How does explicit tropospheric VOC chemistry affect radiative forcing? Investigation of preindustrial climate using the Community Earth System Model version 2
Bibliographic record
Abstract
The representation of volatile organic compound (VOC) chemistry in earth system models directly impacts aerosol formation. Earlier research has shown that explicitly representing VOC chemistry produces significant impacts on simulated climate compared to an implicit ”SOAG scheme” approach that prescribes a bulk gas phase precursor to SOA formation. In this study, we examine the effective radiative forcing (ERF) of explicit VOC chemistry in the Community Earth System Model version 2 under preindustrial conditions using the fixed sea surface temperature (fSST) approach. 150-year fSST simulations were performed in the following two configurations: 1) WF, with explicit VOC chemistry in the troposphere, and 2) MF, with a SOAG scheme in the troposphere. The computed ERF due to explicit VOC chemistry is 0.49 +- 0.033 Wm-2. Additional analysis establishes that this ERF is mostly accounted for by direct aerosol effects, which are in turn likely driven by widespread SOA reductions in the accumulation mode. Earlier studies have also found widespread SOA reductions in response to explicit VOC chemistry, but the precise reasons for these reductions remain unclear. 250-year simulations using fully coupled versions of WF and MF (called ”WC” and ”MC”) are also performed. WC produces 0.40 +- 0.012 K warming of global mean surface temperature (GMST), as is expected given the positive ERF. This result contrasts with an earlier study by the same authors which produced nearly unchanged GMST. We discuss the reasons for this contrast, along with our greater confidence in the results of this study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".