The response of ecosystem water‐use efficiency to rising atmospheric <scp>CO</scp><sub>2</sub> concentrations: sensitivity and large‐scale biogeochemical implications
Bibliographic record
Abstract
Summary Ecosystem water‐use efficiency (WUE) is an important metric linking the global land carbon and water cycles. Eddy covariance‐based estimates of WUE in temperate/boreal forests have recently been found to show a strong and unexpected increase over the 1992–2010 period, which has been attributed to the effects of rising atmospheric CO2 concentrations on plant physiology. To test this hypothesis, we forced the observed trend in the process‐based land surface model JSBACH by increasing the sensitivity of stomatal conductance (gs) to atmospheric CO2 concentration. We compared the simulated continental discharge, evapotranspiration (ET), and the seasonal CO2 exchange with observations across the extratropical northern hemisphere. The increased simulated WUE led to substantial changes in surface hydrology at the continental scale, including a significant decrease in ET and a significant increase in continental runoff, both of which are inconsistent with large‐scale observations. The simulated seasonal amplitude of atmospheric CO2 decreased over time, in contrast to the observed upward trend across ground‐based measurement sites. Our results provide strong indications that the recent, large‐scale WUE trend is considerably smaller than that estimated for these forest ecosystems. They emphasize the decreasing CO2 sensitivity of WUE with increasing scale, which affects the physiological interpretation of changes in ecosystem WUE.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".