05 Mar 2024
 | 05 Mar 2024
Status: this preprint is currently under review for the journal HESS.

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

Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Gokturk

Abstract. Convection-permitting regional climate models (CPRCMs) have demonstrated enhanced capability in capturing extreme precipitation compared to regional climate models (RCMs) with convection-parameterization schemes. Despite this, a comprehensive understanding of their added values in daily or sub-daily extremes, especially at local scale, remains limited. In this study, we conduct a thorough comparison of daily and sub-daily extreme precipitation from HARMONIE-Climate model, cycle 38 at 3 km resolution (HCLIM3) and 12 km resolution (HCLIM12) across Norway’s diverse landscape, divided into eight regions, using both gridded and in-situ observations. Our main focus is to investigate the added value of CPRCMs (i.e., HCLIM3) compared to RCMs (i.e., HCLIM12) for extreme precipitation at daily and sub-daily scales from regional to local scales, and quantify to what extend CPRCM can reproduce the orographic effect on extreme precipitation at daily and sub-daily scale. We find that HCLIM3 better matches observations than HCLIM12 for daily and sub-daily precipitation extreme indices at regional scale in Norway. More specifically, HCLIM3 better captures the maximum 1-day precipitation (Rx1d) at most of the regions except south-western region in Norway. Notably, HCLIM12 shows underestimation in the complex orography for annual Rx1d. For the maximum 1-hour precipitation (Rx1h), the superiority from HCLIM3 have also been found on average, although with slightly higher wet-bias in the western, middle-inland and middle-coastal during summer. In addition, the reverse orography effect on seasonal Rx1h at regional scale can be better reproduced by HCLIM3 than HCLIM12 in most seasons except spring. At the local scale, HCLIM3 can better capture the temporal evolution of Rx1h than HCLIM12 when compared with observations between 1999–2018. However, we see that the benefit from HCLIM3 in capturing seasonal Rx1d within western region diminishes at local scale. Most interesting finding is that the added value from HCLIM3 is clearer in Rx1h than in Rx1d at both regional and local scale, especially in the extreme seasonality. In general, HCLIM3 performs better than HCLIM12 on Rx1d and Rx1h in Norway with the mean of bias distribution closer to zero, although it varies a bit among regions. Specifically, HCLIM3 performs slightly poorer in the south-western region. This study highlights the importance of more realistic convection-permitting regional climate simulations in providing reliable insights into the characteristics of precipitation extremes across Norway's eight regions. Such information is crucial for effective adaptation management to mitigate severe hydro-meteorological hazards, especially for the local extremes.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Gokturk

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-68', Anonymous Referee #1, 24 May 2024 reply
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Gokturk
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Gokturk


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Short summary
We compared extreme precipitations in Norway from convection-permitting models at 3 km resolution (HCLIM3) and regional climate model at 12 km (HCLIM12) and show that the HCLIM3 is more accurate than HCLIM12 in predicting the intense rainfalls that can lead to floods, especially at local scales. This is more clear in hourly extremes than daily. Our research suggests using more detailed climate models could improve forecasts, helping the local society brace for the impacts of extreme weather.