Impact of processes on the weather scale on subseasonal prediction

Abstract:

Variability of the large-scale atmospheric circulation in the midlatitudes is characterised by extratropical Rossby waves and stationary patterns, such as blocking anticyclones, therein. The Rossby wave pattern itself is modulatedy by processes on the weather / synoptic scale, in particular by rapidly ascending air streams in midlatitude cyclones – so-called warm conveyor belts (WCBs). Most often, WCBs are identified by selecting coherent bundles of Lagrangian trajectories. This approach, however, requires data at a high spatio-temporal resolution and is computationally expensive. To identify WCBs in expansive data sets such as the S2S (subseasonal-to-seasonal) ensemble reforecasts or climate model projections, alternative approaches are necessary. In this study, we first introduce a statistical model which is capable of identifying the inflow, ascent, and outflow phase of WCBs based on Eulerian input parameters. Validation against a Lagrangian-based dataset confirms that the statistical model is reliable in replicating the climatological frequency of WCBs as well as the footprints of WCBs at instantaneous time steps. Second, we employ the statistical model to verify the representation of WCBs in ECMWF’s S2S reforecasts (1997-2017). We find WCB frequency biases already at early lead times with an underestimation of WCB outflow over the North Atlantic and East Pacific towards North America. Furthermore, we find that the forecast skill already decays at forecast lead time days 7-10 with a slightly longer forecast horizon for the Pacific compared to the Atlantic region. These results indicate that synoptic-scale activity might hinder accurate forecasts of the large-scale extratropical circulation on subseasonal time scales. Future work will quantify, if and in which situations the represenation of WCBs affects forecast skill for large-scale flow patterns, such as Atlantic-European weather regimes, with a specific focus on atmospheric blocking.