Long-term sea-level rise necessitates a commitment to adaptation: A first order assessment

Haasnoot, Marjolijn; Winter, Gundula; Brown, Sally; Dawson, Richard; Ward, Philip J.; Eilander, Dirk

doi:10.1016/j.crm.2021.100355

Cited by 32 publications

(23 citation statements)

References 66 publications

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“…As a last example, Haasnoot et al. (2021) identify global adaptation needs for different SLR scenarios. Although this scenario‐conditional analysis is appropriate for understanding differences between models, its key limitation is that it places the burden for deciding which probabilistic scenario to design for onto the end user .…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, construct 18 probability distribution functions for future flood risk in New Orleans, considering multiple models for ice sheet dynamics and storm surge and multiple RCP scenarios. As a last example, Haasnoot et al (2021) identify global adaptation needs for different SLR scenarios. Although this scenario-conditional analysis is appropriate for understanding differences between models, its key limitation is that it places the burden for deciding which probabilistic scenario to design for onto the end user.…”

Section: Scenario-conditional Probabilistic Analysismentioning

confidence: 99%

“…Human‐natural systems are never closed and model results are never unique, and thus validation and verification of models representing these systems is necessarily qualitative and subjective (Oreskes et al., 1994). In other words, no model exists that could represent the full truth, and the future is therefore deeply uncertain (Haasnoot et al., 2021; Keller et al., 2021; Lempert, 2002; Walker et al., 2013). To address these challenges, a growing literature on decision making under deep uncertainty (DMDU) emphasizes the value of identifying decisions that are robust, in some sense, to deep uncertainties (Borgomeo et al., 2018; Herman et al., 2015; McPhail et al., 2019; Moody & Brown, 2013).…”

Section: Introductionmentioning

confidence: 99%

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A Subjective Bayesian Framework for Synthesizing Deep Uncertainties in Climate Risk Management

Doss‐Gollin

Keller

2023

Earth's Future

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Projections of nonstationary climate risks can vary considerably from one source to another, posing considerable communication and decision‐analytical challenges. One such challenge is how to present trade‐offs under deep uncertainty in a salient and interpretable manner. Some common approaches include analyzing a small subset of projections or treating all considered projections as equally likely. These approaches can underestimate risks, hide deep uncertainties, and are mostly silent on which assumptions drive decision‐relevant outcomes. Here we introduce and demonstrate a transparent Bayesian framework for synthesizing deep uncertainties to inform climate risk management. The first step of this workflow is to generate an ensemble of simulations representing possible futures and analyze them through standard exploratory modeling techniques. Next, a small set of probability distributions representing subjective beliefs about the likelihood of possible futures is used to weight the scenarios. Finally, these weights are used to compute and characterize trade‐offs, conduct robustness checks, and reveal implicit assumptions. We demonstrate the framework through a didactic case study analyzing how high to elevate a house to manage coastal flood risks.

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Section: Resultsmentioning

confidence: 99%

Section: Scenario-conditional Probabilistic Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Subjective Bayesian Framework for Synthesizing Deep Uncertainties in Climate Risk Management

Doss‐Gollin

Keller

2023

Earth's Future

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“…Coastal ecosystems harbour vast biodiversity, not only in the iconic coral reefs but from the poles to the tropics in kelp forests, maerl and oyster beds and seagrass meadows [3]. In 2020, almost 11% of the global population live in coastal areas, from small-scale settlements to large mega cities, and these numbers are projected to increase [4]. Therefore, our coasts are intersections of the needs of ecosystems and people [5].…”

Section: Introductionmentioning

confidence: 99%

Marine protected areas in the context of climate change: key challenges for coastal social-ecological systems

Schmidt

Pieraccini

Evans

2022

Phil. Trans. R. Soc. B

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Climate and ecological emergencies play out acutely in coastal systems with devastating impacts on biodiversity, and the livelihoods of communities and their cultural values. Marine Protected Areas (MPAs) are one of the key management and regulatory tools against biodiversity loss, playing a role in strengthening bio-cultural diversity and sustainability of coastal social-ecological systems. What is unclear though is the effectiveness of static protections under climate change as species move. Next to ecological uncertainty, regulatory uncertainty may play a role in weakening marine conservation. We asked whether MPAs are ecologically effective now and can sustain or improve to be so in the future while facing key climate and regulatory uncertainties. MPAs can support the protection of cultural values and have an impact on activities of sea-users and the sustainability of social-ecological systems. As such, questions surrounding their legitimacy under a changing climate and increased uncertainty are pertinent. We argue that MPA governance must be cognisant of the interdependency between natural and human systems and their joint reaction to climate change impacts based on an integrated, co-developed, and interdisciplinary approach. Focusing on the UK as a case study, we highlight some of the challenges to achieve effective, adaptive and legitimate governance of MPAs. This article is part of the theme issue ‘Nurturing resilient marine ecosystems’.

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“…Human-natural systems are never closed and model results are never unique, and thus validation and verification of models representing these systems is necessarily qualitative and subjective (Oreskes et al, 1994). In other words, no model exists that could represent the full truth, and the future is thus deeply uncertain (Keller et al, 2021;Walker et al, 2013;Lempert, 2002;Haasnoot et al, 2021). Con-sequently, a growing literature on decision making under deep uncertainty (DMDU) emphasizes the value of identifying decisions that are robust, in some sense, to deep uncertainties (Moody & Brown, 2013;Herman et al, 2015;McPhail et al, 2019;Borgomeo et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A subjective Bayesian framework for synthesizing deep uncertainties in climate risk management

Doss‐Gollin

Keller

2022

Preprint

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Long-term sea-level rise necessitates a commitment to adaptation: A first order assessment

Cited by 32 publications

References 66 publications

A Subjective Bayesian Framework for Synthesizing Deep Uncertainties in Climate Risk Management

A Subjective Bayesian Framework for Synthesizing Deep Uncertainties in Climate Risk Management

Marine protected areas in the context of climate change: key challenges for coastal social-ecological systems

A subjective Bayesian framework for synthesizing deep uncertainties in climate risk management

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