Correcting a bias in a climate model with an augmented emulator

• Published in: Geoscientific Model Development 2020-05, Vol.13 (5), p.2487-2509
• Main Authors: McNeall, Doug, Williams, Jonny, Betts, Richard, Booth, Ben, Challenor, Peter, Good, Peter, Wiltshire, Andy
• Format: Article
• Language: English
• Subjects: Analysis; Bias; Calibration; Climate; Climate models; Computer applications; Computer simulation; Emulators; Errors; Forests; Gaussian process; Land surface models; Parameter identification; Parameter sensitivity; Parameter uncertainty; Parameterization; Parameters; Prejudice; Problems; Rooting; Sensitivity analysis; Setting (Literature); Simulation; Structural models
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-13-2487-2020

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. Setting these parameters traditionally relies heavily on insights from those involved in parameterisation of the underlying climate processes. Given the many degrees of freedom and computational expense involved in evaluating such a selection, this can be imperfect leaving open questions about whether any subsequent simulated biases result from mis-set parameters or wider structural model errors (such as missing or partially parameterised processes). 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.