Pulses of Cold Atlantic Water in the Arctic Ocean from Ocean Model Simulation
Bibliographic record
Abstract
The thermohaline intrusion of the warm and saline Atlantic Water (AW) into the Arctic Ocean, referred to as “Arctic Atlantification”, has significant implications and feedback to the dynamics and thermodynamics of the Arctic Ocean. The AW enters the Arctic Ocean through two gateways: Fram Strait and the Barents Sea Opening (BSO). The relative strength of these two AW branches dominates the oceanic heat contribution to the Arctic Ocean. In conjunction with the measurements in key hydrographic sections, numerical ocean modelling provides us with a useful tool to characterize and corroborate the temporal and spatial variability of the AW branches. Simulations are conducted using the regional configuration Arctic and North Hemispheric Atlantic (ANHA) of the ocean/sea-ice model NEMO running at 1/4° and 1/12° resolution. Online passive tracers from the model configuration are used to trace the pathways of the AW inflow in the Arctic Ocean. With the AW becoming more important to the dynamics of the Arctic Ocean, this study aims to examine its variability, transformation, impacts, and ultimately track how it evolves. While the heat in the Fram Strait Branch Water (FSBW) dissipates in a slower process through the mixing with the ambient cold water below sea surface, the vast majority of the heat loss of the Barents Sea Branch Water (BSBW) takes place in Barents Sea due to the sea surface cooling, leading to Cold Atlantic Water (CAW) production during 2013 and 2014. The CAW pulses result in a significant heat content reduction in the eastern Arctic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".