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

=== 6.5.3 Risk Management and Adaptation ===

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Risk management of ENSO events has focussed on two main aspects: better prediction and early warning systems, and better mechanisms for reducing risks to agriculture, infrastructure, fisheries and aquaculture, wildfire and flood management. Extreme ENSO events are rare, with three such events since 1950 and they are difficult to predict due to the different drivers influencing them (Puy et al., 2017 <sup>[[#fn:r586|586]]</sup> ). The impacts of ENSO events also vary between events and between the different regions affected (Murphy et al., 2014 <sup>[[#fn:r587|587]]</sup> ; Fasullo et al., 2018 <sup>[[#fn:r588|588]]</sup> ; Power and Delage, 2018 <sup>[[#fn:r589|589]]</sup> ) however, there is limited literature on the change in the impacts of extreme ENSO compared to other ENSO events. In addition, there are also no specific risk management and adaptation strategies for human and natural systems for more extreme events other than what is in place for ENSO events (see also Chapter 4, Section 4.4 for the response to sea level change, an observed impact of ENSO). A first step in risk management and adaptation is thus to better understand the impacts these events have and to identify conditions that herald such extreme events that could be used to better predict extreme ENSO events.

Monitoring and forecasting are the most developed ways to manage extreme ENSOs. Several systems are already in place for monitoring and predicting seasonal climate variability and ENSO occurrence. However, the sustainability of the observing system is challenging and currently the Tropical Pacific Observing System 2020 (TPOS 2020) has the task of redesigning such a system, with ENSO prediction as one of its main objectives. These systems could be further elaborated to include extreme ENSO events. Westerly wind events in the Western Tropical Pacific, (Lengaigne et al., 2004 <sup>[[#fn:r590|590]]</sup> ; Chen et al., 2015a <sup>[[#fn:r591|591]]</sup> ; Fedorov et al., 2015 <sup>[[#fn:r592|592]]</sup> ) strong easterly wind events in the tropical Pacific (Hu and Fedorov, 2016 <sup>[[#fn:r593|593]]</sup> ; Puy et al., 2017 <sup>[[#fn:r594|594]]</sup> ), nonlinear interaction between air-sea fluxes and atmospheric deep convection (Bellenger et al., 2014 <sup>[[#fn:r595|595]]</sup> ; Takahashi and Dewitte, 2016 <sup>[[#fn:r596|596]]</sup> ) and advection of mean temperature by anomalous eastward zonal currents (Kim and Cai, 2014 <sup>[[#fn:r597|597]]</sup> ) are some of the factors that play an important role in the evolution of extreme ENSO events, which can be considered while improving the monitoring and forecasting system.

Despite the specificity of each extreme El Niño event, their forecasting is expected to improve through monitoring of recently identified precursory signals that peak in a window of two years before the event (Varotsos et al., 2016 <sup>[[#fn:r598|598]]</sup> ). An early warning system for coral bleaching associated, among other stressors, with extreme ENSO heat stress is provided by the NOAA Coral Reef Watch service with a 5 km resolution (Liu et al., 2018 <sup>[[#fn:r599|599]]</sup> ). The impacts of ENSO-associated extreme heat stress are heterogeneous, indicating the influence of other factors either biotic such as coral species composition, local adaptation by coral taxa reef depth or abiotic such as local upwelling or thermal anomalies (Claar et al., 2018 <sup>[[#fn:r600|600]]</sup> ). When identified and quantified, these factors can be used for risk analysis and risk management for these ecosystems.

In principle, it is easier to transfer the financial risk associated with extreme ENSO events through, for example, insurance products or other risk transfer instruments such as Catastrophe Bonds, than for more moderate events. An accurate prediction system is not required, but the measurement of these events, and quantification of likely impacts is required. As in other types of insurance systems, this can be done through, for example, calculations of average annual losses associated with extreme ENSO, and the design of appropriate financial instruments. Examples of research that can support the design of risk transfer instruments include Anderson et al. (2018) and Gelcer et al. (2018) for specific crops yields, and Aguilera et al. (2018) and Broad et al. (2002) for specific fisheries. Several risk transfer instruments have been implemented to deal with ENSO impacts, including parametric insurance based on SSTs for heavy rainfall damages, and another scheme for agricultural damages, both in Peru. Other examples include forecast-based financial aid (Red Cross Climate Centre, 2016). More broadly, other forms of risk management and governance can be designed with better information about the likely impacts of extreme ENSO events (e.g., Vignola et al., 2018).

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