A multivariate spatial statistical model for statistical downscaling of sea surface temperature in the Great Barrier Reef region

We propose a statistical downscaling method to produce fine-resolution climate projections. A multivariate spatial statistical model is developed to jointly analyse high-resolution remote sensing data and coarse-resolution climate model outputs. With a basis function representation, the resulting model can achieve efficient computation and describe potentially nonstationary spatial dependence. We implement our method to produce downscaled sea surface temperature projections over the Great Barrier Reef region from CMIP6 Earth system models. Compared with the state of the art, our method reduces the mean squared predictive error substantially and produces a predictive distribution enabling holistic uncertainty quantification analyses.

Duke Scholars

Author Peter Kalmus Earth and Climate Sciences