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

=== 11.4.4 Detection and Attribution, Event Attribution ===

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Both SREX (Chapter 3, [[#Seneviratne--2012|Seneviratne et al., 2012]] ) and AR5 (Chapter 10, IPCC, 2014) concluded with ''medium confidence'' that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century. These assessments were based on the evidence of anthropogenic influence on aspects of the global hydrological cycle, in particular, the human contribution to the warming-induced observed increase in atmospheric moisture that leads to an increase in heavy precipitation, and ''limited evidence'' of anthropogenic influence on extreme precipitation of durations of one and five days.

Since AR5 there has been new and ''robust evidence'' and improved understanding of human influence on extreme precipitation. In particular, detection and attribution analyses have provided consistent and ''robust evidence'' of human influence on extreme precipitation of one- and five-day durations at global to continental scales. The observed increases in Rx1day and Rx5day over the Northern Hemisphere land area during 1951–2005 can be attributed to the effect of combined anthropogenic forcing, including greenhouse gases and anthropogenic aerosols, as simulated by CMIP5 models and the rate of intensification with regard to warming is consistent with C-C scaling ( [[#Zhang--2013|Zhang et al., 2013]] ). This is confirmed to be robust when an additional nine years of observational data and the CMIP6 model simulations were used (Cross-Chapter Box 3.2, Figure 1; [[#Paik--2020|Paik et al., 2020]] ). The influence of greenhouse gases is attributed as the dominant contributor to the observed intensification. The global average of Rx1day in the observations is consistent with simulations by both CMIP5 and CMIP6 models under anthropogenic forcing, but not under natural forcing (Cross-Chapter Box 3.2, Figure 1). The observed increase in the fraction of annual total precipitation falling into the top fifth or top first percentiles of daily precipitation can also be attributed to human influence at the global scale ( [[#Dong--2021|Dong et al., 2021]] ). The CMIP5 models were able to capture the fraction of land experiencing a strong intensification of heavy precipitation during 1960–2010 under anthropogenic forcing, but not in unforced simulations ( [[#Fischer--2014|Fischer et al., 2014]] ). But the models underestimated the observed trends ( [[#Borodina--2017a|Borodina et al., 2017a]] ). Human influence also significantly contributed to the historical changes in record-breaking one-day precipitation ( [[#Shiogama--2016|Shiogama et al., 2016]] ). There is also ''limited evidence'' of the influences of natural forcing. Substantial reductions in Rx5day and Simple Daily Intensity Index (SDII) for daily precipitation intensity over the global summer monsoon regions occurred during 1957–2000 after explosive volcanic eruptions ( [[#Paik--2018|Paik and Min, 2018]] ). The reduction in post-volcanic eruption extreme precipitation in the simulations is closely linked to the decrease in mean precipitation, for which both thermodynamic effects (moisture reduction due to surface cooling) and dynamic effects (monsoon circulation weakening) play important roles.

There has been new evidence of human influence on extreme precipitation at continental scales, including the detection of the combined effect of greenhouse gases and aerosol forcing on Rx1day and Rx5day over North America, Eurasia, and mid-latitude land regions ( [[#Zhang--2013|Zhang et al., 2013]] ) and of greenhouse gas forcing in Rx1day and Rx5day in the mid-to-high latitudes, western and eastern Eurasia, and the global dry regions ( [[#Paik--2020|Paik et al., 2020]] ). These findings are corroborated by the detection of human influence in the fraction of extreme precipitation in the total precipitation over Asia, Europe, and North America ( [[#Dong--2021|Dong et al., 2021]] ). Human influence was found to have contributed to the increase in frequency and intensity of regional precipitation extremes in North America during 1961–2010, based on optimal fingerprinting and event attribution approaches ( [[#Kirchmeier-Young--2020|Kirchmeier-Young and Zhang, 2020]] ). [[#Tabari--2020|Tabari et al. (2020)]] found the observed latitudinal increase in extreme precipitation over Europe to be consistent with model-simulated responses to anthropogenic forcing.

Evidence of human influence on extreme precipitation at regional scales is more limited and less robust. In north-west Australia, the increase in extreme rainfall since 1950 can be related to increased monsoonal flow due to increased aerosol emissions, but cannot be attributed to an increase in greenhouse gases ( [[#Dey--2019a|Dey et al., 2019a]] ). Anthropogenic influence on extreme precipitation in China was detected in one study (H. [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|]] [[#Li--2017|Li et al., 2017]] ), but not in another using different detection and data-processing procedures (W. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] a), indicating the lack of robustness in the detection results. A still weak signal-to-noise ratio seems to be the main cause for the lack of robustness, as detection would become robust 20 years in the future (W. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] a). [[#Krishnan--2016|Krishnan et al. (2016)]] attributed the observed increase in heavy rain events (intensity &gt;100 mm day <sup>–1</sup> ) in the post-1950s over central India to the combined effects of greenhouse gases, aerosols, land-use and land-cover changes, and rapid warming of the equatorial Indian Ocean SSTs. Roxyet al. (2017) and [[#Devanand--2019|Devanand et al. (2019)]] showed that the increase in widespread extremes over the South Asian Monsoon during 1950–2015 is due to the combined impacts of the warming of the Western Indian Ocean (Arabian Sea) and the intensification of irrigation water management over India.

Anthropogenic influence may have affected the large-scale meteorological processes necessary for extreme precipitation and the localized thermodynamic and dynamic processes, both contributing to changes in extreme precipitation events. Several new methods have been proposed to disentangle these effects by either conditioning on the circulation state or attributing analogues. In particular, the extremely wet winter of 2013–2014 in the UK can be attributed, approximately to the same degree, to both temperature-induced increases in saturation vapour pressure and changes in the large-scale circulation ( [[#Vautard--2016|Vautard et al., 2016]] ; [[#Yiou--2017|Yiou et al., 2017]] ). There are multiple cases indicating that very extreme precipitation may increase at a rate more than the C-C rate (7% per 1°C of warming) ( [[#Pall--2017|Pall et al., 2017]] ; [[#Risser--2017|Risser and Wehner, 2017]] ; [[#van%20der%20Wiel--2017|van der Wiel et al., 2017]] ; [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ; S.-Y.S. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ).

Event attribution studies found an influence of anthropogenic activities on the probability or magnitude of observed extreme precipitation events, including European winters ( [[#Schaller--2016|Schaller et al., 2016]] ; [[#Otto--2018b|Otto et al., 2018b]] ), extreme 2014 precipitation over the northern Mediterranean ( [[#Vautard--2015|Vautard et al., 2015]] ), parts of the USA for individual events ( [[#Knutson--2014a|Knutson et al., 2014a]] ; [[#Szeto--2015|Szeto et al., 2015]] ; [[#Eden--2016|Eden et al., 2016]] ; [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ), extreme rainfall in 2014 over Northland, New Zealand ( [[#Rosier--2015|Rosier et al., 2015]] ) or China ( [[#Burke--2016|Burke et al., 2016]] ; [[#Sun--2018|Sun and Miao, 2018]] ; [[#Yuan--2018b|Yuan et al., 2018b]] ; [[#Zhou--2018|Zhou et al., 2018]] ). However, for other heavy rainfall events, studies identified a lack of evidence about anthropogenic influences ( [[#Imada--2013|Imada et al., 2013]] ; [[#Schaller--2014|Schaller et al., 2014]] ; [[#Otto--2015c|Otto et al., 2015c]] ; [[#Siswanto--2015|Siswanto et al., 2015]] ). There are also studies where results are inconclusive because of limited reliable simulations ( [[#Christidis--2013b|Christidis et al., 2013b]] ; [[#Angélil--2016|Angélil et al., 2016]] ). Overall, both the spatial and temporal scales on which extreme precipitation events are defined are important for attribution; events defined on larger scales have larger signal-to-noise ratios and thus the signal is more readily detectable. At the current level of global warming, there is a strong enough signal to be detectable for large-scale extreme precipitation events, but the chance of detecting such signals for smaller-scale events decreases ( [[#Kirchmeier-Young--2019|Kirchmeier-Young et al., 2019]] ).

In summary, most of the observed intensification of heavy precipitation over land regions is ''likely'' due to anthropogenic influence, for which greenhouse gases emissions are the main contributor. New and ''robust evidence'' since AR5 includes attribution to human influence of the observed increases in annual maximum one-day and five-day precipitation and in the fraction of annual precipitation falling in heavy events. The evidence since AR5 also includes a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, both of which can only be explained when anthropogenic greenhouse gas forcing is considered. Human influence has contributed to the intensification of heavy precipitation in three continents where observational data are more abundant ( ''high confidence'' ) (North America, Europe and Asia). On the spatial scale of AR6 regions, there is ''limited evidence'' of human influence on extreme precipitation, but new evidence is emerging; in particular, studies attributing individual heavy precipitation events found that human influence was a significant driver of the events, particularly in the winter season.

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