Light-absorbing impurities in Arctic snow
Bibliographic record
Abstract
Abstract. Absorption of radiation by ice is extremely weak at visible and near-ultraviolet wavelengths, so small amounts of light-absorbing impurities in snow can dominate the absorption of solar radiation at these wavelengths, reducing the albedo relative to that of pure snow, contributing to the surface energy budget and leading to earlier snowmelt. In this study Arctic snow is surveyed for its content of light-absorbing impurities, expanding and updating the 1983–1984 survey of Clarke and Noone. Samples were collected in Alaska, Canada, Greenland, Svalbard, Norway, Russia, and the Arctic Ocean during 1998 and 2005–2009, on tundra, glaciers, ice caps, sea ice, frozen lakes, and in boreal forests. Snow was collected mostly in spring, when the entire winter snowpack is accessible for sampling. Sampling was carried out in summer on the Greenland Ice Sheet and on the Arctic Ocean, of melting glacier snow and sea ice as well as cold snow. About 1200 snow samples have been analyzed for this study. The snow is melted and filtered; the filters are analyzed in a specially designed spectrophotometer system to infer the concentration of black carbon (BC), the fraction of absorption due to non-BC light-absorbing constituents and the absorption Ångstrom exponent of all particles. This is done using BC calibration standards having a mass absorption efficiency of 6.0 m2 g−1 at 550 nm and by making an assumption that the absorption Angstrom exponent for BC is 1.0 and for non-BC light-absorbing aerosol is 5.0. The reduction of snow albedo is primarily due to BC, but other impurities, principally brown (organic) carbon, are typically responsible for ~40% of the visible and ultraviolet absorption. The meltwater from selected snow samples was saved for chemical analysis to identify sources of the impurities. Median BC amounts in surface snow are as follows (nanograms of carbon per gram of snow): Greenland 3, Arctic Ocean snow 7, melting sea ice 8, Arctic Canada 8, subarctic Canada 14, Svalbard 13, Northern Norway 21, western Arctic Russia 27, northeastern Siberia 34. Concentrations are more variable in the European Arctic than in Arctic Canada or the Arctic Ocean, probably because of the proximity to BC sources. Individual samples of falling snow were collected on Svalbard, documenting the springtime decline of BC from March through May. Absorption Ångstrom exponents are 1.5–1.7 in Norway, Svalbard, and western Russia, 2.1–2.3 elsewhere in the Arctic, and 2.5 in Greenland. Correspondingly, the estimated contribution to absorption by non-BC constituents in these regions is ~25%, 40%, and 50% respectively. It has been hypothesized that when the snow surface layer melts some of the BC is left at the top of the snowpack rather than being carried away in meltwater. This process was observed in a few locations and would cause a positive feedback on snowmelt. The BC content of the Arctic atmosphere has declined markedly since 1989, according to the continuous measurements of near-surface air at Alert (Canada), Barrow (Alaska), and Ny-Ålesund (Svalbard). Correspondingly, the new BC concentrations for Arctic snow are somewhat lower than those reported by Clarke and Noone for 1983–1984, but because of methodological differences it is not clear that the differences are significant. Nevertheless, the BC content of Arctic snow appears to be no higher now than in 1984, so it is doubtful that BC in Arctic snow has contributed to the rapid decline of Arctic sea ice in recent years.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".