How Much Can Riverine Biogeochemical Fluxes Affect the Arctic Ocean Acidification?
Bibliographic record
Abstract
Abstract Arctic rivers carry not only large amounts of freshwater but also biogeochemical materials into the ocean and play important roles in Arctic ocean acidification (OA). This study quantitatively evaluated the effects of riverine biogeochemical fluxes (R‐BGC; carbon and nutrients) on the Arctic marine carbonate system and OA using multi‐decadal experiments (1979–2018) with a pan‐Arctic sea ice–ocean model. Improved initial and lateral boundary conditions of carbonate properties, observation‐based riverine biogeochemical data, and land model‐based interannually varying riverine freshwater discharge were adopted to enable more realistic experiments. The model simulated negative trends in aragonite saturation state (Ω) and pH in most regions of Arctic Ocean regardless of R‐BGC. The increased riverine freshwater promoted more OA through the higher dilution effect. Compared to the experiment with riverine discharge of only freshwater, the inclusion of R‐BGC caused positive anomalies in Ω and pH (by ∼0.14 and ∼0.03 in central basins, and by ∼0.15 and ∼0.06 in shelf seas, respectively). In the central basins, these anomalies were caused mostly by carbon (total alkalinity and dissolved inorganic carbon) of the R‐BGC. In the shelf seas, nutrient (nitrate and silicate) fluxes also contributed ∼14% and ∼32% of the anomalies owing to the enhanced primary production and a corresponding reduction in seawater pCO 2 . R‐BGC mitigated OA (ΔΩ = −1.53 × 10 −3 year −1 and ΔpH = −0.56 × 10 −3 year −1 ) in regions where riverine freshwater was accumulated (i.e., the Canada Basin, Chukchi Cap, Eurasian Basin, and East Siberian Sea). This study stressed the importance of including R‐BGC for OA model projection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".