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.

[1]  Katharine Hayhoe,et al.  Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing , 2007 .

[2]  M. Déqué,et al.  Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values , 2007 .

[3]  Pierre Friedlingstein,et al.  The new IPSL climate system model: IPSL-CM4 , 2006 .

[4]  R. Barthelmie,et al.  Empirical downscaling of wind speed probability distributions , 2005 .

[5]  D. Lettenmaier,et al.  Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .

[6]  D. Pollard,et al.  Comparison of future climate change over North America simulated by two regional models , 2003 .

[7]  Alex J. Cannon,et al.  Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models , 2002 .

[8]  Christian W. Dawson,et al.  SDSM - a decision support tool for the assessment of regional climate change impacts , 2002, Environ. Model. Softw..

[9]  Alexei G. Sankovski,et al.  Special report on emissions scenarios , 2000 .

[10]  D. Wilks,et al.  The weather generation game: a review of stochastic weather models , 1999 .

[11]  C. W. Richardson,et al.  Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates , 1998 .

[12]  Daniel Rosenfeld,et al.  Optimality of empirical Z‐R relations , 1997 .

[13]  Guido Visconti,et al.  Use of a limited-area model nested in a general circulation model for regional climate simulation over Europe , 1990 .

[14]  G. Brier,et al.  Some applications of statistics to meteorology , 1958 .

[15]  D. Darling.  The Kolmogorov-Smirnov, Cramer-von Mises Tests , 1957 .