Evaluation of Climate Variability Performances using Statistical Climate Models
Uncertainty of the climates nowadays bring the crucial calamities problems especially at unexpected areas and in anytime. Thus, the projection of climate variability becomes significant information especially in the designing, planning and managing of water resources and hydrological systems. Numer...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Kebangsaan Malaysia
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/23960/ http://umpir.ump.edu.my/id/eprint/23960/ http://umpir.ump.edu.my/id/eprint/23960/ http://umpir.ump.edu.my/id/eprint/23960/1/Evaluation%20of%20Climate%20Variability%20Performances.pdf |
| Summary: | Uncertainty of the climates nowadays bring the crucial calamities problems especially at unexpected areas and in anytime. Thus, the projection of climate variability becomes significant information especially in the designing, planning and managing of water resources and hydrological systems. Numerous climate models with varies methods and purposes have been developed to generate the local weather scenarios with considered the greenhouse gasses (GHGs) effect provided by General Circulation Models (GCMs). However, the accuracy and suitability of each climate models are depending on the atmospheric characters’ selection and the variables consideration to form the statistical equation of local-global weather relationship. In this study, there are two well-known statistical climate models were considered; Lars-
WG and SDSM models represent for the regression and weather typing methods, respectively. The main aim was to evaluate the performances among these climate models suit for the Pahang climate variability for the upcoming year ∆2050. The findings proved the Lars-WG as a reliable climate modelling with undemanding data sources and use simpler analysis method compared to the SDSM. It is able to produce better rainfall simulated results with lesser % MAE and higher R value close to 1.0. However, the SDSM lead in the temperature simulation with considered the most influenced meteorological parameters in the analysis. In year ∆2050, the temperature is expected to rise achieving 35°C. The rainfall projection results provided by these models are not consistent whereby it is expecting to increase 2.6% by SDSM and reduce 1.0% by Lars-WG from the historical trend and concentrated on Nov. |
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