Assessing Global Vegetation–Climate Feedbacks from Observations*
Bibliographic record
Abstract
Abstract The feedback between global vegetation greenness and surface air temperature and precipitation is assessed using remote sensing observations of monthly fraction of photosynthetically active radiation (FPAR) for 1982 to 2000 with a 2.5° grid resolution. Lead/lag correlations are used to infer vegetation–climate interactions. Furthermore, a statistical method is used to quantify the efficiency of vegetation feedback on climate in the observations. This feedback analysis provides a first quantitative assessment of global vegetation feedback on climate. In northern mid- and high latitudes, vegetation variability is found to be driven predominantly by temperature; in the meantime, vegetation also exerts a strong positive feedback on temperature with the feedback accounting for over 10%–25% of the total monthly temperature variance. The strongest positive feedback occurs in the boreal regions of southern Canada/northern United States, northern Europe, and southern Siberia, where the feedback efficiency exceeds 1°C (0.1 FPAR)−1. Over most of the Tropics and subtropics (outside the equatorial rain belt), vegetation is driven primarily by precipitation. However, little vegetation feedback is found on local precipitation when averaged year-round, with the feedback explained variance usually accounting for less than 5% of the total precipitation variance. Nevertheless, in a few isolated small regions such as Northeast Brazil, East Africa, East Asia, and northern Australia, there appears to be some positive vegetation feedback on local precipitation, with the feedback efficiency over 1 cm month−1 (0.1 FPAR)−1. Further studies suggest a significant seasonal variation of the vegetation feedback in some regions. A preliminary analysis also seems to suggest an enhanced intensity of the vegetation feedback, especially on precipitation, at longer time scales and over a larger grid box area. Limitations and implications of the assessment of vegetation feedback are also discussed. The assessed vegetation feedback is shown to be valuable for the evaluation of vegetation–climate feedback in coupled climate–vegetation models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".