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

=== 11.3.4 Detection and Attribution, Event Attribution ===

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The SREX (IPCC, 2012) assessed that it is ''likely'' anthropogenic influences have led to the warming of extreme daily minimum and maximum temperatures at the global scale. The AR5 concluded that human influence has ''very likely'' contributed to the observed changes in the intensity and frequency of daily temperature extremes on the global scale in the second half of the 20th century (IPCC, 2014). With regard to individual, or regionally or locally specific events, AR5 concluded that it is ''likely'' human influence has substantially increased the probability of occurrence of heatwaves in some locations.

Studies since AR5 continue to attribute the observed increase in the frequency or intensity of hot extremes and the observed decrease in the frequency or intensity of cold extremes to human influence, dominated by anthropogenic greenhouse gas emissions, on global and continental scales, and for many AR6 regions. These include attribution of changes in the magnitude of annual TXx, TNx, TXn, and TNn, based on different observational datasets including, HadEX2 and HadEX3, CMIP5 and CMIP6 simulations, and different statistical methods ( [[#Kim--2016|Kim et al., 2016]] ; Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] a; [[#Seong--2021|Seong et al., 2021]] ). As is the case for an increase in mean temperature ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1|Section 3.3.1]] ), an increase in extreme temperature is mostly due to greenhouse gas forcing, offset by aerosol forcing. The aerosols’ cooling effect is clearly detectable over Europe and Asia ( [[#Seong--2021|Seong et al., 2021]] ). As much as 75% of the moderate daily hot extremes (above 99.9th percentile) over land are due to anthropogenic warming ( [[#Fischer--2015|Fischer and Knutti, 2015]] ). New results are found to be more robust due to the extended period that improves the signal-to-noise ratio. The effect of anthropogenic forcing is clearly detectable and attributable in the observed changes in these indicators of temperature extremes, even at country and sub-country scales, such as in Canada ( [[#Wan--2019|Wan et al., 2019]] ). Changes in the number of warm nights, warm days, cold nights, and cold days, and other indicators such as the Warm Spell Duration Index (WSDI), are also attributed to anthropogenic influence ( [[#Christidis--2016|Christidis and Stott, 2016]] ; [[#Hu--2020|Hu et al., 2020]] ).

Regional studies, including for Asia ( [[#Dong--2018|Dong et al., 2018]] ; [[#Lu--2018|Lu et al., 2018]] ), Australia ( [[#Alexander--2017|Alexander and Arblaster, 2017]] ), and Europe ( [[#Christidis--2016|Christidis and Stott, 2016]] ), found similar results. A clear anthropogenic signal is also found in the trends in the Combined Extreme Index (CEI) for North America, Asia, Australia, and Europe ( [[#Dittus--2016|Dittus et al., 2016]] ). While various studies have described increasing trends in several heatwave metrics (heatwave duration, the number of heatwave days, etc.) in different regions (e.g., [[#Cowan--2014|Cowan et al., 2014]] ; [[#Bandyopadhyay--2016|Bandyopadhyay et al., 2016]] ; M. [[#Sanderson--2017|]] [[#Sanderson--2017|Sanderson et al., 2017]] ), few recent studies have explicitly attributed these changes to causes; most of them stated that observed trends are consistent with anthropogenic warming. The detected anthropogenic signals are clearly separable from the response to natural forcing, and the results are generally insensitive to the use of different model samples, as well as different data availability, indicating robust attribution. Studies of monthly, seasonal, and annual records in various regions ( [[#Kendon--2014|Kendon, 2014]] ; [[#Lewis--2015|Lewis and King, 2015]] ; [[#Bador--2016|Bador et al., 2016]] ; [[#Meehl--2016|Meehl et al., 2016]] ; [[#Zhou--2019|]] [[#Zhou--2019|C. Zhou et al., 2019]] ) and globally ( [[#King--2017|King, 2017]] ) show an increase in the breaking of hot records and a decrease in the breaking of cold records ( [[#King--2017|King, 2017]] ). Changes in anthropogenically attributablerecord-breaking rates are noted to be largest over the Northern Hemisphere land areas ( [[#Shiogama--2016|Shiogama et al., 2016]] ). Yin and Sun (2018) found clear evidence of an anthropogenic signal in the changes in the number of frost and ice days, when multiple model simulations were used. In some key wheat-producing regions of Southern Australia, increases in frost days or frost season length have been reported ( [[#Dittus--2014|Dittus et al., 2014]] ; [[#Crimp--2016|Crimp et al., 2016]] ); these changes are linked to decreases in rainfall, cloud-cover, and subtropical ridge strength, despite an overall increase in regional mean temperatures ( [[#Dittus--2014|Dittus et al., 2014]] ; [[#Pepler--2018|Pepler et al., 2018]] ).

A significant advance since AR5 has been a large number of studies focusing on extreme temperature events at monthly and seasonal scales, using various extreme event attribution methods. [[#Diffenbaugh--2017|Diffenbaugh et al. (2017)]] found that anthropogenic warming has increased the severity and probability of the hottest month by more than 80% of the available observational area on the global scale. [[#Christidis--2014|Christidis and Stott (2014)]] provide clear evidence that warm events have become more probable because of anthropogenic forcings. [[#Sun--2014|Sun et al. (2014)]] found that human influence has caused a more than 60-fold increase in the probability of the extreme warm 2013 summer in eastern China since the 1950s. Human influence is found to have increased the probability of the historically hottest summers in many regions of the world, both in terms of mean temperature ( [[#Mueller--2016|]] [[#Mueller--2016|B. Mueller et al., 2016]] ) and wet bulb globe temperature (WBGT; [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ). In most regions of the Northern Hemisphere, changes in the probability of extreme summer average WBGT were found to be about an order of magnitude larger than changes in the probability of extreme hot summers estimated by surface air temperature ( [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|C. Li et al., 2017]] ). In addition to these generalized, global-scale approaches, extreme event studies have found an attributable increase in the probability of hot annual and seasonal temperatures in many locations, including Australia ( [[#Knutson--2014b|Knutson et al., 2014b]] ; [[#Lewis--2014|Lewis and Karoly, 2014]] ), China ( [[#Sun--2014|Sun et al., 2014]] ; [[#Sparrow--2018|Sparrow et al., 2018]] ; [[#Zhou--2020|Zhou et al., 2020]] ), Korea (Y.-H. [[#Kim--2018|]] [[#Kim--2018|]] [[#Kim--2018|Kim et al., 2018]] ) and Europe ( [[#King--2015b|King et al., 2015b]] ).

There have also been many extreme event attribution studies that examined short-duration temperature extremes, including daily temperatures, temperature indices, and heatwave metrics. Examples of these events from different regions are summarized in various annual Explaining Extreme Events supplements of the ''Bulletin of the American Meteorological Society'' ( [[#Peterson--2012|Peterson et al., 2012]] , 2013a; [[#Herring--2014|Herring et al., 2014]] , 2015, 2016, 2018, 2019, 2020), including a number of approaches to examine extreme events (described in [[#Easterling--2016|Easterling et al., 2016]] ; [[#Stott--2016|Stott et al., 2016]] ; [[#Otto--2017|Otto, 2017]] ). Several studies of recent events from 2016 onwards have determined an infinite risk ratio (a fraction of attributable risk, or FAR, of 1), indicating that the occurrence probability for such events is close to zero in model simulations without anthropogenic influences (see [[#Herring--2018|Herring et al., 2018]] , 2019, 2020; [[#Imada--2019|Imada et al., 2019]] ; [[#Vogel--2019|Vogel et al., 2019]] ). Though it is difficult to accurately estimate the lower bound of the uncertainty range of the FAR in these cases ( [[#Paciorek--2018|Paciorek et al., 2018]] ), the fact that those events are so far outside the envelop of the models with only natural forcing indicates that it is ''extremely unlikely'' for those events to occur without human influence.

Studies that focused on the attributable signal in observed cold extreme events show human influence reducing the probability of those events. Individual attribution studies on the extremely cold winter of 2011 in Europe ( [[#Peterson--2012|Peterson et al., 2012]] ), in the eastern USA during 2014 and 2015 ( [[#Trenary--2015|Trenary et al., 2015]] , 2016; [[#Wolter--2015|Wolter et al., 2015]] ; [[#Bellprat--2016|Bellprat et al., 2016]] ), in the cold spring of 2013 in the United Kingdom ( [[#Christidis--2014|Christidis et al., 2014]] ), and of 2016 in eastern China ( [[#Qian--2018|Qian et al., 2018]] ; Y. [[#Sun--2018b|]] [[#Sun--2018|Sun et al., 2018]] b ) all showed a reduced probability due to human influence on the climate. An exception is the study of [[#Grose--2018|Grose et al. (2018)]] , which found an increase in the probability of the severe western Australian frost of 2016 due to anthropogenically-driven changes in circulation patterns that drive cold outbreaks and frost probability.

Different event attribution studies can produce a wide range of changes in the probability of event occurrence because of different framing. The temperature event definition itself plays a crucial role in the attributable signal ( [[#Fischer--2015|Fischer and Knutti, 2015]] ; Kirchmeier‐Young et al., 2019). Large-scale, longer-duration events tend to have notably larger attributable risk ratios ( [[#Angélil--2014|Angélil et al., 2014]] , 2018; [[#Uhe--2016|Uhe et al., 2016]] ; [[#Harrington--2017|Harrington, 2017]] ; Kirchmeier‐Young et al., 2019), as natural variability is smaller. While uncertainty in the best estimates of the risk ratios may be large, their lower bounds can be quite insensitive to uncertainties in observations or model descriptions, thus increasing confidence in conservative attribution statements ( [[#Jeon--2016|Jeon et al., 2016]] ).

The relative strength of anthropogenic influences on temperature extremes is regionally variable, in part due to differences in changes in atmospheric circulation, land–surface feedbacks, and other external drivers such as aerosols. For example, in the Mediterranean and over western Europe, risk ratios on the order of 100 have been found ( [[#Kew--2019|Kew et al., 2019]] ; [[#Vautard--2020|Vautard et al., 2020]] ), whereas in the USA, changes are much less pronounced. This is probably a reflection of the land–surface feedback enhanced extreme 1930s temperatures that reduce the rarity of recent extremes, in addition to the definition of the events and framing of attribution analyses (e.g., spatial and temporal scales considered). Local forcing may mask or enhance the warming effect of greenhouse gases. In India, short-lived aerosols or an increase in irrigation may be masking the warming effect of greenhouse gases ( [[#Wehner--2018c|Wehner et al., 2018c]] ). Irrigation and crop intensification have been shown to lead to a cooling in some regions, in particular in North America, Europe, and India ( ''high confidence'' ) (N.D. [[#Mueller--2016|]] [[#Mueller--2016|Mueller et al., 2016]] ; [[#Thiery--2017|Thiery et al., 2017]] , 2020; [[#Chen--2019|Chen and Dirmeyer, 2019]] ). Deforestation has contributed about one third of the total warming of hot extremes in some mid-latitude regions since pre-industrial times ( [[#Lejeune--2018|Lejeune et al., 2018]] ). Despite all of these differences, and larger uncertainties at the regional scale, nearly all studies demonstrated that human influence has contributed to an increase in the frequency or intensity of hot extremes and to a decrease in the frequency or intensity of cold extremes.

In summary, long-term changes in various aspects of long- and short-duration extreme temperatures, including intensity, frequency, and duration have been detected in observations and attributed to human influence at global and continental scales. It is ''extremely likely'' that human influence is the main contributor to the observed increase in the intensity and frequency of hot extremes and the observed decrease in the intensity and frequency of cold extremes on the global scale. It is ''very likely'' that this applies on continental scales as well. Some specific recent hot extreme events would have been ''extremely unlikely'' to occur without human influence on the climate system. Changes in aerosol concentrations have affected trends in hot extremes in some regions, with the presence of aerosols leading to attenuated warming, in particular from 1950 to 1980. Crop intensification, irrigation and no-till farming have attenuated increases in summer hot extremes in some regions, such as Central North America ( ''medi'' ''um confidence'' ).

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