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Impact of Ocean Data Assimilation on Climate Predictions with ICON-ESM
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  • Holger Pohlmann,
  • Sebastian Brune,
  • Kristina Fröhlich,
  • Johann H Jungclaus,
  • Christine Sgoff,
  • Johanna Baehr
Holger Pohlmann
Deutscher Wetterdienst

Corresponding Author:[email protected]

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Sebastian Brune
Universität Hamburg
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Kristina Fröhlich
Deutscher Wetterdienst
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Johann H Jungclaus
Max Planck Institute for Meteorology
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Christine Sgoff
Deutscher Wetterdienst
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Johanna Baehr
Universität Hamburg, Center for Earth System Research and Sustainability
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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.