An Adaptive Procedure for Tuning a Sea Surface Temperature Model

Abstract

To determine the value of the adjustable parameters of an ocean model required to optimally fit the observations, an adaptive inverse method is developed and applied to a sea surface temperature (SST) model of the tropical Atlantic. The best-fit calculation is performed by minimizing a misfit between observed and simulated data, which depends on the observational and the modeling errors. An adaptive procedure is designed in which the model being tuned is also used to construct a model of the observational errors. This is done by performing the optimization on the mean seasonal cycle and using the SST anomalies obtained for different years and plausible forcing fields as additional information to construct a sample estimate of the observational error covariance matrix. Assuming idealized modeling errors, the procedure is applied to the SST model of Blumenthal and Cane, yielding refined estimates for several models and heat flux parameters. The simulation of the mean annual SST is improved but not the simulation of seasonal and interannual variability. The model-observation discrepancies remain too large to be solely attributed to atmospheric and oceanic data uncertainties and are linked to the model's rudimentary geometry and its incorrect representation of SST cooling by upwelling. The existence of larger model deficiencies than was originally assumed in the model errors is confirmed by a statistical test of the correctness of the assumptions in the inverse calculation.