The need for global glacier speed to combine measured velocity with balance velocity
Bibliographic record
Abstract
One of the outstanding glaciological research questions is how glaciers respond dynamically to climate change and how this varies regionally. Ice-velocity changes occur on an interannual scale in response to mass-balance forcing changing the glacier geometry and therefore the driving stress. For a glacier tending towards steady-state the mass flux through a cross-section equals the mass balance upstream of the cross-section. Mass loss will, therefore, usually lead to a slowdown of glaciers. However, a changing climate can also affect the occurrence of sliding and change a glacier’s thermal regime and its marginal processes (ice-ocean, ice-lake and ice-bed interactions). The response of glacier flow to climate change is, therefore, not straightforward, and mass loss combined with increased meltwater production or a transition to a temperate regime may lead to an increase in flow speed. Ultimately, depending on their individual response time, glaciers respond in a delayed dynamical way to changes in mass balance. Various recent publications have addressed the above research question at regional scales by analysing decadal changes in flow speed in relation to glacier mass loss. Only few local works, however, have addressed the question in the context of measured differences between actual and balance velocities. The recent generation of diverse global glacier datasets, such as the Randolph Glacier Inventory (RGI), GoLIVE and ITS_LIVE ice speed, ice thickness, and globally-distributed datasets such as WGMS mass balance data and companion ground measurements, offer opportunities to address outstanding research questions in interregional to global perspectives. We will compare, for the first time, for glaciers in various RGI subregions the difference between the measured glacier velocity, derived from available GoLIVE and ITS-LIVE datasets and additional speckle tracking from SAR scenes, and the balance velocity, derived using mass balance profiles, hypsometry, and ice thickness datasets. We use the standard approach of deriving balance flux along a flowline, and use a scenario-based approach to deal with measurement and model uncertainties. In this poster we present the results for approximately 20 glaciers in Canada and Iceland in detail. Ultimately, we aim to use more than 200 glaciers with WGMS and independent long-term mass balance records, distributed over the 19 RGI first-order regions and as many as the 89 second-order regions as possible.
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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".