多陆面模式、多驱动场对新疆地区陆面过程模拟研究及结果集成
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摘要
陆面作为大气的下垫面,与大气之间进行着物质、能量和动量的交换,深刻影响着大气运动与气候变化,相应地描述陆面过程的陆面模式对全球和区域的数值预报、气候模拟也具有重要影响。新疆地区地处欧亚大陆腹地,属于典型的温带大陆性气候,且其地形复杂多变,80年代以来气温升高,降水也有较大增加,这与北方地区普遍的升温降水减少不同,因此对新疆地区的陆面过程研究具有非常重要的意义。
为了获得新疆地区陆面过程参量,并揭示其特征,本文主要工作分为以下六部分：
第一部分,利用新疆地区相关的台站信息和全球土壤质地资料、全球土壤颜色资料和全国植被覆盖资料构建了新疆地区的地表参数集,为模式提供了边界数据,也可以为以后的相关工作提供借鉴和参考作用。
第二部分,以新疆地区99个站点1960-2005观测资料为基础,通过五次多项式插值构建了高时间分辨率的陆面模式驱动场。并对构建的陆面模式驱动场的特征进行了分析,发现新疆地区的温度、降水、湿度、向下长波辐射近年来都呈现上升趋势,气压场和短波辐射场变化不大,风场风速减小。
第三部分,应用本文建立的地表参数集及高分辨率大气场驱动了BATS、LSM、CoLM三个陆面模式,对新疆地区陆面水热过程进行了off-line模拟研究,并对模拟结果进行了分析,利用观测的土壤温度进行了验证。三个陆面模式对所吸收太阳辐射、感热通量、的年际变化特征比较一致。本文还对三个模式模拟的5cm深处土壤温度与实际观测进行了对比分析,CoLM模拟的年际变化特征最符合实际观测,LSM对空间分布特征模拟效果较好,BATS在数值上最接近观测值。
第四部分,应用4个不同的大气驱动场驱动CLM2.0模拟的陆面过程分量。主要对多驱动场模拟结果中的5cm深处土壤温度、吸收太阳辐射、感热通量以及土壤水分进行了年际特征、不同季节下的年际变化特征及空间特征对比分析。
而以Obs-Q和Obs-P作为驱动场模拟的5cm土壤温度分布图则较好地反应了新疆地形所造成的影响,整体上更接近观测值的分布特点。对于感热通量的区域分布,这四个模拟结果非常接近,区域分布特征也比较一致,只是数量上稍有差异。新疆地区吸收的太阳辐射基本呈北低南高、东高西低型。以obs-Q和Obs-P作为驱动场的模拟结果可以更好地描述出新疆特殊地形对吸收太阳辐射的影响。
第五部分,应用新疆地区4个站点的土壤温度和土壤湿度观测值对模式模拟的7个结果进行对比分析,发现土壤温度的模拟结果和观测值相关性都非常好,可以模拟出各个站点的总体变化趋势；只是数值上有一些差异,总体差值在5℃以内,这也是陆面模式需要改进的地方,有待于进一步对模式进行改进研究。在驱动场中加入观测值后模拟效果有改进。
无论是多模式还是多驱动场对4个台站土壤湿度的模拟结果并不能很好地反应其变化特征,但是从数值上来说,模拟结果还有一定的可信度,跟观测值相差不是太大；对于CLM2.0,在驱动场中加入观测值可以改善陆面模式对土壤湿度的模拟性能。
第六部分,对多模式和多驱动场获得的7个模拟结果进行了集成,采用了MEAN、MLR和BPANN三种集成方法。在新疆地区的阿勒泰、乌兰乌苏、吐鲁番和莎车4个站点上进行了集成结果验证。
从以上4个站点集成效果可以看出,对模拟结果进行集成,选择合适的集成方法可以提高与观测值的相关系数,并在数量上更接近观测值,也就是说适当的集成方法可以改善模拟效果。MLR方法无论从数值还是相关系数,都比BPANN和MEAN方法略占优势,因此本章又利用多元线性回归方法,结合土壤温度观测值,在新疆地区建立了MLR集成,得到新疆地区陆面过程参量的集成结果,并分析了其与大气变量的气候响应。
研究发现在1960年到2000年之间,吸收的太阳辐射近年来变化不大,且其和短波辐射具有很好的正相关,和温度也具有较好的正相关,与气压和降水具有比较好的负相关；感热通量呈现较弱的上升趋势,且其与短波辐射具有很好的正相关,与土壤温度和经向风具有正相关关系,而与气压和纬向风具有较好的负相关；土壤湿度呈现明显上升趋势,特别是1987年后,上升幅度更大,且其与降水和大气相对湿度具有非常好的正相关,与纬向风和气压具有较好的负相关,与气温呈现较弱的正相关性,这与中国大部分地区不同。
As the lower boundary of the atmosphere surfaces, land surface exchanges the material,energy and momentum with atmosphere and plays an important role in atmosphere and climate change.Xinjiang province, locating in the hinterland of the Eurasian continent, as one of the largest provinces in China, has a typical temperate continental climate, and it also has a complicated topography. Since the 1980s,in Xinjiang province,the temperature and precipitation both have increased, which is different from the changes in northern China where the temperature increased, but the precipitation decreased.So the research on the land surface processes in Xinjiang is very meaningful.
In order to obtain the land surface process and show its characteristics of the land surface process, the work is organized in the following six parts.
In section 1,data base on the surface parameters was built basing on the data collection stations in Xinjiang and data on global soil quality and color and vegetation coverage in China, which will be used later as a reference for evaluation of models.
In section 2,a land-surface driven field showing a high resolution on time was set up using a five-degree polynomial interpolation basing on data from 99 sites in Xinjiang from 1960 to 2005.Eight inputs are required for this driven field model. Data on pressure, temperature,relative humidity, wind (U),wind (V),precipitation were taken from the Xinjiang Meteorological Bureau four-time-a-day conventional observation data, a downward long-wave radiation data was calculated according to Swinbank method, and sun radiation data was extracted from the TT.Qian global data. Missing observation data are added by averaging the several years' data of same date, different year from the same site.A model predicting 8 times a day with eight 5-degree polynomial interpolation input data was built.
And further data analysis on the driven field model showed that in Xinjiang province,temperature, precipitation, humidity,downward long-wave radiation increased in recent years, pressure and short-wave radiation changed slightly, and wind velocity decreased.
In section 3,the surface parameters data base and high-resolution time driven filed were applied to three land surface models, BATS,LSM, and CoLM to simulate the heat and precipitation exchanging process off-line between atmosphere and land surface.The results were analysed and the model were verified-using the observation data on soil temperature.Three different modes showed different computational results on the absorbed solar radiation、sensible heat flux、latent heat flux and Bowen ratio.The results presented in this study will provide a reference on the research on land surface models in Xingjiang.These results are also of great importance to deepen the understanding of land surface models in Xingjiang.
In section 4, four different atmospheric forcing schemes are first used to drive the land-surface processes simulated by CLM2.0.Then the spatial and annual variabitlities as well as the interannual variabilities for different seasons are thoroughly analyzed with four simulated parameters, such as soil temperature at 5-cm depth, absorbed solar radiation, sensible heat flux, and soil moisture.
Spatial distribution of modeled soil temperatures at 5-cm depth with Obs-Q and obs-P forcing schemes are more close to observed features than the other two schemes, reasonably illustrating the logical impacts due to the special topography in Xinjiang Province.There is a similar spatial pattern of modeled sensible heat flux for all four schemes,with only slightly differences in magnitude.Significant spatial variations of absorbed solar radiation are found in this study, with higher values in the south and east of Xinjiang Province.In addition,results simulated by Obs-Q and obs-P forcing schemes show better correspondence between orographic structure and absorbed solar radiation than the other two schemes.
In section 5,model result evaluations are performed by comparing 7 model outputs with the soil temperature and humidity data measured in four different stations.Generally, all the model results can reproduce the temporal trend of soil temperature for 4 different sites.However, the magnitude of modeled soil temperature deviates about 5℃from observations, which suggests further improvements for the model configurations are needed,for example,adding observations to the atmospheric forcing fields, etc.
Neither the multiple models nor the models with multiple forcing schemes conducted in this study can reasonably reproduce the general trend of soil moisture variations, though the magnitude between model results and observations are much close.However, the results indicate that land-surface model performance for modeling soil moisture can be improved by adding observations to the atmospheric forcing fields.
In section 6,the ensembles of different simulation results are analyzed.To compare different ensemble methods, observation data from Aletai station, Wulanwusu station, Tulufan station and Shache station are used.Three kinds of ensemble methods,the mean method (MEAN),multiple linear regression (MLR),and back propagation artificial neural networks (BPANN), are applied in this work. The mean method is the one that employs simple mathematical average to the model results.While the other two methods all consider the combinations of land-surface processes and observations.
To conclude, the performance of model simulations can be improved by using proper ensemble simulation methods.This study shows that MLR method does a better job than BPANN and MEAN methods both in the magnitudes and temporal trends.After gaining the confidence on MLR method, this work further analyzed the climatological responses of atmospheric parameters by using combinations of MLR method and measured soil temperature.
Simulation results indicate a weak variation of absorbed solar radiation during 1960-2000.Absorbed solar radiation is found be positively correlated with short-wave radiation and atmosphere temperature, but negatively correlated with atmospheric pressure and precipitation.There is a slightly increasing trend for sensible heat flux, which is well correlated with the short-wave radiation, soil temperature and meridional wind positively. Moreover, both atmospheric pressure and latitudinal wind are found to be negatively correlated with sensible heat flux. Soil moisture increases significantly during 1960-2000, especially after 1987.Soil moisture also demonstrates a strong positive correlation with precipitation and atmospheric relative humidity, but a strong negative correlation with atmospheric pressure and latitudinal wind, as well as a weak positive correlation with atmospheric temperature, which is totally different from the climate patterns observed in most parts of China.
为了获得新疆地区陆面过程参量,并揭示其特征,本文主要工作分为以下六部分：
第一部分,利用新疆地区相关的台站信息和全球土壤质地资料、全球土壤颜色资料和全国植被覆盖资料构建了新疆地区的地表参数集,为模式提供了边界数据,也可以为以后的相关工作提供借鉴和参考作用。
第二部分,以新疆地区99个站点1960-2005观测资料为基础,通过五次多项式插值构建了高时间分辨率的陆面模式驱动场。并对构建的陆面模式驱动场的特征进行了分析,发现新疆地区的温度、降水、湿度、向下长波辐射近年来都呈现上升趋势,气压场和短波辐射场变化不大,风场风速减小。
第三部分,应用本文建立的地表参数集及高分辨率大气场驱动了BATS、LSM、CoLM三个陆面模式,对新疆地区陆面水热过程进行了off-line模拟研究,并对模拟结果进行了分析,利用观测的土壤温度进行了验证。三个陆面模式对所吸收太阳辐射、感热通量、的年际变化特征比较一致。本文还对三个模式模拟的5cm深处土壤温度与实际观测进行了对比分析,CoLM模拟的年际变化特征最符合实际观测,LSM对空间分布特征模拟效果较好,BATS在数值上最接近观测值。
第四部分,应用4个不同的大气驱动场驱动CLM2.0模拟的陆面过程分量。主要对多驱动场模拟结果中的5cm深处土壤温度、吸收太阳辐射、感热通量以及土壤水分进行了年际特征、不同季节下的年际变化特征及空间特征对比分析。
而以Obs-Q和Obs-P作为驱动场模拟的5cm土壤温度分布图则较好地反应了新疆地形所造成的影响,整体上更接近观测值的分布特点。对于感热通量的区域分布,这四个模拟结果非常接近,区域分布特征也比较一致,只是数量上稍有差异。新疆地区吸收的太阳辐射基本呈北低南高、东高西低型。以obs-Q和Obs-P作为驱动场的模拟结果可以更好地描述出新疆特殊地形对吸收太阳辐射的影响。
第五部分,应用新疆地区4个站点的土壤温度和土壤湿度观测值对模式模拟的7个结果进行对比分析,发现土壤温度的模拟结果和观测值相关性都非常好,可以模拟出各个站点的总体变化趋势；只是数值上有一些差异,总体差值在5℃以内,这也是陆面模式需要改进的地方,有待于进一步对模式进行改进研究。在驱动场中加入观测值后模拟效果有改进。
无论是多模式还是多驱动场对4个台站土壤湿度的模拟结果并不能很好地反应其变化特征,但是从数值上来说,模拟结果还有一定的可信度,跟观测值相差不是太大；对于CLM2.0,在驱动场中加入观测值可以改善陆面模式对土壤湿度的模拟性能。
第六部分,对多模式和多驱动场获得的7个模拟结果进行了集成,采用了MEAN、MLR和BPANN三种集成方法。在新疆地区的阿勒泰、乌兰乌苏、吐鲁番和莎车4个站点上进行了集成结果验证。
从以上4个站点集成效果可以看出,对模拟结果进行集成,选择合适的集成方法可以提高与观测值的相关系数,并在数量上更接近观测值,也就是说适当的集成方法可以改善模拟效果。MLR方法无论从数值还是相关系数,都比BPANN和MEAN方法略占优势,因此本章又利用多元线性回归方法,结合土壤温度观测值,在新疆地区建立了MLR集成,得到新疆地区陆面过程参量的集成结果,并分析了其与大气变量的气候响应。
研究发现在1960年到2000年之间,吸收的太阳辐射近年来变化不大,且其和短波辐射具有很好的正相关,和温度也具有较好的正相关,与气压和降水具有比较好的负相关；感热通量呈现较弱的上升趋势,且其与短波辐射具有很好的正相关,与土壤温度和经向风具有正相关关系,而与气压和纬向风具有较好的负相关；土壤湿度呈现明显上升趋势,特别是1987年后,上升幅度更大,且其与降水和大气相对湿度具有非常好的正相关,与纬向风和气压具有较好的负相关,与气温呈现较弱的正相关性,这与中国大部分地区不同。
As the lower boundary of the atmosphere surfaces, land surface exchanges the material,energy and momentum with atmosphere and plays an important role in atmosphere and climate change.Xinjiang province, locating in the hinterland of the Eurasian continent, as one of the largest provinces in China, has a typical temperate continental climate, and it also has a complicated topography. Since the 1980s,in Xinjiang province,the temperature and precipitation both have increased, which is different from the changes in northern China where the temperature increased, but the precipitation decreased.So the research on the land surface processes in Xinjiang is very meaningful.
In order to obtain the land surface process and show its characteristics of the land surface process, the work is organized in the following six parts.
In section 1,data base on the surface parameters was built basing on the data collection stations in Xinjiang and data on global soil quality and color and vegetation coverage in China, which will be used later as a reference for evaluation of models.
In section 2,a land-surface driven field showing a high resolution on time was set up using a five-degree polynomial interpolation basing on data from 99 sites in Xinjiang from 1960 to 2005.Eight inputs are required for this driven field model. Data on pressure, temperature,relative humidity, wind (U),wind (V),precipitation were taken from the Xinjiang Meteorological Bureau four-time-a-day conventional observation data, a downward long-wave radiation data was calculated according to Swinbank method, and sun radiation data was extracted from the TT.Qian global data. Missing observation data are added by averaging the several years' data of same date, different year from the same site.A model predicting 8 times a day with eight 5-degree polynomial interpolation input data was built.
And further data analysis on the driven field model showed that in Xinjiang province,temperature, precipitation, humidity,downward long-wave radiation increased in recent years, pressure and short-wave radiation changed slightly, and wind velocity decreased.
In section 3,the surface parameters data base and high-resolution time driven filed were applied to three land surface models, BATS,LSM, and CoLM to simulate the heat and precipitation exchanging process off-line between atmosphere and land surface.The results were analysed and the model were verified-using the observation data on soil temperature.Three different modes showed different computational results on the absorbed solar radiation、sensible heat flux、latent heat flux and Bowen ratio.The results presented in this study will provide a reference on the research on land surface models in Xingjiang.These results are also of great importance to deepen the understanding of land surface models in Xingjiang.
In section 4, four different atmospheric forcing schemes are first used to drive the land-surface processes simulated by CLM2.0.Then the spatial and annual variabitlities as well as the interannual variabilities for different seasons are thoroughly analyzed with four simulated parameters, such as soil temperature at 5-cm depth, absorbed solar radiation, sensible heat flux, and soil moisture.
Spatial distribution of modeled soil temperatures at 5-cm depth with Obs-Q and obs-P forcing schemes are more close to observed features than the other two schemes, reasonably illustrating the logical impacts due to the special topography in Xinjiang Province.There is a similar spatial pattern of modeled sensible heat flux for all four schemes,with only slightly differences in magnitude.Significant spatial variations of absorbed solar radiation are found in this study, with higher values in the south and east of Xinjiang Province.In addition,results simulated by Obs-Q and obs-P forcing schemes show better correspondence between orographic structure and absorbed solar radiation than the other two schemes.
In section 5,model result evaluations are performed by comparing 7 model outputs with the soil temperature and humidity data measured in four different stations.Generally, all the model results can reproduce the temporal trend of soil temperature for 4 different sites.However, the magnitude of modeled soil temperature deviates about 5℃from observations, which suggests further improvements for the model configurations are needed,for example,adding observations to the atmospheric forcing fields, etc.
Neither the multiple models nor the models with multiple forcing schemes conducted in this study can reasonably reproduce the general trend of soil moisture variations, though the magnitude between model results and observations are much close.However, the results indicate that land-surface model performance for modeling soil moisture can be improved by adding observations to the atmospheric forcing fields.
In section 6,the ensembles of different simulation results are analyzed.To compare different ensemble methods, observation data from Aletai station, Wulanwusu station, Tulufan station and Shache station are used.Three kinds of ensemble methods,the mean method (MEAN),multiple linear regression (MLR),and back propagation artificial neural networks (BPANN), are applied in this work. The mean method is the one that employs simple mathematical average to the model results.While the other two methods all consider the combinations of land-surface processes and observations.
To conclude, the performance of model simulations can be improved by using proper ensemble simulation methods.This study shows that MLR method does a better job than BPANN and MEAN methods both in the magnitudes and temporal trends.After gaining the confidence on MLR method, this work further analyzed the climatological responses of atmospheric parameters by using combinations of MLR method and measured soil temperature.
Simulation results indicate a weak variation of absorbed solar radiation during 1960-2000.Absorbed solar radiation is found be positively correlated with short-wave radiation and atmosphere temperature, but negatively correlated with atmospheric pressure and precipitation.There is a slightly increasing trend for sensible heat flux, which is well correlated with the short-wave radiation, soil temperature and meridional wind positively. Moreover, both atmospheric pressure and latitudinal wind are found to be negatively correlated with sensible heat flux. Soil moisture increases significantly during 1960-2000, especially after 1987.Soil moisture also demonstrates a strong positive correlation with precipitation and atmospheric relative humidity, but a strong negative correlation with atmospheric pressure and latitudinal wind, as well as a weak positive correlation with atmospheric temperature, which is totally different from the climate patterns observed in most parts of China.
引文
[1]Dickinson R E..Land-Atmosphere Interaction. U.S.Nation Report Interation Union of Geophysics 1991-1994.1995:917-922.
[2]Charney, J. G.,1975:Dynamics of deserts and drought in the Sahel. Quart. J. Roy. Meteor. Soc., 101(428):193-292.
[3]Charney,J.G.,Quirk W.K.,Chow S.H.,and Kornfield J.1977:A comparative study of the effects of albedo changes on drought in semi-arid regions.J. Atmos Sci,34:1366-1385.
[4]Shukla, J., Mintz, Y,1982:Influence of land surface evapotranspiration on the earth's climate. Science,215 (4539):1498-1501.
[5]Sud, Y.C.,Smith, W. E..1985a:The influence of surface roughness of deserts on the July circulation models.Atmos.-Ocean.,20:301-316.
[6]Kiehl, J. T.,and K.E.1997:Trenberth. Earth's annual global mean energy budget,Bull. Am. Meteorol.Soc,78:197-208.
[7]Verhoef, A.Reply to the Comments on'A New Method to Determine the Zero-Plane Displacement'by Zhang and Park,Boundary-Layer Meteorology 1999:141-143.
[8]张先恭,李小泉.本世纪中国气温变化的某些特征.气象学报,1982,40(2)：198-208.
[9]王遵娅,丁一汇,何金海,虞俊.近50年来中国气候变化特征的再分析.气象学报,2004,62(2)：228-236.
[10]王绍武,董光荣.中国西部环境评估//中国西部环境特征及其演变(第一卷).北京：科学出版社,2002：73-122.
[11]丁一汇,王守荣.中国西北地区气候与生态环境概论.北京：气象出版社,2001：77-154.
[12]陈文海、柳艳香、马拄国,中国1951-1997年气候变化趋势的季风特征,高原气象,2002,21(3)：251-257.
[13]周子康、汤燕冰、俞连根、徐霜芝,中国气候对全球气温增暖的响应[J],科技通报,1997,13(2)：69-73.
[14]赵宗慈,中国的气候变化及对未来的预测,气候变化和环境问题全国学术讨论会论文汇编,1991.
[15]陈隆勋、周秀骥等,中国近80年来气候变化特征及其形成机制,气象学报,2004,62(5)：634-646.
[16]屠其璞,中国气温异常的区域异常特征研究,气象学报,2000,58(3)：288-290.
[17]陈隆勋,朱文琴,王文等,中国近45年来气候变化的研究[J],气象学报.1998,56(3)：257-271.
[18]苏宏超,魏文寿,韩萍.新疆近50 a来的气温和蒸发变化[J].冰川冻土,2003,25(2)：174-177.
[19]袁玉江,魏文寿,穆桂金.天山山区近40年秋季气候变化特征与南、北疆比较,地理科学,2004(6)：674—679.
[20]胡汝骥,樊自立,王亚俊等.近50a新疆气候变化对环境影响评估.干旱地理,2001,18(2)：23-35.
[21]刘波,马柱国,冯锦明,等.1960年以来新疆地区蒸发皿蒸发与实际蒸发之间的关系.地理学报,2008,63(11)：1131-1139.
[22]马柱国,任晓波.1951-2006年中国区域气候变化与干旱化,气候变化研究进展,2007,3(4)：195-201.
[23]施雅风,沈永平,李栋梁,等.中国西北气候由暖干向暖湿转型的特征和趋势探讨.第四纪研究,2003,23(2)：152-164.
[24]牛国跃,洪钟祥,孙菽芬.陆面过程研究的现状与发展趋势.地球科学进展,1997,12(1)：20-25.
[25]Sellers A H, P itman J A. L and2Surface schemes fo r future climate models:Specification, aggregation and heterogeneity. J. Geophys Res,1992,97(D3):2687-2696.
[26]Charney, and P. Stone(1974) Drought in the Sahel:Biogeophysical feedback mechanism. Science,253:299-301.
[27]Charney, J. G.,Quirk, W.J.,Chow, S.,and Kornfield, J.(1977)."A comparative study of the effects of albedo change on drought in semi-arid regions."Journal of the Atmospheric Sciences 34:1366-1385.
[28]王介民.陆面过程实验和地气相互作用研究—从HEIFE到IMGRASS和AME-Tibet/TIPEX.高原气象,1999,18(3)：280-294.
[29]Budyko, M. I.1956:Heat Balance of the Earth's Surface[M].Gidrometeoizdat Leningrad Russia:255.
[30]Manabe, S.1969:Climate and ocean circulation:1.The atmospheric circulation and the hydrology of the earth's surface. Mon. Wea. Rev.,97:939-805.
[31]Deardorff, J. W.1978:Efficient prediction of ground surface temperature and moisture with inclusion of a layer of vegetation. J. Geophys.Res.,83:1889-1903.
[32]Dickinson, R. E.1984:Modeling evapotranspiration for three-dimensional global climate models.In:Hansen J E,Takahashi T Eds. Climate processes and Climate Sensitivity,58-72. Geophysical Monograph 29,Maurice Ewing Volume 5,American Geophysical Union, Washington,DC.
[33]Dickinson, R. E.,Henderson-Sellers, A.,Kennedy, P. J.,et al.1986:Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model.National Center for Atmospheric Research.
[34]Sellers, P. J.,Mintz, Y.,Sud, Y.C.,et al.1986:A simple biosphere model (SIB) for use within general circulation models. J. Atmos.Sci.,43:505-531.
[35]Sellers, P. J.,Randall, D.A.,Collatz,G.J.,et al..1996:A revised land surface parameterization (SiB2) for atmospheric GCMs. Part Ⅰ:Model formulation. J.Climate,9:676-705.
[36]Sellers, P. J.,Los, S.O.,Tucker, C. J.,et al.1996:A revised land surface parameterization (SiB2) for atmospheric GCMs.Part Ⅱ:The generation of global fields of terrestrial biophysical parameters from satellite data. J.Climate,9:706-737.
[37]Bonan, G.B.,and S.Levis,2006:Evaluating aspects of the Community Land and Atmosphere Models (CLM3 and CAM3)using a dynamic global vegetation model; J. Climate,19: 2290-2301.
[38]Dai Yongjiu,Zeng Xubin,Dickinson E.2001:Common Land Model (Technical Documentation and User's Guide),69.
[39]Houser P R, Shuttleworth W J,Gupta H V. Integration of Soil Moisture Remote Sensing and Hydrologic Modeling Using Data Assimilation. Water Resource Research,1998,34(12): 3405-3420.
[40]Rodell M, Houser P R, Jambor U, et. The Global Land Data Assimilation System.Bulletin of the American Meteorological Society,2004,85 (3):381-394.
[41]Mitchell K E,Lohmann D,Houser P R,et.. The Multi-institution North American Land Data Assimilation System (NLDAS):Utilizing Multiple GCIP Products and Partners in a Continental Distributed Hydrological Modeling System. Journal of Geophysical Research, 2004:109D07S90.
[42]Vanden H B.ELDAS Final Report.KNM I:ECMW F,2005.
[43]YANG K, WATANABE T, KOIKE T, et.. Autocalibration System Developed to Assimilate AMSRE Data into a Land Surface Model for Estimating Soil Moisture and the Surface Energy Budget.Journal of the Meteorological Society of Japan,2007,85A:229-242.
[44]李新,黄春林,车涛,等.中国陆面数据同化系统研究的进展与前瞻.自然科学进展, 2007,17(2)：163-1731.
[45]陈莹莹,施建成,杜今阳,等.基于GLDAS的中国区地表能量平衡数值试验.水科学进展,2009,20(1)：25-31.
[46]Li, X.,C. L. Huang, et al. (2007)."Development of a Chinese land data assimilation system:its progress and prospects."Progress in Natural Science 17(8):881-892.
[47]Yang, K.,T. Watanabe, et al.(2007). "Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget."Journal of the Meteorological Society of Japan 85A:229-242.
[48]Huang, C. L.,X. Li, et al. (2008)."Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter."Remote Sensing of Environment 112(4): 1320-1336.
[49]Bonan, G.B.1995:Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model.J. Geophys.Res.,1995,100:2817-2831.
[50]Bonan, G.B.,Oleson, K.W.,Vertenstein, M.,Levis, S.,Zeng, X.,Dai, Y.,Dickinson, R.E.,and Yang, Z. L.2002a:The land surface climatology of the Community Land Model coupled to the NCAR Community Climate Model. J. Climate 15:3123-3149.
[51]Dai Yongjiu,Zeng Xubin,Dickinson R E,et al.2003:The common land model (CLM).Bull. Amer. Meteor.Soc.,84(8):1013-1023.
[52]Dai Yongjiu.2005:The Common Land Model (CoLM) User's Guide.23pp.
[53]Oleson, K. W.,Dai Yongjiu,Bonan G B, et al.2004:Technical Description of the Community Land Model (CLM). NCAR Technical.Note-461+STR,173pp National Center for Atmospheric Research, Boulder, CO.
[54]邓绶林主编,地学辞典,河北教育出版社,1992.
[55]黄洪峰,土壤·植物·大气相互作用原理及模拟研究,气象出版社,1997.
[56]毛嘉富博士论文：Sheffield动态全球植被模型的改进及应用研究,2006.
[57]周明煜,徐祥德,卞林根,等.青藏高原大气边界层观测分析与动力学研究.北京：气象出版社,2000.
[58]Masson V, Champeaux J L, Chauvin F, et al. A global database of land surface parameters at 1-km resolution in meteorological and climate models. Journal of Climate,2003,16(9):1261～ 1282.
[59]Wiscombe, W.J.,and Warren, S.G.1980:A model for the spectral albedo of snow. I. Pure snow. J.Atmos. Sci.37:2712-2733.
[60]Anderson, E.A.1976:A point energy and mass balance model of a snow cover. NOAA Technical eport NWS 19, Office of Hydrology, National Weather Service, Silver Spring, MD.
[61]盛裴轩.大气物理学.北京大学出版社,2003：135-189.
[62]Qian, T.,A. Dai, K. E.Trenberth, and K. W. Oleson,2006:Simulation of global land surface conditions from 1948-2004.Part Ⅰ:Forcing data and evaluation.J. Hydrometeorology, 7,953-975.
[63]邱金桓.大气物理与大气探测学.气象出版社,2005：127-152.
[64]Swinbank W C.Long-wave radiation from clear skies.Quarterly Journal of the Royal Meteorological Society,1963,89:339～3481.
[65]Jacobs J D. Radiation Climate of Broughton Island. Energy budget studies in relation to fast-ice breakup processes in Davis Strait. Occas.Pap.,Inst. of Arctic and Alp Res.,University of Colorado,Boulder,United States,1978,26:105～120.
[66]秦涛,付宗堂ArcGIS中几种空间内插方法的比较[J].物探化探计算技术,2007(1)：63-70.
[67]林忠辉,莫兴国,李宏轩,等.中国陆地区域气象要素的空间插值[J].地理学报,2002,57(1)：47-56.
[68]潘耀忠,龚道溢,邓磊,等.基于DEM的中国陆地多年平均温度插值方法[J].地理学报,2004,59(3)：366-374.
[69]刘湘南,黄方,王平,等。GIS空间分析原理与方法。北京：科学出版社,2005.
[70]刘波.新疆区域地表水分过程的模拟研究,中国科学院大气物理研究所博士论文,2008.
[71]Henderson Sellers A,Yang Z L,D ick inson R E.The project for intercomparison of land surface parameterization schemes. Bull.Amer. Meteor.Soc.,1993,74:133～1349.
[72]Henderson Sellers, A.,A. J. Pitman, P.K. Love, P. Iranne jad, and T. H Chen, The Project for Intercomparison of Land-Surface(PILPS):Phase 2& 3,Bull.Am.Meteorol.Soc.,1995(73):1962-1970.
[73]Epstein, E. S.1969.Stochastic-dynamic prediction. Tellus,21:739-759.
[74]Leith, C, E.,1974:Theoretical skill of Monte Carto forecasts. Mon. Wea. Rev.,102,409-418.
[75]Kalnay, E.,2003:Atmospheric Modeling, Data Assimilation and Predictability.Cambridge University Press, UK,248.
[76]Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D.R. Bachiochi, Z. Zhang, C.E. Williford, S.Gadgil,and S.Surendran,1999:Improved weather and seasonal climate forecasts from multimodel superensemble. Science,285:1548-1550.
[77]Yun, W. T., L.Stefanova, and T. N. Krishnamurti.,2003:Improvement of the superensemble technique for seasonal forecasts.J. Climate.,16:3834-3840.
[78]Hagedorn, R.,F. J. Doblas-Reyes and T. N. Palmer,2005:The Rationale Behind the...in Seasonal Forecasting-Ⅰ. Basic Concept, Tellus,57A:219-233.
[79]P.K. Goel, M.H. DeGroot, Only normal distributions have linear posterior expectations in linear regression,J. Am.Statist. Assoc.1980(75):895-900.
[80]Sarangi, A.,Bhattacharya, A.K.,2005.Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. Agricultural Water Management,78:195-208.
[81]张立明,人工神经网络的模型及其应用,上海复旦大学出版社,1992：22-25.
[82]Kuan CM, Liu T. Forecasting exchange rates using feed forward and recurrent neural networks. Journal of Applied Econometrics 1995,10:347-364.
[83]Zhang G P.Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003(50):159-175.
[84]Leung M T, Chen A S,Daouk H. Forecasting exchange rates using general regression neural networks. Computers & Operations Research 2000(27):1093-1100.
[85]Rowntree P R, J R Bolton. Simulation of the atmospheric response to soil moisture anomalies over Europe. Quart J Roy Meteor Soc,1983,109:501-526.
[86]马柱国,魏和林,符淙斌.中国东北部区域土壤湿度的变化及其与气候变率的关系.气象学报,2000,58(3)：278-287.
[2]Charney, J. G.,1975:Dynamics of deserts and drought in the Sahel. Quart. J. Roy. Meteor. Soc., 101(428):193-292.
[3]Charney,J.G.,Quirk W.K.,Chow S.H.,and Kornfield J.1977:A comparative study of the effects of albedo changes on drought in semi-arid regions.J. Atmos Sci,34:1366-1385.
[4]Shukla, J., Mintz, Y,1982:Influence of land surface evapotranspiration on the earth's climate. Science,215 (4539):1498-1501.
[5]Sud, Y.C.,Smith, W. E..1985a:The influence of surface roughness of deserts on the July circulation models.Atmos.-Ocean.,20:301-316.
[6]Kiehl, J. T.,and K.E.1997:Trenberth. Earth's annual global mean energy budget,Bull. Am. Meteorol.Soc,78:197-208.
[7]Verhoef, A.Reply to the Comments on'A New Method to Determine the Zero-Plane Displacement'by Zhang and Park,Boundary-Layer Meteorology 1999:141-143.
[8]张先恭,李小泉.本世纪中国气温变化的某些特征.气象学报,1982,40(2)：198-208.
[9]王遵娅,丁一汇,何金海,虞俊.近50年来中国气候变化特征的再分析.气象学报,2004,62(2)：228-236.
[10]王绍武,董光荣.中国西部环境评估//中国西部环境特征及其演变(第一卷).北京：科学出版社,2002：73-122.
[11]丁一汇,王守荣.中国西北地区气候与生态环境概论.北京：气象出版社,2001：77-154.
[12]陈文海、柳艳香、马拄国,中国1951-1997年气候变化趋势的季风特征,高原气象,2002,21(3)：251-257.
[13]周子康、汤燕冰、俞连根、徐霜芝,中国气候对全球气温增暖的响应[J],科技通报,1997,13(2)：69-73.
[14]赵宗慈,中国的气候变化及对未来的预测,气候变化和环境问题全国学术讨论会论文汇编,1991.
[15]陈隆勋、周秀骥等,中国近80年来气候变化特征及其形成机制,气象学报,2004,62(5)：634-646.
[16]屠其璞,中国气温异常的区域异常特征研究,气象学报,2000,58(3)：288-290.
[17]陈隆勋,朱文琴,王文等,中国近45年来气候变化的研究[J],气象学报.1998,56(3)：257-271.
[18]苏宏超,魏文寿,韩萍.新疆近50 a来的气温和蒸发变化[J].冰川冻土,2003,25(2)：174-177.
[19]袁玉江,魏文寿,穆桂金.天山山区近40年秋季气候变化特征与南、北疆比较,地理科学,2004(6)：674—679.
[20]胡汝骥,樊自立,王亚俊等.近50a新疆气候变化对环境影响评估.干旱地理,2001,18(2)：23-35.
[21]刘波,马柱国,冯锦明,等.1960年以来新疆地区蒸发皿蒸发与实际蒸发之间的关系.地理学报,2008,63(11)：1131-1139.
[22]马柱国,任晓波.1951-2006年中国区域气候变化与干旱化,气候变化研究进展,2007,3(4)：195-201.
[23]施雅风,沈永平,李栋梁,等.中国西北气候由暖干向暖湿转型的特征和趋势探讨.第四纪研究,2003,23(2)：152-164.
[24]牛国跃,洪钟祥,孙菽芬.陆面过程研究的现状与发展趋势.地球科学进展,1997,12(1)：20-25.
[25]Sellers A H, P itman J A. L and2Surface schemes fo r future climate models:Specification, aggregation and heterogeneity. J. Geophys Res,1992,97(D3):2687-2696.
[26]Charney, and P. Stone(1974) Drought in the Sahel:Biogeophysical feedback mechanism. Science,253:299-301.
[27]Charney, J. G.,Quirk, W.J.,Chow, S.,and Kornfield, J.(1977)."A comparative study of the effects of albedo change on drought in semi-arid regions."Journal of the Atmospheric Sciences 34:1366-1385.
[28]王介民.陆面过程实验和地气相互作用研究—从HEIFE到IMGRASS和AME-Tibet/TIPEX.高原气象,1999,18(3)：280-294.
[29]Budyko, M. I.1956:Heat Balance of the Earth's Surface[M].Gidrometeoizdat Leningrad Russia:255.
[30]Manabe, S.1969:Climate and ocean circulation:1.The atmospheric circulation and the hydrology of the earth's surface. Mon. Wea. Rev.,97:939-805.
[31]Deardorff, J. W.1978:Efficient prediction of ground surface temperature and moisture with inclusion of a layer of vegetation. J. Geophys.Res.,83:1889-1903.
[32]Dickinson, R. E.1984:Modeling evapotranspiration for three-dimensional global climate models.In:Hansen J E,Takahashi T Eds. Climate processes and Climate Sensitivity,58-72. Geophysical Monograph 29,Maurice Ewing Volume 5,American Geophysical Union, Washington,DC.
[33]Dickinson, R. E.,Henderson-Sellers, A.,Kennedy, P. J.,et al.1986:Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model.National Center for Atmospheric Research.
[34]Sellers, P. J.,Mintz, Y.,Sud, Y.C.,et al.1986:A simple biosphere model (SIB) for use within general circulation models. J. Atmos.Sci.,43:505-531.
[35]Sellers, P. J.,Randall, D.A.,Collatz,G.J.,et al..1996:A revised land surface parameterization (SiB2) for atmospheric GCMs. Part Ⅰ:Model formulation. J.Climate,9:676-705.
[36]Sellers, P. J.,Los, S.O.,Tucker, C. J.,et al.1996:A revised land surface parameterization (SiB2) for atmospheric GCMs.Part Ⅱ:The generation of global fields of terrestrial biophysical parameters from satellite data. J.Climate,9:706-737.
[37]Bonan, G.B.,and S.Levis,2006:Evaluating aspects of the Community Land and Atmosphere Models (CLM3 and CAM3)using a dynamic global vegetation model; J. Climate,19: 2290-2301.
[38]Dai Yongjiu,Zeng Xubin,Dickinson E.2001:Common Land Model (Technical Documentation and User's Guide),69.
[39]Houser P R, Shuttleworth W J,Gupta H V. Integration of Soil Moisture Remote Sensing and Hydrologic Modeling Using Data Assimilation. Water Resource Research,1998,34(12): 3405-3420.
[40]Rodell M, Houser P R, Jambor U, et. The Global Land Data Assimilation System.Bulletin of the American Meteorological Society,2004,85 (3):381-394.
[41]Mitchell K E,Lohmann D,Houser P R,et.. The Multi-institution North American Land Data Assimilation System (NLDAS):Utilizing Multiple GCIP Products and Partners in a Continental Distributed Hydrological Modeling System. Journal of Geophysical Research, 2004:109D07S90.
[42]Vanden H B.ELDAS Final Report.KNM I:ECMW F,2005.
[43]YANG K, WATANABE T, KOIKE T, et.. Autocalibration System Developed to Assimilate AMSRE Data into a Land Surface Model for Estimating Soil Moisture and the Surface Energy Budget.Journal of the Meteorological Society of Japan,2007,85A:229-242.
[44]李新,黄春林,车涛,等.中国陆面数据同化系统研究的进展与前瞻.自然科学进展, 2007,17(2)：163-1731.
[45]陈莹莹,施建成,杜今阳,等.基于GLDAS的中国区地表能量平衡数值试验.水科学进展,2009,20(1)：25-31.
[46]Li, X.,C. L. Huang, et al. (2007)."Development of a Chinese land data assimilation system:its progress and prospects."Progress in Natural Science 17(8):881-892.
[47]Yang, K.,T. Watanabe, et al.(2007). "Auto-calibration system developed to assimilate AMSR-E data into a land surface model for estimating soil moisture and the surface energy budget."Journal of the Meteorological Society of Japan 85A:229-242.
[48]Huang, C. L.,X. Li, et al. (2008)."Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter."Remote Sensing of Environment 112(4): 1320-1336.
[49]Bonan, G.B.1995:Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model.J. Geophys.Res.,1995,100:2817-2831.
[50]Bonan, G.B.,Oleson, K.W.,Vertenstein, M.,Levis, S.,Zeng, X.,Dai, Y.,Dickinson, R.E.,and Yang, Z. L.2002a:The land surface climatology of the Community Land Model coupled to the NCAR Community Climate Model. J. Climate 15:3123-3149.
[51]Dai Yongjiu,Zeng Xubin,Dickinson R E,et al.2003:The common land model (CLM).Bull. Amer. Meteor.Soc.,84(8):1013-1023.
[52]Dai Yongjiu.2005:The Common Land Model (CoLM) User's Guide.23pp.
[53]Oleson, K. W.,Dai Yongjiu,Bonan G B, et al.2004:Technical Description of the Community Land Model (CLM). NCAR Technical.Note-461+STR,173pp National Center for Atmospheric Research, Boulder, CO.
[54]邓绶林主编,地学辞典,河北教育出版社,1992.
[55]黄洪峰,土壤·植物·大气相互作用原理及模拟研究,气象出版社,1997.
[56]毛嘉富博士论文：Sheffield动态全球植被模型的改进及应用研究,2006.
[57]周明煜,徐祥德,卞林根,等.青藏高原大气边界层观测分析与动力学研究.北京：气象出版社,2000.
[58]Masson V, Champeaux J L, Chauvin F, et al. A global database of land surface parameters at 1-km resolution in meteorological and climate models. Journal of Climate,2003,16(9):1261～ 1282.
[59]Wiscombe, W.J.,and Warren, S.G.1980:A model for the spectral albedo of snow. I. Pure snow. J.Atmos. Sci.37:2712-2733.
[60]Anderson, E.A.1976:A point energy and mass balance model of a snow cover. NOAA Technical eport NWS 19, Office of Hydrology, National Weather Service, Silver Spring, MD.
[61]盛裴轩.大气物理学.北京大学出版社,2003：135-189.
[62]Qian, T.,A. Dai, K. E.Trenberth, and K. W. Oleson,2006:Simulation of global land surface conditions from 1948-2004.Part Ⅰ:Forcing data and evaluation.J. Hydrometeorology, 7,953-975.
[63]邱金桓.大气物理与大气探测学.气象出版社,2005：127-152.
[64]Swinbank W C.Long-wave radiation from clear skies.Quarterly Journal of the Royal Meteorological Society,1963,89:339～3481.
[65]Jacobs J D. Radiation Climate of Broughton Island. Energy budget studies in relation to fast-ice breakup processes in Davis Strait. Occas.Pap.,Inst. of Arctic and Alp Res.,University of Colorado,Boulder,United States,1978,26:105～120.
[66]秦涛,付宗堂ArcGIS中几种空间内插方法的比较[J].物探化探计算技术,2007(1)：63-70.
[67]林忠辉,莫兴国,李宏轩,等.中国陆地区域气象要素的空间插值[J].地理学报,2002,57(1)：47-56.
[68]潘耀忠,龚道溢,邓磊,等.基于DEM的中国陆地多年平均温度插值方法[J].地理学报,2004,59(3)：366-374.
[69]刘湘南,黄方,王平,等。GIS空间分析原理与方法。北京：科学出版社,2005.
[70]刘波.新疆区域地表水分过程的模拟研究,中国科学院大气物理研究所博士论文,2008.
[71]Henderson Sellers A,Yang Z L,D ick inson R E.The project for intercomparison of land surface parameterization schemes. Bull.Amer. Meteor.Soc.,1993,74:133～1349.
[72]Henderson Sellers, A.,A. J. Pitman, P.K. Love, P. Iranne jad, and T. H Chen, The Project for Intercomparison of Land-Surface(PILPS):Phase 2& 3,Bull.Am.Meteorol.Soc.,1995(73):1962-1970.
[73]Epstein, E. S.1969.Stochastic-dynamic prediction. Tellus,21:739-759.
[74]Leith, C, E.,1974:Theoretical skill of Monte Carto forecasts. Mon. Wea. Rev.,102,409-418.
[75]Kalnay, E.,2003:Atmospheric Modeling, Data Assimilation and Predictability.Cambridge University Press, UK,248.
[76]Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D.R. Bachiochi, Z. Zhang, C.E. Williford, S.Gadgil,and S.Surendran,1999:Improved weather and seasonal climate forecasts from multimodel superensemble. Science,285:1548-1550.
[77]Yun, W. T., L.Stefanova, and T. N. Krishnamurti.,2003:Improvement of the superensemble technique for seasonal forecasts.J. Climate.,16:3834-3840.
[78]Hagedorn, R.,F. J. Doblas-Reyes and T. N. Palmer,2005:The Rationale Behind the...in Seasonal Forecasting-Ⅰ. Basic Concept, Tellus,57A:219-233.
[79]P.K. Goel, M.H. DeGroot, Only normal distributions have linear posterior expectations in linear regression,J. Am.Statist. Assoc.1980(75):895-900.
[80]Sarangi, A.,Bhattacharya, A.K.,2005.Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. Agricultural Water Management,78:195-208.
[81]张立明,人工神经网络的模型及其应用,上海复旦大学出版社,1992：22-25.
[82]Kuan CM, Liu T. Forecasting exchange rates using feed forward and recurrent neural networks. Journal of Applied Econometrics 1995,10:347-364.
[83]Zhang G P.Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003(50):159-175.
[84]Leung M T, Chen A S,Daouk H. Forecasting exchange rates using general regression neural networks. Computers & Operations Research 2000(27):1093-1100.
[85]Rowntree P R, J R Bolton. Simulation of the atmospheric response to soil moisture anomalies over Europe. Quart J Roy Meteor Soc,1983,109:501-526.
[86]马柱国,魏和林,符淙斌.中国东北部区域土壤湿度的变化及其与气候变率的关系.气象学报,2000,58(3)：278-287.