Projections of Future Climate Change in Singapore Based on a Multi-Site Multivariate Downscaling Approach

Xin Li,Vladan Babovic,Ke Zhang

doi:10.3390/w11112300

Abstract

Estimates of the projected changes in precipitation and temperature have great significance for adaption planning in the context of climate change. To obtain the climate change information at regional or local scale, downscaling approaches are required to downscale the coarse global climate model (GCM) outputs to finer resolutions in both spatial and temporal dimensions. The multi-site, multi-variate downscaling approach has received considerable attention recently due to its advantage in providing distributed, physically coherent downscaled meteorological fields for subsequent impact modeling. In this study, a newly developed multi-site multivariate statistical downscaling approach based on empirical copula was applied to downscale grid-based, monthly precipitation, maximum and minimum temperature outputs from nine global climate models to site-specific, daily data over four weather stations in Singapore. The advantage of this approach lies in its ability to reflect the at-site statistics, inter-site and inter-variable dependencies, and temporal structure in the downscaled data. The downscaling was conducted for two projection periods (i.e., the 2021–2050 and 2071–2100 periods) under two emission scenarios (i.e., representative concentration pathway (RCP)4.5 and RCP8.5 scenarios). Based on the downscaling results, projected changes in daily precipitation, maximum and minimum temperatures were examined. The results show that there is no consensus on the projected change in average precipitation over the two future periods. The major uncertainty for precipitation projection comes from the GCMs. For daily maximum and minimum temperatures, all downscaled GCMs project an increase of average temperature in the future. These change signals could be different from those of the original GCM data, both in magnitude and in direction. These findings could assist in adaption planning in Singapore in response to emerging climate risks.

Highlights

Estimates of the projected changes for the most relevant meteorological variables are essential for stakeholders to anticipate adaptation strategies for emerging climate risks
The Dynamical downscaling (DD) approach is able to generate finer-resolution climate data, the scale discrepancy remains between the regional climate model (RCM) grid-scale and the site-specific scale required by local studies
Observed inter-station correlations are well reproduced in the spatially-temporally downscaled ACCESS1.3 data post-processed by the Empirical Copula (EC) approach (ACCESS1.3-STD-EC) for precipitation occurrence, precipitation amount, and maximum and minimum temperatures at both daily and monthly timescales

Summary

Introduction

Estimates of the projected changes for the most relevant meteorological variables are essential for stakeholders to anticipate adaptation strategies for emerging climate risks. Water 2019, 11, 2300 several hundreds of kilometers) is too coarse to be directly used in regional or local climate change studies, downscaling approaches have to be developed to bridge the scale discrepancy between. GCM grid-scale and the resolution required by regional or local studies. GCM outputs from GCM grid-scale to regional and local scales. The DD approach nests a regional climate model (RCM) (with spatial resolution in the order of tens of kilometers) into the parent. The DD approach is able to generate finer-resolution climate data, the scale discrepancy remains between the RCM grid-scale and the site-specific scale required by local studies. The SD approaches are generally categorized into three groups [4]: perfect prognosis (PP; referred to as “perfect prog”), model output statistics (MOS)

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