Bibliographic record
Abstract
Interannual climate variability during the northern summer season has been investigated in this study. After Walker's pioneering work (Walker and Bliss 1932, 1937), many previous studies have documented and discussed the structure and the dynamical/thermodynamical causes (e.g., Bjerkness 1969). However, relatively less attention have been devoted on the summer climate variability. Thus, two different aspects of the interannual climate variability during the northern summer season have been discussed. One is the interannual variation of the boreal-forest rainbelts, and the other is the interannual variation of the North American monsoon rainfall. Also, the summer climatological aspect of the boreal-forest rainbelts from a hydrological and dynamical perspectives was presented in prior to its interannual variability.;The boreal forests comprise one third of the global woodlands, while the warm-season runoff from the boreal-forest rainbelts provide a major amount of freshwater to the Arctic Ocean. It is shown the boreal-forest rainbelts are maintained by the convergence of water vapor by transients along these rainbelts and the interannual variation of these rainbelts is caused by the collective response of these rainbelts to the North Atlantic Oscillation and the East-Asian teleconnection monsoon pattern in Eurasia and the Nitta-like short-wave train and the North Atlantic Oscillation in the Alaska-Canadian subarctic region. It was hypothesized by our recent study that the North American Monsoon (including both the Mexican and the Southwest U.S. monsoon) is maintained by the east-west differential heating between the Western Tropical Atlantic heating and the Eastern Tropical Pacific cooling. Diagnostic analysis with NCEP/NCAR reanalysis data in this study substantiated this hypothesis. Our current study has primarily focused on diagnostic analysis of observations and reanalysis. Further analysis with the global climate model such as NCAR CAM or NASA NSIPP and the regional climate model is suggested to further substantiate our hypothesis proposed in this study.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".