Editing IPCC:AR6/WGII/Chapter-17 (section)

=== 17.3.2 Integration across Portfolios of Adaptation Responses ===

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In recent years, methods for simultaneously considering multiple societal and sectoral objectives, climate risks and adaptation options have been emerging, often termed ‘integrated’ approaches ( [[#Hadka--2015|Hadka et al., 2015]] ; [[#Garner--2016|Garner et al., 2016]] ; [[#Rosenzweig--2017|Rosenzweig et al., 2017]] ; [[#Giupponi--2017a|Giupponi and Gain, 2017a]] ; [[#Stelzenmuller--2018|Stelzenmuller et al., 2018]] ; [[#Marchau--2019|Marchau et al., 2019]] ). Different decision-making approaches can be complementary ( [[#Kwakkel--2016|Kwakkel et al., 2016]] ), and multiple approaches will be needed to manage risks across sectors, in space and over short to long time scales ( [[#17.6|Section 17.6]] ).

Higher-level integration was first presented in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) ( [[#Burton--2012|Burton et al., 2012]] ; [[#Lal--2012|Lal et al., 2012]] ; [[#O’Brien--2012|O’Brien et al., 2012]] ) and includes concepts of planning, coordination and mainstreaming ( [[#Lal--2012|Lal et al., 2012]] ), consideration of cross-scale dynamics and nested vulnerabilities ( [[#Klein--2014|Klein et al., 2014]] ), and decision-making across governments and sectors ( [[#Denton--2014|Denton et al., 2014]] ; [[#Mimura--2014|Mimura et al., 2014]] ).

Since AR5, recognition of the importance of using integrated adaptation to improve climate risk management across the nexus between many sectors and across regions has increased ( ''high confidence'' ) ( [[#Harrison--2016|Harrison et al., 2016]] ; [[#Challinor--2018|Challinor et al., 2018]] ). This was highlighted in the Special Report on Climate Change and Land ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ); advanced planning and integration of adaptation responses are needed over many levels ( ''medium confidence'' ) ( [[#Göpfert--2019|Göpfert et al., 2019]] ; [[#17.6|Section 17.6]] ; [[#Woodruff--2019|Woodruff and Regan, 2019]] ). The complexity of managing this nexus may be compounded by the potential for antagonistic or synergistic effects among and between climate impacts, and changes arising from local sectoral activities and independent adaptation responses to those risks ( ''high confidence'' ) ( [[#Crain--2008|Crain et al., 2008]] ; [[#Piggott--2015|Piggott et al., 2015]] ; [[#Adger--2018|Adger et al., 2018]] ; [[#Brown--2018|Brown et al., 2018]] ; [[#Stelzenmuller--2018|Stelzenmuller et al., 2018]] ; [[#Simpson--2021|Simpson et al., 2021]] ), such as the cross-sectoral demands for freshwater ( [[#Xue--2015|Xue et al., 2015]] ; [[#Azhoni--2018|Azhoni et al., 2018]] ). Integrated adaptation will also help facilitate management of new and emerging risks, help identify when response plans may need to be changed in light of the dynamics of risk over time, and help identify solutions that are less likely to constrain future options for adapting to future needs ( [[#Wise--2016|Wise et al., 2016]] ).

Implicit to managing cross-sectoral interactions, including the nexus concept, is that the interlinkages between multiple sectors are systemic, and therefore solutions to challenges arising from any one sector can only be satisfactorily addressed by considering the connections to other sectors at the same time ( [[#Wichelns--2017|Wichelns, 2017]] ). Challenges for integrated adaptation include: (1) to sufficiently capture the complexities between the nexus dimensions ( [[#Weitz--2017|Weitz et al., 2017]] ); (2) to adequately consider the time, costs and challenges of coordination and cooperation ( [[#Wichelns--2017|Wichelns, 2017]] ); (3) to consider the political economy in which progress towards more integrated solutions could take place, not only accounting for technological requirements ( [[#Leck--2015|Leck and Roberts, 2015]] ); (4) to obtain sufficient temporal or spatial data to capture the interactions between natural and social processes ( [[#Shannak--2018|Shannak et al., 2018]] ); (5) to connect these considerations to decision-making and policy processes in order to gain insights into the conditions for collaboration and coordination across sectors, including external dynamics and political and cognitive factors determining change ( [[#Weitz--2017|Weitz et al., 2017]] ); and (6) to develop a coherent framework against which to assess results and observations ( [[#Crain--2008|Crain et al., 2008]] ; [[#Wichelns--2017|Wichelns, 2017]] ).

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'''Cross Chapter Box DEEP | Effective adaptation and decision-making under deep uncertainties'''
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Authors: Carolina Adler (Switzerland/Chile/Australia), Robert Lempert (USA), Andrew Constable (Australia), Marjolijn Haasnoot (the Netherlands), Judy Lawrence (New Zealand), Katharine J. Mach (USA), Simon French (UK), Robert Kopp (USA), Camille Parmesan (USA), Mauricio Dominguez Aguilar (Mexico), Elisabeth A. Gilmore (USA), Rachel Bezner Kerr (Canada), Adugna Gemeda (Ethiopia), Cristina Tirado-von der Pahlen (USA/Spain), Debora Ley (Mexico), Rupa Mukerji (India).

'''Decision-relevant uncertainties for managing climate risk'''
Adaptation decision-making can benefit from assessments that support planning for both ‘what is most likely ’ as well as for stress-testing adaptation options over a range of scenarios (Sections 11.7 and 17.3; Cross-Chapter Box.5 in SROCC Chapter 1). This Cross-Chapter Box summarises how deep uncertainties ( [[IPCC:Wg2:Chapter:Chapter-1#1.2|Section 1.2]] ; [[#IPCC--2019a|IPCC, 2019a]] ) can be assessed in decision-making and addressed practically for adaptation.

The concept of deep uncertainty has evolved in IPCC assessments, expanding beyond a focus on reducing uncertainty, to also considering a range of tools and approaches that guide robust and timely decisions to address climate risks. Deep uncertainty is defined as circumstances where experts or stakeholders do not know or cannot agree on one or more of the following: (1) appropriate conceptual models that describe relationships among drivers in a system; (2) the probability distributions used to represent uncertainty about variables and parameters; and/or (3) how to weigh and value desirable alternative outcomes (Cross-Chapter Box 5 in Chapter 1; [[#Lempert--2003|Lempert et al., 2003]] ; [[#IPCC--2019a|IPCC, 2019a]] ; [[#IPCC--2019c|IPCC, 2019c]] ).

Decisions by individuals, households, the private sector, governments and public–private partnerships are generally made with partial or uncertain information. This is also the case for adaptation and development decisions where there is often deep uncertainty about the impacts and the societal conditions, preferences and priorities, and responses over time. Under such conditions, decision makers employ decision processes and scientific information differently from situations where most decision-relevant information is available, uncontested and confidently characterised with single joint probability distribution. Assuming scientific information is certain, when it is not, is a barrier to effective communication of risks and to successful decisions under uncertainty, increasing the potential for failure and regret of investments, lost opportunities and transfers of costs to future generations ( [[#Sarewitz--2000|Sarewitz and Byerly, 2000]] ; [[#Marchau--2019|Marchau et al., 2019]] ; Sections 11.7 and 17.6).

Addressing deep uncertainty is contextual as it depends on the decision options available, outcomes at stake and the available scientific information (Box 1.1. in [[#Marchau--2019|Marchau et al., 2019]] ). The IPCC uncertainty guidance note ( [[#Mastrandrea--2010|Mastrandrea et al., 2010]] ) addresses only the latter (see also [[#Mastrandrea--2011|Mastrandrea and Mach, 2011]] ; [[IPCC:Wg2:Chapter:Chapter-1#1.3.4|Section 1.3.4]] ). Deep uncertainty is generally more salient when policy-relevant statements have ''low confidence'' or lack relevant data or information, or in cases where significant uncertainty contributes to disagreements and disputes ( [[#Sriver--2018|Sriver et al., 2018]] ). Recent work has also included moral uncertainty ( [[#MacAskill--2020|MacAskill et al., 2020]] ) by evaluating the outcomes of alternative strategies with analyses organised around different perspectives on the appropriate principles of justice ( [[#Ciullo--2020|Ciullo et al., 2020]] ; [[#17.3|Section 17.3]] ; [[#Jafino--2021|Jafino et al., 2021]] ; [[#Lempert--2021|Lempert and Turner, 2021]] ).

To better communicate deep uncertainty, WGI AR6 complements projections of likely global mean sea level change, driven by processes in which there is at least ''medium confidence'' , with projections that incorporate ice-sheet processes in which there is ''low confidence'' ( [[IPCC:Wg2:Chapter:Chapter-9#9.6.3|Section 9.6.3]] in [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). The latter are accompanied by storylines to highlight the physical processes that would generate extreme outcomes (Box 9.4 in [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). These ''low confidence'' projections and storylines are useful because the likelihood of high-end (&gt;1.5 m) global mean sea level (GMSL) rise in the 21st century is difficult to determine but important to consider in coastal settings (e.g., Cross-Chapter Paper 2; Cross-Chapter Box SLR in Chapter 3). High-end GMSL rise by 2100 could be caused by earlier-than-projected disintegration of marine ice shelves, the abrupt, widespread onset of marine ice sheet instability and marine ice cliff instability around Antarctica, or faster-than-projected changes in the surface mass balance and dynamical ice loss from Greenland (Box TS.4 in [[#Arias--2021|Arias et al., 2021]] ; Box 9.4 in [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). In a low-likelihood, high-impact storyline and a high CO 2 emissions scenario, such processes could in combination contribute more than one additional metre of sea level rise by 2100 (Box TS.4 in [[#Arias--2021|Arias et al., 2021]] ; [[IPCC:Wg2:Chapter:Chapter-9#9.6.3|Section 9.6.3]] and Box 9.4 in [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ). Other hazards assessed in WGI AR6 that address similar aspects relevant for decision-making under deep uncertainty include drought ( [[IPCC:Wg2:Chapter:Chapter-8#8.4.1|Section 8.4.1.6]] in [[#Douville--2021|Douville et al., 2021]] ; [[IPCC:Wg2:Chapter:Chapter-11#11.6|Section 11.6.5]] in [[#Seneviratne--2021|Seneviratne et al., 2021]] ), flood ( [[IPCC:Wg2:Chapter:Chapter-8#8.4.1|Section 8.4.1.5]] in [[#Douville--2021|Douville et al., 2021]] ; [[IPCC:Wg2:Chapter:Chapter-11#11.5|Section 11.5.5]] in [[#Seneviratne--2021|Seneviratne et al., 2021]] ) and wildfire weather (days) (Section 11.8.3 and Box 11.2 in [[#Seneviratne--2021|Seneviratne et al., 2021]] ), among others.

'''Approaches and information requirements for managing deep uncertainty'''
Many approaches are available for evaluating robust decisions under conditions of deep uncertainty (Sections 17.3 and 11.7; Box 11.5 in Chapter 11). The majority use multiple scenarios to stress-test adaptation options and explore how alternative adaptation pathways might evolve under a range of different conditions (Swanson and Bhadwal, 2009). Approaches differ in terms of their focus, types of strategies best addressed, and data and other resources required ( [[#Marchau--2019|Marchau et al., 2019]] ).

‘Low regret’ options are one relatively simple and common approach to deep uncertainty (Sections 17.3 and 17.6) expected to perform well over a wide range of scenarios and represent one example of robust strategies. However, such options will generally be insufficient for adaptive responses to adapt over long time frames and to avoid lock-in of investments (Section 11.7; Box 11.5 in Chapter 11).

‘Adaptation pathways’ provide another approach for addressing deep uncertainty and staging decisions over time ( [[#Haasnoot--2013|Haasnoot et al., 2013]] ), by linking the choice of near-term adaptation actions with pre-determined future thresholds. Observation of such thresholds trigger subsequent actions in the planning or implementation stages of adaptation strategies. Adaptation pathways can begin with low-regret, near-term actions that aim to create and preserve future options to adjust if and when necessary. Alternative pathways can be explored and evaluated to design an adaptive plan with short-term actions and long-term options.

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Cross Chapter Box DEEP

Climate resilient development (CRD), and the pathways (CRDPs) to it, can also involve decision-making under deep uncertainty. Literature assessed in sectoral and regional chapters of this report present several examples of potential risks to achieving development goals under climate change, at global as well as national and local levels ( ''high confidence'' ) (Chapter 18). Achieving CRD depends on negotiation, contestation and reconciliation of trade-offs among diverse actors, who in turn value preferred outcomes differently with respect to associated climate risks and uncertainties, hence the prospect for deep uncertainty to manifest ( [[IPCC:Wg2:Chapter:Chapter-18#18.5|Section 18.5]] ). Deep uncertainty also characterises the development process itself, given that fundamental changes and disruptions are part of the transformational changes required to shift towards CRDPs.

The ‘keeping options open’ approach, i.e. plans that use a series of sequential decisions and actions in the near term to avoid closing off potentially promising future options ( [[#Rosenhead--2001|Rosenhead, 2001]] ; Section 2.6) or, by using real options, takes near-term actions that create currently unavailable options in the future ( [[#Kwakkel--2020|Kwakkel, 2020]] ). Deep uncertainty approaches use a wide range of storylines as scenarios to test low-regret options and to provide information relevant for potential thresholds for use in adaptation pathways ( [[#Haasnoot--2013|Haasnoot et al., 2013]] ; Boxes 11.4, 11.6; Sections 11.7, 17.3).

Deep uncertainty approaches enhance the value of monitoring to detect signals of change in a timely manner ( ''medium confidence'' ). Actionable warning can come from climate signals, and socioeconomic indicators/signposts, including drivers of change, vulnerability and impacts, best suited for timely, reliable and convincing signals for decision-making that anticipate future changes and the need for adaptation or the potential to seize opportunities ( [[#Hermans--2017|Hermans et al., 2017]] ; [[#Haasnoot--2018|Haasnoot et al., 2018]] ; [[#Stephens--2018|Stephens et al., 2018]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). For early warning signals to be decision relevant, they need to have institutional connectivity to enable action ( [[#Haasnoot--2018|Haasnoot et al., 2018]] ; Sections 1.4, 11.4, 11.7; Table 11.18) ( ''medium confidence'' ).

'''Examples and case studies from across the WGII report'''
There are diverse examples of the practical application of deep uncertainty methods across different climate change hazards in many regions of the world. For instance, low-regret options have been used to address the impacts and risks of landslides and debris flows in mountains (Section [https://www.ipcc.ch/chapter/17#CCP5.2.6 CCP5.2.6] ). Their frequency and magnitude are already widely experienced (Section [https://www.ipcc.ch/chapter/17#CCP5.2.6 CCP5.2.6] ) and projected to increase (Section [https://www.ipcc.ch/chapter/17#CCP5.3.2.1 CCP5.3.2.1] ). However, managing these associated risks also requires joint consideration of projected vulnerabilities and exposure of people and infrastructure, including the multiple and dynamic non-climate-related factors that are relevant for how the impacts manifest in context, such as population growth and land use planning ( [https://www.ipcc.ch/chapter/17#CCP5.2.6 CCP5.2.6] ). Here, context-specific deliberative processes are used that include scenarios to guide and specify preventive measures with higher effectiveness than protective (infrastructure) measures could achieve alone. Low-regret adaptation involves raising awareness and accounting for long planning horizons to address the uncertainties associated with such risks, for instance in mountain regions, including education (Sections [https://www.ipcc.ch/chapter/17#CCP5.4.1 CCP5.4.1] ; [https://www.ipcc.ch/chapter/17#CCP5.2.6 CCP5.2.6] ), with co-benefits such as addressing changes in water availability for supply and demand ( [https://www.ipcc.ch/chapter/17#CCP5.4.1 CCP5.4.1] ).

Adaptation pathways have been used to address SLR and changes in extreme rainfall through flood risk and management (Cross-Chapter Box SLR in Chapter 3; CCP2; Sections 13.2, 11.3 and 11.7): for example, adaptive plans in the Netherlands ( [[#Van%20Alphen--2016|Van Alphen, 2016]] ; [[#Bloemen--2019|Bloemen et al., 2019]] ), climate resilient development in Bangladesh ( [[#Hossain--2018|Hossain et al., 2018]] ; [[#Zevenbergen--2018|Zevenbergen et al., 2018]] ), adaptive spatial pathways for infrastructure retreat and for flood risk management in New Zealand ( [[#Lawrence--2019|Lawrence et al., 2019]] a; [[#Kool--2020|Kool et al., 2020]] ) and adaptive strategies such as in the cities of London ( [[#Ranger--2013|Ranger et al., 2013]] ; [[#Hall--2019|Hall et al., 2019]] ), New York ( [[#Rosenzweig--2014|Rosenzweig and Solecki, 2014]] ) and Los Angeles ( [[#Aerts--2018a|Aerts et al., 2018a]] ). This approach is mainstreamed into guidance documents such as the Climate Risk Informed Decision Analysis (CRIDA) ( [[#Mendoza--2018|Mendoza et al., 2018]] ), national guidance and policy briefs to address coastal hazards and sea level rise planning in New Zealand ( [[#Lawrence--2018|Lawrence et al., 2018]] ; [[#Lawrence--2019b|]] [[#Lawrence--2019|Lawrence et al., 2019]] b ), planning for sea level rise in California (OCP, 2018) and synthesis documents by the government of Canada on marine coasts ( [[#Lemmen--2016|Lemmen et al., 2016]] ). Furthermore, examples from the UK, New Zealand and the Netherlands point to the development of monitoring plans to detect signals for climate adaptation ( [[#Stephens--2017|Stephens et al., 2017]] ; [[#Haasnoot--2018|Haasnoot et al., 2018]] ; [[#Bloemen--2019|Bloemen et al., 2019]] ).

Climate-smart planning, with a focus on keeping options open, can play a role in reducing species extinction rates (Sections 2.5, 2.6). When and where and for whom particular irreversible impacts will occur is deeply uncertain, for example the extinction of a species. Even at the lowest emissions scenarios, some local species will become extinct, but estimates of extinction risk are highly uncertain, typically varying by factors of two to three even for one species ( [[IPCC:Wg2:Chapter:Chapter-2#2.5|Section 2.5]] ) ( ''medium confidence'' ). Risks of species’ extinctions are lowered by reducing emissions, but keeping options open for as long as possible and avoiding irreversible actions are key to developing a climate-resilient adaptive pathway so that real-time climate-driven changes can inform actions. Nature-based solutions (NBS) are emerging as key players for mitigation. With smart planning, NBS offer approaches that not only provide substantial mitigation, but also considerable adaptation benefit to biodiversity, and human health and well-being. Done poorly, such projects can result in large negative impacts on humans and nature. An NBS climate-sensitive decision framework leading to ‘win-win’ solutions for mitigation and adaptation is shown in Figure 1 Cross-Chapter Box NATURAL in [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] (see also Sections 2.4.2.5, 2.5, 2.6, 5.4.4.4 and 5.14.1; Cross-Chapter Box ILLNES in Chapter 2; Cross-Chapter Box COVID in Chapter 7).

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In view of these multiple and diverse examples, it is evident that the application of deep uncertainty methods is enabling decisions to be made in a timely manner that avoid foreseeable and undesirable outcomes and take opportunities as they arise ( ''high confidence'' ).

'''Prospects for adaptation decision-making'''
Deep uncertainty is increasingly salient for decision-making as recognition of climate-related risks and related uncertainties has increased ( ''high confidence'' ). These risks can compound and cascade to become new risks, increasing the breadth, frequency and severity of climate change impacts and the consequently increasing scale and scope of adaptation ( ''high confidence'' ) (Cross-Chapter Box Extremes in Chapter 2; Sections 1.3.1.2, 2.3, 2.5, 2.6, 11.5, 11.7, and [https://www.ipcc.ch/chapter/17#CCP5.3.1 CCP5.3.1] ). Waiting until uncertainties are resolved (if they ever can) may leave little or no time to adapt. The lead time for planning and implementation of adaptation can take decades ( [[#Haasnoot--2020b|Haasnoot et al., 2020b]] ; Cross-Chapter Box SLR in Chapter 3), and socioeconomic developments can lock in undesirable pathways where underlying vulnerabilities and exposure, such as poverty, conflict and their associated displacement of people, remain unaddressed (Sections 5.13.4, 16.5.2.3.8; Cross-Chapter Box Migrate in Chapter 7).

Overall, there is growing evidence that effective implementation of strategies developed for deeply uncertain problems require adequate mandates and funding frameworks, preparedness and disaster response plans, and monitoring and evaluation of the strategy outcomes, against how the future unfolds ( ''medium confidence'' ). Collaborative and adaptive governance arrangements, and education and awareness raising, promote learning environments for community engagement, and are essential for the effective implementation of robust adaptation plans ( ''medium confidence'' ) (Sections 5.14.1, 17.3 and 11.7).

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