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dc.contributor.authorMcNeall, D
dc.contributor.authorWilliams, J
dc.contributor.authorBetts, R
dc.contributor.authorBooth, B
dc.contributor.authorChallenor, P
dc.contributor.authorGood, P
dc.contributor.authorWiltshire, A
dc.date.accessioned2020-07-17T15:44:15Z
dc.date.issued2020-05-29
dc.description.abstractA 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 modelling errors such as missing deep rooting in the Amazon in the land surface component of the climate model, to 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 the climate model from McNeall et al. (2016) using an "augmented" Gaussian process emulator, where temperature and precipitation, variables usually regarded as model outputs, are treated as model inputs alongside land surface input parameters. A sensitivity analysis finds that the forest fraction is nearly as sensitive to climate variables as it is to 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.en_GB
dc.description.sponsorshipAlan Turing Instituteen_GB
dc.identifier.citationVol. 13, no.5, pp. 2487 - 2509en_GB
dc.identifier.doi10.5194/gmd-13-2487-2020
dc.identifier.urihttp://hdl.handle.net/10871/121996
dc.language.isoenen_GB
dc.publisherCopernicus Publicationsen_GB
dc.rights© Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 Licenseen_GB
dc.titleCorrecting a bias in a climate model with an augmented emulatoren_GB
dc.typeArticleen_GB
dc.date.available2020-07-17T15:44:15Z
dc.identifier.issn1991-959X
dc.descriptionThis is the final version. Available from Copernicus Publications via the DOI in this record. en_GB
dc.identifier.eissn1991-9603
dc.identifier.journalGeoscientific Model Developmenten_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2020-04-15
exeter.funder::Alan Turing Instituteen_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2020-04-15
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2020-07-17T15:41:02Z
refterms.versionFCDVoR
refterms.dateFOA2020-07-17T15:44:19Z
refterms.panelBen_GB


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© Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License
Except where otherwise noted, this item's licence is described as © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License