Abstract
When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical statistical downscaling methods (ESD). In this context the question is addressed: Is an empirical statistical downscaling method capable of yielding results that are comparable to those by dynamical RCMs? Based on the presented ESD method, the comparison of RCM and ESD results show a high amount of agreement. In addition the empirical statistical downscaling can be applied directly to a GCM or a GCM-RCM cascade. The paper aims at comparing the consequences of employing various CGM-RCM-ESD combinations on the derived future changes of temperature and precipitation. This adds to the insight on the scale dependency of downscaling strategies. Results for one GCM with several scenario runs driving several RCMs with and without subsequent empirical statistical downscaling are presented. It is shown that there are only small differences between using the GCM results directly or as a GCM-RCM-ESD cascade.
Highlights
Once upon a time there was just a single tool for assessing the character and magnitude of climate change
The authors wish to point out that it is in the “force field” of regionalization approaches and scales where this paper aims at gaining further insight into the sensitivity of climate signals to the pathway in which these regionalized results are arrived at
Section 3.3) are but indicators for the magnitude of the climate signals. These are compared with projections which were obtained through regionalizations forced by the Global Climate Model (GCM)
Summary
Once upon a time there was just a single tool for assessing the character and magnitude of climate change It was a coarse resolution numerical model that enabled extensions of the current climate and projections of a future climate. Despite improvements in their resolution, which have to face and overcome immense computational demands, a further branching out was necessary with respect to the approaches and strategies which need to be applied in order to access local scales This includes the use of high-resolution limited area versions of numerical models (Regional Climate Models, RCM), nested into global models for the regions of interest, described e.g., in [8,9,10]. An alternative approach makes use of statistical connections (see [11] or [12]) that straddle the scales of atmospheric features and attempt to link global and local climate developments
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