Seasonal evaluation of tropospheric CO <sub>2</sub> over the Asia-Pacific region observed by the CONTRAIL commercial airliner measurements
Bibliographic record
Abstract
Abstract. We present climatological carbon dioxide (CO2) distributions over the Asia-Pacific region obtained from the CONTRAIL (Comprehensive Observation Network for Trace gases by Airliner) measurements. The high-frequency in-flight CO2 measurements over 10 years reveal a clear seasonal variation of CO2 in the upper troposphere (UT), with a maximum occurring in April–May and a minimum in August–September. The CO2 mole fraction in the UT north of 40° N is low and highly variable in June–August due to the arrival of air parcels with seasonally low CO2 caused by the summertime biospheric uptake in boreal Eurasia. For August–September in particular, the UT CO2 is noticeably low within the Asian summer monsoon anticyclone associated with the convective transport of strong biospheric CO2 uptake signal over South Asia. During September as the anticyclone decays, a spreading of this low CO2 area in the UT is observed in the vertical profiles of CO2 over the Pacific Rim of the continental East Asia. Simulation results identify the influence of anthropogenic and biospheric CO2 fluxes in the seasonal evolution of the spatial CO2 distribution over the Asia-Pacific region. It is found, for example, that a substantial contribution to the UT CO2 over the northwestern Pacific comes from the continental East Asian emissions in the spring, but switches to South Asian and/or Southeast Asian air masses affected dominantly by the biospheric CO2 uptake in the summer monsoon season. The CONTRAIL CO2 data provide useful constraints to model estimates of surface fluxes and to the evaluation of the satellite observations, in particular for the Asia-Pacific region.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".