo E, Yabu S, Ose T and Kitoh A (2011) Meteorological research institute-earth system model v1 (MRI-ESM1)—model description. Technical Report of MRI, vol 64Google Scholar
  54. Zorita E, von Storch H (1999) The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 12:2474–2489CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  1. 1.Climate Research Foundation (FIC)MadridSpain

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