Future changes in association between atmospheric circulation anomalies and extreme temperature events
Bibliographic record
Abstract
With increasing global temperatures, there has been an observed increase in the quantity and intensity of extreme weather events, particularly heat extremes in the midlatitude regions. Some recent studies have attributed this increase at least partially to an amplification of upper tropospheric jet stream waves. Whilst there is significant scientific uncertainty over causes of recent trends in jet stream waviness, the impact atmospheric waves have on extreme events is clear. Therefore it is key to quantify whether the relative importance of jet stream waviness on the formation of extreme temperature events changes in the future.We achieve this by studying the probability ratio between co-occurring high magnitude geopotential height anomalies at 500 hPa, and coincident surface temperature extremes. We calculate this for the historical period (1980-2015) and the future (2065-2100), and compare how this probability ratio - the association between atmospheric circulation and surface temperature extremes - changes between these two periods. To understand the changes seen, we also look at projected changes in the frequency of high magnitude geopotential height anomalies.Results from three large ensembles show that cold extremes in boreal winter (December-February) exhibit a clear decrease in association between the historical to the future period, indicating that cold extremes at the end of the 21st century become less associated with strong atmospheric circulation anomalies compared to the current historical period. Conversely, hot extremes in boreal summer (June-August) exhibit small regional changes in association for the future period but hemispherically show no clear trend. We further explore the boreal winter trend in CMIP6 models, and explore mechanisms for this trend by comparing across different models with different changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".