Editing IPCC:AR6/WGII/Chapter-16 (section)

=== 16.5.4 RKR Interactions ===

<div id="h2-17-siblings" class="h2-siblings"></div>

Multiple feedbacks between individual risks exist that have the potential to create cascades ( [[#WEF--2018|WEF, 2018]] ; [[#IPCC--2019c|IPCC, 2019c]] p. 680; [[#Simpson--2021|Simpson et al., 2021]] ) and then to amplify systemic risks and impacts far beyond the level of individual RKRs ( ''medium confidence'' ). Scientific research, however, remains limited on whether such interactions would result in increasing or decreasing the initial impact(s), and hence risk severity across systems. Given the scope of this chapter on increasing risk severity, here we focus on assessing RKR interactions that lead to increasing risk. Drawing directly on RKR assessments (Sections 16.5.2.3.2–16.5.2.3.8), this section cites those assessments rather than primary literature. The arrows in Figure 16.11 are derived from a qualitative analysis by three authors of [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-16 Chapter 16] of the material provided by chapters on KRs and RKR assessments ( [[#16.5.2.3|Section 16.5.2.3]] ), and do not result from any systematic and quantitative approach as done in some recent studies (e.g., [[#WEF--2018|WEF, 2018]] ; [[#Yokohata--2019|Yokohata et al., 2019]] ).

<div id="_idContainer038" class="Figure"></div>

[[File:e62e9bd366add2d9fd79c9e2fe619b19 IPCC_AR6_WGII_Figure_16_011.png]]

'''Figure 16.11 |''' '''Illustration of some connections across key risks.''' Panel A describes all the cross-RKR risk cascades that are described in RKR assessments (Sections 16.5.2.3.2–16.5.2.3.8). Panel B builds on [[#16.5.2|Section 16.5.2]] and Table SM16.24 to provide an illustration of such interactions at the key risk level, for example from ecological risk to key dimensions for human societies. The arrows are representative of interactions as qualitatively identified in this chapter; they do not result from any quantitative modelling exercise.

''Interactions at the RKR level'' (Figure 16.11, panel A)—climate change will combine with pre-existing socioeconomic and ecological conditions (grey blocks on the left-hand-side of panel A in Figure 16.10) to generate direct and second-order effects (black plain arrows) both on the structure and/or functioning of ecosystems (RKR-B) and on some natural processes such as the hydrologic cycle (RKR-G), for example. This then translates into implications not only for biodiversity but also for natural resources that support livelihoods, which will in turn affect food security (especially food availability; RKR-F), water security (especially access to adequate quantities of acceptable quality water; RKR-G) and the living standards of already vulnerable groups and aggregate economic outputs at the global level (RKR-D). CIDs ( [[#IPCC--2021|IPCC, 2021]] ) will also directly affect infrastructure that are critical to ensure some basic conditions for economies to function (RKR-C), for example through transportation within and outside the country, energy production and international trade. Such disturbances to socioecological systems and economies pose climate-related risks to human health (RKR-E) as well as to peace and human mobility (RKR-H). Indeed, while health is concerned with direct influence of climate change, for example through hotter air temperatures impacting morbidity and mortality or the spatial distribution of disease vectors such as mosquitos, it is also at risk of being stressed by direct and secondary climate impacts on living standards, food security and water security (RKR-D, RKR-F, RKR-G, respectively). Increased poverty, increased hunger and limited access to drinkable water are well-known drivers of poor health conditions. The role of impact cascades is even more prominent in the case of peace and human mobility (RKR-H), even though the scientific literature does not conclude on any clear and direct climate influence on armed conflict and human migration. Rather, climate-induced degradation of natural resources that are vital for subsistence agriculture and fisheries, transformational and long-term consequences on livelihoods (e.g., new risks, increasing precarious living conditions, gendered inequity, etc.), and erosion of social capital due to exacerbated tension within and between communities are considered among the main drivers of armed conflicts and forced displacement, therefore highlighting links with water security (RKR-G) and living standards (RKR-D), for example.

RKR assessments also suggest that some feedback effects are at work (arrows moving from the right to the left in panel A) that contribute to the potentially long-lasting effects of climate risks. RKR-H assessment, for example, states that there is ''robust evidence'' that major armed conflicts routinely trigger mass displacement, threaten health and food security, and undermine economic activity and livelihoods, often with lasting negative consequences for living standards and socioeconomic development, therefore linking back to risks to living standards (RKR-D), human health (RKR-E) and food security (RKR-F).

''Interactions at the KR level'' (Figure 16.11, panel B)—panel B illustrates risk connections at the Key Risk level ( [[#16.5.2.1|Section 16.5.2.1]] ) and as described in RKR assessments ( [[#16.5.2.3|Section 16.5.2.3]] ). To only take one example here, risk to livelihoods and economies is influenced by the loss of ecosystem services (RKR-B) and the loss or breakdown of critical infrastructures (RKR-C), and it influences risks to human lives and health (RKR-E), food and water security (RKR-F, RKR-G), poverty (RKR-D) and peace and human mobility (RKR-H). As a third-order sequence, RKR assessments show that increased risk to peace and human mobility affects lives and health as well as food security, which in turn threaten livelihoods and economies.

The above suggests that some vicious cycle effects play a central role in explaining impact processes. Cascading effects can indeed lead to cumulative risks that partly feed various drivers of the emergence of severe risks ( [[#16.5.1|Section 16.5.1]] ), such as the acceleration of ecosystem degradation, or the reaching of thresholds and irreversible states in human systems at a decade-to-century time horizon (e.g., when permanent inundation questions the habitability of some low-lying coasts; RKR-A). The extent and duration of risk cascades are, however, expected to substantially vary depending on warming levels and development pathways, both separately ( [[#16.5.3|Section 16.5.3]] ) and when combined (Sections 16.6.1, 16.6.2) (Figure 16.10).

In addition, RKR assessments converge to suggest that regions that are already experiencing climate change impacts will experience severe impact cascades first (e.g., RKR-F), because they are in areas (i) that face development constraints and associated challenges such as poverty, inequity and social discrimination for example, and (ii) where climate change projections are the most intense for the next decades. That is especially a concern for Africa (RKR-F, RKR-G), Asia and Latin America (Chapters 9, 10, 12). RKR-E, for example, concludes that the likelihood of severe risks to human health is especially high for highly susceptible populations, particularly the poor and otherwise marginalised. RKR assessments, however, emphasise that middle- and high-income regions are also to be considered at serious risk because climate change is accelerating at the global level ( [[#IPCC--2021|IPCC, 2021]] ), and because critical dimensions are exposed to severe risks such as major transportation (e.g., international airports) and energy (e.g., nuclear power plants) infrastructure for instance (RKR-C), and because of the interconnectedness of economies.

Finally, all RKR assessments suggest that enhanced adaptation has the potential to contain such feedback effects and cascading processes more broadly, and reduce the duration of the impacts on the system as a whole. There are, however, knowledge gaps on such a potential, as well as on the nature of impact cascades (positive, negative, neutral, mixed).

<div id="cross-working-group-box-srm" class="h2-container box-container"></div>

'''Cross-Working Group Box SRM | Solar Radiation Modification'''
<div id="h2-25-siblings" class="h2-siblings"></div>

Authors: Christopher H. Trisos (South Africa), Oliver Geden (Germany), Sonia I. Seneviratne (Switzerland), Masahiro Sugiyama (Japan), Maarten van Aalst (the Netherlands), Govindasamy Bala (India), Katharine J. Mach (USA), Veronika Ginzburg (Russia), Heleen de Coninck (the Netherlands), Anthony Patt (Switzerland)

'''Proposed solar radiation modification schemes'''
This cross-working group box assesses solar radiation modification (SRM) proposals, their potential contribution to reducing or increasing climate risk, as well as other risks they may pose (categorised as risks from responses to climate change in the IPCC AR6 risk definition in 1.2.1.1), and related perception, ethics and governance questions.

SRM refers to proposals to increase the reflection of shortwave radiation (sunlight) back to space to counteract anthropogenic warming and some of its harmful impacts ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ) (Cross-Chapter Box 10; WGI Chapters 4, 5). A number of SRM options have been proposed, including: stratospheric aerosol interventions (SAI), marine cloud brightening (MCB), ground-based albedo modifications (GBAM) and ocean albedo change (OAC). Although not strictly a form of SRM, cirrus cloud thinning (CCT) has been proposed to cool the planet by increasing the escape of longwave thermal radiation to space and is included here for consistency with previous assessments ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ). SAI is the most-researched proposal. Modelling studies show SRM could reduce surface temperatures and potentially ameliorate some climate change risks (with more confidence for SAI than other options), but SRM could also introduce a range of new risks.

There is ''high agreement'' in the literature that for addressing climate change risks SRM cannot be the main policy response to climate change and is, at best, a supplement to achieving sustained net zero or net negative CO 2 emission levels globally ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ; [[#MacMartin--2018|MacMartin et al., 2018]] ; [[#Buck--2020|Buck et al., 2020]] ; National Academies of Sciences and Medicine, 2021b). SRM contrasts with climate change mitigation activities, such as emission reductions and carbon dioxide removal (CDR), as it introduces a ‘mask’ to the climate change problem by altering the Earth’s radiation budget, rather than attempting to address the root cause of the problem, which is the increase in greenhouse gases (GHGs) in the atmosphere. In addition, the effects of proposed SRM options would only last as long as a deployment is maintained—for example, requiring ca. yearly injection of aerosols in the case of SAI as the lifetime of aerosols in the stratosphere is 1–3 years ( [[#Niemeier--2011|Niemeier et al., 2011]] ) or continuous spraying of sea salt in the case of MCB as the lifetime of sea salt aerosols in the atmosphere is only about 10 d—which contrasts with the long lifetime of CO 2 and its climate effects, with global warming resulting from CO 2 emissions ''likely'' remaining at a similar level for a hundred years or more ( [[#MacDougall--2020|MacDougall et al., 2020]] ) and long-term climate effects of emitted CO 2 remaining for several hundreds to thousands of years ( [[#Solomon--2009|Solomon et al., 2009]] ).

'''Which scenarios?'''
The choice of SRM deployment scenarios and reference scenarios is crucial in assessment of SRM risks and its effectiveness in attenuating climate change risks ( [[#Keith--2015|Keith and MacMartin, 2015]] ; [[#Honegger--2021|Honegger et al., 2021]] ). Most climate model simulations have used scenarios with highly stylised large SRM forcing to fully counteract large amounts of warming in order to enhance the signal-to-noise ratio of climate responses to SRM ( [[#Kravitz--2015|Kravitz et al., 2015]] ; [[#Sugiyama--2018a|Sugiyama et al., 2018a]] ; [[#Tilmes--2018|Tilmes et al., 2018]] ; [[#Krishna-Pillai--2019|Krishna-Pillai et al., 2019]] ).

The effects of SRM fundamentally depend on a variety of choices about deployment ( [[#Sugiyama--2018b|Sugiyama et al., 2018b]] ), including: its position in the portfolio of human responses to climate change (e.g., the magnitude of SRM used against the background radiative forcing), governance of research and potential deployment strategies, and technical details (latitude, materials, and season, among others, see WGI [[IPCC:Wg2:Chapter:Chapter-4#4.6.3|Section 4.6.3.3]] ). The plausibility of many SRM scenarios is highly contested, and not all scenarios are equally plausible because of socio-political considerations ( [[#Talberg--2018b|Talberg et al., 2018b]] ), as with, for example, CDR ( [[#Fuss--2014|Fuss et al., 2014]] ; [[#Fuss--2018|Fuss et al., 2018]] ). Development of scenarios and their selection in assessments should reflect a diverse set of societal values with public and stakeholder inputs ( [[#Sugiyama--2018a|Sugiyama et al., 2018a]] ; [[#Low--2020|Low and Honegger, 2020]] ), as depending on the focus of a limited climate model simulation, SRM could look grossly risky or highly beneficial (Pereira and al., 2021).

In the context of reaching the long-term global temperature goal of the Paris Agreement, there are different hypothetical scenarios of SRM deployment: early, substantial mitigation with no SRM, more limited or delayed mitigation with moderate SRM, unchecked emissions with total reliance on SRM, and regionally heterogeneous SRM. Each scenario presents different levels and distributions of SRM benefits, side effects and risks. The more intense the SRM deployment, the larger is the likelihood for the risks of side effects and environmental risks (e.g., [[#Heutel--2018|Heutel et al., 2018]] ). Regional disparities in climate hazards may result from both regionally deployed SRM options such as GBAM, and more globally uniform SRM such as SAI ( [[#Jones--2018a|Jones et al., 2018a]] ; [[#Seneviratne--2018b|Seneviratne et al., 2018b]] ). There is an emerging literature on smaller forcings of SAI to reduce global average warming, for instance, to hold global warming to 1.5°C or 2°C alongside ambitious conventional mitigation ( [[#Jones--2018a|Jones et al., 2018a]] ; [[#MacMartin--2018|MacMartin et al., 2018]] ), or bring down temperature after an overshoot ( [[#Tilmes--2020|Tilmes et al., 2020]] ). If emissions reductions and CDR are deemed insufficient, SRM may be seen by some as the only option left to ensure the achievement of the Paris Agreement’s temperature goal by 2100.

'''Table Cross-Working Group Box SRM.1''' '''|''' SRM options and their potential climate and non-climate impacts. Description, potential climate impacts, potential impacts on human and natural systems, and termination effects of a number of SRM options: stratospheric aerosol interventions (SAI), marine cloud brightening (MCB), ocean albedo change (OAC), ground-based albedo modifications (GBAM) and cirrus cloud thinning (CCT).

{| class="wikitable"
|-
! ''SRM option''

! ''SAI''

! ''MCB''

! ''OAC''

! ''GBAM''

! ''CCT''

|-
| Description

| Injection of reflective aerosol particles directly into the stratosphere or a gas which then

converts to aerosols that reflect sunlight

| Spraying sea salt or other particles in marine clouds, making them more reflective

| Increase surface albedo of the ocean (e.g., by creating microbubbles or placing reflective foam on the surface)

| Whitening roofs, changes in land use management (e.g., no-till farming, bioengineering to make crop leaves more reflective), desert albedo enhancement, covering glaciers with reflective sheeting

| Seeding to promote nucleation of cirrus clouds, reducing optical thickness and cloud lifetime to allow more outgoing longwave radiation to escape to space

|-
| Potential climate impacts ''other than reduced warming''

| Change precipitation and runoff

pattern; reduced temperature and precipitation extremes; precipitation reduction in some monsoon regions; decrease in direct and increase in diffuse sunlight at surface; changes to stratospheric dynamics and chemistry; potential

delay in ozone hole recovery; changes in surface ozone and UV radiation

| Change in land-sea contrast in temperature and precipitation, regional precipitation and runoff changes

| Change in land–sea contrast in temperature and precipitation, regional, precipitation and runoff changes

| Changes in regional precipitation pattern, regional extremes and regional circulation

| Changes in temperature and precipitation pattern, altered regional water cycle, increase in sunlight reaching the surface

|-
| Potential impacts on human and natural systems

| Changes in crop yields, changes in land and ocean ecosystem productivity, acid rain (if using sulphate), reduced risk of heat stress to corals

| Changes in regional ocean productivity, changes in crop yields, reduced heat stress for corals, changes in ecosystem productivity on land, sea salt deposition over land

| Unresearched

| Altered photosynthesis, carbon uptake and side effects on biodiversity

| Altered photosynthesis and carbon uptake

|-
| Termination effects

| Sudden and sustained termination would result in rapid warming, and abrupt changes to water cycle. Magnitude of termination depends on the degree of warming offset

| Sudden and sustained

termination would result in rapid warming, and abrupt changes to water cycle. Magnitude of termination depends on the degree of warming offset

| Sudden and sustained

termination would result in rapid warming. Magnitude of termination depends on the degree of warming offset

| GBAM can be maintained over several years without major termination effects because of its regional scale of application.

Magnitude of termination depends on the degree of warming offset

| Sudden and sustained

termination would result in rapid warming. Magnitude of termination depends on the degree of warming offset

|-
| References (also see main text of this box)

| [[#Tilmes--2018|Tilmes et al. (2018)]] ; [[#Simpson--2019|Simpson et al. (2019)]] ; Visioni et al. (2017)

| [[#Latham--2012|Latham et al. (2012)]] ; [[#Ahlm--2017|Ahlm et al. (2017)]] ; [[#Stjern--2018|Stjern et al. (2018)]]

| [[#Evans--2010|Evans et al. (2010)]] ; [[#Crook--2015a|Crook et al. (2015a)]]

| [[#Zhang--2016|Zhang et al. (2016)]] ; [[#Field--2018|Field et al. (2018)]] ; [[#Seneviratne--2018a|Seneviratne et al. (2018a)]] ; [[#Davin--2014|Davin et al. (2014)]] ; [[#Crook--2015a|Crook et al. (2015a)]]

| [[#Storelvmo--2014|Storelvmo and Herger (2014)]] ; [[#Crook--2015a|Crook et al. (2015a)]] ; Jackson et al. (2016); [[#Gasparini--2020|Gasparini et al. (2020)]] ; [[#Duan--2020|Duan et al. (2020)]]

|}

'''SRM risks to human and natural systems and potential for risk reduction'''
Since AR5, hundreds of climate modelling studies have simulated effects of SRM on climate hazards ( [[#Kravitz--2015|Kravitz et al., 2015]] ; [[#Tilmes--2018|Tilmes et al., 2018]] ). Modelling studies have shown SRM has the potential to offset some effects of increasing GHGs on global and regional climate, including the increase in frequency and intensity of extremes of temperature and precipitation, melting of Arctic sea ice and mountain glaciers, weakening of Atlantic meridional overturning circulation, changes in frequency and intensity of tropical cyclones, and decrease in soil moisture (WGI, Chapter 4). However, while SRM may be effective in alleviating anthropogenic climate warming either locally or globally, it would neither maintain the climate in its present-day state nor return the climate to a pre-industrial state (climate averaged over 1850–1900, see WGI Chapter 1, Box 1.2) in all regions and in all seasons even when used to fully offset the global mean warming ( ''high confidence'' ) (WGI Chapter 4). This is because the climate forcing and response to SRM options are different from the forcing and response to GHG increase. Because of these differences in climate forcing and response patterns, the regional and seasonal climates of a world with a global mean warming of 1.5°C or 2°C achieved via SRM would be different from a world with similar global mean warming but achieved through mitigation (MacMartin et al.., 2019). At the regional scale and seasonal time scale, there could be considerable residual climate change and/or overcompensating change (e.g., more cooling, wetting or drying than just what is needed to offset warming, drying or wetting due to anthropogenic greenhouse gas emissions), and there is ''low confidence'' in understanding of the climate response to SRM at the regional scale (WGI, Chapter 4).

SAI implemented to partially offset warming (e.g., offsetting half of global warming) may have potential to ameliorate hazards in multiple regions and reduce negative residual change, such as drying compared with present-day climate, that is associated with fully offsetting global mean warming ( [[#Irvine--2020|Irvine and Keith, 2020]] ), but may also increase flood and drought risk in Europe compared with unmitigated warming ( [[#Jones--2021|Jones et al., 2021]] ). Recent modelling studies suggest it is conceptually possible to meet multiple climate objectives through optimally designed SRM strategies (WGI, Chapter 4). Nevertheless, large uncertainties still exist for climate processes associated with SRM options (e.g., aerosol–cloud–radiation interaction) (WGI, Chapter 4) ( [[#Kravitz--2020|Kravitz and MacMartin, 2020]] ).

Compared with climate hazards, many fewer studies have examined SRM risks—the potential adverse consequences to people and ecosystems from the combination of climate hazards, exposure and vulnerability—or the potential for SRM to reduce risk ( [[#Curry--2014|Curry et al., 2014]] ; [[#Irvine--2017|Irvine et al., 2017]] ). Risk analyses have often used inputs from climate models forced with stylised representations of SRM, such as dimming the sun. Fewer have used inputs from climate models that explicitly simulated injection of gases or aerosols into the atmosphere, which include more complex cloud radiative feedbacks. Most studies have used scenarios where SAI is deployed to hold average global temperature constant despite high emissions.

There is ''low confidence'' and large uncertainty in projected impacts of SRM on crop yields due in part to a limited number of studies. Because SRM would result in only a slight reduction in CO 2 concentrations relative to the emission scenario without SRM (Chapter 5, WGI), the CO 2 fertilisation effect on plant productivity is nearly the same in emissions scenarios with and without SRM. Nevertheless, changes in climate due to SRM are likely to have some impacts on crop yields. A single study indicates MCB may reduce crop failure rates compared with climate change from a doubling of CO 2 pre-industrial concentrations ( [[#Parkes--2015|Parkes et al., 2015]] ). Models suggest SAI cooling would reduce crop productivity at higher latitudes compared with a scenario without SRM by reducing the growing season length, but benefit crop productivity in lower latitudes by reducing heat stress ( [[#Pongratz--2012|Pongratz et al., 2012]] ; [[#Xia--2014|Xia et al., 2014]] ; [[#Zhan--2019|Zhan et al., 2019]] ). Crop productivity is also projected to be reduced where SAI reduces rainfall relative to the scenario without SRM, including a case where reduced Asian summer monsoon rainfall causes a reduction in groundnut yields ( [[#Xia--2014|Xia et al., 2014]] ; [[#Yang--2016|Yang et al., 2016]] ). SAI will increase the fraction of diffuse sunlight, which is projected to increase photosynthesis in forested canopy, but will reduce the direct and total available sunlight, which tends to reduce photosynthesis. As total sunlight is reduced, there is a net reduction in crop photosynthesis with the result that any benefits to crops from avoided heat stress may be offset by reduced photosynthesis, as indicated by a single statistical modelling study ( [[#Proctor--2018|Proctor et al., 2018]] ). SAI would reduce average surface ozone concentration ( [[#Xia--2017|Xia et al., 2017]] ) mainly as a result of aerosol-induced reduction in stratospheric ozone in polar regions, resulting in reduced downward transport of ozone to the troposphere ( [[#Pitari--2014|Pitari et al., 2014]] ; [[#Tilmes--2018|Tilmes et al., 2018]] ). The reduction in stratospheric ozone also allows more UV radiation to reach the surface. The reduction in surface ozone, together with an increase in surface UV radiation, would have important implications for crop yields but there is ''low confidence'' in our understanding of the net impact.

Few studies have assessed potential SRM impacts on human health and well-being. SAI using sulphate aerosols is projected to deplete the ozone layer, increasing mortality from skin cancer, and SAI could increase particulate matter due to offsetting warming, reduced precipitation and deposition of SAI aerosols, which would increase mortality, but SAI also reduces surface-level ozone exposure, which would reduce mortality from air pollution, with net changes in mortality uncertain and depending on aerosol type and deployment scenario ( [[#Effiong--2016|Effiong and Neitzel, 2016]] ; [[#Eastham--2018|Eastham et al., 2018]] ; [[#Dai--2020|Dai et al., 2020]] ). However, these effects may be small compared with changes in risk from infectious disease (e.g., mosquito-borne illnesses) or food security due to SRM influences on climate ( [[#Carlson--2020|Carlson et al., 2020]] ). Using volcanic eruptions as a natural analogue, a sudden implementation of SAI that forced the El Niño-Southern Oscillation (ENSO) system may increase risk of severe cholera outbreaks in Bengal ( [[#Trisos--2018|Trisos et al., 2018]] ; [[#Pinke--2019|Pinke et al., 2019]] ). Considering only mean annual temperature and precipitation, SAI that stabilises global temperature at its present-day level is projected to reduce income inequality between countries compared with the highest warming pathway (RCP8.5) ( [[#Harding--2020|Harding et al., 2020]] ). Some integrated assessment model scenarios have included SAI ( [[#Arino--2016|Arino et al., 2016]] ; [[#Emmerling--2018|Emmerling and Tavoni, 2018]] ; [[#Heutel--2018|Heutel et al., 2018]] ; [[#Helwegen--2019|Helwegen et al., 2019]] ; [[#Rickels--2020|Rickels et al., 2020]] ) showing the indirect costs and benefits to welfare dominate, since the direct economic cost of SAI itself is expected to be relatively low ( [[#Moriyama--2017|Moriyama et al., 2017]] ; [[#Smith--2018|Smith and Wagner, 2018]] ). There is a general lack of research on the wide scope of potential risk or risk reduction to human health, well-being and sustainable development from SRM and on their distribution across countries and vulnerable groups ( [[#Carlson--2020|Carlson et al., 2020]] ; [[#Honegger--2021|Honegger et al., 2021]] ).

SRM may also introduce novel risks for international collaboration and peace. Conflicting temperature preferences between countries may lead to counter-geoengineering measures such as deliberate release of warming agents or destruction of deployment equipment ( [[#Parker--2018|Parker et al., 2018]] ). Game-theoretic models and laboratory experiments indicate that a powerful actor or group with a higher preference for SRM may use SAI to cool the planet beyond what is socially optimal, imposing welfare losses on others, although this cooling does not necessarily imply that excluded countries would be worse off relative to a world of unmitigated warming ( [[#Ricke--2013|Ricke et al., 2013]] ; [[#Weitzman--2015|Weitzman, 2015]] ; [[#Abatayo--2020|Abatayo et al., 2020]] ). In this context, counter-geoengineering may promote international cooperation or lead to large welfare losses ( [[#Heyen--2019|Heyen et al., 2019]] ; [[#Abatayo--2020|Abatayo et al., 2020]] ).

Cooling caused by SRM would increase the global land and ocean CO 2 sinks ( ''medium confidence'' ), but this would not stop CO 2 from increasing in the atmosphere or affect the resulting ocean acidification under continued anthropogenic emissions ( ''high confidence'' ) (WGI Chapter 5).

Few studies have assessed potential SRM impacts on ecosystems. SAI and MCB may reduce risk of coral reef bleaching compared with global warming with no SAI ( [[#Latham--2013|Latham et al., 2013]] ; [[#Kwiatkowski--2015|Kwiatkowski et al., 2015]] ), but risks to marine life from ocean acidification would remain, because SRM proposals do not reduce elevated levels of anthropogenic atmospheric CO 2 concentrations. MCB could cause changes in marine net primary productivity by reducing light availability in deployment regions, with important fishing regions off the west coast of South America showing both large increases and decreases in productivity ( [[#Partanen--2016|Partanen et al., 2016]] ; [[#Keller--2018|Keller, 2018]] ).

There is large uncertainty in terrestrial ecosystem responses to SRM. By decoupling increases in atmospheric greenhouse gas concentrations and temperature, SAI could generate substantial impacts on large-scale biogeochemical cycles, with feedbacks to regional and global climate variability and change ( [[#Zarnetske--2021|Zarnetske et al., 2021]] ). Compared with a high-CO 2 world without SRM, global-scale SRM simulations indicate reducing heat stress in low latitudes would increase plant productivity, but cooling would also slow down the process of nitrogen mineralisation, which could decrease plant productivity ( [[#Glienke--2015|Glienke et al., 2015]] ; [[#Duan--2020|Duan et al., 2020]] ). In high-latitude and polar regions, SRM may limit vegetation growth compared with a high-CO 2 world without SRM, but net primary productivity may still be higher than pre-industrial climate ( [[#Glienke--2015|Glienke et al., 2015]] ). Tropical forests cycle more carbon and water than other terrestrial biomes, but large areas of the tropics may tip between savanna and tropical forest depending on rainfall and fire ( [[#Beer--2010|Beer et al., 2010]] ; [[#Staver--2011|Staver et al., 2011]] ). Thus, SAI-induced reductions in precipitation in Amazonia and central Africa are expected to change the biogeography of tropical ecosystems in ways different from both present-day climate and global warming without SAI ( [[#Simpson--2019|Simpson et al., 2019]] ; [[#Zarnetske--2021|Zarnetske et al., 2021]] ). This would have potentially large consequences for ecosystem services ( [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] and Chapter 9). When designing and evaluating SAI scenarios, biome-specific responses need to be considered if SAI approaches are to benefit rather than harm ecosystems. Regional precipitation change and sea salt deposition over land from MCB may increase or decrease primary productivity in tropical rainforests ( [[#Muri--2015|Muri et al., 2015]] ). SRM that fully offsets warming could reduce the dispersal velocity required for species to track shifting temperature niches, whereas partially offsetting warming with SAI would not reduce this risk unless rates of warming were also reduced ( [[#Trisos--2018|Trisos et al., 2018]] ; [[#Dagon--2019|Dagon and Schrag, 2019]] ). SAI may reduce high-fire-risk weather in Australia, Europe and parts of the Americas, compared with global warming without SAI ( [[#Burton--2018|Burton et al., 2018]] ). Yet SAI using sulphur injection could shift the spatial distribution of acid-induced aluminium soil toxicity into relatively undisturbed ecosystems in Europe and North America ( [[#Visioni--2020|Visioni et al., 2020]] ). For the same amount of global mean cooling, SAI, MCB and CCT would have different effects on gross and net primary productivity because of different spatial patterns of temperature, available sunlight, and hydrological cycle changes ( [[#Duan--2020|Duan et al., 2020]] ). Large-scale modification of land surfaces for GBAM may have strong trade-offs with biodiversity and other ecosystem services, including food security ( [[#Seneviratne--2018a|Seneviratne et al., 2018a]] ). Although existing studies indicate SRM will have widespread impacts on ecosystems, risks and potential for risk reduction for marine and terrestrial ecosystems and biodiversity remain largely unknown.

A sudden and sustained termination of SRM in a high CO 2 emissions scenario would cause rapid climate change ( ''high confidence'' ; WGI Chapter 4). More scenario analysis is needed on the potential likelihood of sudden termination ( [[#Kosugi--2013|Kosugi, 2013]] ; [[#Irvine--2020|Irvine and Keith, 2020]] ). A gradual phase-out of SRM combined with emission reduction and CDR could avoid these termination effects ( ''medium confidence'' ) ( [[#MacMartin--2014|MacMartin et al., 2014]] ; [[#Keith--2015|Keith and MacMartin, 2015]] ; [[#Tilmes--2016|Tilmes et al., 2016]] ). Several studies find that large and extremely rapid warming and abrupt changes to the water cycle would occur within a decade if a sudden termination of SAI occurred ( [[#McCusker--2014|McCusker et al., 2014]] ; [[#Crook--2015b|Crook et al., 2015b]] ). The size of this ‘termination shock’ is proportional to the amount of radiative forcing being masked by SAI. A sudden termination of SAI could place many thousands of species at risk of extinction, because the resulting rapid warming would be too fast for species to track the changing climate ( [[#Trisos--2018|Trisos et al., 2018]] ).

'''Public perceptions of SRM'''
Studies on the public perception of SRM have used multiple methods: questionnaire surveys, workshops, and focus group interviews ( [[#Burns--2016|Burns et al., 2016]] ; [[#Cummings--2017|Cummings et al., 2017]] ). Most studies have been limited to Western societies, with some exceptions. Studies have repeatedly found that respondents are largely unaware of SRM ( [[#Merk--2015|Merk et al., 2015]] ). In the context of this general lack of familiarity, the public prefers CDR to SRM ( [[#Pidgeon--2012|Pidgeon et al., 2012]] ), is very cautious about SRM deployment because of potential environmental side effects and governance concerns, and mostly rejects deployment for the foreseeable future. Studies also suggest conditional and reluctant support for research, including proposed field experiments, with conditions of proper governance ( [[#Sugiyama--2020|Sugiyama et al., 2020]] ). Recent studies show that the perception varies with the intensity of deliberation ( [[#Merk--2019|Merk et al., 2019]] ), and that the public distinguishes different funding sources ( [[#Nelson--2021|Nelson et al., 2021]] ). Limited studies for developing countries show a tendency for respondents to be more open to SRM ( [[#Visschers--2017|Visschers et al., 2017]] ; [[#Sugiyama--2020|Sugiyama et al., 2020]] ), perhaps because they experience climate change more directly ( [[#Carr--2018|Carr and Yung, 2018]] ). In some Anglophone countries, a small portion of the public believes in chemtrail conspiracy theories, which are easily found in social media ( [[#Tingley--2017|Tingley and Wagner, 2017]] ; [[#Allgaier--2019|Allgaier, 2019]] ). Since researchers rarely distinguish different SRM options in engagement studies, there remains uncertainty in public perception.

'''Ethics'''
There is broad literature on ethical considerations around SRM, mainly stemming from philosophy or political theory, and mainly focused on SAI ( [[#Flegal--2019|Flegal et al., 2019]] ). There is concern that publicly debating, researching and potentially deploying SAI could involve a ‘moral hazard’, with potential to obstruct ongoing and future mitigation efforts ( [[#Morrow--2014|Morrow, 2014]] ; [[#Baatz--2016|Baatz, 2016]] ; [[#McLaren--2016|McLaren, 2016]] ), while empirical evidence is limited and mostly at the individual, not societal, level ( [[#Burns--2016|Burns et al., 2016]] ; [[#Merk--2016|Merk et al., 2016]] ; [[#Merk--2019|Merk et al., 2019]] ). There is ''low agreement'' whether research and outdoors experimentation will create a ‘slippery slope’ towards eventual deployment, leading to a lock-in to long-term SRM, or can be effectively regulated at a later stage to avoid undesirable outcomes ( [[#Hulme--2014|Hulme, 2014]] ; [[#Parker--2014|Parker, 2014]] ; [[#Callies--2019|Callies, 2019]] ; [[#McKinnon--2019|McKinnon, 2019]] ). Regarding potential deployment of SRM, procedural, distributive and recognitional conceptions of justice are being explored ( [[#Svoboda--2014|Svoboda and Irvine, 2014]] ; [[#Svoboda--2017|Svoboda, 2017]] ; [[#Preston--2018|Preston and Carr, 2018]] ; [[#Hourdequin--2019|Hourdequin, 2019]] ). With the SRM research community’s increasing focus on distributional impacts of SAI, researchers have started more explicitly considering inequality in participation and inclusion of vulnerable countries and marginalised social groups ( [[#Flegal--2018|Flegal and Gupta, 2018]] ; [[#Whyte--2018|Whyte, 2018]] ; [[#Táíwò--2021|Táíwò and Talati, 2021]] ), including considering stopping research ( [[#Stephens--2020|Stephens and Surprise, 2020]] ; National Academies of Sciences and Medicine, 2021a). There is recognition that SRM research has been conducted predominantly by a relatively small number of experts in the Global North, and that more can be done to enable participation from diverse peoples and geographies in setting research agendas and research governance priorities, and undertaking research, with initial efforts to this effect (e.g., [[#Rahman--2018|Rahman et al., 2018]] ), noting unequal power relations in participation could influence SRM research governance and potential implications for policy ( [[#Whyte--2018|Whyte, 2018]] ; [[#Táíwò--2021|Táíwò and Talati, 2021]] ; [[#Winickoff--2015|Winickoff et al., 2015]] ; [[#Frumhoff--2018|Frumhoff and Stephens, 2018]] ; [[#Biermann--2019|Biermann and Möller, 2019]] ; [[#McLaren--2021|McLaren and Corry, 2021]] ; National Academies of Sciences and Medicine, 2021b)

'''Governance of research and of deployment'''
Currently, there is no dedicated, formal international SRM governance for research, development, demonstration or deployment (see WGIII Chapter 14). Some multilateral agreements—such as the UN Convention on Biological Diversity or the Vienna Convention on the Protection of the Ozone Layer—indirectly and partially cover SRM, but none is comprehensive, and the lack of robust and formal SRM governance poses risks ( [[#Ricke--2013|Ricke et al., 2013]] ; [[#Talberg--2018a|Talberg et al., 2018a]] ; [[#Reynolds--2019a|Reynolds, 2019a]] ). While governance objectives range broadly, from prohibition to enabling research and potentially deployment ( [[#Sugiyama--2018b|Sugiyama et al., 2018b]] ; [[#Gupta--2020|Gupta et al., 2020]] ), there is agreement that SRM governance should cover all interacting stages of research through to any potential, eventual deployment with rules, institutions and norms ( [[#Reynolds--2019b|Reynolds, 2019b]] ). Accordingly, governance arrangements are co-evolving with respective SRM technologies across the interacting stages of research, development, demonstration and—potentially—deployment ( [[#Rayner--2013|Rayner et al., 2013]] ; [[#Parker--2014|Parker, 2014]] ; [[#Parson--2014|Parson, 2014]] ). Stakeholders are developing governance already in outdoors research, for example for MCB and OAC experiments on the Great Barrier Reef ( [[#McDonald--2019|McDonald et al., 2019]] ). Co-evolution of governance and SRM research provides a chance for responsibly developing SRM technologies with broader public participation and political legitimacy, guarding against potential risks and harms relevant across a full range of scenarios, and ensuring that SRM is considered only as a part of a broader portfolio of responses to climate change ( [[#Stilgoe--2015|Stilgoe, 2015]] ; [[#Nicholson--2018|Nicholson et al., 2018]] ). For SAI, large-scale outdoor experiments even with low radiative forcing could be transboundary, and those with deployment-scale radiative forcing may not be distinguished from deployment, such that [[#MacMartin--2019|MacMartin and Kravitz (2019)]] argue for continued reliance on modelling until a decision on whether and how to deploy is made, with modelling helping governance development. For further discussion of SRM governance, see Chapter 14, WGIII.

<div id="16.6" class="h1-container"></div>

<span id="reasons-for-concern-across-scales-1"></span>