{"id":{"repo_id":"east-anglia","oai_identifier":"oai:ueaeprints.uea.ac.uk:36354"},"canonical_url":"https://search.dev.ndltd.org/etd/east-anglia/oai:ueaeprints.uea.ac.uk:36354","repository":{"repo_id":"east-anglia","name":"University of East Anglia","base_url":"https://ueaeprints.uea.ac.uk/cgi/oai2"},"display":{"title":"The climatology of Thailand and future climate change projections using the regional climate model precis","abstract":"The climate of Thailand has not been studied in as much depth as in other parts of continental Southeast Asia. The baseline climate of Thailand during 1961-1990 is first analysed using daily observational data from five surface stations, each representing a different region of Thailand, supplemented by the high resolution 0.5° monthly gridded observational dataset, CRUTS2.1. The latter leads to a deeper understanding of the spatial variation in seasonal cycles of key climate variables in Thailand. Also revealed is an increase in the number of tropical depressions crossing Thailand during La Niña years. It was found that there is a statistically significant intensification (reduction) of precipitation during La Niña (El Niño) years at Surat Thani (Chiang Mai) in southern (northern) Thailand during ON (JJAS). This work facilitates the Regional Climate Model validation work which follows. The Providing REgional Climates for Impact Studies regional climate model, PRECIS, was run for the first time over Southeast Asia to specifically study the climate of Thailand. The first phase is model validation during the 1961-1990 baseline period. An ensemble of RCM runs is undertaken to study the sensitivity to the driving GCM. The added value provided by PRECIS in comparison to the coarser driving models is discussed. The possible causes of model bias are investigated. The model projections for the end of this century are undertaken based on high (SRESA2) and low (SRES-B2) emission scenarios which estimate the range of possible climate change in Thailand. These RCM simulations suggest trends in temperature that are broadly in line with those reported by IPCC. PRECIS A2 and B2 simulations mostly produce small precipitation increases in JJAS and small precipitation increases (decreases) during DJF under the A2 (B2) scenario. Wet season precipitation increases appear to be related to higher rain intensity on fewer rain days.","abstract_html":"The climate of Thailand has not been studied in as much depth as in other parts of continental Southeast Asia. The baseline climate of Thailand during 1961-1990 is first analysed using daily observational data from five surface stations, each representing a different region of Thailand, supplemented by the high resolution 0.5° monthly gridded observational dataset, CRUTS2.1. The latter leads to a deeper understanding of the spatial variation in seasonal cycles of key climate variables in Thailand. Also revealed is an increase in the number of tropical depressions crossing Thailand during La Niña years. It was found that there is a statistically significant intensification (reduction) of precipitation during La Niña (El Niño) years at Surat Thani (Chiang Mai) in southern (northern) Thailand during ON (JJAS). This work facilitates the Regional Climate Model validation work which follows. The Providing REgional Climates for Impact Studies regional climate model, PRECIS, was run for the first time over Southeast Asia to specifically study the climate of Thailand. The first phase is model validation during the 1961-1990 baseline period. An ensemble of RCM runs is undertaken to study the sensitivity to the driving GCM. The added value provided by PRECIS in comparison to the coarser driving models is discussed. The possible causes of model bias are investigated. The model projections for the end of this century are undertaken based on high (SRESA2) and low (SRES-B2) emission scenarios which estimate the range of possible climate change in Thailand. These RCM simulations suggest trends in temperature that are broadly in line with those reported by IPCC. PRECIS A2 and B2 simulations mostly produce small precipitation increases in JJAS and small precipitation increases (decreases) during DJF under the A2 (B2) scenario. Wet season precipitation increases appear to be related to higher rain intensity on fewer rain days.","abstract_has_math":false,"creators":["Inthacha, Sujittra"],"institution":"University of East Anglia","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-24T02:11:49Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Inthacha, Sujittra"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011"]},{"key":"dc:date.issued","label":"Date","values":["2011"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Environmental Sciences"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of East Anglia"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://ueaeprints.uea.ac.uk/id/eprint/36354/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://ueaeprints.uea.ac.uk/id/eprint/36354/1/2011InthachaSPhD.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The climate of Thailand has not been studied in as much depth as in other parts of continental Southeast Asia. The baseline climate of Thailand during 1961-1990 is first analysed using daily observational data from five surface stations, each representing a different region of Thailand, supplemented by the high resolution 0.5° monthly gridded observational dataset, CRUTS2.1. The latter leads to a deeper understanding of the spatial variation in seasonal cycles of key climate variables in Thailand. Also revealed is an increase in the number of tropical depressions crossing Thailand during La Niña years. It was found that there is a statistically significant intensification (reduction) of precipitation during La Niña (El Niño) years at Surat Thani (Chiang Mai) in southern (northern) Thailand during ON (JJAS). This work facilitates the Regional Climate Model validation work which follows. The Providing REgional Climates for Impact Studies regional climate model, PRECIS, was run for the first time over Southeast Asia to specifically study the climate of Thailand. The first phase is model validation during the 1961-1990 baseline period. An ensemble of RCM runs is undertaken to study the sensitivity to the driving GCM. The added value provided by PRECIS in comparison to the coarser driving models is discussed. The possible causes of model bias are investigated. The model projections for the end of this century are undertaken based on high (SRESA2) and low (SRES-B2) emission scenarios which estimate the range of possible climate change in Thailand. These RCM simulations suggest trends in temperature that are broadly in line with those reported by IPCC. PRECIS A2 and B2 simulations mostly produce small precipitation increases in JJAS and small precipitation increases (decreases) during DJF under the A2 (B2) scenario. Wet season precipitation increases appear to be related to higher rain intensity on fewer rain days."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["The climatology of Thailand and future climate change projections using the regional climate model precis"]}]}],"canonical_facts":{"dc:creator":["Inthacha, Sujittra"],"dc:date":["2011"],"dc:date.issued":["2011"],"dc:description.abstract":["The climate of Thailand has not been studied in as much depth as in other parts of continental Southeast Asia. The baseline climate of Thailand during 1961-1990 is first analysed using daily observational data from five surface stations, each representing a different region of Thailand, supplemented by the high resolution 0.5° monthly gridded observational dataset, CRUTS2.1. The latter leads to a deeper understanding of the spatial variation in seasonal cycles of key climate variables in Thailand. Also revealed is an increase in the number of tropical depressions crossing Thailand during La Niña years. It was found that there is a statistically significant intensification (reduction) of precipitation during La Niña (El Niño) years at Surat Thani (Chiang Mai) in southern (northern) Thailand during ON (JJAS). This work facilitates the Regional Climate Model validation work which follows. The Providing REgional Climates for Impact Studies regional climate model, PRECIS, was run for the first time over Southeast Asia to specifically study the climate of Thailand. The first phase is model validation during the 1961-1990 baseline period. An ensemble of RCM runs is undertaken to study the sensitivity to the driving GCM. The added value provided by PRECIS in comparison to the coarser driving models is discussed. The possible causes of model bias are investigated. The model projections for the end of this century are undertaken based on high (SRESA2) and low (SRES-B2) emission scenarios which estimate the range of possible climate change in Thailand. These RCM simulations suggest trends in temperature that are broadly in line with those reported by IPCC. PRECIS A2 and B2 simulations mostly produce small precipitation increases in JJAS and small precipitation increases (decreases) during DJF under the A2 (B2) scenario. Wet season precipitation increases appear to be related to higher rain intensity on fewer rain days."],"dc:format":["application/pdf"],"dc:identifier.uri":["https://ueaeprints.uea.ac.uk/id/eprint/36354/1/2011InthachaSPhD.pdf"],"dc:language":["en"],"dc:publisher.department":["School of Environmental Sciences"],"dc:publisher.institution":["University of East Anglia"],"dc:relation.isreferencedby":["https://ueaeprints.uea.ac.uk/id/eprint/36354/"],"dc:title":["The climatology of Thailand and future climate change projections using the regional climate model precis"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T02:11:49Z"}