Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales.

Di Luca, A., D. Argueso, J.P. Evans, R. de Elia and R. Laprise
Journal of Geophysical Research – Atmospheres, 121(4), 1575-1590, doi: 10.1002/2015JD024009, 2015.

Abstract

This study evaluates the added value in the representation of surface climate variables from an ensemble of regional climate model (RCM) simulations by comparing the relative skill of the RCM simulations and their driving data over a wide range of RCM experimental setups and climate statistics. The methodology is specifically designed to compare results across different variables and metrics, and it incorporates a rigorous approach to separate the added value occurring at different spatial scales. Results show that the RCMs’ added value strongly depends on the type of driving data, the climate variable, and the region of interest but depends rather weakly on the choice of the statistical measure, the season, and the RCM physical configuration. Decomposing climate statistics according to different spatial scales shows that improvements are coming from the small scales when considering the representation of spatial patterns, but from the large-scale contribution in the case of absolute values. Our results also show that a large part of the added value can be attained using some simple postprocessing methods.

Key Figure


Figure 5. Total AV terms and the contribution from different spatial scales as a function of the various factors of interest for (a and c) the mse and (b and d) the correlation AV metrics. Results obtained using the 10 and 50 km simulations are shown in Figures 5a and 5b and Figures 5c and 5d, respectively. Note that a square is assigned to each factor and AV scale term and that each quadrant of the square gives the averaged AV for specific values of each factor. Hence, the lower right quadrant of the square located in the “region” column and the “300” AV term row represents the averaged AV over the “flat region” for the large-scale term.


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