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

Supplement

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

Data sets

Code for "A new metric for climate models that includes field and spatial dependencies using Gaussian Markov Random Fields" A. Nosedal-Sanchez, C. S. Jackson, and G. Huerta https://doi.org/10.5281/zenodo.33765

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.