Collaborative Research: Understanding Climate Change: A Data Driven Approach

合作研究：了解气候变化：数据驱动的方法

• 批准号: 1029166
• 负责人: Alok Choudhary
• 金额: $ 90万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029166&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Climate; Change

Understanding Climate Change: A Data Driven ApproachClimate change is the defining environmental challenge now facing our planet. Whether it is an increase in the frequency or intensity of hurricanes, rising sea levels, droughts, floods, or extreme temperatures and severe weather, the social, economic, and environmental consequences are great as the resource-stressed planet nears 7 billion inhabitants later this century. Yet there is considerable uncertainty as to the social and environmental impacts because the predictive potential of numerical models of the earth system is limited. These models are incapable of addressing important questions relating to food security, water resources, biodiversity, mortality, and other socio-economic issues over relevant time and spatial scales.Climate model development has contributed small and incremental improvements; however, extensive modeling gains have not been forthcoming. Modeling limitations have hampered efforts at providing information on climate change impacts and adaptation and mitigation strategies. A new and transformative approach is required to improve prediction of the potential impacts on human welfare. Data driven methods that have been highly successful in other facets of the computational sciences are now being used in the environmental sciences with success. This Expedition project will significantly advance key challenges in climate change science developing exciting and innovative new data driven approaches that take advantage of the wealth of climate and ecosystem data now available from satellite and ground-based sensors, the observational record for atmospheric, oceanic, and terrestrial processes, and physics-based climate model simulations.To realize this ambitious goal, novel methodologies appropriate to climate change science will be developed in four broad areas of data-intensive computer science: relationship mining, complex networks, predictive modeling, and high performance computing. Analysis and discovery approaches will be cognizant of climate and ecosystem data characteristics, such as non-stationarity, nonlinear processes, multi-scale nature, low-frequency variability, long-range spatial dependence, and long-memory temporal processes such as teleconnections. These innovative new approaches will be used to better understand the complex nature of the earth system and the mechanisms contributing to such climate change phenomena as hurricane frequency and intensity in the tropical Atlantic, precipitation regime shifts in the ecologically sensitive African Sahel or the Southern Great Plains, and the propensity for extreme weather events that weaken our infrastructure and result in environmental disasters with economic losses in excess of $100 billion per year in the U.S. alone.Assessments of climate change impacts, which are useful for stakeholders and policymakers, depend critically on regional and decadal scale projections of climate extremes. Thus, climate scientists often need to develop qualitative inferences about inadequately predicted climate extremes based on insights from observations (e.g., increase in hurricane intensity) or conceptual understanding (e.g., relation of wildfires to regional warming or drying and hurricanes to sea surface temperatures). These urgent societal priorities offer fertile grounds for knowledge discovery approaches. In particular, qualitative inferences on climate extremes and impacts may be transformed into quantitative predictive insights based on a combination of hypothesis-guided data analysis and relatively hypothesis-free, yet data-guided discovery processes.A primary focus of this Expedition project will be on uncertainty reduction, which can bring the complementary or supplementary skills of physics-based models together with data-guided insights regarding complex climate processes. The systematic evaluation of climate models and their component processes, as well as uncertainty assessments at regional and decadal scales is a fundamental problem that will be addressed. The ability to translate gains in the predictive skills of climate variables to improvements in impact assessments and attributions is a critical requirement for informing policymakers. Novel methodologies will be developed to gain actionable insights from disparate impacts-related datasets as well as for causal attribution or root-cause analysis. This research will be conducted in close collaboration with the climate science community and will complement insights obtained from physics-based climate models. Improved understanding of salient atmospheric processes will be provided to those contributing to the development and improvement of climate models with the goal of improving predictability. The approaches and formalisms developed in this research are expected to be applicable to a broad range of scientific and engineering problems, which use model simulations to analyze physical processes. This project will also contribute to efforts in education, diversity, community engagement, and dissemination of tools and computer and atmospheric science findings.

了解气候变化：数据驱动的接近气候变化是我们星球面临的定义环境挑战。无论是飓风的频率或强度增加，海平面上升，干旱，洪水还是极端温度和恶劣天气，社会，经济和环境的后果都很大，因为资源胁迫的星球靠近70亿居民世纪。然而，关于社会和环境的影响存在很大的不确定性，因为地球系统数值的预测潜力受到限制。这些模型无法解决与粮食安全，水资源，生物多样性，死亡率和其他社会经济问题相关时间和空间量表的重要问题。ClimateModel Development促进了小小的改进；但是，尚未实现广泛的建模收益。建模限制阻碍了提供有关气候变化影响以及适应和缓解策略的信息的努力。需要一种新的变革性方法来改善对人类福利的潜在影响的预测。现在，在计算科学的其他方面取得了非常成功的数据驱动方法，现在正在使用成功的环境科学。该探险项目将在气候变化科学方面大大提高关键挑战，开发令人兴奋且创新的新数据驱动方法，这些方法利用了现在从卫星和地面传感器，大气，海洋和海洋和海洋的观察记录中获得的大量气候和生态系统数据的优势陆地过程和基于物理的气候模型模拟。要实现这个雄心勃勃的目标，适合气候变化科学的新方法将在数据密集型计算机科学的四个广泛领域中开发：关系挖掘，复杂的网络，预测性建模和高性能计算。 分析和发现方法将认识到气候和生态系统数据特征，例如非平稳性，非线性过程，多尺度性质，低频可变性，远程空间依赖性以及诸如远程连接之类的长期内存时间过程。这些创新的新方法将用于更好地理解地球系统的复杂性质以及导致气候变化现象的机制，以及极端天气事件削弱我们的基础设施并导致环境灾难的倾向，仅在美国，每年经济损失超过1000亿美元。气候变化影响的评估对利益相关者和政策制定者有用，对区域有用和际气候极端的际标准预测。因此，气候科学家通常需要根据观察结果（例如，飓风强度的增加）或概念理解（例如，野火与野火与区域变暖或干燥和飓风与海面温度之间的关系（例如，将飓风强度的增加）（例如，将飓风强度的增加）（例如，将飓风强度的增加）（例如，将飓风强度的增加）或概念理解（例如，飓风强度的增加）或概念性理解的洞察力提出定性推断。这些紧急的社会优先事项为知识发现方法提供了肥沃的理由。特别是，基于假设指导的数据分析和相对无假设的发现过程的结合，对气候极端和影响的定性推断可能会转化为定量预测见解。该探险项目的主要重点将放在降低不确定性，可以将基于物理模型的互补或补充技能以及有关复杂气候过程的数据引导的见解。对气候模型及其组成过程的系统评估，以及区域和十年尺度上的不确定性评估是将要解决的基本问题。将气候变量的预测能力提高到改进影响评估和归因的能力是告知决策者的关键要求。将开发新的方法论，以从与影响相关的数据集以及因果归因或根本原因分析中获得可行的见解。这项研究将与气候科学界密切合作进行，并将补充从基于物理的气候模型中获得的见解。 将对促进气候模型开发和改进的人的明显大气过程的理解提高，目的是提高可预测性。预计本研究中开发的方法和形式主义将适用于广泛的科学和工程问题，这些问题使用模型模拟来分析物理过程。 该项目还将为教育，多样性，社区参与以及工具以及计算机和大气科学发现的努力做出贡献。