Impacts of Aerosol Dry Deposition on Black Carbon Spatial Distributions and Radiative Effects in the Community Atmosphere Model CAM5
Bibliographic record
Abstract
Abstract Dry deposition is an important process affecting the lifetime and spatial distributions of atmospheric aerosols. Black carbon (BC) plays an important role in the Earth's climate, but is subject to large bias in remote regions in model simulations. In this study, to improve the BC simulations, the scheme of Petroff and Zhang ( ) (PZ10) is implemented into the Community Atmospheric Model version 5 (CAM5), and model simulations using PZ10 are compared with the one using the default scheme of Zhang et al. ( ) (Z01) and observations. The PZ10 scheme predicts much lower dry deposition velocity (Vd) than Z01 for fine particles in Aitken, primary carbon, and accumulation modes, resulting in 73.0% lower of global mean BC dry deposition fluxes and 23.2% higher of global mean BC column burdens. CAM5 with PZ10 increases modeled BC concentrations at all altitudes and latitudes compared to Z01, which improves the agreement with observations of BC profiles in the lower troposphere in the Arctic. It also improves the simulation of surface BC concentrations in high‐latitudes remote regions and its seasonality in the Arctic. The global annual mean radiative effects due to aerosol‐radiation interactions (REari) and aerosol‐cloud interactions (REaci) of BC from the CAM5 experiment using Z01 are 0.61 ± 0.007 and −0.11 ± 0.02 W m−2, respectively, compared to slightly larger REari (0.75 ± 0.01 W m−2) and REaci (–0.14 ± 0.02 W m−2) from CAM5 using PZ10. The results suggest that Brownian diffusion efficiency is a key factor for the predictions of Vd, which requires better representation in the global climate models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".