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Title: A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

Abstract

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.

Authors:
; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1266397
Grant/Contract Number:
SC0006985; SC0010843
Resource Type:
Journal Article: Published Article
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 9; Journal Issue: 7; Related Information: CHORUS Timestamp: 2016-07-20 02:36:21; Journal ID: ISSN 1991-9603
Publisher:
Copernicus GmbH
Country of Publication:
Germany
Language:
English

Citation Formats

Nosedal-Sanchez, Alvaro, Jackson, Charles S., and Huerta, Gabriel. A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields. Germany: N. p., 2016. Web. doi:10.5194/gmd-9-2407-2016.
Nosedal-Sanchez, Alvaro, Jackson, Charles S., & Huerta, Gabriel. A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields. Germany. doi:10.5194/gmd-9-2407-2016.
Nosedal-Sanchez, Alvaro, Jackson, Charles S., and Huerta, Gabriel. 2016. "A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields". Germany. doi:10.5194/gmd-9-2407-2016.
@article{osti_1266397,
title = {A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields},
author = {Nosedal-Sanchez, Alvaro and Jackson, Charles S. and Huerta, Gabriel},
abstractNote = {A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.},
doi = {10.5194/gmd-9-2407-2016},
journal = {Geoscientific Model Development (Online)},
number = 7,
volume = 9,
place = {Germany},
year = 2016,
month = 7
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.5194/gmd-9-2407-2016

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