Application Evaluation of a Bias Correction Method in the Correction of CMIP6 Precipitation Data for Summer in Qinghai-Xizang Plateau
Received date: 2023-08-16
Revised date: 2024-03-27
Online published: 2024-09-19
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.
Yumeng LIU , Lin ZHAO , Zhaoguo LI , Shaoying WANG , Yuanyuan MA , Xianhong MENG . Application Evaluation of a Bias Correction Method in the Correction of CMIP6 Precipitation Data for Summer in Qinghai-Xizang Plateau[J]. Plateau Meteorology, 2025 , 44(1) : 16 -31 . DOI: 10.7522/j.issn.1000-0534.2024.00046
null | |
null | |
null | |
null | |
null | |
null | |
null | |
null | |
null | |
null | |
null | |
null | |
null | 鲍艳, 王玉琦, 南素兰, 等, 2023.青藏高原植被对未来气候变暖的反馈[J].高原气象, 42(3): 553-563.DOI: 10.7522/j.issn.1000-0534.2021.00109.Bao Y , |
null | |
null | 陈杰, 许崇育, 郭生练, 等, 2016.统计降尺度方法的研究进展与挑战[J].水资源研究, 5(4): 299-313.DOI: 10.12677/JWRR.2016.54037.Chen J , |
null | |
null | 陈荣, 段克勤, 尚溦, 等, 2023.基于CMIP6模式数据的1961 -2099年青藏高原降水变化特征分析[J].高原气象, 42(2): 294-304 DOI: 10.7522/j.issn.1000-0534.2021.00084.Chen R , |
null | |
null | 段凯, 梅亚东, 2021.几种降水降尺度方法在中国十大流域的适用性分析[J].武汉大学学报(工学版), 54(9): 777-783.DOI: 10.14188/j.1671-8844.2021-09-001.Duan K , |
null | |
null | 范丽军, 符淙斌, 陈德亮, 2005.统计降尺度法对未来区域气候变化情景预估的研究进展[J].地球科学进展, 20(3): 320-329.DOI: 10.11867/j.issn.1001-8166.2005.03.0320.Fan L J , |
null | |
null | 匡志远, 宋振亚, 董昌明, 2020.基于机器学习订正模型的未来百年全球海表温度预估研究[J].气候变化研究快报, 9(4): 270-284.DOI: 10.12677/CCRL.2020.94031.Kuang Z Y , |
null | |
null | 李宛鸿, 徐影, 2023.CMIP6模式对青藏高原极端气温指数模拟能力评估及预估[J].高原气象, 42(2): 305-319.DOI: 10.7522/j.issn.1000-0534.2022.00032.Li W H , |
null | |
null | 潘保田, 李吉均, 1996.青藏高原: 全球气候变化的驱动机与放大器——Ⅲ.青藏高原隆起对气候变化的影响[J].兰州大学学报, 32(1): 108-115. |
null | |
null | 强安丰, 汪妮, 莫淑红, 等, 2020.气候变化对水文水资源影响评价的不确定研究进展[J].水资源研究, 9(2): 169-178.DOI: 10.12677/JWRR.2020.92018. Qiang A F , |
null | |
null | 王予, 李惠心, 王会军, 等, 2021.CMIP6全球气候模式对中国极端降水模拟能力的评估及其与CMIP5的比较[J].气象学报, 79(3): 369-386.DOI: 10.11676/qxxb2021.031.Wang Y , |
null | |
null | |
null | |
null | 肖雨佳, 李建, 李妮娜, 2022.CMIP6 HighResMIP高分辨率气候模式对青藏高原降水模拟的评估[J].暴雨灾害, 41(2): 215-223.DOI: 10.3969/j.issn.1004-9045.2022.02.012.Xiao Y J , |
null | |
null | 徐仁慧, 赵磊, 文小航, 2022.基于 CMIP6 动力降尺度对青藏高原降水的评估[J].气候变化研究快报, 11(6): 1076-1087.DOI: 10.12677/CCRL.2022.116112. Xu R H , |
null | |
null | 徐祥德, 董李丽, 赵阳, 等, 2019.青藏高原“亚洲水塔”效应和大气水分循环特征[J].科学通报, 64(27): 2830-2841. |
null | |
null | 杨明鑫, 肖天贵, 李勇, 等, 2022.CMIP6 模式对我国西南地区夏季气候变化的模拟和预估[J].高原气象, 41(6): 1557-1571.DOI: 10.7522/j.issn.1000-0534.2021.00119.Yang M X , |
null | |
null | 张春雨, 刘爱利, 吕嫣冉, 等, 2023.基于CMIP6 青藏高原腹地气候模拟评估及时空分析[J].高原气象, 42(5): 1144-1159.DOI: 10.7522/j.issn.1000-0534.2022.00104.Zhang C Y , |
null | |
null | 张佳怡, 伦玉蕊, 刘浏, 等, 2022.CMIP6多模式在青藏高原的适应性评估及未来气候变化预估[J].北京师范大学学报(自然科学版), 58(1): 77-89.DOI: 10.12202/j.0476-0301.2021114.Zhang J Y , |
null | |
null | 张雪芹, 彭莉莉, 林朝晖, 2008.未来不同排放情景下气候变化预估研究进展[J].地球科学进展, 23(2): 174-185.DOI: 10.11867/j.issn.1001-8166.2008.02.0174.Zhang X Q , |
null | |
null | 赵丹, 张丽霞, 周天军, 2022.CMIP6模式对中国东部地区水循环的模拟能力评估[J].大气科学, 46(3): 557-572.DOI: 10.3878/j.issn.1006-9895.2106.21030.Zhao D , |
null | |
null | 周天军, 邹立维, 陈晓龙, 2019.第六次国际耦合模式比较计划(CMIP6)评述[J].气候变化研究进展, 15(5): 445-456.DOI: 10.12006/j.issn.1673-1719.2019.193.Zhou T J , |
null |
/
〈 |
|
〉 |