Is spring melting in the Arctic detectable by under-ice radiation?
Bibliographic record
Abstract
A trend towards earlier sea-ice melt is detected in many ice-covered regions in the Arctic. The timing of the melt onset has a strong impact on the sea-ice energy budget. Melt onset changes the radiative properties of the ice due to increasing snow wetness and meltwater. So far, satellite passive microwave data are used to detect the melt onset. We analyzed transmitted radiation spectra as collected underneath drifting sea-ice using a remotely operated vehicle during the ARTofMELT expedition in the Fram Strait in spring 2023. We colocated those spectra with measurements of snow depth, sea ice and surface topography, chlorophyll-a concentration in the water column, and with aerial images. This combined dataset enables us to track down possible subsurface pathways and accumulation pools of meltwater. Areas of low snow load and depressed surface topography are characterized by higher transmitted radiation compared to areas with a thick snow cover. Those areas overlapped with areas that showed the first signs of surface melt. Chlorophyll-a concentrations varied only slightly in magnitude and did not match with the heterogeneous pattern of snow depth and ice topography. Here we discuss how to disentangle the influences of chlorophyll a and the subsurface meltwater on the spectral shape of transmitted radiation. We propose that upon successful disentanglement, the spectra can be used as an indicator for subsurface melting. Our study suggests that sea-ice melting starts subsurface and that measurements of transmitted solar radiation spectra could be used to identify the melt onset prior to surface melting. This can provide an interesting complementary information on melt occurrence and on the location of the water in the snowpack in addition to satellite passive microwave data.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".