Nonlinear Carbon Feedbacks in CMIP6 and Their Impacts on Future Freshwater Availability
Bibliographic record
Abstract
Abstract Some theories and analyses of earlier generations of Earth system models (ESMs) suggest that transpiration will decline with higher atmospheric carbon dioxide concentrations [CO2] due to stomatal closure, thereby enhancing runoff and soil moisture relative to the continental drying predicted by warming alone. Using the latest generation of idealized experiments from the Coupled Climate–Carbon Cycle Model Intercomparison Project forced with increasing [CO2], we show that the opposite effect prevails: Plants themselves contribute to projected soil drying, with smaller negative effects on runoff, and this picture emerges by considering the interactions between radiatively driven warming and the physiological effects of high [CO2] on plants. These interactions act to increase plant-based evapotranspiration (ET) by expanding the leaf area and lengthening and warming growing seasons beyond what would be predicted by radiative or biogeochemical effects alone. Collectively, these interactions increase ecosystem water use and dry soils, compensating for any land water savings from stomatal closure. At the same time, these interactions have grown and become more uncertain across ESM generations. Notably, the simulated strength of these plant–water interactions scales with the resilience of the land carbon sink to warming—a key feedback in the carbon cycle. Our results emphasize that a linearity assumption underpinning the analyses of carbon, plant, and water interactions is not appropriate for the latest generation of ESMs, with implications for model development, as well as the accurate interpretation of projected changes to the carbon cycle and its consequences for future climate, drought, and water availability. Significance Statement Understanding plants and how their water use will respond to climate change is essential to understanding future drought and aridity. We demonstrate that interactions between warming and higher atmospheric carbon dioxide in the latest generation of climate models lead to amplified plant growth and associated plant water use. The simulated strength of this interaction is related to weaker land carbon losses from warming. The net result is that in climate models, plant responses to forcing enhance land surface drying rather than reduce it, as some previous analyses of earlier generations of climate models have found. Our findings highlight that as models become more sophisticated, carbon feedbacks become more uncertain with implications for how we assess plant influences on water cycle changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".