Assessment of tropical cyclone hazard and risk in a changing climate by means of a new global hybrid model
Bibliographic record
Abstract
The future evolution of tropical cyclones (TCs) in a warming world is an important issue, considering their potential socio-economic impacts on the areas hit by these phenomena. Understanding the natural variability and sources of uncertainties over present and future climates and modelling the impacts of TCs remains an important challenge as climate projections do not always provide robust responses about their future evolution. With questions arising about the insurability of coastal communities in the future, risk management requires more robust quantification as to how climate change affects TCs dynamics. It is therefore important to develop TC models that are computationally efficient to provide a full distribution of outcomes for the present and future.Here, we present a global TC wind model based upon statistical models forced with 10 variables from the 40 members of the Community Earth System Model (CESM) Large Ensemble (LE). The model provides a full description of the frequency, spatial cyclogenesis patterns, tracks and intensities from 1980 to 2060 under the RCP 8.5 emissions scenario. The resulting event sets can therefore be used for risk management in the financial services industry. We find that future frequency of TCs in the North Atlantic is heavily dependent upon how Sea Surface Temperature (SST) and vorticity are accounted for to generate cyclogenesis patterns. Nevertheless, we obtain a larger proportion of Cat. 4-5 storms in the future independently on how SST and vorticity are accounted for with greater intensification along the Gulf of Mexico and the east coast of the U.S. This is consistent with a projected increase (decrease) in the SST (wind shear) over those regions in the CESM-LE. Finally, we find that, especially for Cat. 4+ hurricanes, population growth and climate change should both contribute significantly to the increase in TC risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".