Multiple scenarios of climate anomalies over Europe in ensemble seasonal forecasts

Seasonal prediction uses ensemble forecasting to sample the distribution of possible climate outcomes in the upcoming term given the slowly-varying constraints on the atmosphere. However, translating the members’ distribution of a seasonal forecast into meaningful information is a challenge climate services are often faced with. When a large ensemble spread makes the forecast difficult to interpret, highlighting the competing signals from which the uncertainty arises may bear added value to end users. In order to do so, we present an approach to extract alternative seasonal forecast scenarios over Europe (in temperature, precipitation and atmospheric circulation) from ensemble seasonal forecasts. The aim of the scenarios is to refine the ensemble analysis beyond the usual forecast products (e.g ensemble mean, tercile probabilities), and to provide additional guidance for preparation of the seasonal forecast bulletins routinely issued at Météo-France. The seasonal forecast scenarios are determined with a hierarchical clustering of the ensemble members, based on their forecast temperature at 2-m (T2m). The dissimilarity between two members is defined from the spatial correlation between their respective maps of T2m anomalies – relative to model climatology – over a European domain (29.5°W-40.5°E; 30.5°N-70.5°N, land grid points only). The subsequent dissimilarity matrix across the ensemble feeds the clustering algorithm that groups members into clusters eventually defining the scenarios. The seasonal outcomes corresponding to these scenarios are then described through several diagnostics, e.g composites on sensible climate variables (T2m, precipitation), composites on atmospheric circulation variables (Z500, V200), and analysis through modes of variability and weather regimes. In addition, we provide a description of how scenarios diverge in the course of forecast integration and identify teleconnections related to each scenario. Finally, we also assess the skill of the seasonal forecasts assuming that only the subset of members representing the most likely scenario is retained. This methodology has been implemented to the Copernicus Climate Change Services (C3S) real-time seasonal forecasts across the past year for experimental purposes, and it is shown to be a relevant complement for the preparation of the Météo-France operational seasonal bulletins.