Editing IPCC:AR6/WGI/Chapter-8 (section)

== 8.6 What Is the Potential for Abrupt Change? ==

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In this Report, ''abrupt change'' is defined as a regional-to-global scale change in the climate system that occurs faster than the typical rate of changes in its history, implying non-linearity in the climate response (see Glossary). Often, abrupt change arises from positive feedbacks in the climate system that cause the current state to become unstable, and cross a ‘tipping point’ (Lenton et al., 2008); that is, a rapid shift from one climate state to another. The water cycle has several attributes with potential to produce abrupt change. Non-linear interactions between the ocean, atmosphere, and land surface can result in rapid shifts between wet and dry states (Sections 8.6.1 and 8.6.2). Cessation of solar radiation modification could also result in abrupt changes in the water cycle ( [[#8.6.3|Section 8.6.3]] ). This section reviews these types of abrupt shifts and assesses the likelihood that they will occur by 2100.

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=== 8.6.1 Abrupt Water Cycle Responses to a Collapse of Atlantic Meridional Overturning Circulation ===

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Multiple lines of evidence, including both paleoclimate reconstructions and simulations, suggest that a severe weakening or collapse of Atlantic Meridional Overturning Circulation (AMOC, see Glossary) causes abrupt and profound changes in the global hydrological cycle ( [[#Chiang--2005|Chiang and Bitz, 2005]] ; [[#Broccoli--2006|Broccoli et al., 2006]] ; [[#Chiang--2012|Chiang and Friedman, 2012]] ; [[#Jackson--2015|Jackson et al., 2015]] ; [[#Renssen--2018|Renssen et al., 2018]] ). Deep water formation in the North Atlantic is dependent on a delicate balance of heat and salt fluxes ( [[#Buckley--2016|Buckley and Marshall, 2016]] ); disruption in either of these due to melting ice sheets, a change in precipitation and evaporation, or ocean circulation can force AMOC to cross a tipping point (SROCC; [[#Drijfhout--2015|Drijfhout et al., 2015]] ). During the last deglacial transition, one such slowdown in AMOC – during the Younger Dryas event (12,800–11,700 years ago) – caused worldwide changes in precipitation patterns. These included a southward migration of the tropical ITCZ (Peterson et al., 2000; [[#McGee--2014|McGee et al., 2014]] ; [[#Schneider--2014|Schneider et al., 2014]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ; [[#Reimi--2016|Reimi and Marcantonio, 2016]] ; P.X. [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ) and systematic weakening of the African and Asian monsoons (Tierney and DeMenocal, 2013; Otto-Bliesner et al. , 2014; Cheng et al. , 2016; Grandey et al. , 2016; Wurtzel et al. , 2018). Conversely, the Southern Hemisphere (SH) monsoon systems intensified (Cruz et al. , 2005; Ayliffe et al. , 2013; Stríkis et al. , 2015, 2018; Campos et al. , 2019) . Drying occurred in Meso-America (Lachniet et al., 2013) while the North American monsoon system was largely unaffected (Bhattacharya et al., 2018). The mid-latitude region in North America was wetter (Polyak et al. , 2004; Grimm et al. , 2006; Wagner et al. , 2010; Voelker et al. , 2015) , while Europe was drier (Genty et al., 2006; [[#Rach--2017|Rach et al., 2017]] ; [[#Naughton--2019|Naughton et al., 2019]] ). A transient coupled climate model simulation was able to reproduce the large-scale precipitation response to such an event (Figure 8.27a; [[#Liu--2009|Liu et al., 2009]] ).

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[[File:660261de89307e1162d5893b09404793 IPCC_AR6_WGI_Figure_8_27.png]]

'''Figure 8.27 |''' '''Comparison of reconstructed past and idealized future annual mean precipitation responses to an Atlantic Meridional Overturning Circulation (AMOC) collapse. (a)''' Model simulation of precipitation response to the Younger Dryas event relative to the preceding warm Bølling-Allerød period (base colours, calculated as the difference between 12,600–11,700 years before the present (BP) and 14,500–12,900 BP from the Transient Climate Evolution (TraCE) paleoclimate simulation of [[#Liu--2009|Liu et al., 2009]] ), with paleoclimate proxy evidence superimposed on top (dots). '''(b)''' Model simulation of precipitation response to an abrupt collapse in AMOC under a doubling of 1990 CO <sub>2</sub> levels (after W. [[#Liu--2017|]] [[#Liu--2017|Liu et al., 2017]] ). Regions with rainfall rates below 20 mm yr <sup>–1</sup> are masked. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

These patterns of past hydroclimatic change are relevant for future projections because it is ''very likely'' that AMOC will weaken by 2100 in response to increased greenhouse gas emissions ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.3.1|Section 9.2.3.1]] ; Weaver et al. , 2012; Drijfhout et al. , 2015; Bakker et al. , 2016; Reintges et al. , 2017) . Furthermore, there is ''medium confidence'' that the decline in AMOC will not involve an abrupt collapse before 2100 ( [[IPCC:Wg1:Chapter:Chapter-9#9.2.3.1|Section 9.2.3.1]] ). The response of precipitation to hypothetical AMOC collapse under elevated greenhouse gases bears resemblance to the paleoclimate response during the Younger Dryas event, with some important differences due to effects of increased CO <sub>2</sub> on global precipitation patterns (Figure 8.27b). As with the paleoclimate events, AMOC collapse results in a southward shift in the ITCZ that is most pronounced in the tropical Atlantic. This could cause drying in the Sahel region (Defrance et al., 2017) as well as Meso-America and northern Amazonia (Parsons et al., 2014; Y. [[#Chen--2018|]] [[#Chen--2018|]] [[#Chen--2018|Chen et al., 2018]] ). AMOC collapse also causes the Asian monsoon systems to weaken (Figure 8.27b; W. [[#Liu--2017|]] [[#Liu--2017|Liu et al., 2017]] ) counteracting the strengthening expected in response to elevated greenhouse gases (see [[#8.4.2|Section 8.4.2]] ). Europe is projected to experience moderate drying in response to AMOC collapse (Jackson et al., 2015).

In summary, given that there is ''medium confidence'' that the decline in AMOC will not involve an abrupt collapse before 2100, there is ''low confidence'' that an AMOC-driven abrupt change in the water cycle will occur by 2100. However, if AMOC collapse does occur, it is ''very likely'' that there would be large regional impacts on the water cycle.

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=== 8.6.2 Abrupt Water Cycle Responses to Changes in the Land Surface ===

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Changes in the land surface, including vegetation cover and dust emissions, can trigger abrupt changes in the water cycle. Plants regulate the exchange of water and energy between the land surface and the atmosphere ( [[#8.2.3.3|Section 8.2.3.3]] ), such that sudden shifts in plant functions, types, or biomes can trigger feedbacks that have the potential to cause abrupt changes in the regional water cycle. Dust emissions, from either climatic or land use changes, affect the radiation budget and can regionally exacerbate dry extremes. Below, we assess the likelihood of abrupt changes in the water cycle for the well-studied regions of the Amazon and the Sahel, and the potential for dust emissions to amplify drought and aridity.

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==== 8.6.2.1 Amazon Deforestation and Drying ====

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The Amazon forest plays an active role in driving atmospheric moisture transport and generating precipitation in the South American region ( SRCCL; Drumond et al. , 2014; Poveda et al. , 2014; Yin et al. , 2014; Staal et al. , 2018, 2020; Agudelo et al. , 2019; Espinoza et al. , 2019) . This close association between the land surface and the water cycle makes the Amazon a potential hotspot for abrupt change ( [[#Torres--2014|Torres and Marengo, 2014]] ). Both deforestation and drying are projected to increase by 2100, resulting in a worst-case scenario of up to a 50% loss in forest cover by 2050 (Soares-Filho et al. , 2006; Boisier et al. , 2015; ter Steege et al. , 2015; Gomes et al. , 2019) . Deforestation in the Amazon also raises the probability of catastrophic fires ( [[#Brando--2014|Brando et al., 2014]] ). The combination of deforestation, drier conditions, and increased fire can push the rainforest ecosystem past a tipping point, beyond which there is rapid land surface degradation, a sharp reduction in atmospheric moisture recycling, an increase in the fraction of precipitation that runs off, and a further shift towards a drier climate (Staal et al. , 2015; Boers et al. , 2017; Zemp et al. , 2017; Ruiz-Vásquez et al. , 2020) . A rapid drop in precipitation has a direct impact on river flows, driving basin-scale shifts from a regulated to unregulated state ( [[#Salazar--2018|Salazar et al., 2018]] ). Regional climate modeling experiments confirm that increased deforestation leads to a drier climate, although not all models show a true tipping point, at least under present-day climatic conditions ( [[#Lejeune--2015|Lejeune et al., 2015]] ; [[#Spracklen--2015|Spracklen and Garcia-Carreras, 2015]] ).

In AR5, some simulations using a coupled climate–carbon cycle model exhibited an abrupt dieback of the Amazon forest in future climate scenarios ( [[#Oyama--2003|Oyama and Nobre, 2003]] ; [[#Cox--2004|Cox et al., 2004]] ; [[#Malhi--2008|Malhi et al., 2008]] ).However, subsequent work demonstrated that abrupt Amazon dieback does not occur consistently across, or even within, Earth system models ( [[#Lambert--2013|Lambert et al., 2013]] ; [[#Boulton--2017|Boulton et al., 2017]] ). The occurrence of dieback is highly dependent on both how dry the simulated climate is in the present day ( [[#Malhi--2009|Malhi et al., 2009]] ) as well as the representation of forest structure and competitive dynamics ( [[#Levine--2016|Levine et al., 2016]] ). Models with a low diversity of plant characteristics and types have a higher tendency for abrupt change ( [[#Sakschewski--2016|Sakschewski et al., 2016]] ). Abrupt shifts and ecosystem disruptions can occur on the sub-regional level ( [[#Pires--2013|Pires and Costa, 2013]] ), highlighting the need for higher-resolution modelling studies. Since AR5, CMIP6 projections suggest that a tipping point in the Amazon system may be crossed on a local or regional scale ( [[#Staal--2020|Staal et al., 2020]] ) but continue to be highly dependent on model biases in precipitation and the simulation of the land surface. Consequently, the timing, and probability, of an abrupt shift remains difficult to ascertain.

In summary, while there is a strong theoretical expectation that Amazon drying and deforestation can cause a rapid change in the regional water cycle, currently there is ''limited'' model ''evidence'' to verify this response, hence there is ''low confidence'' that such a change will occur by 2100.

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==== 8.6.2.2 Greening of the Sahara and the Sahel ====

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Greening of the Sahara and Sahel regions in North Africa, in response to an increase in precipitation, has long been considered an amplifying mechanism that can lead to abrupt change. Although the high surface albedo of the desert stabilizes the energy balance of the system ( [[#Charney--1975|Charney, 1975]] ), greening can induce strong, positive feedbacks between the land surface and precipitation that can shift the region into a ‘Green Sahara’ state. The fact that the transition phase between a Desert Sahara and Green Sahara is not theoretically stable (Brovkin et al., 1998) creates a tipping point and allows for the possibility of an abrupt shift between dry and wet climate regimes. Paleoclimate reconstructions provide evidence of past Green Sahara states (DeMenocal and Tierney, 2012), under which rainfall rates increased by an order of magnitude (Tierney et al., 2017), leading to a vegetated landscape (Jolly et al., 1998) with large lake basins (Gasse, 2000; [[#Drake--2006|Drake and Bristow, 2006]] ). The underlying driver of the Green Sahara is the periodic increase in summer insolation associated with the orbital precession cycle (Kutzbach, 1981). In this sense, Green Saharas are not direct analogues for a response to anthropogenic greenhouse gas emissions (GHGs), as these past states were forced by natural, seasonal changes in solar radiation. However, the climate dynamics of Green Sahara periods (which have global impacts, [[#Pausata--2020|Pausata et al., 2020]] ), and the speed of the transitions between Desert Saharas and Green Saharas, are relevant for future projections.

Since AR5, paleoclimatic studies have improved our view of the timing, spatial extent, and speed of transitions associated with the early Holocene (11,000–5,000 years ago) Green Sahara. Observed transitions into and out of Green Sahara states are always faster than the underlying forcing, in agreement with theoretical considerations ( ''high confidence'' ) (Tierney and DeMenocal, 2013; [[#Shanahan--2015|Shanahan et al., 2015]] ; [[#Tierney--2017|Tierney et al., 2017]] ). However, there is ''low confidence'' in the duration of the transition because sedimentary records cannot typically resolve changes on decadal to multi-decadal time scales (Tierney and DeMenocal, 2013). Both paleoclimate data and modelling experiments suggest that the timing and speed of the transition was spatially heterogeneous ( ''high confidence'' ), with northern Saharan locations becoming drier thousands of years before more equatorial locations (Shanahan et al., 2015; [[#Tierney--2017|Tierney et al., 2017]] ; [[#Dallmeyer--2020|Dallmeyer et al., 2020]] ). These observations are consistent with theoretical studies suggesting that spatial heterogeneity and diversity in ecosystems can mitigate the probability of catastrophic change (Van Nes and Scheffer, 2005; [[#Bathiany--2013|Bathiany et al., 2013]] ). Conversely, low ecosystem diversity can produce local or regional ‘hot spots’ of abrupt change such as those seen in some paleoclimate records (Claussen et al., 2013).

CMIP5 and CMIP6 models ''',''' some of which include dynamic vegetation schemes, cannot simulate the magnitude, nor the spatial extent, of greening and precipitation change associated with the last Green Sahara under standard mid-Holocene (6,000 years ago) boundary conditions ( ''high confidence'' ) (Figure 3.11; Harrison et al. , 2014; Tierney et al. , 2017; Brierley et al. , 2020). This result remains unchanged since AR4 ( [[#Jansen--2007|Jansen et al., 2007]] ). This may be due to climatological biases in the models ( [[#Harrison--2015|Harrison et al., 2015]] ) or could imply that the strength of the feedbacks between vegetation and the water cycle in the models is too weak (Hopcroft et al., 2017). To date, climate models still only produce the amount and spatial extent of rainfall that is needed to sustain a Green Sahara if they are given prescribed changes in the land surface, such as albedo, soil moisture, vegetation cover and/or dust emissions (Pausata et al., 2016; [[#Skinner--2016|Skinner and Poulsen, 2016]] ; [[#Tierney--2017|Tierney et al., 2017]] ).

Some climate model simulations suggest that under future high-emissions scenarios, CO <sub>2</sub> radiative forcing causes rapid greening in the Sahel and Sahara regions via precipitation change (Claussen et al., 2003; [[#Drijfhout--2015|Drijfhout et al., 2015]] ). For example, in the BNU-ESM RCP8.5 simulation, the change is abrupt with the percentage of bare soil dropping from 45% to 15%, and percentage of tree cover rising from 50% to 75%, within 10 years (2050–2060; Drijfhout et al., 2015). However, other modelling results suggest that this may be a short-lived response to CO <sub>2</sub> fertilization (Bathiany et al., 2014).

In summary, given outstanding uncertainties in how well the current generation of climate models capture land surface feedbacks in the Sahel and Sahara, there is ''low confidence'' that an abrupt change to a greener state will occur in these regions before 2100 or 2300.

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==== 8.6.2.3 Amplification of Drought by Dust ====

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Mineral dust aerosols in the climate system originate from both semi-permanent and transient sources ( [[#Prospero--2002|Prospero et al., 2002]] ; [[#Ginoux--2012|Ginoux et al., 2012]] ). The former are typically arid regions where significant alluvial sediments have accumulated over time, while the latter are often associated with natural (e.g., droughts, wildfires) and anthropogenic (e.g., land use change, desertification) disturbances. Modern-day dust emissions are dominated by natural sources ( [[#Ginoux--2012|Ginoux et al., 2012]] ), although human emissions may contribute 10–60% of the global atmospheric dust load ( [[#Webb--2018|Webb and Pierre, 2018]] ). Paleo-dust records suggest that human factors (land use change and landscape disturbance) may have doubled global dust emissions between 1750 and the last quarter of the 20th century (Section 2.2.6; [[#Hooper--2018|Hooper and Marx, 2018]] ).

Dust aerosols influence the climate system and hydrologic cycle through both direct impacts on radiation (absorbing and scattering longwave and shortwave) and via indirect effects on cloud and precipitation processes (Box 8.1; [[#Choobari--2014|Choobari et al., 2014]] ; [[#Kok--2018|Kok et al., 2018]] ; [[#Schepanski--2018|Schepanski, 2018]] ). The capacity of dust aerosols to suppress precipitation by reducing humidity and energy availability, and increasing stability in the atmosphere ( [[#Cook--2013|Cook et al., 2013]] ; [[#Huang--2014|Huang et al., 2014]] ) can drive positive feedbacks (see also Section 6.3.6). Thus there is strong potential for dust to contribute to abrupt changes in the water cycle, especially in semi-arid regions where wind erosion is highly sensitive to vegetation cover and drought variability ( [[#Yu--2015|Yu et al., 2015]] ). One such event occurred over the Central USA during the 1930s: the Dust Bowl drought, an iconic event characterized by widespread land degradation and historically unprecedented levels of dust storm activity ( [[#Hansen--2004|Hansen and Libecap, 2004]] ; [[#Lee--2015|Lee and Gill, 2015]] ). While initialized by warm sea surface temperatures in the North Atlantic, modeling work indicates that land cover changes and resulting dust emissions contributed to the severity and spatial extent of the drought by further suppressing precipitation ( [[#Cook--2009|Cook et al., 2009]] ; [[#Hu--2018|Hu et al., 2018]] ; [[#Cowan--2020|Cowan et al., 2020]] ). There is also increasing evidence that dust aerosol feedbacks are necessary to explain the magnitude of rainfall increase during the mid-Holocene Green Sahara ( [[#Pausata--2016|Pausata et al., 2016]] ; [[#Tierney--2017|Tierney et al., 2017]] ).

The importance of dust aerosol feedbacks in future abrupt climate events, like droughts or rapid aridification, is unclear. In part, this is because the response of dust aerosol emissions and loading levels in the atmosphere to climate change is highly uncertain (Tegen and [[#Schepanski--2018|Schepanski, 2018]] ; [[#Webb--2018|Webb and Pierre, 2018]] ). This difficulty in predicting future dust responses is rooted in the fact that emissions depend on both changes to the land surface (e.g., land use/land cover change, aridification, ecological responses to climate change) and the state of the atmosphere (Tegen and [[#Schepanski--2018|Schepanski, 2018]] ). While there is some evidence that global dust aerosol concentrations in the future will increase ( [[#Allen--2016|Allen et al., 2016]] ; Tegen and [[#Schepanski--2018|Schepanski, 2018]] ), it is highly dependent on changes in precipitation patterns and atmospheric circulation (see the SRCCL, [[IPCC:Wg1:Chapter:Chapter-2#2.4.1|Section 2.4.1]] ), and it is not clear what the radiative impact will be ( [[#Allen--2016|Allen et al., 2016]] ; [[#Kok--2018|Kok et al., 2018]] ).

In summary, due to ''limited evidence'' , there is ''low confidence'' regarding the role of dust in abrupt climate change events over the next century.

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=== 8.6.3 Abrupt Water Cycle Responses to Initiation or Termination of Solar Radiation Modification ===

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Solar radiation modification (SRM) techniques seek to reduce the impacts of climate change by modifying the Earth’s radiation budget, either by reflecting incoming solar radiation or increasing the amount of heat lost to space. Note that, following SR1.5, the definition of SRM in this Report refers to changes in both solar and longwave radiation ( [[IPCC:Wg1:Chapter:Chapter-4#4.6.3.3|Section 4.6.3.3]] and Glossary). A variety of methods have been proposed, including injection of aerosols or their precursors into the stratosphere, cloud brightening, and cirrus cloud thinning (Table 4.8). Since SRM alters the planetary energy balance, changes in the hydrological cycle are theoretically expected ( [[#8.2|Section 8.2]] ). These changes can be abrupt if the initial magnitude of SRM is large, rather than increased gradually. Since AR5, a diversity of SRM techniques have been tested using climate model simulations, with an increasing focus on consequences for regional water availability. Techniques targeting shortwave radiation (sulfate injection, surface albedo modification, cloud brightening) are ''likely'' to reduce global mean precipitation relative to future CO <sub>2</sub> -emissions scenarios (Bala et al. , 2008; A. Jones et al. , 2013; Tilmes et al. , 2013; Ferraro et al. , 2014; Crook et al. , 2015) . In contrast, cirrus cloud thinning, a longwave radiation technique, results in increased global precipitation as it causes enhanced radiative cooling in the troposphere ( ''medium confidence'' ) ( [[#Crook--2015|Crook et al., 2015]] ; [[#Kristjánsson--2015|Kristjánsson et al., 2015]] ; [[#Jackson--2016|Jackson et al., 2016]] ).

The magnitude of hydrological disruption for both the initiation and termination of SRM depends on the method used, as well as the strength and duration of its implementation ( [[#Ekholm--2016|Ekholm and Korhonen, 2016]] ; [[#Irvine--2019|Irvine et al., 2019]] ). Under abrupt SRM implementation, hydrological shifts are rapid, occurring within the first decade ( [[#Crook--2015|Crook et al., 2015]] ). Artificial enhancement of albedo in Northern Hemisphere desert regions causes a southward shift in the Hadley Cell and ITCZ, and extreme drying in the northern tropics ( [[#Crook--2015|Crook et al., 2015]] ). Uniform or tropical stratospheric sulfate injection weakens the African and Asian summer monsoons and causes drying in the Amazon ( [[#Robock--2008|Robock et al., 2008]] ; [[#Crook--2015|Crook et al., 2015]] ; [[#Dagon--2016|Dagon and Schrag, 2016]] ). Changes in evapotranspiration can produce large deficits or surpluses in soil moisture and runoff in different regions and seasons ( [[#Dagon--2016|Dagon and Schrag, 2016]] ).

Rapid changes (years to decades) in the hydrological cycle are also expected if SRM is terminated abruptly, either purposefully or because of technical failure or political disagreement. We reiterate the AR5 conclusion that if SRM ‘were terminated for any reason, there is ''high confidence'' that surface temperatures would increase rapidly (within a decade or two) to values consistent with the GHG forcing’. The additional global warming caused by SRM termination may result in a rapid increase in global mean precipitation ( ''medium confidence'' ) ( [[#Jones--2013|]] [[#Jones--2013|A. Jones et al., 2013]] ). Heterogenous regional and seasonal changes are also expected, but are model-dependent ( [[#Jones--2013|]] [[#Jones--2013|A. Jones et al., 2013]] ). As with SRM initiation, the impact of SRM termination is expected to be dependent on the technique deployed.

In summary, it is ''very likely'' that abrupt water cycle changes will occur if SRM is abruptly initiated or halted, especially in tropical regions. Further assessment of the potential side-effects of SRM is found in [[IPCC:Wg1:Chapter:Chapter-4#4.6.3.3|Section 4.6.3.3]] .

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