Deciphering the role of evapotranspiration in declining relative humidity trends over land
Bibliographic record
Abstract
In recent decades, relative humidity over land has declined, driving increases in droughts and wildfires. Previous explanations attribute this trend to insufficient moisture advection from the ocean to sustain the current level, but this ignores atmospheric moisture supplied from terrestrial evapotranspiration. Importantly, current state-of-the-art climate models continue to underestimate the observed relative humidity trend over land. Here, we show that changes in specific humidity over land relative to a given baseline, unaccounted for by ocean advection, are quantitatively equivalent to relative changes in evapotranspiration on a global scale. This finding is consistent across climate models and climate reanalysis datasets, despite discrepancies in evapotranspiration trends among them. Differences in evapotranspiration trends are identified as a prominent cause of the bias in relative humidity trend expressed in climate models. These results suggest that current climate models may overestimate evapotranspiration intensifications, leading to an underestimation of atmospheric drying, with critical implications for accurately predicting droughts, wildfires, and climate adaptation. Differences in simulated evapotranspiration may play a key role in explaining why the observed decline in relative humidity over land is underestimated in state-of-the-art climate models, suggest analyses of climate model output and reanalysis data.
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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 | 0.001 | 0.001 |
| 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".