Interannual variations of heat and freshwater content in the cold water dome of the Labrador Sea
Bibliographic record
Abstract
In this paper, we investigate the interannual variability of heat content and freshwater content in the Labrador Sea using ARMOR-3D data-set (1993-2016), which combines in situ and satellite observations. Significant trends of increasing the heat content and of decreasing the freshwater content in the upper 500th layer were detected. With the depth the slope coefficients of the trends decrease, which suggests the upper ocean origin of the processes, forming the observed tendencies. On interannual scales (after removal of the trends), negative correlation between these characteristics were obtained, which can be formed by the intensification of the ocean-atmosphere heat/freshwater exchange, as well as by intensification of oceanic advection of polar and/or subtropical water in the Labrador Sea. The interannual variability in the heat and freshwater content is dominated by 2-4 years and 5-8 years cycles. Significant correlation of the heat/freshwater content with the North Atlantic Oscillation Index (NAOI) is obtained. For the 2-year and 5-8 year cycles, the high coherence between the heat content and the NAOI is detected. For the 2-year it is found a delay of NAOI by a quarter of the period relative to the heat content, interpreted as the reverse effect of the Labrador Sea water temperature on thephase/intensity of NAOI. For 5-8-year cycles, the high coherence between the variations in the heat content and the Atlantic Multidecadal oscillation index (AMOI, characterizing the mean temperature of the North Atlantic) is detected with practically zero phase shift. After the beginning of 2000, the certain coherence with the AMOI was observed also for the 2-4-year cycles. At these time scales, AMOI leads the variations in the heat content in the sea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 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".