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A Fast and Efficient Version of the TwO-Moment Aerosol Sectional (TOMAS) Global Aerosol Microphysics Model

机译:TwO-矩气溶胶截面(TOMAS)全球气溶胶微物理模型的快速高效版本

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摘要

This study develops more computationally efficient versions of the TwO-Moment Aerosol Sectional (TOMAS) microphysics algorithms, collectively called “Fast TOMAS.” Several methods for speeding up the algorithm were attempted, but only reducing the number of size sections was adopted. Fast TOMAS models, coupled to the GISS GCM II-prime, require a new coagulation algorithm with less restrictive size resolution assumptions but only minor changes in other processes. Fast TOMAS models have been evaluated in a box model against analytical solutions of coagulation and condensation and in a 3-D model against the original TOMAS (TOMAS-30) model. Condensation and coagulation in the Fast TOMAS models agree well with the analytical solution but show slightly more bias than the TOMAS-30 box model. In the 3-D model, errors resulting from decreased size resolution in each process (i.e., emissions, cloud processing/wet deposition, microphysics) are quantified in a series of model sensitivity simulations. Errors resulting from lower size resolution in condensation and coagulation, defined as the microphysics error, affect number and mass concentrations by only a few percent. The microphysics error in CN70/CN100 (number concentrations of particles larger than 70/100 nm diameter), proxies for cloud condensation nuclei, range from -5% to 5% in most regions. The largest errors are associated with decreasing the size resolution in the cloud processing/wet deposition calculations, defined as cloud-processing error, and range from -20% to 15% in most regions for CN70/CN100 concentrations. Overall, the Fast TOMAS models increase the computational speed by 2 to 3 times with only small numerical errors stemming from condensation and coagulation calculations when compared to TOMAS-30. The faster versions of the TOMAS model allow for the longer, multi-year simulations required to assess aerosol effects on cloud lifetime and precipitation.
机译:这项研究开发了计算效率更高的TwO-Moment气溶胶截面(TOMAS)微物理算法版本,统称为“快速TOMAS”。尝试了几种加速算法的方法,但仅减少了尺寸的数量通过。快速TOMAS模型与GISS GCM II-prime结合使用时,需要一种新的混凝算法,该算法具有较小的尺寸分辨率假设,但在其他过程中只有很小的变化。快速TOMAS模型已在箱形模型中针对凝结和冷凝的分析解决方案进行了评估,在3-D模型中针对原始TOMAS(TOMAS-30)模型进行了评估。 Fast TOMAS模型中的冷凝和凝结与分析解决方案非常吻合,但显示出比TOMAS-30盒模型稍大的偏差。在3-D模型中,在一系列模型敏感性模拟中量化了每个过程中尺寸分辨率降低所导致的误差(即排放,云处理/湿法沉积,微观物理学)。由缩合和凝结中较低的尺寸分辨率引起的误差(定义为微观物理学误差)仅影响百分之几和质量浓度。在大多数地区,CN70 / CN100(直径大于70/100 nm的粒子的数量浓度)(云凝结核的代理)的微观物理误差范围为-5%至5%。最大的误差与降低云处理/湿沉降计算中的尺寸分辨率有关,定义为云处理误差,对于CN70 / CN100浓度,在大多数区域中,误差范围从-20%到15%。总体而言,与TOMAS-30相比,Fast TOMAS模型将计算速度提高了2到3倍,而冷凝和凝结计算产生的数值误差很小。 TOMAS模型的较快版本允许进行更长的多年模拟,以评估气溶胶对云层寿命和降水的影响。

著录项

  • 来源
    《Aerosol Science and Technology》 |2012年第6期|p.678-689|共12页
  • 作者

    Y. H. Lee P. J. Adams;

  • 作者单位

    NASA Goddard Institute for Space Studies and Center for Climate Systems Research, Columbia University, New York, New York, USA Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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  • 入库时间 2022-08-18 00:57:39

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