Observed and CAM3 GCM Sea Surface Wind Speed Distributions: Characterization, Comparison, and Bias Reduction

Climatological surface wind speed probability density functions (PDFs) estimated from observations are characterized and used to evaluate, for the first time, contemporaneous wind PDFs predicted by a GCM. The observations include NASA’s global Quick Scatterometer (QuikSCAT) dataset, the NCEP/ Department of Energy Global Reanalysis 2 (NCEP-2) 6-hourly reanalysis, and the Tropical Atmosphere Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) moored buoy data, all from 2000 to 2005. Wind speed mean, 90th percentile, standard deviation, and Weibull shape parameter climatologies are constructed from these data. New features that emerge from the analysis include the identification of a stationary pattern to the wind speed variance in the equatorial Pacific. Interestingly, a distinct wind speed shape anomaly migrates with the ITCZ across this stationary background. The GCM despite its coarser spatial and temporal resolution predicts wind speed PDFs in general agreement with observations. Relative to QuikSCAT, the NCAR Community Atmosphere Model, version 3 (CAM3) GCM has a globally averaged positive mean wind speed bias of about 0.2 m s 1 originating primarily within the trades and Southern Hemisphere storm track. Global standard deviation biases are largest in the winter hemisphere storm tracks. The largest shape biases occur along the equatorial peripheries of the Northern Hemisphere and southern Indian Ocean anticyclones. Year-round negative shape and mean wind speed biases persist along the ITCZ. The GCM’s overactive tropical convection and slight subtropical anticyclone displacement contribute to positive mean speed, standard deviation, and shape trade biases. Surface heat and energy fluxes depend nonlinearly on wind speed magnitude, are sensitive to the tails of the wind distribution, and hence vary significantly on spatiotemporal scales not resolved by GCMs. Limited computing resources force the use of coarse-resolution GCMs, which do not resolve finer-scale wind speed fluctuations. Rather, surface fluxes are determined from the mean wind speed computed by averaging spatially and temporally over subgrid-scale features. Some surface flux routines account for gustiness during low mean winds resulting from thermally driven convection. The authors hypothesize that GCMs systematically underestimate surface momentum flux nonlinearities and that this biases surface wind predictions most in regions of strong winds with high variability. To test this, climate simulations that account for surface fluxes due to subgrid-scale GCM winds are performed. This significantly improves climatological surface wind speed statistics, particularly in the Southern Hemisphere storm track, consistent with the hypothesis. These wind speed improvements can be attributed to a reduction in GCM sea level pressure biases throughout the globe.