The Rise and Fall of Alaska and Yukon Glaciers Detected by TOPEX/Poseidon and Jason‐2 Altimeters Using a Novel Glacier‐Threshold Method
Bibliographic record
Abstract
Abstract Radar altimetry has been used to monitor sea level changes and ice sheet elevation changes for decades. Over mountain glaciers, radar altimetry has limited applications due to contaminated waveforms caused by complex glacier surfaces and steep terrains. In this study, we develop a glacier‐threshold method (GTM) to determine glacier elevation changes in Alaska and Yukon. The GTM retracks waveforms, reduces terrain effect and detects invalid elevation observations from the TOPEX/Poseidon (T/P) and Jason‐2 (J2) altimeters, resulting in an average usable rate of 35% from original altimeter‐measured heights. The selected measurements are used to construct time series of glacier elevation changes over 1993–2002 (T/P) and 2008–2016 (J2) at 47 sites. A crossover analysis, validation by airborne laser altimetry observations, and comparisons with recent studies confirm the estimated glacier elevation changes. Our findings suggest that the thinning of glaciers in Alaska and Yukon has been ubiquitous in recent years. The site near Walsh Glacier has the highest thinning rate of −5.71 ± 0.09 m/yr, followed by Chitina Glacier at −4.51 ± 0.21 m/yr. Only 17% of sites show glacier thickening due to surges and mass accumulations. Using a sophisticated data processing algorithm like the GTM, we show that altimeter data from the repeat TOPEX‐Jason missions can be used to monitor long‐term glacier elevation changes at inaccessible spots, creating an additional value for altimeter missions originally purposed for monitoring long‐term sea level change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".