Decomposition of physical processes controlling EASM precipitation changes during the mid-Piacenzian: new insights into data–model integration
Bibliographic record
Abstract
Abstract The mid-Piacenzian warm period (MPWP, ~3.264–3.025 Ma) has gained widespread interest due to its partial analogy with future climate. However, quantitative data–model comparison of East Asian Summer Monsoon (EASM) precipitation during the MPWP is relatively rare, especially due to problems in decoding the imprint of physical processes to climate signals in the records. In this study, pollen-based precipitation records are reconstructed and compared to the multi-model ensemble mean of the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). We find spatially consistent precipitation increase in most simulations but a spatially divergent change in MPWP records. We reconcile proxy data and simulation by decomposing physical processes that control precipitation. Our results 1) reveal thermodynamic control of an overall enhancement of EASM precipitation and 2) highlight a distinct control of thermodynamic and dynamical processes on increases of tropical and subtropical EASM precipitation, reflecting the two pathways of water vapor supply that enhance EASM precipitation, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".