Seasonal weather regimes in the North Atlantic region: towards new seasonality?
Bibliographic record
Abstract
Abstract. European climate variability is shaped by atmospheric dynamics and local physical processes over the North Atlantic region. As both have strong seasonal features, a better insight of their future seasonality is essential to anticipate changes in weather conditions for human and natural systems. We explore the weather seasonality of the North Atlantic over 1979–2017 and 1979–2100 by using seasonal weather regimes (SWRs) defined by clustering year-round daily fields of geopotential height at 500 hPa (Z500) from the ERA-Interim reanalysis and 12 climate models of the Coupled Model Intercomparison Project fifth phase (CMIP5). The spatial and temporal variability of SWR structures is investigated, as well as associated patterns of surface air temperature. Although the climate models have biases, they reproduce structures and evolutions of SWRs similar to the reanalysis over 1979–2017: decreasing frequency of winter conditions (starting later and ending earlier in the year) and increasing frequency of summer conditions (starting earlier and ending later). These changes are stronger over 1979–2100 than over 1979–2017, associated with a large increase of North Atlantic seasonal mean Z500 and temperature. When using more SWRs (i.e. more freedom in the definition of seasonality), the changes over 1979–2100 correspond to a long-term swap between SWRs, resulting in similar structures (seasonal cycle and weather patterns) with respect to the evolution of the seasonal cycle of Z500 and temperature. To understand whether the evolution of the SWRs is linked to uniform Z500 increase (i.e. uniform warming), or to changes in Z500 spatial patterns (i.e. changes in circulation patterns), we remove the calendar trend in the Z500 regional average to define SWRs based on detrended data (d-SWRs). The temporal properties of d-SWRs appear almost constant, whereas their spatial patterns change. This indicates that the calendar Z500 regional trend drives the evolution of the SWRs and that the changing spatial patterns in d-SWRs account for the heterogeneity of this trend. Our study suggests that historical winter conditions will continue to decrease in the future while historical summer conditions continue to increase. It also suggests that according to an increasing seasonal cycle, the seasonality of weather conditions would not change in a major way.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".