Extreme event attribution using km-scale simulations reveals the pronounced role of climate change in the Durban floods
Bibliographic record
Abstract
The Durban floods of 11–12 April 2022 is the worst flood disaster in South Africa’s history and raised questions about the role of climate change in the event. Meso-scale dynamics, involving processes that cannot be resolved at the spatial resolutions of current global climate models, played an important role in the heavy falls of rain. Here we report on the development of a convection-permitting conditional extreme event attribution modelling system, well-suited to explore the role of climate change in meso- and convective-scale extreme weather events. The African-based attribution system makes use of a computationally-efficient variable-resolution atmospheric model and runs on a local high-performance computer in South Africa. Similar systems can potentially be rolled out across the Global South (and North). The system relies on a km-scale perturbed-physics ensemble to describe the simulation/structural uncertainty associated with an extreme weather event in an anthropogenically-warmed world, compared to counterfactual cooler worlds where the effects of anthropogenic forcing are (partially) removed. Simulations reveal a pronounced role of climate change in the Durban floods. Average rainfall in the Durban region is simulated to have been at least 40% higher during the two days of the flood, relative to rainfall in a counterfactual cooler world. Climate change substantially increased rainfall during the Durban floods of 11–12 April 2022 in south Africa, with at least 40% higher rainfall compared to a counterfactual cooler world, according to atmospheric model simulations using a km-scale perturbed-physics ensemble to undertake extreme event attribution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".