Detecting Long-term Characteristics of Marine Heatwaves (1982–2021) in the Indonesian Waters

doi:10.21203/rs.3.rs-2186945/v2

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Detecting Long-term Characteristics of Marine Heatwaves (1982–2021) in the Indonesian Waters

https://doi.org/10.21203/rs.3.rs-2186945/v2

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In this paper, we investigate the characteristics of marine heatwaves (MHWs) (i.e. frequency, maximum intensity, and duration of occurrences) in Indonesian waters and the mechanisms underlying the formation of MHWs generated by local forcing. The study utilizes the remote sensing data from National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature (OISST) dataset for SST and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5) for net surface heat flux components (shortwave radiation, longwave radiation, sensible heat flux, and latent heat flux), wind speed, and total cloud cover. The data covered a period within the last 40 years (1982–2021) during the warm (1982–2007) and cold (2008–2021) phases of the Pacific Decadal Oscillation (PDO) and were processed by statistical analysis methods. This study clearly reveals the highest average frequency and duration of MHWs in the Indonesian waters which were around 3–5 times a year and 10–20 days, respectively, occurring during the cold phase of PDO. Meanwhile, the highest maximum intensity of MHWs in the Indonesian waters was around > 1.5°C during the warm phase of PDO (El Niño dominated in Indonesia) generated by the local net surface heat flux. The most dominant contributors of net surface heat flux were high shortwave radiation (heat gain) and low latent heat flux (heat loss) through the evaporation process, thus keeping the ocean warm. Moreover, this study highlights the highest maximum intensity of MHWs which could reach > 3°C along western Sumatra to southern Java and waters around Papua when El Niño and positive Indian Ocean Dipole (pIOD) occur together. In contrast, the lowest maximum intensity of MHWs in the Indonesian waters occurred when La Niña was happening simultaneously with negative IOD (nIOD).

Global warming

marine heatwaves

Pacific Decadal Oscillation

net surface heat flux

Indonesian waters

Global warming continues to increase every year due to greenhouse gas emissions by human activities (anthropogenic; IPCC 2018) and is characterized by an increase in global average air temperature of 1.1°C within the last 100 years. The rising air temperature affects the increasing temperature in the sea through the ocean-atmosphere interaction. This rising sea surface temperature (SST) is experienced by several regions in various parts of the world, including Indonesia. According to Iskandar et al. (2020), the warming trend of SST in Indonesian waters reaches 0.19 ± 0.04°C/decade for a period of 33 years (1982–2014), exceeding the average warming trend of global SST (0.13°C/decade).

Warm SST areas are vulnerable to extreme events, such as tropical cyclones (Kuleshov et al. 2008; Oliver et al. 2017 and 2018a), intense rainfall, and can even cause floods (Behrens et al. 2019). Another phenomenon due to an increase in SST is the formation of marine heatwaves (MHWs), which are categorized as a phenomenon of warm water anomalies (> 90th percentile) in waters with a duration of ≥ 5 consecutive days (Hobday et al. 2016). Globally, the number of MHWs has increased > 50% (Oliver et al. 2018a) over the last century (1925–2016) and was predicted to continually occur until 2100 in most of the world's oceans due to anthropogenic activities that led to further warming (Frölicher et al. 2018). Moreover, the frequency of MHWs has doubled in 2021 since the 1980s and continues to increase, especially in the coastal areas (IPCC 2021).

MHWs can occur to depths > 100 m (Pearce and Feng 2013; Jackson et al. 2018; Su et al. 2021) and have a significant impact on changes in ocean and atmospheric dynamics (Heidemann and Ribbe 2019; Behrens et al. 2019; Feng and Shinoda 2019; Fewings and Brown 2019; Gawarkiewicz et al. 2019). Additionally, MHWs have various effects on the existence of marine ecosystems, such as mass mortality, species migration, and shifts in ecosystem communities (Smale et al. 2019; Fordyce et al. 2019; Strub et al. 2019; Kendrick et al. 2019; Thomsen et al. 2019; Smith et al. 2019). MHWs can also modify marine components (e.g., alter the carbon cycle and water column stratification, reduce dissolved oxygen concentrations, and prevent sea ice formation) (Brauko et al. 2020; Hu et al. 2020; Carvalho et al. 2021; Mignot et al. 2021), which then strengthen water column stratification in the sea that can cause a weakening of the mixing process and upwelling strength. The impact can be seen on the decreasing of the distribution of nutrients, phytoplankton, and zooplankton to the surface (Doney 2006) and ultimately can lead to a reduction in fisheries resources and aquaculture production (Mills et al. 2013; Caputi et al. 2016; Oliver et al. 2017; Cheung and Frölicher 2020).

The detection of MHWs in various parts of the world has been widely reported in recent years. For example, the occurrence of MHWs in the Tasman Sea in 2015/16 (Oliver et al. 2017), in northern Australia in 2016 (Lewis and Mallela 2018; Oliver et al. 2018b), in the Gulf of Alaska and the Bering Sea in 2016 (Oliver et al. 2018b; Walsh et al. 2018), in California waters in 2016 (Jacox et al. 2018), and the Tasman Sea in 2017/18 (Perkins-Kirkpatrick et al. 2019). However, to the best of our knowledge, research on MHWs in Indonesian waters is relatively new and still limited to local areas, specifically in western Sumatra (1982–2018; Ismail 2021) and southern Java (1982–2019; Iskandar et al. 2021). In addition, the study of MHWs was carried out without distinguishing between the warm and cold phases of the Pacific Decadal Oscillation (PDO). Comprehensive information regarding the characteristics of MHWs in Indonesian waters, which are differentiated based on fluctuations in the PDO index pattern that occurs in the 22–30 year period (Yao et al. 2018) and the mechanism underlying the formation of MHWs, is to date still unknown. It becomes an urgency to understand this matter as part of a mitigation effort to deal with the MHWs disaster in the Indonesian waters due to ongoing climate change. Therefore, the aims of this study is to further investigate the characteristics of MHWs (frequency, intensity, and duration of events) in the Indonesian waters during the last 40 years (1982–2021), which were differentiated based on the warm (1982–2007) and cold (2008–2021) phases of PDO (https://www.daculaweather.com/4_pdo_index.php). The mechanism underlying the formation of MHWs was also identified, particularly due to local forcing.

This paper is organized as follows. The “Data and Methods” section describes all remote sensing and reanalysis data used for this study. In the “Results and Discussions” section, we present an overview of the MHWs during 1982–2021 and elaborate the mechanism of MHW formation generated by local forcing in the form of net surface heat flux. Lastly, we end with “Summary and Conclusions” of the main results of the study.

The characteristics and factors generating MHWs were investigated by using SST and atmospherics data within the Indonesian waters (8°N to 12°S and 95°E to 141°E, as shown in Fig. 1) from January 1982 to December 2021. The SST data were originated from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature (OISST) dataset (https://www.ncei.noaa.gov/data/sea-surface-temperature-opti muminterpolation/v2.1/access/avhrr/) that have a spatial and temporal resolution of 0.25° × 0.25° recorded daily, respectively. The net surface heat flux components consisting of shortwave radiation (SWR), longwave radiation (LWR), sensible heat flux (SHF), and latent heat flux (LHF) were obtained from satellite imagery data from The European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5) available at https://cds.climate.copernicus.eu. The wind speed and total cloud cover were also downloaded from ERA5. They both have a spatial resolution of 0.25°× 0.25° and a time interval of six hours. The Dipole Mode and Oceanic Niño Indices (DMI and ONI, respectively) (https://stateoftheocean.osmc.noaa.gov/) from 1982 to 2021 were used to determine Indian Ocean Dipole (IOD), El Niño Southern Oscillation (ENSO), and neutral events. In this study, all data were calculated during 40 years (1982–2021), 26 years (1982–2007; warm phase), and 14 years (2008–2021; cold phase) using statistical analysis methods.

2.1 Identification of MHWs

In the present study, the definition of MHWs is a prolonged warm water anomalies event exceeding a threshold (90th percentile) during at least five consecutive days (Hobday et al. 2016). The climatology and the threshold values were calculated as an 11-day window-centered average on each specific date for 40 years (1982–2021), 26 years (1982–2007), and 14 years (2008–2021). Following Hobday et al. (2016), MHWs calculated a 30-day moving average to generate a smooth climatology and threshold time series. Spatial MHWs are detected and analyzed based on a Matlab toolbox provided by Zhao and Marin (2019), available at https://github.com/ZijieZhaoMMHW/m_mhw1.0/commit/04491bdc6b34458acbe07afd83d906af29754099. Furthermore, we focused on three metrics. The first metric, frequency, is defined as the number of occurrences of MHWs. The second metric is maximum intensity, defined as the highest temperature anomaly during the MHWs. The third metric, duration, defines the period when the temperature is ≥ 90th percentile threshold without dipping below it for > 2 consecutive days. We also calculated trends in frequency, maximum intensity, and duration occurrences of MHWs from January 1982 to December 2021.

2.2 Net Surface Heat Flux

In addition to observing the role of atmospheric factors on the MHWs formation in the Indonesian waters, we also calculated net surface heat flux based on accumulation from SWR, LWR, SHF, and LHF. The net surface heat flux equation can be written as follows:

$${Q}_{O}={Q}_{SWR}+{Q}_{LWR}+{Q}_{SHF}+{Q}_{LHF}$$

where ${Q}_{O}$ is net surface heat flux at the surface of the ocean, ${Q}_{SWR}$ represents the shortwave radiation (also known as the surface net solar radiation), ${Q}_{LWR}$ is the longwave radiation (also known as the surface net thermal radiation), ${Q}_{SHF}$ is the sensible heat flux, i.e. heat transfer between Earth's surface and atmosphere via turbulent motion and influenced by the gradient of the sea and air temperature, and ${Q}_{LHF}$ denotes the latent heat flux, i.e. heat transfer between Earth's surface and atmosphere via turbulent motion and influenced by the gradient of the sea and air humidity (Iskandar et al. 2020; Pujol et al. 2022). We consider in this study that positive fluxes are heat transfer from the atmosphere to the sea (heat gain) and vice versa, negative fluxes are heat transfer from the ocean to the atmosphere (heat loss).

2.3 Wind Speed and Total Cloud Cover

The wind speed and total cloud cover were obtained from ERA5. Furthermore, we calculated the average of wind speed and total cloud cover during January 1982 to December 2021 using statistical analysis methods and was implemented in Matlab.

2.4 DMI and ONI Classification

The DMI and ONI were provided by the NOAA website (https://stateoftheocean.osmc.noaa.gov/sur/pac/). The DMI was used to monitor positive and negative IOD (pIOD and nIOD) phases, whereas the ONI was to monitor El Niño and La Niña phases. The DMI is defined as the gradient of SST anomalies between the western equatorial Indian Ocean (50°E–70°E, 10°S–10°N) and the south eastern equatorial Indian Ocean (90°E–110°E, 10°S–0°). The ONI is defined as the mean of SST anomalies in the Niño 3.4 regions between 170–120°W and 5°S–5°N. The DMI and ONI were calculated relative to the monthly climatological seasonal cycle during Januari 1982 to December 2021. The pIOD (nIOD) phases are defined when DMI is above (below) one standard deviation (0.48°C), meanwhile El Niño (La Niña) phases are defined when ONI is above 0.5°C (below 0.5°C) (Pant et al. 2015).

3.1 Characteristics of MHWs in the Indonesian waters

In this study, we examined the characteristics of MHWs based on its frequency value (Fig. 2a, b, c), maximum intensity (Fig. 2d, e, f), and duration of occurrence (Fig. 2g, h, i) during the last 40 years (1982–2021), 26 years (1982–2007; warm phase), and 14 years (2008–2021; cold phase). The average frequency (number of events) of MHWs over 40 years in Indonesian waters is dominated by two events per year (Fig. 2a). On average, the frequency of these MHWs decreases during the warm phase, i.e. only one event per year (Fig. 2b). However, in some coastal areas, such as western Sumatra, northern Kalimantan, and the coasts around Sulawesi and Papua, around 2–3 events were found in a year (Fig. 2b). Meanwhile, the average frequency of MHWs during the cold phase of PDO increased significantly to three events per year. We even found > 5 events per year in the northern territorial waters of Indonesian and around the Halmahera Sea, as shown in Fig. 2c.

We also analyzed the characteristics of the MHWs regarding their maximum intensity (Fig. 2d, e, f). The average maximum intensity of MHWs in Indonesian waters over 40 years is 1–1.5 ℃ (Fig. 2d). The highest (lowest) average maximum intensity of MHWs was found during the warm (cold) phase of PDO, as shown in Fig. 2e (2f). The three study periods, as mentioned before, showed similar highest average maximum intensity areas, which occurred in southern Java, reaching > 2 ℃ (Fig. 2d, e, f). In accordance to our study, a previous study showed that the average maximum intensity in southern Java from January 1982 to December 2019 reached > 1.8 ℃ (Iskandar et al. 2021). Interestingly, we found in the present study that the highest maximum intensity occurred during the warm phase of the PDO when El Niño dominated Indonesian waters (Fig. 2e).

In addition to the frequency and maximum intensity of MHWs, we also identified the duration of occurrences of MHWs (Fig. 2g, h, i). Over the last 40 years, the average duration of MHWs throughout Indonesian waters is 10 days (Fig. 2g). Meanwhile, the longest (shortest) duration of MHWs was found during the cold (warm) phase, as shown in Fig. 2i (2h). In the cold phase, the average duration of MHWs in the Indonesian waters varied between 15–20 days, with the most prolonged periods > 25 days occurring in southern Java (Fig. 2i). Whereas the average duration of MHWs is < 10 days during the warm phase of PDO (Fig. 2h).

Our findings suggested that the highest average frequency and duration (maximum intensity) of MHWs occurred during the cold (warm) phase of PDO. We suggested that the high frequency and long duration of MHWs during the last 14 years (2008–2021; cold phase) will result in increases of the ecological impacts in the following years. Some questions remained, including what are the factors influencing the maximum intensity of MHWs in the Indonesian waters? And what is the mechanism underlying the development of MHWs in these regions? We will further discuss these in Section 3.3 from the point of view of ocean-atmosphere interactions, particularly due to local forcing.

3.2 Linear trend of MHWs in the Indonesian waters

To quantify the effect of a long-term trend on the MHWs results, we identified the linear trend based on frequency (Fig. 3a, b, c), maximum intensity (Fig. 3d, e, f), and duration of occurrences (Fig. 3g, h, i), with areas of black dots representing increasing or decreasing events within the 95% confidence intervals. The frequency of MHWs has increased by 1–2 events per decade during the last 40 years (Fig. 3a), occuring in almost all Indonesian waters. Meanwhile, the warm and cold phases of PDO show an increase and decrease in frequency (Fig. 3b, c). For the three study periods, the result clearly reveals the most significant increase in frequency occurred during the cold phase, particularly in the northern territory of Indonesia waters, reaching > 4 events per decade (Fig. 3c) and we believe that this will continue to increase, partly due to global warming. Moreover, the linear trend of the maximum intensity of MHWs shows a negative value over the last 40 years (Fig. 3d), reaching − 0.2 ℃/decade, while the warm and cold phases showed a decrease and increase in the maximum intensity of MHWs ± 0.4 per decade (Fig. 3e, f).

We also examined the linear trend of the duration of MHWs (Fig. 3g, h, i). The average duration of MHWs was five days per decade for the last 40 years, as shown in Fig. 3g, but the linear trend of the duration showed a different pattern during warm and cold phases of PDO (Fig. 3h, i). The linear trend of the duration in the cold phase of PDO can reach > 10 days per decade (Fig. 3i), for example, in the southern Java to Nusa Tenggara and north of Maluku to Papua, whereas, during the warm phase of PDO, the linear trend of the duration is < 5 days per decade (Fig. 3h).

3.3 The formation of MHWs generated by local forcing

3.3.1 Net surface heat flux

The sea-atmosphere interactions in the Indonesian waters were indicated by the value of net surface heat flux, which has a positive value (Fig. 4a, b, c), which means that there is a heat transfer process from the atmosphere to the sea, which plays a role in ocean warming. Meanwhile, the negative value of the net surface heat flux, which occurred in the south of the equator of the Indian Ocean at 12°S, causes the opposite heat transfer from the sea to the atmosphere, thus playing a role in sea cooling. From the three periods of studies, there are differences in spatial characteristics between the western and eastern parts of Indonesian waters (Fig. 4a, b, c). This study found that the net surface heat flux is more significant in the east of Indonesia waters than in the western regions. The average net surface heat flux in the eastern region is > 80 W/m² with some areas, such as the Sawu, Halmahera, and the Sulawesi Seas, reaching > 100 W/m² (Fig. 4a, b, c). Meanwhile, the western regions have lower net surface heat flux, ranging from 0–40 W/m², with negative values occurring at the end of the west of the Aceh waters (~ 5 W/m²) and the southern equatorial Indian Ocean (~ 20 W/m²) for the three study periods (Fig. 4a, b, c). The significant difference in net surface heat flux characteristics between the eastern and western regions is caused by the cloud cover factor. According to Wallace and Hobbs (2006), there is a smaller amount of total cloud cover in the eastern than western regions due to the influence of seasons. Furthermore, the rain and cloud cover pattern will constantly propagate from west to east areas, causing more cloud cover in the west regions. This phenomenon impacts the amount of solar radiation entering the sea, which is more limited in the west than in the eastern regions of the Indonesian waters.

From the three study periods, we found that the highest net surface heat flux occurs during the warm phase of PDO (Fig. 3b) when El Niño dominated Indonesian waters. During El Niño conditions, the western Pacific is cooler than normal, which may contribute to cooling in the Indonesian waters by advection. However, the maximum intensity of MHWs event in the Indonesian waters were most likely not driven by oceanic advection, but by air-sea heat flux into the ocean. In the El Niño phase, its atmospheric conditions can potentially reduce wind speed and cloud coverage, thus leading to increased net surface heat flux into the ocean, which plays a role in warming the ocean. This result was similar to the previous study documented by Haideman and Ribbe (2019), in which El Niño was impacting air-sea heat flux and the local atmosphere significantly, thus generating large MHWs in Queensland, Australia. In contrast, in this study we found that the net surface heat flux was negligible during the La Niña event.

3.3.2 Components of net surface heat flux

In this section, we will describe the role of net surface heat flux components on the maximum intensity of MHWs (Fig. 5). We found that among other components (LWR and SHF), contribution of LHF on the heat loss is the biggest (Fig. 5j, k, l), which results in the significant accumulation of net surface heat flux. The amount of LHF (heat loss) is smaller during the warm phase (Fig. 5k) than in the cold phase of PDO (Fig. 5l). The low evaporation events influenced the low cloud cover amount. The heat absorbed by the ocean is more significant during the warm phase. On the other hand, during the cold phase, a lot of evaporation occurred which generated more clouds. These conditions impact the solar heat radiation which is then reflected by the clouds which then reach the Earth's surface. This is followed by decrease of heat intensity in the ocean and plays a role in ocean cooling.

3.3.3 Interannual variations of MHWs

In this section, we continued our analysis on the interannual variations of MHWs based on IOD, ENSO, and neutral events during September to November (SON) in the period of 1982–2021. We will discuss only the SON in this section to eliminate seasonal variations to obtain a more accurate analysis of the maximum intensity of MHWs and its generating factor in terms of the role of the atmosphere. We selected the SON season based on the DMI and ONI, as shown in Fig. 6, which shows the strongest values compared to other seasons. Details of the classification of the year when IOD, ENSO, and neutral events occurred during SON 1982 to 2021 period, can be seen in Table 1.

3.3.4 The maximum intensity of MHWs during SON in the period of 1982–2021

We analyzed the maximum intensity of MHWs in the Indonesian waters in SON 1988 when the La Niña event occurred (Fig. 7a, left column). The maximum intensity variates between 0–2 ℃ in the inner seas, whereas some areas in the outer seas can reach > 2.5 ℃. The lowest maximum intensity mainly occurred in eastern Indonesian waters, such as Halmahera, Banda, Arafura, and western Sumatra (Fig. 7a, left column). In SON 1990, during the neutral event (no IOD and or ENSO phases), the maximum intensity is greater than the La Niña event, with the average maximum intensity ranging 1–2 ℃ and can reach > 2.5 ℃, especially in the eastern Indonesian waters (Fig. 7a, middle column). Further analysis was on the maximum intensity of MHWs in SON 1996 when the nIOD event happened (Fig. 7a, right column). The maximum intensity characteristics in this period were different from the La Niña event. The lowest value occurred in the southern regions (e.g. the northern and southern waters of Java, Flores Sea, and around the Nusa Tenggara waters), with the average maximum intensity near zero (Fig. 7a, right column).

The El Niño event that occurred at SON 2015 (Fig. 9a, left column), is one of the key drivers of maximum intensity MHWs in the eastern Indonesian waters. The result of the research showed that the high maximum intensity reaching > 1 ℃ occurred in almost all eastern Indonesian waters. Moreover, some areas have the highest maximum intensity, reaching > 2.5 ℃, such as in Sulawesi, Molucca/Maluku, and the Arafura Sea (Fig. 9a, left column). The high maximum intensity was also found on the coasts of southern Java and Kalimantan (Fig. 9a, left column). On the other hand, the pIOD drives the maximum intensity of MHWs in western Indonesian waters (Fig. 9a, right column). Evenmore, this can reach > 3.0 ℃ along the west coast of Sumatra to southern Java (Fig. 9a, right column). This value was the most fantastic compared to the other phases (El Niño, La Niña, and nIOD).

In detail, we also observed the maximum intensity of MHWs when the IOD and ENSO phases attenuate and strengthen each other. In the previous explanation, El Niño (pIOD) significantly impacted the maximum intensity of MHWs in eastern (western) Indonesian waters than La Niña (nIOD) event. The highest maximum intensity of all phases was found during El Niño and pIOD phases, i.e. in SON 1997 (Fig. 8a, left column), reaching > 3.0 ℃ in almost all of the Indonesian waters (e.g. along the west coast of Sumatra to southern Java, the west coast of Kalimantan, Banda, and the Arafura Sea; as shown in Fig. 8a, left column). In contrast, the lowest maximum intensity of all phases was found when La Niña was happening simultaneously with nIOD in SON 1998 (Fig. 8a, middle column). Most of the maximum intensity in the Indonesian waters was almost zero, with some areas showing values of ~ 1.0 ℃ (Fig. 8a, middle column). This condition has the similiar characteristic as the neutral condition in SON 2013 (Fig. 8a, right column) with the maximum intensity of MHWs ranging 0 to 1 ℃ in almost all Indonesian waters.

Moreover, this study found similar patterns between the maximum intensity of MHWs and the net surface heat flux in the Indonesian waters (Figs. 7,8,9). The most dominant contribution of net surface heat flux comes from high SWR as heat gain and low LHF through the evaporation process as heat loss, thus keeping the ocean warm. For all phases, the result showed that high SWR occurred in the southern regions, reaching > 300 W/m² (Figs. 7,8,9c). This condition corresponds to the position of the sun in the south of the equator during the SON season. Additionally, the amount of total cloud cover in the southern regions was the lowest compared to the northern regions in all phases. It makes the amount of SWR absorbed by the ocean become more significant because it is not reflected by the atmosphere, particularly by the cloud cover (Figs. 7,8,9h).

This study provides assessment of the characteristics of MHWs (frequency, maximum intensity, and duration of occurrences) in the Indonesian waters during the last 40 years (1982–2021), 26 years (1982–2007; warm phase), and 14 years (2008–2021; cold phase), as shown in Fig. 2. The highest average frequency and duration of MHWs were around 3–5 times per year and 10–20 days, respectively, occurring during the cold phase of PDO (Fig. 2c, i). On the other hand, the highest maximum intensity, around > 1.5°C, happened during the warm phase of PDO (Fig. 2e) when El Niño dominated the Indonesian waters. In addition, this study revealed the mechanism underlying the formation of maximum intensity of MHWs in terms of the role of local forcing related to the net surface heat flux (Fig. 4a, b, c). We found that the high net surface heat flux occurred during the warm phase of PDO. These conditions were associated with the highest maximum intensity which also happened in this phase (Figs. 4b, 2e). We hypothesize that the El Niño phase significantly influenced the maximum intensity of MHWs, especially in the eastern Indonesian waters, which reached > 2.5 ℃ (Fig. 9a, left column). On the other hand, the pIOD phase contributes to the formation of maximum intensity of MHWs, particularly in the western regions of Indonesian waters, with value reaching > 3.0 ℃ (Fig. 9a, right column). Furthermore, the maximum intensity when the IOD and ENSO phases strengthen and weaken each other was also investigated in this research. The present study showed the highest maximum intensity, reaching > 3.0 ℃ almost in all of the Indonesian waters in SON 1997 when El Niño was happening simultaneously with the pIOD phase (Fig. 8a, left column). In the opposite condition, the lowest maximum intensity occurred during La Niña and nIOD phases in SON 1998 (Fig. 8a, middle column).

Our estimate of the mechanism causing high net surface heat flux within the Indonesian waters suggests that the most dominant contribution of net surface heat flux is high SWR as heat gain and low LWR as heat loss through the evaporation process. The findings suggested that there was an increasing SST in the Indonesian waters, significantly influencing the maximum intensity of MHWs. However, we also found a very significant difference in the net surface heat flux characteristics between eastern and western Indonesian seas during the three study periods, although not investigated in detail during this present study. Therefore, further works would be to show the detailed characteristics for both regions and understand possible mechanisms that influenced MHWs. Additionally, the comprehensive MHWs in the Indonesian waters at the surface have been analyzed. This extreme event allows the occurrence of MHWs in the subsurface, so further studies are needed to identify this phenomenon.

ECMWF: The European Center for Medium-Range Weather Forecasts; OISST: Optimum Interpolation Sea Surface Temperature.

Availability of data and material

The SST data were provided from https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/. The surface heat flux components (shortwave radiation, longwave radiation, sensible heat flux, and latent heat flux), surface wind speed, and total cloud cover were available in the https://cds.climate.copernicus.eu. Meanwhile, the IOD and ENSO indices were obtained from the https://stateoftheocean.osmc.noaa.gov/. A Matlab toolbox to detect and analyze the MHWs were sourced from https://github.com/ZijieZhaoMMHW/m_mhw1.0/commit/04491bdc6b34458acbe07afd83d906af29754099.

Competing interests

The authors declare that they have no competing interest.

Funding

Parts of this research were funded by Bandung Institute of Technology (ITB) under the program of Penelitian, Pengabdian kepada Masyarakat dan Inovasi ITB (P2MI) 2021 and 2022. This research was also supported by the Indonesian Ministry of Education, Culture, Research and Technology (Kemendikbudristek) under the research program of Penelitian Disertasi Doktor (PDD) 2022 (Research Grand Contract No. 083/E5/PG.02.00.PT/2022).

Authors' contributions

EB conducted the main research, wrote the original draft preparation, and further reviewed and edited the manuscript. NSN conceptualized the study and is the main contributor and also the main supervisor of the work and the writing. SRG contributed in developing and preparing the methodology, visualization, and collaborated with the first author in the construction of the manuscript. AT supervised the research project, reviewed and edited the manuscript. All authors read and approved the final manuscript.

Authors' information

¹Doctoral Program in Earth Sciences, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB), Indonesia.

²Research Group of Oceanography, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB), Indonesia.

³Department of Oceanography, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB), Indonesia.

Acknowledgements

Parts of this research were funded by Bandung Institute of Technology (ITB) under the program of Penelitian, Pengabdian kepada Masyarakat dan Inovasi ITB (P2MI) 2021 and 2022. In addition, the authors are also very thankful to the Indonesian Ministry of Education, Culture, Research and Technology (Kemendikbudristek) for supporting this research under the research program of Penelitian Disertasi Doktor (PDD) 2022 (Research Grand Contract No. 083/E5/PG.02.00.PT/2022). We also would like to gratefully acknowledge data support from NOAA and ECMWF.

Behrens E, Fernandez D, Sutton P (2019) Meridional oceanic heat transport influences marine heatwaves in the Tasman Sea on interannual to decadal timescales. Front Mar Sci 6(228):1–17. doi:10.3389/fmars.2019.00228.
Brauko KM, Cabral A, Costa NV, Hayden J, Dias CEP, Leite ES, Westphal RD, Mueller CM, Hall-Spencer JM, Rodrigues RR, Rörig LR, Pagliosa PR, Fonseca AL, Alarcon OE, Horta PA (2020) Marine heatwaves, sewage and eutrophication combine to trigger deoxygenation and biodiversity loss: a SW Atlantic case study. Front Mar Sci 7:590258. doi:10.3389/fmars.2020.590258.
Caputi N, Kangas M, Denham A, Feng M, Pearce A, Hetzel Y, Chandrapavan A (2016) Management adaptation of invertebrate fisheries to an extreme marine heat wave event at a global warming hot spot. Ecol Evol 6(11):3583–3593. doi:10.1002/ece3.2137.
Carvalho KS, Smith TE, Wang, S (2021) Bering Sea marine heatwaves: patterns, trends and connections with the Arctic. J Hydrol 600:126462. doi:10.1016/j.jhydrol. 2021.126462.
Cheung WWL, Frölicher TL (2020) Marine heatwaves exacerbate climate change impacts for fisheries in the Northeast Pacific. Sci Rep 10(1):6678. doi:10.1038/s41598-020-63650-z.
Doney SC (2006) Plankton in warmer world. Nature 444:695–696. doi:10.1038/444695a.
Feng X, Shinoda T (2019) Air-sea heat flux variability in the Southeast Indian Ocean and its relation with Ningaloo Niño. Front Mar Sci 6(266):1–15. doi:10.3389/fmars.2019.00266.
Fewings MR, Brown KS (2019) Regional structure in the marine heat wave of summer 2015 off the Western United States. Front Mar Sci 6(564):1–14. doi:10.3389/fmars.2019.00564.
Fordyce AJ, Ainsworth TD, Heron SF, Leggat W (2019) Marine heatwave hotspots in coral reef environments: physical drivers, ecophysiological outcomes and impact upon structural complexity. Front Mar Sci 6(498). doi:10.3389/fmars.2019.00498.
Frölicher TL, Fischer EM, Gruber N (2018) Marine heatwaves under global warming. Nature 560(7718):360–364. doi:10.1038/s41586-018-0383-9.
Gawarkiewicz G, Chen K, Forsyth J, Bahr F, Mercer AM, Ellertson A, Fratantoni P, Seim H, Haines S, Han L (2019) Characteristics of an advective marine heatwave in the Middle Atlantic Bight in early 2017. Front Mar Sci 6(712):1–14. doi:10.3389/fmars.2019.00712.
Heidemann H, Ribbe J (2019) Marine heat waves and the influence of El Niño off Southeast Queensland, Australia. Front Mar Sci 6(56):1–15. doi:10.3389/ fmars.2019.00056.
Hobday AJ, Alexander LV, Perkins-Kirkpatrick SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Gupta AS (2016) A hierarchical approach to defining marine heatwaves. Prog Oceanogr 141:227–238. doi:10.1016/j.pocean.2015.12.014.
Hu S, Zhang L, Qian S (2020) Marine heatwaves in the Arctic region: variation in different ice covers. Geophys Res Lett 47(16). doi:10.1029/ 2020GL089329.
IPCC (2018) “Climate change 2018,” in The Physical Science Basis, vol. 1. .
IPCC (2021) “Climate change 2021,” in The Physical Science Basis, vol. 1. .
Iskandar I, Mardiansyah W, Lestari DO, Masumoto Y (2020) What did determine the warming trend in the Indonesian sea?. PEPS 7(1). doi:10.1186/s40645-020-00334-2.
Iskandar M, Ismail MFA, Arifin T, Chandra H (2021) Marine heatwaves of sea surface temperature off South Java. Heliyon 7(12):e08618. doi:10.1016/j.heliyon.2021.e08618.
Ismail MFA (2021) Characteristics of marine heatwaves off West Sumatra derived from high-resolution satellite data. J Hunan Univ Nat Sci 48(6):130–136.
Jackson JM, Johnson GC, Dosser HV, Ross T (2018) Warming from recent marine heatwave lingers in deep British Columbia fjord. Geophys Res Lett 45(18):9757–9764. doi:10.1029/2018GL078971.
Jacox MG, Alexander MA, Mantua NJ, Scott JD, Hervieux G, Webb RS, Werner FE (2018) Forcing of multiyear extreme ocean temperatures that impact California current living marine resources in 2016. BAMS 99(1):S27–S33. doi:10.1175/BAMS-D-17-0119.1.
Kendrick GA, Nowicki RJ, Olsen YS, Strydom S, Fraser MW, Sinclair EA, Statton J, Hovey RK, Thomson JA, Burkholder DA, McMahon KM, Kilminster K, Hetzel Y, Fourqurean JW, Heithaus MR, Orth RJ (2019) A Systematic review of how multiple stressors from an extreme event drove ecosystem-wide loss of resilience in an iconic seagrass community. Front Mar Sci 6(455):1–15. doi:10.3389/fmars.2019.00455.
Kuleshov Y, Qi L, Fawcett R, Jones D (2008) On tropical cyclone activity in the Southern Hemisphere: trends and the ENSO connection. Geophys Res Lett 35(14):1–5. doi:10.1029/2007GL032983.
Lewis SC, Mallela J (2018) A multifactor risk analysis of the record 2016 great barrier reef bleaching. BAMS 99(1):S144–S149. doi: 10.1175/BAMS-D-17-0074.1.
Mignot A, Von Schuckmann K, Gasparin F, van Gennip S, Landschützer P, Perruche C, Lamouroux J, Amm T (2021) Decrease in air-sea CO2 fluxes caused by persistent marine heatwaves. Nat Commun 13:4300. doi:10.31223/X5JG7V.
Mills K, Pershing A, Brown C, Chen Y, Chiang FS, Holland D (2013) Fisheries management in a changing climate: lessons from the 2012 ocean heat wave in the Northwest Atlantic. Oceanogr 26(2):191–195. doi:10.5670/oceanog.2013.27.
Oliver ECJ, Benthuysen JA, Bindoff NL, Hobday AJ, Holbrook NJ, Mundy CN, Perkins-Kirkpatrick SE (2017) The unprecedented 2015/16 Tasman Sea marine heatwave. Nat Commun 8(16101):1–12. doi:10.1038/ncomms16101.
Oliver ECJ, Donat MG, Burrows MT, Moore PJ, Smale DA, Alexander LV, Benthuysen JA, Feng M, Sen Gupta A, Hobday AJ, Holbrook NJ, Perkins-Kirkpatrick SE, Scannell HA, Straub SC, Wernberg T (2018a) Longer and more frequent marine heatwaves over the past century. Nat Commun 9(1):1–12. doi:10.1038/s41467-018-03732-9.
Oliver ECJ, Perkins-Kirkpatrick SE, Holbrook NJ, Bindoff NL (2018b) Anthropogenic influences on record 2016 marine heatwave. BAMS 99(1):S44–S48. doi:10.1175/BAMS-D-17-0093.1.
Pant V, Girishkumar MS, Udaya Bhaskar TVS, Ravichandran M, Papa F, Thangaprakash VP (2015) Observed interannual variability of near-surface salinity in the Bay of Bengal. J Geophys Res Oceans 120(5):3315–3329. doi:10.1002/ 2014JC010340.
Pearce AF, Feng M (2013) The rise and fall of the “marine heat wave” off Western Australia during the summer of 2010/2011. J Mar Syst 111–112:139–156. doi:10.1016/j.jmarsys.2012.10.009.
Perkins-Kirkpatrick S, King A, Cougnon E, Grose M, Oliver ECJ, Holbrook NJ, Lewis SC, Pourasghar F (2019) The role of natural variability and anthropogenic climate change in the 2017/18 Tasman Sea marine heatwave. BAMS 100(1):S105–S110. doi:10.1175/BAMS-D-18-0116.1.
Pujol C, Pe´rez-Santos I, Barth A, Alvera-Azca´rate A (2022) Marine heatwaves offshore Central and South Chile: understanding forcing mechanisms during the years 2016-2017. Front Mar Sci 9:800325. doi:10.3389/fmars.2022.800325.
Smale DA, Wernberg T, Oliver ECJ, Thomsen M, Harvey BP, Straub SC, Burrows MT, Alexander LV, Benthuysen JA, Donat MG, Feng M, Hobday AJ, Holbrook NJ, Perkins-Kirkpatrick SE, Scannel HA, Gupta SA, Payne BL, Moore PJ (2019) Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat Climate Chang 9(4):306–312. doi:10.1038/s41558-019-0412-1.
Smith KA, Dowling CE, Brown J (2019) Simmered then boiled: multi decadal poleward shift in distribution by a temperate fish accelerates during marine heatwave. Front Mar Sci 6(407):1–16. doi:10.3389/fmars.2019.00407.
Strub PT, James C, Montecino V, Rutllant JA, Blanco JL (2019) Ocean circulation along the Southern Chile transition region (38°–46°S): mean, seasonal and interannual variability, with a focus on 2014–2016. Prog. Oceanog. 172:159–198. doi: 10.1016/j.pocean.2019.01.004.
Su Z, Pilo GS, Corney S, Holbrook NJ, Mori M, Ziegler P (2021) Characterizing marine heatwaves in the Kerguelen Plateau region. Front Mar Sci 7. doi:10.3389/fmars.2020.531297.
Thomsen MS, Mondardini L, Alestra T, Gerrity S, Tait L, South P, Lilley SA, Sciel DR (2019) Local extinction of bull kelp (Durvillaea spp.) due to a marine heatwave. Front Mar Sci 6(84). doi:10.3389/fmars.2019.00084.
Wallace JM, Hobbs PV (2006) Atmospheric science: an introduction survei: second edition. Elsevier, Amsterdam, 352–353, 357, 367–368.
Walsh JE, Thoman RL, Bhatt US, Bieniek PA, Brettschneider B, Brubaker M, Danielson S, Lader R, Fetterer F, Holderied K, Iken K, Mahoney A, McCammon M, Partain J (2018) The high latitude marine heat wave of 2016 and its impacts on Alaska. BAMS 99(1):S39–S43. doi:10.1175/BAMS-D-17-0105.1.
Yao J, Xiao L, Gou M, Li C, Lian E, Yang S (2018) Pacific decadal oscillation impact on East China precipitation and its imprint in new geological documents. Sci 61(4):473–482. doi:10.1007/s11430-016-9146-2.
Zhao Z, Marin M (2019) A Matlab toolbok to detect and analyze marine heatwaves. J Open Source Softw 4(33):1124. doi:10.21105/joss.01124.

Table 1. IOD, ENSO, and neutral events during the SON 1982–2021 period.

	pIOD	Neutral	nIOD
El-Niño	1997	2015	-
Neutral	2019	1990, 2013	1996
La-Niña	-	1988	1998

PEPS2022graphicalabstractimageBeliyanaetal.png

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Detecting Long-term Characteristics of Marine Heatwaves (1982–2021) in the Indonesian Waters

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Abstract

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1 Introduction

2 Data And Methods

2.1 Identification of MHWs

2.2 Net Surface Heat Flux

2.3 Wind Speed and Total Cloud Cover

2.4 DMI and ONI Classification

3 Results And Discussions

3.1 Characteristics of MHWs in the Indonesian waters

3.2 Linear trend of MHWs in the Indonesian waters

3.3 The formation of MHWs generated by local forcing

3.3.1 Net surface heat flux

3.3.2 Components of net surface heat flux

3.3.3 Interannual variations of MHWs

3.3.4 The maximum intensity of MHWs during SON in the period of 1982–2021

4 Summary And Conclusions

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