Land-atmosphere feedbacks reduce evaporative demand in a warming climate: implications at local and global scales
Bibliographic record
Abstract
The magnitude and extent of runoff reduction, drought intensification, and dryland expansion under climate change are unclear and contentious. A primary reason is disagreement between global circulation models and current potential evaporation (PE) models for evaporative demand under warming climatic conditions. An emerging body of research suggests that current PE models including Penman-Monteith and Priestley-Taylor may overestimate future evaporative demand. However, they are still widely used for climatic impact analysis although the underlying physical mechanisms for PE projections remain unclear. Here, we show that current PE models diverge from observed non-water-stressed evaporation, a proxy of evaporative demand, across site (>1500 flux tower site years), watershed (>10,000 watershed-years), and global (25 climate models) scales. By not incorporating land-atmosphere feedback processes, current models overestimate non-water-stressed evaporation and its driving factors for warmer and drier conditions. To resolve this, we introduce a land-atmosphere coupled PE model that accurately reproduces non-water-stressed evaporation across spatiotemporal scales. We demonstrate that terrestrial evaporative demand will increase at a similar rate to ocean evaporation, but much slower than rates suggested by current PE models. This finding suggests that land-atmosphere feedbacks moderate continental drying trends. Budyko-based runoff projections incorporating our PE model are well aligned with those from coupled climate simulations, implying that land-atmosphere feedbacks are key to improving predictions of climatic impacts on water resources. Our approach provides a simple and robust way to incorporate coupled land-atmosphere processes into water management tools.
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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".