Evolution of Dry and Wet Spells Under Climate Change Over North‐Eastern North America
Bibliographic record
Abstract
Abstract Risks associated to extreme hydrological conditions, such as floods and droughts, are expected to increase in future climate because of projected changes in precipitation and temperature. Assessing how wet and dry persisting conditions (or spells) will evolve in future climate is a crucial step in the study of extreme hydrological events. Projected changes over the north‐eastern part of North America in the annual number of wet days, wet, and dry spells characteristics (number, duration), and wet spells intensities are analyzed. Two regional climate model ensembles are considered: the multimember ensemble from the Canadian RCM v5 (CRCM5‐LE), and 16 simulations from the NA‐CORDEX multimodel ensemble both using the Representative Concentration Pathway 8.5 (RCP8.5) scenario. Comparison of observed and simulated wet and dry spells characteristics is first performed in present climate (1971–2000). Regional Climate Model (RCM)s tend to generate too many wet days and wet or dry spells resulting in shorter dry spells and slightly longer wet spells. Modeling uncertainties are accounted for a bigger contribution to the bias than internal variability since the multimodel ensemble dispersion is the largest. Throughout the 21st century, both ensembles project significant trends in winter at higher latitudes resulting in increasing wet day frequency, increasing number of wet spell, longer wet spells and shorter dry spells. For other seasons, internal variability of the CRCM5‐LE and differences among the various NA‐CORDEX simulations seems to overwhelm the climate change signal. Wet spell intensities increases are projected for all seasons over almost the entire domain. Globally, wetter climate with potential significant hydrological impacts are expected in many regions.
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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 | 0.001 |
| 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) | 0.001 | 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".