Glacier darkening quantified from airborne imaging spectroscopy, Place Glacier, British Columbia, Canada
Bibliographic record
Abstract
Seasonal to long-term changes in albedo, or glacier darkening, is a critical parameter for energy and mass balance models. Yet many of these models employ simple parameterization schemes that darken snow and ice surfaces non-linearly through time. This simplification is not representative of the complex controls on albedo that vary spatially and temporally, driven by atmospheric processes, surface-atmosphere interaction, topography, and timing of glacier ice exposure. Albedo also spectrally varies, controlled by concentrations of light absorbing constituents (LACs) in the visible wavelengths and grain size in the near infrared wavelengths. Radiative forcing by LACs can enhance grain growth, leading to more rapid glacier darkening over the full solar spectrum. This process can accelerate as snow and ice melts because LACs tend to accumulate at the surface which can lead to increased radiative forcing over time for some glaciers. As temperatures warm, and aerosols increase due to land use change, drought, fire, and urbanization, it is likely that glacier darkening will intensify. To better quantify seasonal rates of darkening, and understand controls on intra- and interannual variability, we collected and analyzed a rich dataset obtained from imaging spectroscopy and lidar collected over Place Glacier in the Coast Mountains of British Columbia, Canada. Over the years 2021-2022, we acquired monthly data during the period of snow and glacier melt (March to October for 2021 and July to October 2022) using an aircraft with dedicated lidar (Riegl-780) and hyperspectral (Specim-Fenix; 451 bands) sensors. We processed these monthly acquisitions into 1-m, analysis-ready products. We describe our workflow for these products including development of snow and ice surface property retrievals in complex mountainous terrain. Our workflow yields retrievals that include broadband albedo, radiative forcing by LACs, and grain size. Radiative forcing from LACs can originate from abiotic and biotic sources, and we use the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) to interpret our retrievals with respect to contributions from dust and black carbon. We also highlight how these data can be used to understand seasonal glacier darkening events that occurred during a heat dome, snow algae blooms, and a late start to accumulation season. All these events are expected to increase in frequency or intensity due to climate change and hence, a better understanding of these physical processes will lead to improved physical models for future glacier evolution.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".