Projected decline of CO2 fertilization effects on vegetation carbon sequestration in India
Bibliographic record
Abstract
The increase in vegetation productivity in India (net primary productivity; NPP) has been observed in recent decades; however, substantial uncertainty exists about the continued strength of these land carbon sinks under climate change. The enhanced NPP is driven by the strong positive carbon-concentration feedback (CO2 fertilization effect; CFE), but the temporal dynamics of this feedback are unclear. Using the carbon fluxes from the multiple Earth System Models (ESMs) of Coupled Model Inter-comparison Project (CMIP6), we showed an increasing trend in NPP would continue under climate change with projections of NPP to 2.00 ± 0.12 PgCyr-1 (25% increase) during 2021-2049, 2.36 ± 0.12 PgCyr-1 (18% increase) during 2050-2079, and 2.67 ± 0.07 PgCyr-1 (13% increase) during 2080-2099 in Indian tropic forests under SSP585 scenario. This suggests a significant decline in the growth rate of NPP in future periods. To understand the feedbacks that drive the NPP increase, we analyzed the relative effects of CFE and warming. We compared the simulations from the biogeochemical coupled model (BGC) from ESMs, which exclude the warming effects, with the fully coupled model, which includes both CFE and warming effects. The BGC model projected a 74.7% increase in NPP by the end of the century, significantly higher than the 55.9% increase projected by the fully coupled model. This shows that the consistent increase in the NPP was associated with the rise in atmospheric CO2. More importantly, results reveal that the decrease in the growth rate of NPP was due to the decline in the contribution of CFE across the different vegetations at a rate of -0.62% 100 ppm-1. Such a decline could be attributed to nutrient limitation, negative responses to high temperatures, droughts, heat waves, etc. Additionally, statistically significant shifts in the strength of carbon sinks (at a rate of -1.15% per decade) were identified in abating anthropogenic CO2 emissions. These shifts in land carbon sinks can potentially exacerbate global warming and impose additional challenges on our collective efforts to meet climate policy targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".