Increasing cloudiness in Arctic damps the increase in phytoplankton primary production due to sea ice receding
Bibliographic record
Abstract
Abstract. The Arctic Ocean and its marginal seas are among the marine regions most affected by climate change. Here we present the results of a diagnostic model used to assess the primary production (PP) trends over the 1998–2010 period at pan-Arctic, regional and local (i.e. 9.28 km resolution) scales. Photosynthetically active radiation (PAR) above and below the sea surface was estimated using precomputed look-up tables of spectral irradiance, taking as input satellite-derived cloud optical thickness and cloud fraction parameters from the International Satellite Cloud Climatology Project (ISCCP) and sea ice concentration from passive microwaves data. A spectrally resolved PP model, designed for optically complex waters, was then used to assess the PP trends at high spatial resolution. Results show that PP is rising at a rate of +2.8 TgC yr−1 (or +14% decade−1) in the circum-Arctic and +5.1 TgC yr−1 when sub-Arctic seas are considered. In contrast, incident PAR above the sea surface (PAR(0+)) has significantly decreased over the whole Arctic and sub-Arctic Seas, except over the perennially sea-ice covered waters of the Central Arctic Ocean. This fading of PAR(0+) (−8% decade−1) was caused by increasing cloudiness during summer. Meanwhile, PAR penetrating the ocean (PAR(0−)) increased only along the sea ice margin over the large Arctic continental shelf where sea ice concentration declined sharply since 1998. Overall, PAR(0−) slightly increased in the circum-Arctic (+3.4% decade−1), while it decreased when considering both Arctic and sub-Arctic Seas (−3% decade−1). We showed that rising phytoplankton biomass (i.e. chlorophyll a) normalized by the diffuse attenuation of photosynthetically usable radiation (PUR), accounted for a larger proportion of the rise in PP than did the increase in light availability due to sea-ice loss in several sectors, and particularly in perennially and seasonally open waters. Against a general backdrop of rising productivity over Arctic shelves, significant negative PP trends and the timing of phytoplankton spring-summer bloom were observed in regions known for their great biological importance such as the coastal polynyas of northern Greenland.
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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 | 0.001 |
| 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".