Non-Hydrostatic Regcm4 (Regcm4-NH): Evaluation of Precipitation Statistics at the Convection-Permitting Scale over Different Domains

Abstract

Recent studies over different geographical regions of the world have proven that regional climate models at the convection-permitting scale (CPMs) improve the simulation of precipitation in many aspects, such as the diurnal cycle, precipitation frequency, intensity, and extremes at daily—but even more at hourly—time scales. Here, we present an evaluation of climate simulations with the newly developed RegCM4-NH model run at the convection-permitting scale (CP-RegCM4-NH) for a decade-long period, over three domains covering a large European area. The simulations use a horizontal grid spacing of ~3 km and are driven by the ERA-Interim reanalysis through an intermediate driving RegCM4-NH simulation at ~12 km grid spacing with parameterized deep convection. The km-scale simulations are evaluated against a suite of hourly observation datasets with high spatial resolutions and are compared to the coarse-resolution driving simulation in order to assess improvements in precipitation from the seasonal to hourly scale. The results show that CP-RegCM4-NH produces a more realistic representation of precipitation than the coarse-resolution simulation over all domains. The most significant improvements were found for intensity, heavy precipitation, and precipitation frequency, both on daily and hourly time scales in all seasons. In general, CP-RegCM4-NH tends to correctly produce more intense precipitation and to reduce the frequency of events compared to the coarse-resolution one. On the daily scale, improvements in CP simulations are highly region dependent, with the best results over Italy, France, and Germany, and the largest biases over Switzerland, the Carpathians, and Greece, especially during the summer seasons. At the hourly scale, the improvement in CP simulations for precipitation intensity and spatial distribution is clearer than at the daily timescale. In addition, the representation of extreme events is clearly improved by CP-RegCM4-NH, particularly at the hourly time scale, although an overestimation over some subregions can be found. Although biases between the model simulations at the km-scale and observations still exist, this first application of CP-RegCM4-NH at high spatial resolution indicates a clear benefit of convection-permitting simulations and encourages further assessments of the added value of km-scale model configurations for regional climate change projections.

Dynamic downscaling of climate simulations and projected changes in tropical South America using RegCM4.7

“…The use of CP‐RCMs implies an increase in computational requirements of 2–3 orders of magnitude compared to coarser resolution hydrostatic models (Giorgi, 2019). Although the high demand on computational resources, recent studies have indeed shown that CP‐RCMs provide important AV in simulating regional and local climate processes, reducing persistent biases in larger scale models (e.g., Coppola et al, 2020; Hart et al, 2018; Stocchi et al, 2022).…”

Convection-Permitting Future Climate Simulations for Bulgaria under the RCP8.5 Scenario

“…Several international projects have been launched to produce and compare convection−permitting climate simulations from different climate research institutes and universities in Europe (the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Studies, CORDEX-FPS [1], the European Climate Prediction System, EUCP [10]). Recent studies using 10-year-long convection-permitting simulations reveal that finer resolution reduces the present-day biases in precipitation [11,12]. The results in [12] confirm the improved performance of CP models in simulating important characteristics of daily and hourly precipitation and extreme events.…”

Northwestern Mediterranean Heavy Precipitation Events in a Warmer Climate: Robust Versus Uncertain Changes With a Large Convection‐Permitting Model Ensemble

“…For AROME and RegCM simulations and even more for REMO, a significant overestimation of rain volumes is found, related to long‐lasting and oversized systems. RegCM‐ICTP simulates too high maximum intensities (Stocchi et al., 2022), whereas AROME‐CNRM underestimates them (Caillaud et al., 2021).…”