Editing IPCC:AR6/WGI/Chapter-11 (section)

== 11.8 Compound Events ==

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The SREX (SREX Chapter 3) first defined compound events as: (i) two or more extreme events occurring simultaneously or successively, (ii) combinations of extreme events with underlying conditions that amplify the impact of the events, or (iii) combinations of events that are not themselves extremes but lead to an extreme event or impact when combined.

Further definitions of compound events have emerged since SREX. [[#Zscheischler--2018|Zscheischler et al. (2018)]] defined compound events broadly as ‘the combination of multiple drivers and/or hazards that contributes to societal or environmental risk’. This definition is used in the present assessment, because of its clear focus on the risk framework established by the IPCC, and also highlighting that compound events may not necessarily result from dependent drivers. Compound events have been classified into: preconditioned events, where a weather-driven or climate-driven precondition aggravates the impacts of a climatic impact-driver; multivariate events, where multiple drivers and/or climatic impact-drivers lead to an impact; temporally compounding events, where a succession of hazards leads to an impact; and spatially compounding events, where hazards in multiple connected locations cause an aggregated impact ( [[#Zscheischler--2020|Zscheischler et al., 2020]] ). Drivers include processes, variables, and phenomena in the climate and weather domain that may span over multiple spatial and temporal scales. Hazards (such as floods, heatwaves, wildfires; also termed ´climatic impact-drivers´ in this report, see Chapter 12) are usually the immediate physical precursors to negative impacts, but can occasionally have positive outcomes ( [[#Flach--2018|Flach et al., 2018]] ). The present assessment focuses on the physical dimension of changes in compound events, as it is part of the IPCC AR6 Working Group I Report.

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=== 11.8.1 Overview ===

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The combination of two or more – not necessarily extreme – weather or climate events that occur: i) at the same time; ii) in close succession; or iii) concurrently in different regions, can lead to extreme impacts that are much larger than the sum of the impacts due to the occurrence of individual extremes alone. This is because multiple stressors can exceed the coping capacity of a system more quickly. The contributing events can be of similar types (clustered multiple events) or of different types ( [[#Zscheischler--2020|Zscheischler et al., 2020]] ). Many major weather- and climate-related catastrophes are inherently of a compound nature ( [[#Zscheischler--2019|Zscheischler et al., 2019]] ).This has been highlighted for a broad range of hazards, such as droughts, heatwaves, wildfires, coastal extremes, and floods ( [[#Westra--2016|Westra et al., 2016]] ; [[#AghaKouchak--2020|AghaKouchak et al., 2020]] ; [[#Ridder--2020|Ridder et al., 2020]] ). Co-occurring extreme precipitation and extreme winds can result in infrastructural damage ( [[#Martius--2016|Martius et al., 2016]] ); the compounding of storm surge and precipitation extremes can cause coastal floods ( [[#Wahl--2015|Wahl et al., 2015]] ); the combination of drought and heat can lead to tree mortality ( [[#11.6|Section 11.6]] ; [[#Allen--2015|Allen et al., 2015]] ); and wildfires increase occurrences of hailstorms and lightning (Y. [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] a). Compound storm types consisting of co-located cyclone, front and thunderstorm systems have a higher chance of causing extreme rainfall and extreme winds than individual storm types ( [[#Dowdy--2017|Dowdy and Catto, 2017]] ). Extremes may occur at similar times at different locations ( [[#De%20Luca--2020a|De Luca et al., 2020a]] , b) but affect the same system, for instance, spatially concurrent climate extremes affecting crop yields and food prices ( [[#Singh--2018|Singh et al., 2018]] ; [[#Anderson--2019|Anderson et al., 2019]] ). Studies also show an increasing likelihood for breadbasket regions to be concurrently affected by climate extremes with increasing global warming, even between 1.5°C and 2°C of global warming (Box 11.2; [[#Gaupp--2019|Gaupp et al., 2019]] ). Concomitant extreme conditions at different locations become more probable as changes in climate extremes are emerging over an increasing fraction of the land area (Sections 11.2.3, 11.2.4, 11.8.2 and 11.8.3, and Box 11.4).

Finally, impacts may occur because of large multivariate anomalies in the climate drivers, if systems are adapted to historical multivariate climate variability ( [[#Flach--2017|Flach et al., 2017]] ). For instance, ecosystems are typically adapted to the local covariability of temperature and precipitation such that a bivariate anomaly may have a large impact, even though neither temperature nor precipitation may be extreme based on a univariate assessment ( [[#Mahony--2018|Mahony and Cannon, 2018]] ). Given that almost all systems are affected by weather and climate phenomena at multiple space-time scales ( [[#Raymond--2020|Raymond et al., 2020]] ), it is natural to consider extremes in a compound event framework. It should be noted, however, that multi-hazard dependencies can also decrease risk, for instance when hazards are negatively correlated ( [[#Hillier--2020|Hillier et al., 2020]] ). Despite this recognition, the literature on past and future changes in compound events has been limited, but is growing. This section assesses examples of types of compound events in available literature.

In summary, compound events include the combination of two or more – not necessarily extreme – weather or climate events that occur (i) at the same time, (ii) in close succession, or (iii) concurrently in different regions. The land area affected by concurrent extremes has increased ( ''high confidence'' ). Concurrent extreme events at different locations, but possibly affecting similar sectors (e.g., breadbaskets) in different regions, will become more frequent with increasing global warming, in particular above +2°C of global warming ( ''high'' ''confidence'' ).

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=== 11.8.2 Concurrent Extremes in Coastal and Estuarine Regions ===

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Coastal and estuarine zones are prone to a number of meteorological extreme events and also to concurrent extremes (see also Section 6.8.2 in SROCC). Floods are a major climatic impact-driver in coastal regions around the world (Chapter 12), and flood occurrence may be influenced by the dependence between storm surge, extreme rainfall, and river flow, but also by sea level rise, waves and tides, as well as groundwater for estuaries. Floods with multiple drivers are often referred to as ‘compound floods’ ( [[#Wahl--2015|Wahl et al., 2015]] ; [[#Moftakhari--2017|Moftakhari et al., 2017]] ; [[#Bevacqua--2020c|Bevacqua et al., 2020c]] ).

At USA coasts, the probability of co-occurring storm surge and heavy precipitation is higher for the Atlantic/Gulf coast relative to the Pacific coast ( [[#Wahl--2015|Wahl et al., 2015]] ). Furthermore, six studied locations on the USA coast with long overlapping time series show an increase in the dependence between heavy precipitation and storm surge over the last century, leading to more frequent co-occurring storm surge and heavy precipitation events at the present day ( [[#Wahl--2015|Wahl et al., 2015]] ). Storm surge and extreme rainfall are also dependent in most locations on the Australian coasts ( [[#Zheng--2013|Zheng et al., 2013]] ) and in Europe along the Dutch coasts ( [[#Ridder--2018|Ridder et al., 2018]] ), along the Mediterranean Sea, the Atlantic coast and the North Sea ( [[#Bevacqua--2019|Bevacqua et al., 2019]] ). The probability of flood occurrence can be assessed via the dependence between storm surge and river flow ( [[#Bevacqua--2020b|Bevacqua et al., 2020b]] , c). For instance, the occurrence of a North Sea storm surge in close succession with an extreme Rhine or Meuse river discharge is much more probable due to their dependence, compared to if both events were independent ( [[#Kew--2013|Kew et al., 2013]] ; [[#Klerk--2015|Klerk et al., 2015]] ). Significant dependence between high sea levels and high river discharge are found for more than half of the available station observations, which are mostly located around the coasts of North America, Europe, Australia, and Japan ( [[#Ward--2018|Ward et al., 2018]] ). Combining global river discharge with a global storm surge model, hotspots of compound flooding have been discovered that are not well covered by observations in some regions, including Madagascar, Northern Morocco, Vietnam, and Taiwan of China ( [[#Couasnon--2020|Couasnon et al., 2020]] ). In the Dutch Noorderzijlvest area, there is more than a two-fold increase in the frequency of exceeding the highest warning level compared to the case if storm surge and heavy precipitation were independent ( [[#van%20den%20Hurk--2015|van den Hurk et al., 2015]] ). In other regions and seasons, the dependence can be insignificant (W. [[#Wu--2018|]] [[#Wu--2018|Wu et al., 2018]] ) and there can be significant seasonal and regional differences in the storm surge–heavy precipitation relationship. Assessments of flood probabilities are often not based on actual flood measurements; instead, they are estimated from its main drivers, including astronomical tides, storm surge, heavy precipitation, and high streamflow. Such single driver analyses might underestimate flood probabilities if multiple correlated drivers contribute to flood occurrence (e.g., [[#van%20den%20Hurk--2015|van den Hurk et al., 2015]] ).

Many coastal areas are also prone to the occurrence of compound precipitation and wind extremes, which can cause damage, including to infrastructure and natural environments. A high percentage of co-occurring wind and precipitation extremes are found in coastal regions and in areas with frequent tropical cyclones. Finally, the combination of extreme wave height and duration is also shown to influence coastal erosion processes ( [[#Corbella--2012|Corbella and Stretch, 2012]] ).

Aspects of concurrent extremes in coastal and estuarine environments have increased in frequency and/or magnitude over the last century in some regions. These include an increase in the dependence between heavy precipitation and storm surge over the last century, leading to more frequent co-occurring storm surge and heavy precipitation events in the present day along USA coastlines ( [[#Wahl--2015|Wahl et al., 2015]] ). In Europe, the probability of compound flooding occurrence increases most strongly along the Atlantic coast and the North Sea under strong warming. This increase is mostly driven by an intensification of precipitation extremes and aggravated flooding probability due to sea level rise ( [[#Bevacqua--2019|Bevacqua et al., 2019]] ). At the global scale and under a high-emissions scenario, the concurrence probability of meteorological conditions driving compound flooding would increase by more than 25%, on average, along coastlines worldwide by 2100, compared to the present ( [[#Bevacqua--2020c|Bevacqua et al., 2020c]] ). Sea level extremes and their physical impacts in the coastal zone arise from a complex set of atmospheric, oceanic, and terrestrial processes that interact on a range of spatial and temporal scales and will be modified by a changing climate, including sea level rise ( [[#McInnes--2016|McInnes et al., 2016]] ). Interactions between sea level rise and storm surges ( [[#Little--2015|Little et al., 2015]] ), and sea level and fluvial flooding ( [[#Moftakhari--2017|Moftakhari et al., 2017]] ) are projected to lead to more frequent and intense compound coastal flooding events as sea levels continue to rise.

In summary, there is ''medium confidence'' that, over the last century, the probability of compound flooding has increased in some locations, including along the USA coastline. There is ''high confidence'' that the occurrence and magnitude of compound flooding in coastal regions will increase in the future due to both sea level rise and increases in heavy precipitation.

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=== 11.8.3 Concurrent Droughts and Heatwaves ===

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Concurrent droughts and heatwaves have a number of negative impacts on human society and natural ecosystems. Studies since SREX and AR5 show several occurrences of observed combinations of drought and heatwaves in various regions.

Over most land regions, temperature and precipitation are strongly negatively correlated during summer ( [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ), mostly due to land–atmosphere feedbacks (Sections 11.1.6 and 11.3.2), but also because synoptic-scale weather systems favourable for extreme heat are also unfavourable for rain ( [[#Berg--2015|Berg et al., 2015]] ). This leads to a strong correlation between droughts and heatwaves ( [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ). Drought events characterized by low precipitation and extreme high temperatures have occurred, for example, in California ( [[#AghaKouchak--2014|AghaKouchak et al., 2014]] ), inland Eastern Australia ( [[#King--2014|King et al., 2014]] ), and large parts of Europe ( [[#Orth--2016a|Orth et al., 2016a]] ). The 2018 growing season was both record-breaking dry and hot in Germany ( [[#Zscheischler--2020|Zscheischler and Fischer, 2020]] ).

The probability of co-occurring meteorological droughts and heatwaves has increased in the observational period in many regions and will continue to do so under unabated warming ( [[#Herrera-Estrada--2017|Herrera-Estrada and Sheffield, 2017]] ; [[#Zscheischler--2017|Zscheischler and Seneviratne, 2017]] ; [[#Hao--2018|Hao et al., 2018]] ; [[#Sarhadi--2018|Sarhadi et al., 2018]] ; [[#Alizadeh--2020|Alizadeh et al., 2020]] ; [[#Wu--2021|Wu et al., 2021]] ). Overall, projections of increases in co-occurring drought and heatwaves are reported in northern Eurasia ( [[#Schubert--2014|Schubert et al., 2014]] ), Europe ( [[#Orth--2016a|Orth et al., 2016a]] ; [[#Sedlmeier--2018|Sedlmeier et al., 2018]] ), south-east Australia ( [[#Kirono--2017|Kirono et al., 2017]] ), multiple regions of the USA ( [[#Diffenbaugh--2015|Diffenbaugh et al., 2015]] ; [[#Herrera-Estrada--2017|Herrera-Estrada and Sheffield, 2017]] ), north-west China (X. [[#Li--2019|]] [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ; [[#Kong--2020|Kong et al., 2020]] ) and India ( [[#Sharma--2017|Sharma and Mujumdar, 2017]] ). The dominant signal is related to the increase in heatwave occurrence, which has been attributed to anthropogenic forcing ( [[#11.3.4|Section 11.3.4]] ). This means that, even if drought occurrence is unaffected, compound hot and dry events will be more frequent ( [[#Sarhadi--2018|Sarhadi et al., 2018]] ; [[#Yu--2020|Yu and Zhai, 2020]] ).

Drought and heatwaves are also associated with fire weather, related through high temperatures, low soil moisture, and low humidity. Fire weather refers to weather conditions conducive to triggering and sustaining wildfires, which generally include temperature, soil moisture, humidity, and wind (Chapter 12). Concurrent hot and dry conditions amplify conditions that promote wildfires ( [[#Schubert--2014|Schubert et al., 2014]] ; [[#Littell--2016|Littell et al., 2016]] ; [[#Dowdy--2018|Dowdy, 2018]] ; [[#Hope--2019|Hope et al., 2019]] ). Burnt area extent in western USA forests ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ) and particularly in California ( [[#Williams--2019|Williams et al., 2019]] ) has been linked to anthropogenic climate change via a significant increase in vapour pressure deficit, a primary driver of wildfires. A study of the western USA examined the correlation between historical water-balance deficits and annual area burned, across a range of vegetation types, from temperate rainforest to desert ( [[#McKenzie--2017|McKenzie and Littell, 2017]] ). The relationship between temperature and dryness, and wildfire, varied with ecosystem type, and the fire–climate relationship was nonstationary and vegetation-dependent. In many fire-prone regions, such as the Mediterranean and China’s Daxing’anling region, projections for increased severity of future drought and heatwaves may lead to an increased frequency of wildfires relative to observed climatology (Tian et al., 2017; [[#Ruffault--2018|Ruffault et al., 2018]] ). Observations show a long-term trend towards more dangerous weather conditions for bushfires in many regions of Australia, which is attributable (at least in part) to anthropogenic climate change ( [[#Dowdy--2018|Dowdy, 2018]] ). There is emerging evidence that recent regional surges in wildland fires are being driven by changing weather extremes (Cross-Chapter Box 3; [[#Jia--2019|Jia et al., 2019]] ; SRCCL Chapter 2). Between 1979 and 2013, the global burnable area affected by long fire weather seasons doubled, and the mean length of the fire weather season increased by 19% ( [[#Jolly--2015|Jolly et al., 2015]] ). However, at the global scale, the total burned area has been decreasing between 1998 and 2015 due to human activities mostly related to changes in land use ( [[#Andela--2017|Andela et al., 2017]] ). Given the projected ''high confidence'' increase in compound hot and dry conditions, there is ''high confidence'' that fire weather conditions will become more frequent at higher levels of global warming in some regions. This assessment is also consistent with Chapter 12’s examination of regional projected changes in fire weather. The SRCCL (Chapter 2) assessed with ''high confidence'' that future climate variability is expected to enhance the risk and severity of wildfires in many biomes such as tropical rainforests.

In summary, there is ''high confidence'' that concurrent heatwaves and droughts have increased in frequency over the last century at the global scale due to human influence. There is ''medium confidence'' that weather conditions that promote wildfires (fire weather) have become more probable in southern Europe, northern Eurasia, the USA, and Australia over the last century. There is ''high confidence'' that compound hot and dry conditions become more probable in nearly all land regions as global mean temperature increases. There is ''high confidence'' that fire weather conditions will become more frequent at higher levels of global warming in some regions.

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Box 11.4 | Case Study: Global-scale Concurrent Climate Anomalies – the 2015–2016 Extreme El Niño and 2018 Boreal Spring–Summer

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Occurrence of concurrent or near-concurrent extremes in different parts of a region, or in different locations around the world, challenges adaptation and risk management capacity. This can occur as a result of natural climate variability, as climates in different parts of the world are interconnected through large-scale atmospheric–oceanic teleconnections. In addition, in a warming climate, the probability of having several locations being affected simultaneously by, for example, hot extremes and heatwaves increases strongly as a function of global warming, with detectable changes even for changes as small as +0.5°C of additional global warming (Sections 11.2.4 and 11.3, Cross-chapter Box 11.1). Recent articles have highlighted the risks associated with concurrent extremes over large spatial scales (e.g., [[#Lehner--2015|Lehner and Stocker, 2015]] ; [[#Boers--2019|Boers et al., 2019]] ; [[#Gaupp--2019|Gaupp et al., 2019]] ). There is evidence that such global-scale extremes associated with hot temperature extremes are increasing in occurrence ( [[#Sippel--2015|Sippel et al., 2015]] ; [[#Vogel--2019|Vogel et al., 2019]] ). Hereafter, the focus is on two case studies of recent global-scale events that featured concurrent extremes in several regions across the world. The first focuses on concurrent extremes driven by variability in tropical Pacific sea surface temperatures (SSTs) associated with the 2015–2016 extreme El Niño, while the second addresses the impacts of global warming combined with abnormal atmospheric circulation patterns in the 2018 boreal spring/summer.

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[[File:e0d9c9cd7d9b726fa43bbde1a27c248d IPCC_AR6_WGI_Box_11_4_Figure_1.png]]

'''Box 11.4, Figure 1 |'''

'''| Analysis of the percentage of land area affected by temperature extremes larger than two (blue) or three (orange) standard deviations in June–July–August (JJA) between 30°N and 80°N using a normalization.''' This figure shows a substantial increase in the overall land area affected by very strong hot extremes since 1990. Adapted from Sippel et al. (2015).

'''The extreme El Niño in 2015–2016'''

El Niño–Southern Oscillation (ENSO) is one of the phenomena that have the ability to bring multitudes of extremes in different parts of the world, especially in extreme El Niño (Annex IV.2.3) cases. Additionally, the background climate warming associated with greenhouse gas forcing can significantly exacerbate extremes in parts of the world, even under normal El Niño conditions. The 2015–2016 extreme El Niño event was one of the three extreme El Niño events since the 1980s and the availability of satellite rainfall observations. According to some measures, it was the strongest El Niño in the past 145 years ( [[#Barnard--2017|Barnard et al., 2017]] ). The 2015–2016 warmth was unprecedented at the central equatorial Pacific (Niño4: 5°N–5°S, 150°E–150°W), and this exceptional warmth was ''unlikely'' to have occurred entirely naturally, appearing to reflect an anthropogenically forced trend ( [[#Newman--2018|Newman et al., 2018]] ). In particular, its signal was seen in very high monthly global mean surface temperature (GMST) values in late 2015 and early 2016, contributing to the highest record of GMST in 2016 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] ). Both the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] ), suggesting that global extremes similar to those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. A brief summary of extreme events that happened in 2015–2016 is provided in Sections 6.2.2 and 6.5.1.1 of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC). We provide some highlights illustrating extremes that occurred in different parts of the world during the 2015–2016 extreme El Niño in Box 11.4, Table 1, as well as in the short summary that follows.

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'''Box 11.4, Table 1 |'''

'''List of events related to the 2015–2016 Extreme El Niño in''' '''the literature.'''
{| class="wikitable"
|-
! Region

! Period

! Events

! References

|-
| Indonesia

| July 2015 to June 2016

| Droughts, forest fire

| [[#Field--2016|Field et al. (2016)]] ; [[#Huijnen--2016|Huijnen et al. (2016)]] ; [[#Patra--2017|Patra et al. (2017)]] ; [[#Hartmann--2018|Hartmann et al. (2018)]]

|-
| Northern Australia

| Between late 2015 and early 2016

| High temperature and drought

| [[#Duke--2017|Duke et al. (2017)]]

|-
| Amazon

| September 2015 to May 2016

| Droughts, forest fire

| [[#Jiménez-Muñoz--2016|Jiménez-Muñoz et al. (2016)]] ; [[#Erfanian--2017|Erfanian et al. (2017)]] ; [[#Aragão--2018|Aragão et al. (2018)]] ; [[#Panisset--2018|Panisset et al. (2018)]] ; [[#Ribeiro--2018|Ribeiro et al. (2018)]]

|-
| The entirety of South America north of 20°S

| Austral spring and 2015–2016 summer

| Droughts

| [[#Erfanian--2017|Erfanian et al. (2017)]]

|-
| Ethiopia

| February-September 2015

| Droughts

| [[#Blunden--2016|Blunden and Arndt (2016)]] ; [[#Philip--2018b|Philip et al. (2018b)]]

|-
| Southern Africa

| November 2015–April 2016

| Droughts

| Funk et al. (2016, 2018a); [[#Blamey--2018|Blamey et al. (2018)]] ; [[#Yuan--2018a|Yuan et al. (2018a)]]

|-
| Europe

| Boreal 2015–2016 winter

| Effects on circulation patterns

| [[#Geng--2017|Geng et al. (2017)]] ; [[#Scaife--2017|Scaife et al. (2017)]]

|-
| India

| May 2016

| High temperature

| [[#van%20Oldenborgh--2018|van Oldenborgh et al. (2018)]]

|-
| India

| December 2015

| Extreme rainfall

| [[#van%20Oldenborgh--2016|van Oldenborgh et al. (2016)]] ; [[#Boyaj--2018|Boyaj et al. (2018)]]

|-
| China

| June–July 2016

| Extreme rainfall

| [[#Sun--2018|Sun and Miao (2018)]] ; [[#Yuan--2018b|Yuan et al. (2018b)]] ; [[#Zhou--2018|Zhou et al. (2018)]]

|-
| Western North Pacific

| Boreal summer 2015

| The large number (13) of Category 4 and 5 tropical cyclones

| [[#Blunden--2016|Blunden and Arndt (2016)]] ; [[#Mueller--2016|]] [[#Mueller--2016|B. Mueller et al. (2016)]] ; W. [[#Zhang--2016a|Zhang et al. (2016a)]] ; Hong et al., (2018); [[#Yamada--2019|Yamada et al. (2019)]]

|-
| Eastern North Pacific

| Boreal summer 2015

| A record-breaking number of tropical cyclones

| [[#Collins--2016|Collins et al. (2016)]] ; [[#Murakami--2017b|Murakami et al. (2017b)]]

|-
| Global

| 2015–2016 El Niño

| High CO <sub>2</sub> release to the atmosphere associated with droughts and fires in several affected regions

| [[#Humphrey--2018|Humphrey et al. (2018)]] ; [[#Brando--2019|Brando et al. (2019)]]

|}

Several regions were strongly affected by droughts in 2015, including Indonesia, Australia, the Amazon region, Ethiopia, southern Africa, and Europe. As a result, global measurements of land water anomalies were particularly low in that year ( [[#Humphrey--2018|Humphrey et al., 2018]] ). In 2015, Indonesia experienced a severe drought and forest fire, causing pronounced impact on economy, ecology and human health due to haze crisis (Field et al. , 2016; Huijnen et al. , 2016; Patra et al. , 2017; Hartmann et al. , 2018). The northern part of Australia experienced high temperatures and low precipitation between late 2015 and early 2016, and the extensive mangrove trees were damaged along the Gulf of Carpentaria in Northern Australia ( [[#Duke--2017|Duke et al., 2017]] ). The Amazon region experienced the most intense droughts of this century in 2015–2016. This drought was more severe than the previous major droughts that occurred in the Amazon in 2005 and 2010 ( [[#Lewis--2011|Lewis et al., 2011]] ; [[#Erfanian--2017|Erfanian et al., 2017]] ; [[#Panisset--2018|Panisset et al., 2018]] ). The 2015–2016 Amazon drought impacted the entirety of South America north of 20°S during the austral spring and summer ( [[#Erfanian--2017|Erfanian et al., 2017]] ). It also increased forest fire incidence by 36% compared to the preceding 12 years ( [[#Aragão--2018|Aragão et al., 2018]] ) and, as a consequence, increased the biomass burning outbreaks and the carbon monoxide (CO) concentration in the area, affecting air quality ( [[#Ribeiro--2018|Ribeiro et al., 2018]] ). This out-of-season drought affected the water availability for human consumption and agricultural irrigation. It also left rivers with very low water levels and large sandbanks, preventing ship transportation of food, medicines, and fuels ( [[#INMET--2017|INMET, 2017]] ). Eastern African countries were impacted by drought in 2015. The drought in Ethiopia was the worst in several decades and was associated with the 2015–2016 extreme El Niño ( [[#Blunden--2016|Blunden and Arndt, 2016]] ; [[#Philip--2018b|Philip et al., 2018b]] ). It was suggested that anthropogenic warming contributed to the 2015 Ethiopian and southern African droughts by increasing SSTs and local air temperatures ( [[#Funk--2016|Funk et al., 2016]] , 2018b; [[#Yuan--2018a|Yuan et al., 2018a]] ). It has also been suggested that the 2015–2016 extreme El Niño affected circulation patterns in Europe during the 2015–2016 winter ( [[#Geng--2017|Geng et al., 2017]] ; [[#Scaife--2017|Scaife et al., 2017]] ).

The atmospheric CO <sub>2</sub> growth rate was particularly high in 2015, possibly related to some of the mentioned droughts, in particular in Indonesia and the Amazon region, leading to higher CO <sub>2</sub> release in combination with less CO <sub>2</sub> uptake from land areas ( [[#Humphrey--2018|Humphrey et al., 2018]] ). The impact of the 2015–2016 extreme El Niño on vegetation systems via drought was also shown from satellite data ( [[#Kogan--2017|Kogan and Guo, 2017]] ). Overall, tropical forests were a carbon source to the atmosphere during the 2015–2016 El Niño-related drought, with some estimates suggesting that up to 2.3 PgC were released ( [[#Brando--2019|Brando et al., 2019]] ).

The 2015–2016 extreme El Niño has induced extreme precipitation in some regions. Severe rainfall events were observed in Chennai city in India in Devember 2015, and the Yangtze river region in China in June–July 2016, and it was shown that these rainfall events are partly attributed to the 2015–2016 extreme El Niño (van Oldenborgh et al. , 2016; Boyaj et al. , 2018; [[#Sun--2018|Sun and Miao, 2018]] ; Yuan et al. , 2018b; Zhou et al., 2018).

In 2015, tropical cyclone activity was notably high in the North Pacific ( [[#Blunden--2016|Blunden and Arndt, 2016]] ). Over the western North Pacific, there were 13 Category 4 and 5 tropical cyclones (TCs), more than twice the area’s typical annual value of 6.3 (W. [[#Zhang--2016b|]] [[#Zhang--2016|Zhang et al., 2016]] b ). Similarly, a record-breaking number of TCs were observed in the eastern North Pacific, particularly in the western part of that domain ( [[#Collins--2016|Collins et al., 2016]] ; [[#Murakami--2017b|Murakami et al., 2017b]] ). These extraordinary TC activities were related to the average SST anomaly during that year, which were associated with the 2015–2016 extreme El Niño and the positive phase of the Pacific Meridional Mode ( [[#Murakami--2017b|Murakami et al., 2017b]] ; [[#Hong--2018|Hong et al., 2018]] ; [[#Yamada--2019|Yamada et al., 2019]] ). However, it has been suggested that the intense TC activities in both the western and the eastern North Pacific in 2015 were not only due to the El Niño, but also to a contribution of anthropogenic forcing ( [[#Murakami--2017b|Murakami et al., 2017b]] ; S.-H. [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|Yang et al., 2018]] ). The impact of the Indian Ocean SST was also suggested to contribute to the extreme TC activity in the western North Pacific in 2015 ( [[#Zhan--2018|Zhan et al., 2018]] ). In contrast, in Australia, it was the least active TC season since satellite records began in 1969–1970 ( [[#Blunden--2017|Blunden and Arndt, 2017]] ).

'''Global-scale temperature extremes and concurrent precipitation extremes in boreal 2018 sp''' '''ring and summer'''

In the 2018 boreal spring–summer season (May–August), wide areas of the mid-latitudes in the Northern Hemisphere experienced heat extremes and (in part) enhanced drought (Box 11.4, Figure 2; Kornhuber et al. , 2019; Vogel et al. , 2019). The reported impacts included ( [[#Vogel--2019|Vogel et al., 2019]] ): 90 deaths from heat strokes in Quebec (Canada); 1469 deaths from heat strokes in Japan ( [[#Shimpo--2019|Shimpo et al., 2019]] ); 48 heat-related deaths in the Republic of Korea ( [[#Min--2020|Min et al., 2020]] ); heat warnings affecting 90,000 students in the USA; fires in numerous countries (Canada (British Columbia), USA (California), Finland (Lapland), Latvia); crop losses in the UK, Germany and Switzerland ( [[#Vogel--2019|Vogel et al., 2019]] ) and overall in central and Northern Europe (leading to yield reductions of up to 50% for the main crops ( [[#Toreti--2019|Toreti et al., 2019]] ); fish deaths in Switzerland; and melting of roads in the Netherlands and the UK, among others. In addition to the numerous hot and dry extremes, an extremely heavy rainfall event occurred over wide areas of Japan from 28 June to 8 July 2018 ( [[#Tsuguti--2018|Tsuguti et al., 2018]] ), which was followed by a heatwave ( [[#Shimpo--2019|Shimpo et al., 2019]] ). The heavy precipitation event caused more than 230 deaths in Japan, and was named ‘the Heavy Rain Event of July 2018’.

The heavy precipitation event was characterized by unusually widespread and persistent rainfall and locally anomalous total precipitation led by band-shaped precipitation systems, which are frequently associated with heavy precipitation events in East Asia ( [[#11.7.3|Section 11.7.3]] ; [[#Kato--2020|Kato, 2020]] ). The extreme rainfall in Japan was caused by anomalous moisture transport with a combination of abnormal jet condition ( [[#Takemi--2019|Takemi and Unuma, 2019]] ; [[#Takemura--2019|Takemura et al., 2019]] ; [[#Tsuji--2020|Tsuji et al., 2020]] ; [[#Yokoyama--2020|Yokoyama et al., 2020]] ), which can be viewed as an atmospheric river (Sections 8.2.2.8 and 11.7.2; [[#Yatagai--2019|Yatagai et al., 2019]] ) caused by intensified inflow velocity and high SST around Japan ( [[#Sekizawa--2019|Sekizawa et al., 2019]] ; [[#Kawase--2020|Kawase et al., 2020]] ).

This precipitation event and the subsequent heatwave are related to abnormal condition of the jet stream and North Pacific Subtropical High in this month ( [[#Shimpo--2019|Shimpo et al., 2019]] ; [[#Ren--2020|Ren et al., 2020]] ), which caused extreme conditions from Europe, Eurasia, and North America (Box 11.4, Figure 2; [[#Kornhuber--2019|Kornhuber et al., 2019]] ). A combination of the positive anomaly of the North Atlantic Oscillation (NAO, Annex IV.2.1) and the meandering jets is necessary to explain the pattern of the observed anomalies (Drouard et al. , 2019) . A role of Atlantic SST anomaly on the meandering jets and the subtropical high have been suggested ( [[#Liu--2019|B. Liu et al., 2019]] ). These dynamic and thermodynamic components generally have substantial influence on extreme rainfall in East Asia ( [[#Oh--2018|Oh et al., 2018]] ), but it is under investigation whether these factors were due to anthropogenic forcing.

Regarding the hot extremes that occurred across the Northern Hemisphere in the 2018 boreal May–July period, [[#Vogel--2019|Vogel et al. (2019)]] found that the event was unprecedented in terms of the total area affected by hot extremes (on average, about 22% of populated and agricultural areas in the Northern Hemisphere) for that period, but was consistent with a +1°C climate which was the estimated global mean temperature anomaly around that time (for 2017; SR1.5). This study also found that events similar to the 2018 May–July temperature extremes would approximately occur two out of three years under +1.5°C of global warming, and every year under +2°C of global warming. [[#Imada--2019|Imada et al. (2019)]] also suggest that the mean annual occurrence of extreme hot days in Japan will be expected to increase by 1.8 times under a global warming level of 2°C above pre-industrial levels. [[#Kawase--2020|Kawase et al. (2020)]] showed that the extreme rainfall in Japan during this event was increased by approximately 7% due to recent rapid warming around Japan. Imada et al. (2020) showed that the probability of the Heavy Rain Event of July 2018 in Japan was increased from 0.22% to 2.00% due to anthropogenic warming. Hence, it is ''virtually certain'' that these 2018 concurrent events would not have occurred without human-induced global warming. Concurrent events of this type are also projected to happen more frequently under higher levels of global warming. However, there is currently ''low confidence'' in projected changes in the frequency or strength of the anomalous circulation patterns leading to concurrent extremes (e.g., Cross-Chapter Box 10.1).

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[[File:8b960021e628718fccebc033dea879cd IPCC_AR6_WGI_Box_11_4_Figure_2.png]]

'''Box 11.4, Figure 2 |'''

'''Meteorological conditions in July 2018.''' The colour shading shows the monthly mean near-surface air temperature anomaly with respect to 1981–2010. Contour lines indicate the geopotential height in m, highlighted are the isolines on 12,000 m and 12,300 m, which indicate the approximate positions of the polar-front jet and subtropical jet, respectively. The light blue-green ellipse shows the approximate extent of the strong precipitation event that occurred at the beginning of July in the region of Japan and Korea. All data is from the global European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5, [[#Hersbach--2020|Hersbach et al., 2020]] ).

The case studies presented in this Box illustrate the current state of knowledge regarding the contribution of human-induced climate change to recent concurrent extremes in the global domain. Recent years have seen a more frequent occurrence of such events. The heatwave in Europe in the 2019 boreal summer and its coverage in the global domain is an additional example ( [[#Vautard--2020|Vautard et al., 2020]] ). However, very few studies investigate which types of concurrent extreme events could occur under increasing global warming. It has been noted that such events could also be of particular risk for concurrent impacts in the world’s breadbaskets ( [[#Zampieri--2017|Zampieri et al., 2017]] ; [[#Kornhuber--2020|Kornhuber et al., 2020]] ; see also [[#11.8.1|Section 11.8.1]] ).

In summary, the 2015–2016 extreme El Niño and the 2018 boreal spring/summer extremes were two examples of recent concurrent extremes. The El Niño event in 2015–2016 was one of the three extreme El Niño events since the 1980s, and there are many extreme events concurrently observed in this period including droughts, heavy precipitation, and more frequent intense tropical cyclones. Both the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period ( ''medium confidence'' ), suggesting that global extremes similar to those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. The 2018 boreal spring/summer extremes were characterized by heat extremes and enhanced droughts in wide areas of the mid-latitudes in the Northern Hemisphere and extremely heavy rainfall in East Asia. These concurrent events were generally related to the abnormal condition of the jet and North Pacific Subtropical High, but also amplified by background global warming. It is ''virtually certain'' that these 2018 concurrent extreme events would not have occurred without human-induced global warming. Recent years have seen a more frequent occurrence of such concurrent events. However, it is still unknown which types of concurrent extreme events could occur under increasing global warming.

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