Monthly and seasonal prediction of sea surface temperature anomalies in the Gulf of Mexico
A thermodynamic model is used to predict the sea surface temperature (SST) anomalies in the Gulf of Mexico for extended periods as long as 3 months and for seasonal prediction. The basic equation of the model is the thermodynamic energy equation applied to the upper mixed layer of the ocean, which i...
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Veröffentlicht in: | Journal of marine systems 2000-11, Vol.26 (3), p.289-302 |
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Sprache: | eng |
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Zusammenfassung: | A thermodynamic model is used to predict the sea surface temperature (SST) anomalies in the Gulf of Mexico for extended periods as long as 3 months and for seasonal prediction. The basic equation of the model is the thermodynamic energy equation applied to the upper mixed layer of the ocean, which includes the horizontal transport of heat by mean ocean currents and by eddy turbulence, as well as heating by short and long-wave radiation, evaporation and sensible heat given off to the atmosphere.
An objective verification of the skill of the predictions is presented for the period from March 1986 to February 1987.
As an initial condition for the first month of the prediction we used the observed SST anomalies in the previous month, and for the second and third month we used the predicted monthly value in the previous corresponding month.
Regarding the atmospheric interaction, the results show that the initial atmospheric forcing, which consists of the observed anomalies of surface air temperature and the surface wind in the month previous to the first month of the prediction, plays an important role in the prediction.
The skill of the model is increased in the semi-prediction where we use, as atmospheric forcing, the surface air temperature anomalies and the surface wind anomalies for the current month instead of those belonging at the previous month to the first month of prediction. This result suggests that a coupled model in which were predicted simultaneously the ocean temperature and the atmospheric variables would improve the predictions of the SST anomalies. |
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ISSN: | 0924-7963 1879-1573 |
DOI: | 10.1016/S0924-7963(00)00040-3 |