Non-uniform changes in different daily precipitation events in the contiguous United States
Bibliographic record
Abstract
This study examines changes in characteristics (amount, frequency, intensity) of daily precipitation in the contiguous United States (CONUS) using high-quality records for a long-term period (1900–2018) and a more recent period (1950–2018) at different temporal (annual and seasonal scales) and different spatial scales (national and sub-regional scales). Results show that the patterns of change during the two periods are very similar. First, on the annual basis, we find an overall increase in the total annual precipitation, frequency of wet days, and intensity of precipitation in both periods in the CONUS, with percentages of stations showing significant increasing trends significantly larger than what can be expected by chance. Second, stations with significant increasing trends are mainly concentrated in eastern CONUS, while stations with decreasing trends are located on the west coast and partial southeast coast. Specifically, the amounts and frequencies of light, moderate, and heavy precipitation mostly have significantly increased at more than 10% of stations. In both periods, there is a non-uniform change for three intensity categories of precipitation, with the frequency and total amount of events with higher intensity showing a larger rate of change, resulting in the smaller contribution of light precipitation to annual total precipitation but larger contribution due to heavy precipitation. Such non-uniform changes can also be observed in most sub-regions and seasons. Moreover, the estimated sensitivities of the amount of light, moderate, heavy precipitation, and heaviest precipitation event to global surface temperature increase for the 1900–2018 period is comparable with that for the 1950–2018 period, indicating that sampling period does not have a substantial effect on the scaling relationship between the amount of different precipitation events and global mean temperature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".