Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria

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...

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Bibliographic Details
Published in:Engineering applications of computational fluid mechanics 2020-01, Vol.14 (1), p.90-106
Main Authors: Homsi, Rajab, Shiru, Mohammed Sanusi, Shahid, Shamsuddin, Ismail, Tarmizi, Harun, Sobri Bin, Al-Ansari, Nadhir, Chau, Kwok-Wing, Yaseen, Zaher Mundher
Format: Article
Language:English
Subjects:
Annual precipitation
Climate change
Climate models
Decision analysis
Decision trees
Dry season
general circulation model
General circulation models
Geoteknik
Mapping
Multiple criterion
Precipitation
precipitation projection
Rainy season
random forest
Soil Mechanics
symmetrical uncertainty
Syria
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Summary: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.
ISSN:1994-2060
1997-003X
1997-003X
DOI:10.1080/19942060.2019.1683076