Iterative run-time bias corrections in an atmospheric GCM (LMDZ v6.3)

Abstract. Run-time bias corrections of atmospheric circulation models can be based on nudging (newtonian relaxation) to an atmospheric reanalysis. In this case, the time increments of selected state variables are modified by adding the nudging terms obtained with an uncorrected version the model. This is a well-known method to improve the models’ representation of large-scale circulation patterns. In this work, we propose and evaluate a variant of this method, consisting of iterative nudging: the corrected model is itself nudged towards the reanalysis, and the resulting nudging terms are added to the initial ones to calculate the new, iterated correction terms. This procedure can be iterated an arbitrary number of times. Evaluating the LMDZ atmospheric general circulation model (AGCM) for a varying number of iterations of nudging to the ERA5 reanalysis for the period 1981–2000, we show that the simulated large-scale circulation patterns over the period 2001–2020 are consistently improved when the bias correction procedure is iterated compared to the non-iterated original procedure. However, while there is a clear benefit of one or two iterations of the bias correction method, signs of over-correction appear after about three iterations.