Differences of the inverted terrestrial ecosystem carbon flux between using GOSAT and OCO-2 XCO <sub>2</sub> retrievals
Bibliographic record
Abstract
Abstract. In this study, both the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory 2 (OCO-2) XCO2 retrievals are assimilated within the GEOS-Chem 4D-Var assimilation framework to constrain the terrestrial ecosystem carbon flux during Jul 1, 2014 to Dec 31, 2015. The inverted global and regional carbon fluxes during Jan 1 to Dec 31, 2015 are shown and discussed. Surface CO2 mixing ratios from 47 surface flask sites and XCO2 measurements from 13 TCCON sites are used to evaluate the simulated concentrations with the posteriori carbon fluxes. The results show that globally, the terrestrial ecosystem carbon sink (excluding biomass burning emissions) estimated from GOSAT data is stronger than that inferred from OCO-2 data, and the annual atmospheric CO2 growth rate estimated from GOSAT data is more consistent with the estimate of GCP 2017. Regionally, in most regions, the land sinks inferred from GOSAT data are also stronger than those from OCO-2 data. Compared with the prior fluxes, the carbon fluxes in northern temperate regions change most, followed by tropical and southern temperate regions, and the smallest changes occur in boreal regions. Basically, in temperate regions, the inferred land sinks are significantly increased, while those in tropical regions are decreased. The different changes in different regions are mainly related to the spatial coverage and the amount of XCO2 data in these regions. Compared with CT2016, the inferred carbon sinks are comparable in most temperate regions, but much weaker in boreal and tropical regions. Evaluations using flask and TCCON observations suggest that GOSAT and OCO-2 data, can effectively improve the carbon flux estimates in the northern hemisphere, while in the southern hemisphere the optimized carbon sinks may be overestimated, especially for GOSAT data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".