Assessing Climate Change Impacts on Human‐Perceived Temperature Extremes and Underlying Uncertainties
Bibliographic record
Abstract
Abstract It has been suggested that global warming impacts on human thermal comfort will cause an increase in the heat stress and a decrease in the cold stress in the future. A recent study has shown elevated increases in human‐perceived equivalent temperature (HPET) by using a single index for summer and winter seasons (Li et al., 2018, https://doi.org/10.1038/s41558‐017‐0036‐2 ). However, they have not considered multiple indices with combined effects on deriving HPET, which can result in large uncertainties in assessing climate change impacts on HPET and related extremes. Therefore, we develop a new framework with high‐resolution projections and an ensemble of 10 indices to quantify the impacts of climate change on HPET and related perceived extremes as well as to address uncertainties in both empirical indices and emission scenarios over China. Our findings reveal that different combinations of climatic variables can lead to two opposite conclusions for both normal and extreme conditions. For example, by using indices only considering the combined effect of temperature and relative humidity, China is projected to have an elevated increase in the HPET and in the frequency of high‐temperature extremes. By taking into account wind speed, the country expects to have the HPET even lower than the surface air temperature and an increase in the frequency of low‐temperature extremes. In addition, the resulting range of HPET due to uncertainty in indices is greater than the uncertainty range derived from different emission scenarios for the entire country. Therefore, it is necessary to conduct a comprehensive assessment that explicitly addresses uncertainties in the HPET in order to improve the robustness and reliability of assessing climate change impacts on human‐perceived temperature extremes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".