Editing IPCC:Wg1:Chapter:Chapter-1-comments (section)

==== 1.4.2.2 The Emergence of the Climate Change Signal ====

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In the 1930s it was noted that temperatures were increasing at both local and global scales (Figure 1.8; [[#Kincer--1933|Kincer, 1933]] ; [[#Callendar--1938|Callendar, 1938]] ). At the time it was unclear whether the observed changes were part of a longer-term trend or a natural fluctuation; the ‘signal’ had not yet clearly emerged from the ‘noise’ of natural variability. Numerous studies have since focused on the emergence of changes in temperature using instrumental observations (e.g., [[#Madden--1980|Madden and Ramanathan, 1980]] ; [[#Wigley--1981|Wigley and Jones, 1981]] ; [[#Mahlstein--2011|Mahlstein et al., 2011]] , 2012; [[#Lehner--2015|Lehner and Stocker, 2015]] ; [[#Lehner--2017|Lehner et al., 2017]] ) and paleo-temperature data (e.g., [[#Abram--2016|Abram et al., 2016]] ).

Since the IPCC Third’s Assessment Report in 2001, the observed signal of climate change has been unequivocally detected at the global scale ( [[#1.3|Section 1.3]] ), and this signal is increasingly emerging from the noise of natural variability on smaller spatial scales and in a range of climate variables (FAQ 1.2). In this Report emergence of a climate change signal or trend refers to when a change in climate (the ‘signal’) becomes larger than the amplitude of natural or internal variations (defining the ‘noise’). This concept is often expressed as a ‘signal-to-noise’ ratio (S/N) and emergence occurs at a defined threshold of this ratio (e.g., S/N &gt;1 or 2). Emergence can be estimated using observations and/or model simulations and can refer to changes relative to a historical or modern baseline (Section 12.5.2 and Glossary). The concept can also be expressed in terms of time (the ‘time of emergence’; Glossary) or in terms of a global warming level (Section 11.2.5; [[#Kirchmeier-Young--2019|Kirchmeier-Young et al., 2019]] ) and is also used to refer to a time when we can expect to see a response of mitigation activities that reduce emissions of GHGs or enhance their sinks (emergence with respect to mitigation; [[IPCC:Wg1:Chapter:Chapter-4#4.6.3.1|Section 4.6.3.1]] ). Whenever possible, emergence should be discussed in the context of a clearly defined level of S/N or other quantification, such as ‘the signal has emerged at the level of S/N &gt;2’, rather than as a simple binary statement. For an extended discussion, see [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10 Chapter 10] (Section 10.4.3).

Related to the concept of emergence is the detection of change (Chapter 3). Detection of change is defined as the process of demonstrating that some aspect of the climate, or a system affected by climate, has changed in some defined statistical sense, often using spatially aggregating methods that try to maximize S/N, such as ‘fingerprints’ (e.g., [[#Hegerl--1996|Hegerl et al., 1996]] ), without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small, for example, &lt;10% (Glossary).

An example of observed emergence in surface air temperatures is shown in Figure 1.14. Both the largest changes in temperature and the largest amplitude of year-to-year variations are observed in the Arctic, with lower latitudes showing less warming and smaller year-to-year variations. For the six example regions shown in Figure 1.14, the emergence of changes in temperature is more apparent in Northern South America, East Asia and Central Africa, than for northern North America or Northern Europe. This pattern was predicted by [[#Hansen--1988|Hansen et al. (1988)]] and noted in subsequent observations by [[#Mahlstein--2011|Mahlstein et al. (2011)]] (Sections 10.3.4.3 and 12.5.2). Overall, tropical regions show earlier emergence of temperature changes than at higher latitudes ( ''hi'' ''gh confidence'' ).

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'''Figure 1.14 |''' '''The observed emergence of changes in temperature.''' '''(Top left)''' The total change in temperature estimated for 2020 relative to 1850–1900 (following [[#Hawkins--2020|Hawkins et al., 2020]] ), showing the largest warming occurring in the Arctic. '''(Top right)''' The amplitude of estimated year-to-year variations in temperature. '''(Middle''' '''left)''' The ratio of the observed total change in temperature and the amplitude of temperature variability (the ‘signal-to-noise (S/N) ratio’), showing that the warming is most apparent in the tropical regions (also see FAQ 1.2). '''(Middle right)''' The global warming level at which the change in local temperature becomes larger than the local year-to-year variability. The '''bottom''' panels show time series of observed annual mean surface air temperatures over land in various example regions, as indicated by the boxes in the top-left panel. The 1 and 2 standard deviations ( σ ) of estimated year-to-year variations for that region are shown by the pink shaded bands. Observed temperature data from Berkeley Earth ( [[#Rohde--2020|Rohde and Hausfather, 2020]] ). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).
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Since AR5, the emergence of projected future changes has also been extensively examined, in variables including surface air temperature ( [[#Hawkins--2012|Hawkins and Sutton, 2012]] ; [[#Kirtman--2013|Kirtman et al., 2013]] ; [[#Tebaldi--2013|Tebaldi and Friedlingstein, 2013]] ), ocean temperatures and salinity ( [[#Banks--2002|Banks and Wood, 2002]] ), mean precipitation ( [[#Giorgi--2009|Giorgi and Bi, 2009]] ; [[#Maraun--2013|Maraun, 2013]] ), drought ( [[#Orlowsky--2013|Orlowsky and Seneviratne, 2013]] ), extremes ( [[#Diffenbaugh--2011|Diffenbaugh and Scherer, 2011]] ; [[#Fischer--2014|Fischer et al., 2014]] ; [[#King--2015|King et al., 2015]] ; [[#Schleussner--2020|Schleussner and Fyson, 2020]] ), and regional sea level change ( [[#Lyu--2014|Lyu et al., 2014]] ). The concept has also been applied to climate change impacts such as effects on crop growing regions ( [[#Rojas--2019|Rojas et al., 2019]] ). In AR6, the emergence of oceanic signals such as regional sea level change and changes in water mass properties is assessed in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.6.1.4); emergence of future regional changes is assessed in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10 Chapter 10] (Section 10.4.3); the emergence of extremes as a function of global warming levels is assessed in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] (Section 11.2.5); and the emergence of climatic impact-drivers for AR6 regions and many climate variables is assessed in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] (Section 12.5.2).

Although the magnitude of any change is important, regions which have a larger signal of change relative to the background variations will potentially face greater risks than other regions, as they will see unusual or novel climate conditions more quickly ( [[#Frame--2017|Frame et al., 2017]] ). As in Figure 1.14, the signal of temperature change is often smaller in tropical countries, but their lower amplitude of variability means they may experience the effects of climate change earlier than the mid-latitudes. In addition, these tropical countries are often among the most exposed, due to large populations ( [[#Lehner--2015|Lehner and Stocker, 2015]] ), and often more vulnerable ( [[#Harrington--2016|Harrington et al., 2016]] ; [[#Harrington--2018|Harrington and Otto, 2018]] ; [[#Russo--2019|Russo et al., 2019]] ). Higher levels of exposure and vulnerability increase the risk from climate-related impacts (Cross-Chapter Box 1.3). The rate of change is also important for many hazards (e.g., [[#Loarie--2009|Loarie et al., 2009]] ). Providing more information about changes and variations on regional scales, and the associated attribution to particular causes (Cross-Working Group Box: Attribution), is therefore important for adaptation planning.

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