State-dependent decadal predictability identified with explainable machine learning

通过可解释的机器学习确定依赖于状态的十年可预测性

• 批准号: 2210068
• 负责人: Elizabeth Barnes
• 金额: $ 70万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210068&HistoricalAwards=false
• 关键词: State; dependent; decadal; predictability; identified

Variation in climate from one year to another or even one decade to another can be substantial, including severe winters followed by mild ones and alternations between dry and rainy summers. Efforts to predict such interannual to decadal variations have been a subject of intensive research but with mixed results, suggesting that the predictability of climate variations is not high in general but there may be particular cases in which useful multi-year predictions can be made. For example recent work suggests that multi-year warming and cooling of North Atlantic sea surface temperature (SST) can be anticipated when Atlantic ocean heat transport is unusually strong or weak.Research supported under this award uses machine learning techniques in combination with climate model simulations to identify climate states that lead to enhanced predictability, and understand why climate predictability is enhanced for these states. The work uses Controlled Abstention Networks (CANs), a variant of neural networks developed by the lead Principal Investigator (PI) and others in which the neural network is able to overlook data from the training set in which there are no identifiable relationships between predictors (like ocean heat transport) and predictands (like North Atlantic SST). In effect the CAN says "I don't know" when confronted with ambiguous training data, thereby concentrating on those portions of the training dataset which contain strong, predictable signals. The CAN is ideally suited to the search for state-dependent climate predictability given its underlying assumption that predictable relationships are the exception rather than the norm.The CAN-based search for state-dependent predictability is accompanied by analysis seeking to explain why climate fluctuations evolve more predictably from some climate states than from others. Applications of neural networks to climate science are hampered by the "black box" nature of the networks, which may have uncanny predictive power yet lack credibility because there is no accounting for why a particular set of inputs produces a given result. The PIs address this shortcoming through an explainable artificial intelligence (XAI) technique called layerwise relevance propagation (LRP, developed by the lead PI), which generates "relevance heat maps" showing the spatial patterns of data that are the most influential in producing the predictive relationships found by CAN or other neural networks. For example LRP applied to a neural network predictive scheme for surface temperatures in the Pacific Northwest shows that most of the predictive skill comes from precursor SST patterns along the Kuroshio current and in the northwest Pacific, both regions associated with known modes of decadal Pacific climate variability.A further novelty of the work is the use of climate model output rather than observations. Machine learning methods require large amounts of training data, thus the few decades of the observational record are insufficient for the development of decadal prediction schemes. The PIs take advantage of the 100-member ensemble of simulations from the second version of the Community Earth System Model (CESM2), covering the period 1850 to 2100, along with similar simulations from the Coupled Model Intercomparison Project (CMIP), to provide adequate sample size. A further advantage of the climate model simulations is that they allow examination of changes in decadal predictability as a consequence of anthropogenic climate change.The work is of societal as well as scientific interest due to the potentially severe impacts of climate variability. The Dust Bowl drought of the 1930s is a prime example of decadal climate variability and its societal consequences, which were made worse by the agricultural practices of the era. The work also develops the techniques of XAI, which are relevant to the ethical use of artifical intelligence technology. In addition to the societal value of the research products the project has broader impacts through its partnership with two minority-serving institutions, Metropolitan State University of Denver (MSU) and North Carolina Agricultural and Technical University (NCA&T). The PIs work with collaborators Sam Ng (MSU) and Ademe Mekonnen (NCA&T) to incorporate machine learning methods into undergraduate courses, covering topics including what "machine learning" actually means and why overfitting is bad. The award provides funding for students from MSU and NCA&T to participate in the Reseach Experiences for Undergraduates (REU) program at Colorado State University, where they will spend 10 weeks working on research related to this project. The project also provides support and training to two graduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

气候在一年与另一年之间，甚至在十年与十年之间的变化可能是巨大的，包括严冬之后的暖冬，以及干燥和多雨的夏季之间的交替。 预测这种年际到十年变化的努力一直是深入研究的主题，但结果好坏参半，这表明气候变化的可预测性一般不高，但可能有一些特殊情况，可以做出有用的多年预测。 例如，最近的研究表明，当大西洋海洋热量输送异常强或弱时，可以预测北大西洋海面温度（SST）的多年变暖和变冷。该奖项支持的研究使用机器学习技术与气候模型模拟相结合，以识别导致可预测性增强的气候状态，并了解为什么这些状态的气候可预测性增强。 这项工作使用受控弃权网络（CAN），这是由首席研究员（PI）和其他人开发的神经网络的一种变体，其中神经网络能够忽略来自训练集的数据，其中预测因子（如海洋热传输）和预测因子（如北大西洋SST）之间没有可识别的关系。 实际上，CAN在面对模糊的训练数据时会说“我不知道”，从而集中在训练数据集中包含强可预测信号的部分。 CAN非常适合于寻找依赖于状态的气候可预测性，因为它的基本假设是可预测的关系是例外而不是常态。基于CAN的依赖于状态的可预测性搜索伴随着分析，试图解释为什么气候波动从某些气候状态比从其他国家更可预测地演变。 神经网络在气候科学中的应用受到网络的“黑匣子”性质的阻碍，这种网络可能具有不可思议的预测能力，但缺乏可信度，因为无法解释为什么一组特定的输入会产生给定的结果。 PI通过一种可解释的人工智能（XAI）技术解决了这一缺点，该技术被称为分层相关传播（LRP，由首席PI开发），该技术生成“相关热图”，显示数据的空间模式，这些数据在产生CAN或其他神经网络发现的预测关系方面最具影响力。 例如，LRP应用于西北太平洋表面温度的神经网络预测方案表明，大部分预测技巧来自于沿着黑潮和西北太平洋的SST前兆型，这两个区域都与已知的年代际太平洋气候变率模态有关。这项工作的另一个新奇是使用气候模式输出而不是观测。机器学习方法需要大量的训练数据，因此几十年的观测记录不足以开发十年预测方案。 PI利用了社区地球系统模式（CESM 2）第二版的100个成员的模拟集合，涵盖1850年至2100年期间，沿着与耦合模式相互比较项目（CMIP）的类似模拟，以提供足够的样本量。 气候模式模拟的另一个优点是，它们允许检查人为气候变化造成的年代际可预测性的变化，由于气候变率的潜在严重影响，这项工作具有社会和科学意义。20世纪30年代的沙尘暴干旱是十年气候变化及其社会后果的一个典型例子，而当时的农业实践使其变得更糟。 这项工作还开发了XAI的技术，这些技术与人工智能技术的道德使用有关。 除了研究产品的社会价值外，该项目还通过与丹佛大都会州立大学（MSU）和北卡罗来纳州农业技术大学（NCA T）这两个少数民族服务机构的合作关系产生了更广泛的影响。 PI与合作者Sam Ng（MSU）和Ademe Mekonnen（NCA T）合作，将机器学习方法纳入本科课程，涵盖的主题包括“机器学习”的实际含义以及为什么过拟合是不好的。 该奖项为来自密歇根州立大学和NCA T的学生提供资金，以参加科罗拉多州立大学的本科生研究经验（REU）计划，在那里他们将花10周的时间进行与该项目相关的研究。 该项目还为两名研究生提供支持和培训。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。