Do CMIP6 models have Pacific Water Heat Signatures in the Canada Basin?
Bibliographic record
Abstract
The Arctic Ocean’s Beaufort Gyre is experiencing rapid, anthropogenic change as evidenced by numerous observations including sea ice loss, a more stratified upper ocean, and ocean warming. Climate models struggle to simulate observed changes to ocean stratification, raising the possibility of significant biases in the upper-ocean heat storage. Here, we examine how accurately near-surface ocean heat signatures from local solar absorption and the Pacific Ocean are represented in 34 climate model simulations in comparison to ORAS5 ocean reanalysis. We find that 85\% of the models do not reproduce the heat signature associated with Pacific Water typically found in observed temperature profiles. This bias causes the models to have 62\% (23\%) less heat than observed in the top 100~m in March (September) on average. Our results suggest that this bias may be closely related to the models simulating unrealistically deep vertical mixing in the Beaufort Gyre, causing the surface to be too salty and weakening the lateral density gradient necessary for the subduction of Pacific Water. These results suggest that heat, which would otherwise be stored in the ocean, is instead lost to the atmosphere and to the sea ice and thus has direct implications for simulated Arctic climate in response to climate change.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".