Evolution of future extreme drought frequency in two climate model large ensembles
Bibliographic record
Abstract
<p>Recent studies project a significant increase in drought frequency over most continents over the 21<sup>st</sup> century. However, few studies have specifically looked at extreme droughts, defined here as having a return period larger than 20 years. In this work, two large climate model ensembles, the 50-member Canadian Earth System Model (CanESM2) and the 40-member Community Earth System Model (CESM1), both under the RCP8.5 scenario are used to project the evolution of the extreme drought frequency in the near (2036-2065) and far future (2070-2099) relative to the 1980-2009 historical period. The use of a large ensemble allows for a robust estimation of the frequency of very large droughts. Frequency changes for the 2, 20 and 100-year droughts were computed.</p><p>Extreme meteorological droughts were globally assessed using the short-term (1-month) and long-term (24-month) Standardized Precipitation Index (SPI). Extreme hydrological extreme droughts were assessed by the 1-month Streamflow Drought Index (SDI), using a lumped hydrological model on 5797 North American catchments to transform climate model outputs into catchment streamflows.</p><p>Results show that both climate models project increases of extreme meteorological drought frequency over many of the world’s regions, with a typical two or three-fold increase. The spatial distribution of regions with increasing meteorological drought frequency mostly matches those projected changes in future mean annual precipitation. Changes in future extreme hydrological droughts are dramatically more severe than for meteorological droughts, with up to a 27-times increase in frequency for the 100-year hydrological droughts, outlining the large impact of temperature change. The frequency change is the largest for the 100-year compared to the 2 and 20-year hydrological droughts.</p>
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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 | 0.000 | 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) | 0.003 | 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".