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A Numerical Study on Effects of Land-Surface Heterogeneity from “Combined Approach” on Atmospheric Process Part I: Principle and Method


doi: 10.1007/s00376-000-0047-0

  • A method based on Giorgi (1997a, 1997b) and referred to as ‘combined approach’, which is a combination of mosaic approach and analytical-statistical-dynamical approach, is proposed. Compared with those of other approaches, the main advantage of the combined approach is that it not only can represent both interpatch and intrapatch variability, but also cost less computational time when the land surface heterogeneity is considered. Because the independent variable of probability density function (PDF) is extended to the single valued function of basic meteorological characteristic quantities, which is much more universal, the analytical expressions of the characteristic quantities, (e.g., drag coefficient, snow coverage, leaf surface aerodynamical resistance) affected by roughness length are derived, when the roughness length (and/ or the zero plane displacement) heterogeneity has been mainly taken into account with the approach. On the basis of the rule which the PDF parameters should follow, we choose a function y of the roughness length z0 as the PDF independent variable, and set different values of the two parameters width ratio αn and height ratio γ of PDF (here a linear, symmetric PDF is applied) for sensitivity experiments, from which some conclusions can be drawn, e.g., relevant characteristic terms show different sensitivities to the heterogeneous characteristic (i.e., roughness length), which suggests that we should consider the heterogeneities of the more sensitive terms in our model instead of the heterogeneities of the rest, and which also implies that when the land surface scheme is coupled into the global or regional atmospheric model, sensitivity tests against the distribution of the heterogeneous characteristic are very necessary; when the parameterαn is close to zero, little heterogeneity is represented, andαn differs with cases, which have an upper limit of about 0.6; in the reasonable range ofαn, a peak-like distribution of roughness length can be depicted by a small value ofγ, etc..
  • [1] Zeng Xinmin, Zhao Ming, Su Bingkai, 2000: A Numerical Study on Effects of Land-Surface Heterogeneity from ‘Combined Approach’ on Atmospheric Process Part II: Coupling-Model Simulations, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 241-255.  doi: 10.1007/s00376-000-0007-8
    [2] Zheng Weizhong, Ni Yunqi, 1999: A Numerical Experiment Study for Effects of the Grassland Desertification on Summer Drought in North China, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 251-262.  doi: 10.1007/BF02973086
    [3] LIU Ge, JI Liren, SUN Shuqing, ZHANG Qingyun, 2010: An Inter-hemispheric Teleconnection and a Possible Mechanism for Its Formation, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 629-638.  doi: 10.1007/s00376-009-8172-x
    [4] Cheng Minghu, Shi Guangyu, Zhou Xiuji, 1990: Numerical Experiment of Combined Infrared and Ultraviolet Radiation Remote Sensing to Determine the Profile and Total Content of Atmospheric Ozone, ADVANCES IN ATMOSPHERIC SCIENCES, 7, 305-319.  doi: 10.1007/BF03179763
    [5] LI Hongqi, GUO Weidong, SUN Guodong, ZHANG Yaocun, FU Congbin, 2011: A New Approach for Parameter Optimization in Land Surface Model, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1056-1066.  doi: 10.1007/s00376-010-0050-z
    [6] Liu Ruizhi, 1986: A NUMERICAL EXPERIMENT OF CYCLOGENESIS AND THE DEVELOPMENT OF DISTURBANCES, ADVANCES IN ATMOSPHERIC SCIENCES, 3, 499-504.  doi: 10.1007/BF02657939
    [7] Liu Huizhi, Sang Jianguo, Zhang Boyin, Johnny C.L. Chan, Andrew Y.S.Cheng, Liu Heping, 2002: Influences of Structures on Urban Ventilation:A Numerical Experiment, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 1045-1054.  doi: 10.1007/s00376-002-0063-3
    [8] Xu Yuantai, Li Hongzhou, 1989: A Numerical Experiment of Mesolow on the Eastern Side of the Taihang Mountains, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 133-136.  doi: 10.1007/BF02656924
    [9] Yang Xiaosong, Lin Zhaohui, Dai Yongjiu, Guo Yufu, 2001: Validation of IAP94 Land Surface Model over the Huaihe River Basin with HUBEX Field Experiment Data, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 139-154.  doi: 10.1007/s00376-001-0009-1
    [10] Ji Liren, S.Tibaldi, 1984: NUMERICAL EXPERIMENT ON THE SEASONAL TRANSITION OF GENERAL CIRCULATION OVER ASIA - PART Ⅰ, ADVANCES IN ATMOSPHERIC SCIENCES, 1, 128-149.  doi: 10.1007/BF03187624
    [11] Chen Yuejuan, Zhang Hong, Bi Xunqiang, 1998: Numerical Experiment for the Impact of the Ozone Hole over Antarctica on the Global Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 300-311.  doi: 10.1007/s00376-998-0002-z
    [12] Liu Ruizhi, 1985: NUMERICAL EXPERIMENT OF SIX-LEVEL IMPLICIT PRIMITIVE MODEL, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 178-188.  doi: 10.1007/BF03179750
    [13] Zhao Ming, 1988: A NUMERICAL EXPERIMENT OF THE PBL WITH GEO-STROPHIC MOMENTUM APPROXIMATION, ADVANCES IN ATMOSPHERIC SCIENCES, 5, 47-56.  doi: 10.1007/BF02657345
    [14] Guo Weidong, Sun Shufen, Qian Yongfu, 2002: Case Analyses and Numerical Simulation of Soil Thermal Impacts on Land Surface Energy Budget Based on an Off-Line Land Surface Model, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 500-512.  doi: 10.1007/s00376-002-0082-0
    [15] Shuangmei MA, Congwen ZHU, Juan LIU, 2020: Combined Impacts of Warm Central Equatorial Pacific Sea Surface Temperatures and Anthropogenic Warming on the 2019 Severe Drought in East China, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1149-1163.  doi: 10.1007/s00376-020-0077-8
    [16] Marek PÓŁROLNICZAK, Leszek KOLENDOWICZ, Bartosz CZERNECKI, Mateusz TASZAREK, Gabriella TÓTH, 2021: Determination of Surface Precipitation Type Based on the Data Fusion Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 387-399.  doi: 10.1007/s00376-020-0165-9
    [17] Jiaqi ZHENG, Qing LING, Jia LI, Yerong FENG, 2024: Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 1601-1613.  doi: 10.1007/s00376-023-3085-7
    [18] Shaofeng LIU, Michael HINTZ, Xiaolong LI, 2016: Evaluation of Atmosphere-Land Interactions in an LES from the Perspective of Heterogeneity Propagation, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 571-578.  doi: 10.1007/s00376-015-5212-6
    [19] Jianguo LIU, Binghao JIA, Zhenghui XIE, Chunxiang SHI, 2016: Ensemble Simulation of Land Evapotranspiration in China Based on a Multi-Forcing and Multi-Model Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 673-684.  doi: 10.1007/s00376-016-5213-0
    [20] HAN Guijun, LI Wei, HE Zhongjie, LIU Kexiu, MA Jirui, 2006: Assimilated Tidal Results of Tide Gauge and TOPEX/POSEIDON Data over the China Seas Using a Variational Adjoint Approach with a Nonlinear Numerical Model, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 449-460.  doi: 10.1007/s00376-006-0449-8

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Manuscript History

Manuscript received: 10 January 2000
Manuscript revised: 10 January 2000
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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A Numerical Study on Effects of Land-Surface Heterogeneity from “Combined Approach” on Atmospheric Process Part I: Principle and Method

  • 1. Department of Atmospheric Sciences; Nanjing University; Nanjing; 210093,Department of Atmospheric Sciences; Nanjing University; Nanjing; 210093,Department of Atmospheric Sciences; Nanjing University; Nanjing; 210093

Abstract: A method based on Giorgi (1997a, 1997b) and referred to as ‘combined approach’, which is a combination of mosaic approach and analytical-statistical-dynamical approach, is proposed. Compared with those of other approaches, the main advantage of the combined approach is that it not only can represent both interpatch and intrapatch variability, but also cost less computational time when the land surface heterogeneity is considered. Because the independent variable of probability density function (PDF) is extended to the single valued function of basic meteorological characteristic quantities, which is much more universal, the analytical expressions of the characteristic quantities, (e.g., drag coefficient, snow coverage, leaf surface aerodynamical resistance) affected by roughness length are derived, when the roughness length (and/ or the zero plane displacement) heterogeneity has been mainly taken into account with the approach. On the basis of the rule which the PDF parameters should follow, we choose a function y of the roughness length z0 as the PDF independent variable, and set different values of the two parameters width ratio αn and height ratio γ of PDF (here a linear, symmetric PDF is applied) for sensitivity experiments, from which some conclusions can be drawn, e.g., relevant characteristic terms show different sensitivities to the heterogeneous characteristic (i.e., roughness length), which suggests that we should consider the heterogeneities of the more sensitive terms in our model instead of the heterogeneities of the rest, and which also implies that when the land surface scheme is coupled into the global or regional atmospheric model, sensitivity tests against the distribution of the heterogeneous characteristic are very necessary; when the parameterαn is close to zero, little heterogeneity is represented, andαn differs with cases, which have an upper limit of about 0.6; in the reasonable range ofαn, a peak-like distribution of roughness length can be depicted by a small value ofγ, etc..

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