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.

气溶胶是指悬浮在大气中的微小颗粒，在云的形成中发挥着关键作用，因为当水蒸气凝结到气溶胶上时会产生云滴和冰粒。当存在更多的气溶胶时，云层往往会有更多更小的水滴，使它们更明亮，更有效地将阳光反射回太空。因此，工业活动导致的气溶胶量增加会增加云的亮度，从而对气候产生降温效应。气溶胶-云-相互作用(RFaci)引起的人为辐射强迫在多大程度上抵消了温室气体变暖导致的全球气温升高，是气候科学中一个重要且尚未解决的问题。RFaci研究进展的一个障碍是难以进行计算机模拟，以明确地表示云的特性而覆盖整个地球，以便评估全球气候影响。云的运动是湍流的，需要模型的网格点间距为几分之一公里，而全球模型的网格间距通常为几十到数百公里。为了弥合这一比例差距，PI开发了一个超参数(UP)模型，这意味着具有粗略网格间距的全球模型，其中每个网格框包含一个域大小远远小于网格框的精细比例云解析模型。该模型在科学和计算上都具有挑战性，该项目包括共同努力提高计算效率，以使模拟实用。研究解决了几个关于RFaci的具体问题。一个问题是，为什么气候模型往往比卫星估计的RFaci高估，在某些情况下高估了两倍。UP模型和卫星观测之间的比较将通过一种轻推方法来促进，在这种方法中，外部强迫被用来约束模拟的天气模式，以匹配卫星观测的日期。由于对大尺度环流特征的错误模拟，允许将差异归因于气溶胶-云相互作用，因此微调将模拟的RFaci和卫星估计的RFaci之间的差异最小化。由于RFaci的高和低的社会影响，这项工作具有更广泛的影响：如果工业驱动的RFaci的冷却效应很大，那么温室气体变暖的强度必须处于当前估计的高端，才能解释过去一个世纪出现的变暖。同样，如果上个世纪工业RFaci的降温幅度很小，那么全球气温对温室气体增加的敏感度可能会处于其估计范围的较低端。因此，RFaci是决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，在该项目下开发的软件部分通过共同体地球系统模型的一个版本向研究界提供。该项目为博士后研究学者提供支持和培训，从而提供劳动力发展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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