Editing IPCC:AR6/WGI/Chapter-3 (section)

==== 3.3.2.4 Streamflow ====

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Streamflow is to-date the only variable of the terrestrial water cycle with enough in-situ observations to allow for detection and attribution analysis at continental to global scales. Based on evidence from a few formal detection and attribution studies, particularly on the timing of peak streamflow, and the qualitative evaluation of studies reporting on observed and simulated trends, AR5 concluded that there is ''medium confidence'' that anthropogenic influence on climate has affected streamflow in some middle and high latitude regions ( [[#Bindoff--2013|Bindoff et al., 2013]] ). The AR5 also noted that observational uncertainties are large and that often only a limited number of models were considered.

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.3.6|Section 2.3.1.3.6]] assesses that there have not been significant trends in global average streamflow over the last century, though regional trends have been observed, driven in part by internal variability. Only a limited number of studies have systematically compared observed streamflow trends at continental to global scales with changes simulated by global circulation models in a detection and attribution setting. H. [[#Yang--2017|]] [[#Yang--2017|Yang et al. (2017)]] did not find a significant correlation between observed runoff changes and changes simulated in CMIP5 models in most grid cells, consistent with the assessment that observed changes are dominated by internal variability. In a pan-European assessment, [[#Gudmundsson--2017|Gudmundsson et al. (2017)]] attributed the spatio-temporal pattern of decreasing streamflow in southern Europe and increasing streamflow in northern Europe to anthropogenic climate change, but also concluded that additional effects of human water withdrawals could not be excluded. Focussing on continental runoff between 1958 and 2004, [[#Alkama--2013|Alkama et al. (2013)]] found a significant change only when using reconstructed data over all rivers, and a large uncertainty in the estimate of the global streamflow trend due to opposing changes over different continents. [[#Gedney--2014|Gedney et al. (2014)]] detected the influence of aerosols on streamflow in North America and Europe, with aerosols having driven an increase in streamflow due to reduced evaporation (see Section 8.3.1.5 for details on processes). There is also evidence for a detectable anthropogenic contribution toward earlier winter-spring streamflows in the north-central US ( [[#Kam--2018|Kam et al., 2018]] ) and in western Canada ( [[#Najafi--2017|Najafi et al., 2017]] ). From a model evaluation perspective, [[#Sheffield--2013|Sheffield et al. (2013)]] reported that CMIP5 models reproduce spatial variations in runoff in North America well, though they tend to underestimate it.

Recently, [[#Gudmundsson--2021|Gudmundsson et al. (2021)]] performed a global detection and attribution study on streamflow and found that some regions are drying and others are wetting. Moreover, the simulated streamflow trends are consistent with observations only if externally forced climate change is considered, and the simulated effects of water and land management cannot reproduce the observed trends. The effects of volcanic eruptions in driving reduced streamflow have also been detected in the wet tropics ( [[#Iles--2015|Iles and Hegerl, 2015]] ; [[#Zuo--2019|Zuo et al., 2019]] ).

In summary, there is ''medium confidence'' that anthropogenic climate change has altered local and regional streamflow in various parts of the world and that the associated global-scale trend pattern is inconsistent with internal variability. Moreover, human interventions and water withdrawals, while affecting streamflow, cannot explain the observed spatio-temporal trends ( ''medium confidence'' ).

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'''Cross-Chapter Box 3.2 | Human Influence on Large-scale Changes in Temperature and Precipitation Extremes'''

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'''Contributors:''' Nathan P. Gillett (Canada), Seung-Ki Min (Republic of Korea), Krishnan Raghavan (India), Ying Sun (China), Xuebin Zhang (Canada)

Understanding how temperature and precipitation extremes have changed at large scales and the causes of these changes is an important part of our overall assessment of human influence on the climate system. [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses changes in extremes and their causes, while this Cross-Chapter Box summarizes relevant assessments and supporting evidence in Chapters 8 and 11 and relates changes in extremes to mean changes on global and continental scales.

'''Attribution of temperature extremes'''
One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature ( [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Kharin--2018|Kharin et al., 2018]] ). It is thus no surprise that changes in temperature extremes are consistent with warming mean temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to human influence ( [[#3.3.1|Section 3.3.1]] ), and the connection between changes in mean and extreme temperatures, it is to be expected that anthropogenic forcing has also influenced temperature extremes.

( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses that there is ''high confidence'' that climate models can reproduce the mean state and overall warming of temperature extremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time periods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human influence on various aspects of temperature extremes, at global, continental, and regional scales. This includes attribution to human influence of observed changes in intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand panel of Cross-Chapter Box 3.2, Figure 1 clearly shows that long-term changes in the global mean annual maximum daily maximum temperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic forcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean temperature ( [[#3.3.1|Section 3.3.1]] ), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes globally and over most continents over the 1951–2015 period ( [[#Hu--2020|Hu et al., 2020]] ; [[#Seong--2021|Seong et al., 2021]] ), though greenhouse gas and aerosol influences are less clearly separable in observed changes in cold extremes.

( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses that it is ''virtually certain'' that human-induced greenhouse gas forcing is the main contributor to the observed increase in the likelihood and severity of hot extremes and the observed decrease in the likelihood and severity of cold extremes on global scales, and ''very likely'' that this applies on most continents.

'''Attribution of precipitation extremes'''
An important piece of evidence supporting the SREX and AR5 assessment that there is ''medium confidence'' that anthropogenic forcing has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation is projected to decrease.

The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models ( ''high confidence'' ), and there is ''high confidence'' in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering changes in precipitation intensity averaged over all wet days, there is ''high confidence'' that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is ''likely'' that such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand

Cross-Chapter Box 3.2

panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on extreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the long-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the influence of anthropogenic aerosol and natural forcings ( [[#Dong--2020|Dong et al., 2020]] ; [[#Paik--2020b|Paik et al., 2020b]] ).

[[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses that it is ''likely'' that human influence, in particular due to greenhouse gas forcing, is the main driver of the observed intensification of heavy precipitation in global land regions during recent decades (Section 11.4.4).

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'''Cross-Chapter Box 3.2, Figure 1 | Comparison of observed and simulated changes in global mean temperature and precipitation extremes.''' Time series of globally averaged five-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation (Rx1day as standardized probability index in %) between 1953 and 2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with natural and human forcing '''(top)''' and natural forcing only '''(bottom)''' . For CMIP5, historical simulations for 1953–2005 are combined with corresponding RCP4.5 scenario runs for 2006–2017. For CMIP6, historical simulations for 1953–2014 are combined with SSP2-4.5 scenario simulations for 2015–2017. Numbers in brackets represent the number of models used. The time-fixed observational mask has been applied to model data throughout the whole period. Grid cells with more than 70% of data available between 1953 and 2017 plus data for at least three years between 2013 and 2017 are used. Coloured lines indicate multi-model means, while shading represents 5th–95th percentile ranges, based on all available ensemble members with equal weight given to each model ( [[#3.2|Section 3.2]] ). Anomalies are relative to 1961–1990 means. Figure is updated from [[#Seong--2021|Seong et al. (2021)]] , their Figure 3 and [[#Paik--2020b|Paik et al. (2020b)]] , their Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

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