Assessing the skill of Copernicus seasonal forecast systems in predicting temperature and precipitation anomalies in the Alpine region

The Alpine region is warming faster than the global average, with intensifying heatwaves and declining summer rainfall contributing to increased water shortages and more frequent droughts. Accurate forecasts of meteorological and hydrological variables on a seasonal scale would provide early warnings of extreme seasonal conditions and aid in the management of water resources. The PRIN-2022 SPHERE project¹ is developing a forecasting chain based on Copernicus seasonal forecast systems for meteorological inputs and integrates snow-hydrological models to predict seasonal snowpack evolution, river discharge and water availability in the Po River basin (Italy). In this context, it is crucial to assess the skill of Copernicus seasonal forecast systems in predicting meteorological inputs and to evaluate how this skill propagates through the forecasting chain.

This study evaluates the performance of three state-of-the-art seasonal forecast systems available in the Copernicus Climate Change Service (C3S) archive: ECMWF System 5, Météo-France System 6, and CMCC SPS3. These models provide retrospective seasonal forecasts of near-surface air temperature and precipitation at a spatial resolution of 1° x 1° and a monthly temporal resolution, for the common period 1993–2014. The analysis focuses on seasonal average anomalies and applies a range of deterministic and probabilistic verification metrics, including the anomaly correlation coefficient, Brier score, area under the ROC curve, and continuous ranked probability score.

The results provide a comprehensive assessment of the forecast systems' skill in predicting temperature and precipitation anomalies, with a particular focus on the winter and summer seasons-critical periods for applications in energy, water management, agriculture, and the Alpine ski industry. The forecast systems are compared to a baseline forecasting method based on the ERA5 climatology to quantify their relative skills. Preliminary findings reveal strengths and weaknesses across models, with significant variation in performance metrics depending on the season and parameter.

Future steps include extending the analysis to encompass (i) additional meteorological forcings, such as wind and relative humidity, and (ii) output of the forecast chain, including snow water equivalent, snow depth, and river discharge. This will enable the investigation of forecast skill at different steps of the modelling chain and quantify its overall added value compared to a baseline forecasting method based on the climatology. This research aims to advance and optimize the utility of seasonal forecasts in addressing critical climate-related challenges in the Alpine region.

¹Progetto di Ricerca di rilevante Interesse Nazionale (PRIN-2022): Seasonal Prediction of wateravailability: enHancing watER sEcurity from high mountains to plains (SPHERE)