High Resolution Mapping of Ice Mass Loss in the Gulf of Alaska From Constrained Forward Modeling of GRACE Data
Bibliographic record
Abstract
The resolution of GRACE Terrestrial Water Storage change data is too low to discriminate mass variations at the scale of glaciers, small ensemble of glaciers, or icefields. In this paper, we applied an iterative constraint modelling strategy over the Gulf of Alaska (GOA) to improve the resolution of ice loss estimates derived from GRACE. We assess the effect of the most influential parameters such as the type of GRACE Level-2 solution and the degree of heterogeneity of the distribution map over which the GRACE data is focused. Three GRACE solutions from the most common processing strategies and three ice distribution maps of resolutions ranging from 55,000 km2 to 20,000 km2 are used. First, we present results from a series of simulations with synthetic data or a mix of synthetic/modelled data to validate the focusing strategy and we point out how inaccuracies arise while increasing the spatial resolution of GRACE data. Second, we present the recovery of the total GRACE-derived mass change anomaly at the scale of the GOA. At this scale, all solutions and distribution maps agree, showing ~40 Gt/yr of mean ice mass loss over the 2002-2017 period. This result is similar to studies using GRACE solutions from the latest releases and time-series of more than 8 years. The first studies using GRACE data published during the 2005-2008 era generally overestimated the total ice mass loss. Third, we show results of the three resolutions tested to focus the mass anomaly. Using focusing units (mascon) of ~30,000 km2 or larger, the focusing procedure provides reliable results with errors below 15%. Below this threshold, errors of up to 56% are observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".