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

根据地球系统模型对未来气候变化的预测在应对气候变化构成的威胁方面发挥着至关重要的作用，特别是考虑到需要为历史上没有先例的情况进行规划。但ESM预测具有很大的不确定性，这限制了它们对决策支持的有用性，而最令人担忧的气候变化形式往往是具有最大不确定性的形式。考虑到从云形成到碳循环再到海洋湍流的许多过程，这种不确定性并不出人意料，这些过程影响了气候对人为强迫的反应。这些流程必须在ESM中表示，但没有简单的方法来模拟它们。有些问题，如海洋湍流，很难解决，因为它们所涉及的空间尺度太小，无法以全球模型可访问的分辨率进行模拟。其他的过程，如通过森林树冠的水和二氧化碳的交换，由于科学理解的不完整而被粗略地模拟。相反，不能在ESM中明确表示的过程被结合到“参数”中，这大致表达了它们对解析的模型状态的影响。这种参数化是基于第一原理理论的，但它们也涉及粗略的近似，必须通过为控制其行为的参数分配数值来进行“调整”。这些参数通常缺乏观测和理论约束，它们的值是手动调整的，以改善对当今气候的模拟。即使在调整之后，模型仍然表现出很大的偏差，ESM的复杂性和计算成本已经增加到传统的实际操作调整变得不实用的地步。人工智能(AI)，特别是以机器学习(ML)的形式，为改进参数化和减少气候预测中的不确定性提供了一种新的方法。人工智能是对传统的参数化发展的一个引人注目的补充，传统的参数化发展从理论和物理原理开始，并且在某种程度上很少使用观测数据。相比之下，人工智能的方法是数据驱动的，因此与近年来发生的地球系统数据的爆炸性增长完美匹配。这包括来自卫星、现场网络和实地活动的数据。对于一些过程，特别是云的形成和海洋湍流，小尺度过程模型已经变得足够现实，它们可以提供替代观测来驱动基于人工智能的方法。人工智能与物理学习地球中心(LEAP)将人工智能的力量应用于丰富的可用地球系统数据，以克服传统参数化开发和调整的限制，从而创造了一条新的途径，以实现更好的环境监测和更好地指导决策者。人工智能方法是新颖的，因为它们将物理约束(如守恒定律)构建到数据驱动的算法中。人工智能方法也被用来寻找更具区分性的方法来使用观测数据来评估模型的性能。LEAP与社区地球系统模型的开发者合作，以确保其进展可供全球气候研究人员使用。为了促进有效的气候适应，LEAP通过支持博士后、研究生、高中生、家长和教师，促进对下一代不同级别的下一代学习者的公平培训。此外，该中心支持与公共和私营部门的双向知识转让，为利益攸关方开发与气候相关的量身定做的相关信息，以便他们能够更好地适应气候变化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.