No Proportional Increase of Terrestrial Gross Carbon Sequestration From the Greening Earth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Terrestrial vegetation, as the key component of the biosphere, has a greening trend since the beginning of this century. However, how this substantial greening translated to global gross carbon sequestration or gross primary production (GPP) is not clear. Here we investigated terrestrial GPP dynamics and the respective contributions of climate change and vegetation cover change (VCC) from 2000 to 2015. We adopted a remote sensing based data‐driven model, which was calibrated based on the global eddy flux data set (FLUXNET2015) and Moderate Resolution Imaging Spectroradiometer vegetation index data (Collection 6). A series of simulation experiments were conducted to disaggregate the effects of climate and VCC. We found a much weaker increase in global GPP (0.08%/year; P = 0.07) when compared with the global greening rate (0.23%/year; P < 0.001). The positive effect of VCC on GPP was reduced by 53% due to climate stress. Enhanced global GPP were largely contributed by nonforests, especially croplands. However, tropical forests, once a major driver of the global GPP increase, negatively contributed to global GPP trend due to warming‐induced moisture stress and deforestation. Given the limited potential of cropland carbon storage due to harvest and consumption, the contrasting GPP changes (i.e., cropland GPP increase vs. forest GPP reduction) may have shifted the distribution of the land carbon sink. Our study highlights the potential vulnerability of terrestrial gross carbon sequestration under climate and land use changes and has important implications in the global carbon cycle and climate warming mitigation.
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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 | 0.002 | 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 | 0.001 |
| 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 it