Improved Representation of Cloud-Aerosol Interactions in the Community Earth System Model: A New Sectional Cloud Model that Interacts with Modal and Sectional Aerosol Models

社区地球系统模型中云-气溶胶相互作用的改进表示：与模态和剖面气溶胶模型相互作用的新剖面云模型

基本信息

批准号：
2114638
负责人：
Owen Toon
金额：
$ 65.89万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-15 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114638&HistoricalAwards=false
关键词：
Improved Representation Cloud Aerosol Interactions

项目摘要

Clouds have a profound effect on the energy balance of the Earth, by reflecting sunlight to space and blocking outgoing infrared radiation. Earth's climate is thus sensitive to all the processes, collectively referred to as cloud microphysics, that govern the formation and growth of cloud particles and their removal through evaporation and precipitation. For example, as climate warms the abundance of ice particles in clouds decreases in favor of liquid droplets, which makes the clouds more reflective and thus has a counteracting effect on the warming (a negative feedback). Liquid clouds also tend to last longer as they are less effective in generating precipitation, which could further enhance the negative feedback of the shift from ice particles to droplets.The sensitivity of climate to cloud microphysics poses a challenge for climate research, particularly as climate models must represent the full global climate system while much of the microphysics takes place over distances less than a millimeter. Global models use parameterizations to represent the bulk effects of cloud microphysics but these parameterizations are necessarily crude given the need to perform long and computationally intensive simulations. One concern with microphysics parameterizations is that they use nonphysical parameters to adjust the behavior of the clouds, and these parameters have a direct effect on important climate system behaviors such as the sensitivity of global temperature to greenhouse gas increases. A case in point is autoconversion, a parametric representation of the processes through which cloud particles interact to form precipitation. Autoconversion summarily converts some portion of a cloud's frozen and liquid water into raindrops or snowflakes according to externally imposed threshold criteria. The choice of threshold values for autoconversion affects cloud lifetimes and thus affects the top-of-atmosphere energy balance, thus giving the nonphysical thresholds an outsized effect on the simulated climate.Work performed here develops an alternative cloud microphysics model in which autoconversion and other one-step approximations are replaced by a more detailed formulation in which cloud particles are represented in terms of a size distribution, meaning the model partitions droplets and ice particles into a set of size bins, also referred to as sections of the size distribution, and keeps track of the abundance of particles in each bin. Microphysics is then represented through interactions between bins, for instance if small droplets grow bigger as water vapor condenses on them they are transferred from a bin for small droplets to a bin for larger ones. An advantage of the scheme is that the more explicit representation of cloud microphsyics eliminates many of the nonphysical parameters found in simpler schemes. The scheme is too computationally intensive for use in century-scale climate simulations but is practical for decadal simulations and can be used to inform development of simpler fast schemes.The cloud microphysics model is based on CARMA, the Community Aerosol and Radiation Model for Atmospheres, which uses a size bin scheme to represent the chemistry and microphysics of aerosols. Here the bin scheme is adapted to represent the microphysics of liquid cloud droplets and cloud ice, with the ability to represent the transfer of water between droplet bins and ice particle bins through freezing and thawing. The versions of CARMA used to represent aerosols and clouds are referred to as CARMA-aerosol and CARMA-cloud, respectively, and they are used together to represent the effects of aerosols on cloud particles. This award continues development of CARMA under previous support, most recently through AGS-1640903.Once developed, the model is used to address several issues in cloud physics and climate dynamics. In particular the model is used to consider the effect of cloud microphysics on climate change through simulations in which carbon dioxide concentration is instantaneously doubled, a standard way to assess the sensitivity of simulated climate to greenhouse gas increases. Motivation for the simulations comes from the increased climate sensitivity found in the latest generation of climate models contributing to the Climate Model Intercomparison Project (CMIP), which has been ascribed to changes in cloud microphysics.The work has societal relevance through its effort to improve understanding of the role of cloud microphysics in climate change. Cloud microphysics is a particular concern as clouds are frequently called out as the greatest source of uncertainty in model projections of future climate change used for decision support. The work also benefits the worldwide community of researchers who use and develop CESM. The project has educational value through the development of a stand-alone version of CARMA-cloud model which is suitable for classroom use, and through the support and training of a graduate student.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

云通过反射阳光到空间并阻止外向红外辐射对地球的能量平衡产生深远的影响。因此，地球的气候对所有过程（共同称为云微物理学）敏感，该过程控制云颗粒的形成和生长及其通过蒸发和沉淀的去除。例如，随着气候变暖，云中的冰颗粒的丰度减少了液滴，这使得云使云更加反射，从而对变暖产生抵消作用（负反馈）。液态云在产生降水方面的有效性较小，因此液体云的持续时间往往更长，这可以进一步增强从冰颗粒向液滴转移的负面反馈。气候对云微物理学的敏感性对气候研究构成了挑战，尤其是作为气候模型的挑战必须代表整个全球气候系统，而大部分微型物理学的发生在距离小于毫米的距离上。全球模型使用参数化来表示云微物理学的批量影响，但是鉴于需要进行长时间和计算强度的模拟，这些参数化必然是粗略的。微物理参数化的一个问题是，它们使用非物理参数来调整云的行为，并且这些参数对重要的气候系统行为有直接影响，例如全球温度对温室气体的敏感性增加。一个很好的例子是自动转换，这是云颗粒相互作用以形成沉淀的过程的参数表示。根据外部施加的阈值标准，自动转换即将转换为云冷冻和液态水的某些部分变成雨滴或雪花。自动转换的阈值选择会影响云的寿命，从而影响大气顶能量平衡，从而使非物理阈值对模拟气候产生极大的影响。此处执行的工作开发了一种替代的云微物理学模型，在该模型中，该模型在该模型中进行了自动转化和另一个自动转换模型 - 步骤近似被更详细的配方所取代，在该配方中，云颗粒以尺寸分布表示，这意味着模型分区液滴和冰粒子成一组尺寸箱，也称为大小分布的各个部分，并保持每个垃圾箱中颗粒的丰度轨迹。然后，通过垃圾箱之间的相互作用来表示微物理学，例如，如果小滴较大，随着水蒸气的凝结，它们会从垃圾箱中转移到小滴的垃圾箱到垃圾箱的垃圾箱。该方案的一个优点是，更明确的云微观结晶表示消除了在简单方案中发现的许多非物理参数。该方案在计算上太密集了，无法在世纪级的气候模拟中使用，但对于衰老模拟而言是实用的，可用于为更简单的快速方案的开发提供信息。云微物理学模型基于Carma，基于Carma，Carma，社区气雾和辐射模型，用于大气，，，用于大气，它使用尺寸箱方案来表示气溶胶的化学和微物理学。在这里，bin方案适应了代表液态云液滴和云冰的微物理学，并能够通过冷冻和解冻来表示水之间的水转移。 Carma用来表示气溶胶和云的版本分别称为Carma-Aerosol和Carma-Cloud，它们一起用于表示气溶胶对云颗粒的影响。该奖项继续在先前的支持下，最近通过AGS-1640903。开发了该奖项，该模型用于解决云物理和气候动态中的几个问题。特别地，该模型通过模拟来考虑云微物理学对气候变化的影响，二氧化碳浓度即时加倍，这是评估模拟气候对温室气体增加的敏感性的标准方法。模拟的动机来自最新一代气候模型中提高的气候敏感性，这些气候模型有助于气候模型对比项目（CMIP），该项目归因于云微物理学的变化。云微物理学在气候变化中的作用。云微物理学是一个特别关注的问题，因为在用于决策支持的未来气候变化的模型预测中，云通常被称为最大的不确定性来源。这项工作还受益于使用和开发CESM的全球研究人员社区。该项目通过开发独立版本的Carma-Cloud模型具有教育价值，该模型适合课堂使用，并通过支持和培训研究生。该奖项反映了NSF的法定任务，并被认为值得支持通过使用基金会的智力优点和更广泛影响的评论标准进行评估。