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.

该 NSF 科学技术中心 (STC) 将重点研究气候模型中云过程的表示。 STC 的名称是“大气过程多尺度建模中心”(MMAP)，牵头机构是科罗拉多州立大学 (CSU)。 MMAP的目标是打破数十年来阻碍气候研究进展的“僵局”。气候模型是基于物理的，包括大气、海洋、陆地表面和冰冻圈的表示。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产生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规模大气环流的相互作用，这是气候变化模拟可靠性的最重要限制之一。云系统模拟不佳也会降低天气预报的技巧，尤其是降水。 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，其中细网格云系统解析模型（CSRM）嵌入到大气环流模型的更大网格单元中（GCM）。在 MMF 中，CSRM 取代了当前 GCM 中使用的单列“传统参数化”。传统的参数化基于统计理论，涉及不确定的闭合假设，并且很少或没有有关云场空间结构的信息，而 MMF 可以将云过程明确地解析到几公里的尺度，因此可以明确地表示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 都是二维 (2D) 的，具有周期性边界条件。它代表 GCM 网格列中云的“样本”。 CSRM 对云场的高分辨率描绘可用于计算 GCM 网格列的采样部分的统计数据（例如，降水率和分数云量），并且这些统计数据将应用于整个网格列。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、气溶胶、湍流和辐射等微物理。 MMF 无需闭合假设即可确定深对流活动的强度。它们消除了辐射传输和微物理参数化中云重叠假设的需要。它们有潜力代表云和重力波与地形的相互作用。它们对于模拟云系统内化学物质的传输和转化，以及大气与地表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 仍必须包括关键的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规模上表示的，因此可以通过相对简单的方式对它们进行参数化。 MMF 的另一个非常重要的优势是可以通过比较模拟和观察到的云规模过程来评估产生的结果。科罗拉多州立大学最近的工作表明，相对于传统 GCM 的控制模拟，原型 MMF 可以大大改进对各种时间尺度（从昼夜到季节内）的大气变化的模拟。它还提供了更真实的云量和降水模拟。 MMF 的实验已经表明，辐射加热速率的云尺度变化很重要，对流动量传输也很重要，它包含在使用 3D CSRM 的新版本 MMF 中。该研究的一个关键部分包括进一步发展 MMF 概念，从新版本的 MMF 开始，其中消除了 CSRM 的周期性边界条件，并将多个 2D CSRM 组合起来创建“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。周期性边界条件的去除允许对流系统从一个 GCM 网格列传播到下一网格列，并防止对流在周期性域尺寸减小时被人为“挤压”。因为当 GCM 分辨率增加时没有理由改变嵌入式 CSRM 的公式，所以准 3D MMF 的公式与外部网格的间距无关。此外，可以根据数据规定真实的地形强迫，并用于模拟地形重力波和地形云。最后，随着外部网格尺寸的细化，准 3D MMF 以平滑、自然的方式收敛到全局 CSRM。 PI 强调现有的 GCM 不具备这种收敛特性。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；激发本科生对气候科学研究生教育和职业的兴趣；开发和传播旨在让 K-12 学生（及其教师）了解气候系统性质和气候科学职业机会的教材。在这些领域中，MMAP 将特别努力吸纳来自气候科学专业人士中代表性不足的群体的学生。 MMAP 将开展两个出版项目，这将显着加强我们领域的科学交流：创建一本专门用于全球建模的新型独特在线技术期刊，以及制作一本关于全球气候建模历史的编辑书籍，包括访谈记录与主要参与者。 MMAP 研究的智力价值在于其针对云气候问题的革命性方法。地球系统科学家可以通过从新的、不同的角度处理气候建模问题来了解更多关于全球气候系统的知识，而这些新知识是 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态的理解，以及我们对气候变化的云反馈进行可靠预测的能力。 MMAP 的遗产将包括重要的新建模工具，这些工具将为人为气候变化提供更加可靠的预测。此外，MMAP 将展示将高分辨率观测结果与全球模型结果进行比较的新方法，使运营中心能够改进天气预报，一方面加强全球建模者与云规模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个级别的男女学生对气候研究的兴奋和机会的认识，创办一本独特的新学术期刊，并出版一本记录全球气候模型历史的书，重点是云参数化 问题。