Accelerating Climate Action: A just transition in a post-Covid era. Book of abstracts, 9th SISC Annual Conference (online, 22-24 Set 2021)

Interest in seasonal forecasts has been increasing due to their potential applications in different economic and socially relevant sectors, including water management, agriculture and energy production. This study provides an overall assessment of the skills of the main seasonal forecast systems available in the Copernicus Climate Data Store (C3S) in representing temperature and precipitation anomalies at the monthly and seasonal time scale. The focus area is the Mediterranean, a densely populated region where seasonal forecasts could be helpful in a variety of economic sectors, including water management, hydropower production and agriculture. In this analysis, seasonal forecast systems issued by 5 European institutions (ECMWF, MétéoFrance, UKMO, DWD, CMCC), together with two different Multi-Model Ensembles (MME) derived from them and a persistence (PERS) forecast, have been analysed. The added value of these forecast systems with respect to the simpler forecast approach based on climatology has been investigated. Interest in seasonal forecasts has been increasing due to their potential applications in different economic and socially relevant sectors, including water management, agriculture and energy production. This study provides an overall assessment of the skills of the main seasonal forecast systems available in the Copernicus Climate Data Store (C3S) in representing temperature and precipitation anomalies at the monthly and seasonal time scale. The focus area is the Mediterranean, a densely populated region and a climatic hotspot. In this analysis, seasonal forecast systems issued by 5 European institutions (ECMWF, MétéoFrance, UKMO, DWD, CMCC), together with two different Multi-Model Ensembles (MME) derived from them and a persistence (PERS) forecast, have been analysed. The added value of these forecast systems with respect to the simpler forecast approach based on climatology has been investigated. Different deterministic (Anomaly Correlation Coefficient) and probabilistic scores (Brier Score, Fair Continuous Ranked Probability Score and Receiver Operating Characteristic Curve) have been employed to obtain an overall assessment of the “quality” of the forecasts (Wilks, 2011; Murphy, 1993; WMO, 2018), using ERA5 dataset as a reference. The ensemble quality is assessed through the discussion of rank histograms. We performed the analysis using 6-month forecasts starting on May 1st and November 1st to reproduce the following summer and the winter seasons, respectively, considering the forecasts at both monthly and seasonal time scales. To this purpose, a thorough analysis has been performed to quantify the effect of the data aggregation. In general, temperature anomalies are better reproduced than precipitation anomalies. As shown in rank histograms, the overall ensemble quality is better for temperature, especially during the winter months. After the first month (lead time 0), decreasing skills are evident for almost any skill score, variable, starting date and model. The persistence forecast shows low accuracy and sharpness. Since forecast skills vary in space and time across different models, forecast skills for specific domains should be considered before developing particular applications. Moreover, we recommend using an ensemble of models, such as the MME forecast. References Murphy, A.H. (1993), “What is a good forecast? An essay on the nature of goodness in weather forecasting”, Weather & Forecasting, 8(2), 281–293. WMO (2018), Guidance on Verification of Operational Seasonal Climate Forecasts, Issue WMO-1220. 81 pp. Wilks, D.S. (2011), Statistical Methods in the Atmospheric Sciences, Academic Press Inc.