Perfect model estimates of seasonal forecast skill

Abstract

The skill of seasonal forecasts of midlatitude atmospheric circulation is notoriously intermittent and might be modest on average. The use of seasonal forecasts for real‐time applications can therefore benefit greatly from approaches providing an a priori indication of how skillful a single forecast will be at capturing circulation anomalies. This work introduces a methodology to predict the skill of such a single ensemble forecast. It consists of verifying the ensemble mean against each of its individual members before averaging, in order to provide a “perfect model” estimate of forecast skill. This methodology is applied to seasonal forecasts from the Copernicus Climate Change Service (C3S), for a selection of four variables (mean sea‐level pressure, geopotential height at 500 and 200 hPa, and streamfunction at 200 hPa) characterizing the atmospheric circulation in three domains covering the Northern Hemisphere midlatitudes: Pacific and North America, North Atlantic, and Asia. Skill is defined as the Anomaly Correlation Coefficient (ACC), which quantifies how well the predicted spatial patterns of atmospheric circulation match the observed ones. Results show that the prediction of forecast skill is quite successful for upper‐troposphere variables but is diversely successful in the mid‐troposphere and the surface, depending on the region under study. Moreover, the capacity to predict forecast skill exhibits seasonal dependence, with generally better performance in boreal summer and winter and an even greater summer peak for the North Atlantic region. These results are very similar across the six C3S models under consideration and their multi‐model combination, which is an indication of robustness.