Formaldehyde in the Tropical Western Pacific: Chemical Sources and Sinks, Convective Transport, and Representation in CAM‐Chem and the CCMI Models
Bibliographic record
Abstract
Abstract Formaldehyde (HCHO) directly affects the atmospheric oxidative capacity through its effects on HOx. In remote marine environments, such as the tropical western Pacific (TWP), it is particularly important to understand the processes controlling the abundance of HCHO because model output from these regions is used to correct satellite retrievals of HCHO. Here we have used observations from the Convective Transport of Active Species in the Tropics (CONTRAST) field campaign, conducted during January and February 2014, to evaluate our understanding of the processes controlling the distribution of HCHO in the TWP as well as its representation in chemical transport/climate models. Observed HCHO mixing ratios varied from ~500 parts per trillion by volume (pptv) near the surface to ~75 pptv in the upper troposphere. Recent convective transport of near surface HCHO and its precursors, acetaldehyde and possibly methyl hydroperoxide, increased upper tropospheric HCHO mixing ratios by ~33% (22 pptv); this air contained roughly 60% less NO than more aged air. Output from the CAM‐Chem chemistry transport model (2014 meteorology) as well as nine chemistry climate models from the Chemistry‐Climate Model Initiative (free‐running meteorology) are found to uniformly underestimate HCHO columns derived from in situ observations by between 4 and 50%. This underestimate of HCHO likely results from a near factor of two underestimate of NO in most models, which strongly suggests errors in NOx emissions inventories and/or in the model chemical mechanisms. Likewise, the lack of oceanic acetaldehyde emissions and potential errors in the model acetaldehyde chemistry lead to additional underestimates in modeled HCHO of up to 75 pptv (~15%) in the lower troposphere.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".