Editing IPCC:AR6/SROCC/Chapter-6 (section)

== 6.4 Marine Heatwaves and their Implications ==

<div id="article-6-4marine-heatwaves-and-their-implications-block-1"></div>

AR5 concluded that it is ''virtually certain'' that the global ocean temperature in the upper few hundred meters has increased from 1971 – 2010 (Rhein et al., 2013 <sup>[[#fn:r307|307]]</sup> ), and that the temperature is projected to further increase during the 21st century (Collins et al., 2013 <sup>[[#fn:r308|308]]</sup> ). For an update on observed and projected long-term changes in ocean temperature and heat, see Chapter 5.

Superimposed onto the long-term ocean warming trend are short-term extreme warming events, called MHWs, during which ocean temperatures are extremely high. Whereas the response of marine organisms and ecosystems to gradual trends in temperature has been assessed in AR5 (e.g., Hoegh-Guldberg et al., 2014; Pörtner et al., 2014 <sup>[[#fn:r309|309]]</sup> ), research on the response of the natural, physical and socioeconomic systems to MHWs has newly emerged since AR5. Notable exceptions are studies on the effect of MHWs on intertidal systems and tropical coral reef ecosystems, which have been already assessed in AR5 (Gattuso et al., 2014 <sup>[[#fn:r310|310]]</sup> ; Pörtner et al., 2014 <sup>[[#fn:r311|311]]</sup> ).

MHWs are periods of extremely high ocean temperatures that persist for days to months, can extend up to thousands of kilometres and can penetrate multiple hundreds of metres into the deep ocean (see SROCC Glossary; Hobday et al., 2016a <sup>[[#fn:r312|312]]</sup> ; Scannell et al., 2016 <sup>[[#fn:r313|313]]</sup> ; Benthuysen et al., 2018 <sup>[[#fn:r314|314]]</sup> ). A MHW is an event at a particular place and time of the year that is rare and predominately, but not exclusively, defined with a relative threshold; that is, an event rarer than 90th or 99th percentile of a probability density function. By definition, the characteristics of what is called a MHW may therefore vary from place to place in an absolute sense. Different metrics are used to quantify changes in MHW characteristics, such as frequency, duration, intensity, spatial extent and severity. To monitor and predict coral bleaching risk, the metric degree heating week (DHW; e.g., Eakin et al., 2010) is often used, which combines the effect of duration and magnitude of the heatwave.

<span id="observations-and-key-processes-detection-and-attribution-projections"></span>
=== 6.4.1 Observations and Key Processes, Detection and Attribution, Projections ===

<div id="section-6-4-1-1recent-documented-mhws-and-key-driving-mechanisms"></div>

<span id="recent-documented-mhws-and-key-driving-mechanisms"></span>
==== 6.4.1.1 Recent Documented MHWs and Key Driving Mechanisms ====

<div id="section-6-4-1-1recent-documented-mhws-and-key-driving-mechanisms-block-1"></div>

MHWs have been observed and documented in all ocean basins over the last two decades (Figure 6.3a, Figure 6.2, Table 6.2). Prominent examples include the Northeast Pacific 2013–2015 MHW (often called ‘The Blob’; Bond et al. 2015 <sup>[[#fn:r315|315]]</sup> ), the Yellow Sea/East China Sea 2016 MHW (KMA, 2016; KMA, 2017; KMA, 2018), the Western Australia 2011 MHW (Pearce and Feng, 2013 <sup>[[#fn:r316|316]]</sup> ; Kataoka et al. 2014 <sup>[[#fn:r317|317]]</sup> ), and the Northwest Atlantic 2012 MHW (Mills et al. 2013 <sup>[[#fn:r318|318]]</sup> ).

The dominant ocean and/or atmospheric processes leading to the buildup, persistence and decay of MHWs vary greatly among the individual MHWs and depend on the location and time of occurrence. One of the most important global driver of MHWs are El Niño events (Oliver et al., 2018a <sup>[[#fn:r319|319]]</sup> ). During El Niño events, the SST, in particular of the central and eastern equatorial Pacific and the Indian Ocean, are anomalously warm (see Section 6.5). MHWs may also be associated with other large-scale modes of climate variability, such as the Pacific Decadal Oscillation (PDO), AMO, Indian Ocean Dipole (IOD), North Pacific Oscillation and NAO, which modulate ocean temperatures at the regional scale (Benthuysen et al., 2014 <sup>[[#fn:r320|320]]</sup> ; Bond et al., 2015 <sup>[[#fn:r321|321]]</sup> ; Chen et al., 2015b <sup>[[#fn:r322|322]]</sup> ; Di Lorenzo and Mantua, 2016). These modes can change the strength, direction and location of ocean currents that build up areas of extreme warm waters, or they can change the air-sea heat flux, leading to a warming of the ocean surface from the atmosphere. For example, predominant La Niña conditions in 2010 and 2011 strengthened and shifted the Leeuwin Current southward along the west coast of Australia leading to the Western Australia 2011 MHW (Pearce and Feng, 2013 <sup>[[#fn:r323|323]]</sup> ; Kataoka et al., 2014 <sup>[[#fn:r324|324]]</sup> ). Another example is The Blob, which emerged in 2013 in response to teleconnections between the North Pacific and the weak El Niño that drove strong positive sea level pressure anomalies across the northeast Pacific inducing a smaller heat loss from the ocean (Bond et al., 2015 <sup>[[#fn:r325|325]]</sup> ; Di Lorenzo and Mantua, 2016). Low sea ice concentrations in the Arctic, however, may have also played a role (Lee et al., 2015a <sup>[[#fn:r326|326]]</sup> ).

The buildup and decay of extreme warm SSTs may also be caused by small-scale atmospheric and oceanic processes, such as ocean mesoscale eddies or local atmospheric weather patterns (Carrigan and Puotinen, 2014 <sup>[[#fn:r327|327]]</sup> ; Schlegel et al., 2017a <sup>[[#fn:r328|328]]</sup> ; Schlegel et al., 2017b <sup>[[#fn:r329|329]]</sup> ). For example, the Tasman Sea 2015–2016 MHW was caused by enhanced southward transport in the East Australian current driven by increased wind stress curl across the mid-latitude South Pacific (Oliver and Holbrook, 2014 <sup>[[#fn:r330|330]]</sup> ; Oliver et al., 2017 <sup>[[#fn:r331|331]]</sup> ) with local downwelling-favourable winds also having played a role in the subsurface intensification of the MHW (Schaeffer and Roughan, 2017 <sup>[[#fn:r332|332]]</sup> ). In addition, the 2016 MHW in the southern part of the Great Barrier Reef was mitigated by the ETC Winston that passed over Fiji on February 20th. The cyclone caused strong winds, cloud cover and rain, which lowered SST and prevented corals from bleaching (Hughes et al., 2017b <sup>[[#fn:r333|333]]</sup> ).

<div id="section-6-4-1-2detection-and-attribution-of-mhw-events"></div>

<span id="detection-and-attribution-of-mhw-events"></span>
==== 6.4.1.2 Detection and Attribution of MHW Events ====

<div id="section-6-4-1-2detection-and-attribution-of-mhw-events-block-1"></div>

The upper ocean temperature has significantly increased in most regions over the last few decades, with anthropogenic forcing ''very likely'' being the main driver (Bindoff et al., 2013 <sup>[[#fn:r334|334]]</sup> ). Concurrent with the long-term increase in upper ocean temperatures, MHWs have become more frequent, extensive and intense (Frölicher and Laufkötter, 2018 <sup>[[#fn:r335|335]]</sup> ; Oliver et al., 2018a <sup>[[#fn:r336|336]]</sup> ; Smale et al., 2019 <sup>[[#fn:r337|337]]</sup> ). Analysis of satellite daily SST data reveal that the number of MHW days exceeding the 99th percentile, calculated over the 1982–2016 period, has doubled globally between 1982 and 2016, from about 2.5 heatwave days yr–1 to 5 heatwave days yr–1 (Frölicher et al. 2018 <sup>[[#fn:r338|338]]</sup> ; Oliver et al. 2018a <sup>[[#fn:r339|339]]</sup> ).  At the same time, the maximum intensity of MHWs has increased by 0.15°C and the spatial extent by 66% (Frölicher et al., 2018 <sup>[[#fn:r340|340]]</sup> ). Using a classification system to separate MHWs into categories (I-IV, depending on the level to which SSTs exceed local averages), Hobday et al. (2018) <sup>[[#fn:r341|341]]</sup> show that the occurrence of MHWs has increased for all categories over the past 35 years with the largest increase (24%) in strong (Category II) MHW events. In 2016, about a quarter of the surface ocean experienced either the longest or most intense MHW (Hobday et al., 2016a; Figure 6.3b).

The observed trend towards more frequent, intense and extensive MHWs, defined relative to a fixed baseline period, is ''very likely'' due to the long-term anthropogenic increase in mean ocean temperatures, and cannot be explained by natural climate variability (Frölicher et al., 2018 <sup>[[#fn:r342|342]]</sup> ; Oliver et al., 2018a <sup>[[#fn:r343|343]]</sup> ; Oliver, 2019 <sup>[[#fn:r344|344]]</sup> ). As climate models project a long-term increase in ocean temperatures over the 21st century (Collins et al., 2013 <sup>[[#fn:r345|345]]</sup> ), a further increase in the probability of MHWs under continued global warming can be expected (see Section 6.4.1.3). Extending the analysis to the pre-satellite period (before 1982) by using a combination of daily ''in situ'' measurements and gridded monthly ''in situ'' based data sets, Oliver et al. (2018a) show that the global frequency and duration of MHWs have increased since 1925. At regional scale, MHWs have become more common in 38% of the world’s coastal ocean over the last few decades (Lima and Wethey, 2012 <sup>[[#fn:r346|346]]</sup> ). In tropical reef systems, the interval between recurrent MHWs and associated coral bleaching events has diminished steadily since 1980, from once every 25 to 30 years in early 1980s to once every 6 years in 2016 (Hughes et al., 2018a <sup>[[#fn:r347|347]]</sup> ). Due to the scarcity of below surface temperature data with high temporal and spatial resolution, it is currently unknown if and how MHWs at depth have changed over the past decades.

Several attribution studies (summarised in Table 6.2) have investigated if the likelihood of individual MHW events has changed due to anthropogenic warming. On a global scale and at present day (2006–2015), climate models suggest that 84–90% ( ''very likely'' range) of all globally occurring MHWs are attributable to the temperature increase since 1850–1900 (Fischer and Knutti, 2015 <sup>[[#fn:r348|348]]</sup> ; Frölicher et al., 2018 <sup>[[#fn:r349|349]]</sup> ). Attribution studies on individual MHW events show that the intensity of the western tropical Pacific MHW in 2014 (Weller et al., 2015 <sup>[[#fn:r350|350]]</sup> ), the intensity of the Alaskan Sea 2016 MHW (Oliver et al., 2018b <sup>[[#fn:r351|351]]</sup> ; Walsh et al., 2018 <sup>[[#fn:r352|352]]</sup> ) and the extreme SSTs in the central equatorial Pacific in 2015–2016 can be fully attributed to anthropogenic warming. In other words, the aforementioned studies show that such events could not have occurred without the temperature increase since 1850–1900. In addition, extreme SSTs in the northeast Pacific in 2014 have become about five times more likely with human-induced global warming (Wang et al., 2014a <sup>[[#fn:r353|353]]</sup> ; Kam et al., 2015 <sup>[[#fn:r354|354]]</sup> ; Weller et al., 2015 <sup>[[#fn:r355|355]]</sup> ). The Tasman Sea 2015–2016 MHW was 330 times (for duration) and 6.8 times (for intensity) more likely with anthropogenic climate change than without (Oliver et al., 2017 <sup>[[#fn:r356|356]]</sup> ), and the northern Australia 2016 MHW was up to fifty times more likely due to anthropogenic climate change (Weller et al., 2015 <sup>[[#fn:r357|357]]</sup> ; King et al., 2017 <sup>[[#fn:r358|358]]</sup> ; Lewis and Mallela, 2018 <sup>[[#fn:r359|359]]</sup> ; Newman et al., 2018 <sup>[[#fn:r360|360]]</sup> ; Oliver et al., 2018b <sup>[[#fn:r361|361]]</sup> ). Also the risk of the Great Barrier Reef bleaching event in 2016 was increased due to anthropogenic climate change (King et al., 2017 <sup>[[#fn:r362|362]]</sup> ; Lewis and Mallela, 2018 <sup>[[#fn:r363|363]]</sup> ). Even though natural variability is still needed for the events to occur, these studies show that most of the individual MHW events analysed so far have a clear human-induced signal. However, such attribution studies have not been undertaken for all major individual MHW events yet (e.g., five out of ten MHWs indicated in Figure 6.3a have not been assessed), and it is therefore still unknown for some of the observed individual MHW events if they have an anthropogenic signal or not (labelled as ‘unknown’ in Figure 6.3a).

We conclude that it is ''very likely'' that MHWs have increased in frequency, duration and intensity since pre-industrial (1850–1900), and that between 2006 – 2015 most MHWs (84–90%; ''very likely'' range) are attributable to the temperature increase since 1850–1900. Only few studies on the attribution of individual MHW events exist, but they all point to human influence on recent MHW events.

<div id="section-6-4-1-2detection-and-attribution-of-mhw-events-block-2"></div>

<span id="figure-6.3"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 6.3'''

<span id="figure-6.3-examples-of-recent-marine-heatwaves-mhws-and-their-observed-impacts.-a-examples-of-documented-mhws-over-the-last-two-decades-and-their-impacts-on-natural-physical-and-socioeconomic-systems.-the-colour-map-shows-the-maximum-sea-surface-temperature-sst-anomaly-during-the-mhw-using-the-national-oceanic-and-atmospheric-administrations-noaa-daily"></span>
<!-- IMG CAPTION -->
'''Figure 6.3 | Examples of recent marine heatwaves (MHWs) and their observed impacts. (a) Examples of documented MHWs over the last two decades and their impacts on natural, physical and socioeconomic systems. The colour map shows the maximum sea surface temperature (SST) anomaly during the MHW using the National Oceanic and Atmospheric Administration’s (NOAA) daily […]'''
<!-- IMG FILE -->
[[File:181956bc46f381ac899f5e3c70d6580f IPCC-SROCC-CH_6_3.jpg]]

Figure 6.3 | Examples of recent marine heatwaves (MHWs) and their observed impacts. (a) Examples of documented MHWs over the last two decades and their impacts on natural, physical and socioeconomic systems. The colour map shows the maximum sea surface temperature (SST) anomaly during the MHW using the National Oceanic and Atmospheric Administration’s (NOAA) daily Optimum Interpolation SST dataset (Reynolds et al. 2007 <sup>[[#fn:r364|364]]</sup> ; Banzon et al. 2016 <sup>[[#fn:r365|365]]</sup> ). A MHW is defined here as a set of spatially and temporally coherent grid points exceeding the 99th percentile. The 99th percentile is calculated over the 1982–2011 reference period after de-seasonalising the data. Red shading of the boxes indicates if the likelihood of MHW occurrence has increased due to anthropogenic climate change, and symbols denote observed impacts on physical systems over land, marine ecosystems, and socioeconomic and human systems. Figure is updated from Frölicher and Laufkötter (2018) <sup>[[#fn:r366|366]]</sup> and is not a complete compilation of all documented MHWs. (b) The record warming years 2015 and 2016 and the global extent of mass bleaching of corals during these years. The colour map shows the Degree Heating Week (DHW) annual maximum over 2015 and 2016 from NOAA’s Coral Reef Watch Daily Global 5 km Satellite Coral Bleaching Heat Stress Monitoring Product Suite v.3.1 (Liu et al. 2014a <sup>[[#fn:r367|367]]</sup> ). The DHW describes how much heat has accumulated in an area over the past twelve weeks by adding up any temperatures that exceed 1oC above the maximum summertime mean (e.g., Eakin et al. 2010). Symbols show reef locations that are assessed in Hughes et al. (2018a) and indicate where severe bleaching affected more than 30% of corals (purple circles), moderate bleaching affected less than 30% of corals (blue circles), and no substantial bleaching was recorded (light blue circles).

<!-- END IMG -->
<div id="section-6-4-1-3future-changes"></div>

<span id="future-changes"></span>
==== 6.4.1.3 Future Changes ====

<div id="section-6-4-1-3future-changes-block-1"></div>

MHWs will increase in frequency, duration, spatial extent and intensity throughout the ocean under future global warming (Oliver et al., 2017 <sup>[[#fn:r368|368]]</sup> ; Ramírez and Briones, 2017 <sup>[[#fn:r369|369]]</sup> ; Alexander et al., 2018 <sup>[[#fn:r370|370]]</sup> ; Frölicher et al., 2018 <sup>[[#fn:r371|371]]</sup> ; Frölicher and Laufkötter, 2018 <sup>[[#fn:r372|372]]</sup> ; Darmaraki et al., 2019 <sup>[[#fn:r373|373]]</sup> ). Projections based on 12 CMIP5 Earth system models suggest that, on global scale, the probability of MHWs exceeding the pre-industrial (1850–1900) 99th percentile will ''very likely'' increase by a factor of 20–27 by 2031–2050 and ''very likely'' by a factor of 46–55 by 2081–2100 under the RCP8.5 greenhouse gas (GHG) scenario (Figure 6.4a; Frölicher et al. 2018 <sup>[[#fn:r374|374]]</sup> ). In other words, a one-in-hundred-day event at pre-industrial levels is projected to become a one-in-four-day event by 2031–2050 and a one-in-two-day event by 2081–2100. The duration of MHW is projected to ''very likely'' increase from 8–10 days at 1850–1900, to 126–152 days in 2081–2100 under the RCP8.5 scenario (Frölicher et al., 2018 <sup>[[#fn:r375|375]]</sup> ). The maximum intensity (maximum exceedance of the 1850–1900 99th percentile) will ''very likely'' increase from 0.3°C–0.4°C in 1850–1900, to 3.1°C–3.8°C in 2081–2100 under the RCP8.5 scenario. Under the RCP2.6 scenario, the magnitude of changes in the different MHW metrics would be substantially reduced (Frölicher et al., 2018 <sup>[[#fn:r376|376]]</sup> ). For example, the probability ratio would ''very likely'' increase by a factor of 16–24 by 2081–2100 for RCP2.6; less than half of that is projected for the RCP8.5. The magnitude of changes in the probability ratio scales with global mean atmospheric surface temperature and is independent of the warming path (Figure 6.4b), that is, it does not depend on whether a particular warming level is reached sooner (RCP8.5) or later (RCP2.6).

<div id="section-6-4-1-3future-changes-block-2"></div>

<span id="figure-6.4"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 6.4'''

<span id="figure-6.4-global-and-regional-changes-in-the-probability-ratio-of-marine-heatwaves-mhws.-the-probability-ratio-is-the-fraction-by-which-the-number-of-mhw-days-yr1-has-changed-since-18501900.-a-changes-in-the-annual-mean-probability-ratio-of-mhws-exceeding-the-99th-percentile-of-pre-industrial-local-daily-sea-surface-temperature-sst"></span>
<!-- IMG CAPTION -->
'''Figure 6.4 | Global and regional changes in the probability ratio of marine heatwaves (MHWs). The probability ratio is the fraction by which the number of MHW days yr–1 has changed since 1850–1900. (a) Changes in the annual mean probability ratio of MHWs exceeding the 99th percentile of pre-industrial local daily sea surface temperature (SST) […]'''
<!-- IMG FILE -->
[[File:47954e6bb8e31c80e0e78b9bc7354e7b IPCC-SROCC-CH_6_4.jpg]]

Figure 6.4 | Global and regional changes in the probability ratio of marine heatwaves (MHWs). The probability ratio is the fraction by which the number of MHW days yr–1 has changed since 1850–1900. (a) Changes in the annual mean probability ratio of MHWs exceeding the 99th percentile of pre-industrial local daily sea surface temperature (SST) averaged over the ocean. The thick lines represent the multi-model averages of 12 climate models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) covering the 1861–2100 period for the Representative Concentration Pathway (RCP) 8.5 and RCP2.6 scenarios, respectively. The shaded bands indicate the 90% confidence interval of the standard error of the mean. The black line shows an observational-based estimate. As daily SST data are available only for the 1982–2016 period, we assume that the observed mean temperature change is the main cause of the change in frequency of extremes (Frölicher et al. 2018; Oliver, 2019). We therefore subtracted first the differences between 1854–1900 and 1982–2016 obtained from the extended reconstructed SST Version 4 dataset (ERSSTv4; Huang et al. 2015a) from the daily satellite data before calculating the 99th percentile for the observations. (b) Same as (a), but the probability ratio is plotted for different levels of global surface atmospheric warming and for the individual models. The simulated time series in (b) are smoothed with a 10-year running mean. (c,d) Simulated regional changes in the multi-model mean probability ratio of MHWs exceeding the preindustrial 99th percentile in 2081–2100 for the (c) RCP2.6 scenario and the (d) RCP8.5 scenario. The grey contours in (c,d) highlight the spatial pattern. Figure is modified from Frölicher et al. (2018) <sup>[[#fn:r380|380]]</sup> .

<!-- END IMG -->
<div id="section-6-4-1-3future-changes-block-3"></div>

The changes in MHWs will not be globally uniform. CMIP5 models project that the largest increases in the probability of MHWs will occur in the tropical ocean, especially in the western tropical Pacific, and the Arctic Ocean (Figure 6.4c,d), and that most of the large marine ecosystems will also experience large increases in the number of MHW days (Alexander et al., 2018 <sup>[[#fn:r381|381]]</sup> ; Frölicher et al., 2018 <sup>[[#fn:r382|382]]</sup> ). Smallest increases are projected for the Southern Ocean. In addition, MHW events in the Great Barrier Reef, such as the one associated with the bleaching in 2016, are projected to be at least twice as frequent under 2°C global warming than they are today (King et al., 2017 <sup>[[#fn:r383|383]]</sup> ). The magnitude of projected changes at the local scale is uncertain, partly due to issues of horizontal and vertical resolution of CMIP5-type Earth system models. Only a few studies have used higher resolution oceanic models (eddy-resolving) to assess the local-to-regional changes in MHW characteristics. For example, regional high-resolution coupled climate model simulations suggest that the Mediterranean Sea will experience at least one long lasting MHW every year by the end of the 21st century under the RCP8.5 scenario (Darmaraki et al., 2019 <sup>[[#fn:r384|384]]</sup> ), and eddy-resolving ocean model simulations project a further increase in the likelihood of extreme temperature events in the Tasman Sea (Oliver et al., 2014 <sup>[[#fn:r385|385]]</sup> ; Oliver et al., 2015 <sup>[[#fn:r386|386]]</sup> ; Oliver et al., 2017 <sup>[[#fn:r387|387]]</sup> ).

Most of the global changes in the probability of MHWs, when defined relative to a fixed temperature climatology and using coarse resolution CMIP5-type climate models, are driven by the global-scale shift in the mean ocean temperature (Alexander et al., 2018 <sup>[[#fn:r388|388]]</sup> ; Frölicher et al., 2018 <sup>[[#fn:r389|389]]</sup> ). However, previously ice-covered regions, such as the Arctic Ocean, will exhibit larger SST variability under future global warming. This is because of an enhanced SST increase in summer due to sea ice retreat, but SST remaining near the freezing point in winter (Carton et al., 2015 <sup>[[#fn:r390|390]]</sup> ; Alexander et al., 2018 <sup>[[#fn:r391|391]]</sup> ). When contrasting the changes in the probability of MHWs with land-based heatwaves (Fischer and Knutti, 2015 <sup>[[#fn:r392|392]]</sup> ), it is evident that MHWs are projected to occur more frequently (Frölicher et al., 2018 <sup>[[#fn:r393|393]]</sup> ; Frölicher and Laufkötter, 2018 <sup>[[#fn:r394|394]]</sup> ). This is because the temperature variability is much smaller in ocean surface waters than in the atmosphere (Frölicher and Laufkötter, 2018 <sup>[[#fn:r395|395]]</sup> ) .

We conclude that there is ''very'' ''high confidence'' that MHWs will increase in frequency, duration, spatial extent and intensity in all ocean basins under future global warming, mainly because of an increase in mean ocean temperature. However, higher resolution models are needed to make robust projections at the local-to-regional scale.

<span id="impacts-on-natural-physical-and-human-systems"></span>
=== 6.4.2 Impacts on Natural, Physical and Human Systems ===

<div id="section-6-4-2-1impacts-on-marine-organisms-and-ecosystems"></div>

<span id="impacts-on-marine-organisms-and-ecosystems"></span>
==== 6.4.2.1 Impacts on Marine Organisms and Ecosystems ====

<div id="section-6-4-2-1impacts-on-marine-organisms-and-ecosystems-block-1"></div>

Temperature plays an essential role in the biology and ecology of marine organisms (e.g., Pörtner, 2002; Pörtner and Knust, 2007 <sup>[[#fn:r396|396]]</sup> ; Poloczanska et al., 2013 <sup>[[#fn:r397|397]]</sup> ; Hoegh-Guldberg et al., 2014 <sup>[[#fn:r398|398]]</sup> ), and therefore extreme high ocean temperature can have large impacts on marine ecosystems. Recent studies show that MHWs have strongly impacted marine organisms and ecosystem services in all ocean basins (Smale et al., 2019 <sup>[[#fn:r399|399]]</sup> ) over the last two decades. Impacts include coral bleaching and mortality (Hughes et al., 2017b <sup>[[#fn:r400|400]]</sup> ; Hughes et al., 2018a <sup>[[#fn:r401|401]]</sup> ; Hughes et al., 2018b <sup>[[#fn:r402|402]]</sup> ), loss of seagrass and kelp forests (Smale et al., 2019 <sup>[[#fn:r403|403]]</sup> ), shifts in species range (Smale and Wernberg, 2013 <sup>[[#fn:r404|404]]</sup> ), and local (Wernberg et al., 2013 <sup>[[#fn:r405|405]]</sup> ; Wernberg et al., 2016 <sup>[[#fn:r406|406]]</sup> ) and potentially global extinctions of coral species (Brainard et al., 2011 <sup>[[#fn:r407|407]]</sup> ).

A growing number of studies have reported that MHWs negatively affect corals and coral reefs through bleaching, disease, and mortality (see Chapter 5 for an extensive discussion on coral reefs and coral bleaching). The recent (2014–2017) high ocean temperatures in the tropics and subtropics triggered a pan-tropical episode of unprecedented mass bleaching of corals (100s of km 2 ), the third global-scale event after 1997–1998 and 2010 (Heron et al., 2016 <sup>[[#fn:r408|408]]</sup> ; Eakin et al., 2017 <sup>[[#fn:r409|409]]</sup> ; Hughes et al., 2017b <sup>[[#fn:r410|410]]</sup> ; Eakin et al., 2018 <sup>[[#fn:r411|411]]</sup> ; Hughes et al., 2018a <sup>[[#fn:r412|412]]</sup> ). The heat stress during this event was sufficient to cause bleaching at 75% of global reefs (Hughes et al., 2018a; Figure 6.3b) and mortality at 30% (Eakin et al., 2017 <sup>[[#fn:r414|414]]</sup> ), much more than any previously documented global bleaching event. In some locations, many reefs bleached extensively for the first time on record, and over half of the reefs bleached multiple times during the three year event. However, there were distinct geographical variations in bleaching, mainly determined by the spatial pattern and magnitude of the MHW (Figure 6.3b). For example, bleaching was extensive and severe in the northern regions of the Great Barrier Reef, with 93% of the northern Australian Great Barrier Reef coral suffering bleaching in 2016, but impacts were moderate at the southern coral reefs of the Great Barrier Reef (Brainard et al., 2018 <sup>[[#fn:r415|415]]</sup> ; Stuart-Smith et al., 2018 <sup>[[#fn:r416|416]]</sup> ).

Apart from strong impacts on corals, recent MHWs have demonstrated their potential impacts on other marine ecosystems and ecosystems services (Ummenhofer and Meehl, 2017 <sup>[[#fn:r417|417]]</sup> ; Smale et al., 2019 <sup>[[#fn:r418|418]]</sup> ). Two of the best studied MHWs with extensive ecological implications are the Western Australia 2011 MHW and the Northeast Pacific 2013–2015 MHW. The Western Australia 2011 MHW resulted in a regime shift of the temperate reef ecosystem (Wernberg et al., 2013 <sup>[[#fn:r419|419]]</sup> ; Wernberg et al., 2016 <sup>[[#fn:r420|420]]</sup> ). The abundance of the dominant habitat-forming seaweeds ''Scytohalia dorycara'' and ''Ecklonia radiata'' became significantly reduced and ''Ecklonia'' kelp forest was replaced by small turf-forming algae with wide ranging impacts on associated sessile invertebrates and demersal fish. The sea grass ''Amphibolis antarctica'' in Shark Bay underwent defoliation after the MHW (Fraser et al., 2014 <sup>[[#fn:r421|421]]</sup> ), and together with the loss of other sea grass species, these lead to releases of 2–9 Tg CO 2 to the atmosphere during the subsequent three years after the MHW (Arias-Ortiz et al., 2018 <sup>[[#fn:r422|422]]</sup> ). In addition, coral bleaching and adverse impacts on invertebrate fisheries were documented (Depczynski et al., 2013 <sup>[[#fn:r423|423]]</sup> ; Caputi et al., 2016 <sup>[[#fn:r424|424]]</sup> ). The Northeast Pacific 2013–2015 MHW also caused extensive alterations to open ocean and coastal ecosystems (Cavole et al., 2016 <sup>[[#fn:r425|425]]</sup> ). Impacts included increased mortality events of sea birds (Jones et al., 2018 <sup>[[#fn:r426|426]]</sup> ), salmon and marine mammals (Cavole et al., 2016 <sup>[[#fn:r427|427]]</sup> ), very low ocean primary productivity (Whitney, 2015 <sup>[[#fn:r428|428]]</sup> ; Jacox et al., 2016 <sup>[[#fn:r429|429]]</sup> ), an increase in warm water copepod species (Di Lorenzo and Mantua, 2016) and novel species compositions (Peterson et al., 2017 <sup>[[#fn:r430|430]]</sup> ). In addition, a coast wide bloom of the toxigenic diatom ''Pseudo-nitzschia'' resulted in the largest ever recorded outbreak of domoic acid along the North American west coast (McCabe et al., 2016 <sup>[[#fn:r431|431]]</sup> ). Domoic acid was detected in many marine mammals, such as whales, dolphins, porpoises, seals and sea lions. The elevated toxins in commercially harvested fish and invertebrates resulted in prolonged and geographically extensive closure of razor clam and crab fisheries.

Other MHWs also demonstrated the vulnerability of marine organisms and ecosystems to extremely high ocean temperatures. The Northwest Atlantic 2012 MHW strongly impacted coastal ecosystems by causing a northward movement of warm water species and local migrations of some species (e.g., lobsters) earlier in the season (Mills et al., 2013 <sup>[[#fn:r432|432]]</sup> ; Pershing et al., 2015) <sup>[[#fn:r433|433]]</sup> . The Mediterranean Sea 2003 MHW lead to mass mortalities of macro-invertebrate species (Garrabou et al., 2009 <sup>[[#fn:r434|434]]</sup> ) and the Tasman Sea 2015–2016 MHW had impacts on sessile, sedentary and cultured species in the shallow, near-shore environment including outbreaks of disease in commercially viable species (Oliver et al., 2017 <sup>[[#fn:r435|435]]</sup> ). ''Vibrio'' outbreaks were also observed in the Baltic Sea in response to elevated SSTs (Baker-Austin et al., 2013 <sup>[[#fn:r436|436]]</sup> ). The Alaskan Sea 2016 MHW favoured some phytoplankton species, leading to harmful algal blooms, shellfish poisoning events and mortality events in seabirds (Walsh et al., 2018 <sup>[[#fn:r437|437]]</sup> ; see chapter 3 for more details). Also, lower than average size of multiple groundfish species were observed including Pollock, Pacific cod, and Chinook salmon (Zador and Siddon, 2016 <sup>[[#fn:r438|438]]</sup> ). The Yellow Sea/East China Sea 2016 MHW killed a large number of different marine organisms in coastal and bay areas around South Korea (Kim and Han, 2017 <sup>[[#fn:r439|439]]</sup> ) and the Southwest Atlantic 2017 MHW lead to toxic algal blooms (Manta et al., 2018 <sup>[[#fn:r440|440]]</sup> ). The Coastal Peruvian 2017 MHW affected anchovies, which showed decreased fat content and early spawning as a reproductive strategy (IMPARPE, 2017), a behaviour usually seen during warm El Niño conditions (Ñiquen and Bouchon, 2004 <sup>[[#fn:r442|442]]</sup> ).

Based on the examples described above we conclude with ''very high confidence'' that a range of organisms and ecosystems have been impacted by MHWs across all ocean basins over the last two decades. Given that MHWs will ''very likely'' increase in intensity and frequency with further climate warming, we conclude with ''high confidence'' that this will push some marine organisms, fisheries and ecosystem beyond the limits of their resilience. These impacts will occur on top of those expected from a progressive shift in global mean ocean temperatures.

<div id="section-6-4-2-2impacts-on-the-physical-system"></div>

<span id="impacts-on-the-physical-system"></span>
==== 6.4.2.2 Impacts on the Physical System ====

<div id="section-6-4-2-2impacts-on-the-physical-system-block-1"></div>

MHWs can impact weather patterns over land via teleconnections causing drought, heavy precipitation or heat wave events. For example, the Northeast Pacific 2013–2015 MHW and the associated persistent atmospheric high-pressure ridge prevented normal winter storms from reaching the West Coast of the US and may have contributed to the drought conditions across the entire West Coast (Seager et al., 2015 <sup>[[#fn:r443|443]]</sup> ; Di Lorenzo and Mantua, 2016). The Tasman Sea 2015–2016 MHW has increased the intensity of rainfall that caused flooding in northeast Tasmania in January 2016 (see Box 6.1) and the Coastal Peruvian 2017 MHW caused heavy rainfall and flooding on the west coast of tropical South America (ENFEN, 2017 <sup>[[#fn:r444|444]]</sup> ; Echevin et al., 2018 <sup>[[#fn:r445|445]]</sup> ; Garreaud, 2018 <sup>[[#fn:r446|446]]</sup> ; Takahashi et al., 2018 <sup>[[#fn:r447|447]]</sup> ). Similarly, MHWs in the Mediterranean Sea may have amplified heatwaves (Feudale and Shukla, 2007 <sup>[[#fn:r448|448]]</sup> ; García-Herrera et al., 2010 <sup>[[#fn:r449|449]]</sup> ) and heavy precipitation events over central Europe (Messmer et al., 2017 <sup>[[#fn:r450|450]]</sup> ), as well as trigger intense ETCs over the Mediterranean Sea (González ‐ Alemán et al., 2019 <sup>[[#fn:r451|451]]</sup> ). Such physical changes induced by MHWs may then also affect ecosystems and human systems on land (Reimer et al., 2015 <sup>[[#fn:r452|452]]</sup> ).

It should be noted that past and future impacts of MHWs on weather patterns over land depend not only on the duration and intensity of MHWs, but also on a wide range of different additional processes in the climate system such as the large-scale circulation of the atmosphere and oceans, and changes in the mean climate. Therefore, we conclude that there is currently ''low confidence'' in how MHWs impact the weather systems over land.

<div id="section-6-4-2-3impacts-on-the-human-system"></div>

<span id="impacts-on-the-human-system"></span>
==== 6.4.2.3 Impacts on the Human System ====

<div id="section-6-4-2-3impacts-on-the-human-system-block-1"></div>

MHWs can also lead to significant socioeconomic ramifications when affecting aquaculture or important fishery species, or when triggering heavy rain or drought events on land. The Northwest Atlantic 2012 MHW, for example, had major economic impacts on the US lobster industry in 2015 (Mills et al., 2013). The MHWs lead to changes in lobster fishing practices and harvest patterns, because the lobsters moved from the deep offshore waters into shallower coastal areas much earlier in the season than usual causing a rapid rise in lobster catch rates. Together with a supply chain bottleneck, the record catch outstripped market demand and contributed to a collapse in lobster prices (Mills et al., 2013 <sup>[[#fn:r453|453]]</sup> ). Even though high catch volumes were reported, the price collapse threatened the economic viability of many US and Canadian lobster fisheries. Economic impacts through changes in fisheries were also reported during the Northeast Pacific 2013–2015 MHW and the Alaskan Sea 2016 MHW. The Northeast Pacific 2013–2015 MHW led to closing of both commercial and recreational fisheries resulting in millions of USD in losses among fishing industries (Cavole et al., 2016 <sup>[[#fn:r455|455]]</sup> ). In addition, the toxin produced by the harmful algal blooms can be transferred through the marine food web and humans who eat contaminated fish, shellfish or crustaceans (Berdalet et al., 2016 <sup>[[#fn:r456|456]]</sup> ; Du et al., 2016 <sup>[[#fn:r457|457]]</sup> ; McCabe et al., 2016 <sup>[[#fn:r458|458]]</sup> ). The ingestion of such contaminated seafood products, the inhalation of aerosolised toxins or the skin contact with toxin-contaminated water may cause toxicity in humans. Symptoms in human associated with the ingestion of the contaminated seafood range from mild gastrointestinal distress to seizures, coma, permanent short-term memory loss and death (Perl et al., 1990 <sup>[[#fn:r459|459]]</sup> ). The ecological changes associated with the Alaskan Sea 2016 MHW impacted subsistence and commercial activities. For example, ice-based harvesting of seals, crabs and fish in western Alaska was delayed due to the lack of winter sea ice. MHWs can also impact the socioeconomic and human system through changes to weather patterns. For example, heavy rain associated with the Coastal Peruvian 2017 MHW triggered numerous landslides and flooding, which resulted in a death toll of several hundred, and widespread damage to infrastructure and civil works (United Nations, 2017 <sup>[[#fn:r460|460]]</sup> ).

Studies on the impact of MHWs on human systems are still relatively scarce, even though many show negative impacts on human health and economy. We therefore conclude with ''medium confidence'' that MHWs can negatively impact human health and economy.

<span id="risk-management-and-adaptation-monitoring-and-early-warning-systems"></span>
=== 6.4.3 Risk Management and Adaptation, Monitoring and Early Warning Systems ===

<div id="section-6-4-3risk-management-and-adaptation-monitoring-and-early-warning-systems-block-1"></div>

Risk management strategies to respond to MHWs include early warning systems as well as seasonal (weeks to several months) and multi-annual predictions systems. Since 1997, the National Oceanic and Atmospheric Administration’s (NOAA) Coral Reef Watch has used satellite SST data to provide near real-time warning of coral bleaching (Liu et al., 2014a <sup>[[#fn:r461|461]]</sup> ). These satellite-based products, along with NOAA Coral Reef Watch’s four month coral bleaching outlook based on operational climate forecast models (Liu et al., 2018 <sup>[[#fn:r462|462]]</sup> ), and coral disease outbreak risk (Heron et al., 2010 <sup>[[#fn:r463|463]]</sup> ) provide critical guidance to coral reef managers, scientists, and other stakeholders (Tommasi et al., 2017b <sup>[[#fn:r464|464]]</sup> ; Eakin et al., 2018 <sup>[[#fn:r465|465]]</sup> ). These products are also used to implement proactive bleaching response plans (Rosinski et al., 2017 <sup>[[#fn:r466|466]]</sup> ), brief stakeholders, and allocate monitoring resources in advance of bleaching events, such as the 2014–2017 global coral bleaching event (Eakin et al., 2017 <sup>[[#fn:r467|467]]</sup> ). For example, Thailand closed ten reefs for diving in advance of the bleaching peak in 2016, while Hawaii immediately began preparation of resources both to monitor the 2015 bleaching and to place specimens of rare corals in climate controlled, onshore nurseries in response to these forecast systems (Tommasi et al., 2017b <sup>[[#fn:r468|468]]</sup> ). New measurement techniques, such as Argo and deep Argo floats, may help to further develop prediction systems for subsurface MHWs, but such systems are not yet in place.

SST forecasts ranging from seasonal to decadal (5–10 years) have also been used or are planned to be used as early warning systems for multiple other ecosystems and fisheries in addition to coral reefs, including aquaculture, lobster, sardine, and tuna fisheries (Hobday et al., 2016b <sup>[[#fn:r469|469]]</sup> ; Payne et al., 2017 <sup>[[#fn:r470|470]]</sup> ; Tommasi et al., 2017b <sup>[[#fn:r471|471]]</sup> ). For example, seasonal forecasts of SST around Tasmania may help farm managers of salmon aquaculture to prepare and respond to upcoming MHWs by changing stocking densities, varying feed mixes, transferring fish to different locations in the farming region and implementing disease management (Spillman and Hobday, 2014 <sup>[[#fn:r472|472]]</sup> ; Hobday et al., 2016b <sup>[[#fn:r473|473]]</sup> ). Skilful multi-annual to decadal SST predictions may also inform and improve decisions about spatial and industrial planning, as well as the management of various extractive sectors such as the adjustments to quotas for internationally shared fish stocks (Tommasi et al., 2017a <sup>[[#fn:r474|474]]</sup> ). It has been shown that global climate forecasts have significant skill in predicting the occurrence of above average warm or cold SST events at decadal timescales in coastal areas (Tommasi et al., 2017a <sup>[[#fn:r475|475]]</sup> ), but barriers to their widespread usage in fishery and aquaculture industry still exist (Tommasi et al., 2017b <sup>[[#fn:r476|476]]</sup> ).

Even with a monitoring and prediction system in place, MHWs have developed without warning and had catastrophic effects (Payne et al., 2017 <sup>[[#fn:r477|477]]</sup> ). For example, governmental agencies, socioeconomic sectors, public health officials and citizens were not forewarned of the Coastal Peruvian 2017 MHW, despite a basin-wide monitoring system across the Pacific. The reason was partly due to a coastal El Niño definition problem and a new government (in Nicaragua) that may have hindered actions (Ramírez and Briones, 2017 <sup>[[#fn:r478|478]]</sup> ). Therefore, early warning systems should not only provide predictions of physical changes, but should also connect different institutions to assist decision makers in performing time-adaptive measures (Chang et al., 2013 <sup>[[#fn:r479|479]]</sup> ).

Monitoring and prediction systems are important and can be advanced by the use of common metrics to describe MHWs. So far, MHWs are often defined differently in the literature, and it is only recently that a categorising scheme (Categories I to IV; based on the degree to which temperatures exceed the local climatology), similar to what is used for hurricanes, has been developed (Hobday et al., 2018 <sup>[[#fn:r480|480]]</sup> ). Such a categorising scheme, can easily be applied to real data and predictions, and may facilitate comparison, public communication and familiarity with MHWs. Similar metrics (e.g., DHW) have been successfully developed and used to identify ocean regions where conditions conducive to coral bleaching are developing.

<span id="extreme-enso-events-and-other-modes-of-interannual-climate-variability"></span>