Adjoint data assimilation in coupled atmosphere‐ocean models: Determining model parameters in a simple equatorial model

Abstract

AbstractSimple equatorial coupled atmosphere‐ocean models have been used to simulate the El Niño phenomenon. the possibility of determining the empirical parameters in these coupled models is explored by the adjoint data‐assimilation method. Identical twin experiments are conducted to retrieve six parameters (coupling and damping parameters) in a simple coupled model with the atmosphere and the ocean each represented by a single‐layer linear shallow‐water model with perfect initial conditions specified. Wind and sea level height (SLH) data, generated from a 40‐day unstable local‐growth simulation of a warm event, are assimilated to test the effects of data sparsity and noise on parameter retrieval.The temporal sparsity of both wind and SLH data is generally more detrimental for parameter estimation than spatial sparsity, and sparse wind data are more detrimental than sparse SLH data. Longer assimilation windows improve the parameter estimation, but the best window length for the wind data is not the best for the SLH data. A priori information for individual parameters as implemented in the cost function is useful in providing information for the size of the parameters and in enhancing the convexity of the cost function, but not as a substitute for inadequate data.