Expanding Amazon dry-hot season under anthropogenic climate change
Bibliographic record
Abstract
The Amazon rainforest, a crucial global carbon sink, plays a vital role in the global climate system. As ongoing climate change and local deforestation push the Amazon toward a critical tipping point, understanding the region's changing climate patterns becomes increasingly important. In this study, we reveal a significant expansion of the dry-hot season across the Amazon rainforest from 1980-2022, creating prolonged adverse climate conditions for the ecosystem and local communities. A machine learning clustering algorithm is used to define the dry-hot season automatically by considering the temperature, precipitation, and soil moisture simultaneously.The land-atmosphere interaction predominates the dry-hot season expansion in the Amazon. During the dry season (Aug-Oct), the daily maximum temperature has warmed by ~1 degree per decade, much faster than that in the wet seasons (~0.4 degree per decade). By the surface heat budget analysis, we found that intensive dry-season warming is predominantly driven by reduced evapotranspiration, leading to decreased surface latent heat flux and increased shortwave radiation due to diminished cloud cover. The declining evapotranspiration rates stem from a combination of increasing soil moisture deficits and local deforestation.By large-ensemble climate model simulations, we further demonstrate that this dry-hot season expansion is highly unlikely to occur without anthropogenic climate change and this expansion will exacerbate under future warming scenarios. By single-forcing experiment, we further confirm the critical role of local deforestation in amplifying this expansion. These findings emphasize the urgent need for targeted mitigation and adaptation strategies to protect this vital ecosystem from the compounding effects of climate change and deforestation.
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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 | 0.000 |
| 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".