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Article Dans Une Revue Geophysical Research Letters Année : 2009

Probabilistic downscaling approaches: Application to wind cumulative distribution functions

Résumé

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 largescale 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.
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Dates et versions

hal-03197853 , version 1 (15-04-2021)

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P.-A. Michelangeli, M. Vrac, H. Loukos. Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 2009, 36 (11), ⟨10.1029/2009GL038401⟩. ⟨hal-03197853⟩
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