Abstract

Grassland environments constitute approximately 40% of the earth’s vegetated surface, and they play a key role in a number of processes linking the land surface with the atmosphere. To investigate these linkages, a variety of techniques, including field and modeling studies, are required. Using data collected at the Central Plains Experimental Range (CPER) in northeastern Colorado from 25 March to 10 November 2004, this study compares two common ways of measuring turbulent fluxes of latent heat, sensible heat, and carbon dioxide in the field: the eddy covariance (EC) and Bowen ratio energy balance (BREB) methods. The turbulent fluxes measured by each of these methods were compared in terms of magnitude and seasonal behavior and were combined to calculate eddy diffusivities and examine turbulent transport. Relative to the EC method, the BREB method tended to overestimate the magnitude of the sensible heat, latent heat, and carbon dioxide fluxes. As a result, substantial differences in both the diurnal pattern and long-term magnitudes of the water and carbon budgets were apparent depending on which method was used. These differences arise from (i) the forced closure of the surface energy balance and (ii) the assumption of similarity between the eddy diffusivities required by the BREB method. An empirical method was developed that allows the BREB and EC datasets to be reconciled; this method was tested successfully using data collected at the CPER site during 2005. Ultimately, however, the BREB and EC methods show important differences that must be recognized and taken into account when analyzing issues related to the energy, water, or carbon cycles.

Abstract

Sources of differences between observations and simulations for a case study using the Noah land surface model–based High-Resolution Land Data Assimilation System (HRLDAS) are examined for sensible and latent heat fluxes H and LE, respectively; surface temperature T_s ; and vertical temperature difference T ₀ − T_s , where T ₀ is at 2 m. The observational data were collected on 29 May 2002, using the University of Wyoming King Air and four surface towers placed along a sparsely vegetated 60-km north–south flight track in the Oklahoma Panhandle. This day had nearly clear skies and a strong north–south soil-moisture gradient, with wet soils and widespread puddles at the south end of the track and drier soils to the north. Relative amplitudes of H and LE horizontal variation were estimated by taking the slope of the least squares best-fit straight line ΔLE/ΔH on plots of time-averaged LE as a function of time-averaged H for values along the track. It is argued that observed H and LE values departing significantly from their slope line are not associated with surface processes and, hence, need not be replicated by HRLDAS. Reasonable agreement between HRLDAS results and observed data was found only after adjusting the coefficient C in the Zilitinkevich equation relating the roughness lengths for momentum and heat in HRLDAS from its default value of 0.1 to a new value of 0.5. Using C = 0.1 and adjusting soil moisture to match the observed near-surface values increased horizontal variability in the right sense, raising LE and lowering H over the moist south end. However, both the magnitude of H and the amplitude of its horizontal variability relative to LE remained too large; adjustment of the green vegetation fraction had only a minor effect. With C = 0.5, model-input green vegetation fraction, and our best-estimate soil moisture, H, LE, ΔLE/ΔH, and T ₀ − T_s , were all close to observed values. The remaining inconsistency between model and observations—too high a value of H and too low a value of LE over the wet southern end of the track—could be due to HRLDAS ignoring the effect of open water. Neglecting the effect of moist soils on the albedo could also have contributed.

Abstract

Comparisons of 10-m above ground level (AGL) wind speeds from numerical weather prediction (NWP) models to point observations consistently show that model daytime wind speeds are slow compared to observations, even after improving model physics and going to smaller grid spacing. Previous authors have attributed the discrepancy to differences between the areas represented by model and observations, and the small surface roughness upstream of wind vanes compared with the corresponding model grid value. Using daytime fair-weather data from the May–June 2002 International H₂O Experiment (IHOP_2002), the effect of wind-vane exposure is explored by comparing observed 10-m winds from nine surface-flux towers in well-exposed locations to modeled 10-m winds found by applying Monin–Obukhov (MO) similarity for unstable conditions to flight-track-averaged data collected by the University of Wyoming King Air over flat to rolling terrain with occasional trees and buildings. In the calculations, King Air winds and fluxes are supplemented with thermodynamic means and fluxes from the surface-flux towers. After exercising considerable care in characterizing and reducing biases in aircraft winds and fluxes, the authors found that MO-based surface winds averaged 0.5–0.7 ± 0.2 m s⁻¹ less than those measured—about the same as the smaller reported discrepancies between NWP models and observed winds.

Abstract

Nearly one-half of the earth’s terrestrial surface is susceptible to drought, which can have significant social, economic, and environmental impacts. Therefore, it is important to develop better descriptions and models of the processes linking the land surface and atmosphere during drought. Using data collected during the International H₂O Project, the study presented here investigates the effects of variations in the environmental factors driving the latent heat flux (λE) during drought conditions at a rangeland site located in the panhandle of Oklahoma. Specifically, this study focuses on the relationships of λE with vapor pressure deficit, wind speed, net radiation, soil moisture content, and greenness fraction. While each of these environmental factors has an influence, soil moisture content is the key control on λE. The role of soil moisture in regulating λE is explained in terms of the surface resistance to water vapor transfer. The results show that λE transitioned between being water or energy limited during the course of the drought. The implications of this on the ability to understand and model drought conditions and transitions into or out of droughts are discussed.

Abstract

Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth’s surface and play an important role in land–atmosphere exchange processes. This study focuses on one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (r _{c min} ). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, r _{c min} is determined from observational data collected during the 2002 International H₂O Project (IHOP_2002). The results indicate that r _{c min} is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s m⁻¹ while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s m⁻¹. The mean r _{c min} for all the sites is 72 s m⁻¹ with a standard deviation of 39 s m⁻¹. This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in r _{c min} has important implications for land surface modeling where r _{c min} is often parameterized as a constant. For example, the Noah land surface model parameterizes r _{c min} for the grasslands and croplands types in this study as 40 s m⁻¹. Tests with the coupled Weather Research and Forecasting (WRF)–Noah model indicate that the using the modified values of r _{c min} from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the r _{c min} values for grasslands and croplands, updating the r _{c min} will not fully account for the variability in r _{c min} observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance.

Abstract

Land–atmosphere interactions play a critical role in regulating numerous meteorological, hydrological, and environmental processes. Investigating these processes often requires multiple measurement sites representing a range of surface conditions. Before these measurements can be compared, however, it is imperative that the differences among the instrumentation systems are fully characterized. Using data collected as a part of the 2008 Bushland Evapotranspiration and Agricultural Remote Sensing Experiment (BEAREX08), measurements from nine collocated eddy covariance (EC) systems were compared with the twofold objective of 1) characterizing the interinstrument variation in the measurements, and 2) quantifying the measurement uncertainty associated with each system. Focusing on the three turbulent fluxes (heat, water vapor, and carbon dioxide), this study evaluated the measurement uncertainty using multiple techniques. The results of the analyses indicated that there could be substantial variability in the uncertainty estimates because of the advective conditions that characterized the study site during the afternoon and evening hours. However, when the analysis was limited to nonadvective, quasi-normal conditions, the response of the nine EC stations were remarkably similar. For the daytime period, both the method of Hollinger and Richardson and the method of Mann and Lenschow indicated that the uncertainty in the measurements of sensible heat, latent heat, and carbon dioxide flux were approximately 13 W m⁻², 27 W m⁻², and 0.10 mg m⁻² s⁻¹, respectively. Based on the results of this study, it is clear that advection can greatly increase the uncertainty associated with EC flux measurements. Since these conditions, as well as other phenomena that could impact the measurement uncertainty, are often intermittent, it may be beneficial to conduct uncertainty analyses on an ongoing basis.

Abstract

The computation of turbulent fluxes of heat, momentum, and greenhouse gases requires measurements taken at high sampling frequencies. An important step in this process involves the detection and removal of sudden, short-lived variations that do not represent physical processes and that contaminate the data (i.e., spikes). The objective of this study is to assess the performance of several noteworthy despiking methodologies in order to provide a benchmark assessment and to provide a recommendation that is most applicable to high-frequency micrometeorological data in terms of efficiency and simplicity. The performance of a statistical time window–based algorithm widely used in micrometeorology is compared to three other methodologies (phase space, wavelet based, and median filter). These algorithms are first applied to a synthetic signal (a clean reference version and then one with spikes) in order to assess general performance. Afterward, testing is done on a time series of actual CO₂ concentrations that contains extreme systematic spikes every hour owing to instrument interference, as well as several smaller random spike points. The study finds that the median filter and wavelet threshold methods are most reliable, and that their performance by far exceeds statistical time window–based methodologies that use the median or arithmetic mean operator (−34% and −71% reduced root-mean-square deviation, respectively). Overall, the median filter is recommended, as it is most easily automatable for a variety of micrometeorological data types, including data with missing points and low-frequency coherent turbulence.

Abstract

The utility of a snow–vegetation energy balance model for estimating surface energy fluxes is evaluated with field measurements at two sites in a rangeland ecosystem in southwestern Idaho during the winter of 2007: one site dominated by aspen vegetation and the other by sagebrush. Model parameterizations are adopted from the two-source energy balance (TSEB) modeling scheme, which estimates fluxes from the vegetation and surface substrate separately using remotely sensed measurements of land surface temperature. Modifications include development of routines to account for surface snowmelt energy flux and snow masking of vegetation. Comparisons between modeled and measured surface energy fluxes of net radiation and turbulent heat showed reasonable agreement when considering measurement uncertainties in snow environments and the simplified algorithm used for the snow surface heat flux, particularly on a daily basis. There was generally better performance over the aspen field site, likely due to more reliable input data of snow depth/snow cover. The model was robust in capturing the evolution of surface energy fluxes during melt periods. The model behavior was also consistent with previous studies that indicate the occurrence of upward sensible heat fluxes during daytime owing to solar heating of vegetation limbs and branches, which often exceeds the downward sensible heat flux driving the snowmelt. However, model simulations over aspen trees showed that the upward sensible heat flux could be reversed for a lower canopy fraction owing to the dominance of downward sensible heat flux over snow. This indicates that reliable vegetation or snow cover fraction inputs to the model are needed for estimating fluxes over snow-covered landscapes.

The May–June 2002 International H₂O Project was held in the U.S. Southern Great Plains to determine ways that moisture data could be collected and utilized in numerical forecast models most effectively. We describe the surface and boundary layer components, and indicate how the data can be acquired. These data document the eddy transport of heat and water vapor from the surface to the atmosphere (in terms of sensible heat flux H and latent heat flux LE), as well as radiative, atmospheric, soil, and vegetative factors that affect it, so that the moisture and heat supply to the atmosphere can be related to surface properties both for observational studies and tests of land surface models. The surface dataset was collected at 10 surface flux towers at locations representing the major types of land cover and extending from southeast Kansas to the Oklahoma Panhandle. At each location, the components of the surface energy budget (H, LE, net radiation, and soil heat flux) are documented each half-hour, along with the weather (wind, temperature, mixing ratio, air pressure, and precipitation), soil temperature, moisture, and matric potential down to 70–90 cm beneath the surface at 9 of the 10 sites. Observations of soil and vegetation properties and their horizontal changes were taken near all 10 towers during periodic visits. Aircraft measurements of H and LE from repeated low-level flight tracks along three tracks collocated with the surface sites extend the flux tower measurements horizontally. We illustrate the effects of vegetation and soil moisture on the H and LE and their horizontal variability.

Abstract

The assimilation of L-band surface brightness temperature (Tb) into the land surface model (LSM) component of a numerical weather prediction (NWP) system is generally expected to improve the quality of summertime 2-m air temperature (T2m) forecasts during water-limited surface conditions. However, recent retrospective results from the European Centre for Medium-Range Weather Forecasts (ECMWF) suggest that the assimilation of L-band Tb from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) mission may, under certain circumstances, degrade the accuracy of growing-season 24-h T2m forecasts within the central United States. To diagnose the source of this degradation, we evaluate ECMWF soil moisture (SM) and evapotranspiration (ET) forecasts using both in situ and remote sensing resources. Results demonstrate that the assimilation of SMOS Tb broadly improves the ECMWF SM analysis in the central United States while simultaneously degrading the quality of 24-h ET forecasts. Based on a recently derived map of true global SM–ET coupling and a synthetic fraternal twin data assimilation experiment, we argue that the spatial and temporal characteristics of ECMWF SM analyses and ET forecast errors are consistent with the hypothesis that the ECMWF LSM overcouples SM and ET and, as a result, is unable to effectively convert an improved SM analysis into enhanced ET and T2m forecasts. We demonstrate that this overcoupling is likely linked to the systematic underestimation of root-zone soil water storage capacity by LSMs within the U.S. Corn Belt region.