Land-Atmosphere Interactions: A Core Program in Support of Community Climate Models

摘要ATM-9419715 Dickinson，Robert E. 土地-大气相互作用-支持社区气候建模的核心计划 该项目的重点是在全球变化模型的范围内了解土地和大气之间的相互作用并确定其参数。 其目标是：通过数据分析、数值过程研究和模式模拟，从与陆面过程耦合的角度确定最重要的大气过程的功能;并在此基础上，开发新的或改进的GCM参数化，在NCAR社区气候系统模式中实施和检验这些参数化，并为NCAR气候建模社区提供成功的参数化。 该提案汇集并支持一个强大的研究团队，该团队具有广泛的背景和经验，每个人都具有重要的个人技能。 拟议的工作将：扩大对陆-气耦合过程的基本理解;确定这些过程在NCAR社区模式中如何运作;确定这些过程与其他具有可比价值的气候模式中的过程有何不同;确定这些观测、敏感性研究和模式相互比较对于澄清不同参数化的相对正确性是必要的;并在大气环流模式的背景下，开发出适用于科学界未来改进和升级的新治疗方法。 为实现这些目标而设计的各个分项目包括：研究行星边界中陆地表面和云之间的相互作用;云辐射通量的特征及其与陆地表面的耦合;大气环流模式中中尺度环流影响的参数化，包括潮湿过程、云和降水;云的性质、大气热力学性质和土地覆盖之间关系的观测特征;亚马逊森林砍伐对云过程影响的观测研究;进一步探索亚马逊森林砍伐对气候的影响，以更好地了解陆地表面的相互作用;以及古气候的联系。 这些研究将利用卫星、无线电探空仪和地面数据进行观测分析。 卫星数据分析将强调使用当地的高分辨率（30公里）ISCCP数据档案。 他们还将使用中尺度和全球气候模式的集成，后者的重点是NCAR社区模式。 理论和参数化分析将使用中尺度模式积分和各种一维对流参数化。 拟议的工作将大大有助于更好地理解和信心，在使用NCAR气候系统模型预测未来的气候。 要做到这一点，就必须推动在从气候模拟的科学认识和实际方面来看，目前可以说是模式性能最薄弱的方面之一取得重大进展。 拟议的研究将为一代研究生和博士后学员提供关于这些问题的培训和教育。 该奖项是根据USGCRP，气候建模，分析和预测（CMAP）项目。

Abstract ATM-9419715 Dickinson, Robert E. University of Arizona Title: Land-Atmosphere Interactions - A Core Program in Support of Community Climate Modeling This project focuses on understanding and parameterizing the interactions between land and the atmosphere in the context of global change models. Its objectives are: to establish, through data analyses, numerical process studies and model stimulations, the function of the most important atmospheric processes, from the viewpoint of coupling to land-surface processes; and on the basis of these studies, to develop new or improved parameterizations for GCMs, implement and test these parameterizations in NCAR community climate system models, and provide successful parameterizations to the NCAR climate modeling community. This proposal brings together and supports a strong research team with a wide variety of background and experience, each with important individual skills. The proposed work will: expand the basic understanding of the processes responsible for land-atmosphere coupling; determine how these operate in NCAR community models; determine how these processes differ, if they do, from those in other climate models of comparable merit; determine how these observations, sensitivity studies, and model intercomparisons are necessary to clarify the relative correctness of differing parameterizations; and develop indicated new treatments that are suitable, in the context of GCM's, for future improvements and upgrades by the scientific community. Individual subprojects designed to pursue these objectives include: studies of interactions between the land surface and clouds in the planetary boundary (PBL); characterization of cloud radiative fluxes and their coupling to the land surface; parameterization of the effects of mesoscale circulations in a GCM, including moist processes, clouds and precipitation; observation characterization of relationships between cloud properties, atmospheric thermodynamics propert ies, and land cover; observational search for effects of Amazon deforestation on cloud processes; further exploration of climate effects of Amazon deforestation to better understand land-surface interactions; and paleoclimatic linkages. These studies will use satellite, radiosonde, and surface data for their observational analyses. Satellite data analyses will emphasize the use of a local archive of high-resolution (30 km) ISCCP data. They will also use integrations of mesoscale and global climate models, with an emphasis for the latter on NCAR community models. Theoretical and parameterizations analyses will use mesoscale model integration and various 1-D convection parameterizations. The proposed work will contribute substantially to great understanding of and confidence in the use of NCAR climate system models for projections of future climate. This will be done by promoting major advances in what is arguably now one of the weakest aspects of model performance from the viewpoint of scientific understanding and practical aspects of climate simulation. The performance of the proposed research will provide training and education on these questions for a generation of graduate students and post-doctoral trainees. This award is under the USGCRP, Climate Modeling, Analysis and Prediction (CMAP) project.