Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO <sub>2</sub> Retrievals
Bibliographic record
Abstract
TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO 2 dry air mole fraction (XCO 2 ). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO 2 retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO 2 . The inversion estimates global NEE at −3.46 PgC yr -1 , evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO 2 growth rate. Regionally, our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands, especially in Africa and India peninsula. The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season, and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year. Evaluations against independent CO 2 observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. These results suggest that TanSat XCO 2 retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa, and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".