A significant change in sea ice diffuse attenuation coefficient with temperature and its implications for the <scp>Arctic Ocean</scp>
Bibliographic record
Abstract
Abstract Sea ice transparency is essential to the ecology of the ice‐covered sea. Research suggests that the diffuse attenuation coefficient of downwelling irradiance () varies with ice temperature (); however, the characteristics of its response are not well understood yet, particularly from a quantitative perspective. In an attempt to fill this gap, three independent laboratory experiments were performed between 2016 and 2022 to investigate the variations of with changing . Validation experiments were further performed in Liaodong Bay in 2018 and 2022. To explore the dominant factors controlling this phenomenon, corresponding changes in scattering properties were estimated through a two‐stream radiative transfer model. To examine its implications for local ice algal primary production and radiation transport, of Arctic sea ice was parameterized as a function of . Our results from the laboratory and field experiments showed that decreases (increases) with ice warming (cooling), and a 1°C change in results in a 0.29 m−1 average change in . The response of to between −9°C and −2°C is more notable than that observed between −24°C and −9°C. This is associated with the different variations in the scattering properties of sea ice. It reveals a significant effect on ice algal primary production under severe light‐limited conditions but a weaker effect on radiation transport. Knowledge of this ‐temperature dependence would further improve our understanding of the optical properties of sea ice and the parameterization of ice transparency in large‐scale climate models.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".