Linking Historical and Projected Trends in Extreme Precipitation with Cumulative Carbon Dioxide Emissions
Bibliographic record
Abstract
Extreme weather events are expected to increase in frequency and intensity in response to higher global temperatures, augmenting societal exposure to these events. While the magnitude of projected changes in extremes varies considerably among future emission scenarios, a large part of this uncertainty is driven by the choice of scenario, rather than by the climate response to a particular emission scenario. A growing body of research has identified robust linear relationships between climate changes and cumulative carbon emissions; for global average temperature change, this relationship is known as the transient climate response to cumulative carbon emissions (TCRE). Extensions of the TCRE framework to other variables, such as regional and seasonal temperature and precipitation changes, have also shown to be effective, raising the possibility that changes in weather extremes could be linked to cumulative carbon dioxide (CO2) emissions. Here, we estimate changes in historical and projected trends in one-day (Rx1day) and five-day maximum precipitation (Rx5day) events as a function of cumulative carbon emissions across a range of future emission scenarios and global climate models. Our results show that median Rx1day and Rx5day generally increases linearly with increasing cumulative emissions, consistent with studies that have previously employed the TCRE framework to estimate changes in precipitation extremes, as well as other climate indicators. Overall, we show that a linear response to cumulative CO2 emissions is a good approximation for both historical and future trends in precipitation extremes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".