Evaluation of interdecadal trends in sea ice, surface winds and ocean waves in the Arctic in 1980--2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it