Framing Extreme Precipitation Events in the Context of Cumulative Emissions
Bibliographic record
Abstract
Heavy to extreme precipitation events are often-destructive forms of weather that, despite their infrequency, can lead to significant losses of human life and infrastructural damage. Such events are expected to increase in a warmer world as cumulative carbon emissions continue to rise. However, the extent to which this increase occurs varies considerably across scenarios and spatial scales, especially for the most extreme precipitation. The Transient Response to Cumulative CO2 Emissions (TCRE) has proven to be a powerful metric that characterizes the linear response of global mean temperature to cumulative carbon emissions, and previous research has shown its potential applicability to other climate indicators, such as regional temperature and precipitation, and heat extremes. By using simulations from nine Coupled Model Intercomparison Project Phase 5 (CMIP5) models, I intend to quantify extreme precipitation indices of one-day maximum (Rx1day) and five-day maximum (Rx5day) events against cumulative CO2 emissions. I show that the TCRE framework can be applied to represent changes in these precipitation extremes, with validation of this approach at sub-global scales across emissions scenarios. In Chapter 3, I determine whether precipitation extremes respond linearly to cumulative CO2 emissions, at global to local scales, using simple linear regression modelling. In Chapter 4, I conduct a Generalized Extreme Value (GEV) analysis to model the behavior of the most extreme values of Rx1day and Rx5day and evaluate whether trends in location parameter estimates and specified return levels can be approximated by (regional) TCRE values. For Chapter 5, I extend this analysis to estimate remaining carbon budgets (RCBs) associated with avoiding particular extreme precipitation levels. Overall, my results suggest that extreme precipitation work well within a TCRE framework, and that global and sub-global changes can be well approximated by linear responses to cumulative CO2 emissions, though with less robustly linear trends at local scales. My results further highlight that location parameter estimates and return levels of Rx1day and Rx5day scale approximately linearly to increasing cumulative carbon emissions. My findings also show that RCBs are generally small to avoid specified present-day 20-year and 100-year return levels. This suggests that such events are becoming commonplace with global warming.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".