Center for Multi-Scale Modeling of Atmospheric Processes (MMAP)

大气过程多尺度模拟中心 (MMAP)

• 批准号: 0425247
• 负责人: David Randall
• 金额: $ 1896万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0425247&HistoricalAwards=false
• 关键词: Center; Multi; Scale; Modeling; Atmospheric

This NSF Science and Technology Center (STC) will focus on the representation of cloud processes in climate models. The STC's name is the "Center for Multi-Scale Modeling of Atmospheric Processes" (MMAP), and the lead institution is Colorado State University (CSU). The goal of MMAP is to break the "deadlock" that has stalled the progress of climate research for several decades. Climate models are physically based and include representations of the atmosphere, the ocean, the land-surface, and the cryosphere. They run on the most powerful computers available. They are now providing predictions of future climate change due to anthropogenic changes in the composition of the Earth's atmosphere. These predictions are being used as input to policy decisions that have enormous economic implications for the U.S. and the world. It has been true for decades now that our inability to simulate the interactions of clouds with large-scale atmospheric circulations is one of the most important limitations on the reliability of climate-change simulations. Poor simulations of cloud systems also reduce the skill of weather forecasts, especially for precipitation. MMAP will address this problem through a revolutionary new approach called the "multi-scale modeling framework" (MMF), in which fine-grid Cloud-System Resolving Models (CSRMs) are embedded within the much larger grid cells of an atmospheric general circulation model (GCM). In an MMF, the CSRM takes the place of the single-column "conventional parameterizations" that are used in current GCMs. Whereas conventional parameterizations are based on statistical theories involving uncertain closure assumptions and little or no information about the spatial structure of the cloud field, MMFs resolve cloud processes explicitly down to a scale of a few kilometers, and so represent some aspects of the spatial structure explicitly. The first MMF was created by MMAP scientist W. Grabowski of NCAR. In most of the prototype studies carried out to date, the CSRM is two-dimensional (2D), with periodic boundary conditions. It represents a "sample" of the clouds in a GCM grid column. The CSRM's high-resolution depiction of a cloud field can be used to compute statistics (e.g., the precipitation rate and fractional cloudiness) for the sampled portion of the GCM's grid column, and these statistics are applied to the entire grid column. A key point is that MMFs can represent the cloud-scale interactions among the many physical and chemical processes that are active in cloud systems, including cloud dynamics, microphysics including aerosols, turbulence, and radiation. MMFs eliminate the need for closure assumptions to determine the strength of deep convective activity. They eliminate the need for cloud-overlap assumptions in the radiative transfer and microphysics parameterizations. They have the potential to represent the interactions of both clouds and gravity waves with orography. They are also particularly attractive for the simulation of chemical species transports and transformations within cloud systems, as well as small-scale interactions between the atmosphere and the biological and hydrological processes of the land-surface. MMFs must still include parameterizations of critical sub-cloud-scale processes, including microphysics, turbulence, and radiative transfer. Because these processes are represented on the cloud-scale, however, they can be parameterized in relatively straightforward ways. A further very important strength of an MMF is that the results produced can be evaluated by comparison of simulated and observed cloud-scale processes. Recent work at CSU has shown that, relative to a control simulation with a conventional GCM, a prototype MMF produces greatly improved simulations of atmospheric variability on a variety of time scales, from diurnal to intra-seasonal. It also gives more realistic simulations of cloudiness and precipitation. Experiments with the MMF have already shown that cloud-scale variability of the radiative heating rate is important, as is convective momentum transport, which is included in a new version of the MMF that uses a 3D CSRM. A key part of the research consists of further development of the MMF concept, beginning with a new version of the MMF in which the periodic boundary conditions of the CSRM are eliminated, and multiple 2D CSRMs are combined to create a "quasi-3D" MMF. Removal of the 2D constraint permits convective systems to have arbitrary orientation and to vertically transport horizontal momentum. Removal of the periodic boundary conditions allows convective systems to propagate from one GCM grid column to the next, and prevents the convection from being artificially "squeezed" as the periodic domain decreases in size. Because there is no reason to alter the formulation of the embedded CSRM when the GCM's resolution is increased, the formulation of the quasi-3D MMF is independent of the spacing of the outer grid. In addition, realistic topographic forcing can be prescribed from data, and used to simulate orographic gravity waves and orographic clouds. Finally, a quasi-3D MMF converges in a smooth and natural way to a global CSRM, as the size of the outer grid is refined. The PIs emphasize that no existing GCM has this convergence property. They plan to develop, evaluate, and apply a quasi-3D MMF as the central, organizing activity of the research. MMAP's education and human-resource goals are to provide first-rate graduate education in Atmospheric Science; to interest undergraduates in graduate education and careers in climate science; and to develop and disseminate teaching materials designed to inform K-12 students (and their teachers) about the nature of the climate system and the career opportunities in climate science. In each of these areas, MMAP will make a special effort to include students from groups that are under-represented among climate- science professionals. MMAP will undertake two publishing projects that will significantly enhance scientific communication in our field: the creation of a new and unique online technical journal devoted to global modeling, and the production of an edited book on the history of global climate modeling, including transcripts of interviews with the key participants. The intellectual merit of MMAP's research lies in its revolutionary approach to the cloud-climate problem. Earth system scientists can learn a lot about the global climate system by approaching the problem of climate modeling from a new and different perspective, and this new knowledge is the most valuable thing that will flow from MMAP's research. The research will have broad impacts on both science and society because it will increase both our understanding of climate dynamics, and our ability to make reliable predictions of cloud feedbacks on climate change. The legacy of MMAP will include important new modeling tools that will provide substantially more reliable predictions of anthropogenic climate change. In addition, MMAP will demonstrate new ways to compare high resolution observations with global model results, enable improved weather forecasts by the operational centers, strengthen the scientific interactions between global modelers on the one hand and cloud-scale observers and cloud modelers on the other, create a heightened awareness of the excitement and opportunities of climate research among both female and male students from all ethnic backgrounds and at all levels, inaugurate a unique new scholarly journal, and produce a book that captures the history of global climate modeling with an emphasis on the cloud parameterization problem.

这个NSF科学技术中心（STC）将专注于云过程在气候模式中的表现。STC的名字是“大气过程多尺度模拟中心”（MMAP），牵头机构是科罗拉多州立大学（CSU）。MMAP的目标是打破几十年来阻碍气候研究进展的“僵局”。气候模式以物理为基础，包括大气、海洋、陆地表面和冰冻圈的表征。它们在最强大的计算机上运行。他们现在正在预测由于地球大气成分的人为变化而导致的未来气候变化。这些预测被用作对美国和世界具有巨大经济影响的政策决策的输入。几十年来，我们无法模拟云与大尺度大气环流之间的相互作用，这是影响气候变化模拟可靠性的最重要限制之一。糟糕的云系统模拟也降低了天气预报的能力，尤其是降水预报。MMAP将通过一种称为“多尺度建模框架”（MMF）的革命性新方法来解决这个问题，在这种方法中，细网格云系统解析模型（csrm）嵌入到大气环流模型（GCM）的更大网格单元中。在MMF中，CSRM取代了当前gcm中使用的单列“常规参数化”。传统的参数化基于统计理论，涉及不确定的封闭假设和很少或根本没有关于云场空间结构的信息，而mmf明确地将云过程分解到几公里的尺度，因此明确地表示了空间结构的某些方面。第一个MMF是由NCAR的MMAP科学家W. Grabowski创建的。在迄今为止进行的大多数原型研究中，CSRM是二维的（2D），具有周期性边界条件。它代表了GCM网格列中的云的“样本”。CSRM对云场的高分辨率描述可用于计算GCM网格列采样部分的统计数据（例如，降水率和分数云量），这些统计数据应用于整个网格列。关键的一点是，mmf可以代表云系统中活跃的许多物理和化学过程之间的云尺度相互作用，包括云动力学、微物理（包括气溶胶、湍流和辐射）。MMFs消除了对确定深层对流活动强度的闭合假设的需要。它们消除了在辐射传输和微物理参数化中对云重叠假设的需要。它们有可能代表云和重力波与地形学的相互作用。它们对于模拟云系统内化学物质的运输和转化以及大气与陆地表面生物和水文过程之间的小规模相互作用也特别有吸引力。mmf还必须包括关键亚云尺度过程的参数化，包括微物理、湍流和辐射传输。然而，由于这些过程是在云尺度上表示的，因此可以以相对直接的方式对它们进行参数化。MMF的另一个非常重要的优点是，产生的结果可以通过比较模拟和观测到的云尺度过程来评估。CSU最近的工作表明，相对于传统GCM的控制模拟，原型MMF在各种时间尺度（从日到季节）上大大改善了大气变化的模拟。它还提供了更真实的云和降水模拟。MMF的实验已经表明，辐射加热速率的云尺度变化是重要的，对流动量输送也是重要的，这包括在使用3D CSRM的新版本MMF中。研究的一个关键部分是MMF概念的进一步发展，从一个新的MMF版本开始，其中消除了CSRM的周期性边界条件，并将多个2D CSRM组合成一个“准3d”MMF。消除二维约束允许对流系统具有任意方向并垂直传输水平动量。去除周期边界条件允许对流系统从一个GCM网格柱传播到下一个，并防止对流被人为地“挤压”，因为周期域的大小减小。由于当GCM的分辨率增加时，没有理由改变嵌入式CSRM的公式，因此准三维MMF的公式与外网格的间距无关。此外，真实的地形强迫可以从数据中规定，并用于模拟地形重力波和地形云。最后，随着外网格的细化，准三维MMF以平滑自然的方式收敛到全局CSRM。pi强调没有现有的GCM具有这种收敛性。他们计划开发、评估和应用准3d MMF作为研究的中心组织活动。MMAP的教育和人力资源目标是提供一流的大气科学研究生教育；使本科生对气候科学的研究生教育和职业感兴趣；编写和传播教材，使K-12学生（及其教师）了解气候系统的性质和气候科学领域的就业机会。在这些领域中，MMAP将作出特别努力，吸纳来自气候科学专业人员中代表性不足的群体的学生。MMAP将承担两个出版项目，这将大大加强我们领域的科学交流：创建一个新的和独特的在线技术期刊，专门研究全球模型，以及制作一本关于全球气候模型历史的编辑书籍，包括对主要参与者的采访记录。MMAP研究的智力价值在于它对云气候问题的革命性方法。地球系统科学家可以通过从一个新的和不同的角度来解决气候建模问题，从而对全球气候系统有很多了解，而这些新知识将是MMAP研究中最有价值的东西。这项研究将对科学和社会产生广泛的影响，因为它将增加我们对气候动力学的理解，以及我们对气候变化的云反馈做出可靠预测的能力。MMAP的遗产将包括重要的新建模工具，这些工具将提供更可靠的人为气候变化预测。此外，MMAP将展示将高分辨率观测结果与全球模式结果进行比较的新方法，使业务中心能够改进天气预报，加强全球建模者与云尺度观测者和云建模者之间的科学互动，提高来自所有种族背景和各级别的男女学生对气候研究的兴奋和机会的认识。创办一份独特的新学术期刊，并出版一本书，以云参数化问题为重点，捕捉全球气候模型的历史。