Articles | Volume 9, issue 7
Geosci. Model Dev., 9, 2407–2414, 2016
Geosci. Model Dev., 9, 2407–2414, 2016

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-Sanchez1,2, Charles S. Jackson3, and Gabriel Huerta1 Alvaro Nosedal-Sanchez et al.
  • 1Department of Mathematics and Statistics, The University of New Mexico, Albuquerque, USA
  • 2Department of Mathematical and Computational Sciences, University of Toronto, Mississauga, USA
  • 3Institute for Geophysics, The University of Texas at Austin, Austin, USA

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.

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.