Editing IPCC:AR6/SROCC/Chapter-6 (section)

== 6.8 Compound Events and Cascading Impacts ==

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=== 6.8.1 Concepts ===

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Compound events refer to events that are characterised by multiple failures that can amplify overall risk and/or cause cascading impacts (Helbing, 2013; Gallina et al. 2016; Figure 6.1). These impacts may be triggered by multiple hazards that occur coincidently or sequentially and can lead to substantial disruption of natural or human systems (Leonard et al., 2014; Oppenheimer et al., 2014; Gallina et al., 2016; Zscheischler et al., 2018). These concepts are illustrated in a series of recent case studies that show how compound events interact with multiple elements of the ecosystem and society to create compound risk and cascading impacts (Box 6.1).

Compound events and cascading impacts are examples of deep uncertainty because data deficiency often prevents the assessment of probabilities and consequences of the risks from compound events. Furthermore, climate drivers that contribute to compound events could cross tipping points in the future (e.g., Cai et al., 2016; Cross-Chapter Box 4 in Chapter 1). Concepts and methods for addressing compound events and cascading impacts have a solid foundation in disaster risk reduction frameworks (Scolobig, 2017) where they may be assessed with scenarios, risk mapping, and participatory governance (Marzocchi et al., 2012; Komendantova et al., 2014). However, these approaches have tended to not consider the effects of climate change, rather considering hazards and vulnerability as stationary entities (Gallina et al., 2016). Trends in geophysical and meteorological extreme events and their interaction with more complex social, economic and environmental vulnerabilities overwhelm existing governance and institutional capacities (Shimizu and Clark, 2015) because of the aggregated cascading impacts.

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=== 6.8.2 Multiple Hazards ===

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Understanding regions where changes in the climate system could increase the likelihood or severity of multiple hazards is relevant to understanding compound events (Figure 6.1). Several recent studies have highlighted coastal regions that are becoming more susceptible to multiple hazards from changes in regional climate. Warming and poleward expansion of the warm western boundary current regions (WBCs; Yang et al., 2016a) together with intensified cyclogenesis in these WBC regions; the Gulf Stream (Booth et al., 2012), the Kuroshio (Hirata et al., 2016) and the East Australian Current (EAC; Pepler et al., 2016a) can increase the likelihood of multiple hazards. These include increased rates of SLR (Brunnabend et al., 2017; Zhang et al., 2017b) together with increases in severe rainfall, storm surges and associated flooding (Thompson et al., 2013; Oey and Chou, 2016; Pepler et al., 2016a). WBCs have undergone an intensification and poleward expansion in all but the Gulf Stream where the weakening of the AMOC cancelled this effect (Seager and Simpson, 2016; Yang et al., 2016a).

Acknowledging the dual role of regional SLR and TCs frequency and intensity changes for future flood risk, Little et al. (2015) developed a flood index that takes account of local projected SLR along with TC frequency and intensity changes in a CMIP5 multi-model ensemble. They find that relative to 1986–2005, the Flood Index is 4–75 times higher by 2080–2099 for RCP2.6 (10–90th percentile range) and 35–350 times higher for RCP8.5. In the vicinity of the East Australian Current, Pepler et al. (2016b) found warmer SSTs boost the intensification of weak to moderate ETC’s. Neglecting the compounding effects of flood and extreme sea level drivers can cause significant underestimation of flood risk and projected failure probability (Wahl et al., 2016; Moftakhari et al., 2017).

Over the last decade, several efforts have been made to address long-term shoreline change driven by the cascading impact of SLR, waves and MSL. Ranasinghe et al. (2012) presented the Probabilistic Coastline Recession model, which provides probabilistic estimates of coastline recession in response to both storms and SLR in the 21st century. Dune recession is estimated for each storm considering the recovery between storms, which is obtained empirically. More recently, Toimil et al. (2017) developed a methodology to address shoreline change over this century due to the action of waves, storm surges, astronomical tides in combination with SLR. The methodology considers the generation of thousands of multi-variate hourly time series of waves and storm surges to reconstruct future shoreline evolution probabilistically, which enables estimates of extreme recessions and long-term coastline change to be obtained. The model proposed by Vitousek et al. (2017) integrates longshore and cross-shore transport induced by GCM-projected waves and SLR, which allows it to be applied to both long and pocket sandy beaches. The analysis provides only one instance of what coastline change over the 21st century may be.

To summarise, new studies highlight regions such as coasts including those adjacent to WBCs, that are experiencing larger changes to multiple phenomena simultaneously such as SLR and cyclone intensity linked to higher SST increases ( ''medium confidence'' ), which increases the likelihood of extremes from multiple hazards occurring ( ''medium confidence'' ). Failing to account for the multiple factors responsible for extreme events will lead to an underestimation of the probabilities of occurrence ( ''high confidence'' ).

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=== 6.8.3 Cascading Impacts on Ecosystems ===

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Damage and loss of ecosystems (mangrove, coral reefs, polar deserts, wetlands and salt marshes); or regime shifts in ecosystem communities lead to reduced resilience of all the ecosystems and possible flow-on effects to human systems. For example, recent studies showed that living corals and reef structures have experienced significant losses from human-related drivers such as coastal development; sand and coral mining; overfishing, acidification, and climate-related storms and bleaching events (Smith, 2011; Nielsen et al., 2012; Hilmi et al., 2013; Graham et al., 2015; Lenoir and Svenning, 2015; Hughes et al., 2017b). As a consequence, reef flattening is taking place globally due the loss of corals and from the bio-erosion and dissolution of the underlying reef carbonate structures (Alvarez-Filip et al., 2009). Reef mortality and flattening due to non-climate and climate-related drivers trigger cascading impacts and risks due to the loss of the protection services provided to coastal areas. High emission scenarios are expected to lead to almost the complete loss of coral cover by 2100, although policies aiming to lower the combined aerosol-radiation interaction and aerosol-cloud interaction (e.g., IPCC RCP 6.0) may partially limit the impacts on coral reefs and the associated habitat loss, thereby preserving an estimated 14 to 20 billion USD in consumer surplus 2100 (2014 USD, 3% discount; Speers et al., 2016). Moreover, projected SLRwill increase flooding risks, and these risks will be even greater if reefs that now help protect coasts from waves are lost due to bleaching-induced mortality.

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=== 6.8.4 Cascading Impacts on Social Systems ===

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Impacts of compound events also have significant multi-effects in the societal system. Cascading impacts are particularly driven by the loss or (temporary) disruption of critical infrastructure (Pescaroli and Alexander, 2018), such as communications, transport, and power supply, on housing, dams and flood protection; as well as health provision. Repeated extreme and compound events are leading to critical transitions in social systems (Kopp et al., 2016) which may cause the disruption of (local) communities, creating cascading impacts consisting of short-term impacts as well as long-lasting economic effects, and in some cases migration. When the responses of the economic sector to short term weather variations are applied to long term-climate projections, risks associated with climate change on different sectors are projected to result in an average 1.2% of decrease of US GDP per degree Celsius of warming. Furthermore, broad geographical discrepancies generate a large transfer of value northwards and westwards with the expected consequence of increased economic inequality (Hsiang et al., 2017). The severity and intensity of the cascading impacts also depend on the affected societies’ vulnerability and resilience. For example, the intensity and influence of compound events are dependent on the size and scale of the affected society and the percentage of economy or GDP impacted (Handmer et al., 2012 in IPCC SREX). Smaller countries and especially small islands face the challenge of being unable to ‘hedge’ the risk through geographical redistribution (see Cross-Chapter Box 9).

Impacts from the natural system can descend into a cascade of disasters. For example, in 2005, Hurricane Katrina led to heavy flooding in the coastal area, dike breaches, emergency response failures, chaos in evacuation (traffic jams) and social disruption. Flooding in Thailand in 2011 led to the closure of many factories which not only impacted on the country’s economy but impaired the global automobile and electronic industry (Kreibich et al., 2014). Female-owned establishments are more challenged with failures than businesses owned by men due to less experience, shorter duration and smaller size of businesses (Haynes et al., 2011; Marshall et al., 2015). The impact of compound events on ecosystems can also, in the long run, have devastating impacts on societal systems, for example, impacts from tropical storms can lead to coral degradation, which leads to increased wave impact and subsequent accelerated coastal erosion and impacts on fishing resources. This subsequently can have an impact on local economies, potentially leading to social disruption and migration (Saha, 2017). Impacts on marine ecosystems and habitats will also affect subsistence and commercial fisheries and, as a result, food security (Barrow et al., 2018). Climate-induced community relocations in Alaska stem from repeated extreme weather events coupled with climate change-induced coastal erosion and these impact the habitability of the whole community (Bronen, 2011; Durrer and Adams, 2011; Marino, 2011; Marino, 2012; Bronen and Chapin, 2013; see also Cross-Chapter Boxes 2 and 5 in Chapter 1).

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=== 6.8.5 Risk Management and Adaptation, Sustainable and Resilient Pathways ===

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The management of compound events and cascading impacts in the context of governance poses challenges, partly because it is place dependent and heavily influenced by local parameters such as hazard experience and cultural values. Moreover, in some cases, people perceive that their community or country is less affected than others, leading to a ‘spatial optimism bias’ that delays or reduces the scope of actions (Nunn et al., 2016). In other cases it is unclear who will take responsibility when compound events and cascading impacts occur (Scolobig, 2017), although for some compound risks (e.g.,. na-tech disasters – when natural hazards trigger technological disasters), the private sector cooperates with governments to manage and respond to risks (Krausmann et al., 2017). Considerable variations exist among and inside countries. The level of engagement depends on the process of cascading impacts and the role of governance arrangement at the country level (Lawrence et al., 2018) countries’ capacity to develop integrated risk and disaster frameworks and regulations, viable multi-stakeholder and public-private partnership in the case of multiple technological and natural hazards (Gerkensmeier and Ratter, 2018), the initiatives of local governments to exercise compound risk operations, and experience in interagency cooperation (Scolobig, 2017). The importance of local knowledge and traditional practices in disaster risk prevention and reduction is widely recognised (Hiwasaki et al., 2014; Hilhorst et al., 2015; Audefroy and Sánchez, 2017) ( ''high confidence).'' The need to strengthen DRM is evident and can be improved and communicated effectively by integrating local knowledge such as Inuit’s indigenous knowledge and local knowledge in Alaska (Pearce et al., 2015; Cross-Chapter Box 3 in Chapter 1) since it is easier for communities to accept than pure science-based DRM (Ikeda et al., 2016).

Despite difficulties of governance and decision making, many researchers and policy makers have recognised the need to study combined climatic and other hazards and their impacts. Several methods are now being employed to assess climatic hazards and compound events simultaneously, and also in combination (Klerk et al., 2015; van den Hurk et al., 2015; Wahl et al., 2015; Zscheischler and Seneviratne, 2017; Wu et al., 2018; Zscheischler et al., 2018). Policy makers can also begin to plan for disaster risk reduction and adaptation, based on these analyses of compound events and risks. Addressing limitations in understanding the compound hazards, as well as adequate mechanisms of the cascading impacts is needed. Finally, there are limits to resources to study these complex interactions in sufficient detail, as well as limits to data and information on past events that would allow the simulation of these effects, including economic impacts.

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=== 6.8.6 Global Impact of Tipping Points ===

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A small number of studies (Lontzek et al., 2015; Cai et al., 2016; Lemoine and Traeger, 2016) use different versions of the Dynamic Integrated Climate-Economy assessment model (Nordhaus, 1992; Nordhaus, 2017) to assess the impact of diverse sets of tipping points and causal interactions between them on the socially optimal reduction of gas emissions and the present social cost of carbon, representing the economic cost caused by an additional ton of CO 2 emissions or its equivalent.

Cai et al. (2016) consider five interacting, stochastic, potential climate tipping points: reorganisation of the AMOC; disintegration of the GIS; collapse of the WAIS; dieback of the Amazon Rain Forest; and shift to a more persistent El Niño regime. The deep uncertainties associated with the likelihood of each of these tipping points and the dependence of them on the state of the others is addressed through expert elicitation. There ''is limited evidence'' , but ''high agreement'' that present costs of carbon are clearly underestimated. Double (Lemoine and Traeger, 2016), triple (Ceronsky et al., 2011), to eightfold (Cai et al., 2016) increase of the carbon price are suggested, depending on the working hypothesis. Cai et al. (2016) indicate that with the prospect of multiple interacting tipping points, the present social cost of carbon increases from 15–116 USD per tonne of CO 2 , and conclude that stringent efforts are needed to reduce CO 2 emission if these impacts are to be avoided.

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