设为首页
加入收藏
Assessment of Two Aerosol Modules of CAM5
-
摘要: 公共大气模式 (CAM) 被广泛用于气候变化研究中，其5.0版中包含两个气溶胶模块MAM3和MOZART。利用AeroCom (2000年) 多模式中值、IMPROVE (1988—2005年) 和EMEP (2002—2008年) 站点资料对两模块进行了评估。结果表明：MAM3和MOZART模块都能很好地模拟硫酸盐气溶胶的季节变化, 与观测资料相比，模拟结果均在夏季偏高, 相关系数均在0.89左右，2倍误差内。两模块能较好地模拟黑碳气溶胶的时空分布特征, 与观测资料相比，模拟结果偏低，相关系数均在0.62左右, 排放源偏小而清除率偏大是造成黑碳气溶胶偏低的主要原因。两模块对有机碳气溶胶的模拟结果差别较大，大部分站点在3倍误差内，MAM3的结果偏高92.1%，MOZART则偏低58.1%；两模块一次有机碳的结果接近，差异主要源自对二次有机碳的模拟。两模块模拟的海盐气溶胶偏大，这主要是清除率偏小造成的。两模块采用相同的起沙机制，但起沙系数有差异, MAM3的沙尘源强偏大近两倍，模拟总量较大；MOZART的沙尘源强则偏低40%左右，模拟沙尘总量较低。即模式中气溶胶的输送和扩散过程偏弱，说明清除机制还有待改进。Abstract: 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.
-
Key words:
- CAM5;
- aerosol;
- climate effect
-
图 1 模拟硫酸盐气溶胶月平均质量浓度与观测值的比较
(误差棒是该月观测值的标准差)
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)
图 2 模拟硫酸盐气溶胶地面质量浓度与观测值年平均比较的散点图
(实线为1:1比率，虚线为2:1和1:2的比率；R是相关系数，b是相对平均偏差)
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)
图 3 硫酸盐气溶胶柱质量浓度年平均值全球分布
(a) AEROCOM多模式中值，(b) MAM3模块，(c) MOZART模块
Fig. 3 Annual average global distribution of column concentration of sulfate aerosol according to AEROCOM_MEDIAN (a), MAM3(b) and MOZART (c)
图 4 模拟黑碳气溶胶地面质量浓度与观测值年平均比较的散点图(虚线为3:1和1:3比率)
Fig. 4 The same as in Fig 2, but for black carbon aerosol(two dashed lines are 1:3 or 3:1)
图 5 黑碳气溶胶柱质量浓度年平均全球分布
(a) AEROCOM多模式中值，(b) MAM3模块，(c) MOZART模块
Fig. 5 The same as in Fig. 3, but for black carbon aerosol
图 6 模拟有机碳气溶胶地面质量浓度 (包括POC和SOC) 与观测值年平均比较的散点图
(虚线为3:1和1:3比率)
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)
图 7 有机颗粒物柱质量浓度年平均全球分布
(a) AEROCOM多模式中值，(b) MAM3模块，(c) MOZART模块
Fig. 7 The same as in Fig. 3, but for organic matter (OM)
表 1 两模块采用的排放源清单
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。
下载: 导出CSV
表 2 硫酸盐气溶胶的年收支情况
Table 2 Annual budget for sulfate aerosol
模式 大气总
量/Tg源 汇 生命
期/d年排放
总量/Tg直接排
放/TgSO2气相
氧化/TgSO2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-1MAM3 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
下载: 导出CSV
表 3 黑碳气溶胶的年收支情况
Table 3 Annual budget for black carbon aerosol
模式 大气总
量/Tg年排放
总量/Tg汇 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-1MAM3 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
下载: 导出CSV
表 4 一次和二次有机碳颗粒物的全球年收支情况
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汇 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
下载: 导出CSV
表 5 海盐和沙尘气溶胶的全球年收支情况
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汇 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
下载: 导出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 -
计量
- 摘要浏览量: 4091
- HTML全文浏览量: 1125
- PDF下载量: 2466
- 被引次数: 0
