Correcting a bias in a climate model with an augmented emulator
Geoscientific Model Development Discussions (2020)
A key challenge in developing flagship climate model configurations is the process of setting uncertain input parameters at values that lead to credible climate simulations.
Here, we present a complementary approach to identifying plausible climate model parameters, with a method of bias correcting subcomponents of a climate model using a Gaussian process emulator that allows credible values of model input parameters to be found even in the presence of a significant model bias.
The augmented emulator allows bias correction of an ensemble of climate model runs and reduces the risk of choosing poor parameter values because of an error in a subcomponent of the model.