Simulation of the tropospheric sulfur cycle in a global model with a physically based cloud scheme
Bibliographic record
Abstract
The treatment of the sulfur cycle in the CSIRO global climate model (GCM) is described. It is substantially based on the scheme developed previously for the European Center/Hamburg (ECHAM) model, but the treatment of wet scavenging has been completely rewritten to better reflect the different properties of liquid and frozen precipitation, and the treatment of these in the model's cloud microphysical scheme. The model is able to reproduce the observed finding that wet deposition of sulfur over Europe and North America is larger in summer than in winter, but the seasonal cycle of sulfate over Europe is not well simulated. The latter is improved when the amplitude of the seasonal cycle of European emissions is increased. Below‐cloud scavenging makes an important contribution in our scheme: On omitting it, the global sulfate burden increases from 0.67 to 0.93 Tg S. On reverting to the less efficient scavenging treatment used in ECHAM, the global sulfate burden again increases from 0.67 to 0.93 Tg S, and excessive sulfate concentrations are obtained in Europe and North America. Some deficiencies in the simulation are investigated via further sensitivity tests. In particular, during the Arctic winter, the modeled sulfur dioxide (SO2) concentrations are too large, and the modeled sulfate concentrations are too small (as in most global sulfur‐cycle models). Recent laboratory experiments suggest that SO2 oxidation in ice clouds is nonnegligible. We obtain a much improved Arctic simulation when a simple treatment of SO2 oxidation in ice clouds is included.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".