Using Petascale Computing Capabilities to Address Climate Change Uncertainties

该奖项使用IBM正在开发并计划部署在伊利诺伊大学的名为Blue Waters的大型、新的计算资源来促进科学研究。它提供差旅资金，以支持主要调查人员、Blue Waters项目团队和供应商技术团队之间的技术协调。该项目通过描述和说明当前模型的局限性，满足了解和减少与气候变化数值模拟相关的不确定性的持续和关键需求，以减少与气候预测趋势相关的不确定性。模拟和分析旨在使用Blue Waters Petascale计算资源。该项目的两个主要目标将解决与理解气候敏感性和模型解决有关的长期公认的问题。在气候敏感性研究中使用的独特的超集合方法和在高分辨率研究中使用的高级动力学模型核心都需要千万亿级的能力。该项目将使用最先进的计算资源，即Blue Waters，和尖端的建模软件来推进气候研究，这将有助于国际(和国家)评估本世纪气候变化的潜在影响。拟议的研究将由一组领先的气候科学家合作进行，他们还将通过跨学科培养学生和博士后研究员的思维和想法，并通过该项目的密切互动来培养他们的跨学科培训。这将为他们提供对研究和教育过程的宝贵见解。模拟产生的数据将提供给更大的研究科学家社区。

This award facilitates scientific research using the large, new, computational resource named Blue Waters being developed by IBM and scheduled to be deployed at the University of Illinois. It provides travel funds to support technical coordination between the principal investigators, the Blue Waters project team and the vendor technical team.The project addresses on-going and crucial needs for understanding and reducing uncertainties associated with numerical modeling of climate change through characterizing and accounting for current model limitations in order to reduce uncertainty associated with projected trends in our climate. The simulations and analyses are designed to use the Blue Waters petascale computing resources. The two major objectives of the project will address long recognized issues associated with understanding climate sensitivity and with model resolution. The unique super-ensemble approach used in the climate sensitivity studies and the advanced dynamical model cores used in the high resolution studies both require petascale capabilities.This project will use state-of-the-art computational resources, namely Blue Waters, and cutting-edge modeling software in advancing the study of climate that will contribute to international (and national) assessments of the potential impacts of climate change over this century. The proposed research will be carried out collaboratively by a team of leading climate scientists who will also provide interdisciplinary training to students and postdoctoral fellows through cross-disciplinary fertilization in both thinking and ideas that will be fostered through the close interactions on this project. This will provide them with invaluable insights into both the research and educational processes. The data produced by the simulations will be made available to the larger community of research scientists.