Weakly coupled data assimilation for climate predictions with ICON-XPP

We present the development of the initialisation strategy for seasonal to decadal climate predictions based on the ICON-XPP model, within DWD’s Innovation in the Applied Research and Development (IAFE) program. The ICON-XPP model is based on several well-established model components:  the ICON-NWP, operational weather forecast model at the DWD, as atmospheric model, ICON-O as ocean model, JSBACH as land model and uses a hydrological discharge model. To develop a weakly coupled data assimilation system for ICON-XPP, we use the experience build on a former ICON-ESM version (Pohlmann et al 2023). In our weakly coupled data assimilation framework, we use two different assimilation methods for atmosphere and ocean. We initialize the ocean component of the climate system through a monthly assimilation of salinity and temperature profiles from the EN4 dataset. For this we use a localised singular evolutive interpolated Kalman filter implemented via the Parallel Data Assimilation Framework (PDAF, Nerger 2020). The atmosphere component is initialised by nudging temperature, pressure and horizontal wind fields of the ERA5 reanalysis. We conduct our experiments with a 25-member ensemble, which we put together from three different historical runs started from different states of our piControl run. In the atmosphere ICON-XPP is run as R2B5 (~80km resolution) with 130 vertical levels and in the ocean we use a resolution of ~40km (R2B6) with 72 vertical levels. Our assimilation experiments start from 1990 after a ten-year assimilation spin-up of the ocean. We show the results of our experiments with the weakly coupled data assimilation system and discuss its challenges.