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.
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.
This page is maintaind by Jason Evans |
Last updated 29 November 2013