Spatio-temporal Downscaling Emulator for Regional Climate Models: a Comparative Study

TL;DR

This paper introduces a spatio-temporal statistical downscaling emulator for Regional Climate Models, comparing two estimation methods, and demonstrates its effectiveness and flexibility using NARCCAP data.

Contribution

It proposes a novel spatio-temporal model with varying coefficients as a downscaling emulator for RCMs, comparing INLA and varycoef methods for estimation.

Findings

The emulator accurately estimates non-stationary effects across space.

The model effectively predicts spatially and temporally varying climate variables.

INLA is faster and more accurate for parameter estimation.

Abstract

Regional Climate Models (RCM) describe the meso scale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from General Circulation Models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of computer time more than statistical climate downscaling. In this paper we describe how to use a spatio-temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using varying coefficients. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non-stationary marginal effects, which means that the downscaling output can vary over space. Furthermore, the model has flexibility to estimate the mean of any variable in space and time, and has good prediction results. INLA was the fastest method for all the cases, and the approximation with best accuracy to estimate the different parameters from the model and the posterior distribution of the response variable.