Nonseasonal fluctuations of the Arctic Ocean mass observed by the GRACE satellites
Bibliographic record
Abstract
Time variable gravity observations from the GRACE satellites reveal strong nonseasonal fluctuations of bottom pressure in the Arctic Ocean on the time scales from 2 to 6 months and a record‐high bottom pressure anomaly in February of 2011. Here, we examine the nature and driving forces behind those fluctuations. We find that the nonseasonal variability of the Arctic Ocean mass is strongly coupled to wind forcing. The zonal wind pattern is correlated with a dipole pattern of Arctic Ocean mass changes. Westerly wind intensification over the North Atlantic at about 60°N as well as over the Russian Arctic continental shelf break cause the ocean mass to decrease in the Nordic seas and in the central Arctic, and to increase over the Russian Arctic shelf. Basin‐wide Arctic Ocean mass fluctuations are correlated with northward wind anomalies over the northeastern North Atlantic and Nordic seas, and over the Bering Sea. We show that positive (negative) Arctic Ocean mass anomalies are associated with anticyclonic (cyclonic) anomalies of the large‐scale ocean circulation pattern. Based on ocean model simulations, we conclude that the observed nonseasonal Arctic Ocean mass variability is mostly explained by the net horizontal wind‐driven transports, and the contribution of fresh water fluxes is negligible. We demonstrate that transport anomalies across both the Atlantic and Pacific gateways were equally important for generating large Arctic Ocean mass anomalies in 2011.
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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".