An improved carbon greenhouse gas simulation in GEOS-Chem version 12.1.1
Bibliographic record
Abstract
Abstract. Understanding greenhouse gas–climate processes and feedbacks is a fundamental step in understanding climate variability and its links to greenhouse gas fluxes. Chemical transport models are the primary tool for linking greenhouse gas fluxes to their atmospheric abundances. Hence accurate simulations of greenhouse gases are essential. Here, we present a new simulation in the GEOS-Chem chemical transport model that couples the two main greenhouse gases: carbon dioxide (CO2) and methane (CH4), along with the indirect effects of carbon monoxide (CO), based on their chemistry. Our updates include the online calculation of the chemical production of CO from CH4 and the online production of CO2 from CO, both of which were handled offline in the previous versions of these simulations. We discuss differences between the offline (uncoupled) and online (coupled) calculation of the chemical terms and perform a sensitivity simulation to identify the impact of OH on the results. We compare our results with surface measurements from the NOAA Global Greenhouse Gas Reference Network (NOAA GGGRN), total column measurements from the Total Carbon Column Observing Network (TCCON) and aircraft measurements from the Atmospheric Tomography Mission (ATom). Relative to the standard uncoupled simulation, our coupled results show better agreement with measurements. We use the remaining measurement-model differences to identify sources and sinks that are over or underestimated in the model. We find underestimated OH fields when calculating the CH4 loss and CO production from CH4. Biomass burning emissions and secondary production are underestimated for CO in the Southern Hemisphere and we find enhanced anthropogenic sources in the Northern Hemisphere. We also find significantly stronger chemical production of CO2 in tropical land regions, especially in the Amazon. The model-measurement differences also highlight biases in the calculation of CH4 in the stratosphere and in vertical mixing that impacts all three gases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".