A hybrid statistical-dynamical method to downscale global climate models over Europe

To characterize the impacts of climate change, robust high-resolution climate change information is generally needed. The resolution of global climate models is currently too coarse to provide directly such information. A specific spatial downscaling step is therefore generally needed, either (1) dynamical downscaling with regional climate models or (2) statistical downscaling.

In this study, we present a new hybrid statistical-dynamical downscaling approach, intended to combine the respective strengths of statistical and dynamical downscaling, while overcoming their respective limitations. This hybrid method aims to emulate regional climate models and is based on a constructed analogues method.

Contrary to dynamical downscaling, the computational cost of the method is low, allowing to downscale a large number of global climate projections and therefore to correctly assess the climate uncertainties in impact studies. Contrary to statistical downscaling, the method does not rely on the assumption that the downscaling relationship established in the present climate with observations remains valid in the future climate perturbed by anthropogenic forcings. Therefore, the hybrid approach should be as robust as regional climate models in projecting future climate change.

In this presentation, the hybrid statistical-dynamical downscaling method is first presented. Elements of evaluation, in a perfect model framework based on an ensemble of regional climate models over Europe, are then shown and discussed to demonstrate the interest of the method and its applicability to study future climate changes over western Europe. Finally, results of the application of the method to downscale global climate projections over western Europe are shown, and important implications of the results are discussed.