03-03-2015

Partially coupled spin-up of Earth-System-Models (Modini)

Large-scale fully coupled Earth System Models (ESMs) are usually applied for climate projections like those presented in the reports of the Intergovernmental Panel on Climate Change.

In these models the internal variability is often within the correct order of magnitude compared with the observed climate, but due to internal variability and arbitrary initial conditions they are not able to reproduce the observed timing of climate events or shifts as for instance observed in the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), or the Atlantic Meridional Overturning Circulation (AMOC). Additional information about the real climate history is necessary to constrain ESMs; not only to emulate the past climate, but also to introduce a potential forecast skill into these models through a proper initialisation. The authors attempt to do this by extending the MPI-ESM using a partial coupling technique (Model initialization by partially coupled spin-up “Modini”). This method is implemented by adding observed (reanalysis) wind-field anomalies to the MPI-ESM's inherent climatological wind-field when computing the surface wind stress that is used to drive the ocean and sea ice model. Using anomalies instead of the full wind-field reduces potential model drifts, using total observed wind stress rather than anomalies could lead to different mean climate states of the unconstrained MPI-ESM and the partially-coupled Modini-MPI-ESM. The atmospheric component of Modini-MPI-ESM remains unconstrained and produces its own wind field. The advantage of this method, according to the authors, is that it is relatively easily transferable to other coupled systems. The authors show that Modini-MPI-ESM has a significant skill over the time period 1980 to 2013 in reproducing historical climate fluctuations, indicating the potential of the method for initialising seasonal to decadal forecasts. With this method, it is thus not only possible to better reproduce the observed climate of the past decades; it also ensures a more realistic initial state for climate predictions using ESMs.

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