North American Monsoon Response to Antecedent Soil Moisture and Snow in the Colorado Plateau
Bibliographic record
Abstract
Abstract This study explores the relationship between spring soil moisture and snowpack in the Colorado Plateau (CP) and the onset of the North American Monsoon (NAM) using reanalysis data. We find that increased spring soil moisture in the CP corresponds to later NAM onset dates throughout much of the southwestern United States (US) and northwestern Mexico. Anomalously moist spring soils in the CP increase the seasonal‐average surface latent heat flux and decrease sensible heat flux, resulting in lower late spring and early summer surface temperatures and a weakened thermal gradient between the land and ocean. Decreased spring sensible heat flux also corresponds to decreased early summer geopotential heights over the southwestern US, a weaker NAM circulation, and reduced moisture flux into the NAM region. NAM onset dates in years with the highest spring soil moisture or snow depth content in the CP are delayed 1 week on average in the NAM region. Conversely, NAM onset dates in years with the lowest spring soil moisture or snow depth content in the CP occur 1 week earlier on average. While increased spring snow in the CP is correlated with a delayed NAM onset, the relationship between spring albedo and NAM onset lacks statistical significance, suggesting that CP snow anomalies affect the NAM by contributing soil moisture via snowmelt, rather than directly affecting the surface energy budget. Results indicate a mechanistic pathway that links spring soil moisture and snowpack anomalies in the CP to NAM development, which will be tested in future numerical simulations.
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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 | 0.000 |
| 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".