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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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2016
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| Online Access: | http://umpir.ump.edu.my/id/eprint/15881/1/Prediction%20of%20future%20climate%20trend%20using%20stochastic%20weather%20generator-FKASA-Ahmad%20Saifuddin%20Othman-CD10296.pdf http://umpir.ump.edu.my/id/eprint/15881/ |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English |
| Summary: | 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%. |
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