Global vegetation productivity response to climatic oscillations during the satellite era
Bibliographic record
Abstract
Climate control on global vegetation productivity patterns has intensified in response to recent global warming. Yet, the contributions of the leading internal climatic variations to global vegetation productivity are poorly understood. Here, we use 30 years of global satellite observations to study climatic variations controls on continental and global vegetation productivity patterns. El Niño-Southern Oscillation (ENSO) phases (La Niña, neutral, and El Niño years) appear to be a weaker control on global-scale vegetation productivity than previously thought, although continental-scale responses are substantial. There is also clear evidence that other non-ENSO climatic variations have a strong control on spatial patterns of vegetation productivity mainly through their influence on temperature. Among the eight leading internal climatic variations, the East Atlantic/West Russia Pattern extensively controls the ensuing year vegetation productivity of the most productive tropical and temperate forest ecosystems of the Earth's vegetated surface through directionally consistent influence on vegetation greenness. The Community Climate System Model (CCSM4) simulations do not capture the observed patterns of vegetation productivity responses to internal climatic variations. Our analyses show the ubiquitous control of climatic variations on vegetation productivity and can further guide CCSM and other Earth system models developments to represent vegetation response patterns to unforced variability. Several winter time internal climatic variation indices show strong potentials on predicting growing season vegetation productivity two to six seasons ahead which enables national governments and farmers forecast crop yield to ensure supplies of affordable food, famine early warning, and plan management options to minimize yield losses ahead of time.
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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 | 0.001 | 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 | 0.001 |
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".