STC: Center for Learning the Earth with Artificial Intelligence and Physics (LEAP)

STC：利用人工智能和物理学习地球中心 (LEAP)

• 批准号: 2019625
• 负责人: Pierre Gentine
• 金额: $ 2500万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019625&HistoricalAwards=false
• 关键词: STC; Center; Learning; Earth; Artificial

Projections of future climate change from Earth system models (ESMs) play a critical role in addressing the threats posed by climate change, especially given the need to plan for conditions that have no historical precedent. But ESM projections have large uncertainties which limit their usefulness for decision support, and the most worrisome forms of climate change are often the ones with the greatest uncertainties. Such uncertainty is not unexpected considering the many processes, from cloud formation to carbon cycling to ocean turbulence, that affect the climatic response to anthropogenic forcing. These processes must be represented in ESMs but there is no easy way to simulate them. Some, like ocean turbulence, are hard simply because they involve spatial scales too small to be simulated at the resolutions accessible to global models. Others, like the exchange of water and carbon dioxide through a forest canopy, are crudely simulated due to incomplete scientific understanding.Processes which cannot be explicitly represented in ESMs are instead incorporated through “parameterizations”, which roughly express their effects on the resolved model state. Such parameterizations are based on first-principles theory but they also involve crude approximations and must be “tuned” by assigning numerical values to parameters which control their behavior. The parameters typically lack observational and theoretical constraints, and their values are manually adjusted to improve the simulation of present-day climate. Even after tuning models still exhibit substantial bias, and the complexity and computational expense of ESMs has increased to the point where traditional hands-on tuning is becoming impractical.Artificial Intelligence (AI), particularly in the form of Machine Learning (ML), offers a new way forward for improving parameterizations and reducing uncertainty in climate projections. AI is a compelling complement to traditional parameterization development, which begins with theory and physical principles and uses observational data somewhat sparingly. In contrast, the methods of AI are data driven and thus a perfect match for the explosive growth in earth system data that has occurred in recent years. This includes data from satellites, in situ networks, and field campaigns. For some processes, particularly cloud formation and ocean turbulence, small-scale process models have become sufficiently realistic that they can provide surrogate observations to drive AI-based methods.The Center for Learning the Earth with Artificial Intelligence and Physics (LEAP) applies the power of AI to the wealth of available earth system data to overcome the limitations of traditional parameterization development and tuning, thus creating a new pathway to better ESMs and better guidance to decision-makers. The AI methods are novel in that they build physical constraints such as conservation laws into data-driven algorithms. AI methods are also used to find more discriminating ways to use observational data to evaluate model performance. LEAP works with the developers of the Community Earth System Model to ensure that its advances are made available to the worldwide community of climate researchers.In order to promote effective climate adaptation, LEAP fosters equitable training of the next generation of diverse learners across multiple scales by supporting post docs, graduate students, high-school students, parents, and teachers. Furthermore, the Center supports bidirectional knowledge transfer with the public and private sectors to develop tailored and relevant climate-related information for stakeholders so that they can better adapt to climate change.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.

地球系统模型（ESM）对未来气候变化的预测在应对气候变化所定位的威胁方面起着关键作用，尤其是考虑到没有历史先例的条件的计划。但是ESM项目的不确定性限制了其对决策支持的有用性，而气候变化的最邪恶形式通常是最不确定性的情况。形成碳循环到海洋湍流的云形成，这影响了对人为强迫的杂志反应。这些过程必须在ESM中表示，但是没有简单的方法来模拟它们。有些（例如海洋湍流）很难仅仅是因为它们涉及太小的空间尺度，无法在全球模型可访问的决议上进行模拟。其他的，例如由于不完整的科学理解而对水和二氧化碳通过森林冠层进行了粗略模拟。而不是通过“参数化”纳入ESMS中无法明确表示的过程，该过程大致表达了其对解决模型状态的影响。这样的参数基于第一原理理论，但它们也涉及粗近似，必须通过将数值分配给控制其行为的参数来“调整”。这些参数通常缺乏观察性和理论约束，并且它们的值是手动调整以改善当今气候的模拟。即使在调整模型仍然暴露了实质性偏见之后，ESM的复杂性和计算费用也增加了到传统的动手调整变得不切实际。人工智能（AI），尤其是以机器学习的形式（ML），为改善参数并降低气候预测中的不确定的不确定。 AI是传统参数化发展的令人信服的完成，该完成始于理论和物理原理，并在某种程度上使用观察数据。相反，AI的方法是数据驱动的，因此是近年来发生的地球系统数据爆炸性增长的完美匹配。这包括来自卫星，原位网络和现场活动的数据。对于某些过程，尤其是云形成和海洋湍流，小规模的过程模型已经变得足够现实，可以提供替代观察以推动基于AI的方法。通过人工智能和物理学（LEAP）学习地球的中心，将AI的力量应用于可用地球系统的财富，以更好地限制传统参数的限制，从而更好地开发和调整新的态度。 AI方法是新颖的，因为它们将物理约束（例如保护定律）构建到数据驱动的算法中。 AI方法还用于查找使用观察数据来评估模型性能的更多区分方法。 LEAP与社区地球系统模型的开发商合作，以确保其进步可以为全球气候研究人员提供可用。为了促进有效的气候适应性，LEAP通过支持邮政文档，研究生，高中生，高中生，教师和老师来促进对下一代潜水员学习的公平培训。此外，该中心支持与公共和私营部门的双向知识转移，以为利益相关者开发量身定制的和相关的气候相关信息，以便他们可以更好地适应气候变化。该奖项反映了NSF的法定任务，并通过使用该基金会的智力功能和广泛的影响来评估NSF的法定任务。

项目成果

Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML

• DOI: 10.3390/w13233393
• 发表时间: 2021-12
• 期刊: Water
• 影响因子: 3.4
• 作者: Hoang Tran;E. Leonarduzzi;Luis De la Fuente;R. B. Hull;Vineet Bansal;Calla Chennault;P. Gentine;Peter Melchior;L. Condon;R. Maxwell

Trends and variability in the ocean carbon sink

• DOI: 10.1038/s43017-022-00381-x
• 发表时间: 2023-01-24
• 期刊: NATURE REVIEWS EARTH & ENVIRONMENT
• 影响因子: 42.1
• 作者: Gruber, Nicolas;Bakker, Dorothee C. E.;Mueller, Jens Daniel
• 通讯作者: Mueller, Jens Daniel

Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

• DOI: 10.1029/2021ms002847
• 发表时间: 2022-05
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Yu Cheng;M. Giometto;Pit Kauffmann;Ling Lin;Chengyu Cao;Cody Zupnick;Harold Li;Qi Li;Y. Huang;R. Abernathey;P. Gentine

Variability in the Global Ocean Carbon Sink From 1959 to 2020 by Correcting Models With Observations

• DOI: 10.1029/2022gl098632
• 发表时间: 2022-07-28
• 期刊: GEOPHYSICAL RESEARCH LETTERS
• 影响因子: 5.2
• 作者: Bennington, Val;Gloege, Lucas;McKinley, Galen A.
• 通讯作者: McKinley, Galen A.

Explicit Physical Knowledge in Machine Learning for Ocean Carbon Flux Reconstruction: The pCO 2 ‐Residual Method

海洋碳通量重建机器学习中的显式物理知识：pCO 2 – 残差法

• DOI: 10.1029/2021ms002960
• 发表时间: 2022
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Bennington, Val;Galjanic, Tomislav;McKinley, Galen A.
• 通讯作者: McKinley, Galen A.