Decadal Covariability of the Northern Wintertime Land Surface Temperature and Atmospheric Circulation
Bibliographic record
Abstract
Abstract The decadal covariability of northern wintertime land surface temperature and 500-hPa geopotential anomalies is examined using the National Centers for Environmental Prediction–National Center for Atmospheric Research and the Twentieth-Century Reanalyses over the twentieth century and a 996-yr preindustrial climate simulation from the Canadian Earth System Model. Based on the reanalysis data, the covariability is dominated by two leading maximum covariance analysis (MCA) modes. MCA1 is characterized by temperature anomalies over most of Canada, the eastern United States, Mexico, and Eurasian mid- to high latitudes, accompanied by anomalies of opposite sign elsewhere over northern landmasses. MCA2 features temperature anomalies over most of North America, Eurasia, and Greenland with opposite anomalies elsewhere. In the upper troposphere the synoptic vorticity fluxes reinforce the anomalous circulation, while in the lower troposphere advection by the anomalous mean flow offsets the eddy forcing and maintains the decadal temperature perturbation. The MCA1-associated variability has a broad spectrum over decadal–interdecadal time scales, while the MCA2-related variability has a significant power peak around 20 yr. The change of temperature and geopotential trends around 1990 tends to be a decadal-scale shift in winter and has significant features of the leading mode of the decadal covariability. The climate model has broadly similar decadal covariability, including the leading MCA patterns as well as the temporal evolution of the patterns. The decadal temperature and geopotential anomalies primarily covary with the North Atlantic Oscillation but also with the variability of the North Pacific index, while the Southern Oscillation index variability tends to be the least important predictor for the northern decadal temperature and geopotential anomalies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".