Can long-term tropical land carbon-climate feedback uncertainties be constrained from interannual variability?
Bibliographic record
Abstract
Whether tropical land carbon sink will persist in the future to slow climate change remains elusive in Earth System Model (ESM) projections, largely due to carbon-climate feedback uncertainties. Unraveling drivers of interannual variability (IAV) of the land carbon cycle can inform tropical land carbon-climate feedbacks. Here we utilize two generations of factorial ESM experiments to show that the IAV of the tropical land carbon uptake under both present and future climate is consistently dominated by terrestrial water variations in ESMs. The magnitude of this interannual sensitivity of tropical land carbon uptake to water variations (γIAV,W) under future climate shows a large spread across the latest 16 ESMs (2.3 ± 1.5 PgC/yr/Tt H2O). Based on the identified significant emergent relationship between γIAV,Wunder future climate and present climate, the mean and spread of future γIAV,Ware reduced by about 41% and 44%, respectively (1.3 ± 0.8 PgC/yr/Tt H2O), using observations and the emergent constraint methodology. However, the long-term tropical land carbon-climate feedback uncertainties in the latest 16 ESMs can no longer be directly constrained by land carbon cycle IAV compared with previous generations of ESMs, given that additional important processes such as tree mortality are not well represented in IAV but could determine long-term tropical land carbon storage. This result highlights the importance of recommended out-of-sample testing for validating previously diagnosed emergent constraint. In summary, our results suggest the limited implication of IAV for long-term tropical land carbon-climate feedbacks and help isolate remaining uncertainties with respect to the effects of water limitation on tropical land sink in ESMs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".