Impact of snow darkening via dust, black carbon, and organic carbon on boreal spring climate in the Earth system
Bibliographic record
Abstract
Abstract Dust, black carbon (BC), and organic carbon (OC) aerosols, when deposited onto snow, are known to reduce the albedo of the snow (i.e., snow darkening effect (SDE)). Here using the NASA Goddard Earth Observing System Model, Version 5 (GEOS‐5) with aerosol tracers and a state‐of‐the‐art snow darkening module (GOddard SnoW Impurity Module: GOSWIM) for the land surface, we examine the role of SDE on climate in the boreal spring snowmelt season. SDE is found to produce significant surface warming (over 15 W m−2) over broad areas in midlatitudes, with dust being the most important contributor to the warming in central Asia and the western Himalayas and with BC having larger impact in the Europe, eastern Himalayas, East Asia, and North America. The contribution of OC to the warming is generally low but still significant mainly over southeastern Siberia, northeastern East Asia, and western Canada (~19% of the total solar visible absorption by these snow impurities). The simulations suggest that SDE strengthens the boreal spring water cycle in East Asia through water recycling and moisture advection from the ocean and contributes to the maintenance of dry conditions in parts of a region spanning Europe to central Asia, partially through feedback on the model's background climatology. Overall, our study suggests that the existence of SDE in the Earth system associated with dust, BC, and OC contributes significantly to enhanced surface warming over continents in northern hemisphere midlatitudes during boreal spring, raising the surface skin temperature by approximately 3–6 K near the snowline.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".