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Assessing ENSO Simulations and Predictions Using Adjoint Ocean State Estimation

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  • 1 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
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Abstract

Simulations and seasonal forecasts of tropical Pacific SST and subsurface fields that are based on the global Consortium for Estimating the Circulation and Climate of the Ocean (ECCO) ocean-state estimation procedure are investigated. As compared to similar results from a traditional ENSO simulation and forecast procedure, the hindcast of the constrained ocean state is significantly closer to observed surface and subsurface conditions. The skill of the 12-month lead SST forecast in the equatorial Pacific is comparable in both approaches. The optimization appears to have better skill in the SST anomaly correlations, suggesting that the initial ocean conditions and forcing corrections calculated by the ocean-state estimation do have a positive impact on the predictive skill. However, the optimized forecast skill is currently limited by the low quality of the statistical atmosphere. Progress is expected from optimizing a coupled model over a longer time interval with the coupling statistics being part of the control vector.

Current affiliation: Leibniz-Institut für Meereswissenschaften (IFM-GEOMAR), Kiel, Germany

Current affiliation: Institut für Meereskunde, Zentrum für Meeres-und Klimaforschung, Universität Hamburg, Hamburg, Germany

Corresponding author address: Dietmar Dommenget, Leibniz-Institut für Meereswissenschaften (IFM-GEOMAR), Düsternbrooker Weg 20, 24015 Kiel, Germay. Email: ddommenget@ifm.uni-kiel.de

Abstract

Simulations and seasonal forecasts of tropical Pacific SST and subsurface fields that are based on the global Consortium for Estimating the Circulation and Climate of the Ocean (ECCO) ocean-state estimation procedure are investigated. As compared to similar results from a traditional ENSO simulation and forecast procedure, the hindcast of the constrained ocean state is significantly closer to observed surface and subsurface conditions. The skill of the 12-month lead SST forecast in the equatorial Pacific is comparable in both approaches. The optimization appears to have better skill in the SST anomaly correlations, suggesting that the initial ocean conditions and forcing corrections calculated by the ocean-state estimation do have a positive impact on the predictive skill. However, the optimized forecast skill is currently limited by the low quality of the statistical atmosphere. Progress is expected from optimizing a coupled model over a longer time interval with the coupling statistics being part of the control vector.

Current affiliation: Leibniz-Institut für Meereswissenschaften (IFM-GEOMAR), Kiel, Germany

Current affiliation: Institut für Meereskunde, Zentrum für Meeres-und Klimaforschung, Universität Hamburg, Hamburg, Germany

Corresponding author address: Dietmar Dommenget, Leibniz-Institut für Meereswissenschaften (IFM-GEOMAR), Düsternbrooker Weg 20, 24015 Kiel, Germay. Email: ddommenget@ifm.uni-kiel.de

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