Extreme Weather and Climate Events: Dynamics, Predictability and Ensemble Simulations
Bibliographic record
Abstract
This special issue was motivated by the recent World Climate Research Programme (WCRP) workshop on Extremes in Climate Prediction Ensembles (ExCPns) 25-27 October 2021.Extreme weather and climate events have significant impacts on society and the economy (Lee et al. 2022;Oh et al. 2022;Ranalkar et al. 2022;Shelton et al. 2022;Zhao et al. 2022).These events are also projected to become more intense and frequent in a globally warmer world (Park et al. 2023;Liu et al. 2023).Together with many socio-economic changes, this means that the associated risks are changing and likely are increasing.Extreme events are difficult to analyze due to their rarity.However, ensemble predictions and ensemble climate simulations can greatly multiply the number of realizations of climate system behavior beyond the one that is available from observations.This can provide powerful means to analyze changes in extreme events and potentially to make more skillful predictions on subseasonal and multi-annual time scales.Ensembles also open avenues to better understand the underlying mechanisms of extreme events and for the attribution of such events.With this special issue we aim to advance our understanding and predictive skill of extreme weather and climate events using ensemble and modern statistical methods on various time scales.The special issue consists of 8 contributions which cover the full range of spatial scales from micro-scales to continental scales.Wang et al. (2023) describe the microphysical structures of an extreme rainfall event in China.On larger scales Liu et al. (2023) describe changes to precipitation extremes and the East Asian monsoon in high-resolution global warming simulations and demonstrate the advantage of high-resolution modeling by reducing biases.Cadiou et al. (2023) attempt to attribute a rain bomb.Event to global warming and describe the challenges doing so.Akter and Rafiuddin (2023) emphasize the need to better understand and predict tornado outbreaks during tropical cyclone events especially in developing countries.Ke et al. (2023) show how an abnormally cyclonic largescale circulation feature affected regional predictability due to persistent convection.Ma et al. (2023) use modern timeseries analysis tools to better understand the Pacific Decadal Oscillation and how it might change in a warmer climate.Park and Kam (2023) explore sub-seasonal predictability of drought conditions in South Korea while Finke et al. (2023) examine the link between persistent Eurasian cold events and stratospheric anomalies.As our special issue demonstrates extremes cover a wide range of scales and topics.We hope that the contributions in this special issue will encourage further research in this exciting area.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".