Variability of summer-time Arctic sea ice: the drivers and the contribution to the sea ice trend and extremes
Bibliographic record
Abstract
Understanding the variability of summer-time Arctic sea ice at interannual to multidecadal time scales in the midst of anthropogenically forced sea ice decline is crucial for better predictions of sea ice conditions in the future climate and rapid changes in sea ice. Here, we apply time-frequency analysis to study the modes of variability, extreme events and the trend in the September Arctic sea ice in 100–150 year datasets. We extract the non-linear trend for the sea ice area and provide an estimate for the anthropogenic-driven sea ice loss. For the used dataset, the anthropogenic-related sea ice loss is found to have a rate of ~-0.25 million km2 per decade in the 1980’s and accelerating to ~-0.47 million km2 per decade in 2010’s. By assuming the same rate of sea ice loss in the future, and without the contribution of the internal variability and feedbacks, we can approximate the occurrence of summer sea-ice free Arctic to be around 2060. Regarding the dominant modes of variability for the September sea ice, we find that they have periods of around 3, 6, 17, 28 and 55 years, and show what drives these modes and how they contribute to sea ice extreme events. The main atmospheric and oceanic drivers of sea ice modes include the Arctic oscillation and Arctic dipole anomaly for the 3-year mode, variability of sea surface temperature (SST) in Gulf Stream region for the 6-year mode, decadal SST variability in the northern North Atlantic Ocean for the 17-year mode, Pacific decadal oscillation (PDO) for the 28-year mode, and Atlantic multidecadal Oscillation (AMO) for the 55-year mode. Results show that changes in the sea ice due to internal variability can be as large as forced changes thus can slow down or accelerate the background anthropogenic-driven sea ice loss. By applying the same method, we also present modes of variability and trend of sea ice in the large ensemble global model simulations of EC-Earth model (SMHI-LENS) for the future climate projections and different climate scenarios.
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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 | 0.000 | 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".