A globally complete, spatially and temporally resolved estimate of glacier mass change: 2000 to 2019
Bibliographic record
Abstract
The world’s glaciers distinct from the Greenland and Antarctic ice sheets are shrinking rapidly, altering regional hydrology and raising global sea level. Yet, due to the scarcity of globally consistent observations, their recent evolution is only known as a heterogeneous temporal and geographic patchwork and future projections are thus not optimally constrained. Here, we present the first globally complete, consistent and resolved estimate of glacier mass change derived from more than half a million digital elevation models (DEMs) generated or extracted from multiple satellite archives including ASTER, ArcticDEM and REMA. Combining state-of-the-art numerical photogrammetry and novel statistical approaches, we reconstruct two decades of glacier surface elevation change at an unprecedented spatial and temporal resolution. We validate our results by comparing them to independent, high-precision elevation measurements from the ICESat and IceBridge campaigns, as well as to very high resolution DEM differences from LiDAR, Pléiades, and SPOT-6. The elevation time series are integrated to volume changes for every single glacier on Earth and, by assuming an average density, aggregated to regional and global mass changes. We compare our revised glacier mass changes to earlier estimates derived from altimetry, gravimetry, geodetic and field data. As an illustration, our integrated geodetic mass loss over all Icelandic glaciers yields -8.3 +- 1.1 Gt yr-1 over the period 2002-2016 in agreement with a recent gravimetry estimate of -8.3 +- 1.8 Gt yr-1 (Wouters et al., 2019), known to perform well in this region. Both estimates are more negative than -5.7 +- 1.2 Gt yr-1, compiled from glaciological observations and geodetic data (Zemp et al., 2019). Our global estimate of glacier mass change constitutes a new benchmark dataset that will help to: (i) assess present-day and future climate change impacts on glaciers; (ii) close the sea-level rise budget; (iii) assess the threat on water resources and (iv) facilitate research on natural hazards related to glaciers. Our results specifically provide a strong observational basis that holds a great potential to further our understanding of the multi-scale morphologic and climatic drivers of glacier mass change, essential to improve physically-based glaciological modelling and calibrate future projections.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.001 |
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".