scholarly journals CONNECTING LAND-ATMOSPHERE INTERACTIONS TO SURFACE HETEROGENEITY IN CHEESEHEAD19

Author(s):  
BRIAN J. BUTTERWORTH ◽  
ANKUR R. DESAI ◽  
STEFAN METZGER ◽  
PHILIP A. TOWNSEND ◽  
MARK D. SCHWARTZ ◽  
...  

CAPSULE SUMMARYA regional-scale observational experiment designed to address how the atmospheric boundary layer responds to spatial heterogeneity in surface energy fluxes.

2020 ◽  
Author(s):  
Brian Butterworth ◽  
Ankur Desai ◽  
Sreenath Paleri ◽  
Stefan Metzger ◽  
David Durden ◽  
...  

<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>


2014 ◽  
Vol 15 (3) ◽  
pp. 973-989 ◽  
Author(s):  
Lennert B. Stap ◽  
Bart J. J. M. van den Hurk ◽  
Chiel C. van Heerwaarden ◽  
Roel A. J. Neggers

Abstract Observations have shown that differences in surface energy fluxes over grasslands and forests are amplified during heat waves. The role of land–atmosphere feedbacks in this process is still uncertain. In this study, a single-column model (SCM) is used to investigate the difference between forest and grassland in their energy response to heat waves. Three simulations for the period 2005–11 were carried out: a control run using vegetation characteristics for Cabauw (the Netherlands), a run where the vegetation is changed to 100% forest, and a run with 100% short grass as vegetation. A surface evaporation tendency equation is used to analyze the impact of the land–atmosphere feedbacks on evapotranspiration and sensible heat release under normal summer and heat wave conditions with excessive shortwave radiation. Land–atmosphere feedbacks modify the contrast in surface energy fluxes between forest and grass, particularly during heat wave conditions. The surface resistance feedback has the largest positive impact, while boundary layer feedbacks generally tend to reduce the contrast. Overall, forests give higher air temperatures and drier atmospheres during heat waves. In offline land surface model simulations, the difference between forest and grassland during heat waves cannot be diagnosed adequately owing to the absence of boundary layer feedbacks.


2019 ◽  
Author(s):  
Kristina Bohm ◽  
Joachim Ingwersen ◽  
Josipa Milovac ◽  
Thilo Streck

Abstract. Land surface models are essential parts of climate and weather models. The widely used Noah-MP land surface model requires information on the leaf area index (LAI) and green vegetation fraction (GVF) as key inputs of its evapotranspiration scheme. The model aggregates all agricultural areas into a land use class termed Cropland and Pasture. In a previous study we showed that, on a regional scale, GVF has a bimodal distribution formed by two crop groups differing in phenology and growth dynamics: early covering crops (ECC, ex.: winter wheat, winter rapeseed, winter barley) and late covering crops (LCC, ex.: corn, silage maize, sugar beet). That result can be generalized for Central Europe. The present study quantifies the effect of splitting the land use class Cropland and Pasture of Noah-MP into ECC and LCC on surface energy fluxes and temperature. We further studied the influence of increasing the LCC share, which in the study area (the Kraichgau region, southwest Germany) is mainly the result of heavily subsidized biomass production, on energy partitioning at the land surface. We used the GVF dynamics derived from high-resolution (5 m × 5 m) RapidEye satellite data and measured LAI data for the simulations. Our results confirm that GVF and LAI strongly influence the partitioning of surface energy fluxes, resulting in pronounced differences between ECC and LCC simulations. Splitting up the generic crop into ECC and LCC had the strongest effect on land surface exchange processes in July–August. During this period, ECC are at the senescence growth stage or already harvested, while LCC have a well-developed, ground-covering canopy. The generic crop resulted in humid bias, i.e. an increase of evapotranspiration by +0.5 mm d−1 (LE: 1.3 MJ m−2 d−1), decrease of H by 1.2 MJ m−2 d−1 and decrease of surface temperature by −1 °C. The bias increased as the shares of ECC and LCC became similar. The observed differences will impact the simulations of processes in the planetary boundary layer. Increasing the LCC share from 28 to 38 % in the Kraichgau region led to a decrease of LE and a heating up of the land surface in the early growing season. Over the second part of the season, LE increased and the land surface cooled down by up to 1 °C.


2008 ◽  
Vol 9 (6) ◽  
pp. 1443-1463 ◽  
Author(s):  
Susan Frankenstein ◽  
Anne Sawyer ◽  
Julie Koeberle

Abstract Numerical experiments of snow accumulation and depletion were carried out as well as surface energy fluxes over four Cold Land Processes Experiment (CLPX) sites in Colorado using the Snow Thermal model (SNTHERM) and the Fast All-Season Soil Strength model (FASST). SNTHERM is a multilayer snow model developed to describe changes in snow properties as a function of depth and time, using a one-dimensional mass and energy balance. The model is intended for seasonal snow covers and addresses conditions found throughout the winter, from initial ground freezing in the fall to snow ablation in the spring. It has been used by many researchers over a variety of terrains. FASST is a newly developed one-dimensional dynamic state-of-the-ground model. It calculates the ground’s moisture content, ice content, temperature, and freeze–thaw profiles as well as soil strength and surface ice and snow accumulation/depletion. Because FASST is newer and not as well known, the authors wanted to determine its use as a snow model by comparing it with SNTHERM, one of the most established snow models available. It is demonstrated that even though FASST is only a single-layer snow model, the RMSE snow depth compared very favorably against SNTHERM, often performing better during the accumulation phase. The surface energy fluxes calculated by the two models were also compared and were found to be similar.


2016 ◽  
Vol 73 (11) ◽  
pp. 4553-4571 ◽  
Author(s):  
Diana R. Stovern ◽  
Elizabeth A. Ritchie

Abstract This study uses the WRF ARW to investigate how different atmospheric temperature environments impact the size and structure development of a simulated tropical cyclone (TC). In each simulation, the entire vertical virtual temperature profile is either warmed or cooled in 1°C increments from an initial specified state while the initial relative humidity profile and sea surface temperature are held constant. This alters the initial amount of convective available potential energy (CAPE), specific humidity, and air–sea temperature difference such that, when the simulated atmosphere is cooled (warmed), the initial specific humidity and CAPE decrease (increase), but the surface energy fluxes from the ocean increase (decrease). It is found that the TCs that form in an initially cooler environment develop larger wind and precipitation fields with more active outer-core rainband formation. Consistent with previous studies, outer-core rainband formation is associated with high surface energy fluxes, which leads to increases in the outer-core wind field. A larger convective field develops despite initializing in a low CAPE environment, and the dynamics are linked to a wider field of surface radial inflow. As the TC matures and radial inflow expands, large imports of relative angular momentum in the boundary layer continue to drive expansion of the TC’s overall size.


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