The changing seasonality of mixed layer temperature and its driving mechanism in the Arctic shelf seas
Bibliographic record
Abstract
This study analyzes the changing seasonality of the upper ocean temperature in the Arctic shelf seas in response to the warming climate. The study is conducted by using a global ocean-sea ice coupled model based on the Nucleus for European Modelling of the Ocean framework. The model decently simulates the variability of the Sea Surface Temperature (SST) in each shelf sea over 1995-2022, which agree with the observations from the NOAA SST dataset. The modelled and observational results consistently demonstrate that the significant warming trend of the SST appears in the Barents, Kara, and Laptev Seas, corresponding to the maximum atmospheric warming trend in the Arctic as some studies have previously found. The amplified seasonal cycle occurs in the East Siberian, Laptev, and Kara Seas. We perform mixed layer heat budget calculations to probe the role of the contributing processes on the seasonal amplitude change of the Mixed Layer Temperature (MLT) in the shelf seas. The seasonal amplitude change of MLT in the shelf seas is primarily governed by the heat flux forcing, with various degrees of contribution from the horizontal advection and minimal contribution from the vertical entrainment. The heat flux forcing term is controlled by the changes in the heat fluxes. The solar heat flux dictates the forcing term change mostly in the summer, while the nonsolar heat flux plays a crucial role in the fall. This study helps us understand the thermodynamic change in the Arctic shelf seas in a changing climate based on a modelling perspective.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".