Linking future Gulf Stream warming and increased European winter precipitation in an eddy-rich model
Bibliographic record
Abstract
This contribution discusses future changes in Gulf Stream temperatures, winter precipitation over northwestern Europe, and their connection. We compare HighResMIP historical and ssp5-8.5 scenario simulations generated with five different configurations of the global coupled model HadGEM3-GC3.1, including one at a pioneering 50-km-atmosphere–1/12°-ocean global resolution. The highest resolution model projects an increase in winter rainfall over Europe outside or to the extremes of multimodel ensembles, such as CMIP6 and HighResMIP, for which both the highest ocean and atmosphere resolutions are essential: on the one hand, only the eddy-rich ocean (1/12°) projects a progressive northward shift of the Gulf Stream and substantial surface warming of the region; on the other, only the 50-km atmosphere translates such warming into strengthened extratropical cyclone activity over the North Atlantic and, hence, increased rainfall over Europe. The results suggest that climate projections relying on traditional ~100-km-resolution models might underestimate climate changes in the North Atlantic and Europe, demonstrating the importance of improved Gulf Stream representation for robust uncertainty estimates of climate risk.We also present the first results of the STREAM project, which aims to study the role of the ocean mesoscale in driving North Atlantic and European climate variability and predictability. We describe the results of the HighResMIP simulations generated with the EC-Earth global climate model at the T1270-ORCA12 resolution (about 15 km in both the atmosphere and the ocean) and explore the main model biases and response to climate change, as well as the variability in the North Atlantic circulation associated with subpolar oceanic deep mixing.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".