Climate Dynamics

, Volume 45, Issue 1–2, pp 441–453 | Cite as

Evaluating wind extremes in CMIP5 climate models

  • Devashish Kumar
  • Vimal Mishra
  • Auroop R. Ganguly


Wind extremes have consequences for renewable energy sectors, critical infrastructures, coastal ecosystems, and insurance industry. Considerable debates remain regarding the impacts of climate change on wind extremes. While climate models have occasionally shown increases in regional wind extremes, a decline in the magnitude of mean and extreme near-surface wind speeds has been recently reported over most regions of the Northern Hemisphere using observed data. Previous studies of wind extremes under climate change have focused on selected regions and employed outputs from the regional climate models (RCMs). However, RCMs ultimately rely on the outputs of global circulation models (GCMs), and the value-addition from the former over the latter has been questioned. Regional model runs rarely employ the full suite of GCM ensembles, and hence may not be able to encapsulate the most likely projections or their variability. Here we evaluate the performance of the latest generation of GCMs, the Coupled Model Intercomparison Project phase 5 (CMIP5), in simulating extreme winds. We find that the multimodel ensemble (MME) mean captures the spatial variability of annual maximum wind speeds over most regions except over the mountainous terrains. However, the historical temporal trends in annual maximum wind speeds for the reanalysis data, ERA-Interim, are not well represented in the GCMs. The historical trends in extreme winds from GCMs are statistically not significant over most regions. The MME model simulates the spatial patterns of extreme winds for 25–100 year return periods. The projected extreme winds from GCMs exhibit statistically less significant trends compared to the historical reference period.


CMIP5 models Wind extremes Gumbel distribution Model evaluation 



The research was primarily funded by the United States National Science Foundation Expeditions in Computing Grant # 1029711, as well as Northeastern University’s Tier 1 Grant from the Office of the Provost, and the Varahamihir Research Fellowship awarded to the second author by the Government of India. The climate model data sets were downloaded from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archive of the US Department of Energy’s Lawrence Livermore National Laboratory.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Devashish Kumar
    • 1
  • Vimal Mishra
    • 1
    • 2
  • Auroop R. Ganguly
    • 1
  1. 1.Sustainability and Data Sciences Laboratory, Civil and Environmental EngineeringNortheastern UniversityBostonUSA
  2. 2.Civil EngineeringIndian Institute of TechnologyGandhinagarIndia

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