Closing the energy balance gap at scale

众所周知，地表条件会影响天气和气候。地表和大气之间的水分、能量和气体交换取决于地表类型(例如，水、森林、草原)、植被、土壤条件等。为了研究这些交换，科学家们建立了仪表塔来测量不同高度的各种特性，以了解陆地表面和大气是如何相互作用的。然而，许多地点的地表条件不均匀，使得解释这些数据变得困难。本研究项目将尝试使用一种先进的数值方法，以提供一种更好地确定具有量化不确定性和低偏差的地面-大气通量的空间和时间变化的方法。一个成功的项目将对生态学、水文学、地球系统建模、自然气候解决方案以及石油和天然气行业的排放产生影响。多名学生和职业生涯早期的科学家将参与这项工作，研究小组计划扩大范围以加强公众对地球系统的了解。该项目的总体目标是通过开发整体尺度方法来改进理解区域尺度地表-大气能量、水和碳通量的大小和驱动因素的方法，该方法以大涡模拟(LES)模式为基准，并应用于涡旋协方差通量、边界层剖面和表面特性观测的密集网络。研究小组将评估环境响应函数(ERF)，以测试所有能量平衡组件的综合定标方法如何既能解决通量测量中的已知偏差，又能改进使用通量塔数据的模型数据基准。这项技术将与大涡模拟和空间涡旋协方差方法进行比较，并将应用于在Chequamegon异质生态系统能量平衡研究期间通过高密度广泛探测器阵列(CheeseHead-19)实地活动收集的涡旋协方差塔和空气中通量的广泛数据集和其他数据。将检验三个主要假设：1.能量平衡闭合在时空尺度上进行评估时会得到改善，这与下垫面可变性和上覆气象学中的光谱差距是一致的。一致尺度的湍流热通量、净辐射、土壤热通量以及空气、土壤和生物量中的相关储存项，与涡旋协方差塔址的平均能量不平衡相比，将改善能量平衡闭合。一组有限的与能量传输、陆地表面热量和生态系统异质性有关的地表和大气变量可以使用ERF-虚拟控制体积来衡量涡旋协方差通量，其方式可以增加与建模、大气逆变换和基于清单的碳的一致性。该项目由地球科学局共同资助，以支持地学领域的AI/ML进步。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。

Land surface conditions are known to impact weather and climate. The exchange of moisture, energy, and gases between the surface and atmosphere depends on the types of surfaces (e.g., water, forest, grassland), vegetation, soil conditions, and more. To study these exchanges, scientists have set up instrumented towers to measure various properties at different heights to get a sense for how the land surface and atmosphere are interacting. However, the heterogeneous conditions of the land surface in many locations makes interpreting this data difficult. This research project will attempt to use an advanced numerical approach to provide a method to better determine the space and time variations of surface-atmosphere fluxes with quantified uncertainties and low bias. A successful project would have implications for ecology, hydrology, Earth system modeling, natural climate solutions, and oil and gas industry emissions. Multiple students and early-career scientists would be involved in the work and the research team plans outreach to enhance public understanding of Earth systems.The overarching goal of this project is to improve approaches to understanding the magnitude and drivers of regional-scale surface-atmosphere fluxes of energy, water, and carbon through development of a holistic scaling approach benchmarked within a large eddy simulation (LES) model and applied to a dense network of eddy covariance flux, boundary-layer profile, and surface property observations. The research team will evaluate the Environmental Response Function (ERF) to test how a comprehensive scaling approach of all energy balance components can both resolve the known bias in flux measurements and improve model-data benchmarking with flux tower data. The technique will be compared to LES and spatial eddy covariance approaches and be applied to an extensive dataset of eddy covariance tower and airborne fluxes and other data collected during the Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD-19) field campaign. Three main hypotheses will be tested:1. Energy balance closure improves when evaluated over a space-time scale that is consistent with spectral gaps in underlying surface variability and overlying meteorology.2. Consistently scaled turbulent heat fluxes, net radiation, soil heat flux, and associated storage terms in air, soil, and biomass will improve energy balance closure compared to the average energy imbalance at eddy covariance tower sites.3. A restricted set of land surface and atmospheric variables related to energy transport and land surface thermal and ecosystem heterogeneity can scale eddy covariance fluxes using ERF-Virtual Control Volume in ways that increases consistency with modeled, atmospheric inverse, and inventory-based, including of carbon.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.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.