Predictability of the Minimum Sea Ice Extent from Late Winter Fram Strait Ice Export: Model vs Observations
Bibliographic record
Abstract
We assess whether the observed seasonal predictability of the September sea ice extent arising from Fram Strait ice area export, a proxy for coastal divergence along the Eurasian coastline, is present in Global Climate Models, namely the CESM2-LE, GISS-E2.1-G, GFDL FLOR-LE, CNRM-CM6-1 and CanESM5. Results show distinct periods where winter Fram Strait ice area export anomalies are negatively correlated with the May sea ice thickness anomalies along the Eurasian coastline (the source region of the Transpolar Drift Stream), and the following September Arctic sea ice extent, as shown in observations. Counter-intuitively, periods where winter Fram Strait ice area export anomalies are positively correlated with the following September sea ice extent anomalies are also present in several models. This occurs early in the record when the mean Arctic sea ice thickness is large and ice area exported out of the Arctic (or recirculated in the Beaufort Gyre) survives the following summer melt leading to positive sea ice anomalies in the Greenland and Beaufort seas. Later in the record, when sea ice is thinner, winter Fram Strait ice area export anomalies are correlated with enhanced ridging and convergence of sea ice north of the Canadian Arctic Archipelago, leading to positive SIE anomalies in the late summer in the Lincoln Sea. Finally, there are several periods where the Fram Strait ice area export and coastal divergence are weakly coupled, resulting in no (statistically significant) seasonal predictability of the September SIE. In general, we find that the coupling between the Fram Strait ice area export and the September SIE is present across models and changes in the statistical relationship as a function of the mean Arctic sea ice thickness state
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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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".