Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam

The Quantile Mapping (QM) bias correction (BC) technique was applied for the first time to address biases in the simulated precipitation over Vietnam from the Regional Climate Model (RegCM) driven by five Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Model (GCM) products. The...

Full description

Saved in:
Bibliographic Details
Main Authors: Trinh-Yuan, Long, Matsumoto, Jun, Tangang, Fredolin T, Juneng, Liew, Cruz, Faye T, Narisma, Gemma T, Santisirisomboon, Jerasorn, Phan-Van, Tan, Gunawan, Dodo, Aldrian, Edvin, Ngo-Duc, Thanh
Format: text
Published: Archīum Ateneo 2019
Subjects:
Online Access:https://archium.ateneo.edu/physics-faculty-pubs/114
https://www.jstage.jst.go.jp/article/sola/15/0/15_2019-001/_article
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Ateneo De Manila University
id ph-ateneo-arc.physics-faculty-pubs-1111
record_format eprints
spelling ph-ateneo-arc.physics-faculty-pubs-11112022-04-19T11:40:22Z Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam Trinh-Yuan, Long Matsumoto, Jun Tangang, Fredolin T Juneng, Liew Cruz, Faye T Narisma, Gemma T Santisirisomboon, Jerasorn Phan-Van, Tan Gunawan, Dodo Aldrian, Edvin Ngo-Duc, Thanh The Quantile Mapping (QM) bias correction (BC) technique was applied for the first time to address biases in the simulated precipitation over Vietnam from the Regional Climate Model (RegCM) driven by five Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Model (GCM) products. The QM process was implemented for the period 1986−2005, and subsequently applied to the mid-future period 2046−2065 under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. Comparison with the original model outputs during the independent validation period shows a large bias reduction from 45% to 3% over Vietnam and significant improvements in representing precipitation indices (PI) after applying the QM technique. Moreover, the ensemble average of the BC products generally performed better than an individual BC member in capturing the spatial distribution of the PIs. A drier condition with a longer rainfall break, and shorter consecutive rainfall events are anticipated over Northern and Central Vietnam during their respective wet seasons in the mid-future. Furthermore, this study showed that the QM method minimally modified the future changes in PIs over most of Vietnam; thus, these corrected projections could be used in climate impacts and adaptation studies. 2019-01-01T08:00:00Z text https://archium.ateneo.edu/physics-faculty-pubs/114 https://www.jstage.jst.go.jp/article/sola/15/0/15_2019-001/_article Physics Faculty Publications Archīum Ateneo Oceanography and Atmospheric Sciences and Meteorology Physics
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Oceanography and Atmospheric Sciences and Meteorology
Physics
spellingShingle Oceanography and Atmospheric Sciences and Meteorology
Physics
Trinh-Yuan, Long
Matsumoto, Jun
Tangang, Fredolin T
Juneng, Liew
Cruz, Faye T
Narisma, Gemma T
Santisirisomboon, Jerasorn
Phan-Van, Tan
Gunawan, Dodo
Aldrian, Edvin
Ngo-Duc, Thanh
Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam
description The Quantile Mapping (QM) bias correction (BC) technique was applied for the first time to address biases in the simulated precipitation over Vietnam from the Regional Climate Model (RegCM) driven by five Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Model (GCM) products. The QM process was implemented for the period 1986−2005, and subsequently applied to the mid-future period 2046−2065 under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. Comparison with the original model outputs during the independent validation period shows a large bias reduction from 45% to 3% over Vietnam and significant improvements in representing precipitation indices (PI) after applying the QM technique. Moreover, the ensemble average of the BC products generally performed better than an individual BC member in capturing the spatial distribution of the PIs. A drier condition with a longer rainfall break, and shorter consecutive rainfall events are anticipated over Northern and Central Vietnam during their respective wet seasons in the mid-future. Furthermore, this study showed that the QM method minimally modified the future changes in PIs over most of Vietnam; thus, these corrected projections could be used in climate impacts and adaptation studies.
format text
author Trinh-Yuan, Long
Matsumoto, Jun
Tangang, Fredolin T
Juneng, Liew
Cruz, Faye T
Narisma, Gemma T
Santisirisomboon, Jerasorn
Phan-Van, Tan
Gunawan, Dodo
Aldrian, Edvin
Ngo-Duc, Thanh
author_facet Trinh-Yuan, Long
Matsumoto, Jun
Tangang, Fredolin T
Juneng, Liew
Cruz, Faye T
Narisma, Gemma T
Santisirisomboon, Jerasorn
Phan-Van, Tan
Gunawan, Dodo
Aldrian, Edvin
Ngo-Duc, Thanh
author_sort Trinh-Yuan, Long
title Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam
title_short Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam
title_full Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam
title_fullStr Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam
title_full_unstemmed Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam
title_sort application of quantile mapping bias correction for mid-future precipitation projections over vietnam
publisher Archīum Ateneo
publishDate 2019
url https://archium.ateneo.edu/physics-faculty-pubs/114
https://www.jstage.jst.go.jp/article/sola/15/0/15_2019-001/_article
_version_ 1731309314102001664