A Novel Spatial Downscaling Approach for Climate Change Assessment in Regions With Sparse Ground Data Networks

Abstract

Due to the increased variability in the climate caused by global warming, the number of natural disasters has risen over the past four decades (Brown et al., 2008). Extreme events beyond the historical record and the bounds of natural variability have led to human casualties, property damages, and socioeconomic problems, creating international disagreements (AghaKouchak et al., 2014;Meehl et al., 2000;Oki & Kanae, 2006). It has become increasingly important to consider the changes in extreme events to design for safety from natural disasters as the climate gets warmer.Climate models (e.g., global climate models [GCMs] and regional climate models [RCMs]) represent the main tools used to assess the future climate and the associated changes in the hydrological circulation over a long-term planning horizon (Borgomeo et al., 2014;Haro-Monteagudo et al., 2020;Steinschneider et al., 2015). These climate models attempt to simulate accurately the current climate as well as the response of the climate system to projected greenhouse gas concentrations into the future (Kattsov et al., 2007;Kripalani et al., 2007). However, model simulations are known to exhibit systematic bias, which has limited the direct use of especially precipitation from climate models (S. Kim, Eghdamirad, et al., 2020;Woldemeskel et al., 2016). GCMs often have a low spatial resolution (100-300 km), with which regional climate may not be well reproduced (Diaconescu et al., 2018). In this context, RCMs with higher resolutions of 50 km or less can provide a better representation of localized extreme rainfall events at finer spatial scales (Hadjinicolaou Abstract This study proposes a novel approach that expands the existing QDM (quantile delta mapping) to address spatial bias, using Kriging within a Bayesian framework to assess the impact of using a point reference field. Our focus here is to spatially downscale daily rainfall sequences simulated by regional climate models (RCMs), coupled to the proposed QDM-spatial bias-correction, in which the distribution parameters are first interpolated onto a fine grid (rather than the observed daily rainfall). The proposed model is validated through a cross-validatory (CV) evaluation using rainfall data from a set of weather stations in South Korea and climate change scenarios simulated by three alternate RCMs. The results demonstrate the efficacy of the proposed model to simulate the bias-corrected daily rainfall sequences over large regions at fine resolutions. A discussion of the potential use of the proposed approach in the field of hydrometeorology is also offered.Plain Language Summary Climate models can simulate biased representations of atmospheric processes, necessitating procedures for correction before use in hydrological applications. Such spatial bias can be caused for many reasons, one of which is the use of point data in establishing a spatial reference field to compare model simulations against. The most straightforward way to address this bias is to interpolate the locally observed data at the weat...

Direct and Indirect Application of Univariate and Multivariate Bias Corrections on Heat-stress Indices based on Multi-RCM Simulations

“…A variety of BC methods with different levels of complexity and performance have been developed and implemented for both global and regional climate simulations (François et al, 2020; Y.-T. Kim et al, 2021;Teutschbein & Seibert, 2012). Generally, their aim is to correct certain features in the target's distribution, such as the simple statistics of the mean (Linear Scaling, LS, Teutschbein & Seibert, 2012) and variance (Variance Scaling, VA, Chen & Dudhia, 2001), or the more advanced quantiles (Quantile Mapping, QM) for adjusting the entire distribution by parametric (PQM) or empirical (EQM) transformation (Gudmundsson et al, 2012;Switanek et al, 2017).…”