Efficient identification of ocean thermodynamics in a physical/biogeochemical ocean model with an iterative Importance Sampling method
Efficient identification of parameters in numerical models remains a computationally demanding problem. Here we present an iterative Importance Sampling approach and demonstrate its application to estimating parameters that control the heat uptake efficiency of a physical/biogeochemical ocean model coupled to a simple atmosphere. The algorithm has similarities to a previously-developed ensem- ble Kalman filtering (EnKF) method applied to similar problems, but is more flexible and powerful in the case of nonlinear models and non-Gaussian uncertainties. The method is somewhat more computation- ally demanding than the EnKF but may be preferred in cases where the approximations that the EnKF relies upon are unsound. Our results suggest that the three-dimensional structure of ocean tracer fields may act as a useful constraint on ocean mixing and consequently the heat uptake of the climate system under anthropogenic forcing.
Ocean Modelling, 32, 205-215
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