TY - JOUR
T1 - Precipitation projection using a CMIP5 GCM ensemble model
T2 - a regional investigation of Syria
AU - Homsi, Rajab
AU - Shiru, Mohammed Sanusi
AU - Shahid, Shamsuddin
AU - Ismail, Tarmizi
AU - Harun, Sobri Bin
AU - Al-Ansari, Nadhir
AU - Chau, Kwok Wing
AU - Yaseen, Zaher Mundher
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - general circulation model
KW - precipitation projection
KW - random forest
KW - symmetrical uncertainty
KW - Syria
UR - http://www.scopus.com/inward/record.url?scp=85075007804&partnerID=8YFLogxK
U2 - 10.1080/19942060.2019.1683076
DO - 10.1080/19942060.2019.1683076
M3 - Journal article
AN - SCOPUS:85075007804
SN - 1994-2060
VL - 14
SP - 90
EP - 106
JO - Engineering Applications of Computational Fluid Mechanics
JF - Engineering Applications of Computational Fluid Mechanics
IS - 1
ER -
