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Parameter uncertainty quantification in an idealized GCM with a seasonal cycle

Howland, Michael F. and Dunbar, Oliver R. A. and Schneider, Tapio (2021) Parameter uncertainty quantification in an idealized GCM with a seasonal cycle. . (Unpublished)

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Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude, compared with anthropogenic climate change, in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof-of-concept with an idealized general circulation model (GCM). Uncertainty quantification is performed using the calibrate-emulate-sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect-model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the size of the parameter posterior distributions leads to a reduction in the uncertainty of climate model predictions.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper ItemCode
Howland, Michael F.0000-0002-2878-3874
Dunbar, Oliver R. A.0000-0001-7374-0382
Schneider, Tapio0000-0001-5687-2287
Additional Information:The copyright holder for this preprint is the author/funder. Published Online: Wed, 4 Aug 2021. This work was supported by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program and by the National Science Foundation (NSF, award AGS-1835860). We thank Andrew Stuart for thoughtful comments on the work and the manuscript. Data Availability: All computer code used in this paper is open source. The code is available at: An open-source Julia implementation of CES is accessible at:
Funding AgencyGrant Number
Schmidt Futures ProgramUNSPECIFIED
Subject Keywords:Atmospheric Sciences, Atmospheric Sciences / Numerical Modelling, Atmospheric Sciences / Atmospheric Dynamics, Information and Computing Sciences / Machine Learning, Information and Computing Sciences
Record Number:CaltechAUTHORS:20210823-173258629
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110383
Deposited By: Tony Diaz
Deposited On:24 Aug 2021 18:58
Last Modified:24 Aug 2021 18:58

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