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008			200709t20192019my ab||fram|| 000 0 eng d
020			_aTHE0008725(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aFKASA .I94 2019 r Bc.
100	0		_aMuhammad Izhar Ismail, _eauthor.
245	1	0	_aPerformances difference on climate between AR4 and AR5 / _cMuhammad Izhar Ismail
264		1	_aKuantan, Pahang : _bUMP, _c2019
264		4	_c© 2019
300			_axii, 99 pages : _billustrations, maps (some color) ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Civil Engineering and Earth Resources
502			_aProject Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2019
504			_aIncludes bibliographical references
520	3		_aThe Intergovernmental Panel on Climate Change (IPCC) revised the impact of greenhouse gases (GHGs) into the climate system and came out with the Fifth Assessment Report (AR5) in year 2014. By AR5, the climate changes impact were classified based on the level of radiation forcing known as Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). This version was slightly difference with the AR4 version which based on the GHGs groups known as A1, B1, A2, and B2. The modification in the climate changes assessment will affecting the accuracy of the climate projection in the long term. Therefore, the main aim of this study was to determine the impact in the climate change assessment between Fourth Assessment Report (AR4) with AR5. The study was focused on Eastern Malaysia including Kelantan and Terengganu. In this study, the Statistical Downscaling Model (SDSM) was used as a statistical climate model to assess the differences of the climate performances. Meanwhile, the general circulation model (GCM) provided by Climate Modelling and Analysis (CanESM2) was used for the long-term climate generation. Referring to the results, the predictor of p-u, r500, and r850 are the most influence variables in forming the local temperature and rainfall at the regions. The accuracy of the climate generation was controlled by the lower %MAE with high Correlation (R) in the calibrated and validated results. The temperature simulation was successfully to produce 0.6% of %MAE with R close to 1.0. Meanwhile the rainfall at Terengganu and Kelantan were produced less than 14% of %MAE with 0.99 of R. Based on the comparison performances between GCMs and historical data, the RCP4.5 (AR5) and SRES A2 (AR4) have been selected as the best radiation forcing level at Kelantan representative for different ARs. Meanwhile for Terengganu, the RCP2.6 (AR5) and SRES B2 (AR4) have been selected due to least %MAE performances. The projected climate results were expected to have minimum increment in the max (0.79%), mean (0.43%) and min (0.2%) temperature. The local rainfall shows increasing pattern with (9.37%) in Station Gunong Barat Bachok, (5.04%) for Station Rumah Pam Salor Pengkalan Kubor, (9.11%) for Station Sg. Simpang Ampat in Kelantan. For Terengganu, the pattern shows an increment of (4.43%) for Station Sek Men. Bukit Sawa, (5.25%) for Station Rumah Pam Pulau Musang, and (42.07%) for Station Kg Peringat.
610	2	0	_aFaculty of Civil Engineering and Earth Resources _xDisertations
650		0	_aUniversities and colleges _xDisertations
650		0	_aTheses
942			_2lcc _cPSM