Ensemble-based estimation of sea-ice volume variations in the Baffin Bay
Bibliographic record
Abstract
Sea ice in the Baffin Bay plays an important role in deep water formation in the Labrador Sea and contributes to the variation of the Atlantic meridional overturning circulation (AMOC) on larger scales. Sea-ice data from locally merged satellite observations (Sat-merged SIT) in the eastern Canadian Arctic and three state-of-the-art sea ice–ocean models are used to quantify sea-ice volume variations from 2011 to 2016. Ensemble-based sea-ice volume (SIV) fluxes and the related standard deviations in the Baffin Bay are generated from four different estimates of SIV fluxes that were derived from Sat-merged SIT, three modeled SITs and satellite-based ice-drift data. Results show that the net increase in the SIV in Baffin Bay occurs from October to early April with the largest SIV increase in December (113 ± 17 km 3 month −1 ) followed by a reduction from May to September with the largest SIV decline in July (−160 ± 32 km 3 month −1 ). The maximum SIV inflow occurs in winter with the amount of 236 (±38) km 3 while ice outflow reaches the maximum in spring with a mean value of 168 (±46) km 3 . The ensemble mean SIV inflow reaches its maximum (294 ± 59 km 3 ) in winter 2013 caused by high ice velocity along the north gate while the largest SIV outflow (229 ± 67 km 3 ) occurs in spring of 2014 due to the high ice velocity and thick ice along the south gate. The long-term annual mean ice volume inflow and outflow are 411 (±74) km 3 yr −1 and 312 (±80) km 3 yr −1 , respectively. Our analysis also reveals that, on average, sea ice in the Baffin Bay melts from May to September with a net reduction of 335 km 3 in volume while it freezes from October to April with a net increase of 218 km 3 . In the melting season, there is about 268 km 3 freshwater produced by local melting of sea ice in the Baffin Bay. In the annual mean, the mean freshwater converted from SIV outflow that enters the Labrador Sea is about 250 km 3 yr −1 (i.e., 8 mSv), while it is only about 9 % of the net liquid freshwater flux through the Davis Strait. The maximum freshwater flux derived from SIV outflow peaks in March is 65 km 3 (i.e., 25 mSv).
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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) | 0.001 | 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 | 0.000 | 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".