Expert judgment in climate science: How it is used and how it can be justified

Majszak, Mason; Jebeile, Julie

doi:10.1016/j.shpsa.2023.05.005

Studies in History and Philosophy of Science

2023

DOI: 10.1016/j.shpsa.2023.05.005

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Expert judgment in climate science: How it is used and how it can be justified

Mason Majszak

Julie Jebeile

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Cited by 8 publications

References 31 publications

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Climate tipping points and expert judgment

Lam

Majszak

2022

WIREs Climate Change

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Expert judgment can be seen throughout climate science and even more prominently when discussing climate tipping points. To provide an accurate characterization of expert judgment we begin by evaluating the existing literature on expertise as it relates to climate science as a whole, before then focusing the literature review on the role of expert judgment in the unique context of climate tipping points. From this we turn our attention to the structured expert elicitation protocols specifically developed for producing expert judgments about tipping points. We highlight that expert elicitation is not only used for the quantification of uncertainty in this context, but also for the very identification and characterization of tipping points and their interactions, making expert judgment in itself a genuine scientific output. The central role of expert judgment in this domain raises several epistemic issues that require careful attention. Among other topics, we discuss the relationship between expert judgment and modeling, as well as the nonepistemic values that are involved in the production of expert judgments, highlighting how the elicitation protocols can be used to manage these values. In the perspective of climate change, clarifying the epistemic foundations of expert judgment in this context can help to navigate the epistemic situation between self-defeating alarmism and blind dismissal, thus contributing to a better understanding of the challenges related to climate (and Earth system) tipping points.

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Climate tipping points and expert judgment

Lam

Majszak

2022

WIREs Climate Change

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Integrating artificial intelligence with expert knowledge in global environmental assessments: opportunities, challenges and the way ahead

Muccione,

Vaghefi,

Bingler

et al. 2024

Reg Environ Change

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With new cycles of global environmental assessments (GEAs) recently starting, including GEO-7 and IPCC AR7, there is increasing need for artificial intelligence (AI) to support in synthesising the rapidly growing body of evidence for authors and users of these assessments. In this article, we explore recent advances in AI and connect them to the different stages of GEAs showing how some processes can be automatised and streamlined. The meticulous and labour-intensive nature of GEAs serves as both a valuable strength and a challenge to staying pertinent and current in today’s era of urgency and the pursuit of the latest knowledge. Utilising AI tools for reviewing and synthesizing scientific literature holds the evident promise of substantially lessening the workload for experts and expediting the assessment process. This, in turn, could lead to more frequent report releases and a smoother integration of the latest scientific advancements into actionable measures. However, successful outcomes can only be achieved if domain experts co-develop and oversee the deployment of such tools together with AI researchers. Otherwise, these tools run the risk of producing inaccurate, incomplete, or misleading information with significant consequences. We demonstrate this through a few examples that compare recently deployed large language models (LLMs) based tools in their performance in capturing nuanced concepts in the context of the reports of the Intergovernmental Panel on Climate Change (IPCC). We recommend establishing ethical committees and organising dedicated expert meetings to develop best practice guidelines, ensuring responsible and transparent integration of AI into GEAs.

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Fusion of Probabilistic Projections of Sea‐Level Rise

Grandey,

Dauwels,

Koh

et al. 2024

Earth's Future

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A probabilistic projection of sea‐level rise uses a probability distribution to represent scientific uncertainty. However, alternative probabilistic projections of sea‐level rise differ markedly, revealing ambiguity, which poses a challenge to scientific assessment and decision‐making. To address the challenge of ambiguity, we propose a new approach to quantify a best estimate of the scientific uncertainty associated with sea‐level rise. Our proposed fusion combines the complementary strengths of the ice sheet models and expert elicitations that were used in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). Under a low‐emissions scenario, the fusion's very likely range (5th–95th percentiles) of global mean sea‐level rise is 0.3–1.0 m by 2100. Under a high‐emissions scenario, the very likely range is 0.5–1.9 m. The 95th percentile projection of 1.9 m can inform a high‐end storyline, supporting decision‐making for activities with low uncertainty tolerance. By quantifying a best estimate of scientific uncertainty, the fusion caters to diverse users.

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Expert judgment in climate science: How it is used and how it can be justified

Cited by 8 publications

References 31 publications

Climate tipping points and expert judgment

Climate tipping points and expert judgment

Integrating artificial intelligence with expert knowledge in global environmental assessments: opportunities, challenges and the way ahead

Fusion of Probabilistic Projections of Sea‐Level Rise

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