• An, Z. S., 2000: The history and variability of the East Asian paleomonsoon climate. Quat. Sci. Rev., 19, 171187, https://doi.org/10.1016/S0277-3791(99)00060-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • An, Z. S., J. E. Kutzbach, W. L. Prell, and S. C. Porter, 2001: Evolution of Asian monsoons and phased uplift of the Himalaya-Tibetan plateau since late Miocene times. Nature, 411, 6266, https://doi.org/10.1038/35075035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, A., and Coauthors, 2016: Land-atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Climate Change, 6, 869874, https://doi.org/10.1038/nclimate3029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., D. Y. Yu, M. Georgescu, and J. G. Wu, 2018: Substantial impacts of landscape changes on summer climate with major regional difference: The case of China. Sci. Total Environ. 625, 416427, https://doi.org/10.1016/j.scitotenv.2017.12.290.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., J. G. Wu, D. Y. Yu, and W. Wang, 2019: The biophysical effects of the vegetation restoration program on regional climate metrics in the Loess Plateau, China. Agric. For. Meteor., 268, 169180, https://doi.org/10.1016/j.agrformet.2019.01.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface-hydrology model with the Penn State–NCAR MM5 modeling system. Part II: Preliminary model validation. Mon. Wea. Rev., 129, 587604, https://doi.org/10.1175/1520-0493(2001)129<0587:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheng, S. J., X. D. Guan, J. P. Huang, F. Ji, and R. X. Guo, 2015: Long-term trend and variability of soil moisture over East Asia. J. Geophys. Res. Atmos., 120, 86588670, https://doi.org/10.1002/2015JD023206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dickinson, R. E., A. Henderson-Sellers, P. J. Kennedy, and M. F. Wilson, 1986: Biosphere–Atmosphere Transfer Scheme (BATS) for the Community Climate Model. NCAR Tech. Note NCAR/TN-275+STR, 72 pp., https://doi.org/10.5065/D6668B58.

    • Crossref
    • Export Citation
  • Donohoe, A., and D. S. Battisti, 2011: Atmospheric and surface contributions to planetary albedo. J. Climate, 24, 44024418, https://doi.org/10.1175/2011JCLI3946.1.

    • Search Google Scholar
    • Export Citation
  • Duan, A. M., R. Z. Sun, and J. H. He, 2017: Impact of surface sensible heating over the Tibetan Plateau on the western Pacific subtropical high: A land-air-sea interaction perspective. Adv. Atmos. Sci., 34, 157168, https://doi.org/10.1007/s00376-016-6008-z.

    • Search Google Scholar
    • Export Citation
  • Farouki, O. T., 1981: Thermal properties of soils. CRREL Monograph 81-1, U.S. Army Corps of Engineers, 136 pp., https://apps.dtic.mil/sti/pdfs/ADA111734.pdf.

  • Fu, B., S. Wang, Y. Liu, J. B. Liu, W. Liang, and C. Y. Miao, 2017: Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annu. Rev. Earth Planet. Sci., 45, 223243, https://doi.org/10.1146/annurev-earth-063016-020552.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, C. B., and F. Penning De Vries, Eds., 2006: Initial science plan of the Monsoon Asia Integrated Regional Study. MAIRS Working Paper Series 1, IAP-CAS, 86 pp.

    • Crossref
    • Export Citation
  • Fu, C. B., and G. Wen, 2002: Some key issues of aridity trend in northern China (in Chinese). Climatic Environ. Res., 7, 2229.

  • Fu, C. B., and Z. G. Ma, 2008: Global change and regional aridfication (in Chinese with English abstract). Chin. J. Atmos. Sci., 32, 752760.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ge, J., A. J. Pitman, W. D. Guo, B. L. Zan, and C. B. Fu, 2020: Impact of revegetation of the Loess Plateau of China on the regional growing season water balance. Hydrol. Earth Syst. Sci., 24, 515533, https://doi.org/10.5194/hess-24-515-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Getirana, A. C. V., and Coauthors, 2014: Water balance in the Amazon basin from a land surface model ensemble. J. Hydrometeor., 15, 25862614, https://doi.org/10.1175/JHM-D-14-0068.1.

    • Search Google Scholar
    • Export Citation
  • Getirana, A. C. V., and Coauthors, 2020: GRACE improves seasonal groundwater forecast initialization over the United States. J. Hydrometeor., 21, 5971, https://doi.org/10.1175/JHM-D-19-0096.1.

    • Search Google Scholar
    • Export Citation
  • Guan, X. D., J. Huang, N. Guo, J. R. Bi, and G. Y. Wang, 2009: Variability of soil moisture and its relationship with surface albedo and soil thermal parameters over the Loess Plateau. Adv. Atmos. Sci., 26, 692700, https://doi.org/10.1007/s00376-009-8198-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Z. T., and Coauthors, 2002: Onset of Asian desertification by 22 Myr ago inferred from loess deposits in China. Nature, 416, 159163, https://doi.org/10.1038/416159a.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Y., 1994: Research advance about the energy budget and transportation of water vapor in the HEIFE area (in Chinese). Adv. Earth Sci., 9, 3034, http://www.adearth.ac.cn/CN/Y1994/V9/I4/30.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., and Y. Gao, 1994: Some new understandings of processes at the land surface in arid area from the HEIFE (in Chinese). Acta Meteor. Sin., 52, 285296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, F., Z. Xu, and W. Guo, 2019: Evaluating vector winds in the Asian-Australian monsoon region simulated by 37 CMIP5 models. Climate Dyn., 53, 491507, https://doi.org/10.1007/s00382-018-4599-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, J. P., and Coauthors, 2008: An overview of the semi-arid climate and environment research observatory over the Loess Plateau. Adv. Atmos. Sci., 25, 906921, https://doi.org/10.1007/s00376-008-0906-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, J. P., M. X. Ji, Y. Z. Liu, L. Zhang, and D. Y. Gong, 2013: An overview of arid and semi-arid climate change (in Chinese). Adv. Climate Change Res., 9, 914.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, J. P., M. X. Ji, Y. K. Xie, S. S. Wang, Y. L. He, and J. T. Ran, 2016a: Global semi-arid climate change over last 60 years. Climate Dyn., 46, 11311150, https://doi.org/10.1007/s00382-015-2636-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, J. P., H. P. Yu, X. D. Guan, G. Y. Wang, and R. X. Guo, 2016b: Accelerated dryland expansion under climate change. Nat. Climate Change, 6, 166171, https://doi.org/10.1038/nclimate2837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, J. P., and Coauthors, 2017a: Dryland climate change: Recent progress and challenges. Rev. Geophys., 55, 719778, https://doi.org/10.1002/2016RG000550.

    • Search Google Scholar
    • Export Citation
  • Huang, J. P., H. P. Yu, A. G. Dai, Y. Wei, and L. T. Kang, 2017b: Drylands face potential threat under 2°C global warming target. Nat. Climate Change, 7, 417422, https://doi.org/10.1038/nclimate3275.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, J. P., J. R. Ma, X. D. Guan, Y. Li, and Y. L. He, 2019: Progress in semi-arid climate change studies in China. Adv. Atmos. Sci., 36, 922937, https://doi.org/10.1007/s00376-018-8200-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ji, M., J. P. Huang, Y. Xie, and J. Liu, 2015: Comparison of dryland climate change in observations and CMIP5 simulations. Adv. Atmos. Sci., 32, 15651574, https://doi.org/10.1007/s00376-015-4267-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johansen, O., 1975: Thermal conductivity of soils. Ph.D. thesis, Norwegian University of Science and Technology, 291 pp.

  • Kumar, S. V., D. M. Mocko, S. Wang, C. D. Peters-Lidard, and J. Borak, 2019: Assimilation of remotely sensed leaf area index into the Noah-MP land surface model: Impacts on water and carbon fluxes and states over the Continental United States. J. Hydrometeor., 20, 13591377, https://doi.org/10.1175/JHM-D-18-0237.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., B. J. Choudhury, M. S. Moran, R. J. Reginato, and H. L. Weaver, 1989: Determination of sensible heat flux over sparse canopy using thermal infrared data. Agric. For. Meteor., 44, 197216, https://doi.org/10.1016/0168-1923(89)90017-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwon, Y., Z. L. Yang, L. Zhao, T. J. Hoar, A. M. Toure, and M. Rodell, 2016: Estimating snow water storage in North America using CLM4, DART, and snow radiance data assimilation. J. Hydrometeor., 17, 28532874, https://doi.org/10.1175/JHM-D-16-0028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, T. S., 1985: Regional record of aeolian processes: The distribution of loess. Loess and the Environment, China Ocean Press, 14–15.

  • Lu, S., and H. C. Zuo, 2018: Improvement and validation of the Common Land Model on cropland covered by plastic film in the arid region of China. J. Appl. Meteor. Climatol., 57, 20712089, https://doi.org/10.1175/JAMC-D-17-0185.1.

    • Search Google Scholar
    • Export Citation
  • Lu, S., and H. C. Zuo, 2021: Sensitivity of South Asian summer monsoon simulation to land surface schemes in Weather Research and Forecasting model. Int. J. Climatol., 41, 68056824, https://doi.org/10.1002/joc.7278.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, S., W. D. Guo, Y. K. Xue, F. Huang, and J. Ge, 2021: Simulation of summer climate over Central Asia shows high sensitivity to different land surface schemes in WRF. Climate Dyn., 57, 22492268, https://doi.org/10.1007/s00382-021-05876-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, S. Q., and Coauthors, 2009: Soil thermal conductivity parameterization establishment and application in numerical model of central Tibetan Plateau (in Chinese). Chin. J. Geophys., 52, 919928, https://doi.org/10.3969/j.issn.0001-5733.2009.04.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lv, M. X., Z. G. Ma, M. X. Li, and Z. Y. Zheng, 2019a: Quantitative analysis of terrestrial water storage changes under the Grain for Green Program in the Yellow River basin. J. Geophys. Res. Atmos., 124, 13361351, https://doi.org/10.1029/2018JD029113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lv, M. X., Z. G. Ma, and S. M. Peng, 2019b: Responses of terrestrial water cycle components to afforestation within and around the Yellow River basin. Atmos. Ocean. Sci. Lett., 12, 116123, https://doi.org/10.1080/16742834.2019.1569456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, Z. G., and C. B. Fu, 2006: Some evidence of drying trend over northern China from 1951 to 2004. Chin. Sci. Bull., 51, 29132925, https://doi.org/10.1007/s11434-006-2159-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, Z. G., and C. B. Fu, 2007: Global aridification in the second half of the 20th century and its relationship to large-scale climate background. Sci. China, 50D, 776788, https://doi.org/10.1007/s11430-007-0036-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maestre, F. T., and Coauthors, 2013: Changes in biocrust cover drive carbon cycle responses to climate change in drylands. Global Change Biol., 19, 38353847, https://doi.org/10.1111/gcb.12306.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., and H. Pan, 1984: A two-layer model of soil hydrology. Bound.-Layer Meteor., 29, 120, https://doi.org/10.1007/BF00119116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Passerat de Silans, A. M. B., B. A. Monteny, and J. P. Lhomme, 1996: Apparent soil thermal diffusivity, a case study: HAPEX-Sahel experiment. Agric. For. Meteor., 81, 201216, https://doi.org/10.1016/0168-1923(95)02323-2.

    • Search Google Scholar
    • Export Citation
  • Piao, S. L., and Coauthors, 2010: The impacts of climate change on water resources and agriculture in China. Nature, 467, 4351, https://doi.org/10.1038/nature09364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., R. Avissar, M. R. Raupach, H. A. Dolman, X. B. Zeng, and S. Denning, 1998: Interactions between the atmosphere and terrestrial ecosystems: Influence on weather and climate. Global Change Biol., 4, 461475, https://doi.org/10.1046/j.1365-2486.1998.t01-1-00176.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reed, S. C., K. K. Coe, J. P. Sparks, D. C. Housman, T. J. Zelikova, and J. Belnap, 2012: Changes to dryland rainfall result in rapid moss mortality and altered soil fertility. Nat. Climate Change, 2, 752755, https://doi.org/10.1038/nclimate1596.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Safriel, U., and Z. Adeel, 2005: Dryland systems. Ecosystems and Human Well-being. Current State and Trend, R. Hassan et al., Eds., Island Press, 623662.

    • Crossref
    • Export Citation
  • Schaaf, C. B., and Coauthors, 2002: First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ., 83, 135148, https://doi.org/10.1016/S0034-4257(02)00091-3.

    • Search Google Scholar
    • Export Citation
  • Sheppard, P. A., 1958: Transfer across the Earth’s surface and through the air above. Quart. J. Roy. Meteor. Soc., 84, 205224, https://doi.org/10.1002/qj.49708436102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sridhar, V., R. L. Elliott, C. Fei, and J. A. Brotzge, 2002: Validation of the NOAH-OSU land surface model using surface flux measurements in Oklahoma. J. Geophys. Res., 107, 4418, https://doi.org/10.1029/2001JD001306.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237273, https://doi.org/10.1175/JCLI-3243.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, G., J. Huang, W. Guo, J. Zuo, J. Wang, J. Bi, Z. Huang, and J. Shi, 2010: Observation analysis of land-atmosphere interactions over the Loess Plateau of northwest China. J. Geophys. Res., 115, D00K17, https://doi.org/10.1029/2009JD013372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J. M., and Y. Mitsuta, 1991: Turbulence structure and transfer characteristics in the surface layer of the HEIFE Gobi area. J. Meteor. Soc. Japan, 69, 587593, https://doi.org/10.2151/jmsj1965.69.5_587.

    • Search Google Scholar
    • Export Citation
  • Wang, J. M., and Y. Mitsuta, 1992: Evaporation from the desert: Some preliminary results of HEIFE. Bound.-Layer Meteor., 59, 413418, https://doi.org/10.1007/BF02215461.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., P. D’Odorico, J. P. Evans, D. J. Eldridge, M. F. McCabe, K. K. Caylor, and E. G. King, 2012: Dryland ecohydrology and climate change: Critical issues and technical advances. Hydrol. Earth Syst. Sci., 16, 25852603, https://doi.org/10.5194/hess-16-2585-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., X. Zeng, M. Barlage, R. E. Dickinson, and C. B. Schaaf, 2004: Using MODIS BRDF and albedo data to evaluate global model land surface albedo. J. Hydrometeor., 5, 314, https://doi.org/10.1175/1525-7541(2004)005<0003:UMBAAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., M. Barlage, X. Zeng, R. E. Dickinson, and C. B. Schaaf, 2005: The solar zenith angle dependence of desert albedo. Geophys. Res. Lett., 32, L05403, https://doi.org/10.1029/2004GL021835.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., T. Wong, N. Loeb, P. Minnis, K. Priestley, and R. Kandel, 2005: Changes in Earth’s albedo measured by satellite. Science, 308, 825, https://doi.org/10.1126/science.1106484.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, G. X., Y. M. Liu, X. Zhu, W. Li, R. Ren, A. M. Duan, and X. Liang, 2009: Multi-scale forcing and the formation of subtropical desert and monsoon. Ann. Geophys., 27, 36313644, https://doi.org/10.5194/angeo-27-3631-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, X., H. C. Zuo, Q. D. Yang, S. J. Wang, L. J. Wang, J. W. Chen, B. L. Chen, and B. D. Zhang, 2012: On the factors influencing surface-layer energy closure and their seasonal variability over the semi-arid Loess Plateau of Northwest China. Hydrol. Earth Syst. Sci., 16, 893910, https://doi.org/10.5194/hess-16-893-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, Z. X., and A. M. Duan, 2016: Impacts of Tibetan Plateau snow cover on the interannual variability of the East Asian summer monsoon. J. Climate, 29, 84958514, https://doi.org/10.1175/JCLI-D-16-0029.1.

    • Search Google Scholar
    • Export Citation
  • Xu, Z., Z. Hou, Y. Han, and W. Guo, 2016: A diagram for evaluating multiple aspects of model performance in simulating vector fields. Geosci. Model Dev., 9, 43654380, https://doi.org/10.5194/gmd-9-4365-2016.

    • Search Google Scholar
    • Export Citation
  • Xu, Z., Y. Han, and C. Fu, 2017: Multivariable integrated evaluation of model performance with the vector field evaluation diagram. Geosci. Model Dev., 10, 38053820, https://doi.org/10.5194/gmd-10-3805-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, K., and Coauthors, 2008: Turbulent flux transfer over bare-soil surfaces: characteristics and parameterization. J. Appl. Meteor. Climatol., 47, 276290, https://doi.org/10.1175/2007JAMC1547.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X. S., 1979: The Tibetan Plateau and the vegetation of China-geographical distribution characteristics of the Plateau in China related to the Plateau’s Role in atmospheric circulation (in Chinese). J. Xinjiang Agric. Univ., 1, 413, https://doi.org/CNKI:SUN:XJNY.0.1979-01-001.

    • Search Google Scholar
    • Export Citation
  • Zhang, X. S., 1993: A vegetation-climate classification system for global change studies in China. Quat. Sci., 13, 157169.

  • Zhou, L., C. J. Tucker, R. K. Kaufmann, D. Slayback, N. V. Shabanov, and R. B. Myneni, 2001: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res., 106, 20 06920 083, https://doi.org/10.1029/2000JD000115.

    • Search Google Scholar
    • Export Citation
  • Zilitinkevich, S., 1995: Non-local turbulent transport: Pollution dispersion aspects of coherent structure of convective flows. Trans. Ecol. Environ., 6, 5360, https://doi.org/10.2495/AIR950071.

    • Search Google Scholar
    • Export Citation
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Impacts of Land Surface Parameterizations on Simulations over the Arid and Semiarid Regions: The Case of the Loess Plateau in China

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  • 1 aSchool of Atmospheric Sciences, Nanjing University, Nanjing, China
  • | 2 bJoint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing, China
  • | 3 cPlateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, China
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Abstract

The arid and semiarid areas of the Loess Plateau are extremely sensitive to climate change. Land–atmosphere interactions of these regions play an important role in the regional climate. However, most present land surface models (LSMs) are not reasonable and accurate enough to describe the surface characteristics in these regions. In this study, we investigate the effects of three key land surface parameters including surface albedo, soil thermal conductivity, and additional damping on the Noah LSM in simulating the land surface characteristics. The observational data from June to September from 2007 to 2009 collected at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) station in northwest China are used to validate the Noah LSM simulations. The results suggest that the retrieved values of surface albedo, soil thermal conductivity, and additional damping based on observations are in closer agreement with those of the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping, respectively. Furthermore, the model performance is not obviously affected by surface albedo parameterization schemes, while the scheme of soil thermal conductivity is vital to simulations of latent heat flux and soil temperature and the scheme of additional damping is crucial for simulating net radiation flux, sensible heat flux, and surface soil temperature. A set of optimal parameterizations is proposed for the offline Noah LSM at the SACOL station when the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping are combined simultaneously, especially in the case of sensible heat flux and surface soil temperature simulations.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Weidong Guo, guowd@nju.edu.cn

Abstract

The arid and semiarid areas of the Loess Plateau are extremely sensitive to climate change. Land–atmosphere interactions of these regions play an important role in the regional climate. However, most present land surface models (LSMs) are not reasonable and accurate enough to describe the surface characteristics in these regions. In this study, we investigate the effects of three key land surface parameters including surface albedo, soil thermal conductivity, and additional damping on the Noah LSM in simulating the land surface characteristics. The observational data from June to September from 2007 to 2009 collected at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) station in northwest China are used to validate the Noah LSM simulations. The results suggest that the retrieved values of surface albedo, soil thermal conductivity, and additional damping based on observations are in closer agreement with those of the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping, respectively. Furthermore, the model performance is not obviously affected by surface albedo parameterization schemes, while the scheme of soil thermal conductivity is vital to simulations of latent heat flux and soil temperature and the scheme of additional damping is crucial for simulating net radiation flux, sensible heat flux, and surface soil temperature. A set of optimal parameterizations is proposed for the offline Noah LSM at the SACOL station when the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping are combined simultaneously, especially in the case of sensible heat flux and surface soil temperature simulations.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Weidong Guo, guowd@nju.edu.cn
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