Prediction of future climate trend using stochastic weather generator

The issue of climate change and its effects on various aspects of the environment has become more challenges for society. It is desirable to analyse and predict the changes of critical climatic variables, such as rainfall, temperature and potential evapotranspiration affect in the content of global...

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Main Author:	Ahmad Saifuddin, Othman
Format:	Undergraduates Project Papers
Language:	English English English
Published:	2016
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/15881/ http://umpir.ump.edu.my/id/eprint/15881/ http://umpir.ump.edu.my/id/eprint/15881/1/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Table%20of%20contents-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf http://umpir.ump.edu.my/id/eprint/15881/2/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Abstract%20-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf http://umpir.ump.edu.my/id/eprint/15881/3/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Chapter%201-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf

id	ump-15881
recordtype	eprints
spelling	ump-158812016-12-20T07:04:19Z http://umpir.ump.edu.my/id/eprint/15881/ Prediction of future climate trend using stochastic weather generator Ahmad Saifuddin, Othman TA Engineering (General). Civil engineering (General) The issue of climate change and its effects on various aspects of the environment has become more challenges for society. It is desirable to analyse and predict the changes of critical climatic variables, such as rainfall, temperature and potential evapotranspiration affect in the content of global climate change. This change is also affected by the increment of gas carbon dioxide (CO2) and other gases GHGs emissions. This study is focus of analyse the prediction patterns of rainfall, temperature and potential evapotranspiration of Pahang state. The rainfall pattern can be estimate the future climate change, general circulation models (GCMs) are applied. Therefore, Long Ashton Research Station Weather Generator (LARS-WG), which utilized the Stochastic Weather Generators approach, is applied in order to convert the coarse spatial resolution of the GCMs output into a fine resolution. The result show that the changes in rainfall, temperature and potential evapotranspiration can be consider is state to the trend of change in the respective by years. Therefore, the quantity of annual rainfall decreases had reached above 64%, while the distribution of temperature can increases had reached above 10% and potential evapotranspiration raise had reached above 44% increases of the end of century. In this study, have seen different results from the PRECIS and LARS-WG models though we have used the same GCMs (HadCM3) and emission scenario, which reveals the uncertainties due to the downscaling method. The LARS-WG result shows difference with PRECIS for rainfall and temperature. However the monthly rainfall prediction by LARS-WG is performed well closer to the history compare to the PRECIS. The annually LARS-WG is performance well closer with 1.19% to the history compare to the PRECIS with 32.86%. 2016-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/15881/1/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Table%20of%20contents-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/15881/2/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Abstract%20-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/15881/3/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Chapter%201-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf Ahmad Saifuddin, Othman (2016) Prediction of future climate trend using stochastic weather generator. Faculty of Civil Engineering and Earth Resources, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:98167&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Ahmad Saifuddin, Othman Prediction of future climate trend using stochastic weather generator
description	The issue of climate change and its effects on various aspects of the environment has become more challenges for society. It is desirable to analyse and predict the changes of critical climatic variables, such as rainfall, temperature and potential evapotranspiration affect in the content of global climate change. This change is also affected by the increment of gas carbon dioxide (CO2) and other gases GHGs emissions. This study is focus of analyse the prediction patterns of rainfall, temperature and potential evapotranspiration of Pahang state. The rainfall pattern can be estimate the future climate change, general circulation models (GCMs) are applied. Therefore, Long Ashton Research Station Weather Generator (LARS-WG), which utilized the Stochastic Weather Generators approach, is applied in order to convert the coarse spatial resolution of the GCMs output into a fine resolution. The result show that the changes in rainfall, temperature and potential evapotranspiration can be consider is state to the trend of change in the respective by years. Therefore, the quantity of annual rainfall decreases had reached above 64%, while the distribution of temperature can increases had reached above 10% and potential evapotranspiration raise had reached above 44% increases of the end of century. In this study, have seen different results from the PRECIS and LARS-WG models though we have used the same GCMs (HadCM3) and emission scenario, which reveals the uncertainties due to the downscaling method. The LARS-WG result shows difference with PRECIS for rainfall and temperature. However the monthly rainfall prediction by LARS-WG is performed well closer to the history compare to the PRECIS. The annually LARS-WG is performance well closer with 1.19% to the history compare to the PRECIS with 32.86%.
format	Undergraduates Project Papers
author	Ahmad Saifuddin, Othman
author_facet	Ahmad Saifuddin, Othman
author_sort	Ahmad Saifuddin, Othman
title	Prediction of future climate trend using stochastic weather generator
title_short	Prediction of future climate trend using stochastic weather generator
title_full	Prediction of future climate trend using stochastic weather generator
title_fullStr	Prediction of future climate trend using stochastic weather generator
title_full_unstemmed	Prediction of future climate trend using stochastic weather generator
title_sort	prediction of future climate trend using stochastic weather generator
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/15881/ http://umpir.ump.edu.my/id/eprint/15881/ http://umpir.ump.edu.my/id/eprint/15881/1/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Table%20of%20contents-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf http://umpir.ump.edu.my/id/eprint/15881/2/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Abstract%20-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf http://umpir.ump.edu.my/id/eprint/15881/3/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-Chapter%201-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf
first_indexed	2023-09-18T22:21:03Z
last_indexed	2023-09-18T22:21:03Z
_version_	1777415658557931520

Prediction of future climate trend using stochastic weather generator

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