Inter‐comparison and validation of high‐resolution surface air temperature reanalysis fields over Italy - Francesco Cavalleri; Francesca Viterbo; Michele Brunetti; Riccardo Bonanno; Veronica Manara; Cristian Lussana; Matteo Lacavalla; Maurizio Maugeri (International Journal of Climatology, 2024)
Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to