Abstract
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.
Highlights
Climate scientists are interested in developing new metrics for assessing how well climate simulations reproduce observed climate for purposes of comparing models, driving model development, and evaluating model prediction uncertainties (Gleckler et al, 2008; Reichler and Kim, 2008; Santer et al, 2009; Knutti et al, 2010; Weigel et al, 2010; Braverman et al, 2011)
Where v is the vector of differences between model output and observations with a length given by the product of the number of observational fields and number of grid points, nobs npts, α is a scalar with a value close to zero, I stands for an identity matrix of dimension npts corresponding to v, and Q is a precision operator of dimension npts × npts from a Gaussian Markov random fields (GMRFs) induced by a firstorder neighborhood structure
We introduce a “witch hat” graph that provides a compact summary of variance–covariance information between these two methods in order to show that GMRFs do a reasonable job approximating observed field and space relationships
Summary
Climate scientists are interested in developing new metrics for assessing how well climate simulations reproduce observed climate for purposes of comparing models, driving model development, and evaluating model prediction uncertainties (Gleckler et al, 2008; Reichler and Kim, 2008; Santer et al, 2009; Knutti et al, 2010; Weigel et al, 2010; Braverman et al, 2011). Where v is the vector of differences between model output and observations with a length given by the product of the number of observational fields and number of grid points, nobs npts , α is a scalar with a value close to zero, I stands for an identity matrix (a diagonal matrix of ones) of dimension npts corresponding to v, and Q is a precision operator of dimension npts × npts from a GMRF induced by a firstorder neighborhood structure.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
