地球科学进展

地球科学进展 ›› 2017, Vol. 32 ›› Issue (9): 983 -995. doi: 10.11867/j.issn.1001-8166.2017.09.0983

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全球降水中氢氧稳定同位素GCM模拟空间分布的比较
王学界( ), 章新平( ), 张婉君, 张新主, 罗紫东   
  1. 湖南师范大学资源与环境科学学院, 湖南师范大学地理空间大数据挖掘与应用湖南省重点实验室,湖南 长沙 410081
  • 收稿日期:2017-04-11 修回日期:2017-08-01 出版日期:2017-09-20
  • 基金资助:
    国家自然科学基金项目“湘江流域水稳定同位素的取样、模拟和比较”(编号:41571021);湖南省重点学科建设项目“地理学”(编号:2016001)资助

Comparison on Spatial Distribution of Hydrogen and Oxygen Stable Isotope GCM Simulation in Global Precipitation

Xuejie Wang( ), Xinping Zhang( ), Wanjun Zhang, Xinzhu Zhang, Zidong Luo   

  1. Key Laboratory of Geospatial Big Data Mining and Application, College of Resources and Environmental Science, Hu’nan Normal University, Changsha 410081, China
  • Received:2017-04-11 Revised:2017-08-01 Online:2017-09-20 Published:2017-09-20
  • About author:

    First author:Wang Xuejie(1992-), male, Xinshao County, Hu’nan Province, Master student.Research areas include isotopic meteorology and climate change.E-mail:xuejiewang2015@163.com

  • Supported by:
    Foundation item:Project supported by the National Natural Science Foundation of China“Stable isotope sampling, simulation and compared in Xiangjiang River Basin”(No.41571021);Key Discipline Construction Projects in Hunan Province“Geography”(No.2016001)
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利用大气环流模式模拟降水中氢氧稳定同位素可以深入了解水循环过程中水稳定同位素的迁移变化规律并弥补实测数据在空间和时间方面的不连续性。利用10个引入水稳定同位素循环的GCM(General Circulation Models)模拟数据,分析了全球降水中稳定同位素效应的空间分布特征,对不同模式的模拟结果之间以及模拟结果与全球降水同位素监测网络(GNIP)的实际监测结果之间进行了比较,旨在对稳定同位素大气环流模式模拟结果的有效性进行评价,改善对水循环中水稳定同位素效应的理解和认识。结果显示,在δ18O的全球空间分布模拟方面,isoGSM,ECHAM4,LMDZ4和HadAM3模拟效果较佳;在δ18O的季节差的空间分布模拟方面各模式模拟效果总体较好,仅HadAM3模拟效果稍差;在δ18O与气温相关关系的空间分布模拟方面,isoGSM,GISS E-F,ECHAM4,GISS E-N和LMDZ4模拟结果与实测较匹配;在δ18O与降水量相关关系的空间分布模拟方面LMDZ4,isoGSM,GISS E-F,ECHAM4和MUGCM模拟能力较强;在全球大气降水线模拟方面GISS E-F,isoGSM和GISS E-N优势明显。

关键词: 氢氧稳定同位素, iGCMs, GNIP, 全球大气降水线

The general circulation models are used to simulate hydrogen and oxygen stable isotope in precipitation, which can enhance our understanding of the migratory processes of water stable isotope in water cycle and remedy disadvantages of measured data in spatial and temporal discontinuity. We used ten GCM (General Circulation Models) simulated data including stable isotope water cycle, and analyzed the spatial distribution characteristics of oxygen stable isotope effect in global precipitation. Meanwhile, we compared different simulated results as well as simulated results and the GNIP (Global Network of Isotopes in Precipitation) actual monitoring results. Our main purposes were to evaluate the simulative validity of stable isotope atmospheric circulation and improve our understanding and cognition for stable isotopic effect in water cycle. The results indicated that isoGSM, ECHAM4, LMDZ4 and HadAM3 showed good performances in simulating δ18O. Expect HadAM3, other simulated conclusion of models had good performances in the aspect of simulate seasonal difference of δ18O in spatial distribution. The simulated results of isoGSM, GISS E-F, ECHAM4, GISS E-N and LMDZ4 matched monitoring results more in the aspect of simulating relationship between δ18O and air temperature in spatial distribution. LMDZ4, isoGSM, GISS E-F, ECHAM4 and MUGCM had stronger capacity in the aspect of simulating relationship between δ18O and precipitation in spatial distribution. GISS E-F, isoGSM and GISS E-N had more advantage of simulate global meteoric water line.

Key words: Hydrogen and oxygen stable isotope, iGCMs, GNIP, GMWL.

中图分类号: 

表1 各稳定同位素大气环流模式(iGCMs)的基本信息
Table 1 The essential information of each stable isotope general circulation model(iGCMs)
模式名称 来源说明 网格精度 步长/min 模拟时间段
CAM2 美国国家大气研究中心(NCAR) 2.81°×2.81° 20 1958—2003年
ECHAM4 德国马普研究所(MPI) 2.0°×2.0° 24 1870—2003年
GENESIS3 美国科罗拉多大学 1.875°×1.875° 30 1950—1999年
GISS E-F 美国戈达德太空研究所(GISS) 2.5°×2.0° 30 1870—2009年
GISS E-N 美国戈达德太空研究所(GISS) 2.5°×2.0° 30 1870—2009年
HadAM3 英国哈得莱气候研究与预测中心(Hadley) 3.75°×2.75° 30 1870—2001年
isoGSM 日本东京大学 1.875°×1.875° 30 1979—2008年
LMDZ4 法国国家气象动力实验室(LMD/IPSL) 3.75°×2.75° 3 1979—2007年
MIROC3.2 日本东京大学气候系统研究中心 2.84°×2.80° 20 1979—2009年
MUGCM 澳大利亚墨尔本大学(UniMelb) 2.0°×2.0° 20 1870—2003年
图1 GNIP实测的和iGCMs模拟的多年平均降水中δ 18O的空间分布
(a) CAM2;(b) ECHAM4;(c) GENESIS3;(d) GISS E-F;(e) GISS E-N;(f) HadAM3;(g) isoGSM;(h) LMDZ4;(i) MIROC3.2;(j) MUGCM;散点代表实测值,有色阴影代表模拟值,且实测值与模拟值的颜色分级相同
Fig.1 Spatial distribution of mean annual δ 18O in precipitation measured by GNIP and simulated by iGCMs
(a) CAM2; (b) ECHAM4; (c) GENESIS3; (d) GISS E-F; (e) GISS E-N; (f) HadAM3; (g) isoGSM; (h) LMDZ4; (i) MIROC3.2;(j) MUGCM; Color points represent measured values and shaded patterns represent simulated values
图2 GNIP实测的与iGCMs模拟的全球降水中多年平均δ 18O之间的有效性比较
字母A~J分别代表CAM2,ECHAM4,GENESIS3,GISS E-F,GISS E-N,HadAM3,isoGSM,LMDZ4,MIROC3.2,MUGCM
Fig.2 Effectiveness comparisons between global mean annual δ 18O in precipitation measured by GNIP and simulated by iGCMs
Alphabet A~J represent CAM2, ECHAM4, GENESIS3, GISS E-F, GISS E-N, HadAM3, isoGSM, LMDZ4, MIROC3.2, MUGCM, respectively
图3 GNIP实测的和iGCMs模拟的降水中δ 18O季节差的空间分布
(a) CAM2;(b) ECHAM4;(c) GENESIS3;(d) GISS E-F;(e) GISS E-N;(f) HadAM3;(g) isoGSM;(h) LMDZ4;(i) MIROC3.2;(j) MUGCM;散点代表实测值,有色阴影代表模拟值
Fig.3 Spatial distribution of mean seasonal difference of δ 18O in precipitation measured by GNIP and simulated by iGCMs
(a) CAM2; (b) ECHAM4; (c) GENESIS3; (d) GISS E-F; (e) GISS E-N; (f) HadAM3; (g) isoGSM; (h) LMDZ4; (i) MIROC3.2; (j) MUGCM; Color points represent measured values and shaded patterns represent simulated values
图4 GNIP实测的与iGCMs模拟的全球降水中δ 18O季节差的有效性比较
字母A~J分别代表CAM2,ECHAM4,GENESIS3,GISS E-F,GISS E-N, HadAM3,isoGSM,LMDZ4,MIROC3.2,MUGCM
Fig.4 Effectiveness comparisons between global mean seasonal difference of δ 18O in precipitation measured by GNIP and simulated by iGCMs
Alphabet A-J represent CAM2, ECHAM4, GENESIS3, GISS E-F, GISS E-N, HadAM3, isoGSM, LMDZ4, MIROC3.2, MUGCM, respectively
图5 GNIP实测的和iGCMs模拟的降水中δ 18O与气温之间相关系数的空间分布
(a) CAM2;(b) ECHAM4;(c) GENESIS3;(d) GISS E-F;(e) GISS E-N;(f) HadAM3;(g) isoGSM;(h) LMDZ4;(i) MIROC3.2;(j) MUGCM;散点代表实测值,有色阴影代表模拟值
Fig.5 Spatial distribution of correlation coefficient between δ 18O in precipitation and air temperature measured by GNIP and simulated by iGCMs
(a) CAM2; (b) ECHAM4; (c) GENESIS3; (d) GISS E-F; (e) GISS E-N; (f) HadAM3; (g) isoGSM; (h) LMDZ4; (i) MIROC3.2;(j) MUGCM; Color points represent measured values and shaded patterns represent simulated values
表2 实测的与模拟的降水中δ 18O与气温相关系数符号统计表( R=0.2时的信度约为0.05)
Table 2 Statistical data between measured and simulated δ 18O/ T correlated coefficients (The confidence level is about 0.05 at R=0.2)
模式名称 温度效应显著
站点数(R>0.2)		 温度效应不
显著站点数
(0.2>R>0)		 反温度效应不
显著站点数
(0>R>-0.2)		 反温度效应
显著站点数
(R<-0.2)		 总体模
拟效果
GNIP实测 284 29 29 51 -
CAM2 192(68%) 8(28%) 9(31%) 34(67%) 62%
ECHAM4 260(92%) 7(24%) 7(24%) 25(49%) 76%
GENESIS3 187(66%) 6(21%) 10(34%) 29(57%) 59%
GISS E-F 259(91%) 5(17%) 3(10%) 34(67%) 77%
GISS E-N 255(90%) 3(10%) 3(10%) 38(75%) 76%
HadAM3 156(55%) 6(21%) 6(21%) 44(86%) 54%
isoGSM 254(89%) 7(24%) 7(24%) 35(69%) 77%
LMDZ4 251(88%) 4(14%) 7(24%) 36(71%) 76%
MIROC3.2 222(78%) 5(17%) 5(17%) 44(86%) 70%
MUGCM 221(78%) 4(14%) 5(17%) 40(78%) 69%
图6 GNIP实测的和iGCMs模拟的降水中δ 18O与降水量之间相关系数的空间分布
(a) CAM2;(b) ECHAM4;(c) GENESIS3;(d) GISS E-F;(e) GISS E-N;(f) HadAM3;(g) isoGSM;(h) LMDZ4;(i) MIROC3.2;(j) MUGCM;散点代表实测值,有色阴影代表模拟值
Fig.6 Spatial distribution of correlation coefficients between δ 18O in precipitation and precipitation amount measured by GNIP and simulated by iGCMs
(a)CAM2; (b) ECHAM4; (c) GENESIS3; (d) GISS E-F; (e) GISS E-N; (f) HadAM3; (g) isoGSM; (h) LMDZ4; (i) MIROC3.2;(j) MUGCM; Color points represent measured values and shaded patterns represent simulated values
表3 实测与模拟的降水中δ 18O与降水量相关系数符号统计表( R=0.2时的信度约为0.05)
Table 3 Statistical data between measured and simulated δ 18O/ P correlated coefficients (The confidence level is about 0.05 at R=0.2)
模式名称 降水量效应显
著的站点数
(R<-0.2)		 降水量效应不
显著的站点数
(-0.2<R<0)		 反降水量效应
不显著的站点数
(0>R>0.2)		 反降水量效应
显著的站点数
(0.2<R)		 总体模
拟效果
GNIP实测 264 55 90 69 -
CAM2 171(65%) 25(45%) 3(3%) 39(57%) 50%
ECHAM4 250(95%) 5(9%) 5(6%) 33(48%) 61%
GENESIS3 192(73%) 26(47%) 22(24%) 33(48%) 57%
GISS E-F 214(81%) 23(42%) 15(17%) 45(65%) 62%
GISS E-N 188(71%) 22(40%) 25(28%) 45(65%) 59%
HadAM3 216(82%) 15(27%) 11(12%) 29(42%) 57%
isoGSM 248(94%) 19(35%) 9(10%) 28(41%) 64%
LMDZ4 246(93%) 22(40%) 7(8%) 36(52%) 65%
MIROC3.2 206(78%) 14(25%) 15(17%) 46(67%) 59%
MUGCM 254(96%) 5(9%) 6(7%) 20(29%) 60%
[1] Dansgaard W.Stable isotopes in precipitation[J].Tellus, 1964, 16(4): 436-468.
[2] Ding Xiaodong, Zheng Liwei, Gao Shuji.A review on the Younger Dryas event[J]. Advances in Earth Science, 2014, 29(10): 1 095-1 109.
[丁晓东, 郑立伟, 高树基. 新仙女木事件研究进展[J]. 地球科学进展,2014, 29(10): 1 095-1 109.]
[3] Luan Yihua, Yu Yongqiang, Zheng Weipeng.Review of development and application of high resolution global climate system model[J].Advances in Earth Science, 2016, 31(3): 258-268.
[栾贻花, 俞永强, 郑伟鹏. 全球高分辨率气候系统模式研究进展[J]. 地球科学进展, 2016, 31(3): 258-268.]
[4] Li Yue, Wang Rujian, Li Wenbao.Review on research on paleo-sea level reconstruction based on foraminiferal oxygen isotope in deep sea sediments[J]. Advances in Earth Science, 2016, 31(3): 310-319.
[李悦, 王汝建, 李文宝. 利用有孔虫氧同位素重建古海平面变化的研究进展[J]. 地球科学进展, 2016, 31(3): 310-319.]
[5] Liu Jingfeng, Ding Minghu, Xiao Cunde.Review on atmospheric water vapor isotopic observation and research: Theory, method and modeling[J].Progress in Geography, 2015, 34(3): 340-353.
[柳景峰, 丁明虎, 效存德. 大气水汽氢氧同位素观测研究进展——理论基础、观测方法和模拟[J]. 地理科学进展, 2015, 34(3): 340-353.]
[6] Zhang Xinping, Guan Huade, Zhang Xinzhu, et al.Comparisons and assessment on stable isotopic effects in precipitation by different models[J]. Quaternary Sciences, 2016, 36(6): 1 343-1 357.
[章新平, 关华德, 张新主,等. 不同模式模拟的降水稳定同位素效应的比较和评估[J]. 第四纪研究, 2016, 36(6): 1 343-1 357.]
[7] Joussaume S, Sadourny R, Jouzel J.A general circulation model of water isotope cycles in the atmosphere[J]. Nature, 1984, 311: 24-29.
[8] Hoffmann G, Werner M, Heimann M.Water isotope module of the ECHAM atmospheric general circulation model: A study on timescales from days to several years[J].Journal of Geophysical Research: Atmospheres, 1998, 103(D14): 16 871-16 896.
[9] Risi C, Noone D, Worden J, et al.Process-evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopologues: 1. Comparison between models and observations[J]. Journal of Geophysical Research: Atmospheres, 2012, 117(D5): 1-26.
[10] Zhang Xinping, Sun Zhian, Zhang Xinzhu, et al.Intercomparison of δ18O in precipitation simulated by isotopic GCMs with GNIP observations over East Asia[J]. Quaternary Sciences, 2012, 32(1): 67-80.
[章新平, 孙治安, 张新主,等. 东亚降水中δ18O的GCM模拟及其与GNIP实测值的比较[J]. 第四纪研究,2012, 32(1): 67-80.]
[11] Conroy J L, Cobb K M, Noone D.Comparison of precipitation isotope variability across the tropical Pacific in observations and SWING2 model simulations[J].Journal of Geophysical Research: Atmospheres, 2013, 118(11): 5 867-5 892.
[12] Xi X.A review of water isotopes in atmospheric general circulation Models: Recent advances and future prospects[J].International Journal of Atmospheric Sciences, 2014: 1-16,doi:10.1155/2014/250920.
[13] Bowen G J, Wilkinson B.Spatial distribution of δ18O in meteoric precipitation[J]. Geology, 2002, 30(4): 315-318.
[14] Meng Yuchuan, Liu Guodong.Effect of below-cloud secondary evaporation on stable isotopes in precipitation over the Yangtze River Basin[J].Advances in Water Science, 2010, 21(3): 327-334.
[孟玉川, 刘国东. 长江流域水稳定同位素的云下二次蒸发效应[J]. 水科学进展, 2010, 21(3): 327-334.]
[15] Taylor K E.Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research: Atmospheres, 2001, 106(D7): 7 183-7 192.
[16] Craig H.Isotopic variations in meteoric waters[J]. Science, 1961, 133(3 465): 1 702-1 703.
[17] Zhang Xinping, Yao Tandong.Relations of oxygen isotopic composition in precipitation with temperature and precipitation amount in some regions of China[J].Journal of Glaciology and Geocryology, 1994, 16(1): 31-40.
[章新平, 姚檀栋. 我国部分地区降水中氧同位素成分与温度和降水量之间的关系[J]. 冰川冻土, 1994, 16(1): 31-40.]
[18] Che Y, Zhang M, Wang S, et al.Stable water isotopes of precipitation in China simulated by SWING2 models[J]. Arabian Journal of Geosciences, 2016, 9(19): 1-12.
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