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

=== 1.4.4 Considering an Uncertain Future ===

<div id="h2-23-siblings" class="h2-siblings"></div>

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.

<div id="1.4.4.1" class="h3-container"></div>

<span id="low-likelihood-outcomes"></span>
==== 1.4.4.1 Low-Likelihood Outcomes ====

<div id="h3-17-siblings" class="h3-siblings"></div>

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).

<div id="_idContainer049" class="_idGenObjectStyleOverride-1"></div>

[[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).

<div id="1.4.4.2" class="h3-container"></div>

<span id="storylines"></span>
==== 1.4.4.2 Storylines ====

<div id="h3-18-siblings" class="h3-siblings"></div>

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).

<div id="1.4.4.3" class="h3-container"></div>

<span id="abrupt-change-tipping-points-and-surprises"></span>
==== 1.4.4.3 Abrupt Change, Tipping Points and Surprises ====

<div id="h3-19-siblings" class="h3-siblings"></div>

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]]).

<div id="_idContainer051" class="_idGenObjectStyleOverride-1"></div>

[[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.

<div id="cross-chapter-box-1.3" class="h2-container box-container"></div>
<div class="container-box col-cross">