ICON-XPP in the CERISE project: a first set of seasonal hindcasts

The Copernicus Climate Change Service Evolution (CERISE) project, as an EU-HORIZON project, aims to enhance the quality of the C3S (Copernicus Climate Change Service) reanalysis and seasonal forecast portfolio, with a focus on land-atmosphere coupling. CERISE will develop new and innovative ensemble-based coupled land-atmosphere data assimilation approaches and land surface initialisation techniques to pave the way for the next generations of the C3S reanalysis and seasonal prediction systems. Deutscher Wetterdienst is developing its land data assimilation for the initialisation of seasonal forecasts with ICON-XPP. ICON-XPP (ICON eXtended Predictions and Projections) is not only the state-of-art climate modelling system in Germany but also an effort to unify knowledge and experiences from many institutes in one model system that can deliver seamless weather and climate simulations. We aim to include snow analysis, soil moisture analysis and leaf area index assimilation into our climate forecast data assimilation system. Here we present a baseline version of multi-year historical forecasts, so-called hindcasts for a period of 1993 to 2022. The current system to generate initial conditions consists of an Ensemble Kalman Filter for the ocean while nudging is applied in the atmosphere. Further, annually changing ESA-CCI land cover replaces the LUH2 data during this time, which is processed into plant functional types (PFTs) for use with the land model JSBACH. Results from these first seasonal hindcasts using the ICON-XPP model will be shown and discussed. Of special interest are the start months for the boreal spring and autumn forecasts, as the impact of land data assimilation is expected to be strongest.