Regional and Phased Vegetation Responses to Climate Change Are Different in Southwest China
Bibliographic record
Abstract
Southwestern China (SW) is simultaneously affected by the East Asian monsoon, South Asian monsoon and westerly winds, forming a complex and diverse distribution pattern of climate types, resulting in a low interpretation rate of vegetation changes by climate factors in the region. This study explored the response characteristics of vegetation to climatic factors in the whole SW and the core area of typical climate type and the phased changes in response, adopting the form of “top-down”, using linear trend method, moving average method and correlation coefficient, and based on the climate data of CRU TS v. 4.02 for the period 1982–2017 and the annual maximum, 3/4 quantile, median, 1/4 quantile, minimum and average (abbreviated as P100, P75, P50, P25, P5 and Mean) of GIMMS NDVI, which were to characterize vegetation growth conditions. Coupling with the trend and variability of climate change, we identified four major types of climate change in the SW, including the significant increase in both temperature and precipitation (T+*-P+*), the only significant increase in temperature and decrease (T+*-P−) or increase (T+*-P+) of precipitation and no significant change (NSC). We then screened out nine typical areas of climate change types (i.e., core areas (CAs)), followed by one T+*-P+* area, which was located in the center of the lake basin of the Qiangtang Plateau. The response of vegetation to climatic factors in T+*-P+* area/T+*-P+ areas and T+*-P− areas/NSC areas were mainly manifested in an increase and a significant decrease, which makes the response characteristics of vegetation to climatic factors in the whole SW have different directionality at different growth stages. Our results may provide new ideas for clearly showing the complexity and heterogeneity of the vegetation response to climate change in the region under the background of global warming.
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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) | 0.000 | 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".