Probabilistic downscaling approaches: Application to wind cumulative distribution functions

A statistical method is developed to generate local cumulative distribution functions (CDFs) of surface climate variables from large-scale fields. Contrary to most downscaling methods producing continuous time series, our "probabilistic downscaling methods'' (PDMs), named "CDF-transform'', is designed to deal with and provide local-scale CDFs through a transformation applied to large-scale CDFs. First, our PDM is compared to a reference method (Quantile-matching), and validated on a historical time period by downscaling CDFs of wind intensity anomalies over France, for reanalyses and simulations from a general circulation model (GCM). Then, CDF-transform is applied to GCM output fields to project changes in wind intensity anomalies for the 21st century under A2 scenario. Results show a decrease in wind anomalies for most weather stations, ranging from less than 1% (in the South) to nearly 9% (in the North), with a maximum in the Brittany region. Citation: Michelangeli, P.-A., M. Vrac, and H. Loukos (2009), Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophys. Res. Lett., 36, L11708, doi:10.1029/2009GL038401.