Wide-spread Occurrence and Increasing Trend of Biogenic Aerosol Precursors in the Arctic Ocean Simulated by an Ocean Biogeochemical Model
Bibliographic record
Abstract
Biogenic aerosol precursors from phytoplankton production can affect cloud properties, especially in remote regions such as the Arctic Ocean. Reliable estimates on variability and trend of these precursors are required as extensive measurements in the Arctic are still scarce. We present a setup of the coupled ocean biogeochemical model FESOM2.1-REcoM3 where we integrated dissolved carboxylic acid containing polysaccharides (PCHO) and Transparent Exopolymer Particles (TEP) to describe these precursors in the upper ocean. We define PCHO as one part of the excreted organic carbon, which can then aggregate to form larger particles, TEP. Compared to observations, the simulation provides a valid TEP estimate with mean concentrations of 200-400 µg C L-1 on the continental shelves and 10-50 µg C L-1 in the central basins (0-30 m depth range). Further, the simulation for 1990-2019 reveals a significant positive trend of TEP of 0.5-3 µg C L-1 yr-1 during July-September in the Amerasian Basin (+3.5% yr-1), the Canadian Archipelago (+1.2% yr-1) and the Kara Sea (+0.8% yr-1), in contrast to the eastern Fram Strait (-0.4% yr-1), the Barents Sea (-0.3% yr-1), and parts of the Eurasian Basin with a significant decrease of -0.5-2 µg C L-1 yr-1. Our study provides for the first time an integration of TEP formation, aggregation and remineralization processes into a global ocean biogeochemical model. This simulation assembles valuable data on biogenic aerosol precursors, and as such, fills a gap on which Earth System Models can greatly benefit to improve the understanding of aerosol feedbacks within the Arctic climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".