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Title: Precipitation projection using a CMIP5 GCM ensemble model : a regional investigation of Syria
Authors: Homsi, R
Shiru, MS
Shahid, S
Ismail, T
Bin Harun, S
Al-Ansari, N
Chau, KW 
Yaseen, ZM
Issue Date: 2020
Source: Engineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 90-106
Abstract: The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by -30 to -85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to -30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by -12 to -93%, which indicated a drier climate for the country in the future.
Keywords: Precipitation projection
General circulation model
Random forest
Symmetrical uncertainty
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2019.1683076
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cite.
The following publication Rajab Homsi, Mohammed Sanusi Shiru, Shamsuddin Shahid, TarmiziIsmail, Sobri Bin Harun, Nadhir Al-Ansari, Kwok-Wing Chau & Zaher Mundher Yaseen(2020) Precipitation projection using a CMIP5 GCM ensemble model: a regional investigationof Syria, Engineering Applications of Computational Fluid Mechanics, 14:1, 90-106 is available at
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