Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling
05 July 2017

The Climatological Distribution of Extreme Arctic Winds and Implications for Ocean and Sea Ice Processes


With an eye towards future higher resolution RASM simulations we evaluated extreme surface wind statistics in three reanalyses (ERA-I, CFSR, and CORE2) and two regional climate simulations (WRF50 and WRF10). This analysis compares wind speed probability distribution function, 99th percentile extreme wind speeds, and surface turbulent fluxes associated with extreme wind speeds.


50th and 99th percentile winds were found to be weakest in ERA-I, similar in WRF50 and CFSR, and strongest in CORE2. The similar grid spacing of WRF50 and CFSR likely contribute to the similar wind speed statistics in these two datasets. Two WRF simulations, with 10 and 50 km horizontal grid spacing were compared. WRF10 has stronger extreme winds than WRF50 across the model domain and consistently higher winds than WRF50 near areas of elevated terrain. The stronger extreme winds alter surface turbulent fluxes with implications for future high resolution coupled climate simulations.


Some of the strongest near-surface winds on Earth form in the Arctic and sub-Arctic due to intense midlatitude cyclones and mesoscale processes, and these strong surface winds have important impacts on ocean and sea ice processes. We examine the climatological distribution of over-ocean, near-surface wind speeds within a Pan-Arctic domain for 18 years (1990–2007) in four gridded data sets: the European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-I), the Climate Forecast System Reanalysis, version 2 of the Common Ocean-Ice Reference Experiment data set, and a regional climate simulation generated using the Weather Research and Forecasting (WRF) model run at 50 km (WRF50) horizontal resolution with ERA Interim as lateral boundary conditions. We estimate probability density functions, the annual cycle, and map the 50th and 99th percentile winds. We then perform the same statistical analysis of winds for 2 years when 10 km WRF data are available (June 2005 to May 2007); despite the much shorter time period, the Pan-Arctic statistics are very similar to those from the 18 year analysis. We repeat the wind speed statistical analysis within a subdomain surrounding Greenland and find that WRF10 has consistently larger maximum wind speeds, but this difference only appears at wind speed percentiles higher than 99%. Differences in the 99th percentile wind speeds are spatially heterogeneous. An investigation of surface fluxes within WRF50 and WRF10 reveals unrealistically large sensible heat fluxes along the sea ice edge, and the geographic distribution and magnitude of these fluxes is shown to be sensitive to sea ice representation in WRF. 

John Cassano
University of Colorado at Boulder