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Record W3021451744 · doi:10.1029/2019gl086926

Continuity of the Mass Loss of the World's Glaciers and Ice Caps From the GRACE and GRACE Follow‐On Missions

2020· article· en· W3021451744 on OpenAlexaboutno aff
E. Ciracì, I. Velicogna, Sean Swenson

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierArcticClimatologyGlacier mass balanceIce capsArchipelagoGeologyPhysical geographyOceanographyGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.273
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations171
Published2020
Admission routes1
Has abstractyes

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