论文

多套土壤温湿度资料在青藏高原的适用性

  • 刘川 ,
  • 余晔 ,
  • 解晋 ,
  • 周欣 ,
  • 李江林 ,
  • 葛骏
展开
  • 中国科学院寒区旱区环境与工程研究所 寒旱区陆面过程与气候变化重点实验室, 兰州 730000;2. 中国科学院大学, 北京 100049;3. 中国科学院平凉陆面过程与灾害天气观测研究站, 平凉 744015

收稿日期: 2015-01-27

  网络出版日期: 2015-06-28

基金资助

国家重大科学研究计划项目(2013CB956004)

Applicability of Soil Temperature and Moisture in Several Datasets over Qinghai-Xizang Plateau

  • LIU Chuan ,
  • YU Ye ,
  • XIE Jing ,
  • ZHOU Xin ,
  • LI Jianglin ,
  • GE Jun
Expand
  • Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Pingliang Land Surface Process & Severe Weather Research Station, Chinese Academy of Sciences, Pingliang 744015, China

Received date: 2015-01-27

  Online published: 2015-06-28

摘要

利用青藏高原中部和东部土壤温度和湿度观测资料, 通过计算两套再分析资料(ERA-Interim和CFSR)和六套陆面模式资料(ERA/land、 MERRA/land、 GLDAS-NOAH、 GLDAS-CLM、 GLDAS-MOSAIC和GLDAS-VIC)分别与观测资料之间的平均偏差、 偏差标准差、 相关系数、 标准差比等统计参数, 结合Brunke排名法, 综合评估了再分析资料和陆面模式资料中土壤温湿度数据在青藏高原的适用性.结果表明: 对于土壤温度, CFSR与观测值最接近, 其次是MERRA/land和GLDAS-CLM, 而ERA-Interim和ERA/land与观测值相差较大; 除GLDAS-CLM土壤温度比观测值偏高外, 其他资料土壤温度在大部分站点比观测值偏低, 其中ERA-Interim和ERA/land土壤温度比观测值偏低较多, 部分站点平均偏差超过-20℃.对于非冻结期(5-10月)土壤湿度, GLDAS-CLM与观测值最接近, 其次是GLDAS-NOAH或ERA-Interim; 与观测值相比, CFSR、 ERA-Interim和ERA/land的土壤湿度偏湿, 平均偏差大部分在0.05~0.20 m3·m-3之间, 而GLDAS-NOAH、 GLDAS-CLM和GLDAS-MOSAIC的土壤湿度偏干.

本文引用格式

刘川 , 余晔 , 解晋 , 周欣 , 李江林 , 葛骏 . 多套土壤温湿度资料在青藏高原的适用性[J]. 高原气象, 2015 , 34(3) : 653 -665 . DOI: 10.7522/j.issn.1000-0534.2015.00034

Abstract

In situ soil temperature and moisture observations at 7 stations and one region (Naqu) over the Qinghai-Xizang Plateau are used to validate two reanalysis products (i.e. ERA Interim and CFSR) and six land surface model products (i.e. ERA/land, MERRA/land, GLDAS-NOAH, GLDAS-CLM, GLDAS-MOSAIC and GLDAS-VIC). Four statistical quantities, i.e. mean bias (BIAS), standard deviation of differences (σd), correlation coefficient (R) and ratio of standard deviations (σrobs), are calculated at each site, and the Brunke ranking method is applied to quantify the relative performance of the eight datasets for each variable and statistical quantity. The results show that for daily soil temperature CFSR has the best overall performance, followed by MERRA/land and GLDAS-CLM, while ERA Interim and ERA/land perform the worst. GLDAS-CLM tends to overestimate daily soil temperatures, while other datasets tend to underestimate soil temperatures at most observation sites, with ERA Interim and ERA/land having large cold bias exceeding -20℃. For soil moisture during unfreezing period (May to October), GLDAS-CLM shows the best overall performance, followed by GLDAS-NOAH and ERA Interim. CFSR, ERA Interim, and ERA/land have wet biases, with most of the biases between 0.05 and 0.20 m3·m-3, while GLDAS-NOAH, GLDAS-CLM and GLDAS-MOSAIC tend to be drier than observations.

参考文献

[1]李崇银. 气候动力学引论[M]. 北京: 气象出版社, 1995: 290-296.
[2]Henderson-sellers A, Yang Z L, Dickinson R E. The project for intercomparison of land-surface parameterization schemes[J]. Bull Amer Meteor Soc, 1994, 74(7): 1335-1350.
[3]Chahine M T. The hydrological cycle and its influence on climate[J]. Nature, 1992, 359: 373-380.
[4]Trenberth K E. Atmospheric moisture recycling: Role of advection and local evaporation[J]. J Climate, 1999, 12: 1368-1381.
[5]Bengtsson L. The global atmospheric water cycle[J]. Environ Res Lett, 2010, 5(2), doi: 10.1088/1748-9326/5/2/025002.
[6]De R P, Drusch M, Boone A, et al. AMMA land surface model intercomparison experiment coupled to the community microwave emission model: ALMIP-MEM[J]. J Geophys Res: Atmospheres, 2009, 114, D05105, doi: 10.1029/2008JD010724.
[7]叶笃正, 高由禧. 青藏高原气象学[M]. 北京: 科学出版社, 1979: 1-278.
[8]Wang S L, Jin H J, Li S X, et al. Permafrost degradation on the Qinghai-Tibet Plateau and its environmental impacts[J]. Permafrost and Periglacial Processes, 2000, 11(1): 43-53.
[9]Zhao L, Ping C L, Yang D Q, et al. Changes of climate and seasonally frozen ground over the past 30 years in Qinghai-Xizang (Tibetan) Plateau, China[J]. Global and Planetary Change, 2004, 43(1-2): 19-31.
[10]Xu W X, Gu S, Zhao X Q, et al. High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the three-river source region on the Qinghai-Tibetan Plateau[J]. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(4): 528-535.
[11]Cheng G, Wu Tonghua. Responses of permafrost to climate change and their environmental significance, Qinghai-Tibet Plateau[J]. J Geophys Res, 2007, 112, F02S03, doi: 10.1029/2006JF000631.
[12]张少波, 陈玉春, 吕世华, 等. 青藏高原植被变化对中国东部夏季降水影响的模拟研究[J]. 高原气象, 2013, 32(5): 1236-1245, doi: 10.7522/j.issn.1000-0534.2012.00119.
[13]Dee D P, Uppala S M, Simmons A J, et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system[J]. Quart J Roy Meteor Soc, 2011, 137(656): 553-597.
[14]Kalnay E, Kanamitsu M, Kistler R, et al. The NCEP/NCAR 40-year reanalysis project[J]. Bull Amer Meteor Soc, 1996, 77(3): 437-471.
[15]Saha S, Moorthi S, Pan H L, et al. The NCEP climate forecast system reanalysis[J]. Bull Amer Meteor Soc, 2010, 91(8): 1015-1057.
[16]Wang A H, Zeng X B. Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau[J]. J Geophys Res: Atmospheres, 2012, 117, D05102, doi: 10.1029/2011JD016553.
[17]Zou H, Zhu J, Zhou L, et al. Validation and application of reanalysis temperature data over the Tibetan Plateau[J]. J Meteor Res, 2014, 28 (1): 139-149.
[18]荀学义, 胡泽勇, 吴学宏, 等. 三套位势高度再分析资料在青藏高原地区的对比分析[J]. 高原气象, 2011, 30(6): 1444-1452.
[19]李瑞青, 吕世华, 韩博, 等. 青藏高原东部三种再分析资料与地面气温观测资料的对比分析[J]. 高原气象, 2012, 31 (6): 1488-1502.
[20]Zhu X Y, Liu Y M, Wu G X. An assessment of summer sensible heat flux on the Tibetan Plateau from eight data sets[J]. Science China: Earth Sciences, 2012, 55(5): 779-786.
[21]Su Z, De R P, Wen J, et al. Evaluation of ECMWF's soil moisture analyses using observations on the Tibetan Plateau[J]. J Geophys Res: Atmospheres, 2013, 118(11): 5304-5318.
[22]Chen Y Y, Yang K, Qin J, et al. Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau[J]. J Geophys Res: Atmospheres, 2013, 118(10): 4466-4475.
[23]Yang K, Qin J, Zhao L, et al. A multi-scale soil moisture and freeze-thaw monitoring network on the third pole[J]. Bull Amer Meteor Soc, 2013, 94(12): 1907-1916.
[24]Hirose N, Koike T, Ishidaira. Study on spatially averaged evaporation under soil moisture heterogeneity affected by permafrost micro-topography[J]. J Meteor Soc Japan, 2002, 80: 191-203.
[25]中国科学院中国植被图编辑委员会. 中国植被图集1: 1000000[Z]. 北京: 科学出版社, 2001.
[26]王青霞, 吕世华, 鲍艳, 等. 青藏高原不同时间尺度植被变化特征及其与气候因子的关系分析[J]. 高原气象, 2014, 33(2): 301-312, doi: 10.7522/j.issn.1000-0534.2014.00002.
[27]肖瑶, 赵林, 李韧, 等. 藏北高原多年冻土区地表反照率特征分析[J]. 冰川冻土, 2010, 29(3): 480-488.
[28]Reichle R H, Koster R D, De Lannoy G J M, et al. Assessment and enhancement of MERRA land surface hydrology estimates[J]. J Climate, 2011, 24(24): 6322-6338.
[29]Rodell M, Houser P R, Jambor U, et al. The global land data assimilation system[J]. Bull Amer Meteor Soc, 2004, 85(3): 381-394.
[30]Balsamo G, Albergel C, Beljaars A, et al. ERA-Interim/Land: A global land water resources dataset[J]. Hydrology and Earth System Sciences Discussions, 2013, 10: 14705-14745.
[31]魏凤英. 现代气候统计诊断与预测技术[M]. 北京: 气象出版社, 2007: 1-298.
[32]Brunke M A, Fairall C W, Zeng X B, et al. Which bulk aerodynamic algorithms are least problematic in computing ocean surface turbulent fluxes[J]. J Climate, 2003, 16(4): 619-635.
[33]Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. J Geophys Res: Atmospheres, 2001, 106(D7): 7183-7192.
[34]Decker M, Brunke M A, Wang Z, et al. Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations[J]. J Climate, 2012, 25(6): 1916-1944.
[35]陈渤黎, 吕世华, 罗斯琼. CLM3.5模式对青藏高原玛曲站陆面过程的数值模拟研究[J]. 高原气象, 2012, 31(6): 1511-1522.
[36]熊建胜, 张宇, 王少影, 等. CLM4.0土壤水分传输方案改进在青藏高原陆面过程模拟中的效应[J]. 高原气象, 2014, 33(2): 323-336, doi: 10.7522/j.issn.1000-0534.2014.00012.
[37]Sheffield J, Goteti G, Wood E F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling[J]. J Climate, 2006, 19(13): 3088-3111.
文章导航

/