Bibliographic record
Abstract
Abstract. Quantifying the total liquid water amounts (LWA) in the Greenland ice sheet (GrIS) is critical for understanding GrIS firn processes, mass balance, and global sea-level rise. Although satellite microwave observations are very sensitive to ice sheet melt and thus can provide a way of monitoring the ice sheet melt globally, estimating total LWA, especially the sub-surface LWA, remains a challenge. Here, we present a microwave retrieval of LWA over Greenland using enhanced resolution L-band brightness temperature (TB) data products from the NASA Soil Moisture Active Passive (SMAP) satellite for the 2015–2023 period. L-band signals receive emission contributions deep in the ice sheet and are sensitive to the liquid water content (LWC) in the firn column. Therefore, they can estimate the surface-to-subsurface LWA, unlike higher frequency signals (e.g., 18 and 37 GHz bands), which are limited to the top few centimeters of the surface snow during the melt. We used vertically polarized TB (V-pol TB) with empirically derived thresholds to detect liquid water and identify distinct ice sheet zones. A forward model based on radiative transfer in the ice sheet was used to simulate TB. The simulated TB was then used in an inversion algorithm to estimate LWA. Finally, the retrievals were compared with the LWA obtained from two sources. The first source was a locally calibrated ice sheet energy and mass balance (EMB) model, which was forced by in situ measurements from six automatic weather stations (AWS) of the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and Greenland Climate Network (GC-Net) located in the percolation zone of the GrIS. The second source was the corresponding LWA obtained from the Glacier Energy and Mass Balance (GEMB) model within the National Aeronautics and Space Administration’s (NASA) Ice-sheet and Sea-Level System Model (ISSM). The retrievals show generally good agreement with both the references, demonstrating the potential for advancing our understanding of ice sheet physical processes to better project Greenland’s contribution to the global sea level rise in response to the warming climate.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.362 | 0.275 |
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".