Atmospheric feedbacks reverse the sensitivity of modeled photosynthesis to stomatal function
Bibliographic record
Abstract
Stomata on leaves mediate fluxes of carbon and water between terrestrial plants and the atmosphere. These fluxes are governed by stomatal function and can be modulated in many Earth system models (ESMs) by an empirical parameter within the calculation of stomatal conductance, the stomatal slope ( g 1M ). Intuitively, g 1M represents the marginal water cost of carbon, relating it to the emergent plant property of water use efficiency. Observations show that g 1M can range widely across and within plant types in varying environments, and this distribution of g 1M is not captured within ESMs which represent each plant type with a single g 1M value. Here we examine how g 1M influences photosynthesis using coupled ESM simulations by perturbing g 1M to observed 5th and 95th percentiles for each plant type. We find that high g 1M reduces photosynthesis nearly everywhere, while low g 1M has regionally dependent responses. Under fixed atmospheric conditions, low g 1M increases photosynthesis in the Amazon and central North America but decreases photosynthesis in boreal Canada. These responses reverse when the atmosphere responds interactively due to spatially differing sensitivity to increases in temperature and vapor pressure deficit. Stomatal function also influences photosynthetic response to changes in atmospheric carbon dioxide (CO 2 ), with lower and higher g 1M modifying total global response to elevated 2x preindustrial CO 2 by 6.4% and -9.6%, respectively. Our work demonstrates that atmospheric feedbacks are critical for determining the photosynthetic response to assumptions about stomatal function and some regions are particularly sensitive to choice of g 1M .
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".