Preprints
https://doi.org/10.5194/egusphere-2022-1037
https://doi.org/10.5194/egusphere-2022-1037
 
17 Oct 2022
17 Oct 2022
Status: this preprint is open for discussion.

How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?

Eleonora Dallan1, Francesco Marra2, Giorgia Fosser3, Marco Marani4, Giuseppe Formetta5, Christoph Schär6, and Marco Borga1 Eleonora Dallan et al.
  • 1Department of Land Environment Agriculture and Forestry, University of Padova, Padova, Italy
  • 2National Research Council of Italy - Institute of Atmospheric Sciences and Climate (CNR-ISAC), Bologna, Italy
  • 3University School for Advanced Studies - IUSS Pavia, Pavia, Italy
  • 4Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, Italy
  • 5Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
  • 6Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland

Abstract. Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state-of-the-art for these projections as they resolve convective processes key to short-duration extremes. Recent studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are sub-grid even for CPMs. It is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands, however, CPM simulations are still too short for analysing extremes using conventional methods. We introduce the use of a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value, SMEV) for the analysis of extremes from short time slices such as the ones of CPM simulations. We analyse an ERA-Interim-driven COSMO-crCLM simulation (2000–2009, 2.2 km resolution) and we use hourly precipitation from 174 rain gauges in an orographically-complex area in Northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes as compared to observational data. We focus on extremes as high as the 20-year return levels. While an overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias correction approaches should account for orography. SMEV capability of estimating reliable rare extremes from short periods promises further application on short time-slice CPM projections, and model ensembles.

Eleonora Dallan et al.

Status: open (until 12 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1037', Anonymous Referee #1, 12 Nov 2022 reply
  • RC2: 'Comment on egusphere-2022-1037', Anonymous Referee #2, 18 Nov 2022 reply

Eleonora Dallan et al.

Eleonora Dallan et al.

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Short summary
Climate models at convection-permitting resolutions could represent future changes in extreme short-duration precipitation critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite a good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider orography.