Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2016-20
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
04 Mar 2016
Review status
This discussion paper is a preprint. It has been under review for the journal Geoscientific Model Development (GMD). A final paper in GMD is not foreseen.
Empirical Bayes approach to climate model calibration
Charles S. Jackson1 and Gabriel Huerta2 1Institute for Geophysics, University of Texas at Austin, Austin, TX, USA
2Department of Math and Statistics, University of New Mexico, NM, USA
Abstract. Climate data is highly correlated through the physics and dynamics of the atmosphere. Model evaluation often involves averages of various quantities over different regions and seasons making it difficult from a statistical perspective to quantify the significance of differences that arise between a model and observations. Here we present a strategy that makes use of a set of perfect modeling experiments to quantify the effects of these correlations on model evaluation metrics. This information is incorporated into Bayesian inference through a precision parameter with informative priors. These concepts are illustrated through an example of fitting a line through data that includes either uncorrelated or correlated noise as well as to the calibration of CAM3.1. The concept of a precision parameter may be applied as a strategy to weight different climate model evaluation metrics within a multivariate normal framework. From the example with CAM3.1, the precision parameter plays a central role in rescaling the estimated parametric uncertainties to better accommodate modeling structural errors.

Citation: Jackson, C. S. and Huerta, G.: Empirical Bayes approach to climate model calibration, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-20, 2016.
Charles S. Jackson and Gabriel Huerta
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'Review of "Empirical Bayes approach..."', Anonymous Referee #1, 20 May 2016 Printer-friendly Version 
 
EC1: 'Editor comment', Klaus Gierens, 14 Jul 2016 Printer-friendly Version 
 
AC1: 'Response to reviewers #1 and #2.', Charles Jackson, 15 Aug 2016 Printer-friendly Version Supplement 
Charles S. Jackson and Gabriel Huerta

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Jackson, C. S. and Huerta, G.
https://doi.org/10.5281/zenodo.33545
Charles S. Jackson and Gabriel Huerta

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Short summary
Climate data is highly correlated which can make it difficult from a statistical perspective to quantify the significance of differences that arise between a model and observations. Here we explore a common device in Bayesian inference for assessing the statistical significance of a fit between a model and data and suggest how this approach may be applied to the calibration of a climate model.
Climate data is highly correlated which can make it difficult from a statistical perspective to...
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