More than Magnitude: Towards a multidimensional understanding of unprecedented weather to better support disaster management
Bibliographic record
Abstract
The 1900 Galveston Texas Hurricane, the 2021 Pacific Northwest heatwave, and the 2023 Tropical Cyclone Freddy were all events that were unprecedented in diverse ways and had severe humanitarian impacts. Understanding past and future risk of unprecedented weather is an emerging question across climate science disciplines but use of this research by the humanitarian sector has been limited. This cross-disciplinary paper is an effort by climate scientists and humanitarian practitioners to address this gap. For it, we combined narrative and scoping literature reviews with structured practitioner engagement to develop a working definition and typology of unprecedented weather through a disaster management lens. We qualitatively coded over 400 peer-reviewed articles to highlight the current state of research on unprecedented weather, and then discussed these findings in a workshop with 48 humanitarian practitioners. Our results show that, while analyses of past and future unprecedented weather often focus on the magnitude of such events, extreme weather can be unprecedented in many other dimensions, all which have significant implications for early warning, anticipatory action, and disaster response planning. We conclude with a call for more imagination and diversity in research on extreme weather risks, and for closer collaboration between climate scientists and disaster managers to design and answer questions that matter for humanitarian outcomes.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.013 | 0.030 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".