Modeling spatial heterogeneity in surface turbulent heat flux in the US Southern Great Plains
Williams, Ian N. — Lawrence Berkeley National Laboratory
Area of research
General Circulation and Single Column Models/Parameterizations
Advances in modeling cloud dynamics call 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.
Combining ARM observations and land-modeling improves prediction of spatial heterogeneity in surface turbulent heat fluxes. Modeled vegetation processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can help initiate convective clouds.
Satellite and ground-based vegetation remote-sensing data were combined to better characterize land-surface spatial heterogeneity in the Community Land Model (CLM4.0). The new sub-grid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground-based flux measurements in the US 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 than 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. This ‘patchiness’ acts to broaden the size spectrum of surface turbulent heat flux, 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 high spatial resolution.