Attributing Weather Extremes to Climate Change and the Future of Adaptation Policy
Bibliographic record
Abstract
Until recently, climate scientists were unable to link the occurrence of extreme weather events to anthropogenic climate change. In recent years, however, climate science has made considerable advancements, making it possible to assess the influence of anthropogenic climate change on single weather events. Using a new technique called ‘probabilistic event attribution’, scientists are able to assess whether anthropogenic climate change has changed the likelihood of the occurrence of a recorded extreme weather event (e.g. an extreme storm season, extreme rainfall, heatwave, drought, etc.). These advancements raise the expectation that this branch of climate science can contribute to climate adaptation efforts. This paper examines the normative underpinnings of these policy discussions. To date, the debates revolve around whether the findings of attribution science can be used to establish moral liability for harms resulting from climate change. On close analysis, this normative framework has serious shortcomings. The paper rejects the moral liability framework and suggests, through a review of the international climate negotiations under the UNFCCC, that the science of event attribution can inform adaptation policy within a risk-pooling and climate risk insurance framework. The proposed framework is defended both on normative grounds and on the basis of its potential application within the Warsaw International Mechanism for Loss and Damage under the Cancun Adaptation Framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".