Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition.
Bibliographic record
Abstract
Warm air intrusions over Arctic sea ice can rapidly change the snow and ice surfaceconditions and can alter sea ice concentration (SIC) estimates derived from satellite-based microwave radiometry without altering the true SIC.Here we focus on two warm moist air intrusions that produced surface glazing duringthe Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC)expedition that reached the research vessel Polarstern in mid-April 2020. After theevents, we observe increased SIC deviations between different satellite products,including climate data records, and especially an underestimation of SIC for algorithmsbased on polarization difference.To examine the causes of this underestimation, we use the extensive MOSAiC snowand ice measurements to computationally model the brightness temperatures of thesurface on a local scale. We further investigate the brightness temperatures observedby ground-based radiometers at frequencies 6.9 GHz, 19 GHz and 89 GHz.We show that the drop in the retrieved sea ice concentration of some satellite productscan be attributed to large-scale surface glazing, i.e., the formation of a thin ice crust atthe top of the snowpack, caused by the warming events.Another mechanism affecting satellite products which are mainly based on gradientratios of brightness temperatures, is the interplay of the changed temperature gradientin the snow and snow metamorphism.From the two analyzed climate data record products, one is less affected by thewarming events.The low frequency channels at 6.9 GHz were less sensitive to these snow surfacechanges, which could be exploited in future retrievals of sea ice concentration.
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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) | 0.001 | 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".