Spatial and temporal characteristics of extreme rainfall: Added benefits with sub‐kilometre‐resolution climate model simulations?

Abstract

Local, short‐duration extreme precipitation events can cause floodings and have massive economic consequences. The climate change impact on such events is of great interest, but due to the small spatio‐temporal scales involved, these are challenging to properly represent in climate models. This study analyses a new sub‐kilometre (750 m) HARMONIE‐Climate model simulation driven by ERA5 reanalysis data. Three convection‐permitting models at 750 m, 3 and 5 km grid distance are analysed and compared with driving reanalyses, intermediate model simulations and a dense rain gauge network. The representation of convective events is analysed by a range of metrics categorised as spatial, temporal and event‐focused. Precipitation events are analysed at both hourly and sub‐hourly scales and a clear difference between model performance on these scales is found. Overall, we find a better performance for HCLIM750m for most metrics, yet the added benefits of the computationally intensive sub‐kilometre scale simulation seems limited compared to the convection‐permitting models at 3 and 5 km.

Enhanced Evaluation of Sub-daily and Daily Extreme Precipitation in Norway from Convection-Permitting Models at Regional and Local Scales

“…As highlighted by Konstali and Sorteberg (2022), there can be significant uncertainties associated with the interpolation of grided precipitation data. Besides, the benefit for precipitation spatial evaluation based on in-situ observation has also been reported in Thomassen et al (2023). Therefore, the evaluation of extreme precipitation from HCLIM3 and HCLIM12 here, is based on both gridded precipitation and in-situ observation.…”

“…The study by Thomassen et al (2023) utilizing SPHEAR with a 2.2 km resolution echoes challenges in accurately simulating extreme precipitation over complex terrain in the northern Italy. Our findings confirm the https://doi.org/10.5194/hess-2024-68 Preprint.…”

“…A potential reason for the observed biases is related to the limitations of pseudo-observation SeNorge, which may inadequately represent steep valleys at the 3 km grid space (Thomassen et al, 2023). Sparse station distribution in complex mountain areas may contribute to unrealistic observations, concealing the benefits from HCLIM3.…”

“…However, the evaluation and conclusions from Lind et al (2020) and Mé dus et al (2022) were mainly focused on the large regional and country scale of Fenno-Scandinavia, overlooking the added values of CPRCM at local scale. Furthermore, Thomassen et al (2023) observed that HCLIM3 tends to exhibit underestimations in monthly precipitation and a later evening peak compared to sub-kilometre models. They found that the advantages of sub-kilometer models were not outstanding.…”