Algal photophysiology drives darkening and melt of the Greenland Ice Sheet
Bibliographic record
Abstract
Blooms of Zygnematophycean “glacier algae” lower the bare ice albedo of the Greenland Ice Sheet (GrIS), amplifying summer energy absorption at the ice surface and enhancing meltwater runoff from the largest cryospheric contributor to contemporary sea-level rise. Here, we provide a step change in current understanding of algal-driven ice sheet darkening through quantification of the photophysiological mechanisms that allow glacier algae to thrive on and darken the bare ice surface. Significant secondary phenolic pigmentation (11 times the cellular content of chlorophyll a ) enables glacier algae to tolerate extreme irradiance (up to ∼4,000 µmol photons⋅m −2 ⋅s −1 ) while simultaneously repurposing captured ultraviolet and short-wave radiation for melt generation. Total cellular energy absorption is increased 50-fold by phenolic pigmentation, while glacier algal chloroplasts positioned beneath shading pigments remain low-light–adapted ( E k ∼46 µmol photons⋅m −2 ⋅s −1 ) and dependent upon typical nonphotochemical quenching mechanisms for photoregulation. On the GrIS, glacier algae direct only ∼1 to 2.4% of incident energy to photochemistry versus 48 to 65% to ice surface melting, contributing an additional ∼1.86 cm water equivalent surface melt per day in patches of high algal abundance (∼10 4 cells⋅mL −1 ). At the regional scale, surface darkening is driven by the direct and indirect impacts of glacier algae on ice albedo, with a significant negative relationship between broadband albedo (Moderate Resolution Imaging Spectroradiometer [MODIS]) and glacier algal biomass ( R 2 = 0.75, n = 149), indicating that up to 75% of the variability in albedo across the southwestern GrIS may be attributable to the presence of glacier algae.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".