Process‐Based Climate Model Development Harnessing Machine Learning: II. Model Calibration From Single Column to Global

Abstract

Abstract We demonstrate a new approach for climate model tuning in a realistic situation. Our approach, the mathematical foundations and technical details of which are given in Part I, systematically uses a single‐column configuration of a global atmospheric model on test cases for which reference large‐eddy‐simulations are available. The space of free parameters is sampled running the single‐column model from which metrics are estimated in the full parameter space using emulators. The parameter space is then reduced by retaining only the values for which the emulated metrics match large eddy simulations within a given tolerance to error. The approach is applied to the 6A version of the LMDZ model which results from a long investment in the development of physics parameterizations and by‐hand tuning. The boundary layer is revisited by increasing the vertical resolution and varying parameters that were kept fixed so far, which improves the representation of clouds at process scale. The approach allows us to automatically reach a tuning of this modified configuration as good as that of the 6A version. We show how this approach helps accelerate the introduction of new parameterizations. It allows us to maintain the physical foundations of the model and to ensure that the improvement of global metrics is obtained for a reasonable behavior at process level, reducing the risk of error compensations that may arise from over‐fitting some climate metrics. That is, we get things right for the right reasons.