Feedback between surface air temperature and atmospheric circulation in high‐temperature weather in East China: a diurnal perspective
Bibliographic record
Abstract
Abstract This study proposes the generality of surface air temperature ( SAT )–atmospheric circulation feedback during high‐temperature weather in late July 2003 over East China by using the Advanced Research Weather Research and Forecasting model ( WRF ; Version 3) simulations with a succession of 24‐h integrations, i.e. on a daily scale, the SAT increase leads to a weakened ridge of the western Pacific subtropical high in the lower troposphere (i.e. negative feedback), whereas it leads to a strengthened ridge in the upper troposphere (i.e. positive feedback) and vice versa. Additionally, using the balance equation of temperature, the feedbacks are clarified from the diurnal‐variation perspective. This shows many complex details, e.g. the changes in geopotential heights are more complex than those in air temperatures, and the overall daily feedback appears to be dominated by the feedback during the phase with intense daytime surface heating. All of the WRF ‐modified land surface conditions can lead to large changes in the maximum, minimum, and average SATs over the mean diurnal scale, with generally larger differences induced by land surface schemes than those induced by initial soil moisture, suggesting that the SAT –circulation feedback can be greatly reduced (or amplified) by different land conditions over the diurnal weather scale and that diurnal variations could substantially contribute to longer timescale climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".