Greenland, Antarctica and Glaciers and Ice Caps mass balance from GRACE/GRACE-FO and other data
Bibliographic record
Abstract
We discuss the state of mass balance of glaciers and ice sheets from 2002 to present using data from GRACE/GRACE-FO missions, after filling the gap between missions. We compare data processed by different centers (JPL, CSR, GFZ) and evaluate various Glacial Isotatic Adjustment models. In Greenland, the data indicate a persistent mass loss at 251 Gt/yr, with an acceleration of 3 Gt/yr/yr, and large summer losses (400-600 Gt) in 2012, 2017, 2019. The mass balance regime has been evolving significantly in recent years, especially in the North, which holds the largest potential for rapid sea level rise. In Antarctica, ongoing mass losses in the Amundsen Sea Embayment of West Antarctica (122 Gt/yr), Antarctic Peninsula (26 Gt/yr), and Wilkes Land in East Antarctica (33 Gt/yr) dominate a small but significant increase in snowfall in the Queen Maud Land sector of East Antarctica since 2009 (47 Gt/yr). For the GIC, the mass loss averages 274 Gt/yr, with an acceleration of 4 Gt/yr/yr. The largest contributors are in the Arctic: Canadian Archipelago (70 Gt/yr), Alaska (72 Gt/yr), Russian Arctic (21 Gt/yr), Svalbard and Iceland (29 Gt/yr)) versus the southern hemisphere which is dominated by Patagonia (35 Gt/yr). High Mountain Asia averages 22 Gt/yr mass loss, with a large inter-annual variability. In regions not dominated by ice dynamics, the GRACE results compare better every year with output products from regional climate models (MAR, RACMO) forced by ERA5 and with global models such as NASA’s MERRA-2, which offers interesting perspectives for model development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.006 | 0.003 |
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".