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

=== 6.2.2 Recent Anomalous Extreme Climate Events and their Causes ===

<div id="section-6-2-2recent-anomalous-extreme-climate-events-and-their-causes-block-1"></div>

The attribution of changes in the observed statistics of extremes are generally addressed using well-established detection-attribution methods. In contrast, record-breaking weather and climate events are by definition unique, and can be expected to occur with or without climate change as the observed record lengthens. Therefore, event attribution begins with the premise that the climate is changing, the goal being to determine statistically how much climate change has contributed to the severity of the event (Trenberth et al., 2015 <sup>[[#fn:r5|5]]</sup> ; Shepherd, 2016 <sup>[[#fn:r6|6]]</sup> ). Annual reports dedicated to extreme event attribution (Peterson et al., 2012 <sup>[[#fn:r7|7]]</sup> ; Peterson et al., 2013 <sup>[[#fn:r8|8]]</sup> ; Herring et al., 2014 <sup>[[#fn:r9|9]]</sup> ; Herring et al., 2015 <sup>[[#fn:r10|10]]</sup> ; Herring et al., 2018 <sup>[[#fn:r11|11]]</sup> ) have helped stimulate studies that adopt recognised methods for extreme event attribution. The increasing pool of studies allows different approaches to be contrasted and builds consensus on the role of climate change when individual climate events are studied by multiple teams using different methods. A number of these events are summarised in Table 6.2 and Figure 6.2. Collectively, these studies show that the role of climate change in the ocean and cryosphere extreme events is increasingly driving extreme climate and weather events across the globe including compound events ( ''high confidence'' ).  Some regions including Africa and the Pacific have had relatively fewer event attribution studies undertaken, possibly reflecting the lack of capacity by regional and national technical institutions. A caveat of this approach is that there is a potential for ‘null results’, that is, cases where attribution is not possible, to be reported. Nevertheless, there is no evidence that this is the case, and the number of recent studies and wide range of phenomena addressed suggests increasing influence of climate change on extreme events.

<div id="section-6-2-2recent-anomalous-extreme-climate-events-and-their-causes-block-2"></div>

<span id="figure-6.2"></span>
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'''Figure 6.2'''

<span id="figure-6.2-locations-where-extreme-events-with-an-identified-link-to-ocean-changes-have-been-discussed-in-table-6.2."></span>
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'''Figure 6.2 | Locations where extreme events with an identified link to ocean changes have been discussed in Table 6.2.'''
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[[File:8cc39f00ff6ed8c9204317248906f3b7 IPCC-SROCC-CH_6_2.jpg]]

Figure 6.2 | Locations where extreme events with an identified link to ocean changes have been discussed in Table 6.2.

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<div id="section-6-2-2recent-anomalous-extreme-climate-events-and-their-causes-block-3"></div>

<span id="table-6.2"></span>
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'''Table 6.2'''

A selection of extreme events with links to oceans and cryosphere. In many of these studies the method of event attribution has been used to estimate the role of climate change using either a probabilistic approach (using ensembles of climate models to assess how much more likely the event has become with anthropogenic climate change compared to a world without) or a storyline approach which examines the components of the climate system that contribute to the events and how changes in the climate system affect them (Shepherd, 2016 <sup>[[#fn:r12|12]]</sup> ).

<!-- TABLE -->
{| class="wikitable"
|-
| Year/type of hazard
| Region
| Severe hazard
| Attribution to anthropogenic climate change
| Impact, costs
|-
| 1998
[[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]

| Western equatorial Pacific, Great Barrier Reef, Australia
| Extreme sea surface temperatures (SSTs)
| Unknown if global warming has increased the probability.
| Coral bleaching
|-
| 2003 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Mediterranean Sea
| June to August with sea water temperatures 1°C–3°C above climatological mean (Olita et al., 2007 <sup>[[#fn:r13|13]]</sup> ; Garrabou et al., 2009 <sup>[[#fn:r14|14]]</sup> ; Galli et al., 2017 <sup>[[#fn:r15|15]]</sup> )
| Increase in air temperature and a reduction of wind stress and air-sea exchanges (Olita et al., 2007 <sup>[[#fn:r16|16]]</sup> ). Unknown if global warming has increased the probability
| Mass mortality of macro-invertebrate species; amplified heatwave over central Europe in 2003
|-
| 2004 [[File:9caae6f34b74b711a15e10bd9e43efe7 icon-hurricane-150x150.png]]
| South Atlantic
| First hurricane in the South Atlantic since 1970
| Increasing trend to positive Southern Annular Mode (SAM) could favour the synoptic conditions for such events in the future (Pezza and Simmonds, 2005 <sup>[[#fn:r17|17]]</sup> )
| Three deaths, 425 million USD damage (McTaggart-Cowan et al., 2006 <sup>[[#fn:r20|20]]</sup> )
|-
| 2005 [[File:019c4c15fc474e50e8acabd69c7527d6 icon-hurricanes-150x150.png]]
| North Atlantic
| Record number of tropical storms, hurricanes and Category 5 hurricanes since 1970
| Trend in SST due to global warming contributed to half of the total SST anomaly. Atlantic Multidecadal Variability (AMV) and the after-effects of the 2004–2005 El Niño also played a role (Trenberth and Shea, 2006 <sup>[[#fn:r18|18]]</sup> )
| Costliest US natural disaster; 1,836 deaths and 30 billion USD in direct economic costs in Louisiana due to Hurricane Katrina (Link, 2010 <sup>[[#fn:r21|21]]</sup> )
|-
| 2007 [[File:9caae6f34b74b711a15e10bd9e43efe7 icon-hurricane-150x150.png]]
| Arabian Sea
| Strongest tropical cyclone (TC) (Gonu) attaining sustained winds of 270 kph and gustiness of 315 kph
| No attribution study done, although it was noted that this Category 5 TC had followed an unusual path
(Dibajnia et al., 2010 <sup>[[#fn:r19|19]]</sup> )

| Caused around 4 billion USD in damages (Fritz et al., 2010 <sup>[[#fn:r22|22]]</sup> ; Coles et al., 2015 <sup>[[#fn:r23|23]]</sup> )
|-
| 2008 [[File:80e0c0e6a7b2754e3fa59c30f149fee4 icon-wave-150x150.png]]
| Western Pacific Islands
| North Pacific generated wave-swell event
| Event shown to have been made more extreme compared to other historical events due to La Niña and SLR (Hoeke et al., 2013 <sup>[[#fn:r24|24]]</sup> )
| Wave-induced inundation in islands of six Pacific nations (Kiribati, Marshall Islands, Micronesia, Nauru, Papua New Guinea, Solomon Islands), salt water flooding of food and water supplies in Kosrae, Micronesia, 1,408 houses damaged and 63,000 people affected across eight provinces in Papua New Guinea (Hoeke et al., 2013 <sup>[[#fn:r25|25]]</sup> )
|-
| 2010 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Western equatorial Pacific, Great Barrier Reef, Australia
| Extreme SST
| Unknown if global warming increased the probability
| Coral bleaching
|-
| 2010 [[File:1fe32f32e663e3d311e7dd9999a82dd7 icon-sun-150x150.png]]
| Southern Amazon
| Widespread drought in the Amazon led to lowest river levels of major Amazon tributaries on record (Marengo et al., 2011 <sup>[[#fn:r26|26]]</sup> )
| Model-based attribution indicates human influences and SST natural variability increased probabilities of the 2010 severe drought in the South Amazon region whereas aerosol emissions had little effect (Shiogama et al., 2013 <sup>[[#fn:r27|27]]</sup> )
| Relative to the long-term mean, the 2010 drought resulted in a reduction in biomass carbon uptake of 1.1 Pg C, compared to 1.6 Pg C for the 2005 event which was driven by an increase in biomass mortality (Feldpausch et al., 2016 <sup>[[#fn:r28|28]]</sup> )
|-
| 2010–2011 [[File:e2cac7f216102765183d902d0f0cf453 icon-rain-150x150.png]]
| Eastern Australia
| Wettest spring since 1900 (Leonard et al., 2014 <sup>[[#fn:r29|29]]</sup> )
| Based on La Niña SSTs during satellite era, La Niña alone is insufficient to explain total rainfall. 25% of rainfall was attributed to SST trend in region (Evans and Boyer-Souchet, 2012 <sup>[[#fn:r30|30]]</sup> )
| Brisbane river catchment flooding in January 2011, costing 23 lives and an estimated 2.55 billion USD (van den Honert and McAneney, 2011 <sup>[[#fn:r31|31]]</sup> )
|-
| 2010–2011 [[File:4148063e3469466e9a29fb15bfb8c4c3 icon-snow-150x150.png]]
| UK
| Severely cold winter (coldest December since 1910 and second coldest since 1659)
| Model results indicate that human influence reduced the odds by at least 20% and possibly by as much as 4 times with a best estimate that the odds have been halved (Christidis and Stott, 2012 <sup>[[#fn:r32|32]]</sup> )
| Many adverse consequences of the extreme temperatures, including closed schools and airports (Christidis and Stott, 2012 <sup>[[#fn:r33|33]]</sup> )
|-
| 2011 [[File:9caae6f34b74b711a15e10bd9e43efe7 icon-hurricane-150x150.png]]
| Western North Pacific
| Tropical Storm Washi (also known as TS Sendong) was world’s deadliest storm in 2011
| No attribution done; disaster was the outcome of interplay of climatic, environmental and social factors (Espinueva et al., 2012 <sup>[[#fn:r34|34]]</sup> )
| Fatalities: &gt;1,250; injured: 2,002; missing: 1,049 (Rasquinho et al., 2013 <sup>[[#fn:r35|35]]</sup> ).
Socioeconomic costs: 63.3 million USD (Espinueva et al., 2012 <sup>[[#fn:r36|36]]</sup> )

|-
| 2011 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Western Australia
| Most extreme warming event in the region in the last 140 years during which sea temperature anomalies of 2°C–4°C persisted for more than 10 weeks along &gt;2,000 km of coastline.
from Ningaloo (22°S) to Cape Leeuwin (34°S); up to 5°C warmer SSTs than normal (Feng et al., 2013 <sup>[[#fn:r43|43]]</sup> ; Pearce and Feng, 2013 <sup>[[#fn:r38|38]]</sup> ; Benthuysen et al., 2014 <sup>[[#fn:r39|39]]</sup> ; Caputi et al., 2016 <sup>[[#fn:r40|40]]</sup> ; Perkins-Kirkpatrick et al., 2016 <sup>[[#fn:r41|41]]</sup> )

| Warming of poleward-flowing Leeuwin Current in Austral summer forced by oceanic and atmospheric teleconnections associated with the 2010–2011 La Niña (Feng et al., 2013 <sup>[[#fn:r37|37]]</sup> ). Conditions increased since 1970’s by negative Interdecadal Pacific Oscillation (IPO) and anthropogenic global warming (Feng et al., 2015 <sup>[[#fn:r42|42]]</sup> ). Shift of temperate marine ecosystem was climate-driven
| Widespread coral bleaching and fish kills.
Biodiversity patterns of temperate seaweeds, sessile invertebrates and demersal fish were altered leading to reduced abundance of habitat-forming seaweeds

(Wernberg et al., 2013 <sup>[[#fn:r44|44]]</sup> )

|-
| 2011 [[File:e2cac7f216102765183d902d0f0cf453 icon-rain-150x150.png]]
| Golden Bay, New Zealand
| In December, Extreme two day
total rainfall was experienced (one in 500-year event)

| Model based attribution indicated total moisture available for precipitation in Golden Bay, New Zealand was 1–5% higher due to anthropogenic emissions (Dean et al., 2013 <sup>[[#fn:r45|45]]</sup> )
| In town of Takaka, 453 mm was recorded in 24 hours and 674 mm in 48 hours (Dean et al. 2013 <sup>[[#fn:r46|46]]</sup> )
|-
| 2012 [[File:92d1017eff61c41fffe53156e2db3353 icon-ice-150x150.png]]
| Arctic
| Arctic sea ice minimum
| Model-based attribution indicated the exceptional 2012 sea ice loss was due to sea ice memory and positive feedback of warm atmospheric conditions, both contributing approximately equally (Guemas et al., 2013 <sup>[[#fn:r47|47]]</sup> ) and ''extremely unlikely'' to have occurred due to internal climate variability alone based on observations and model-based attribution (Zhang and Knutson, 2013 <sup>[[#fn:r48|48]]</sup> )
| Up to 60% higher contribution of sea ice algae in the central Arctic (Fernández-Méndez et al., 2015 <sup>[[#fn:r49|49]]</sup> ; see also chapter 3.2.3)
|-
| 2012 [[File:9caae6f34b74b711a15e10bd9e43efe7 icon-hurricane-150x150.png]]
| US East coast
| Hurricane Sandy
| Relative SLR shown to have increased probabilities of exceeding peak impact elevations since the mid-20th century (Sweet et al., 2013 <sup>[[#fn:r50|50]]</sup> ; Lackmann, 2015 <sup>[[#fn:r51|51]]</sup> )
| Repair and mitigation expenditures funded at 60.2 billion USD. Losses of fishing vessels estimated at 52 million USD (Sainsbury et al., 2018 <sup>[[#fn:r52|52]]</sup> )
|-
| 2012 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Northwest Atlantic
| First half of 2012, record-breaking SSTs (1°C–3°C above normal) from the Gulf of Maine to Cape Hatteras (Mills et al., 2013 <sup>[[#fn:r53|53]]</sup> ; Chen et al., 2014 <sup>[[#fn:r54|54]]</sup> ; Pershing et al., 2015 <sup>[[#fn:r55|55]]</sup> ; Zhou et al., 2015 <sup>[[#fn:r56|56]]</sup> )
| Local warming from the atmosphere due to anomalous atmospheric jet stream position (Chen et al., 2014 <sup>[[#fn:r57|57]]</sup> ) . Unknown if global warming increased the probability
| Northward movement of warm water species and local migrations of lobsters earlier in the season (Mills et al., 2013 <sup>[[#fn:r58|58]]</sup> ; Pershing et al., 2015 <sup>[[#fn:r58|58]]</sup> )
|-
| 2013 [[File:e2cac7f216102765183d902d0f0cf453 icon-rain-150x150.png]]
| UK
| Extreme winter rainfall
| Some evidence for a human-induced increase in extreme winter rainfall in the UK for events with time scales of 10 days (Christidis and Stott, 2015 <sup>[[#fn:r60|60]]</sup> )
| Tidal surges,
widespread floodplain

inundation, and pronounced river flows leading to damages in transport infrastructure, business and residential properties and a cost of 560 million GBP in recovery schemes (Department for Communities and Local Government, 2014 <sup>[[#fn:r61|61]]</sup> ; Huntingford et al., 2014 <sup>[[#fn:r62|62]]</sup> ). Unprecedented deaths of over 4,400 Puffins found on UK and Scottish coasts linked to cold and strong winds during this event (Harris and Elkins, 2013 <sup>[[#fn:r63|63]]</sup> )

|-
| 2013 [[File:9caae6f34b74b711a15e10bd9e43efe7 icon-hurricane-150x150.png]]
| Western North Pacific
| Strongest and fastest Super Typhoon Haiyan (Category 5) in the region
| Occurred in a season with remarkably warm SSTs, (David et al., 2013 <sup>[[#fn:r64|64]]</sup> ; Takagi and Esteban, 2016 <sup>[[#fn:r65|65]]</sup> ). Ocean heat content and sea levels had increased since 1998 due to the negative Pacific Decadal Oscillation (PDO) phase but impacts were worsened by thermodynamic effects on SSTs, SLR and storm surges due to climate change (Trenberth et al., 2015 <sup>[[#fn:r66|66]]</sup> )
| Deadliest and most expensive natural disaster in the Philippines (Fatalities: 6,245; Injured: 28,626; Missing: 1,039). Damage to mangroves was still apparent 18 months after the storm (Sainsbury et al., 2018 <sup>[[#fn:r67|67]]</sup> )
|-
| 2013–2015 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Northeast Pacific Ocean
| Largest heatwave ever recorded (often called ‘The Blob’; Bond et al. 2015), with maximum SST anomalies of 6°C off Southern California (Jacox et al., 2016 <sup>[[#fn:r69|69]]</sup> ; Gentemann et al., 2017 <sup>[[#fn:r70|70]]</sup> ; Rudnick et al., 2017 <sup>[[#fn:r71|71]]</sup> ) and subsurface warm anomalies in the deep British Columbia Fjord that persisted through the beginning of 2018 (Jackson and Wood, 2018 <sup>[[#fn:r72|72]]</sup> )
| Emerged in 2013 in response to teleconnections between North Pacific and the weak El Niño that drove strong positive sea level pressure anomalies across the northeast Pacific inducing smaller heat loss (Bond et al., 2015 <sup>[[#fn:r73|73]]</sup> ; Di Lorenzo and Mantua, 2016). Global warming increased the probability of occurrence for regional parts of the MHW (Weller et al., 2015 <sup>[[#fn:r74|74]]</sup> ; Jacox et al., 2018 <sup>[[#fn:r75|75]]</sup> ; Newman et al., 2018 <sup>[[#fn:r76|76]]</sup> )
| Major impacts on entire marine food web. Caused a major outbreak of a toxic algal bloom along the US West Coast leading to impacts on fisheries (McCabe et al., 2016 <sup>[[#fn:r77|77]]</sup> ) . Increased mortality of sea birds (Jones et al., 2018 <sup>[[#fn:r78|78]]</sup> ). Contributed to drought conditions across the US West Coast
|-
| 2014 [[File:019c4c15fc474e50e8acabd69c7527d6 icon-hurricanes-150x150.png]]
| Hawaiian hurricane season
| Extremely active hurricane season in the eastern and central Pacific Ocean, particularly around Hawaii
| Anthropogenic forcing could have contributed to the unusually large number of hurricanes in Hawaii in 2015, in combination with the moderately favourable El Niño event conditions (Murakami et al., 2015 <sup>[[#fn:r79|79]]</sup> )
| Acute disturbance of coral along Wai‘ōpae coastline (southeastern tip of Hawai‘i Island) due to passages of Hurricanes Iselle, Julio and Ana that caused high waves, increased runoff and elevated SSTs associated with the 2014–2015 El Niño (Burns et al., 2016 <sup>[[#fn:r80|80]]</sup> ).
|-
| 2014 [[File:9caae6f34b74b711a15e10bd9e43efe7 icon-hurricane-150x150.png]]
| Arabian Sea
| Cyclone Nilofar was the first severe TC to be recorded in the Arabian Sea in post-monsoon cyclone season (Murakami et al., 2017 <sup>[[#fn:r81|81]]</sup> )
| Anthropogenic global warming has been shown to have increased the probability of post-monsoon TCs over the Arabian Sea (Murakami et al., 2017 <sup>[[#fn:r82|82]]</sup> )
| Cyclone did not make landfall but produced heavy rainfall on western Indian coasts (Bhutto et al., 2017 <sup>[[#fn:r83|83]]</sup> )
|-
| 2014 [[File:e2cac7f216102765183d902d0f0cf453 icon-rain-150x150.png]]
| Northland New Zealand
| Extreme five day rainfall in Northland
| Extreme five day rainfall over Northland, New Zealand was influenced by human-induced climate change (Rosier et al., 2015 <sup>[[#fn:r84|84]]</sup> )
| 18.8 million NZD in insurance claims (Rosier et al., 2015 <sup>[[#fn:r85|85]]</sup> )
|-
| 2014–2017 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Western equatorial Pacific, Great Barrier Reef, Australia
| Extreme SSTs
| Global warming increased probability of occurrence for regional parts of the MHW (Weller et al., 201 <sup>[[#fn:r86|86]]</sup> 5; Oliver et al., 2018b <sup>[[#fn:r87|87]]</sup> )
| Anthropogenic greenhouse gas (GHG) emission increased the risk of coral bleaching through anomalously high SSTs and accumulation of heat stress (Lewis and Mallela, 2018 <sup>[[#fn:r88|88]]</sup> )
|-
| 2015 [[File:4148063e3469466e9a29fb15bfb8c4c3 icon-snow-150x150.png]]
| North America
| Anomalously low temperatures with intense snowstorms
| Reduced Arctic sea ice and anomalous SSTs may have contributed to establishing and sustaining the anomalous meander of the jet stream, and could enhance the probability of such extreme cold spells over North America (Bellprat et al., 2016 <sup>[[#fn:r89|89]]</sup> )
| Several intense snowstorms resulting in power outages and large economic losses (Munich RE, 2016 <sup>[[#fn:r90|90]]</sup> )
|-
| 2015 [[File:92d1017eff61c41fffe53156e2db3353 icon-ice-150x150.png]]
| Arctic
| Record low Northern Hemisphere (NH) sea ice extent in March 2015
| Record low in NH sea ice maximum could not have been reached without human-induced change in climate, with the surface atmospheric conditions, on average, contributing 54% to the change (Fuckar et al., 2016 <sup>[[#fn:r91|91]]</sup> )
| March NH sea ice content reached the lowest winter maximum in 2015. Emerging evidence of increased snow fall over regions outside the Arctic (see 3.4.1.1) due to sea ice reduction as well as changes in the timing, duration and intensity of primary production, which affect secondary production (3.2.3.1)
|-
| 2015 [[File:809ce8226f602b9baae5b48acc4ca057 icon-flood-150x150.png]]
| Florida
| Sixth largest flood in Virginia Key, Florida since 1994, with the fifth highest in response to hurricanes
| The probability of a 0.57 m flood has increased by 500% (Sweet et al., 2016 <sup>[[#fn:r92|92]]</sup> )
| Flooding in several Miami-region communities with 0.57 m of ocean water on a sunny day
|-
| 2015–/2016 [[File:1fe32f32e663e3d311e7dd9999a82dd7 icon-sun-150x150.png]]
| Ethiopia and Southern Africa
| One of the worst droughts in 50 years, also intensified flash droughts characterised by severe heatwaves
| Anthropogenic warming contributed substantially to the very warm 2015–2016 El Niño SSTs, land local air temperatures thereby reducing Northern Ethiopia and Southern Africa rainfall and runoff (Funk et al., 2018 <sup>[[#fn:r93|93]]</sup> ; Yuan et al., 2018 <sup>[[#fn:r94|94]]</sup> )
| A 9 million tonne cereal deficit resulted in more than 28 million people in need of humanitarian aid (Funk et al. 2018 <sup>[[#fn:r95|95]]</sup> )
|-
| 2015 [[File:9caae6f34b74b711a15e10bd9e43efe7 icon-hurricane-150x150.png]]
| Eastern North Pacific
| TC Patricia, the most intense and rapidly intensifying storm in the Western Hemisphere (estimated mean sea level (MSL) pressure of 872 hPa (Rogers et al., 2017 <sup>[[#fn:r96|96]]</sup> ), intensified rapidly into a Category 5 TC (Diamond and Schreck, 2016 <sup>[[#fn:r97|97]]</sup> )
| A near-record El Niño combined with a positive Pacific Meridional Mode provided extreme record SSTs and low vertical wind shear that fuelled the 2015 eastern North Pacific hurricane season to near-record levels (Collins et al., 2016 <sup>[[#fn:r98|98]]</sup> )
| Approximately 9,000 homes and agricultural croplands, including banana crops, were damaged by wind and rain from Patricia that made landfall near Jalisco, Mexico (Diamond and Schreck, 2016 <sup>[[#fn:r99|99]]</sup> )
|-
| 2015 [[File:019c4c15fc474e50e8acabd69c7527d6 icon-hurricanes-150x150.png]]
| Arabian Sea, Somalia and Yemen
| Cyclones Chapala and Megh occurred within a week of each other and both tracked westward across Socotra Island and Yemen. Rainfall from Chapala was seven times the annual average
| Anthropogenic global warming has been shown to have increased the probability of post-monsoon TCs over the Arabian Sea (Murakami et al., 2017 <sup>[[#fn:r100|100]]</sup> )
| Death toll in Yemen from Chapala and Megh was 8 and 20 respectively. Thousands of houses and businesses damaged or destroyed by both cyclones and fishing disrupted. The coastal town of Al Mukalla experienced a 10 m storm surge that destroyed the seafront (Kruk, 2016 <sup>[[#fn:r101|101]]</sup> ). Flooding in Somalia led to thousands of livestock killed and damage to infrastructure (IFRC, 2016)
|-
| 2015–2016 [[File:f9e6362aa864fef9cc025a66f3f15952 icon-rainfall-150x150.png]]
| Northern Australia (Gulf of Carpentaria)
| High temperatures, low rainfall, extended drought period and low sea levels
| Attributed to anomalously high temperatures and low rainfall and low sea levels associated with El Niño (Duke et al., 2017 <sup>[[#fn:r103|103]]</sup> )
| 1,000 km of mangrove tidal wetland dieback (&gt;74,000 ha). with potential flow-on consequences to Gulf of Carpentaria fishing industry worth 30 million AUS yr-1 due to loss of recruitment habitat
|-
| 2015–2016 [[File:a848db5838c4ffbe9cfe03e5120ce10c icon-raintemp-150x150.png]]
| Tasman Sea
| MHW lasted for 251 days with maximum SSTs of 2.9°C above the 1982–2005 average (Oliver et al., 2017 <sup>[[#fn:r104|104]]</sup> )
| Enhanced southward transport in the East Australian current driven by increased wind stress (Oliver et al., 2017 <sup>[[#fn:r105|105]]</sup> ). The intensity and duration of the MHW were unprecedented and both had a clear human signature (Oliver et al., 2017 <sup>[[#fn:r106|106]]</sup> )
| Disease outbreaks in farmed shellfish, mortality in wild shellfish and species found further south than previously recorded. Drought followed by severe rainfall caused severe bushfires and flooding in northeast Tasmania (see Box 6.1).
|-
| 2016 [[File:92d1017eff61c41fffe53156e2db3353 icon-ice-150x150.png]]
| Arctic
| Record high air temperatures and record low sea ice were observed in the Arctic winter/spring of 2016 (Petty et al., 2017 <sup>[[#fn:r107|107]]</sup> )
| Would not have been possible without anthropogenic forcing (Kam et al., 2018 <sup>[[#fn:r108|108]]</sup> ), however the relative role of preconditioning, seasonal atmospheric/ocean forcing and storm activity in determining the evolution of the Arctic sea ice cover is still highly uncertain (Petty et al., 2018 <sup>[[#fn:r109|109]]</sup> )
| Impacts on Arctic ecosystems (e.g., Post et al., 2013; Meier et al., 2014), potential changes to mid-latitude weather (e.g., Cohen et al., 2014; Francis and Skific, 2015 <sup>[[#fn:r111|111]]</sup> ; Screen et al., 2015 <sup>[[#fn:r112|112]]</sup> ) and human activities in the Arctic
|-
| 2016 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Bering Sea/Gulf of Alaska,
| Record-setting warming with peak SSTs of 6°C above the 1981–2010 climatology (Walsh et al., 2017 <sup>[[#fn:r113|113]]</sup> ; Walsh et al., 2018 <sup>[[#fn:r114|114]]</sup> )
| Nearly fully attributed to human-induced climate change (Oliver et al., 2018b <sup>[[#fn:r115|115]]</sup> ; Walsh et al., 2018 <sup>[[#fn:r116|116]]</sup> )
| Impacts on marine ecosystems in Alaska, included favouring some phytoplankton species, but resulted in one of the largest harmful algal blooms on record which reached the Alaska coast in 2015 (Peterson et al., 2017 <sup>[[#fn:r117|117]]</sup> ), uncommon paralytic shellfish poisoning events in Kachemak Bay and oyster farm closures in 2015 and 2016, dramatic mortality events in seabird species such as common murres in 2015–2016 (Walsh et al., 2018 <sup>[[#fn:r118|118]]</sup> )
|-
| 2016 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| East China Sea
| MHW
| Warming predominantly attributable to combined effects of oceanic advection
(-0.18°C, 24%) and net heat flux (-0.44°C, 58%; Tan and Cai, 2018)

| Impacts on marine organisms (Kim and Han, 2017 <sup>[[#fn:r120|120]]</sup> )’
|-
| 2016 [[File:4148063e3469466e9a29fb15bfb8c4c3 icon-snow-150x150.png]]
| Eastern China
| Super cold surge
| This cold surge would have been stronger if there was no anthropogenic warming (Qian et al., 2018 <sup>[[#fn:r121|121]]</sup> ; Sun and Miao, 2018 <sup>[[#fn:r122|122]]</sup> )
| Extreme weather brought by the cold surge caused significant impacts on &gt;1 billion people in China in terms of transportation and electricity transmission systems, agriculture and human health (Qian et al. 2018 <sup>[[#fn:r123|123]]</sup> )
|-
| 2016 [[File:92d1017eff61c41fffe53156e2db3353 icon-ice-150x150.png]]
| Antarctic
| Antarctic sea ice extent decreased at a record rate 46% faster than the mean rate and 18% faster than any spring rate in the satellite era producing a record minimum for the satellite period (1979–2016) (Turner et al., 2017 <sup>[[#fn:r124|124]]</sup> )
| Largely attributable to thermodynamic surface forcing (53%), while wind stress and the sea ice and oceanic conditions from the previous summer (January 2016) explain the remaining 34% and 13%, respectively (Kusahara et al., 2018 <sup>[[#fn:r125|125]]</sup> ) linked with a shift to positive phase of PDO and negative SAM in late 2016 (Meehl et al., 2019 <sup>[[#fn:r126|126]]</sup> ; see also 3.2.1.1)
| Potential impacts on ecosystems and fisheries are poorly known (chapter 3.6)
|-
| 2017 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Yellow Sea/East China Sea
| SSTs 2°C–7°C higher than normal (Kim and Han, 2017 <sup>[[#fn:r127|127]]</sup> ; Tan and Cai, 2018 <sup>[[#fn:r128|128]]</sup> )
| Unknown if global warming has increased the probability
| Impacts on marine organisms
|-
| 2017 [[File:019c4c15fc474e50e8acabd69c7527d6 icon-hurricanes-150x150.png]]
| Western North Atlantic
| Hurricanes Harvey, Irma and Maria
| Rainfall intensity in Harvey attributed to climate change and winds for Irma and Maria attributed to climate change. (Emanuel, 2017 <sup>[[#fn:r129|129]]</sup> ; Risser and Wehner, 2017 <sup>[[#fn:r130|130]]</sup> ; van Oldenborgh et al., 2017 <sup>[[#fn:r131|131]]</sup> ; see Box 6.1)
| Extensive impacts (see Box 6.1)
|-
| 2017 [[File:9caae6f34b74b711a15e10bd9e43efe7 icon-hurricane-150x150.png]]
| Europe
| Storm Ophelia
| In agreement with projections of increase of cyclones of tropical origin hitting European coasts (Haarsma et al., 2013 <sup>[[#fn:r132|132]]</sup> )
| Largest ever recorded hurricane in East Atlantic; extreme winds and coastal erosion in Ireland
|-
| 2017 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Persian Gulf
| Severe warming in the Gulf with reef bottom temperatures resulting in 5.5°C-weeks of thermal stress as degree heating weeks
(Burt et al., 2019 <sup>[[#fn:r134|134]]</sup> )

| Mortality of corals shown to have been caused by increases in sea-bottom temperatures (Burt et al., 2019 <sup>[[#fn:r133|133]]</sup> )
| 94.3% of corals bleached in the Gulf
|-
| 2017 [[File:1fe32f32e663e3d311e7dd9999a82dd7 icon-sun-150x150.png]]
| East Africa
| Drought (across Tanzania, Ethiopia, Kenya and Somalia)
| Extremely warm ‘Western V’ (stretching poleward and eastward from a point near the Maritime Continent) SST doubled the probability of drought (Funk et al., 2018 <sup>[[#fn:r135|135]]</sup> )
| Contributed to extreme food insecurity (Funk et al., 2018 <sup>[[#fn:r136|136]]</sup> ) approaching near-famine conditions (FEWS NET and FSNAU, 2017 <sup>[[#fn:r137|137]]</sup> ; WFP et al., 2017 <sup>[[#fn:r138|138]]</sup> )
|-
| 2017 [[File:e2cac7f216102765183d902d0f0cf453 icon-rain-150x150.png]]
| Peru
| Extremely wet rainy season
| Human influence is estimated to make such events at least 1.5 times more likely (Christidis et al., 2018a <sup>[[#fn:r139|139]]</sup> )
| Widespread flooding and landslides 1.7 million people, a death toll of 177 and an estimated damages of 3.1 billion USD (Christidis et al., 2018a <sup>[[#fn:r140|140]]</sup> )
|-
| 2017 [[File:e2cac7f216102765183d902d0f0cf453 icon-rain-150x150.png]]
| Bangladesh
| Pre-monsoon extreme six day rainfall event
| The likelihood of this 2017 pre-monsoon extreme rainfall is nearly doubled by anthropogenic climate change; although this contribution is sensitive to the climatological period used (Rimi et al., 2018 <sup>[[#fn:r141|141]]</sup> )
| Triggered flash floods affecting 850,000 households and 220,000 hectares of harvestable crops leading to a 30% rice price hike (FAO, 2017)
|-
| 2017 [[File:e2cac7f216102765183d902d0f0cf453 icon-rain-150x150.png]]
| Uruguay, South America
| April-May heavy precipitation
| The risk of the extreme rainfall in the Uruguay River increased two-fold by anthropogenic climate change
| Triggered wide-spread overbank flooding along the Uruguay River causing economic loss of 102 million USD (FAMURS, 2017 <sup>[[#fn:r142|142]]</sup> ) and displacement of 3,500 people (de Abreu et al., 2019)
|-
| 2017 [[File:1fe32f32e663e3d311e7dd9999a82dd7 icon-sun-150x150.png]]
| Northeast China
| Persistent summer-spring hot and dry extremes
| Risk of persistent spring-summer hot and dry extremes is increased by 5–55% and 37–113%, respectively, by anthropogenic climate change (Wang et al., 2018 <sup>[[#fn:r143|143]]</sup> )
| Affected more than 7.4 million km² of crops and herbage and direct economic loss of about 10 billion USD (Zhang et al., 2017c <sup>[[#fn:r144|144]]</sup> )
|-
| 2017 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Coastal Peru
| Strong shallow ocean warming of up to 10°C off the northern coast of Peru
| Unknown if global warming increased the probability
| Caused heavy rainfall and flooding (ENFEN, 2017 <sup>[[#fn:r145|145]]</sup> ; Garreaud, 2018 <sup>[[#fn:r146|146]]</sup> ). Affected anchovies (decreased fat content and early spawning as a reproductive strategy; IMPARPE, 2017 <sup>[[#fn:r147|147]]</sup> )
|-
| 2017 [[File:e7f0c73bfcad95d15ffebd7d5fb50829 icon-temp-150x150.png]]
| Southwestern Atlantic
| SSTs were 1.7°C higher than previous maximum from February to March 2017 between 32°S–38°S (Manta et al., 2018 <sup>[[#fn:r148|148]]</sup> )
| High air temperature and low wind speed led to MHW. Unknown if global warming increased the probability
| Fish species mass mortalities
|}
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