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Meng Wang

Arizona State University



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Yiannis Kamarianakis

Arizona State University



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Matei Georgescu

Arizona State University



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Alex Mahalov

Arizona State University



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244 – Data Fusion for Environmental Applications

Spatio-Temporal Modeling for Regional Climate Model Comparison: Application on Perennial Bioenergy Crop Impacts

Sponsor: Section on Statistics and the Environment
Keywords: Bayesian, spatio-temporal, regional climate models, biofuel

Meng Wang

Arizona State University

Yiannis Kamarianakis

Arizona State University

Matei Georgescu

Arizona State University

Alex Mahalov

Arizona State University

This article presents spatio-temporal Bayesian models for analyzing regional climate model outputs. WRF simulated temperatures associated with control simulation bias, as well as biofuel impacts, were modeled using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes displayed satisfactory performance in capturing spatio-temporal associations. The effects of microphysics parameterizations in reproducing near-surface climatic conditions were found statistically significant. Simulated temperature impacts due to perennial bioenergy crop expansion were robust to physics parameterization schemes.

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