Assessment of the spatio-temporal variability of the added value on precipitation of convection-permitting simulation over the Iberian Peninsula using the RegIPSL regional earth system model

Abstract

In this study, we have assessed the added value on the spatio-temporal distribution of the precipitation of convection-permitting simulation (3 km) compared to the parent coarse-scale parameterized convection simulation (20 km) with the high-resolution observational datasets i.e., SPREAD (5 km) and IBERIA01 (10 km) over the Iberian Peninsula (IP) in all four seasons during 2000–2009. Both simulations are evaluation runs based on ERA-Interim reanalysis and performed with the RegIPSL regional earth system model in the frame of the European Climate Prediction system (EUCP) H2020 project and COordinated Regional climate Downscaling Experiment (CORDEX). We have not found significant improvement in the convection-permitting simulation compared to the parent coarse-scale simulation for the seasonal mean precipitation of the IP except the spatial variation over mountainous peaks. The kilometer-scale simulation significantly underestimates the observed seasonal mean precipitation over the western parts of the IP compared to the coarse-scale simulation, which may be attributed to a change of local dynamics in the kilometer-scale simulation with a weakening and southward shift of the moisture-laden westerly winds approaching from the Atlantic Ocean over the IP (i.e., a decline in atmospheric moisture transport from the Atlantic Ocean towards the IP). However, the added value of kilometer-scale simulation over the driving coarse-scale simulation is obtained for various indices; in the representation of the spatio-temporal distribution of the wet-day precipitation frequency and intensity, and the extreme/heavy precipitation events for each season at both resolutions i.e., downscaled and upscaled. It has also been noted that the spatio-temporal distribution of precipitation for all metrics used varies between the two observational datasets for all seasons.