structural and environmental characteristics of

Structural and Environmental Characteristics of Extratropical Cyclones that CauseTornado Outbreaks in the Warm Sector: A Composite Study

EIGO TOCHIMOTO AND HIROSHI NIINO

Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan

(Manuscript received 9 January 2015, in final form 27 November 2015)

ABSTRACT

The structural and environmental characteristics of extratropical cyclones that cause tornado outbreaks

[outbreak cyclones (OCs)] and that do not [nonoutbreak cyclones (NOCs)] are examined using the Japanese

55-year Reanalysis dataset (JRA-55). Composite analyses show differences between OCs and NOCs: for

OCs, storm relative environmental helicity (SREH) and convective available potential energy (CAPE) are

notably larger, and the areas in which these parameters have significant values are wider in the warm sector

than they are for NOCs. The larger CAPE inOCs is due to larger amounts of low-level water vapor, while the

greater SREH is due to stronger low-level southerly wind.

The composite analyses for environmental fields defined by 20-day means suggest that environmental

meridional flows have the potential to advect large amounts of warm and moist air northward, creating at-

mospheric instability in the troposphere that contributes to the occurrence of a tornado outbreak.A piecewise

potential vorticity (PV) diagnosis shows that low- to midlevel PV anomalies are the main contributor to the

difference in the low-level winds between OCs and NOCs, whereas upper-level PV anomalies make only a

minor contribution.

An examination of the structures of the extratropical cyclones and the upper-level jet stream suggests that

the difference in the low-level winds between OCs and NOCs is due to differences in the structure of the jet

stream. The OCs develop when the jet stream displays larger anticyclonic shear. This causes a more merid-

ionally elongated structure of OCs, resulting in stronger low-level winds in the southeastern quadrant of the

cyclones.

1. Introduction

Typical springtime tornado outbreaks in the United

States (e.g., Carr 1952; Fujita et al. 1970; Galway 1975,

1977; Grazulis 1993) are associated with several synoptic

features: extratropical cyclones (ECs), frontal systems,

and upper-level troughs together with their associated

jet stream. For instance, from 3 to 11 May 2003, an ex-

tended outbreak occurred across the central and eastern

United States in association with ECs and caused 41

fatalities, 642 injuries, and ;$829 million (U.S. dollars)

in damage (Hamill et al. 2005). This outbreak occurred

in the warm sector of the ECs where atmospheric in-

stability and vertical shear are strong. Despite the

recent availability of long-term reanalysis data and

sophisticated numerical simulations, it is still not fully

understood how synoptic environments cause tornado

outbreaks.

Numerous studies have investigated the relationships

between synoptic fields and tornado outbreaks (e.g.,

Miller 1972; Johns and Doswell 1992; Stensrud et al.

1997; Roebber et al. 2002; Gaffin and Parker 2006;

Corfidi et al. 2010). Uccellini and Johnson (1979)

performed a case study of a severe weather outbreak

over Indiana and Ohio on 10–11 May 1973. They

pointed out that the coupling between upper-level and

low-level jet streaks influenced the development of se-

vere convective storms. Using a mesoscale model,

Stensrud et al. (1997) examined the effectiveness of

convective weather parameters in distinguishing torna-

dic from nontornadic thunderstorms for nine severe

weather episodes in the United States. They concluded

that storm-relative environmental helicity (SREH;

Davies-Jones et al. 1990) is useful for determining re-

gions where supercell storms are likely to occur.

Corresponding author address: Eigo Tochimoto, Atmosphere

and Ocean Research Institute, The University of Tokyo, 5-1-5,

Kashiwanoha, Kashiwa, Chiba 277-8564, Japan.

E-mail: [email protected]

MARCH 2016 TOCH IMOTO AND N I I NO 945

DOI: 10.1175/MWR-D-15-0015.1

� 2016 American Meteorological SocietyUnauthenticated | Downloaded 12/28/21 02:25 AM UTC

mailto:[email protected]

Roebber et al. (2002) showed that upper-level potential

vorticity (PV) influenced the occurrence of a tornado

outbreak event on 3–4 May 1999. These previous stud-

ies, however, are mainly individual case studies.

In recent studies, statistical analyses have been per-

formed on the relationships between tornado out-

breaks and synoptic features (Mercer et al. 2009, 2012;

Shafer et al. 2009, 2010b). Shafer et al. (2009) per-

formed numerical simulations of 50 tornadic outbreaks

(TOs) and 50 primarily nontornadic outbreaks (NTOs)

associated with a few tornadoes, convective wind gusts,

and hail to investigate the differences in prevailing

synoptic features between TOs and NTOs. They

showed that dynamical parameters, including vertical

wind shear and SREH, are most useful for dis-

tinguishing between the outbreak types. Mercer et al.

(2012) classified synoptic patterns for outbreaks into

several types and identified synoptic-scale differences

between TOs and NTOs: TOs are accompanied by

stronger upper-level troughs and low-level thermal

advection than NTOs. They also emphasized the im-

portance of SREH and vertical shear in the develop-

ment of TOs. These studies demonstrated that

synoptic-scale conditions play a substantial role in the

occurrence of tornado outbreaks.

The present study focuses on ECs, which are one of

the primary synoptic features leading to tornado out-

breaks. Figure 1 shows a synoptic pattern that typically

results in tornado outbreak (e.g., Newton 1967). This

pattern is similar toMiller’s (1972) type-B pattern and is

characterized by strong progressive ECs. An upper-level

jet streak, located near the center of the EC, strengthens

vertical shear and advects cool, dry air into the upper

andmiddle troposphere, while low-level southerly winds

advect warm and moist air into the warm sector,

creating a region of convective instability. As a result,

the warm sector becomes a favorable region for tornado

outbreak (e.g., Johns and Doswell 1992). Although

earlier studies have pointed out that many tornado

outbreaks are accompanied by ECs with an upper-level

trough and a jet streak (e.g., Roebber et al. 2002; Lee

et al. 2006; Corfidi et al. 2010), not all ECs produce

tornado outbreaks. Thus, the differences in the structure

and environment of ECs that cause and do not cause

tornado outbreaks remain poorly understood.

In the present study, we use composite analyses of

reanalysis data between April and May from 1995 to

2012 in the United States to investigate differences in

the structure and environment between ECs that cause

tornado outbreaks and those that do not, and we con-

sider the physical mechanisms for the differences. The

remainder of this paper is organized as follows. The

analysis methods are described in section 2, the results

are outlined in section 3, a discussion is presented in

section 4, and finally a summary is given in section 5.

2. Methodology

a. Dataset and detection of ECs

We used Japanese 55-year Reanalysis data (JRA-55;

Ebita et al. 2011; Kobayashi et al. 2015) that provides

6-hourly gridpoint values with a resolution of 1.258 3 1.258at 37 levels. The vertical grid spacing is 25 hPa from 1000

to 750 hPa, 50 hPa from 750 to 250hPa, and 25hPa from

250 to 100 hPa. To calculate SREH, meridional and

zonal components of winds are interpolated linearly to

height levels at 250-m intervals.

The ECs are detected by applying the tracking algo-

rithm of Hodges (1994, 1995, 1999) to 6-hourly relative

vorticity at 900 hPa, where the relative vorticity is

truncated to T42 horizontal resolution to focus on

synoptic-scale cyclones (Jung et al. 2012; Yanase et al.

2014). Hodges’s tracking algorithm is used in many

studies of ECs (e.g., Hoskins and Hodges 2002;

Bengtsson et al. 2006; Hodges et al. 2011).Moreover, the

algorithm using 900-hPa vorticity can detect ECs rea-

sonably well (Yanase et al. 2014). The tracking method

is applied to the region 308–47.58N, 1208–408W. Tornado

data are taken from the Severe Weather Database

produced by the StormPredictionCenter at NOAA.We

only use the data after the year 1995 in the majority of

the present analyses as enhanced Fujita (EF0) tornadoes

drastically increased after 1995 and there is no obvious

trend from 1995 to 2012 (not shown).

b. Categorization of tornadic ECs

In what follows, a tornadic EC (TEC) is defined as

an EC that is accompanied by a tornado within 158 inlongitude and latitude from the center of the EC, and

within 3 h of the 6-hourly analysis time of JRA-55. We

will be focusing on ECs that cause many tornadoes in

the warm sector, because the structural and environ-

mental characteristics of ECs that cause many tor-

nadoes in the frontal zones may differ. Thus, we

exclude tornadoes occurring in regions with a tem-

perature gradient exceeding 2K (100 km)21. If ECs

and their associated fronts are assumed to have

speeds of;20–50 km h21, they move 60–150 km in 3 h.

Thus, the locations of tornadoes would not depart

much from the frontal zones defined by the present

study in the JRA-55, which has a horizontal resolu-

tion of 120 km. Note that the above criterion may not

exclude tornadoes associated with drylines or wind

shift lines that are not accompanied by large tem-

perature gradients.

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The TECs obtained based on the above criteria are

then categorized into two groups according to the

number of associated (nonfrontal) tornadoes occur-

ring between 0900 UTC on one day and 0900 UTC on

the following day. If the number is $15, the TEC is

called an outbreak cyclone (OC). If it is #5, the TEC

is called a nonoutbreak cyclone (NOC). These two

categories of TECs will be studied in detail. The

strength of tornadoes is not considered in the present

study; the purpose of our study is to identify differ-

ences in the structure and environment between ECs

that cause many tornadoes and those that cause a

small number of tornadoes regardless of the tornado

strength. It may be argued that the choice of 15 as a

threshold is somewhat arbitrary. However, we have

checked the sensitivity to the threshold by changing

the criterion to .10 and .20 for OCs and have con-

firmed that the results do not change qualitatively. In

appendix C we show that the 14 EC cases fall below

the 15-tornado OC criterion solely because of the

exclusion of tornadoes in the frontal zones. The tor-

nadoes associated these cases occur mainly in the

warm frontal zones and the warm sector, with very

few along the cold fronts. Although the composite

structures of the 14 ECs that cause tornadoes in the

warm frontal zones exhibit interesting features that

are different from those for OCs, their detailed study

will be left for future work.

c. Composite analysis

Composite analysis is performed to identify differ-

ences in the structural and environmental fields between

OCs and NOCs. In the analysis, physical variables are

superposed with respect to the TEC center, which is

defined by the vorticity maximum at 900 hPa. The time

at which the largest number of tornadoes occurred

within 24h is defined as the key time (KT), where the

time will be given in units of hours. To exclude weak

ECs, only TECs with a 900-hPa relative vorticity

of $3 3 1025 s21 at KT are analyzed. In addition, only

TECs with a central sea level pressure (SLP) lower than

1005hPa are superposed to investigate the differences

betweenOCs andNOCs, as it is desirable to examine the

differences for TECs of similar intensity. The sensitivity

of the results to the vorticity and SLP criteria is dis-

cussed in appendix A.

The numbers of OCs and NOCs thus selected are 55

and 41, respectively. The composite analysis is per-

formed fromKT2 12 to KT. Although we use a simple

FIG. 2. Geographical distribution of the centers of outbreak cyclones (OCs; red circles) and

nonoutbreak cyclones (NOCs; blue circles) at key time (KT) defined in the text.

FIG. 1. A typical synoptic pattern for tornado outbreaks

(Newton 1967).



average technique to obtain the composite field, it will

be shown that common and important characteristics

of OCs and NOCs are obtained. Permutation testing

(Efron and Tibshirani 1993), which is a statistical

technique to determine if the means of two distribu-

tions are different, is performed on the meteorological

variables. In what follows, p values of 0.1, 0.05, and

0.01 correspond to the 90%, 95%, and 99% confidence

levels, respectively. This test is superior to the t test in

the present study as the distribution of the variables is

unknown and the t test requires the variable to

follow a normal distribution (Mercer et al. 2009).

d. Mesoscale environmental parameters

Of the many dynamic and thermodynamic parame-

ters that characterize tornadic storm environments,

SREH, convective available potential energy (CAPE),

and energy helicity index (EHI) are used in the present

study. SREH gives a potential for rotational charac-

teristics of convective storms (Davies-Jones et al.

1990), and is defined as

SREH5

ðh0

k3›V

›z� (V2 c) dz , (2.1)

whereV is the environmental wind vector, c is the storm

motion vector, h is an assumed inflow depth, and k is a

unit vertical vector. We adopt h5 1 km and estimate c

using a method developed by Bunkers et al. (2000).

CAPE is defined as the positive buoyant energy

available to a parcel that rises from its initial height level

to the level of neutral buoyancy (LNB), as follows:

CAPE5 g

ðLNB

LFC

u(z)2 u(z)

u(z)dz , (2.2)

where u(z) is the potential temperature of the parcel,

u(z) is the potential temperature of the environment, g is

the acceleration due to gravity, and LFC is the level of

free convection.

Furthermore, EHI (Hart and Korotky 1991) defined

by

EHI5SREH3CAPE

1:63 105(2.3)

is used as an environmental parameter. Rasmussen

(2003) suggested that a formulation of EHI as a combi-

nation of the 0–1-km SREH and CAPE is the best dis-

criminator between tornadic and nontornadic storms.

Shafer et al. (2009) also found that EHI is a useful pa-

rameter for distinguishing outbreak types.

TABLE 1. Mean latitude and longitude of the locations of OCs

and NOCs centers at KT, and standard deviation of their latitude

(slat) and longitude (slon).

Mean lat slat Mean lon slon

OC 40.5 3.5 268.3 5.6

NOC 40.7 4.3 269.5 8.2

FIG. 3. Distribution of tornadoes (red dots) for (a) OCs and (b) NOCs at KT. Grayscale shows the composite vorticity

(s21) and contour lines show geopotential height (m) at 900 hPa. The centers of the TECs are located at (0, 0).



e. Piecewise PV inversion

A piecewise PV inversion (Davis and Emanuel 1991)

is used to investigate the relative contribution of indi-

vidual components of the PV anomaly to the TEC

structure. Ertel (1942) defines PV as

q51

rh � =u , (2.4)

where q is PV, r is the density of air, h is the absolute

vorticity, u is potential temperature, and = is the three-

dimensional vector differential operator.

A nonlinear balance equation, derived by Charney

(1955), is used to invert Ertel’s PV. Assuming a hydro-

static balance and the irrotational wind component to be

much smaller than the nondivergent component, the

divergence equation and Ertel’s PV can be rewritten in

spherical coordinates as

=2F5= � f=C12

a4 cos2f

"›2C

›l2

›2C

›f22

�›2C

›l›f

�2#,

(2.5)

and

q5gkp

p

�( f 1=2C)

›2F

›p22

1

a2 cos2f

›2C

›l›p

›2F

›l›p

21

a2›2C

›f›p

›2F

›f›p

�, (2.6)

respectively, where F is the geopotential, C is the non-

divergent streamfunction, l is longitude, f is latitude, a is

the radius of the earth,k5R/cp is thePoisson constant, f is

theCoriolis parameter,p is pressure, andp5 (p/p0)k is the

Exner function. This study assumes homogeneous lateral

boundary and Neumann-type upper and lower boundary

conditions (Davis and Emanuel 1991). At each grid point,

PV anomalies are defined as deviations from the basic PV

field, which is defined by the 20-day running mean.

Equations (2.5) and (2.6) are linearized with respect to PV

field anomalies as described byDavis andEmanuel (1991).

In the present study, PV anomalies are divided into

three components: the upper-level component (UPV)

between 500 and 1hPa, the lower boundary component

(LBT) at 987.5 hPa, and the lower to midtroposphere

perturbation component (MPV) between 975 and

550 hPa. Potential temperature anomalies in the upper

and lower boundaries are included in UPV and LBT,

respectively, because they are equivalent to PV anom-

alies (Bretherton 1966).

3. Results

a. Distribution of TECs

Most OCs and NOCs occur between the central plains

and the East Coast. However, OCs are concentrated

more in the central plains (Fig. 2). The difference is

presented quantitatively in Table 1. Although the mean

latitude and longitude of OCs are similar to those of

FIG. 4. SREH (color shading; m2 s22) at KT for (a)OCs and (b) NOCs, and (c) p values for difference in SREHbetweenOCs andNOCs

(color shading). Black solid lines in (a) and (b) indicate contours of geopotential height (m) at 900 hPa, and those in (c) indicate the

differences in SREHbetweenOCs andNOCs. The contour interval in (a) and (b) is 20m, and that in (c) is 20m2 s22. Only contours above

20m2 s22 are shown in (c).



NOCs, the standard deviation is larger in NOCs. Thus,

the locations of NOCs are more dispersed than those of

OCs, especially meridionally.

b. Composite analysis

1) DISTRIBUTION OF TORNADOES

Composite geopotential height and vorticity fields for

OCs andNOCs, together with tornado locations relative

to the cyclone center at KT, are shown in Fig. 3. Most

tornadoes associated with OCs occur in the southeast

quadrant, corresponding to the warm sector, and par-

ticularly within 58 latitude and longitude from the OC

center (Fig. 3a). Conversely, tornadoes associated with

NOCs appear to occur more sporadically (Fig. 3b).

However, a kernel density estimate analysis (Wilks

2006) indicates that there is no notable difference in the

spatial density distribution of tornadoes between OCs

FIG. 5. Horizontal distribution of CAPE (m2 s22) for (left) OCs, (middle) NOCs, and (right) p values for the difference between OCs

and NOCs at (a)–(c) KT 2 12, (d)–(f) KT 2 6, and (g)–(i) KT. Contours in (a) and (b) indicate geopotential height at 900 hPa (m), and

those in (c) differences in CAPE between OCs and NOCs. The contour interval in (a) and (b) is 20m, and in (c) it is 200m2 s22.



and NOCs (not shown). Thus, the apparently sporadic

distribution is due to the small number of tornadoes

associated with NOCs, which reflects a relatively less

favorable environment for tornadogenesis, as wewill see

in the next section in more detail. The area of vorticity

exceeding 1 3 1025 s21 extends farther southward for

OCs than for NOCs.

2) ENVIRONMENTAL PARAMETERS

Mesoscale environmental parameters from the com-

posite fields are examined to identify significant envi-

ronmental conditions that lead to tornado outbreaks.

Significant differences exist between OCs and NOCs in

both dynamic and thermodynamic parameters. A region

of large SREH, exceeding 150m2 s22 and extending at

least 500km to the south, exists in the east-southeast

quadrant of theOC centers (Fig. 4a). SREH for NOCs is

notably smaller (Fig. 4b). Furthermore, the area in

which the SREH exceeds 100m2 s22 is narrower for

NOCs. The differences in SREH values between OCs

and NOCs are 100m2 s22 at most and the area of sta-

tistical significance exceeding the 95% or 99% confi-

dence level extends over the east-southeast quadrant of

the OC centers (Fig. 4c). Thus, OCs provide a dynami-

cally more favorable environment for supercell and

tornado formation than NOCs. These results are con-

sistent with studies using the areal coverage of envi-

ronmental parameters (e.g., Hamill et al. 2005; Shafer

et al. 2009, 2010a).

Notable differences also exist in the distribution of

CAPE between OCs and NOCs (Fig. 5). For OCs, at

KT 2 12 the area in which CAPE exceeds 600m2 s22

extends southward from the center (Fig. 5a), while for

NOCs there is no region with significant CAPE ex-

ceeding this value (Fig. 5b). CAPE for both OCs and

NOCs increases with time. Since KTs in most of the

OCs and NOCs occur at 1800 or 0000 UTC, this sig-

nificant increase in CAPE from KT 2 12 to KT 2 6 or

KT is likely to be caused by daytime solar radiative

heating. At KT the maximum value of CAPE is

;1000m2 s22 for OCs but ,400m2 s22 for NOCs. The

difference is statistically significant (exceeding 99%

confidence level) in the south-southeast region of the

cyclone center from KT 2 12 to KT. The values of the

difference exceed 500m2 s22 at KT 2 6 and KT. Thus,

regions of tornadogenesis in OCs are thermodynami-

cally unstable and have the potential for strong con-

vection to develop.

Differences in CAPE betweenOCs andNOCs appear

to be partly explained by low-level water vapor fields

(Fig. 6). In OCs, low-level specific humidity of 0.012–

0.014 kgkg21 intrudes 200km south of the cyclone cen-

ter. In NOCs this value of low-level humidity occurs

1000km south of the cyclone center. Although more

water vapor is found between 2000 and 2500 km south of

the cyclone center in OCs, the CAPE is largest at

;1000km south of the center of OCs. Figures 7a and 7b

show vertical cross sections of specific humidity and

FIG. 6. Specific humidity (color shading; kg kg21) at 950 hPa and CAPE (contours; m2 s–2) at KT for (a) OCs and

(b) NOCs. The lines A–A0 and B–B0 show the location of the cross sections presented in Fig. 7.



temperature at;1000 and 2500km south of the cyclone

center (lines A–A0 and B–B0 in Fig. 6), respectively.

Specific humidity near the surface along B–B0 exceeds0.014 kgkg21, which is higher than along A–A0. Thenear-surface temperature 200–1000km west of the cy-

clone center is 300K for both cross sections, whereas the

temperature at 500 hPa along A–A0 is ;5K colder than

along B–B0. Thus, the largest CAPE is located approx-

imately 1000km south of the cyclone center near the line

A–A0.The difference in CAPE between OCs and NOCs

could also be caused by differences in temperature

stratification. To explore this possibility, vertical cross

sections along the same latitude as A–A0 (Fig. 7c) areplotted of the difference in temperature between OCs

and NOCs, and the temperature for NOCs. The tem-

perature at midlevel is warmer for OCs than for NOCs.

Thus, the larger CAPE for OCs near A–A0 is not ex-

plained by the temperature stratification, which is more

unstable for NOCs.

The overlap of regions with large SREH andCAPE in

OCs (Figs. 4 and 6) suggests that OCs are indeed more

favorable than NOCs for severe storm development.

This may also be explained by the difference in distri-

butions of EHI between OCs and NOCs. The region

with EHI. 0.1 is much wider for OCs than for NOCs; in

fact a region with EHI. 0.4 for OCs exists southeast of

the cyclone center (Fig. 8).

3) STRUCTURE OF TECS

Composite structures of TECs are now examined to

understand the differences in convective parameters

between OCs and NOCs (Fig. 9). The geopotential

height and PV for OCs are more meridionally elongated

at both KT2 12 and KT than those for NOCs, resulting

in a stronger zonal pressure gradient and resultant me-

ridional geostrophic wind. The low-level PV for OCs

differs from that for NOCs at a statistically significant

level (exceeding 95%) in the south-southeast region of

the cyclone center. Although the distributions of p

FIG. 7. Vertical–zonal cross sections of specific humidity (color shading; kg kg21) and temperature (contours; K) along (a) A–A0 and(b) B–B0 (Fig. 6) at KT. (c) The vertical–zonal cross section of differences in temperature between OCs and NOCs, and temperature for

NOCs along the latitude of A–A0.



values are noisy, we think that those around the warm

sector are meaningful since they correspond to the region

in which the PV associated with the EC is large, while the

distributions of p values in other regions do not show a

good correspondence. Similar differences in the warm

sector are also found at other low levels (e.g., 875 and

850hPa).

Meridional winds at 900hPa for KT 2 12 and KT are

presented in Fig. 10. A low-level southerly wind de-

velops to the southeast of the OC’s center, with maxi-

mum speeds in excess of 15ms21 at KT, which is

reflected in the distribution of SREH (Fig. 4). In con-

trast to OCs, NOCs are characterized by weaker winds

of about 10–12m s21 with a narrower meridional extent

in the south to east-southeast region. The difference in

the southerly wind exceeds 4ms21 and the difference is

statistically significant at the 99% confidence level

over a wide area (Fig. 10c). This suggests that OCs have

stronger low-level vertical shear, which contributes to

larger SREH, and could potentially transport a larger

amount of water vapor from the south.

To understand the relationship between low-level

meridional wind and SREH, the equation for SREH is

reconsidered. The right-hand side (rhs) of Eq. (2.2) can

be written as follows:

SREH5

ðh0

�(y2 c

y)du

dz2 (u2 c

x)dy

dz

�dz , (3.1)

where cx(cy) is the zonal (meridional) component of the

storm motion. The first (second) term on the rhs of Eq.

(3.1) is associated with vertical shear of the zonal (me-

ridional) wind. Figure 11 shows the magnitude of each

term for OCs and NOCs. For both OCs and NOCs, the

term associated with vertical shear of the meridional

wind is the primary contributor to the SREH, indicating

that the differences in SREH between OCs and NOCs

are primarily due to meridional wind speeds at low

levels.

Now we examine upper-level features of TECs

(Fig. 12). At KT 2 12, high PV (.3.5PVU) is located

slightly to the west of the OCs’ center. With time, PV

increases to 4PVU and intrudes south-southeastward.

For NOCs, in contrast, high PV (.4PVU) is located

approximately 1000km to the northwest of the center at

KT 2 12. The high PV approaches the NOCs’ surface

center with time, but is still located several hundred

kilometers west of the center at KT, which is again

farther than in OCs. The vertical cross sections of the

difference of PV between OCs and NOCs along the line

C–C0 at KT2 12 and KT are shown in Figs. 12c and 12f,

respectively. The region in which statistical significance

exceeds 95% is found west of the cyclone center in the

upper levels.

Upward motion associated with TECs extends from

the northeast to the southeast region of their centers

(Fig. 13). The peak vertical pressure velocity is located

FIG. 8. EHI (color shading) at KT for (a) OCs and (b) NOCs. Contours indicate geopotential height. The contour

interval is 20m.



to the east of the center for both OCs and NOCs, but

vertical motion in OCs is slightly stronger than in

NOCs. Furthermore, the upward velocity is equal to or

stronger than 20.1 Pa s21 over a wider area for OCs.

The stronger and wider synoptic-scale upward motion

may contribute to destabilization of the lower tropo-

sphere (Markowski and Richardson 2010). As these

differences persist from KT 2 12 to KT (not shown),

OCs have a larger potential for severe convective storm

formation over a wider area. The differences have

statistical significance exceeding the 95% or 99%

confidence level within areas in the warm sector of the

cyclone (Fig. 13c). These areas of upward motion may

be related, in part, to the location of the upper-level

disturbances shown in Fig. 12. As described in Hoskins

et al. (1985), an upper-level PV disturbance induces

upward motion in front of its direction of movement.

Since the upper-level PV is located closer to the warm

sector in OCs than in NOCs, stronger upward motion is

likely to occur over a wider area.

4) ENVIRONMENTAL FIELDS

To examine the environmental fields for both OCs

and NOCs, a 20-day running mean was calculated. A

large-scale high pressure system covers the southeastern

quadrant for both OCs and NOCs (Figs. 14a,b). In the

southwestern quadrant, a large-scale trough extends

southward. Strong south-southwesterly flows are evi-

dent along the western edge of the high and the eastern

edge of the trough; these are stronger forOCs (Figs. 14a,b).

There is more low-level moisture to the south of the

cyclone center for OCs than for NOCs (Figs. 14c,d).

Low-level moist air with humidity exceeding 0.01kgkg21

intrudes from the south to about 350km to the south of

the center for OCs, while it is located about 500km to the

south for NOCs.

FIG. 9. Horizontal distribution of PV (potential vorticity units; 1 PVU 5 1026 K kg21m2 s21) for (left) OCs and (middle) NOCs, and

(right) p values for the difference in PV betweenOCs and NOCs. Contours in (a) and (b) indicate geopotential height at 900 hPa (m), and

those in (c) indicate differences in PV betweenOCs and NOCs (PVU). The contour interval in (a) and (b) is 20m, and in (c) it is 0.1 PVU.

(a)–(c) KT 2 12 and (d)–(f) KT.



The horizontal structure of the jet stream at the upper

levels also shows some difference between OCs and

NOCs (Fig. 15). Both OC and NOC centers are located

in the north of the jet streak at KT 2 12; however, the

OCs center is located slightly closer to the exit region of

the jet streak. Between KT2 12 and KT, the OCs move

toward the exit region, whileNOCs remain in themiddle

part of the jet streak. Another important difference

between OCs and NOCs is that the jet streak for OCs

displays greater meridional shear. In particular, anticy-

clonic shear on the southern side of the jet streak is

stronger. The relationship between the meridional

structure of the jet streak and the TECs is discussed in

section 4.

c. Piecewise PV inversion

A piecewise PV inversion (Davis and Emanuel

1991) is performed to identify the relative contribu-

tions of PV anomalies in the UPV, LBT, and MPV

(see section 2e) to the low-level southerly winds that

affect the SREH. Figure 16 compares the ‘‘total

southerly winds,’’ defined as the sum of recovered

meridional winds induced by all of the PV anomalies,

with the southerly wind from JRA-55. Total meridi-

onal winds in the range 10–14m s21 are found in the

east-southeast region of the OCs center and those in

the range 8–10m s21 extend over the east-southeast

region of the NOCs center. Although the total me-

ridional winds are 20%–30% weaker than those in

JRA-55, their horizontal distribution and the quali-

tative differences between OCs and NOCs are similar

to those for JRA-55. Thus, we consider it would be

meaningful to examine the winds induced by each

component of the PV anomaly. It seems that the dif-

ferences in magnitude of total wind recovered from

the PV anomalies and that from JRA-55 are due to the

assumption of the nonlinear balance equation [Eq.

(2.5)], in which irrotational winds are assumed to be

smaller than the nondivergent winds. This nonlinear

balance assumption may become less accurate in cases

when irrotational winds are stronger and they are not

negligible compared with the nondivergent winds.

FIG. 10. As in Fig. 9, but for meridional winds (color shading; m s21) at 900 hPa and the difference in meridional winds (contours; m s21).



The significant differences in the total southerly winds

induced by the total of UPV, MPV, and LBT between

OCs and NOCs are found in the east-southeast region of

the cyclone center (Figs. 17a,b). The southerly winds in

OCs are larger than NOCs and the difference exceeds at

least 3m s21. The differences are statistically significant

at the 95% or 99% confidence level (Fig. 17c).

UPV-induced meridional winds extend from north-

east to southeast of the low center for both OCs and

NOCs (Figs. 17d,e). Induced southerly winds in OCs are

stronger throughout the period KT 2 12 to KT. The

difference is 1–1.5m s21 in the east-southeast region of

the cyclone center. Although there are small areas in this

region with statistical significance exceeding the 95%

level (Fig. 17f), this difference explains only a minor

fraction of the differences in total southerly winds be-

tween OCs and NOCs.

The sum of meridional winds induced by MPV and

LBT is presented in Figs. 17g–i. For OCs, the

southerly winds extending from south to north on the

east side of the center are stronger and cover a me-

ridionally wider area than for NOCs. These stronger

southerly winds are due to the southward elongation

of positive PV at low levels (Fig. 9). The positive PV

anomaly seems to be associated with the cold front

extending south-southwest from the cyclone center.

A considerable fraction of the differences in the

southerly wind seems to be explained by

MPT 1 LBT.

4. Discussion

The present composite analysis indicates that sig-

nificant differences exist between the structures of OCs

FIG. 11. SREH (m2 s22) for (top)OCs and (bottom)NOCs.Only contours above 100m2 s22 are drawn. Color shading

shows the contribution of vertical shear of (a),(c) meridional and (b),(d) zonal winds.



and NOCs, which result in larger SREH for OCs and

smaller SREH for NOCs.Mercer et al. (2009, 2012) and

Shafer et al. (2009, 2010b) also discussed synoptic-scale

structure of dynamic and thermodynamic fields. How-

ever, they were mainly interested in the identification

of the most distinguishing parameters that determine

tornado outbreaks. We have contributed to explicitly

and quantitatively revealing the connection between

EC structures and important parameters for tornado

outbreaks.

Our analyses also identify CAPE as a significant fac-

tor in distinguishing OCs and NOCs, which was not

identified in these previous studies (Monteverdi et al.

2003; Mercer et al. 2009; Shafer et al. 2009, 2010b). The

previous studies such as Mercer et al. (2009, 2012) and

Shafer et al. (2009, 2010b) primarily compared tornadic

and nontornadic outbreaks without concentrating on

the EC environment. As the present study focuses

specifically on the differences between ECs that cause

outbreaks of tornadoes and those that do not, the find-

ings of our study are not necessarily inconsistent with the

previous results.

We would like to emphasize that the differences in

the environmental parameters for composite analyses

between OCs and NOCs are mainly due to the struc-

ture of ECs and depend less strongly on the strength of

the ECs (although we note that all ECs considered in

this study meet a minimum threshold). Although OCs

tend to have lower central pressure than NOCs, the

differences in EC strength between OCs and NOCs

have little impact on the composite fields. This is con-

firmed by a more sophisticated composite analysis in

which weighted averages are used (see appendix A for

more details).

Our results suggest that strong low-level south-

erly winds cause larger SREH and also increase

FIG. 12. Horizontal distributions of PV (PVU) at 250 hPa for (left)OCs and (middle)NOCs, and (right) the vertical–zonal cross sections

of p values for the difference in PV between OCs and NOCs along the latitude of the EC center. Contours indicate (a),(b) geopotential

height at 900 hPa (m) and (c) the vertical cross section of differences in PV between OCs and NOCs (PVU). The contour interval in

(a) and (b) is 20m, and that in (c) is 0.1 PVU. (a)–(c) KT 2 12 and (d)–(f) for KT.



atmospheric instability, through moisture transport.

The piecewise PV inversion indicates that the UPV

disturbances are not the main contributor to the dif-

ferences in the low-level southerly flow (Fig. 4) be-

tween OCs and NOCs. Instead, the difference is caused

mainly by the sum of MPV and LBT anomalies asso-

ciated with EC structure and their accompanying warm

and cold fronts.

These structural differences between OCs and

NOCs are influenced by the upper-level jet stream.

Two paradigms exist regarding the relationship be-

tween the structure of ECs and the jet stream. The first,

suggested by Schultz et al. (1998) and Schultz and

Zhang (2007), is that ECs developing at the exit region

of a jet streak display meridionally elongated struc-

tures, while ECs developing at the entrance region

display zonally elongated structures. Background dif-

fluent (confluent) flow at the exit (entrance) stretches

the ECs and associated fronts meridionally (zonally).

The second paradigm is suggested by several authors

(e.g., Davies et al. 1991; Wernli et al. 1998) who ex-

amined the sensitivity of ECs and surface frontal

structures to background barotropic shear of the jet

stream. When the background shear is anticyclonic,

ECs develop with strong cold fronts, and are meridi-

onally elongated. When the shear is cyclonic, ECs

develop with strong warm fronts having a northwest–

southeast axis.

Clear differences are identified in EC structure and

associated cold fronts between OCs and NOCs.

Figure 18 shows equivalent potential temperature

and frontogenesis associated with deformation fields

at 950 hPa. The OCs display meridionally elongated

pressure fields (Fig. 3) and cold fronts (Fig. 18a),

while those of NOCs display less meridional elon-

gation (Fig. 18b). In contrast, there is no notable

difference between the magnitudes of the de-

formation fields for warm fronts associated with OCs

and NOCs. These features are consistent with pre-

vious studies on the relationship between the struc-

ture of ECs and that of the jet stream (Wernli et al.

1998), which is notably different for OCs and NOCs

in this study. For OCs, the jet stream displays a

meridionally sharper structure with stronger merid-

ional anticyclonic shear on the southern side of the

jet streak. In contrast, in NOCs the jet stream dis-

plays significantly weaker meridional anticyclonic

shear, suggesting that the meridionally elongated

structure and stronger southerly winds in OCs are

caused by the stronger anticyclonic shear of the

jet stream.

There is a possibility that, in the case of stronger

cyclonic shear, a warm front oriented from north-

west to southeast plays an important role in the

formation of severe convection: near the front there

is strong vertical shear and warm moist air is

advected. In addition, low-level convergence at the

front contributes to the initiation of convection. In

the present study, however, the cases associated with

the warm front are not studied in detail since we

focus on the tornadoes in the warm sector (see

appendix C).

FIG. 13. As in Fig. 4, but for (a),(b) vertical pressure velocity at 850 hPa (color shading; Pa s21) at KT and (c) the difference in the vertical

pressure velocity between OCs and NOCs (contours).



Large-scale circulation may also influence the occur-

rence of tornado outbreaks. The south-southwesterly

wind along the western edge of the high pressure system

advects warm moist air northward, forming an unstable

layer in the troposphere.MostOCspropagate through the

southern central plains, while the tracks of NOCs are

more dispersed (Fig. 19). This difference suggests that

most OCs propagate through the region where large

amounts of low-level water vapor intrude from the south

along the high pressure system, resulting in larger CAPE.

5. Summary

The structures and environmental fields of ECs that

affect tornado outbreaks were identified by a composite

analysis of JRA-55 data. The ECs accompanied by 15 or

FIG. 14. Environmental fields of the cyclones: (left) OCs and (right) NOCs. (a),(b) Southerly winds at 900 hPa

(color shading; m s21) and (c),(d) specific humidity at 950 hPa (color shading; kg kg21). Contours drawn at 10-m

intervals indicate the environmental geopotential height (m) at 900 hPa.



more tornadoes were defined as OCs, and those ac-

companied by 5 or fewer as NOCs. Between 1995 and

2012, 55 OCs and 41 NOCs of similar strengths were

selected from ECs in April and May in the United

States. We also examined the relationship between EC

structure and convective parameters.

The structures of OCs and NOCs exhibit clear dif-

ferences: OCs display meridionally elongated struc-

tures, while the NOCs are less meridionally elongated.

Low-level southerly winds are stronger in OCs, facili-

tating the transport of warm moist air into the warm

sector of the EC. Indeed, the highest low-level specific

humidity is found in the south-southeast region of

the OC.

We found significant differences in convective pa-

rameters between OCs and NOCs. In OCs, values of

SREH and CAPE are both larger and significant over a

wider portion of the warm sector than in NOCs. It is

suggested that this larger CAPE associated with OCs is

due to larger amounts of low-level water vapor, while

the larger SREH is due to stronger low-level southerly

winds. Accordingly, EHI, which is a combination of

SREH and CAPE, is also larger for OCs. It is noted that

daytime solar radiative heating may also contribute to

increase the CAPE for both OCs and NOCs.

Upper-level disturbances may also influence the

occurrence of a tornado outbreak by inducing upward

motion in front of the disturbance. The UPV for OCs

is located closer to the center of the surface cyclone

than in NOCs. As a result, the upward motion for OCs

is induced over a wider area in the east-southeast

sector and is likely to play an important role in pro-

viding an environment favorable for severe convec-

tive storms.

FIG. 15. As in Fig. 9, but for environmental wind speed at 250 hPa (color shading; m s21).



We also showed that the large-scale circulation as-

sociated with a high pressure system over the Atlantic

Ocean affects the occurrence of tornado outbreaks.

The large-scale meridional flow transports water va-

por northward, creating unstable layers in the tro-

posphere. This large-scale system has a time scale

longer than that of a typical EC life cycle. Thus, a

tornado outbreak is associated not only with EC

structure but also with large-scale circulation such as

the meridional flows along the edge of the high

pressure system.

A piecewise PV inversion suggested that low-level

southerly flows are associated with EC structures and

accompanying fronts. The sum of MPV and LBT is

the main contributor to stronger southerly winds for

OCs but not for NOCs. The MPV anomalies display

meridionally elongated structures in OCs, consistent

with the meridionally elongated low-level southerly

winds, while UPV anomalies contribute little to dif-

ferences in low-level southerly winds between OCs

and NOCs.

The structures of OCs and NOCs appear to be

related to the strength of anticyclonic shear in the

jet stream. Since the anticyclonic shear in the jet

stream is stronger for OCs than for NOCs, the

structure of the OCs is meridionally more elon-

gated, resulting in stronger southerly wind in the

southeastern quadrant. To assess the relationship

between the EC structure and the meridional shear

of the jet stream in more detail, an idealized nu-

merical experiment is under way, and will be re-

ported elsewhere.

FIG. 16. Total southerly winds at 900 hPa (color shading; m2 s22) and total geopotential height anomaly (con-

tours; m) at 900 hPa for (a) OCs and (b) NOCs. Southerly winds (color shading; m s21) and geopotential height

anomaly (contours; m) for JRA-55 at 900 hPa for (c) OCs and (d) for NOCs.



The present study clarifies important relationships

between the structure of ECs that cause tornado out-

breaks in April and May in the United States and the

environmental fields. Since environmental factors

such as the upper-level jet stream, low-level water

vapor, and temperature fields change seasonally, the

structural characteristics of ECs that cause outbreaks

may also have seasonal variability. A study to examine

the seasonal variability of the structural and environ-

mental characteristics of ECs that cause a tornado

outbreak is being undertaken and will also be reported

elsewhere.

FIG. 17. Southerly winds (m s21) induced by (a) the sum of UPV, MPV, and LBT; (d) by UPV; and (g) the sum of MPV and LBT for

OCs. (b),(e),(h) As in (a),(d),(g), but for NOCs. (c),(f),(i) The p values for difference between OCs and NOCs. Contours indicate

(left),(middle) geopotential height and (right) differences in the southerly winds between OCs and NOCs.



Acknowledgments. We thank Dr. K. I. Hodges of

Analysis of Weather Systems in Climate Models and

Numerical Weather Prediction, University of Read-

ing for providing his tracking tools, and Prof. C. Davis

of MMM, the Earth and Sun Systems Laboratory at

NCAR for providing the program for the piecewise

PV inversion technique. We are also grateful to

Dr. W. Yanase of the Atmosphere and Ocean Re-

search Institute, the University of Tokyo and Dr.

A. Kuwano-Yoshida of the Earth Simulator Center,

Japan Agency for Marine–Earth Science and Tech-

nology for their useful comments. We also would like

to thank Dr. Y. P. Richardson and three anonymous

reviewers for helpful comments, which have greatly

improved the manuscript. This study was supported

in part by JSPS KAKENHI Grant 24244074, and

Field 3, Strategic Programs for Innovative Research,

Ministry of Education, Culture, Sports, Science and

Technology.

APPENDIX A

Sensitivity of the Results to the SLP and VorticityCriteria

As is evident from Fig. 3, the composite central SLP

for OCs is lower than that for NOCs. In this appen-

dix, we first examine the impact of the difference in

FIG. 18. Deformation fields [color shading; K (100 km)21 s21] and equivalent potential temperature (contours; K) at

950 hPa for (a) OCs and (b) NOCs at KT 2 12.

FIG. 19. The EC tracks for (a) OCs and (b) NOCs. Red and blue circles indicate the distributions of ECs at KT.



the composite central SLP between OCs and NOCs

on the overall results in the present study by using the

following weighted-average technique. The OCs and

NOCs are classified into 5 bins based on their central

SLP (980–985, 985–990, 990–995, 995–1000, and

1000–1005 hPa; see Fig. A1). The numbers of OCs

and NOCs contained in bin i (i 5 1, 2, . . . , 5) are

denoted by nOi and nNi, respectively. If a three-

dimensional field of a particular physical variable

around an OC or NOC is denoted by qOij(x, y, z) or

qNik(x, y, z), the composite field of q for OC or NOC

is calculated by

FIG. A1. Scatter diagram between 900-hPa vorticity and sea level pressure (SLP) for the EC

centers. Red diamonds indicate OCs and blue squares NOCs.

FIG. A2. SREH (color shading; m2 s22) and geopotential height (contours; m) for (a) OCs and (b) NOCs obtained by

composite analysis using a weighted-average technique.



qO(x, y, z)5

�4

i51

nOi1 n

Ni

2nOi

�nOi

j51

qOij(x, y, z)

�4

i51

nOi1 n

Ni

2nOi

,

qN(x, y, z)5

�4

i51

nOi1 n

Ni

2nNi

�nNi

k51

qNik

(x, y, z)

�4

i51

nOi1 n

Ni

2

,

where j5 1, 2, . . . , nOi and k5 1, 2, . . . , nNi.

The composite fields of the SREH obtained by this

weighted-average technique for OCs and NOCs

(Fig. A2) turn out to be quite similar to those from the

simple average technique used in section 3 (Fig. 4). Note

that the composite central SLPs of OCs and NOCs in

Fig. A2 are nearly equal. Therefore, the differences in

the SLP do not explain the differences in environmental

parameters between OCs and NOCs.

Figure A1 also indicates that OCs tend to have stronger

vorticity at 900hPa, especially for lower SLP (less than

995hPa), which seems to be one of the statistically impor-

tant differences in the cyclone structure between OCs and

FIG. A3. (top) SREH (color shading; m2 s22) for (a) OCs and (b) NOCs with central SLP between 995 and 1005 hPa and vertical

vorticity exceeding 53 1025 s21, and (c) p values for the difference between OCs and NOCs (color shading). Contours in (a),(b) indicate

geopotential height (m), and those in (c) indicate the difference in SREH between OCs and NOCs. (bottom) As in (top), but for CAPE

(color shading; m2 s22).



NOCs. We performed a t test to examine whether or not

the averages of vorticity and SLP are different for OCs and

NOCs. The results of the t test show that differences in

vorticity and SLP between OCs and NOCs are statistically

significant, exceeding the 95% confidence level. Thus, the

vorticity and SLP anomalies for OCs are significantly

stronger and deeper, respectively. The difference in the

distributions ofOCs andNOCs in terms of vertical vorticity

and central SLP motivates an examination of the differ-

ences in cyclone structure between OCs and NOCs that

FIG. C1. (a) SREH (color shading; m2 s22) and (b) CAPE (color shading; m2 s22) for composite ECs that are

accompanied by 15 or more tornadoes only when tornadoes near frontal zones are included. Contours indicate

geopotential height (m). Blue dots indicate the distribution of tornadoes.

FIG. B1. (a) SREH (color shading; m2 s22) and (b) CAPE (color shading; m2 s22) for composite ECs associated with

only F/EF2 or stronger tornadoes. Contours indicate geopotential height (m).



have similar intensity in both vorticity and central SLP. For

this purpose, we pick out the OCs and NOCs that have

central SLP between 995 and 1005hPa and vorticity ex-

ceeding 5 3 1025 s21, and perform a composite analysis.

The results of the composite analysis (Fig.A3) are basically

similar to Figs. 4, 5, andA2: there are significant differences

in SREH and CAPE between OCs and NOCs. For OCs,

the SREH is larger and the area in which its value exceeds

100m2s22 is wider in the southeast region of the cyclone

center (Figs. A3a,b), and the CAPE is also larger in the

south-southeast region of the cyclone center (Figs. A3d,e).

Areas in which differences of SREH and CAPE between

OCs and NOCs are statistically significant also exist

(Figs. A3c,f), although they are somewhat narrower than in

Figs. 4 and 5.

We also performed a composite analysis in which only

strong ECs (central SLP is lower than 1000hPa) are

considered. The results indicated that the differences

betweenOCs and NOCs are similar to those described in

section 3 (not shown). Furthermore, another composite

analysis without the upper SLP threshold of 1005hPawas

performed. The results show that the differences in the

environments between OCs and NOCs increase (not

shown), because NOCs include a larger number of

weaker ECs with central SLP of higher than 1005hPa. In

conclusion, the differences in the characteristics of OCs

and NOCs in the present composite analysis are robust

and depend onlyweakly on the choice of the thresholds of

central SLP and vertical vorticity.

APPENDIX B

Composite for ECs Associated with the Strong(F/EF2) Tornadoes

We selected ECs that are accompanied by F/EF2 or

stronger tornadoes (hereafter, F2C) from April to May be-

tween 1995 and 2012 and made a similar composite analysis

for their structure and environment.Overall features such as

the spatial structure of ECs and distributions of SREH and

CAPE (Fig. B1) are similar to those for OCs (Figs. 4 and 5).

The maxima of SREH and CAPE for F2C are ;200 and

800m2s22, respectively. It is rather unexpected that these

values are slightly smaller than those for OCs.

APPENDIX C

Characteristics of ECs that Cause a TornadoOutbreak Including Tornadoes near Frontal Zones

In the present study, there are 14 ECs that are ac-

companied by 15 or more tornadoes but are not

categorized as OCs, because the number of the torna-

does drops below 15 when tornadoes that occur near the

frontal zones are excluded. The tornado distributions

with respect to the cyclone center, the composite cy-

clone structures, and the composite fields of environ-

mental parameters for these 14 ECs are shown in

Figs. C1a and C1b. The contours of geopotential height

show that a low pressure trough associated with a warm

frontal zone extends eastward from the cyclone center,

and most of the tornadoes associated with this category

of ECs are located near the warm frontal zone or the

warm sector. Note that few tornadoes are located near

the cold frontal zone in these 14 cases, indicating that,

for ECs that spawned more than 15 tornadoes, most

tornadoes associated with the cold frontal zone occur

when a sufficient number also occur in the warm sector

to meet OC criterion.

Values of SREH exceeding 150m2 s22 are found to

the east of the cyclone center, while the SREH in the

warm sector is smaller than for OCs (Fig. 4). Values of

CAPE exceeding 600m2 s22 intrude farther north-

ward than for OCs (Fig. 5). Although these results

may indicate interesting features of ECs that cause

tornadoes near the warm frontal zone, the sample size

(14) may be somewhat small to generalize the results

statistically, so their detailed study will be left for

the future.

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