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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2016-20
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-2016-20
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: methods for assessment of models 04 Mar 2016

Submitted as: methods for assessment of models | 04 Mar 2016

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This preprint has been withdrawn by the authors.

Empirical Bayes approach to climate model calibration

Charles S. Jackson1 and Gabriel Huerta2 Charles S. Jackson and Gabriel Huerta
  • 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.

This preprint has been withdrawn.

Charles S. Jackson and Gabriel Huerta

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Interactive discussion

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Charles S. Jackson and Gabriel Huerta

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Code for "Empirical Bayes approach to climate model calibration" 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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