Probabilistic downscaling approaches: Application to wind cumulative distribution functions

Abstract

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.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

“…Considerable effort has been expended developing these types of bias correction algorithms (Michelangeli et al 2009;Li et al 2010;Hempel et al 2013), evaluating their performance (Piani et al 2010;Gudmundsson et al 2012;Chen et al 2013), and determining their limitations (Ehret et al 2012;Eden et al 2012;Maraun 2013;Maraun and Widmann 2015;Chen et al 2015). A recent critical review is offered by Maraun (2016).…”

Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms

“…First, it is highly sensitive to weather and production is variable. It is potentially sensitive to climate change [2][3][4] . Then a challenge, which largely remains to be investigated, is to assess the effects of wind farms on climate and environment, especially in regions with intense wind power development.…”

A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

“…In this work, we use the CDF-t method developed by Michelangeli et al (2009) to adjust climate models. It consists in matching the CDF of a climate variable simulated by a model (here the GCM) to the CDF of this variable in observations (here WFDEI) through a mathematical function.…”

“…The bias correction has been performed using the CDF-t method (Michelangeli et al, 2009), a method that has been widely used and validated for various variables and in various contexts (e.g. Kallache et al, 2011;Vrac et al, 2012;Lavaysse et al, 2012;Vautard et al, 2013;Vrac and Friederichs, 2015;Vrac et al, 2016), including tropical areas (Oettli et al, 2011;Vigaud et al, 2013), but not Africa.…”