Characteristics and Formation of Two Leading Marine Heatwave Modes in the North Indian Ocean during Summer and Their Implications for Local Precipitation

Jayarathna W. N. D. Sandaruwan aGuy Carpenter Asia–Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China

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Wen Zhou bDepartment of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China

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Paxson K. Y. Cheung aGuy Carpenter Asia–Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China

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Yan Du cState Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
dCollege of Marine Science, University of Chinese Academy of Sciences, Beijing, China
eSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China

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Xuan Wang aGuy Carpenter Asia–Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China

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Abstract

Marine heatwaves (MHWs) are extreme climatic events that can have a significant impact on marine ecosystems and their services across the world. We examine the spatiotemporal variation of summer MHWs in the north Indian Ocean (NIO) and find that the whole NIO Basin exhibits a pronounced spatial variability as well as a significant increasing trend in MHW frequency. We show that the NIO has two leading MHW modes linked to two distinct sea surface temperature (SST) patterns during summer. The first MHW mode is associated with basinwide warming, which is preconditioned by a decaying El Niño–Southern Oscillation (ENSO) and sustained throughout the summer by anomalous northeasterlies extending from the anticyclonic circulation of the western North Pacific subtropical high (WNPSH). The combined effect of thermocline warming due to downwelling oceanic planetary waves, decreased wind-induced evaporative cooling, and enhanced insolation cause basinwide summer MHWs. The second MHW mode exhibits a zonal dipole pattern, which has unfavorable cooling conditions in the previous seasons. The second MHW mode is associated with a phase change of ENSO and is greatly influenced by the formation of an interhemispheric pressure difference (IHPD) due to strengthening of the Australian high (AH) and weakening of the WNPSH. The IHPD induces cross-equatorial southerly winds across the eastern Indian Ocean. These winds favor the transformation of basinwide cooling conditions into zonal SST patterns via wind–evaporation–SST and thermocline–SST feedback, causing MHWs with a zonal dipole pattern. These MHW modes have a significant influence on the distribution and intensity of summer precipitation in the NIO.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wen Zhou, wen_zhou@fudan.edu.cn

Abstract

Marine heatwaves (MHWs) are extreme climatic events that can have a significant impact on marine ecosystems and their services across the world. We examine the spatiotemporal variation of summer MHWs in the north Indian Ocean (NIO) and find that the whole NIO Basin exhibits a pronounced spatial variability as well as a significant increasing trend in MHW frequency. We show that the NIO has two leading MHW modes linked to two distinct sea surface temperature (SST) patterns during summer. The first MHW mode is associated with basinwide warming, which is preconditioned by a decaying El Niño–Southern Oscillation (ENSO) and sustained throughout the summer by anomalous northeasterlies extending from the anticyclonic circulation of the western North Pacific subtropical high (WNPSH). The combined effect of thermocline warming due to downwelling oceanic planetary waves, decreased wind-induced evaporative cooling, and enhanced insolation cause basinwide summer MHWs. The second MHW mode exhibits a zonal dipole pattern, which has unfavorable cooling conditions in the previous seasons. The second MHW mode is associated with a phase change of ENSO and is greatly influenced by the formation of an interhemispheric pressure difference (IHPD) due to strengthening of the Australian high (AH) and weakening of the WNPSH. The IHPD induces cross-equatorial southerly winds across the eastern Indian Ocean. These winds favor the transformation of basinwide cooling conditions into zonal SST patterns via wind–evaporation–SST and thermocline–SST feedback, causing MHWs with a zonal dipole pattern. These MHW modes have a significant influence on the distribution and intensity of summer precipitation in the NIO.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wen Zhou, wen_zhou@fudan.edu.cn

1. Introduction

Marine heatwaves (MHWs) are extended periods of anomalously high sea surface temperature (SST) events that exceed a seasonally varying threshold (90th percentile) (Hobday et al. 2016; Scannell et al. 2016). These events have a significant and long-lasting negative impact on marine ecosystems (Hughes et al. 2018; Oliver et al. 2018a; Smale et al. 2019; Benthuysen et al. 2020), including coral reef bleaching, mass mortality of marine organisms, benthic habitat loss, and changes in species diversity around the globe (McWilliams et al. 2005; Garrabou et al. 2009; Mills et al. 2013; Caputi et al. 2016; Frölicher and Laufkötter 2018; Wernberg 2021). These extreme MHWs can also have disastrous consequences for humans through environmental and socioeconomic damage (Smith et al. 2021).

Under global warming, MHW properties exhibit a diverse distribution across the global oceans (Holbrook et al. 2019). Oliver et al. (2018a) discovered that the duration and frequency of MHWs have increased by more than 15% and 30%, respectively, causing a more than 50% increase in global oceanic annual MHW days from 1925 to recent years because of rising mean temperatures.

Several studies have implied that the formation of MHWs in the Indian and Pacific Oceans is strongly associated with large-scale climate modes like El Niño–Southern Oscillation (ENSO), the Indian Ocean dipole (IOD), and the Pacific decadal oscillation (PDO) (Holbrook et al. 2019, 2020). Hence, SST anomaly (SSTa) variability in the tropical Indo-Pacific region is closely tied to the variability of ENSO, IOD, and Indian Ocean warming through the remote teleconnection of atmospheric and oceanic circulation (Holbrook et al. 2019, 2020).

During these climate modes, favorable atmospheric forcing for the generation of MHWs includes the persistence of anomalous high pressure systems over the ocean, which can lead to a notable rise in insolation due to less cloud cover and suppression of latent heat loss as a result of weaker wind speed, which causes SST to rise (Sen Gupta et al. 2020; Holbrook et al. 2019; Liu et al. 2022). Anomalous oceanic conditions like horizontal warm water advection (Feng et al. 2013; Oliver et al. 2017), coastal upwelling (Yao and Wang 2021), and mixed layer variability (Amaya et al. 2021) can also provide preconditioning for local MHWs.

The phase of the climate mode is also crucial for MHW variability (Holbrook et al. 2019). For example, an MHW event that lasted over several years in the northeast Pacific was triggered by El Niño–induced anomalous atmospheric forcing associated with a long-lasting anticyclonic system over this region (Bond et al. 2015; Di Lorenzo and Mantua 2016; Schmeisser et al. 2019; Amaya et al. 2020). The majority of MHW days on Australia’s northeast coast occurred during strong El Niño years (Heidemann and Ribbe 2019). Moreover, MHWs off the coast of western Australia are not solely due to La Niña; they were linked to both La Niña (Feng et al. 2013) and local air–sea interaction (Kataoka et al. 2014).

The north Indian Ocean (NIO) is a large, semienclosed sea with high primary productivity, especially during the summer monsoon season (Roxy et al. 2016). However, the Indian Ocean has exhibited rapid and notably faster warming compared with other ocean basins over the last few decades (Levitus et al. 2012; Roxy et al. 2014; Dhame et al. 2020). This temperature rise negatively affects the ocean’s primary production (Roxy et al. 2016) and influences the intensity of regional precipitation (Ajayamohan and Rao 2008; Roxy et al. 2015; Li and Zhou 2012, 2015; Zhang et al. 2022).

Various studies have looked at extreme MHW occurrences in the Pacific Ocean (Di Lorenzo and Mantua 2016; Amaya et al. 2020; Chen et al. 2021) and Atlantic Ocean (Mills et al. 2013; Rodrigues et al. 2019; Schlegel et al. 2021). Recent studies in marginal seas like the South China Sea (SCS) (Lee et al. 2020; Liu et al. 2022; Yao and Wang 2021; Tan et al. 2022) and Red Sea (Bawadekji et al. 2021; Mohamed et al. 2021) have emphasized the significant positive trend in MHW frequency and duration. However, only a few studies have been conducted in the Indian Ocean. Chatterjee et al. (2022) found a significant increase in MHW frequency and number of MHW days in the Arabian Sea, whereas the western Indian Ocean (WIO) shows a considerably higher prevalence of MHWs, followed by the eastern Indian Ocean (EIO) in recent years (Saranya et al. 2022; Gao et al. 2022). Extended MHWs in the western tropical Indian Ocean (TIO) were initiated by oceanic planetary waves, specifically in 2015/16 and 2019/20, coupled with major climate modes in the region (Zhang et al. 2021). There is evidence of coral bleaching brought on by MHWs in the southeast Indian Ocean, notably in relation to ENSO and indicating global warming (Zhang et al. 2017). Higher SSTs have been seen in the Andaman Sea, the Arabian Sea, and the Bay of Bengal, where thermal stress has caused excessive coral bleaching during summer (Krishnan et al. 2011; Patterson Edward et al. 2018).

In the NIO, the boreal summer, referred to as the June–August (JJA) period in previous studies, for example, Schott and McCreary (2001), is crucial for heavy rainfall, coastal upwelling, and coral bleaching (Zhang et al. 2017; Paparella et al. 2019). The ocean–atmosphere conditions in the NIO that modulate SST are strongly altered by major climate modes via various teleconnection processes on an interannual time scale (Schott and McCreary 2001). However, their potential impact in driving extreme anomalous warming and inducing NIO MHWs during summer is not yet fully understood. In this paper, we explore the spatial and temporal characteristics of summer MHWs in the NIO and their variation on an interannual time scale due to large-scale climate variability. We show that the major climate modes substantially regulate interannual variation of summertime MHW modes by altering regional ocean–atmospheric processes in the NIO. In addition, this study explores local climate responses to these extreme anomalous warming events in the NIO during summer.

The rest of the paper is organized as follows: section 2 describes the data and analysis methods used for MHW detection. Section 3 describes the spatiotemporal characteristics of MHWs in the NIO. The MHW modes and related ocean–atmospheric interactions are explained in sections 4 and 5. Finally, section 6 summarizes and discusses the current findings.

2. Data and methodology

a. Data sources

The primary daily SST dataset for MHW detection is derived from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) Sea Surface Temperature, version 2 (OISSTv2), data with a resolution of 0.25° × 0.25° for the period 1982–2020 (Reynolds et al. 2002). We only use daily data of NOAA OISST to detect MHWs, and to analyze the underlying ocean–atmosphere interactions, we use monthly variables. The National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) Reanalysis (Kalnay et al. 1996) is used to provide daily atmospheric reanalysis data, including 850-hPa geopotential height fields, surface winds, outgoing longwave radiation, net shortwave radiation flux (NSWRS), net longwave radiation flux (NLWRS), latent heat flux (LHTFL), and sensible heat flux (SHTFL) at a resolution of 2.5° × 2.5° for 1982–2020. The NCEP Global Ocean Data Assimilation System (GODAS) reanalysis dataset with a resolution of 0.33° × 0.33° for 1982–2020 (Behringer and Xue 2004) is used to derive sea surface height (SSH). SSH product from GODAS is used instead of Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) by Centre National d’Etudes Spatiales (CNES) data center, as it covers the study period (1982–2020). Monthly precipitation data are obtained from the Global Precipitation Climatology Project (GPCP) v2.3 with a resolution of 2.5° × 2.5° from 1982 to 2020 (Adler et al. 2003). We compare the results of the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5) with NCEP/NCAR products (see Fig. S1 in the online supplemental material). Only results from the NCEP/NCAR are presented in the main text as they have been used for the previous MHW studies by researchers over the Indian Ocean (Saranya et al. 2022). The study period is from 1982 to 2020, and anomalies for the data fields are calculated by subtracting the corresponding mean seasonal cycle.

b. Methods

An MHW event is detected as an anomalous warm event that exceeds the 90th percentile of the seasonally varying threshold for at least five successive days based on the climatological mean (Hobday et al. 2016; Wang et al. 2014, 2016) from 1982 to 2020.

The 90th percentile of the climatological threshold is calculated using daily SST data within an 11-day frame centered on data from 1982 to 2020 and then smoothed using a 31-day moving average (Lee et al. 2020; Liu et al. 2022; Yao and Wang 2021; Tan et al. 2022). A consecutive event with an interruption of fewer than two days is still considered a single MHW event in the Indian Ocean. Based on this definition, statistically consistent metrics for extreme MHW events can be developed to provide some characteristics for a warming event, which is then important for assessing its impact on specific sectors such as marine ecosystems and services. Metrics such as MHW frequency (occurrence), severity, duration, and days are examined (detailed definitions are available in Hobday et al. 2016). To obtain MHWs during summer, we consider only the events that exceed the seasonal threshold during the period June–August (92 days) for the 39-yr period. Summertime MHW frequency is computed as the number of MHW events occurring in the period June–August from 1982 to 2020.

An empirical orthogonal function (EOF) analysis, regression and correlation analyses, and linear trend analyses are also carried out to find the interannual variance.

Niño-3.4 is calculated using OISST data from January 1982 to December 2020, with SST anomalies in the Niño-3.4 region (5°N–5°S, 170°–120°W). The Indian Ocean Basin mode (IOBM) index is expressed as the SSTa over the TIO (20°S–20°N, 40°–100°E) (Zheng et al. 2011; Du et al. 2013; Zhang et al. 2022). The Indian Ocean dipole mode index (DMI) is designated as the difference in area-mean SSTa averaged between the western (10°S–10°N, 50°–70°E) and eastern (10°S–0°, 90°–110°E) region (Saji et al. 1999).

3. Spatial and temporal properties of summer MHWs in the NIO

The seasonal mean SST (Fig. 1a) and seasonal mean threshold (Fig. 1b) (seasonally varying 90th percentile) show a robust zonal gradient. The mean SST in the NIO is lowest (<26°C) near the western boundary (coast of Somalia and Oman) and highest (>30°C) in the central equatorial and EIO. The seasonal mean SST and the MHW threshold are separated by only 1°–1.5°C in the NIO (Fig. 1b), and the existence of MHWs in the area is indicated if the SSTa is significantly above the seasonal mean. Southwest monsoon winds dominate the NIO (Fig. 1a), bringing precipitation to the region during the summer (Schott and McCreary 2001). These winds (Fig. 1a) cause severe wind stress maxima along the coasts of Somalia and Oman (Bauer et al. 1991). Due to offshore Ekman transport, warm water at the surface is replaced by cold water from deeper ocean layers (Schott et al. 2002). Changes in these local processes such as upwelling eventually lead to changes in the mean SST and Indian Ocean warm pool (McCreary et al. 1996; Schott et al. 2002; Vinayachandran et al. 2004). Thus, rising mean SSTs over the summer are likely to provide favorable conditions for frequent MHWs in this region.

Fig. 1.
Fig. 1.

Summertime average of (a) SST (shading; unit: °C) and surface wind (vectors; unit m s−1); (b) 90th-percentile threshold value of daily SST for the 1982–2020 base period, and detected (c) MHW duration, (d) total MHW days, (e) MHW mean intensity, and (f) MHW frequency in the NIO during 1982–2020.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

During the summer, the average MHW characteristics in the NIO exhibit prominent spatial variability. The average duration of MHWs in the WIO, northern Arabian Sea, and northern Bay of Bengal ranges from 10 to 15 days (Fig. 1c). The average total number of MHW days varies from 15 to 20 (Fig. 1d) and has spatial variability similar to MHW duration. MHW intensity has a notable distribution, with areas of high intensity (1.5°–2°C per unit time) seen at the western boundary of the NIO, specifically near the Oman coast and Gulf of Oman (Fig. 1e). MHW frequency also has substantial spatial variability, with mean MHW occurrences ranging from 1 to 1.5 times per year, depending on the region (Fig. 1f). A notable increase in the likelihood of summer MHWs (>1.5 times per year) is observed in the northern Arabian Sea and northern Bay of Bengal (Fig. 1f). As the 90th-percentile threshold SST level (Fig. 1b) variability tends to be 1°–2°C above the seasonal mean value, the increase in the detected MHW frequency during the summer indicates extremely high SST conditions, which are clearly distinguished from positive SST anomalies that are simply over the seasonal mean.

A robust positive trend is observed in both mean SST (Fig. 2a) and MHW frequency (Fig. 2b), with a stronger trend (>0.5 decade−1) in the western and northern NIO, confirming recent studies of MHWs in the region (Chatterjee et al. 2022; Saranya et al. 2022). The area-averaged time series also shows a consistent, rapidly increasing trend in mean SST and MHW frequency with noticeable interannual variation (Fig. 2c). After 1998, both the mean SST and MHW frequency show a robust increase in the NIO Basin. Although MHWs have already become long lasting (Zhang et al. 2021), no decline in their frequency has been observed in recent years. The rise in MHW frequency is clearly explained by growth in the regional mean temperature, with a strong correlation (R = 0.92, p < 0.05), which is the highest among other metrics (Fig. 2d). Rising MHW trends in the NIO may be a result of global warming, but data availability is insufficient to distinguish the effects of decadal variability. Hence, the occurrence of MHWs has increased in the NIO, and the consequent ecological harm demands more attention.

Fig. 2.
Fig. 2.

Spatial trend in (a) mean SST and (b) MHW frequency from 1982 to 2020, (c) regionally averaged (5°S–30°N, 40°–100°E) summer SST and MHW frequency, and (d) scatterplot of summer mean SST and MHW frequency (green line shows the best-fit linear curve). Stippling in (a) and (b) reflects areas where the SST and MHW frequency trend is significant at the 95% confidence level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

4. EOF analysis of summer MHWs in the NIO

To investigate the spatiotemporal patterns of summer MHWs in the NIO, EOF analysis is applied over the detected MHW frequency detrended datasets. The MHW frequency metric is chosen because it more comprehensively includes the discrete occurrence of extreme MHWs, which is crucial for the ecological environment and its rate of adaptation (Oliver et al. 2021). In addition, it also has a strong relationship with mean SST variability in the region. We can determine the unique regional oceanic and atmospheric interaction associated with each MHW frequency mode by regressing the specific variables (SST, surface wind, atmospheric fluxes, etc.) onto each EOF mode. A number of other researchers have used this method and have confirmed that it can be used to evaluate regional ocean–atmosphere interactions and their relationship to the variance in MHWs on an interannual time scale (Oliver et al. 2018b; Lee et al. 2020; Yao and Wang 2021). The first two leading modes and their respective principal component (PC) time series are shown in Fig. 3. Mode 1 and mode 2 account for 28.3% (Fig. 3a) and 10.2% (Fig. 3c) of the total variance, respectively. According to the North equation (North et al. 1982), these two characteristics are substantially separated from one another, while the remaining modes are less significant, with many local features.

Fig. 3.
Fig. 3.

First two EOF modes of variability of total summer MHW frequency. (a),(c) Spatial pattern of the Indian Ocean (5°S–30°N, 40°–100°E) and (b),(d) the related PC time series.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

The first EOF pattern indicates a monosign over the entire NIO Basin (hereafter called the basinwide mode), with more weight in the central Indian Ocean (Fig. 3a). The corresponding PC1 (Fig. 3b) consists of strong year-to-year variability, with highlighted years in 1983, 1998, 2015, and 2020. The second EOF mode consists of a zonal dipole pattern with a positive pole centered across the western region and a negative pole across the eastern region (hereafter called the zonal dipole mode), where variability is associated with opposite polarity (Fig. 3c). The corresponding PC2 also shows robust interannual variation, with positive peaks in 2012 and 2015 (Fig. 3d).

5. Contributing factors and processes for summer MHWs

a. Seasonal evolution of SST

The interaction of large-scale physical factors associated with each mode is acquired by projecting these variable data (detrended) onto each mode. Lee et al. (2020) have shown that the prior seasons and months may provide prerequisite conditions for summertime MHWs in East Asia. Hence, to check the preconditioning status of SSTa for MHW genesis in the NIO, linear regression maps for SSTa for the preceding winter (DJF[−1]) and spring (MAM[−1]) and the simultaneous summer (JJA[0]) are created with respect to the two PCs (Fig. 4).

Fig. 4.
Fig. 4.

Regressed patterns of anomalous SSTs (shading; unit: °C) for the earlier seasons of DJF[−1] and MAM[−1] and the simultaneous summer (JJA[0]) against the MHW frequency (left) PC1 and (right) PC2 time series. The number in the square brackets denotes the seasons relative to summer: 0 for the simultaneous summer and −1 for the preceding season. Stippling indicates statistical significance exceeding the 95% confidence level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

For the first MHW frequency mode, a positive SSTa signal in the Indian Ocean first appears south of the equator during winter, when the mature phase of an El Niño–like SSTa pattern (anomalously warm) emerges in the eastern Pacific (Fig. 4a). The warming pattern in the Indian Ocean persists through spring (Fig. 4c) and then becomes firmly established and shifts north of the equator, spanning the whole basin during summer (Fig. 4e). Meanwhile, the El Niño–like pattern in the eastern Pacific dissipates to a neutral condition in summer (Fig. 4e). It is clear that during the 1 MHW mode, the consecutive persistence of a positive SSTa in the NIO during the preceding seasons and the development of basinwide ocean warming are associated with the decaying El Niño in the eastern Pacific. These results imply that anomalous warming in the NIO a few months before summer can provide favorable preconditioning for a summertime rise in MHW frequency at the basin scale.

In the second MHW mode (Fig. 4b), a negative SSTa pattern emerges across much of the northern and western Indian Ocean during the winter (DJF[−1]). At the same time, the eastern equatorial Pacific indicates a La Niña–like negative SSTa condition. The negative SSTa condition in the WIO then develops and becomes more prominent over the entire NIO, indicating mature basinwide cooling during boreal spring (Fig. 4d). However, an abrupt increase in SSTa (warming) is observed over the WIO and negative SSTa (cooling) near the EIO, with a remarkable zonal SSTa gradient from east to west during summer, indicating the initial stage of positive IOD-like development (Fig. 4f). The second MHW mode exhibits abrupt warming and an SSTa gradient over the east and west, in contrast to the first MHW mode’s persistent warming. The collapse of the persistent cooling (warming) pattern and the sudden emergence of the IOD-like pattern, accompanied by the phase change from La Niña to El Niño in the second mode, suggest the influence of large-scale atmospheric circulation (anomalous wind) and local ocean dynamics (upwelling) (Guo et al. 2015, 2018; Lu and Ren 2020), raising the ocean temperature above the MHW threshold.

b. Major ocean–atmosphere interactions

It is evident that the period from spring to summer is crucial for both MHW modes. Warming of the Indian Ocean is also highly related to ocean dynamics, especially the variability of the thermocline and seasonal upwelling. The sea surface height anomaly (SSHa) in the TIO can be used as a clear indicator of thermocline variability (Du et al. 2009; Guo et al. 2018; Du et al. 2020). Hence, regressions between SSHa, surface wind anomalies (SWa), and MHW mode (Fig. 5) are provided to investigate the role of the thermocline in modulating the extreme SSTa related to MHWs in the region from March to August.

Fig. 5.
Fig. 5.

SSHa (shading; unit: m) and SWa (vectors; unit: m s−1) regressed with (left) PC1 and (right) PC2 from March to August. Hatching indicates areas where the regression of SSH exceeds the 95% confidence level, and for wind, only those regions exceeding the 95% confidence level are displayed.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

The positive SSHa in the WIO during March and April in the first MHW mode indicates that the initial warming of the NIO SST is related to thermocline warming (Fig. S2a) and the presence of downwelling planetary waves (Figs. 5a,c) from the preceding winter. The WIO warming pattern is characterized by distinct asymmetric (C-shaped) SWa (Wu et al. 2008; Du et al. 2009) over the northern/southern equatorial Indian Ocean during March and April (Figs. 5a,c). In addition, this leads to westward-propagating downwelling Rossby waves in the southern Indian Ocean (Xie et al. 2002) and is crucial in maintaining the warming until summer. From May to July, positive SSHa arises both in the WIO and along the equator, whereas asymmetric SWa diminishes south of the equator and evolves into strong anomalous easterlies over the NIO (Figs. 5e,g,i). These anomalous easterlies have the potential to suppress the climatological southwesterly monsoon winds, resulting in a second SST warming due to reduced evaporative cooling (Du et al. 2013, 2009). Positive SSHa during the period June–August indicates deepening of the thermocline in most of the NIO (Figs. 5i,k). Together, downwelling Rossby waves and anomalous easterlies contribute to the warming of the vast area of the NIO including the western and equatorial Indian Ocean by inhibiting seasonal upwell cooling and wind–evaporation–SST feedback (WES) (Zhang and Du 2021), which in turn creates ideal conditions for extreme warming in the region.

In the second MHW mode, anomalous equatorial westerlies (Fig. 5b) over the EIO appear to be protruding across the eastern equatorial Indian Ocean from the preceding winter. The evolution of negative SSHa over the WIO during March (Fig. 5b) and April (Fig. 5d) suggests the presence of upwelling Rossby waves (Fig. S2b), which cause basinwide cooling during La Niña, as previously documented by Chowdary et al. (2006). However, by the end of May, a strong easterly SWa is distinct over the equatorial Indian Ocean (Fig. 5f). This easterly wind becomes increasingly dominant from June to August, transforming into southeasterlies and shifting toward Java and Sumatra (Figs. 5h,j,l).

The southeasterlies enhance the wind speed and intensify the climatological surface wind (Figs. 5h,j). As a result, SSTa in the eastern Indian Ocean may decrease due to WES feedback (Li et al. 2003). Meanwhile, negative SSHa indicates a lifted thermocline along the Java–Sumatra coast due to the southeast SWa (Fig. 5h). This shallow thermocline promotes upwelling and hence significantly cools SST in the EIO via thermocline–SST feedback (Li et al. 2003; Saji et al. 1999). The long-lasting southeasterly SWa strengthens the Bjerknes feedback and upwelling, resulting in continued cooling of the SST in the EIO throughout the summer (Figs. 5h,j,l). Previous studies have found that anomalous easterlies play an essential role in driving positive Bjerknes feedback and subsurface ocean dynamics, such as equatorial nonlinear zonal and vertical advection (Bjerknes 1969; Fischer et al. 2005; Wang et al. 2020; Yang et al. 2020; Saji et al. 1999).

This easterly SWa also generates oceanic planetary waves (Xie et al. 2002). Once these waves reach the WIO thermocline dome during May (Fig. 5f), they deepen the thermocline and suppress vertical entrainment, resulting in increased surface and subsurface warming (Xie et al. 2002; Guo et al. 2015; Du et al. 2020) and an east–west zonal SSTa gradient in the NIO (Fig. 4f). Rossby waves persisting through the summer (Figs. 5j,l) raise the SST in the WIO by anomalous weakening of summertime upwelling, resulting in an increased frequency of MHWs in the region.

MHWs are often driven by a mix of local processes such as air–sea heat flux, horizontal warm water advection, and large-scale atmospheric interactions (Holbrook et al. 2020; Schlegel et al. 2021).

We can clearly distinguish the regions where oceanic and atmospheric processes play an important role in regional SST changes, when net surface heat fluxes (Qnet) are regressed with both MHW modes (Fig. S3). The majority of the northern Arabian Sea (north of 15°N) and all of the Bay of Bengal have strong downward anomalies of −4 to −6 W m−2 (p < 0.05) in the Qnet of PC1 (Fig. S3a), suggesting that the ocean is gaining heat during the summer, which is a major reason for sustaining the basinwide warming in the NIO. This suggests that atmospheric forcing drives air–sea interaction over this region, providing favorable conditions for frequent MHWs. At the same time, WIO near the thermocline dome exhibits heat loss with an upward Qnet of 2 to 3 W m−2 (p < 0.05), indicating ocean dominates the atmosphere in air–sea interactions. The spatial patterns of atmospheric heat flux regressed with PCs are provided in Fig. 6, where the NSWRS (Fig. 6a) and NLWRS (Fig. 6c) patterns are the opposite of each other during summer (JJA). The NSWRS anomalies in most of the Indian Ocean (north of 8°N), SCS, and western North Pacific (WNP) show −2 to −4 W m−2 (Fig. 6a), while NLWRS anomalies show 2 to 3 W m−2. The negative NLWRS (−2 to −6 W m−2) in the south and the WIO indicates suppression of emission due to convective-induced cloud cover (Fig. 6c). The LHTFL flux anomalies are very similar to the Qnet anomalies (Fig. 6e), indicating that Qnet loss of the equatorial WIO can be attributed to the increase in LHTFL of the region. However, aside from the equatorial WIO, the majority of the other NIO regions undergo reduced latent heat loss due to anomalous subsidence and northeasterlies, which can be a significant factor in maintaining basinwide warming and more frequent MHWs. Anomalous SHTFL has a slight positive significance over the WIO (Fig. 6g). Due to less cloud cover, most of the northern NIO is warmed by radiative warming, with excess incoming solar radiation and less latent heat loss, which causes the atmosphere to have a forcing effect over the ocean surface warming. At the same time, seawater warmed by thermocline warming in the western and equatorial Indian Ocean heats the atmosphere by releasing latent heat and dominating air–sea interaction through ocean forcing over the atmosphere during summer.

Fig. 6.
Fig. 6.

Regression between summer (JJA) anomalous atmospheric fluxes (shading; unit: W m−2; upward is considered positive), including NSWRS, NLWRS, LHTFL, and SHTFL with (left) PC1 and (right) PC2. Hatching indicates areas where statistical significance exceeds the 95% confidence level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

The significant positive anomalies in surface Qnet along the western equatorial Indian Ocean, notably along the Somalian coast (Fig. S3b), and in the EIO near Java and Sumatra, provide evidence of considerable ocean forcing over the atmosphere in terms of atmospheric fluxes during the second MHW mode in summer. A significant negative anomaly in Qnet (−4 to 5 W m−2; p < 0.05) appears near the central Indian Ocean and south of Sri Lanka, indicating that the atmosphere is dominant in the ocean–atmosphere interaction (Fig. S3b). The anomalous increase in NSWRS in the EIO reaches −4 to −7 W m−2 (Fig. 6b), indicating clear skies with fewer clouds, while in the WIO, there is a reduction of NSWRS anomalies from 4 to 6 W m−2. As a result of clear skies, the NLWRS anomalies are positive over the EIO (Fig. 6d) but negative near the western equatorial Indian Ocean, indicating increased cloudiness in the region. Due to the strong southeasterly winds that are prevalent in the area (Figs. 5j,l), the positive anomalous LHTFL fields in the EIO (Fig. 6f) in Java and Sumatra approach 5 to 6 W m−2 (p < 0.05), while in the WIO, warm water releases heat through latent heat loss (Fig. 6f). Due to the lack of northeasterly subsidence winds as in MHW mode 1, the northern NIO is resistant to basinwide warming despite having negative LHTFL loss (Chowdary and Gnanaseelan 2007). Over the eastern and northern Indian Ocean, the SHTFL anomalies have negative values (Fig. 6i). SST decreases significantly at the start of summer due to the wind–evaporation–SST feedback and thermocline feedback in the EIO, particularly in the Java–Sumatra region. Strong southeasterly coastal winds promote upwelling while also increasing evaporation and cold SST. This unique thermodynamic forcing cools the adjacent lower atmosphere and makes it clear and stable. This cold environment reduces cloud cover and is conductive to high insolation (Figs. 6b,d), followed by dry and cold weather over the EIO. Simultaneously, the WIO SST is warmed by the deepening thermocline due to downwelling Rossby waves, and the warmed seawater heats the atmosphere by releasing latent heat, which then leads to robust convective activity in the region.

It is crucial to focus on the association between the variability of summer rainfall and the two prominent MHW modes. To that end, we examined the vertical velocity (omega), meridional and equatorial vertical circulation, OLR, and precipitation during summer (Fig. 7). The results demonstrate that precipitation has two distinct variations during the two MHW modes.

Fig. 7.
Fig. 7.

Regression results of summertime (JJA) atmospheric (a),(b) vertical velocity (shading; omega level 500 hPa; unit: Pa s−1), meridional vertical velocity (shading; unit: Pa s−1), and circulation (vectors; unit: m s−1) averaged over 50°–80°E; the equatorial vertical velocity (1°S and 1°N) (shading; unit: Pa s−1), atmospheric circulation (vectors; unit: m s−1), OLR (shading; unit: W m−2), and precipitation (contours, positive: red; negative: green; unit: 0.5 mm day−1) with respect to summer MHW frequency (left) PC1 and (right) PC2 from 1982 to 2020. Hatching and dotted areas indicate regions where the regression of (a),(b) omega and (g),(h) OLR exceed the 95% confidence level. In the area inside the green dashed line in (c)–(f), the regression coefficient results are significant at the 95% confidence level. For precipitation, only those areas where the convection pattern exceeds the 95% confidence level are indicated.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

The negative anomalies of vertical velocity omega (500 hPa) (Fig. 7a) in the western and equatorial Indian Ocean indicate robust convective activities, while anomalous positive values indicate a strong downdraft with less atmospheric circulation and convection across the northern and northeastern Indian Ocean during the first MHW mode (Figs. 6a,c). The strong downdraft further specifies a reduction in cloud cover and winds, resulting in an increase in solar radiation that is absorbed by the top layers of the northeastern Indian Ocean. Hence, this leads to warming in most of the TIO Basin as a result of cloud–radiation–SST feedback (Klein et al. 1999; Tokinaga and Tanimoto 2004; Liu et al. 2022). At the same time, anomalous northeasterlies during summer suppress the climatological westerlies, followed by an increase in SST due to WES feedback, thereby increasing SST warming in the NIO (Du et al. 2009).

Moreover, with the presence of basinwide MHWs, the basinwide extremely warm ocean warmed the adjacent atmosphere and increased convective activities, which are indicated by ascending motion in both the meridional and equatorial vertical circulations (Figs. 7c,e). At the same time, the warmer ocean attracts moisture-containing winds toward the region, which results in anomalously higher precipitation across a vast area of the NIO, except in the northeastern Indian Ocean where the first MHW mode induces dry conditions (positive anomaly in OLR) during late summer (Fig. 7g). This is a clear indication that the preceding seasons provide a favorable environment for basinwide MHW genesis during the first MHW mode, leading to anomalously rising precipitation (negative anomaly in OLR) in most of the NIO at the end of summer (Fig. 7g).

The positive anomalies of omega (Fig. 7b) across the Indo-Pacific (EIO) region during the second MHW mode reflect the dissipation of the prior convection caused by the anomalous downward vertical motion (Fig. 7c) during summer. This has been discussed in several previous studies and identified as a typical characteristic of the Indian Ocean, associated with the development phase of El Niño (Klein et al. 1999; Chowdary and Gnanaseelan 2007; Yang et al. 2010; Liu et al. 2013). Strong positive vertical velocity anomalies (Fig. 7f) over the EIO indicate a clear and stable atmosphere because of the cooler ocean surface caused by strong upwelling and wind–evaporative cooling. This cold environment reduces cloud cover and is also conducive to high insolation, as indicated through the positive anomaly of OLR (Fig. 7h), resulting in dry and cold weather over the EIO. At the same time, the deepening thermocline vigorously warms the WIO (warm pole of the second MHW mode) and increases the favorable conditions for MHW occurrence in the region. Extreme SST warming associated with MHWs heats the atmosphere and increases convection (Fig. 7f) through latent heat loss (Guan et al. 2003). The warmed atmosphere produces more clouds, reducing the incoming solar radiation as shown by negative OLR anomalies near the WIO (Fig. 7h). Furthermore, results indicate that the zonal dipole pattern in the second MHW mode has a substantial impact on rainfall across the Maritime Continent (MC), the Indian subcontinent, Australia, and East Africa. Rainfall is scarce in the MC and Australia due to the dry and cold weather, but abundant in India and East Africa as the atmosphere heats up due to latent heat loss (Fig. 7h).

c. Influence from climate modes and large-scale atmospheric circulations

Events like ENSO and IOD have a considerable impact on MHWs in the Indian Ocean through several dynamic processes on an interannual time scale (Hobday et al. 2016; Holbrook et al. 2019). Understanding their origin is crucial given the significance of abnormal wind patterns associated with both climatic modes in triggering and maintaining positive feedback for both MHW modes.

In the first MHW mode, the western North Pacific subtropical high (WNPSH) and Australian high (AH) become stronger over the SCS and MC, respectively, throughout boreal summer, as seen in Fig. 8a, which indicates an elevated geopotential height peak at 850 hPa. Since the first MHW mode is closely associated with decaying El Niño, the TIO warming scenario during the following summer may result in an anomalous anticyclonic circulation (AAC) and anomalous WNPSH (Fig. 8a) over the SCS and WNP (Watanabe and Jin 2002).

Fig. 8.
Fig. 8.

The 850-hPa geopotential height (shading; unit: m) and corresponding wind (vectors; unit: m s−1), regression with (a) PC1 and (b) PC2. In (a), the WNPSH is delineated as a red box within the area of 10°–30°N, 110°–160°E, and (b) the IHPD is outlined by two red boxes within the areas of(10°–30°N, 105°–140°E and 40°–10°S, 100°–140°E. Hatching indicates where the regression of geopotential height exceeds the 95% confidence level, and for wind, only those regions exceeding the 95% confidence level are displayed. The lead–lag correlation analysis between the 3-month running-mean (c) WNPSH (blue bars), IOBM (solid red line), Niño-3.4 (dashed black line), and PC1 in boreal summer (JJA); and (d) IHPD (blue bars), DMI (solid green line), and Niño-3.4 (dashed black line) and PC2 in JJA.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

Anomalous northeasterlies that originate at the southern edge of this AAC may inhibit the Indian Ocean’s climatological southwest monsoon winds (Fig. 8a), delaying the onset of the Indian summer monsoon (Annamalai et al. 2005) and weakening the wind stress curls that are essential for WIO upwelling. In the SCS, Yao and Wang (2021) noticed a similar suppression of the summer monsoon as a result of the prevailing WNPSH and AAC, which led to severe basinwide MHWs due to the lack of midsummer cooling. The effect of anomalous northeasterlies due to strengthening of the WNPSH is critical in activating positive feedback for basinwide MHWs during the decaying phase of ENSO in the NIO. Hence, a WNPSH index (Fig. 8a) is defined to determine the influence of anomalous northeasterlies related to the WNPSH on the development of basinwide warming by calculating the 850-hPa anomaly over the SCS and WNP (10°–30°N, 110°–160°E).

Understanding the genesis of the southeasterly SWa off the southwest coast of Sumatra is critical in the second MHW mode because it promotes positive feedback for the IOD and converts basinwide cooling into a dipole SSTa pattern in the NIO. The negative 850-hPa anomalies over the SCS suggest a weakening of the WNPSH, while positive values over Australia suggest a reinforced AH throughout the season (Fig. 8b). The asymmetric low-level pressure anomaly in both hemispheres generates cross-equatorial winds due to an interhemispheric pressure difference (IHPD) (Fig. 8b). Lu and Ren (2020) also observed a similar IHPD during the developing phase of the 2019 positive IOD. The wind pattern associated with the IHPD during the onset of summer, characterized by this cross-equatorial flow, induces the southeasterly wind anomalies off the Java and Sumatra coasts and eventually leads to the initial characteristics of the second MHW mode through a positive zonal dipole-like SST pattern. To assess the intensity of this specific cross-hemispheric pressure difference, we calculate an IHPD index (Fig. 8b) by computing the 850-hPa anomaly difference between Western Australia (40°–10°S, 100°–140°E) and the SCS/WNP (10°–30°N, 105°–140°E).

To evaluate the stability of the link between the derived indexes and MHW modes, a lead–lag correlation is conducted between the WNPSH, IHPD, IOBM, DMI, Niño-3.4, and boreal summer (JJA) PCs (Fig. 8). As mentioned before, the IOBM is the dominant mode in Indian Ocean SST. The typical IOBM develops during boreal winter, matures during spring, and lasts until summer (Fig. 8c). It is strongly correlated with the first MHW mode and remains significant throughout the year. In a typical El Niño, which peaks during boreal winter and decays in the following seasons, a strong correlation exists with PC1 for the preceding seasons, but not the simultaneous summer (Fig. 8c). This result clarifies that El Niño during the prior winter has a pronounced influence over basinwide MHW occurrence during summer. The WNPSH index develops during winter, becomes prominent during boreal spring, and reduces the correlation with PC1 after spring but remains significant until the end of summer (Fig. 8c). The correlation between the WNPSH and PC1 (JJA) is significantly high (p < 0.05) during previous seasons, indicating that the WNPSH is a reliable key for predicting basinwide warming followed by the first MHW mode in the NIO during summer. El Niño also modulates the strength of the WNPSH (Li et al. 2007), as well as the IOBM, due to its strong interannual variability (Fig. 8c) (Chowdary and Gnanaseelan 2007; Du et al. 2009).

The IHPD index correlates positively with PC2 during MAM when DMI also increases dramatically (Fig. 8d). The correlation of the IHPD is abrupt and becomes significant from April to June and remains positive until the end of the year. The DMI becomes prominent and influential during the simultaneous summer and after, while Niño-3.4 also increases and remains significant during JJA (Fig. 8d). These findings clearly suggest that the zonal dipole SST pattern is influenced by continuous northward cross-equatorial wind, which is followed by the second MHW mode. Since 1982, the lead–lag correlation between the IHPD index and boreal summer PC2 has shown a strong correlation when the IHPD index precedes the peak phase of the PC2 mode by 1–2 months. As a result, the existence of the IHPD from May to August (MJJA) leads to a negative SSTa near the EIO and the positive SSTa near the WIO (positive IOD-like SST pattern), with the peak in SON in the NIO (Fig. 7d) more likely attributable to long-lasting IODs (Du et al. 2013) and MHWs (Zhang et al. 2021).

The SSTa and SWa associated with the WNPSH (JJA) and IHPD (MJJA) are given in Fig. 8. The SSTa pattern is distinguished by positive values across the NIO and WNP, including the SCS, whereas the SWa pattern is distinguished by northeasterlies over the NIO that extend from the WNP due to the existence of anticyclonic circulation across the SCS (Figs. 9a,c). During the summer, the occurrence of anomalous northeasterlies reduces the climatological southwesterlies. By reducing evaporative cooling and seasonal upwelling, a decrease in wind speed over the NIO provides positive feedback for basinwide warming (Fig. 9c). The regression results of the WNPSH with the SSTa and SWa results show similar features, like the first MHW mode, suggesting the WNPSH’s robustness in maintaining favorable conditions for basinwide MHWs in summer.

Fig. 9.
Fig. 9.

Regressed pattern of anomalous WNPSH (JJA) and (a) summer (JJA) SST (unit: °C), (c) corresponding wind speed (shading; unit: m s−1) and surface wind (vectors; unit: m s−1); (b),(d) as in (a) and (c), but with anomalous IHPD (MJJA). Stippling indicates areas where the regression of SST and wind speed exceeds the 95% confidence level, and for wind (vectors) in (c) and (d), only those regions exceeding the 95% confidence level are displayed.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

Regression between the IHPD (MJJA) and SSTa is characterized by a zonal SSTa pattern, where warmer water aggregates near the WIO and cold water near the EIO (Fig. 9b). The eastern equatorial Pacific is significant, with positive SSTa allied with anomalous westerlies, indicating the development of El Niño. The northward-moving winds are similar to the winds developed during the IHPD in the second MHW mode in summer (Fig. 9d). The positive wind speed anomalies around the region off Sumatra imply that climatological southeasterlies are stronger and vital for positive feedback to wind evaporative cooling (Fig. 9d). The regression results have general characteristics similar to the analysis of PC2 of the second MHW mode, signaling the strength of the IHPD a few months ahead in triggering the positive IOD-like SSTa anomaly pattern in the Indian Ocean, where it leads to summertime MHWs in a zonal dipole manner.

6. Summary and discussion

There are a couple of studies in the Indian Ocean about MHWs based on the given definition (Hobday et al. 2016), and they have pointed out significant increases in MHW frequency and duration and indicate that ENSO and IOD may have potential influence on MHW genesis in the region (Chatterjee et al. 2022; Saranya et al. 2022; Gao et al. 2022). This study reveals how these large-scale climate phenomena facilitate the MHW genesis in the NIO on the interannual time scale, where it had not previously been examined. In our work, we explore the underlying ocean–atmosphere interactions that can facilitate extreme ocean warming and hence frequent MHWs in the NIO interannually.

We first look at the spatial and temporal aspects of summer MHWs across a 39-yr period, from 1982 to 2020. We find significant spatial variability in the frequency, duration, days, and intensity of MHWs in the NIO during summer. The NIO experiences a basinwide positive increasing trend in MHW frequency during the summer months, and the trend is more prominent in the WIO, northern Arabian Sea, and northern Bay of Bengal, where the SST has also exhibited strong warming in the last few decades. In the NIO, MHW frequency exhibits robust interannual variability similar to mean SST. The positive trend in MHW frequency is clearly evident after 2000 and has become more prominent during recent years. Extremely warm events are occurring more frequently in the NIO, and this is tied to both rising mean temperatures and regional climate variability, which may be the main factors causing an increase in MHW frequency in the area. Increased frequency indicates that the regional SST is under extreme warming conditions, which is directly harmful to living organisms and ecosystems in the region, based on their adaptability. Our findings are useful for determining how extreme warming variability in NIO affects the associated marine ecosystem.

Then an EOF analysis is performed on detected MHWs, and the underlying influences of regional oceanic and atmospheric interactions with each mode are analyzed by regressing them onto the EOF modes. Our study reveals the association between two modes of MHW frequency in the NIO and local and remote SST from the preceding seasons. We conduct the EOF analysis on MHW frequency, which is a primary metric of MHW characteristics and allows us to get a general idea of these extreme events, for scientists as well as the public. The first mode is associated with a basinwide pattern, and the second is associated with a zonal dipole pattern.

The first MHW mode is accompanied by favorable warm oceanic conditions for MHW generation from prior seasons due to the persistence of positive SSTa. However, despite unfavorable conditions from the previous seasons, the second MHW mode is supplemented by abrupt ocean–atmospheric dynamics just 1–2 months before summer. Based on these results, the two leading MHW modes in the NIO strongly rely on dynamic and thermodynamic warming.

As the first MHW mode is associated with decaying El Niño in the eastern Pacific, it warms the Indian Ocean during the following seasons asymmetrically across the equator, causing asymmetric SWa, which generates downwelling planetary waves (Du et al. 2009; Xie et al. 2009). These oceanic planetary waves deepen the thermocline in the western and equatorial Indian Ocean and raise surface and subsurface temperatures through thermocline–SST feedback from winter to the onset of summer. When the TIO discharges its energy as a discharging capacitor, it anchors an AAC over the WNP due to the eastward propagation of baroclinic Kelvin waves into the Pacific (Xie et al. 2009), which can both induce anomalous northeasterlies and reduce the total cloud cover over the eastern NIO (Fig. 10a), favoring higher insolation warming. These anomalies further weaken wind–evaporative cooling and summer upwelling by suppressing the climatological westerlies. The combined impact of thermocline warming, reduced wind–evaporative cooling, and increased solar radiation, which are associated with El Niño and IOBM-induced SWa, causes basinwide intense MHWs in summer.

Fig. 10.
Fig. 10.

This schematic figure depicts two distinct physical mechanisms of large-scale atmospheric processes and ocean dynamics associated with NIO summer MHWs. (a) The genesis of basinwide MHWs is due to El Niño–induced AAC, anomalous easterlies, and upwelling suppression due to planetary waves (Rossby) with persistently high WNPSH. (b) The genesis of zonal dipole MHWs linked to an IHPD generated by lower WNPSH and higher AH, which is followed by cross-equatorial winds and westward-propagating downwelling Rossby waves.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0574.1

In contrast, we show that the second MHW mode is linked to the phase transition of ENSO and turns the basinwide cooling SSTa pattern into a zonal dipole pattern during boreal summer due to abrupt atmospheric and ocean dynamics. The pressure difference caused by the strengthening of the AH and weakening of the WNPSH generates cross-equatorial winds, leading to cooler SST by WES feedback and thermocline–SST feedback in the Java–Sumatra region. Concurrently, due to the westward-propagating downwelling Rossby waves, the WIO heats up because of reduced seasonal upwelling, inducing zonal dipole patterns in SST and increasing the number of MHWs in the region (Fig. 10b).

Why can we not see the development of anomalous southeasterlies in the first MHW mode? Anomalous southeasterlies appear to be a critical factor in the establishment of the second MHW mode in the NIO, which is closely linked with the persistence of the AH. However, as previously stated for the first mode, the basinwide warming from March to the following month anchors an anomalous anticyclonic circulation over the WNP through atmospheric Kelvin wave adjustment (Xie et al. 2009). Concurrently, for the first mode, warming in the Indian Ocean can also anchor the anticyclonic circulation over the AH over the southeast Indian Ocean and Australia (Fig. 8a), caused by the roughly symmetric tropospheric temperature anomalies across the equator in the Indian Ocean (Guo et al. 2018). Because of the weak pressure difference, the wind anomalies of the anticyclonic circulation are not strong enough to blow across the equator, hence maintaining the basinwide warming due to the persistence of northeasterlies through the summer followed by the first MHW mode.

We clarify that atmospheric and oceanic influences, as well as global climate mode teleconnection patterns, can be used to explain the relationship between MHWs in the NIO. Both MHW modes significantly influence the variability of Indian Ocean summer monsoon rainfall. The first MHW mode is associated with higher precipitation over the western and central Indian Ocean with anomalous vertical motion, while drying conditions are associated with the northeastern Indian Ocean. However, for the second MHW mode, drying conditions are observed near the EIO due to anomalous downdrafts, especially near Indonesia and Australia, and also contribute to increased precipitation across the western Indian Ocean.

To check the sensitivity of the domain, we performed an EOF analysis for the summertime MHW frequency in a larger domain (20°S–30°N, 40°–110°E), representing the TIO. The result reveals that the larger domain also consists of the similar EOF patterns of the two leading MHW frequency modes explained in the main text, with a basinwide and zonal dipole variability pattern (see supplemental Fig. S4). However, the total variance of the leading MHW frequency modes of TIO are lower (mode 1, 21.8%; mode 2, 9.9%), and slight changes can be seen in the PCs as well. Current findings of EOF and regression results lead us to presume that the underlying mechanism explained in the main text regarding the two leading modes are also in charge of the MHW genesis in TIO, except for the second MHW mode, where eastern Pacific SSTa is less sensitive. Thus, more study is required for a complete comprehension of the associate mechanism for the larger domain in the Indian Ocean.

Our findings emphasize the relevance of early warnings of extreme MHW modes in the Indian Ocean, where it is vital to examine the development of MHWs and precisely forecast them ahead of time to prevent their damaging impacts.

In addition, the MHW modes in the NIO also have a significant impact on SSTa variability in regional ocean basins such as the SCS, as well as the WNPSH and the AAC expansion from the SCS to the Indian Ocean, demonstrating strong interbasin dynamics influencing MHW genesis throughout the summer. Furthermore, the diversity of ENSO and IOD may also have a significant impact on TIO warming. Thus, more research is needed to understand the likely different responses of MHWs to the variability of these climatic modes, as well as the associated interbasin linkages for the development of MHWs in the NIO. We provide findings only from statistical analysis and reanalysis data; therefore, subsequent analysis should be continued with numerical model simulations.

Acknowledgments.

This work was supported by Key National Natural Science Foundation of China Grants (42192563 and 42120104001). Y. D. is supported by the Chinese Academy of Sciences (133244KYSB20190031) and the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0303 and 2019BT02H594). Part of this work is based on the Ph.D. dissertation of the lead author conducted at the City University of Hong Kong.

Data availability statement.

The data used in this study are freely available to the public. The OISST data are obtained from https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/. NCEP–GODAS data can be accessed from https://psl.noaa.gov/data/gridded/data.godas.html. GPCP data are collected from https://psl.noaa.gov/data/gridded/data.gpcp.html. The NCEP/NCAR reanalysis product can be obtained from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.

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