Application Evaluation of a Bias Correction Method in the Correction of CMIP6 Precipitation Data for Summer in Qinghai-Xizang Plateau
Bibliographic record
Abstract
We bias-corrected and assessed summer precipitation data over the Qinghai-Xizang Plateau （QXP） based on 18 models from the Coupled Model Intercomparison Project Phase 6 （CMIP6）.Our assessment of CMIP6 data， conducted for the period 1979-2014， centered on the performance of both the ensemble and individual models.We evaluated the CMIP6 data before and after bias correction， according to considering mean precipitation and extreme precipitation.The results highlight the correction method's dependence on ERA5 reanalysis data quality over the QXP.Although corrected mean summer precipitation over the QXP shows improvement in bias and bias rate， it exhibits inferior interannual time-varying characteristics compared to pre-corrected data.Most of the models were able to better simulate the spatial variability characteristics of mean precipitation over the QXP， gradually increasing from northwest to southeast from 1979 to 2014.Pre-correction precipitation data overestimates precipitation over the QXP with a bias rate of 60.4%， while corrected data is relatively underestimated with a deviation rate of -13.9%.The mean bias of the corrected data from ERA5 is only 0.003 mm·d-1， with a spatial correlation as high as 0.999.Spatial trend analysis of observed data indicates a slight increase in summer precipitation over most of the TP from 1979 to 2014， with a significant decreasing trend only along the eastern edge.Both pre- and post-corrected data generally capture this spatial distribution， though pattern correlation coefficients of most individual uncorrected CMIP6 models do not exceed 0.5.Comparing with the interannual variability of the precipitation data obtained from observations， the pre-corrected data overestimate the precipitation on the QXP， while the post-corrected data are underestimated in comparison with the observation results.Extreme precipitation is selected by determining the 95% thresholds， a revealing a spatial distribution similar to the mean annual precipitation， increasing from northwest to southeast.This feature is well captured by some models， such as MRI-ESM2-0 （The Meteorological Research Institute Earth System Model version 2.0） and ACCESS-CM2 （Australian Community Climate and Earth System Simulator Climate Model Version 2.0）.Earth System Simulator Climate Model Version 2）， the spatial correlation coefficients are 0.851 and 0.821， respectively， compared with the observations， but the spatial correlation of the corrected data decreases from 0.861 to 0.730， failing to accurately characterize the stepwise increase of extreme precipitation on the QXP.The deviation distribution of the corrected extreme precipitation data is similar to pre-correction data， with lower areas concentrated in the southern hinterland and eastern part of the QXP.The analysis of extreme precipitation contribution shows that both the observation results and the CMIP6 precipitation data indicate that the trend of extreme precipitation contribution is not obvious during 1979-2014.Among individual models， EC-Earth3-Veg （European Community Earth-Vegetation model version 3） and EC-Earth3 （European Community Earth Model version 3） and CanESM5 （The Canadian Earth System Model version 5） ranked high in several parameters， showing better simulation capability， while IPSL-CM6A-LR （Institute Pierre-Simon Laplace Climate Model 6A Low Resolution） ranked high in the mean precipitation deviation and extreme precipitation deviation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 teacher head, 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".