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    Map of the mother domain (D01) and nested domain (D02) used in this study. The mother domain comprised 175 and 250 grid points in the north–south and east–west directions, respectively, and had a 12-km grid spacing. The nested domain contained 181 and 151 grid points in the north–south and east–west directions, respectively, and had a 4-km grid spacing.

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    Land-cover characterization from (a) the North America Land Cover Characteristics database 2.0 of the USGS and (b) the representation of USGS dominant vegetation categories within the nested model domain. In (a), dark red pixels represent winter wheat, and nearby light orange pixels represent a mix of winter wheat and grassland. Black squares are locations of Oklahoma Mesonet sites. In (b), land-use categories of interest to this study included “mixed dry/irrigated cropland/pasture” (light yellow), “grassland” (green), and “savanna” (pink). The dataset was derived from satellite measurements and categorized water bodies, snow or ice, urban, and 21 general types of vegetation, including cropland and mixed vegetation.

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    Redefined land-use extent and categories employed in this study. The wheat belt was composed of 2438 grid points defined as MM5 category (a) “mixed dry/irrigated cropland/pasture” (dark green) to represent growing wheat and (b) “grassland” (light green) to represent natural vegetation. Light yellow represents MM5 category “savanna.” The white outline depicts the boundary of the wheat belt.

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    Vegetation fraction used for (a) the wheat run and (b) the natural vegetation run for both spring cases (27 Mar and 5 Apr 2000). Values shown for the wheat run were defined by the Oregon State University/NCEP Eta land surface model as the monthly climatological vegetation fraction for Apr. Note the high percentage of vegetation (80%–90%) through the heart of Oklahoma’s winter wheat belt (across north-central and west-central Oklahoma).

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    Initial thermodynamic profiles for the model simulation of 27 Mar 2000 for the Mesonet sites at (a) CHER and (b) BREC. Locations of these sites are shown in Fig. 2a.

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    Dewpoint fields (colored contours) and isodrosotherms (thin black lines, contoured every 2°C) at the lowest sigma level for the (left) wheat run and the (right) natural vegetation run for 27 Mar 2000. The times displayed are (a) 1200 UTC on 27 Mar and (b) 1500, (c) 1800, (d) 2100, and (e) 0000 UTC on 28 Mar. The white outline depicts the boundary of the wheat belt.

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    (Continued)

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    (Continued)

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    Model results for (a) the difference field of near-surface dewpoints and (b) the near-surface winds for the wheat run at 2100 UTC on 27 Mar 2000. The black outline depicts the boundary of the wheat belt. Positive values of the dewpoint difference field (solid shading) indicate locations where the wheat run was moister than the natural vegetation run.

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    Simulated thermodynamic profiles at 2100 UTC on 27 Mar 2000 for (left) the wheat run and (right) the natural vegetation run. The profiles correspond to the Mesonet sites located (a) within the western wheat belt at CHER, (b) within the eastern wheat belt at BREC, and (c) east of the wheat belt at PAWN. Locations of these sites are shown in Fig. 2a.

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    (Continued)

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    (a) Difference field of the model-calculated PBL heights (contours) and horizontal winds at 1 km (barbs) at 2100 UTC on 27 Mar 2000. Negative values (in solid grays) indicate that PBL heights were lower for the wheat run than the natural vegetation run. Horizontal speed differences less than 1.25 m s–1 are displayed as open circles. (b) Wind field (barbs) from the wheat run at 1 km above sea level for 2100 UTC on 27 Mar 2000. Note that winds across the wheat belt over north-central Oklahoma were westerly. The east–west and north–south lines on Fig. 9a mark the location of the axes of vertical cross sections in Fig. 10. The east–west cross section is parallel to the prevailing flow; the north–south cross section is perpendicular to the prevailing flow. The black outline depicts the boundary of the winter wheat belt.

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    East–west vertical cross sections of potential temperature (K) at 2100 UTC on 27 Mar 2000 for (a) the wheat run and (b) the natural vegetation run. Arrows represent wind vectors parallel to the cross section. North–south vertical cross sections of potential temperature (K) at 2100 UTC on 27 Mar 2000 for (c) the wheat run and (d) the natural vegetation run. Arrows represent wind vectors parallel to the cross section. (e) East–west and (f) north–south vertical cross sections of the potential temperature (K) and wind vector (m s–1 horizontal wind; cm s–1 vertical wind) difference fields at 2100 UTC. Negative values of potential temperature difference (shaded blue) indicate locations where the wheat run was cooler than the natural vegetation run. Positive values of potential temperature difference (shaded gold) indicate locations where the wheat run was warmer than its counterpart. Vectors display the wind vector difference field resulting from velocities from the natural vegetation run subtracted from those from the wheat run. The locations of the cross sections are shown in Fig. 9a. The light gray box above the x axis denotes the location of the wheat belt.

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    (Continued)

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    Horizontal plot of (a) the velocity field as simulated for the wheat run and (b) the velocity difference field at 2100 UTC on 27 Mar 2000. In (a), color-filled contours represent vertical velocities at 1 km above sea level: yellow represents upward motion and green represents downward motion. Horizontal velocities at a height of 1 km above sea level are plotted as barbs. In (b), color-filled contours represent vertical velocity differences at 1 km above sea level: orange represents positive differences and purple represents negative differences. Horizontal velocity differences (wheat run minus natural vegetation run) at a height of 1 km above sea level are plotted as barbs. Horizontal speed differences less than 1.25 m s–1 are displayed as open circles in (b). The black outline depicts the boundary of the winter wheat belt.

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    Dewpoint fields (colored contours) and isodrosotherms (thin black lines, contoured every 2°C) at the lowest sigma level for (left) the wheat run and (right) the natural vegetation run for 5 Apr 2000. The times displayed are (a) 1500, (b) 1800, and (c) 2100 UTC on 5 Apr. The white outline depicts the boundary of the wheat belt.

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    (Continued)

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    Difference fields of the model-calculated PBL heights at (a) 1800 and (b) 2100 UTC on 5 Apr 2000. Negative values (solid shading) indicate that PBL heights were lower for the wheat run than the natural vegetation run. The east–west and north–south lines mark the location of the axes of vertical cross sections in Fig. 14. The black outline depicts the boundary of the winter wheat belt.

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    East–west vertical cross sections of potential temperature (K) at 2100 UTC on 5 Apr 2000 for (a) the wheat run and (b) the natural vegetation run. The location of the cross sections is shown in Fig. 13b. Arrows represent wind vectors parallel to the cross section. Red contours represent positive vertical velocities (cm s–1; ascent); blue contours represent negative vertical velocities (cm s–1; descent). (c) An east–west vertical cross section of the potential temperature (K) and wind vector (m s–1 horizontal wind; cm s–1 vertical wind) difference fields at 2100 UTC. Negative values of potential temperature difference (shaded blue) indicate locations where the wheat run was cooler than the natural vegetation run. Positive values of potential temperature difference (shaded gold) indicate locations where the wheat run was warmer than its counterpart. Vectors display the wind vector difference field resulting from velocities from the natural vegetation run subtracted from those from the wheat run. The locations of the cross sections are shown in Fig. 13. The light gray box above the x axis denotes the location of the wheat belt.

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Influences of a Winter Wheat Belt on the Evolution of the Boundary Layer

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  • 1 Oklahoma Climatological Survey, Norman, Oklahoma
  • 2 NOAA/National Severe Storms Laboratory, Norman, Oklahoma
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Abstract

Evidence exists that a large-scale alteration of land use by humans can cause changes in the climatology of the region. The largest-scale transformation is the substitution of native landscape by agricultural cropland. This modeling study examines the impact of a direct substitution of one type of grassland for another—in this case, the replacement of tallgrass prairie with winter wheat. The primary difference between these grasses is their growing season: native prairie grasses of the U.S. Great Plains are warm-season grasses whereas winter wheat is a cool-season grass.

Case study simulations were conducted for 27 March 2000 and 5 April 2000—days analyzed in previous observational studies. The simulations provided additional insight into the physical processes involved and changes that occurred throughout the depth of the planetary boundary layer. Results indicate the following: 1) with the proper adjustment of vegetation parameters, land-use type, fractional vegetation coverage, and soil moisture, the numerical simulations were able to capture the overall patterns measured near the surface across a growing wheat belt during benign springtime conditions in Oklahoma; 2) the impacts of the mesoscale belt of growing wheat included increased values of latent heat flux and decreased values of sensible heat flux over the wheat, increased values of atmospheric moisture near the surface above and downstream of the wheat, and a shallower planetary boundary layer (PBL) above and downstream of the wheat; 3) in the sheared environments that were examined, a shallower PBL that resulted from growing wheat (rather than natural vegetation) led to reduced entrainment of higher momentum air into the PBL and, thus, weaker winds within the PBL over and downwind from the growing wheat; 4) for the cases studied, gradients in sensible heat were insufficient to establish an unambiguous vegetation breeze or its corresponding mesoscale circulation; 5) the initialization of soil moisture within the root zone aided latent heat fluxes from growing vegetation; and 6) reasonable specification of land surface parameters was required for the correct simulation and prediction of surface heat fluxes and resulting boundary layer development.

Corresponding author address: Dr. Renee A. McPherson, Oklahoma Climatological Survey, University of Oklahoma, 100 East Boyd Street, Suite 1210, Norman, OK 73019-1012. Email: renee@ou.edu

Abstract

Evidence exists that a large-scale alteration of land use by humans can cause changes in the climatology of the region. The largest-scale transformation is the substitution of native landscape by agricultural cropland. This modeling study examines the impact of a direct substitution of one type of grassland for another—in this case, the replacement of tallgrass prairie with winter wheat. The primary difference between these grasses is their growing season: native prairie grasses of the U.S. Great Plains are warm-season grasses whereas winter wheat is a cool-season grass.

Case study simulations were conducted for 27 March 2000 and 5 April 2000—days analyzed in previous observational studies. The simulations provided additional insight into the physical processes involved and changes that occurred throughout the depth of the planetary boundary layer. Results indicate the following: 1) with the proper adjustment of vegetation parameters, land-use type, fractional vegetation coverage, and soil moisture, the numerical simulations were able to capture the overall patterns measured near the surface across a growing wheat belt during benign springtime conditions in Oklahoma; 2) the impacts of the mesoscale belt of growing wheat included increased values of latent heat flux and decreased values of sensible heat flux over the wheat, increased values of atmospheric moisture near the surface above and downstream of the wheat, and a shallower planetary boundary layer (PBL) above and downstream of the wheat; 3) in the sheared environments that were examined, a shallower PBL that resulted from growing wheat (rather than natural vegetation) led to reduced entrainment of higher momentum air into the PBL and, thus, weaker winds within the PBL over and downwind from the growing wheat; 4) for the cases studied, gradients in sensible heat were insufficient to establish an unambiguous vegetation breeze or its corresponding mesoscale circulation; 5) the initialization of soil moisture within the root zone aided latent heat fluxes from growing vegetation; and 6) reasonable specification of land surface parameters was required for the correct simulation and prediction of surface heat fluxes and resulting boundary layer development.

Corresponding author address: Dr. Renee A. McPherson, Oklahoma Climatological Survey, University of Oklahoma, 100 East Boyd Street, Suite 1210, Norman, OK 73019-1012. Email: renee@ou.edu

1. Introduction

Evidence exists that a large-scale alteration of land use by humans can cause changes in the climatology of the region (e.g., Bonan 2001; Pielke 2002; McPherson et al. 2004a; Haugland and Crawford 2005). Although the most noticeable change in land use may be the growth of urban areas, the largest-scale transformation is the substitution of native landscape by agricultural cropland—in many cases, irrigated cropland. This modeling study examines the impact of a direct substitution of one type of grassland for another—in this case, the replacement of tallgrass prairie with winter wheat. The primary difference between these grasses is their growing season: native prairie grasses of the U.S. Great Plains are warm-season grasses (i.e., they are dormant in the winter and actively grow in the summer) whereas winter wheat is a cool-season grass (i.e., it grows throughout the winter and reaches maturity in late spring). Because winter wheat typically is not irrigated throughout the wheat belt of the southern Great Plains, the results of this study are directly applicable to understanding the impact of the wheat belt on Oklahoma’s climate.

A mesoscale crop belt of winter wheat dominates the majority of the western half of Oklahoma. During early spring, a mature wheat crop forms a swath about 150 km wide that extends from southwest Oklahoma into north-central Oklahoma and southern Kansas (Rabin et al. 1990; McPherson et al. 2004a, b). On either side of this band of nonirrigated cropland is sparse or dormant vegetation, especially across extreme western Oklahoma, western Kansas, and much of the Texas and Oklahoma panhandles. After the late spring harvest of wheat occurs, short stubble and bare soil dominate the wheat belt whereas, surrounding the wheat belt, previously dormant grasslands are alive with mature prairie grasses. Hence, this wheat belt affords scientists the opportunity to study the impact of a band of either abundant or sparse vegetation when compared to adjacent lands.

McPherson et al. (2004a) discuss observational evidence for the impact of the Oklahoma winter wheat belt on its mesoscale environment. Using data from the Oklahoma Mesonet (Brock et al. 1995) from 1994 through 2001, the authors documented cooler maximum daily temperatures across the growing wheat from November to April and warmer maximum daily temperatures across the harvested wheat from June to August, as compared to adjacent grasslands. In addition, during March, maximum daily dewpoints were higher across the wheat belt than across the neighboring dormant vegetation. Case studies from 27 March 2000, 5 April 2000, and 10 July 2000 demonstrated the evolution of near-surface temperature and moisture fields across and near Oklahoma’s wheat belt during clear days when the winter wheat most influenced its mesoscale environment. Results from the spring case studies indicated that dewpoint temperatures across the boundary of the wheat belt could differ by 2°–10°C in the absence of a dryline. During the summer, maximum daily temperatures were 2°–5°C warmer over the wheat belt than over nearby growing grasslands. McPherson et al. (2004b) documented similar results for 4 April 2000 and 14 July 2000.

The observations, however, did not document fully the land surface’s influence on the structure and evolution of the planetary boundary layer; nor did the observations reveal the physical processes that caused the reported surface anomalies of temperature and moisture. To better understand the observational results, the authors of this paper conducted a series of numerical model simulations. These simulations also afforded the unique opportunity to examine how the natural environment might evolve from identical initial conditions in the absence of a wheat belt.

Previous numerical studies indicated that the depth of the planetary boundary layer (PBL) was shallowest over an irrigated area of vegetation and deepest over dry, bare land (Segal et al. 1989). Hence, the mixing ratio throughout the PBL was largest over vegetated surfaces, where evapotranspiration was enhanced, dry air entrainment was reduced, and air was mixed within a shallower layer (Segal et al. 1995).

Focusing on the development of severe storms, Chang and Wetzel (1991) modeled the impact of spatial variations of soil moisture and vegetation on boundary layer evolution. They determined that vegetation discontinuities on the meso-α scale either created new or enhanced existing surface gradients both by compacting the spatial rate of temperature change into mesoscale regions and by accumulating moisture within a shallower boundary layer. The differential heating intensified the surface pressure gradient, which, in turn, enhanced both warm advection and convergence across the weak surface boundary. By destabilizing the local environment, these foliage gradients became preferred regions for convective initiation in conditionally unstable environments. Without vegetation, surface evaporation alone was insufficient to establish strong thermal gradients at the surface.

On the mesoscale, differential heating caused by sensible heat gradients across adjacent regions of active vegetation and dry, bare soil can generate a sea-breeze-like circulation (Purdom and Gurka 1974; Mahfouf et al. 1987; Segal et al. 1988; Hong et al. 1995; Lee and Kimura 2001), hereafter termed a “vegetation breeze.” Similar circulations have been modeled across boundaries of distinct vegetation types (Pielke et al. 1991). Observations indicate that vegetation breezes have an appreciable effect on the formation of shallow cumulus clouds (Garrett 1982; Rabin et al. 1990). Numerical simulations demonstrate that these circulations can provide preferred regions to focus atmospheric instabilities and to initiate convective development (Sun and Ogura 1979; Garrett 1982; Mahfouf et al. 1987; Chang and Wetzel 1991; Chen and Avissar 1994).

The following numerical simulations were conducted in concert with the observational analyses of McPherson et al. (2004a, b) and were designed to provide additional insight into the physical mechanisms by which the regional wheat belt influenced the mesoscale atmosphere. Two hypotheses were examined: 1) surface temperature and moisture fields are affected by the evolution (e.g., during growth and after harvest) of Oklahoma’s winter wheat crop, and 2) surface fluxes from Oklahoma’s winter wheat belt modify the depth of the planetary boundary layer.

2. Methodology

a. Overview of the numerical model

The fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) was used because of its well-established history, its incorporation of land surface parameterizations, and its documented ability to simulate both mesoscale phenomena and land–atmosphere interactions. The MM5 is a community mesoscale model that has been upgraded from that documented by Anthes and Warner (1978). It is a three-dimensional, limited-area, nonhydrostatic model devised to simulate atmospheric circulations on the meso- and regional scales.

The vertical coordinate, sigma (σ), is terrain following and is defined as
i1520-0493-133-8-2178-eq1
where p is the pressure, ptop is a specified constant pressure at the top of the model domain, and psfc is the surface pressure. An Arakawa–Lamb (1977) B-staggered grid is used in the horizontal. In the vertical, sigma, the horizontal velocity components, and all scalars are defined on each sigma level whereas vertical velocity is defined halfway between sigma levels. A thorough discussion of the model’s governing equations is presented by Grell et al. (1994).

MM5 features two-way nesting and can support both nested and adjacent domains. Lateral boundary conditions for a given subdomain are supplied by its mother domain.1 The boundary values for the main domain are provided by either an analysis, a previous coarser-mesh simulation, or another model’s forecast. In this case, boundary values were prescribed by Eta Model analyses at 3-h intervals provided by the National Centers for Environmental Prediction (NCEP; NCEP 2000).

Two domains were defined (Fig. 1): 1) a mother domain with 175 and 250 grid points in the north–south and east–west directions, respectively, and a 12–km grid spacing, and 2) a nested domain with 181 and 151 grid points in the north–south and east–west directions, respectively, and a 4–km grid spacing. Equations were computed at time steps of 36 and 12 s for the mother and nested domains, respectively. Forty sigma (σ) levels defined the vertical resolution for both domains (Δσ ≈ 0.02 in the boundary layer), and ptop was set to 100 hPa. To minimize the impact of the lateral boundary of the nested domain, the wheat belt was centered within the higher-resolution domain.

The NCEP Eta Model analyses were interpolated to model levels to serve as both initial conditions and boundary conditions through time. NCEP Eta Model analyses also provided the initial soil moisture (at 10- and 200-cm depths) and soil temperature (at 10-, 200-, and 400-cm depths) fields. National Weather Service (NWS) surface and upper-air observations and hourly Oklahoma Mesonet data (Brock et al. 1995) were assimilated into the model to enhance the gridded, first-guess field. All atmospheric analyses and observations were input in pressure coordinates prior to a preprocessing step that converted the pressure-level fields into sigma coordinates.

A suite of physics options was available in MM5. For the mother domain, this study used the Grell cumulus parameterization (Grell et al. 1994), which was designed for horizontal grid sizes on the mesoscale (e.g., 10–30 km) and allows for both explicit and parameterized rainfall. Cumulus parameterization was not permitted within the nested domain. Cloud water, rainwater, and ice fields were predicted explicitly with microphysical processes on both domains. During the days studied, deep convection was not observed within the region encompassed by the nested domain. Parameterization of the PBL used nonlocal closure from Hong and Pan (1996), a scheme appropriate when high vertical resolution defines the PBL. Longwave and shortwave interactions between cloud and clear air were computed explicitly. For longwave interactions, the Rapid Radiative Transfer Model of Mlawer et al. (1997) accounted for the absorption spectra of water vapor, carbon dioxide, and ozone.

b. The land surface module and surface characteristics

MM5’s version of the Eta land surface model (LSM; Chen and Dudhia 2001a, b) from Oregon State University and NCEP was activated for all simulations. It provided enhancements to depict land–atmosphere interaction more realistically. The MM5 LSM couples three representations of surface physics: a Penman potential evaporation approach that is diurnally dependent (Mahrt and Ek 1984), a multilayer soil model (Mahrt and Pan 1984), and a primitive canopy model (Pan and Mahrt 1987). Calculations within the vegetation canopy (i.e., the combination of leaves, branches, and other vegetative matter) are enhanced by the canopy resistance approach of Noilhan and Planton (1989) and Jacquemin and Noilhan (1990). Prognostic variables include soil moisture, soil temperature, and water stored on the canopy. The soil variables are calculated for four soil layers (surface to 10, 10–30, 30–60, and 60–100 cm). A schematic representation and details of the model can be found in Chen and Dudhia (2001a).

For the current study, evaporation of precipitation from canopy plants is negligible; thus, total evaporation is the sum of the direct evaporation from the top soil layer and the transpiration from the plants. A green vegetation fraction partitions the total evaporation between these components. Transpiration is proportional to the vegetation fraction, the potential evaporation, and the inverse of the canopy and air resistances.2

The spatial distribution of land use and soil types dictated the values of secondary vegetation and soil parameters, such as albedo, minimum stomatal resistance3, and roughness length. The LSM was initialized with an annual-mean surface temperature adjusted to the elevation of the model terrain, a monthly climatological vegetation fraction, and a dominant soil type in each model grid cell. The resolution of the vegetation fraction dataset was 10 min, and values were interpolated from the monthly values to the date of interest. United States Geological Survey (USGS) 5-min (∼9 km) and 2-min (∼4 km) gridded data for elevation and land use defined these static fields for the mother domain and the nested domain, respectively. The USGS land-use dataset had categories for water bodies, snow or ice fields, urban areas, and 21 general types of vegetation, including cropland and mixed vegetation. Parameter values depended on only two seasons: winter (15 October–15 April) and summer (15 April–15 October).

A 16-point, two-dimensional parabolic fit was employed to interpolate land use, soil type, and vegetation fraction from the latitude–longitude datasets to the mesoscale grid. The parabolic interpolation directly computed the vegetation fraction at the grid point. For land use and soil type, defined by categories, the interpolation calculated the percentage of each category on the grid. When water coverage was greater than 50% within a grid cell surrounding a given grid point, then water was assigned to that point. Otherwise, the category with the largest percentage coverage within the cell (excluding water) was assigned to the grid point.

c. Simulation design

Simplicity and realism were the guiding characteristics of the design of the numerical model studies. Previous authors had examined a variety of parameter spaces related to the impact of vegetation on the atmosphere. Rather than conduct an exhaustive set of simulations, this study simply compared numerical runs both with and without a simulated wheat belt. In this manner, the impact of the vegetation region could be identified directly and analyzed through difference fields. To link the simulations to reality, observations and Eta analyses were input into the model from three case study days. Modeled surface fields of near-surface air temperature and dewpoint temperature were compared to observed fields from the Oklahoma Mesonet to verify that the model was simulating the near-surface atmosphere adequately.

Two case study days were selected: 27 March 2000 and 5 April 2000. McPherson et al. (2004a) detailed these days through observational analyses. For each case, two simulations were conducted: one with a simulated wheat belt—hereafter called a wheat run—and one with simulated natural grassland that substituted for the wheat belt—hereafter called a natural vegetation run. Hence, the numerical modeling study involved four simulations (Table 1). These spring cases represented clear days with weak to moderate winds, respectively. A maturing winter wheat crop contrasted with the adjacent dormant grasslands (McPherson et al. 2004a). Initial atmospheric and soil conditions were identical for both the wheat and natural vegetation runs on any given day.

The model was initiated at 1200 UTC for every simulation. Although the model runs extended through 12 h of simulated time, the investigation focused on model hours 3, 6, and 9. At model hour 3, representing 1500 UTC, small differences between the wheat and natural vegetation runs became apparent. At model hour 6, representing 1800 UTC, incoming solar energy was near its daily maximum. By model hour 9, representing 2100 UTC, the maximum differences between the two runs were occurring.

A comparison of Figs. 2a and 2b highlights the limited capability of the 25-category USGS land-use dataset (used in MM5) to delineate the wheat belt. Note that grassland defines a substantial portion of the simulated wheat belt (Fig. 2b) in the regions of mixed winter wheat and grassland (represented in Fig. 2a by light orange pixels). In addition, the physical parameters defined by the 25-category dataset (e.g., albedo, roughness length) were identical for winter wheat in Oklahoma and corn in southern Iowa (not shown) even though the crops have remarkably different growing seasons. Xue et al. (1996), Tsvetsinskaya et al. (2001), and others demonstrated the need to distinguish physical parameters adequately between substantially different crops to obtain representative simulation results in the lower atmosphere. Equally as restrictive for MM5, these parameters were not permitted to change throughout the growing season, yet the physical characteristics of plants vary by vegetation type and stage of growth. Hence, for this study, several of the default vegetation parameters were deemed inadequate and were modified to obtain more representative results.

Model results were sensitive to values of both soil moisture within the root zone and minimum stomatal conductance; these sensitivities were consistent with other modeling studies (e.g., Collins and Avissar 1994; Basara 2001; Crawford et al. 2001; Chen and Dudhia 2001a). In most cases, these values were altered until the simulated near-surface temperatures and dewpoints aligned well with patterns and amplitudes of near-surface fields observed by the Oklahoma Mesonet at 1200 UTC. At that point, the authors deemed that the chosen values were appropriate for the resulting simulations to be used to better understand the physical processes. Table 2 lists some of the parameters used to define the three most significant land-use types in this study: mixed dryland/irrigated cropland/pasture (representing the wheat belt), grassland, and savanna. The parameter values were physically viable, but final values were selected to best simulate the observed near-surface conditions (McPherson et al. 2004a, b) in the wheat runs.

In addition to updating some of the default vegetation parameters, the extent of the winter wheat belt across Oklahoma, southern Kansas, and north-central Texas was redefined (Fig. 3a). The extensive but realistic expansion of the wheat region, originally limited to north-central Oklahoma and south-central Kansas, is evident from a comparison of Figs. 2b and 3a. To obtain this expanded region, grid points that represented a mixture of winter wheat and grassland (light orange pixels on Fig. 2a) were shifted from the “grassland” category in the model to the “mixed dry/irrigated cropland/pasture” category. The reassignment of grid points to a different vegetation category was justified by the observational evidence presented in McPherson et al. (2004a). More precisely, the mesonet observations indicated that the atmosphere across the region of mixed winter wheat and grassland responded more like the winter wheat region than it did the grassland region. Hence, the USGS dataset was modified to be more representative of the actual land use. The simulated wheat belt was defined by 2438 selected grid points (on the nested domain), and it was straightforward to swap those points to become grassland for the natural vegetation runs (Fig. 3b).

For the natural vegetation runs, the vegetation fraction was altered to better represent the lack of a wheat belt. In particular, high values of vegetation fraction across north-central Oklahoma, where there was growing wheat in the wheat run, were reduced to values similar to those of the surrounding grassland or savannah. Figures 4a and 4b display the spring vegetation fractions for the wheat and natural vegetation runs.

Difference fields were computed for many of the simulation variables. For this study, a difference field was defined as a variable field from the natural vegetation run subtracted from that of the wheat run.

d. Ability of the model to simulate reality

To justify confidence in the model results, several fields from the wheat run were compared to observations at selected times. Recall that the modeled wheat belt did not distinguish cropland from a cropland/grassland mixture—the pervasive land use across the southern half of Oklahoma’s wheat belt. As a result, the model was not expected to simulate the observations perfectly, but rather to simulate overall patterns and relative amplitudes in near-surface atmospheric fields.

The model evolution of the patterns and amplitudes of air temperature and dewpoint compared well with those detected by observations during the 27 March and 5 April (McPherson et al. 2004a). Simulations of 27 March and 5 April demonstrated cooler temperatures and higher dewpoints over the wheat belt than over adjacent lands, most notably during midday through late afternoon.

3. Results

Model simulations of 27 March 2000 and 5 April 2000 provided the basis for examining the impact of the wheat crop on the mesoscale environment during the growing season. The atmospheric conditions during 27 March and 5 April were typical of clear days during March or April. The primary differences between the near-surface synoptic conditions on 27 March versus 5 April were higher wind speeds and a stronger inversion on 5 April. Because real data were used to initialize the model, dewpoints across the wheat belt were elevated by the existing wheat crop at the start of both the wheat and the natural vegetation simulations.

a. Simulation of 27 March 2000 conditions

The near-surface field of atmospheric moisture for the 27 March simulation was initialized with the remnants of a moisture plume over the wheat belt. Near-surface dewpoints at 1200 UTC were 1°–2°C higher over the wheat belt than about 30 km beyond the boundary of the wheat belt. Higher dewpoints also were evident across southeast Oklahoma, where moisture advection from the Gulf of Mexico during 26 March might have impacted the region. Subsurface moisture measured by the Oklahoma Mesonet was abundant statewide from 5 to 75 cm below ground on 27 March. As a result of these measurements, soil moisture was set to field capacity in the deepest three (out of four) MM5 soil layers. Consequently, the simulated vegetation would have adequate water for transpiration, yet direct evaporation from the surface would not add unrealistic moisture to the model results.

Model initial condition across the wheat belt region showed some small variability in the heights of the top of the inversion layers (cf. Figs. 5a,b), amounting to a difference of about 50 hPa but no large differences in atmospheric structure. The minimal variability was not surprising, owing to the relatively small size of the wheat belt compared to the density of the upper-air observations. However, it is important to note that any differences in the evaluations of the wheat and natural vegetation runs were not caused by differences in the atmospheric initial conditions and resulted from other sources.

By 1500 UTC, winds had veered from westerly near the surface to northwesterly between 875 and 900 hPa at the May Ranch (MAYR), Cherokee (CHER), Breckinridge (BREC), and Pawnee (PAWN) mesonet sites (locations displayed in Fig. 2a). In addition, speed shear was evident between the surface (∼2.5 m s–1) and 700 hPa (20–25 m s–1).

Figure 6 displays the dewpoint field at the lowest sigma level at 1200, 1500, 1800, and 2100 UTC on 27 Mar and 0000 UTC on 28 Mar for both the wheat run (left side of Fig. 6) and the natural vegetation run (right side of Fig. 6). Dewpoints well beyond the wheat belt were identical between the two runs. Within the wheat belt, however, dewpoint temperatures from 1500 to 2100 UTC were 1°–3°C higher for the wheat run than its natural vegetation counterpart. Indeed, by 2100 UTC, a swath of higher dewpoints appeared on the wheat run as a distorted image of the wheat belt itself (cf. to Fig. 3a).

In agreement with observations (McPherson et al. 2004a), after an initial increase of near-surface dewpoints, substantial drying of the near-surface air occurred between 1300 and 1500 UTC for both runs, particularly across the eastern third of the nested domain. The only exception to this trend was a slight moistening near the surface over the wheat belt in the wheat run. Consequently, the moisture gradient between the wheat belt and its surrounding area increased. Values of near-surface dewpoints from the wheat run were approximately 1°C greater over central and southern sections of the wheat belt as compared to the same region for the natural vegetation run.

The results from the wheat run were consistent in patterns and amplitudes with measured changes in surface dewpoints from the Oklahoma Mesonet. Hence, the model did capture the initial moistening of the PBL followed by the drying of the mixed layer as a result of entrainment (Lilly 1968; Tennekes 1973; Willis and Deardorff 1974).

From 1500 to 1800 UTC, the surface dried across the entire domain for the natural vegetation run. This drying trend was similar for the wheat run except across the wheat belt. Although the northern half of the wheat belt was less moist at 1800 UTC than at 1500 UTC for the wheat run, dewpoint values ranged 2°–3°C higher across this region than those for the natural vegetation run. Moistening of the surface layer was most evident across the southern half of the wheat belt.

Between 1800 and 2100 UTC, dewpoints across all but the northern edge of the wheat belt increased a few degrees Celsius in the wheat run. At this time, the largest difference between the wheat and natural vegetation runs occurred across the wheat belt in both north-central and south-central Oklahoma. Over these two areas, dewpoints ranged from 4° to 5°C higher for the wheat run than for the natural vegetation run (Fig. 7a). Across the far western and far northern edges of the wheat belt, differences between the wheat and natural vegetation runs were minimal. This lack of appreciable dewpoint differences was attributed to dry-air advection by the westerly low-level winds (Fig. 7b). Overall, dewpoint differences of more than 1°C extended across an area about 1.5 times the size of the wheat belt.

By 2300 UTC, surface sensible heat flux was minimal and the convective boundary layer transitioned to a nocturnal boundary layer. As a result, near-surface moisture was confined within a shallow layer (20–30 hPa in depth), causing surface dewpoints to increase domainwide by 1°C from 2200 to 2300 UTC. Between 2300 UTC on 27 March and 0000 UTC on 28 March, surface dewpoints continued to increase by about 2°C domainwide. Similar to the results at 2100 UTC, the largest differences between the wheat and natural vegetation runs occurred across the wheat belt, where dewpoints ranged to 5°C higher than in the natural vegetation run.

Higher dewpoints over the wheat belt for the wheat run resulted from the simulated transpiration of growing plants. Surface latent heat values (not shown) were larger across the wheat belt for the wheat run than for the natural vegetation run. By 2100 UTC, latent heat fluxes across the wheat belt ranged from 300 to 400 W m−2 for the wheat run as compared to 200 to 275 W m−2 for the natural vegetation run. Values of sensible heat flux ranged from 25 to 125 W m−2 for the wheat run and from 100 to 200 W m−2 for the natural vegetation run (not shown). These results were consistent with observations during Cooperative Atmosphere Surface Exchange Study/Argonne Boundary Layer Experiment (CASES/ABLE; LeMone et al. 2000). During that May experiment, latent and sensible heat fluxes over winter wheat were approximately 400 and 150 W m−2, respectively. Over grassland, latent and sensible heat fluxes both were measured to be about 200 W m−2.

Simulated soundings at 2100 UTC (Fig. 8) were compared to the initial soundings over the wheat belt region (Fig. 5). As expected for an upwind location, the soundings to the west of the wheat belt (e.g., MAYR) showed no detectable difference between the wheat and natural vegetation runs (not shown). In fact, the lifting condensation level (LCL) was identical between the two runs. In contrast, the depth of the mixed layer was shallower over the western wheat belt by 21 hPa (Fig. 8a), over the eastern wheat belt by 45 hPa (Fig. 8b), and just east of the wheat belt by 20 hPa (Fig. 8c) for the wheat run in comparison to the natural vegetation run. Over the wheat belt, the differences in LCL values between the two runs were consistent with the associated differences in the values of surface sensible heat flux. Just to the east of the wheat belt, the shallower PBL was attributed to advection by westerlies within the mixed layer from the wheat belt toward the east. Doran et al. (1995) observed that the growth of the mixed layer could be modified significantly by upwind surface fluxes.

The model-calculated heights of the planetary boundary layer for both the wheat and natural vegetation simulations were between 1250 and 3000 m across the nested domain. Although the height patterns (not shown) did not appear substantially different, the impact of the wheat belt was evident in the difference field of PBL heights (Fig. 9a). Height differences ranged predominantly from –100 to –400 m, with a maximum difference of –600 m located above portions of north-central Oklahoma near the eastern boundary of the wheat belt, indicating that the PBL was shallower in the wheat run than in the natural vegetation run over the wheat belt in Oklahoma.

As a result of both a shallower mixed layer over the wheat and advection from the wheat belt eastward, the convective boundary layer just to the east of the wheat belt was about 0.5 g kg−1 moister for the wheat run than the natural vegetation run. Similarly, because of a shallower mixed layer and the increased latent heat flux for the wheat run, the atmosphere over the eastern wheat belt was almost 1 g kg−1 moister throughout the convective boundary layer, as compared to the natural vegetation run. In addition, temperatures were about 2°C cooler within the mixed layer over the eastern wheat belt for the wheat run than for the natural vegetation run. Over the western wheat belt, where the boundary layer could have been influenced by less surface evapotranspiration upstream, mixing ratios were about 0.5 g kg−1 moister for the wheat run than for the natural vegetation run. Thus, according to the model simulations, the winter wheat belt significantly modified the characteristics of the convective boundary layer. The modification was not limited to the atmosphere directly over the wheat belt; it also extended up to 150 km downstream (Fig. 9a).

The PBL height differences above and downstream from the wheat belt (Fig. 9a) not only affected thermodynamic variables, but they directly influenced the transfer of momentum into the mixed layer. Recall that the initial wind profiles displayed low-level speed shear (Fig. 5). For the wheat run, a shallower mixed layer resulted in less entrainment of higher-momentum air into the mixed layer. It is important to note that because previous modeling studies of vegetation-breeze circulations focused on environments without vertical wind shear, the results from the current study, with its sheared environment, appeared different than those discussed in the earlier scientific literature.

Based on PBL height values across the wheat belt over north-central Oklahoma, winds at 1 km above sea level were selected to be representative of those throughout the mixed layer above this region. At 2100 UTC, across north-central Oklahoma, westerly winds dominated at 1 km (Fig. 9b). Hence, east–west and north–south cross sections (locations shown in Fig. 9a) through the core of Oklahoma’s wheat belt were selected to provide insight to the circulations parallel and perpendicular to the prevailing flow.

A shallower PBL for the wheat run as compared to the natural vegetation run was evident for both cross sections (Fig. 10). Both runs exhibited a cooling of the PBL from west to east (Figs. 10a,b). In addition, the height of the PBL, characterized by a tight vertical gradient of potential temperature, was lower to the south than to the north for both runs (Figs. 10c,d) as a result of a stronger inversion toward the south. Wind vectors displayed a predominantly west-to-east motion from the surface to 5 km above sea level (Figs. 10a,b).

The difference fields of potential temperature and wind vectors for these cross sections are displayed in Figs. 10e and 10f. Across central Oklahoma, potential temperatures ranged from 0.5° to 1.5°C cooler throughout the PBL for the wheat run across the eastern half of the wheat belt (Fig. 10e). These results were consistent with observations detailed in Segal et al. (1989). Although potential temperature differences between the two runs did not exist to the west of the wheat belt boundary, the boundary layer to the east of the wheat belt was modified by upwind conditions. The eastward advection of a shallower PBL also was evident on the north–south cross section of the potential temperature difference field (Fig. 10f). The southernmost 100 to 120 km of this cross section resided east of the wheat belt (Fig. 9a). Yet the difference field clearly indicated that potential temperatures were as much as 0.8°C cooler across this region for the wheat run than the natural vegetation run. Apparently, the cooler boundary layer upwind (and over the southern leg of the wheat belt) was advected over this area just east of the wheat belt.

Of particular interest were the difference field wind vectors depicted on the east–west cross section of Fig. 10e. Based on previous idealized studies (e.g., Segal et al. 1988) in calm or light wind conditions, one would expect descending motion over the wheat belt, ascending motion to the west of and adjacent to the wheat belt, and, to a lesser intensity, rising motion to the east of and adjacent to the wheat belt. In this manner, two circulation cells would be established, with the descending branch of both cells occurring over the wheat belt. As a result, vegetation breezes near the surface would expand outward from the crop belt.

The model difference fields, however, indicated that a single-cell circulation dominated the mixed layer (Fig. 10e). The ascending branch of this circulation was near the center of the wheat belt, and a region of descent existed over the eastern boundary of the wheat belt. Other east–west cross sections (not shown) displayed a similar, dominant single-cell circulation. Although weak solenoidal circulations were seen to the west and east of the main single-cell circulation (Fig. 10e), another physical process (described later) apparently was dominating the difference circulation on the meso-β scale.

The velocity and velocity difference fields at a height of 1 km above sea level (Fig. 11) provided insight into the difference circulation. Vertical velocities at 2100 UTC for the wheat run were weak on 27 March 2000, as expected on a synoptically benign day (Fig. 11a). It was evident that no intense circulation was generated by the wheat belt. However, examination of the vertical velocity difference field (Fig. 11b) revealed several weak velocity differences between the two runs. First, along the western boundary of the wheat belt, numerous localized areas of ascending motion were evident as compared to the natural vegetation run. Second, along the eastern boundary, numerous localized areas of descending motion were apparent as compared to the natural vegetation run. Finally, horizontal velocity differences extended 150 km downstream of the northern half of the wheat belt (Fig. 11b).

To examine the differences in horizontal velocities between the runs, the horizontal wind fields for the wheat run at heights of 1.8 and 2.2 km were compared. Over north-central Oklahoma, westerly winds at 1.8 km above sea level averaged 10 to 15 m s–1 and were within the mixed layer (see Fig. 10a); westerly and northwesterly winds at 2.2 km averaged 15 to 25 m s–1 and were above the top of the convective boundary layer (see Fig. 10a). In the natural vegetation run, however, winds at 2.2 km were entrained into the mixed layer (see Fig. 10b). Hence, higher speed winds were entrained into the mixed layer of the natural vegetation run than into that of the wheat run. As a result, vectors representing the horizontal wind differences at 1 km (Fig. 11b) generally were oriented opposite to the flow at 2.2 km (not shown).

The locations of horizontal wind differences greater than 1.25 m s–1 between the wheat and natural vegetation runs were well aligned with the locations of lower PBL heights for the wheat run (Fig. 9a). These wind differences, however, were not as aligned with differences in either the surface fluxes of latent or sensible heat (not shown) or differences in the near-surface dewpoints (Fig. 7a). The largest differences in the vertical velocity fields for the wheat and natural vegetation runs (Fig. 10f) coincided with the tightest gradients in the PBL height difference field (Fig. 9a). Hence, in this environment with low-level shear, the first-order difference of the mixed-layer winds between the wheat and natural vegetation runs resulted from the change in the PBL height and, consequently, the entrainment of different wind velocities within the mixed layer.

b. Simulation of 5 April 2000 conditions

Initial dewpoints at 1200 UTC on 5 April 2000 were slightly higher across the winter wheat belt than adjoining lands (not shown). Similar to 27 March, soils were moist from 5 to 75 cm statewide; hence, soil moisture again was set to field capacity in the deepest three (out of four) MM5 soil layers. Initial thermodynamic profiles identified a strong inversion over the region, as temperatures at 850 hPa ranged from 11° to 14°C warmer than those at the lowest model level. Low-level winds veered from southwesterly to westerly between the surface and 900 hPa over the western wheat belt, between the surface and 850 hPa over the eastern wheat belt, and between the surface and 800 hPa east of the wheat belt. Wind speeds increased from about 5 m s−1 at the surface for these three sites to about 20 m s−1 at 850 hPa. A region of relatively dry air extended across eastern Oklahoma into Arkansas as a result of subsidence accompanying a large, high pressure system.

By 1500 UTC, dewpoints were distinctly different over the wheat belt between the wheat and natural vegetation runs (Fig. 12a). By 1800 UTC, for the natural vegetation run, moderate southwest winds (not shown) advected drier air (dewpoints less than 3°C) over most of the wheat belt except for the section across southern Kansas (Fig. 12b). For the wheat run, southwesterly dry-air advection also occurred by 1800 UTC, but the eastern half of the wheat belt experienced dewpoints in excess of 4°C. Similar to the 27 March simulation, added moisture for the wheat run resulted from increased transpiration from the simulated winter wheat as compared to the grassland.

From 1800 to 2100 UTC, latent heat flux values across the wheat belt (not shown) ranged from about 100 to 200 W m−2 higher for the wheat run than for the natural vegetation run. As a result, near-surface dewpoints were 2°–3°C higher at 1800 UTC and 2°–5°C higher at 2100 UTC (Fig. 12c) over about half of the wheat belt in the wheat run as compared to the natural vegetation run. Values of sensible heat flux over the wheat belt were negligible for the wheat run and ranged from 50 to 125 W m−2 for the natural vegetation run. Latent heat values over the wheat ranged from 350 to 425 W m−2 for the wheat run, about 25 W m−2 higher than those for the 27 March simulation. These results were consistent with observational findings of Doran et al. (1995) whereby latent heat fluxes over wheat and steppe increased with increased surface winds.

The boundary layer over the wheat field did not deepen as quickly during the wheat run for 5 April as it did for 27 March. By 2100 UTC, the mixed layer was about 1 km shallower for 5 April than it was on 27 March. However, similar to the 27 March case, boundary layer heights above and downwind of the wheat belt (not shown) were lower for the wheat run than they were for the natural vegetation run. Height differences of the PBL for 5 April ranged from –50 to –150 m over the wheat belt at 1800 UTC (Fig. 13a) and from –100 to –350 m at 2100 UTC (Fig. 13b), roughly half of those seen on 27 March (Fig. 9a). At 2100 UTC, these differences accounted for about 10% suppression of the PBL on 5 April for the wheat run as opposed to about 20% of the PBL on 27 March. The stronger inversion on 5 April suppressed the vertical development of the PBL as compared to 27 March.

Because PBL height differences were substantially less at 2100 UTC on 5 April than on 27 March, the entrainment of air above the PBL was more uniform between the wheat and natural vegetation runs on 5 April (e.g., Figs. 14a,b), as demonstrated by the difference field of the wind vectors (not shown). Unlike 27 March, a difference circulation on the meso-β scale was not evident (Fig. 14c). In addition, differences in the horizontal wind field between the two runs were weak.

On 5 April at 2100 UTC, Mesonet and radar observations near the western boundary of the wheat belt indicated the possibility of a vegetation breeze (McPherson et al. 2004b) in the vicinity of the east–west vertical cross section (Fig. 13a). The difference field of wind vectors parallel to the cross section (Fig. 14c) also demonstrated a possible vegetation breeze superimposed on the background field over the western boundary of the wheat belt. Unfortunately, because of both the spatial resolution of the model and the temporal resolution of the model output, a significant, near-surface dewpoint gradient observed by the Oklahoma Mesonet could not be simulated adequately. Hence, the authors note that the model results, although not conclusive, were consistent with a vegetation breeze over the western boundary of the wheat belt.

4. Conclusions

The numerical model results generally agreed with observations for the case studies of 27 March 2000 and 5 April 2000. Moreover, the simulations provided additional insight into the physical processes involved and, in particular, into the changes that occurred throughout the depth of the planetary boundary layer. The study was strengthened by the comparisons of model runs that were initiated identically but incorporated different land uses (i.e., anthropogenically modified or natural coverage) over the defined wheat belt. The key results from the modeling study are as follows:

  1. With proper adjustment of vegetation parameters, land-use type, fractional vegetation coverage, and soil moisture, numerical simulations were able to capture the overall patterns measured near the surface across a growing wheat belt during benign springtime conditions in Oklahoma.
  2. The impacts of the mesoscale belt of growing wheat included increased values of latent heat flux and decreased values of sensible heat flux over the wheat, increased values of atmospheric moisture near the surface above and downstream of the wheat, and a shallower PBL above and downstream of the wheat.
  3. In the sheared environments that were examined, a shallower PBL that resulted from growing wheat (rather than natural vegetation) led to reduced entrainment of higher momentum air into the PBL and, thus, weaker winds within the PBL over and downwind from the growing wheat.
  4. For the cases studied, gradients in sensible heat were insufficient to establish an unambiguous vegetation breeze or its corresponding mesoscale circulation.
  5. The initialization of soil moisture within the root zone aided latent heat fluxes from growing vegetation.
  6. Reasonable specification of land surface parameters was required for the correct simulation and prediction of surface heat fluxes and resulting boundary layer development.

Acknowledgments

This manuscript is based upon work supported by the National Science Foundation under NSF-0074852. Additional resources were provided by the Oklahoma Climatological Survey and the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma. Data were provided by the Oklahoma Mesonet, funded by the Oklahoma State Regents for Higher Education. The PSU/NCAR MM5 system is maintained by scientists at both NCAR and PSU who have our thanks for their hard work and dedication in making this system easily accessible. We appreciate the computer hardware and software support of Jared Bostic, David Demko, and Jessica Thomale at the Oklahoma Climatological Survey. The edits by and comments from Dr. Kenneth Crawford have enhanced this manuscript.

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Fig. 1.
Fig. 1.

Map of the mother domain (D01) and nested domain (D02) used in this study. The mother domain comprised 175 and 250 grid points in the north–south and east–west directions, respectively, and had a 12-km grid spacing. The nested domain contained 181 and 151 grid points in the north–south and east–west directions, respectively, and had a 4-km grid spacing.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 2.
Fig. 2.

Land-cover characterization from (a) the North America Land Cover Characteristics database 2.0 of the USGS and (b) the representation of USGS dominant vegetation categories within the nested model domain. In (a), dark red pixels represent winter wheat, and nearby light orange pixels represent a mix of winter wheat and grassland. Black squares are locations of Oklahoma Mesonet sites. In (b), land-use categories of interest to this study included “mixed dry/irrigated cropland/pasture” (light yellow), “grassland” (green), and “savanna” (pink). The dataset was derived from satellite measurements and categorized water bodies, snow or ice, urban, and 21 general types of vegetation, including cropland and mixed vegetation.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 3.
Fig. 3.

Redefined land-use extent and categories employed in this study. The wheat belt was composed of 2438 grid points defined as MM5 category (a) “mixed dry/irrigated cropland/pasture” (dark green) to represent growing wheat and (b) “grassland” (light green) to represent natural vegetation. Light yellow represents MM5 category “savanna.” The white outline depicts the boundary of the wheat belt.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 4.
Fig. 4.

Vegetation fraction used for (a) the wheat run and (b) the natural vegetation run for both spring cases (27 Mar and 5 Apr 2000). Values shown for the wheat run were defined by the Oregon State University/NCEP Eta land surface model as the monthly climatological vegetation fraction for Apr. Note the high percentage of vegetation (80%–90%) through the heart of Oklahoma’s winter wheat belt (across north-central and west-central Oklahoma).

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 5.
Fig. 5.

Initial thermodynamic profiles for the model simulation of 27 Mar 2000 for the Mesonet sites at (a) CHER and (b) BREC. Locations of these sites are shown in Fig. 2a.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 6.
Fig. 6.

Dewpoint fields (colored contours) and isodrosotherms (thin black lines, contoured every 2°C) at the lowest sigma level for the (left) wheat run and the (right) natural vegetation run for 27 Mar 2000. The times displayed are (a) 1200 UTC on 27 Mar and (b) 1500, (c) 1800, (d) 2100, and (e) 0000 UTC on 28 Mar. The white outline depicts the boundary of the wheat belt.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 6.
Fig. 6.

(Continued)

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 6.
Fig. 6.

(Continued)

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 7.
Fig. 7.

Model results for (a) the difference field of near-surface dewpoints and (b) the near-surface winds for the wheat run at 2100 UTC on 27 Mar 2000. The black outline depicts the boundary of the wheat belt. Positive values of the dewpoint difference field (solid shading) indicate locations where the wheat run was moister than the natural vegetation run.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 8.
Fig. 8.

Simulated thermodynamic profiles at 2100 UTC on 27 Mar 2000 for (left) the wheat run and (right) the natural vegetation run. The profiles correspond to the Mesonet sites located (a) within the western wheat belt at CHER, (b) within the eastern wheat belt at BREC, and (c) east of the wheat belt at PAWN. Locations of these sites are shown in Fig. 2a.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 8.
Fig. 8.

(Continued)

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 9.
Fig. 9.

(a) Difference field of the model-calculated PBL heights (contours) and horizontal winds at 1 km (barbs) at 2100 UTC on 27 Mar 2000. Negative values (in solid grays) indicate that PBL heights were lower for the wheat run than the natural vegetation run. Horizontal speed differences less than 1.25 m s–1 are displayed as open circles. (b) Wind field (barbs) from the wheat run at 1 km above sea level for 2100 UTC on 27 Mar 2000. Note that winds across the wheat belt over north-central Oklahoma were westerly. The east–west and north–south lines on Fig. 9a mark the location of the axes of vertical cross sections in Fig. 10. The east–west cross section is parallel to the prevailing flow; the north–south cross section is perpendicular to the prevailing flow. The black outline depicts the boundary of the winter wheat belt.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 10.
Fig. 10.

East–west vertical cross sections of potential temperature (K) at 2100 UTC on 27 Mar 2000 for (a) the wheat run and (b) the natural vegetation run. Arrows represent wind vectors parallel to the cross section. North–south vertical cross sections of potential temperature (K) at 2100 UTC on 27 Mar 2000 for (c) the wheat run and (d) the natural vegetation run. Arrows represent wind vectors parallel to the cross section. (e) East–west and (f) north–south vertical cross sections of the potential temperature (K) and wind vector (m s–1 horizontal wind; cm s–1 vertical wind) difference fields at 2100 UTC. Negative values of potential temperature difference (shaded blue) indicate locations where the wheat run was cooler than the natural vegetation run. Positive values of potential temperature difference (shaded gold) indicate locations where the wheat run was warmer than its counterpart. Vectors display the wind vector difference field resulting from velocities from the natural vegetation run subtracted from those from the wheat run. The locations of the cross sections are shown in Fig. 9a. The light gray box above the x axis denotes the location of the wheat belt.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 10.
Fig. 10.

(Continued)

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 11.
Fig. 11.

Horizontal plot of (a) the velocity field as simulated for the wheat run and (b) the velocity difference field at 2100 UTC on 27 Mar 2000. In (a), color-filled contours represent vertical velocities at 1 km above sea level: yellow represents upward motion and green represents downward motion. Horizontal velocities at a height of 1 km above sea level are plotted as barbs. In (b), color-filled contours represent vertical velocity differences at 1 km above sea level: orange represents positive differences and purple represents negative differences. Horizontal velocity differences (wheat run minus natural vegetation run) at a height of 1 km above sea level are plotted as barbs. Horizontal speed differences less than 1.25 m s–1 are displayed as open circles in (b). The black outline depicts the boundary of the winter wheat belt.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 12.
Fig. 12.

Dewpoint fields (colored contours) and isodrosotherms (thin black lines, contoured every 2°C) at the lowest sigma level for (left) the wheat run and (right) the natural vegetation run for 5 Apr 2000. The times displayed are (a) 1500, (b) 1800, and (c) 2100 UTC on 5 Apr. The white outline depicts the boundary of the wheat belt.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 12.
Fig. 12.

(Continued)

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 13.
Fig. 13.

Difference fields of the model-calculated PBL heights at (a) 1800 and (b) 2100 UTC on 5 Apr 2000. Negative values (solid shading) indicate that PBL heights were lower for the wheat run than the natural vegetation run. The east–west and north–south lines mark the location of the axes of vertical cross sections in Fig. 14. The black outline depicts the boundary of the winter wheat belt.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Fig. 14.
Fig. 14.

East–west vertical cross sections of potential temperature (K) at 2100 UTC on 5 Apr 2000 for (a) the wheat run and (b) the natural vegetation run. The location of the cross sections is shown in Fig. 13b. Arrows represent wind vectors parallel to the cross section. Red contours represent positive vertical velocities (cm s–1; ascent); blue contours represent negative vertical velocities (cm s–1; descent). (c) An east–west vertical cross section of the potential temperature (K) and wind vector (m s–1 horizontal wind; cm s–1 vertical wind) difference fields at 2100 UTC. Negative values of potential temperature difference (shaded blue) indicate locations where the wheat run was cooler than the natural vegetation run. Positive values of potential temperature difference (shaded gold) indicate locations where the wheat run was warmer than its counterpart. Vectors display the wind vector difference field resulting from velocities from the natural vegetation run subtracted from those from the wheat run. The locations of the cross sections are shown in Fig. 13. The light gray box above the x axis denotes the location of the wheat belt.

Citation: Monthly Weather Review 133, 8; 10.1175/MWR2968.1

Table 1.

Overview of numerical model simulations conducted.

Table 1.
Table 2.

Selected parameters used to define the vegetative state within the model. Original model values, if different, are denoted in parentheses.

Table 2.
1

 The mother domain is the smallest domain in which a given subdomain is completely embedded.

2

 In this context, air resistance is the opposition to the transport of moisture through the atmospheric surface layer.

3

 Stomatal resistance is the opposition to the transport of water vapor to or from the leaf stomata and is minimized when the vegetation is well supplied with water.

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