History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble
History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble
We apply an established statistical methodology called history matching to constrain the parameter space of a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3) by using a 10,000-member perturbed physics ensemble and observational metrics. History matching uses emulators (fast statistical representations of climate models that include a measure of uncertainty in the prediction of climate model output) to rule out regions of the parameter space of the climate model that are inconsistent with physical observations given the relevant uncertainties. Our methods rule out about half of the parameter space of the climate model even though we only use a small number of historical observations. We explore 2 dimensional projections of the remaining space and observe a region whose shape mainly depends on parameters controlling cloud processes and one ocean mixing parameter. We find that global mean surface air temperature (SAT) is the dominant constraint of those used, and that the others provide little further constraint after matching to SAT. The Atlantic meridional overturning circulation (AMOC) has a non linear relationship with SAT and is not a good proxy for the meridional heat transport in the unconstrained parameter space, but these relationships are linear in our reduced space. We find that the transient response of the AMOC to idealised CO2 forcing at 1 and 2 % per year shows a greater average reduction in strength in the constrained parameter space than in the unconstrained space. We test extended ranges of a number of parameters of HadCM3 and discover that no part of the extended ranges can by ruled out using any of our constraints. Constraining parameter space using easy to emulate observational metrics prior to analysis of more complex processes is an important and powerful tool. It can remove complex and irrelevant behaviour in unrealistic parts of parameter space, allowing the processes in question to be more easily studied or emulated, perhaps as a precursor to the application of further relevant constraints.
Bayesian uncertainty quantification, History matching, Implausibility, Observations, NROY space
1703-1729
Williamson, Daniel
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Goldstein, Michael
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Allison, Lesley
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Blaker, Adam
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Challenor, Peter
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Jackson, Laura
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Yamazaki, Kuniko
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October 2013
Williamson, Daniel
4c0c5b3b-69ac-48d5-aa6e-1b52219f2c81
Goldstein, Michael
59aacc18-5a0d-4595-8d60-e0924bf48cb6
Allison, Lesley
7dc74756-12bb-4519-b5d3-146e0fa576b5
Blaker, Adam
94efe8b2-c744-4e90-87d7-db19ffa41200
Challenor, Peter
a7e71e56-8391-442c-b140-6e4b90c33547
Jackson, Laura
48fca1af-6cc5-4314-80b4-b21ad3245749
Yamazaki, Kuniko
2fafeed3-7672-4eb2-a832-2bd101c19324
Williamson, Daniel, Goldstein, Michael, Allison, Lesley, Blaker, Adam, Challenor, Peter, Jackson, Laura and Yamazaki, Kuniko
(2013)
History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble.
Climate Dynamics, 41 (7-8), .
(doi:10.1007/s00382-013-1896-4).
Abstract
We apply an established statistical methodology called history matching to constrain the parameter space of a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3) by using a 10,000-member perturbed physics ensemble and observational metrics. History matching uses emulators (fast statistical representations of climate models that include a measure of uncertainty in the prediction of climate model output) to rule out regions of the parameter space of the climate model that are inconsistent with physical observations given the relevant uncertainties. Our methods rule out about half of the parameter space of the climate model even though we only use a small number of historical observations. We explore 2 dimensional projections of the remaining space and observe a region whose shape mainly depends on parameters controlling cloud processes and one ocean mixing parameter. We find that global mean surface air temperature (SAT) is the dominant constraint of those used, and that the others provide little further constraint after matching to SAT. The Atlantic meridional overturning circulation (AMOC) has a non linear relationship with SAT and is not a good proxy for the meridional heat transport in the unconstrained parameter space, but these relationships are linear in our reduced space. We find that the transient response of the AMOC to idealised CO2 forcing at 1 and 2 % per year shows a greater average reduction in strength in the constrained parameter space than in the unconstrained space. We test extended ranges of a number of parameters of HadCM3 and discover that no part of the extended ranges can by ruled out using any of our constraints. Constraining parameter space using easy to emulate observational metrics prior to analysis of more complex processes is an important and powerful tool. It can remove complex and irrelevant behaviour in unrealistic parts of parameter space, allowing the processes in question to be more easily studied or emulated, perhaps as a precursor to the application of further relevant constraints.
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Published date: October 2013
Keywords:
Bayesian uncertainty quantification, History matching, Implausibility, Observations, NROY space
Organisations:
Marine Systems Modelling
Identifiers
Local EPrints ID: 359716
URI: http://eprints.soton.ac.uk/id/eprint/359716
ISSN: 0930-7575
PURE UUID: ebb4095c-b37c-4c4d-8a8b-617355a2ba06
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Date deposited: 08 Nov 2013 16:05
Last modified: 14 Mar 2024 15:27
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Contributors
Author:
Daniel Williamson
Author:
Michael Goldstein
Author:
Lesley Allison
Author:
Adam Blaker
Author:
Peter Challenor
Author:
Laura Jackson
Author:
Kuniko Yamazaki
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