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

==== 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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'''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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'''Cross-Chapter Box 1.3 | Risk Fram''' '''ing in IPCC AR6'''

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'''Contributing Authors:''' Andy Reisinger (New Zealand), Maisa Rojas (Chile), Aïda Diongue-Niang (Senegal), Maarten K. van Aalst (The Netherlands), Mathias Garschagen (Germany), Mark Howden (Australia), Margot Hurlbert (Canada), Katharine Mach (United States of America), Sawsan Khair Elsied Abdel Rahim Mustafa (Sudan), Brian O’Neill (United States of America), Roque Pedace (Argentina), Jana Sillmann (Norway/Germany), Carolina Vera (Argentina), David Viner (United Kingdom)

The IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX; [[#IPCC--2012|IPCC, 2012]] ) presented a framework for assessing risks from climate change, which linked hazards (due to changes in climate) with exposure and vulnerability ( [[#Cardona--2012|Cardona et al., 2012]] ). This framework was further developed by AR5 WGII ( [[#IPCC--2014b|IPCC, 2014b]] ), while AR5 WGI focussed only on the hazard component of risk. As part of AR6, a cross-Working Group process expanded and refined the concept of risk to allow for a consistent risk framing to be used across the three IPCC Working Groups ( [[#IPCC--2019b|IPCC, 2019b]] ; Box 2 in [[#Abram--2019|Abram et al., 2019]] ; [[#Reisinger--2020|Reisinger et al., 2020]] ).

In this revised definition, risk is defined as:

The potential for adverse consequences for human or ecological systems, recognizing the diversity of values and objectives associated with such systems. In the context of climate change, risks can arise from potential impacts of climate change as well as human responses to climate change. Relevant adverse consequences include those on lives, livelihoods, health and well-being, economic, social and cultural assets and investments, infrastructure, services (including ecosystem services), ecosystems and species.

In the context of climate change impacts, risks result from dynamic interactions between climate-related hazards with the exposure and vulnerability of the affected human or ecological system to the hazards. Hazards, exposure and vulnerability may each be subject to uncertainty in terms of magnitude and likelihood of occurrence, and each may change over time and space due to socio-economic changes and human decision-making (see also risk management, adaptation and mitigation).

In the context of climate change responses, risks result from the potential for such responses not achieving the intended objective(s), or from potential trade-offs with, or negative side-effects on, other societal objectives, such as the Sustainable Development Goals (SDGs) (see also risk trade-off). Risks can arise, for example, from uncertainty in implementation, effectiveness or outcomes of climate policy, climate-related investments, technology development or adoption, and system transitions.

Cross-Chapter Box 1.3

The following concepts are also relevant for the definition of risk (Glossary):

'''Exposure:''' The presence of people; livelihoods; species or ecosystems; environmental functions, services, and resources; infrastructure; or economic, social, or cultural assets in places and settings that could be adversely affected.

'''Vulnerability:''' The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt.

'''Hazard:''' The potential occurrence of a natural or human-induced physical event or trend that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources.

'''Impacts:''' The consequences of realized risks on natural and human systems, where risks result from the interactions of climate-related hazards (including extreme weather/climate events), exposure, and vulnerability. Impacts generally refer to effects on lives, livelihoods, health and well-being, ecosystems and species, economic, social and cultural assets, services (including ecosystem services), and infrastructure. Impacts may be referred to as consequences or outcomes and can be adverse or beneficial.

''''''Risk in AR6 WGI''''''
The revised risk framing clarifies the role and contribution of WGI to risk assessment. ‘Risk’ in IPCC terminology applies only to human or ecological systems, not to physical systems on their own.

'''Climatic impact-drivers (CIDs):''' CIDs are physical climate system conditions (e.g., means, events, extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral or a mixture of each across interacting system elements and regions.

InAR6, WGI uses the term ‘climatic impact-drivers’ to describe changes in physical systems rather than ‘hazards’, because the term hazard already assumes an adverse consequence. The terminology of ‘climatic impact-driver’ therefore allows WGI to provide a more value-neutral characterization of climatic changes that may be relevant for understanding potential impacts, without pre-judging whether specific climatic changes necessarily lead to adverse consequences, as some could also result in beneficial outcomes depending on the specific system and associated values. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-12 Chapter 12] and the [[IPCC:Wg1:Chapter:Atlas|Atlas]] assess and provide information on climatic impact-drivers for different regions and sectors to support and link to the WGII assessment of the impacts and risks (or opportunities) related to the changes in the climatic impact-drivers. Although CIDs can lead to adverse or beneficial outcomes, focus is given to CIDs connected to hazards, and hence inform risk.

‘Extremes’ are a category of CID, corresponding to unusual events with respect to the range of observed values of the variable. [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] assesses changes in weather and climate extremes, their attribution and future projections.

As examples of the use of this terminology, the term ‘flood risk’ should not be used if it only describes changes in the frequency and intensity of flood events (a hazard); the risk from flooding to human and ecological systems is caused by the flood hazard, the exposure of the system affected (e.g., topography, human settlements or infrastructure in the area potentially affected by flooding) and the vulnerability of the system (e.g., design and maintenance of infrastructure, existence of early warning systems). As another example, climate-related risk to food security can arise from both potential climate change impacts and responses to climate change and can be exacerbated by other stressors. Drivers for risks related to climate change impacts include climatic impact- drivers (e.g., drought, temperature extremes, humidity) mediated by other climatic impact-drivers (e.g., increased CO <sub>2</sub> fertilization of certain types of crops may help increase yields), the potential for indirect climate-related impacts (e.g., pest outbreaks triggered by ecosystem responses to weather patterns), exposure of people (e.g., how many people depend on a particular crop) and vulnerability or adaptability (how able are affected people to substitute other sources of food, which may be related to financial access and markets).

Information provided by WGI may or may not be relevant to understand risks related to climate change responses. For example, the risk to a company arising from emissions pricing, or the societal risk from reliance on an unproven mitigation technology, is not directly dependent on actual or projected changes in climate but arise largely from human choices. However, WGI climate information may be relevant to understand the potential for maladaptation, such as the potential for specific adaptation responses not achieving the desired outcome or having negative side effects. For example, WGI information about the range of sea level rise can help inform understanding of whether coastal protection, accommodation, or retreat would be the most effective risk management strategy in a particular context.

Cross-Chapter Box 1.3

From a WGI perspective, low-likelihood, high-impact outcomes and the concept of deep uncertainty are also relevant for risk assessment.

'''Low-likelihood, high-impact (LLHI) outcomes:''' Outcomes/events whose probability of occurrence is low or not well known (as in the context of deep uncertainty) but whose potential impacts on society and ecosystems could be high. To better inform risk assessment and decision-making, such low-likelihood outcomes are considered if they are associated with very large consequences and may therefore constitute material risks, even though those consequences do not necessarily represent the most likely outcome.

The AR6 WGI Report provides more detailed information about these types of events compared to AR5 (Table 1.1, [[#1.4.4|Section 1.4.4]] ).

Recognizing the need for assessing and managing risk in situations of high uncertainty, SROCC advanced the treatment of situations with deep uncertainty ( [[#1.2.3|Section 1.2.3]] ; [[#IPCC--2019b|IPCC, 2019b]] ; Box 5 in [[#Abram--2019|Abram et al., 2019]] ). A situation of deep uncertainty exists when experts or stakeholders do not know or cannot agree on: (i) appropriate conceptual models that describe relationships among key driving forces in a system; (ii) the probability distributions used to represent uncertainty about key variables and parameters; and/or (iii) how to weigh and value desirable alternative outcomes ( [[#Abram--2019|Abram et al., 2019]] ). The concept of deep uncertainty can complement the IPCC calibrated uncertainty language and thereby broaden the communication of risk.

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'''Cross-Working Group B''' '''ox | Attribution'''

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'''Contributing Authors:''' Pandora Hope (Australia), Wolfgang Cramer (France/Germany), Gregory M. Flato (Canada), Katja Frieler (Germany), Nathan P. Gillett (Canada), Christian Huggel (Switzerland), Jan Minx (Germany), Friederike Otto (United Kingdom/Germany), Camille Parmesan (France, United Kingdom/United States of America), Joeri Rogelj (United Kingdom/Belgium), Maisa Rojas (Chile), Sonia I. Seneviratne (Switzerland), Aimée B.A. Slangen (The Netherlands), Daithi Stone (New Zealand), Laurent Terray (France), Maarten K. van Aalst (The Netherlands), Robert Vautard (France), Xuebin Zhang (Canada)

''''''Introduction''''''
Changes in the climate system are becoming increasingly apparent, as are the climate-related impacts on natural and human systems. Attribution is the process of evaluating the contribution of one or more causal factors to such observed changes or events. Typical questions addressed by the IPCC include: ‘To what extent is an observed change in global temperature induced by anthropogenic GHG and aerosol concentration changes, or influenced by natural variability?’ and ‘What is the contribution of climate change to observed changes in crop yields, which are also influenced by changes in agricultural management?’ Changes in the occurrence and intensity of extreme events can also be attributed, addressing questions such as: ‘Have human GHG emissions increased the likelihood or intensity of an observed heatwave?’

This Cross-Working Group Box briefly describes why attribution studies are important. It also describes some new developments in the methods used in those studies and provides recommendations for interpretation.

Attribution studies serve to evaluate and communicate linkages associated with climate change, for example: between the human-induced increase in GHG concentrations and the observed increase in air temperature or extreme weather events (AR6 WGI Chapters 3, 10 and 11); or between observed changes in climate and changing species distributions and food production (AR6 WGII Chapters 2 and others, summarized in WGII Chapter 16; e.g., [[#Verschuur--2021|Verschuur et al., 2021]] ); or between climate change mitigation policies and atmospheric GHG concentrations (AR6 WGI Chapter 5; AR6 WGIII Chapter 14). As such, they support numerous statements made by the IPCC (AR6 WGI [[#1.3|Section 1.3]] and Appendix 1A; [[#IPCC--2013b|IPCC, 2013b]] , 2014b).

Attribution assessments can also serve to monitor mitigation and assess the efficacy of applied climate protection policies (AR6 WGI [[IPCC:Wg1:Chapter:Chapter-4#4.6.3|Section 4.6.3]] ; e.g., [[#Nauels--2019|Nauels et al., 2019]] ; [[#Banerjee--2020|Banerjee et al., 2020]] ), inform and constrain projections (WGI [[IPCC:Wg1:Chapter:Chapter-4#4.2.3|Section 4.2.3]] ; [[#Gillett--2021|Gillett et al., 2021]] ; [[#Ribes--2021|Ribes et al., 2021]] ) or inform the loss and damages estimates and potential climate litigation cases by estimating the costs of climate change ( [[#Huggel--2015|Huggel et al., 2015]] ; [[#Marjanac--2017|Marjanac et al., 2017]] ; [[#Frame--2020|Frame et al., 2020]] ). These findings can thus inform mitigation decisions as well as risk management and adaptation planning (e.g., [[#CDKN--2017|CDKN, 2017]] ).

''''''Steps towards an attribu''' '''tion assessment''''''
The unambiguous framing of what changes are being attributed to what causes is a crucial first step for an assessment ( [[#Easterling--2016|Easterling et al., 2016]] ; [[#Hansen--2016|Hansen et al., 2016]] ; [[#Stone--2021|Stone et al., 2021]] ), followed by the identification of the possible and plausible drivers of change and the development of a hypothesis or theory for the linkage (Cross-Working Group Box: Attribution, Figure 1). The next step is to clearly define the indicators of the observed change or event and note the quality of the observations. There has been significant progress in the compilation of fragmented and distributed observational data, broadening and deepening the data basis for attribution research (WGI [[#1.5|Section 1.5]] ; e.g., [[#Poloczanska--2013|Poloczanska et al., 2013]] ; [[#Ray--2015|Ray et al., 2015]] ; [[#Cohen--2018|Cohen et al., 2018]] ). The quality ofthe observational record of drivers should also be considered (e.g., volcanic eruptions: WGI [[IPCC:Wg1:Chapter:Chapter-2#2.2.2|Section 2.2.2]] ). Impacted systems also change in the absence of climate change; this baseline and its associated modifiers – such as agricultural developments or population growth – need to be considered, alongside the exposure and vulnerability of people depending on these systems.

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'''Cross-Working Group Box: Attribution, Figure 1 |'''

'''Schematic of the steps to develop an attribution assessment, and the purposes of such assessments. Methods and systems used to test the attribution hypothesis or theory include: model-based fingerprinting; other model-based methods; evidence-based fingerprinting; process-based approaches; empirical or decomposition methods; and the use of multiple lines of evidence.''' Many of the methods are based on the comparison of the observed state of a system to a hypothetical counterfactual world that does not include the driver of interest to help estimate the causes of the observed response.

There are many attribution approaches, and several methods are detailed below. In physical and biological systems, attribution often builds on the understanding of the mechanisms behind the observed changes and numerical models are used, while in human systems other methods of evidence-building are employed. Confidence in the attribution can be increased if more than one approach is used and the model is evaluated as fit-for-purpose (WGI [[#1.5|Section 1.5]] , WGI Section 3.8, WGI Section 10.3.3.4 ; Hegerl et al. , 2010; Vautard et al. , 2019; Otto et al. , 2020; Philip et al. , 2020) . The final step includes appropriate communication of the attribution assessment and the accompanying confidence in the result (e.g., [[#Lewis--2019|Lewis et al., 2019]] ).

''''''Attribution methods''''''
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