Evolution of Melt Pond Fraction and Depth on Multiyear Ice in 2020 from High Resolution Satellite Observations
Bibliographic record
Abstract
Observations reveal end of summer Arctic sea ice extent is declining at an accelerating rate. Model projections underestimate this decline and continue to have a broad spread in forecasted September sea ice extent. This suggests some important summer processes, such as melt pond formation and evolution, may not be properly represented in current models. Melt ponds form on the sea ice surface as snow melts, and pools in low lying areas on the sea ice surface. The evolution of the ponds depends on snow depth, ice thickness, and surface conditions. Melt water may spread across a level surface, or be confined to depressions between sea ice ridges. Ponds decrease the albedo of the surface and enhance the positive ice albedo feedback, accelerating further melt. Until recently, Arctic-wide observations of individual melt ponds were not available. ICESat-2, a photon counting laser altimeter launched in 2018, provides high resolution detail of sea ice and snow topography due to its unique combination of a small footprint (~12 m) and high-resolution along-track sampling (0.7 m). The green laser (532 nm) is able to penetrate water, enabling melt pond depth measurements. We have developed methods to track the melt pond surface and bathymetry in ICESat-2 data to determine melt pond depth. We also track melt pond evolution through application of a sea ice classification algorithm to 10 m resolution Sentinel-2 imagery. The combination of these two datasets allows for an evolving, three-dimensional view of the melting sea ice surface. We focus on the evolution of summer melt on multiyear ice in the Central Arctic north of Greenland and Canada in 2020. Our findings are put in context of existing literature on melt pond depth, volume, and evolution. We also discuss our results in relation to the melt pond fraction north of the Fram Strait, where we expect different ice conditions in the vicinity of the 2020 MOSAiC field studies. Observational data products comprising melt pond fraction and pond depth are being developed for public distribution. These products may be of interest to those studying under-ice light and biology, as well as modelers who are interested in understanding the evolution of melt pond parameters for model initialization and validation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".