Increasing Arctic River Discharge and Its Role for the Phytoplankton Responses in the Present and Future Climate Simulations
Bibliographic record
Abstract
Arctic amplification is known to accelerate the hydrological cycle in high-latitude landmass, which eventually leads to increased river discharge into the Arctic Ocean. However, the majority of climate models in Coupled Model Intercomparison Project 5 (CMIP5) tend to underestimate Arctic river discharge. This study elucidates the role of additional Arctic river discharge for the phytoplankton responses in the present and future climate simulations. In the present climate simulation, the additional freshwater input showed a decrease in the phytoplankton in spring due to the increasing sea ice, and in summer, it showed an increase in phytoplankton due to the surplus nitrate leftover from spring and induced vertical mixing. Similar processes occurred in future climate simulations. However, in those simulations, the major response region of phytoplankton to additional freshwater input was altered from the Eurasian Basin to the Canadian Basin and the East-Siberian Sea. This is because the current marginal ice zone in the Barents-Kara Sea, where phytoplankton mainly responds, moves toward the East-Siberian-Chukchi Sea. We suggest that Arctic river discharge is potentially an important contributing factor for Arctic ecosystems in both present and future climate that controls sea ice and nutrient distribution.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".