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

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

doi:https://doi.org/10.5194/gmd-9-2407-2016

Articles | Volume 9, issue 7

https://doi.org/10.5194/gmd-9-2407-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-9-2407-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 7

Methods for assessment of models

|

20 Jul 2016

Methods for assessment of models |

| 20 Jul 2016

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

Alvaro Nosedal-Sanchez, Charles S. Jackson, and Gabriel Huerta

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Final revised paper (published on 20 Jul 2016)
Supplement to the final revised paper
Preprint (discussion started on 15 Jan 2016)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of "A new metric for climate models that includes field and spatial dependencies using Gaussian Markov Random Fields" by Nosedal-Sanchez et al', Anonymous Referee #1, 26 Feb 2016
RC2: 'Nosedal et al 2016 GMRF review', Anonymous Referee #2, 11 Mar 2016
AC1: 'Response to reviewers #1 and #2.', Charles Jackson, 09 Apr 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Charles Jackson on behalf of the Authors (21 May 2016) Author's response Manuscript

ED: Publish subject to minor revisions (Editor review) (08 Jun 2016) by Paul Ullrich

AR by Charles Jackson on behalf of the Authors (10 Jun 2016) Author's response Manuscript

ED: Publish as is (12 Jun 2016) by Paul Ullrich

AR by Charles Jackson on behalf of the Authors (21 Jun 2016)

Short summary

We have developed a new metric for climate model evaluation that quantifies the significance of modeling errors across multiple fields. Our approach dramatically reduces the amount of data that is required to evaluate field and space dependencies and increases the community's potential to make use of the extremely valuable but limited satellite observational record. Our objective is to improve the strategies that currently exist for more formal data-driven model development.