Drivers of regional Australian marine heatwaves on sub-seasonal to seasonal timescales

doi:10.21203/rs.3.rs-3589514/v1

Download PDF

Research Article

Drivers of regional Australian marine heatwaves on sub-seasonal to seasonal timescales

https://doi.org/10.21203/rs.3.rs-3589514/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

As marine heatwaves (MHWs) become more intense and longer lasting due to global warming, understanding the drivers and impacts of these events is crucial for effective marine resource management. This study investigates the influence of El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), Sub-Tropical Ridge High (STRH), and Madden Julian Oscillation (MJO) on sea surface temperature (SST) anomalies and MHWs around Australia. The aim of this research is to improve our understanding of the drivers of MHWs on sub-seasonal to seasonal (S2S) timescales, which bridges the gap between short-term weather and interannual to long-term climate variability. By analysing SST anomalies and MHWs characteristics during specific driver phases, a simple MHW hazard index is developed. Our findings support previous research indicating that La Niña plays a role in driving MHWs off the coast of Western Australia and reveals a previously unrecognised connection between ocean warming off Queensland and Tasman Sea low-pressure systems associated with the negative phase of the STRH. Our research emphasizes the importance of considering multiple drivers and their compounding effects on MHWs by showing significant changes to typical La Niña MHW patterns with the additional influence of the MJO. By considering drivers acting in the S2S timescale, forecasts can more accurately capture the timing, intensity, and spatial extent of MHW events within a season. These improved forecasts can enhance the ability of marine managers to adapt and allocate resources based on evolving climate conditions, enabling effective implementation of harm minimisation strategies.

Accelerating ocean warming over the past few decades has raised concerns due to the increased likelihood of devastating impacts on marine ecosystems and biodiversity (Doney et al., 2012). Prolonged and discrete warm ocean temperature extremes, also known as marine heatwaves (MHWs), have been linked to a range of ecosystem consequences, including mass mortality of marine species (Garrabou et al., 2022), outbreaks of harmful algal blooms (Roberts et al., 2019), distribution shifts of species (Frölicher and Laufkötter, 2018; Jacox et al., 2020), coral bleaching (Le Nohaïc et al., 2017; Hughes et al., 2018), kelp mortality (Wernberg et al., 2013; Wernberg et al., 2016; Arafeh-Dalmau et al., 2019; Thomsen et al., 2019; Arafeh-Dalmau et al., 2020) and seagrass die-back (Arias-Ortiz et al., 2018). Australia, with its diverse ocean habitats and one of the world’s largest economic exclusion zones, faces significant threats from these warm extreme events. For example, Tasmania, the country's southernmost state, characterized by temperate marine waters, has experienced several substantial MHW events, including during the summers of 2015/16 (Oliver et al., 2017) and 2017/18 (Perkins-Kirkpatrick et al., 2019; Kajtar et al., 2022). These MHW events resulted in severe impacts on the marine ecosystems, such as the introduction of predatory sea urchins (Byrne et al., 2022). To the north, the Great Barrier Reef (GBR), renowned for its biodiversity, cultural significance, and economic value, has experienced six mass coral bleaching events since 1998 (Hughes et al., 2017), including most recently during the summer of 2021/22 (McGowan and Theobald, 2023).

To effectively manage marine resources, including mitigation of the impacts of MHWs, it is crucial to have early warning systems in place. For these efforts to be successful, the availability of reliable and timely forecasts is paramount. These forecasts serve to provide managers with insight as to the conditions of the upcoming season to support their decision making process (Spillman and Hobday, 2014). In 2021, Kajtar and Holbrook introduced an index to understand MHW hazard around Tasmania under projected climate change (Kajtar and Holbrook, 2021). When the assessment of potential risks related to environmental hazards is integrated with species and ecosystem-specific attributes, there is a greater understanding of the explicit threats that may emerge.

While long-term decisions such as establishing marine protected areas (Game et al., 2008), selecting target species for fisheries (Caputi et al., 2019), and aiding the recovery of vulnerable ecosystems (Arias-Ortiz et al., 2018) are made on the interannual to decadal time scale, sub-seasonal to seasonal (S2S) forecasts are vital for equipping managers with the necessary insights to make informed shorter term decisions. These S2S forecasts are usually in the range of 1–2 weeks to several months, bridging the gap between short-term weather forecasts and longer-term climate projections (Vitart et al., 2017).

Predictions of extreme events can be improved by understanding their connection to different modes of climate variability. El Niño–Southern Oscillation (ENSO) has been shown to enhance or suppress the likelihood of MHWs around the world, including Australia (Holbrook et al., 2019; Holbrook et al., 2020). There are also different types, or ‘flavours’, of ENSO (e.g. Capotondi et al. 2015), which have been seen to result in different distributions of MHWs. For example, in the southeast Indian Ocean, tropical coral reefs are more likely to be impacted by MHWs during a eastern Pacific El Niño, while subtropical reefs in this region experience an increase in these extreme events during a central Pacific La Niña (Zhang et al., 2017).

The Indian Ocean Dipole (IOD) is another important source of climate variability in Australia (Luo et al., 2010), with the negative phase linked to MHWs in the northern region (Zhang et al., 2018). Furthermore, recent research has shown that the positive IOD may also be a useful predictor of increased MHW likelihood off Western Australia up to 20 months in advance (Wang et al., 2023). The positive phase of the IOD, which is generally stronger than the negative phase, is a defined pattern of anomalously high SSTs in the western Indian Ocean, between 10°S-10°N and 50°E-70°E, with anomalously low SSTs in the tropical south-eastern Indian Ocean, between 10°S-0° and 90°E-110°E (Saji et al., 1999).

While there is the potential for skilful prediction of ENSO up to one year in advance (Wang et al., 2012) and the IOD up to six months in advance (Zhao et al., 2019), it is important to note that several other climate modes, which influence climate on shorter timescales ranging from weeks to months, often have limited predictability. The Southern Annular Mode (SAM), for example, strongly influences the climate of the southern hemisphere (SH) and describes the latitudinal positioning of the westerly winds in the mid- to high-latitudes of the SH (Gong and Wang, 1999). During the negative phase of SAM, these westerly winds are seen to contract towards the equator, which has been linked to increased rainfall in eastern Australia (Speer et al., 2011; Min et al., 2013), while the positive SAM phase shows an expansion of these winds to the south. This southward expansion has also been linked to a multi-decadal spin-up and poleward extension of the East Australian Current (EAC) (Cai et al., 2005; Roemmich et al., 2007), with shorter term variations also influencing the generation of MHWs in the Tasman Sea (Li et al., 2022) and the positioning and persistence of enhanced downward air-sea heat fluxes on the occurrence of MHWs in the Tasman Sea region (Gregory et al., 2023).

Other short-term atmospheric drivers of anomalous heat exchange into the ocean include persistent anti-cyclones over the Tasman Sea, which have been seen to transport dry, hot air from the interior desert region of Australia southward (Hudson et al., 2011). As these high-pressure systems are located within the vicinity of the subtropical ridge, Marshall et al. (2014) labelled this phenomenon the Subtropical Ridge High (STRH) and developed an index to show that the STRH contributed to increased heating over Australia during all seasons.

Lastly, the Madden Julian Oscillation (MJO), which is the primary mode of influence on tropical weather at the sub-seasonal timescale (Madden and Julian, 1971), has been shown to alter ocean conditions through changes to surface heat fluxes (Weller and Anderson, 1996; Hendon et al., 1998). It possesses a distinct seasonal nature and is easily distinguishable as a slow, eastward moving pattern of atmospheric circulation and deep convection along the equator associated with cloud and rainfall, with a period of propagation around 30 to 80 days (Hendon and Salby, 1994). It is described by its strength (amplitude) and the central position of the convective energy (phase). Its convective centre develops over the Indian Ocean (Phases 2–3) before crossing the maritime continent (Phases 4–5), weakening over the Pacific Ocean (Phases 6–7) before eventually dissipating east of the International Dateline (Phases 8 and 1) (Oliver and Thompson, 2010).

When considering the impacts of these drivers, it is important to note that the climate modes themselves are often correlated at some level. For example, ENSO and the IOD are strongly correlated, with less than a third of all IOD events occurring independently from ENSO (Loschnigg et al., 2003; Stuecker et al., 2017). Correlations between drivers can also be useful to improve the potential predictability of the drivers themselves. The predictability of SAM, for example, is generally considered low, at only several weeks. However, due to its strong connection with eastern Pacific La Niña and the southern stratospheric polar vortex during the austral warm seasons, models are able to skillfully predict the seasonal variation of SAM in early summer at least one season in advance (Lim et al., 2021).

The key aim of this paper is to provide an initial analysis of how each of these climate modes influence SST and the occurrences, or lack thereof, of MHWs in Australian waters. We also consider the relative importance of compounding drivers and their combined influence on ocean temperature variability. The dynamics of these interactions are complex and sometimes complicated, and so we focus on creating the groundwork from which further research can be undertaken to explore these complexities in more detail.

From here, the paper is organized as follows. Section 2, Data and Methods, includes detailed descriptions of all the processes used for the analysis. Section 3 presents our results, where we show composite maps of oceanic and atmospheric anomalies and levels of MHW hazard for each of the drivers, separated by season. In this section we also present a case study to explore the compounding impacts of multiple drivers by showing La Niña impressions that are modulated by the MJO. Finally, Section 4 provides a summary and discussion.

2.1 Data

SST data used in this study were from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature V2.1 (OISSTv2.1) dataset. These 0.25° gridded global SST data are from high-resolution infrared and microwave satellites blended with in-situ observational data from ships and buoys and comprise daily SSTs from 1982 to the present day (Reynolds et al., 2007; Banzon et al., 2016; Huang et al., 2021).

The atmospheric data were taken from the National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) Reanalysis 2 product. These data were generated using a forecast model that assimilates observational data (Kanamitsu et al., 2002) and, with a relatively coarse spatial grid of 2.5 degrees, is well suited for analysis of large scale processes (Dufek et al., 2008). Variables from the NCEP/NCAR reanalysis used in this study include mean sea level pressure (MSLP) to calculate the SAM and STRH indices, zonal wind data to calculate the MJO index, and the meridional wind fields to show anomalous wind patterns. Outgoing longwave radiation (OLR) data used to calculate the MJO index were taken from NOAA’s OLR dataset estimated from polar-orbiting satellites and interpolated to include global spatial coverage on a daily timescale (Liebmann and Smith, 1996).

For our heat budget and Ekman transport analysis, we used model output from Bluelink ReANalysis (BRAN) 2020 (Chamberlain et al., 2021) which incorporates observations into an eddy-resolving, near-global ocean circulation model, the Ocean Forecasting Australia Model (OFAM) v3 (Oke et al., 2013). This model is forced by atmospheric fluxes from the Japanese Meteorological Agency 55-year reanalysis (JRA-55) (Kobayashi et al., 2015). Ocean variables are available on a daily timescale at 0.1° resolution.

2.2 Calculation of climate indices

2.2.1 El Niño Southern-Oscillation (ENSO) index

The phase of ENSO was determined using the Oceanic Niño Index (ONI). Using a three month running mean of SST anomalies derived from the OISSTv2.1 dataset in the Niño 3.4 region (5°S - 5°N, 170°W-120°W), an El Niño state is detected with a period of at least five consecutive months with SST anomalies >+0.5°C, and a La Niña state detected when SST anomalies <-0.5°C for a period of at least five consecutive months, and all other periods are considered neutral (Barnston, 1997).

The daily ENSO index was calculated as the daily SST anomalies (temperature deviations from the mean, calculated using the full daily climatology from 1982-2022), averaged over the Niño 3.4 region (Philander, 1983).

2.2.2 Indian Ocean Dipole (IOD) index

The phases of the IOD were determined from the differences between SST values, from OISSTv2.1 dataset, in the western Indian Ocean (between 10°S-10°N and 50°E-70°E) and the tropical south-eastern Indian Ocean (between 10°S-0° and 90°E-110°E). The positive phase shows higher temperatures in the western Indian Ocean and cooler temperatures around Indonesia in the east (Saji et al., 1999). The IOD is considered to be in the positive phase when this difference is >+0.4°C, and in the negative phase when this difference is <-0.4°C for at least eight consecutive weeks, with all other times considered as neutral (Saji et al., 1999).

2.2.3 Southern Annular Mode (SAM) index

The daily SAM index was calculated by projecting daily MSLP anomalies, from NCEP/NCAR Reanalysis 2, onto the principal component (PC) time series of the leading empirical orthogonal function (EOF) of observed monthly MSLP anomalies between 25°S and 75°S (see Marshall et al., 2014). SAM positive events are identified when the index is one standard deviation, or greater, above the mean and SAM negative events when the index is at least one standard deviation below the mean. Anything in between is characterised as SAM neutral. While there is more variability in the SAM index using daily data, the general spatial structure of the mode does not differ from one generated using monthly values.

2.2.4 Subtropical Ridge High (STRH) index

The STRH index was devised by Marshall et al. (2014) to describe the persistent high pressure blocking systems over the Tasman Sea located within the region of the subtropical ridge. The index is calculated using area averaged MSLP data, from NCEP/NCAR Reanalysis 2, in the region of 150°E-165°E, y°S ± 7.5° (where y is the latitudinal centre of the subtropical ridge, which varies monthly from the northernmost extent of 27.5°S in August to southernmost of 40°S in February). The positive phase is determined when the index is greater than one standard deviation above the mean, and the negative phase when the index is less than one standard deviation below the mean.

2.2.5 Madden Julian Oscillation (MJO) index

The phase and amplitude of the MJO is determined by the leading pair of EOFs from equatorially-averaged (15°S-15°N) fields of 200 hPa and 850 hPa zonal winds and OLR, which are used to compute the Real-time Multivariate MJO (RMM) index (Wheeler and Hendon, 2004). The pair of PCs that go with the leading EOFs are referred to as RMM1 and RMM2, and effectively describe the evolution of the MJO along the equator. To determine the relationship between the phases of the MJO with other drivers, we calculated each of the phases of the MJO as its own index, following the same method used by Virts and Wallace (2014) and Wang and Hendon (2020).

Figure 1 shows a 3D schematic summary of these drivers, based on the figure by McKay et al. (2023), with the addition of the EAC and the Leeuwin Current, two poleward flowing warm-water boundary currents that can also contribute to MHWs.

2.3 Calculation of MHW metrics

To calculate MHW metrics we followed the methodology of Hobday et al. (2016), who defined a MHW occurrence when ocean temperature exceeds the 90^th percentile relative to daily climatological values for at least five consecutive days. We calculated MHW statistics in the Australian region (0° - 50°S, 100°E - 180°) from NOAA OISSTv2.1 data from 1982-2022. Where a MHW occurs, the intensity (i.e., the difference between the observed temperature and the climatological mean), was recorded at each grid point. (note: the absence of a MHW event has an intensity of 0).

2.4 MHW hazard index

Here, we created a simple MHW hazard index by considering the increased weekly heat stress that a region is exposed to during each of the driver phases during each season. First, we summed the daily MHW intensity at each grid point for each week. We then composited these weekly intensities based on the phases of the weekly climate mode indices and separated by season. Finally, we removed the average seasonal MHW signal (far right column Fig.2), which is dominated by the west Australian and Tasman Sea regions in all seasons.

The MHW hazard index developed here was created using the average temperature (^oC) per week excess or deficit for the driver phase and season of interest. When the value is <-1.5°C, we say the MHW hazard level is very low. When the value is between -0.5°C and -1.5°C, it is seen as lower than average. When it is between -0.5°C and 0.5°C, we consider there is no effect. When it is between +0.5°C and +1.5°C, it is seen as higher than average. When between +1.5°C and +2.5°C, it is seen as very high. And finally, when it is >+2.5°C, we classify it as extreme.

2.5 Average oceanic and atmospheric states during phases of climate modes

Our study area encompasses the broader Australian region bounded by latitudes 0° - 50°S and longitudes 100°E - 180°. Figure 2 provides an overview of the study region, with the top map outlining the key regions mentioned throughout. These include the northwest shelf (NWS), Western Australia (WA), the Great Australian Bight (GAB), Tasman Sea west (TAS-W), Tasman Sea east (TAS-E), the East Australian Current region (EAC) and northeast Queensland (NE QLD). The columns below show a visual representation of average and anomalous temperatures categorized by season. Given our focus on the southern hemisphere (SH), we employ austral seasons: summer (DJF), autumn (MAM), winter (JJA), and spring (SON). The first column shows the average SST for each season highlighting a consistent trend of higher temperatures in the northern Australian tropical waters, gradually transitioning to cooler temperatures in the southern temperate zones. The second column illustrates the maximum SST anomalies, emphasizing regions that frequently experience recurring MHW incidents. Positive ocean temperature anomalies are particularly evident along the Western Australian coast during summer and autumn, aligning with 'Ningaloo Niño' occurrences (Marshall et al., 2015). The Tasman Sea's eastern and western edges experience substantial SST anomalies during spring and summer. Additionally, the EAC and its extension show strong positive SST anomalies throughout all seasons. The third column shows the standard deviation (std) of SST anomalies, closely matching the patterns exhibited in the final column depicting average MHW intensities per season. On the western coast, elevated std and MHW average intensity are concentrated near the northwest shelf in summer, shifting slightly southward in autumn, and enveloping the southwest coast in winter. The EAC region demonstrates elevated variance and MHW intensities across all seasons, prominently spanning the Tasman Sea, particularly in summer. Notably, during spring, an exceptional maximum SST anomaly is evident in the eastern Tasman Sea. However, the slight divergence between the std and average MHW intensity maps in this instance implies the possibility of a single, disproportionately intense, event compared to the typical spring pattern extreme event.

Understanding the individual influence of any single driver is complicated by the fact that they rarely exist in isolation. Figure 3 shows the time series of the ONI, IOD, SAM and STRH from 1982 to 2022, highlighting the difference in the variability of the modes, with the resident times of the ONI phases being the longest and those of the STRH the shortest. In Figure 3 we can also identify periods in which various modes are in phase. For example, in 1997/98 (during the period of extreme El Niño) we can see alignment of the positive ONI, positive IOD, negative SAM and positive STRH. Statistically significant correlation coefficients between these various modes are shown in Table 1, including phases 1-4 of the MJO (phases 5-8 are the inverse of these first four phases). To account for the serial correlation in the time series, we considered the autocorrelation (ρ) to calculate the effective sample size (n_eff = n (see Marshall et al. 2014). This effective sample size was used to assess statistical significance using a two-tailed t-test (Student, 1908). To eliminate signals from other drivers that showed strong correlations in the same seasons, we followed Werner et al.'s (2012) approach and used a regression analysis to remove the signals associated with any other driver with a significant correlation during corresponding seasons. The significant influence of each of the drivers is identified through composite maps of daily SST anomalies during the relevant positive and negative phases. To each of these maps, we also include MSLP and wind speed anomalies to assess the mechanisms that could lead to the accumulation of heat in any region.

Table 1 Correlation coefficients between daily climate driver indices. Those with p-values <0.05 are shown in bold, those with p-values>0.1 are not shown

Summer (DJF)	Niño34	IOD	SAM	STRH	Phase1	Phase2	Phase3	Phase4
Niño34	1					0.061	0.061	0.032
IOD		1			-0.12	-0.11	-0.049	0.038
SAM	-0.19	-0.083	1		-0.077	-0.14	-0.12	-0.043
STRH	0.078	-0.033	0.069	1		0.11	0.13	0.097
Autumn (MAM)	Niño34	IOD	SAM	STRH	Phase1	Phase2	Phase3	Phase4
Niño34	1				0.073	0.12	0.096
IOD	-0.04	1			0.044		-0.04	-0.057
SAM			1		-0.05			0.045
STRH		0.054	0.042	1		0.15	0.19	0.11
Winter (JJA)	Niño34	IOD	SAM	STRH	Phase1	Phase2	Phase3	Phase4
Niño34	1
IOD	0.3	1				0.078	0.1	0.05
SAM		0.066	1			0.057	0.1	0.074
STRH	0.074	0.15	0.12	1	0.3	0.32	0.18	-0.12
Spring (SON)	Niño34	IOD	SAM	STRH	Phase1	Phase2	Phase3	Phase4
Niño34	1				0.18	0.28	0.22
IOD	0.45	1			0.18	0.14		-0.13
SAM	-0.13	-0.12	1		-0.083	-0.066		0.059
STRH	0.07	-0.11	0.077	1	0.15	0.25	0.22	0.033

2.6 Understanding the mechanisms driving extreme regional ocean warming.

Lastly, our analysis looks at understanding the compounding influences of ENSO and the MJO. The sources of excess heat in the Ocean’s upper layer can be evaluated using the heat budget equation (Eq. 1). We use this heat budget equation to better understand the mechanisms that drive ocean warming in the WA region (see Fig. 2) during La Niña states with the various phases of the MJO, following the methodology of Marshall and Hendon (2014).

\(\frac{\partial T}{\partial t}=\frac{{Q}_{net}}{\rho {C}_{p}H}-\left[u\frac{\partial T}{\partial x}+v\frac{\partial T}{\partial y}\right]-w\frac{\partial T}{\partial z}+res\) (Eq. 1)

where the temperature tendency (\(\frac{\partial T}{\partial t})\) is due to contributions from the total heat flux (Q_net) (scaled by the density of seawater (ρ = 1025kg/m³), the heat capacity of water (C_p=3850J/kgC) and the mixed layer depth (H)), the horizontal advection \(\left(u\frac{\partial T}{\partial x}+v\frac{\partial T}{\partial y}\right),\) and the vertical advection (\(w\frac{\partial T}{\partial z}),\) plus smaller contributions from entrainment and diffusion and second order terms – here captured as the residual.

We also consider the combined influence of La Niña with the MJO in the NE QLD region (see Fig. 2). As this region is directly impacted by changes to tropical wind patterns, we investigate the role of Ekman transport by considering the proportion of total horizontal convergence (\(\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y})\) (calculated from the anomalous geostrophic meridional and zonal currents) that is due to Ekman convergence (\(\frac{\partial {u}_{E}}{\partial x}+\frac{\partial {v}_{E}}{\partial y})\). Here, the Ekman convergence is directly related to the Ekman currents arising from anomalous meridional (τ_y) and zonal (τ_x) wind stress using equations 2 &3.

\({v}_{E}=- \frac{1}{f\rho }\frac{\partial {\tau }_{x}}{\partial z}\) (Eq. 2)

\({u}_{E}=\frac{1}{f\rho }\frac{\partial {\tau }_{y}}{\partial z}\) (Eq. 3)

3.1 Average seasonal states with each driver

3.1.1 ENSO

The average daily atmospheric and oceanic states during the phases of ENSO are shown in Fig. 4. ENSO is strongly correlated to the IOD in winter and spring (Table 1). However, this correlation is significant with the IOD lagged from − 3 to + 6 months (Werner et al., 2012), and so we have removed the IOD signal (including lags) from the entire time series. The influence of SAM has also been removed during austral spring and summer. The phases of ENSO have a strong seasonal cycle, with event build-up in the late winter and spring months, peak during summer, and decline through autumn (Philander, 1983). The composite maps for El Niño (Fig. 4) show general cooling in the northeast during all seasons, and cooling along the west coast during summer and autumn as well as in the eastern Tasman Sea during summer, autumn and winter. In autumn, as the El Niño event is declining, there is evidence of warming in the northwest region and along the east coast.

The composite maps for La Niña states show broader regions of anomalous warming. During spring, as La Niña is building up, we see positive SST anomalies in the NE QLD and NWS regions and starting to develop on the west coast. During austral summer and autumn, in the following year, the positive SST anomalies show the strong signal associated with ‘Ningaloo Niño,’ which persists into the winter. There is also a strong warming signal in the Tasman Sea during autumn and winter. The wind patterns show stronger northeasterlies during La Niña in the northeast region in all seasons (Fig. 4, second column). During austral spring, these winds bring warm water onshore and contribute to the warming through promotion of downwelling (Fig. 4b). However, during summer and autumn, these winds divert further east from the Australian mainland and the heating signal remains offshore.

The MHW hazard index values associated with El Niño or La Niña phases in each season can be seen in Fig. 5. The higher hazard rating during La Niña closely matches the SST anomaly maps in Fig. 4. There is a very high MHW hazard rating in spring and winter in the northeast region, and higher than average during summer. The hazard rating in the WA region builds up during summer and reaches an extreme rating during autumn before starting to decline again in winter.

During El Niño, we can see a lower-than-average MHW hazard rating in the northeast region during spring and winter and a lower-than-average to very low hazard rating along the west coast during summer and autumn. During autumn (Fig. 5e), we also see regions of very high MHW hazard in the northwest of Australia, particularly around the Indonesian island of Java, as well as stretching across the Tasman Sea. As there isn’t a corresponding SST warming signal in the Tasman Sea (Fig. 4e), it is likely that these MHWs are due to teleconnections in the region.

3.1.2 IOD

The IOD significantly impacts oceanic and atmospheric patterns, influencing Australian climate most noticeably from May through to November, with December to April dominated by the Australian monsoon (Saji et al., 1999). The ENSO signal (including lags) was removed from the entire timeseries for the IOD analysis, as well as the influences of SAM during spring and the STRH during winter.

Changes to atmospheric circulation during the phases of the IOD are shown in Fig. 6. During austral winter and spring seasons, enhanced westerlies draw warm waters to the northwest coast during the negative phase. Conversely, enhanced westerlies during the positive phase have the opposite effect. During the summer season, although we also see the accumulation of heat in the ocean surface on the northwest coast (Fig. 6d), the wind patterns are not consistent with those associated with the IOD as they are dominated by the Australian monsoon during these months. The same can be seen during autumn (MAM), however to a lesser extent, as these months are impacted by a combination of the IOD and the Australian monsoon.

The impacts on the SST anomalies are most apparent in the northwest region, with positive SST anomalies during the negative IOD phase and negative SST anomalies during the positive phase in all seasons. However, the MHW hazard maps shows that the increased risk only reaches the coastline in this region during winter (Fig. 6). There is also region of high hazard on the northeast coast during spring, which may be due to the likelihood of La Niña co-occurring with negative IOD events, with the reverse shown during the positive phase. The very low hazard region in the western Tasman Sea during spring is more likely due cooling experienced in the Tasman Sea during the negative phase of SAM (Fig. 8b), which is statistically significantly correlated with the positive IOD in spring (Table 1).

3.1.3 SAM

Composite maps of atmospheric and oceanic anomalies during the phases of SAM are shown in Fig. 8, with the signals of ENSO during spring and summer, the IOD during spring, and the STRH during winter removed.

From the number of days (n) of each phase, we can see that the positive SAM is more likely during austral spring, while the negative SAM occurs more often during winter. The most obvious pattern of SST anomalies during the positive phase of SAM is the heating signal evident across the Tasman Sea in spring, summer, and autumn. Conversely there is noticeable cooling in the same region during the negative phase in summer and spring.

During the positive phase of SAM, there are several regions with a positive MHW hazard rating. The Tasman Sea shows a very high MHW hazard rating during summer, autumn and winter, and the northeast shows a very high hazard rating during spring and summer. However, this pattern seems to closely resemble the warming pattern associated with La Niña during those times (Fig. 4b and 4d). Despite strong anomalous cooling in the Tasman Sea during the negative phase of SAM in spring and summer (Fig. 8b and 8d), the MHW hazard level rating does not show any significant reduction during these periods (Fig. 9b and 9d).

3.1.4 STRH

The influences of the SAM and IOD in winter were first removed as they are significantly correlated to the STRH index during this season (Table 1). The atmospheric patterns are the most distinctive compared to the other drivers (Fig. 10). The positive STRH phase shows the high pressure, anti-cyclonic system centred over the Tasman Sea, with corresponding winds moving in an anti-clockwise direction. These high-pressure systems are associated with clearer skies and increasing descending warm air, contributing to extra heating in the TAS-E and in the GAB (Fig. 2 for locations) during spring and summer. The wind vectors also show strong winds moving warm air from the Australian continent south into the region of the GAB, contributing to the extra warming in this region.

During spring and summer, we can also see a heating signal off the NWS, and in an extended region of WA during summer. However, the wind patterns during these phases do not explain this phenomenon. From Table 1, we see that there is a strong and significant correlation between the STRH and the MJO in phases 2, 3 and 4 during summer. These phases are associated with warmer than usual SSTs in this northwest region (Fig. 12) and, could be the cause of these anomalies.

The opposite anomalous atmospheric pattern is apparent during the negative phase of the STRH (Fig. 10). Here, we can see low-pressure, cyclonic systems centred over the Tasman Sea, along with strong anomalous winds moving in a clockwise direction around these low-pressure cells. We can see signals of warming in the NE QLD region during spring, summer, and autumn as warm, dry air is being transported north from the centre of the Australian continent into this region.

Despite the consistency of these warming patterns, the MHW uniformity of the hazard ratings for this driver are low in most seasons (Fig. 11). Summer shows the strongest influence, with a higher than average to very high hazard off the northeast coast during the negative phase (Fig. 11d), and a lower-than-average MHW hazard during the positive phase (Fig. 11c). The low sample size for the weekly samples compared to the daily sample sizes in Fig. 10 suggests that these STRH phases are short lived and restrict their ability to contribute to sustained warmer ocean temperatures, i.e., MHWs.

3.1.5 MJO

The MJO is significantly correlated to the STRH in both spring and summer, and with Niño34 index during spring (Table 1), and so the influences of these have been removed. The MJO is distinctive from the drivers we have considered above, as its eight phases are defined by the region of the centre of convection of the eastward propagating storm system. To simplify this analysis, we have grouped the phases in pairs and restricted our analysis to the austral spring (SON) and summer (DJF) months when the MJO’s convective centre shifts to the south of the equator and has the greatest impacts on the Australian region (Zangvil, 1975; Wheeler and Hendon, 2004).

The strongest warming signal is seen on the NWS in summer during MJO phases two and three. Marshall and Hendon (2014) found this to be attributed to vertical advection, due to Ekman transport in response to the strengthened easterly winds. Heating off the northeast coast can be seen in response to extremely low wind speeds in phases four and five during spring, and in summer during phases six and seven, although the anomalously low winds is less striking.

Interestingly, in spring, a low-pressure system over the Tasman Sea can be seen to contribute to warming off the northeast region, by pushing warm, continental air into the region in much the same way as the STRH, with the opposite seen in phases two and three, despite our removal of the STRH signal.

During the summer months in phases six and seven, there is also a strong heating signal in the Tasman Sea. This region is especially interesting as it is the strongest signal shown in our MHW hazard analysis (Fig. 13).

3.2 Leading driver of SST variability at each location

From our analysis so far, we can see that there are several regions influenced by multiple drivers. To qualitatively show the drivers of most importance, we have calculated the simultaneous, daily correlation between each of the driver indices and positive SST anomalies at each grid point.

Figure 14 shows the driver with the highest correlation with positive SST anomalies based on daily values (left column) and the corresponding correlation coefficient between the index of the leading driver and SST anomalies (right column). Autocorrelation is accounted for using a reduced effective degrees of freedom in the statistical significance test (Marshall et al., 2014). The prevalence of La Niña is evident, indicating its dominance in driving positive SST anomalies in the NE QLD and WA regions for all seasons. During the summer months (DJF), we observe the highest number of individual leading drivers, with most drivers being present (Fig. 14c). While La Niña is dominant over a smaller proportion of the domain in summer compared to spring, it still influences the NE QLD, WA, and TAS-E regions. Our previous analysis suggests that La Niña has the highest correlation value in summer in the TAS-E and TAS-W regions due to its connection with the positive phase of the SAM, which has been associated with significant SST heating and MHWs in the Tasman Sea during spring and summer.

The negative phase of the STRH emerges as the dominant driver in the NE QLD region during summer (Fig. 14c), continuing into autumn and winter, and the positive phase of the STRH is shown to be the strongest influence of positive SST anomalies in the GAB. The positive phase of the IOD does not appear to correspond with positive anomalies in any region, while the negative IOD dominates the NWS domain. The MJO is only a primary influence when in phase 3 (other phases not shown), where it is the leading driver during summer in the NWS (Fig. 14c). El Niño also emerges as the strongest driver in specific regions, such as the NWS and the NE QLD regions during autumn and winter (Fig. 14e, g).

It is also important to note that there are regions that are not significantly correlated to any driver on this simultaneous, daily timescale. The EAC region, for example, does not show any statistically significant correlations in any season and the GAB is blank during autumn and winter.

3.3 Compound drivers: modulation of ENSO influences by the MJO

Building on our earlier evaluation of individual drivers, we now shift our focus towards understanding how multiple drivers work together to influence SST distributions. For this analysis, we examine how the MJO can alter the influence of La Niña events in two different regions during summer. In Fig. 15, the left column shows composites of SST and wind anomalies corresponding to each of the phases of the MJO. The middle column shows the phases of the MJO during La Niña periods. The right column shows these same composites with the average summer La Niña conditions (Fig. 4d) removed to represent the explicit influence of the MJO. The La Niña state is the leading driver of positive SST anomalies in the WA region (Fig. 4), but we can see from both the middle and right columns that the MJO can influence this signal, with stronger anomalies evident in phases 5–6 and weaker anomalies in phases 1, 2 and 8. We have seen from our previous analysis that SSTs to the northeast of Australia are generally positive during La Niña in all seasons (Fig. 4), and remaining offshore during summer. In the middle column of Fig. 15, we can see this warming coming onshore during MJO phases 5–8, with the MJO contributing to this warming. Finally, we can see a warming pattern in the Tasman Sea during MJO phases 2, 5, 7–8.

Both ENSO and the MJO alter tropical wind patterns, with the easterly trade winds strengthened during La Niña and tropical winds converging towards the convective centre of the MJO, where the location is dependent on the phase of the MJO. In the left column (Fig. 15), MJO phases 1–3 show strengthened easterlies, and phases 4–5 show winds converging over Australia, causing stronger easterlies in the east and stronger westerlies in the west. During MJO phases 6–7, the direction changes to stronger westerlies, and finally in phase 8 the winds divert from Australia causing stronger westerlies in the east and stronger easterlies in the west.

To better understand the mechanisms that drive these changes to the warming signal in the Western Australia region, we performed a heat budget analysis (Eq. 1) in the WA box region 110°-116°E, 21°-34°S (Fig. 2) to a depth of 47m, the area averaged mixed layer depth from BRAN2020.

We calculated the average contributions of each of the components of the heat budget equation corresponding to the phases of the MJO during La Niña periods in summer and removed the average of all La Niña summer events. The results are shown in Fig. 16a, with the x-axis showing the MJO phase and the coloured lines representing the different components of the heat budget.

The temperature within the mixed layer in this region increases sharply in phases 4–5 before dropping and reaching a minimum in phase 8. During the positive temperature change (phases 4–5), positive heat fluxes and vertical advection play prominent roles, whereas the negative temperature change (phase 6–8) is primarily governed by the vertical advection term. This vertical advection is directly linked to wind changes induced by the propagation of the MJO. Phases 4–5 are characterized by westerly winds in the northwest region, with the winds shifting southward along the west coast in phase 5. These wind patterns contribute to the positive vertical advection observed in these phases, primarily driven by downwelling processes. Conversely, the wind direction shifts in phases 8 − 1, where strong southerly winds lead to a decrease in vertical advection through enhanced upwelling.

It is noteworthy that even after isolating each MJO phase and accounting for the average La Niña summer imprint, a positive horizontal advection contribution persists across all phases, primarily associated with the intensification of the Leeuwin Current during La Niña periods.

In our analysis of ENSO, we have observed a consistent pattern of warming in the NE QLD region during La Niña (refer to Fig. 4), though typically, these warmer waters do not reach inshore. However, during MJO phases 6–8, we have detected a distinct warming signal in this region (see Fig. 15, third column). Considering the substantial impact of the MJO on tropical climate through its modulation of wind patterns, we also investigated the role of Ekman transport of this warmer water towards inshore NE QLD (142°-150°E, 12°-24°S; Fig. 2).

The average Ekman convergence and total convergence was calculated during each phase of the MJO during La Niña (Equations 2 and 3), with the average summer La Niña values (Fig. 4d) removed, as in the right column in Fig. 15.

The results in Fig. 16b show the average Ekman convergence and total convergence with phases of the MJO. The convergence is negative in MJO phases 1–3, positive in phase 4 before reaching its maximum in phase 5, remains positive in phase 6, and falls below zero again in phases 7–8. The signal of the convergence shows a one MJO phase lag to the winds, which have their maximum westerly strength in MJO phases 3–4. The SST expression is lagged two phases to the convergence due to the thermal inertia of the ocean, with the positive SST anomalies shown in MJO phases 6–8 (Fig. 15).

These results provide evidence affirming the influence of the MJO in modulating the effects of La Niña on ocean temperatures surrounding Australia during the summer season. The profound shifts in wind patterns associated with MJO phases underpin this relationship. While extreme heat is typically expected in the Western Australian region during the summer months of La Niña periods, by incorporating our understanding of the added influence of the MJO we gain a clearer view of the sub-seasonal outlook. Moreover, in the Queensland context, this insight shows the mechanisms through which the MJO, in conjunction with La Niña, triggers warming by facilitating the onshore movement of anomalously warm waters via Ekman transport. This represents a significant advancement in understanding the complex dynamics of these climate drivers and their cascading influences on ocean temperature extremes.

In this study, we have conducted a comprehensive analysis of various climate drivers that influence ocean temperatures, including marine heatwaves (MHWs), around Australia on sub-seasonal to seasonal timescales. Understanding the individual effects of climate drivers, and their compounding influences, is crucial for advancing MHW forecasting capabilities.

We analysed connections between drivers and their influences on MHW metrics (occurrence, intensity), and found that drivers with an extended phase duration have a stronger influence on the development of MHWs. As the residence time of any climate mode decreases (i.e., the amount of time a mode spends in a particular phase becomes shorter), the likelihood of this mode to contribute to the occurrence of a MHW also decreases, given that the processes responsible for heat accumulation in a given region tend to be shorter-lived. From Fig. 3 we can see the residence time decrease from the ONI through to the STRH, and from Figs. 4 to 11 we see the corresponding decrease in the connection between SST anomalies and MHW hazard ratings in the same order.

The spatial patterns of MHW hazard rating more closely resemble those of SST anomalies in ENSO (Figs. 4 and 5) than those associated with the sub-seasonal drivers. These results were insensitive to the index type selected, as a variety of daily Niño indices showed very similar results (not shown). During La Niña, positive SST anomalies in the northeast of Australia and down Australia’s west coast significantly contribute to MHWs in this region (Fig. 4 and Fig. 5). The impact of this heating in the northeast region during summer is less dramatic, as the warming remains offshore. The negative IOD phase is also a strong influence on the formation of MHWs along the Northwest Shelf during winter and spring, when the absence of the Australian monsoon allows the IOD to dominate. The positive SAM phase was the leading driver of warming in the Tasman Sea, presenting a higher-than-average MHW hazard in most seasons (Fig. 9). The connection between the positive phase of SAM and La Niña is shown to be strong, as the patterns of heightened MHW hazard levels is strongly coherent between the two, especially in spring and summer (Tabel 1).

We also assessed the collective influence of these drivers in the same region and their role in the occurrences of MHWs around Australia. Our findings indicate that, although the MJO does not exhibit a strong correlation with MHWs alone, except in the Northwest Shelf region (Fig. 12 and Fig. 13), its modulation of the La Niña signal becomes influential in the evolution of MHWs through Ekman transport, upwelling, and heat advection. Particularly when these oceanic conditions are preconditioned by La Niña, the MJO's influence on tropical winds plays a pivotal role. While we highlight the MJO's potential to compound warming along both the east and west coasts of Australia, it is important to acknowledge that the background ENSO state remains dominant. Our results do not indicate that the MJO has the capacity to fundamentally change the overall seasonal MHWs associated with La Niña events, but instead that the MJO can modulate the background influence of ENSO on regional MHWs on sub-seasonal timescales.

We have introduced a simple MHW hazard index in this study, based on the average weekly excess heat exposure associated with MHWs during each season and considering different phases of various drivers. However, it is important to note that translating this hazard index into a comprehensive risk assessment for marine species and ecosystems requires careful consideration of species-specific factors, including their exposure and vulnerability. For example, a MHW on a coral reef may have very different impacts on the various species that make up the reef ecosystem, depending on the species as well as the timing, duration, and intensity of the heatwave. A MHW occurring in the northern Australian coral reef regions during winter may not have significant impacts if the actual temperature does not exceed the thermal maximum of species of the region (Spillman and Alves, 2009; Perkins-Kirkpatrick et al., 2016). On the other hand, a MHW occurring during a period of high biological productivity, such as during a breeding season or a major migration, may have more severe impacts on a species, regardless of the actual maximum temperature (Barbeaux et al., 2020; Gabriele et al., 2022).

Across the Australian region, we observed strong influences on SST variability by at least one of the examined drivers. However, an interesting exception was identified in the EAC region, with Fig. 14 illustrating that none of the drivers exhibited statistically significant correlations to daily SST anomalies in this region in any season. Although we did not find any significant driver of SST anomalies in this region, our MHW hazard index indicates that during periods of declining El Niño in autumn, the EAC region is at a very high or extreme MHW hazard level (Fig. 5e). This result suggests that SST variability in the EAC region may be due to other processes such as local forcing due to current/wind driven upwelling/downwelling (Roughan and Middleton, 2004; Schaeffer and Roughan, 2017)or the propagation of meanders and frontal eddies (Schaeffer et al., 2017).

The Northwest Shelf region exhibits a pronounced temperature transition from cooler spring to warmer summer conditions, which often extends into the austral autumn months (Zhang et al., 2017). We have seen that during this transition phase, there is the potential for several drivers to contribute to increased warming in this region. We observe elevated warming during the decline of El Niño in autumn and winter (Figs. 4 and 5), during the negative phase of the IOD (Fig. 6) and corresponding to MJO phases 2 and 3 (Fig. 13). Figure 14 illustrates this further, as the maps of the leading drivers reveal a scenario in which multiple drivers compete for dominance as the most influential during the summer, autumn, and winter seasons. While both a declining El Niño and the negative phase of the IOD can each contribute to significant warming along the Northwest Shelf, the simultaneous occurrence of these phases is rare (Fig. 3). Nevertheless, these phases have coincided in the past, contributing to the development of intense MHWs in northern Australia during 2015-16 (Benthuysen et al., 2018).

Understanding which drivers decrease the likelihood or intensity of MHWs is also essential for managers to identify favourable conditions that could be used strategically for ecosystem management, fisheries, aquaculture, and other marine resource-related activities. Our analysis has indicated several drivers can reduce the likelihood of MHWs. These include El Niño on Australia’s west coast during summer and autumn (Fig. 4c &e) and in the northeast region during spring (Fig. 4b), the positive IOD on the Northwest Shelf during all seasons (Fig. 6), the negative SAM on the Tasman Sea during spring and summer (Fig. 8b and 8d), and the positive STRH on the Queensland region during spring and summer (Fig. 10a and 10c).

Our focus has been on presenting results that hold both statistical significance and real-world applicability. However, due to constraints in space, we have intentionally refrained from discussing here the underlying physical mechanisms in detail. This paper serves to lay the groundwork upon which future research can build, such as exploring the more intricate details of the dynamical relationships between drivers and their combined influences including teleconnections and subsurface MHWs.

Overall, our research contributes to a broader understanding of the intricate interplay between climate drivers and their influence on warm ocean temperature extremes, including MHWs, in the Australian region. By scrutinizing the influences of individual drivers and assessing their combined effects, our study underscores the critical need to comprehend the complexity of interactions occurring on shorter timescales within the atmosphere-ocean system.

5. Acknowledgments

CHG was supported by an ARC Centre of Excellence for Climate Extremes (CLEX) PhD scholarship, and additional top-up support from the joint CSIRO-UTAS Quantitative Marine Science program and the Australian Bureau of Meteorology.

NJH was supported by funding from the ARC Centre of Excellence for Climate Extremes (CE170100023) and the National Environmental Science Program Climate Systems Hub.

AGM and CMS would like to acknowledge support from the Australian Bureau of Meteorology.

We would like to acknowledge computational support from Australia’s National Computational Infrastructure (NCI) and the CLEX Computational Modelling Support (CMS) team. We would also like to thank CLEX graphic designer Georgina Harmer for her assistance with the driver schematic.

6. Author Information

Institute for Marine and Antarctic Studies, University of Tasmania, Tasmania, Hobart, Australia

Catherine H. Gregory & Neil J. Holbrook

University of Southern Queensland, Queensland, Toowoomba, Australia

Andrew G. Marshall

Bureau of Meteorology, Victoria, Melbourne, Australia

Claire M. Spillman

Contributions

All authors contributed to the study conception and design. Catherine Gregory analyzed the data and made the figures. The first draft of the manuscript was written by Catherine Gregory and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

7. Data Availability Statement

All datasets used in this study are open-source and available to be downloaded online.

The OISST v2, NOAA OLR, and NCEP/NCAR datasets were provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, via their website at https://psl.noaa.gov/

The BRAN202 dataset is publicly available at https://dapds00.nci.org.au/thredds/catalog/gb6/BRAN/catalog.html

8. Code Availability Statement

MHW event statistics were computed using the xarray python module (https://github.com/coecms/xmhw), based on the original marine heatwave module (http://github.com/ecjoliver/marineHeatWaves).

For access to the any of the code used, please contact the corresponding author.

9. Declarations of Interest

The authors declare that there are no known competing interests, whether personal, professional, or financial, that would influence the findings of this paper.

Arafeh-Dalmau, N., Montaño-Moctezuma, G., Martínez, J.A., Beas-Luna, R., Schoeman, D.S., and Torres-Moye, G. (2019). Extreme marine heatwaves alter kelp forest community near its equatorward distribution limit. Frontiers in Marine Science 6, 499.
Arafeh-Dalmau, N., Schoeman, D.S., Montaño-Moctezuma, G., Micheli, F., Rogers-Bennett, L., Olguin-Jacobson, C., and Possingham, H.P. (2020). Marine heat waves threaten kelp forests. Science 367, 635-635.
Arias-Ortiz, A., Serrano, O., Masqué, P., Lavery, P.S., Mueller, U., Kendrick, G.A., Rozaimi, M., Esteban, A., Fourqurean, J.W., and Marbà, N. (2018). A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nature Climate Change 8, 338.
Banzon, V., Smith, T.M., Chin, T.M., Liu, C., and Hankins, W. (2016). A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth System Science Data 8, 165-176.
Barbeaux, S.J., Holsman, K., and Zador, S. (2020). Marine heatwave stress test of ecosystem-based fisheries management in the Gulf of Alaska Pacific cod fishery. Frontiers in Marine Science 7, 703.
Barnston, A.G. (1997). Documentation of a highly ENSO-related SST region in the equatorial Pacific. Atmosphere-ocean 35, 367-383.
Benthuysen, J.A., Oliver, E.C.J., Feng, M., and Marshall, A.G. (2018). Extreme Marine Warming Across Tropical Australia During Austral Summer 2015–2016. Journal of Geophysical Research: Oceans 123, 1301-1326.
Byrne, M., Gall, M.L., Campbell, H., Lamare, M.D., and Holmes, S.P. (2022). Staying in place and moving in space: Contrasting larval thermal sensitivity explains distributional changes of sympatric sea urchin species to habitat warming. Global Change Biology 28, 3040-3053.
Cai, W., Shi, G., Cowan, T., Bi, D., and Ribbe, J. (2005). The response of the Southern Annular Mode, the East Australian Current, and the southern mid‐latitude ocean circulation to global warming. Geophysical Research Letters 32.
Capotondi, A., Wittenberg, A.T., Newman, M., Di Lorenzo, E., Yu, J.-Y., Braconnot, P., Cole, J., Dewitte, B., Giese, B., Guilyardi, E., Jin, F.-F., Karnauskas, K., Kirtman, B., Lee, T., Schneider, N., Xue, Y., and Yeh, S.-W. (2015). Understanding ENSO Diversity. Bulletin of the American Meteorological Society 96, 921-938.
Caputi, N., Kangas, M., Chandrapavan, A., Hart, A., Feng, M., Marin, M., and Lestang, S.D. (2019). Factors Affecting the Recovery of Invertebrate Stocks From the 2011 Western Australian Extreme Marine Heatwave. Frontiers in Marine Science 6, 484.
Doney, S.C., Ruckelshaus, M., Emmett Duffy, J., Barry, J.P., Chan, F., English, C.A., Galindo, H.M., Grebmeier, J.M., Hollowed, A.B., and Knowlton, N. (2012). Climate change impacts on marine ecosystems. Annual review of marine science 4, 11-37.
Dufek, A.S., Ambrizzi, T., and Da Rocha, R.P. (2008). Are Reanalysis Data Useful for Calculating Climate Indices over South America? Annals of the New York Academy of Sciences 1146, 87-104.
Frölicher, T.L., and Laufkötter, C. (2018). Emerging risks from marine heat waves. Nature communications 9, 1-4.
Gabriele, C.M., Amundson, C.L., Neilson, J.L., Straley, J.M., Baker, C.S., and Danielson, S.L. (2022). Sharp decline in humpback whale (Megaptera novaeangliae) survival and reproductive success in southeastern Alaska during and after the 2014–2016 Northeast Pacific marine heatwave. Mammalian Biology, 1-19.
Game, E.T., Watts, M.E., Wooldridge, S., and Possingham, H.P. (2008). Planning for persistence in marine reserves: a question of catastrophic importance. Ecological applications 18, 670-680.
Garrabou, J., Gómez‐Gras, D., Medrano, A., Cerrano, C., Ponti, M., Schlegel, R., Bensoussan, N., Turicchia, E., Sini, M., and Gerovasileiou, V. (2022). Marine heatwaves drive recurrent mass mortalities in the Mediterranean Sea. Global Change Biology 28, 5708-5725.
Gong, D., and Wang, S. (1999). Definition of Antarctic oscillation index. Geophysical research letters 26, 459-462.
Gregory, C.H., Holbrook, N.J., Marshall, A.G., and Spillman, C.M. (2023). Atmospheric Drivers of Tasman Sea Marine Heatwaves. Journal of Climate, 1-39.
Hendon, H.H., Liebmann, B., and Glick, J.D. (1998). Oceanic Kelvin Waves and the Madden–Julian Oscillation. Journal of the Atmospheric Sciences 55, 88-101.
Hendon, H.H., and Salby, M.L. (1994). The life cycle of the Madden–Julian oscillation. Journal of Atmospheric Sciences 51, 2225-2237.
Holbrook, N.J., Scannell, H.A., Sen Gupta, A., Benthuysen, J.A., Feng, M., Oliver, E.C.J., Alexander, L.V., Burrows, M.T., Donat, M.G., Hobday, A.J., Moore, P.J., Perkins-Kirkpatrick, S.E., Smale, D.A., Straub, S.C., and Wernberg, T. (2019). A global assessment of marine heatwaves and their drivers. Nature Communications 10, 2624.
Holbrook, N.J., Sen Gupta, A., Oliver, E.C.J., Hobday, A.J., Benthuysen, J.A., Scannell, H.A., Smale, D.A., and Wernberg, T. (2020). Keeping pace with marine heatwaves. Nature Reviews Earth & Environment 1, 482-493.
Huang, B., Liu, C., Freeman, E., Graham, G., Smith, T., and Zhang, H.-M. (2021). Assessment and intercomparison of NOAA daily optimum interpolation sea surface temperature (DOISST) version 2.1. Journal of Climate 34, 7421-7441.
Hudson, D., Marshall, A., and Alves, O. (2011). Intraseasonal forecasting of the 2009 summer and winter Australian heat waves using POAMA. Weather and Forecasting 26, 257-279.
Hughes, T.P., Kerry, J.T., Álvarez-Noriega, M., Álvarez-Romero, J.G., Anderson, K.D., Baird, A.H., Babcock, R.C., Beger, M., Bellwood, D.R., Berkelmans, R., Bridge, T.C., Butler, I.R., Byrne, M., Cantin, N.E., Comeau, S., Connolly, S.R., Cumming, G.S., Dalton, S.J., Diaz-Pulido, G., Eakin, C.M., Figueira, W.F., Gilmour, J.P., Harrison, H.B., Heron, S.F., Hoey, A.S., Hobbs, J.-P.A., Hoogenboom, M.O., Kennedy, E.V., Kuo, C.-Y., Lough, J.M., Lowe, R.J., Liu, G., Mcculloch, M.T., Malcolm, H.A., Mcwilliam, M.J., Pandolfi, J.M., Pears, R.J., Pratchett, M.S., Schoepf, V., Simpson, T., Skirving, W.J., Sommer, B., Torda, G., Wachenfeld, D.R., Willis, B.L., and Wilson, S.K. (2017). Global warming and recurrent mass bleaching of corals. Nature 543, 373-377.
Hughes, T.P., Kerry, J.T., Baird, A.H., Connolly, S.R., Dietzel, A., Eakin, C.M., Heron, S.F., Hoey, A.S., Hoogenboom, M.O., Liu, G., Mcwilliam, M.J., Pears, R.J., Pratchett, M.S., Skirving, W.J., Stella, J.S., and Torda, G. (2018). Global warming transforms coral reef assemblages. Nature 556, 492-496.
Jacox, M.G., Alexander, M.A., Bograd, S.J., and Scott, J.D. (2020). Thermal displacement by marine heatwaves. Nature 584, 82-86.
Kajtar, J., and Holbrook, N. (2021). Future projections of marine heatwave hazards to aquaculture and fisheries in Tasmania.
Kajtar, J.B., Bachman, S.D., Holbrook, N.J., and Pilo, G.S. (2022). Drivers, dynamics, and persistence of the 2017/2018 Tasman Sea marine heatwave. Journal of Geophysical Research: Oceans 127, e2022JC018931.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J.J., Fiorino, M., and Potter, G.L. (2002). NCEP–DOE AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society 83, 1631-1644.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., and Endo, H. (2015). The JRA-55 reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II 93, 5-48.
Le Nohaïc, M., Ross, C.L., Cornwall, C.E., Comeau, S., Lowe, R., Mcculloch, M.T., and Schoepf, V. (2017). Marine heatwave causes unprecedented regional mass bleaching of thermally resistant corals in northwestern Australia. Scientific Reports 7, 1-11.
Li, J., Roughan, M., and Kerry, C. (2022). Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current. Journal of Climate 35, 1097-1111.
Liebmann, B., and Smith, C.A. (1996). Description of a complete (interpolated) outgoing longwave radiation dataset. Bulletin of the American Meteorological Society 77, 1275-1277.
Lim, E.-P., Hendon, H.H., Butler, A.H., Thompson, D.W.J., Lawrence, Z.D., Scaife, A.A., Shepherd, T.G., Polichtchouk, I., Nakamura, H., Kobayashi, C., Comer, R., Coy, L., Dowdy, A., Garreaud, R.D., Newman, P.A., and Wang, G. (2021). The 2019 Southern Hemisphere Stratospheric Polar Vortex Weakening and Its Impacts. Bulletin of the American Meteorological Society 102, E1150-E1171.
Loschnigg, J., Meehl, G.A., Webster, P.J., Arblaster, J.M., and Compo, G.P. (2003). The Asian Monsoon, the Tropospheric Biennial Oscillation, and the Indian Ocean Zonal Mode in the NCAR CSM. Journal of Climate 16, 1617-1642.
Luo, J.-J., Zhang, R., Behera, S.K., Masumoto, Y., Jin, F.-F., Lukas, R., and Yamagata, T. (2010). Interaction between El Nino and extreme Indian ocean dipole. Journal of Climate 23, 726-742.
Madden, R.A., and Julian, P.R. (1971). Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. Journal of Atmospheric Sciences 28, 702-708.
Marshall, A., and Hendon, H. (2014). Impacts of the MJO in the Indian Ocean and on the Western Australian coast. Climate dynamics 42, 579-595.
Marshall, A., Hudson, D., Wheeler, M., Alves, O., Hendon, H., Pook, M., and Risbey, J. (2014). Intra-seasonal drivers of extreme heat over Australia in observations and POAMA-2. Climate dynamics 43, 1915-1937.
Marshall, A.G., Hendon, H.H., Feng, M., and Schiller, A. (2015). Initiation and amplification of the Ningaloo Niño. Climate Dynamics 45, 2367-2385.
Mcgowan, H., and Theobald, A. (2023). Atypical weather patterns cause coral bleaching on the Great Barrier Reef, Australia during the 2021–2022 La Niña. Scientific Reports 13, 6397.
Mckay, R.C., Boschat, G., Rudeva, I., Pepler, A., Purich, A., Dowdy, A., Hope, P., Gillett, Z.E., and Rauniyar, S. (2023). Can southern Australian rainfall decline be explained? A review of possible drivers. WIREs Climate Change 14, e820.
Min, S.-K., Cai, W., and Whetton, P. (2013). Influence of climate variability on seasonal extremes over Australia. Journal of Geophysical Research: Atmospheres 118, 643-654.
Oke, P.R., Griffin, D.A., Schiller, A., Matear, R.J., Fiedler, R., Mansbridge, J., Lenton, A., Cahill, M., Chamberlain, M.A., and Ridgway, K. (2013). Evaluation of a near-global eddy-resolving ocean model. Geosci. Model Dev. 6, 591-615.
Oliver, E., and Thompson, K. (2010). Madden-Julian Oscillation and sea level: Local and remote forcing. Journal of Geophysical Research 115.
Oliver, E.C.J., Benthuysen, J.A., Bindoff, N.L., Hobday, A.J., Holbrook, N.J., Mundy, C.N., and Perkins-Kirkpatrick, S.E. (2017). The unprecedented 2015/16 Tasman Sea marine heatwave. Nature Communications 8, 16101.
Perkins-Kirkpatrick, S., King, A.D., Cougnon, E., Grose, M.R., Oliver, E., Holbrook, N., Lewis, S., and Pourasghar, F. (2019). The role of natural variability and anthropogenic climate change in the 2017/18 Tasman Sea marine heatwave.
Perkins-Kirkpatrick, S., White, C., Alexander, L., Argüeso, D., Boschat, G., Cowan, T., Evans, J., Ekström, M., Oliver, E., and Phatak, A. (2016). Natural hazards in Australia: heatwaves. Climatic Change 139, 101-114.
Philander, S.G.H. (1983). El Niño Southern Oscillation phenomena. Nature 302, 295-301.
Reynolds, R.W., Smith, T.M., Liu, C., Chelton, D.B., Casey, K.S., and Schlax, M.G. (2007). Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate 20, 5473-5496.
Roberts, S.D., Van Ruth, P.D., Wilkinson, C., Bastianello, S.S., and Bansemer, M.S. (2019). Marine heatwave, harmful algae blooms and an extensive fish kill event during 2013 in South Australia. Frontiers in Marine Science 6, 610.
Roemmich, D., Gilson, J., Davis, R., Sutton, P., Wijffels, S., and Riser, S. (2007). Decadal spinup of the South Pacific subtropical gyre. Journal of Physical Oceanography 37, 162-173.
Roughan, M., and Middleton, J.H. (2004). On the East Australian Current: Variability, encroachment, and upwelling. Journal of Geophysical Research: Oceans 109.
Saji, N., Goswami, B.N., Vinayachandran, P., and Yamagata, T. (1999). A dipole mode in the tropical Indian Ocean. Nature 401, 360-363.
Schaeffer, A., Gramoulle, A., Roughan, M., and Mantovanelli, A. (2017). Characterizing frontal eddies along the East Australian Current from HF radar observations. Journal of Geophysical Research: Oceans 122, 3964-3980.
Schaeffer, A., and Roughan, M. (2017). Subsurface intensification of marine heatwaves off southeastern Australia: The role of stratification and local winds. Geophysical Research Letters 44, 5025-5033.
Speer, M.S., Leslie, L.M., and Fierro, A.O. (2011). Australian east coast rainfall decline related to large scale climate drivers. Climate Dynamics 36, 1419-1429.
Spillman, C.M., and Alves, O. (2009). Dynamical seasonal prediction of summer sea surface temperatures in the Great Barrier Reef. Coral Reefs 28, 197-206.
Spillman, C.M., and Hobday, A.J. (2014). Dynamical seasonal ocean forecasts to aid salmon farm management in a climate hotspot. Climate Risk Management 1, 25-38.
Student (1908). The probable error of a mean. Biometrika 6, 1-25.
Stuecker, M.F., Timmermann, A., Jin, F.-F., Chikamoto, Y., Zhang, W., Wittenberg, A.T., Widiasih, E., and Zhao, S. (2017). Revisiting ENSO/Indian Ocean Dipole phase relationships. Geophysical Research Letters 44, 2481-2492.
Thomsen, M.S., Mondardini, L., Alestra, T., Gerrity, S., Tait, L., South, P.M., Lilley, S.A., and Schiel, D.R. (2019). Local extinction of bull kelp (Durvillaea spp.) due to a marine heatwave. Frontiers in Marine Science 6, 84.
Virts, K.S., and Wallace, J.M. (2014). Observations of temperature, wind, cirrus, and trace gases in the tropical tropopause transition layer during the MJO. Journal of the Atmospheric Sciences 71, 1143-1157.
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C., Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H.S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., Manoussakis, M., Mastrangelo, D., Maclachlan, C., Mclean, P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A.W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D., Woolnough, S., Wu, T., Won, D.J., Xiao, H., Zaripov, R., and Zhang, L. (2017). The Subseasonal to Seasonal (S2S) Prediction Project Database. Bulletin of the American Meteorological Society 98, 163-173.
Wang, G., and Hendon, H.H. (2020). Impacts of the Madden–Julian Oscillation on wintertime Australian minimum temperatures and Southern Hemisphere circulation. Climate Dynamics 55, 3087-3099.
Wang, S.-Y., L'heureux, M., and Chia, H.-H. (2012). ENSO prediction one year in advance using western North Pacific sea surface temperatures. Geophysical Research Letters 39.
Wang, Y., Holbrook, N.J., and Kajtar, J.B. (2023). Predictability of marine heatwaves off Western Australia using a linear inverse model. Journal of Climate, 1-40.
Weller, R.A., and Anderson, S.P. (1996). Surface Meteorology and Air-Sea Fluxes in the Western Equatorial Pacific Warm Pool during the TOGA Coupled Ocean-Atmosphere Response Experiment. Journal of Climate 9, 1959-1990.
Wernberg, T., Bennett, S., Babcock, R.C., De Bettignies, T., Cure, K., Depczynski, M., Dufois, F., Fromont, J., Fulton, C.J., and Hovey, R.K. (2016). Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169-172.
Wernberg, T., Smale, D.A., Tuya, F., Thomsen, M.S., Langlois, T.J., De Bettignies, T., Bennett, S., and Rousseaux, C.S. (2013). An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nature Climate Change 3, 78-82.
Werner, A., Maharaj, A.M., and Holbrook, N.J. (2012). A new method for extracting the ENSO-independent Indian Ocean Dipole: application to Australian region tropical cyclone counts. Climate Dynamics 38, 2503-2511.
Wheeler, M.C., and Hendon, H.H. (2004). An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Monthly weather review 132, 1917-1932.
Zangvil, A. (1975). Temporal and spatial behavior of large-scale disturbances in tropical cloudiness deduced from satellite brightness data. Mon. Wea. Rev. 103, 904-920.
Zhang, L., Han, W., Li, Y., and Shinoda, T. (2018). Mechanisms for generation and development of the Ningaloo Niño. Journal of Climate 31, 9239-9259.
Zhang, N., Feng, M., Hendon, H.H., Hobday, A.J., and Zinke, J. (2017). Opposite polarities of ENSO drive distinct patterns of coral bleaching potentials in the southeast Indian Ocean. Scientific Reports 7, 2443.
Zhao, S., Jin, F.-F., and Stuecker, M.F. (2019). Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts. Geophysical Research Letters 46, 9980-9990.

Download PDF

Editorial decision: Minor Revision
11 Jan, 2024
Reviewers agreed at journal
14 Nov, 2023
Reviewers invited by journal
13 Nov, 2023
Editor assigned by journal
09 Nov, 2023
First submitted to journal
08 Nov, 2023

You are reading this latest preprint version

Drivers of regional Australian marine heatwaves on sub-seasonal to seasonal timescales

Status:

Version 1

Abstract

Figures

1. Introduction

2. Material and Methods

2.6 Understanding the mechanisms driving extreme regional ocean warming.

3. Results

3.1 Average seasonal states with each driver

3.1.1 ENSO

3.1.2 IOD

3.1.3 SAM

3.1.4 STRH

3.1.5 MJO

3.2 Leading driver of SST variability at each location

3.3 Compound drivers: modulation of ENSO influences by the MJO

4. Discussion and Conclusions

Declarations

References

Status:

Version 1