Rising Atmospheric <scp>CO<sub>2</sub></scp> Alleviates Drought Impact on Autumn Leaf Senescence Over Northern Mid‐High Latitudes
Bibliographic record
Abstract
ABSTRACT Aim Drought reduces plant growth and hastens the process of leaf senescence in autumn. Concurrently, increasing atmospheric CO2 concentrations likely amplifies photosynthetic activity while increasing plant water‐use efficiency. However, how drought affects the date of leaf senescence (DLS) and whether elevated CO2 can alleviate this remain unknown. Here, we explore the effect of drought on DLS under recent climate change and explore the underlying mechanisms. Location Northern mid‐high latitudes. Time Period 2000–2019. Major Taxa Studied Plants. Methods We conducted comprehensive analyses based on satellite remote sensing, eddy covariance flux observations, in situ phenology observations and land‐surface models. Linear regression analysis and a ten‐year moving window were adapted to investigate the spatiotemporal patterns in DLS sensitivity to drought (Sdd). The partial least squares regression method was used to attribute the main factors for the variation in Sdd, and land‐surface models in different scenarios were used to verify the robustness of the results. Results Our study presented divergent spatial patterns of Sdd, where the highest Sdd was concentrated in dry and warm regions. Temporally, multiple datasets consistently illustrate a significant decrease in the Sdd during recent decades (p < 0.05). We also observed a nonlinear relationship between the trend of Sdd and aridity gradient, which presented a slightly positive Sdd trend in dry regions but a negative trend in wet regions. We found these observed changes were primarily attributed to elevated CO2, alleviating the drought stress on DLS in nearly 40% of the study area. Main Conclusions Our findings demonstrate the complex role that atmospheric CO2 plays in regulating plant leaf senescence during drought stress, highlighting the need to incorporate the effects of elevated CO2 on vegetation autumn phenology into land‐surface models for projecting vegetation growth and carbon uptake under continued global 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 | 0.000 | 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) | 0.001 | 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".