Insights from NEMO simulations on the recent hydrographic changes in the Beaufort region and Bering Strait
Bibliographic record
Abstract
The storage/release of freshwater in the Beaufort region has changed rapidly since the early 2000s. Using a suite of coupled ocean-sea ice model Nucleus for European Modelling of the Ocean (NEMO) simulations with different configurations, resolutions, sea-ice modules, and atmospheric and runoff forcing, we review hydrographic changes in the Beaufort region and examine model performance across various runs from 2004 to 2019. We find that the global configuration (eORCA025) can better reproduce the observed freshwater changes, whereas the regional configurations (ANHA4 and ANHA12), regardless of their resolution or forcing, underestimate stored freshwater and fail to depict recent observed changes. The global configuration also has an improved representation of the vertical thermohaline structure in the Beaufort region with reduced upper ocean model biases. We further investigate the drivers of freshwater changes in the global simulation. Seasonal freshwater changes are primarily modulated by sea ice changes, while interannual freshwater variability is mainly driven by lateral freshwater flux. Comparisons with mooring data and derived transports near Bering Strait reveal that eORCA025 simulations underestimate volume transport but simulate fresher and warmer water than the observations, whereas regional ANHA4 simulations better capture Bering Strait inflow and more closely match observed temperature and salinity. These results highlight the importance of regional processes within the Arctic in shaping freshwater storage in the Beaufort Gyre. Our study provides insights into recent hydrographic changes in the Beaufort region and Bering Strait, offering suggestions for improving models’ ability to represent these changes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".