Time and space variability of freshwater content, heat content and seasonal ice melt in the Arctic Ocean from 1991 to 2011
Bibliographic record
Abstract
Abstract. The Arctic Ocean gains freshwater mainly through river discharge, precipitation and the inflowing low salinity waters from the Pacific Ocean. In addition the recent reduction in sea ice volume is likely to influence the surface salinity and thus contribute to the freshwater content in the upper ocean. The present day freshwater storage in the Arctic Ocean appears to be sufficient to maintain the upper ocean stratification and to protect the sea ice from the deep ocean heat content. The recent freshening has not, despite the established strong stratification, been able to restrain the accelerating ice loss and other possible heat sources besides the Atlantic Water, such as the waters advecting from the Pacific Ocean and the solar insolation warming the Polar Mixed Layer, are investigated. Since the ongoing freshening, oceanic heat sources and the sea ice melt are closely related, this study, based on hydrographic observations, attempts to examine the ongoing variability in time and space in relation to these three properties. The largest time and space variability of freshwater content occurs in the Polar Mixed Layer and the upper halocline. The freshening of the upper ocean during the 2000s is ubiquitous in the Arctic Ocean although the most substantial increase occurs in the Canada Basin where the freshwater is accumulating in the thickening upper halocline. Whereas the salinity of the upper halocline is nearly constant, the freshwater content in the Polar Mixed Layer is increasing due to decreasing salinity. The decrease in salinity is likely to result from the recent changes in ice formation and melting. In contrast, in the Eurasian Basin where the seasonal ice melt has remained rather modest, the freshening of both the Polar Mixed Layer and the upper halocline is mainly of advective origin. While the warming of the Atlantic inflow was widespread in the Arctic Ocean during the 1990s, the warm and saline inflow events in the early 2000s appear to circulate mainly in the Nansen Basin. Nevertheless, even in the Nansen Basin the seasonal ice melt appears independent of the continuously increasing heat content in the Atlantic layer. As no other oceanic heat sources can be identified in the upper layers, it is likely that increased absorption of solar energy has been causing the ice melt prior to the observations.
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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 | 0.000 |
| 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".