Aerosol size-dependent below-cloud scavenging by rain and snow in the ECHAM5-HAM
Bibliographic record
Abstract
Abstract. Wet deposition processes are highly efficient in the removal of aerosols from the atmosphere, and thus strongly influence global aerosol concentrations, and clouds, and their respective radiative forcings. In this study, physically detailed size-dependent below-cloud scavenging parameterizations for rain and snow are implemented in the ECHAM5-HAM global aerosol-climate model. Previously, below-cloud scavenging by rain in the ECHAM5-HAM was simply a function of the aerosol mode, and then scaled by the rainfall rate. The below-cloud scavenging by snow was a function of the snowfall rate alone. The global mean aerosol optical depth, and sea salt burden are sensitive to the below-cloud scavenging coefficients, with reductions near to 15% when the more vigorous size-dependent below-cloud scavenging by rain and snow is implemented. The inclusion of a prognostic rain scheme significantly reduces the fractional importance of below-cloud scavenging since there is higher evaporation in the lower troposphere, increasing the global mean sea salt burden by almost 15%. Thermophoretic effects are shown to produce increases in the global and annual mean below-cloud number removal of Aitken size particles of near to 15%, but very small increases (near 1%) in the global mean below-cloud mass scavenging of carbonaceous and sulfate aerosols. Changes in the assumptions about the below-cloud scavenging of ultra-fine particles by rain do not cause any significant changes to the global mean aerosol mass or number burdens, despite a change in the below-cloud number removal rate for nucleation mode particles by near to 10%. For nucleation mode particles, changes to the assumptions about the below-cloud scavenging by snow produce a greater change in the number removal rate, in excess of one order of magnitude. Closer agreement with different observations is found when the more physically detailed below-cloud scavenging parameterization is employed in the ECHAM5-HAM model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".