To enable climate models for decadal climate predictions is the central aim of MiKlip. The respective climate model needs to resemble the observed climate state and its tendency at the initialization of the prediction. This is only possible through the incorporation of observations into oceanic and/or atmospheric components of the climate model.
The initialization of weather prediction systems has a long tradition and is based on sophisticated techniques. The current development in atmospheric initialization is focused on variational methods and better representation of error covariances and their development in time, which is partly covered in 4D variational (4DVar) by singular vectors, but increasingly more based on ensemble initialization techniques like Ensemble Transform and different types of Ensemble Kalman Filter (EnKF).
The central aim of the project AODA-PENG is threefold. First, we will incorporate state-of-the-art ensemble techniques into the recent but still very simple medium range climate prediction system using the experience gained during the development of a weather prediction system. The consortium formed by MPI for Meteorology (MPI-M), Meteorological Institute University Bonn (MIUB), and the German National Weather Service (DWD) has already successfully developed an ensemble forecasting scheme for the global weather forecast model GME of DWD within the DFG funded priority program PP1167-PQP “Quantitative Precipitation Forecast”. Second, we will apply data assimilation techniques such as 3D variational (3DVar) and EnKF schemes, and latest available observations jointly with ensemble generation by breeding to provide a state-of-the-art initialization for the planned decadal prediction system. This will be done systematically for the atmospheric and oceanic components. We consider this combination of coupled data assimilation and ensemble generation techniques as the particular strength of our approach. Third, we will analyse the aerosol forcing being an important but still very uncertain external forcing factor on the long time scales of climate projections with respect to its relevance for medium range climate predictions.
Meteorologisches Institut Universität Bonn (MIUB)
Prof. Dr. Andreas Hense
Dr. Vanya Romanova
Institut für Meereskunde Universität Hamburg
Prof. Dr. Johanna Baehr
Max Planck Institut für Meteorologie
Dr. Luis Kornblueh
Dr. Wolfgang Müller
Dr. Jin-Song von Storch
Dr. Jürgen Kröger
Deutscher Wetterdienst (DWD)
Dr. Andreas Rhodin