Projected changes to wintertime air-sea turbulent heat fluxes over the subpolar North Atlantic Ocean
Bibliographic record
Abstract
<!--!introduction!--> In wintertime over the subpolar North Atlantic Ocean (SPNA), the strongest surface sensible and latent heat fluxes typically occur just downstream of the ice edge. The recent retreat in Arctic wintertime sea ice is changing the distribution of these turbulent heat fluxes, with consequences for the formation of the dense waters that feed into the Atlantic Meridional Overturning Circulation. Projections of turbulent heat flux over the SPNA are investigated using output from the HadGEM3-GC3.1 climate model, produced as part of the 6th phase of the Coupled Model Inter-Comparison Project. Comparison of two model resolutions (MM: 60 km atmosphere - 1/4° ocean and HH: 25 km – 1/12°) shows that the HH configuration more accurately simulates historic sea ice and turbulent heat flux distributions. The MM configuration tends to produce too much sea ice in the SPNA, affecting the turbulent heat flux distribution; however, it displays improved performance during winters with less sea ice, increasing confidence in future projections when less sea ice is predicted. Future projections are presented for low (SSP1-2.6) and high (SSP5-8.5) emissions pathways. The simulations agree in predicting that with climate change the SPNA will see significant reductions in wintertime sea ice and air-sea turbulent fluxes later in the 21st century, particularly in the Labrador and Irminger Seas and the interior of the Nordic Seas, and a notable reduction in their decadal variability. These effects are more severe under the SSP5-8.5 pathway. The implications for SPNA ocean circulation are discussed.
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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) | 0.001 | 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) | 0.002 | 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".