The role of OCO-3 XCO <sub>2</sub> retrievals in estimating global terrestrial net ecosystem exchanges
Bibliographic record
Abstract
Satellite-based column-averaged dry-air CO 2 mole fraction (XCO 2 ) retrievals are frequently used to improve the estimates of terrestrial net ecosystem exchanges (NEEs). The Orbiting Carbon Observatory 3 (OCO-3) satellite, launched in May 2019, was designed to address important questions about the distribution of carbon fluxes on Earth, but its role in estimating global terrestrial NEE remains unclear. Here, using the Global Carbon Assimilation System, version 2, we investigate the impact of OCO-3 XCO 2 on the estimation of global NEE by assimilating the OCO-3 XCO 2 retrievals alone and in combination with the OCO-2 XCO 2 retrievals. The results show that when only the OCO-3 XCO 2 is assimilated (Exp_OCO3), the estimated global land sink is significantly lower than that from the OCO-2 experiment (Exp_OCO2). The estimate from the joint assimilation of OCO-3 and OCO-2 (Exp_OCO3&2) is comparable on a global scale to that of Exp_OCO2. However, there are significant regional differences. Compared to the observed global annual CO 2 growth rate, Exp_OCO3 has the largest bias and Exp_OCO3&2 shows the best performance. Furthermore, validation with independent CO 2 observations shows that the biases of Exp_OCO3 are significantly larger than those of Exp_OCO2 and Exp_OCO3&2 at middle and high latitudes. The reasons for the poor performance of assimilating OCO-3 XCO 2 alone include the lack of observations beyond 52° S and 52° N, the large fluctuations in the number of data, and the varied observation time. Our study indicates that assimilating OCO-3 XCO 2 retrievals alone leads to an underestimation of land sinks at high latitudes and that a joint assimilation of OCO-2 XCO 2 and the OCO-3 XCO 2 retrievals observed in the afternoon is required for a better estimation of global terrestrial NEE.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".