Black carbon-induced snow albedo reduction over the Tibetan Plateau: Uncertainties from snow grain shape and aerosol-snow mixing state based on an updated SNICAR model
Bibliographic record
Abstract
Abstract. We implement a set of new parameterizations into the widely used SNow, ICe, and Aerosol Radiative (SNICAR) model to account for effects of snow grain shape (spherical versus nonspherical) and black carbon (BC)-snow mixing state (external versus internal). We find that nonspherical snow grains lead to higher pure albedo but weaker BC-induced albedo reductions relative to spherical snow grains, while BC-snow internal mixing significantly enhances albedo reductions relative to external mixing. The combination of snow nonsphericity and internal mixing suggests an important interactive effect on BC-induced albedo reduction. Comparisons with observations of clean and BC-contaminated snow albedo show that model simulations accounting for both snow nonsphericity and BC-snow internal mixing perform better than those using the common assumption of spherical snow grains and external mixing. We further apply the updated SNICAR model with comprehensive in-situ measurements of BC concentrations in the Tibetan Plateau snowpack to quantify the present-day (2000–2015) BC-induced snow albedo effects from a regional and seasonal perspective. The BC concentrations show distinct and substantial sub-regional and seasonal variations, with higher values in the non-monsoon season and low altitudes. As a result, the BC-induced regional mean snow albedo reductions and surface radiative effects vary by up to an order of magnitude across different sub-regions and seasons, with values of 0.7–30.7 (1.4–58.4) W m−2 for BC externally mixed with fresh (aged) snow spheres. The BC radiative effects are further complicated by uncertainty in snow grain shape and BC-snow mixing state. BC-snow internal mixing enhances the mean albedo effects over the plateau by 30–60 % relative to external mixing, while nonspherical snow grains decrease the mean albedo effects by up to 31 % relative to spherical grains. Based on this study, extensive measurements and improved model characterization of snow grain shape and aerosol-snow mixing state are urgently needed in order to precisely evaluate BC-snow albedo effects.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".