通用陆面模式(CoLM)湖泊过程方案与性能评估
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摘要
对新版通用陆面模式CoLM(the Common Land Model)中的湖泊过程方案CoLM-Lake做了介绍,并通过10个湖泊的观测数据评估了它的模拟性能,同时讨论了模拟结果对模型关键参数的敏感性.模拟结果显示,CoLM-Lake对于浅湖的模拟效果非常理想,对于表层温度、湍流通量和垂直热力结构的量级和季节变化特征模拟的比较准确,同时对湖泊冻融循环过程给出了合理的刻画.CoLM-Lake对于深湖垂直结构及其变化的模拟亦较为准确,但对垂直混合强度的刻画仍然不足.虽然模型对于北美五大湖表层温度的模拟误差相对明显,但其量级和季节变化特征仍在合理的范围之内.通过模型参数的敏感性分析发现,动力学地表粗糙度可通过改变湍流通量的大小修正湖泊表层温度的模拟.湖泊深度、辐射消光系数和热力扩散系数可共同影响湖泊的热力结构.较大的湖泊深度,较小的消光系数和较大的热力扩散系数均使得湖泊内部传递和存储更多的热量,从而增加湖泊的热力惯性,降低湖泊各层温度的季节震荡和变化幅度,推迟湖泊冬季的冻结时间.湖泊到达最大密度时的温度受湖泊盐度的影响,调整此温度同样可使得模型的模拟结果得到改进.以上敏感性分析说明CoLM-Lake对于湖泊热力过程的模拟可以通过模型参数优化进行改进.总体上,CoLM-Lake可以合理地刻画各个湖泊的主要特征,模拟的误差均在参数取值的不确定性范围之内,因此CoLM-Lake在全球尺度上适用于对湖泊物理过程的模拟.
The Common Land Model(CoLM) has been updated to a new version, but the lake scheme in CoLM(CoLM-Lake) has never been evaluated. This paper introduces the structure and physical processes of CoLM-Lake, and evaluates its simulation performance through the observations from 10 lakes over different regions. It also discusses the sensitivities of simulated results to some important parameters in the model. Results show that CoLM-Lake performs very well over the three shallow lakes(Kossenblatter, Taihu and Sparkling Lake) where the model accurately captures the magnitudes and seasonal variations of lake surface temperature, turbulent fluxes, and vertical thermal structure. The freeze-thaw cycle of Sparkling Lake is reproduced at a reasonable level as well. Also, CoLM-Lake show acceptable performance in simulating vertical structures and their variations for deep lakes, although the vertical mixing strength remains underestimated. The biases of surface temperatures in the Great Lakes of North America are relatively larger, but the magnitudes and seasonal variations in temperature fall within a reasonable range. In general, CoLM-Lake is suitable for the simulation of lake physical processes on the global scale. The simulated results have strong sensitivities to some parameters with values that have large uncertainties. The surface roughness lengths determine the amounts of turbulent fluxes which are emitted into the atmosphere, and thus affecting the simulated lake surface temperature. Although the surface roughness based on the dynamic diagnosis of lake properties in the model has made the simulated turbulent fluxes very accurate, the further adjustment of surface roughness can improve the realism of the simulated turbulent fluxes and temperature on lake surface. Lake depth, optical extinction coefficient and thermal diffusivity affect the simulated lake thermal structure from different angles. Lake depth determines the amount of water in vertical mixing; optical extinction coefficient determines the distribution of solar radiation at different lake depths; and thermal diffusivity determines the simulated vertical mixing strength that a lake can achieve. Either a larger lake depth, a smaller extinction coefficient, or a larger thermal diffusivity enables a lake to transfer and store more heat internally, thereby increasing lake thermal inertia, reducing daily and seasonal variations of lake water temperatures, and causing late freeze-up dates in winter. Therefore, the biases of simulated lake thermal structure can be corrected by adjusting the above three parameters. Note that lake depth and optical extinction coefficient are lake inherent attributes, and thus it is important to collect high-resolution and high-precision data for these two parameters in the future. The modification of thermal diffusivity should focus on increasing simulated vertical mixing strength for deep lakes, while the relatively accurate simulation for shallow lakes should be maintained, and thus more complex processes in deep lakes need to be considered in the model than simply multiplying the background thermal diffusivity of entire lake. The temperature at which lake water reaches maximum density is affected by lake salinity, and adjusting this temperature also improves simulated results. Hence, the effects of lake salinity on lake water physical properties should be considered in the development of future lake schemes. As three-dimensional lake schemes become more sophisticated, it is increasingly important to couple them into regional and global climate models. However, the description of important processes such as lake vertical mixing, phase change and snow hydrology are still too simple in one-dimensional schemes, and thus the upper limit of accuracy that one-dimensional schemes can achieve in deep lake simulations remains unclear, and the necessity of coupling the three-dimensional schemes with climate models is under dispute. If one-dimensional schemes are found to have better performance in simulating the characteristics of deep lakes, then the large amount of computational resources that need to be consumed to run three-dimensional models can be saved. Therefore, developing one-dimensional lake schemes at current stage is extremely important.
The Common Land Model(CoLM) has been updated to a new version, but the lake scheme in CoLM(CoLM-Lake) has never been evaluated. This paper introduces the structure and physical processes of CoLM-Lake, and evaluates its simulation performance through the observations from 10 lakes over different regions. It also discusses the sensitivities of simulated results to some important parameters in the model. Results show that CoLM-Lake performs very well over the three shallow lakes(Kossenblatter, Taihu and Sparkling Lake) where the model accurately captures the magnitudes and seasonal variations of lake surface temperature, turbulent fluxes, and vertical thermal structure. The freeze-thaw cycle of Sparkling Lake is reproduced at a reasonable level as well. Also, CoLM-Lake show acceptable performance in simulating vertical structures and their variations for deep lakes, although the vertical mixing strength remains underestimated. The biases of surface temperatures in the Great Lakes of North America are relatively larger, but the magnitudes and seasonal variations in temperature fall within a reasonable range. In general, CoLM-Lake is suitable for the simulation of lake physical processes on the global scale. The simulated results have strong sensitivities to some parameters with values that have large uncertainties. The surface roughness lengths determine the amounts of turbulent fluxes which are emitted into the atmosphere, and thus affecting the simulated lake surface temperature. Although the surface roughness based on the dynamic diagnosis of lake properties in the model has made the simulated turbulent fluxes very accurate, the further adjustment of surface roughness can improve the realism of the simulated turbulent fluxes and temperature on lake surface. Lake depth, optical extinction coefficient and thermal diffusivity affect the simulated lake thermal structure from different angles. Lake depth determines the amount of water in vertical mixing; optical extinction coefficient determines the distribution of solar radiation at different lake depths; and thermal diffusivity determines the simulated vertical mixing strength that a lake can achieve. Either a larger lake depth, a smaller extinction coefficient, or a larger thermal diffusivity enables a lake to transfer and store more heat internally, thereby increasing lake thermal inertia, reducing daily and seasonal variations of lake water temperatures, and causing late freeze-up dates in winter. Therefore, the biases of simulated lake thermal structure can be corrected by adjusting the above three parameters. Note that lake depth and optical extinction coefficient are lake inherent attributes, and thus it is important to collect high-resolution and high-precision data for these two parameters in the future. The modification of thermal diffusivity should focus on increasing simulated vertical mixing strength for deep lakes, while the relatively accurate simulation for shallow lakes should be maintained, and thus more complex processes in deep lakes need to be considered in the model than simply multiplying the background thermal diffusivity of entire lake. The temperature at which lake water reaches maximum density is affected by lake salinity, and adjusting this temperature also improves simulated results. Hence, the effects of lake salinity on lake water physical properties should be considered in the development of future lake schemes. As three-dimensional lake schemes become more sophisticated, it is increasingly important to couple them into regional and global climate models. However, the description of important processes such as lake vertical mixing, phase change and snow hydrology are still too simple in one-dimensional schemes, and thus the upper limit of accuracy that one-dimensional schemes can achieve in deep lake simulations remains unclear, and the necessity of coupling the three-dimensional schemes with climate models is under dispute. If one-dimensional schemes are found to have better performance in simulating the characteristics of deep lakes, then the large amount of computational resources that need to be consumed to run three-dimensional models can be saved. Therefore, developing one-dimensional lake schemes at current stage is extremely important.
引文
1 Hostetler S W,Giorgi F,Bates G T,et al.Lake-atmosphere feedbacks associated with paleolakes Bonneville and Lahontan.Science,1994,263:665-668
2 Bonan G B.Sensitivity of a GCM simulation to inclusion of inland water surfaces.J Chem Ecol,1995,8:2691-2704
3 Dai Y J,Zeng X B,Dickinson R E,et al.The Common Land Model.Bull Am Meteorol Soc,2003,84:1013-1023
4 Long Z,Perrie W,Gyakum J,et al.Northern lake impacts on local seasonal climate.J Hydrometeorol,2007,8:881-896
5 Samuelsson P,Kourzeneva E,Mironov D.The impact of lakes on the European climate as simulated by a regional climate model.Boreal Environ Res,2015,15:113-129
6 Subin Z M,Riley W J,Mironov D.An improved lake model for climate simulations:Model structure,evaluation,and sensitivity analyses in CESM1.J Adv Model Earth Syst,2012,4:1-27
7 Deng B,Liu S,Xiao W,et al.Evaluation of the CLM4 lake model at a large and shallow freshwater lake.J Hydrometeorol,2012,14:636-649
8 Xiao C,Lofgren B M,Wang J,et al.Improving the lake scheme within a coupled WRF-lake model in the Laurentian great lakes.J Adv Model Earth Syst,2016,8:1969-1985
9 Notaro M,Holman K,Zarrin A,et al.Influence of the Laurentian Great Lakes on regional climate.J Clim,2013,26:789-804
10 Beletsky D,Hawley N,Rao Y R.Modeling summer circulation and thermal structure of Lake Erie.J Geophys Res Oceans,2013,118:6238-6252
11 Huang A,Rao Y R,Lu Y.Evaluation of a 3-D hydrodynamic model and atmospheric forecast forcing using observations in Lake Ontario.J Geophys Res,2010,115:C02004
12 Bai X Z,Wang J,Schwab D J,et al.Modeling 1993-2008 climatology of seasonal general circulation and thermal structure in the Great Lakes using FVCOM.Ocean Model,2013,65:40-63
13 Martynov A,Sushama L,Laprise R.Simulation of temperate freezing lakes by one-dimensional lake models:Performance assessment for interactive coupling with regional climate models.Boreal Environ Res,2010,15:143-164
14 Mackay M D,Neale P J,Arp C D,et al.Modeling lakes and reservoirs in the climate system.Limnol Oceanogr,2009,54:2315-2329
15 Kiehl J T,Hack J J,Bonan G B,et al.Description of the NCAR Community Climate Model(CCM3).NCAR Technical Note NCAR/TN-420+STR,1996
16 Henderson-Sellers B.New formulation of eddy diffusion thermocline models.Appl Math Model,1985,9:441-446
17 Hostetler S W,Bartlein P J.Simulation of lake evaporation with application to modeling lake level variations of Harney-Malheur Lake,Oregon.Water Resour Res,1990,26:2603-2612
18 Fang X,Stefan H G.Long-term lake water temperature and ice cover simulations/measurements.Cold Reg Sci Technol,1996,24:289-304
19 Gu H,Jin J,Wu Y,et al.Calibration and validation of lake surface temperature simulations with the coupled WRF-lake model.Clim Change,2015,129:471-483
20 Stepanenko V M,Goyette S,Martynov A,et al.First steps of a Lake Model Intercomparison Project:LakeMIP.Boreal Environ Res,2010,15:191-202
21 Dai Y,Yuan H,Wei N,et al.The Common Land Model(Version 2014):User’s guide and source codes,2014.http://globalchange.bnu.edu.cn/research/models
22 Liang X Z,Xu M,Yuan X,et al.Regional climate-weather research and forecasting model.Bull Amer Meteor Soc,2012,93:1363-1387
23 Shen X,Su Y,Hu J,et al.Development and operation transformation of GRAPES global middle-range forecast system(in Chinese).JAppl Meteorol Sci,2017,28:1-10[沈学顺,苏勇,胡江林,等.GRAPES_GFS全球中期预报系统的研发和业务化.应用气象学报,2017,28:1-10]
24 Ji D,Wang L,Feng J,et al.Basic evaluation of Beijing Normal University Earth System Model(BNU_ESM)version 1.Geosci Model Dev,2014,7:2039-2064
25 Sun H C,Zhou G Q,Zeng Q C.Assessments of the climate system model(CAS-ESM-C)using IAP AGCM4 as its atmospheric component(in Chinese).Chin J Atmos Sci,2012,36:215-233[孙泓川,周广庆,曾庆存.IAP第四代大气环流模式的耦合气候系统模式模拟性能评估.大气科学,2012,36:215-233]
26 Zhang X X,Dai Y J,Cui H Z,et al.Evaluating common land model energy fluxes using FLUXNET data.Adv Atmos Sci,2017,34:1035-1046
27 Xin F Y,Bian L G,Zhang X H.The application of Colm to arid region of Northwest China and Qinghai-Xizang Plateau.Plateau Meteorol,2006,25:567-574
28 Luo S,LüS,Yu A Z.Development and validation of the frozen soil parameterization scheme in common land model.Cold Reg Sci Technol,2009,55:130-140
29 Song Y M,Guo W D,Zhang Y C.Simulation of latent heat flux exchange between land surface and atmosphere in temperate mixed forest and subtropical artificial coniferous forest sites in China by CoLM.Plateau Meteorol,2008,8:60429-60433
30 Bonan G B.A land surface model(LSM version 1.0)for ecological,hydrological,and atmospheric studies:Technical description and user’s guide.NCAR Technical Note NCAR/TN-417+STR,National Center for Atmospheric Research,Boulder,CO.1996
31 Hostetler S W,Bates G T,Giorgi F.Interactive coupling of a lake thermal model with a regional climate model.J Geophys Res,1993,98:5045-5057
32 Li C,Lu H,Yang K,et al.Evaluation of the common land model(CoLM)from the perspective of water and energy budget simulation:Towards inclusion in CMIP6.Atmosphere,2017,8:141
33 Wei N,Dai Y,Zhang M,et al.Impact of precipitation-induced sensible heat on the simulation of land-surface air temperature.J Adv Model Earth Syst,2014,6:1311-1320
34 Zilitinkevich S S.Dynamics of the Atmospheric Boundary Layer.Leningrad Gidrometeor,1970
35 Andreas E L.A theory for the scalar roughness and the scalar transfer-coefficients over snow and sea ice.Boundary Layer Meteorol,1987,38:159-184
36 Hakanson L.Models to predict Secchi depth in small glacial lakes.Aquat Sci,1995,57:31-53
37 Beyrich F,Leps J P,Mauder M,et al.Area-averaged surface fluxes over the litfass region based on eddy-covariance measurements.Boundary Layer Meteorol,2006,121:33-65
38 Lazhu,Yang K,Wang J,et al.Quantifying evaporation and its decadal change for Lake Nam Co,central Tibetan Plateau.J Geophys Res Atmos,2016,121:7578-7591
39 Shangguan W,Dai Y,Duan Q,et al.A global soil data set for earth system modeling.J Adv Model Earth Syst,2014,6:249-263
40 Ataktürk S S,Katsaros K B.Wind Stress and Surface Waves Observed on Lake Washington.J Phys Oceanogr,1999,29:633-650
41 Li Z,Lyu S,Zhao L,et al.Turbulent transfer coefficient and roughness length in a high-altitude lake,Tibetan Plateau.Theor Appl Climatol,2015,124:723-735
42 Stepanenko V M,Martynov A,Johnk K D,et al.A one-dimensional model intercomparison study of thermal regime of a shallow turbid midlatitude lake.Geosci Model Dev Discuss,2012,5:3993-4035
43 Kourzeneva E,Asensio H,Martin E,et al.Global gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modeling.Tellus A,2012,64:36-38
44 Cui Y Y,Zhu L P,Ju J T,et al.Seasonal variations of water balance and supply process based upon discharge monitoring in Ranwu Lake of Southeast Tibet(in Chinese).Acta Geogr Sin,2017,72:1221-1234[崔颖颖,朱立平,鞠建廷,等.基于流量监测的西藏东南部然乌湖水量平衡季节变化及其补给过程分析.地理学报,2017,72:1221-1234]
45 Fang N,Yang K,Lazhu,et al.Research on the application of WRF-lake modeling at Nam Co Lake on the Qinghai-Tibetan Plateau(in Chinese).Plateau Meteor,2017,36:610-618[方楠,阳坤,拉珠,等.WRF湖泊模型对青藏高原纳木措湖的适用性研究.高原气象,2017,36:610-618]
2 Bonan G B.Sensitivity of a GCM simulation to inclusion of inland water surfaces.J Chem Ecol,1995,8:2691-2704
3 Dai Y J,Zeng X B,Dickinson R E,et al.The Common Land Model.Bull Am Meteorol Soc,2003,84:1013-1023
4 Long Z,Perrie W,Gyakum J,et al.Northern lake impacts on local seasonal climate.J Hydrometeorol,2007,8:881-896
5 Samuelsson P,Kourzeneva E,Mironov D.The impact of lakes on the European climate as simulated by a regional climate model.Boreal Environ Res,2015,15:113-129
6 Subin Z M,Riley W J,Mironov D.An improved lake model for climate simulations:Model structure,evaluation,and sensitivity analyses in CESM1.J Adv Model Earth Syst,2012,4:1-27
7 Deng B,Liu S,Xiao W,et al.Evaluation of the CLM4 lake model at a large and shallow freshwater lake.J Hydrometeorol,2012,14:636-649
8 Xiao C,Lofgren B M,Wang J,et al.Improving the lake scheme within a coupled WRF-lake model in the Laurentian great lakes.J Adv Model Earth Syst,2016,8:1969-1985
9 Notaro M,Holman K,Zarrin A,et al.Influence of the Laurentian Great Lakes on regional climate.J Clim,2013,26:789-804
10 Beletsky D,Hawley N,Rao Y R.Modeling summer circulation and thermal structure of Lake Erie.J Geophys Res Oceans,2013,118:6238-6252
11 Huang A,Rao Y R,Lu Y.Evaluation of a 3-D hydrodynamic model and atmospheric forecast forcing using observations in Lake Ontario.J Geophys Res,2010,115:C02004
12 Bai X Z,Wang J,Schwab D J,et al.Modeling 1993-2008 climatology of seasonal general circulation and thermal structure in the Great Lakes using FVCOM.Ocean Model,2013,65:40-63
13 Martynov A,Sushama L,Laprise R.Simulation of temperate freezing lakes by one-dimensional lake models:Performance assessment for interactive coupling with regional climate models.Boreal Environ Res,2010,15:143-164
14 Mackay M D,Neale P J,Arp C D,et al.Modeling lakes and reservoirs in the climate system.Limnol Oceanogr,2009,54:2315-2329
15 Kiehl J T,Hack J J,Bonan G B,et al.Description of the NCAR Community Climate Model(CCM3).NCAR Technical Note NCAR/TN-420+STR,1996
16 Henderson-Sellers B.New formulation of eddy diffusion thermocline models.Appl Math Model,1985,9:441-446
17 Hostetler S W,Bartlein P J.Simulation of lake evaporation with application to modeling lake level variations of Harney-Malheur Lake,Oregon.Water Resour Res,1990,26:2603-2612
18 Fang X,Stefan H G.Long-term lake water temperature and ice cover simulations/measurements.Cold Reg Sci Technol,1996,24:289-304
19 Gu H,Jin J,Wu Y,et al.Calibration and validation of lake surface temperature simulations with the coupled WRF-lake model.Clim Change,2015,129:471-483
20 Stepanenko V M,Goyette S,Martynov A,et al.First steps of a Lake Model Intercomparison Project:LakeMIP.Boreal Environ Res,2010,15:191-202
21 Dai Y,Yuan H,Wei N,et al.The Common Land Model(Version 2014):User’s guide and source codes,2014.http://globalchange.bnu.edu.cn/research/models
22 Liang X Z,Xu M,Yuan X,et al.Regional climate-weather research and forecasting model.Bull Amer Meteor Soc,2012,93:1363-1387
23 Shen X,Su Y,Hu J,et al.Development and operation transformation of GRAPES global middle-range forecast system(in Chinese).JAppl Meteorol Sci,2017,28:1-10[沈学顺,苏勇,胡江林,等.GRAPES_GFS全球中期预报系统的研发和业务化.应用气象学报,2017,28:1-10]
24 Ji D,Wang L,Feng J,et al.Basic evaluation of Beijing Normal University Earth System Model(BNU_ESM)version 1.Geosci Model Dev,2014,7:2039-2064
25 Sun H C,Zhou G Q,Zeng Q C.Assessments of the climate system model(CAS-ESM-C)using IAP AGCM4 as its atmospheric component(in Chinese).Chin J Atmos Sci,2012,36:215-233[孙泓川,周广庆,曾庆存.IAP第四代大气环流模式的耦合气候系统模式模拟性能评估.大气科学,2012,36:215-233]
26 Zhang X X,Dai Y J,Cui H Z,et al.Evaluating common land model energy fluxes using FLUXNET data.Adv Atmos Sci,2017,34:1035-1046
27 Xin F Y,Bian L G,Zhang X H.The application of Colm to arid region of Northwest China and Qinghai-Xizang Plateau.Plateau Meteorol,2006,25:567-574
28 Luo S,LüS,Yu A Z.Development and validation of the frozen soil parameterization scheme in common land model.Cold Reg Sci Technol,2009,55:130-140
29 Song Y M,Guo W D,Zhang Y C.Simulation of latent heat flux exchange between land surface and atmosphere in temperate mixed forest and subtropical artificial coniferous forest sites in China by CoLM.Plateau Meteorol,2008,8:60429-60433
30 Bonan G B.A land surface model(LSM version 1.0)for ecological,hydrological,and atmospheric studies:Technical description and user’s guide.NCAR Technical Note NCAR/TN-417+STR,National Center for Atmospheric Research,Boulder,CO.1996
31 Hostetler S W,Bates G T,Giorgi F.Interactive coupling of a lake thermal model with a regional climate model.J Geophys Res,1993,98:5045-5057
32 Li C,Lu H,Yang K,et al.Evaluation of the common land model(CoLM)from the perspective of water and energy budget simulation:Towards inclusion in CMIP6.Atmosphere,2017,8:141
33 Wei N,Dai Y,Zhang M,et al.Impact of precipitation-induced sensible heat on the simulation of land-surface air temperature.J Adv Model Earth Syst,2014,6:1311-1320
34 Zilitinkevich S S.Dynamics of the Atmospheric Boundary Layer.Leningrad Gidrometeor,1970
35 Andreas E L.A theory for the scalar roughness and the scalar transfer-coefficients over snow and sea ice.Boundary Layer Meteorol,1987,38:159-184
36 Hakanson L.Models to predict Secchi depth in small glacial lakes.Aquat Sci,1995,57:31-53
37 Beyrich F,Leps J P,Mauder M,et al.Area-averaged surface fluxes over the litfass region based on eddy-covariance measurements.Boundary Layer Meteorol,2006,121:33-65
38 Lazhu,Yang K,Wang J,et al.Quantifying evaporation and its decadal change for Lake Nam Co,central Tibetan Plateau.J Geophys Res Atmos,2016,121:7578-7591
39 Shangguan W,Dai Y,Duan Q,et al.A global soil data set for earth system modeling.J Adv Model Earth Syst,2014,6:249-263
40 Ataktürk S S,Katsaros K B.Wind Stress and Surface Waves Observed on Lake Washington.J Phys Oceanogr,1999,29:633-650
41 Li Z,Lyu S,Zhao L,et al.Turbulent transfer coefficient and roughness length in a high-altitude lake,Tibetan Plateau.Theor Appl Climatol,2015,124:723-735
42 Stepanenko V M,Martynov A,Johnk K D,et al.A one-dimensional model intercomparison study of thermal regime of a shallow turbid midlatitude lake.Geosci Model Dev Discuss,2012,5:3993-4035
43 Kourzeneva E,Asensio H,Martin E,et al.Global gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modeling.Tellus A,2012,64:36-38
44 Cui Y Y,Zhu L P,Ju J T,et al.Seasonal variations of water balance and supply process based upon discharge monitoring in Ranwu Lake of Southeast Tibet(in Chinese).Acta Geogr Sin,2017,72:1221-1234[崔颖颖,朱立平,鞠建廷,等.基于流量监测的西藏东南部然乌湖水量平衡季节变化及其补给过程分析.地理学报,2017,72:1221-1234]
45 Fang N,Yang K,Lazhu,et al.Research on the application of WRF-lake modeling at Nam Co Lake on the Qinghai-Tibetan Plateau(in Chinese).Plateau Meteor,2017,36:610-618[方楠,阳坤,拉珠,等.WRF湖泊模型对青藏高原纳木措湖的适用性研究.高原气象,2017,36:610-618]