Effects of Global Warming on Drought Frequency and Duration in the Northeast United States
Bibliographic record
Abstract
Over the past century, the northeast United States experienced several major droughts, with great economic damage as a consequence. We use the Standardized Precipitation Index to study the change of drought conditions in the northeast United States, using observations and simulations from eight state-of-the-art general circulation models for the period 1901 to 2050. We separated the droughts into two different time scales, 3 months and 12 months, and three different severities, moderate, severe, and extreme. While the models behave quite differently from each other, the ensemble averages of the model simulations showed decreases in the frequencies of droughts in the future. The models project the frequencies of 3-month moderate, severe, and extreme droughts to decrease by about 12%, 20%, and 5% respectively for the first quarter of the 21st century and 13%, 19%, and 11% for the second quarter compared with the 20th century; and 12-month droughts to decrease by 2%, 14%, and 7% during the first quarter and 18.5%, 36%, and 37% during the second quarter of this century. While only the 12-month severe and extreme decreases are statistically significant at the 80% level, and none of the other decreases is statistically significant, the results are consistent with projected precipitation increases, and serve as valuable guidance for water availability planning for the future. This study uses only a precipitation-based index and does not account for potential changes in evaporation. As climate models improve, this study should be repeated analyzing soil moisture simulated by the models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".