玉米农田下垫面动力与热力参数动态对陆面过程模拟的影响研究
|
摘要
地表反照率(α)直接控制太阳辐射能在地表和大气之间的分配,是计算陆面与大气系统中能量和物质交换中重要的热力参数；空气动力学粗糙度(z0)和零平面位移(d)是影响陆一气通量交换的主要动力参数。地表热力与动力参数的准确表达直接影响陆—气通量的估算,进而影响各气象要素的模拟。现有陆面模型中的α、z0和d在同类下垫面大多使用固定值,与实际情况存在较大差距,从而导致陆面过程模拟的不确定性。玉米农田因其冠层高度(h)、叶面积指数(LAI)和植被覆盖度(FVEG)等下垫面结构和性质随生育期变化较大,使得α、z0和d等参数在一年中不断发生变化,导致辐射、水分、热量的分配和传输等一系列物理过程随之变化。本研究利用锦州玉米农田生态系统野外观测站2006-2008年的观测资料,分析了整个玉米生育期的α、z0,和d动态变化特征及其与相关影响因子的关系,建立了各参数的动态参数化方案,对BATSle模型原有静态赋值方案进行了改进,定量评价动力与热力参数变化对陆面过程模拟的影响。主要结论如下：
(1)研究分析了BATSle模型对动态LAI、FVEG、Ze和α的敏感性。模型基本可以模拟出表层土壤温度(Tg)、净入射短波辐射(frs)及感热(Hs)的日动态及日际变化,但对表层土壤湿度(SWC)和潜热(λE)尤其在非降水日模拟能力较差。当LAI、FVEG较小时,各变量模拟误差较大,随着模型设定值与实际值逐渐接近,模拟误差不断减小,说明真实的参数设置对提高模拟精度非常必要。z0动态变化对Tg和Hs模拟有一定改进作用,动态LAI对改善Tg、Hs、A.E和SWC的模拟效果作用显著,FVEG将显著改善对Tg、frs、Hs和SWC的模拟。农田从裸土向有植被覆盖转变时各变量对参数动态敏感性更大。反照率的动态赋值可不同程度影响Tg、Hs和λE的模拟,其中Hs对其变化敏感性最大。
(2)建立了基于太阳高度角(hθ)、SWC和LAI三因子的玉米农田α的动态参数化方案。非生长季,hθ和SWC与α分别呈对数和线性关系所建立的模型能较好地模拟出α的日动态特征,但在初春土壤刚化通时,对α模拟能力较差。生长季考虑h。、SWC和LAI与a为对数、线性和指数关系所建立的模型模拟精度较高,但在营养生长阶段很差,总体效果不理想。引入FVEG对裸土和植被分别赋权重所建立的α综合模型可反映α的季节动态变化,在整个生长季尤其营养生长阶段模拟精度改善明显。通过与双层模型和简化双层模型比较发现,综合模型除玉米生育后期模拟能力略小于简化双层模型外,其他大部分时段模拟能力都较强,对实现玉米农田α动态参数化更为理想。
(3)利用α综合模型并引入动态LAI改进了BATSle模型,定量评价了模型改进对陆-气通量模拟的影响。改进模型实现了α的动态模拟,全年模拟误差明显减小,生长季内由于动态LAI的引入使α模拟结果更为真实；frs模拟精度改进明显,改进年总量占年总辐射的1.7%；净长波吸收辐射(frl)在5、6月FVEG快速变化时模拟精度提高显著；净辐射(nr)在生长季和全年白天模拟精度有所提高。Tg年平均改进量为0.62K,多数月份月平均改进量在1K以上。模型改进使热通量模拟过程更接近真实情况,而原模型模拟结果表面上看与实测值趋势较一致,但实际上是“虚假正确”。热分量中Hs模拟精度改善最明显,生长季好于非生长季,下垫面性质变化明显的6月和10月改善显著。λE改善程度小于Hs,生长季略好于非生长季,但误差仍较大,主要是模型对非降水日SWC明显低估所致,表明BATSle模型中对土壤水分的模拟需要改进。土壤热通量(G)模拟值对实测值的解释能力提高4%,模拟精度在非生长季高于生长季。
(4)建立了基于最优方法估算的动力参数与影响因子关系的动态参数化方案。利用不同高度组合求得理查逊数(Ri)所计算的z0差异明显,z。随Ri增大而减小,2m和10m两层高度求取Ri更为合理。通过比较摩擦风速(u*)实测与模拟值的相关性选择最优方法计算z0和d。z0值在抽雄前小于0.2m,乳熟前后达最大,约为0.4m,d值在拔节后10天左右、h约为1.4m时开始出现,为0.8-1m,抽雄后为1-1.4m。从量级和变化趋势上看,本研究结果与相关研究基本一致。在d值出现前,z0与风速、LAI和h分别呈负指数和线性正相关关系,h和风速对z。作用的累加形式所建立的z0参数化模型模拟精度最高。d值出现后,风速与z0、d之和的关系明显大于与它们各自的关系,z0、d与LAI和h都呈指数正相关关系,LAI和h对z的影响大于d和z0+d,h对z0、d和z0+d影响大于LAI,d/h和z0/h分别为0.4-0.54和0.1-0.14；在h达到最大值前,d/h和z0/h分别随LAI减小和增大,LAI和风速与z0都呈指数关系建立的z0参数化模型模拟精度更高,h和风速与d都呈指数关系的d参数化模型模拟精度更高；当h不变时,LAI变化幅度较小且风速测量误差的增大使d参数化模拟模型精度降低,因z0日际变动很小使所建立的模型无法对其模拟。
(5)评估了动力参数动态参数化方案对BATSle模型的改进作用。G改进最为明显,其次是Hs,而XE改进相对较少,生长季总改进量分别占总辐射的1.24%,0.36%和0.19%,其中G在7、8月改进最明显,改进量分别占月总辐射的2.35%和3.36%；Hs改进最明显的8月改进量占月总辐射1.68%；λE在7、8月改进最明显,改进量分别占月总辐射的0.67%和2.29%。当h小于1.6m时,模型对d的敏感性较弱,d可以忽略。
(6)评估了动力与热力参数动态参数化方案对BATSle模型的改进作用。利用所建立的α、z0和d动态参数化方案同时对模型改进后,α、nr和Tg模拟精度有所提高；热通量各分量中Hs改进幅度最大,其次是G,λE改进最小。nr和Hs因α动态参数化而改进的幅度大于z0和d动态参数化,Tg、λE和G模拟精度则因z0和d改进而提高明显。在α、z0和d得到改善的情况下,一些时段λE模拟精度却有所下降,主要是原模型对SWC模拟不合理所引起,这一情况表明,陆面过程模拟中,一个或几个参量的改进可能会使一些变量的模拟得到改善,但因陆面过程的复杂性某些要素的模拟精度反而下降,一个很重要原因就是原模型对某些要素模拟过程中出现误差相抵的问题,使模拟结果出现“虚假正确”,而某些参量的改进恰恰打破了原有的“平衡”,导致所谓的误差增大,而实际上模型是向着更为合理的过程改进,改进后模型模拟误差的增大并不完全代表改进过程的无效。因此,陆面过程模型的一些过程仍不完善,需要不断加以改进。
Surface albedo (a) controls directly distribution of solar radiation energy between the earth surface and atmosphere, and it is a very important thermal parameter used to calculate exchanges of energy and materials between terrestrial ecosystems and atmosphere. Aerodynamics roughness (z0) and zero plane displacement (d) are significant dynamic parameters influencing flux exchanges bwteeen terrestrial ecosystems and atmosphere. Accurate description of these parameters can improve simulation accuracy of the exchanges of energy and materials between terrestrial ecosystems and atmosphere as well as meteorological elements. Usually, a,zo and d are expressed with fixed value during the same kind of underlying surface in existing land surface models, and do not consider the change with time. Rainfed maize agroecosystem is a typical and representative underlying surface type in northeast China because its extreme changes in surface construction and properties including canopy height (h), leaf area index (LAI) and vegetation coverage (FVEG) with growth of maize cause variations of a, zo and d and then lead to changes in a series of physical process such as distributing and transferring processes of radiation, water and heat. Based on continuous observation data of land-atmosphere flux exchanges, meteorological and biological elements during2006-2008from Jinzhou agricultural ecosystem research station, dynamic characteristics and relationships with relevant influence factors of a,zo and d in rainfed maize whole growth period are analyzed and their dynamic parameterization schemes are set up and used to improve BATSle. At the last, the effect of improved model on simulating land surface process is investigated. The main conclusions are listed as follows.
(1) Revealing the sensitivity of BATSle to dynamic LAI, FVEG, a and z0. BATSle is able to simulate reasonable daily pattern and interdiurnal change of surface soil temperature(Tg), net absorbed solar energy flux(Frs) and sensible heat flux(Hs) as well as undesirable surface soil water content(SWC) and latent heat flux(λE) especially on no-precipitation day. The simulation errors is greater and decreasing when LAI and FVEG are smaller and approaching ground truth, indicating that it is very necessary for increase simulation precision to use more real parameter settings in the model. Dynamic assignment of zo, LAI and FVEG plays an important role in improving simulation precision respectively to Tg, Hs and Tg, Hs, λE, SWC and Tg, frs, Hs, SWC. On the whole, every variable is sensitive to parameter dynamic when rainfed maize ecosystem surface change from bare soil to vegetation. In addition, a assigned with dynamic value affects to varying degrees to simulation of Tg, λE and Hs, especially the latter.
(2) Developing a dynamic parameterization scheme of a based on solar altitude(ho), SWC and LAI. The bare soil a scheme founded considering respectively logarithm and linear relationship between a and he and SWC is better than those considering other relationships and is able to simulate diurnal pattern of a with smaller error in most of the non-growing season except early spring. In the growing season, the simulation precision of a scheme founded with statistical regression method considering respectively logarithm, linear and exponential relationship between a and he, SWC and LAI those play an important role to a is higher than those considering other relationships. For the limitation of data, the scheme underestimates evidently a in most of the study periods especially in vegetative growth phase of maize. As FVEG is introduced and used to bestow weighing to soil and vegetation, the synthesis model whose simulation error decreases significantly in whole growing season especially in vegetative growth phase is able to reflect seasonal variation of a and has dynamic simulation ability, which change an untrue hypothesis that vegetation a is only fixed value in many land surface model and makes the model universal-adapted to simulate dynamic a in different phases of rainfed maize ecosystem. Compared with the double-layer and simplified double-layer model of a, simulation ability of the synthesis model is stronger than that of another two in most of time expect in later growing period when is weaker than that of simplified double-layer model.
(3) Evaluating the simulation of improving BATSle model through introducing the synthesis model and dynamic LAI. The results show that the improving model realizes dynamic simulation of a whose annual simulation error decreases obviously and the simulation value is more accurate for dynamic LAI introduced in growing season, which improves the simulation precision of radial component such as frs whose annual improving quantity(IQ) accounts for1.7percent of annual global radiation, net absorb long wave radiation(frl) in May and June when FVEG changes quickly and nr in growing season and daytime,respectively. IQ of yearly and monthly Tg is0.62K and above1K, respectively. Concidering difference of canopy heat flux between bare soil and vegetation, their simulation results analyzed respectively show that simulation process of heat flux by the improve model is more close to the fact including Hs whose improvement is the most obvious, especially in the growing season in June and August when underlying surface characteristics change evidently than in non-growing season, and λE whose improvement is less than the former and circumstance is consistent mainly with Hs, but simulation error is large because of notable underestimation of SWC on no precipitation day, which demonstrates that expression of soil water content of BATSle must be improved, as well as soil heat flux(G) whose simulation precision is higher in the non-growing season than in the growing season and the explaining ability of simulation to observation increases4%. Yet simulation process of the primary model is consistent with the fact in trend seen from the outside but is an indisguise in facts.
(4) Establishing dynamic parameterization schemes of zo and d by the optimization methods. It is found that zo from bulk Rickardson number (Ri) considering different heights combination is discrepant, decrease with increasing Ri and is reasonable simulated with the height combination of2m and10m. z0is smaller than0.2m before tasseling stage and comes to the maximum about0.4m before and after milk stage. d value begins to appear10days after jointing when h is1.4m height and at this time its height is0.8to1m then1to1.4m after tasseling stage. For magnitude and change trend, zo and d in this study are consistent with related research results. Before d appears, negative exponent and positive linear relationships between zo and wind speed, LAI, h are found. Simulation precision of zo parameterization scheme considering accumulation form of the effect of h and wind speed on zo is highest. After d appears, relationship between wind speed and zo+d is more notable than those between wind speed and zo or d. At the same time, positive exponent relationships between zo or d and LAI or h are found. LAI and h have more influence to zo than d and zo+d for the former greater than the latter. In addition, d/h and zo/h is0.4to0.54and0.1to0.14respectively. Before h comes to the maximum, d/h and zo/h are decreasing and increasing with LAI respectively. Simulation precisions of z0and d parameterization model considering multiplicative form of exponent relationships between zo and LAI, wind speed as well as d and h, wind speed respectively are highest, respectively. When h is invariable, simulation precisions of d and zo parameterization model is decreasing and can't be simulated respectively as a result of small variation of LAI and greater measure error of wind speed for the former and smaller diurnal variation for the latter.
(5) Evaluating the simulation of improving BATSle model through introducing dynamic aerodynamic parameter scheme. The simulation precision of each component of land surface heat flux is in different degree improved with the order of G, Hs and λE whose growing season IQ account for1.24,0.36and0.19percent of global radiation respectively when original BATSle model is modified with newly-built dynamic zo and d parameterization scheme. IQ of G, Hs and λE are larger in July and August, in August and September, in July and August accounting for2.35and3.36,1.68and0.4as well as0.67and2.29percent of monthly global radiation respectively than in other months. Furthermore, we come to a conclusion that d is able to be ignored when that is smaller than1.6m because of slow response from BATSle model.
(6) Evaluating the simulation of improving BATSle model through introducing dynamic aerodynamic and thermodynamic parameter scheme. Simulation precision of a, nr and Tg are improved when original BATSle model is simultaneously modified with newly-built dynamic a, zo and d parameterization scheme. As a result, simulation of each component of land surface heat flux is improved with the order of Hs, G, and λE. Considering contribution of each parameter, a dynamic parameterization contributes more than that of z0and d to nr and Hs, on the contrary, z0and d dynamic parameterization contribute more than that of a to simulation of Tg, λE and G. Simulation precision of λE decreases in some periods though that parameterization of a, zo and d are improved, which is owed to SWC unreasonably simulated by original BATSle model. Those show that simulation of some variables may be improved but those of another deteriorative when one or several parameters are mended because the fictitious balance situation canceling out simulation errors between different variables and making simulation result seem to be true is destroyed by improving of some variables, which leads error to amplify. But, the model is being more reasonably improved. As a result, enlargement of simulation error does n't absolutely represent that the improvement of model is invalid. Altogether, many processes in land surface model are still not perfect and in need to be improved
(1)研究分析了BATSle模型对动态LAI、FVEG、Ze和α的敏感性。模型基本可以模拟出表层土壤温度(Tg)、净入射短波辐射(frs)及感热(Hs)的日动态及日际变化,但对表层土壤湿度(SWC)和潜热(λE)尤其在非降水日模拟能力较差。当LAI、FVEG较小时,各变量模拟误差较大,随着模型设定值与实际值逐渐接近,模拟误差不断减小,说明真实的参数设置对提高模拟精度非常必要。z0动态变化对Tg和Hs模拟有一定改进作用,动态LAI对改善Tg、Hs、A.E和SWC的模拟效果作用显著,FVEG将显著改善对Tg、frs、Hs和SWC的模拟。农田从裸土向有植被覆盖转变时各变量对参数动态敏感性更大。反照率的动态赋值可不同程度影响Tg、Hs和λE的模拟,其中Hs对其变化敏感性最大。
(2)建立了基于太阳高度角(hθ)、SWC和LAI三因子的玉米农田α的动态参数化方案。非生长季,hθ和SWC与α分别呈对数和线性关系所建立的模型能较好地模拟出α的日动态特征,但在初春土壤刚化通时,对α模拟能力较差。生长季考虑h。、SWC和LAI与a为对数、线性和指数关系所建立的模型模拟精度较高,但在营养生长阶段很差,总体效果不理想。引入FVEG对裸土和植被分别赋权重所建立的α综合模型可反映α的季节动态变化,在整个生长季尤其营养生长阶段模拟精度改善明显。通过与双层模型和简化双层模型比较发现,综合模型除玉米生育后期模拟能力略小于简化双层模型外,其他大部分时段模拟能力都较强,对实现玉米农田α动态参数化更为理想。
(3)利用α综合模型并引入动态LAI改进了BATSle模型,定量评价了模型改进对陆-气通量模拟的影响。改进模型实现了α的动态模拟,全年模拟误差明显减小,生长季内由于动态LAI的引入使α模拟结果更为真实；frs模拟精度改进明显,改进年总量占年总辐射的1.7%；净长波吸收辐射(frl)在5、6月FVEG快速变化时模拟精度提高显著；净辐射(nr)在生长季和全年白天模拟精度有所提高。Tg年平均改进量为0.62K,多数月份月平均改进量在1K以上。模型改进使热通量模拟过程更接近真实情况,而原模型模拟结果表面上看与实测值趋势较一致,但实际上是“虚假正确”。热分量中Hs模拟精度改善最明显,生长季好于非生长季,下垫面性质变化明显的6月和10月改善显著。λE改善程度小于Hs,生长季略好于非生长季,但误差仍较大,主要是模型对非降水日SWC明显低估所致,表明BATSle模型中对土壤水分的模拟需要改进。土壤热通量(G)模拟值对实测值的解释能力提高4%,模拟精度在非生长季高于生长季。
(4)建立了基于最优方法估算的动力参数与影响因子关系的动态参数化方案。利用不同高度组合求得理查逊数(Ri)所计算的z0差异明显,z。随Ri增大而减小,2m和10m两层高度求取Ri更为合理。通过比较摩擦风速(u*)实测与模拟值的相关性选择最优方法计算z0和d。z0值在抽雄前小于0.2m,乳熟前后达最大,约为0.4m,d值在拔节后10天左右、h约为1.4m时开始出现,为0.8-1m,抽雄后为1-1.4m。从量级和变化趋势上看,本研究结果与相关研究基本一致。在d值出现前,z0与风速、LAI和h分别呈负指数和线性正相关关系,h和风速对z。作用的累加形式所建立的z0参数化模型模拟精度最高。d值出现后,风速与z0、d之和的关系明显大于与它们各自的关系,z0、d与LAI和h都呈指数正相关关系,LAI和h对z的影响大于d和z0+d,h对z0、d和z0+d影响大于LAI,d/h和z0/h分别为0.4-0.54和0.1-0.14；在h达到最大值前,d/h和z0/h分别随LAI减小和增大,LAI和风速与z0都呈指数关系建立的z0参数化模型模拟精度更高,h和风速与d都呈指数关系的d参数化模型模拟精度更高；当h不变时,LAI变化幅度较小且风速测量误差的增大使d参数化模拟模型精度降低,因z0日际变动很小使所建立的模型无法对其模拟。
(5)评估了动力参数动态参数化方案对BATSle模型的改进作用。G改进最为明显,其次是Hs,而XE改进相对较少,生长季总改进量分别占总辐射的1.24%,0.36%和0.19%,其中G在7、8月改进最明显,改进量分别占月总辐射的2.35%和3.36%；Hs改进最明显的8月改进量占月总辐射1.68%；λE在7、8月改进最明显,改进量分别占月总辐射的0.67%和2.29%。当h小于1.6m时,模型对d的敏感性较弱,d可以忽略。
(6)评估了动力与热力参数动态参数化方案对BATSle模型的改进作用。利用所建立的α、z0和d动态参数化方案同时对模型改进后,α、nr和Tg模拟精度有所提高；热通量各分量中Hs改进幅度最大,其次是G,λE改进最小。nr和Hs因α动态参数化而改进的幅度大于z0和d动态参数化,Tg、λE和G模拟精度则因z0和d改进而提高明显。在α、z0和d得到改善的情况下,一些时段λE模拟精度却有所下降,主要是原模型对SWC模拟不合理所引起,这一情况表明,陆面过程模拟中,一个或几个参量的改进可能会使一些变量的模拟得到改善,但因陆面过程的复杂性某些要素的模拟精度反而下降,一个很重要原因就是原模型对某些要素模拟过程中出现误差相抵的问题,使模拟结果出现“虚假正确”,而某些参量的改进恰恰打破了原有的“平衡”,导致所谓的误差增大,而实际上模型是向着更为合理的过程改进,改进后模型模拟误差的增大并不完全代表改进过程的无效。因此,陆面过程模型的一些过程仍不完善,需要不断加以改进。
Surface albedo (a) controls directly distribution of solar radiation energy between the earth surface and atmosphere, and it is a very important thermal parameter used to calculate exchanges of energy and materials between terrestrial ecosystems and atmosphere. Aerodynamics roughness (z0) and zero plane displacement (d) are significant dynamic parameters influencing flux exchanges bwteeen terrestrial ecosystems and atmosphere. Accurate description of these parameters can improve simulation accuracy of the exchanges of energy and materials between terrestrial ecosystems and atmosphere as well as meteorological elements. Usually, a,zo and d are expressed with fixed value during the same kind of underlying surface in existing land surface models, and do not consider the change with time. Rainfed maize agroecosystem is a typical and representative underlying surface type in northeast China because its extreme changes in surface construction and properties including canopy height (h), leaf area index (LAI) and vegetation coverage (FVEG) with growth of maize cause variations of a, zo and d and then lead to changes in a series of physical process such as distributing and transferring processes of radiation, water and heat. Based on continuous observation data of land-atmosphere flux exchanges, meteorological and biological elements during2006-2008from Jinzhou agricultural ecosystem research station, dynamic characteristics and relationships with relevant influence factors of a,zo and d in rainfed maize whole growth period are analyzed and their dynamic parameterization schemes are set up and used to improve BATSle. At the last, the effect of improved model on simulating land surface process is investigated. The main conclusions are listed as follows.
(1) Revealing the sensitivity of BATSle to dynamic LAI, FVEG, a and z0. BATSle is able to simulate reasonable daily pattern and interdiurnal change of surface soil temperature(Tg), net absorbed solar energy flux(Frs) and sensible heat flux(Hs) as well as undesirable surface soil water content(SWC) and latent heat flux(λE) especially on no-precipitation day. The simulation errors is greater and decreasing when LAI and FVEG are smaller and approaching ground truth, indicating that it is very necessary for increase simulation precision to use more real parameter settings in the model. Dynamic assignment of zo, LAI and FVEG plays an important role in improving simulation precision respectively to Tg, Hs and Tg, Hs, λE, SWC and Tg, frs, Hs, SWC. On the whole, every variable is sensitive to parameter dynamic when rainfed maize ecosystem surface change from bare soil to vegetation. In addition, a assigned with dynamic value affects to varying degrees to simulation of Tg, λE and Hs, especially the latter.
(2) Developing a dynamic parameterization scheme of a based on solar altitude(ho), SWC and LAI. The bare soil a scheme founded considering respectively logarithm and linear relationship between a and he and SWC is better than those considering other relationships and is able to simulate diurnal pattern of a with smaller error in most of the non-growing season except early spring. In the growing season, the simulation precision of a scheme founded with statistical regression method considering respectively logarithm, linear and exponential relationship between a and he, SWC and LAI those play an important role to a is higher than those considering other relationships. For the limitation of data, the scheme underestimates evidently a in most of the study periods especially in vegetative growth phase of maize. As FVEG is introduced and used to bestow weighing to soil and vegetation, the synthesis model whose simulation error decreases significantly in whole growing season especially in vegetative growth phase is able to reflect seasonal variation of a and has dynamic simulation ability, which change an untrue hypothesis that vegetation a is only fixed value in many land surface model and makes the model universal-adapted to simulate dynamic a in different phases of rainfed maize ecosystem. Compared with the double-layer and simplified double-layer model of a, simulation ability of the synthesis model is stronger than that of another two in most of time expect in later growing period when is weaker than that of simplified double-layer model.
(3) Evaluating the simulation of improving BATSle model through introducing the synthesis model and dynamic LAI. The results show that the improving model realizes dynamic simulation of a whose annual simulation error decreases obviously and the simulation value is more accurate for dynamic LAI introduced in growing season, which improves the simulation precision of radial component such as frs whose annual improving quantity(IQ) accounts for1.7percent of annual global radiation, net absorb long wave radiation(frl) in May and June when FVEG changes quickly and nr in growing season and daytime,respectively. IQ of yearly and monthly Tg is0.62K and above1K, respectively. Concidering difference of canopy heat flux between bare soil and vegetation, their simulation results analyzed respectively show that simulation process of heat flux by the improve model is more close to the fact including Hs whose improvement is the most obvious, especially in the growing season in June and August when underlying surface characteristics change evidently than in non-growing season, and λE whose improvement is less than the former and circumstance is consistent mainly with Hs, but simulation error is large because of notable underestimation of SWC on no precipitation day, which demonstrates that expression of soil water content of BATSle must be improved, as well as soil heat flux(G) whose simulation precision is higher in the non-growing season than in the growing season and the explaining ability of simulation to observation increases4%. Yet simulation process of the primary model is consistent with the fact in trend seen from the outside but is an indisguise in facts.
(4) Establishing dynamic parameterization schemes of zo and d by the optimization methods. It is found that zo from bulk Rickardson number (Ri) considering different heights combination is discrepant, decrease with increasing Ri and is reasonable simulated with the height combination of2m and10m. z0is smaller than0.2m before tasseling stage and comes to the maximum about0.4m before and after milk stage. d value begins to appear10days after jointing when h is1.4m height and at this time its height is0.8to1m then1to1.4m after tasseling stage. For magnitude and change trend, zo and d in this study are consistent with related research results. Before d appears, negative exponent and positive linear relationships between zo and wind speed, LAI, h are found. Simulation precision of zo parameterization scheme considering accumulation form of the effect of h and wind speed on zo is highest. After d appears, relationship between wind speed and zo+d is more notable than those between wind speed and zo or d. At the same time, positive exponent relationships between zo or d and LAI or h are found. LAI and h have more influence to zo than d and zo+d for the former greater than the latter. In addition, d/h and zo/h is0.4to0.54and0.1to0.14respectively. Before h comes to the maximum, d/h and zo/h are decreasing and increasing with LAI respectively. Simulation precisions of z0and d parameterization model considering multiplicative form of exponent relationships between zo and LAI, wind speed as well as d and h, wind speed respectively are highest, respectively. When h is invariable, simulation precisions of d and zo parameterization model is decreasing and can't be simulated respectively as a result of small variation of LAI and greater measure error of wind speed for the former and smaller diurnal variation for the latter.
(5) Evaluating the simulation of improving BATSle model through introducing dynamic aerodynamic parameter scheme. The simulation precision of each component of land surface heat flux is in different degree improved with the order of G, Hs and λE whose growing season IQ account for1.24,0.36and0.19percent of global radiation respectively when original BATSle model is modified with newly-built dynamic zo and d parameterization scheme. IQ of G, Hs and λE are larger in July and August, in August and September, in July and August accounting for2.35and3.36,1.68and0.4as well as0.67and2.29percent of monthly global radiation respectively than in other months. Furthermore, we come to a conclusion that d is able to be ignored when that is smaller than1.6m because of slow response from BATSle model.
(6) Evaluating the simulation of improving BATSle model through introducing dynamic aerodynamic and thermodynamic parameter scheme. Simulation precision of a, nr and Tg are improved when original BATSle model is simultaneously modified with newly-built dynamic a, zo and d parameterization scheme. As a result, simulation of each component of land surface heat flux is improved with the order of Hs, G, and λE. Considering contribution of each parameter, a dynamic parameterization contributes more than that of z0and d to nr and Hs, on the contrary, z0and d dynamic parameterization contribute more than that of a to simulation of Tg, λE and G. Simulation precision of λE decreases in some periods though that parameterization of a, zo and d are improved, which is owed to SWC unreasonably simulated by original BATSle model. Those show that simulation of some variables may be improved but those of another deteriorative when one or several parameters are mended because the fictitious balance situation canceling out simulation errors between different variables and making simulation result seem to be true is destroyed by improving of some variables, which leads error to amplify. But, the model is being more reasonably improved. As a result, enlargement of simulation error does n't absolutely represent that the improvement of model is invalid. Altogether, many processes in land surface model are still not perfect and in need to be improved
引文
1 Abramopoulos F, C Rosenweig and B Choudhury. Improved groud hydrology calculations for global climate models(GCMs):Soil water content and evaporation. J. Climate, 1988,1(9):921-941.
2 Andre J C, Goutorbe J P, Perrier A, et al. Evaporation over land surfaces:first results from HAPEX-MOBILHY special observing period. Ann. Geophys.,1988,6:477-492.
3 Andre J C, Goutorbe J P, Perrier A, et al. HAPEX-MOBILHY:A hydrological atmospheric experiments for the study of water budget and evaporation flux at the climatic scale. Bull. Amer. Meteor. Soc.,1986,67(2):138-144.
4 Avissar R. Concep tual aspects of a statistical-dynamical app roach to rep resent landscape subgrid heterogeneities in atmospheric models. J Geophys Res,1992,97:2729-2742.
5 Baldocchi D, Falge E, Gu L, et al. Fluxnet:a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 2001,82:2415-2434.
6 Baldocci D, Kelliher F M, Black T A, et al. Climate and vegetation controls on boreal zone energy exchange. Glob Change Biol,6(suppll):2000,69-83.
7 Bartelink, H. H., Radiation interception by forest trees:a simulation study on effects of stand density and foliage clustering on absorption and transmission. Ecol. Model. 1998,105,213-225.
8 Betts A K, Ball J H. Albedo over the boreal forest. J Geophys Res, 1997,102(24):28901-28910.
9 Blondin C and H Boetter. The surface and sub-surface parameterization scheme in the ECMWF forecasting systerm. Rivsion and operational assessment of weather elements, ECWMF Tech. Memo.,1987,135, p48.
10 Bonan G B. A Land Surface Model(LSM) for ecological, hydrological and atmospheric study:technical description and user's guide. NCAR/TN-417+-STR, NCAR Technical note, Climate and global dynamics division, national center for atmospheric research, boulder, Colorado.1996.
11 Bonan G B. Comparison of two land surface process models using prescribed forcing. J. Geophys. Res.,1994,99:25803-25818.
12 Bonan G B. Land-atmosphere C02 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. J. Geophys.Res.,1995,100:2817-2831.
13 Bonan GB. A Land Surface Model (LSM Version 1.0) for Eco-logical, Hydrological and Atmospheric Studies:Technical Description and User's Guide[R]. NCAR Technical Note NCAR/TN-417 + STR, Boulder, Colorado.1996.
14 Cess R D, Potter G G, Zhang M-H and other 30 Co-authers. Interpretation of snow-climate feedback as produced by 17 general circulation models. Science,1991,253(5022):888-892.
15 Charney J G, Quirk W J, Chow S H, et al. A Comparative study of the effects of albedo change on drought in semi-arid regions. J Atmos Sci,1977,34:1366-1385.
16 Charney J G. Dynamics of desert s and drought in t he Sahel. Quarterly Journal of t he Royal Meteorological Society,1975,101:193-202.
17 Charney J G. Dynamics of deserts and drought in the Sahel. Quart. J. Roy. Meteor. Soc., 1975,101(428):193-292.
18 Chen X H. Relationship between surface albedo and some meteorological factors. Journal of Chengdu Institute of Meteorology,1999,14 (3):233-238.
19 Collatz G J, Ball J T, Grivet C, et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration:A model that includes the laminar boundary layer. Agri. Forest Meteor.,1991,54:107-136.
20 Collatz G J, Ribas-Carbo M, Berry J A. Coupled photosynthesis-stomatal conductance model for leaves of C4 plant. Aust. J. Plant Physiol.1992,19:519-538.
21 Colman R A. Snow and cloud feedbacks modeled by an atmospheric general circulation model. Climate Dynamics,1994,9(4):253-265.
22 Counihan J. Wind tunnel determination of the roughness fetch as a function of the fetch and the roughness densities of three dimensional roughness elements. Atmospheric environment,1971,5:637-642.
23 Cuf AD, Fisch G, Hodnett MG. The albedo of Amazonian forest and rangeland. Journal of climate,1995,8:1544-1554.
24 Dai YJ, Zeng QC. A land surface model(IAP94)for climate studies. Part I:Formulation and validation in offline experiments. Advances in Atmospheric Sciences, 1997,14:443-460.
25 Dai Yongjiu,Zeng Xubin, Dickinson R E, et al. The common land model (Cl.M). Bull. Amer. Meteor. Soe,2003,84 (8):1013-1023.
26 Dai Yongjiu, Zeng Xubin, Dickinson R E. Common Land Model (Technical Documentation and Userps Guide)[R]. The Georgia Institute of Technology,2001,69pp
27 Dai Yongjiu. The Common Land Model(CoLM) UsersGuide [R]. School of geography Beijing Normal University,2005,23pp.
28 De bruin HAR, Verhoef A. A new method to determine the zero-plane displacement. Boundary-layer Meteorol.,1997,82:159-164.
29 Deardoff J W. Efficient prediction of ground surface tempera-ture and moisture, wit h inclusion of a layer of vegetation. Journal of Geophysical Research, 1978,83(4):1889-1904.
30 Deardorff,J. W. Three-dimensional numerical study of the hutent Kinetic energy budget in unstable planetary boundary layer. J. Atmos. Sci.1974,31,465-474
31 Dickinson R E, Henderson-sellers A, Kennedy P J. Biosphere Atmosphere Transfer Scheme (BATS) Version le as Coupled to the NCAR Community Climate Model[R]. NCAR Technical Note,NCAR/ TN2387+STR,1993,72.
32 Dickinson R E, Kennedy P. Impacts on regional climate of Amazon deforestation. Geophys ResLett,1992,19 (19):1947-1950.
33 Dickinson R E. Land surface processes and climate-surface albedos and energy balance. Adv Geophys,1983,25:305-353.
34 Dickinson R. E. Land surface process and climate-surface albedos and energy balance. Advance in Geophysics,1983,25,305-353, Academic Press.
35 Dickinson RE, Sellers HA, Kennedy PJ. Biosphere Atmosphere Transfer Scheme (BATS) Version le as Coupled to the NCAR Community Climate Model[R]. NCAR Technical Note, NCARPTN-387 + STR.1993.
36 Dirmeyer P A, Shuk la J. Albedo as a modulator of climate response to tropical defo-restation. J Geophys Res,1994,99 (D10):20863-20877.
37 Dong B, Valdes P. J.. Modelling the Asian summer monsoon rainfall and Eurasian Winter/spring snow mass. Quart. J. R. Meteor. Soc.,1998,24(552):2567-2598.
38 Dong ZB, Liu XP, Wang XM. Aerodynamic roughness length of gravel beds. Geomorphology,2002,43 (1-2):17-31.
39 Dong zhibao,Gao Shangyu, Fryrear D W. Drag coefficients and Roughness length as disturbed by artificial standing vegetation. Journal of Arid Environments,2001,49(3):485-5051.
40 Douglas B C, Xue Y K, Richard J H, et al. Modeling the impact of land surface degradation on the climate of tropical North Africa. J Climate,2001,14(8):1809-1822.
41 Douville H, Royer J F and Mahfouf J F. Sensitivity of the Asian summer monsoon to an anomalous Eurasian snow cover within the Meteo-France GCM. Climate Dynamics, 1996,12(7):449-466.
42 Ducoudre N I, Laval K, Perrier A. SECH IBA a new set of parameterizations of the hydrologic exchanges at the land-atmosphere interface with in the LMD atmospheric general circulation model. J. Climate.,1993,6:248-273
43 Erbs, D.G., Klein, S.A., Duffie, J. A. Estimation of the diffuse radiation fraction for hourly, daily and monthly average global radiation. Solar energy,1982,28,293-302.
44 Famiglietti J S, Wood E F. Multi-scale modeling of spatially-variable water and energy balance p rocesses. Water Resour Res,1994,30:3061-3078.
45 Farquhar G D, Sharkey T D. Stomatal conductance and photosynthesis. Ann. Rev. Plant. Physiol.,1982,33:317-345.
46 Farquhar G D, Von Caemmerer S. Modeling of photosynthesis response to environment conditions. In:Lange O L, Nobel P S, Osmond C B, et al. eds. Physiological Plant Ecology π,550-588. Encyclopedia of Plant Physiology new series. Vol.12B. Springer, Berlin.1982.
47 Farquhar G D, Wong S C. An empirical model of stomatal conductance. Australian J. Plant Physiology,1984,11:191-209.
48 Flerchinger, G. H. and K E Saxton. Simultaneous heat and water model of a freezing snow-residue-soil system I. Theory and development. Trans of ASAE,1989,32(2):565-571.
49 Foley I A, Prentice I C, Ramankutty N, et. al. An integrated biospher model of land surface p rocesses, terrestrial carbon balance, and vegetation dynamics. Global Biogeochemical Cycles,1996,10(4):603-628.
50 Gao Z, Wang J, Ma Y, et al. Study of roughness lengths and drag coefficients over Nansha Sea Region, Gobi, Desert, Oasis and Tibetan Plateau. Physics and Chemistry of the Earth(B),2000,25:141-145.
51 Garratt J R,The atmospheric boundary layer, Cambridge University press,1992,pp:316.
52 Giorgi F, Marinuci M R. Validation of a regional atmospheric model over Europe sensitivity of wintertime and summertime simulations to selected physics parameterizations and lower boundary conditions. Quart J.Roy. Meteor.Soc.,1991,117 1171-1206。
53 Giorgi F, Marinuci M R. Validation of a regional atmospheric model over Europe sensitivity of wintertime and summertime simulations to selected physics parameterizations and lower boundary conditions. Quart J.Roy. Meteor.Soc. 1991,117:1171-1206.
54 Giorgi F, Marinucci M R, Bates G T. Development of a second-generation regional climate model (RegCM2). Part I:Boundary-layer and radiation t ransfer process. Mon. Wea. Rev. 1993,121:2794-2813。
55 Giorgi F, Marinucci M R, Bates G T. Development of a second-generation regional climate model (RegCM2). Part I:Boundary-layer and radiation t ransfer process. Mon. Wea. Rev. 1993,121:2794-2813.
56 Giorgi F, Marinucci M R, Canio G D, et al. Development of a second-generation regional climate model (RegCM2). Part II:Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev,1993,121:2814-2832.
57 Giorgi F, Marinucci M R, Canio G D, et al. Development of a second-generation regional climate model (RegCM2). Part II:Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev,1993,121:2814-2832.
58 Gordon W S. Climate change, hydrology and ecological models:intercomparison and validation. Dissertation, Doctor of Philosophy, The University of Texas at Austin.2003.
59 Gordon W S. Climate change, hydrology and ecological models:intercomparison and validation. Dissertation, Doctor of Philosophy, The University of Texas at Austin.2003.
60 Haeltine A, Prentice I C, Creswell I D. A coupled carbon and water flux model to predict vegetation structure. J. of Vegetation Science,1996,7:651-666.
61 Hao Y B, Wang Y F, Huang X Z, et al. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agricultural and Forest Meteorology,2007,146(12):57-69.
62 Hao Y B, Wang Y F, Huang X Z, et al. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agricultural and Forest Meteorology,2007,146(12):57-69.
63 Hao Y B, Wang Y F, Mei X R, et al. C02, H20 and energy exchange of an Inner Mongolia steppe ecosystem during a dry and wet year. Acta ecologica,2008,33(2):133-143.
64 Hao YB, Wang YF, Huang XZ, et al. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agricultural and Forest Meteorology,2007,146(12):57-69.
65 Henderson-Sellers A, Dickinson R E, Durbidge T B, et al. Tropical deforestation: modeling local2to regional scale climate change. J Geophys Res,1993,98 (D4):7289-7315.
66 Henderson-Sellers A. The project for intercomparison of land-surface parameterization schemes.Bull Amer Meteor Soc,1993,74(7):1335-1348.
67 Houghton, J. T.,and others[EDS]. Radiative forcing of climate change and an evaluation of the IPCC IS92 emission scenarios. Cambridge Univ. Press.1995.
68 Idso S B, Jackson R D, Reginato R J, Nakayama F S. The dependence of bare soil albedo on soil water content. Journal of Applied Meteorology,1975,14(1),109-113.
69 Irannejad P,Shao Y. Description and validation of t he atmosphere land surface interaction scheme (ALSIS) with HAPEX and Cabauw data. Global Planet Change,1998,19: 87-114.
70 Jacobs AFG and van Pul WAJ. Seasonal changes in the albedo of a maize crop during two seasons. Agricultural and Forest Meteorology,1990,49:351-360.
71 Jacobs AFG, van Pul WAJ. Seasonal changes in the albedo of a maize crop during two seasons. Agric For Meteorol,1990,49:351-360.
72 Jarvis P G, James G B, Landsberg J J. Coniferous fores[A]. in:Monteith J L, ed. Vegetation and the atmosphere. Vol.2[C]. London:academic press,1976,171-240.
73 Ji jinjun. A climate-vegetation interaction model:simulating physical and biological process at the surface. J. Biogeography,1995,22:445-451.
74 Ji JJ and Hu YC. A simple land surface process model for use in climate studied. Acta Meteorologica Sinica,1989,3(3):344-353.
75 Keith W. Oleson, Yongjiu Dai, Gordon Bonan, et al. Technical Description of the Community Land Model (CLM)[R]. NCAR Technical Note,NCAR/ TN461 + STR,2004,23.
76 Kim J, E k M. A simulation of the surface energy budget and soil water content over the hydro logic atmospheric pilot experiments-modification du Bilan Hydrique forest site. J. Geophys. Res.,1995,10:20845-20854.
77 Kimura R, Otsuki K, Kamichika M. Relationships between the zero-plane displacement and the roughness length over sorghum and alfalfa canopies. J. Agric. Meteorol. 1999,55:15-24.
78 Klink,K. Evaluating climate-vegetation interactions at climate model sub-grid scales. VolXLV, No.1, University of Delaware, Center for Climatic Research, Newark, DE.1992.
79 Kowalczyk E A, Garratt J R, Krummel P B A. Soil-canopy scheme for use in numerical model of the atmosphere-ID stand-alone model. In:CS IRO, DAR, Technical Paper.1991,32,59.
80 Kurbatkin GP, Manabe S, Hahn D G. The moisture content of the continents and the intensi ty of summer monsoon circulation. Soviet Meteor And Hydrology,1979, (11):16.
81 Laval K, Picon L. Effect of a change of the surface albedo of the Sahelon climate. J Atmos Sci,1986,43:2418-2429.
82 Laval K. General circulation model experiments with surface albedo changes. Climatic Change,1986,9:91-102.
83 Lean J, Rowntree P R. A GCM simulation of the impact of Amazonian deforestation on climate using an improved canopy representation. Q J R Meteorol Soc,1993,119 (512):509-530.
84 Lettau H. Note on aerodynamic roughness parameter estimation on the basis of roughness element description. Journal of Applied Areorology,1969,8:828-832.
85 Li Bin, Avissar R. The impact of spatial variability of land surfacecharacteristics on land-surface heat fluxes. J Climate,1994,7:527-537.
86 Li S G., Eugster W, Asanuma J, Kontani A, Davaa G, Oyunbaatar D, Sugita M. Energy partitioning and its biophysical controls above a grazing steppe in central Mongolia. Agricultural and Forest Meteorology,2006,37(1-2):89-106.
87 Li SG, Eugster W, Asanuma J, et al. Energy partitioning and its biophysical controls above a grazing steppe in central Mongolia. Agricultural and Forest Meteorology, 2006,37 (1-2):89-106.
88 Li Y N, Zhao L, Zhou H K, Xu S X, Zhang F W. Changes in Reflected Radiation and Reflectivity for Growing Season of Alpine Swamp in the Northern Qinghai. Journal Of Glaciology And Geogryology,2007,29(1):137-143.
89 Li Y, Hu Z Y.2009. A Study on Parameterization of Surface Albedo over Grassland Surface in the Northern Tibetan Plateau. Advances in atmospheric sciences,26(1):161-168.
90 Liu H Z, WANG B M, FU C B. Relationships between surface albedo, soil thermal parameters and soil moisture in the semi-arid area of Tongyu, northeastern China. Advance in Atmospheric Sciences,2008,25(5):757-764.
91 Lofgren B M. Sensitivity of land2ocean circulations, precipitation and soil moisture to perturbed land surface albedo.J Climate,1995,8:2521-2542.
92 Lu Daren. Inner Mongolia semi-arid grassland soil-vegetation-atmosphere interaction (IMGRASS). Global Change News Letter.1997,31:4-5.
93 M S Roxy, V B Sumithranand, G Renuka. Variability of soil moisture and its relationship with surface albedo and soil thermal diffusivity at astronomical observatory, thiruvananthapuram, south kerala. J. Earth Syst. Sci.2010,119, No.4, august pp.507-517.
94 Manzi A 0. Introduction d'un schema des transferts sol-vegetation-atmosphere dans un modele de circulation generale et application a la simulation de la deforestation Amazonienne.1993.
95 Martano P. Estimation of surface roughness length and displacement height from single-level sonic anemometer data. Journal of Applied Meteorology,2000,39:708-715.
96 Mason P J. The formation of a really averaged roughness lengths. Quarterly Journal of the royal meteorological society,1988,114:399-420.
97 Molion L C B, Moore C J. Estimation the zero-plane displacement for tall vegetation using a mass conservation method. Boundary Layer Meteorology,1983,26(7):115-125.
98 Monteith. J. L., Unsworth, M. H. Principles of Environmental physics,2nd edn., Edward A rnold, London.1990.
99 Monteith J L, Unsworth M H. Principles of environmental physics[M]. London:Edward Arnold. Garratt J R,The atmospheric boundary layer, Cambridge University press, 1992,pp:316.
100 Monteith J L, Unsworth M H. Principles of environmental physics[M]. London:Edward Arnold. 1990.
101 Monteith J L. Principles of environmental physics. London:Edward Arnold.1973.
102 Neilson R P. A model for predicting continental-scale vegetation distribution and water balance. Ecological Application,1995,5:362-385.
103 Nobre C A, Sellers P J, Shukla J. Amazonian deforestation and regional climate change. J Climate,1991,4 (10):957-988.
104 Noilhan J, Planon S. A simple parameterization of land2surface processes for meterological models. Mon. W e Rev.,1989,117:536-550.
105 Otterman J, Chou M D, Arking A. Effects of nontropical forest cover on climate. J Clim Appl Meteorol,1984,23:762-767.
106 Philip G.Oguntunde, Nick van de Giesen. Crop growth and development effects on surface albedo for maize and cowpea fields in Ghana, West Africa. Int J Biometeorol, 2004,49:106-112.
107 Pitman A J, Yang Z-L, Cogley J G, Henderson-Sellers. Description of bare essentials of surface transfer for the bureau of meteorology Research Centre A GCM. In:BMRC research report,1991,32. Melbourne, Australia.
108 Raupach M R, Thom A S, Edwards I. A wind tunnel study of turbulent flow close to regularly arrayed roughness surface. Boundary Layer Ateorology,1980,18:373-393.
109 Reijmer C H, Van Meijgaard E, Van den Broeke M R. Numerical studies with a regional atmospheric climate model based on changes in the roughness length for momentum and heat over Antarctica. Bound layer meteor.,2004,111(2):313-337.
110 Rind D. The influence of ground moisture conditions in North America on summer climate as modeled in the GISS GCM. Mon. Wea. Rev.,1982,110:1487-1494.
111 Ross, J. Radiative transfer in plant communities.In:Monteith,J. L. (Ed.), Vegetation and the Atmosphere. Vol.1, Principles, Academic Press, London,1975, pp.13-55.
112 Saito T, Nagai Y, Isobe S, et al. An investigation of turbulence within a crop canopy. J. Agric. Meteorol.,1970,25:205-214.
113 Saxton, K. E. Sensitivity analysis of the combination evapotranspiration equation, Agric. Meteorol.1975,15(3):343-353.
114 Sellers P J, Mintz Y, Sud Y C, et al. A simple biosphere model (SiB) for use within general circulation models. J Atmos Sci,1986,43:505-531.
115 Sellers P J, Hall F G, Asrar G, et al. The first ISLSCP field experiment(FIFE). Bull. Amer. Meteor. Soc.,1988,69(1):22-27.
116 Sellers P J, RandallD A, Collatz C J, et al. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 1:Model formulation. Journal of Climate, 1996,9:676-705.
117 Seth A, Giorgi F, Dickinson R E. Simulating fluxes from heterogeneous land surface:Exp icit subgrid method emp loying the biosphere - atmosphere scheme (BATS). J Geoph Res, 1999,18651-18667.
118 Shao Y. Physics and Modeling of Wind Erosion [M]. London:Kluwer Academic Publishers: 2000,99-100.
119 Shaw RH and Pereira AR. Aerodynamic roughness of a plant canopy:a numerical experiment. Agricultural and Forest Meteorology,1982,26:51-65.
120 Shukla J,Nobre C, Sellers P J. Amazon deforestation and climate change. Science, 1990,247(4948):1322-1325.
121 Shukla J, Mintz Y. Influence of land surface evapotranspiration on the earth's climate. Science,1982,215 (4539):1498-1501.
122 Sivapalan M, Wood R A. Evaluation of the effects of general circulation model subgrid variability and patchiness of rainfall and soil moisture on land surface water balance fluxes[A]. Scale Issues in HydrologicalModeling[M], Kalma J D, Sivapalan M Eds. John Wiley and Sons.1995,453-473.
123 Song J. Diurnal asymmetry in surface albedo. Agric For Meteorol,1998,92:181-189.
124 Stull R B. An introduction to boundary layer meteorology[M]. New York:Kluwer academic publishers,1988:378.
125 Sub Y C, Shukla J, Mintz Y. The influence of land surface roughness on atmospheric circulation and rainfall:a sensitivity experiment with a GCM. NASA Tech. Memo, 1985,86219.
126 Sub Y C, Smith W E. Influence of local and surface processes on the Indian monsoon-A numerical study. J. Climate Appl. Meteor.,1985,24(10):1015-1036.
127 Sub Y C, Smith W E. The influence of surface roughness of deserts on the July circulation-A numerical study. Bound.-Layer Meteor.1985,33(1)15-49.
128 Sud Y C, Fennessy M. A study of the influence of surface albedo on July circulation in semi-arid regions using the GLAS GCM. J Climatol,1982,2:105-125.
129 Tajchman S J. Comments on measuring turbulent exchange within and above forest canopy. Bull Am Meteorol Soc,1981,62:1550-1559.
130 Takagi K, Miyata A, Harazono Y, et al. An alternative approach to determining zero-plane displacement and its application to a lotus paddy field. Agric. For. Meteorol. 2003,115:173-181.
131 Tanner C D. Potential evapotranspiration estimates by the approximate energy balance method of Penman. J. Geophysical Research,1990,65:3391-3413.
132 Taylor P A. On the parameterization of drag over small-scale topography in neutrally-strantified boundary-layer flow. Boundary layer Meterology,1989,48:409-422.
133 Thom A S, Stewart J B, Oliver H R, et al. Comparison of Aerodynamic and Energy budget estimaters of fluxes over a pine forest. Quart J R Meteorol Soc,1975,101:93-105.
134 Uchijima Z. Maize and rice. In:Monteith JL(ed) Vegetation and the atmosphere, vol.2 Principles. Academic press, London,1976,pp 33-64.
135 Verseghy D L. CLA SS:a Canadian land surface scheme for GCM s:I soil model, Int. J. Climatol.,1991,11:111-133.
136 Wand K C, WANG P C, LIU J M, Sparrow M, Haginoya S, Zhou X J. Variation of surface albedo and soil thermal parameters with soil moisture content at a semi-desert site on the western Tibetan Plateau. Bound Layer Meteorology,2005,116:117-129.
137 Wang J, Y Gao, Y Hu, et al. An overview of the HEIFE experiments in the People's of republic:Exchange processes at the land surface for a range of space and time scales. IAHS Pub1.1993,212:397-403.
138 Wang K C, Wang P C, Liu J M, Sparrow M, Haginoya S, Zhou X J. Variation of surface albedo and soil thermal parameters with soil moisture content at a semi-desert site on the western Tibetan Plateau. Bound Layer Meteorology,2005,116:117-129.
139 Wang S S. Dynamics of surface albedo of a boreal forest and its simulation. Ecological Modeling,2005,183:477-494.
140 Wang S, Grant R F, Verseghy D L, et al. Modeling plant carbon and nitrogen dynamics of a boreal aspen forest in CLASS the Canadian Land surface scheme. Ecol Model, 2001,142:135-142.
141 Wang S, Grant R F, Verseghy D L, et al. Modeling carbon dynamics of boreal forest ecosystems using the Canadian Land surface scheme. Clim Change,2002,55:451-477.
142 Wang S, Grant R F, Verseghy D L, et al. Modeling carbon-coupled energy and water dynamics of a boreal aspen forest in a general Circulation Model Land surface scheme. Int J Climatol, 2002,22:1249-1265.
143 Wang Z, Barlage M, Zeng X, Dickinson R E, Schaaf C B. The solar zenith angle dependence of desert albedo. Geophysical Research Letters,2005,32(L05403):1-4.
144 Warrilow D.A., A.B. Sangster, Slingo. A Modelling of land surface processes and their influence on European climate[A]. DCTN 38, Dynamical Climatology Branch, United Kingdom Meteorological Office, Bracknell, Berkshire RG122SZ,UK.1986.
145 Williams J. The influence of snow cover on the atmospheric circulation and its role in climate change:an analysis based on results form the NCAR global circulation model. J. Appl. Meteor.,1975,14(2):137-152.
146 Woodward F I, Smith T M and Emaneul W R. A global land primary productivity and phytogeography model. Global Biogeochemical Cycles,1995,9:471-490.
147 Xue Y, Sellers P J and Kinter J L. A simplified biosphere model for global climate studies. J. Climate,1991,4:345-364.
148 Xue Y, Shukla J. The influence of land-surface properties on Sahel Climate, Part Ⅰ:Desertification. J Climate, 1993,6 (12):2232-2245.
149 Xue Y, Shukla J. The influence of land-surface properties on Sahel Climate, Part Ⅱ: Defo-restation. J Climate,1996,9 (12):3260-3275.
150 Xue Y. The impact of desertification in theMongo lian and the InnerMongo lian Grassland on the regional climate. J Climate,1996,9 (9):2173-2189.
151 Yamazaki T, Kondo J, Watanabe T. Heat balance model with a canopy of one or two layer and its application to field experiment.J. Appl. Meteor.,1992,31:86-103.
152 Yasunari T, Kitoh A, Tokioka T. Local and remote responses to excessive snow cover mass over Eurasia appearing in the northern spring and summer climate-A study with the MRI. GCM. J. Meteor. Soc. Japan,1991,69 (4):473-487.
153 Yeh T C, Wetherald R and Manabe S. Model study of the short-term climatic and hydrologic effects of sudden smow cover removal. Mon. Wea. Rev.,1983,111 (5):1013-1024.
154 Yin X W. The albedo of vegetated land surfaces:systems analysis and mathematical modeling. Theoretical and applied climatology,1998,60,121-140.
155 Zeng X, zhao M, Dickinson R E. Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J. Climate. 1998,11:2628-2644.
156 Zhang H, Henderson-Sellers A, McGuffie K. Impacts of tropical deforestation, Part I: The role of large-scale dynamics. J Climate,1996,9 (10):2498-2521.
157鲍艳,吕吐华,奥银焕,等.反照率参数化改进对裸土地表能量和热过程模拟的影响.太阳能学报,2007,28(7)：775-782.
158蔡福,于慧波,矫玲玲,等.降水要素空间插值精度的比较-以东北地区为例[J].资源科学,2006,28(6)：73-79.
159蔡福,张淑杰,于贵瑞,等.基于空间化技术对中国近50年平均气温时空演变特征的研究[J].高原气象,2006,25(6)：1168-1175.
160蔡福,张玉书,陈鹏狮,等.近50年辽宁热量资源时空演变特征分析[J].自然资源学报,2009,24(9)：1635-1646.
161蔡福,周广胜,李荣平,等.东北玉米农田下垫而参数动态特征[J].生态学杂志,2011b,30(3)：494-501.
162蔡福,周广胜,李荣平,等.陆而过程模型对下垫而参数动态变化的敏感性分析[J].地球科学进展,2011c,26(3)：300-310.
163陈斌,徐祥德,丁裕国,等.地表粗糙度非均匀性对模式湍流通量计算的影响[J].高原气象,2010,29(2)：340-348.
164陈海山,倪东鸿,李忠贤,等.植被覆盖异常变化影响陆面状况的数值模拟.南京气象学院学报,2006,29(6)：725-734.
165陈家宜,王介民,光田宁.一种确定地表粗糙度的独立方法.大气科学,1993,17(1)：21-26.
166陈建绥.中国地表反射率的分布及变化[J].地理学报,1964,30(2)：85-93.
167陈泮勤,程邦波,王芳,等.全球气候变化的几个关键问题辨析[J].地球科学进展,2010,25(1)：69-95.
168陈潜,赵鸣,汤剑平等.陆面过程模式BATS中的地气通量计算方案的一个改进试验[J].南京大学学报(自然科学),2004,40(3)：330-340.
169陈厦,桑卫国.暖温带地区3种森林群落叶面积指数和林冠开阔度的季节动态[J].植物生态学报,2007,31(3)：431-436.
170陈渝蓉,高国栋.我国辐射平衡各分量的计算方法及时空分布特征(1)[J].南京大学学报(自然科学版),1976,(2)：89-110.
171陈云浩,李晓兵,谢峰.我国西北地区地表反照率的遥感研究[J].地理科学,2001,21(4)327-332.
172董玉祥.国际风沙研究的新进展—记第六届风沙研究国际会议[J].中国沙漠,2007,27(2)：347-348.
173董治宝,Donald WF,高尚玉.直立植物防沙措施粗糙特征的模拟实验.中国沙漠,2000,20(3)：260-263.
174董治宝,陈广庭.生物防沙物理学研究进展.中国沙漠,1996,16(3)：44-48.
175范广洲,吕世华,罗四维.西北地区绿化对该区及东亚、南亚区域气候影响的数值模拟[J].高原气象,1998,17(3)：300-309.
176房云龙,孙菽芬,李倩,等.干旱区陆面过程模型参数优化和地气相互作用特征的模拟研究[J].大气科学,2010,34(2)：290-306.
177符淙斌,魏和林,郑维忠,等.中尺度模式对中国大陆地表覆盖类型的敏感性试验[C].全球变化与我国未来的生存环境.北京：气象出版社：1996,286.
178傅伯杰,牛栋,于贵瑞.生态系统观测研究网络在地球系统科学中的作用[J].地理科学进展,2007,26(1)：1-16.
179高学杰,张冬峰,陈仲新,等.中国当代土地利用对区域气候影响的数值模拟[J].中国科学D辑：地球科学,2007,37(3)：397-404.
180高志球,工介民,马耀明,等.不同下垫面的粗糙度和中性曳力系数研究[J].高原气象,2000,19(1)：17-24.
181关德新,金明淑,徐浩.长白山阔叶红松林生长季反射率特征[J].应用生态学报,2002,13(12)：1544-1546.
182郭建侠,卞林根,戴永久.在华北玉米生育期观测的16m高度C02浓度及通量特征[J].大气科学,2007,31(4)：695-708.
183郭建侠.华北玉米下垫面湍流输送特征及参数化方案比较研究[M].博士学位论文.2006
184何奇瑾,周广胜,周莉,等.盘锦芦苇湿地空气动力学参数动态特征及其影响因素分析.气象与环境学报,2007,23(4)：7-12.
185何清,缪启龙,张瑞军,等.塔克拉玛干沙漠肖塘地区空气动力学粗粗糙度分析.中国沙漠,2008,28(6)：1011-1016.
186何勇,姜允迪,丹利,等.中国气候、陆地生态系统碳循环研究[M].北京：气象出版社,2006,1pp.
187何玉斐,张宏升,刘明星,等.戈壁下垫面空气动力学参数确定的在研究[J].北京大学学报,2009,45(3)439-443.
188何玉斐,张宏升,刘明星,等.戈壁下垫面空气动力学参数确定的在研究[J].北京大学学报,2009,45(3)439-443.
189胡隐樵,高由禧.黑河实验(HEIFE)——对干旱地区陆面过程的一些新认识[J].气象学报, 1994,52(3)：285-296.
190胡隐樵,奇跃进,杨选利.河西戈壁小气候和热量平衡特征的初步分析.高原气象,1990.9：113-119.
191黄安宁,张耀存BATSle陆面模式对p-σ九层区域气候模式性能的影响[J].大气科学,2007,31(1)：155-166.
192季国良,马晓燕,邹基玲,等.黑河地区绿洲和沙漠地面辐射收支的若干特征[J].干旱气象,2003,21(3)：29-33.
193季劲钧,余莉.地表面物理过程与生物化学过程耦合反馈机理的模拟研究.大气科学,1999,23(4)：439-448.
194季劲钧.陆地表面物理和生物学过程年度变程的模拟,全球变化与我国未来的生存环境,符淙斌、严中伟编.北京：气象出版社,1996,248-258.
195李崇银.气候动力学[M].北京：气象出版社,2002,311-337.
196李根柱,王贺新,朱教君.辽东山区长白落叶松叶面积指数和林冠开阔度的月动态[J].东北林业大学学报,2009,37(7)：20-22.
197李国平,段廷扬,巩远发.青藏高原近地层通量特征的合成分析[J].气象学报,2002,60(4)453-460.
198李国平,段廷扬,吴贵芬.青藏高原西部的地面热源强度及地面热量平衡[J].地理科学,2003,23(1)：13-18.
199李锁锁,吕世华,柳媛普.黄河上游玛曲地区空气动力学参数的确定及其在陆面过程模式中的应用[J].高原气象,2010,29(6)：1408-1413.
200李伟平,吴国雄,刘辉.地表反照率的改变影响夏季北非副热带高压的数值模拟.气象学报,2000,58(1)：26-39.
201李祎君,许振柱,工云龙,等.玉米农田水热通量动态与能量闭合分析[J].植物生态学报,2007,31(6)：1132-1144.
202林朝晖,杨小松,郭裕福HUBEX试验期间淮河流域陆面过程特征的初步分析[J].自然科学进展,2001,11(6)：588-594.
203林而达,许吟隆,蒋金荷,等.气候变化国家评估报告Ⅱ-气候变化的影响与适应.气候变化研究进展,2006,2(2)：51-56.
204林忠辉,项月琴,莫兴国,等.夏玉米叶面积指数增长模型的研究.中国生态农业学报,2004,11(4)：69-72.
205刘和平,刘树华.森林冠层空气动力学参数的确定[J].北京大学学报：自然科学版,1997,33(4)：522-528.
206刘辉志,涂钢,董文杰.半干旱区不同下垫面地表反照率变化特征[J].科学通报,2008,53(10)：1220-1227.
207刘树华,蒋浩宇,胡非,等.区域大气模式中陆面子模式起转过程的研究[J].气象学报,2008,66(3)：351-358.
208刘树华,李新荣,刘立超,等.陆面过程参数化模式的研究[J].中国沙漠,2001,21(3)：303-311.
209刘树华,邓毅,胡非,等.森林下垫面陆面物理过程及局地气候效应的数值模拟试验[J].气象学报,2005,63(1)：1-12.
210刘树华,李新荣,刘立超,等.陆面过程参数化模式的研究.中国沙漠,2001,21(3)：303-311.
211刘树华,文平辉,张云雁,等.陆面过程和大气边界层相互作用敏感性实验[J].气象学报,2001,59(5)：533-548.
212刘小平,董治宝.砾石床面的空气动力学粗糙度[J].中国沙漠,2003,23(1)：37-44.
213吕达仁,陈左忠,王庚辰,等.内蒙古半干旱草原土壤-植被-大气相互作用科学问题与实验计划概述[J].气候与环境研究,1997,2(3)：100-209.
214吕萍,董治宝.戈壁风蚀面与植被扭盖地表性质粗糙度长度的确定[J].中国沙漠,2004,24(3)：279-285.
215吕世华,陈玉春.西北植被覆盖对我国区域气候变化影响的数值模拟[J].高原气象,1999,8(3)：416-424.
216茅宇豪,刘树华.不同下垫面空气动力学参数的研究[J].气象学报,2006,64(3)：325-334.
217米娜,陈鹏狮,张玉书,等.几种蒸散模型在玉米农田蒸散量计算中的应用比较[J].资源科学,2009,31(9)：1599-1606.
218幕青松,王建成,苗天德.粗糙度动力学特征的初步研究.力学学报,2003,35(2)：129-134.
219倪允琪.气候动力学[M].北京：气象出版社,1993,335-380.
220牛国跃,洪钟祥,孙菽芬.陆而过程研究的现状与发展趋势[J].地球科学进,1997,12(1)：20-25.
221平晓燕,周广胜,孙敬松等.基于功能平衡假说的玉米光合产物分配动态模拟[J].应用生态学报,2010,21(1)：129-135.
222乔娟,张强,张杰.非均匀下垫而陆面过程参数化问题研究进展[J].干旱气象,2008,26(1)：73-78.
223邱玉珺,吴风巨,刘志.梯度法计算空气动力学粗糙度存在的问题[J].大气科学学报,2010,33(6)：697-702.
224施伟来,王汉杰.中国西部退耕还林(草)与沙漠绿化的区域性气候效应[C].西部开发与生态建设.北京：中国林业出版社：2001,592.
225石雪峰,夏建新,吉祖稳.空气动力学粗糙度与植被特征关系的研究进展.中央民族大学学报,2006,15(3)：218-225.
226孙鸿烈.中国生态系统研究网络为生态系统评估提供科技支撑[J].资源科学,2006,28(4)：2-3.
227孙菽芬,金继明.陆面过程模式研究中的几个问题[A].陶诗言,陈联寿,徐祥德,等.第二次青藏高原大气科学实验理论研究进展(二)[M].北京：气象出版社,2000,76-84.
228孙菽芬.陆面过程的物理、生化机制和参数化模型[M].北京：气象出版社,2005,307pp.
229孙菽芬.陆面过程研究的进展[J].新疆气象,2002,(6)：1-6.
230孙治安,翁笃鸣.我国地表反照率的气侯计算及其时空分布特征[J].南京气象学院学报,1987,10(2)：189-200.
231覃文汉.应用压力中心法确定农田空气动力参数.气象学报,1994,52(1)：99-106.
232王介民,邱华盛.中日合作亚洲季风实验—青藏高原实验(GAME - Tibet) [J].中国科学院院刊,2000,5：386-388.
233王介民.陆面过程实验和地气相互作用研究—从HEIFE到IMGRASS和GAME - Tibet/ TIPEX[J].高原气象,1999,18(3)：280-294.
234王玲,谢德体,刘海隆,等.玉米叶面积指数的普适增长模型.西南农业大学学报,2004,26(3)：303-311.
235王涛. 干旱区主要陆表过程与人类活动和气候变化研究进展[J].中国沙漠,2007,27(5)：711-718.
236王宇,周广胜.雨养玉米农田生态系统的蒸散特征及其作物系数.应用生态学报,2010,21(3)：647-653.
237魏和林,符淙斌.下垫面非均匀性的模拟[J].气候与环境研究,1997,2(2)：106-114.
238夏建新,石雪峰,吉祖稳,等.植被条件对下垫面空气动力学粗糙度影响实验研究[J].应用基础与工程科学学报,2007,15(1)：23-31.
239熊伟,居辉,许吟隆,等.气候变化对中国农业温度阈值影响研究及其不确定性分析[J].地球科学进展,2006,21(1)：70-76.
240徐兴奎,李素红.中国地表月平均反照率的遥感反演[J].气象学报,2002,60(2)：215-300.
241薛根元,周锁铨,孙照渤,等.陆面过程研究的新进展[J].科学通报,2005,21(4)：378-386.
242阳伏林,周广胜,张峰,等.内蒙古温带荒漠草原地表反照率特征及数值模拟[J].应用生态学报,2009,20(12)：2847-2852.
243杨兴国,牛生杰,郑有飞.陆而过程观测试验研究进展[J].干旱气象,2003,21(3)：83-89.
244于贵瑞,方华军,伏玉玲,工秋凤.区域尺度陆地生态系统碳收支及其循环过程研究进展.生态学报,2011,31(19)：5449-5459.
245于贵瑞,张雷明,孙晓敏,等.业洲区域陆地生态系统碳通量观测研究进展[J].中国科学(D辑),2004,34(增刊Ⅱ)：15-29.
246曾新民,赵鸣,苏炳凯.“结合法”表示的下垫面温湿非均匀对夏季风气候影响的数值试验[J].大气科学,2002,26(1)：41-55.
247张存来,邹学勇,董光荣,等.耕作土壤表面的空气动力学粗糙度及其对十壤风蚀的影响[J].中国沙漠,2002,22(5)：473-475.
248张果,周广胜,阳伏林.内蒙古荒漠草原地表反照率变化特征[J].生态学报,2010,30(24)：6943-6951.
249张果,周广胜,阳伏林.内蒙古温带荒漠草原生态系统水热通量动态.应用生态学报,2010,21(3)：597-603.
250张宏升,陈家宜.非单一水平均匀下垫面空气动力学参数的确定.应用气象学报,1997,8(3)：310-315.
251张厚瑄,林而达.中国农业响应全球气候变化的策略问题[J].农业环境保护,1997,16(1)：35-39.
252张华,李峰瑞,伏乾科.沙质草地植被防风抗蚀生态效应的野外观测研究[J].环境科学,2004,25(2)：119-124.
253张井勇,董文杰,符淙斌.中国北方和蒙古南部植被退化对区域气候的影响.科学通报,2005,50(1)：53—58.
254张克存,屈建军,董治宝,等.风沙流中风速脉动对输沙量的影响[J].中国沙漠,2006,26(3)：336-340.
255张强,卫国安.荒漠戈壁大气总体曳力系数和输送系数观测研究[J].高原气象,2004,23(3)305-312.
256张强,胡隐樵.大气边界层气象学的研究进展和面临的主要科学问题.地球科学进展,2001,16(4)：526-532.
257张强,王胜,卫国安.西北地区戈壁局地陆面物理参数的研究.地球物理学报,2003,46(5)：616-623.
258张强.大气边界层气象学研究综述.干旱气象,2003,21(3)：74-78.
259张琼,钱永甫.用NCEPPNCAR再分析辐射资料估算月平均地表反照率[J].地理学报,1999,54(4)：309-317.
260张述文,邱崇践,张卫东.用变分方法估算淮河流域总体输送系数[J].高原气象,2004,23(4)：506-511.
261张雅静,申向东.植被覆盖地表空气动力学粗糙度与零平面位移高度的模拟分析[J].中国沙漠,2008,28(1)：21-26.
262赵晓松,关德新,吴家兵,等.长白山阔叶红松林的零平面位移和粗糙度[J].生态学杂志,2004,23(5)：84-88.
263郑益群,钱永甫,苗曼倩.植被变化对中国区域气候的影响E：初步模拟结果[J].气象学报,2002,60(1)：1-16.
264钟中,韩士杰.长白山阔叶红松林冠层空气动力学参数的计算[J].南京大学(自然科学版)2002,38(4)：565-571.
265周明焊,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[M].北京：气象出版社.：.2000.
266周艳莲,孙晓敏,朱治林,等.几种不同下垫面地表粗糙度动态变化及其对通量机理模型模拟的影响.中国科学(D辑),2006,36(增刊)：244-254.
267周艳莲,孙晓敏,朱治林,等.几种典型地表粗糙度计算方法的比较研究[J].地理研究,2007,26(5)：888-897.
268左洪超,胡隐樵.黑河试验区沙漠和戈壁的总体输送系数.高原气象,1992,11：371-380.
269左洪超,吕世华,胡隐樵.中国近50年气温及降水量的变化趋势分析.高原气象,2004,23(2)：238-244.
2 Andre J C, Goutorbe J P, Perrier A, et al. Evaporation over land surfaces:first results from HAPEX-MOBILHY special observing period. Ann. Geophys.,1988,6:477-492.
3 Andre J C, Goutorbe J P, Perrier A, et al. HAPEX-MOBILHY:A hydrological atmospheric experiments for the study of water budget and evaporation flux at the climatic scale. Bull. Amer. Meteor. Soc.,1986,67(2):138-144.
4 Avissar R. Concep tual aspects of a statistical-dynamical app roach to rep resent landscape subgrid heterogeneities in atmospheric models. J Geophys Res,1992,97:2729-2742.
5 Baldocchi D, Falge E, Gu L, et al. Fluxnet:a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 2001,82:2415-2434.
6 Baldocci D, Kelliher F M, Black T A, et al. Climate and vegetation controls on boreal zone energy exchange. Glob Change Biol,6(suppll):2000,69-83.
7 Bartelink, H. H., Radiation interception by forest trees:a simulation study on effects of stand density and foliage clustering on absorption and transmission. Ecol. Model. 1998,105,213-225.
8 Betts A K, Ball J H. Albedo over the boreal forest. J Geophys Res, 1997,102(24):28901-28910.
9 Blondin C and H Boetter. The surface and sub-surface parameterization scheme in the ECMWF forecasting systerm. Rivsion and operational assessment of weather elements, ECWMF Tech. Memo.,1987,135, p48.
10 Bonan G B. A Land Surface Model(LSM) for ecological, hydrological and atmospheric study:technical description and user's guide. NCAR/TN-417+-STR, NCAR Technical note, Climate and global dynamics division, national center for atmospheric research, boulder, Colorado.1996.
11 Bonan G B. Comparison of two land surface process models using prescribed forcing. J. Geophys. Res.,1994,99:25803-25818.
12 Bonan G B. Land-atmosphere C02 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. J. Geophys.Res.,1995,100:2817-2831.
13 Bonan GB. A Land Surface Model (LSM Version 1.0) for Eco-logical, Hydrological and Atmospheric Studies:Technical Description and User's Guide[R]. NCAR Technical Note NCAR/TN-417 + STR, Boulder, Colorado.1996.
14 Cess R D, Potter G G, Zhang M-H and other 30 Co-authers. Interpretation of snow-climate feedback as produced by 17 general circulation models. Science,1991,253(5022):888-892.
15 Charney J G, Quirk W J, Chow S H, et al. A Comparative study of the effects of albedo change on drought in semi-arid regions. J Atmos Sci,1977,34:1366-1385.
16 Charney J G. Dynamics of desert s and drought in t he Sahel. Quarterly Journal of t he Royal Meteorological Society,1975,101:193-202.
17 Charney J G. Dynamics of deserts and drought in the Sahel. Quart. J. Roy. Meteor. Soc., 1975,101(428):193-292.
18 Chen X H. Relationship between surface albedo and some meteorological factors. Journal of Chengdu Institute of Meteorology,1999,14 (3):233-238.
19 Collatz G J, Ball J T, Grivet C, et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration:A model that includes the laminar boundary layer. Agri. Forest Meteor.,1991,54:107-136.
20 Collatz G J, Ribas-Carbo M, Berry J A. Coupled photosynthesis-stomatal conductance model for leaves of C4 plant. Aust. J. Plant Physiol.1992,19:519-538.
21 Colman R A. Snow and cloud feedbacks modeled by an atmospheric general circulation model. Climate Dynamics,1994,9(4):253-265.
22 Counihan J. Wind tunnel determination of the roughness fetch as a function of the fetch and the roughness densities of three dimensional roughness elements. Atmospheric environment,1971,5:637-642.
23 Cuf AD, Fisch G, Hodnett MG. The albedo of Amazonian forest and rangeland. Journal of climate,1995,8:1544-1554.
24 Dai YJ, Zeng QC. A land surface model(IAP94)for climate studies. Part I:Formulation and validation in offline experiments. Advances in Atmospheric Sciences, 1997,14:443-460.
25 Dai Yongjiu,Zeng Xubin, Dickinson R E, et al. The common land model (Cl.M). Bull. Amer. Meteor. Soe,2003,84 (8):1013-1023.
26 Dai Yongjiu, Zeng Xubin, Dickinson R E. Common Land Model (Technical Documentation and Userps Guide)[R]. The Georgia Institute of Technology,2001,69pp
27 Dai Yongjiu. The Common Land Model(CoLM) UsersGuide [R]. School of geography Beijing Normal University,2005,23pp.
28 De bruin HAR, Verhoef A. A new method to determine the zero-plane displacement. Boundary-layer Meteorol.,1997,82:159-164.
29 Deardoff J W. Efficient prediction of ground surface tempera-ture and moisture, wit h inclusion of a layer of vegetation. Journal of Geophysical Research, 1978,83(4):1889-1904.
30 Deardorff,J. W. Three-dimensional numerical study of the hutent Kinetic energy budget in unstable planetary boundary layer. J. Atmos. Sci.1974,31,465-474
31 Dickinson R E, Henderson-sellers A, Kennedy P J. Biosphere Atmosphere Transfer Scheme (BATS) Version le as Coupled to the NCAR Community Climate Model[R]. NCAR Technical Note,NCAR/ TN2387+STR,1993,72.
32 Dickinson R E, Kennedy P. Impacts on regional climate of Amazon deforestation. Geophys ResLett,1992,19 (19):1947-1950.
33 Dickinson R E. Land surface processes and climate-surface albedos and energy balance. Adv Geophys,1983,25:305-353.
34 Dickinson R. E. Land surface process and climate-surface albedos and energy balance. Advance in Geophysics,1983,25,305-353, Academic Press.
35 Dickinson RE, Sellers HA, Kennedy PJ. Biosphere Atmosphere Transfer Scheme (BATS) Version le as Coupled to the NCAR Community Climate Model[R]. NCAR Technical Note, NCARPTN-387 + STR.1993.
36 Dirmeyer P A, Shuk la J. Albedo as a modulator of climate response to tropical defo-restation. J Geophys Res,1994,99 (D10):20863-20877.
37 Dong B, Valdes P. J.. Modelling the Asian summer monsoon rainfall and Eurasian Winter/spring snow mass. Quart. J. R. Meteor. Soc.,1998,24(552):2567-2598.
38 Dong ZB, Liu XP, Wang XM. Aerodynamic roughness length of gravel beds. Geomorphology,2002,43 (1-2):17-31.
39 Dong zhibao,Gao Shangyu, Fryrear D W. Drag coefficients and Roughness length as disturbed by artificial standing vegetation. Journal of Arid Environments,2001,49(3):485-5051.
40 Douglas B C, Xue Y K, Richard J H, et al. Modeling the impact of land surface degradation on the climate of tropical North Africa. J Climate,2001,14(8):1809-1822.
41 Douville H, Royer J F and Mahfouf J F. Sensitivity of the Asian summer monsoon to an anomalous Eurasian snow cover within the Meteo-France GCM. Climate Dynamics, 1996,12(7):449-466.
42 Ducoudre N I, Laval K, Perrier A. SECH IBA a new set of parameterizations of the hydrologic exchanges at the land-atmosphere interface with in the LMD atmospheric general circulation model. J. Climate.,1993,6:248-273
43 Erbs, D.G., Klein, S.A., Duffie, J. A. Estimation of the diffuse radiation fraction for hourly, daily and monthly average global radiation. Solar energy,1982,28,293-302.
44 Famiglietti J S, Wood E F. Multi-scale modeling of spatially-variable water and energy balance p rocesses. Water Resour Res,1994,30:3061-3078.
45 Farquhar G D, Sharkey T D. Stomatal conductance and photosynthesis. Ann. Rev. Plant. Physiol.,1982,33:317-345.
46 Farquhar G D, Von Caemmerer S. Modeling of photosynthesis response to environment conditions. In:Lange O L, Nobel P S, Osmond C B, et al. eds. Physiological Plant Ecology π,550-588. Encyclopedia of Plant Physiology new series. Vol.12B. Springer, Berlin.1982.
47 Farquhar G D, Wong S C. An empirical model of stomatal conductance. Australian J. Plant Physiology,1984,11:191-209.
48 Flerchinger, G. H. and K E Saxton. Simultaneous heat and water model of a freezing snow-residue-soil system I. Theory and development. Trans of ASAE,1989,32(2):565-571.
49 Foley I A, Prentice I C, Ramankutty N, et. al. An integrated biospher model of land surface p rocesses, terrestrial carbon balance, and vegetation dynamics. Global Biogeochemical Cycles,1996,10(4):603-628.
50 Gao Z, Wang J, Ma Y, et al. Study of roughness lengths and drag coefficients over Nansha Sea Region, Gobi, Desert, Oasis and Tibetan Plateau. Physics and Chemistry of the Earth(B),2000,25:141-145.
51 Garratt J R,The atmospheric boundary layer, Cambridge University press,1992,pp:316.
52 Giorgi F, Marinuci M R. Validation of a regional atmospheric model over Europe sensitivity of wintertime and summertime simulations to selected physics parameterizations and lower boundary conditions. Quart J.Roy. Meteor.Soc.,1991,117 1171-1206。
53 Giorgi F, Marinuci M R. Validation of a regional atmospheric model over Europe sensitivity of wintertime and summertime simulations to selected physics parameterizations and lower boundary conditions. Quart J.Roy. Meteor.Soc. 1991,117:1171-1206.
54 Giorgi F, Marinucci M R, Bates G T. Development of a second-generation regional climate model (RegCM2). Part I:Boundary-layer and radiation t ransfer process. Mon. Wea. Rev. 1993,121:2794-2813。
55 Giorgi F, Marinucci M R, Bates G T. Development of a second-generation regional climate model (RegCM2). Part I:Boundary-layer and radiation t ransfer process. Mon. Wea. Rev. 1993,121:2794-2813.
56 Giorgi F, Marinucci M R, Canio G D, et al. Development of a second-generation regional climate model (RegCM2). Part II:Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev,1993,121:2814-2832.
57 Giorgi F, Marinucci M R, Canio G D, et al. Development of a second-generation regional climate model (RegCM2). Part II:Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev,1993,121:2814-2832.
58 Gordon W S. Climate change, hydrology and ecological models:intercomparison and validation. Dissertation, Doctor of Philosophy, The University of Texas at Austin.2003.
59 Gordon W S. Climate change, hydrology and ecological models:intercomparison and validation. Dissertation, Doctor of Philosophy, The University of Texas at Austin.2003.
60 Haeltine A, Prentice I C, Creswell I D. A coupled carbon and water flux model to predict vegetation structure. J. of Vegetation Science,1996,7:651-666.
61 Hao Y B, Wang Y F, Huang X Z, et al. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agricultural and Forest Meteorology,2007,146(12):57-69.
62 Hao Y B, Wang Y F, Huang X Z, et al. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agricultural and Forest Meteorology,2007,146(12):57-69.
63 Hao Y B, Wang Y F, Mei X R, et al. C02, H20 and energy exchange of an Inner Mongolia steppe ecosystem during a dry and wet year. Acta ecologica,2008,33(2):133-143.
64 Hao YB, Wang YF, Huang XZ, et al. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agricultural and Forest Meteorology,2007,146(12):57-69.
65 Henderson-Sellers A, Dickinson R E, Durbidge T B, et al. Tropical deforestation: modeling local2to regional scale climate change. J Geophys Res,1993,98 (D4):7289-7315.
66 Henderson-Sellers A. The project for intercomparison of land-surface parameterization schemes.Bull Amer Meteor Soc,1993,74(7):1335-1348.
67 Houghton, J. T.,and others[EDS]. Radiative forcing of climate change and an evaluation of the IPCC IS92 emission scenarios. Cambridge Univ. Press.1995.
68 Idso S B, Jackson R D, Reginato R J, Nakayama F S. The dependence of bare soil albedo on soil water content. Journal of Applied Meteorology,1975,14(1),109-113.
69 Irannejad P,Shao Y. Description and validation of t he atmosphere land surface interaction scheme (ALSIS) with HAPEX and Cabauw data. Global Planet Change,1998,19: 87-114.
70 Jacobs AFG and van Pul WAJ. Seasonal changes in the albedo of a maize crop during two seasons. Agricultural and Forest Meteorology,1990,49:351-360.
71 Jacobs AFG, van Pul WAJ. Seasonal changes in the albedo of a maize crop during two seasons. Agric For Meteorol,1990,49:351-360.
72 Jarvis P G, James G B, Landsberg J J. Coniferous fores[A]. in:Monteith J L, ed. Vegetation and the atmosphere. Vol.2[C]. London:academic press,1976,171-240.
73 Ji jinjun. A climate-vegetation interaction model:simulating physical and biological process at the surface. J. Biogeography,1995,22:445-451.
74 Ji JJ and Hu YC. A simple land surface process model for use in climate studied. Acta Meteorologica Sinica,1989,3(3):344-353.
75 Keith W. Oleson, Yongjiu Dai, Gordon Bonan, et al. Technical Description of the Community Land Model (CLM)[R]. NCAR Technical Note,NCAR/ TN461 + STR,2004,23.
76 Kim J, E k M. A simulation of the surface energy budget and soil water content over the hydro logic atmospheric pilot experiments-modification du Bilan Hydrique forest site. J. Geophys. Res.,1995,10:20845-20854.
77 Kimura R, Otsuki K, Kamichika M. Relationships between the zero-plane displacement and the roughness length over sorghum and alfalfa canopies. J. Agric. Meteorol. 1999,55:15-24.
78 Klink,K. Evaluating climate-vegetation interactions at climate model sub-grid scales. VolXLV, No.1, University of Delaware, Center for Climatic Research, Newark, DE.1992.
79 Kowalczyk E A, Garratt J R, Krummel P B A. Soil-canopy scheme for use in numerical model of the atmosphere-ID stand-alone model. In:CS IRO, DAR, Technical Paper.1991,32,59.
80 Kurbatkin GP, Manabe S, Hahn D G. The moisture content of the continents and the intensi ty of summer monsoon circulation. Soviet Meteor And Hydrology,1979, (11):16.
81 Laval K, Picon L. Effect of a change of the surface albedo of the Sahelon climate. J Atmos Sci,1986,43:2418-2429.
82 Laval K. General circulation model experiments with surface albedo changes. Climatic Change,1986,9:91-102.
83 Lean J, Rowntree P R. A GCM simulation of the impact of Amazonian deforestation on climate using an improved canopy representation. Q J R Meteorol Soc,1993,119 (512):509-530.
84 Lettau H. Note on aerodynamic roughness parameter estimation on the basis of roughness element description. Journal of Applied Areorology,1969,8:828-832.
85 Li Bin, Avissar R. The impact of spatial variability of land surfacecharacteristics on land-surface heat fluxes. J Climate,1994,7:527-537.
86 Li S G., Eugster W, Asanuma J, Kontani A, Davaa G, Oyunbaatar D, Sugita M. Energy partitioning and its biophysical controls above a grazing steppe in central Mongolia. Agricultural and Forest Meteorology,2006,37(1-2):89-106.
87 Li SG, Eugster W, Asanuma J, et al. Energy partitioning and its biophysical controls above a grazing steppe in central Mongolia. Agricultural and Forest Meteorology, 2006,37 (1-2):89-106.
88 Li Y N, Zhao L, Zhou H K, Xu S X, Zhang F W. Changes in Reflected Radiation and Reflectivity for Growing Season of Alpine Swamp in the Northern Qinghai. Journal Of Glaciology And Geogryology,2007,29(1):137-143.
89 Li Y, Hu Z Y.2009. A Study on Parameterization of Surface Albedo over Grassland Surface in the Northern Tibetan Plateau. Advances in atmospheric sciences,26(1):161-168.
90 Liu H Z, WANG B M, FU C B. Relationships between surface albedo, soil thermal parameters and soil moisture in the semi-arid area of Tongyu, northeastern China. Advance in Atmospheric Sciences,2008,25(5):757-764.
91 Lofgren B M. Sensitivity of land2ocean circulations, precipitation and soil moisture to perturbed land surface albedo.J Climate,1995,8:2521-2542.
92 Lu Daren. Inner Mongolia semi-arid grassland soil-vegetation-atmosphere interaction (IMGRASS). Global Change News Letter.1997,31:4-5.
93 M S Roxy, V B Sumithranand, G Renuka. Variability of soil moisture and its relationship with surface albedo and soil thermal diffusivity at astronomical observatory, thiruvananthapuram, south kerala. J. Earth Syst. Sci.2010,119, No.4, august pp.507-517.
94 Manzi A 0. Introduction d'un schema des transferts sol-vegetation-atmosphere dans un modele de circulation generale et application a la simulation de la deforestation Amazonienne.1993.
95 Martano P. Estimation of surface roughness length and displacement height from single-level sonic anemometer data. Journal of Applied Meteorology,2000,39:708-715.
96 Mason P J. The formation of a really averaged roughness lengths. Quarterly Journal of the royal meteorological society,1988,114:399-420.
97 Molion L C B, Moore C J. Estimation the zero-plane displacement for tall vegetation using a mass conservation method. Boundary Layer Meteorology,1983,26(7):115-125.
98 Monteith. J. L., Unsworth, M. H. Principles of Environmental physics,2nd edn., Edward A rnold, London.1990.
99 Monteith J L, Unsworth M H. Principles of environmental physics[M]. London:Edward Arnold. Garratt J R,The atmospheric boundary layer, Cambridge University press, 1992,pp:316.
100 Monteith J L, Unsworth M H. Principles of environmental physics[M]. London:Edward Arnold. 1990.
101 Monteith J L. Principles of environmental physics. London:Edward Arnold.1973.
102 Neilson R P. A model for predicting continental-scale vegetation distribution and water balance. Ecological Application,1995,5:362-385.
103 Nobre C A, Sellers P J, Shukla J. Amazonian deforestation and regional climate change. J Climate,1991,4 (10):957-988.
104 Noilhan J, Planon S. A simple parameterization of land2surface processes for meterological models. Mon. W e Rev.,1989,117:536-550.
105 Otterman J, Chou M D, Arking A. Effects of nontropical forest cover on climate. J Clim Appl Meteorol,1984,23:762-767.
106 Philip G.Oguntunde, Nick van de Giesen. Crop growth and development effects on surface albedo for maize and cowpea fields in Ghana, West Africa. Int J Biometeorol, 2004,49:106-112.
107 Pitman A J, Yang Z-L, Cogley J G, Henderson-Sellers. Description of bare essentials of surface transfer for the bureau of meteorology Research Centre A GCM. In:BMRC research report,1991,32. Melbourne, Australia.
108 Raupach M R, Thom A S, Edwards I. A wind tunnel study of turbulent flow close to regularly arrayed roughness surface. Boundary Layer Ateorology,1980,18:373-393.
109 Reijmer C H, Van Meijgaard E, Van den Broeke M R. Numerical studies with a regional atmospheric climate model based on changes in the roughness length for momentum and heat over Antarctica. Bound layer meteor.,2004,111(2):313-337.
110 Rind D. The influence of ground moisture conditions in North America on summer climate as modeled in the GISS GCM. Mon. Wea. Rev.,1982,110:1487-1494.
111 Ross, J. Radiative transfer in plant communities.In:Monteith,J. L. (Ed.), Vegetation and the Atmosphere. Vol.1, Principles, Academic Press, London,1975, pp.13-55.
112 Saito T, Nagai Y, Isobe S, et al. An investigation of turbulence within a crop canopy. J. Agric. Meteorol.,1970,25:205-214.
113 Saxton, K. E. Sensitivity analysis of the combination evapotranspiration equation, Agric. Meteorol.1975,15(3):343-353.
114 Sellers P J, Mintz Y, Sud Y C, et al. A simple biosphere model (SiB) for use within general circulation models. J Atmos Sci,1986,43:505-531.
115 Sellers P J, Hall F G, Asrar G, et al. The first ISLSCP field experiment(FIFE). Bull. Amer. Meteor. Soc.,1988,69(1):22-27.
116 Sellers P J, RandallD A, Collatz C J, et al. A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 1:Model formulation. Journal of Climate, 1996,9:676-705.
117 Seth A, Giorgi F, Dickinson R E. Simulating fluxes from heterogeneous land surface:Exp icit subgrid method emp loying the biosphere - atmosphere scheme (BATS). J Geoph Res, 1999,18651-18667.
118 Shao Y. Physics and Modeling of Wind Erosion [M]. London:Kluwer Academic Publishers: 2000,99-100.
119 Shaw RH and Pereira AR. Aerodynamic roughness of a plant canopy:a numerical experiment. Agricultural and Forest Meteorology,1982,26:51-65.
120 Shukla J,Nobre C, Sellers P J. Amazon deforestation and climate change. Science, 1990,247(4948):1322-1325.
121 Shukla J, Mintz Y. Influence of land surface evapotranspiration on the earth's climate. Science,1982,215 (4539):1498-1501.
122 Sivapalan M, Wood R A. Evaluation of the effects of general circulation model subgrid variability and patchiness of rainfall and soil moisture on land surface water balance fluxes[A]. Scale Issues in HydrologicalModeling[M], Kalma J D, Sivapalan M Eds. John Wiley and Sons.1995,453-473.
123 Song J. Diurnal asymmetry in surface albedo. Agric For Meteorol,1998,92:181-189.
124 Stull R B. An introduction to boundary layer meteorology[M]. New York:Kluwer academic publishers,1988:378.
125 Sub Y C, Shukla J, Mintz Y. The influence of land surface roughness on atmospheric circulation and rainfall:a sensitivity experiment with a GCM. NASA Tech. Memo, 1985,86219.
126 Sub Y C, Smith W E. Influence of local and surface processes on the Indian monsoon-A numerical study. J. Climate Appl. Meteor.,1985,24(10):1015-1036.
127 Sub Y C, Smith W E. The influence of surface roughness of deserts on the July circulation-A numerical study. Bound.-Layer Meteor.1985,33(1)15-49.
128 Sud Y C, Fennessy M. A study of the influence of surface albedo on July circulation in semi-arid regions using the GLAS GCM. J Climatol,1982,2:105-125.
129 Tajchman S J. Comments on measuring turbulent exchange within and above forest canopy. Bull Am Meteorol Soc,1981,62:1550-1559.
130 Takagi K, Miyata A, Harazono Y, et al. An alternative approach to determining zero-plane displacement and its application to a lotus paddy field. Agric. For. Meteorol. 2003,115:173-181.
131 Tanner C D. Potential evapotranspiration estimates by the approximate energy balance method of Penman. J. Geophysical Research,1990,65:3391-3413.
132 Taylor P A. On the parameterization of drag over small-scale topography in neutrally-strantified boundary-layer flow. Boundary layer Meterology,1989,48:409-422.
133 Thom A S, Stewart J B, Oliver H R, et al. Comparison of Aerodynamic and Energy budget estimaters of fluxes over a pine forest. Quart J R Meteorol Soc,1975,101:93-105.
134 Uchijima Z. Maize and rice. In:Monteith JL(ed) Vegetation and the atmosphere, vol.2 Principles. Academic press, London,1976,pp 33-64.
135 Verseghy D L. CLA SS:a Canadian land surface scheme for GCM s:I soil model, Int. J. Climatol.,1991,11:111-133.
136 Wand K C, WANG P C, LIU J M, Sparrow M, Haginoya S, Zhou X J. Variation of surface albedo and soil thermal parameters with soil moisture content at a semi-desert site on the western Tibetan Plateau. Bound Layer Meteorology,2005,116:117-129.
137 Wang J, Y Gao, Y Hu, et al. An overview of the HEIFE experiments in the People's of republic:Exchange processes at the land surface for a range of space and time scales. IAHS Pub1.1993,212:397-403.
138 Wang K C, Wang P C, Liu J M, Sparrow M, Haginoya S, Zhou X J. Variation of surface albedo and soil thermal parameters with soil moisture content at a semi-desert site on the western Tibetan Plateau. Bound Layer Meteorology,2005,116:117-129.
139 Wang S S. Dynamics of surface albedo of a boreal forest and its simulation. Ecological Modeling,2005,183:477-494.
140 Wang S, Grant R F, Verseghy D L, et al. Modeling plant carbon and nitrogen dynamics of a boreal aspen forest in CLASS the Canadian Land surface scheme. Ecol Model, 2001,142:135-142.
141 Wang S, Grant R F, Verseghy D L, et al. Modeling carbon dynamics of boreal forest ecosystems using the Canadian Land surface scheme. Clim Change,2002,55:451-477.
142 Wang S, Grant R F, Verseghy D L, et al. Modeling carbon-coupled energy and water dynamics of a boreal aspen forest in a general Circulation Model Land surface scheme. Int J Climatol, 2002,22:1249-1265.
143 Wang Z, Barlage M, Zeng X, Dickinson R E, Schaaf C B. The solar zenith angle dependence of desert albedo. Geophysical Research Letters,2005,32(L05403):1-4.
144 Warrilow D.A., A.B. Sangster, Slingo. A Modelling of land surface processes and their influence on European climate[A]. DCTN 38, Dynamical Climatology Branch, United Kingdom Meteorological Office, Bracknell, Berkshire RG122SZ,UK.1986.
145 Williams J. The influence of snow cover on the atmospheric circulation and its role in climate change:an analysis based on results form the NCAR global circulation model. J. Appl. Meteor.,1975,14(2):137-152.
146 Woodward F I, Smith T M and Emaneul W R. A global land primary productivity and phytogeography model. Global Biogeochemical Cycles,1995,9:471-490.
147 Xue Y, Sellers P J and Kinter J L. A simplified biosphere model for global climate studies. J. Climate,1991,4:345-364.
148 Xue Y, Shukla J. The influence of land-surface properties on Sahel Climate, Part Ⅰ:Desertification. J Climate, 1993,6 (12):2232-2245.
149 Xue Y, Shukla J. The influence of land-surface properties on Sahel Climate, Part Ⅱ: Defo-restation. J Climate,1996,9 (12):3260-3275.
150 Xue Y. The impact of desertification in theMongo lian and the InnerMongo lian Grassland on the regional climate. J Climate,1996,9 (9):2173-2189.
151 Yamazaki T, Kondo J, Watanabe T. Heat balance model with a canopy of one or two layer and its application to field experiment.J. Appl. Meteor.,1992,31:86-103.
152 Yasunari T, Kitoh A, Tokioka T. Local and remote responses to excessive snow cover mass over Eurasia appearing in the northern spring and summer climate-A study with the MRI. GCM. J. Meteor. Soc. Japan,1991,69 (4):473-487.
153 Yeh T C, Wetherald R and Manabe S. Model study of the short-term climatic and hydrologic effects of sudden smow cover removal. Mon. Wea. Rev.,1983,111 (5):1013-1024.
154 Yin X W. The albedo of vegetated land surfaces:systems analysis and mathematical modeling. Theoretical and applied climatology,1998,60,121-140.
155 Zeng X, zhao M, Dickinson R E. Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J. Climate. 1998,11:2628-2644.
156 Zhang H, Henderson-Sellers A, McGuffie K. Impacts of tropical deforestation, Part I: The role of large-scale dynamics. J Climate,1996,9 (10):2498-2521.
157鲍艳,吕吐华,奥银焕,等.反照率参数化改进对裸土地表能量和热过程模拟的影响.太阳能学报,2007,28(7)：775-782.
158蔡福,于慧波,矫玲玲,等.降水要素空间插值精度的比较-以东北地区为例[J].资源科学,2006,28(6)：73-79.
159蔡福,张淑杰,于贵瑞,等.基于空间化技术对中国近50年平均气温时空演变特征的研究[J].高原气象,2006,25(6)：1168-1175.
160蔡福,张玉书,陈鹏狮,等.近50年辽宁热量资源时空演变特征分析[J].自然资源学报,2009,24(9)：1635-1646.
161蔡福,周广胜,李荣平,等.东北玉米农田下垫而参数动态特征[J].生态学杂志,2011b,30(3)：494-501.
162蔡福,周广胜,李荣平,等.陆而过程模型对下垫而参数动态变化的敏感性分析[J].地球科学进展,2011c,26(3)：300-310.
163陈斌,徐祥德,丁裕国,等.地表粗糙度非均匀性对模式湍流通量计算的影响[J].高原气象,2010,29(2)：340-348.
164陈海山,倪东鸿,李忠贤,等.植被覆盖异常变化影响陆面状况的数值模拟.南京气象学院学报,2006,29(6)：725-734.
165陈家宜,王介民,光田宁.一种确定地表粗糙度的独立方法.大气科学,1993,17(1)：21-26.
166陈建绥.中国地表反射率的分布及变化[J].地理学报,1964,30(2)：85-93.
167陈泮勤,程邦波,王芳,等.全球气候变化的几个关键问题辨析[J].地球科学进展,2010,25(1)：69-95.
168陈潜,赵鸣,汤剑平等.陆面过程模式BATS中的地气通量计算方案的一个改进试验[J].南京大学学报(自然科学),2004,40(3)：330-340.
169陈厦,桑卫国.暖温带地区3种森林群落叶面积指数和林冠开阔度的季节动态[J].植物生态学报,2007,31(3)：431-436.
170陈渝蓉,高国栋.我国辐射平衡各分量的计算方法及时空分布特征(1)[J].南京大学学报(自然科学版),1976,(2)：89-110.
171陈云浩,李晓兵,谢峰.我国西北地区地表反照率的遥感研究[J].地理科学,2001,21(4)327-332.
172董玉祥.国际风沙研究的新进展—记第六届风沙研究国际会议[J].中国沙漠,2007,27(2)：347-348.
173董治宝,Donald WF,高尚玉.直立植物防沙措施粗糙特征的模拟实验.中国沙漠,2000,20(3)：260-263.
174董治宝,陈广庭.生物防沙物理学研究进展.中国沙漠,1996,16(3)：44-48.
175范广洲,吕世华,罗四维.西北地区绿化对该区及东亚、南亚区域气候影响的数值模拟[J].高原气象,1998,17(3)：300-309.
176房云龙,孙菽芬,李倩,等.干旱区陆面过程模型参数优化和地气相互作用特征的模拟研究[J].大气科学,2010,34(2)：290-306.
177符淙斌,魏和林,郑维忠,等.中尺度模式对中国大陆地表覆盖类型的敏感性试验[C].全球变化与我国未来的生存环境.北京：气象出版社：1996,286.
178傅伯杰,牛栋,于贵瑞.生态系统观测研究网络在地球系统科学中的作用[J].地理科学进展,2007,26(1)：1-16.
179高学杰,张冬峰,陈仲新,等.中国当代土地利用对区域气候影响的数值模拟[J].中国科学D辑：地球科学,2007,37(3)：397-404.
180高志球,工介民,马耀明,等.不同下垫面的粗糙度和中性曳力系数研究[J].高原气象,2000,19(1)：17-24.
181关德新,金明淑,徐浩.长白山阔叶红松林生长季反射率特征[J].应用生态学报,2002,13(12)：1544-1546.
182郭建侠,卞林根,戴永久.在华北玉米生育期观测的16m高度C02浓度及通量特征[J].大气科学,2007,31(4)：695-708.
183郭建侠.华北玉米下垫面湍流输送特征及参数化方案比较研究[M].博士学位论文.2006
184何奇瑾,周广胜,周莉,等.盘锦芦苇湿地空气动力学参数动态特征及其影响因素分析.气象与环境学报,2007,23(4)：7-12.
185何清,缪启龙,张瑞军,等.塔克拉玛干沙漠肖塘地区空气动力学粗粗糙度分析.中国沙漠,2008,28(6)：1011-1016.
186何勇,姜允迪,丹利,等.中国气候、陆地生态系统碳循环研究[M].北京：气象出版社,2006,1pp.
187何玉斐,张宏升,刘明星,等.戈壁下垫面空气动力学参数确定的在研究[J].北京大学学报,2009,45(3)439-443.
188何玉斐,张宏升,刘明星,等.戈壁下垫面空气动力学参数确定的在研究[J].北京大学学报,2009,45(3)439-443.
189胡隐樵,高由禧.黑河实验(HEIFE)——对干旱地区陆面过程的一些新认识[J].气象学报, 1994,52(3)：285-296.
190胡隐樵,奇跃进,杨选利.河西戈壁小气候和热量平衡特征的初步分析.高原气象,1990.9：113-119.
191黄安宁,张耀存BATSle陆面模式对p-σ九层区域气候模式性能的影响[J].大气科学,2007,31(1)：155-166.
192季国良,马晓燕,邹基玲,等.黑河地区绿洲和沙漠地面辐射收支的若干特征[J].干旱气象,2003,21(3)：29-33.
193季劲钧,余莉.地表面物理过程与生物化学过程耦合反馈机理的模拟研究.大气科学,1999,23(4)：439-448.
194季劲钧.陆地表面物理和生物学过程年度变程的模拟,全球变化与我国未来的生存环境,符淙斌、严中伟编.北京：气象出版社,1996,248-258.
195李崇银.气候动力学[M].北京：气象出版社,2002,311-337.
196李根柱,王贺新,朱教君.辽东山区长白落叶松叶面积指数和林冠开阔度的月动态[J].东北林业大学学报,2009,37(7)：20-22.
197李国平,段廷扬,巩远发.青藏高原近地层通量特征的合成分析[J].气象学报,2002,60(4)453-460.
198李国平,段廷扬,吴贵芬.青藏高原西部的地面热源强度及地面热量平衡[J].地理科学,2003,23(1)：13-18.
199李锁锁,吕世华,柳媛普.黄河上游玛曲地区空气动力学参数的确定及其在陆面过程模式中的应用[J].高原气象,2010,29(6)：1408-1413.
200李伟平,吴国雄,刘辉.地表反照率的改变影响夏季北非副热带高压的数值模拟.气象学报,2000,58(1)：26-39.
201李祎君,许振柱,工云龙,等.玉米农田水热通量动态与能量闭合分析[J].植物生态学报,2007,31(6)：1132-1144.
202林朝晖,杨小松,郭裕福HUBEX试验期间淮河流域陆面过程特征的初步分析[J].自然科学进展,2001,11(6)：588-594.
203林而达,许吟隆,蒋金荷,等.气候变化国家评估报告Ⅱ-气候变化的影响与适应.气候变化研究进展,2006,2(2)：51-56.
204林忠辉,项月琴,莫兴国,等.夏玉米叶面积指数增长模型的研究.中国生态农业学报,2004,11(4)：69-72.
205刘和平,刘树华.森林冠层空气动力学参数的确定[J].北京大学学报：自然科学版,1997,33(4)：522-528.
206刘辉志,涂钢,董文杰.半干旱区不同下垫面地表反照率变化特征[J].科学通报,2008,53(10)：1220-1227.
207刘树华,蒋浩宇,胡非,等.区域大气模式中陆面子模式起转过程的研究[J].气象学报,2008,66(3)：351-358.
208刘树华,李新荣,刘立超,等.陆面过程参数化模式的研究[J].中国沙漠,2001,21(3)：303-311.
209刘树华,邓毅,胡非,等.森林下垫面陆面物理过程及局地气候效应的数值模拟试验[J].气象学报,2005,63(1)：1-12.
210刘树华,李新荣,刘立超,等.陆面过程参数化模式的研究.中国沙漠,2001,21(3)：303-311.
211刘树华,文平辉,张云雁,等.陆面过程和大气边界层相互作用敏感性实验[J].气象学报,2001,59(5)：533-548.
212刘小平,董治宝.砾石床面的空气动力学粗糙度[J].中国沙漠,2003,23(1)：37-44.
213吕达仁,陈左忠,王庚辰,等.内蒙古半干旱草原土壤-植被-大气相互作用科学问题与实验计划概述[J].气候与环境研究,1997,2(3)：100-209.
214吕萍,董治宝.戈壁风蚀面与植被扭盖地表性质粗糙度长度的确定[J].中国沙漠,2004,24(3)：279-285.
215吕世华,陈玉春.西北植被覆盖对我国区域气候变化影响的数值模拟[J].高原气象,1999,8(3)：416-424.
216茅宇豪,刘树华.不同下垫面空气动力学参数的研究[J].气象学报,2006,64(3)：325-334.
217米娜,陈鹏狮,张玉书,等.几种蒸散模型在玉米农田蒸散量计算中的应用比较[J].资源科学,2009,31(9)：1599-1606.
218幕青松,王建成,苗天德.粗糙度动力学特征的初步研究.力学学报,2003,35(2)：129-134.
219倪允琪.气候动力学[M].北京：气象出版社,1993,335-380.
220牛国跃,洪钟祥,孙菽芬.陆而过程研究的现状与发展趋势[J].地球科学进,1997,12(1)：20-25.
221平晓燕,周广胜,孙敬松等.基于功能平衡假说的玉米光合产物分配动态模拟[J].应用生态学报,2010,21(1)：129-135.
222乔娟,张强,张杰.非均匀下垫而陆面过程参数化问题研究进展[J].干旱气象,2008,26(1)：73-78.
223邱玉珺,吴风巨,刘志.梯度法计算空气动力学粗糙度存在的问题[J].大气科学学报,2010,33(6)：697-702.
224施伟来,王汉杰.中国西部退耕还林(草)与沙漠绿化的区域性气候效应[C].西部开发与生态建设.北京：中国林业出版社：2001,592.
225石雪峰,夏建新,吉祖稳.空气动力学粗糙度与植被特征关系的研究进展.中央民族大学学报,2006,15(3)：218-225.
226孙鸿烈.中国生态系统研究网络为生态系统评估提供科技支撑[J].资源科学,2006,28(4)：2-3.
227孙菽芬,金继明.陆面过程模式研究中的几个问题[A].陶诗言,陈联寿,徐祥德,等.第二次青藏高原大气科学实验理论研究进展(二)[M].北京：气象出版社,2000,76-84.
228孙菽芬.陆面过程的物理、生化机制和参数化模型[M].北京：气象出版社,2005,307pp.
229孙菽芬.陆面过程研究的进展[J].新疆气象,2002,(6)：1-6.
230孙治安,翁笃鸣.我国地表反照率的气侯计算及其时空分布特征[J].南京气象学院学报,1987,10(2)：189-200.
231覃文汉.应用压力中心法确定农田空气动力参数.气象学报,1994,52(1)：99-106.
232王介民,邱华盛.中日合作亚洲季风实验—青藏高原实验(GAME - Tibet) [J].中国科学院院刊,2000,5：386-388.
233王介民.陆面过程实验和地气相互作用研究—从HEIFE到IMGRASS和GAME - Tibet/ TIPEX[J].高原气象,1999,18(3)：280-294.
234王玲,谢德体,刘海隆,等.玉米叶面积指数的普适增长模型.西南农业大学学报,2004,26(3)：303-311.
235王涛. 干旱区主要陆表过程与人类活动和气候变化研究进展[J].中国沙漠,2007,27(5)：711-718.
236王宇,周广胜.雨养玉米农田生态系统的蒸散特征及其作物系数.应用生态学报,2010,21(3)：647-653.
237魏和林,符淙斌.下垫面非均匀性的模拟[J].气候与环境研究,1997,2(2)：106-114.
238夏建新,石雪峰,吉祖稳,等.植被条件对下垫面空气动力学粗糙度影响实验研究[J].应用基础与工程科学学报,2007,15(1)：23-31.
239熊伟,居辉,许吟隆,等.气候变化对中国农业温度阈值影响研究及其不确定性分析[J].地球科学进展,2006,21(1)：70-76.
240徐兴奎,李素红.中国地表月平均反照率的遥感反演[J].气象学报,2002,60(2)：215-300.
241薛根元,周锁铨,孙照渤,等.陆面过程研究的新进展[J].科学通报,2005,21(4)：378-386.
242阳伏林,周广胜,张峰,等.内蒙古温带荒漠草原地表反照率特征及数值模拟[J].应用生态学报,2009,20(12)：2847-2852.
243杨兴国,牛生杰,郑有飞.陆而过程观测试验研究进展[J].干旱气象,2003,21(3)：83-89.
244于贵瑞,方华军,伏玉玲,工秋凤.区域尺度陆地生态系统碳收支及其循环过程研究进展.生态学报,2011,31(19)：5449-5459.
245于贵瑞,张雷明,孙晓敏,等.业洲区域陆地生态系统碳通量观测研究进展[J].中国科学(D辑),2004,34(增刊Ⅱ)：15-29.
246曾新民,赵鸣,苏炳凯.“结合法”表示的下垫面温湿非均匀对夏季风气候影响的数值试验[J].大气科学,2002,26(1)：41-55.
247张存来,邹学勇,董光荣,等.耕作土壤表面的空气动力学粗糙度及其对十壤风蚀的影响[J].中国沙漠,2002,22(5)：473-475.
248张果,周广胜,阳伏林.内蒙古荒漠草原地表反照率变化特征[J].生态学报,2010,30(24)：6943-6951.
249张果,周广胜,阳伏林.内蒙古温带荒漠草原生态系统水热通量动态.应用生态学报,2010,21(3)：597-603.
250张宏升,陈家宜.非单一水平均匀下垫面空气动力学参数的确定.应用气象学报,1997,8(3)：310-315.
251张厚瑄,林而达.中国农业响应全球气候变化的策略问题[J].农业环境保护,1997,16(1)：35-39.
252张华,李峰瑞,伏乾科.沙质草地植被防风抗蚀生态效应的野外观测研究[J].环境科学,2004,25(2)：119-124.
253张井勇,董文杰,符淙斌.中国北方和蒙古南部植被退化对区域气候的影响.科学通报,2005,50(1)：53—58.
254张克存,屈建军,董治宝,等.风沙流中风速脉动对输沙量的影响[J].中国沙漠,2006,26(3)：336-340.
255张强,卫国安.荒漠戈壁大气总体曳力系数和输送系数观测研究[J].高原气象,2004,23(3)305-312.
256张强,胡隐樵.大气边界层气象学的研究进展和面临的主要科学问题.地球科学进展,2001,16(4)：526-532.
257张强,王胜,卫国安.西北地区戈壁局地陆面物理参数的研究.地球物理学报,2003,46(5)：616-623.
258张强.大气边界层气象学研究综述.干旱气象,2003,21(3)：74-78.
259张琼,钱永甫.用NCEPPNCAR再分析辐射资料估算月平均地表反照率[J].地理学报,1999,54(4)：309-317.
260张述文,邱崇践,张卫东.用变分方法估算淮河流域总体输送系数[J].高原气象,2004,23(4)：506-511.
261张雅静,申向东.植被覆盖地表空气动力学粗糙度与零平面位移高度的模拟分析[J].中国沙漠,2008,28(1)：21-26.
262赵晓松,关德新,吴家兵,等.长白山阔叶红松林的零平面位移和粗糙度[J].生态学杂志,2004,23(5)：84-88.
263郑益群,钱永甫,苗曼倩.植被变化对中国区域气候的影响E：初步模拟结果[J].气象学报,2002,60(1)：1-16.
264钟中,韩士杰.长白山阔叶红松林冠层空气动力学参数的计算[J].南京大学(自然科学版)2002,38(4)：565-571.
265周明焊,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[M].北京：气象出版社.：.2000.
266周艳莲,孙晓敏,朱治林,等.几种不同下垫面地表粗糙度动态变化及其对通量机理模型模拟的影响.中国科学(D辑),2006,36(增刊)：244-254.
267周艳莲,孙晓敏,朱治林,等.几种典型地表粗糙度计算方法的比较研究[J].地理研究,2007,26(5)：888-897.
268左洪超,胡隐樵.黑河试验区沙漠和戈壁的总体输送系数.高原气象,1992,11：371-380.
269左洪超,吕世华,胡隐樵.中国近50年气温及降水量的变化趋势分析.高原气象,2004,23(2)：238-244.