Circumpolar polynya regions and ice production in the Arctic: Results from MODIS thermal infrared imagery for 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea
Bibliographic record
Abstract
Abstract. High-resolution MODIS thermal infrared satellite data are used to infer spatial and temporal characteristics of 16 prominent coastal polynya regions over the entire Arctic basin. Thin-ice thickness distributions (≤ 20 cm) are calculated from MODIS ice-surface temperatures, combined with ECMWF ERA-Interim atmospheric reanalysis data in an energy balance model for 13 winter-seasons (2002/2003 to 2014/2015; November to March). From all available swath-data, (quasi-) daily thin-ice thickness composites are computed in order to derive quantities such as polynya area and total thermodynamic (i.e. potential) ice production. A gap-filling approach is applied to account for cloud and data gaps in the MODIS composites. All polynya regions combined cover an average thin-ice area (POLA) of 184.3 ± 35.6 × 103 km2 in winter. This allows for an average total wintertime accumulated ice production of about 1444 ± 258 km3, whereby the Kara Sea region and the North Water polynya (both 19 %) as well as scattered smaller polynyas in the Canadian Arctic Archipelago (all combined 15 %) are the main contributors. Other well-known sites of polynya formation (Laptev Sea, Chukchi Sea) show smaller contributions and range between 2 and 7 %. Compared to an earlier study on pan-Arctic polynya characteristics that utilized lower resolution passive microwave remote sensing data, our estimates are about 22 % larger on average. This partly originates from the use of high-resolution MODIS data, which increases the capability to resolve small scale (> 2 km) thin-ice features such as large leads. Despite the short record of 13 winter-seasons, positive trends in ice production are detected for several regions of the eastern Arctic (most significant in the Laptev Sea region with an increase of 6.8 km3/yr) and the North Water polynya, while other polynyas in the western Arctic show a more pronounced variability with varying trends. We emphasize the role of the Laptev Sea polynyas as being a major influence on Transpolar Drift characteristics through a distinct relation between increasing ice production and ice area export. Overall, our study contains the most accurate characterization of circumpolar polynya dynamics and ice production to date which should be valuable for future modeling efforts on atmosphere–sea ice–ocean interactions in the Arctic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".