Year: 2022
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Barboza, Luis A., Chen, Shu Wei Chou, Córdoba, Marcela Alfaro, Alfaro, Eric J., Hidalgo, Hugo G.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Awards: G3637520558, G5074946852, G8725670311
OpenAlex ID: W4281942166