Intensified future heat extremes linked with increasing ecosystem water limitation
Bibliographic record
Abstract
Abstract. Heat extremes have severe implications for human health, ecosystems, and the initiation of wildfires. While they are mostly introduced by atmospheric circulation patterns, the intensity of heat extremes is modulated by terrestrial evaporation associated with soil moisture availability. Thereby, ecosystems provide evaporative cooling through plant transpiration and soil evaporation, which can be reduced under water stress. While it has been shown that regional ecosystem water limitation is projected to increase in the future, the respective repercussions on heat extremes remain unclear. In this study, we use projections from 12 Earth system models to show that projected changes in heat extremes are amplified by increasing ecosystem water limitation in regions across the globe. We represent the ecosystem water limitation with the ecosystem limitation index (ELI) and quantify temperature extremes through the differences between the warm-season mean and maximum temperatures. We identify hotspot regions in tropical South America and across Canada and northern Eurasia where relatively strong trends towards increased ecosystem water limitation jointly occur with amplifying heat extremes. This correlation is governed by the magnitude of the ELI trends and the present-day ELI which denotes the land–atmosphere coupling strength determining the temperature sensitivity to evaporative cooling. Many regions where ecosystem functioning is predominantly energy-limited or transitional in the present climate exhibit strong trends towards increasing the water limitation and simultaneously experience the largest increases in heat extremes. Sensitivity of temperature excess trends to ELI trends is highest in water-limited regions, such that in these regions relatively small ELI trends can amount to drastic temperature excess trends. Therefore, considering the ecosystem's water limitation is key for assessing the intensity of future heat extremes and their corresponding impacts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".