SGER: Upgrading Community Atmosphere Model (CAM) Cloud Macrophysics

SGER：升级社区大气模型 (CAM) 云宏观物理

• 批准号: 0841237
• 负责人: Christopher Bretherton
• 金额: $ 3.35万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0841237&HistoricalAwards=false
• 关键词: SGER; Upgrading; Community; Atmosphere; Model

A climate model is a complex software system whose parts must work properly together. This is a particular challenge for its atmospheric 'moist physics' parameterizations - turbulence, cumulus convection, cloud formation, cloud-aerosol interaction and precipitation, because these processes tightly interact on length scales much smaller than a climate model grid cell. The Community Atmosphere Model (CAM) is the atmospheric component of the world's leading community-developed climate model. Coordinated efforts by several research groups, loosely organized into a CAM Microphysics Initiative, are completely revamping most of the moist physics parameterizations. Their aim is to reduce CAM's cloud and precipitation biases and to enable credible global simulation of the effect of human-produced aerosol on clouds. During these efforts, some important shortcomings have been found in the CAM formulation for 'cloud macrophysics'- the parameterizations that determine the fractional cloud cover and the masses of liquid and ice cloud particles that condense and evaporate in each model grid cell. These shortcomings involve inconsistencies between the calculation of cloud fraction and cloud condensate that often result in 'empty clouds' (cloud area with no cloud particles), especially in regions of shallow cumulus convection. In high-latitude regions, CAM cloud predictions are sensitive to arbitrary changes to the order of calls to different parameterizations. The goal of this research is to implement and test a set of detailed code changes that will: (1) Better treat the interaction between cumulus and stratiform cloud cover, ameliorating issues of 'empty clouds' with finite cloud fraction but no condensate.(2) Ensure that the cloud macrophysics is operating on an appropriate state. (3) Use a more justifiable closure to calculate the rate of condensation /evaporation in the cloudy part of grid cells. (4) Use a relationship between cloud fraction and relative humidity that is analogous to schemes used by other leading climate centers. This research must be completed within the next several months in order to be evaluated in time to be included in the next generation Community Climate System Model 4 (CCSM 4), to be frozen in early 2009 and applied to the Intergovenmental Panel on Climate Change (IPCC) fifth assessment.Intellectual merit. The model changes will improve the internal consistency of the treatment of cloud processes in CAM. They will allow all moist processes to contribute to a consistent and physically realizable subgrid spatial distribution of cloud condensate within each grid cell. All changes are based on recommendations from the published literature. Broader Impacts. Many NCAR, DOE and university scientists use the CCSM as their primary climate model. This research will provide a solid basis for further code improvements in the microphysics, cumulus convection, and moist turbulence parameterizations that are needed to produce a cutting-edge simulation of human-induced aerosol effects on clouds and climate and better projections of 21st century climate change. These changes will be ready to use for model simulations in support of the 5th IPCC Assessment, for which better simulation of cloud-aerosol feedbacks is emerging as an important thrust.

气候模型是一个复杂的软件系统，其各个部分必须一起正常工作。这对其大气“潮湿物理”参数化（湍流、积云对流、云形成、云-气溶胶相互作用和降水）来说是一个特殊的挑战，因为这些过程在比气候模型网格单元小得多的长度尺度上紧密相互作用。社区大气模型 (CAM) 是世界领先的社区开发的气候模型的大气组成部分。多个研究小组的协调努力，松散地组织成 CAM 微观物理计划，正在彻底修改大多数潮湿物理参数化。他们的目标是减少 CAM 的云和降水偏差，并能够对人类产生的气溶胶对云的影响进行可靠的全球模拟。在这些努力中，我们发现了“云宏观物理”的 CAM 公式中的一些重要缺陷，即确定分数云覆盖以及在每个模型网格单元中凝结和蒸发的液体和冰云颗粒质量的参数化。这些缺点涉及云分数和云凝结物计算之间的不一致，常常导致“空云”（没有云颗粒的云区域），特别是在浅积云对流区域。在高纬度地区，CAM 云预测对不同参数化调用顺序的任意更改很敏感。本研究的目标是实施和测试一组详细的代码更改，这些更改将：（1）更好地处理积云和层状云层之间的相互作用，改善云分数有限但无凝结物的“空云”问题。（2）确保云宏观物理在适当的状态下运行。 (3)采用更合理的闭包计算网格单元阴暗部分的凝结/蒸发率。 (4) 使用云量和相对湿度之间的关系，类似于其他主要气候中心使用的方案。这项研究必须在未来几个月内完成，以便及时评估并纳入下一代共同体气候系统模型4（CCSM 4），并于2009年初冻结并应用于政府间气候变化专门委员会（IPCC）第五次评估。智力价值。 模型更改将提高 CAM 中云流程处理的内部一致性。它们将允许所有潮湿过程有助于每个网格单元内一致且物理上可实现的云凝结水子网格空间分布。所有更改均基于已发表文献的建议。更广泛的影响。 许多 NCAR、DOE 和大学科学家使用 CCSM 作为他们的主要气候模型。 这项研究将为进一步改进微物理、积云对流和潮湿湍流参数化方面的代码提供坚实的基础，这些参数化是对人类引起的气溶胶对云层和气候的影响进行前沿模拟以及更好地预测 21 世纪气候变化所需的。这些变化将可用于模型模拟，以支持第五次 IPCC 评估，更好地模拟云-气溶胶反馈正在成为该评估的一个重要推动力。