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Journal articles
Regional climate downscaling with prior statistical correction of the global climate forcing
Abstract : A novel climate downscaling methodology that attempts to correct climate simulation biases is proposed. By combining an advanced statistical bias correction method with a dynamical downscaling it constitutes a hybrid technique that yields nearly unbiased, high-resolution, physically consistent, three-dimensional fields that can be used for climate impact studies. The method is based on a prior statistical distribution correction of large-scale global climate model (GCM) 3-dimensional output fields to be taken as boundary forcing of a dynamical regional climate model (RCM). GCM fields are corrected using meteorological reanalyses. We evaluate this methodology over a decadal experiment. The improvement in terms of spatial and temporal variability is discussed against observations for a past period. The biases of the downscaled fields are much lower using this hybrid technique, up to a factor 4 for the mean temperature bias compared to the dynamical downscaling alone without prior bias correction. Precipitation biases are subsequently improved hence offering optimistic perspectives for climate impact studies.
https://hal-ineris.archives-ouvertes.fr/ineris-00961787
Contributor : Gestionnaire Civs <>
Submitted on : Thursday, March 20, 2014 - 2:57:16 PM
Last modification on : Tuesday, November 10, 2020 - 3:12:09 PM
Long-term archiving on: : Friday, June 20, 2014 - 11:41:21 AM
Contributor : Gestionnaire Civs <>
Submitted on : Thursday, March 20, 2014 - 2:57:16 PM
Last modification on : Tuesday, November 10, 2020 - 3:12:09 PM
Long-term archiving on: : Friday, June 20, 2014 - 11:41:21 AM
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2013-004_post-print.pdf
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