Collaborative Research: Advancing Understanding of Aerosol-Cloud Feedback Using the World's First Global Climate Model with Explicit Boundary Layer Turbulence

合作研究：利用世界上第一个具有明确边界层湍流的全球气候模型增进对气溶胶云反馈的理解

• 批准号: 1912134
• 负责人: Michael Pritchard
• 金额: $ 47.23万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912134&HistoricalAwards=false
• 关键词: Collaborative; Research; Advancing; Understanding; Aerosol

Aerosols, meaning tiny particles suspended in the atmosphere, play a key role in cloud formation, as cloud droplets and ice particles are produced when water vapor condenses onto aerosols. When more aerosols are present clouds tend to have a larger number of smaller droplets, making them brighter and more effective in reflecting sunlight back to space. Thus increases in aerosol amount due to industrial activity can increase the brightness of clouds, resulting in a cooling effect on climate. The extent to which the global temperature increase from greenhouse warming has been offset by human-induced radiative forcing from aerosol-cloud-interactions (RFaci) is an important and unsolved problem in climate science.One obstacle to progress on RFaci is the difficulty of performing computer simulations which explicitly represent cloud properties yet cover the whole earth, so that global climatic effects can be assessed. Cloud motions are turbulent and require models with grid points spaced a fraction of a kilometer apart, while global model grid spacing is typically tens to hundreds of kilometers. To bridge this scale gap the PIs have developed an ultraparameterized (UP) model, meaning a global model with coarse grid spacing in which each grid box contains a fine-scale cloud resolving model with a domain size much smaller than the grid box. The model is challenging both scientifically and computationally, and the project includes a concerted effort to improve computational efficiency to make simulations practical.The research addresses several specific questions regarding RFaci. One question is why climate models tend to overestimate RFaci compared to estimates from satellites, in some cases by a factor of two. Comparisons between the UP model and satellite observations will be facilitated by a nudging methodology, in which external forcing is used to constrain the simulated weather patterns to match the days when the satellite observations were taken. The nudging minimizes differences between simulated and satellite-estimated RFaci due to incorrect simulation of large-scale circulation features, allowing attribution of differences to aerosol-cloud interactions.The work has broader impacts due to the societal implications of high versus low RFaci: if the cooling effect of industrially-driven RFaci is large, the strength of greenhouse warming must be at the high end of current estimates in order to explain the warming seen over the past century. Likewise, if industrial RFaci cooling was small over the last century, the sensitivity of global temperature to greenhouse gas increase is likely to be on the lower end of its estimated range. RFaci is thus among the largest uncertainties in determining climate sensitivity and the severity of climate change impacts. In addition, software developed under the project is made available to the research community, in part through a version of the Community Earth System Model. The project provides support and training for a postdoctoral research scholar, thereby providing workforce development.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.

气溶胶（意味着悬浮在大气中的微小颗粒）在云形成中起关键作用，因为当水蒸气凝结到气溶胶上时会产生云滴和冰颗粒。 当更多的气溶胶存在时，云层往往会有较大的较小液滴，从而使它们更明亮，更有效地反射阳光回到太空。因此，由于工业活动而导致的气溶胶量增加可以提高云的亮度，从而导致对气候的冷却影响。 在气候科学中，人类引起的辐射强迫（RFACI）被人类引起的辐射强迫（RFACI）抵消了全球温度变暖的全球温度升高的程度。明确表示云特性但覆盖整个地球的计算机模拟，以便可以评估全球气候效应。云动作是湍流的，需要带网格点的模型相距一公里，而全球模型网格间距通常数十亿至数百公里。 为了弥合此规模差距，PI开发了一个超碳酸盐（UP）模型，这意味着一个具有粗网格间距的全局模型，其中每个网格框中包含一个优质的云解析模型，其域大小比网格盒小得多。 该模型在科学和计算上都在挑战，该项目包括一致的努力，以提高计算效率以使模拟实用。该研究解决了有关RFACI的几个具体问题。 一个问题是，与卫星的估计相比，气候模型倾向于高估RFACI，在某些情况下要高两个。 UP模型和卫星观察之间的比较将通过一种轻度的方法来促进，其中使用外部强迫来限制模拟天气模式，以匹配进行卫星观测值的日子。 由于对大规模循环特征的模拟错误的模拟，由于模拟的模拟不正确，因此可以最大程度地减少模拟和卫星估计的RFACI之间的差异，从而允许将差异归因于Aerosol-Cloud相互作用。由于高RFACI的社会影响，这项工作具有更大的影响。工业驱动的RFACI的冷却效果很大，温室变暖的强度必须处于当前估计的高端，以解释过去一个世纪中看到的变暖。 同样，如果上个世纪工业RFACI冷却很小，那么全球温度对温室气体升高的敏感性很可能在其估计范围的下端。因此，RFACI是确定气候敏感性和气候变化影响严重程度的最大不确定性之一。此外，项目下开发的软件可供研究社区提供，部分通过社区地球系统模型的版本。 该项目为博士后研究学者提供了支持和培训，从而提供了劳动力发展。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。

项目成果

Load‐Balancing Intense Physics Calculations to Embed Regionalized High‐Resolution Cloud Resolving Models in the E3SM and CESM Climate Models

负载平衡密集物理计算，将区域化高分辨率云解析模型嵌入到 E3SM 和 CESM 气候模型中

• DOI: 10.1029/2021ms002841
• 发表时间: 2022
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Peng, Liran;Pritchard, Michael;Hannah, Walter M.;Blossey, Peter N.;Worley, Patrick H.;Bretherton, Christopher S.
• 通讯作者: Bretherton, Christopher S.

Lower Tropospheric Processes: A Control on the Global Mean Precipitation Rate

对流层低层过程：对全球平均降水率的控制

• DOI: 10.1029/2020gl091169
• 发表时间: 2021
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Hendrickson, Jacob M.;Terai, Christopher R.;Pritchard, Michael S.;Caldwell, Peter M.
• 通讯作者: Caldwell, Peter M.

The Impact of Resolving Subkilometer Processes on Aerosol‐Cloud Interactions of Low‐Level Clouds in Global Model Simulations

• DOI: 10.1029/2020ms002274
• 发表时间: 2020-11
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: C. Terai;M. Pritchard;P. Blossey;C. Bretherton

Conservation of Dry Air, Water, and Energy in CAM and Its Potential Impact on Tropical Rainfall

CAM 中干燥空气、水和能源的保护及其对热带降雨的潜在影响

• DOI: 10.1175/jcli-d-21-0512.1
• 发表时间: 2022
• 期刊: Journal of Climate
• 影响因子: 4.9
• 作者: Harrop, Bryce E.;Pritchard, Michael S.;Parishani, Hossein;Gettelman, Andrew;Hagos, Samson;Lauritzen, Peter H.;Leung, L. Ruby;Lu, Jian;Pressel, Kyle G.;Sakaguchi, Koichi
• 通讯作者: Sakaguchi, Koichi