The Atlantic inflow across the Greenland-Scotland ridge in global climate models (CMIP5)
Bibliographic record
Abstract
Oceanic heat transport from the North Atlantic to the Arctic through the Nordic Seas is a key component of the climate system that has to be modelled accurately in order to predict, for example, future Arctic sea ice changes or European climate. Here we quantify biases in the climatological state and dynamics of the transport of oceanic heat into the Nordic Seas across the Greenland-Scotland ridge in 23 state-of-the-art global climate models that participated in the Climate Model Intercomparison Project phase 5. The mean poleward heat transport, its seasonal cycle and interannual variability are inconsistently represented across these models, with a vast majority underestimating them and a few models greatly overestimating them. The main predictor for these biases is the resolution of the model via its representation of the Greenland-Scotland ridge bathymetry: the higher the resolution, the larger the heat transport through the section. The second predictor is the large-scale ocean circulation, which is also connected to the bathymetry: models with the largest heat transport import water from the European slope current into all three straits of the Greenland-Scotland ridge, whereas those with a weak transport import water from the Labrador Sea. The third predictor is the spatial pattern of their main atmospheric modes of variability (North Atlantic Oscillation, East Atlantic and Scandinavian patterns), where the models with a weak inflow have their atmospheric low-pressure centre shifted south towards the central Atlantic. We argue that the key to a better representation of the large-scale oceanic heat transport from the North Atlantic to the Arctic in global models resides not only in higher resolution, but also in a better bathymetry and representation of the complex ocean-ice-atmosphere interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 teacher head, 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".