Comparison of high-resolution climate reanalysis datasets for hydro-climatic impact studies

Abstract

Continuous high-quality meteorological information is needed to describe and understand extreme hydro-climatic events, such as droughts and floods. Observation-based information of the highest quality is often only available on a national level and for a few meteorological variables. As an alternative, large-scale climate reanalysis datasets that blend model simulations with observations are often used. However, their performance can be biased due to coarse spatial resolutions, model uncertainty, and data assimilation biases. Previous studies on the performance of reanalysis datasets either focused on the global scale, on single variables, or on a few aspects of the hydro-climate. Therefore, we here conduct a comprehensive spatio-temporal evaluation of different precipitation, temperature, and snowfall metrics for four state-of-the-art reanalysis datasets (ERA5, ERA5-Land, CERRA, and CHELSA-v2.1) over complex terrain. We consider the climatologies of mean and extreme climate metrics, daily to inter-annual variability, as well as consistency in long-term trends. Further, we compare the representation of extreme events, namely, the intensity and severity of the 2003 and 2018 meteorological droughts as well as the 1999 and 2005 heavy precipitation events that triggered flooding in Switzerland. The datasets generally show a satisfactory performance for most of these characteristics, except for the representation of snowfall (solid precipitation) and the number of wet days in ERA5 and ERA5-Land. Our results show that there are clear differences in the representation of precipitation among datasets, with CERRA showing a substantial improvement in the representation of precipitation compared to the other datasets. In contrast to precipitation, temperature is more comparable across datasets, with CERRA and CHELSA showing smaller biases but a clear increase in bias with elevation. All the datasets were able to identify the 2003 and 2018 drought events; however, ERA5, ERA5-Land, and CHELSA overestimated their intensity and severity, while CERRA underestimated them. The 1999 and 2005 floods were overall well represented by all the datasets, with CERRA showing the best agreement with observations, and the other datasets overestimating the spatial extent of the events. We conclude that, overall, CERRA is the most reliable dataset and suitable for a broad range of analyses, particularly for regions where snow processes are relevant and for applications where the representation of daily to inter-annual precipitation variability is important.