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A Global Overview of Marine Heatwaves in a Changing Climate

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Antonietta Capotondi1,2,†, Regina R. Rodrigues3, Alex Sen Gupta4,5, Jessica A. Benthuysen6,
Clara Deser7, Thomas L. Frölicher8,9, Nicole S. Lovenduski10, Dillon J. Amaya2, Natacha Le
Grix8,9, Tongtong Xu1,2, Juliet Hermes11, Neil J. Holbrook12,13, Roxy Mathew Koll14, CristianMartinez-Villalobos15,16, Simona Masina17, Amandine Schaeffer18,19, Robert W. Schlegel20,
Kathryn E. Smith21, Chunzai Wang22,23,24

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1. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder,
Boulder, CO, USA.
2. NOAA/Physical Sciences Laboratory, Boulder, CO, USA
3. Federal University of Santa Catarina, Florianópolis, Brazil
4. Climate Change Research Centre and Centre for Marine Science and Innovation, University of New
South Wales
5. Australian Research Council, Centre of Excellence for Climate Extremes
6. Australian Institute of Marine Science, Indian Ocean Marine Research Centre, Crawley, Western
Australia 6009, Australia
7. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder CO,
USA
8. Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland
9. Oeschger Centre for Climate Change Research, University of Bern, Bern Switzerland
10. Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research,
University of Colorado Boulder, Boulder, CO, USA.
11. South African Environmental Observation Network, Cape Town, South Africa
12. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
13. ARC Centre of Excellence for Climate Extremes, University of Tasmania, Hobart, Tasmania, Australia
14. Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth
Sciences, Pune, India
15. Faculty of Engineering and Science, Universidad Adolfo Ibáñez, Santiago, Chile
16. Data Observatory Foundation, ANID Technology Center No. DO210001, Santiago, Chile
17. CMCC Foundation - Euro-Mediterranean Center on Climate Change, Bologna, Italy
18. School of Mathematics and Statistics, UNSW Sydney, Sydney, Australia
19. Center for Marine Science and Innovation, UNSW Sydney, Sydney, Australia
20. Laboratoire d’Océanographie de Villefranche, Sorbonne University, CNRS, Villefranche-sur-mer,France21. Marine Biological Association of the United Kingdom, Plymouth, PL1 2PB, UK
22. State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese
Academy of Sciences, Guangzhou, China
23. Global Ocean and Climate Research Center, South China Sea Institute of Oceanology, Chinese
Academy of Sciences, Guangzhou, China
24. Guangdong Key Laboratory of Ocean Remote Sensing, South China Sea Institute of Oceanology,
Chinese Academy of Sciences, Guangzhou, China†

email: antonietta.capotondi@noaa.gov

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Abstract
Marine heatwaves (MHWs) have dramatically impacted marine ecosystems and ecosystemservices over large areas of the world oceans. In this paper, we review the recent substantial
advances in this active area of research, including the exploration of the three-dimensional
structure and evolution of MHWs, their local and remote drivers, their connection with otherextremes in the ocean and over land, future projections by climate model, and assessment of theirpredictability and current prediction skill. Ongoing discussions involve the attribution of thesetemperature extremes to internal versus anthropogenic factors, their definition in the context ofchanging baselines, their combination with other ecosystem stressors, and the reliability of future
projections from state-of-the-art climate models. To make progress on predicting and projecting
MHWs, a more complete mechanistic understanding of these extremes over the full ocean depth
and at the relevant spatial and temporal scales is needed, together with models that can realistically
capture the leading mechanisms at those scales. Sustained observing systems, as well as measuring
platforms that can be rapidly deployed, are essential to achieve comprehensive event
characterizations while also chronicling the evolving nature of these temperature extremes andtheir impacts in our changing climate.
Introduction
In recent decades, episodes of warm ocean temperature extremes have been associated with more
intense and frequent impacts on organisms, ecosystems and reliant human industries around theworld1-3. By analogy with their atmospheric counterpart, these extreme ocean temperature events
have been termed “marine heatwaves” (MHWs)4,5. Some of the most prominent events, together
with the unprecedented warming during the boreal summer of 2023 are presented in Box 1. MHWs
influence regional climate phenomena and often drive substantial impacts on the marine
environment. For example, MHWs in the Indian Ocean are found to modulate the monsoon winds
and rains over the Indian subcontinent, impacting water and food security over the region6. MHWsinteract with and intensify tropical cyclones, making them more destructive7-10. Biological MHW
impacts include mass mortality events in invertebrates, fish, birds and marine mammals1,11-13, coralbleaching14,15, declines in foundation species3,16,17 and entire ecosystem restructuring18,19, with farreaching socioeconomic impacts20.
Recent reviews and perspectives 13,21,22 have outlined major steps forward in understanding MHW
characteristics, drivers, and predictability, along with the economic impacts they cause. However,in this rapidly evolving field, more recent research has provided new insights into MHWs, while
generating important new questions and research avenues. Although MHW research has primarily
considered temperature extremes at the ocean surface, subsurface temperature extremes may be
more intense and longer-lasting than their surface counterparts23-27. Given their critical importancefor marine ecology, subsurface MHWs need to be closely observed, mechanistically understood,
and predicted. In addition, while the physical characterization of MHWs has mainly focused onlarge-scale events (Box 1), MHWs are now also studied in more localized coastal areas, marginalseas, and fjords28-31, where they are negatively impacting the local ecology and coastal
communities3,16. MHWs are also increasingly being examined along with other extreme

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conditions, like high acidity or low-oxygen32,33, sea level extremes34, floods35, droughts36, severe
weather events37 or even terrestrial heat waves over the adjacent land38. These “compound events”
act as multiple stressors for marine life and society.
The ability to predict MHWs and compound events from days to seasons in advance is key forstakeholder preparation and mitigation efforts39. Skillful forecasts require enhanced understandingof MHW drivers to assess their predictability, and prediction systems that realistically capture theprocesses underpinning that predictability21,40,41. While progress has been made in prediction
activities42,43, additional improvements could be achieved through a deepened understanding of
the relative roles of different MHW drivers, and dynamical model improvements, which include
an assessment of the sensitivity of MHW forecasts to model resolution44-47.
As the oceans continue to warm with anthropogenic climate change48,49, defining MHWs under
non-stationary conditions becomes increasingly challenging, as commonly used definitions willlead to a permanent MHW state in areas experiencing enhanced warming50 (Fig. 1). In addition,
separating the processes internal to the climate system from those of anthropogenic origin51,52 is
key to the mechanistic understanding of the nature of MHWs and the assessment of theirpredictability and their future changes.
This article extends previous reviews13,21,22 by highlighting the new emerging areas in MHW
research outlined above, including: a critical re-evaluation of MHW definitions and their detection,
both at the surface and in the subsurface, in the presence of climate change; observational needs
and new emerging “observing” strategies; advances in the understanding of both surface andsubsurface MHW drivers to aid prediction efforts; compound events and their prediction; andinvestigations to assess future MHW projections using empirical approaches and state-of-the-art
modeling systems. This review also provides a perspective on new and promising avenues foradvancing our understanding and prediction capabilities of ocean extremes in the context of ourchanging climate.
Defining a marine heatwave
Defining a MHW involves multiple choices, each leading to outcomes with distinct implications.
These choices may be motivated by the need to understand the physical drivers or impacts of a
MHW, or they can be constrained by the characteristics of the available data, like record length or
temporal resolution. For simplicity, MHWs have typically been analyzed using local definitions53.
However, since MHWs have a three-dimensional structure that evolves over time, otherapproaches are emerging25,54,55 to facilitate the tracking of extended surface or subsurface events
over time.
Over the past decade, the majority of studies have adopted a common framework for defining
MHWs. Following the widely used Hobday et al. (2016)53 framework, a MHW occurs at a given
location when daily sea surface temperature anomalies exceed the seasonally-varying 90th
percentile climatology for five days or more (with dips below this threshold for two days or less
ignored). The 90th percentile climatology is typically based on a fixed reference period, or“baseline”.

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These threshold criteria were chosen in analogy with atmospheric heatwaves56, and were notnecessarily dictated by specific impacts in the marine environment. As such, other definitions havealso been employed22, including, for example, definitions using the 99th percentile57, approaches
using monthly data41,42,51,52,58,59 instead of daily data (Fig. 1), annual maximum temperatures60, or
cumulative temperatures exceeding fixed thresholds, a criterion commonly used for coralbleaching monitoring and prediction61-63. Attempts to incorporate information on biologicalimpacts has led to the creation of MHW hazard indices, where species-tailored metrics were codeveloped with stakeholders using absolute temperatures64. With a fixed baseline, MHW
conditions will become increasingly common as the ocean warms57,65, potentially leading to a“permanent” MHW state in some regions experiencing enhanced warming10 (Fig. 1). Thesechanging characteristics may reflect the risk these events pose to some marine organisms,
particularly those with slow adaptation rates13. However, considering a fixed baseline limits ourability to distinguish the slow climate change-related processes from the faster processes
associated with internal modes of climate variability or synoptic weather conditions40, with
implications for understanding events’ predictability and assessing their prediction skill66. Thus,
there has been a recent call to remove the effects of mean warming when defining MHWs bydetrending temperature time series67 or using a shifting baseline period68, especially for future
projections. To define MHW characteristics, the decision to use a temperature threshold thatremains fixed or changes over time will ultimately depend on the application being studied, theimportance of maintaining consistency with past studies and the characteristics of the data record.
Given the availability of satellite-derived sea surface temperature (SST), many MHW studies haverelied on daily gridded satellite data, starting in the early 1980s. However, different datasets will
have varying temporal and spatial resolutions and/or may be sporadic and contain data gaps.
Modified MHW definitions may be appropriate for different datasets and specific applications. For
instance, monthly means can be used in regions characterized by long ocean memory (e.g., thetropical Pacific), or when the focus is on long-lasting MHWs20,69. In all definitions, the temporaland spatial scales of the dynamics at play need to be considered.
Spatially, MHWs can cover large horizontal distances and extend deep into the water column.
MHW structure is linked to their drivers and needs to be included in their characterization. Whilehorizontal extent has been considered in studies assessing MHW projections57,70, a number of newtechniques have been developed to track connected MHW regions at the surface54,55,71 or in threedimensional space25,72, including the splitting and merging of MHW regions, referred to as “MHW
systems”72. These techniques treat MHWs as objects that evolve in space and time providing an
illustration of their areas of influence. Similar algorithms have also been applied to ocean
acidification extremes in the Northeast Pacific73, allowing an assessment of the severity of theirimpacts. To date, these tracking algorithms are purely statistical and do not incorporate information
about event dynamics, but the use of tracking will provide new opportunities for understanding
the extent of MHW systems as they evolve through time, and facilitate the identification of theirunderlying dynamics.
Observations for characterizing marine heatwaves
Observations are the foundation for characterizing and understanding MHWs. For more than acentury, a diversity of ways to measure ocean temperature have been developed from in situ

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stationary and moving platforms (both passive and active) to remotely sensed methods (Box 2;
Table 1 of Oliver et al. 202122).

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An alternative approach for increasing the sample size of extreme events is to use empirical models
trained on observations, like Linear Inverse Models (LIMs90), to produce multi-millennia synthetic
time series. These synthetic data share similar statistical properties (covariances, autocorrelation,
event evolution) with observations35,51,58 and allow to explore the full range of possible MHW
realizations that are consistent with the dynamics and noise structure of the training data.

The challenge for observing MHWs is to measure ocean temperature at high temporal resolution
and over a long period (decades) to define a threshold for extremes, while accounting for theinherent variability of temperature at different timescales. Advances in understanding MHWs
globally have relied largely on satellite derived sea surface temperature products blended withnear-surface in situ data provided from surface drifting buoys and ship underway systems (e.g.
products such as the Operational SST and Sea Ice Analysis (OSTIA) system74,75 and NOAA Daily
Optimum Interpolation SST v2.1 dataset76). On the other hand, long-term in situ temperature
measurements from water samples and moorings have been crucial for characterizing temperature
extremes at daily timescale, although not representative of large areas. There are coastal locations
distributed worldwide where ocean temperatures have been recorded since before the satellite
era65,77-79, providing insight into long-term trends of local ocean temperatures and changes in thefrequency of temperature extremes. Only a few sites include sustained measurements extending
through the water column, which have been crucial to understand sub-surface MHWcharacteristics and drivers23,24,80. Globally, the network of Argo floats has provided atransformative capability to study subsurface events by sampling ocean temperatures27,81 over theupper 2000 m for more than two decades82. However, Argo-derived gridded datasets are primarily
available at monthly time resolution and lack coverage over continental shelves and marginal seas.
The combination of different observational platforms, for example Argo and coastal moorings29,
is proving extremely valuable to achieve a comprehensive view of MHWs (Box 2).
To overcome issues associated with sparse and inconsistent observations, many studies haveleveraged ocean reanalyses—models constrained by observations through data assimilation— tounderstand MHW drivers and dynamical processes41,83,84, and analyze MHW characteristics atboth the ocean surface and in the subsurface25,26. Ocean reanalyses offer uniform data coverage intime and space, in some cases at high-resolution (e.g., GLORYS12v185), thus also facilitating thecharacterization of MHWs on continental shelves86,87. Reanalysis products are subject to model
errors and biases, and should be used with care to study extremes, especially in areas where limited
observations were assimilated.
On longer time-scales, paleoclimate proxies may offer an opportunity to extend the observational
record into the past, thereby increasing sample sizes and generating more robust MHW statistics.
For example, ocean temperature reconstructions based on the geochemical properties of corals
offer a robust measure of thermal stress in the past88. Planktonic foraminifera also correlate withupper-ocean temperature89, and their fossil samples at some locations may provide insights into
MHW statistics in the past, albeit with limitations due to coarse temporal resolution.

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Drivers of surface and subsurface marine heatwaves
The processes driving MHWs affect their characteristics, including duration, intensity and verticalstructure, and are key for predicting their evolution. MHWs are driven by local heat fluxes
associated with synoptic atmospheric conditions or ocean advective and mixing processes, and are
sensitive to the ocean state (e.g., mixed layer depth). These local drivers may themselves be
modulated by large-scale modes of climate variability or anthropogenic warming, and vary
regionally and seasonally. In the extra-tropics, intense MHWs are commonly associated withpersistent atmospheric highs, resulting in increased insolation and decreased wind speeds that
reduce turbulent heat losses and vertical ocean mixing40,91-96. Associated shallower mixed layers
can further amplify the warming from surface heat fluxes59,83,97,98. More broadly, heat budget
analyses indicate that increased insolation and reduced evaporative cooling typically dominate thebuild-up of MHWs while decay is generally driven by increased turbulent heat losses99. In
boundary current regions, anomalous warm oceanic advection is often important. Key examples
include the 2011 Ningaloo Niño5,100,101 and the long-lived 2015/16 Tasman Sea MHW102,103.
Advection-driven MHWs typically have a smaller surface area, but last longer21 and reachdeeper104 than atmospherically-driven events21. In the tropical Pacific, MHWs associated with El
Niño Southern Oscillation (ENSO) are dynamically driven105, with surface heat fluxes damping
temperatures during both the onset and decay phases99,106. In high-energy regions, like westernboundary currents, oceanic mesoscale eddies and meanders, which often cross onto the shelf, cancontribute to the onset, intensity, and longevity of MHWs at small temporal and spatial
scales8,47,81,107.
Large-scale modes of climate variability can affect the likelihood of MHW occurrences
regionally6,40, by modulating the local drivers and the initial upper-ocean stratification. For
example, the 2013/14 MHW in the southwest Atlantic was forced by atmospheric conditions
associated with a wave train triggered by the Madden-Julian Oscillation in the tropical Indian
Ocean36. ENSO events are associated with a significant increase in the frequency, intensity andduration of MHWs across many parts of the global oceans65,91, enhancing the forecast skill of
MHWs and ocean acidity extremes42,108, and influencing MHW projections52. For example,
stronger equatorial Pacific easterly winds during La Niña events lead to an enhancement of theIndonesian Throughflow and Leeuwin Current, thereby transporting warm tropical waters to theWestern Australian coast, creating favorable conditions for the development of MHWs100. ENSO
also affects the Northeast Pacific Ocean through both oceanic and atmospheric pathways109.
However, ENSO’s influence on MHWs may depend on the spatial pattern of ENSO SST
anomalies84, and may be mediated by other modes of variability at interannual or decadal
timescales40,41,95,110,111. For example, a pre-existing positive Indian Ocean Dipole can increase thelikelihood and predictability of MHWs off Western Australia up to 20 months in advance111, whilein the Northeast Pacific, MHW onset is influenced by low-frequency variability related to the
Pacific Decadal Oscillation (PDO)41,110. Interactions between tropical basins112,113 can also
contribute to MHW development, as exemplified by the 2020 MHWs in the Northwest Pacific andSouth China Sea114,115 and the unprecedented Northwest Pacific event of 2022116. Finally, MHWs
in the far-eastern tropical Pacific (“coastal El Niño events”), result primarily from the constructive
interference of the North and South Pacific Meridional Modes117,118, and are not necessarily related
to basin-wide ENSO conditions35. Assessing the relative contributions and links between large-

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scale drivers is critical to fully understand and exploit the inherent system predictability andimprove predictions119.
MHWs extend into the subsurface ocean, with depth structures that may vary considerably
depending on the region27, the leading driving mechanisms and the local bathymetry, whether inopen ocean or on the shelf. MHWs can be confined to the mixed layer (“shallow MHWs”), driven
by enhanced air-sea heat fluxes, ocean advection or reduced wind-induced turbulent mixing (Fig.
2a, e), or they can penetrate well below the mixed layer27. In shallow coastal regions, they caneven extend to the ocean bottom (“Extended” events; Fig. 2c, d)24,86 due to the intrusion of warm
eddies and western boundary current meanders onto the shelf (Fig. 2c) or through warm advection
by alongshore currents (Fig. 2d), as shown by data from a near-shore mooring site in eastern
Australia24. Near the shelf, deep warm anomalies can result from downwelling processes andcoexist with surface cooling24 (Fig. 2b). More generally, subsurface intensification through the
dynamical movement of the thermocline results from local Ekman pumping or from the passageof large-scale planetary waves, processes that occur ubiquitously throughout the ocean26 (Fig. 2f).
These subsurface anomalies are often larger than surface anomalies, due to the movement of thestrong vertical temperature gradients around the thermocline23,24,26,27,81,86,120. As they evolve overtime120 (Fig. 2e-h), subsurface MHWs can extend below the mixed layer during its seasonal
shoaling and persist at depth even though the surface layer cools (Fig. 2g). They may sometimes
be entrained back into a deepening mixed layer, and produce a delayed surface warming, a process
known as “re-emergence”121 (Fig. 2h). Such evolutions were found in a model of the eastern
tropical and North Pacific120 and in Argo observations of Northeast Pacific MHWs during 20042020122, and are likely to occur in other regions123.
Compound and cascading events
Compound events are generally defined as a combination of extreme conditions and/or hazards
that contribute to societal or environmental risk124. As such, they stress both natural and humansystems, causing socioeconomic impacts such as loss of essential ecosystem services andincome124. Understanding their underlying physical processes is thus critical for predictability
assessments. During a compound event, extreme conditions can occur simultaneously (e.g., highocean temperatures and low oxygen concentrations in the ocean) or in close sequence, where one
event can increase the system vulnerability to a successive event. For instance, droughts andheatwaves can lead to a higher risk of flash floods over land. Events can also occur concurrently
over different regions with large-scale consequences, as exemplified by the widespread impacts
on fisheries caused by MHWs and low upper ocean nutrient levels during El Niño events.
Marine heatwaves and terrestrial extremes
Our understanding is more advanced for extremes and compound events over land125. However,work into ocean-land compound events is growing. For instance, atmospheric blocking overeastern South America and the western South Atlantic is associated with persistent high-pressure
centers that can reduce cloud cover and latent heat loss, leading to simultaneous drought conditions
over land and heatwaves in the adjacent ocean36. Similarly, synoptic conditions driving terrestrial
heatwaves in some locations around Australia are also conducive to the warming of the ocean,
increasing the likelihood of a concurrent MHW38. As many extreme extra-tropical MHWs are

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associated with persistent high-pressure centers91, such systems, straddling the land and ocean,might plausibly lead to compound marine-terrestrial temperature extremes in coastal regions.
MHWs can also be related to enhanced evaporation and transport of humidity, inducing heavy
rainfall along coastal regions, such as during the Tasman Sea MHW in 2015-16102 or the coastal
MHW off Peru in 2017126.
Marine heatwaves and ocean biogeochemical extremes
Given their potential impacts on marine organisms, there is growing interest in ocean
biogeochemical extremes that can co-occur with temperature extremes (Fig. 3), including highacidity (OAX)73,127-130, low oxygen (LOX)131 and low chlorophyll extremes (LChl)132,133. These
stressors may act additively or synergistically134. For example, compound MHW-LOX events canhave detrimental effects on aerobic metabolic rates, especially in ectotherms, i.e., cold-blooded
organisms135-137. Additionally, compound MHW-OAX events can adversely affect molluscs138 or
warm-water corals139, while MHW-LChl events are often associated with extremely low fishbiomass conditions140. Moreover, it is plausible that the concurrent extreme ocean acidification
conditions amplified the devastating effects of the Northeast Pacific Marine Heatwave of 2014–
2015141 (Box 1, top panel), also known as the “Blob”83.
Compound MHW-OAX events are more likely to occur in the subtropics than in the equatorial
Pacific and mid-to-high latitudes, as high temperatures in the subtropics strongly increase thehydrogen ion [H ] concentration (i.e., acidity)142. At higher-latitudes, lower backgroundtemperatures limit this effect, while in the equatorial Pacific, reduced Dissolved Inorganic Carbon
due to weaker upwelling leads to a decreased [H ] concentration, counteracting the effect oftemperature. Conversely, hotspots of compound MHW-LChl events are found in the equatorial
Pacific, along the boundaries of the subtropical gyres and in the northern Indian Ocean, often
associated with El Niño events132 and enhanced nutrient limitation on phytoplankton growth133,143.
Notably, the North Pacific MHW in 2014-2016 was identified as a quadruple compound event
during some phases of its development, involving high temperature, low oxygen, high acidity andlow chlorophyll levels32,132,144. For example, in January 2014, the extreme warming of the Blob
(Box 1, top panel) co-occurred with low chlorophyll over part of the MHW area (Fig. 3c).
++

Climate model projections indicate that long-term trends in acidification, deoxygenation, andnutrient decline in the low-latitude upper ocean will persist for decades145,146, amplifying thefrequency, intensity and scale of compound MHWs and biogeochemical extremes32,142. Notably,
even when using a shifting baseline, whereby the effect of long-term warming and OAX are
removed, OAX events and, therefore, compound MHW-OAX events are expected to increase due
to projected increases in the seasonal and diurnal variations in [H ]129,147,148.
+

Despite initial studies, understanding ocean compound extreme events is still in its infancy32. A
global perspective on the temporal and spatial characteristics of these events, especially at depth,
and a mechanistic understanding of relevant processes and their cascading impacts on ecosystems,
are currently missing, mainly due to the lack of available subsurface data.

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Climate models representations of MHWs
Given the sparsity of long-term observational records, especially at depth, numerical models canhelp us to better understand MHW characteristics and their drivers. Global Earth system models(ESMs), which include both physical and biogeochemical components, are essential to provide
future projections of both MHWs and biogeochemical extremes. But how well do climate models
represent MHWs? Global coupled climate models vary in their degree of fidelity in representing
climatological characteristics of basic MHW metrics (frequency, intensity and duration) at bothdaily44,60, and monthly51,52 timescales. CMIP-type ESMs tend to overestimate the duration of
MHWs47,52 (Fig. 4). In addition, regions of strong ocean currents are especially problematic inmodels without eddy-permitting resolution44,45,47, due to the models’ inability to capture theinfluence of mesoscale eddies on MHW development in those regions107. Observed changes in
MHW characteristics over the historical period are also challenging for models to simulate,although those stemming from mean state changes are better represented than those due to changes
in internal variability51. However, the observational record of surface MHWs is relatively short,consisting of approximately 40 years for daily SSTs derived from satellite remote sensing, andapproximately 100 years for monthly SSTs measured by ships of opportunity, with subsurface
MHW records being even shorter. Such observational records only provide a limited sample of all
the possible realizations that are consistent with the dynamics and noise of the climate system.
Unlike observations, coupled climate models offer the potential for multiple realizations of thepast and future, thereby enhancing sample sizes of extreme events. In particular, so-called “Single
Model Initial-condition Large Ensembles” (SMILEs) have become a powerful tool in climate
research for studying the simulated characteristics of internal variability and forced responses on
local and regional scales149. SMILEs consist of many historical and future scenario simulations(generally 30-100) for a particular model, each starting from slightly different initial conditions,and allow a clean separation between the forced signal (the ensemble mean) and internalvariability/extremes (departures from the ensemble mean). The power of SMILEs is only
beginning to be exploited for the study of MHWs and their projected changes52,57,142,150.
Figure 4 illustrates the effect of sampling uncertainty on MHW characteristics during the historicalperiod 1950-2020, obtained from the 100-member CESM2 SMILE52, after the forced signal isremoved. The simulated composite MHW intensity (Fig. 4a) and duration (Fig. 4g) metrics basedon all ensemble members mask the considerable range found across individual realizations (Fig. 4
d-f and j-l), underscoring the need for SMILEs to guard against sampling uncertainty. Since thesingle observational record (Fig. 4b, h) provides a limited sample size of extreme events, it may
be challenging to separate true model biases from apparent biases stemming from inadequate
sampling. One approach is to assess whether the characteristics of the single observed composite
MHW lie outside the plausible (5th-95th percentile) range across SMILE members, in which casethe model shows a likely bias (Fig. 4c, i). ESMs are also used for seasonal predictions of MHWs
and other biogeochemical extremes. Thus, the fidelity of models in accurately simulating suchextremes is critical for assessing the reliability of their predictions.

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Prediction of MHWs and associated biogeochemical extremes
Understanding MHW predictability and building effective prediction systems can greatly benefit
marine management21. For example, accurate seasonal predictions of MHWs have the capacity totransform resource management practices that affect ecosystem services such as fisheries,
aquaculture, and tourism21,43. Motivated by many potential benefits, recent research has quantified
subseasonal-to-seasonal MHW predictability and forecast skill using dynamical and statistical
approaches41,42,151-153.
Forecast systems based on global climate models have been used to estimate MHW probabilistic
forecast skill and errors by comparing initialized hindcasts (i.e. retrospective forecasts) with theactual evolution of historical temperature anomalies. Results indicate that, for many open-ocean
regions, these dynamical forecast systems are capable of skillfully predicting MHW onset,
intensity, and duration several months in advance in both the surface and subsurface
ocean42,151,152. Forecast skill, quantified by the correlation of ensemble-mean SST with that of theobservations, is generally higher in the tropical and northeast extratropical Pacific, beating the skill
associated with statistical (damped persistence) forecasts42,108,152. MHW forecast skill is also
higher in the subsurface (0-40 m) than the surface when compared to a reanalysis product, thoughsubsurface skill outside the tropics is primarily due to persistence151.
While in some cases dynamical forecast systems can produce skillful predictions of MHWs
multiple months in advance, this is not always true. For example, 9-month lead forecasts initializedin July 1997 predicted an elevated likelihood of surface ocean MHW occurrence in the eastern
Tropical Pacific, Gulf of Alaska, California Current, subtropical Atlantic and Indian Oceans, and
the Pacific sector of the Southern Ocean during March 1998 (Figure 5a). However, dynamical 8month lead forecasts initialized in March 2013 predicted low probability of surface ocean MHWs
nearly everywhere in the global ocean for November 2013 (Figure 5b). The observed SST
anomalies in March 1998 (Figure 5c) and November 2013 (Figure 5d) indicate that the forecasts
generated in July 1997 were more accurate than the March 2013 forecasts. The accuracy of theJuly 1997 initialized forecast is primarily due to the development of the 1997/1998 El Niño event,as ENSO predictability imparts prediction skill to initialized forecasts of MHWs21,42. The March2013 initialized forecast provides little indication of the development of the Blob in late-201383,154.
This comparison highlights the difficulties in forecasting MHWs that are driven by stochastic
atmospheric processes, like the Blob153, or energized by modes of variability not accurately
captured by the models. On the other end, surface and subsurface MHWs that are associated with
ENSO variability and/or oceanic teleconnections may be predictable several months inadvance21,42,108,151.
Statistical MHW forecasts may have similar skill as forecasts from dynamical models, while
requiring substantially less computational resources. McAdam et al. (2023)151 showed that asimple statistical persistence forecast can skillfully predict the number of subsurface MHW days
one season in advance in approximately half of the ocean, but it underestimates the number of
events compared to the reanalysis product used as validation. More complex statistical models,
including empirical-dynamical models such as Linear Inverse Models (LIMs), can be used to probe
sources of predictive skill for particular regions or events. For example, LIM-based studies
showed that a decadal mode of variability was a precursor for MHW growth in the Northeast

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Pacific Blob region41, and that predictability of MHWs off Western Australia was enhanced up to
20 months in advance by the presence of a positive Indian Ocean Dipole155.
ESM dynamical forecast systems display promising levels of forecast skill for surface andsubsurface biogeochemical properties affected by MHWs, such as oxygen, acidity, or
productivity156-158. Recent studies have explored dynamical forecast skill of ocean biogeochemical
extremes. For example, Mogen et al. (2024)108 showed that a coupled model produces skillfulforecasts of OAX events associated with aragonite saturation state anomalies, at lead times of up
to twelve months in some regions, and further identify ENSO events as playing a key role inpredicting this type of extremes. Such findings inspire efforts to include biogeochemical
predictions in operational forecasting systems for MHWs.
Marine heatwaves in a changing climate
The ocean has stored more than 91% of the excess heat159 that has accumulated in the Earth System
due to human-induced increases in greenhouse gases, resulting in widespread ocean warming (Fig.
6a). Such slow background warming exacerbates naturally-occurring temperature excursions,
resulting in increased frequency, intensity and duration of extreme SST events. Indeed, attribution
studies have shown that the majority of the most impactful MHWs worldwide over recent decades
could not have occurred without the influence of global warming50,57,70. In the presence of the
global warming trend, climate models project large increases in the frequency, intensity, durationand spatial extent of warm temperature extremes, with the magnitude of the increase becoming
progressively larger at higher warming levels57.
In addition to the long-term ocean warming trend, climate change can also affect ocean extremes
through changes in variability. An increase in mean SSTs, relative to, for example, to pre-industrial
levels or early historical periods, will result in a shift of the probability density function (PDF)
toward larger values, enhancing the likelihood of more severe events (Fig. 6c-e). Changes invariability, however, alter the PDF’s width, which can also affect the probability of SST extremes(Fig. 6c-e). Moreover, changes in internal SST variability may be asymmetric, and lead toincreased probabilities for either warm or cold extremes (Fig. 6d). The relative role of the warming
trend vs. anthropogenically-induced changes in internal variability in MHW statistics varies
geographically51,52, with the long-term warming trend often accounting for more than 90% of thetotal changes46,50-52,57,142,160, as illustrated for CESM2 in Fig. 6b. Exceptions are the Arctic, whereinternal variability can account for 30-40% of MHW intensity changes, and the Northeast Atlantic,
with values up to 80% (Fig. 6b). While separating the effects of the temperature trend and internalvariability on MHW characteristics is critical for the mechanistic understanding of MHWs, andfor assessing events’ predictability, this separation is challenging. The climate change trend may
be nonlinear51,79, and failure to accurately account for such nonlinearity may result in an apparentchange in internal variability51. Approaches used to estimate the forced trend in observations for
MHW studies include the use of univariate50 or multivariate51,161,162 statistical approaches, while
SMILEs can be used in the modeling context.
There are several ways by which anthropogenic forcing can alter internal climate variability.
Mixed layer shoaling may occur with global warming98,150, resulting in increased mixed-layer
temperatures for the same level of atmosphere-to-ocean heat exchange. The projected increase in

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upper-ocean stratification163 and ocean heat content160 can alter the characteristics of key largescale drivers of MHWs. For example, increased stratification in the equatorial Pacific has been
related to future enhancements of ENSO amplitude in several climate models164, while in the extratropics, stronger stratification will result in faster oceanic Rossby waves and shorter adjustment
processes, potentially leading to reduced growth and predictability of decadal modes of variability
like the PDO165. In addition, changes in extra-tropical atmospheric circulation variability driven
by internal atmospheric dynamics and by teleconnections from changing ENSO behavior, could
alter MHW characteristics through impacts on air-sea heat and momentum exchange166. Dramaticchanges in Arctic sea-ice coverage and amplified Arctic warming may have been responsible forthe changes in atmospheric circulation and Northeast Pacific surface heat fluxes that led to theunprecedented MHWs in that region in recent decades167. The reduction in sea-ice will also resultin an increase in MHW activity near the marginal ice zone52,123.
Changes in ENSO characteristics are particularly critical for future MHWs. Consistent with theobserved association between El Niño events and the enhanced likelihood of MHW
occurrence40,65, multi-model large ensembles project a significant reduction in MHW areal
coverage, intensity and duration during ENSO-neutral periods relative to all periods, when themean warming component is removed52. Thus, changes in ENSO variability can be expected tosignificantly influence the statistics of MHWs in the future, highlighting the critical need of
constraining the spread in expected ENSO changes168, and achieving more reliable future
projections.
Summary and Future Perspectives
MHWs are an active and fast evolving area of research, and significant progress has been made in
the last few years. Definitions of MHWs have been critically re-evaluated to best characterize these
events and their drivers in the presence of the climate change trend, and approaches have beendeveloped that incorporate spatial dimensions and time evolution25,54,55. On the observational side,
novel ideas for integrated observing systems capable of providing the three-dimensional structureof MHWs in near real-time are now emerging (Box 2). Additional advances include a deepened
understanding of local and remote drivers of MHWs41,111,119,169,170, explorations of subsurface
MHWs and their possible structures, investigations of land-ocean and physical-biogeochemicalcompound events, future projections of MHWs and related uncertainty52, and evolving efforts in
MHW prediction42,108,151,152. Yet, to achieve a more robust assessment of MHW predictability,
additional investigations are needed to better understand large-scale drivers of MHWs in different
regions and during different seasons, and their interplay in altering local processes responsible for
MHW growth, evolution and persistence. MHW definitions should also be extended to reflect
MHW mechanisms, and allow event characterization based on their primary drivers.
A key question for MHW prediction and projection, is whether climate models currently used forseasonal predictions and for future projections can realistically simulate MHW mechanisms
beyond basic, local surface statistics (like frequency, intensity and duration). For example, canmodels simulate events similar to the most prominent and impactful MHWs in the historical
record? Are these events driven by the same local and remote influences as in nature? Do they
have similar subsurface characteristics? Assessing models’ fidelity in simulating modes ofvariability that can influence MHW development is also critical. Given the strong association

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between ENSO events and MHW occurrences40,65,91, the reliability of simulated MHWs in bothpresent and future scenarios strongly depend on the models’ ability to realistically simulate ENSO.
However, ENSO representations in climate models still show significant biases, and its future
projections vary significantly across models168, calling for an in-depth understanding of model
differences and biases, and concerted efforts toward model improvement.
In order to provide forecasts that are useful for stakeholders, greater focus is needed on higher
resolution global and regional models, that are able to resolve processes occurring on the shelf or
at scales relevant for coastal topography (e.g., embayments, fjords, coral atolls, etc.). Regionalmodels, which are currently under development for some regions171, should include
biogeochemistry, and be used for prediction and projections applications. The availability ofobservations at these scales is also critical for model development and validation.
While many studies have discussed MHW impacts, this area of research is still evolving. For
example, some long-lasting MHW impacts are just emerging, like the decline of the humpback
whales in the North Pacific since 2014, attributed to loss of prey after the 2014-16 MHW172.
Conversely, other research suggests that MHWs are not a dominant driver of change in demersal
fishes over the recent decades87. Aspects in need of further research include: 1) influence of
MHWs on local atmospheric extremes, like atmospheric rivers and heatwaves; 2) connections
between temperature extremes and oceanic biogeochemical extremes32,132; and 3) long-term andcumulative consequences of MHWs on marine life across trophic levels, as well as assessment ofrecovery times in different regions. Also, given the reported impact of MHWs on air-sea CO2
fluxes69 and their association with cloud feedback in some regions173, a deeper exploration of
possible MHW influences on the Earth’s carbon and radiation budgets may be important.
Observations of physical, biogeochemical and ecological quantities, at the surface and especiallyin the ocean subsurface, are key to all of the above aspects of MHW research. In particular, they
are needed to constrain ocean reanalyses and assess model performance. Sustained observations,both globally (e.g., satellite) or regionally (e.g., moorings), in conjunction with ocean reanalyses
and long time series from paleo reconstructions or empirical models, are necessary to monitor
long-term changes in ocean properties and robustly assess the statistics of extreme warm events
around those long-term changes. On the other hand, systems that can be rapidly deployed for realtime monitoring (Box 2), provide not only a comprehensive characterization of individual events,
but also immediate guidance to decision-makers. Ideally, such systems should be developed inother regions where prominent MHW events tend to occur.
Enhanced understanding of MHWs and their impacts is essential to guide and support adaptationand mitigation strategies. The tremendous level of ecological, economical and societal losses
resulting from these ocean extremes calls for urgent actions to drastically reduce greenhouse gas
emissions in order to limit the devastating consequences of climate change.

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Box 1. Historical marine heatwaves and the unprecedented summer of 2023
Recent decades have witnessed the occurrence of MHWs that were particularly intense, longlasting and impactful (top panel of Box figure, showing SST anomalies above 1°C at the peakmonth of each MHW). These most prominent MHWs generally occurred in different regions at
different times. However, the boreal summer of 2023 recorded global monthly-mean SSTs atrecord high since the beginning of the instrumental record174, with a large fraction of the ocean
experiencing extreme conditions, as illustrated by the widespread SST anomalies76 above the 90th
percentile (1982-2011 baseline) during July 2023 (bottom panel of Box figure). In particular,
average North Atlantic (0°-60°N, 0°-80°W) temperatures reached levels of warming that exceeded
four standard deviations of the 1980-2011 period during parts of July and September 2023175, withan annual average ~0.23°C higher than in 2022176.
What caused this unprecedented warming? The developing El Niño in 2023 can be expected tohave caused an increase in radiative heating due to the influence of the El Niño SST pattern on
atmospheric static stability and low-level clouds177. In addition, El Niño can alter the atmosphericcirculation and cause the development of SST anomalies in different regions of the world, like thenortheast Pacific109 and the tropical North Atlantic178, although warming in the tropical North
Atlantic usually occurs after the peak of an El Niño event rather than during its development phase.
The pattern of Atlantic warming is consistent with the negative phase of the North Atlantic
Oscillation179, which was indeed strongly negative from mid-April to mid-May and most of July
2023. The concentration of the 2023 warming in near-surface waters176 suggests that upper ocean
stratification, possibly modulated by large-scale climate modes, may have played an important rolein preventing the excess heat absorbed by the ocean from being effectively distributed downward,
resulting in enhanced surface warming. Other hypotheses regarding the unprecedented 2023
warming include a decreased transport of Saharan dust to the western Atlantic, and a reduction ofship emissions following a 2020 international agreement, leading to an increase in radiative
forcing180, although the influence of these factors on Atlantic warming has yet to be demonstrated.
Another hypothesis pertains to the aftermath of the January 2022 Hunga Tonga-Hunga Ha’apai
volcanic eruption in Tonga181. This eruption emitted aerosols, which had cooling effects, while
simultaneously releasing stratospheric water vapor, which had warming effects. However, thesefactors are estimated to explain, at most, a marginal net cooling of a few hundredths of a degree,
rather than a warming181. In addition to these mostly natural drivers, the ocean is estimated to haveabsorbed about 91% of the excess heat associated with global warming159, causing an averagewarming of the upper 2000m of the global ocean of ~6.6 1021J/year over 1958-2023176. Thus, it isvery likely that climate change has contributed to the intensity and widespread coverage of the2023 MHWs.

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Box 2. An Integrated Observing System for Monitoring MHWs
Near real time ocean temperature observations and readily accessible visualizations are critical formonitoring MHWs, as they enable responsive activities such as local sampling for research andmanagement. Gliders provide near real time data for a suite of oceanographic variables important
for assessing MHW impacts on the marine environment. In Australia, ocean gliders have beenused to sample the water column during a MHW over several weeks, through Australia’s
Integrated Marine Observing System (IMOS) Event Based Sampling sub-facility (panel d of Box
figure). In addition, combining different types of temperature observations relative toclimatologies over the same reference period offers a comprehensive view of a temporally andspatially evolving MHW182. We illustrate the diversity of temperature observations used to monitor
a MHW in the Box figure for the 2020 Great Barrier Reef MHW off northeast Australia.
Over the whole Great Barrier Reef, the MHW intensity peaked on 19 February 2020183,184. On thatday, extremely warm surface waters encompassed a wide extent of the Great Barrier Reef andCoral Sea, based on sea surface temperature (SST) percentiles relative to the 1992-2016
climatological distributions for that day of the year 185,186 (panel a of the Box figure, also indicating
the location of other near real time measuring platforms on that day). Several types of monitoring
platforms collected temperature data during February 2020 (Panel b of Box figure). The AustralianInstitute of Marine Science (AIMS) R/V Cape Ferguson measured near-surface water temperatures
between 7 February 2020 (north) and 26 February 2020 (south)187 (small circles shaded with thetemperature values). These near-surface temperature measurements were complemented by thosefrom Argo floats (larger circles in panel b, also shaded according to the temperature values), witha pink outline marking the position of one Argo float (5905849) on 20 February 2020188,189. The
Argo float near -15.4°N, 147.9°E in the Coral Sea also provided subsurface profiles (shown for
the upper 1000 m in panel c of the Box figure).
A glider (GBR20200123) was deployed through IMOS Event Based Sampling to monitor the
MHW over the shelf190. The glider traversed the continental shelf and slope from 23 January 2020
(north) to 24 February 2020 (south), as indicated by the colored (by temperature) track in panel b,measuring subsurface temperature (panel d, same colorbar as in panel b) and a suite of biophysicalvariables. The glider location on 19 February is indicated by the square with the pink outline (panelb). Finally, the IMOS National Reference Station Yongala (labeled NRSYON in panel b) provided
near real time and delayed-mode near-surface temperatures, displayed in panel e of the Box figure
relative to 1 January 2020. These measurements during the MHW could be compared against thetemperatures recorded at this station from 19 September 2013 to 31 January 2024191,192, indicating
the importance of sustained observations for detecting extremes. These datasets werecomplemented by in situ water temperature data collected from stationary sources (indicated inpanel b) at other IMOS moorings, AIMS reef weather stations193 and coral reef sites194. Together,
these temperature measurements provided a comprehensive dataset for assessing the MHW’s
characteristics and impacts during the 2020 GBR mass coral bleaching event.

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Figuresa) SSTa July 2022 with trend
SSTA (oC)b) With trend
90th percentile

Baselinec) SSTa July 2022 detrended
SSTA (oC)d) Detrended
90th percentile

Baseline
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Figure 1. Influence of trends on marine heatwave definition. a) SST anomaly (SSTA, °C) relative to the
1982-2011 climatology over the Mediterranean during July 2022 (dashed line in b) from NOAAOISSTv2.176. Anomalies include the trend signal. b) SST anomalies averaged over the western
Mediterranean (the region bounded by the black lines and the coast, i.e., north of 40°N, and west of 12°E).
c) As in a), but with SST anomalies linearly detrended. d) as in b), but with anomalies linearly detrended.
In b) and d), the thick blue horizontal line indicates the baseline period used to compute the climatology
and to define the 90th percentile (thin horizontal blue lines), which for simplicity is chosen to be seasonallyindependent. In the presence of a trend, the western Mediterranean tends to be in a quasi-permanent MHW
state toward the end of the record, while removal of the trend highlights isolated events since 2015, andalso reveals more pronounced extreme events at the beginning of the record. The MHW in the boreal
summer of 2003 emerges as an extremely intense event, irrespective of the presence of a trend signal.

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Figure 2. Vertical structures of MHWs. a)-d) Possible vertical structures of MHWs near the shelf,
including: “shallow” MHWs which do not penetrate below the mixed layer (a); “Bottom” intensified events
due to a downwelling thermocline near the bottom, due, for example to alongshore winds, as illustrated for
the Southern Hemisphere (b); “Extended” profiles from the surface to the bottom due to intrusion of warm
eddies or western boundary meanders into the shelf (c) or due to warm alongshore advection (d). e)-h)Temporal evolution of subsurface MHWs associated with changes in upper-ocean mixing for shallow
events (e); propagation of oceanic Rossby waves causing variations in thermocline depth (f); persistence ofdeep anomalies with no surface signature due to mixed layer shoaling (g); and re-emergence of deep
anomalies at the surface when the mixed layer deepens (h).The subsurface structure of MHWs depends on
the processes involved in their formation, as well as the region’s stratification and circulation.

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Figure 3. Near-Surface biogeochemical anomalies and compound conditions during some impactful
MHWs. The biogeochemical quantities are shown for the month and the area of the MHWs displayed in
the top panel of Box1’s figure. The footprint of those MHWs is indicated by grey lines. a) Percentile
associated with the mean chlorophyll anomaly during the MHWs, compared to the local empirical
distribution of chlorophyll monthly anomalies from 1998 to 2018. b) Percentile associated with the mean
[H+] anomaly during the MHWs, compared to the local empirical distribution of [H+] monthly anomalies
from 1982 to 2019, based on observationally-derived data142. c) Extent of the MHWs co-occurring with alow chlorophyll extreme event (MHW-LChl, in blue), a high acidity event (MHW-OAX, in red), and both
(MHW-LChl-OAX, in yellow). LChl events are defined as events with chlorophyll anomaly percentiles on
panel (a) lower than their 10th percentile, and OAX events as events with [H+] anomaly percentiles
exceeding their 90th percentile. The chlorophyll data, corresponding to the mean chlorophyll concentration
within the mixed layer, are obtained from the NASA Ocean Biogeochemical Model reconstruction195, andare publicly available for 1998-2021(https://gmao.gsfc.nasa.gov/gmaoftp/rousseaux/Carlos/NOBM/).

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Figure 4. Fidelity in climate models’ representation of MHW statistics. (a-f) Composite MHW intensity(ºC) and (g-l) composite MHW duration (months) during 1950-2020 from the 100-member of theCommunity Earth System Model version 2 (CESM2) SMILE and observations (ERSSTv5)196. (a, g)
Ensemble average; (b, h) Observations; (c, i) Ensemble average minus Observations; (d, j) Ensemble
maximum; (e, k) Ensemble minimum; (f, l) Ensemble maximum minus minimum. Gray shading in (c, i)
indicates that observations lie within the 5th-95th percentile range of the CESM2 Large Ensemble. Adaptedfrom Deser et al. (2024)52.

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Figure 5. Dependence of MHW forecast skill on El Niño. (a-b) Forecasted MHW probabilities fortwo periods: (a) March 1998, and (b) November 2013, based on probabilistic forecasts of linearly
detrended anomalies from the North American Multimodel Ensemble197 initialized 8.5 months prior(i.e., July 1997 and March 2013, respectively). White contour indicates 30% probability of occurring
MHW conditions. (c-d) Observed monthly SST anomalies (°C) for the two forecasted periods. Black
contours indicate observed MHW conditions. Adapted from (Jacox et al. 2022)42.

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Figure 6. Projected changes in MHWs in one climate model Large Ensemble. a) Changes in mean SST(2070-2100 minus 1970-2000) based on the ensemble mean of the CESM2 large ensemble according to the
SSP370 scenario. b) Changes in composite MHW intensity (2070-2100 minus 1970-2000) due to internalvariability divided by the intensity changes due to both changes in variability-plus-mean state. (c-e)
Histograms of area-averaged SST (°C) from the CESM2 large ensemble for (c) Arctic (poleward of 67°N),(d) western tropical Pacific (8°S–6°N, 155°E–175°W), and (e) northeast Atlantic (35°–62°N, 30°–0°W)
based on all months from all 100 ensemble members during 1970–2000 (gray) and 2070–2100 (blue) after
removing the ensemble-mean climatological seasonal cycle for each period. The regions considered forcomputing the histograms are shown by the boxes in panel b). Purple histograms are the same as the blue
histograms but with the mean state change (2070–2100 minus 1970–2000) added back in. The 10th and90th percentiles of each distribution are shown as vertical solid lines, and the 50th percentile is shown as avertical dashed line. The number in the upper right of panel a) indicates the global mean ocean temperature
difference (°C), while the number in the upper right of panel b) indicates the fractional area (%) of values
in the range -0.1 and +0.1. (Adapted from Deser et al. 2024)52.

Competing interests
The authors declare no competing interests.
Acknowledgements
This paper is a product of the CLIVAR “Marine Heatwaves in the Global Ocean” Research Focus
Group. The authors thank CLIVAR for their sponsorship and the CLIVAR Program Office for theirsupport. A.C. was supported by the NOAA Climate Program Office’s CVP and MAPP programs,
DOE Award #DE-SC0023228, and NASA Physical Oceanography grant #80NSSC21K0556.
N.S.L. was supported by U.S. National Science Foundation (OCE-1752724) and the NOAA CVP
program (NA20OAR4310405). CM acknowledges support from Proyecto ANID Fondecyt código
3200621, and Data Observatory Foundation ANID Technology Center No. DO210001. TLF
acknowledges funding from AtlantECO (project number: 862923) as well as TipESM, which are

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both funded by the European Union. Views and opinions expressed are however those of theauthor(s) only and do not necessarily reflect those of the European Union or the European Climate,
Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the
granting authority can be held responsible for them. NJH acknowledges support from the ARC
Centre of Excellence for Climate Extremes (CE170100023) and the National Environmental
Science Program Climate Systems Hub. NCAR is sponsored by the National Science Foundation
under Cooperative Agreement 1852977. We thank Adam S. Phillips (NCAR) for helping with the
preparation of Figs. 4 and 6. Argo data were collected and made freely available by theInternational Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu,
https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System.
Data was sourced from Australia's Integrated Marine Observing System (IMOS) - IMOS is enabledby the National Collaborative Research Infrastructure strategy (NCRIS).
Author contributions
A.C. and R.R.R. conceived the study with input from all the authors. A.C., A. S., J.A.B., R.R.R,
T.L.F., C.D., and N.S.L. coordinated the writing of various sections. A.C., A.S., J.A.B., N.L., T.X.,
N.S.L., D.J.A., and C.D. contributed to the preparation of the figures. All authors contributed tothe discussion and interpretation of the material and assisted with the writing of the manuscript,
led by A.C.
References
1
2
3
4
5
6

7
8

Smith, K. E. et al. Socioeconomic impacts of marine heatwaves: Global issues andopportunities. Science 374, eabj3593 (2021). https://doi.org:doi:10.1126/science.abj3593
Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nature
Communications 9, 650 (2018). https://doi.org:10.1038/s41467-018-03163-6
Smith, K. E. et al. Global impacts of marine heatwaves on shallow coastal foundation
species. Nature Communications, Accepted (2024 ).
Pearce, A. et al. The “Marine Heat Wave” Off Western Australia during the Summer of
2010/11. Department of Fisheries, Western Australia 222, 36pp (2011).
Pearce, A. F. & Feng, M. The rise and fall of the “marine heat wave” off Western Australia
during the summer of 2010/2011. Journal of Marine Systems 111-112, 139-156 (2013).
https://doi.org:https://doi.org/10.1016/j.jmarsys.2012.10.009
Saranya, J. S., Roxy, M. K., Dasgupta, P. & Anand, A. Genesis and Trends in Marine
Heatwaves Over the Tropical Indian Ocean and Their Interaction With the Indian Summer
Monsoon. Journal of Geophysical Research: Oceans 127, e2021JC017427 (2022).
https://doi.org:https://doi.org/10.1029/2021JC017427
Singh, V. K., Roxy, M. K. & Deshpande, M. Role of warm ocean conditions and the MJO
in the genesis and intensification of extremely severe cyclone Fani. Scientific Reports 11,
3607 (2021). https://doi.org:10.1038/s41598-021-82680-9
Mawren, D., Hermes, J. & Reason, C. J. C. Marine heat waves and tropical cyclones - Twodevastating types of coastal hazard in South-eastern Africa. Estuarine, Coastal and Shelf
Science 277, 108056 (2022). https://doi.org:https://doi.org/10.1016/j.ecss.2022.108056

24

926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970

9
10
11
12
13
14
15

16
17
18
19
20
21
22
23
24

Rathore, S. et al. Interactions Between a Marine Heatwave and Tropical Cyclone Amphanin the Bay of Bengal in 2020. Frontiers in Climate 4 (2022).
https://doi.org:10.3389/fclim.2022.861477
Roxy, M. K. et al. in The Indian Ocean and its Role in the Global Climate System (eds
Caroline C. Ummenhofer & Raleigh R. Hood) 469-482 (Elsevier, 2024).
Jones, T. et al. Massive Mortality of a Planktivorous Seabird in Response to a Marine
Heatwave.
GeophysicalResearch
Letters
45,
3193-3202(2018).
https://doi.org:https://doi.org/10.1002/2017GL076164
Garrabou, J. et al. Marine heatwaves drive recurrent mass mortalities in the Mediterranean
Sea.
GlobalChangeBiology28,
5708-5725(2022).
https://doi.org:https://doi.org/10.1111/gcb.16301
Smith, K. E. et al. Biological Impacts of Marine Heatwaves. Annual Review of Marine
Science 15, 119-145 (2023). https://doi.org:10.1146/annurev-marine-032122-121437
Eakin, C. M., Sweatman, H. P. A. & Brainard, R. E. The 2014–2017 global-scale coralbleaching
event:
insights
andimpacts.
Coral
Reefs
(2019).
https://doi.org:https://doi.org/10.1007/s00338-019-01844-2
Moriarty, T., Leggat, W., Heron, S. F., Steinberg, R. & Ainsworth, T. D. Bleaching,mortality and lengthy recovery on the coral reefs of Lord Howe Island. The 2019 marine
heatwave suggests an uncertain future for high-latitude ecosystems. PLOS Climate 2,e0000080 (2023). https://doi.org:10.1371/journal.pclm.0000080
Filbee-Dexter, K. et al. Marine heatwaves and the collapse of marginal North Atlantic kelp
forests. Scientific Reports 10, 13388 (2020). https://doi.org:10.1038/s41598-020-70273-x
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of
ecosystem
services.
NatureClimateChange9,306-312(2019).
https://doi.org:10.1038/s41558-019-0412-1
Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global
biodiversity
hotspot.
NatureClimateChange3,78-82(2013).
https://doi.org:10.1038/nclimate1627
Stuart-Smith, R. D., Brown, C. J., Ceccarelli, D. M. & Edgar, G. J. Ecosystem restructuring
along the Great Barrier Reef following mass coral bleaching. Nature 560, 92-96 (2018).
https://doi.org:10.1038/s41586-018-0359-9
Cheung, W. W. L. et al. Marine high temperature extremes amplify the impacts of climatechange on fish and fisheries. Science Advances 7, eabh0895 (2021).
https://doi.org:doi:10.1126/sciadv.abh0895
Holbrook, N. J. et al. Keeping pace with marine heatwaves. Nature Reviews Earth &Environment 1, 482-493 (2020). https://doi.org:10.1038/s43017-020-0068-4
Oliver, E. C. J. et al. Marine Heatwaves. Annual Review of Marine Science 13, 313-342(2021). https://doi.org:10.1146/annurev-marine-032720-095144
Schaeffer, A. & Roughan, M. Subsurface intensification of marine heatwaves off
southeastern Australia: The role of stratification and local winds. Geophysical Research
Letters 44, 5025-5033 (2017). https://doi.org:https://doi.org/10.1002/2017GL073714
Schaeffer, A., Sen Gupta, A. & Roughan, M. Seasonal stratification and complex local
dynamics control the sub-surface structure of marine heatwaves in Eastern Australiancoastal waters. Communications Earth & Environment 4, 304 (2023).
https://doi.org:10.1038/s43247-023-00966-4

25

971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015

25
26
27
28
29
30
31

32
33
34
35
36
37
38
39
40

Sun, D., Li, F., Jing, Z., Hu, S. & Zhang, B. Frequent marine heatwaves hidden below thesurface of the global ocean. Nature Geoscience 16, 1099-1104 (2023).
https://doi.org:10.1038/s41561-023-01325-w
Fragkopoulou, E. et al. Marine biodiversity exposed to prolonged and intense subsurface
heatwaves. Nature Climate Change 13, 1114-1121 (2023). https://doi.org:10.1038/s41558023-01790-6
Zhang, Y., Du, Y., Feng, M. & Hobday, A. J. Vertical structures of marine heatwaves.
Nature Communications 14, 6483 (2023). https://doi.org:10.1038/s41467-023-42219-0
Reed, D. et al. Extreme warming challenges sentinel status of kelp forests as indicators ofclimatechange.
NatureCommunications
7,13757(2016).
https://doi.org:10.1038/ncomms13757
Jackson, J. M., Johnson, G. C., Dosser, H. V. & Ross, T. Warming From Recent Marine
Heatwave Lingers in Deep British Columbia Fjord. Geophysical Research Letters 45,
9757-9764 (2018). https://doi.org:https://doi.org/10.1029/2018GL078971
Yao, Y. & Wang, C. Variations in Summer Marine Heatwaves in the South China Sea.
Journal of Geophysical Research: Oceans 126, e2021JC017792 (2021).
https://doi.org:https://doi.org/10.1029/2021JC017792
Koehlinger, J. A., Newton, J., Mickett, J., Thompson, L. & Klinger, T. Large and transient
positive temperature anomalies in Washington’s coastal nearshore waters during the 2013–2015 northeast Pacific marine heatwave. PLOS ONE 18, e0280646 (2023).
https://doi.org:10.1371/journal.pone.0280646
Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes andcompoundeventsintheocean.
Nature600,
395-407(2021).
https://doi.org:10.1038/s41586-021-03981-7
Hauri, C. et al. More Than Marine Heatwaves: A New Regime of Heat, Acidity, and Low
Oxygen Compound Extreme Events in the Gulf of Alaska. AGU Advances 5,
e2023AV001039 (2024). https://doi.org:https://doi.org/10.1029/2023AV001039
Han, W. et al. Sea level extremes and compounding marine heatwaves in coastal Indonesia.
Nature Communications 13, 6410 (2022). https://doi.org:10.1038/s41467-022-34003-3
Martinez-Villalobos, C., Dewitte, B., Garreaud, R. G. & Loyola, L. Extreme coastal El
Niño events are tightly linked to the development of the Pacific Meridional Modes. npj
Climate and Atmospheric Science, In press (2024).
Rodrigues, R. R., Taschetto, A. S., Sen Gupta, A. & Foltz, G. R. Common cause for severe
droughts in South America and marine heatwaves in the South Atlantic. Nature Geoscience12, 620-626 (2019). https://doi.org:10.1038/s41561-019-0393-8
Dzwonkowski, B. et al. Compounding impact of severe weather events fuels marine
heatwave in the coastal ocean. Nature Communications 11, 4623 (2020).
https://doi.org:10.1038/s41467-020-18339-2
Pathmeswaran, C., Sen Gupta, A., Perkins-Kirkpatrick, S. E. & Hart, M. A. Exploring
Potential Links Between Co-occurring Coastal Terrestrial and Marine Heatwaves inAustralia. Frontiers in Climate 4 (2022). https://doi.org:10.3389/fclim.2022.792730
Hobday, A. J. et al. With the arrival of El Nino, prepare for stronger marine heatwaves.
Nature 621, 38-41 (2023). https://doi.org:https://doi.org/10.1038/d41586-023-02730-2
Holbrook, N. J. et al. A global assessment of marine heatwaves and their drivers. Nature
Communications 10, 2624 (2019). https://doi.org:10.1038/s41467-019-10206-z

26

1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060

41

42
43

44
45
46
47
48
49

50
51
52
53
54
55

Capotondi, A., Newman, M., Xu, T. & Di Lorenzo, E. An Optimal Precursor of Northeast
Pacific Marine Heatwaves and Central Pacific El Niño Events. Geophysical Research
Letters49,
e2021GL097350(2022).
https://doi.org:https://doi.org/10.1029/2021GL097350
Jacox, M. G. et al. Global seasonal forecasts of marine heatwaves. Nature 604, 486-490(2022). https://doi.org:10.1038/s41586-022-04573-9
Hartog, J. R., Spillman, C. M., Smith, G. & Hobday, A. J. Forecasts of marine heatwaves
for marine industries: Reducing risk, building resilience and enhancing management
responses. Deep Sea Research Part II: Topical Studies in Oceanography 209, 105276(2023). https://doi.org:https://doi.org/10.1016/j.dsr2.2023.105276
Pilo, G. S., Holbrook, N. J., Kiss, A. E. & Hogg, A. M. Sensitivity of Marine Heatwave
Metrics to Ocean Model Resolution. Geophysical Research Letters 46, 14604-14612(2019). https://doi.org:https://doi.org/10.1029/2019GL084928
Hayashida, H., Matear, R. J., Strutton, P. G. & Zhang, X. Insights into projected changes
in marine heatwaves from a high-resolution ocean circulation model. Nature
Communications 11, 4352 (2020). https://doi.org:10.1038/s41467-020-18241-x
Guo, X. et al. Threat by marine heatwaves to adaptive large marine ecosystems in an eddyresolving
model.
NatureClimateChange12,
179-186(2022).
https://doi.org:10.1038/s41558-021-01266-5
Bian, C. et al. Oceanic mesoscale eddies as crucial drivers of global marine heatwaves.
Nature Communications 14, 2970 (2023). https://doi.org:10.1038/s41467-023-38811-z
Collins, M. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing
Climate (eds H.-O. Portner et al.) Ch. Extremes, Abrupt Changes and Managing Risk, pp.
589–655 (Cambridge University Press, 2019).
Foc-Kemper, B. et al. in Climate Change 2021: The Physical Science Basis. Contributionof Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change (eds V. Masson-Delmotte et al.) Ch. Ocean, Cryosphere and Sea Level
Change., pp. 1211–1362 (Cambridge University Press, 2021).
Oliver, E. C. J. et al. Projected Marine Heatwaves in the 21st Century and the Potential for
Ecological
Impact.
FrontiersinMarineScience
6(2019).
https://doi.org:10.3389/fmars.2019.00734
Xu, T. et al. An increase in marine heatwaves without significant changes in surface ocean
temperature
variability.
NatureCommunications
13,
7396(2022).
https://doi.org:10.1038/s41467-022-34934-x
Deser, C. et al. Future Changes in the Intensity and Duration of Marine Heat and Cold
Waves: Insights from Coupled Model Initial-Condition Large Ensembles. Journal of
Climate 37, 1877-1902 (2024). https://doi.org:https://doi.org/10.1175/JCLI-D-23-0278.1
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Progress in
Oceanography141,
227-238(2016).
https://doi.org:https://doi.org/10.1016/j.pocean.2015.12.014
Sun, D., Jing, Z., Li, F. & Wu, L. Characterizing global marine heatwaves under a spatiotemporal framework. Progress in Oceanography 211, 102947 (2023).
https://doi.org:https://doi.org/10.1016/j.pocean.2022.102947
Scannell, H. A. et al. Spatiotemporal evolution of marine heatwaves globally. ESS Open
Archive (July 23, 2023). https://doi.org:10.22541/essoar.169008275.57053412/v1

27

1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106

56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73

Perkins, S. E. & Alexander, L. V. On the Measurement of Heat Waves. Journal of Climate
26, 4500-4517 (2013). https://doi.org:https://doi.org/10.1175/JCLI-D-12-00383.1
Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming.
Nature 560, 360-364 (2018). https://doi.org:10.1038/s41586-018-0383-9
Xu, T., Newman, M., Capotondi, A. & Di Lorenzo, E. The Continuum of Northeast Pacific
Marine Heatwaves and Their Relationship to the Tropical Pacific. Geophysical Research
Letters 48, 2020GL090661 (2021). https://doi.org:https://doi.org/10.1029/2020GL090661
Amaya, D. J., Miller, A. J., Xie, S.-P. & Kosaka, Y. Physical drivers of the summer 2019
North Pacific marine heatwave. Nature Communications 11, 1903 (2020).
https://doi.org:10.1038/s41467-020-15820-w
Cael, B. B., Burger, F. A., Henson, S. A., Britten, G. L. & Frölicher, T. L. Historical andfuture maximum sea surface temperatures. Science Advances 10, eadj5569 (2024).
https://doi.org:doi:10.1126/sciadv.adj5569
Glynn, P. W. & D'Croz, L. Experimental evidence for high temperature stress as the causeof El Niño-coincident coral mortality. Coral Reefs 8, 181-191 (1990).
https://doi.org:10.1007/BF00265009
Liu, G. et al. Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km
Global Products from NOAA Coral Reef Watch. Remote Sensing 6, 11579-11606 (2014).
Langlais, C. E. et al. Coral bleaching pathways under the control of regional temperature
variability.
NatureClimateChange7,839-844(2017).
https://doi.org:10.1038/nclimate3399
Kajtar, J. B. et al. A stakeholder-guided marine heatwave hazard index for fisheries andaquaculture. Climatic Change 177, 26 (2024). https://doi.org:10.1007/s10584-024-036848
Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century.
Nature Communications 9, 1324 (2018). https://doi.org:10.1038/s41467-018-03732-9
Wulff, C. O., Vitart, F. & Domeisen, D. I. V. Influence of trends on subseasonal temperature
prediction skill. Quarterly Journal of the Royal Meteorological Society 148, 1280-1299(2022). https://doi.org:https://doi.org/10.1002/qj.4259
Jacox, M. G. Marine heatwaves in a changing climate. Nature 571, 485-487 (2019).
Amaya, D. J. et al. Marine heatwaves need clear definitions so coastal community canadapt. Nature 616, 29-32 (2023).
Mignot, A. et al. Decrease in air-sea CO2 fluxes caused by persistent marine heatwaves.
Nature Communications 13, 4300 (2022). https://doi.org:10.1038/s41467-022-31983-0
Laufkötter, C., Zscheischler, J. & Frölicher, T. L. High-impact marine heatwaves
attributable to human-induced global warming. Science 369, 1621-1625 (2020).
https://doi.org:doi:10.1126/science.aba0690
Bonino, G., Masina, S., Galimberti, G. & Moretti, M. Southern Europe and western Asian
marine heatwaves (SEWA-MHWs): a dataset based on macroevents. Earth Syst. Sci. Data
15, 1269-1285 (2023). https://doi.org:10.5194/essd-15-1269-2023
Prochaska, J. X., Beaulieu, C. & Giamalaki, K. The rapid rise of severe marine heat wave
systems.
Environmental
Research:
Climate2,021002(2023).
https://doi.org:10.1088/2752-5295/accd0e
Desmet, F., Münnich, M. & Gruber, N. Spatiotemporal heterogeneity in the increase inocean acidity extremes in the northeastern Pacific. Biogeosciences 20, 5151-5175 (2023).
https://doi.org:10.5194/bg-20-5151-2023

28

1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152

74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89

Donlon, C. J. et al. The Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) system. Remote Sensing of Environment 116, 140-158 (2012).
https://doi.org:https://doi.org/10.1016/j.rse.2010.10.017
Good, S. et al. The Current Configuration of the OSTIA System for Operational Productionof Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sensing
12, 720 (2020).
Huang, B. et al. Improvements of the Daily Optimum Interpolation Sea Surface
Temperature (DOISST) Version 2.1. Journal of Climate 34, 2923-2939 (2021).
https://doi.org:https://doi.org/10.1175/JCLI-D-20-0166.1
Cook, F. et al. Marine heatwaves in shallow coastal ecosystems are coupled with theatmosphere: Insights from half a century of daily in situ temperature records. Frontiers in
Climate 4 (2022). https://doi.org:10.3389/fclim.2022.1012022
Goebeler, N., Norkko, A. & Norkko, J. Ninety years of coastal monitoring reveals baseline
and extreme ocean temperatures are increasing off the Finnish coast. Communications
Earth & Environment 3, 215 (2022). https://doi.org:10.1038/s43247-022-00545-z
Hemming, M., Roughan, M. & Schaeffer, A. Exploring multi-decadal time series oftemperature extremes in Australian coastal waters. Earth Syst. Sci. Data 16, 887-901(2024). https://doi.org:10.5194/essd-16-887-2024
Hu, S. et al. Observed strong subsurface marine heatwaves in the tropical western Pacific
Ocean. Environmental Research Letters 16, 104024 (2021). https://doi.org:10.1088/17489326/ac26f2
Elzahaby, Y. & Schaeffer, A. Observational Insight Into the Subsurface Anomalies ofMarineHeatwaves.
FrontiersinMarineScience
6(2019).
https://doi.org:10.3389/fmars.2019.00745
Johnson, G. C. et al. Argo—Two Decades: Global Oceanography, Revolutionized. Annual
ReviewofMarineScience14,379-403(2022).
https://doi.org:https://doi.org/10.1146/annurev-marine-022521-102008
Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014
warm anomaly in the NE Pacific. Geophysical Research Letters 42, 3414-3420 (2015).
https://doi.org:https://doi.org/10.1002/2015GL063306
Capotondi, A., Sardeshmukh, P. D., Di Lorenzo, E., Subramanian, A. C. & Miller, A. J.
Predictability of US West Coast Ocean Temperatures is not solely due to ENSO. Scientific
Reports 9, 10993 (2019). https://doi.org:10.1038/s41598-019-47400-4
Jean-Michel, L. et al. The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12
Reanalysis. Frontiers in Earth Science 9 (2021). https://doi.org:10.3389/feart.2021.698876
Amaya, D. J. et al. Bottom marine heatwaves along the continental shelves of North
America. Nature Communications 14, 1038 (2023). https://doi.org:10.1038/s41467-02336567-0
Fredston, A. L. et al. Marine heatwaves are not a dominant driver of change in demersal
fishes. Nature 621, 324-329 (2023). https://doi.org:10.1038/s41586-023-06449-y
Zinke, J. et al. Coral record of southeast Indian Ocean marine heatwaves with intensified
Western Pacific temperature gradient. Nature Communications 6, 8562 (2015).
https://doi.org:10.1038/ncomms9562
Lane, M. K. et al. Planktonic foraminiferal assemblages reflect warming during two recent
mid-latitude marine heatwaves. Frontiers in Marine Science 10 (2023).
https://doi.org:10.3389/fmars.2023.1155761

29

1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198

90
91
92

93

94
95
96
97

98
99
100
101

102
103
104

Penland, C. & Sardeshmukh, P. D. The Optimal Growth of Tropical Sea Surface
Temperature
Anomalies.
Journalof
Climate8,
1999-2024(1995).
https://doi.org:https://doi.org/10.1175/1520-0442(1995)008<1999:TOGOTS>2.0.CO;2
Sen Gupta, A. et al. Drivers and impacts of the most extreme marine heatwave events.
Scientific Reports 10, 19359 (2020). https://doi.org:10.1038/s41598-020-75445-3
Perkins-Kirkpatrick, S. E. et al. The Role of Natural Variability and Anthropogenic Climate
Change in the 2017/18 Tasman Sea Marine Heatwave. Bulletin of the American
MeteorologicalSociety100,
S105-S110(2019).
https://doi.org:https://doi.org/10.1175/BAMS-D-18-0116.1
Kajtar, J. B., Bachman, S. D., Holbrook, N. J. & Pilo, G. S. Drivers, Dynamics, andPersistence of the 2017/2018 Tasman Sea Marine Heatwave. Journal of GeophysicalResearch:
Oceans127,e2022JC018931(2022).
https://doi.org:https://doi.org/10.1029/2022JC018931
Salinger, M. J. et al. The unprecedented coupled ocean-atmosphere summer heatwave in
the New Zealand region 2017/18: drivers, mechanisms and impacts. Environmental
Research Letters 14, 044023 (2019). https://doi.org:10.1088/1748-9326/ab012a
Gregory, C. H., Holbrook, N. J., Marshall, A. G. & Spillman, C. M. Atmospheric Driversof Tasman Sea Marine Heatwaves. Journal of Climate 36, 5197-5214 (2023).
https://doi.org:https://doi.org/10.1175/JCLI-D-22-0538.1
Carrasco, D., Pizarro, O., Jacques-Coper, M. & Narváez, D. A. Main drivers of marine heatwaves in the eastern South Pacific. Frontiers in Marine Science 10 (2023).
https://doi.org:10.3389/fmars.2023.1129276
Dasgupta, P., Nam, S., Saranya, J. S. & Roxy, M. K. Marine Heatwaves in the East Asian
Marginal Seas Facilitated by Boreal Summer Intraseasonal Oscillations. Journal ofGeophysicalResearch:
Oceans129,e2023JC020602(2024).
https://doi.org:https://doi.org/10.1029/2023JC020602
Amaya, D. J. et al. Are Long-Term Changes in Mixed Layer Depth Influencing North
Pacific Marine Heatwaves? Bulletin of the American Meteorological Society 102, S59-S66(2021). https://doi.org:https://doi.org/10.1175/BAMS-D-20-0144.1
Vogt, L., Burger, F. A., Griffies, S. M. & Frölicher, T. L. Local Drivers of Marine
Heatwaves: A Global Analysis With an Earth System Model. Frontiers in Climate 4 (2022).
https://doi.org:10.3389/fclim.2022.847995
Feng, M., McPhaden, M. J., Xie, S.-P. & Hafner, J. La Niña forces unprecedented Leeuwin
Current
warmingin2011.
Scientific
Reports
3,
1277(2013).
https://doi.org:10.1038/srep01277
Benthuysen, J., Feng, M. & Zhong, L. Spatial patterns of warming off Western Australia
during the 2011 Ningaloo Niño: Quantifying impacts of remote and local forcing.
Continental
Shelf
Research91,
232-246(2014).
https://doi.org:https://doi.org/10.1016/j.csr.2014.09.014
Oliver, E. C. J. et al. The unprecedented 2015/16 Tasman Sea marine heatwave. Nature
Communications 8, 16101 (2017). https://doi.org:10.1038/ncomms16101
Li, Z., Holbrook, N. J., Zhang, X., Oliver, E. C. J. & Cougnon, E. A. Remote Forcing of
Tasman Sea Marine Heatwaves. Journal of Climate 33, 5337-5354 (2020).
https://doi.org:https://doi.org/10.1175/JCLI-D-19-0641.1
Elzahaby, Y., Schaeffer, A., Roughan, M. & Delaux, S. Oceanic Circulation Drives the
Deepest and Longest Marine Heatwaves in the East Australian Current System.

30

1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243

105
106
107
108
109
110

111
112
113
114
115
116
117
118
119

GeophysicalResearch
Letters48,
e2021GL094785(2021).
https://doi.org:https://doi.org/10.1029/2021GL094785
Jin, F.-F. An Equatorial Ocean Recharge Paradigm for ENSO. Part I: Conceptual Model.
Journalofthe
Atmospheric
Sciences
54,811-829(1997).
https://doi.org:https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2
Capotondi, A. ENSO diversity in the NCAR CCSM4 climate model. Journal ofGeophysicalResearch:
Oceans118,4755-4770(2013).
https://doi.org:https://doi.org/10.1002/jgrc.20335
Bian, C., Jing, Z., Wang, H. & Wu, L. Scale-Dependent Drivers of Marine Heatwaves
Globally.
GeophysicalResearch
Letters51,
e2023GL107306(2024).
https://doi.org:https://doi.org/10.1029/2023GL107306
Mogen, S. C. et al. Multi-month forecasts of marine heatwaves and ocean acidification
extremes. Nature Geoscience, Revised (2024).
Capotondi, A. et al. Observational Needs Supporting Marine Ecosystems Modeling and
Forecasting: From the Global Ocean to Regional and Coastal Systems. Frontiers in Marine
Science 6 (2019). https://doi.org:10.3389/fmars.2019.00623
Ren, X., Liu, W., Capotondi, A., Amaya, D. J. & Holbrook, N. J. The Pacific Decadal
Oscillation modulated marine heatwaves in the Northeast Pacific during past decades.
Communications Earth & Environment 4, 218 (2023). https://doi.org:10.1038/s43247-02300863-w
Wang, Y., Holbrook, N. J. & Kajtar, J. B. Predictability of Marine Heatwaves off Western
Australia Using a Linear Inverse Model. Journal of Climate 36, 6177-6193 (2023).
https://doi.org:https://doi.org/10.1175/JCLI-D-22-0692.1
Wang, C. Three-ocean interactions and climate variability: a review and perspective.
Climate Dynamics 53, 5119-5136 (2019). https://doi.org:10.1007/s00382-019-04930-x
Cai, W. et al. Pantropical climate interactions. Science 363, eaav4236 (2019).
https://doi.org:doi:10.1126/science.aav4236
Yao, Y., Wang, C. & Wang, C. Record-breaking 2020 summer marine heatwaves in thewestern North Pacific. Deep Sea Research Part II: Topical Studies in Oceanography 209,
105288 (2023). https://doi.org:https://doi.org/10.1016/j.dsr2.2023.105288
Song, Q., Yao, Y. & Wang, C. Response of Future Summer Marine Heatwaves in the South
China Sea to Enhanced Western Pacific Subtropical High. Geophysical Research Letters
50, e2023GL103667 (2023). https://doi.org:https://doi.org/10.1029/2023GL103667
Song, Q., Wang, C., Yao, Y. & Fan, H. Unraveling the Indian monsoon’s role in fueling theunprecedented 2022 Marine Heatwave in the Western North Pacific. npj Climate andAtmospheric Science 7, 90 (2024). https://doi.org:10.1038/s41612-024-00645-x
Chiang, J. C. H. & Vimont, D. J. Analogous Pacific and Atlantic Meridional Modes of
Tropical Atmosphere–Ocean Variability. Journal of Climate 17, 4143-4158 (2004).
https://doi.org:https://doi.org/10.1175/JCLI4953.1
Zhang, H., Clement, A. & Di Nezio, P. The South Pacific Meridional Mode: A Mechanism
for ENSO-like Variability. Journal of Climate 27, 769-783 (2014).
https://doi.org:https://doi.org/10.1175/JCLI-D-13-00082.1
Gregory, C. H., Holbrook, N. J., Marshall, A. G. & Spillman, C. M. Sub-seasonal toseasonal drivers of regional marine heatwaves around Australia. Climate Dynamics (2024).
https://doi.org:10.1007/s00382-024-07226-x

31

1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288

120

121
122

123
124
125

126
127
128
129
130
131

132
133

Köhn, E. E., Vogt, M., Münnich, M. & Gruber, N. On the Vertical Structure andPropagation of Marine Heatwaves in the Eastern Pacific. Journal of Geophysical Research:
Oceans129,e2023JC020063(2024).
https://doi.org:https://doi.org/10.1029/2023JC020063
Alexander, M. A., Deser, C. & Timlin, M. S. The Reemergence of SST Anomalies in the
North
Pacific
Ocean.
Journalof
Climate12,2419-2433(1999).
https://doi.org:https://doi.org/10.1175/1520-0442(1999)012<2419:TROSAI>2.0.CO;2
Scannell, H. A., Johnson, G. C., Thompson, L., Lyman, J. M. & Riser, S. C. Subsurface
Evolution and Persistence of Marine Heatwaves in the Northeast Pacific. GeophysicalResearch
Letters47,
e2020GL090548(2020).
https://doi.org:https://doi.org/10.1029/2020GL090548
Richaud, B. et al. Drivers of Marine Heatwaves in the Arctic Ocean. Journal ofGeophysicalResearch:
Oceans129,e2023JC020324(2024).
https://doi.org:https://doi.org/10.1029/2023JC020324
Zscheischler, J. et al. Future climate risk from compound events. Nature Climate Change
8, 469-477 (2018). https://doi.org:10.1038/s41558-018-0156-3
Seneviratne, S. I. et al. in Climate Change 2021: The Physical Science Basis. Contributionof Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change (eds V. Masson-Delmotte et al.) Ch. Weather and Climate Extreme Eventsin a Changing Climate, 1513-1766 (Cambridge University Press, 2021).
Peng, Q., Xie, S.-P., Wang, D., Zheng, X.-T. & Zhang, H. Coupled ocean-atmosphere
dynamics of the 2017 extreme coastal El Niño. Nature Communications 10, 298 (2019).
https://doi.org:10.1038/s41467-018-08258-8
Hauri, C., Gruber, N., McDonnell, A. M. P. & Vogt, M. The intensity, duration, and severityof low aragonite saturation state events on the California continental shelf. GeophysicalResearch Letters 40, 3424-3428 (2013). https://doi.org:https://doi.org/10.1002/grl.50618
Negrete-García, G., Lovenduski, N. S., Hauri, C., Krumhardt, K. M. & Lauvset, S. K.
Sudden emergence of a shallow aragonite saturation horizon in the Southern Ocean. Nature
Climate Change 9, 313-317 (2019). https://doi.org:10.1038/s41558-019-0418-8
Burger, F. A., John, J. G. & Frölicher, T. L. Increase in ocean acidity variability andextremes under increasing atmospheric CO2. Biogeosciences 17, 4633-4662 (2020).
https://doi.org:10.5194/bg-17-4633-2020
Burger, F. A. & Frölicher, T. L. Drivers of Surface Ocean Acidity Extremes in an Earth
System Model. Global Biogeochemical Cycles 37, e2023GB007785 (2023).
https://doi.org:https://doi.org/10.1029/2023GB007785
Köhn, E. E., Münnich, M., Vogt, M., Desmet, F. & Gruber, N. Strong Habitat Compression
by Extreme Shoaling Events of Hypoxic Waters in the Eastern Pacific. Journal ofGeophysicalResearch:
Oceans127,e2022JC018429(2022).
https://doi.org:https://doi.org/10.1029/2022JC018429
Le Grix, N., Zscheischler, J., Laufkötter, C., Rousseaux, C. S. & Frölicher, T. L. Compoundhigh-temperature and low-chlorophyll extremes in the ocean over the satellite period.
Biogeosciences 18, 2119-2137 (2021). https://doi.org:10.5194/bg-18-2119-2021
Le Grix, N., Zscheischler, J., Rodgers, K. B., Yamaguchi, R. & Frölicher, T. L. Hotspots
and drivers of compound marine heatwaves and low net primary production extremes.
Biogeosciences 19, 5807-5835 (2022). https://doi.org:10.5194/bg-19-5807-2022

32

1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332

134

Boyd, P. W. & Brown, C. J. Modes of interactions between environmental drivers andmarinebiota.
FrontiersinMarineScience2(2015).
https://doi.org:10.3389/fmars.2015.00009
135
Pörtner, H. O. & Knust, R. Climate change affects marine fishes through the oxygen
limitation of thermal tolerance. Science (New York, N.Y.) 315, 95-97 (2007).
https://doi.org:10.1126/science.1135471
136
Deutsch, C., Ferrel, A., Seibel, B., Pörtner, H.-O. & Huey, R. B. Climate change tightens a
metabolic constraint on marine habitats. Science 348, 1132-1135 (2015).
https://doi.org:doi:10.1126/science.aaa1605
137
Morée, A. L., Clarke, T. M., Cheung, W. W. L. & Frölicher, T. L. Impact of deoxygenation
and warming on global marine species in the 21st century. EGUsphere 2022, 1-36 (2022).
https://doi.org:10.5194/egusphere-2022-1364
138
Bednaršek, N. et al. Natural Analogues in pH Variability and Predictability across the
Coastal Pacific Estuaries: Extrapolation of the Increased Oyster Dissolution under
Increased pH Amplitude and Low Predictability Related to Ocean Acidification.
Environmental
Science&Technology
56,9015-9028(2022).
https://doi.org:10.1021/acs.est.2c00010
139
Hoegh-Guldberg, O. et al. Coral Reefs Under Rapid Climate Change and Ocean
Acidification.
Science318,
1737-1742(2007).
https://doi.org:doi:10.1126/science.1152509
140
Le Grix, N., Cheung, W. L., Reygondeau, G., Zscheischler, J. & Frölicher, T. L. Extreme
and compound ocean events are key drivers of projected low pelagic fish biomass. Global
Change Biology 29, 6478-6492 (2023). https://doi.org:https://doi.org/10.1111/gcb.16968
141
Cavole, L. M. et al. Biological Impacts of the 2013–2015 Warm-Water Anomaly in the
Northeast Pacific
Winners, Losers, and the Future. Oceanography 29, 273-285 (2016).
142
Burger, F. A., Terhaar, J. & Frölicher, T. L. Compound marine heatwaves and ocean acidity
extremes. Nature Communications 13, 4722 (2022). https://doi.org:10.1038/s41467-02232120-7
143
Wyatt, A. M., Resplandy, L. & Marchetti, A. Ecosystem impacts of marine heat waves in
the northeast Pacific. Biogeosciences 19, 5689-5705 (2022). https://doi.org:10.5194/bg-195689-2022
144
Mogen, S. C. et al. Ocean Biogeochemical Signatures of the North Pacific Blob.
GeophysicalResearch
Letters49,
e2021GL096938(2022).
https://doi.org:https://doi.org/10.1029/2021GL096938
145
Frölicher, T. L., Rodgers, K. B., Stock, C. A. & Cheung, W. W. L. Sources of uncertainties
in 21st century projections of potential ocean ecosystem stressors. Global Biogeochemical
Cycles 30, 1224-1243 (2016). https://doi.org:https://doi.org/10.1002/2015GB005338
146
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation,and upper-ocean nutrient and primary production decline from CMIP6 model projections.
Biogeosciences 17, 3439-3470 (2020). https://doi.org:10.5194/bg-17-3439-2020
147
Kwiatkowski, L. & Orr, J. C. Diverging seasonal extremes for ocean acidification during
the twenty-first century. Nature Climate Change 8, 141-145 (2018).
https://doi.org:10.1038/s41558-017-0054-0

33

1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378

148
149
150

151
152
153
154
155
156
157
158
159
160
161

162

Kwiatkowski, L., Torres, O., Aumont, O. & Orr, J. C. Modified future diurnal variabilityof the global surface ocean CO2 system. Global Change Biology 29, 982-997 (2023).
https://doi.org:https://doi.org/10.1111/gcb.16514
Deser, C. et al. Insights from Earth system model initial-condition large ensembles andfutureprospects.
NatureClimateChange10,277-286(2020).
https://doi.org:10.1038/s41558-020-0731-2
Alexander, M. A. et al. Projected sea surface temperatures over the 21st century: Changes
in the mean, variability and extremes for large marine ecosystem regions of Northern
Oceans.
Elementa:
ScienceoftheAnthropocene
6(2018).
https://doi.org:10.1525/elementa.191
McAdam, R., Masina, S. & Gualdi, S. Seasonal forecasting of subsurface marine
heatwaves.
Communications
Earth&
Environment
4,225(2023).
https://doi.org:10.1038/s43247-023-00892-5
de Boisseson, E. & Balmaseda, M. Predictability of Marine Heatwaves: assessment basedon the ECMWF seasonal forecast system. EGUsphere 2023, 1-19 (2023).
https://doi.org:10.5194/egusphere-2023-2232
Hu, Z.-Z., Kumar, A., Jha, B., Zhu, J. & Huang, B. Persistence and Predictions of theRemarkable Warm Anomaly in the Northeastern Pacific Ocean during 2014–16. Journalof Climate 30, 689-702 (2017). https://doi.org:https://doi.org/10.1175/JCLI-D-16-0348.1
Amaya, D. J., Bond, N. A., Miller, A. J. & De Florio, M. J. The evolution and known
atmospheric forcing mechanisms behind the 2013-2015 North Pacific warm anomalies. US
CLIVAR Variations 14 (2016).
Wang, Y., Kajtar, J. B., Alexander, L. V., Pilo, G. S. & Holbrook, N. J. Understanding theChanging Nature of Marine Cold-Spells. Geophysical Research Letters 49,
e2021GL097002 (2022). https://doi.org:https://doi.org/10.1029/2021GL097002
Park, J.-Y., Stock, C. A., Dunne, J. P., Yang, X. & Rosati, A. Seasonal to multiannual marine
ecosystem prediction with a global Earth system model. Science 365, 284-288 (2019).
https://doi.org:doi:10.1126/science.aav6634
Frölicher, T. L., Ramseyer, L., Raible, C. C., Rodgers, K. B. & Dunne, J. Potential
predictability of marine ecosystem drivers. Biogeosciences 17, 2061-2083 (2020).
https://doi.org:10.5194/bg-17-2061-2020
Mogen, S. C. et al. Skillful Multi-Month Predictions of Ecosystem Stressors in the Surface
andSubsurface
Ocean.
Earth's
Future11,
e2023EF003605(2023).
https://doi.org:https://doi.org/10.1029/2023EF003605
Forster, P. M. et al. Indicators of Global Climate Change 2022: annual update of large-scale
indicators of the state of the climate system and human influence. Earth Syst. Sci. Data 15,2295-2327 (2023). https://doi.org:10.5194/essd-15-2295-2023
Yao, Y., Wang, C. & Fu, Y. Global Marine Heatwaves and Cold-Spells in Present Climate
to
FutureProjections.
Earth's
Future10,
e2022EF002787(2022).
https://doi.org:https://doi.org/10.1029/2022EF002787
Frankignoul, C., Gastineau, G. & Kwon, Y.-O. Estimation of the SST Response to
Anthropogenic and External Forcing and Its Impact on the Atlantic Multidecadal
Oscillation and the Pacific Decadal Oscillation. Journal of Climate 30, 9871-9895 (2017).
https://doi.org:https://doi.org/10.1175/JCLI-D-17-0009.1
Wills, R. C. J., Battisti, D. S., Armour, K. C., Schneider, T. & Deser, C. Pattern Recognition
Methods to Separate Forced Responses from Internal Variability in Climate Model

34

1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423

163

164
165
166

167
168
169
170
171
172
173
174
175

176

Ensembles and Observations. Journal of Climate 33, 8693-8719 (2020).
https://doi.org:https://doi.org/10.1175/JCLI-D-19-0855.1
Capotondi, A., Alexander, M. A., Bond, N. A., Curchitser, E. N. & Scott, J. D. Enhanced
upper ocean stratification with climate change in the CMIP3 models. Journal ofGeophysicalResearch:
Oceans117(2012).
https://doi.org:https://doi.org/10.1029/2011JC007409
Cai, W. et al. Changing El Niño–Southern Oscillation in a warming climate. Nature
Reviews Earth & Environment 2, 628-644 (2021). https://doi.org:10.1038/s43017-02100199-z
Geng, T., Yang, Y. & Wu, L. On the Mechanisms of Pacific Decadal Oscillation Modulation
in a Warming Climate. Journal of Climate 32, 1443-1459 (2019).
https://doi.org:https://doi.org/10.1175/JCLI-D-18-0337.1
O’Brien, J. P. & Deser, C. Quantifying and Understanding Forced Changes to Unforced
Modes of Atmospheric Circulation Variability over the North Pacific in a Coupled Model
Large
Ensemble.
Journalof
Climate36,
19-37(2023).
https://doi.org:https://doi.org/10.1175/JCLI-D-22-0101.1
Song, S.-Y., Yeh, S.-W., Kim, H. & Holbrook, N. J. Arctic warming contributes to increasein Northeast Pacific marine heatwave days over the past decades. Communications Earth& Environment 4, 25 (2023). https://doi.org:10.1038/s43247-023-00683-y
Maher, N. et al. The future of the El Niño–Southern Oscillation: using large ensembles toilluminate time-varying responses and inter-model differences. Earth Syst. Dynam. 14,
413-431 (2023). https://doi.org:10.5194/esd-14-413-2023
Zhao, Y. & Yu, J.-Y. Two Marine Heatwave (MHW) Variants under a Basinwide MHW
Conditioning Mode in the North Pacific and Their Atlantic Associations. Journal of
Climate 36, 8657-8674 (2023). https://doi.org:https://doi.org/10.1175/JCLI-D-23-0156.1
Shi, J. et al. Northeast Pacific warm blobs sustained via extratropical atmosphericteleconnections. Nature Communications 15, 2832 (2024). https://doi.org:10.1038/s41467024-47032-x
Ross, A. C. et al. A high-resolution physical–biogeochemical model for marine resource
applications in the northwest Atlantic (MOM6-COBALT-NWA12 v1.0). Geosci. Model
Dev. 16, 6943-6985 (2023). https://doi.org:10.5194/gmd-16-6943-2023
Cheeseman, T. et al. Bellwethers of change: population modelling of North Pacifichumpback whales from 2002 through 2021 reveals shift from recovery to climate response.
Royal Society Open Science 11 (2024). https://doi.org:10.1098/rsos.231462
Myers, T. A., Mechoso, C. R., Cesana, G. V., DeFlorio, M. J. & Waliser, D. E. Cloud
Feedback Key to Marine Heatwave off Baja California. Geophysical Research Letters 45,
4345-4352 (2018). https://doi.org:https://doi.org/10.1029/2018GL078242
Ripple, W. J. et al. The 2023 state of the climate report: Entering uncharted territory.
BioScience 73, 841-850 (2023). https://doi.org:10.1093/biosci/biad080
Kuhlbrodt, T., Swaminathan, R., Ceppi, P. & Wilder, T. A Glimpse into the Future: The
2023 Ocean Temperature and Sea Ice Extremes in the Context of Longer-Term Climate
Change. Bulletin of the American Meteorological Society 105, E474-E485 (2024).
https://doi.org:https://doi.org/10.1175/BAMS-D-23-0209.1
Cheng, L. et al. New Record Ocean Temperatures and Related Climate Indicators in 2023.
Advances in Atmospheric Sciences (2024). https://doi.org:10.1007/s00376-024-3378-5

35

1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468

177
178
179
180

181
182

183

184
185

186
187
188
189

Ceppi, P. & Fueglistaler, S. The El Niño–Southern Oscillation Pattern Effect. GeophysicalResearch
Letters48,
e2021GL095261(2021).
https://doi.org:https://doi.org/10.1029/2021GL095261
Zhang, W., Jiang, F., Stuecker, M. F., Jin, F.-F. & Timmermann, A. Spurious North TropicalAtlantic precursors to El Niño. Nature Communications 12, 3096 (2021).
https://doi.org:10.1038/s41467-021-23411-6
Deser, C., Alexander, M. A., Xie, S.-P. & Phillips, A. S. Sea Surface Temperature
Variability: Patterns and Mechanisms. Annual Review of Marine Science 2, 115-143(2010). https://doi.org:https://doi.org/10.1146/annurev-marine-120408-151453
Diamond, M. S. Detection of large-scale cloud microphysical changes within a major
shipping corridor after implementation of the International Maritime Organization 2020
fuel sulfur regulations. Atmos. Chem. Phys. 23, 8259-8269 (2023).
https://doi.org:10.5194/acp-23-8259-2023
Schoeberl, M. R. et al. The Estimated Climate Impact of the Hunga Tonga-Hunga Ha'apai
Eruption Plume. Geophysical Research Letters 50, e2023GL104634 (2023).
https://doi.org:https://doi.org/10.1029/2023GL104634
Benthuysen, J. A., Oliver, E. C. J., Feng, M. & Marshall, A. G. Extreme Marine Warming
Across Tropical Australia During Austral Summer 2015–2016. Journal of GeophysicalResearch:
Oceans123,1301-1326(2018).
https://doi.org:https://doi.org/10.1002/2017JC013326
Benthuysen, J. A., Steinberg, C., Spillman, C. M. & Smith, G. A. Oceanographic driversof bleaching in the GBR: from observations to prediction. Volume 4: Observations andpredictions of marine heatwaves. Report to the National Environmental Science Program.
Reef and Rainforest Research Centre Limited, Cairns (47pp). Accessed from:https://nesptropical.edu.au/wp-content/uploads/2021/2006/2024.2022-Volume-2024Final-Report_COMPLETE.pdf (2021).
Benthuysen, J. A., Smith, G. A., Spillman, C. M. & Steinberg, C. R. Subseasonal predictionof the 2020 Great Barrier Reef and Coral Sea marine heatwave. Environmental Research
Letters 16, 124050 (2021). https://doi.org:10.1088/1748-9326/ac3aa1
Wijffels, S. E. et al. A fine spatial-scale sea surface temperature atlas of the Australianregional seas (SSTAARS): Seasonal variability and trends around Australasia and New
Zealand revisited. Journal of Marine Systems 187, 156-196 (2018).
https://doi.org:https://doi.org/10.1016/j.jmarsys.2018.07.005
IMOS2024a. IMOS - SRS - SST - L3S - Multi Sensor - 6 day - night time - Australia.
https://portal.aodn.org.au/search?uuid=e1908591-b3cf-42aa-a32f-424322b28165.
Accessed 17 March 2024
IMOS2024b. Sensors on Tropical Research Vessels: Enhanced Measurements from Shipsof Opportunity (SOOP). https://portal.aodn.org.au/search?uuid=8af21108-c535-43bf8dab-c1f45a26088c. Accessed 22 March 2024
Argo2020. Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)
Snapshotof ArgoGDAC
ofFebruary
10st
2020.
SEANOE.
https://doi.org/10.17882/42182#70590
IMOS2024c.
IMOS
–
Argo
Profiles
–
core
data.
https://portal.aodn.org.au/search?uuid=4402cb50-e20a-44ee-93e6-4728259250d2.
Accessed 26 March 2024

36

1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495

190
191

192

193
194
195

196
197

IMOS2024d. IMOS - Australian National Facility for Ocean Gliders (ANFOG) - delayedmode glider deployments. https://portal.aodn.org.au/search?uuid=c317b0fe-02e8-4ff996c9-563fd58e82ac. Accessed 18 March 2024
IMOS2024e. IMOS - ANMN National Reference Stations - Darwin and Yongala - Near
real-time
meteorology
andoceanographicaldata.
https://portal.aodn.org.au/search?uuid=006bb7dc-860b-4b89-bf4c-6bd930bd35b7.
Accessed 17 March 2024
IMOS2024f. IMOS NRS. IMOS - Australian National Mooring Network (ANMN) Facility
WQM
and
CTD
burst
averageddata
products.
https://portal.aodn.org.au/search?uuid=8964658c-6ee1-4015-9bae-2937dfcc6ab9.
Accessed 19 March 2024
AIMS2009. Northern Australia Automated Marine Weather and Oceanographic Stations.
https://doi.org/10.25845/5c09bf93f315d. Accessed 17 March 2024
AIMS2017. AIMS Sea Water Temperature Observing System (AIMS Temperature Logger
Program). https://doi.org/10.25845/5b4eb0f9bb848. Accessed 28 November 2023
Watson, G. & Rousseaux, C. S. NASA Ocean Biogeochemical Model assimilating satellite
chlorophyll global data daily VR2017, Goddard Earth Sciences Data and Information
Services
Center(GES
DISC),
Greenbelt,
MD,USA,
https://doi.org/10.5067/PT6TXZKSHBW9. (2017). https://doi.org:
Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5):
Upgrades, Validations, and Intercomparisons. Journal of Climate 30, 8179-8205 (2017).
https://doi.org:https://doi.org/10.1175/JCLI-D-16-0836.1
Kirtman, B. P. et al. The North American Multimodel Ensemble: Phase-1 Seasonal-toInterannual Prediction; Phase-2 toward Developing Intraseasonal Prediction. Bulletin oftheAmericanMeteorologicalSociety95,
585-601(2014).
https://doi.org:https://doi.org/10.1175/BAMS-D-12-00050.1

37