Summertime Surface Wind Variability over Northeastern North America at Multidecadal to Centennial Time Scales via Statistical Downscaling
Bibliographic record
Abstract
Abstract The variability of the surface zonal and meridional wind components over northeastern North America during June–October is analyzed through a statistical downscaling (SD) approach that relates the main wind and large-scale circulation modes. An observational surface wind dataset of 525 sites over 1953–2010 provides the local information. Twelve global reanalyses provide the large-scale information. The large-to-local variability of the wind field can be explained, to a large extent, in terms of four coupled modes of circulation explaining a similar amount of variance. The SD method is mostly sensitive to the number of retained modes and subregionally to the large-scale information variable, but not to the reanalysis source. The SD methodological uncertainty based on the use of multiple configurations is directly related to the variability of the wind, similar in relative terms for both components. With an adequate choice of parameters the SD estimates provide more realistic variances than the reanalysis wind, although their correlations with respect to observations are lower than the latter. Additionally, while these different SD estimations are very similar on the reanalysis used, the various reanalysis wind fields show noticeable differences, especially in their variances. The wind variability is reconstructed back to 1850, making use of century-long reanalyses and two additional SLP gridded datasets, which allows estimating the variability at decadal to multidecadal time scales. Recent negative (significant) trends in the zonal component do not stand out in the multidecadal context, but they are consistent with a global stilling process, and are partially attributable to changes in the large-scale dynamics.
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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 | 0.001 | 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".