Abstract

ERA5 is widely considered as a valid proxy of observation at region scales. Surface air temperature from the E-OBS database and 196 meteorological stations across Europe are being applied for evaluation of the fifth-generation ECMWF reanalysis ERA5 temperature data in the period of 1981–2010. In general, ERA5 captures the mean and extreme temperatures very well and ERA5 is reliable for climate investigation over Europe. High correlations ranging from 0.995 to 1.000 indicate that ERA5 could capture the annual cycle very well. However, the high mean biases and high Root Mean Square Error (RMSE) for some European sub-regions (e.g., the Alps, the Mediterranean) reveal that ERA5 underestimates temperatures. The biases can be mainly attributed to the altitude differences between ERA5 grid points and stations. Comparing ERA5 with the other two datasets, ERA5 temperature presents more extreme temperature and small outliers for regions southern of 40° latitude and less extreme temperatures in areas over the Black Sea. In Scandinavia, ERA5 temperatures are more frequently extreme than the observational ones.

Highlights

  • One of the main issues that are addressed in the largest percentage of climatological studies, and one of the main challenges that climate scientists face to conduct their research is to have accurate and long-term datasets

  • As mentioned in the previous paragraph, we utilized daily mean temperature data provided by three sources (ERA5, European Climate Assessment and Dataset (ECA&D), European daily highresolution gridded observational dataset (E-OBS))

  • Data for andextreme daily observations of the mean temperature, an additional analysis was made maximum and ECA&D stations is summarized in Table

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Summary

Introduction

One of the main issues that are addressed in the largest percentage of climatological studies, and one of the main challenges that climate scientists face to conduct their research is to have accurate and long-term datasets Such data that lead to robust results and conclusions and can provide critical information on the monitoring of the trends of the climatic parameters, need to have a homogenous spatial resolution over each domain of interest, and should be long-term, and have no gaps. It should be highlighted that these reanalysis data are not used indiscriminately [5], since they may be affected by the potential inhomogeneities of the observational data, errors in satellite radiance, incomplete model physics, and insufficient representation of topography [6] Their evaluation is fundamental to assess the potential uncertainty due to the interpolation

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