Trends in the occurrence of <scp>pan‐Arctic</scp> warm extremes in the past four decades
Bibliographic record
Abstract
Abstract The most recent historic heat wave in Siberia with record‐shattering temperatures is a reflection that, as a manifestation of global warming, the Arctic is experiencing more frequent and severe warm‐temperature extremes that could have global consequences. Here, we apply the self‐organizing map (SOM) clustering method to 6‐hr data from ERA‐Interim from 1979 to 2017 to document the spatial and seasonal variations of the trends in the number of warm‐extreme days over the pan‐Arctic region, and to apportion the trends into a dynamic component representing changes in atmospheric circulation patterns, a thermodynamic component not directly related to circulations, and an interaction component. We show significant upward trends in the occurrence of warm extremes across much of the Arctic Ocean in all seasons except for summer when regions of significant upward trends move from the Arctic Ocean to the Canadian Arctic Archipelago, Greenland, and the northern North Atlantic. The direction and magnitude of the trends in seasonal warm extremes as well as their seasonal and spatial variations are dominated by the thermodynamic component, with the dynamic component and the interaction component at least an order of magnitude smaller. Although negligible to the long‐term, pan‐Arctic averaged trend, the dynamic component may be comparable with, or even larger than, the thermodynamic component at some locations and under certain atmospheric circulation patterns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".