IPCC AR5 全球气候模式模拟的中国地区日平均降水精度评价

doi:10.3724/SP.J.1047.2016.00227

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (2): 227-237.doi: 10.3724/SP.J.1047.2016.00227

IPCC AR5 全球气候模式模拟的中国地区日平均降水精度评价

孙侦^1,²(), 贾绍凤^1,*^*(), 吕爱锋¹, 朱文彬^1,², 高彦春¹

1. 中国科学院地理科学与资源研究所中国科学院陆地水循环及地表过程重点实验室,北京 100101
2. 中国科学院大学,北京 100049

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: 040400126@163.com
基金资助:
基金项目：国家自然科学基金项目“中国人口高峰期粮食安全的水土资源配置研究”(41471463)

Precision Estimation of the Average Daily Precipitation Simulated by IPCC AR5 GCMs in China

SUN Zhen^1,²(), JIA Shaofeng^1,^*(), LV Aifeng¹, ZHU Wenbin^1,², GAO Yanchun¹

1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academic of Sciences, Beijing 100049, China

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/Abstract

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。

关键词: IPCC AR5, 全球气候模式, 降水, 精度

Abstract:

This article estimated the precision of the precipitation simulated by 15 IPCC AR5 (the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC AR5) GCMs (Global Climate Models) and the multi-model ensemble (MME), based on the observed precipitation from 660 stations in China during 1996 to 2005. We firstly extracted the model simulation value at the corresponding position of the meteorological station, using the bilinear interpolation method, and took the average value of different models at the same station as the multi-model ensemble simulation value, then estimated the precision of the precipitation simulated by 15 IPCC AR5 GCMs and MME based on the observation of meteorological station. There were four evaluation parameters, including Corr (correlation coefficient), Bias, MRE (Mean Relative Error), and RMSE (Root Mean Square Error). Results show that the biases of the average daily precipitations simulated by IPCC AR5 GCMs present a gradually downward trend from northwest to southeast, and the RMSEs show a gradually increasing trend from northwest to southeast, while MREs in the east are less than those in the west. 82.3% of the average daily precipitations simulated by MRI-CGCM3 have relatively small biases, ranging from -0.5 to 0.5. The precisions of average daily precipitations simulated by BNU and MIROC-ESM are lower than that of others. Compared with other models, the MME simulation has the largest percentages of which the correlation coefficients are more than 0.5, MREs are less than 0.5, and RMSEs are less than 4mm, which accounted for 64.8%, 25.8% and 86.4% respectively. And the percentage of the biases ranging from -0.5 to 0.5 is relatively large, which is 56.7%, indicating that the simulation precision of MME is better than that of any other GCMs, and the MME can reduce the uncertainty of a single GCM simulation in future scenarios. Therefore, it is more scientific and reasonable to select the precipitation simulated by MME as the climate change condition, while studying subjects related to climate change.

Key words: IPCC AR5, GCMs, precipitation, precision

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

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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

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

IPCC AR5 全球气候模式模拟的中国地区日平均降水精度评价

Precision Estimation of the Average Daily Precipitation Simulated by IPCC AR5 GCMs in China

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