Interdecadal Connection between Arctic Temperature and Summer Precipitation over the Yangtze River Valley in the CMIP5 Historical Simulations
Bibliographic record
Abstract
Abstract This study assesses the ability of the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations in capturing the interdecadal precipitation enhancement over the Yangtze River valley (YRV) and investigates the contributions of Arctic temperature and mid- to high-latitude warming to the interdecadal variability of the East Asian summer monsoon rainfall. Six CMIP5 historical simulations including models from the Canadian Centre for Climate Modeling and Analysis (CCCma), the Beijing Climate Center, the Max Planck Institute for Meteorology, the Meteorological Research Institute, the Met Office Hadley Centre, and NCAR are used. The NCEP–NCAR reanalysis and observed precipitation are also used for comparison. Among the six CMIP5 simulations, only CCCma can approximately simulate the enhancement of interdecadal summer precipitation over the YRV in 1990–2005 relative to 1960–75; the various relationships between the summer precipitation and surface temperature (Ts), 850-hPa winds, and 500-hPa height field (H500); and the relationships between Ts and H500 determined using regression, correlation, and singular value decomposition (SVD) analyses. It is found that CCCma can reasonably simulate the interdecadal surface warming over the boreal mid- to high latitudes in winter, spring, and summer. The summer Baikal blocking anomaly is postulated to be the bridge that links the winter and spring surface warming over the mid- to high latitude and Arctic with the enhancement of summer precipitation over the YRV. Models that missed some or all of these relationships found in CCCma and the reanalysis failed to simulate the interdecadal enhancement of precipitation over the YRV. This points to the importance of Arctic and mid- to high-latitude processes on the interdecadal variability of the East Asian summer monsoon and the challenge for global climate models to correctly simulate the linkages.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".