Antarctic sea ice types from active and passive microwave remote sensing
Bibliographic record
Abstract
Abstract. Polar sea ice is one of the Earth’s climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014 and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly of MYI. Due to differences in physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently, no method has existed to monitor the distribution and temporal development of Antarctic ice types, particularly MYI throughout the freezing season and on decadal time scales. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The first circumpolar, long-term time series of Antarctic sea ice types; MYI, first-year ice and young ice is being established, so far covering years 2013–2019. Qualitative comparison with synthetic aperture radar data, with charts of the development stage of the sea ice, and with Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval for the first time allows insight into the evolution and dynamics of Antarctic sea ice types. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".