Constraining the carbon cycle in JULES-ES-1.0

Land surface models are widely used to study climate change and its impacts, but uncertainties in input parameter settings and model errors hamper their use. We use 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 and carbon cycle. Using a Gaussian Process emulator trained on the ensemble to predict the model output, we can repeat this process for parts of parameter space where the model is not yet tested. 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 and carbon cycle observations. We rule out 88% of the initial input parameter space as being statistically inconsistent with observed land surface behaviour. We use the emulator to perform 3 types of 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, eliminate model biases, and make better carbon cycle projections.