Collaborative Research: HDR: Data-Driven Earth System Modeling

全球天气和气候模型在水平间距约为 100 公里的计算网格上代表大气，这些网格堆叠成厚度超过一公里的层。 这样的网格足以捕捉气旋、锋面和其他大规模大气现象的动态，但这些现象严重依赖于空间尺度远小于网格间距的过程。 小规模过程必须通过参数化方案来间接表示，参数化方案估计它们对解析的大气状态的净影响。 例如，云对于网格间距来说通常太小，但它们对于将水分从海洋表面移动到对流层中部至关重要，因此云参数化在确定大气湿度方面发挥着关键作用，即使在最大的空间尺度上也是如此。 参数化方案本质上是近似的，开发能够产生真实模拟的方案是模型开发的核心挑战。 参数化的缺点限制了天气和气候模型在科学研究和社会应用中的实用性。大多数参数化方案严重依赖于各种参数，这些参数的值无法先验确定，而必须通过反复试验来找到。 这项任务称为“调整”，非常费力，因为它是针对每个参数化方案单独执行的，并且涉及模型在多种配置中的多次集成。它在使用观测数据方面也效率低下，考虑到卫星和其他来源提供的大量观测数据，这是不幸的。当所有方案在全局模拟中相互作用时，所得的参数集可能不是最优的，并且可能产生意想不到的结果。 最后，手动调整不利于不确定性量化，这对于估计未来气候变化预测的不确定性很有价值。该项目的目标是通过数据同化、机器学习和使用大涡模拟 (LES) 模型的精细过程建模相结合来取代临时手动调整。 LES模型的网格间距为几十米，可以明确模拟参数化方案所代表的云和湍流。 这些成分组合在一起创建了一个全局机器学习大气模型 (MLAM)，其中嵌入全局模型的选定网格列中的 LES 模型显式模拟由其他列中的参数化方案表示的子网格尺度过程。机器学习用于调整方案以模拟 LES 模拟的行为，以便显式模拟成为参数化的在线基准。 通过这种方式，所有方案都可以在运行的全局模拟中一起进行交互调整。 在模型集成过程中，来自各种来源的观测数据被同化，以提供对参数值的进一步约束，并且作为自动调整的一部分，生成参数不确定性的估计。 在海洋环流模型中实施类似的调整过程，并将两者结合起来产生机器学习气候模型。 模型调整通常被视为必要但平凡的活动，其本身并不是一个研究主题。 但是，能够从观测和过程模型中学习参数的模型为更好的模型和更好的模型使用方式提供了一条新的前进道路。由于天气和气候模型更好的预报和预测的社会价值，这项工作具有更广泛的影响。 这项工作直接解决了决策者用于规划天气和气候影响的预测和预测的不确定性。 此外，这里开发的建模策略适用于广泛的研究领域，这些领域面临着将大规模行为与小规模未解决的过程相关联的问题（例如，在进化生物学中将基因型与表型相关联的问题）。 此外，PI 将建立一个关于数据驱动的地球系统建模的跨学科研究生项目。该计划弥合了目前阻碍环境建模进展的环境科学和计算科学之间的差距。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Global weather and climate models represent the atmosphere on computational grids with horizontal spacing of perhaps 100km, stacked in layers which can be over a kilometer thick. Such grids suffice to capture the dynamics of cyclones, fronts, and other large-scale atmospheric phenomena, but these phenomena depend critically on processes with spatial scales much smaller than the grid spacing. The small-scale processes must be represented indirectly, through parameterization schemes which estimate their net impact on the resolved atmospheric state. For example clouds are typically too small for the grid spacing yet they are critical for moving moisture from the ocean surface to the mid-troposphere, thus cloud parameterizations play a key role in determining atmospheric humidity even on the largest spatial scales. Parameterization schemes are inherently approximate, and the development of schemes which produce realistic simulations is a central challenge of model development. Shortcomings in parameterization limit the usefulness of weather and climate models both for scientific research and for societal applications.Most parameterization schemes depend critically on various parameters whose values cannot be determined a priori but must instead be found through trial and error. This task, referred to as "tuning", is laborious as it is performed separately for each parameterization scheme and involves multiple integrations of the model in multiple configurations. It is also inefficient in its use of observations, which is unfortunate given the large amount of observational data available from satellites and other sources. The resulting parameter sets may not be optimal and may produce unexpected results when all the schemes interact with each other in global simulations. Finally, manual tuning is not conducive to uncertainty quantification, which would be valuable for estimating the uncertainty in future climate change projections. The goal of this project is to replace ad hoc manual tuning with a combination of data assimilation, machine learning, and fine-scale process modeling using large eddy simulation (LES) models. LES models have grid spacings of a few tens of meters and can explicitly simulate the clouds and turbulence represented by parameterization schemes. These ingredients are combined to create a global Machine Learning Atmospheric Model (MLAM), in which LES models embedded in selected grid columns of a global model explicitly simulate subgrid-scale processes which are represented by parameterization schemes in the other columns. Machine learning is used to tune the schemes to emulate the behavior of the LES simulations, so that explicit simulations become an online benchmark for parameterization. In this way all the schemes can be tuned together and interactively within a running global simulation. Observational data from a variety of sources is assimilated during the model integration to provide a further constraint on parameter values, and estimates of parameter uncertainty are generated as part of the automated tuning. A similar tuning process is implemented in an ocean general circulation model, and the two are combined to produce a machine learning climate model. Model tuning is generally viewed as a necessary but mundane activity which is not in itself a research topic. But a model capable of learning its parameters from observations and process models offers a new path forward, toward both better models and better ways of using models.The work has broader impacts due to the societal value of better forecasts and projections from weather and climate models. The work directly addresses uncertainty in forecasts and projections used by decision makers to plan for weather and climate impacts. In addition, the modeling strategy developed here is applicable to a broad class of research areas which face the problem of relating large-scale behaviors to small-scale unresolved processes (the problem of relating genotypes to phenotypes in evolutionary biology, for example). In addition, the PIs will establish a cross-disciplinary graduate program on data-driven Earth system modeling. The program bridges the gap between environmental and computational sciences which currently hinders progress in environmental modeling.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.