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Coupled Ocean–Atmosphere Forecasts in the Presence of Climate Drift

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  • 1 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
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Abstract

Two different coupled atmosphere–ocean GCMs are used to forecast SST anomalies with lead times of up to one year. The initialization procedure does not balance the ocean and atmosphere components, nor is the coupled model flux corrected to maintain the correct mean state. Rather, the coupled model is allowed to evolve freely during the forecast. The inevitable climate drift is estimated across an ensemble of forecasts and subtracted to give the true forecast. Although the climate drift is often bigger than the interannual signal, the method works. This is true for a drift toward both warmer and colder SSTs, as exemplified by the two models.

The best way of establishing the mean bias correction from a small sample of prior forecasts is discussed. In some circumstances the sample median may be a more robust estimator than the sample mean. For the limited set of forecasts here, use of the median bias in the cross-correlated forecasts reduces forecast error, when compared to use of the mean bias.

Corresponding author address: Dr. Timothy N. Stockdale, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire RG2 9AX, United Kingdom.

Email: net@ecmwf.int

Abstract

Two different coupled atmosphere–ocean GCMs are used to forecast SST anomalies with lead times of up to one year. The initialization procedure does not balance the ocean and atmosphere components, nor is the coupled model flux corrected to maintain the correct mean state. Rather, the coupled model is allowed to evolve freely during the forecast. The inevitable climate drift is estimated across an ensemble of forecasts and subtracted to give the true forecast. Although the climate drift is often bigger than the interannual signal, the method works. This is true for a drift toward both warmer and colder SSTs, as exemplified by the two models.

The best way of establishing the mean bias correction from a small sample of prior forecasts is discussed. In some circumstances the sample median may be a more robust estimator than the sample mean. For the limited set of forecasts here, use of the median bias in the cross-correlated forecasts reduces forecast error, when compared to use of the mean bias.

Corresponding author address: Dr. Timothy N. Stockdale, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire RG2 9AX, United Kingdom.

Email: net@ecmwf.int

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