An Analysis of the Importance of a Fully-Coupled Atmosphere and Land-Surface When Considering the Impact of Multi-Scale Land Spatial Heterogeneity on Cloud Development

<p><span>Land-surface heterogeneity is known to play an important role in land-surface hydrology, which drives the bottom boundary condition for atmospheric models in numerical weather prediction (NWP) applications. However, the ultimate impact of land-surface heterogeneity on atmospheric boundary layer (ABL) development is still an open problem with implications for sub-grid scale (SGS) parameterizations for both NWP and climate modeling. Large-eddy simulation (LES) is often used to study the effects of land-surface heterogeneity on ABL development, most typically via specified surface fields which are not influenced by the atmosphere (i.e. semi-coupled). Heterogeneous land surfaces have been seen in previous studies to have a significant influence on ABL dynamics, particularly cloud production, in certain cases when semi-coupled to the atmosphere. </span></p><p><span>Here we use the Weather Research and Forecasting (WRF) model as an LES with both semi-coupled and fully-coupled land surfaces to investigate the impact of two-way coupling on the interaction between heterogeneous land surfaces and daytime ABLs. For semi-coupled simulations, the HydroBlocks land-surface model is run offline, drive</span><span>n by 4-km NLDAS-2 meteorology with Stage-IV radar rainfall data, and then used to specify the bottom boundary in WRF. The WRF-Hydro model is used for cases where the land surface is fully coupled to the WRF model. Both land-surface models use the Noah-MP model as their underlying physics package and add both subsurface and overland flow routing. </span><span>The WRF model uses a 100-m horizontal resolution, and the land-surface models use </span><span>high resolution (30 m) datasets that were upscaled to match the LES resolution for elevation, landcover, and soil type using NED, NLCD, and POLARIS respectively. </span><span>These LES experiments are performed over the ARM Southern Great Plains Site</span><span> atmospheric observatory in Oklahoma during the Summer of 2017 with a grid size of 100 km x 100 km to imitate a single cell in a modern climate model. </span><span>The impact of land-surface heterogeneity on the atmosphere is evaluated by comparing simulations using the fully heterogeneous land surfaces with simulations where the land surface is homogenized at each timestep, taking a domain-wide spatial mean value at every grid cell. </span><span>Results are evaluated primarily by the differences in the development of clouds and evolution of turbulent kinetic energy in the ABL. </span></p>

On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.

Using Spatial Eddy Covariance to Investigate Energy Balance Closure over a Heterogeneous Ecosystem

<p>Land surface heterogeneity influences patterns of sensible and latent heat flux, which in turn affect processes in the atmospheric boundary layer. However, gridded atmospheric models often fail to incorporate the influence of land surface heterogeneity due to differences between the temporal and spatial scales of models compared to the local, sub-grid processes. Improving models requires the scaling of surface flux measurements; a process made difficult by the fact that surface measurements usually find an imbalance in the energy budget.</p><p>The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) was an observational experiment designed to investigate how the atmospheric boundary layer responds to scales of spatial heterogeneity in surface-atmosphere heat and water exchanges. The campaign was conducted from June – October 2019, measuring surface energy fluxes over a heterogeneous forest ecosystem as fluxes transitioned from latent heat-dominated summer through sensible heat-dominated fall. Observations were made by ground, airborne, and satellite platforms within the 10 x 10 km study region, which was chosen to match the scale of a typical model grid cell. The spatial distribution of energy fluxes was observed by an array of 20 eddy covariance towers and a low-flying aircraft. Mesoscale atmospheric properties were measured by a suite of LiDAR and sounding instruments, measuring winds, water vapor, temperature, and boundary layer development. Plant phenology was measured in-situ and mapped remotely using hyperspectral imaging.</p><p>The dense set of multi-scale observations of land-atmosphere exchange collected during the CHEESEHEAD field campaign permits combining the spatial and temporal distribution of energy fluxes with mesoscale surface and atmospheric properties. This provides an unprecedented data foundation to evaluate theoretical explanations of energy balance non-closure, as well as to evaluate methods for scaling surface energy fluxes for improved model-data comparison. Here we show how fluxes calculated using a spatial eddy covariance technique across the 20-tower network compare to those of standard temporal eddy covariance fluxes in order to characterize of the spatial representativeness of single tower eddy covariance measurements. Additionally, we show how spatial EC fluxes can be used to better understand the energy balance over heterogeneous ecosystems.</p>

An investigation into the influence of high-resolution land-surface heterogeneity on atmospheric dynamics

<p>Land-surface heterogeneity is known to play an important role in land surface hydrology and thus the boundary conditions for numerical weather prediction (NWP) and climate modeling. For this reason, there have been considerable efforts over the past two decades to improve its representation in large scale models. However, to date, the inclusion of sub-grid heterogeneity in modeling land-atmosphere interactions in regional and global models has been limited to sub-grid spatial means and thus have almost entirely disregarded its multi-scale impact on the simulated atmospheric dynamics. To begin to address this challenge, here we use large-eddy simulations (LES) coupled to a land-surface model to gain a more complete understanding of its role in the coupled land-atmosphere system. In this work, we illustrate its impact over the Southern Great Plains (SGP) site in the United States and present a path forward for using these modeling experiments to guide the development of a complementary coupling parameterization within climate models.</p><p>More specifically, over the SGP site, we use high-resolution LES to investigate the impact of SGS land heterogeneity under different atmospheric and surface conditions to inform the development of land-surface and planetary boundary layer (PBL) parameterizations for coarser, operational-scale weather and climate modeling efforts. The experiment methodology uses a high-resolution land-surface model (WRF-Hydro), spun-up over multiple years using reanalysis data, which is then coupled to the Weather Research and Forecasting (WRF) model for high-resolution LES. Cases are considered using both the fully heterogeneous land model as well as using a homogeneous surface with domain-averaged flux values at all grid points, allowing the dynamical effects of land-surface heterogeneity on the atmosphere to be isolated, and the land/atmospheric conditions under which land-surface heterogeneity plays a role to be studied. Results are evaluated primarily by the differences in the development of the planetary boundary layer and the extent, duration and intensity of developing rainfall events.</p>

Modelling multi-scale atmosphere and land-surface interactions

<p>As land-surface properties are heterogeneous over a broad range of length-scales, surface-induced fluxes are heterogeneous too. Representing land-surface heterogeneity and the corresponding fluxes is a challenging task in numerical prediction of weather and projection of climate. </p><p>In this work, we introduce the approach of <em>'para-real' ensemble modelling</em> to investigate the dynamic effect of land-surface heterogeneity. We perform a large ensemble of high-resolution simulations using the Weather research and forecast model (WRF-ARW-LSM). The para-real simulation ensembles are externally forced by a reanalysis of a real case in spring 2013, but become exposed to different synthesized surface patterns (SP) generated as quasi-fractal Brownian surfaces (quasi-fBs) with exact control of the dominant wave length and fractal persistence.</p><p>The focus of this study is on the three inter-related land-surface and atmosphere coupling mechanisms--the <em>thermodynamic coupling</em>, <em>aerodynamic coupling</em>, and <em>hydrological coupling</em>. For each mechanism, a corresponding surface property is identified, namely surface albedo (α) for thermodynamic coupling, roughness length (z<sub>0</sub>) for aerodynamic coupling, and soil type (s<sub>t</sub>) for hydrological coupling. For each surface property, we generate a set of quasi-fBs with different dominant length scale and fractal persistence. In our para-real ensembles, the original fields of the surface properties are replaced by the quasi-fBs, for which we estimate the control parameters from the original data, i.e., the probability density distribution of the original data matches that of the quasi-fBs which eliminates the flux aggregation effect and allows us to focus on the dynamic effect. </p><p>We find, first, a strong impact of the length scale of the surface forcing on the intensity of coupling: while the dynamic effect of surface heterogeneity significantly impacts the state of the atmospheric boundary layer for all cases investigated,  the impact of the surface signal on the atmospheric state  grows with the length-scale of the surface heterogeneity. Second, we demonstrate that larger fractal persistence of the surface signal also strengthens the atmosphere--surface coupling. Third, the qualitative impact of the surface forcing is shown to depend on time, which eliminates the possibility of a simple linear forward propagation of the surface signal; there is strong sensitivity to the diurnal cycle, in particular with respect to the horizontal wind components: The maximum intensity of atmosphere--surface coupling (measured in terms of correlation) is found around noon for the atmospheric temperature, and some hours later (in the early afternoon) for water vapor. Fourth, among the different surface forcing investigated, we find that the heterogeneity of soil type is the most important to the atmospheric state--surface exchanges and its signal are detected in the atmospheric water-vapor up to 2km height; in particular, the soil-type pattern with the smallest length-scale causes a doubling of cloud-water above 500m height  whereas no impact on the bulk atmospheric state is found for patterns with other length-scales and fractal persistence or forcing of other surface variables. This illustrates the key part that hydrological coupling plays in connecting the atmosphere to the surface, and it underlines the relevance of improved hydrological process-level representation for improved parameterization of the coupled land--atmosphere system.</p>

Modelling Regional Water and Energy balance in East Africa

<p>Effective modeling of surface water and energy balance is crucial in planning and management of regional resources. However, the heterogeneous and clumped vegetation structure controls the portioning of land surface water and energy fluxes, which leads to large variations of local radiative and hydrological processes. The aim of this study is to characterize the land surface heterogeneity in East Africa and examine the impact of the spatially and temporally varying vegetation parameters on energy and water balance in the region.  We used MODIS datasets on Leaf Area Index (LAI), Enhanced Vegetation Index (EVI) and albedo to derive time-varying vegetation parameters for the period 2001 – 2011 period at 0.05° resolution. These parameters were integrated with the Variable Infiltration Capacity (VIC) model to characterize the effects of varying vegetation properties on surface water and energy fluxes. A twin simulation was also carried based on seasonally averaged vegetation parameters to isolate the effects of time-varying and spatially heterogeneous parameters on the water and energy fluxes. The simulation results were compared to rigorously validated global datasets on evapotranspiration and sensible heat. Results showed that the time-varying and spatially heterogeneous vegetation parameters provided surface water and energy fluxes which were more consistent with the validation datasets. The simulated evapotranspiration matched reasonably well with the observed values particularly in areas characterized by sparse vegetation and which are more prone to human influence. The improvements were highly noticeable in grassland and savanna land cover types. However, due to intensive human activities in region which affect not only the lad cover but also the vegetation structure, there is need for characterization of the land cover parameters based on high resolution data which can better capture the land surface heterogeneity in the region.</p>