Contribution of the Tibetan Plateau Winter Snow Cover to Seasonal Prediction of the East Asian Summer Monsoon
Bibliographic record
Abstract
How to improve the prediction skill of the East Asian summer monsoon (EASM) is a challenging but essential issue. This study examines the impact of the winter Tibetan Plateau (TP) snow cover (TPSC) on the subsequent EASM during the past two decades. Based on the high-resolution MODIS/Terra snow cover data, a new snow cover critical area (76°−83°E, 28°−35°N) is identified in the southwestern TP for the EASM seasonal prediction. Results show that the increase of the TPSC within this critical area during prior winter significantly increases summer precipitation over the Yangtze River Basin (YRB). The TPSC anomaly induces anomalous cooling in the overlying atmospheric column, leading to an anomalous cyclonic circulation in the upper troposphere. Such anomalous cyclonic circulation may further contribute to the local snow cover increase, and through such a snow-albedo feedback process, the excessive TPSC anomaly is strengthened and persists through the following summer. Coexisting with the positive anomalous TPSC, the South Asian High, the western Pacific Subtropical High, and the Subtropical Westerly Jet shift southward. A deep cyclonic circulation is induced in northeastern China by the excessive TPSC anomaly, which is reproduced in the linear baroclinic model simulation. Northerly flow is crucial for accumulating water vapour and favours more rainfall over the YRB. A physical empirical prediction model is established to quantify the TPSC contribution to the seasonal prediction of the EASM. Empirical hindcast output shows the prediction skill of the EASM is significantly improved with the additional predictor of the winter TPSC. In particular, the TPSC has greatly improved the prediction of the extreme EASM in 2020. The above results indicate that the prior winter TPSC anomaly in this critical area can provide another predictability source for the EASM, besides El Niño-Southern Oscillation and the North Atlantic Oscillation.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".