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    Skew T–logp diagram showing the sounding used to initialize the horizontally homogeneous base state for the control simulation. The thermodynamic sounding is based on a composite sounding from six extreme rain events that occurred near midlevel circulations, described in Schumacher and Johnson (2009) and S09. The parcel path for the parcel with the highest in the lowest 3 km is shown by the dashed line.

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    (a) Time series showing the maximum convergence (solid line) and maximum upward vertical velocity (dashed line) in a dry simulation using the momentum forcing described in the text. For convergence, the units are ×10−5 s−1, and for vertical velocity they are ×10−2 m s−1; both are plotted on the same axis. (b) Divergence (shaded every 2 × 10−5 s−1) and wind perturbations (m s−1; reference vector at bottom) at km and vertical velocity (line contours every 0.01 m s−1) at km at h in the dry simulation. (c) North–south vertical section of potential temperature and vertical velocity in the lowest 4 km of the dry simulation. The section is taken through the center of the domain, and values have been averaged over an area 20 km on either side of this line. The thin solid contours represent potential temperature at h; the thin dashed contours show the potential temperature of the base state. Thick contours represent vertical velocity (m s−1), with negative values dashed. (d) Vertical profiles of divergence (×10−5 s−1), averaged over a 150 km × 150 km box centered on the maximum convergence. The solid line is the divergence in the dry simulation after 6 h, the dashed line is from the RUC composites of Schumacher and Johnson (2009) at 6 h before the heaviest rainfall, and the dashed–dotted line is from the NOLATENT simulation of the 6–7 May 2000 case described in Schumacher and Johnson (2008). (e) As in (d), but for vertical velocity (×10−2 m s−1). This figure is directly comparable to Fig. 3 of S09.

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    Skew T–logp diagram showing the modified dewpoint profiles for the LOWDRY (solid green) and LOWDRY_SHALLOW (dashed green) experiments and the modified temperature profile for the LOWCOOL (solid red) experiment, in comparison with the control temperature and dewpoint profiles (solid black). At the left, the vertical profile of relative humidity is also shown for the control simulation (black), LOWDRY (solid green), LOWDRY_SHALLOW (dashed green), and LOWCOOL (solid red). Note that the diagram is zoomed in to show only below 650 hPa.

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    Simulated composite radar reflectivity from the control simulation at t = (a) 3, (b) 5, (c) 7, and (d) 9 h. The portion of the domain shown is the same in all panels; however, recall that the domain is being translated toward the east-northeast, as discussed in the text. In (a), the −2 × 10−5 s−1 divergence contour from the dry simulation at h and km (black line) is included to illustrate the location of the imposed convergence.

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    (a) As in Fig. 4, except at h and zoomed in on the region of deep convection. (b) Vertical velocity at km at h in the control simulation. (c) Potential temperature perturbations on the lowest model level at the same time. (d) Potential temperature perturbations at km.

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    North–south vertical section through the convective line at h in the control simulation. The location of the cross section is shown by line A–A′ in Fig. 5b, and values have been averaged over an area 5 km on either side of this line. Shown are potential temperature perturbations (shaded), vertical velocity (contours at −4, −2, −1, 1, 2, 3, 5, 8, 12, and 16 m s−1, with negative contours dashed), and system-relative, line-perpendicular flow vectors (shown at every fifth horizontal grid point).

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    Total accumulated rainfall in (a) the control simulation, (b) LOWDRY, (c) LOWDRY_SHALLOW, and (d) LOWCOOL. The white plus signs indicate the location of the point maximum in each simulation.

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    (a) Area-averaged rainwater mixing ratio at (left profiles) and h (right profiles). (b) Area-averaged increase in the water vapor mixing ratio owing to evaporation of rain. The profiles on the left side of the graph are for the 60 min ending at h, and the profiles on the right side are for the 60 min ending at h. The area-averaging region is from x = 180 to 360 km and from y = 200 to 350 km (see Fig. 4 for these locations).

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    Vertical mass flux over the model domain at z = (a) 8, (b) 5, and (c) 2 km above the surface. The scale of the ordinate is different for each panel.

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    Time–longitude (Hovmöller) diagram of vertical velocity (m s−1) at km in (a) the control simulation, (b) LOWDRY, (c) LOWDRY_SHALLOW, and (d) LOWCOOL. Values are adjusted to account for the moving model domain and have been averaged over an 80-km band in the y direction within which the deep convection occurred. The labels on the x axis are provided for a sense of distance, but because of the adjustment to account for the translating domain, they do not correspond to the locations within the domain shown in other figures (i.e., x = 100 km in this figure does not correspond to x = 100 km in Fig. 5).

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    Vertical velocity (shaded) and near-surface tracer concentration (the 1% contour is shown) at km and h in (a) the control simulation, (b) LOWDRY, (c) LOWDRY_SHALLOW, and (d) LOWCOOL. The locations of the vertical sections shown in Fig. 12 (solid) and Fig. 13 (dashed) are also shown.

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    North–south vertical section through the convective line at h in (a) the control simulation, (b) LOWDRY, (c) LOWDRY_SHALLOW, and (d) LOWCOOL. Shown are concentration (color shading) of passive tracer released from the lowest 750 m, vertical velocity (contoured as in Fig. 6), and potential temperature perturbation (−1 and −0.5 K contours in blue). The locations of the vertical sections are shown by the solid lines in Fig. 11; all panels show an average of these fields over an area 5 km on either side of the respective line.

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    As in Fig. 12, but for vertical sections through the eastern portion of the MCS. The locations of the vertical sections are shown by the dashed lines in Fig. 11; all panels show an average of these fields over an area 5 km on either side of the respective line.

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    As in Fig. 10, but for potential temperature perturbation on the lowest model level, and the averaging band in the y direction is 120 km wide. Only hours 4–9 are shown.

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    As in Fig. 10, but for concentration at km of passive tracer released from the lowest 750 m. Only hours 6–9 are shown.

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    (a),(c) As in Figs. 15a and 15b, but for concentration at km of passive tracer released between and 2.5 km. (b),(d) As in Figs. 12a and 12b, except the shaded field is concentration of tracer released between and 2.5 km.

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    Vertical section at 7 h of CAPE for parcels lifted from each level (shaded), isentropes at (solid black) and h (dashed black; , contoured every 1 K), potential temperature perturbation (thick blue contours every 0.25 K; only negative values contoured), and vertical velocity (the 1 m s−1 contour is shown in red) for (a) control and (b) LOWCOOL. The locations of the sections are as in Figs. 12a and 12d.

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    Schematic depiction of the key processes responsible for rainfall differences between (a) the control simulation and (b) the experiments with a slight reduction in near-surface moisture in the initial condition. Both (a) and (b) depict a north–south vertical section through the western portion of the mature MCS. Near-surface and elevated air parcel trajectories are shown by thick black arrows; a gust front is shown by the blue curve; and the LFCs for elevated and surface-based parcels are denoted by LFCelev and LFCsfc, respectively.

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Sensitivity of Precipitation Accumulation in Elevated Convective Systems to Small Changes in Low-Level Moisture

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  • 1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
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Abstract

Using a method for initiating a quasi-stationary, heavy-rain-producing elevated mesoscale convective system in an idealized numerical modeling framework, a series of experiments is conducted in which a shallow layer of drier air is introduced within the near-surface stable layer. The environment is still very moist in the experiments, with changes to the column-integrated water vapor of only 0.3%–1%. The timing and general evolution of the simulated convective systems are very similar, but rainfall accumulation at the surface is changed by a much larger fraction than the reduction in moisture, with point precipitation maxima reduced by up to 29% and domain-averaged precipitation accumulations reduced by up to 15%. The differences in precipitation are partially attributed to increases in the evaporation rate in the shallow subcloud layer, though this is found to be a secondary effect. More importantly, even though the near-surface layer has strong convective inhibition in all simulations and the convective available potential energy of the most unstable parcels is unchanged, convection is less intense in the experiments with drier subcloud layers because less air originating in that layer rises in convective updrafts. An additional experiment with a cooler near-surface layer corroborates these findings. The results from these experiments suggest that convective systems assumed to be elevated are, in fact, drawing air from near the surface unless the low levels are very stable. Considering that the moisture differences imposed here are comparable to observational uncertainties in low-level temperature and moisture, the strong sensitivity of accumulated precipitation to these quantities has implications for the predictability of extreme rainfall.

Corresponding author address: Prof. Russ Schumacher, Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO 80523. E-mail: russ.schumacher@colostate.edu

Abstract

Using a method for initiating a quasi-stationary, heavy-rain-producing elevated mesoscale convective system in an idealized numerical modeling framework, a series of experiments is conducted in which a shallow layer of drier air is introduced within the near-surface stable layer. The environment is still very moist in the experiments, with changes to the column-integrated water vapor of only 0.3%–1%. The timing and general evolution of the simulated convective systems are very similar, but rainfall accumulation at the surface is changed by a much larger fraction than the reduction in moisture, with point precipitation maxima reduced by up to 29% and domain-averaged precipitation accumulations reduced by up to 15%. The differences in precipitation are partially attributed to increases in the evaporation rate in the shallow subcloud layer, though this is found to be a secondary effect. More importantly, even though the near-surface layer has strong convective inhibition in all simulations and the convective available potential energy of the most unstable parcels is unchanged, convection is less intense in the experiments with drier subcloud layers because less air originating in that layer rises in convective updrafts. An additional experiment with a cooler near-surface layer corroborates these findings. The results from these experiments suggest that convective systems assumed to be elevated are, in fact, drawing air from near the surface unless the low levels are very stable. Considering that the moisture differences imposed here are comparable to observational uncertainties in low-level temperature and moisture, the strong sensitivity of accumulated precipitation to these quantities has implications for the predictability of extreme rainfall.

Corresponding author address: Prof. Russ Schumacher, Department of Atmospheric Science, Colorado State University, 1371 Campus Delivery, Fort Collins, CO 80523. E-mail: russ.schumacher@colostate.edu

1. Introduction

In the midlatitude warm season, a large fraction of the heavy precipitation is produced by mesoscale convective systems (MCSs; Houze 2004), which are organized clusters or lines of deep convective cells (e.g., Fritsch et al. 1986; Ashley et al. 2003; Schumacher and Johnson 2005, 2006; Stevenson and Schumacher 2014). Furthermore, much of this heavy precipitation falls at night (e.g., Stevenson and Schumacher 2014), suggesting that often the MCSs responsible for the rainfall are “elevated,” meaning that the air that ascends in deep convection originates not at the surface (as that layer typically stabilizes after dark, owing to radiational cooling), but above the surface (e.g., Corfidi et al. 2008). Such elevated convective systems generally require a synoptic- or mesoscale lifting mechanism, such as a front (e.g., Maddox et al. 1979; Trier and Parsons 1993; Augustine and Caracena 1994; Junker et al. 1999; Laing and Fritsch 2000; Moore et al. 2003; Schumacher and Johnson 2005) or a mesoscale convective vortex (MCV; e.g., Fritsch et al. 1994; Trier and Davis 2002; Schumacher and Johnson 2009) to initiate and maintain convection, although some MCSs may be surface-based early in their life cycle but become elevated as they move into a nocturnal stable layer (e.g., Parker 2008; French and Parker 2010).

Whereas a large amount of attention has been given to understanding MCSs that are maintained by lift at the downshear edge of a convectively generated cold pool, as outlined in the pioneering study of Rotunno et al. (1988), comparatively less attention has been given to the variety of mechanisms that can initiate and maintain elevated MCSs within a region of larger-scale ascent. Crook and Moncrieff (1988) showed that, if large-scale convergence is allowed to lift a mesoscale region to saturation, an organized convective system could be maintained without an evaporatively driven cold pool, and even the large-scale ascent was not needed to maintain the convection after it had been initiated. They also showed that the structure at low levels of the MCS may transition from a gravity wave to a cold pool through its life cycle. Schumacher (2009, hereinafter S09) conducted more sophisticated three-dimensional simulations of an elevated MCS in an environment with mesoscale convergence and found that convection was organized into a line by a low-level gravity wave and that new convective cells repeatedly formed upstream along this gravity wave (a process known as back building). These organization and motion characteristics of the MCS resulted in heavy rainfall, with rainfall accumulations similar to those in observed extreme rainfall events.

Other recent studies have examined the processes responsible for the organization and maintenance of elevated MCSs. Using radar observations, Marsham et al. (2010) identified a low-level wave structure in an elevated MCS in the United Kingdom that closely resembled the simulated wave of S09. Marsham et al. (2011) and Trier et al. (2011) investigated the initiation of an elevated MCS during the International H2O Project in 2002 and documented the development of gravity waves as well as the transition of the convection from elevated at night to surface-based after sunrise. Parker (2008) and French and Parker (2010) also found that gravity waves or bores can organize or maintain elevated convection as a squall line moves from a well-mixed boundary layer representative of daytime conditions into a cool, stable nocturnal boundary layer.

A remaining question raised by some of these past studies is how to determine whether an MCS with near-surface inflow that has substantial convective inhibition (CIN) but still has nonnegligible convective available potential energy (CAPE) is truly elevated or is still ingesting near-surface air. Corfidi et al. (2008) argue that a continuum exists between surface-based and elevated convection rather than a sharp distinction. Consistent with this idea, Parker (2008) showed that several hours of low-level cooling were required before a simulated squall line became truly elevated, and Nowotarski et al. (2011) and Billings and Parker (2012) showed that supercell thunderstorms may similarly continue to ingest near-surface air even when the boundary layer has stabilized considerably. These questions are of both scientific interest for increased understanding of MCS dynamics and practical importance for the prediction of hazardous weather, such as tornadoes, heavy local rainfall, and severe winds.

In addition to the mechanisms responsible for organizing and maintaining MCSs, there remain questions about the sensitivity of the convection and its attendant hazards to the details of the environment surrounding the convection—in other words, the predictability. Wandishin et al. (2008, 2010) showed that perturbing the vertical profiles of temperature, moisture, and wind with representative analysis uncertainties could lead to vastly different MCS evolutions in idealized simulations. James and Markowski (2010) and Grant and van den Heever (2015) showed that both storm structure and accumulated precipitation are substantially altered when layers of dry air are introduced into the environment. Melhauser and Zhang (2012) used progressively smaller initial-condition perturbations to demonstrate the limitations to the practical and intrinsic predictability of a squall line, and Schumacher et al. (2013) examined the influence of small differences in the prediction of an earlier convective system on the development of heavy precipitation associated with an MCV the following night. In a somewhat different context, Gilmore et al. (2004) and several subsequent studies have demonstrated that small changes to the way that microphysical processes are parameterized can lead to large changes in precipitation accumulation at the surface, even for storms within the same environmental conditions, again suggesting limited predictability for heavy precipitation. Finally, although some studies have attempted to quantify precipitation efficiency in convective storms (e.g., Market et al. 2003; McCaul et al. 2005), this remains a difficult quantity to estimate or predict.

In this study, small perturbations to the initial near-surface environmental moisture profile will be made to assess the sensitivity of accumulated rainfall in a simulated elevated MCS. Because detailed observations of the vertical profile of environmental moisture above the surface are relatively sparse, these experiments will offer insights into the predictability of heavy precipitation. Furthermore, because these changes to the moisture and temperature profiles affect the CAPE and CIN of near-surface air parcels (but not the CAPE and CIN of the more unstable elevated parcels), the distinction between surface-based and elevated convection and the importance of this distinction for the production of heavy precipitation, will be addressed. In other words, this research will address the following two questions:

  • How do small changes in the low-level environmental moisture profile affect the distribution of accumulated precipitation in an elevated MCS?
  • Do these moisture changes also affect the dynamical evolution of elevated MCSs, and if so, how?
Section 2 of the manuscript outlines the design of the numerical model experiments, and the results of the experiments are presented in section 3. Section 4 concludes the paper.

2. Methods and experimental design

a. Model configuration

The numerical simulations conducted for this study are very similar to those presented by S09; an overview of the methods will be given here along with the few changes that were made to the configuration used in that study. The reader is directed to S09 for a more detailed explanation of the model configuration. The simulations are conducted with version 1.17 of Cloud Model 1 (Bryan and Fritsch 2002). In these simulations, a momentum forcing is applied to a portion of the model domain that causes a three-dimensional convergence field with a circular shape to gradually develop. This eventually leads to the initiation of deep convective cells within the region of convergence. The momentum forcing is motivated by the findings of Crook and Moncrieff (1988) and the implementation discussed by Loftus et al. (2008), and it is designed to emulate the ascent associated with an MCV or other mesoscale circulation without adding the complexities of the circulation itself. The momentum forcing is identical to that outlined in S09, except that in this study all simulations use a horizontal grid spacing of 500 m, but no random perturbations are applied to speed the process of convection initiation. In other words, the simulation proceeds identically to the 1-km simulations shown in S09 but at twice the horizontal resolution. As in S09, 61 levels with a stretched grid are used in the vertical. The initial environmental conditions for the control simulation are horizontally homogeneous and are defined by the composite sounding from six observed extreme rainfall cases calculated by Schumacher and Johnson (2009), with the wind field slightly idealized as in S09 (Fig. 1). In this sounding, the lowest 1.1 km reflects a nocturnal stable layer, with the most unstable air located above this at approximately 875 hPa. There is a reverse-shear wind profile, with weak winds near the surface increasing to a low-level wind maximum, and then decreasing above. The domain translates at u = 7.5 m s−1 and υ = 2.5 m s−1, similar to the typical motion of MCVs. All simulations (except for the dry simulation discussed in the next paragraph) are integrated for 9 h.

Fig. 1.
Fig. 1.

Skew T–logp diagram showing the sounding used to initialize the horizontally homogeneous base state for the control simulation. The thermodynamic sounding is based on a composite sounding from six extreme rain events that occurred near midlevel circulations, described in Schumacher and Johnson (2009) and S09. The parcel path for the parcel with the highest in the lowest 3 km is shown by the dashed line.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Figure 2 summarizes the structure and evolution of the convergence forcing and associated vertical motion in a dry simulation; it can be directly compared with Fig. 3 of S09. The characteristics of the dry simulation are nearly identical to those of S09, with the maximum convergence and vertical motion being very slightly stronger in this run. This, along with some smaller-scale features apparent in Fig. 2b, is likely a result of the higher spatial resolution of this simulation. This comparison provides confidence that the simulations with moisture will progress similarly to those shown by S09.

Fig. 2.
Fig. 2.

(a) Time series showing the maximum convergence (solid line) and maximum upward vertical velocity (dashed line) in a dry simulation using the momentum forcing described in the text. For convergence, the units are ×10−5 s−1, and for vertical velocity they are ×10−2 m s−1; both are plotted on the same axis. (b) Divergence (shaded every 2 × 10−5 s−1) and wind perturbations (m s−1; reference vector at bottom) at km and vertical velocity (line contours every 0.01 m s−1) at km at h in the dry simulation. (c) North–south vertical section of potential temperature and vertical velocity in the lowest 4 km of the dry simulation. The section is taken through the center of the domain, and values have been averaged over an area 20 km on either side of this line. The thin solid contours represent potential temperature at h; the thin dashed contours show the potential temperature of the base state. Thick contours represent vertical velocity (m s−1), with negative values dashed. (d) Vertical profiles of divergence (×10−5 s−1), averaged over a 150 km × 150 km box centered on the maximum convergence. The solid line is the divergence in the dry simulation after 6 h, the dashed line is from the RUC composites of Schumacher and Johnson (2009) at 6 h before the heaviest rainfall, and the dashed–dotted line is from the NOLATENT simulation of the 6–7 May 2000 case described in Schumacher and Johnson (2008). (e) As in (d), but for vertical velocity (×10−2 m s−1). This figure is directly comparable to Fig. 3 of S09.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

The most substantive difference between the simulations in this study and those presented by S09 is that a more sophisticated two-moment microphysics parameterization (Morrison et al. 2009) is used here instead of the single-moment scheme used by S09. However, the initiation and evolution of the convective system in the control simulation are very similar to the simulations of S09, and the accumulated rainfall distribution is also very similar. Additional subtle differences between these simulations will be addressed later in the paper, but in general this control simulations can be thought of as the same as that in S09, except using higher resolution, an updated model version, and a more sophisticated microphysics parameterization.

b. Moisture sensitivity experiments

To test the sensitivity of MCS processes and rainfall accumulation to the vertical distribution of moisture, several experiments were conducted with simple modifications to the initial moisture profile (the experiments are summarized in Table 1). Two experiments were designed in which the water vapor mixing ratio in the near-surface layer is assumed to be well-mixed, which makes this layer slightly drier than in the control simulation. In the first of these, referred to as LOWDRY, the mixing ratio in the lowest 1.1 km is given a constant value of 14.62 g kg−1 (Fig. 3). (This is equal to the mixing ratio in the control sounding at 1.1 km, but the mixing ratio in the control sounding increases below this level to a maximum of 16.18 g kg−1 at the surface.) The second experiment is referred to as LOWDRY_SHALLOW and includes a similar modification, except that the initial mixing ratio is only constant in the lowest roughly 700 m, with a value of 15.05 g kg−1 (Fig. 3). The moisture modifications were made in this way for several reasons. First, they were motivated by simplicity, in that assuming a well-mixed profile for moisture is a simple, yet reasonable, modification to apply. Second, modifications were made only below the level with the maximum so that the CAPE and CIN of the most unstable parcel are unchanged. Or, in other words, the environment is only altered in the layer below where the unstable inflow air is presumably located. (The validity of this assumption will be addressed later in the paper, however.) Finally, Coniglio (2012) showed that, in convective storm environments, analyses from the Rapid Update Cycle (RUC; Benjamin et al. 2004), which were originally used to develop the composite sounding shown in Fig. 1, tend to have a slight moist bias near the surface, so these experiments will test the sensitivity of rainfall forecasts in an idealized framework to moisture differences of a similar magnitude. (They showed a median dewpoint error of about 1 K, which corresponds to a mixing ratio difference of about 1 g kg−1 at typical temperatures for these events.)

Table 1.

Details regarding the initial soundings for the numerical experiments. Differences from the control sounding are given in parentheses; percentage differences are given for IWV. The prefix MU refers to parcels lifted from the level with the highest ; SB refers to parcels lifted from the lowest model level.

Table 1.
Fig. 3.
Fig. 3.

Skew T–logp diagram showing the modified dewpoint profiles for the LOWDRY (solid green) and LOWDRY_SHALLOW (dashed green) experiments and the modified temperature profile for the LOWCOOL (solid red) experiment, in comparison with the control temperature and dewpoint profiles (solid black). At the left, the vertical profile of relative humidity is also shown for the control simulation (black), LOWDRY (solid green), LOWDRY_SHALLOW (dashed green), and LOWCOOL (solid red). Note that the diagram is zoomed in to show only below 650 hPa.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Motivated by the outcomes of the moisture sensitivity experiments (which will be presented in the next section), an additional experiment was designed in which the near-surface temperature profile was altered but the moisture profile was the same as that in the control simulation. In particular, the surface temperature was reduced by 0.8 K, with the magnitude of this cooling linearly decreasing to zero at 1.1 km above the surface. This experiment is referred to as LOWCOOL, and the altered temperature profile is shown in Fig. 3. This experiment is designed to test whether the differences in precipitation found in the moisture sensitivity experiments are primarily related to changes in relative humidity [note that the near-surface RH is higher in the LOWCOOL simulation than in the control, while the integrated water vapor (IWV) is unchanged] or to changes in the low-level stability.

3. Results

a. General characteristics of control simulation

In the control simulation, convection initiates and evolves very similarly to the simulation of S09. Air is lifted within the region of convergence forcing, deep convection initiates at approximately 2.5 h into the integration (t = 3 h is shown in Fig. 4a), and it begins to organize between t = 4 and 5 h (Fig. 4b). The convection on the southern flank of this initial cluster organizes into a southwest–northeast-oriented convective line between t = 5 and 7 h, and the organization takes place along a low-level gravity wave (Figs. 5 and 6), with back-building convection on the southwestern flank of the line (see S09 for further discussion of the mechanisms responsible for this behavior). At about t = 7 h, a surface cold pool begins to develop on the eastern end of the convective line (Fig. 5c), and this cold pool continues to strengthen and expand through the remainder of the simulation, with convection then surging southward along its gust front in the last hour of the simulation (Fig. 4d). While the eastern end of the line is surging southward, new cells continue to develop on the western flank through the conclusion of the simulation. Concurrent forward- and backward-propagating convective lines such as these are occasionally observed in radar imagery, with the backward-propagating convection capable of producing locally heavy rainfall (e.g., Corfidi 2003; Keene and Schumacher 2013; Peters and Schumacher 2015a,b). Figures 4, 5, and 6 are directly comparable to Figs. 5, 7, and 8a of S09 and reveal that the overall structure and evolution of this simulation are very similar to that one, despite the use of a different microphysics scheme and model version. With these similarities established, we can proceed to analyze the differences between the control simulation and the moisture sensitivity experiments.

Fig. 4.
Fig. 4.

Simulated composite radar reflectivity from the control simulation at t = (a) 3, (b) 5, (c) 7, and (d) 9 h. The portion of the domain shown is the same in all panels; however, recall that the domain is being translated toward the east-northeast, as discussed in the text. In (a), the −2 × 10−5 s−1 divergence contour from the dry simulation at h and km (black line) is included to illustrate the location of the imposed convergence.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Fig. 5.
Fig. 5.

(a) As in Fig. 4, except at h and zoomed in on the region of deep convection. (b) Vertical velocity at km at h in the control simulation. (c) Potential temperature perturbations on the lowest model level at the same time. (d) Potential temperature perturbations at km.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Fig. 6.
Fig. 6.

North–south vertical section through the convective line at h in the control simulation. The location of the cross section is shown by line A–A′ in Fig. 5b, and values have been averaged over an area 5 km on either side of this line. Shown are potential temperature perturbations (shaded), vertical velocity (contours at −4, −2, −1, 1, 2, 3, 5, 8, 12, and 16 m s−1, with negative contours dashed), and system-relative, line-perpendicular flow vectors (shown at every fifth horizontal grid point).

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

b. Rainfall accumulation

The control simulation produces a broad area of rainfall exceeding 25 mm, with a band of heavier amounts that includes a point maximum of 125.4 mm (Fig. 7a; Table 2). The region with the largest rainfall totals was associated with the back-building convection on the southwestern flank of the simulated MCS. In the control simulation, measurable rain did not begin falling at the surface until after t = 3 h into the simulation, so the totals shown here effectively represent approximately 6 h of accumulation. Local accumulations of 125 mm in 6 h are generally consistent with the rainfall totals in observed MCSs associated with MCVs (e.g., Schumacher and Johnson 2009; Schumacher et al. 2013).

Fig. 7.
Fig. 7.

Total accumulated rainfall in (a) the control simulation, (b) LOWDRY, (c) LOWDRY_SHALLOW, and (d) LOWCOOL. The white plus signs indicate the location of the point maximum in each simulation.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Table 2.

Maximum accumulated precipitation at a point and area-averaged precipitation in the numerical experiments, accounting for the translation of the domain. The area-averaged precipitation was calculated over the region shown in Fig. 7. The differences are with respect to the control run.

Table 2.

All of the experiments show a similar spatial distribution of precipitation to the control run, with a band of heavier rainfall at the southern edge of a larger swath of moderate precipitation amounts (Fig. 7). In all of the experiments, convection initiated and organized at approximately the same time as in the control simulation (this will be discussed in more detail later). However, despite these similarities and the relatively small changes to the vertical moisture profile in their initial conditions, the rainfall accumulation was substantially altered in the experiments (Fig. 7; Table 2). For example, the LOWDRY_SHALLOW simulation, which had only 0.3% less IWV in its base state than the control simulation, produced 6% less total precipitation, and there was a 28% reduction in the point rainfall maximum (Fig. 7b; Table 2). Similarly, the LOWDRY simulation had a 15% reduction in total rainfall and 29% less rain at the location of the point maximum (Fig. 7c; Table 2). In the experiments, the location of the point maximum was also shifted to the east of where it occurred in the control simulation (Fig. 7). In LOWCOOL, which had a cooler low-level environment but an unchanged moisture profile, the precipitation was also reduced considerably (Fig. 7d). These large changes to the accumulated rainfall in response to relatively small changes in the initial condition point toward limitations in the predictability of heavy precipitation and also raise questions about the reasons for these differences.

Possible reasons for the large differences in accumulated rainfall in response to a slight drying of the low levels are (i) increased evaporation of rain owing to lower relative humidity, especially in the subcloud layer; (ii) changes in the intensity of convection; and (iii) differences in the organization and motion of convection resulting from evaporatively driven cold pools or other related mechanisms. One could also envision feedbacks between two or more of these processes. The following section will address which of these mechanisms are at work in these experiments.

c. Examination of causes of the rainfall reduction

Perhaps the most direct mechanism for reduced rainfall in the LOWDRY and LOWDRY_SHALLOW simulations is the evaporation of rain in the drier subcloud layer; in other words, the same amount of rain could be produced within the convective system, but if more of that rain evaporates as it falls, then the surface accumulation will be reduced. Early in the life cycle of the convective system (prior to t = 7 h), this is indeed what occurs, as the LOWDRY and LOWDRY_SHALLOW runs exhibit increased evaporation in the lowest 500 m (Fig. 8b, left profiles). However, once the simulated convective systems have reached maturity, this is no longer the case. Instead, the control simulation shows more evaporation throughout the lowest 4 km after t = 7 h (Fig. 8b, right profiles). By t = 8 h, the control simulation had a higher rainwater mixing ratio in this layer than did all of the other simulations (Fig. 8a, right profiles); thus, the fact that the convection in the control simulation produced more rain outweighed the effect of subcloud evaporation. Similarly, despite low-level evaporation being reduced in the LOWCOOL run because of its higher environmental RH, substantially less rainwater was produced in this simulation compared with the control run. Therefore, the next question to answer is: what caused the control simulation to produce a larger volume of rain?

Fig. 8.
Fig. 8.

(a) Area-averaged rainwater mixing ratio at (left profiles) and h (right profiles). (b) Area-averaged increase in the water vapor mixing ratio owing to evaporation of rain. The profiles on the left side of the graph are for the 60 min ending at h, and the profiles on the right side are for the 60 min ending at h. The area-averaging region is from x = 180 to 360 km and from y = 200 to 350 km (see Fig. 4 for these locations).

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

To assess the overall intensity of the convection in the experiments, the domain-integrated vertical mass flux at different levels is examined. In all simulations, upward mass flux begins to increase rapidly after t = 3.5 h with the initiation of deep convection; this increase is slightly greater in the control simulation (Fig. 9). At low levels and midlevels, the mass flux fluctuates throughout the simulations, with a peak at 5.5–6 h, a relative minimum at 6.5–7 h, and a subsequent increase toward the end of the simulation, with the details varying from run to run (Figs. 9b,c). In contrast, at z = 8 km there is a steady increase in upward mass flux throughout the simulations (Fig. 9a).

Fig. 9.
Fig. 9.

Vertical mass flux over the model domain at z = (a) 8, (b) 5, and (c) 2 km above the surface. The scale of the ordinate is different for each panel.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

With these details in mind, a consistent theme of this analysis is that the control simulation has greater upward mass flux than the sensitivity simulations at all vertical levels for nearly the entire model integration (Fig. 9), followed by the LOWDRY_SHALLOW, LOWDRY, and LOWCOOL runs. (The LOWDRY_SHALLOW simulation does, however, have greater mass flux than the control for a limited time between approximately t = 6 and 7 h.) Hovmöller diagrams of vertical velocity at z = 8 km (Fig. 10) reveal that, in all simulations, deep convection initiates in a similar location around t = 4 h with initially slow movement. At subsequent times, two primary areas of convection are present, with some convection moving relatively quickly eastward, while other convection is nearly stationary. (On the diagrams in Fig. 10, the slow-moving convection appears as a near-vertical region of upward motion, whereas the faster-moving convection appears on a diagonal path; these processes can be visualized spatially in the control simulation in Fig. 4.) The intensity of the eastward-propagating convection is similar among all of the simulations, with perhaps the most notable differences being slightly slower eastward propagation in LOWDRY_SHALLOW and LOWCOOL compared with the other runs. Much larger differences are found for the coverage and intensity of the slow-moving convection. In particular, the control simulation shows intense convection on the upstream flank of the MCS from approximately t = 7.5 h to the end of the run (Figs. 10a and 4), whereas the LOWDRY and LOWCOOL simulations have essentially no deep convection in this area (west of approximately x = 70 km in Figs. 10b and 10d). The LOWDRY_SHALLOW simulation shows an intermediate pathway, with slow-moving convection that is less intense than in the control simulation (Fig. 10c).

Fig. 10.
Fig. 10.

Time–longitude (Hovmöller) diagram of vertical velocity (m s−1) at km in (a) the control simulation, (b) LOWDRY, (c) LOWDRY_SHALLOW, and (d) LOWCOOL. Values are adjusted to account for the moving model domain and have been averaged over an 80-km band in the y direction within which the deep convection occurred. The labels on the x axis are provided for a sense of distance, but because of the adjustment to account for the translating domain, they do not correspond to the locations within the domain shown in other figures (i.e., x = 100 km in this figure does not correspond to x = 100 km in Fig. 5).

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

These results point to the convective intensity (in terms of spatial coverage of convection, strength of individual updrafts, or both), rather than the magnitude of rainwater evaporation, being the key reason that more rain reached the ground in the control simulation compared to the experiments with dry layers near the surface. Of particular interest is what causes there to be intense back-building convection in the control simulation but not in the simulations with drier or cooler near-surface layers.

As discussed previously and shown in Table 1, the CAPE and CIN for the most unstable parcels are identical for all of the experiments, and presumably the convection is primarily elevated. Although near-surface parcels have large CIN in all of the initial thermodynamic profiles, the control sounding has considerable CAPE for surface-based parcels, whereas LOWDRY and LOWDRY_SHALLOW do not, and the level of free convection (LFC) for surface-based parcels is higher in LOWDRY and LOWDRY_SHALLOW. Therefore, one possible reason for the differences in convective structure and intensity is that there are differences in whether near-surface air is ingested into deep convective updrafts. To address this question, passive tracers are released in the model in the lowest 750 m, and the amount of near-surface tracer that is transported to upper levels is analyzed. In the control simulation, near-surface tracer is indeed ingested and transported to upper levels in both the forward-propagating and back-building parts of the convective system by t = 8.5 h (Fig. 11a) Vertical sections reveal that up to 25% of the initial concentration reaches above 10 km in the western portion of the MCS (Fig. 12a) and up to 50% in the eastern portion (Fig. 13a) The experiments also show near-surface tracer at upper levels in the eastward-propagating part of the MCS (Figs. 11b–d and 13b–d), but in contrast, they have few to none on the western, back-building flank, and the convection there is generally weaker (Figs. 12b–d).

Fig. 11.
Fig. 11.

Vertical velocity (shaded) and near-surface tracer concentration (the 1% contour is shown) at km and h in (a) the control simulation, (b) LOWDRY, (c) LOWDRY_SHALLOW, and (d) LOWCOOL. The locations of the vertical sections shown in Fig. 12 (solid) and Fig. 13 (dashed) are also shown.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Fig. 12.
Fig. 12.

North–south vertical section through the convective line at h in (a) the control simulation, (b) LOWDRY, (c) LOWDRY_SHALLOW, and (d) LOWCOOL. Shown are concentration (color shading) of passive tracer released from the lowest 750 m, vertical velocity (contoured as in Fig. 6), and potential temperature perturbation (−1 and −0.5 K contours in blue). The locations of the vertical sections are shown by the solid lines in Fig. 11; all panels show an average of these fields over an area 5 km on either side of the respective line.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Fig. 13.
Fig. 13.

As in Fig. 12, but for vertical sections through the eastern portion of the MCS. The locations of the vertical sections are shown by the dashed lines in Fig. 11; all panels show an average of these fields over an area 5 km on either side of the respective line.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Figure 12 also shows differences in the low-level temperature field on the western flank of the MCS between the control simulation and the moisture sensitivity experiments. In the control simulation, the signature of the low-level gravity wave discussed previously is apparent, whereas the moisture sensitivity experiments show a near-surface cold pool that is spreading southward. The development of stronger cold pools with drier near-surface air is unsurprising considering the increased evaporation of rain in that layer that occurred prior to t = 7 h, as discussed above. Indeed, among the moisture sensitivity experiments, the cold pool across the entire MCS is weakest and slowest to develop in the control simulation (Fig. 14a) and strongest in LOWDRY (Fig. 14b), with LOWDRY_SHALLOW falling in between (Fig. 14c). Furthermore, once a cold pool begins to develop and move away from its convective source, it generally becomes the dominant lifting mechanism, overwhelming the gravity wave signal. The delay of cold pool development in the control run allows for the lifting to persist along the nearly stationary wave and for the wave to be reinforced in the same location, amplifying the rainfall differences between the simulations.

Fig. 14.
Fig. 14.

As in Fig. 10, but for potential temperature perturbation on the lowest model level, and the averaging band in the y direction is 120 km wide. Only hours 4–9 are shown.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

In the eastward-propagating convection, there are more similarities among the simulations, with near-surface air being ingested into each of the convective systems (Fig. 13). In all of the simulations, there is gradual ascent along the spreading low-level gravity wave suggested by the negative at approximately 1 km and then more focused ascent at the leading edge of the developing cold pool. The cold pool—which has a depth of approximately 2 km and a large horizontal extent in the control run, LOWDRY, and LOWDRY_SHALLOW—is the dominant lifting mechanism in this part of the MCS. Based on the much larger concentrations of near-surface tracer in Fig. 13 compared with Fig. 12, it appears that the mature cold pool is much more effective at lifting near-surface air than the low-level gravity wave found on the western flank of the MCS, which has a smaller horizontal and vertical extent.

Hovmöller diagrams confirm that the characteristics described above are representative of the full life cycle of the MCS. Although all simulations show near-surface air rising in the eastward-propagating portion of the convective system (Fig. 15), there are major differences on the western, slower-moving flank of the MCS. Relatively large amounts of tracer are consistently ingested into the convection in the control simulation (Fig. 15a), but little to no tracer is seen in this area in LOWDRY_SHALLOW and LOWDRY (Figs. 15b,c). If the fate of the passive tracer released within the layer with the most CAPE (from 1 to 2.5 km above the surface) is analyzed, it shows that in the control and LOWDRY simulations, similar fractions of air from this layer rise within deep convection (Fig. 16). The concentration of elevated tracer is considerably larger than that from the near-surface layer (cf. Figs. 16b,d and 12a,b), confirming that the convection is mainly, but not completely, elevated.

Fig. 15.
Fig. 15.

As in Fig. 10, but for concentration at km of passive tracer released from the lowest 750 m. Only hours 6–9 are shown.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

Fig. 16.
Fig. 16.

(a),(c) As in Figs. 15a and 15b, but for concentration at km of passive tracer released between and 2.5 km. (b),(d) As in Figs. 12a and 12b, except the shaded field is concentration of tracer released between and 2.5 km.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

The results showing large differences in the behavior of near-surface air between the control simulation and those with drier low levels motivated an additional experiment in which the near-surface layer was cooled slightly (the LOWCOOL experiment outlined in section 2). The purpose of this experiment was to further test the hypothesis that the stability of air near the surface is the primary reason for these differences. As shown above, LOWCOOL also had substantially less precipitation than the control simulation (Fig. 7), as well as reduced upward mass flux. As with the LOWDRY simulation, there is little back-building convection in the LOWCOOL MCS (Fig. 10), although in LOWCOOL this cannot be attributed to changes to the cold pool, since there is effectively no cold pool until very late in the model integration (Fig. 14d). Analysis of the passive tracer released in the near-surface layer shows that essentially none of this tracer is ingested into deep convection on the western flank of the simulated MCS (Figs. 12d and 15d). There are, however, subtle differences in the structure of the low-level gravity wave in LOWCOOL compared with the control simulation. With the more stable near-surface temperature profile, the effects of the wave are confined to a shallower layer that does not extend all the way to the surface (e.g., Fig. 12 shows that the θ′ = −0.5-K contour reaches the surface in the control run but remains above 0.5 km in LOWCOOL.) Closer examination reveals that, just prior to convection initiation, in LOWCOOL (Fig. 17b) the vertical displacement of isentropes (i.e., the magnitude of θ′) in the wave is also muted such that near-surface air parcels are lifted less than in the control run (Fig. 17a), despite requiring more displacement to reach their LFCs. Thus, surface-based convection is inhibited in LOWCOOL. The influence of this modification to the low-level wave is not possible to completely separate from the effect of the reduced near-surface stability on the development of convection in LOWCOOL, and a full analysis of the reasons for these differences is beyond the scope of this study. Nonetheless, the fact that LOWCOOL in many ways behaves similarly to LOWDRY demonstrates that the stability below the most unstable layer is more important to determining the distribution of heavy rainfall than is the low-level relative humidity.

Fig. 17.
Fig. 17.

Vertical section at 7 h of CAPE for parcels lifted from each level (shaded), isentropes at (solid black) and h (dashed black; , contoured every 1 K), potential temperature perturbation (thick blue contours every 0.25 K; only negative values contoured), and vertical velocity (the 1 m s−1 contour is shown in red) for (a) control and (b) LOWCOOL. The locations of the sections are as in Figs. 12a and 12d.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

In total, these analyses demonstrate several reasons why the accumulated precipitation at the surface is strongly sensitive to the moisture profile in the near-surface layer. There is a direct effect, whereby increased evaporation of rain in the subcloud layer leads to less accumulated precipitation in the experiments with dry low levels, but this effect is only important early in the simulated MCS life cycle and is not the primary cause of the rainfall differences. However, this increased evaporation does lead to stronger near-surface cold pools in the experiments with drier air near the surface. In this environment, a stronger cold pool disrupts the lifting along the low-level gravity wave that develops on the upstream flank of the MCS in the control simulation by moving the lifting mechanism away from convection. And even more importantly, when near-surface air has more moisture and thus more CAPE, less CIN, and a lower LFC—even when the CIN is relatively large and the convection is primarily elevated—there is more back-building (slow moving) convection, the convection is more intense, and the accumulated rainfall is greater. These differences are summarized schematically in Fig. 18.

Fig. 18.
Fig. 18.

Schematic depiction of the key processes responsible for rainfall differences between (a) the control simulation and (b) the experiments with a slight reduction in near-surface moisture in the initial condition. Both (a) and (b) depict a north–south vertical section through the western portion of the mature MCS. Near-surface and elevated air parcel trajectories are shown by thick black arrows; a gust front is shown by the blue curve; and the LFCs for elevated and surface-based parcels are denoted by LFCelev and LFCsfc, respectively.

Citation: Journal of the Atmospheric Sciences 72, 6; 10.1175/JAS-D-14-0389.1

These results offer further support for the idea that convective systems that develop in an environment with a stable layer near the surface may not necessarily be completely elevated. Similar to the results of Parker (2008), Nowotarski et al. (2011), and Billings and Parker (2012), these simulations demonstrate that air within this stable layer can still play an important part in the convective system despite the stability if it has sufficient CAPE. In the numerical experiments presented here, the distinction between surface-based and elevated convection is not merely academic but has a profound effect on whether back-building convection develops, on the intensity of the convective system, and ultimately on the amount of rain that falls at the surface. Considering that detailed observations of moisture above the surface are sparse in space and time, these results suggest an important limitation on the predictability of extreme precipitation.

4. Conclusions

This study uses a series of numerical simulations of convective systems that develop within mesoscale forcing for ascent to examine the sensitivity of precipitation accumulation to small changes in the vertical profile of moisture in the environment. In particular, slightly drier air is introduced into the initial condition in the near-surface stable layer without altering the CAPE, CIN, or moisture content of the unstable layer above. This decreases the vertically integrated water vapor by 1% or less but leads to a much larger reduction in accumulated precipitation at the surface. Some of this rainfall reduction can be attributed to increased evaporation in the subcloud layer, but a more important factor is that the convection is more intense, and there is much more back building of convection, in the control simulation. Examination of passive tracer and parcel trajectories reveals that, in the control simulation, near-surface air ascends into the deep convection even though it has considerable CIN. In contrast, with a drier near-surface layer (and accordingly less CAPE and even more CIN), little to no near-surface air is drawn into the simulated convective system, and back building is limited. Convectively generated cold pools are also stronger in the experiments with drier air near the surface, which further disrupts the development of back-building convection, whereas back-building convection continues along a low-level gravity wave in the control simulation. The result is that, in the experiments with drier air near the surface, rainfall amounts are reduced both at the location of the local maximum (because of the reduction in slow-moving, back-building convection) and over the entire model domain (because the convection is weaker overall).

This study has only addressed a limited part of the environmental parameter space in which heavy-rain-producing convective systems form, and future work will be undertaken to examine the sensitivity of rainfall accumulation to a broader range of modifications to the initial conditions. It is also difficult with the present simulations to quantify whether the participation of surface-based updrafts in the back-building convection contribute to more rainfall by virtue of being surface based or because they simply provide a greater opportunity for a greater number strong updrafts to develop. These questions could be addressed with controlled numerical experiments. In addition to environmental influences, the sensitivity of these results to the method for parameterizing cloud microphysics should also be explored. Finally, a set of carefully designed experiments within this semi-idealized modeling framework using a combination of variations in the environmental initial condition, the strength of the imposed mesoscale forcing, and the cloud microphysical parameterization would yield a more thorough understanding of the predictability of heavy rainfall in elevated MCSs.

Acknowledgments

Discussion with and suggestions from John Peters, Sue van den Heever, Stacey Hitchcock, and Chris Davis led to valuable improvements to this manuscript. Thanks go to George Bryan for supporting and answering questions about CM1 and to Matt Parker and Adam French for providing code to assist in analyzing CM1 simulations. Three anonymous reviewers offered very insightful and helpful suggestions that improved the quality of the manuscript. This research was supported by National Science Foundation Grant AGS-1157425.

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