Satellite Observed Land Surface Greening in Summer Controlled by the Precipitation Frequency Rather Than Its Total Over Tibetan Plateau
Bibliographic record
Abstract
Abstract Land surface greening has substantially changed the carbon sequestration and hydrological processes over Tibetan Plateau (TP). Previous studies have revealed that the increased total precipitation (Ptotal) is the main driver of the enhanced peak growth in TP. However, the role of precipitation pattern, especially the frequency of precipitation (Pfreq, number of rainy days [>0.1 mm]), has not been well analyzed. We used time series of satellite‐derived Normalized Difference Vegetation Index (NDVI) to investigate the effect of Pfreq in controlling the peak growth (the maximum of the NDVI [NDVImax]) of different vegetation types in TP for the period of 1982–2015. We found that the widespread greening trend with Ptotal disappeared when Pfreq was introduced as a controlling variable, and that Pfreq alone contributed more than Ptotal to the increase of NDVImax. The underlying mechanism for the higher contribution of Pfreq than Ptotal to NDVImax is that increased Pfreq significantly improved soil moisture, reduced daytime temperature while increased nighttime temperature, thereby alleviating summer drought. Our results highlight the importance of Pfreq in interpreting the variation of peak growth, and these effects might be better represented in ecosystem models by considering Pfreq rather than Ptotal alone with future climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".