Climate risk assessment needs urgent improvement

Arribas, Alberto; Fairgrieve, Ross; Dhu, Trevor; Bell, Juliet; Cornforth, Rosalind; Gooley, Geoff J.; Hilson, Chris; Luers, Amy; Shepherd, Theodore G.; Street, Roger; Wood, Nicholas

doi:10.1038/s41467-022-31979-w

Cited by 18 publications

(13 citation statements)

References 14 publications

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“…This may be especially true in cases using AI/ML, where the phenomena introduced to control and categorize big data must be managed in such a way that local users' interests, values, and rights are respected and maintained in the information processing practices (Leonelli 2016, Lloyd et al 2022. Note that AI/ML methods need not be incompatible with these local approaches, so long as they are not 'black boxes' (Arribas et al 2022). Finally, RQ3 opens the door to further research in a much more effective way than does RQ2.…”

Section: Discussionmentioning

confidence: 99%

Foundations of attribution in climate-change science

Lloyd,

Shepherd

2023

Environ. Res.: Climate

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Attribution — the explanation of an observed change in terms of multiple causal factors — is the cornerstone of climate-change science. For anthropogenic climate change (ACC), the central causal factor is evidently ACC itself, and one of the primary tools used to reveal ACC is aggregation, or grouping together, of data, e.g. global mean surface temperature. Whilst this approach has served climate-change science well, the landscape is changing rapidly. First, there is an increasing focus on regional or local aspects of climate change, and on singular or unprecedented events, which require varying degrees of disaggregation. Relatedly, climate change is increasingly apparent in observations at the local scale, which is challenging the primacy of climate model simulations. Finally, the explosion of climate data is leading to more phenomena-laden methodologies such as machine learning. All this demands a re-think of how attribution is performed and causal explanations are constructed. Here we use Lloyd’s ‘Logic of Research Questions’ framework to show how the way in which the attribution question is framed can strongly constrain its possible and responsive answers. To address the Research Question “What was the effect of ACC on X?” (RQ1), scientists generally consider the question “What were the causal factors leading to X, and was ACC among them?”. If the causal factors include only external forcing and internal variability (RQ2), then answering RQ2 also answers RQ1. However, this unconditional attribution is not always possible. In such cases, allowing the causal factors to include elements of the climate system itself (RQ3) — the conditional, storyline approach — is shown to allow for a wider range of possible and responsive answers than RQ2, including that of singular causation. This flexibility is important when uncertainties are high. As a result, the conditional RQ3 mitigates against the sort of epistemic injustice that can arise from the unconditional RQ2.

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Section: Discussionmentioning

confidence: 99%

Foundations of attribution in climate-change science

Lloyd,

Shepherd

2023

Environ. Res.: Climate

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“…A practical climate risk assessment requires information on climate hazards at multiple spatial and temporal scales, exposure, vulnerability, their associates, and uncertainty to help communicate risk and prioritize investments (Arribas et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Methodological approaches to assess climate vulnerability and cumulative impacts on coastal landscapes

Olivares-Aguilar

Sánchez-Dávila²,

Wildermann³

et al. 2022

Front. Clim.

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Natural and human-induced hazards and climatic risks threaten marine and coastal ecosystems worldwide, with severe consequences for these socio-ecological systems. Therefore, assessing climate vulnerability (exposure, sensitivity and adaptive capacity) and the cumulative environmental impacts of multiple hazards are essential in coastal planning and management. In this article, we review some approaches used in climate vulnerability assessment and marine and coastal cumulative environmental impacts to learn about state-of-the-art on the subject. Besides, we qualitatively evaluated the climatic vulnerability of five coastal regions of Venezuela using the IPCC concept of Reasons for Concern (RFCs) to determine their level of climatic exposure. We also assessed the cumulative environmental impact of multiple stressors on marine and terrestrial ecosystems using a well-known impact assessment method partially modified to explore the feasibility of this model in data-poor areas. However, we found no standardization of the methodologies used in evaluating Coastal Climate Vulnerability or Cumulative Environmental Impacts in coastal landscapes or frameworks that operationally link them with socio-ecological systems. Most studied coastal regions are at risk from at least three RFCs, loss of unique ecosystems (RFC1), risks associated with extreme events (RFC2) and risks associated with global aggregate impacts (RFC4). Furthermore, the assessment showed that areas with accumulated impact cover about 10 percent ranging from moderate to high in urban areas, growth zones, industrial oil settlements, port areas and aquaculture areas with fishing activity. Moreover, areas with moderate to low cumulative impact cover half of the study area, dominated by uninhabited regions and vegetation of the thorny scrub and coastal grassland types. Therefore, we consider it essential to implement regional climate risk management that incorporates these assessments into the ordinance in countries that are particularly vulnerable to climate change, such as Venezuela, which has an extensive line of low-lying coastlines (where 60% of the country's population lives) and coastal regions with harsh climates and poor economic conditions. Finally, we present the scope and limitations of implementing these evaluations and highlight the importance of incorporating them into regional strategies for adaptation to climate change.

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“…At this point in time, most DRM practice often fall short of encapsulating these multifaceted dynamics (Arribas et al, 2022;Hochrainer-Stigler et al, 2023;Poljansek et al, 2021;Ward et al, 2022). Schlumberger et al (2022) sought to bridge this gap by tailoring the Dynamic Adaptive Policy Pathways framework (DAPP) (DAPP, Haasnoot et al, 2013) for multi-risk systems.…”

Section: Introductionmentioning

confidence: 99%

“…At this point in time, most DRM practice often fall short of encapsulating these multifaceted dynamics (Arribas et al., 2022; Hochrainer‐Stigler et al., 2023; Poljansek et al., 2021; Ward et al., 2022). Schlumberger et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Adaptation Pathways in a Complex Multi‐Risk System

Schlumberger,

Haasnoot,

Aerts

et al. 2024

Earth's Future

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Disaster Risk Management (DRM) is complex due to interacting climate risks from interacting hazards and sectors. We develop a synthetic multi‐risk test case to explore the effects of these interactions on decision‐making under deep uncertainty. The test case accounts for changes in hazard impacts and occurrence frequency due to interactions between floods and droughts. Interactions between the shipping, housing and agricultural sectors are considered as well. We use this test case to explore the utility of the Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR) framework. DAPP‐MR has been introduced to assess DRM policies' effectiveness under deep uncertainties and to develop integrated adaptive strategies considering interactions across hazards, sectors and time. With the test case, we show that the stage‐wise approach of DAPP‐MR, which gradually increases the complexity of the analysis, can facilitate the evaluation process. Earlier stages of the analysis can be used to establish the direct cause‐effect relations, later stages allow us to identify whether additional interacting factors have a significant effect on the direct cause‐effect relations. As a result, decision‐makers can gain insights into dependencies and their relevance for developing short‐to long‐term strategies under deep uncertainty. We show that multi‐risk interactions can lead to non‐linear effects that influence the outcome of the policy analysis, for example, the indirect influence of a decision in one sector on another. Future work could investigate further improving the operationalization of this staged approach as well as extending the set of uncertainties, dynamics and decision‐rules accounted for in the multi‐risk test case.

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Climate risk assessment needs urgent improvement

Cited by 18 publications

References 14 publications

Foundations of attribution in climate-change science

Foundations of attribution in climate-change science

Methodological approaches to assess climate vulnerability and cumulative impacts on coastal landscapes

Evaluating Adaptation Pathways in a Complex Multi‐Risk System

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