Comparison of <scp>CMIP6</scp> and <scp>CMIP5</scp> simulations of precipitation in China and the East Asian summer monsoon
Bibliographic record
Abstract
Abstract We evaluate and compare the simulation of summer precipitation in China and the East Asian summer monsoon (EASM) by eight climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and the corresponding eight previous models from CMIP5. Skill metrics are calculated to assess the climatology, interannual variation and linear trends during the time period 1961–2005. The CMIP6 multimodel ensemble (MME) is more skillful than the CMIP5 MME in the spatial correlation and standard deviation ( SD) of the climatological precipitation over Eastern China. All the CMIP6 models improve the skill scores in the climatological pattern of the EASM relative to the previous models of CMIP5, which is related to their smaller sea surface temperature (SST) biases over the Northwestern Pacific Ocean. The models with a higher capability in reproducing the climatological pattern of the EASM tend to have a better skill in simulating summer precipitation over Eastern China. Most (six of eight) of the CMIP6 models have advantages over the previous CMIP5 models in reproducing the interannual anomalous rainfall pattern over Eastern China related to the EASM. Ten of the 16 models partly reproduce the weakening trend of the EASM during 1961–2005. The high‐skill models (GISS‐E2‐H, GISS‐E2‐1‐H) that simulate a clear weakening trend in the EASM also reasonably simulate the negative correlation between the EASM and the SST over Eastern Indian and the Western Pacific Oceans (EIWP). By contrast, the two models (CESM2 and CESM2‐WACCM) that simulate a positive correlation over the EIWP both produce increasing trends in the EASM indices. This indicates the importance of climate models in simulating the relationship between the EASM and the SST over the EIWP. Among the 16 models, only 2 CMIP6 models (BCC‐CSM2‐MR and GISS‐E2‐1‐H) partly reproduce the linear trend of precipitation over Eastern China, featured by the pattern of “southern flood and northern drought.”
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".