A spatial analysis of multivariate output from regional climate models

TL;DR

This paper introduces a new multivariate hierarchical statistical model using Markov random fields to analyze and characterize the spatial dependencies and uncertainties in regional climate model outputs, specifically temperature and precipitation.

Contribution

It presents a novel multivariate Markov random field representation for flexible modeling of spatial dependencies in climate model ensembles, addressing limited sample sizes.

Findings

Effective modeling of spatial dependencies in climate data

Application to regional climate projections over the western US

Insights into temperature and precipitation changes

Abstract

Climate models have become an important tool in the study of climate and climate change, and ensemble experiments consisting of multiple climate-model runs are used in studying and quantifying the uncertainty in climate-model output. However, there are often only a limited number of model runs available for a particular experiment, and one of the statistical challenges is to characterize the distribution of the model output. To that end, we have developed a multivariate hierarchical approach, at the heart of which is a new representation of a multivariate Markov random field. This approach allows for flexible modeling of the multivariate spatial dependencies, including the cross-dependencies between variables. We demonstrate this statistical model on an ensemble arising from a regional-climate-model experiment over the western United States, and we focus on the projected change in seasonal temperature and precipitation over the next 50 years.