地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (2): 227-237.doi: 10.3724/SP.J.1047.2016.00227
孙侦1,2(), 贾绍凤1,**(
), 吕爱锋1, 朱文彬1,2, 高彦春1
收稿日期:
2015-04-16
修回日期:
2015-06-04
出版日期:
2016-02-10
发布日期:
2016-02-04
通讯作者:
贾绍凤
E-mail:040400126@163.com;jiasf@igsnrr.ac.cn
作者简介:
作者简介:孙 侦(1985-),女,博士生,研究方向为水文水资源。E-mail:
基金资助:
SUN Zhen1,2(), JIA Shaofeng1,*(
), LV Aifeng1, ZHU Wenbin1,2, GAO Yanchun1
Received:
2015-04-16
Revised:
2015-06-04
Online:
2016-02-10
Published:
2016-02-04
Contact:
JIA Shaofeng
E-mail:040400126@163.com;jiasf@igsnrr.ac.cn
摘要:
本文利用中国区域660个站点逐日地面降水资料,评估了由IPCC(the Intergovernmental Panel on Climate Change)数据中心于2014年最新发布的15个全球气候模式(Global Climate Models, GCMs)以及多模式集合(Multi-Model Ensemble, MME)对中国降水的模拟精度。首先,从全球范围数据集中读取研究区范围内的GCMs降水模拟数据;然后,提取各个气象站点处的GCMs模拟值;其次,将GCMs在同一站点的模拟值取平均,得到MME模拟值;最后,以气象站点实际观测值为基准,对GCMs的模拟值精度进行评估。研究结果表明:IPCC AR5 GCMs 1996-2005年平均日降水模拟值偏差在中国地区的空间分布均呈现出西北向东南逐渐减小的特征,东部地区平均相对误差较小,平均相对误差较大的点主要分布在西部,但均方根误差呈现出从西北向东南增加的趋势;MRI-CGCM3有82.3%的日平均降水模拟值偏差都比较小,偏差介于-0.5到0.5之间;对于中国地区1996-2005年平均日降水量,BNU和MIROC-ESM模拟精度最低;MME模式模拟值的相关系数>0.5、平均相对误差<0.5和均方根误差<4 mm的百分率均为最高,分别达到64.8%、25.8%和86.4%,偏差介于-0.5到0.5之间的比例为56.7%,说明MME对中国地区日平均降水的模拟精度优于大部分模式,MME模式可在一定程度上减少单个模式未来情景模拟的不确定性。
孙侦, 贾绍凤, 吕爱锋, 朱文彬, 高彦春. IPCC AR5 全球气候模式模拟的中国地区日平均降水精度评价[J]. 地球信息科学学报, 2016, 18(2): 227-237.DOI:10.3724/SP.J.1047.2016.00227
SUN Zhen,JIA Shaofeng,LV Aifeng,ZHU Wenbin,GAO Yanchun. Precision Estimation of the Average Daily Precipitation Simulated by IPCC AR5 GCMs in China[J]. Journal of Geo-information Science, 2016, 18(2): 227-237.DOI:10.3724/SP.J.1047.2016.00227
表1
IPCC AR5GCMs基本信息描述"
模式名称 | 国家 | 研究机构 | 空间分辨率(纬向×经向) | 时间步长(√考虑闰年) |
---|---|---|---|---|
BCC | 中国 | 国家气候中心 | 64×128 | |
BNU | 中国 | 北京师范大学 | 64×128 | |
CanESM2 | 加拿大 | CCCma | 64×128 | |
CCSM4 | 美国 | NCAR | 192×288 | |
CNRM-CM5 | 法国 | CNRM | 128×256 | √ |
CSIRO-MK3.6.0 | 澳大利亚 | CSIRO | 96×192 | |
FGOALS-g2 | 中国 | 中国科学院大气物理研究所和清华大学 | 60×128 | |
GISS-E2-R | 美国 | GISS/ NASA | 90×144 | |
IPSL-CM5A-LR | 法国 | IPSL | 96×96 | |
MIROC5 | 日本 | AORI/NIES/JAMSTE | 128×256 | |
MIROC-ESM | 日本 | 64×128 | √ | |
MIROC-ESM-CHEM | 日本 | 64×128 | √ | |
MPI-ESM-LR | 德国 | MPI | 96×192 | √ |
MRI-CGCM3 | 日本 | MRI | 160×320 | √ |
NorESM1-M | 挪威 | NCC | 96×144 |
表2
IPCC AR5 GCMs日平均降水模拟值精度评估结果 / (%)"
模型 | r>0.5 | r<0 | |Bias|<0.5 | Bias>3 | RMSE<4 | RMSE>5 | MRE<0.5 | MRE>1.5 |
---|---|---|---|---|---|---|---|---|
BCC | 23.8 | 5.6 | 51.8 | 7.7 | 67.3 | 12.0 | 2.3 | 1.7 |
BNU | 32.7 | 5.2 | 43.8 | 11.4 | 58.3 | 14.4 | 4.7 | 5.6 |
CanESM2 | 31.7 | 1.1 | 58.0 | 4.1 | 62.3 | 11.8 | 3.8 | 1.5 |
CCSM4 | 19.7 | 0.8 | 53.6 | 5.0 | 68.3 | 12.7 | 3.8 | 2.1 |
MPI-ESM-LR | 28.2 | 3.9 | 55.8 | 4.8 | 66.1 | 11.5 | 3.8 | 3.3 |
MRI-CGCM3 | 24.4 | 3.6 | 82.3 | 2.4 | 67.6 | 12.6 | 3.8 | 1.5 |
NorESM1-M | 34.5 | 0.5 | 47.9 | 7.1 | 65.9 | 13.3 | 3.3 | 5.3 |
CNRM-CM5 | 27.1 | 0.2 | 65.9 | 2.4 | 65.2 | 14.4 | 7.0 | 1.4 |
CSIRO-Mk3.6.0 | 28.0 | 2.6 | 70.3 | 2.7 | 69.8 | 9.5 | 4.4 | 0.9 |
FGOALS-g2 | 21.8 | 4.4 | 57.4 | 5.8 | 78.3 | 8.8 | 5.3 | 1.4 |
GISS-E2-R | 18.0 | 2.0 | 67.0 | 7.6 | 67.9 | 13.8 | 5.5 | 3.5 |
IPSL-CM5A-LR | 29.4 | 1.1 | 64.5 | 3.6 | 74.5 | 8.0 | 3.8 | 0.3 |
MIROC5 | 26.1 | 0.3 | 76.8 | 4.1 | 72.6 | 9.2 | 9.5 | 1.2 |
MIROC-ESM | 19.1 | 0.2 | 45.8 | 9.7 | 72.4 | 10.5 | 2.4 | 4.8 |
MIROC-ESM-CHEM | 18.9 | 1.4 | 46.7 | 8.8 | 68.3 | 10.3 | 1.5 | 3.6 |
MME | 64.8 | 0.2 | 56.7 | 5.3 | 86.4 | 4.5 | 25.8 | 1.2 |
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