Continuity of the Mass Loss of the World's Glaciers and Ice Caps From the GRACE and GRACE Follow‐On Missions
Bibliographic record
Abstract
Abstract We use time series of time‐variable gravity from the Gravitational Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GRACE‐FO) missions to evaluate the mass balance of the world's glaciers and ice caps (GIC) for the time period April 2002 to September 2019, excluding Antarctica and Greenland peripheral glaciers. We demonstrate continuity of the mass balance record across the GRACE/GRACE‐FO data gap using independent data from the GMAO Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) reanalysis. We report an average mass loss of 281.5 ± 30 Gt/yr, an acceleration of 50 ± 20 Gt/yr per decade, and a 13‐mm cumulative sea level rise for the analyzed period. Seven regions dominate the mass loss, with the largest share from the Arctic: Alaska (72.5 ± 8 Gt/yr), Canadian Arctic Archipelago (73.0 ± 9 Gt/yr), Southern Andes (30.4 ± 13 Gt/yr), High Mountain Asia (HMA) (28.8 ± 11 Gt/yr), Russian Arctic (20.2 ± 6 Gt/yr), Iceland (15.9 ± 4 Gt/yr), and Svalbard (12.1 ± 4 Gt/yr). At the regional level, the analysis of acceleration is complicated by a strong interannual to decadal variability in mass balance that is well reproduced by the GRACE‐calibrated MERRA‐2 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".