Dominant Modes of Subseasonal Variability of East Asian Summertime Surface Air Temperature and Their Predictions
Bibliographic record
Abstract
Subseasonal variability of surface air temperature (SAT) over East Asia is analyzed using the NCEP–NCAR reanalysis of 34 Northern Hemisphere extended summers. An empirical orthogonal function (EOF) analysis is performed with pentad SAT data to identify the leading modes of subseasonal SAT variability. The first (EOF1) and second (EOF2) modes, which together account for about 35% of the total variance, correspond to a monopole structure of SAT anomaly in the whole East Asian region and a dipole structure with opposite signs of variability over the north and south East Asian continent, respectively. Lead–lag regressions are calculated in order to analyze how the large-scale atmospheric circulation evolves in association with the development of the leading SAT modes. An eastward propagation of the Rossby wave from the midlatitude Atlantic Ocean is observed about three pentads before EOF1. EOF2 is influenced by both the tropical Madden–Julian oscillation (MJO) and a midlatitude wave train. These results indicate that there is potential for prediction of the dominant SAT modes on the subseasonal time scale. The subseasonal prediction of the two dominant modes is further evaluated in the operational monthly forecasting system of Environment and Climate Change Canada (ECCC). The model shows a better forecast skill than the persistence forecast. The strength of the subseasonal signal in initial conditions impacts the forecast skill. The forecasts starting with strong EOF in the initial condition are more skillful than those initialized with weak EOF. The findings in the study contribute to improving the understanding of the subseasonal variability and SAT subseasonal forecasting in East Asia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".