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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 29 Jul 2019

Submitted as: methods for assessment of models | 29 Jul 2019

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A revised version of this preprint is currently under review for the journal GMD.

Correcting a bias in a climate model with an augmented emulator

Doug McNeall1, Jonny Williams2, Richard Betts1,3, Ben Booth1, Peter Challenor3, Peter Good1, and Andy Wiltshire1 Doug McNeall et al.
  • 1Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
  • 2NIWA, 301 Evans Bay Parade, Hataitai, Wellington 6021, New Zealand
  • 3University of Exeter, North Park Road, Exeter, EX4 4QE, UK

Abstract. 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.

A previous study (McNeall et al., 2016) found that a climate model had to be run using land surface input parameter values from very different, almost non-overlapping parts of parameter space to satisfactorily simulate the Amazon and other forests respectively. As the forest fraction of modelled non-Amazon forests was broadly correct at the default parameter settings and the Amazon too low, that study suggested that the problem most likely lay in the model's treatment of non-plant processes in the Amazon region. This might be due to (1) modelling errors such as missing deep-rooting in the Amazon in the land surface component of the climate model, (2) a warm-dry bias in the Amazon climate of the model, or a combination of both.

In this study we bias correct the climate of the Amazon in a climate model using an augmented Gaussian process emulator, where temperature and precipitation, variables usually regarded as model outputs, are treated as model inputs alongside regular land surface input parameters. A sensitivity analysis finds that the forest fraction is nearly as sensitive to climate variables as changes in its land surface parameter values. Bias correcting the climate in the Amazon region using the emulator corrects the forest fraction to tolerable levels in the Amazon at many candidates for land surface input parameter values, including the default ones, and increases the valid input space shared with the other forests. We need not invoke a structural model error in the land surface model, beyond having too dry and hot a climate in the Amazon region.

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. We discuss the potential of the augmented emulator to act as a translational layer between model subcomponents, simplifying the process of model tuning when there are compensating errors, and helping model developers discover and prioritise model errors to target.

Doug McNeall et al.

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Doug McNeall et al.

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Analysis code and data for "Correcting a bias in a climate model with an augmented emulator" D. McNeall, J. Williams, R. Betts, B. Booth, P. Challenor, P. Good, and A. Wiltshire

Doug McNeall et al.


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Latest update: 28 Mar 2020
Publications Copernicus
Short summary
In the climate model FAMOUS, matching the modelled Amazon rainforest to observations required different land-surface parameter settings than for other forests. It was unclear if the problem was due to a bias in the modelled climate, or an error in the land surface component of the model. We use an emulator (a statistical model) to correct the climate in the Amazon, resulting in correcting the simulation of the Amazon forest, even at default parameter settings.
In the climate model FAMOUS, matching the modelled Amazon rainforest to observations required...