Towards an advanced representation of precipitation over Morocco in a global climate model with resolution enhancement and empirical run-time bias corrections

Abstract

Morocco, as a Mediterranean and North African country, is acknowledged as a climate change hotspot, where increased drought and related water resource shortages present a real challenge for human and natural systems. However, its geographic position and regional characteristics make the simulation of the distribution and variability of precipitation particularly challenging in the region. In this study, we propose an approach where the Laboratoire de Météorologie Dynamique Zoom (LMDZ) GCM is run with a stretched grid configuration developed with enhanced resolution (35 km) over the region, and we apply run-time bias correction to deal with the atmospheric model's systematic errors on large-scale circulation. The bias-correction terms for wind and temperature are built using the climatological mean of the adjustment terms on tendency errors in an LMDZ simulation relaxed towards ERA5 reanalyses. The free reference run with the zoomed configuration is compared to two bias-corrected runs. The free run exhibits noticeable improvements in mean low-level circulation, high frequency variability and moisture transport and compares favourably to precipitation observations at the local scale. The mean simulated climate is substantially improved after bias correction w.r.t. to the uncorrected runs. At the regional scale, the bias-correction showed improvements in moisture transport and precipitation distribution, but no noticeable effect was observed in mean precipitation amounts, interannual variability and extreme events. To address the latter, model tuning after grid refinement and developing more “scale-aware” parameterizations are necessary. The observed improvements on the large-scale circulation suggest that the run-time bias correction can be used to drive regional climate models for a better representation of regional and local climate. It can also be combined with “a posteriori” bias correction methods to improve local precipitation simulation, including extreme events.