Causes and Evolution of Winter Polynyas over North of Greenland
Bibliographic record
Abstract
Abstract. During the 42-year period (1979–2020) of satellite measurements, only three winter polynyas have ever been observed north of Greenland and they all occurred in the last decade, i.e. February of 2011, 2017 and 2018. The 2018 polynya was unparalleled by its magnitude and duration compared to the two previous events. Combined with the limited weather station and remotely-sensed sea ice data, a fully-coupled Regional Arctic System Model (RASM) hindcast simulation was utilized to examine the causality and evolution of these recent extreme events. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact on the development of these winter open water episodes in the study region (i.e., no significant ice melting). Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds of greater than 10 m/s) blowing persistently for at least 2 days or more, were required over the study region to mechanically redistribute some of the thickest sea ice out of the region and thus to create open water areas (a latent heat polynya). In order to assess the role of internal variability versus external forcing of such events, we additionally simulated and examined results from two RASM ensembles forced with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles, initialized 30 years apart, one in December 1985 and another in December 2015, respectively, 17 and 14 winter polynyas were produced over north of Greenland. The frequency of polynya occurrence and no apparent sensitivity to the initial sea ice thickness in the study area point to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations and probabilistic forecast of Arctic events, such as the ones being investigated here and elsewhere.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".