Long‐term variability of Arctic Ocean waters: Evidence from a reanalysis of the EWG data set
Bibliographic record
Abstract
We have examined interannual to decadal variability of water properties in the Arctic Ocean using an enhanced version of the 1948–1993 data released earlier under the Gore‐Chernomyrdin environmental bilateral agreement. That earlier data set utilized gridded fields with decadal time resolution, whereas we have developed a data set with annual resolution. We find that beginning about 1976, most of the upper Arctic Ocean became significantly saltier, possibly related to thinning of the arctic ice cover. There are also indications that a more local upper ocean salinity increase in the Eurasian Basin about 1989 may not have originated on the shelf, as had been suggested earlier. In addition to the now well‐established warming of the Atlantic layer during the early 1990s, there was a similar cyclonically propagating warm event during the 1950s. More remarkable, however, was a pervasive Atlantic layer warming throughout most of the Arctic Ocean from 1964–1969, possibly related to reduced vertical heat loss associated with increased upper ocean stratification. A cold period prevailed during most of the 1970s and 1980s, with several very cold events appearing to originate near the Kara and Laptev shelves. Finally, we find that the silicate maximum in the central Arctic Ocean halocline eroded abruptly in the mid‐1980s, demonstrating that the redistribution of Pacific waters and the warming of the Atlantic layer reported from other observations during the 1990s were distinct events separated in time by perhaps 5 years. We have made the entire data set publicly available.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".