Arctic climate and its interaction with lower latitudes under different levels of anthropogenic warming in a global coupled climate model
Bibliographic record
Abstract
Three quasi-equilibrium simulations using constant greenhouse gas forcing corresponding to years 2000, 2015 and 2030 have been performed with the global coupled model EC-Earth in order to analyze the Arctic climate and interactions with lower latitudes under different levels of anthropogenic warming. The model simulations indicate an accelerated warming and ice extent reduction in the Arctic between the year-2030 and year-2015 simulations compared to the change between the year-2015 and year-2000 simulations. Both Arctic warming and sea ice reduction are closely linked to the increase of ocean heat transport into the Arctic, particularly through the Barents Sea Opening. Decadal variations of Arctic sea ice extent and ice volume are of the same order of magnitude as the observed ice extent reductions in the last 30 years and are dominated by the variability of the ocean heat transports through the Barents Sea Opening and the Bering Strait. Despite a general warming of mid and high northern latitudes, a substantial cooling is found in the subpolar gyre of the North Atlantic under year-2015 and year-2030 conditions. This cooling is related to a strong reduction in the AMOC, itself due to reduced deep water formation in the Labrador Sea. The observed trend towards a more negative phase of the North Atlantic Oscillation (NAO) and the observed linkage between autumn Arctic ice variations and NAO are reproduced in our model simulations for selected 30-year periods but are not robust over longer time periods. This indicates that the observed linkages between ice and NAO might not be robust in reality either, and that the observational time period is still too short to reliably separate the trend from the natural variability.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".