Relative humidity gradients as a key constraint on terrestrial water and energy fluxes
Bibliographic record
Abstract
Earth’s climate and water cycle are highly dependent on the latent heat flux (LE) associated with terrestrial evapotranspiration. While the widely-used Penman-Monteith LE model is useful to explore vegetative controls on LE, land-atmosphere interactions are difficult to interpret due to the complex role of biological controls on underlying physical processes. Here, we present a novel LE model that defines LE as a combination of diabatic (heat-driven) and adiabatic (relative humidity (rh) gradient-driven) processes using only abiotic variables. This approach yields new insights on the fundamental characteristics of LE. Here we show that the ratio of LE to available energy is mainly controlled by vertical rh gradients, but the spatiotemporal variability in rh gradients are small due to equilibration at the land-atmosphere boundary. Consequently, the global mean vertical rh gradient is near zero, implying land-atmosphere equilibrium at the global-scale. As a result, the spatiotemporal variability of LE is largely determined by the diabatic term, which can be readily determined by standard meteorological measurements. Our proposed model and findings provide a fundamental benchmark for LE predictions. By demonstrating how land surface conditions become encoded in the atmospheric state, our model will also help to improve our understanding of Earth’s climate system and water cycle.
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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 | 0.001 |
| 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".