A statistical approach to multi‐site multivariate downscaling of daily extreme temperature series

Abstract

Downscaling methods for describing the linkage between global-scale climate variables and local climatic conditions have been frequently used in climate-related impact assessment studies. Previous works, however, have been mainly dealing with downscaling of climatic processes for a single site, but very few studies are concerned with the downscaling of these processes for multi-sites because of the complexity in accurately describing both observed at-site temporal persistence and spatial dependence between different locations. In the present study, a multi-site multivariate statistical downscaling (SD) approach was developed for simulating daily maximum (T max) and minimum (T min) temperature series at many sites concurrently. The proposed approach consists of a combination of a linear regression component to describe the linkage between global climate predictors and local temperature extremes, and a stochastic component based on a spatial moving average process to reproduce the observed spatial dependence between temperature extremes at different sites. The feasibility of the suggested SD method was assessed using observed daily extreme temperature data available at 10 weather stations located in the southwest region of Quebec and the southeast region of Ontario in Canada, as well as climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset for the 1961-1990 period. It was found that the proposed SD approach was able to accurately describe various T max and T min characteristics, including their spatial and temporal variation as well as their interannual anomalies. In addition, comparison of the results from the proposed multi-site multivariate SD method and one simulation series from the Canadian Regional Climate Model (CRCM) at a 45-km resolution (a dynamic downscaling procedure) has indicated that the suggested SD approach was able to more accurately describe the observed spatial and temporal characteristics of extreme temperature series at the regional scale than the CRCM-based dynamic downscaling method.

A spatial temporal downscaling approach to development of IDF relations for Perth airport region in the context of climate change

“…Among them, the Statistical Downscaling Model (SDSM) proposed by Wilby et al (2002) is the most popular tool that is based on the regression concept (Khalili et al 2013). According to the comparisons of SDSM rainfall downscaling performance with Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model (Khan et al 2006), LARS-WG model (Hashmi et al 2011), Smooth Support Vector Machine (SSVM) (Chen et al 2012), SDSM shows high performance in future rainfall downscaling.…”

Genetic Programming for the Downscaling of Extreme Rainfall Events on the East Coast of Peninsular Malaysia

“…A number of studies on downscaling extreme indices has been carried out in recent years [12][13][14][15][16][17]. However, this is still new in Malaysia and Southeast Asian countries, where studies of statistical downscaling have focused mainly on mean climate.…”

“…Two major downscaling approaches are often used: dynamical downscaling methods that are based on high-resolution regional climate models [8,9] and statistical downscaling methods based on some established statistical relationships between large-scale atmospheric variables (predictors) and local climate variables (predictands) [10,11]. Compared to dynamic downscaling, statistical downscaling requires simple computational skills to downscale GCM outputs in order to understand possible future changes in climate at the local scale [12].…”

Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China

“…Numerous multisite downscaling methods have been developed, e.g., dynamic methods based on regional climate models (Cooley and Sain, 2010;Bárdossy and Pegram, 2012;Pegram and Bárdossy, 2013), empirical scaling methods (Allerup, 1996;Bürger and Chen, 2005), generalized linear models Yang et al, 2005;Lu and Qin, 2014;Asong et al, 2016), artificial neural networks (Harpham and Wilby, 2005;Cannon, 2008), nonhomogeneous hidden Markov models (Charles et al, 1999;Bellone et al, 2000;Fu et al, 2013), and weather generators (Wilks, 1999a;Qian et al, 2002;Mehrotra and Sharma, 2010;Khalili et al, 2013;Srivastav and Simonovic, 2015). Thus far, the application to hydrological modeling of most of these methods has been limited, except for the stochastic weather generator.…”