Model-consistent ocean data assimilation for seasonal to decadal climate prediction

Abstract

<p>In Earth's climate system, the slowly varying ocean represents an important source of memory for predictions on the seasonal to decadal time scale. The ocean picks up atmospheric variability on a broad range of scales and feeds back on the large-scale atmospheric circulation. While today’s comprehensive Earth system models (ESMs) used in climate prediction are able to simulate this atmosphere-ocean feedback in a broad sense, data assimilation - which brings the climate model close to the observed state – allows the use of ESMs for climate predictions. We propose that the quality of climate predictions can be improved by initializing the ESMs using a model-consistent assimilation of observations resulting in (1) an initialization of the ESM with a model state close to the observed one, while (2) minimizing a potential initialization shock resulting from a mismatch between the simulated climate state and observations.<br />Here we demonstrate our approach towards a model-consistent assimilation of two ESMs used in climate prediction at Universität Hamburg and Deutscher Wetterdienst: MPI-ESM and ICON-ESM. Central to our approach is a weakly coupled assimilation setup, consisting of an Ensemble Kalman filter assimilation scheme in the ocean component (MPI-ESM, ICON-ESM) and a nudging assimilation scheme in the atmospheric component (MPI-ESM). We show that our approach facilitates a large part of atmosphere-ocean interaction already within the assimilation, allowing for a quick adaption of the assimilation in case of unrealistic behaviour of key processes. For two key large-scale oceanic processes, Atlantic meridional overturning circulation and oceanic Rossby waves, we analyze how sensitive they are to the degree of atmosphere-ocean interaction allowed for during assimilation and what this implies for the respective climate predictions. </p>