Li Xin, Liu Yu. Assessment of two aerosol modules of CAM5. J Appl Meteor Sci, 2013, 24(1): 75-86.
Citation: Li Xin, Liu Yu. Assessment of two aerosol modules of CAM5. J Appl Meteor Sci, 2013, 24(1): 75-86.

Assessment of Two Aerosol Modules of CAM5

  • Received Date: 2012-03-01
  • Rev Recd Date: 2012-11-06
  • Publish Date: 2013-02-28
  • The Community Atmosphere Model (CAM) is widely employed in research of climate simulation and climate change. The latest version 5.0, provides two modules to simulate atmosphere aerosol, named MAM3 and MOZART, respectively. Several main atmosphere aerosols are simulated by these two modules, and the simulated surface concentrations of these aerosols are examined by Interagency Monitoring of Protected Visual Environments Program (IMPROVE) and European Monitoring and Evaluation Program (EMEP). The simulated global distributions of aerosol column concentration, as well as aerosol global budgets are compared with median model results on AeroCom website.Both MAM3 and MOZART modules can capture the seasonal distribution of sulfate aerosol; the simulated surface concentrations are in reasonable agreement with observations, although the values in summer are usually high. The correlation coefficients between models and observations for two modules are both around 0.89. Also, both MAM3 and MOZART can capture spatial and temporal distribution of black carbon aerosol. However, these two modules both underestimate surface concentration of black carbon by a factor of 2—3. The correlation coefficients between models and observations for two modules are both around 0.62, which are believed to be caused by smaller emission fluxes and higher rates of wet removal. The two modules have large difference in simulating organic matter, both having a bias by a factor of 2—3. MAM3 overestimates surface concentrations of organic matter with a normalized mean bias of 92.1%, while MOZART makes an underestimation of 58.1%. It's found that both of these biases usually happen in summer and autumn. A separate analysis demonstrates that the primary organic matter simulated by these two modules are very close, while MAM3 and MOZART have serious differences on simulation of the secondary organic carbon (SOC), which primarily contributes to the bias of total organic matter. Sea salt global budgets by MAM3 and MOZART are close, but the total content of sea salt is larger than median model results on AeroCom. The most likely cause is that lower rates of dry removal and wet removal in the CAM5. With similar mechanism but different emission factor, the two modules perform differently in simulating mineral dust; flux of mineral dust emission in MAM3 is nearly three times as large as that in median model results on AeroCom, and thus overestimates the total content, while MOZART underestimates mineral dust burden, because its emission flux is 40% smaller than that in median model results on AeroCom.According to the comparison in global distribution and global budget, it indicates that CAM5 has a weaker intensity of aerosol translation and diffusion, thus, the removal mechanism should be improved.
  • Fig. 1  Comparison of simulated sulfate aerosol monthly mean concentration with IMPROVE

    (error bar on the solid line denotes IMPROVE monthly mean data along with standard deviation)

    Fig. 2  Annual average simulated surface concentration versus observations from IMPROVE and EMEP

    (the solid line and two dashed lines are 1:1, 1:2 and 2:1, respectively; R is the correlation coefficient, b is mean bias)

    Fig. 3  Annual average global distribution of column concentration of sulfate aerosol according to AEROCOM_MEDIAN (a), MAM3(b) and MOZART (c)

    Fig. 4  The same as in Fig 2, but for black carbon aerosol(two dashed lines are 1:3 or 3:1)

    Fig. 5  The same as in Fig. 3, but for black carbon aerosol

    Fig. 6  The same as in Fig. 2, but for organic carbon aerosol (POC and SOC)

    (two dashed lines are 1:3 or 3:1)

    Fig. 7  The same as in Fig. 3, but for organic matter (OM)

    Table  1  Emission used in two modules

    成分 年排放总量/Tg
    DMS 14.13
    SO2 57.0
    BC 7.7
    POC 35.2
    注:MAM3模块考虑了一部分SO2直接以硫酸盐颗粒物的
    形式排放进入大气,比例设为2.5%,并考虑了一些排放
    情况的高度分布[7];而MOZART中2005年排放源没有
    上述处理。
    DownLoad: Download CSV

    Table  2  Annual budget for sulfate aerosol

    模式 大气总
    量/Tg
    生命
    期/d
    年排放
    总量/Tg
    直接排
    放/Tg
    SO2气相
    氧化/Tg
    SO2液相
    氧化/Tg
    年清除
    总量/Tg
    干沉降
    /Tg
    干清除
    率/d-1
    湿沉降
    /Tg
    湿清除率
    /d-1
    MAM3 0.43 36.7 1.40 12.3 23.0 36.7 7.74 0.030 32.0 0.20 4.3
    MOZART 0.45 46.0 0.00 9.4 36.6 46.0 5.53 0.034 40.5 0.25 3.6
    AEROCOM 0.63 2.11 57.6 6.20 0.027 49.0 0.21 4.0
    注:清除率=年清除总量/大气总量/365;生命期=大气总量/年清除总量×365
    DownLoad: Download CSV

    Table  3  Annual budget for black carbon aerosol

    模式 大气总
    量/Tg
    年排放
    总量/Tg
    生命期
    /d
    年清除
    总量/Tg
    干沉降
    /Tg
    干清除率
    /d-1
    湿沉降
    /Tg
    湿清除率
    /d-1
    MAM3 0.10 7.7 7.9 1.37 0.036 6.53 0.17 4.8
    MOZART 0.12 7.7 7.6 1.66 0.039 5.97 0.14 5.6
    AEROCOM 0.18 8.7 9.2 1.65 0.025 7.65 0.12 7.0
    DownLoad: Download CSV

    Table  4  Global budget for primary and secondary organic matter

    有机碳 模式 大气总
    量/Tg
    年排放
    总量/Tg
    生命
    期/d
    年清除总量/Tg 干沉降/Tg 干清除率/d-1 湿沉降/Tg 湿清除率/d-1
    一次 MAM3 0.71 49.3 50.0 8.1 0.030 41.9 0.16 5.3
    MOZART 0.74 49.3 49.6 10.5 0.039 39.1 0.14 5.5
    LWPD 1.11 49.3 49.4 10.1 0.025 39.3 0.10 8.2
    二次 MAM3 1.34 102.7 102.6 12.7 0.03 89.9 0.18 4.8
    MOZART 0.11 9.51 9.51 1.60 0.04 7.91 0.21 4.0
    LWPD 1.26 68.6 68.5 9.8 0.02 0.21 0.13 6.7
    DownLoad: Download CSV

    Table  5  Global budget for sea salt and sand dust

    气溶胶 模式 大气
    总量/Tg
    年排放
    总量/Tg
    生命
    期/d
    年清除总量/Tg 湍流清除率/d-1 重力清除率/d-1 湿清除率/d-1
    海盐 MAM3 11.2 4696 4692 0.24 0.34 0.58 0.87
    MOZART 10.3 4728 4760 0.39 0.37 0.51 0.79
    AEROCOM 6.1 6218 6206 0.63 0.71 0.81 0.36
    沙尘 MAM3 22.80 3028 3028 0.023 0.209 0.132 2.8
    MOZART 9.08 622 626 0.049 0.060 0.080 5.3
    AEROCOM 15.94 1126 1261 0.068 0.054 0.062 4.6
    DownLoad: Download CSV
  • [1]
    张小曳, 张养梅, 曹国良.北京PM1中的化学组成及其控制对策思考.应用气象学报, 2012, 23(3): 257-264. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120301&flag=1
    [2]
    颜鹏, 郇宁, 张养梅, 等.北京乡村地区分粒径气溶胶OC及EC分析.应用气象学报, 2012, 23(3): 285-293. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120304&flag=1
    [3]
    颜鹏, 刘桂清, 周秀骥, 等.上甸子秋冬季雾霾期间气溶胶光学特性.应用气象学报, 2010, 21(3): 257-265. doi:  10.11898/1001-7313.20100301
    [4]
    徐晓斌, 刘希文, 林伟立.输送对区域本底站痕量气体浓度的影响.应用气象学报, 2009, 20(6): 657-664. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20090602&flag=1
    [5]
    黄健, 刘作挺, 黄敏辉.珠江三角洲区域大气输送和扩散的季节特征.应用气象学报, 2010, 21(6): 698-708. doi:  10.11898/1001-7313.20100606
    [6]
    Wang Z L, Zhang H, Shen X, et al. Modeling study of aerosol indirect effects on global climate with an AGCM. Adv Atmos Sci, 2010, 27(5): 1064-1077. doi:  10.1007/s00376-010-9120-5
    [7]
    Neale R B, Chen C C, Gettlelman A, et al. Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Technical Note, 2010, 486: 1-268. https://www.researchgate.net/publication/224017878_Description_of_the_NCAR_Community_Atmosphere_Model
    [8]
    Liu X, Wang J. How important is organic aerosol hygroscopicity to aerosol indirect forcing. Environ Res Let, 2010, 5(4), 044010, doi: 10.1088/1748-9326/5/4/044010.
    [9]
    Meskhidze N, Xu J, Gantt B, et al. Global distribution and climate forcing of marine organic aerosol—Part 1: Model improvements and evaluation. Atmos Chem Phys, 2011, 11: 11689-11705. doi:  10.5194/acp-11-11689-2011
    [10]
    Liu X H, Easter R C, Ghan S J, et al. Toward a minimal representation of aerosol direct and indirect effects. Geosci Model Dev, 2011, 4: 3485-3598. doi:  10.5194/gmdd-4-3485-2011
    [11]
    Wang M, Ghan S, Easter R, et al. The multi-scale aerosol-climate Model PNNL-MMF: Model description and evaluation. Geosci Model Dev, 2011, 4: 137-168. http://www.oalib.com/paper/2157044#.WQ--Yfl6-0I
    [12]
    Martensson E M, Nilsson E D, de Leeuw G, et al. Laboratory simulations and parameterization of the primary marine aerosol production. Journal of Geophysical Research-Atmospheres, 2003, 108 (D9): 4297. doi:  10.1029/2002JD002263/citedby
    [13]
    Monahan E C, Spiel D E, Davidson K L. Oceanic Whitecaps and Their Role in Air-Sea Exchange Processes. 1986: 167-174.
    [14]
    Zender C S, Bian H, Newman D. The mineral dust entrainment and deposition (DEAD) model: Description and 1990's dust climatology. J Geophys Res, 2003, 108(D14), 4416, doi:  10.1029/2002JD002775.
    [15]
    Rasch P J, Barth M C, Kiehl J T, et al. A description of the global sulfur cycle and its controlling processes in the national center for atmospheric research community climate model, version 3. J Geophys Res, 2000, 105: 1367-1385. doi:  10.1029/1999JD900777
    [16]
    Barth M C, Rasch P J, Kiehl J T, et al. Sulfur chemistry in the national center for atmospheric research community climate model: Description, evaluation, features and sensitivity to aqueous chemistry. J Geophys Res, 2000, 105: 1387-1415. doi:  10.1029/1999JD900773
    [17]
    Zhang L M, Gong S L, Padro J, et al. A size-segregated particle dry deposition scheme for an atmospheric aerosol module. Atmospheric Environment, 2001, 35(3): 549-560. doi:  10.1016/S1352-2310(00)00326-5
    [18]
    Collins W D, Rasch P J, Boville B A, et al., Description of the NCAR Community Atmosphere Model (CAM3). NCAR Technical Note, 2004, 464: 1-214. https://www.mendeley.com/research-papers/description-ncar-community-atmosphere-model-cam-30/
    [19]
    Emmons L K, Walters S, Hess P G, et al. Description and evaluation of the model for ozone and related chemical tracers, version 4 (MOZART-4). Geosci Model Dev, 2010, 3: 43-67. doi:  10.5194/gmd-3-43-2010
    [20]
    Riahi K, Gruebler A, Nakicenovic N. Scenarios of long-term socio-economic and environmental development under climate stabilization. Technological Forecasting and Social Change, 2007, 74: 887-935. doi:  10.1016/j.techfore.2006.05.026
    [21]
    Dentener F, Kinne S, Bond T, et al. Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom. Atmos Chem Phys, 2006, 6: 4321-4344. doi:  10.5194/acp-6-4321-2006
  • 加载中
  • -->

Catalog

    Figures(7)  / Tables(5)

    Article views (4091) PDF downloads(2466) Cited by()
    • Received : 2012-03-01
    • Accepted : 2012-11-06
    • Published : 2013-02-28

    /

    DownLoad:  Full-Size Img  PowerPoint