Mid-21st century anthropogenic changes in extreme precipitation and snowpack projections over Newfoundland
Bibliographic record
Abstract
Extreme precipitation events, including probable maximum precipitation (PMP) and probable maximum snow accumulation (PMSA) and 1/100 annual exceedance probability (AEP) values for precipitation (P100) and snow accumulation (expressed in snow water equivalent; SWE100) were analyzed over Newfoundland to compute the projected changes from 1971–2000 to 2041–2070. PMP and PMSA of various storm durations were simulated based on the moisture maximization of high efficiency storms. Also, P100 and SWE100 data were calculated based on the frequency analysis of liquid precipitation and snowpack data during each 30-year period. The required meteorological variables, including liquid and solid precipitation, precipitable water content, and snow accumulation, defined over a 50 × 50 km grid, were extracted from an ensemble of six regional climate model simulations provided by the North American Regional Climate Change Assessment Program (NARCCAP). Projections indicated that while PMP and P100 are intensifying in the future period, PMSA and SWE100 are declining. This is the first study which quantifies the impact of climate change on extreme-value characteristics of precipitation in Newfoundland. The results of the study can help stakeholders throughout the province to gain a better understanding of the impact of global warming on extreme meteorological events. Such an understanding is prerequisite to build resiliency and understand the uncertainty related to standard probable maximum flood analysis in the region.
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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 | 0.001 | 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".