Sensitivity of Simulated Arctic Ocean Salinity and Strait Transport to Interannually Variable Hydrologic Model Based Runoff
Bibliographic record
Abstract
Abstract As the Arctic warms at an increased rate compared to the rest of the globe, freshwater runoff has been shown to be increasing into the Arctic Ocean. The effects of this contemporary increase in riverine freshwater into the Arctic Ocean are estimated from ocean model simulations, using two runoff data sets. One runoff data set is based on older climatological data, which has no inter‐annual variability after 2007 and as such does not represent the observed increases in river runoff into the Arctic. The other data set comes from a hydrological model developed for the Arctic drainage basin, which includes contemporary changes in the climate. At the Pan‐Arctic scale this new data set represents an approximately 11% increase in runoff, compared with the older climatological data. Comparing two ocean model runs forced with the different runoff data sets, overall changes in different freshwater markers across the basin were found to be between 5% and 10%, depending on the regions. The strongest increases were seen from the Siberian rivers, which in turn caused the strongest freshening in the Eastern Arctic. As the surface waters of the Arctic Ocean are sensitive to runoff, incorporating hydrological model data can help to better understand current changes and potential future impacts from increased runoff with climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".