Can we trust CMIP5/6 future projections of European winter precipitation?
Bibliographic record
Abstract
Abstract IPCC models project a likely increase in winter precipitation over northern Europe under a high-emission scenario. These projections, however, typically rely on relatively coarse ∼100 km resolution models that can misrepresent important processes driving precipitation, such as extratropical cyclone activity, and ocean eddies. Here, we show that a pioneering 50 km atmosphere–1/12° ocean global coupled model projects a substantially larger increase in winter precipitation over northwestern Europe by mid-century than lower-resolution configurations. For this increase, both the highest ocean and atmosphere model resolutions are essential: only the eddy-rich (1/12°) ocean projects a progressive northward shift of the Gulf Stream. This leads to a strong regional ocean surface warming that intensifies air–sea heat fluxes and baroclinicity. For this then to translate into a strengthening of North Atlantic extratropical cyclone activity, the 50 km atmosphere is essential, as it enables enhanced diabatic heating from water vapor condensation and an acceleration of the upper-level mean flow, which weaken vertical stability. Our results suggest that all recent IPCC climate projections using traditional ∼100 km resolution models could be underestimating the precipitation increase over Europe in winter and, consequently, the related potential risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".