Recent estimates of the sea ice volume and solid freshwater flux across the Arctic’s major export passageways
Bibliographic record
Abstract
Sea ice export from the Arctic Ocean is important to the ice mass balance and freshwater budget of the Arctic Ocean and the delivery of freshwater to the North Atlantic. Historically, estimates of the sea ice volume and solid freshwater flux across the Arctic’s major export passageways were temporally limited in terms of available ice thickness data together with low spatial resolution satellite derived sea ice motion data. However, observational advances now provide year-round estimates of ice thickness from CryoSat-2 and high spatiotemporal estimates of sea ice motion can be derived from Senitnel-1 and the RADARSAT Constellation Mission (RCM) synthetic aperture radar (SAR) satellites. In this presentation, we present the results of merging these datasets that provide new high-quality annual and monthly estimates of the sea ice volume flux across the Arctic’s major export passageways of Fram Strait, Nares Strait, Davis Strait and the Canadian Arctic Archipelago from 2016-2022. Over our study period, the annual average volume export at Fram Strait was 1586 km3 that agrees with its longer-term decline. The annual average volume export at Nares Strait and the Canadian Arctic Archipelago was 160 km3, and 43 km3, respectively that is in agreement with longer-term increases and indicates a divergent trajectory compared to Fram Strait. The annual average sea ice volume flux through Davis Strait was 816 km3, nearly double previous estimates. Annually, a total of 1912 km3 of solid freshwater was delivered to the North Atlantic from the passageways of Fram Strait and Davis Strait. Overall, our new high-quality estimates of these sea ice variables provide updated quantities for understanding recent changes in ice mass balance and freshwater budget of the Arctic Ocean and the freshwater balance of the North Atlantic, where overturning is critical to the global climate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".