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Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM

Dunbar, Oliver R. A. and Garbuno-Inigo, Alfredo and Schneider, Tapio and Stuart, Andrew (2021) Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210113-143919927

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Abstract

Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes an optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov chain Monte Carlo (MCMC) methods typically require O(10⁵) model runs, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only O(10²) model runs and can accommodate internal climate variability. The approach consists of three stages: (i) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (ii) an emulation stage emulates the parameter-to-data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (iii) a sampling stage approximates the Bayesian posterior distributions by using the GP emulator and then samples using MCMC. We demonstrate the feasibility and computational efficiency of this calibrate-emulate-sample (CES) approach in a perfect-model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from data surrogates generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1002/essoar.10505626.1DOIDiscussion Paper
https://arxiv.org/abs/2012.13262arXivDiscussion Paper
https://doi.org/10.5281/zenodo.4393029DOICode
ORCID:
AuthorORCID
Dunbar, Oliver R. A.0000-0001-7374-0382
Garbuno-Inigo, Alfredo0000-0003-3279-619X
Schneider, Tapio0000-0001-5687-2287
Additional Information:License: Attribution 4.0 International. Published Online: Mon, 4 Jan 2021. This work was supported by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program, by the Hopewell Fund, the Paul G. Allen Family Foundation, and the National Science Foundation (NSF, award AGS1835860). A.M.S. was also supported by the Office of Naval Research (award N00014-17-1-2079). We thank Emmet Cleary for his preliminary work underlying some of the results shown here. Data Availability: All computer code used in this paper is open source. The code for the idealized GCM, the Julia code for the CES algorithm, the plot tools, and the slurm/bash scripts to run both GCM and CES are available at https://doi.org/10.5281/zenodo.4393029.
Funders:
Funding AgencyGrant Number
Schmidt Futures ProgramUNSPECIFIED
Hopewell FundUNSPECIFIED
Paul G. Allen Family FoundationUNSPECIFIED
NSFAGS-1835860
Office of Naval Research (ONR)N00014-17-1-2079
Subject Keywords:Atmospheric Sciences, Atmospheric Sciences / Numerical Modelling, Informatics
Record Number:CaltechAUTHORS:20210113-143919927
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210113-143919927
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107459
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:13 Jan 2021 22:53
Last Modified:13 Jan 2021 22:53

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