The Qinghai-Tibet Plateau may have already shifted to carbon source: Evidence from OCO-2 satellite XCO2 observations
Bibliographic record
Abstract
As one of the most sensitive regions to climate change on the Earth's surface, the Qinghai-Tibet Plateau is experiencing lasting warming, which has been evidenced to enhance surface carbon uptake but also could lead to carbon emission due to accelerated permafrost degradation and ecosystem respiration. Due to the difficulties of limited observations and imperfect modeling techniques, whether the Qinghai-Tibet Plateau is a carbon sink or source has been an ongoing debate. The recent satellite XCO2 Observations could provide some useful constraints on the carbon budget in this region. Here, based on the recent OCO-2 XCO2 observations and the inversion results from the OCO-2 v10 MIP, we estimated the net biome carbon fluxes for the Qinghai-Tibet Plateau. Our results suggest that this region has become a carbon source (around -0.10 PgC/year) already, which is supported by an upscaling estimate with intensified eddy covariance flux measurements over China. Meanwhile, we found this carbon source signal is not detected by either in-situ CO2 inversions or terrestrial biosphere model simulations. Currently, although some studies based on flux measurements report this region is a carbon sink and even keeps increasing recently, many others hold opposite viewpoints about it. Our result provides an important piece of evidence supporting that the Qinghai-Tibet Plateau becomes a carbon source, albeit additional evidence is needed, especially from in-situ CO2 observations and aerial CO2 observations (e.g., by aircraft, unmanned aerial vehicle, and AirCore). In principle, atmospheric CO2 measurements could provide a more complete picture of the carbon budget in this region compared to discrete and limited eddy flux measurements. In the future, enhanced in-situ and aerial CO2 observations are expected to disentangle the puzzle of this carbon budget issue in this region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".