Abstract
We develop a data assimilation scheme with the Icosahedral
Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal
and seasonal climate predictions at the German weather service. For this
purpose, we implement an Ensemble Kalman Filter to the ocean component
as a first step towards a weakly coupled data assimilation. We performed
an assimilation experiment over the period 1960-2014. This ocean-only
assimilation experiment serves to initialize 10-year long retrospective
predictions (hindcasts) started each year on 1 November. On multi-annual
time scales, we find predictability of sea surface temperature and
salinity as well as oceanic heat and salt contents especially in the
North Atlantic. The mean Atlantic Meridional Overturning Circulation is
realistic and the variability is stable during the assimilation. On
seasonal time scales, we find high predictive skill in the tropics with
highest values in variables related to the El Niño/Southern Oscillation
phenomenon. In the Arctic, the hindcasts correctly represent the
decreasing sea ice trend in winter and, to a lesser degree, also in
summer, although sea ice concentration is generally much too low in both
hemispheres in summer. However, compared to other prediction systems,
prediction skill is relatively low in regions apart from the tropical
Pacific due to the missing atmospheric assimilation. In addition, we
expect a better fine-tuning of the sea ice and the oceanic circulation
in the Southern Ocean in ICON-ESM to improve the predictive skill. In
general, we demonstrate that our data assimilation method is
successfully initializing the oceanic component of the climate system.