Atmosphere and Ocean Origins of North American Droughts*
Bibliographic record
Abstract
Abstract The atmospheric and oceanic causes of North American droughts are examined using observations and ensemble climate simulations. The models indicate that oceanic forcing of annual mean precipitation variability accounts for up to 40% of total variance in northeastern Mexico, the southern Great Plains, and the Gulf Coast states but less than 10% in central and eastern Canada. Observations and models indicate robust tropical Pacific and tropical North Atlantic forcing of annual mean precipitation and soil moisture with the most heavily influenced areas being in southwestern North America and the southern Great Plains. In these regions, individual wet and dry years, droughts, and decadal variations are well reproduced in atmosphere models forced by observed SSTs. Oceanic forcing was important in causing multiyear droughts in the 1950s and at the turn of the twenty-first century, although a similar ocean configuration in the 1970s was not associated with drought owing to an overwhelming influence of internal atmospheric variability. Up to half of the soil moisture deficits during severe droughts in the southeast United States in 2000, Texas in 2011, and the central Great Plains in 2012 were related to SST forcing, although SST forcing was an insignificant factor for northern Great Plains drought in 1988. During the early twenty-first century, natural decadal swings in tropical Pacific and North Atlantic SSTs have contributed to a dry regime for the United States. Long-term changes caused by increasing trace gas concentrations are now contributing to a modest signal of soil moisture depletion, mainly over the U.S. Southwest, thereby prolonging the duration and severity of naturally occurring droughts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".