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

气溶胶，即悬浮在大气中的微小颗粒，在云的形成中起着关键作用，因为当水蒸气凝结在气溶胶上时，会产生云滴和冰粒。当更多的气溶胶存在时，云往往有更多的小液滴，使它们更亮，更有效地将阳光反射回太空。因此，由于工业活动而增加的气溶胶量可以增加云的亮度，从而对气候产生冷却效应。温室效应导致的全球温度升高在多大程度上被气溶胶-云相互作用（RFaci）引起的人为辐射强迫所抵消，这是气候科学中一个重要的尚未解决的问题。阻碍RFaci进展的一个障碍是很难进行计算机模拟，而计算机模拟既要明确地表示云的性质，又要覆盖整个地球，这样才能评估全球气候的影响。云的运动是动荡的，需要网格点间隔不到一公里的模型，而全球模型的网格间距通常是几十到几百公里。为了弥合这一尺度差距，pi开发了一种超参数化（UP）模型，这意味着一个具有粗网格间距的全局模型，其中每个网格框包含一个精细尺度的云解析模型，其域尺寸远小于网格框。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可行。该研究解决了关于RFaci的几个具体问题。一个问题是，为什么气候模型倾向于高估RFaci与卫星的估计相比，在某些情况下是两倍。在UP模式和卫星观测之间的比较将通过轻推方法来促进，在这种方法中，使用外部强迫来约束模拟的天气模式，使其与卫星观测时的天气模式相匹配。由于对大尺度环流特征的不正确模拟，使得模拟的RFaci与卫星估计的RFaci之间的差异最小化，从而允许将差异归因于气溶胶与云的相互作用。由于高RFaci与低RFaci的社会含义，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所见的变暖。同样，如果工业RFaci冷却在上个世纪很小，那么全球温度对温室气体增加的敏感性可能处于其估计范围的低端。因此，在确定气候敏感性和气候变化影响的严重性方面，RFaci是最大的不确定因素之一。此外，在该项目下开发的软件部分通过社区地球系统模型的一个版本提供给研究界。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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.