Climatic Change

, Volume 148, Issue 1–2, pp 235–247 | Cite as

Maps of wind hazard over South Eastern South America considering climate change

  • L. Augusto SanabriaEmail author
  • Andrea F. Carril


Wind is one of the most dangerous natural phenomena for the built environment in South Eastern South America. The hazard posed by wind depends on the extreme wind speeds on the surface and can be quantified by calculating the Average Recurrence Interval—more commonly known as return period—of these winds. Maps of return period of extreme wind speeds are used by planning authorities to enforce appropriate standards for infrastructure construction in most countries of the world. These maps are usually built up from wind speeds recorded at a network of weather stations. In some countries, however, the quality of the records is poor or the stations have not been in operation for long enough to give appropriate data for wind hazard studies. In this paper, we discuss an alternative approach based on wind speeds calculated by climate models. The approach provides longer datasets and facilitates assessment of the impact of climate change on wind hazard, a matter of great of importance for planning and emergency authorities. Map quality is evaluated by comparing results from the climate simulations with results from reanalysis. The comparison shows that the climate simulations produce results close enough to the reanalysis and hence they can be used for wind hazard assessment. The results also show that we could expect little variation in wind hazard in South Eastern South America during most of this century.



We acknowledge the CLARIS-LPB project for providing the data from the regional climate models. The authors wish to thank our colleague G. Pita (Johns Hopkins University, USA) and three anonymous referees for their comments and helpful advice to improve the paper.

Funding information

This research was supported by ANPCyT (PICT-2015-3097 and PICT-2014-0887) and CONICET (PIP-112-2015-0100402CO). The stage of L.A. Sanabria in Argentina was supported by the program “Stays for foreign researchers and/or experts during sabbatical periods,” CONICET, Argentina. Thanks to our colleague Bruno Natalini (UNNE) for developing the application.

Supplementary material

10584_2018_2174_MOESM1_ESM.docx (4.2 mb)
ESM 1 (DOCX 4296 kb)


  1. Bao X, Zhang F (2013) Evaluation of NCEP–CFSR, NCEP–NCAR, ERA-interim, and ERA-40 reanalysis datasets against independent sounding observations over the Tibetan Plateau. J Clim 26:206–214CrossRefGoogle Scholar
  2. Boulanger JP, Brasseur G, Carril AF et al (2010) A Europe–South America network for climate change assessment and impact studies. Clim Chang 98:307–329. CrossRefGoogle Scholar
  3. Boulanger JP, Carril AF, Sanchez E (2016) CLARIS-La Plata Basin: regional hydroclimate variability, uncertainties and climate change scenarios. Clim Res 68:93–94. CrossRefGoogle Scholar
  4. Brower MC (2013) A study of wind speed variability using global reanalysis data. AWS Truepower, LLC, AlbanyGoogle Scholar
  5. Carril AF, Menéndez CG et al (2012) Performance of a multi-RCM ensemble for South Eastern South America. Clim Dyn 39:2747–2768. CrossRefGoogle Scholar
  6. 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. CrossRefGoogle Scholar
  7. 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. CrossRefGoogle Scholar
  8. Cechet R.P., Sanabria L.A., Divi C.B., Thomas C., Yang T., et al (2012) Climate Futures for Tasmania: severe wind hazard and risk technical report, Geoscience Australia Record 2012/43Google Scholar
  9. Cheng CS, Li G, Li Q, Auld H, Fu C (2012) Possible impacts of climate change on wind gusts under downscaled future climate conditions over Ontario, Canada. J Clim 25:3390–3408. CrossRefGoogle Scholar
  10. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, LondonCrossRefGoogle Scholar
  11. Cvitan L (2003) Determining wind gusts using mean hourly wind speed. Geofizika 20:63–74Google Scholar
  12. Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. CrossRefGoogle Scholar
  13. Durañona V (2015) Extreme Wind climate of Uruguay. Ph.D. Thesis. Facultad de Ingenieria, Universidad de la Republica. Montevideo. UruguayGoogle Scholar
  14. Ferreira V, Nascimento EL (2016) Convectively-induced severe wind gusts in southern Brazil: surface observations, atmospheric environment, and association with distinct convective modes. 28th Conference on Severe Local Storms. 7–11 November 2016 Portland, OR, USAGoogle Scholar
  15. Garreaud RD, Vuille M, Compagnucci R, Marengo J (2009) Present-day South American climate. Palaeogeogr Palaeoclimatol Palaeoecol 281(2009):180–195CrossRefGoogle Scholar
  16. Gatey DA (2011) The analysis of extreme synoptic winds. Ph.D. Thesis. Western University. Electronic Thesis and Dissertation Repository. 268.
  17. Gilleland E, Katz RW (2005) Analysing seasonal to interannual extreme weather and climate variability with the extremes toolkit. National Center for Atmospheric. Research (NCAR), BoulderGoogle Scholar
  18. Gilleland E, Katz RW (2011) New software to analyze how extremes change over time. Eos 92(2):13–14CrossRefGoogle Scholar
  19. Giorgi F (2006) Regional climate modeling: status and perspectives. J Phys IV France 139(101):118. Google Scholar
  20. Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework. Bull World Meteorol Organ 58:175–183Google Scholar
  21. Halfdan A, Haraldur O (2004) Mean gust factors in complex terrain. Meteorol Z 13(2):149–155 (in English)CrossRefGoogle Scholar
  22. Holmes JD (2007) Wind Loading of Structures. Taylor & FrancisGoogle Scholar
  23. Holmes JD, Moriarty WW (1999) Application of the generalised Pareto distribution to extreme value analysis in wind engineering. J Wind Eng Indus Aerodyn 83:1–10CrossRefGoogle Scholar
  24. Katz RW (2013) Statistical methods for nonstationary extremes. In: AghaKouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S (Eds.) Extremes in a changing climate. Detection, Analysis and Uncertainty. SpringerGoogle Scholar
  25. Kunz M, Mohr S, Rauthe M, Luz R, Kottmeier C (2010) Assessment of extreme wind speeds from regional climate models—part 1: estimation of return values and their evaluation. Nat Hazards Earth Syst Sci 10:907–922CrossRefGoogle Scholar
  26. Liléo S, Berge E, Undheim O, Klinkert R, Bredesen RE (2013) Long-term correction of wind measurements. State-of-the-art, guidelines and future work. Elforsk report 13:18Google Scholar
  27. López-Franca N, Zaninelli PPG, Carril AA, Menéndez CG, Sánchez E (2016) Changes in temperature extremes for 21st century scenarios over South America derived from a multi-model ensemble of regional climate models. Clim Res 68:151–167. (Accessed September 23, 2016)CrossRefGoogle Scholar
  28. Menéndez CG, De Castro M, Sörensson A, Boulanger JP (2010) CLARIS project: towards climate downscaling in South America. Meteorol Zeitschrift 19:357–362. CrossRefGoogle Scholar
  29. Menéndez CG, Zaninelli PG, Carril AF, Sánchez E (2016) Hydrological cycle, temperature, and land surface-atmosphere interaction in the La Plata Basin during summer: response to climate change. Clim Res.
  30. Nakicenovic N, Alcamo J, Grubler A, Riahi K, Roehrl RA, Rogner HH, Victor N (2000) Special report on emissions scenarios (SRES), A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, 599 ppGoogle Scholar
  31. Natalini BM, Natalini B, Atencio BA, Zaracho JI (2016) Análisis de velocidades de viento extremas de 11 estaciones en Argentina – perspectivas para una actualización del mapa de vientos extremos. Proceedings of the XXIV Jornadas Argentinas de Ingeniería Estructural, Buenos Aires, Sept 28-30Google Scholar
  32. Pita G, Schwarzkopf MLA (2016) Urban downburst vulnerability and damage assessment from a case study in Argentina. Nat Hazards.
  33. Pryor SC, Barthelmie RJ, Clausen NE et al (2012) Analyses of possible changes in intense and extreme wind speeds over northern Europe under climate change scenarios. Clim Dyn 38:189. CrossRefGoogle Scholar
  34. Rockel B, Woth K (2007) Extremes of near-surface wind speed over Europe and their future changes as estimated from an ensemble of RCM simulations. Clim Chang 81(Suppl 1):267. CrossRefGoogle Scholar
  35. Sanabria LA, Cechet RP (2007) A Statistical Model of Severe Winds. Geoscience Australia. GeoCat # 65052 (accessed May 2016)
  36. Sánchez E, Solman S, Remedio ARC, Berbery H, Samuelsson P, Da Rocha RP, Mourão C, Li L, Marengo J, de Castro M, Jacob D (2015) Regional climate modelling in CLARIS-LPB: a concerted approach towards twentyfirst century projections of regional temperature and precipitation over South America. Clim Dyn.
  37. Solman SA (2016) Systematic temperature and precipitation biases in the CLARIS-LPB ensemble simulations over South America and possible implications for climate projections. Clim Res.
  38. Solman SA, Sanchez E, Samuelsson P et al (2013) Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: model performance and uncertainties. Clim Dyn 41:1–19. CrossRefGoogle Scholar
  39. Song L, Liu Z, Wang F (2015) Comparison of wind data from ERA-Interim buoys in the Yellow and East China Seas. Chin J Oceanol Limnol 33(1):282–288CrossRefGoogle Scholar
  40. Taylor KE (2001) J Geophys Res 106(D7):7183–7192CrossRefGoogle Scholar
  41. Wang CH, Wang X, Khoo YB (2013) Extreme wind gust hazard in Australia and its sensitivity to climate change. Nat Hazards 67:549. CrossRefGoogle Scholar
  42. Watson SJ (2014) Quantifying the variability of wind energy. Wiley Interdiscip Rev: Energy Environ 3(4):330–342Google Scholar
  43. Zhang X, Zwiers FW (2013) Statistical indices for the diagnosing and detecting changes in extremes. In: AghaKouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S Eds. Extremes in a changing climate, detection, analysis and uncertainty. SpringerGoogle Scholar
  44. Zipser EJ et al (2006) Where are the most intense thunderstorms on earth? BAMS. doi:

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Facultad de Ciencias Exactas y Naturales, FCEN/UBAUniversidad de Buenos AiresBuenos AiresArgentina
  2. 2.CONICET - Universidad de Buenos Aires, Centro de Investigaciones del Mar y la Atmósfera (CIMA/CONICET-UBA)Buenos AiresArgentina
  3. 3.CIMABuenos AiresArgentina
  4. 4.Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI 3351-IFAECI/CNRS-CONICET-UBA)Buenos AiresArgentina

Personalised recommendations