Pacific Waters Pathways and Vertical Mixing in the CESM1‐LE: Implication for Mixed Layer Depth Evolution and Sea Ice Mass Balance in the Canada Basin
Bibliographic record
Abstract
Abstract We compare the vertical hydrography of the Community Earth System Model Large Ensemble (CESM1‐LE) with observations from two specific periods: the Arctic Ice Dynamics Joint Experiment (AIDJEX; 1975–1976) and Ice‐Tethered Profilers (ITP; 2004–2018). A comparison between simulated and observed salinity and potential temperature profiles highlights two key model biases in all ensemble members: (a) an absence of Pacific Waters in the water column and (b) a slight deepening of the May mixed layer contrary to observations, which show a large reduction in the mixed‐layer depth and an increase in stratification over the same time period. We examine processes controlling the sea ice mass balance using a one‐dimensional vertical heat budget in the light of the model limitations implied by these two biases. Results indicate that remnant solar heat trapped beneath the halocline is mostly ventilated to the surface by mixing before the following melt season. Furthermore, we find that vertical advection associated with Ekman pumping has only a small effect on the vertical heat transport, even in early fall when the winds are strong and the pack ice is weak. Lastly, we estimate the impact of the missing Pacific Waters at 0.40 m of reduced winter ice growth.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".