Publications

Regional climate simulations with high horizontal resolutions are becoming increasingly common and although model development has continually enhanced the representation of atmospheric phenomena, the model improvements are variable, region and time scale-dependant. The high computational costs of increasingly smaller grid-spacing underline the need for a robust assessment of the benefits or losses associated to the dynamical downscaling of coarser resolution models (reanalysis, global climate models or ~tenths km runs), that is, quantitative added value evaluation. In the current study, a probability density function (PDF) matching score is used to determine the distribution added value (DAV) of the EURO-CORDEX maximum and minimum temperatures from the hindcast simulations at 0.44° and 0.11° resolutions. The gridded maximum and minimum temperatures from European Climate Assessment & Dataset (E-Obs) were used as benchmarks for the matching scores and DAVs were determined for the 0.44° and 0.11° simulations individually against ERA-Interim as well as against each other. Temperature added value against ERA-Interim is difficult to find due to the assimilation of surface temperatures in the reanalysis. Nonetheless, there is positive added value in Tmax for half of the models at the yearly time scale, and the increase in resolution also implies positive DAV in half of the models, with DAVs between 0.1 and 3.8%. More importantly, the benefits of downscaling are significantly visible in the extreme end tail of Tmax, where all models have high added value at all resolutions and seasons except in summer (from ~1 to ~21%). In minimum temperature there is added value in winter and autumn at both resolutions. The Mediterranean and the British Isles are the regions where larger positive DAV values for both variables and resolutions are identified. In Tmax, added value is also associated to the increase in resolution (from 50 to 10 km) for half of the models in all regions. Scandinavia, the Alps, and eastern Europe are regions where negative added value is clearly linked with the highest mountain peaks for both variables