Large Eddy Simulation of Heat Entrainment Under Arctic Sea Ice
Bibliographic record
Abstract
Abstract Arctic sea ice has declined rapidly in recent decades. The faster than projected retreat suggests that free‐running large‐scale climate models may not be accurately representing some key processes. The small‐scale turbulent entrainment of heat from the mixed layer could be one such process. To better understand this mechanism, we model the Arctic Ocean's Canada Basin, which is characterized by a perennial anomalously warm Pacific Summer Water (PSW) layer residing at the base of the mixed layer and a summertime Near‐Surface Temperature Maximum (NSTM) within the mixed layer trapping heat from solar radiation. We use large eddy simulation (LES) to investigate heat entrainment for different ice‐drift velocities and different initial temperature profiles. The value of LES is that the resolved turbulent fluxes are greater than the subgrid‐scale fluxes for most of our parameter space. The results show that the presence of the NSTM enhances heat entrainment from the mixed layer. Additionally there is no PSW heat entrained under the parameter space considered. We propose a scaling law for the ocean‐to‐ice heat flux which depends on the initial temperature anomaly in the NSTM layer and the ice‐drift velocity. A case study of “The Great Arctic Cyclone of 2012” gives a turbulent heat flux from the mixed layer that is approximately 70% of the total ocean‐to‐ice heat flux estimated from the PIOMAS model often used for short‐term predictions. Present results highlight the need for large‐scale climate models to account for the NSTM layer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".