A framework for attributing explosive cyclones to climate change: the case study of Alex storm 2020
Bibliographic record
Abstract
The Extreme Event Attribution field aims at evaluating the impact of global warming linked to anthropogenic emissions on extreme events. This work performs an attribution to climate change of the storm Alex, an explosive extratropical cyclone [1] that hit especially Southern France and Northern Italy at the beginning of October 2020. We apply the analogues method on sea-level pressure maps [2] to identify 30 cyclones that match the dynamical structure of Alex for two periods, the counterfactual and the factual world, namely 1950-1985 and 1985-2021, using 6-hourly ERA5 data. Results show that in the factual period the anticyclonic circulation over the North Atlantic and the cyclonic circulation over Northern Africa are deeper than in the counterfactual. Precipitation differences depict a significant increase over North Italy and the Alps. 2-meter air temperature differences consist of a positive non-uniform pattern, with a significant increase over the Alps and east of Newfoundland. We also have computed two indices in the frame of dynamical systems theory for each period: the persistence, which characterizes the average time that the sea-level pressure pattern remains stationary, and the local dimension, which gives a measure of the predictability of the storm [3]. We found that in the factual world there is a significant increase in the persistence and a modest decrease in the local dimension with respect to the counterfactual. Hence, storms like Alex are more persistent and more predictable in present-like conditions. Cyclone tracking shows that the backward trajectories of the analogues in the factual world are more meridional than in the counterfactual one, while the response for the forward trajectories is less clear. This suggests that under current conditions patterns like Alex are more wavy than in the past. Finally, using the metrics to identify explosive cyclones in [1] , we found the same number of analogues that are explosive cyclones in both periods, although in the counterfactual world they come from lower latitudes and the deepening rates are significantly larger. References [1] Reale, M., M. L. Liberato, P. Lionello, J. G. Pinto, S. Salon, and S. Ulbrich, A global climatology of explosive cyclones using a multi-tracking approach, Tellus A: Dynamic Meteorology and Oceanography, 71 (1), 1611,340, 2019. [2] Yiou, P., AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531–543, 2014. [3] Faranda, D., G. Messori, and P. Yiou, Dynamical proxies of North Atlantic predictability and extremes, Sci Rep, 7, 41,278, 2017. Acknowledgments This work is part of the EU International Training Network (ITN) European weather extremes: drivers, predictability and impacts (EDIPI). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 956396.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".