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 ( P total ) is the main driver of the enhanced peak growth in TP. However, the role of precipitation pattern, especially the frequency of precipitation ( P freq , 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 P freq in controlling the peak growth (the maximum of the NDVI [NDVI max ]) of different vegetation types in TP for the period of 1982–2015. We found that the widespread greening trend with P total disappeared when P freq was introduced as a controlling variable, and that P freq alone contributed more than P total to the increase of NDVI max . The underlying mechanism for the higher contribution of P freq than P total to NDVI max is that increased P freq significantly improved soil moisture, reduced daytime temperature while increased nighttime temperature, thereby alleviating summer drought. Our results highlight the importance of P freq in interpreting the variation of peak growth, and these effects might be better represented in ecosystem models by considering P freq rather than P total alone with future climate change.
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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 | 0.000 | 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) | 0.002 | 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".