Surface Moistening Trends in the Northern North American Great Plains Increase the Likelihood of Convective Initiation
Bibliographic record
Abstract
Abstract Land management impacts atmospheric boundary layer processes, and recent trends reducing the practice of summer fallow have led to increases in precipitation and decreases in temperature in the Canadian Prairie provinces during summer. It is unclear if such trends also impact the hydrometeorology of the adjacent U.S. northern Great Plains, parts of which have seen similar changes in land management. Here, MERRA-2 reanalysis data, eddy covariance observations, and a mixed-layer (ML) atmospheric modeling framework are combined to demonstrate that the likelihood of convectively preconditioned conditions has increased by approximately 10% since the mid-1980s and is now more sensitive to further decreases in the Bowen ratio (Bo) and maximum daily net radiation in northeastern Montana. Convective season Bo in the study area has decreased from approximately 2 to 1 from the 1980s until the present, largely due to simultaneous increases in latent heat flux and decreases in sensible heat flux, consistent with observed decreases of summer fallow and increases in cropping. Daily net radiation has not changed despite a significant decrease in May and June humidity lapse rates from the 1980s to present. Future research should determine the area of the U.S. Great Plains that has seen changes in the dynamics of the atmospheric boundary layer height and lifted condensation level and their crossings as a necessary condition for convective precipitation to occur and ascertain if ongoing changes in land management will lead to future changes in convective outcomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".