Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM

Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM

Abstract Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive fo...

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Bibliographic Details
Main Authors: Oliver R. A. Dunbar, Alfredo Garbuno‐Inigo, Tapio Schneider, Andrew M. Stuart
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2021-09-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
uncertainty quantification
model calibration
machine learning
general circulation model
parametric uncertainty
inverse problem
Online Access:https://doi.org/10.1029/2020MS002454

Internet

https://doi.org/10.1029/2020MS002454

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