Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia
Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the E...
Saved in:
Main Authors: | , , , , , , , , , , , , |
---|---|
Format: | Article |
Published: |
Springer
2024
|
Online Access: | http://psasir.upm.edu.my/id/eprint/112897/ https://link.springer.com/article/10.1007/s00704-024-04946-2 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.upm.eprints.112897 |
---|---|
record_format |
eprints |
spelling |
my.upm.eprints.1128972024-10-28T07:56:54Z http://psasir.upm.edu.my/id/eprint/112897/ Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia Sa’adi, Zulfaqar Hamed, Mohammed Magdy Muhammad, Mohd Khairul Idlan Chow, Ming Fai Mohamad, Nur Athirah Basri, Mohd Hadi Akbar Ahmad, Mohamad Faizal Sa’adi, Nurzalikha Alias, Nor Eliza Yusop, Zulkifli Houmsi, Mohamad Rajab Shukla, Prabhakar Aris, Azmi Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the ERA5 dataset accurately represented the spatial pattern of Level 1 heatwaves, showing widespread occurrence. Historically, Level 1 heatwaves prevailed at 63.0%, followed by Level 2 at 27.7%, concentrated in northwestern states and the enclave between the Tahan and Titiwangsa mountain ranges. During very strong El Niño events in 1982/83, 1997/98, and 2015/16, Level 2 heatwave distributions were 10.4%, 26.8%, and 15.0%, respectively. For future projection, the model ensemble was created by selecting top-performing Global Climate Models (GCMs) using Kling-Gupta efficiency (KGE), ranked re-aggregation with compromise programming index (CPI), and GCM subset selection via Fisher-Jenks. The linear scaling bias-corrected GCMs (BC-GCMs), NorESM2-LM, ACCESS-CM2, MPI-ESM1-2-LR, ACCESS-ESM1-5, and FGOALS-g3, were found to exhibit better performance, and then ensemble. March to May show the highest increase in all scenarios, ranging from 3.3 °C to 4.4 °C for Level 1 heatwaves and 4.1 °C to 10.7 °C for Level 2 heatwaves. In the near future, SSP5-8.5 projects up to a 40.5% spatial increase for Level 1 heatwaves and a 2.3% increase for Level 2 heatwaves, affecting 97.1% and 57.2% of the area, respectively. In the far future, under SSP2-4.5 and SSP5-8.5, Tmax is projected to rise rapidly (1.5–4.5 °C) in the northern, western, and central regions, with increasing population exposure anticipated in the northern and western regions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer 2024 Article PeerReviewed Sa’adi, Zulfaqar and Hamed, Mohammed Magdy and Muhammad, Mohd Khairul Idlan and Chow, Ming Fai and Mohamad, Nur Athirah and Basri, Mohd Hadi Akbar and Ahmad, Mohamad Faizal and Sa’adi, Nurzalikha and Alias, Nor Eliza and Yusop, Zulkifli and Houmsi, Mohamad Rajab and Shukla, Prabhakar and Aris, Azmi (2024) Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia. Theoretical and Applied Climatology, 155 (6). pp. 5251-5278. ISSN 0177-798X; eISSN: 1434-4483 https://link.springer.com/article/10.1007/s00704-024-04946-2 10.1007/s00704-024-04946-2 |
institution |
Universiti Putra Malaysia |
building |
UPM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Putra Malaysia |
content_source |
UPM Institutional Repository |
url_provider |
http://psasir.upm.edu.my/ |
description |
Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the ERA5 dataset accurately represented the spatial pattern of Level 1 heatwaves, showing widespread occurrence. Historically, Level 1 heatwaves prevailed at 63.0%, followed by Level 2 at 27.7%, concentrated in northwestern states and the enclave between the Tahan and Titiwangsa mountain ranges. During very strong El Niño events in 1982/83, 1997/98, and 2015/16, Level 2 heatwave distributions were 10.4%, 26.8%, and 15.0%, respectively. For future projection, the model ensemble was created by selecting top-performing Global Climate Models (GCMs) using Kling-Gupta efficiency (KGE), ranked re-aggregation with compromise programming index (CPI), and GCM subset selection via Fisher-Jenks. The linear scaling bias-corrected GCMs (BC-GCMs), NorESM2-LM, ACCESS-CM2, MPI-ESM1-2-LR, ACCESS-ESM1-5, and FGOALS-g3, were found to exhibit better performance, and then ensemble. March to May show the highest increase in all scenarios, ranging from 3.3 °C to 4.4 °C for Level 1 heatwaves and 4.1 °C to 10.7 °C for Level 2 heatwaves. In the near future, SSP5-8.5 projects up to a 40.5% spatial increase for Level 1 heatwaves and a 2.3% increase for Level 2 heatwaves, affecting 97.1% and 57.2% of the area, respectively. In the far future, under SSP2-4.5 and SSP5-8.5, Tmax is projected to rise rapidly (1.5–4.5 °C) in the northern, western, and central regions, with increasing population exposure anticipated in the northern and western regions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. |
format |
Article |
author |
Sa’adi, Zulfaqar Hamed, Mohammed Magdy Muhammad, Mohd Khairul Idlan Chow, Ming Fai Mohamad, Nur Athirah Basri, Mohd Hadi Akbar Ahmad, Mohamad Faizal Sa’adi, Nurzalikha Alias, Nor Eliza Yusop, Zulkifli Houmsi, Mohamad Rajab Shukla, Prabhakar Aris, Azmi |
spellingShingle |
Sa’adi, Zulfaqar Hamed, Mohammed Magdy Muhammad, Mohd Khairul Idlan Chow, Ming Fai Mohamad, Nur Athirah Basri, Mohd Hadi Akbar Ahmad, Mohamad Faizal Sa’adi, Nurzalikha Alias, Nor Eliza Yusop, Zulkifli Houmsi, Mohamad Rajab Shukla, Prabhakar Aris, Azmi Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia |
author_facet |
Sa’adi, Zulfaqar Hamed, Mohammed Magdy Muhammad, Mohd Khairul Idlan Chow, Ming Fai Mohamad, Nur Athirah Basri, Mohd Hadi Akbar Ahmad, Mohamad Faizal Sa’adi, Nurzalikha Alias, Nor Eliza Yusop, Zulkifli Houmsi, Mohamad Rajab Shukla, Prabhakar Aris, Azmi |
author_sort |
Sa’adi, Zulfaqar |
title |
Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia |
title_short |
Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia |
title_full |
Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia |
title_fullStr |
Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia |
title_full_unstemmed |
Employing gridded-based dataset for heatwave assessment and future projection in Peninsular Malaysia |
title_sort |
employing gridded-based dataset for heatwave assessment and future projection in peninsular malaysia |
publisher |
Springer |
publishDate |
2024 |
url |
http://psasir.upm.edu.my/id/eprint/112897/ https://link.springer.com/article/10.1007/s00704-024-04946-2 |
_version_ |
1814936532457357312 |
score |
13.214268 |