Closing the energy balance gap at scale

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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.