THE ANALYSIS OF VARIABILITY AND TREND OF MARINE HEATWAVES IN INDONESIA 1982-2020
Ocean temperatures have increased during the 20th century and are predicted to continue to rise during the 21st century. Simultaneously, there are also extreme phenomena of shorter-time ocean warming known as Marine Heatwaves (MHWs). MHWs are periods when ocean temperatures pass the 90th percenti...
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Ocean temperatures have increased during the 20th century and are predicted to
continue to rise during the 21st century. Simultaneously, there are also extreme
phenomena of shorter-time ocean warming known as Marine Heatwaves (MHWs).
MHWs are periods when ocean temperatures pass the 90th percentile. Factors
generating the occurrence of MHWs vary, depending on the location and time of
the event. However, with long data, the dominant drivers that influence the
occurrence of MHWs and how they change along with increasing global ocean
temperatures can be identified. This study was conducted to analyze the trend and
variability of MHWs in Indonesian waters from 1982-to 2020.
The data used is Daily Optimum Interpolation Sea Surface Temperature (DOISST)
v2.1 with a spatial resolution of 0.25?. The research location is the waters of
Indonesia and its surroundings with boundaries of 16?LS-16?N and 90?E-15?E.
Variability analysis will be carried out by parsing each independent variability
using the Empirical Orthogonal Function (EOF) method. Each dominant mode will
be correlated with a representative climate phenomenon so that it can explain what
phenomena dominantly affect the variability of MHWs in Indonesia. On the
respective grids, trends were analyzed by comparing the different conditions of
MHWs in 1982-1999 and 2003-2020 to minimize bias due to irregularity in the
occurrence of MHWs. To see the average condition in an area, analysis of the trend
of MHWs occurrence was carried out using the least-square method.
From the results obtained, it is found that the variability of MHWs in Indonesia is
dominantly influenced by ENSO events. There are three dominant EOF modes with
different locations and intensities. The first EOF mode explains 27.3 % of the total
variance of MHWs with the center of the anomaly being in the southern region of
Java. The maximum correlation between Principal Component 1 (PC1) and the
Niño 3.4 index (r=0.41) occurs at a time lag of 7 months. The main presumption
for this correlation is the sea teleconnection between the Pacific Ocean to the
Indian Ocean driven by the ENSO event. There is also the influence of the Indian
Ocean Dipole (IOD) in the first mode because it is located in the eastern Indian
Ocean. The maximum correlation between PC1 and the Dipole Mode Index (DMI)
occurred without any lag (r=-0.21). Meanwhile, the second mode explains 13.2 %
of the total variance of MHWs with the center of the anomaly in the western part of
the Pacific Ocean. The third mode EOF explained 7.2 % of the total variance of
MHWs in the study area, with the center of the anomaly in the South China Sea.
The effect of ENSO on MHWs events in the third mode arrived with a time lag of 6
months (r=0.4) and the effect of IOD arrived with a time lag of 2 months (r=-0.24).
MHWs with category I (moderate) most often occur in the western Pacific Ocean
with an accumulated duration of up to 6 years out of 39 years of data used. MHWs
category II (strong) most often occurred in the southern region of Java with an
accumulated duration of up to 8 months, followed by category III (severe) which
most frequently occurred in the South China Sea with an accumulated duration of
up to 30 days. Category IV (extreme) MHWs are extremely rare, with the longest
duration found in the Andaman Sea region (20 days of accumulated duration).
It was also found that there has been an increase in the duration and frequency of
occurrence of MHWs, especially in the three dominant areas based on the results
of EOF calculations. The accumulated annual duration in the western Pacific
Ocean has increased by 24 days per decade, south of Java by 20 days per decade,
and the South China Sea by 14 days per decade. This means that there is an increase
of 50 to 100 days of MHWs at the end of 2020 compared to 1982. The frequency of
MHWs in the western Pacific Ocean has increased by 1.6 annual events per decade,
southern Java by 0.8 annual events per decade, and The South China Sea by 1.4
annual events per decade. This means that there is an increase in the occurrence of
MHWs by 3 to 6 annual events by the end of 2020 compared to 1982.
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Dhuha Habibullah, Ahmad |
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Dhuha Habibullah, Ahmad THE ANALYSIS OF VARIABILITY AND TREND OF MARINE HEATWAVES IN INDONESIA 1982-2020 |
author_facet |
Dhuha Habibullah, Ahmad |
author_sort |
Dhuha Habibullah, Ahmad |
title |
THE ANALYSIS OF VARIABILITY AND TREND OF MARINE HEATWAVES IN INDONESIA 1982-2020 |
title_short |
THE ANALYSIS OF VARIABILITY AND TREND OF MARINE HEATWAVES IN INDONESIA 1982-2020 |
title_full |
THE ANALYSIS OF VARIABILITY AND TREND OF MARINE HEATWAVES IN INDONESIA 1982-2020 |
title_fullStr |
THE ANALYSIS OF VARIABILITY AND TREND OF MARINE HEATWAVES IN INDONESIA 1982-2020 |
title_full_unstemmed |
THE ANALYSIS OF VARIABILITY AND TREND OF MARINE HEATWAVES IN INDONESIA 1982-2020 |
title_sort |
analysis of variability and trend of marine heatwaves in indonesia 1982-2020 |
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id-itb.:664362022-06-28T10:23:41ZTHE ANALYSIS OF VARIABILITY AND TREND OF MARINE HEATWAVES IN INDONESIA 1982-2020 Dhuha Habibullah, Ahmad Indonesia Theses Marine heatwaves, trend, variability, regional climate INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/66436 Ocean temperatures have increased during the 20th century and are predicted to continue to rise during the 21st century. Simultaneously, there are also extreme phenomena of shorter-time ocean warming known as Marine Heatwaves (MHWs). MHWs are periods when ocean temperatures pass the 90th percentile. Factors generating the occurrence of MHWs vary, depending on the location and time of the event. However, with long data, the dominant drivers that influence the occurrence of MHWs and how they change along with increasing global ocean temperatures can be identified. This study was conducted to analyze the trend and variability of MHWs in Indonesian waters from 1982-to 2020. The data used is Daily Optimum Interpolation Sea Surface Temperature (DOISST) v2.1 with a spatial resolution of 0.25?. The research location is the waters of Indonesia and its surroundings with boundaries of 16?LS-16?N and 90?E-15?E. Variability analysis will be carried out by parsing each independent variability using the Empirical Orthogonal Function (EOF) method. Each dominant mode will be correlated with a representative climate phenomenon so that it can explain what phenomena dominantly affect the variability of MHWs in Indonesia. On the respective grids, trends were analyzed by comparing the different conditions of MHWs in 1982-1999 and 2003-2020 to minimize bias due to irregularity in the occurrence of MHWs. To see the average condition in an area, analysis of the trend of MHWs occurrence was carried out using the least-square method. From the results obtained, it is found that the variability of MHWs in Indonesia is dominantly influenced by ENSO events. There are three dominant EOF modes with different locations and intensities. The first EOF mode explains 27.3 % of the total variance of MHWs with the center of the anomaly being in the southern region of Java. The maximum correlation between Principal Component 1 (PC1) and the Niño 3.4 index (r=0.41) occurs at a time lag of 7 months. The main presumption for this correlation is the sea teleconnection between the Pacific Ocean to the Indian Ocean driven by the ENSO event. There is also the influence of the Indian Ocean Dipole (IOD) in the first mode because it is located in the eastern Indian Ocean. The maximum correlation between PC1 and the Dipole Mode Index (DMI) occurred without any lag (r=-0.21). Meanwhile, the second mode explains 13.2 % of the total variance of MHWs with the center of the anomaly in the western part of the Pacific Ocean. The third mode EOF explained 7.2 % of the total variance of MHWs in the study area, with the center of the anomaly in the South China Sea. The effect of ENSO on MHWs events in the third mode arrived with a time lag of 6 months (r=0.4) and the effect of IOD arrived with a time lag of 2 months (r=-0.24). MHWs with category I (moderate) most often occur in the western Pacific Ocean with an accumulated duration of up to 6 years out of 39 years of data used. MHWs category II (strong) most often occurred in the southern region of Java with an accumulated duration of up to 8 months, followed by category III (severe) which most frequently occurred in the South China Sea with an accumulated duration of up to 30 days. Category IV (extreme) MHWs are extremely rare, with the longest duration found in the Andaman Sea region (20 days of accumulated duration). It was also found that there has been an increase in the duration and frequency of occurrence of MHWs, especially in the three dominant areas based on the results of EOF calculations. The accumulated annual duration in the western Pacific Ocean has increased by 24 days per decade, south of Java by 20 days per decade, and the South China Sea by 14 days per decade. This means that there is an increase of 50 to 100 days of MHWs at the end of 2020 compared to 1982. The frequency of MHWs in the western Pacific Ocean has increased by 1.6 annual events per decade, southern Java by 0.8 annual events per decade, and The South China Sea by 1.4 annual events per decade. This means that there is an increase in the occurrence of MHWs by 3 to 6 annual events by the end of 2020 compared to 1982. text |