P2.4
Parameterization of subsurface temperatures in the Lamont ocean model using neural networks
Shuyong Li, University of British Columbia, Vancouver, BC, Canada; and W. W. Hsieh and A. Wu
In the Lamont coupled model of the tropical Pacific, the ocean model uses a simple parameterization scheme for the subsurface temperature, which is replaced here by a neural network (NN) scheme. The inputs of the NNs are the six leading principal components (PCs) (i.e. EOF time series) of the 20oC isotherm depth, and the output is one of the five leading PCs of the subsurface temperature anomalies. Forced by the Florida State University wind stress, the ocean models were run from January 1964 to August 2001. The NN-enhanced Lamont ocean model simulated the sea surface temperature anomalies (SSTA) better than the original Lamont ocean model in the central and western equatorial Pacific. Improvements by the NN-enhanced model is also seen from the principal component analysis (PCA) and nonlinear principal component analysis (NLPCA) of the SSTA.
Poster Session 2, Seasonal to interannual climate prediction with emphasis on the 2002 El Nino (Hall 4AB)
Thursday, 15 January 2004, 9:45 AM-9:45 AM, Hall 4AB
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