Response of nutrients and primary production to high wind and upwelling-favorable wind in the Arctic Ocean: A modeling perspective
Bibliographic record
Abstract
Both remote sensing and numerical models revealed increasing net primary production (NPP) in the Arctic Ocean due to declining sea ice cover and increasing ice-free days. The NPP increases in some parts of the Arctic Ocean are also hypothesized to link to high wind (>10 m/s) and upwelling-favorable wind, however, the mechanism remains unclear. Using Regional Arctic System Model (RASM) to investigate the relationship between NPP and wind, we found that the seasonal NPP are statistically correlated to high wind frequency (HWF) in the Barents (Br) and Southern Chukchi Seas (SC) due to their high subsurface nutrients in the 20-50 m layer. Five high and five low HWF years along a zonally averaged section were chosen to understand the spatial variation of the correlation between HWF, NO3, and NPP in the SC. During high HWF years, the decrease in subsurface NO3 exceeds its increase in surface, implying the utilization by biological productivity. A more positive response of NPP to HWF in north SC than south was also found because more subsurface nutrients were entrained into the surface by higher HWF. The NPP are statistically correlated to easterly wind frequency (EWF) in the Beaufort and Canada Basin (BC), where the stronger EWF-induced upwelling could bring up higher nutrients from >100 m depth. While the nutrients and NPP in the south BC are normally higher than in the north, an increase of EWF can further enhance the nutrients and NPP in the south much more than those in the north. Differences between five high and five low EWF years reveal that the increase of EWF is most important around the shelf break region, where NO3 and NPP are also most enhanced. The enhancement of NPP by higher HWF in the Br and SC is less than that by higher ice-free days ratio (IFR), while the enhancement of NPP by higher EWF in BC is of similar magnitude to that by IFR. As the trend of declining sea ice cover continues, it’s necessary to advance our understanding on the nutrients and NPP response to changing wind regimes in different Arctic regions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".