Arctic warming: Evolution and spreading of the 1990s warm event in the Nordic seas and the Arctic Ocean
Bibliographic record
Abstract
Observations in the Arctic Ocean revealed changes in oceanic temperature, salinity and ice cover of the 1990s as compared with earlier data. With a numerical model, we favorably reproduce the development and subsequent propagation of temperature anomalies in water of Atlantic origin in the 1980s and 1990s. These propagated into the Arctic Ocean via the Barents Sea and the Fram Strait. Two warm anomalies entered the Arctic Ocean through these passages. While the first smaller anomaly only warmed up the western Eurasian Basin, the second large anomaly spread far into the eastern Eurasian Basin and across the Lomonossov Ridge into the western Arctic basins. Intensified boundary currents during the high NAO state in the first half of the 1990s significantly influenced the amplitude and speed of propagation of the temperature anomalies inside the Arctic Ocean. In contrast to the notion of a continuous warming process during the 1990s, our model results suggest the warming of the Atlantic Layer in the Arctic Ocean occurred in the form of events. The event with the largest anomalous heat input during the modeled period entered the Arctic between 1989 and 1994. It is possible to trace back the additional heat input into the Arctic to an increased volume inflow via the Faroer‐Scotland passage and reduced heat loss to the atmosphere in the early 1990s. After a weaker warm inflow in the second half of the 1990s, the most recent observations and the model results point to a recurring warm anomaly in the inflow from 1999 onward.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".