Understanding eddy field in the Arctic Ocean from high-resolution satellite observations
Bibliographic record
Abstract
The Arctic Ocean is a host to major ocean circulation systems, many of which generate eddies that can transport water masses and corresponding tracers over long distances from their formation sites. However, comprehensive observations of critical eddy characteristics are currently not available and are limited to spatially and temporally sparse in situ observations. Here we use multi-mission high‐resolution spaceborne synthetic aperture radar (SAR) measurements to detect eddies over open ocean and marginal ice zones (MIZ) of Fram Strait and Beaufort Gyre regions. We provide the first estimate of eddy properties, including their locations, size, vorticity sign and monthly distribution during summer period (from June to October). The results of historical Envisat ASAR observations for 2007 and 2011 are then compared to Sentinel-1 and ALOS-2 PALSAR-2 measurements acquired in 2016 and 2018, to infer the possible changes in the intensity and locations of eddy generation over the last decade. The most prominent feature of the obtained results is that cyclonic eddies strongly dominate over anticyclones. Eddies range in size between 0.5 and 100 km and are frequently found over the shelf and near continental slopes but also present in the deep basin. For MIZ eddies, the number of eddies clearly depends on sea ice concentration with more eddies detected at the ice edge and over low ice concentration regions. The obtained results clearly show that eddies are ubiquitous in the Arctic Ocean even in the presence of sea ice and emphasize the need for improved ocean observations and modeling at eddy scales. A special focus is also given to infer eddy dynamics over the Arctic marginal ice zones. The use of sequential Sentinel-1 SAR images enables to retrieve high-resolution velocity field over MIZ on a daily basis and observe eddy-driven MIZ dynamics down to submesoscales. The obtained eddy orbital velocities are in agreement with historical observations and may reach up to 0.5-0.7 m/s. We believe that this information is critical for better understanding of the key dynamical processes governing the MIZ state, as well as for improving and validation of sea ice and coupled ice-ocean models. The analysis of eddies in this work was supported by RFBR grant 18‐35‐20078. Processing and analysis of Sentinel‐1 and ALOS‐2 Palsar‐2 data were done within RSF grant 18‐77‐00082. Software development for data analysis in this work was made under the Ministry of Science and Higher Education of the Russian Federation contract 0555‐2019‐0001.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".