Seasonal CO<sub>2</sub> rectifier effect and large‐scale extratropical atmospheric transport
Bibliographic record
Abstract
In atmospheric transport models, the covariation of the atmospheric transport and annually neutral biospheric CO2 flux is usually evident as the annual zonal mean surface CO2 concentration gradient. Using the NIES transport model and CO2 flux from the Biome‐BGC model, the covariations of different transport mechanisms and CO2 flux were examined and quantified. Including the covariation of the total transport (processes included in the NIES model) and CO2 flux, the annual average pole to pole CO2 concentration gradient is 3.5 ppm and interhemispheric difference of the average extratropical surface concentration is 2.5 ppm. The conventional covariation mechanism of the seasonal variation of planetary boundary layer mixing height and CO2 flux accounts for approximately 45% of the CO2 concentration gradient. Another important contribution to the CO2 concentration gradient in this model is the covariation of the extratropical anomaly transport (mainly by cyclones and anticyclones) and the biospheric flux, which accounts for about 55%. This alternate physical mechanism is the association of stronger meridional (north–south) anomaly transport (under strong baroclinic instability condition) with higher CO2 concentration from soil respiration in the winter and weaker anomaly transport (weak baroclinic instability condition) with lower CO2 concentration from photosynthetic uptake in the summer. The net result of the meridional transport and flux covariation is a north–south annual zonal mean CO2 concentration gradient.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".