Abstract
Abstract. The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models (RCMs) are known to exhibit systematic biases in precipitation. Hence, RCM simulations need to be post-processed in order to produce reliable estimates of local scale climate. Popular post-processing approaches are based on statistical transformations, which attempt to adjust the distribution of modelled data such that it closely resembles the observed climatology. However, the diversity of suggested methods renders the selection of optimal techniques difficult and therefore there is a need for clarification. In this paper, statistical transformations for post-processing RCM output are reviewed and classified into (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations, each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in RCM precipitation.
Highlights
It is well established that precipitation simulations from regional climate models (RCMs) are biased (e.g. due to limited process understanding or insufficient spatial resolution (Rauscher et al, 2010)) and need to be post processed before being used for climate impact assessment (e.g Christensen et al, 2008; Maraun et al, 2010; Teutschbein and Seibert, 2010; Winkler et al, 2011a,b)
The three approaches using statistical transformation to postprocess RCM output that were assessed in this paper differ substantially with respect to their underlying assumptions, despite the fact that they are all designed to transform RCM output such that its empirical distribution matches the distribution of observed values
It was demonstrated that the performance of the methods differ substantially
Summary
It is well established that precipitation simulations from regional climate models (RCMs) are biased (e.g. due to limited process understanding or insufficient spatial resolution (Rauscher et al, 2010)) and need to be post processed (i.e. statistically adjusted, bias corrected) before being used for climate impact assessment (e.g Christensen et al, 2008; Maraun et al, 2010; Teutschbein and Seibert, 2010; Winkler et al, 2011a,b). Ines and Hansen, 2006; Engen-Skaugen, 2007; Schmidli et al, 2007; Dosio and Paruolo, 2011; Themeßl et al, 2011; Turco et al, 2011; Chen et al, 2011b; Teutschbein and Seibert, 2012). There is an urgent need for clarifying the relation among different approaches as well as for an objective assessment of their performance
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