Is destabilisation risk increasing in land carbon sinks?
Bibliographic record
Abstract
Global net biome production (NBP), or net land carbon uptake, has been repeatedly shown to increase during recent decades. However, whether the temporal variability and autocorrelation of NBP has changed during this period remains elusive. Answering this question is particularly relevant given that an increase in both could indicate destabilising C sinks and potentially lead to abrupt changes. We investigated the trends and controls of net land C uptake and its temporal variability and autocorrelation, from 1981 to 2018, using two atmospheric inversion models, the amplitude of the seasonal cycle of atmospheric CO2 derived from nine monitoring stations distributed across the Pacific Ocean, and 12 dynamic global vegetation models. Spatially, we found that plant biodiversity presented a convex parabolic relationship with NBP and its temporal variability at the global scale while nitrogen deposition generally increased annual NBP. We also found that annual NBP and its interdecadal temporal variability globally increased, but temporal autocorrelation decreased. Regions characterized by increasingly variable NBP were usually with warmer and with increasingly variable temperatures, and lower and weaker trends in NBP compared to those where NBP variability did not increase, where NBP became stronger. Annual temperature increase and its increasing temporal variability were the most important drivers of declining NBP and increasingly its variability. Our results show that increasing regional NBP variability can be mostly attributed to climate change.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".