Estimated regional CO <sub>2</sub> flux and uncertainty based on an ensemble of atmospheric CO <sub>2</sub> inversions
Bibliographic record
Abstract
Abstract. Global and regional sources and sinks of carbon across the earth’s surface have been studied extensively using atmospheric carbon dioxide (CO2) observations and chemistry-transport model (ACTM) simulations (top-down/inversion method). However, the uncertainties in the regional flux (+ve: source to the atmosphere; −ve: sink on land/ocean) distributions remain unconstrained mainly due to the lack of sufficient high-quality measurements covering the globe in all seasons and the uncertainties in model simulations. Here, we use a suite of 16 inversion cases, derived from a single transport model (MIROC4-ACTM) but different sets of a priori (bottom-up) terrestrial biosphere and oceanic fluxes, as well as prior flux and observational data uncertainties (50 sites) to estimate CO2 fluxes for 84 regions over the period 2000–2020. The ensemble inversions provide a mean flux field that is consistent with the global CO2 growth rate, land and ocean sink partitioning of −2.9 ± 0.3 (±1σ uncertainty on mean) and −1.6 ± 0.2 PgC yr−1, respectively, for the period 2011–2020 (without riverine export correction), offsetting about 22–33 % and 16–18 % of global fossil-fuel CO2 emissions. Aggregated fluxes for 15 land regions compare reasonably well with the best estimations for (approx. 2000–2009) given by the REgional Carbon Cycle Assessment and Processes (RECCAP), and all regions appeared as a carbon sink over 2011–2020. Interannual variability and seasonal cycle in CO2 fluxes are more consistently derived for different prior fluxes when a greater degree of freedom is given to the inversion system (greater prior flux uncertainty). We have evaluated the inversion fluxes using independent aircraft and surface measurements not used in the inversions, which raises our confidence in the ensemble mean flux rather than an individual inversion. Differences between 5-year mean fluxes show promises and capability to track flux changes under ongoing and future CO2 emission mitigation policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".