Evaluation of interdecadal trends in sea ice, surface winds and ocean waves in the Arctic in 1980--2019
Bibliographic record
Abstract
Sea ice loss in the Arctic region is one of the well documented consequences of climate change that also affects local atmospheric dynamics and wind-driven surface gravity waves.In this study, we perform the comparative assessment of linear trends in mean and extreme characteristics of 10-m winds and sea ice concentrations from ERA5, ERA-Interim, MERRA2 and NCEP CFSR reanalyses as well as significant wave heights from wind wave hindcasts performed with the spectral wave model WAVEWATCH III forced by these reanalyses in 1980-2019.The largest decline in sea ice concentration in all four reanalyses is observed in autumn and summer in the Chukchi and Beaufort Seas.In winter, all reanalyses and hindcasts agree on positive trends in both 10-m winds and wave heights in the Bering, Okhotsk and Labrador Seas.In spring, all datasets show negative trends in extreme wave heights in the North Pacific Ocean and positive trends in mean winds and wave heights in the western North Atlantic.In summer, positive trends in extreme 10-m winds and wave heights are observed in the Northeast Atlantic, and positive trends in extreme wave heights are revealed in the Sea of Okhotsk.In autumn, positive trends in both mean and extreme winds are observed in the Chukchi and Beaufort Seas as well as along the western coast of Greenland, which coincides with areas with the largest decline in sea ice concentrations.Positive trends in wind speed and wave heights in the Bering Seas are also revealed in all datasets.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".