Hydroxyl radicals in the tropical troposphere over the Suriname rainforest: comparison of measurements with the box model MECCA
Bibliographic record
Abstract
Abstract. As a major source region of the hydroxyl radical OH, the Tropics largely control the oxidation capacity of the atmosphere on a global scale. However, emissions of hydrocarbons from the tropical rainforest that react rapidly with OH can potentially deplete the amount of OH and thereby reduce the oxidation capacity. The airborne GABRIEL field campaign in equatorial South America (Suriname) in October 2005 investigated the influence of the tropical rainforest on the HOx budget (HOx = OH + HO2). The first observations of OH and HO2 over a tropical rainforest are compared to steady state concentrations calculated with the atmospheric chemistry box model MECCA. The important precursors and sinks for HOx chemistry, measured during the campaign, are used as constraining parameters for the simulation of OH and HO2. Significant underestimations of HOx are found by the model over land during the afternoon, with mean ratios of observation to model of 12.2 ± 3.5 and 4.1 ± 1.4 for OH and HO2, respectively. The discrepancy between measurements and simulation results is correlated to the abundance of isoprene. While for low isoprene mixing ratios (above ocean or at altitudes >3 km), observation and simulation agree fairly well, for mixing ratios >200 pptV (<3 km over the rainforest) the model tends to underestimate the HOx observations as a function of isoprene. Box model simulations have been performed with the condensed chemical mechanism of MECCA and with the detailed isoprene reaction scheme of MCM, resulting in similar results for HOx concentrations. Simulations with constrained HO2 concentrations show that the conversion from HO2 to OH in the model is too low. However, by neglecting the isoprene chemistry in the model, observations and simulations agree much better. An OH source similar to the strength of the OH sink via isoprene chemistry is needed in the model to resolve the discrepancy. A possible explanation is that the oxidation of isoprene by OH not only dominates the removal of OH but also produces it in a similar amount. Several additional reactions which directly produce OH have been implemented into the box model, suggesting that upper limits in producing OH are still not able to reproduce the observations (improvement by factors of ≈2.4 and ≈2 for OH and HO2, respectively). We determine that OH has to be recycled to 94% instead of the simulated 38% to match the observations, which is most likely to happen in the isoprene degradation process, otherwise additional sources are required.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".