Improving Multiyear Sea Ice Concentration Estimates with Sea Ice Drift
Bibliographic record
Abstract
Multiyear ice (MYI) concentration can be retrieved from passive or active microwave remote sensing observations. One of the algorithms that combines both observations is the Environmental Canada Ice Concentration Extractor (ECICE). However, factors such as ridging, snow wetness and metamorphism can cause significant changes in brightness temperature and backscatter, leading to misidentification of FYI as MYI, hence increasing the estimated MYI concentrations suddenly. This study introduces a correction scheme to restore the MYI concentrations under these conditions. The correction utilizes ice drift records to constrain the MYI changes and uses two thresholds of passive microwave radiometric parameters to account for snow wetness and metamorphism. The correction is applied to MYI concentration retrievals from ECICE with inputs from QuikSCAT and AMSR-E observations, acquired over the Arctic region in a series of winter seasons (October to May) from 2002 to 2009. Qualitative comparison with the Radarsat-1 SAR images and quantitative comparison against results from previous studies show that the correction works well by removing the anomalous high MYI concentrations. On average, the correction reduces the estimated MYI area by 5.2 × 105 km2 (14.3%) except for the April–May time frame, when the reduction is larger as the warmer weather prompts the condition of the anomalous snow radiometric signature. Due to the long-lasting (i.e., from one to several weeks) effect of the warm spells on FYI, the correction could be important in climatological research and operational applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".