Multi-model based estimation of sea ice volume variations in theBaffin Bay
Bibliographic record
Abstract
Abstract. Sea ice in Baffin Bay plays an important role in the deep water formation in the Labrador Sea and contributes to the variation of the Atlantic meridional overturning circulation (AMOC) on larger scales. To quantify the sea ice volume variations in Baffin Bay, a major driver of the deep water formation, three state-of-the-art sea ice models (CMST, NAOSIM, and PIOMAS) are investigated in the melt and freezing season from 2011 to 2016. An ensemble of three estimates of the sea ice volume fluxes in Baffin Bay is generated from the three modeled sea ice thickness and NSIDC satellite derived ice drift data. Results show that the net increase of the ensemble mean sea ice volume (SIV) in Baffin Bay occurs from October to April with the largest SIV increase in December (116 ± 16 km3 month−1) and the reduction occurs from May to September with the largest SIV decline in July (−160 ± 32 km3 month−1). The maximum SIV inflow occurs in winter in all the model data consistently. The ensemble mean SIV inflow (322 ± 4 km3) reaches its maximum in winter 2013 caused by high ice velocities while the largest SIV outflow (244 ± 61 km3) occurs in spring of 2014. The long-term annual mean ice volume inflow and outflow are 437(± 53) km3 and 339(± 68) km3, respectively. Our analysis also reveals that on average, sea ice in Baffin Bay melts from May to October with a net reduction of 335 km3 in volume while it freezes from November to April with a net increase of 251 km3.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".