The challenge of simulating the sensitivity of the Amazonian cloud microstructure to cloud condensation nuclei number concentrations
The challenge of simulating the sensitivity of the Amazonian cloud microstructure to cloud condensation nuclei number concentrations
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DOI：
10.5194/acp-20-1591-2020
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发表时间：
2020-02
影响因子：
6.3
通讯作者：
P. Polonik;C. Knote;T. Zinner;Florian Ewald;T. Kölling;B. Mayer;M. Andreae;T. Jurkat-Witschas;T. Klimach;C. Mahnke;S. Molleker;C. Pöhlker;M. Pöhlker;U. Pöschl;D. Rosenfeld;C. Voigt;R. Weigel;M. Wendisch
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文献类型：
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作者：
P. Polonik;C. Knote;T. Zinner;Florian Ewald;T. Kölling;B. Mayer;M. Andreae;T. Jurkat-Witschas;T. Klimach;C. Mahnke;S. Molleker;C. Pöhlker;M. Pöhlker;U. Pöschl;D. Rosenfeld;C. Voigt;R. Weigel;M. Wendisch
抽象的。气溶胶-云相互作用的真实表现对于准确的气候模式预测至关重要。对亚马逊地区清洁和污染大气条件形成强烈对比的这些相互作用的研究是几项实地活动的动机之一，包括“气溶胶、云、降水和辐射的相互作用以及对流云系统的动力学-巴西主要降水系统的云过程：对云解析建模和GPM（全球降水测量）（ACRIDICON-CHUVA）活动的贡献，该活动于2014年9月在巴西马瑙斯举行。在这项工作中，我们结合联合收割机在现场和遥感的气溶胶，云，和大气辐射数据收集ACRIDICON-CHUVA与区域，在线耦合的化学传输模拟，以评估模型的能力，代表间接影响的生物质燃烧气溶胶云微物理和光学特性（液滴数浓度和有效半径）。我们发现，对于较低的CDNC值，模拟的和观察到的中值云滴数浓度（CDNC）之间存在一致性，即，低污染水平。在一般情况下，建模和观察到的CDNC与0.3的斜率之间的线性关系被发现，这意味着一个系统的低估建模CDNC相比，测量。云凝结核（CCN）数浓度的变化也被低估，云滴有效半径（reff）被高估的模式。模拟的有效半径剖面在云底约500 CCN cm −3开始饱和，表明模型灵敏度的上限远低于亚马逊盆地燃烧季节达到的CCN浓度。额外的云凝结核排放的地方火灾并没有造成显着的变化，模拟云滴有效半径。最后，我们还评估了CDNC在云底使用更容易获得的云微物理特性的参数化，表明我们能够推导出云底CDNC从云侧遥感观测。
Abstract. The realistic representation of aerosol–cloud interactions is of primary importance for accurate climate model projections. The investigation of these interactions in strongly contrasting clean and polluted atmospheric conditions in the Amazon region has been one of the motivations for several field campaigns, including the airborne “Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems–Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (Global Precipitation Measurement) (ACRIDICON-CHUVA)” campaign based in Manaus, Brazil, in September 2014. In this work we combine in situ and remotely sensed aerosol, cloud, and atmospheric radiation data collected during ACRIDICON-CHUVA with regional, online-coupled chemistry-transport simulations to evaluate the model's ability to represent the indirect effects of biomass burning aerosol on cloud microphysical and optical properties (droplet number concentration and effective radius). We found agreement between the modeled and observed median cloud droplet number concentration (CDNC) for low values of CDNC, i.e., low levels of pollution. In general, a linear relationship between modeled and observed CDNC with a slope of 0.3 was found, which implies a systematic underestimation of modeled CDNC when compared to measurements. Variability in cloud condensation nuclei (CCN) number concentrations was also underestimated, and cloud droplet effective radii ( reff ) were overestimated by the model. Modeled effective radius profiles began to saturate around 500 CCN cm −3 at cloud base, indicating an upper limit for the model sensitivity well below CCN concentrations reached during the burning season in the Amazon Basin. Additional CCN emitted from local fires did not cause a notable change in modeled cloud droplet effective radii. Finally, we also evaluate a parameterization of CDNC at cloud base using more readily available cloud microphysical properties, showing that we are able to derive CDNC at cloud base from cloud-side remote-sensing observations.