Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter

We describe the development of an efficient method for parameter estimation and ensemble forecasting in climate modelling. The technique is based on the ensemble Kalman filter and is several orders of magnitude more efficient than many others which have been previously used to address this problem. As well as being theoretically (near-)optimal, the method does not suffer from the `curse of dimensionality' and can comfortably handle multivariate parameter estimation. We demonstrate the potential of this method in identical twin testing with an intermediate complexity coupled AOGCM. The model's climatology is successfully tuned via the simultaneous estimation of 12 parameters. Several minor modifications arc described by which the method was adapted to a steady state (temporally averaged) case. The method is relatively simple to implement, and with only O(50) model runs required, we believe that optimal parameter estimation is now accessible even to computationally demanding models.