Seasonal and regional biases in CMIP5 precipitation simulations
Bibliographic record
Abstract
This study provides insight into how CMIP5 climate models perform in simulating summer and winter precipitation at different geographical locations and climate conditions. Precipitation biases in the CMIP5 historical (1901 to 2005) simulations relative to the Climatic Research Unit (CRU) observations are evaluated over 8 regions exhibiting distinct seasonal hydroclimates: moist tropical (Amazonia and central Africa), monsoonal (southern China), moist continental (central Europe), semi-arid (western United States and eastern Australia), and polar (Siberia and Canada). While the bias and monthly quantile bias (MQB) reflect no substantial differences in CMIP5 summer and winter precipitation simulations at the global scale, strong seasonality and high inter-model variability are found over the selected moist tropical regions (i.e. Amazonia and central Africa). In the semi-arid regions, high inter-model precipitation variability is also displayed, especially in summer, while the median of simulations is an overestimate of both winter and summer precipitation. In Siberia and central Europe, most CMIP5 models underestimate summer precipitation, and overestimate it in winter. Also, the MQB values decrease as the choice of quantile thresholds increase, implying that the underestimation of summer precipitation is primarily associated with biases in lower quantiles of the precipitation distribution. While the CMIP5 models exhibit similar behaviors in simulating high-latitude winter precipitation, they differ substantially in summer simulations for the selected Canadian and Siberian regions. Finally, in the monsoonal southern China region, CMIP5 models exhibit large overall precipitation biases in both summer and winter, as well as at higher quantiles.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".