Intercomparison of a High-Resolution Regional Climate Model Ensemble for Catchment-Scale Water Cycle Processes under Human Influence

TL;DR

This study compares regional climate models ICON and TSMP1 over Europe, assessing their accuracy in simulating water cycle processes like temperature, precipitation, and evapotranspiration, revealing biases and the impact of model configurations.

Contribution

It provides a coordinated intercomparison of high-resolution regional climate models for water cycle analysis, highlighting structural differences and the effects of irrigation and resolution on model performance.

Findings

ICON outperforms TSMP1 in most cases

Precipitation biases are substantial, especially in summer

Including irrigation and higher-resolution SST improves simulation accuracy

Abstract

Understanding regional hydroclimatic variability and its drivers is essential for anticipating the impacts of climate change on water resources and sustainability. Yet, considerable uncertainty remains in the simulation of the coupled land atmosphere water and energy cycles, largely due to structural model limitations, simplified process representations, and insufficient spatial resolution. Within the framework of the Collaborative Research Center 1502 DETECT, this study presents a coordinated intercomparison of regional climate model simulations designed for water cycle process analysis over Europe. We analyze the performance of simulations using the ICON and TSMP1 model systems and covering the period from 1990 to 2020, comparing against reference datasets (E-OBS, GPCC, and GLEAM). We focus on 2 m air temperature, precipitation and evapotranspiration over four representative basins, the Ebro, Po, Rhine, and Tisa, within the EURO CORDEX domain. Our analysis reveals systematic cold biases across all basins and seasons, with ICON generally outperforming TSMP1. Precipitation biases exhibit substantial spread, particularly in summer, reflecting the persistent challenge of accurately simulating precipitation. ICON tends to underestimate evapotranspiration, while TSMP1 performs better some seasons. Sensitivity experiments further indicate that the inclusion of irrigation improves simulation performance in the Po basin, which is intensively irrigated, and that higher-resolution sea surface temperature forcing data improves the overall precipitation representation. This baseline evaluation provides a first assessment of the DETECT multimodel ensemble and highlights key structural differences influencing model skill across hydroclimatic regimes.