Evolving Bayesian Emulators for Structured Chaotic Time Series, with Application to Large Climate Models
Evolving Bayesian Emulators for Structured Chaotic Time Series, with Application to Large Climate Models
We develop Bayesian dynamic linear model Gaussian processes for emulation of time series output for computer models that may exhibit chaotic behavior, but where this behavior retains some underlying structure. The statistical technology is particularly suited to emulating the time series output of large climate models that exhibit this feature and where we want samples from the posterior of the emulator to evolve in the same way as dynamic processes in the computer model do. The methodology combines key features of good uncertainty quantification (UQ) methods such as using complex mean functions to capture large-scale signals within parameter space, with dynamic linear models in a way that allows UQ to borrow strength from the Bayesian time series literature. We present an MCMC algorithm for sampling from the posterior of the emulator parameters when the roughness lengths of the Gaussian process are unknown. We discuss an interpretation of the results of this algorithm that allows us to use MCMC to fix the correlation lengths, making future online samples from the emulator tractable when used in practical applications where online MCMC is infeasible. We apply this methodology to emulate the Atlantic Meridional Overturning Circulation (AMOC) as a time series output of the fully coupled non--flux-adjusted atmosphere-ocean general circulation model HadCM3.
Read More: http://epubs.siam.org/doi/abs/10.1137/120900915
dynamic emulation, uncertainty quantification, climate models, Bayesian analysis
1-28
Williamson, Daniel
4c0c5b3b-69ac-48d5-aa6e-1b52219f2c81
Blaker, Adam T.
9135d534-3d5a-431b-b4c2-879a41e70a44
May 2014
Williamson, Daniel
4c0c5b3b-69ac-48d5-aa6e-1b52219f2c81
Blaker, Adam T.
9135d534-3d5a-431b-b4c2-879a41e70a44
Williamson, Daniel and Blaker, Adam T.
(2014)
Evolving Bayesian Emulators for Structured Chaotic Time Series, with Application to Large Climate Models.
SIAM/ASA Journal on Uncertainty Quantification, 2 (1), .
(doi:10.1137/120900915).
Abstract
We develop Bayesian dynamic linear model Gaussian processes for emulation of time series output for computer models that may exhibit chaotic behavior, but where this behavior retains some underlying structure. The statistical technology is particularly suited to emulating the time series output of large climate models that exhibit this feature and where we want samples from the posterior of the emulator to evolve in the same way as dynamic processes in the computer model do. The methodology combines key features of good uncertainty quantification (UQ) methods such as using complex mean functions to capture large-scale signals within parameter space, with dynamic linear models in a way that allows UQ to borrow strength from the Bayesian time series literature. We present an MCMC algorithm for sampling from the posterior of the emulator parameters when the roughness lengths of the Gaussian process are unknown. We discuss an interpretation of the results of this algorithm that allows us to use MCMC to fix the correlation lengths, making future online samples from the emulator tractable when used in practical applications where online MCMC is infeasible. We apply this methodology to emulate the Atlantic Meridional Overturning Circulation (AMOC) as a time series output of the fully coupled non--flux-adjusted atmosphere-ocean general circulation model HadCM3.
Read More: http://epubs.siam.org/doi/abs/10.1137/120900915
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Published date: May 2014
Keywords:
dynamic emulation, uncertainty quantification, climate models, Bayesian analysis
Organisations:
Marine Systems Modelling
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Local EPrints ID: 365714
URI: http://eprints.soton.ac.uk/id/eprint/365714
PURE UUID: ab260947-e80a-464d-90f8-b9f6f17e3558
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Date deposited: 13 Jun 2014 10:22
Last modified: 14 Mar 2024 17:00
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Author:
Daniel Williamson
Author:
Adam T. Blaker
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