Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains. Issue 13 (27th June 2020)

• Record Type: Journal Article
• Authors: Williams, Ian N.; Lee, Jungmin M.; Tadić, Jovan; Zhang, Yunyan; Chu, Housen
• Is Part Of: Journal of geophysical research. Volume 125:Issue 13(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-27
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2019JD032255
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Advances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. In this study, satellite and ground‐based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground‐based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground‐based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions. Key Points: Vegetation heterogeneity broadens the size spectrum of surface latent and sensible heat fluxes depending on model parameters Higher‐resolution vegetation data has greater impact on heat flux heterogeneity when combined with improved soil and stomatal parameters Heterogeneity responses to rain events are muted (at small scales) in models capturing observed stronger vegetation controls on fluxes