Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains

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.

Long‐Term Observations of Turbulence Vertical Velocity Spectra in a Convective Mixed Layer: Dependence on Land‐Surface Forcing in the U.S. Southern Great Plains

“…Half‐hour surface sensible fluxes and EF were obtained from an eddy‐covariance instrument over a winter wheat field (from the Carbon Dioxide Flux Measurement System data stream, SGP30CO2FLX; Chan & Biraud, 2022), and an energy balance Bowen ratio system (data stream SGP30EBBR; Cook & Sullivan, 2019) over a grass field, at the ARM central facility. The average of the two measurements (located about 250 m apart) was used to reflect the approximate land cover percentages near the lidar site (e.g., Williams et al., 2020). We calculated the daily afternoon mean EF, averaged from 12:00 to 18:00 LST.…”

Simulating the Effects of Surface Energy Partitioning on Convective Organization: Case Study and Observations in the US Southern Great Plains

“…Although newer versions of CLM are available, CLM4 was the latest supported version coupled to WRF (Lu & Kueppers, 2012) at the start of our study. For this study, modifications were made to CLM4 in WRF to better represent the land surface in the Southern Great Plains (as described in Williams et al., 2020). Notably, stomatal and soil resistance parameters were changed to better represent surface energy partitioning compared to observations, and higher‐resolution input soil and vegetation datasets were used.…”

“…The latter include a new plant functional type and leaf area index data set developed to capture the heterogeneous distribution of winter crops and grasses across much of Oklahoma and Kansas. WRF‐CLM was initialized with a spun‐up offline run of CLM forced with meteorological data including 4 km gridded hourly precipitation data combining WSR‐88D NEXRAD radar and rain gauge estimates (see Williams et al., 2020).…”

“…including 4 km gridded hourly precipitation data combining WSR-88D NEXRAD radar and rain gauge estimates (see Williams et al, 2020).…”

Local scale evaluation of the simulated interactions between energy, water and vegetation in land surface models

“…With the emergence of freely available data from eddy covariance network, the use of local datasets is an increasingly standardized approach to evaluate the performance of land surface models (Balzarolo et al, 2014;Napoly et al, 2017;Williams et al, 2020;Chen et al, 2018;Joetzjer et al, 2015). However, the eddy covariance observations notoriously suffer from substantial biases and non-closure of the energy balance (Foken, 2008;Mauder et al, 2020).…”