Recent global CO <sub>2</sub> flux inferred from atmospheric CO <sub>2</sub> observations and its regional analyses
Bibliographic record
Abstract
Abstract. The net surface exchange of CO2 for the years 2002–2007 is inferred from 12 181 atmospheric CO2 concentration data with a time-dependent Bayesian synthesis inversion scheme. Monthly CO2 fluxes are optimized for 30 regions of the North America and 20 regions for the rest of the globe. Although there have been many previous multiyear inversion studies, the reliability of atmospheric inversion techniques has not yet been systematically evaluated for quantifying regional interannual variability in the carbon cycle. In this study, the global interannual variability of the CO2 flux is found to be dominated by terrestrial ecosystems, particularly by tropical land, and the variations of regional terrestrial carbon fluxes are closely related to climate variations. These interannual variations are mostly caused by abnormal meteorological conditions in a few months in the year or part of a growing season and cannot be well represented using annual means, suggesting that we should pay attention to finer temporal climate variations in ecosystem modeling. We find that, excluding fossil fuel and biomass burning emissions, terrestrial ecosystems and oceans absorb an average of 3.63 ± 0.49 and 1.94 ± 0.41 Pg C yr−1, respectively. The terrestrial uptake is mainly in northern land while the tropical and southern lands contribute 0.62 ± 0.47, and 0.67 ± 0.34 Pg C yr−1 to the sink, respectively. In North America, terrestrial ecosystems absorb 0.89 ± 0.18 Pg C yr−1 on average with a strong flux density found in the south-east of the continent.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".