Poster Open Access
Initialization is a key step when performing climate predictions, and for this the use of the latest high-quality observations and their assimilation in the model realm is of paramount importance. Less attention has been paid to other essential aspects of initialization that are equally important. For example, inconsistencies between the initial conditions (ICs) used for the different model components can cause important initialization shocks, hindering the prediction capacity during the first weeks of the forecast. In this study we investigate this and other different contributions to the forecast error in a seasonal prediction system with the EC-Earth general circulation model where sea ice is initialized via Ensemble Kalman filter assimilation of European Space Agency (ESA) derived sea ice concentrations. Large initial forecast errors in Arctic sea ice appear in regions of high observational uncertainty and little model spread, a combination that brings the assimilation, and in turn the ICs, close to the model attractor and far from the observations. We also investigated the development of the model drift during the first forecast month, and how it competes with the initial shock due to the inconsistency in ICs. After 24 (19) days the drift, as characterized by the systematic model error, becomes the largest contributor to the forecast error for the May (November) initialized forecasts, while the initial inconsistency dominates in the previous days. However, there are regions like the Greenland Sea for which the impact of the ICs inconsistency is still present after one month. Moreover, the development of both types of errors is sensitive to the month of initialization: the shock is more pronounced in November than in May. The main differences between both months relate to the systematic error, which is much higher in November, as well as to the direction of the shock with respect to the seasonal trend. In both cases the shock leads to sea ice melting, but, unlike in May, in November it happens in a context of sea ice expansion. The results stress that this opposing effect during November might be enhancing the generation of the drift. Our findings also highlight the importance of looking at high frequency data to disentangle the evolution of errors within the first forecast month, whose effects are harder to detect with the monthly averages.