Climate Change Determines Future Population Exposure to Summertime Compound Dry and Hot Events
Bibliographic record
Abstract
Abstract Compound dry and hot events (CDHEs) have increased significantly and caused agricultural losses and adverse impacts on human health. It is thus critical to investigate changes in CDHEs and population exposure in responding to climate change. Based on the simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6), future changes in CDHEs and population exposure are estimated under four Shared Socioeconomic Pathways climate scenarios (SSPs) at first. And then the driving forces behind these changes are analyzed and discussed. The results show that the occurrence of CDHEs is expected to increase by larger magnitudes by the end of the 21st century (the 2080s) than that by the mid‐21st century (2050s). Correspondingly, population exposure to CDHEs is expected to increase significantly responding to higher global warming (SSP3‐7.0 and SSP5‐8.5) but is limited to a relatively low level under the modest emission scenarios (SSP1‐2.6). Globally, compared to 1985–2014, the exposure is expected to increase by 8.5 and 7.7 times under SSP3‐7.0 and SSP5‐8.5 scenarios by the 2080s, respectively. Regionally, Sahara has the largest increase in population exposure to CDHEs, followed by the Mediterranean, Northeast America, Central America, Africa, and Central Asia. The contribution of climate change to the increase of exposure is about 75% by the 2080s under the SSP5‐8.5 scenarios, while that of population change is much lower. The conclusion highlights the importance and urgency of implementing mitigation strategies to alleviate the influence of CDHEs on human society.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".