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

=== 6.4.1 Observations and Key Processes, Detection and Attribution, Projections ===

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==== 6.4.1.1 Recent Documented MHWs and Key Driving Mechanisms ====

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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>

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==== 6.4.1.2 Detection and Attribution of MHW Events ====

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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.

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'''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>
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'''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 […]'''
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[[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).

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==== 6.4.1.3 Future Changes ====

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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).

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'''Figure 6.4'''

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'''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) […]'''
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[[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> .

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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.

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