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Open AccessArticle

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

by Xin Li 1,*, Ke Zhang 2,* and Vladan Babovic 3
1
College of Hydrology and Water Resources and CMA-HHU Joint Laboratory for HydroMeteorological Studies, Hohai University, Nanjing 210098, China
2
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, CMA-HHU Joint Laboratory for HydroMeteorological Studies, and College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
3
Department of Civil and Environmental Engineering, National University of Singapore, Singapore 119077, Singapore
*
Authors to whom correspondence should be addressed.
Water 2019, 11(11), 2300; https://doi.org/10.3390/w11112300
Received: 26 September 2019 / Revised: 25 October 2019 / Accepted: 30 October 2019 / Published: 3 November 2019
(This article belongs to the Section Hydrology)
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. View Full-Text
Keywords: multi-site multivariate downscaling; Empirical Copula; inter-site correlation; inter-variable dependence; temporal structure; Singapore multi-site multivariate downscaling; Empirical Copula; inter-site correlation; inter-variable dependence; temporal structure; Singapore
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Li, X.; Zhang, K.; Babovic, V. Projections of Future Climate Change in Singapore Based on a Multi-Site Multivariate Downscaling Approach. Water 2019, 11, 2300.

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