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

== Cross-Chapter Box 1.2 | Changes in Global Temperature Between 1750 and 1850 ==

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'''Contributing Authors:''' Ed Hawkins (United Kingdom), Paul Edwards (United States of America), Piers Forster (United Kingdom), Darrell S. Kaufman (United States of America), Jochem Marotzke (Germany), Malte Meinshausen (Australia/Germany), Maisa Rojas (Chile), Bjørn H. Samset (Norway), Peter Thorne (Ireland/United Kingdom)

The Paris Agreement aims to limit global temperatures to specific thresholds ‘above pre-industrial levels’. In AR6 WGI, as in previous IPCC reports, observations and projections of changes in global temperature are generally expressed relative to 1850–1900 as an approximate pre-industrial state (SR1.5, [[#IPCC--2018|IPCC, 2018]]). This is a pragmatic choice based upon data availability considerations, though both anthropogenic and natural changes to the climate occurred before 1850. The remaining carbon budgets, the chance of crossing global temperature thresholds, and projections of extremes and sea level rise at a particular level of global warming can all be sensitive to the chosen definition of the approximate pre-industrial baseline ([[#Millar--2017b|Millar et al., 2017b]]; [[#Schurer--2017|Schurer et al., 2017]]; [[#Pfleiderer--2018|Pfleiderer et al., 2018]]; [[#Rogelj--2019|Rogelj et al., 2019]]; [[#Tokarska--2019|Tokarska et al., 2019]]). This Cross-Chapter Box assesses the evidence on change in radiative forcing and global temperature from the period around 1750 to 1850–1900; variations in the climate before 1750 are discussed in Chapter 2.

Although there is some evidence for human influence on climate before 1750 (e.g., [[#Ruddiman--2001|Ruddiman and Thomson, 2001]]; [[#Koch--2019|Koch et al., 2019]]), the magnitude of the effect is still disputed (Section 5.1.2.3; e.g., [[#Joos--2004|Joos et al., 2004]]; J. [[#Beck--2018|]] [[#Beck--2018|Beck et al., 2018]]), and most studies analyse the human influence on climate over the industrial period. Historically, the widespread use of coal-powered machinery started the Industrial Revolution in Britain in the late 18th century ([[#Ashton--1997|Ashton, 1997]]), but the global effects were small for several decades. In line with this, previous IPCC assessment reports considered changes in radiative forcing relative to 1750, and temperature changes were often reported relative to the ‘late 19th century’. The AR5 and SR1.5 made the specific pragmatic choice to approximate pre-industrial global temperatures by using the average of the 1850 – 1900 period, when permanent surface observing networks emerged that provide sufficiently accurate and continuous measurements on a near-global scale (Sections [[#1.3.1|1.3.1]] and [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|2.3.1.1]]), and because model simulations of the historical period used 1850 as their start date. For the same reasons, to ensure continuity with previous assessments, and because of larger uncertainties and lower confidence in climatic changes before 1850 than after, AR6 makes the same choice to approximate pre-industrial global temperatures by using the the average of the 1850–1900 period.

Here weassess improvements in our understanding of climatic changes in the period 1750–1850. Anthropogenic influences on climate between 1750 and 1900 were primarily increased anthropogenic GHG and aerosol emissions, and changes in land use. Between 1750 and 1850 atmospheric CO <sub>2</sub> levels increased from about 278 ppm to about 285 ppm (equivalent to around 3 years of current rates of increase; Chapter 2, [[IPCC:Wg1:Chapter:Chapter-2#2.2.3|Section 2.2.3]]), corresponding to about 55 GtCO <sub>2</sub> in the atmosphere. Estimates of emissions from fossil fuel burning (about 4 GtCO <sub>2</sub> , [[#Boden--2017|Boden et al., 2017]]) cannot explain the pre-1850 increase, so CO <sub>2</sub> emissions from land-use changes are implicated as the dominant source. The atmospheric concentration of other GHGs also increased over the same period, and there was a cooling influence from other anthropogenic radiative forcings (such as aerosols and land-use changes), but with a larger uncertainty than for GHGs (Sections 2.2.6 and 7.3.5.2, and Cross-Chapter Box 1.2, Figure 1; e.g., [[#Carslaw--2017|Carslaw et al., 2017]]; [[#Owens--2017|Owens et al., 2017]]; [[#Hamilton--2018|Hamilton et al., 2018]]). It is ''likely'' that there was a net anthropogenic forcing of 0.0 – 0.3 Wm <sup>–2</sup> in 1850 – 1900 relative to 1750 (''medium confidence''). The net radiative forcing from changes in solar activity and volcanic activity in 1850 – 1900, compared to the period around 1750, is estimated to be smaller than ±0.1 W m <sup>–2</sup>, but note there were several large volcanic eruptions between 1750 and 1850 (Cross-Chapter Box 1.2, Figure 1).

Several studies since AR5 have estimated changes in global temperatures following industrialisation and before 1850. [[#Hawkins--2017|Hawkins et al. (2017)]] used observations, radiative forcing estimates and model simulations to estimate the warming from 1720–1800 until 1986–2005 and assessed a ''likely'' range of 0.55°C–0.80°C, slightly broader than the equivalent range starting from 1850–1900 (0.6°C–0.7°C). From proxy evidence, [[#PAGES%202k%20Consortium--2019|PAGES 2k Consortium (2019)]] found that GMST for 1850–1900 was 0.02 [–0.22 to +0.16] °C warmer than the 30-year period centred on 1750. [[#Schurer--2017|Schurer et al. (2017)]] used climate model simulations of the last millennium to estimate that the increase in GHG concentrations before 1850 caused an additional ''likely'' range of 0.0°C –0.2°C global warming when considering multiple reference periods. [[#Haustein--2017|Haustein et al. (2017)]] implies an additional warming of around 0.05°C attributable to human activity from 1750 to 1850–1900, and the AR6 emulator (Section 7.3.5.3) estimates the ''likely'' range of this warming to be 0.04°C–0.14°C.

Combining these different sources of evidence, we assess that from the period around 1750 to 1850–1900 there was a change in global temperature of around 0.1 [–0.1 to +0.3] °C (''medium confidence''), with an anthropogenic component in a ''likely'' range of 0.0°C–0.2°C (''medi'' ''um confidence'').

[[File:6a7bf6431e46d4e07cd5a74e974a4398 IPCC_AR6_WGI_CCBox_1_2_Figure_1.png]]

'''Cross-Chapter Box 1.2, Figure 1''' | '''Changes in radiative forcing from 1750–2019''' . The radiative forcing estimates from the AR6 emulator (Cross-Chapter Box 7.1) are split into GHG, other anthropogenic (mainly aerosols and land use) and natural forcings, with the average over the 1850–1900 baseline shown for each. 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 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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[[File:fc7c3fbc2db6d0af8a18d4187bea1d57 IPCC_AR6_WGI_Figure_1_13.png]]

'''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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[[File:f99318b822c49734ff81d7990164dfbb IPCC_AR6_WGI_Figure_1_14.png]]

'''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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=== 1.4.3 Sources of Uncertainty in Climate Simulations ===

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When evaluating and analysing simulations of the physical climate system, several different sources of uncertainty need to be considered (e.g., [[#Hawkins--2009|Hawkins and Sutton, 2009]]; [[#Lehner--2020|Lehner et al., 2020]]). Broadly, these sources are: uncertainties in radiative forcings (both those observed in the past and those projected for the future); uncertainty in the climate response to particular radiative forcings; internal and natural variations of the climate system (which may be somewhat predictable); and interactions among these sources of uncertainty.

Ensembles of climate simulations ([[#1.5.4.2|Section 1.5.4.2]]), such as those produced as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), can be used to explore these different sources of uncertainty and estimate their magnitude. Relevant experiments with climate models include both historical simulations constrained by past radiative forcings, and projections of future climate which are constrained by specified drivers, such as GHG concentrations, emissions, or radiative forcings. (The term ‘prediction’ is usually reserved for estimates of the future climate state which are also constrained by the observed initial conditions of the climate system, analogous to a weather forecast.)

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==== 1.4.3.1 Sources of Uncertainty ====

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===== 1.4.3.1.1 Radiative forcing uncertainty =====

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Future radiative forcing is uncertain due to as-yet-unknown societal choices that will determine future anthropogenic emissions; this is considered ‘scenario uncertainty’. The RCP and SSP scenarios, which form the basis for climate projections assessed in this Report, are designed to span a plausible range of future pathways ([[#1.6|Section 1.6]]) and can be used to estimate the magnitude of scenario uncertainty, but the real world may also differ from any one of these example pathways.

Uncertainties also exist regarding past emissions and radiative forcings. These are especially important for simulations of paleoclimate time periods, such as the Pliocene, Last Glacial Maximum or the last millennium, but are also relevant for the CMIP historical simulations of the instrumental period since 1850. In particular, historical radiative forcings due to anthropogenic and natural aerosols are less well constrained by observations than the GHG radiative forcings. There is also uncertainty in the size of large volcanic eruptions (and in the location for some that occurred before around 1850), and the amplitude of changes in solar activity, before satellite observations. The role of historical radiative forcing uncertainty was considered previously ([[#Knutti--2002|Knutti et al., 2002]]; [[#Forster--2013|Forster et al., 2013]]) but, since AR5, specific simulations have been performed to examine this issue, particularly for the effects of uncertainty in anthropogenic aerosol radiative forcing (e.g., [[#Jiménez-de-la-Cuesta--2019|Jiménez-de-la-Cuesta and Mauritsen, 2019]]; [[#Dittus--2020|Dittus et al., 2020]]).

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===== 1.4.3.1.2 Climate response uncertainty =====

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Under any particular scenario ([[#1.6.1|Section 1.6.1]]), there is uncertainty in how the climate will respond to the specified emissions or radiative forcing combinations. A range of climate models is often used to estimate the range of uncertainty in our understanding of the key physical processes and to define the ‘model response uncertainty’ (Sections [[#1.5.4|1.5.4]] and [[IPCC:Wg1:Chapter:Chapter-4#4.2.5|4.2.5]]). However, this range does not necessarily represent the full ‘climate response uncertainty ''’'' in how the climate may respond to a particular radiative forcing or emissions scenario. This is because, for example, the climate models used in CMIP experiments have structural uncertainties not explored in a typical multi-model exercise (e.g., [[#Murphy--2004|Murphy et al., 2004]]) and are not entirely independent of each other ([[#1.5.4.8|Section 1.5.4.8]]; [[#Masson--2011|Masson and Knutti, 2011]]; [[#Abramowitz--2019|Abramowitz et al., 2019]]); there are small spatial-scale features which cannot be resolved; and long time-scale processes or tipping points are not fully represented. [[#1.4.4|Section 1.4.4]] discusses how some of these issues can still be considered in a risk assessment context. For some metrics, such as equilibrium climate sensitivity (ECS), the CMIP6 model range is found to be broader than the ''very likely'' range assessed by combining multiple lines of evidence (Sections 4.3.4 and 7.5.6).

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===== 1.4.3.1.3 Natural and internal climate variations =====

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Even without any anthropogenic radiative forcing, there would still be uncertainty in projecting future climate because of unpredictable natural factors such as variations in solar activity and volcanic eruptions. For projections of future climate, such as those presented in Chapter 4, the uncertainty in these factors is not normally considered. However, the potential effects on the climate of large volcanic eruptions (Cross-Chapter Box 4.1; [[#Zanchettin--2016|Zanchettin et al., 2016]]; [[#Bethke--2017|Bethke et al., 2017]]) and large solar variations ([[#Feulner--2010|Feulner and Rahmstorf, 2010]]; [[#Maycock--2015|Maycock et al., 2015]]) are studied. On longer time scales, orbital effects and plate tectonics also play a role.

Further, even in the absence of any anthropogenic or natural changes in radiative forcing, Earth’s climate fluctuates on time scales from days to decades or longer. These ‘internal’ variations, such as those associated with modes of variability (e.g., ENSO, Pacific Decadal Variability (PDV), or Atlantic Multi-decadal Variability (AMV); Annex IV) are unpredictable on time scales longer than a few years ahead and are a source of uncertainty for understanding how the climate might become in a particular decade, especially regionally. The increased use of ‘large ensembles’ of complex climate model simulations to sample this component of uncertainty is discussed above in [[#1.4.2.1|Section 1.4.2.1]] and further in Chapter 4.

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===== 1.4.3.1.4 Interactions between variability and radiative forcings =====

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It is plausible that there are interactions between radiative forcings and climate variations, such as influences on the phasing or amplitude of internal or natural climate variability ([[#Zanchettin--2017|Zanchettin, 2017]]). For example, the timing of volcanic eruptions may influence Atlantic Multi-decadal Variability (e.g., [[#Otterå--2010|Otterå et al., 2010]]; [[#Birkel--2018|Birkel et al., 2018]]) or ENSO (e.g., [[#Maher--2015|Maher et al., 2015]]; [[#Khodri--2017|Khodri et al., 2017]]; [[#Zuo--2018|Zuo et al., 2018]]), and anthropogenic aerosols may influence decadal modes of variability in the Pacific (e.g., [[#Smith--2016|Smith et al., 2016]]). In addition, melting of glaciers and ice caps due to anthropogenic influences has been speculated to increase volcanic activity (e.g., a specific example for Iceland is discussed in [[#Swindles--2018|Swindles et al., 2018]]).

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==== 1.4.3.2 Uncertainty Quantification ====

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Not all of these listed sources of uncertainty are of the same type. For example, internal climate variations are an intrinsic uncertainty that can be estimated probabilistically, and could be more precisely quantified, but cannot usually be reduced. However, advances in decadal prediction offer the prospect of narrowing uncertainties in the trajectory of the climate for a few years ahead ([[IPCC:Wg1:Chapter:Chapter-4#4.2.3|Section 4.2.3]]; e.g., [[#Meehl--2014|Meehl et al., 2014]]; [[#Yeager--2017|Yeager and Robson, 2017]]).

Other sources of uncertainty, such as model response uncertainty, can in principle be reduced, but are not amenable to a frequency-based interpretation of probability, and Bayesian methods to quantify the uncertainty have been considered instead (e.g., [[#Tebaldi--2004|Tebaldi, 2004]]; [[#Rougier--2007|Rougier, 2007]]; [[#Sexton--2012|Sexton et al., 2012]]). The scenario uncertainty component is distinct from other uncertainties, given that future anthropogenic emissions can be considered as the outcome of a set of societal choices ([[#1.6.1|Section 1.6.1]]).

For climate model projections it is possible to approximately quantify the relative amplitude of various sources of uncertainty (e.g., [[#Hawkins--2009|Hawkins and Sutton, 2009]]; [[#Lehner--2020|Lehner et al., 2020]]). A range of different climate models are used to estimate the model response uncertainty to a particular emissions pathway, and multiple pathways are used to estimate the scenario uncertainty. The unforced component of internal variability can be estimated from individual ensemble members of the same climate model ([[#1.5.4.8|Section 1.5.4.8]]; e.g., [[#Deser--2012|Deser et al., 2012]]; [[#Maher--2019|Maher et al., 2019]]).

Figure 1.15 illustrates the relative size of these different uncertainty components using a ‘cascade of uncertainty’ ([[#Wilby--2010|Wilby and Dessai, 2010]]), with examples shown for global mean temperature, Northern South American annual temperatures and East Asian summer precipitation changes. For global mean temperature, the role of internal variability is small, and the total uncertainty is dominated by emissions scenario and model response uncertainties. Note that there is considerable overlap between individual simulations for different emissions scenarios, even for the mid-term (2041–2060). For example, the slowest-warming simulation for SSP5-8.5 produces less mid-term warming than the fastest-warming simulation for SSP1-1.9. For the long term, emissions scenario uncertainty becomes dominant.

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[[File:5e6738df5d3cb730b505b1733656e44f IPCC_AR6_WGI_Figure_1_15.png]]

'''Figure 1.15 |''' '''The ‘cascade of uncertainties’ in CMIP6 projections.''' Changes in: GSAT '''(left)''' ; Northern South America temperature '''(middle)''' ; and East Asia summer (June–July–August, JJA) precipitation '''(right)''' . These are shown for two time periods: 2041–2060 '''(top)''' and 2081–2100 '''(bottom)''' . The SSP–radiative forcing combination is indicated at the top of each cascade at the value of the multi-model mean for each scenario. This branches downwards to show the ensemble mean for each model, and further branches into the individual ensemble members, although often only a single member is available. These diagrams highlight the relative importance of different sources of uncertainty in climate projections, which varies for different time periods, regions and climate variables. See ([[#1.4.5|Section 1.4.5]] for the definition of the regions used. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

Therelative uncertainty due to internal variability and model uncertainty increases for smaller spatial scales. In the regional example shown in Figure 1.15 for changes in temperature, the same scenario and model combination has produced two simulations which differ by 1°C in their projected 2081–2100 averages due solely to internal climate variability. For regional precipitation changes, emissions scenario uncertainty is often small relative to model response uncertainty. In the example shown in Figure 1.15, the SSPs overlap considerably, but SSP1-1.9 shows the largest precipitation change in the near term, even though global mean temperature warms the least; this is due to differences between regional aerosol emissions projected in this and other scenarios ([[#Wilcox--2020|Wilcox et al., 2020]]). These cascades of uncertainty would branch out further if applying the projections to derive estimates of changes in hazard (e.g., [[#Wilby--2010|Wilby and Dessai, 2010]]; [[#Halsnæs--2018|Halsnæs and Kaspersen, 2018]]; [[#Hattermann--2018|Hattermann et al., 2018]]).

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=== 1.4.4 Considering an Uncertain Future ===

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Since AR5 there have been developments in how to consider and describe future climate outcomes which are considered possible but ''very unlikely ,'' highly uncertain, or potentially surprising. To examine such futures there is a need to move beyond the usual ''likely'' or ''very likely'' assessed ranges and consider low-likelihood outcomes, especially those that would result in significant impacts if they occurred (e.g., [[#Sutton--2018|Sutton, 2018]]; [[#Sillmann--2021|Sillmann et al., 2021]]). This section briefly outlines some of the different approaches used in the AR6 WGI.

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==== 1.4.4.1 Low-Likelihood Outcomes ====

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In the AR6, certain low-likelihood outcomes are described and assessed because they may be associated with high levels of risk, and the greatest risks may not be associated with the most likely outcome. The aim of assessing these possible futures is to better inform risk assessment and decision-making. Two types are considered: (i) low-likelihood high-warming (LLHW) scenarios, which describe the climate in a world with very high climate sensitivity; and (ii) low-likelihood, high-impact outcomes that have a low likelihood of occurring, but would cause large potential impacts on societies or ecosystems.

An illustrative example of how low-likelihood outcomes can produce significant additional risks is shown in Figure 1.16. The Reasons for Concern (RFCs) produced by the IPCC AR5 WGII define the additional risks due to climate change at different global warming levels. These have been combined with [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] assessments of projected global temperature for different emissions scenarios (SSPs; [[#1.6|Section 1.6]]), and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] assessments about ECS. For example, even following an intermediate emissions scenario could result in high levels of additional risk if ECS is at the upper end of the ''very likely'' range. However, not all possible low-likelihood outcomes relate to ECS, and AR6 considers these issues in more detail than previous IPCC assessment reports (see Table 1.1 and [[#1.4.4.2|Section 1.4.4.2]] for some examples).

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[[File:35397456082e6f0e68f69d968a9044f0 IPCC_AR6_WGI_Figure_1_16.png]]

'''Figure 1.16 |''' '''Illustrating concepts of low-likelihood outcomes.''' '''Left:''' schematic likelihood distribution consistent with the IPCC AR6 assessments that equilibrium climate sensitivity (ECS) is ''likely'' in the range 2.5°C to 4.0°C, and ''very likely'' between 2.0°C and 5.0°C (Chapter 7). ECS values outside the assessed ''very likely'' range are designated low-likelihood outcomes in this example (light grey). '''Middle''' and '''right-hand columns:''' additional risks due to climate change for 2020–2090 using the Reasons For Concern (RFCs, see [[#IPCC--2014b|IPCC, 2014b]]), specifically RFC1 describing the risks to unique and threatened systems and RFC3 describing risks from the distribution of impacts ([[#O’Neill--2017b|O’Neill et al., 2017b]]; [[#Zommers--2020|Zommers et al., 2020]]). The projected changes of GSAT used are the 95%, median and 5% assessed ranges from [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] for each SSP (top, middle and bottom); these are designated High ECS, Mid-range ECS and Low ECS respectively. The ‘burning-ember’ risk spectrum of graduated colours is usually associated with levels of committed GSAT change; instead, this illustration associates the risk spectrum with the GSAT temperature reached in each year from 2020 to 2090. Note that this illustration does not include the vulnerability aspect of each SSP scenario. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

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==== 1.4.4.2 Storylines ====

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As societies are increasingly experiencing the impacts of climate change-related events, the climate science community is developing climate information tailored for particular regions and sectors. There is a growing focus on explaining and exploring complex physical chains of events or on predicting climate under various future socio-economic developments. Since AR5, ‘storylines’ or ‘narratives’ approaches have been used to better inform risk assessment and decision-making, to assist understanding of regional processes, and represent and communicate climate projection uncertainties more clearly. The aim is to help build a cohesive overall picture of potential climate change pathways that moves beyond the presentation of data and figures (Glossary; Fløttum and Gjerstad, 2017; [[#Moezzi--2017|Moezzi et al., 2017]]; [[#Dessai--2018|Dessai et al., 2018]]; T.G. [[#Shepherd--2018|]] [[#Shepherd--2018|Shepherd et al., 2018]]).

In the broader IPCC context, the term ‘scenario storyline’ refers to a narrative description of one or more scenarios, highlighting their main characteristics, relationships between key driving forces and the dynamics of their evolution (e.g., emissions of short-lived climate forcers assessed in [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] are driven by ‘scenario storylines’; see [[#1.6|Section 1.6]]). The AR6 WGI is mainly concerned with ‘physical climate storylines’. A physical climate storyline is a self-consistent and plausible physical trajectory of the climate system, or a weather or climate event, on time scales from hours to multiple decades (T.G. [[#Shepherd--2018|]] [[#Shepherd--2018|Shepherd et al., 2018]]). This approach can be used to constrain projected changes or specific events on specified explanatory elements such as projected changes of large-scale indicators (Box 10.2). For example, [[#Hazeleger--2015|Hazeleger et al. (2015)]] suggested using ‘tales of future weather’, blending numerical weather prediction with a climate projection to illustrate the potential behaviour of future high-impact events (also see [[#Hegdahl--2020|Hegdahl et al., 2020]]). Several studies describe how possible large changes in atmospheric circulation would affect regional precipitation and other climate variables, and discuss the various climate drivers that could cause such a circulation response ([[#James--2015|James et al., 2015]]; [[#Zappa--2017|Zappa and Shepherd, 2017]]; [[#Mindlin--2020|Mindlin et al., 2020]]). Physical climate storylines can also help frame the causal factors of extreme weather events ([[#Shepherd--2016|Shepherd, 2016]]) and then be linked to event attribution (Section 11.2.2 and Cross-Working Group Box: Attribution).

Storyline approaches can be used to communicate and contextualize climate change information in the context of risk for policymakers and practitioners (Box 10.2; e.g., [[#de%20Bruijn--2016|de Bruijn et al., 2016]]; [[#Dessai--2018|Dessai et al., 2018]]; [[#Scott--2018|Scott et al., 2018]]; [[#Jack--2020|Jack et al., 2020]]). They can also help in assessing risks associated with LLHI events ([[#Weitzman--2011|Weitzman, 2011]]; [[#Sutton--2018|Sutton, 2018]]), because they consider the ‘physically self-consistent unfolding of past events, or of plausible future events or pathways’ ([[#Shepherd--2018|]] [[#Shepherd--2018|Shepherd et al., 2018]]), which would be masked in a probabilistic approach. These aspects are important as the greatest risk need not be associated with the highest-likelihood outcome, and in fact will often be associated with low-likelihood outcomes. The storyline approach can also acknowledge that climate-relevant decisions in a risk-oriented framing will rarely be taken on the basis of physical climate change alone; instead, such decisions will normally take into account socio-economic factors as well ([[#Shepherd--2019|Shepherd, 2019]]).

In the AR6 WGIAssessment Report, these different storyline approaches are used in several places (see Table 1.1). [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] uses a storyline approach to assess the upper tail of the distribution of global warming levels (the storylines of high global warming levels) and their manifestation in global patterns of temperature and precipitation changes. [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] uses a storyline approach to examine the potential for, and early warning signals of, a high-end sea level scenario, in the context of deep uncertainty related to our current understanding of the physical processes that contribute to long-term sea level rise. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10 Chapter 10] assesses the use of physical climate storylines and narratives as a way to explore uncertainties in regional climate projections, and to link to the specific risk and decision context relevant to a user, for developing integrated and context-relevant regional climate change information. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] uses the term storyline in the framework of extreme event attribution. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] assesses the use of a storylines approach with narrative elements for communicating climate (change) information in the context of climate services (Cross-Chapter Box 12.2).

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==== 1.4.4.3 Abrupt Change, Tipping Points and Surprises ====

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An ‘abrupt change’ is defined in this report as a change that takes place substantially faster than the rate of change in the recent history of the affected component of a system (Glossary). In some cases, abrupt change occurs because the system state actually becomes unstable, such that the subsequent rate of change is independent of the forcing. We refer to this class of abrupt change as a ‘tipping point’ '','' defined as a critical threshold beyond which a system reorganizes, often abruptly and/or irreversibly (Glossary; [[#Lenton--2008|Lenton et al., 2008]]). Some of the abrupt climate changes and climate tipping points discussed in this Report could have severe local climate responses, such as extreme temperature, droughts, forest fires, ice-sheet loss and collapse of the thermohaline circulation (Sections 4.7.2, 5.4.9, 8.6 and 9.2.3).

There is evidence of abrupt changes in Earth’s history, and some of these events have been interpreted as tipping points ([[#Dakos--2008|Dakos et al., 2008]]). Some of these are associated with significant changes in the global climate, such as deglaciations in the Quaternary (past 2.5 million years) and rapid warming at the Palaeocene–Eocene Thermal Maximum (around 55.5 million years ago; [[#Bowen--2015|Bowen et al., 2015]]; [[#Hollis--2019|Hollis et al., 2019]]). Such events changed the planetary climate for tens to hundreds of thousands of years, but at a rate that is actually much slower than projected anthropogenic climate change over this century, even in the absence of tipping points.

Such paleoclimate evidence has even fuelled concerns that anthropogenic GHGs could tip the global climate into a permanent hot state ([[#Steffen--2018|Steffen et al., 2018]]). However, there is no evidence of such non-linear responses at the global scale in climate projections for the next century, which indicates a near-linear dependence of global temperature on cumulative GHG emissions (Sections 1.3.5, 5.5 and 7.4.3.1). At the regional scale, abrupt changes and tipping points, such as Amazon rainforest dieback and permafrost collapse, have occurred in projections with Earth System Models ([[IPCC:Wg1:Chapter:Chapter-4#4.7.3|Section 4.7.3]]; [[#Drijfhout--2015|Drijfhout et al., 2015]]; [[#Bathiany--2020|Bathiany et al., 2020]]). In such simulations, tipping points occur in narrow regions of parameter space (e.g., CO <sub>2</sub> concentration or temperature increase), and for specific climate background states. This makes them difficult to predict using Earth system models (ESMs) relying on parmeterizations of known processes. In some cases, it is possible to detect forthcoming tipping points through time-series analysis that identifies increased sensitivity to perturbations as the tipping point is approached (e.g., ‘critical slowing-down’, [[#Scheffer--2012|Scheffer et al., 2012]]).

Some suggested climate tipping points prompt transitions from one steady state to another (Figure 1.17). Transitions can be prompted by perturbations such as climate extremes which force the system outside of its current well of attraction in the stability landscape; this is called noise-induced tipping (Figure 1.17a,b; [[#Ashwin--2012|Ashwin et al., 2012]]). For example, the tropical forest dieback seen in some ESM projections is accelerated by longer and more frequent droughts over tropical land ([[#Good--2013|Good et al., 2013]]).

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[[File:23aa7d4ef86b70d128c44c971f6234a3 IPCC_AR6_WGI_Figure_1_17.png]]

'''Figure 1.17 |''' '''Illustration of two types of tipping points: noise-induced (a, b) and bifurcation (c, d).''' '''(a)''' and '''(c)''' are example time-series (coloured lines) through the tipping point, with solid-black lines indicating stable climate states (e.g., low or high rainfall) and dashed lines representing the boundary between stable states. '''(b)''' and '''(d)''' are stability landscapes, which provide an intuitive understanding of the different types of tipping point. The ‘valleys’ represent different climate states the system can occupy, with ‘hilltops’ separating the stable states. The resilience of a climate state is implied by the depth of the valley. The current state of the system is represented by a ball. Both scenarios assume that the ball starts in the left-hand valley (dashed-black lines) and then through different mechanisms dependent on the type of tipping transitions to the right-hand valley (coloured lines). Noise-induced tipping events (a, b), for instance drought events causing sudden dieback of the Amazon rainforest, develop from fluctuations within the system. The stability landscape in this scenario remains fixed and stationary. A series of perturbations in the same direction, or one large perturbation, are required to force the system over the hilltop and into the alternative stable state. Bifurcation tipping events (c, d), such as a collapse of the thermohaline circulation in the Atlantic Ocean under climate change, occur when a critical level in the forcing is reached. Here the stability landscape is subjected to a change in shape. Under gradual anthropogenic forcing the left-hand valley begins to shallow and eventually vanishes at the tipping point, forcing the system to transition to the right-hand valley.

Alternatively, transitions from one state to another can occur if a critical threshold is exceeded; this is called ‘bifurcation tipping’ (Figure 1.17c,d; [[#Ashwin--2012|Ashwin et al., 2012]]). The new state is defined as ‘irreversible’ on a given time scale if the recovery from this state takes substantially longer than the time scale of interest, which is decades to centuries for the projections presented in this report. A well-known example is the modelled irreversibility of the ocean’s thermohaline circulation in response to North Atlantic changes such as freshwater input from rainfall and ice-sheet melt ([[#Rahmstorf--2005|Rahmstorf et al., 2005]]; [[#Alkhayuon--2019|Alkhayuon et al., 2019]]), which is assessed in detail in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.2.3).

The tipping point concept is most commonly framed for systems in which the forcing changes relatively slowly. However, this is not the case for most scenarios of anthropogenic forcing projected for the 21st century. Systems with inertia lag behind rapidly increasing forcing, which can lead to the failure of early warning signals or even the possibility of temporarily overshooting a bifurcation point without provoking tipping ([[#Ritchie--2019|Ritchie et al., 2019]]).

‘Surprises’ are a class of risk that can be defined as low-likelihood but well-understood events: they are events that cannot be predicted with current understanding. The risk from such surprises can be accounted for in risk assessments ([[#Parker--2015|Parker and Risbey, 2015]]). Examples relevant to climate science include: a series of major volcanic eruptions or a nuclear war, either of which would cause substantial planetary cooling ([[#Robock--2007|Robock et al., 2007]]; [[#Mills--2014|Mills et al., 2014]]); significant 21st century sea level rise due to marine ice sheet instability (MISI; Box 9.4); the potential for collapse of the stratocumulus cloud decks ([[#Schneider--2019|Schneider et al., 2019]]) or other substantial changes in climate feedbacks (Section 7.4); and unexpected biological epidemics among humans or other species, such as the COVID-19 pandemic (Cross-Chapter Box 6.1; [[#Forster--2020|Forster et al., 2020]]; [[#Le%20Quéré--2020|Le Quéré et al., 2020]]). The discovery of the hole in the ozone layerwas also a surprise even though some of the relevant atmospheric chemistry was known at the time. The term ‘unknownunknowns’ ([[#Parker--2015|Parker and Risbey, 2015]]) is also sometimes used in this context to refer to events that cannot be anticipated with presentknowledge or were of an unanticipated nature before they occurred.

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