Regularisation of the 4DEnVar Data Assimilation method for Calibration of Land Surface Models

Abstract. The four-dimensional ensemble variational (4DEnVar) data assimilation method is an attractive choice for land surface model calibration due to its ease of use, speed, and its circumvention of tangent linear model and adjoint calculations. However, in certain circumstances, this method may prove ineffective when implemented with a highly non-linear model, leading to out-of-range or non-physical posterior parameter values. To address this, the 4DEnVar cost function can be adapted through the introduction of a hyperparameter, which inflates the weight of the background term. In this study, we explore the anticipated challenges of applying 4DEnVar with in situ eddy-covariance flux measurements and present some explanations of the expected behaviour. We show that, when using a hyperparameter to regularise the optimisation, 4DEnVar is able to successfully calibrate two complex land surface models, JULES and ORCHIDEE, with comparable accuracy in its ability to produce model runs with an improved match to the observations. To our knowledge, this is the first study of its kind to compare parameter calibration of two different land surface models in the same experimental setting. In addition to the aforementioned benefits of using 4DEnVar, we show that the method also exhibits considerable versatility, not only with regard to the land surface model to which it is applied, but also in terms of parameter set selection and ensemble size.