Editing IPCC:AR6/WGI/Chapter-1 (section)

=== 1.4.2 Variability and Emergence of the Climate Change Signal ===

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Climatic changes since the pre-industrial era are a combination of long-term anthropogenic changes and natural variations on time scales from days to decades. The relative importance of these two factors depends on the climate variable or region of interest. Natural variations consist of both natural radiatively forced trends (e.g., due to volcanic eruptions or solar variations) and ‘internal’ fluctuations of the climate system which occur even in the absence of any radiative forcings. The internal ‘modes of variability’, such as the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), are discussed further in Annex IV.

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==== 1.4.2.1 Climate Variability Can Influence Trends Over Short Periods ====

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Natural variations in both weather and longer time scale phenomena can temporarily mask or enhance any anthropogenic trends (e.g., [[#Deser--2012|Deser et al., 2012]] ; [[#Kay--2015|Kay et al., 2015]] ). These effects are more important on small spatial and temporal scales but can also occur on the global scale (Cross-Chapter Box 3.1).

Since AR5, many studies have examined the role of internal variability through the use of ‘large ensembles’. Each such ensemble consists of many different simulations by a single climate model for the same time period and using the same radiative forcings. These simulations differ only in their phasing of the internal climate variations (also see [[#1.5.4.2|Section 1.5.4.2]] ). A set of illustrative examples using one such large ensemble ( [[#Maher--2019|Maher et al., 2019]] ) demonstrates how variability can influence trends on decadal time scales (Figure 1.13). The long-term anthropogenic trends in this set of climate indicators are clearly apparent when considering the ensemble as a whole (grey shading), and all the individual ensemble members have very similar trends for ocean heat content (OHC), which is a robust estimate of the total energy stored in the climate system (e.g., [[#Palmer--2014|Palmer and McNeall, 2014]] ). However, the individual ensemble members can exhibit very different decadal trends in global surface air temperature (GSAT), UK summer temperatures, and Arctic sea ice variations. More specifically, for a representative 11-year period, both positive and negative trends can be found in all these surface indicators, even though the long-term trend is for increasing temperatures and decreasing sea ice. Periods in which the long-term trend is substantially masked or enhanced for more than 20 years are also visible in these regional examples. This highlights the fact that observations are expected to exhibit short-term trends which are larger or smaller than the long-term trend or that differ from the average projected trend from climate models, especially on continental spatial scales or smaller (Cross-Chapter Box 3.1). The actual observed trajectory can be considered as one realization of many possible alternative worlds that experienced different weather; this is also demonstrated by the construction of ‘observation-based large ensembles’, which are alternate possible realizations of historical observations that retain the statistical properties of observed regional weather (e.g., [[#McKinnon--2018|McKinnon and Deser, 2018]] ).

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'''Figure 1.13 |''' '''Simulated changes in various climate indicators under historical and RCP4.5 scenarios using the MPI ESM Grand Ensemble.''' The grey shading shows the 5–95% range from the 100-member ensemble. The coloured lines represent individual example ensemble members, with linear trends for the 2011–2021 period indicated by the dashed lines. Changes in ocean heat content (OHC) over the top 2000 m represents the integrated signal of global warming '''(left)''' . The '''top row''' shows surface air temperature-related indicators (annual GSAT change and UK summer temperatures) and The '''bottom row''' shows Arctic sea ice-related indicators (annual ice volume and September sea ice extent). For smaller regions and for shorter time-period averages the variability increases and simulated short-term trends can temporarily mask or enhance anthropogenic changes in climate. Data from [[#Maher--2019|Maher et al. (2019)]] . Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

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==== 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).

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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