Assessing the Impact of Climate Modes on Extreme Arctic Sea Ice Using Reanalysis Data
Bibliographic record
Abstract
ABSTRACT Arctic Sea ice variability arises from both anthropogenic forcing and natural climate modes such as the El Niño‐Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Arctic Oscillation (AO). While these modes are known to influence sea ice concentration (SIC) and thickness (SIT), their impacts on seasonal extremes remain less understood. In this study, the extreme SIC and SIT are investigated using ERA5 and CMEMS reanalysis products, applying a non‐stationary generalised extreme value (GEV) framework with climate indices as covariates. Results indicate that winter and spring sea‐ice variability is most pronounced in the Barents and Greenland Seas, where strong Atlantic inflows and dynamic atmospheric conditions make the marginal ice zone highly sensitive to even minor perturbations. Conversely, in the central and peripheral Arctic, variability maximises in summer and autumn, when melt processes, ice‐albedo feedback and delayed freeze‐up intensify interannual fluctuations. ENSO exerts notable seasonal effects: El Niño events enhance extreme SIC in the Laptev Sea but reduce it in the East Siberian Sea during summer, while SIT extremes increase in the Canadian Arctic Archipelago (CAA) and decline in the East Siberian Sea across all seasons. NAO‐related anomalies include stronger SIC and SIT extremes in the Beaufort Sea and CAA and reductions in the Chukchi Sea during autumn. AO effects include increased SIC in the Chukchi Sea during summer and autumn, but decreases in the Beaufort and CAA in summer; SIT extremes rise in the CAA during spring but fall in the Beaufort Sea in summer. Composite analysis further reveals that out‐of‐phase NAO‐AO states intensify autumn sea ice extremes, whereas in‐phase conditions exert weaker influences. These results emphasise the distinct and seasonally varying roles of climate modes in shaping Arctic Sea ice extremes, offering insights into future Arctic climate variability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".