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  • View in gallery

    ASOS and SAO stations (circles), Oklahoma Mesonet stations (triangles), and West Texas Mesonet stations (squares). Oklahoma Mesonet stations were used for both dryline placement and soil moisture measurements, while West Texas Mesonet data were used only for soil moisture measurements. ASOS and SAO stations were used only for dryline placement. Because the West Texas Mesonet was only able to provide limited data, the locations from this source were chosen to maximize the coverage in this region.

  • View in gallery

    Example of the manual identification of a dryline from 30 May 2013. The dryline is shown with a thick dashed line, isolines of specific humidity are thin and gray (g kg−1), and bolded values indicate volumetric soil moisture [m3water (m3soil)−1].

  • View in gallery

    An example of Thiessen polygons for mesonet stations (points) used in the year 2015. The rectangle containing the polygons represents the study area.

  • View in gallery

    Frequency of days with a dryline during the 2006–15 period. The dashed line is the frequency, and the black line is the exponential smoothed average. Exponential smoothing uses weighted averages in which the weight of an observation decreases exponentially as it moves further toward the past.

  • View in gallery

    Average dryline longitude over April–June. The dashed line is the daily average longitude, and the black line is the polynomial smoothed average. Breaks in the dashed line are a result of zero drylines on a specific day.

  • View in gallery

    Frequency of (a) dryline longitudes in 0.5° increments, and (b) all April–June dryline longitudes at 0000 UTC.

  • View in gallery

    Frequency of specific humidity gradients [g kg−1 (100 km)−1], measured at one representative location across each dryline, at 0000 UTC during April–June from 2006 to 2015.

  • View in gallery

    Average volumetric water content from April–June for the entire study area from 2006 to 2015.

  • View in gallery

    Scatterplot and r of dryline longitude (°) and volumetric soil moisture content [m3water (m3soil)−1] from 2006 to 2015.

  • View in gallery

    Scatterplot and r of the east–west soil moisture gradient [m3water (m3soil)−1] and the specific humidity gradient [g kg−1 (100 km)−1] associated with drylines.

  • View in gallery

    Scatterplot and r of the gradient of specific humidity [g kg−1 (100 km)−1] and the gradient of soil moisture [m3water (m3soil)−1] near the dryline.

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Effects of Soil Moisture on the Longitudinal Dryline Position in the Southern Great Plains

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  • 1 Department of Geography, Ball State University, Muncie, Indiana
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Abstract

The dryline is among the most important meteorological phenomena in the Great Plains because of its significance in tornadogenesis, severe weather, and consistent rainfall. Past research has extensively examined the dynamics of the dryline; however, recent meteorological research looks beyond dynamics and focuses on land–atmosphere interactions. This study focuses on how soil moisture, a surrogate for evapotranspiration, affects the climatological longitudinal positioning of the dryline, presenting a climatological study for the months of April–June during 2006–15 in the southern Great Plains. Here, drylines are defined as specific humidity gradients exceeding 3 g kg−1 (100 km)−1 that do not deviate more than 30° from a north–south orientation; they were found to occur on 33.4% of spring days, and the most favorable position was −100.9° at 0000 UTC. Specific humidity gradients ranged from 3.0 to 15.2 g kg−1 (100 km)−1, with an average value of 6.8 g kg−1 (100 km)−1. A relationship between the dryline longitudinal position and soil moisture was found; as soil moisture values increased, the dryline was located farther west, which suggests soil moisture may influence the longitudinal positioning of the dryline. There was also a relationship between the gradient of soil moisture and the intensity (specific humidity gradient) of the dryline, such that when longitudinal soil moisture gradients were strong (increasing from west to east), the dryline intensity increased.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Nathan M. Hitchens, nmhitchens@bsu.edu

Abstract

The dryline is among the most important meteorological phenomena in the Great Plains because of its significance in tornadogenesis, severe weather, and consistent rainfall. Past research has extensively examined the dynamics of the dryline; however, recent meteorological research looks beyond dynamics and focuses on land–atmosphere interactions. This study focuses on how soil moisture, a surrogate for evapotranspiration, affects the climatological longitudinal positioning of the dryline, presenting a climatological study for the months of April–June during 2006–15 in the southern Great Plains. Here, drylines are defined as specific humidity gradients exceeding 3 g kg−1 (100 km)−1 that do not deviate more than 30° from a north–south orientation; they were found to occur on 33.4% of spring days, and the most favorable position was −100.9° at 0000 UTC. Specific humidity gradients ranged from 3.0 to 15.2 g kg−1 (100 km)−1, with an average value of 6.8 g kg−1 (100 km)−1. A relationship between the dryline longitudinal position and soil moisture was found; as soil moisture values increased, the dryline was located farther west, which suggests soil moisture may influence the longitudinal positioning of the dryline. There was also a relationship between the gradient of soil moisture and the intensity (specific humidity gradient) of the dryline, such that when longitudinal soil moisture gradients were strong (increasing from west to east), the dryline intensity increased.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Nathan M. Hitchens, nmhitchens@bsu.edu

1. Introduction

The dryline, a transition zone that separates dry air from moist air, is an important meteorological phenomenon in the Great Plains of the United States (Fujita 1958) and is a focus for research because of its effect on severe and fire weather forecasting in that region. Many violent tornadoes have occurred as a result of dryline convection; therefore, an emphasis of past research has been on dryline dynamics. However, interactions between the land and atmosphere, as well as the climatology of drylines, have not been researched as comprehensively. There have only been a few studies that focused on the evapotranspiration or soil moisture effect on the dryline (Ziegler et al. 1995; Shaw 1995; Grasso 2000), and all of them incorporated atmospheric models. Curiously, no studies used observed soil moisture data and its effect on the dryline. This study examines observed soil moisture values as a surrogate for evapotranspiration and relates them to the daily position of the dryline.

Moisture gradients associated with the dryline typically exceed 10°C (100 km)−1; however, gradients of 10°C km−1 have been observed (Pietrycha and Rasmussen 2001; Hoch and Markowski 2005). Drylines have been found to occur on 30%–40% of springtime days and have an average longitudinal position of −101° (Schaefer 1974a; Hoch and Markowski 2005; Coffer et al. 2013). In the Great Plains, one of the main mechanisms for an increased gradient of moisture is the confluence of surface winds from a lee trough, which is the same geostrophic deformation that is important to frontogenesis (Schultz et al. 2007). Dry air from the southwestern United States and moist air from the Gulf of Mexico meet, creating a sharp, narrow, nonfrontal moisture discontinuity, which is situated on a north–south line orthogonal to the sloped elevation gradient. Initial moisture advection into the dry air is caused by evaporation from both the ocean and precipitation, but there is also a contribution by evapotranspiration to the moisture being advected (Schaefer 1974b). Soil moisture and vegetation generally decrease from east to west, and as a result, latent heat flux also decreases from east to west, aiding in the creation of the moisture gradient (Grasso 2000). Evapotranspiration affects the latent heat flux by introducing moisture into the atmosphere (Ershadi et al. 2011). However, these processes do not explain the extreme gradients of moisture at the dryline, with dewpoint temperature gradients sometimes exceeding 10°C km−1. The strong moisture and virtual potential temperature gradients, along with weaker temperature gradients, help create vertical solenoids across the dryline, which strengthen the moisture gradient to extreme values and act as frontogentical forcing to create extreme gradients of moisture despite the vertical mixing (Parsons et al. 1991; Ziegler and Hane 1993; Ziegler et al. 1995).

Several studies have shown that land–atmosphere interactions strongly affect weather in the boundary layer (Zhang and Anthes 1982; Segal et al. 1989; Chang and Wetzel 1991; Pielke 2001; Holt et al. 2006). Although a significant portion of atmospheric moisture is advected from the Gulf of Mexico, there is an evapotranspiration component, one of the many land–atmosphere processes. Plants only use 0.5%–1% of water absorbed by the root for growth purposes, while the remainder is released by the plant’s leaves into the air through a process called transpiration (Burton 1982). Transpiring vegetation provides a more sustained source of atmospheric moisture than evaporation from bare soil; therefore, a model incorporating the plant biosphere is needed to predict the weather accurately (Chang and Wetzel 1991).

Ziegler et al. (1995) found the dryline to be sensitive to sensible heat in their model, and thus sensitive to soil moisture. In their land–atmosphere model, a domain of increasing soil moisture with depth counterbalanced the drying of soil due to daytime heating, which suggests evapotranspiration is an important boundary layer moisture quantity. They also found that when soil moisture was reduced, sensible heat increased, which caused a deeper convective boundary layer (CBL) and weakened the moisture gradient at the dryline. In a different land–atmosphere model, Shaw (1995) studied the evapotranspiration effect on dryline morphology and found that increases in soil moisture strengthened the moisture gradient of the dryline. He found that in his simulation with increased soil moisture, the dryline shifted farther west and the moisture gradient was stronger compared to other model parameters, concluding that evapotranspiration is important to the formation and movement of the dryline moisture gradient. Hoch and Markowski (2005) hypothesized that the westward shift in the dryline over the course of spring was due to effects of evapotranspiration from crop growth. They found that by the last week in June, the longitude of the dryline ranged from −102.6° to −100.4°, compared to their seasonal average of −101°. Grasso (2000) ran simulations to determine whether soil moisture had an effect on the dryline and surmised that the formation of the dryline was sensitive to past precipitating events because they correlate with soil moisture. These results show that dryline morphology may respond to horizontal moisture variations, which leads to the question, does the longitudinal positioning of the dryline change during days, months, or years of low or high soil moisture?

The purpose of this study is to show how soil moisture influences the longitudinal position of the dryline, using volumetric soil moisture values as a surrogate for evapotranspiration rates. To do this, a 10-yr climatology of the dryline in the southern Great Plains during the months of April–June was constructed using strict guidelines for the identification of this feature. Then, comparisons were made between soil moisture values throughout the study area and the longitudinal dryline position to identify possible relationships between them.

2. Data and methods

a. Dryline definition

A dryline is broadly defined as a transition zone tens of kilometers in width and hundreds of kilometers in length, separating dry air from moist air (Fujita 1958). Over the years, different definitions were used to identify drylines based on meteorological variables. For instance, in Rhea’s (1966) study describing thunderstorms forming off the dryline, he defined it as a dewpoint temperature difference of at least 5.6°C (10°F) between reporting stations. Schaefer (1974a) also required a 5.6°C dewpoint difference between reporting stations and additionally required that the moist air east of the dryline be “fairly uniform,” with an additional requirement that the mean dewpoint temperature exceed 10°C (50°F). As part of his definition, Schaefer excluded classic fronts by ensuring minimal virtual temperature gradients across the dryline, because at the time, it was believed that drylines showed little to no change in virtual temperature gradients across them, while cold fronts did. However, by using cloud-scale modeling and detailed observation, Coffer et al. (2013) showed that there are indeed large virtual temperature gradients across the dryline. Other studies that strictly defined drylines include Koch and McCarthy (1982), who used the leading edge of a sharp equivalent potential temperature gradient to determine the dryline (usually at the 355-K isopleth); Grasso (2000), who used the strong mixing ratio gradients to determine the dryline due to its conservative properties compared to dewpoint temperature; and Hoch and Markowski (2005), who used horizontal specific humidity gradients of at least 3 g kg−1 (100 km)−1 to determine longitudinal dryline positioning. Although methods for identifying drylines have changed over time, the use of specific humidity gradients appears to be the preferred method for dryline identification in recent studies, because it is a measure that is conserved with pressure (elevation) changes, unlike dewpoint temperature (Hoch and Markowski 2005).

Following Hoch and Markowski (2005), this study defined drylines as areas of specific humidity gradients of at least 3 g kg−1 (100 km)−1. To avoid incorrectly identifying cold fronts as drylines, areas meeting the specific humidity gradient criterion were further examined using plots of temperature, wind vectors, and surface pressure. Cold fronts and drylines both have significant specific humidity gradients; however, cold fronts have greater wind shifts and temperature changes across them compared to drylines. Additionally, outflow boundaries, the leading edge of thunderstorm-cooled air, can affect drylines and were identified using Doppler radar imagery (Glickman and Walter 2000). Finally, daytime drylines are often associated with increased temperatures in the dry sector as a result of the diurnal temperature cycle, especially at 0000 UTC; therefore, if the temperatures were higher on the dry side, that was used as affirmation of the presence of a dryline (Schaefer 1986).

The specific location of a dryline was defined as the furthest eastward extent of a line with the greatest horizontal specific humidity gradient, as described in Hoch and Markowski (2005), and at this location, the longitude of the dryline was recorded at the middle of the specific humidity gradient. Drylines tend to be oriented in a north–south direction in the Great Plains, but the dryline can tilt as much as 30° from this orientation (Tegtmeier 1974; Schaefer 1986). Those drylines that deviated more than 30° from a north–south orientation were excluded from this study.

b. Criteria for determining a dryline

The following are the criteria that were used in this study to identify drylines:

  • specific humidity gradient of at least 3 g kg−1 (100 km)−1 (Hoch and Markowski 2005);
  • not associated with a cold front;
  • not associated with an outflow boundary;
  • dewpoint temperatures in moist sector had a mean value greater than 10°C and were close to uniform (±3°C; Schaefer 1974a);
  • if there were two drylines, the one with the greatest specific humidity gradient was used, assuming all other criteria were satisfied;
  • there was a surface wind shift between the dry and moist sectors, even if it was minimal (Coffer et al. 2013); and
  • a dryline that meets the above criteria must not have deviated more than 30° from a north–south orientation (Schaefer 1986).

c. Data

In this study, the position of the dryline was identified using specific humidity, atmospheric pressure, dewpoint temperature, air temperature, and wind barbs from Oklahoma Mesonet, Automated Surface Observing System (ASOS), and Surface Aviation Observation (SAO) stations. In addition, when available, radar reflectivity was used to identify the dryline. The Oklahoma Mesonet is a network of environmental monitoring stations that goes beyond the guidelines of normal ASOS or SAO station placement (Brock et al. 1995). Most importantly, these mesonet stations have a much finer grid spacing compared with ASOS and SAO stations.

Following Hoch and Markowski (2005), data were analyzed at 0000 UTC each day during the months of April–June over the period of 2006–15. The Oklahoma Mesonet records specific humidity, while ASOS and SAO stations derive specific humidity from dewpoint temperature and pressure (Wallace and Hobbs 1977; Bolton 1980):
e1
e2
where e is the vapor pressure (mb; 1 mb = 1 hPa), Td is the dewpoint temperature (°C), p is the surface pressure (mb), and q is the specific humidity (g kg−1).

Soil moisture was chosen as a surrogate for evapotranspiration because the moisture released into the atmosphere from plants originates from within the soil (Wetzel and Chang 1987). There are equations to calculate evapotranspiration, as described by Penman (1948) and Monteith (1981), but they require over 20 variables, many of which are not measured in great density and are not within the scope of this research. Thornthwaite (1948) introduced potential evapotranspiration as a measure, which describes the amount of evaporation that would occur if there was a sufficient water source available; however, in the Great Plains, many days lacked sufficient water.

Soil moisture observations were obtained from the Oklahoma Mesonet, which has a dense network of soil moisture data, and the West Texas Mesonet, which also has an extensive network; however, these two mesonets use different soil moisture measurements. The preferred measurement for soil moisture is a normalized form of the “raw” sensor response ΔTref, which is measured using the calibrated Campbell Scientific 229-L heat dissipation matric potential sensor. This measure is used to calculate the fractional water index (FWI), a unitless measure ranging from 0 (completely dry) to 1 (saturated), and is calculated as
e3
where ΔTref is the normalized reference sensor response, ΔTd is the reference response when the sensor is dry, and ΔTw is the reference response when the sensor is wet, all measured in degrees Celsius (Schneider et al. 2003). The FWI is useful for approximating evapotranspiration because it takes into consideration soil texture and covers the total range of soil moisture values. It is also a function of matric potential, the adhesive intermolecular force with which water is absorbed onto the soil particles, which is an indication of evapotranspiration.
The Oklahoma Mesonet is among the few networks that use the FWI, but the West Texas Mesonet does not. Therefore, FWI values from the Oklahoma Mesonet were converted to volumetric water, the measure used by the West Texas Mesonet. First, using , matric potential (MP; measured in bars; 1 bar = 1000 hPa) is calculated as (Schneider et al. 2003)
e4
where a = 1.788, c = 0.717, and ΔTref is the reference temperature as described in (3). Next, MP is converted to volumetric water content (WC):
e5
where WCr is residual water content, and WCs is the saturated water content [all in m3water (m3soil)−1]. The empirical constants α and n, WCr, and WCs all depend on location, because the coefficients are functions of soil properties such as texture, density, porosity, and permeability. Thus, the coefficients may change if the soil properties change. For this study, the coefficients were obtained from the Oklahoma Mesonet and held constant at each location, although they are subject to change on the geologic time scale, or if more precise and accurate sensors are installed in the future. The preferred depth of soil moisture measurements was 5 cm below the surface, because the shallow nature of this depth takes into consideration transpiration from the root and evaporation of soil moisture by insolation. Average daily soil moisture from 5 cm underground was calculated each day.

The study area comprised the entire state of Oklahoma, the Texas Panhandle, eastern New Mexico, eastern Arkansas, and extreme southwestern Missouri (Fig. 1). The south-central Great Plains was the preferred domain for dryline placement because drylines are most common in the Texas Panhandle and western Oklahoma (Rhea 1966). Although soil moisture data were only obtained in Oklahoma and the Texas Panhandle, due to the fluid nature of the atmosphere and the dryline, the study area also included areas slightly to the west of Oklahoma and the Texas Panhandle where moisture from evapotranspiration is commonly advected (Sun and Wu 1992; Schultz et al. 2007). Surface data from ASOS and SAO stations were used in all parts of the study area that were not covered by the Oklahoma Mesonet.

Fig. 1.
Fig. 1.

ASOS and SAO stations (circles), Oklahoma Mesonet stations (triangles), and West Texas Mesonet stations (squares). Oklahoma Mesonet stations were used for both dryline placement and soil moisture measurements, while West Texas Mesonet data were used only for soil moisture measurements. ASOS and SAO stations were used only for dryline placement. Because the West Texas Mesonet was only able to provide limited data, the locations from this source were chosen to maximize the coverage in this region.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

The time frame of 2006–15 was chosen because it captured both record dry and wet periods in the study area. According to the Oklahoma Climatological Survey (2016), anomalous precipitation years included 2011, 2012, and 2015. Statewide, annual average precipitation in Oklahoma ranged from only 660 mm (26 in.) in 2012 to 1370 mm (54 in.) in 2015.

d. Analysis techniques

Following Coffer et al. (2013) and Clark et al. (2015), a manual approach to dryline identification was used in this study. First, low pressure centers and their associated boundaries were identified based on temperature gradients, wind shifts, and mean surface-level pressure. This was important because drylines tend to form in the warm sector of a lee-side low (Schaefer 1986). Next, isopleths of specific humidity were examined to determine if a dryline existed; if a gradient exceeded 3 g kg−1 (100 km)−1 within the study area, then the boundary was further examined. If there were two boundaries that exceed the required specific humidity value, then that with the strongest gradient was used if all other criteria were met. Next, dewpoint temperatures in the moist sector were examined for uniformity, with a mean value of at least 10°C required. If necessary, a loop of radar reflectivity was overlaid on the specific humidity map to ensure the dryline was not associated with an outflow boundary. Wind barbs were also inspected for a shift in wind direction at the dryline, as required within the criteria. Finally, the orientation of the boundary was examined for deviations greater than 30° from a north–south orientation, with those exceeding this threshold excluded from the dataset.

An example of the manual approach to dryline identification is shown in Fig. 2, in which a clear gradient of specific humidity was evident from central to western Oklahoma, exceeding the 3 g kg−1 (100 km)−1 threshold. Further, the wind barbs indicate the confluence of winds at the boundary, and it was noticeably hotter and drier west of the dryline, while warm and humid east of the dryline (not shown). The soil was drier west of the moisture boundary, with values ranging from 0.04 to 0.30 m3water (m3soil)−1 west of the dryline, and from 0.21 to 0.43 m3water (m3soil)−1 to the east. The farthest eastward extent of the dryline within the study area for this day was −98.4° of longitude.

Fig. 2.
Fig. 2.

Example of the manual identification of a dryline from 30 May 2013. The dryline is shown with a thick dashed line, isolines of specific humidity are thin and gray (g kg−1), and bolded values indicate volumetric soil moisture [m3water (m3soil)−1].

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

For each day during the study period, if a dryline was identified, its longitude was recorded, and the weighted mean center of the soil moisture values from that day was calculated. The location of the weighted mean was calculated as
e6
e7
where and are the weighted mean locations of X and Y, fi is the volumetric soil moisture value (weight) of point i, and Xi and Yi are the X and Y coordinates of point i. Converted Cartesian coordinates of longitude and latitude were used for Xi and Yi. This allowed for a single point each day to represent the mean center of soil moisture and volumetric water content. If a station was in an area isolated from the dense network, then that station would have been heavily weighted compared to other stations in a denser area (Fig. 3; McGrew et al. 2014). Thiessen polygons (Thiessen 1911) were used for weighted averages, because they approximate weights based on distance from other stations; this approach has been frequently employed in spatial applications using data from mesonets (Zhao and Shepherd 2012; Kornelsen et al. 2015). When interpreting results using this approach, because soil moisture generally decreased toward the west, a westward shift in the weighted mean suggests higher soil moisture values, while an eastward shift would be suggestive of lower values.
Fig. 3.
Fig. 3.

An example of Thiessen polygons for mesonet stations (points) used in the year 2015. The rectangle containing the polygons represents the study area.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

Finally, Pearson’s product-moment correlation coefficient was used to determine whether a relationship exists between the longitude of the dryline and soil moisture. As described in McGrew et al. (2014), the equation of Pearson’s correlation coefficient r is defined as
e8
where N is the number of paired data values. This same approach was also used to determine relationships between the latitude of the mean center of soil moisture and the longitude of the dryline.

3. Results and discussion

a. Dryline climatology

During the 10-yr study period, there were 317 drylines identified out of 910 possible days (33.4%). This percentage is similar to those observed in other climatological dryline studies, including 45% of spring days in Rhea (1966), 41% spring days in Schaefer (1974a), and 32% of April–June days in Hoch and Markowski (2005). The decrease in dryline days from older to newer studies was due to the use of more specific dryline definitions and methods of identifying them. When looking at the frequency of drylines identified during the study period, there was a peak of dryline days from mid- to late June, with a rapid decline toward the end of the 3-month period (Fig. 4). The last 10 days of June (days 21–30) had a daily dryline frequency of 21%, while early June (days 1–10) had a frequency of 49% and mid-June (days 11–20) had a frequency of 51%. By month, the frequency of drylines steadily increased throughout the spring season, with 27% in April, 37% in May, and 40% in June.

Fig. 4.
Fig. 4.

Frequency of days with a dryline during the 2006–15 period. The dashed line is the frequency, and the black line is the exponential smoothed average. Exponential smoothing uses weighted averages in which the weight of an observation decreases exponentially as it moves further toward the past.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

In contrast, Hoch and Markowski (2005) found a broad peak of dryline frequency from mid- to late May, while in this study it was found to occur in mid-June. The difference is likely due to the large study area and longer study period of Hoch and Markowski; for instance, in this study, no drylines were identified on 14 May, while nearly three were typical of mid-May during 2006–15 from Hoch and Markowski. However, there is agreement that dryline frequency increases throughout the spring period, followed by a rapid decrease in late June. Hoch and Markowski hypothesized that the summer monsoon moistens the typical source regions of dry continental air, weakening the moisture gradient; the finding from this study supports their hypothesis.

b. Dryline longitudinal position

The average longitudinal position of drylines was found to be −100.9°, ranging from −105° to −95°, or from the eastern edge of the Rocky Mountains to the Oklahoma–Arkansas border. Several times there appeared to be drylines farther east, but they did not satisfy this study’s dryline criteria, mainly because the moisture gradient was associated with a cold front. Over the course of the April–June period, the dryline tended to shift westward (Fig. 5). In fact, there was a clear peak in frequency between −100.5° and −101° (Fig. 6a), which seems to be associated with the Caprock Escarpment in West Texas (Fig. 6b), an area of strong elevation gradients sloping upward of 1.9 m km−1. Another peak in frequency was observed between −102.5° and −103° in the western Texas Panhandle, although it is not as clear why this area was favored. The annual average dryline position (Table 1) ranged from −102.3° in 2015—approximately the same longitude as Amarillo, Texas—to −99° in 2011, placing it just west of Wichita Falls, Texas, and Lawton, Oklahoma. In 2011, the dryline was much farther east, suggesting that precipitation was occurring more consistently in the east, which would affect agriculture in western Oklahoma and the Texas Panhandle.

Fig. 5.
Fig. 5.

Average dryline longitude over April–June. The dashed line is the daily average longitude, and the black line is the polynomial smoothed average. Breaks in the dashed line are a result of zero drylines on a specific day.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

Fig. 6.
Fig. 6.

Frequency of (a) dryline longitudes in 0.5° increments, and (b) all April–June dryline longitudes at 0000 UTC.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

Table 1.

Number of drylines, average dryline longitude, average specific humidity gradient, and average soil moisture for each year during the study period.

Table 1.

In addition, an effort was made to differentiate between weak and strong synoptic conditions on days with drylines, as there is evidence that drylines are associated with distinct synoptic processes (Schultz et al. 2007). A weak synoptic setup was classified from the 500- and 300-hPa levels by identifying features that included ridging, zonal winds without jet streaks, or weak troughing without jet streaks; a strong synoptic setup included strong troughing, weak troughing with jet streaks, or zonal winds with jet streaks. To do this, jet streaks were examined at 300 hPa, defined by winds exceeding 38.6 m s−1 (75 kt), and geopotential height was examined at 500 hPa. Of the total 317 dryline days from 2006 to 2015, there were 86 drylines within strong synoptic conditions and 231 drylines in weak synoptic conditions. The average longitudinal position of drylines identified with strong synoptic conditions was −100°, while the position of those identified with weak synoptic conditions was −101°. This supports the findings of Schultz et al. (2007), with strong synoptic conditions favoring drylines developing farther east.

c. Climatology of specific humidity gradients

According to the dryline criteria specified in this study, gradients of specific humidity were required to be at least 3 g kg−1 (100 km)−1 to be classified as a dryline, assuming all other criteria were satisfied. The range of specific humidity gradients associated with drylines began at 3.0 and extended to 15.2 g kg−1 (100 km)−1, with an average of 6.8 g kg−1 (100 km)−1 (Fig. 7). Most drylines (90%) were associated with specific humidity gradients between 3 and 10 g kg−1 (100 km)−1, showing extreme gradients of specific humidity to be rare. Drylines with strong synoptic conditions had average specific humidity gradients of 7.67 g kg−1 (100 km)−1, while those occurring under weak synoptic conditions averaged 6.52 g kg−1 (100 km)−1, suggesting strong synoptic conditions favor more intense drylines.

Fig. 7.
Fig. 7.

Frequency of specific humidity gradients [g kg−1 (100 km)−1], measured at one representative location across each dryline, at 0000 UTC during April–June from 2006 to 2015.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

d. Soil moisture climatology

Typically, soil moisture values were lowest in dry years and highest in wet years in the western part of the study area. However, toward the east, soil moisture values remained mostly constant throughout the study period (Oklahoma Climatological Survey 2016). This is expected because the dryline is typically the initiation point of convection, which then causes precipitation to occur farther eastward. Average soil moisture values from the more notable dry years in the southern Great Plains, such as 2006 or 2011, differed greatly in the west compared to other years, but very little in the east (Table 2). The longitudes used to define west (−101°, or the western side of the Texas Panhandle) and east (−96°, or eastern Oklahoma) in Table 2 were selected because they include ~2° on the eastern and western edges of the soil moisture study area. West of −101°, there were 5 stations, and east of −96°, there were 32.

Table 2.

Annual average soil moisture values [m3water (m3soil)−1].

Table 2.

The average amount of soil moisture decreased from the beginning of April through the end of the month, remaining relatively constant through June (Fig. 8). Although soil moisture decreased, potential evapotranspiration, the amount of evapotranspiration that would occur if there was sufficient soil moisture source available, likely increased (Illston et al. 2004). This is because the increased insolation later in the period likely evaporated more soil moisture, and there was an increase in transpiration later in the period due to crop growth, which also removed moisture from the soil. As vegetation and agriculture greens during the spring, more precipitation is needed to sustain high soil moisture values due to the increased evapotranspiration rate.

Fig. 8.
Fig. 8.

Average volumetric water content from April–June for the entire study area from 2006 to 2015.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

e. Soil moisture effect

One of the questions motivating this study was whether the position of the longitudinal weighted mean center of soil moisture was related to the longitudinal position of the dryline, specifically: if the weighted mean center of soil moisture moved to the west, would the longitudinal dryline position also move to the west? After comparing the locations of these two variables each year throughout the study period, no relationship was found, with the average correlation coefficient being very low (r = 0.17), and some yearly correlation values being negative. This suggests that the weighted center of soil moisture has little effect on dryline positioning.

A stronger correlation was found between the longitude of the dryline and the total volumetric soil moisture content, with a 10-yr r value of −0.51 (Fig. 9). The negative correlation coefficient values suggest that as the total volumetric soil moisture increased within the study area, the longitude of the dryline moved west. Physically, if soil moisture is high, evapotranspiration can occur at a higher rate, resulting in an increase in the amount of moisture in the moist boundary layer. This supports previous idealized model studies that looked at soil moisture’s effect on the dryline (Shaw 1995). If the study area were reduced to only include areas west of −101°, then the 10-yr average r value decreased further to −0.66, which is attributed to the responsiveness of drylines located toward western Oklahoma and the Texas Panhandle to soil moisture values, while drylines in eastern Oklahoma had less association due to the homogenous soil moisture values. Strong drylines had slightly better correlation (r = −0.60) than weak drylines (r = −0.53) when comparing longitude and total volumetric soil moisture. Both of these moderate correlation values suggest a relationship between soil moisture and the dryline after eliminating the possibility that longitudinal differences are solely due to distinct synoptic conditions. However, more extensive examination is required to better attribute the change in dryline position to soil moisture and evapotranspiration.

Fig. 9.
Fig. 9.

Scatterplot and r of dryline longitude (°) and volumetric soil moisture content [m3water (m3soil)−1] from 2006 to 2015.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

There appeared to be a positive feedback system in the southern Great Plains between dryline position and evapotranspiration: as the soil moisture increased, the dryline formed farther to the west, and, in turn, precipitation from storms initiating along the dryline increased soil moisture amounts farther west. Essentially, this caused an overall increase in soil moisture within the study area. Positive and negative feedbacks related to soil moisture and precipitation, such as those found in this study, were described by Findell and Eltahir (2003) and have also been described using teleconnections. Because the dryline’s longitudinal position was moderately correlated with soil moisture (Fig. 9), it is hypothesized that soil moisture affects the latent heat flux, which then affects the vertical circulations needed to shift the dryline eastward. Latent heat flux is dependent on land surface properties such as soil moisture, albedo, leaf area index, and aerodynamic roughness (Pielke et al. 1991; Ziegler et al. 1995). Thus, the dryline may not make advancements toward the east during the day if vertical circulations fail to penetrate through the moist boundary layer to the dry elevated mixed layer. Future work should focus on how evapotranspiration or soil moisture affect the vertical circulations at the dryline, and whether that has an effect on dryline propagation.

The position of the dryline was also sensitive to soil moisture gradients, even across a large area. To determine a single value representing the soil moisture gradient across the entire study area, those stations west of −101° and east of −96° were selected. Each day, weighted means were calculated for both the western and eastern stations, allowing for the calculation of the gradient; the steeper the gradient, the more soil moisture values decreased rapidly toward the west (weighted mean soil moisture values were never larger in the west than the east). Correlation coefficient values were found for each year between the soil moisture gradient and the specific humidity gradient (Table 3), with the observed moderately positive correlations supporting the results of previous studies: the east-to-west decrease in soil moisture may initially help to create the dryline (Schaefer 1974a), but, because of year-to-year variation in correlation coefficients (Fig. 9), there are clearly other processes at work. The moderately positive correlation coefficient values, with a 10-yr r value of 0.50 (Fig. 10), suggest that the strong soil moisture gradients across the study area may have contributed to more intense drylines.

Table 3.

Correlation coefficient r values for each year during the study period and 95% confidence intervals (CIs) for the entirety of 2006–15.

Table 3.
Fig. 10.
Fig. 10.

Scatterplot and r of the east–west soil moisture gradient [m3water (m3soil)−1] and the specific humidity gradient [g kg−1 (100 km)−1] associated with drylines.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

To analyze the possible effect that soil moisture has in the vicinity of the dryline, weighted means were calculated for stations within 1° longitude of the dryline, both to the east and west, with the requirement that there must be at least three stations included on each side of the dryline. Soil moisture gradient values across the dryline were calculated from the weighted means and were compared to the specific humidity gradient of each dryline (Fig. 11). The results from the correlation analysis suggest that the strength of the dryline (specific humidity gradient) is sensitive to soil moisture changes, such that negative gradients (or no gradient) in soil moisture meant the specific humidity gradient of the dryline was smaller (r = 0.51). However, if the gradient of moisture was strong (larger positive difference), then the specific humidity gradient was large. It is suggested that as soil moisture values east of the dryline increased, low-level moisture within the boundary layer also increased, perhaps through evapotranspiration, strengthening the gradient of the dryline.

Fig. 11.
Fig. 11.

Scatterplot and r of the gradient of specific humidity [g kg−1 (100 km)−1] and the gradient of soil moisture [m3water (m3soil)−1] near the dryline.

Citation: Journal of Hydrometeorology 19, 2; 10.1175/JHM-D-17-0091.1

Examining the year 2012 further, there were a total of 20 days on which soil moisture gradients were calculated (out of 36 days with drylines), with only 4 days of negative gradient values, meaning more soil moisture was present in the west than the east. The average soil moisture difference across the dryline was 0.01 m3water (m3soil)−1. However, there were extreme cases, with the highest soil moisture difference of 0.12 m3water (m3soil)−1 associated with a 9.2 g kg−1 (100 km)−1 specific humidity gradient. The weakest soil moisture difference, −0.05 m3water (m3soil)−1, was associated with a dryline with a 3.8 g kg−1 (100 km)−1 specific humidity gradient. The middle 50% of soil moisture values fell between 0.004 and 0.020 m3water (m3soil)−1. It is believed that extreme values of volumetric soil moisture played a significant role in the high correlation coefficient value for that year.

4. Summary

The focus of this study was the effects of soil moisture, a surrogate for evapotranspiration, on the longitudinal positioning of the dryline in the southern Great Plains during the months of April, May, and June from 2006 to 2015. A component of this study was a climatology similar to that produced by Hoch and Markowski (2005), albeit more concentrated both spatially and temporally. This study yielded the following conclusions:

  • The average dryline was located at −100.9° and occurred on 33.4% of spring days during the study period.
  • Drylines located farther east had stronger specific humidity gradients than those located farther west, with an average gradient value of 6.8 g kg−1 (100 km)−1 across the study area.
  • Soil moisture values remained constant in the east, but fluctuated greatly in the west, suggesting precipitation influences average soil moisture values toward the west.
  • As soil moisture values increased across the entire study area, the dryline formed farther to the west, which suggests soil moisture may play a role in the longitudinal positioning of the dryline.
  • When soil moisture gradients were strong from east to west, the dryline (specific humidity gradient) was stronger.
  • There was little association between the weighted mean centers of soil moisture and the dryline longitude.

The results from this study can be applied in many ways, including to severe weather, agriculture, and the energy business. Specifically, since the dryline is a boundary that is often a focal point for the initiation of thunderstorms, it helps provide sufficient rainfall for crops. It can also be an important temperature boundary, with the identification of the dryline being crucial during times of peak energy usage in the Great Plains (Tribble 2003). Future work should continue to focus on land–atmosphere properties and how they affect the dryline. For example, “wobbles” on the dryline may be associated with land–atmosphere properties like evapotranspiration, and it has been hypothesized in other studies that these areas may play a role in initiating supercell thunderstorms (Hane et al. 1997; Murphey et al. 2006). The results of this study are suggestive of possible relationships between soil moisture and the position of the dryline, but more extensive study is necessary to better identify what contribution soil moisture (and evapotranspiration) has in affecting the position of the dryline, if any.

Acknowledgments

The authors thank Dr. David Call, Dr. Petra Zimmermann, and Dr. Adam Berland for their helpful comments to improve this manuscript, as well as the Oklahoma Mesonet and the West Texas Mesonet for providing soil moisture data. The helpful feedback from three anonymous reviewers also improved this manuscript. Financial support for publication costs was kindly provided by Ball State University’s College of Sciences and Humanities, Department of Geography, and Sponsored Projects Administration.

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