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 ensemble 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 computationally 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.