The goal of Module A during the second phase of MiKlip (MiKlip II) is (i) to improve estimates of initial conditions for the ocean, sea ice and the land soil moisture, (ii) based on those initial conditions to improve initialization procedures and finally (iii) to optimize procedures to span the MiKlip forecast ensemble. The workpackage WP3 of Modul A is the subproject Atmospheric and Oceanic Data Assimilation & Ensembles Generation (AODA-PENG2). Its focus will be on the ensemble generation. Using two different approaches in MPI-ESM1.1, ensemble simulations will be performed and investigated to sample the sources of initial uncertainty through appropriate ensemble generation. Then the subsequent error growth during the predictions is used to quantify the development of prediction uncertainties.
As one approach, for dynamically oriented ensemble generation, the breeding method will be tested. The method differs from the classical breeding in the sense that specific time scales and spatial scales can be selected a priori to generate growing uncertainty modes. The breeding technique is a method used for constructing disturbances on the ocean prognostic variables. These disturbances are added to the initial model state of each forecast. The method extracts error modes which develop most strongly in space and time where the long term variability of the ocean physical processes is responsible for interannual and decadal changes.
A second approach will be based on the already implemented Ensemble Kalman Filter technique in the oceanic component of MPI-ESM 1.1. The current global implementation of the Ensemble Kalman Filter shows hindcast skill when the inherent data assimilation ensemble is used for forecast initialization. However, recent studies have shown that the quality of the Ensemble Kalman data assimilation can be considerably improved in two ways: the use of the localized variant together with artificial covariance inflation and the increase of the ensemble size to more than 30 realisations. After upgrading our current implementation accordingly, we will further investigate the impact on hindcast skill and also analyse the ensemble spread and error growth.
Institut für Meereskunde Universität Hamburg
Prof. Dr. Johanna Baehr
Dr. Sebastian Brune
Meteorologisches Institut Universität Bonn
Prof. Dr. Andreas Hense
Dr. Vanya Romanova
Müller, V. | H. Pohlmann, A. Düsterhus, D. Matei, J. Marotzke, W. A. Müller, M. Zeller and J. Baehr
Brune, S. | L. Nerger and J. Baehr
Romanova, V. | A. Hense
Hazeleger, W. | B. Wouters, G. van Oldenborgh, S. Corti, D. Smith, N. Dunstone, J. Kroeger, H. Pohlmann, and J.-S. von Storch