Sea Ice Roughness Overlooked as a Key Source of Uncertainty in CryoSat‐2 Ice Freeboard Retrievals
Bibliographic record
Abstract
Abstract ESA's CryoSat‐2 has transformed the way we monitor Arctic sea ice, providing routine measurements of the ice thickness with near basin‐wide coverage. Past studies have shown that uncertainties in the sea ice thickness retrievals can be introduced at several steps of the processing chain, for instance, in the estimation of snow depth, and snow and sea ice densities. Here, we apply a new physical model to CryoSat‐2, which further reveals sea ice surface roughness as a key overlooked feature of the conventional retrieval process. High‐resolution airborne observations demonstrate that snow and sea ice surface topography can be better characterized by a lognormal distribution, which varies based on the ice age and surface roughness within a CryoSat‐2 footprint, than a Gaussian distribution. Based on these observations, we perform a set of simulations for the CryoSat‐2 echo waveform over “virtual” sea ice surfaces with a range of roughness and radar backscattering configurations. By accounting for the variable roughness, our new lognormal retracker produces sea ice freeboards that compare well with those derived from NASA's Operation IceBridge airborne data and extends the capability of CryoSat‐2 to profile the thinnest/smoothest sea ice and thickest/roughest ice. Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first‐year ice and 30% over multiyear ice, representing one of the principal sources of pan‐Arctic sea ice thickness uncertainty.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".