Slow-down of the greening trend in natural vegetation with further rise in atmospheric CO2
Bibliographic record
Abstract
Satellite data reveal widespread changes in vegetation cover of Earth’s land surfaces. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change and consequent episodic disturbances (e.g. fires and droughts) are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at biome-level that can be scaled into a global picture of what is behind the observed changes is currently lacking. Therefore, we analyze here the longest available satellite record of global leaf area index (LAI, 1981-2017) and identify several clusters of significant long-term changes at the biome scale. Using process-based model simulations (fully-coupled MPI-M Earth system model and 13 stand-alone land surface models), we disentangle the effects of rising CO2 on LAI in a probabilistic setting applying Causal Counterfactual Theory. Our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last two decades (2000-2017). The decreases in LAI are primarily concentrated in regions of high LAI (i.e. tropical forests), whereas the increases are in low LAI regions (i.e. northern and arid lands). These opposing trends are reducing the LAI texture of natural vegetation at the global scale. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems and rainfall anomalies in tropical biomes. Our results do not support previously published accounts of dominant global-scale effects of CO2 fertilization. Most models largely underestimate vegetation browning, especially in the tropical rainforests. The leaf area loss in these productive ecosystems could be an early indicator of a slow-down in the terrestrial carbon sink. Models need to better account for this effect to realize plausible Earth system projections of the 21st century.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".