Submitted as: model evaluation paper
03 Feb 2023
Submitted as: model evaluation paper |  | 03 Feb 2023
Status: this preprint is currently under review for the journal GMD.

Constraining the carbon cycle in JULES-ES-1.0

Douglas McNeall, Eddy Robertson, and Andy Wiltshire

Abstract. 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.

Douglas McNeall et al.

Status: open (until 31 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-280', Michel Crucifix, 08 Mar 2023 reply
  • RC2: 'Comment on gmd-2022-280', Anonymous Referee #2, 15 Mar 2023 reply

Douglas McNeall et al.

Douglas McNeall et al.


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Short summary
We can run simulations of the land surface using computer models, to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.