New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

Abstract

In applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of sub-grid variability and the spatial and temporal dependence at the finer scale. Here, a post-processing procedure is proposed for temperature projections that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In a first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Secondly, residual spacetime dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study where projections of two regional climate models from the EURO-CORDEX ensemble are bias-corrected and downscaled to a 1 × 1 km grid in the Trøndelag area of Norway.A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time than empirical quantile mapping.

Stochastic downscaling of gridded precipitation to spatially coherent subgrid precipitation fields using a transformed Gaussian model

“…Our method presented here may be applied to (regional or global) climate model output, adjusts for biases and enables downscaling to finer spatially coherent fields. It shares conceptual ideas with the method for temperature by Yuan et al (2019), but is designed for generating precipitation fields. The method can be considered a conditional weather generator and shares all the advantages of this approach.…”

“…It shares conceptual ideas with the method for temperature by Yuan et al . (2019), but is designed for generating precipitation fields. The method can be considered a conditional weather generator and shares all the advantages of this approach.…”

“…A possible alternative strand employs stochastic models, borrowed from multisite weather generators (Onof et al, 2000;Ferraris et al, 2003;Wilks, 2012;Chen et al, 2018;Maraun and Widmann, 2018;Li and Babovic, 2019). Yuan et al (2019) developed a first implementation of such a model for daily temperature based on the Gaussian distribution and using an ARMA process to model temporal dependence.…”

“…Yuan et al . (2019) developed a first implementation of such a model for daily temperature based on the Gaussian distribution and using an ARMA process to model temporal dependence.…”

Comment on hess-2020-673

“…Climate change information from an RCM output may be added in a transparent manner by updating each component of the weather generator based on estimated climate change in the corresponding component at the coarser RCM scale. Yuan et al (2019) propose a similar model for obtaining high-resolution daily mean temperature projections.…”