Closing the energy balance gap at scale

大规模缩小能源平衡差距

• 批准号: 2313772
• 负责人: Ankur Desai
• 金额: $ 84.12万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313772&HistoricalAwards=false
• 关键词: Closing; energy; balance; gap; scale

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.

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、草地）、植被、土壤条件等。 为了研究这些交换，科学家们建立了仪器塔来测量不同高度的各种特性，以了解地表和大气如何相互作用。 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。 该研究项目将尝试使用先进的数值方法来提供一种方法，以更好地确定表面大气通量的空间和时间变化，并具有量化的不确定性和低偏差。 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工业排放产生影响。 多名学生和早期职业科学家将参与这项工作，研究团队计划开展外展活动，以增强公众对地球系统的了解。该项目的总体目标是改进了解区域尺度表面大气的大小和驱动因素的方法通过开发以大涡模拟 (LES) 模型为基准的整体标度方法，并将其应用于涡流协方差通量、边界层轮廓和表面特性观测的密集网络，可以测量能量、水和碳的通量。 研究团队将评估环境响应函数（ERF），以测试所有能量平衡组件的综合缩放方法如何解决通量测量中的已知偏差并利用通量塔数据改进模型数据基准。 该技术将与 LES 和空间涡流协方差方法进行比较，并应用于涡流协方差塔和空气通量的广泛数据集以及在由高密度广泛探测器阵列支持的 Chequamegon 异质生态系统能量平衡研究期间收集的其他数据（ CHEESEHEAD-19）野外战役。 将检验三个主要假设： 1．当在与下垫面变化和上覆气象的光谱间隙一致的时空尺度上进行评估时，能量平衡闭合会得到改善。2．与涡流协方差塔站点的平均能量不平衡相比，一致缩放的湍流热通量、净辐射、土壤热通量以及空气、土壤和生物质中的相关存储条件将改善能量平衡闭合。3。与能量传输以及地表热量和生态系统异质性相关的一组有限的地表和大气变量可以使用 ERF 虚拟控制体积来缩放涡流协方差通量，从而提高与建模、大气反演和基于库存（包括碳）的一致性该项目由地球科学理事会共同资助，以支持地球科学领域的 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评估进行评估，认为值得支持。影响审查标准。

项目成果

Scalar Flux Profiles in the Unstable Atmospheric Surface Layer Under the Influence of Large Eddies: Implications for Eddy Covariance Flux Measurements and the Non‐Closure Problem

• DOI: 10.1029/2023gl106649
• 发表时间: 2023-12
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Heping Liu;Cheng Liu;Jianping Huang;Ankur R. Desai;Qianyu Zhang;K. Ghannam;G. Katul

Characterizing energy balance closure over a heterogeneous ecosystem using multi-tower eddy covariance

• DOI: 10.3389/feart.2023.1251138
• 发表时间: 2024-01
• 期刊: Frontiers in Earth Science
• 影响因子: 2.9
• 作者: Brian J. Butterworth;Ankur R. Desai;D. Durden;H. Kadum;Danielle LaLuzerne;M. Mauder;Stefan Metzger;S. Paleri;Luise Wanner