Comment on gmd-2022-280

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Land surface models are an important tool in the study of climate change and its impacts, but their use can be hampered by uncertainties in input parameter settings and by errors in the models. We apply Uncertainty Quantification (UQ) techniques to constrain the input parameters of JULES-ES-1.0, the land surface component of the UK Earth system model UKESM1.0. We use an ensemble of historical simulations of the land surface model to rule out ensemble members and corresponding input parameter settings that do not match modern observations of the land surface. As JULES-ES-1.0 is computationally expensive, we use a cheap statistical proxy termed an emulator, trained on the ensemble of model runs, to rule out untested parts of parameter space. We use history matching, an iterated approach to constraining JULES-ES-1.0, running an initial ensemble and training the emulator, before choosing a second wave of ensemble members consistent with historical land surface observations. We rule out 88 % of the initial input parameter space as being statistically inconsistent with observed land surface behaviour. We use sensitivity analysis to identify the most (and least) important input parameters for controlling the global output of JULES-ES-1.0, and provide information on how parameters might be varied to improve the performance of the model and eliminate model biases.