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Changes in Cyclone Characteristics in Response to Modified SSTs
L. S. GRAFF AND J. H. LACASCE
Meteorology and Oceanography Section, Department of Geosciences, University of Oslo, Oslo, Norway
(Manuscript received 20 June 2013, in final form 20 February 2014)
ABSTRACT
The impact of changes in sea surface temperature (SST) on the statistics of extratropical cyclones is investigated. The cyclones were identified in an atmospheric general circulation model (AGCM) using an
objective Lagrangian tracking algorithm, applied to the 850-hPa relative vorticity. The statistics were generated for several 20-yr simulations, in which the SSTs were warmed or cooled by 2 K in latitudinal bands. The
response was studied in both hemispheres, during summer and winter.
Changes in the position of the storm tracks are largely consistent with those seen in previous studies.
Increasing SSTs uniformly or increasing the midlatitude SST gradient results in a poleward shift in the storm
tracks, with the clearest trends seen in the Southern Hemisphere (SH). Here it is demonstrated that the SST
modifications alter the cyclone characteristics as well. When the warming includes the low latitudes and/or the
midlatitude gradient is increased, there are more short-lived cyclones. These are also on average more intense
and translate faster, both poleward and eastward.
The poleward displacement is correlated with cyclone intensity, so that stronger cyclones translate to higher
latitudes. This is suggestive of vortex self-advection in the presence of a mean potential vorticity (PV) gradient. The increased eastward translation is correlated with the depth-averaged zonal velocity, and so is likely
related to an increase in the steering-level velocity. These changes in cyclone translation probably contribute
to the changes in the storm tracks seen previously.
1. Introduction
Storm tracks are regions of heightened cyclone activity, located at midlatitudes in both hemispheres and
intensified over the major oceans. The Northern Hemisphere (NH) contains two main storm tracks, over the
North Atlantic (NA) and the North Pacific (NP). These
stretch from the east coast of North America and Asia
across the ocean basins. The NA storm track, in particular, displays a characteristic poleward tilt. An additional
track, associated with the Mediterranean Sea, is present
during winter. In the Southern Hemisphere (SH), a single
storm track encircles Antarctica at approximately 508S.
This is more zonally symmetric than its NH counterparts,
but its intensity does vary somewhat with longitude.
The storm tracks are linked to regions of strong surface
temperature contrast, such as between the land and ocean
in the NH entrance regions (e.g., Hoskins and Valdes
Corresponding author address: Lise Seland Graff, Meteorology
and Oceanography Section, Department of Geosciences, University of Oslo, P.O. Box 1022, Blindern, N-0315 Oslo, Norway.
E-mail: l.s.graff@geo.uio.no
DOI: 10.1175/JCLI-D-13-00353.1
Ó 2014 American Meteorological Society
1990) and in oceanic frontal zones (e.g., Nakamura and
Shimpo 2004; Nakamura et al. 2008; Sampe et al. 2010;
Hotta and Nakamura 2011; Ogawa et al. 2012). The latter
occur both near land, as with the Gulf Stream and
Kuroshio, and over the open ocean, as with the Antarctic
polar frontal zone (APFZ) in the southern Atlantic and
Indian Oceans. The frontal zones exert a restoring effect
on low-level atmospheric baroclinicity, maintaining the
meridional temperature gradients against the mixing induced by the storms. They therefore act as an ‘‘anchor’’
for the storm tracks (Nakamura and Shimpo 2004;
Nakamura et al. 2004, 2008; Hotta and Nakamura 2011;
Sampe et al. 2010). Model simulations show that the storm
tracks weaken and shift equatorward when frontal zones,
for instance based on Indian Ocean climatology, are removed (e.g., Nakamura et al. 2008; Sampe et al. 2010).
The literature suggests that the storm tracks have
shifted poleward in recent years, both in observations
and reanalysis data (e.g., McCabe et al. 2001; Fyfe 2003;
Chang 2007; Wang et al. 2006; Ulbrich et al. 2009; Vilibic
c 2010; Bender et al. 2012). Similar shifts have
and Sepi
been found in models forced under global warming
scenarios (e.g., Yin 2005; Bengtsson et al. 2006; Ulbrich
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et al. 2008; Schuenemann and Cassano 2010; Wu et al.
2010; Chang et al. 2012). The shift is particularly clear in
the SH (e.g., Chang et al. 2012).
A number of mechanisms have been proposed to explain the shift. Among these is increased atmospheric
heating by the ocean. That the ocean is warming is becoming clear (e.g., Levitus et al. 2005; Bindoff et al.
2007). Increasing sea surface temperatures (SSTs) in
models warms the overlying atmosphere, changing tropospheric baroclinicity and thus affecting the storm
tracks (e.g., Caballero and Langen 2005; Kodama and
Iwasaki 2009; Lu et al. 2010; Graff and LaCasce 2012,
hereafter GL12). When the meridional SST gradients
are altered, the resulting change in storm-track position
depends on the location and the sign of the change (e.g.,
Brayshaw et al. 2008; Chen et al. 2010; GL12). Increasing the gradient at midlatitudes causes the tracks to
shift poleward, whereas decreasing the gradient at
midlatitudes or increasing it in the tropics produces an
equatorward shift.
The storm tracks, and their changes, are usually diagnosed in two ways. The first involves using bandpassfiltered fields, such as geopotential height or eddy heat
and momentum fluxes, to isolate variability in the 2–7-day
band (e.g., Blackmon 1976; Blackmon et al. 1977; Yin
2005; Sampe et al. 2010; GL12). The second focuses on
changes in the cyclones themselves, their positions and
regions of cyclogenesis and cyclolysis (e.g., Hoskins and
Hodges 2002, 2005; McCabe et al. 2001; Bengtsson et al.
2006; Wang et al. 2006; Bengtsson et al. 2009; Catto et al.
2011). The first approach is an Eulerian analysis, based
on averaging fields by location and height; the second,
which involves tracking individual storms, is closer to a
Lagrangian one.
In addition to knowing how the storm tracks will shift,
it is also of interest to know how the cyclones themselves will change in the future climate. While the total
number of cyclones has been projected to decrease
(e.g., Bengtsson et al. 2006; Pinto et al. 2007; Ulbrich
et al. 2009), projections regarding their intensities differ
substantially, with some indicating an increase (e.g.,
Lambert and Fyfe 2006; Mizuta et al. 2011) and others
finding weak or no changes (e.g., Bengtsson et al. 2006,
2009; Pinto et al. 2007; Catto et al. 2011). Indeed, trends
differ substantially between regions, with increases in
intensity in some places and decreases in others. The
picture is further complicated by the use of different
tracking algorithms and measures of intensity (Ulbrich
et al. 2009, 2013), although the results for the strongest
cyclones are more consistent (Ulbrich et al. 2013).
The present study examines how cyclone characteristics change under controlled SST forcing in an atmospheric general circulation model (AGCM). The cyclones
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are identified using an objective Lagrangian tracking
routine (Hodges 1994, 1995, 1999) applied to anomalies
in relative vorticity on the 850-hPa surface (z850 hPa). The
AGCM is the Community Atmosphere Model, version 3
(CAM3), and the runs are the same as those analyzed by
GL12, who examined changes in bandpass-filtered statistics during boreal winter. The cyclone-based results
verify the changes described by GL12, but additionally
reveal changes in the cyclone characteristics. Furthermore, while GL12 focused on the zonally averaged response in the latitude–pressure plane, we concentrate on
the horizontal (latitude–longitude) plane and describe
regional (e.g., Atlantic versus Pacific in the NH) variations. We also expand the analysis to include both the
summer and winter seasons in both hemispheres.
The data and methods are described in section 2. The
results are presented in section 3, and are summarized
and discussed in section 4.
2. Data and methods
a. Model and reanalysis data
For the analysis, we use 6-hourly data from the simulations presented in GL12. These were carried out for
a 20-yr period1 using the National Center for Atmospheric Research (NCAR) CAM3, with climatological
input data from the 1980s and 1990s (Collins et al. 2004).
The model was run with a spectral T42 horizontal and 26level vertical (T42L26) resolution, and was integrated
with an active land surface (the Community Land
Model), a thermodynamic sea ice model, and the Data
Ocean Model (DOM). The latter reads and interpolates
prescribed time-varying SST data, in this case the built-in
climatological dataset derived from the Smith/Reynold
EOF dataset for 1981–2001 (Reynolds et al. 2002; Collins
et al. 2004, 2006). The dataset includes 12 monthly
samples that are cycled throughout the integration
period. Note that the sea ice coverage is prescribed
when using DOM. As such, the primary function of the
sea ice model is to compute the surface fluxes between
the ice and atmosphere.
To validate the CAM3 results, we used 6-hourly
reanalysis data from the European Centre for
Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim; Dee et al. 2011).
ERA-Interim is simulated using the 2006 version of the
ECMWF Integrated Forecast System (IFS), including fully
1
Control simulations were also performed over a 30-yr period,
and for a 20-yr period with and without random initial perturbations to the initial temperature field, yielding similar results. Thus
the 20-yr integration was deemed sufficient.
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FIG. 1. The CAM3 climatological SST field averaged over (a) DJF and (b) JJA, respectively. The thick black horizontal lines mark the
6458 latitudes (see discussion in the text).
coupled components for the atmosphere, land surface, and
ocean waves. The IFS is integrated with a fourdimensional variational data assimilation (4DVAR)
scheme at spectral T255 horizontal and 60-level vertical
(T255L60) resolution. Although data are available for
a longer time period, we consider the same 20-yr period
from which the climatological input data in CAM3 were
constructed. We also interpolated the ERA-Interim data
to the CAM3 grid, to simplify comparisons.
b. SST perturbations
We consider five CAM3 runs, one control and four
sensitivity runs.2 The SSTs from the control run are
shown in Fig. 1. The left and right panels show the field
averaged over the NH winter and summer periods in the
three-month blocks December–February (DJF) and
June–August (JJA), respectively. In the sensitivity runs,
a 2-K modification was imposed to the climatological
SSTs in different regions (see Figs. 1 and 2 in GL12). In
the 2K run, the SSTs were increased uniformly over the
oceans, while in the three remaining runs the SSTs were
increased/decreased equatorward or poleward of 458
(indicated by the dark horizontal lines in Fig. 1). The
modifications were tapered to zero over an 88 range.
Thus the three latter forcings alter the SST gradients at
458, where the climatological SSTs have a largely zonal
orientation.
In the 2K-lowlat run, the low latitudes were warmed
equatorward of 458. Thus both the low-latitude SSTs and
the midlatitude SST gradients were increased. In the
2
The GOGA/TOGA/MOGA SST perturbation experiments
described by Lau (1997) (and references therein) are similar in
spirit to those studied here. The focus in those experiments, however, was the response to SST anomalies in prescribed regions,
rather than to fixed warmings/coolings, as here and in, for example,
Brayshaw et al. (2008).
62K-highlat runs, the SSTs were warmed and cooled
poleward of 458. Cooling the high-latitude SSTs, in the
22K-highlat run, allows investigating the effect of
stronger midlatitude SST gradients without increased
low-latitude heating. The opposite effect is examined in
the 2K-highlat run, as the midlatitude gradient is
weakened. Note too that only the SSTs were altered; the
sea ice distribution was unchanged in all cases.
The modifications to the midlatitude SST gradients in
the 2K-lowlat and 62K-highlat runs actually vary between ocean basins, depending on the climatology (not
shown). During DJF, the zonally averaged unperturbed
midlatitude SST gradient in the SH has one large maximum at about 438S, which strengthens and broadens
toward the pole in the 2K-lowlat and 22K-highlat runs.
In the 2K-highlat run, the gradient weakens between 428
and 558, and shifts equatorward by about 38.
The NA winter gradient has two minima, one at 438N
(reflecting the Gulf Stream) and a weaker one at 518N
(reflecting the North Atlantic Current). The general
picture is similar to SH DJF: the strongest gradient intensifies and broadens in the 2K-lowlat and 22K-highlat
runs; in the 2K-highlat run the gradient generally
weakens but also shifts somewhat equatorward.
In the NP, the winter gradient is strongest at 408N. The
minimum intensifies and broadens toward the pole in
the 2K-lowlat run. The 62K-highlat changes are too far
north to affect the maximum amplitude, but they do
induce a broadening/contraction on the poleward side,
consistent with the NA. The changes are generally the
same during JJA in all three basins.
c. Cyclone tracking
We computed the individual cyclone trajectories using
the objective tracking routine of Hodges (1994, 1995,
1999). This employs a two-step process: first, the feature
points (the positions of the cyclone centers) are identified;
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second, they are linked into trajectories by minimizing
a cost function that restricts variations in direction and
speed between consecutive time steps. Off-grid feature
points are identified through interpolation/smoothing
and treated as well.
Different fields may be used for tracking, such as
geopotential height, mean sea level pressure (MSLP), or
relative vorticity (e.g., Hoskins and Hodges 2002, 2005;
Bengtsson et al. 2006, 2009; Catto et al. 2011). These
reflect different aspects of the flow field. MSLP is more
influenced by large-scale features like the Icelandic low
and strong mean flows (e.g., Hoskins and Hodges 2002).
Relative vorticity better captures small-scale features
and is less influenced by the background flow (e.g.,
Hoskins and Hodges 2002). It also allows for identifying
the cyclones earlier in their life cycle and better reflects
the centers of rotation (Hodges 1996).
We track cyclonic vortices in the z850 hPa field in both
hemispheres, during their respective winter and summer
seasons. We neglect weak, short-lived, and slow-moving
systems, requiring that the cyclones 1) have a z850 hPa
exceeding j1025j s21 in magnitude, 2) last for at least 2
days, and 3) move more than 108 geodesic. The z850 hPa
field was spatially filtered prior to the tracking process,
removing scales with planetary wavenumbers less than 5
and larger than 42 (e.g., Hoskins and Hodges 2002). All
z850 hPa values reported in the following stem from the
filtered fields.
We focus initially on the track density fields, which indicate the concentration of cyclone trajectories (Hodges
1996). This facilitates the comparison with GL12 and other
studies. Thereafter we concentrate on the cyclones themselves, examining quantities averaged among a given
population (e.g., from a selected storm track). This is effectively a more Lagrangian approach.
d. Statistics
We make use of probability density functions (PDFs)
to illustrate the range of values present, and PDFs from
different runs are compared. To determine where changes
with respect to the control run are significant, we use two
different tests: the two-sample Kolmogorov–Smirnov test
(KS test) and the Wilcoxon–Mann–Whitney rank-sum test
for two independent samples (WMW test) (e.g., Wilks
2006). The former determines the probability that the
largest difference between two cumulative density functions (CDFs) can occur. The CDF is the cumulative integral of the PDF. An advantage with the KS test is that it
is nonparametric; that is, it makes no assumptions about
the underlying distribution.
The WMW test, like the Student’s t test, is used to
determine whether one sample population tends to have
significantly larger values than the other. It determines
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the probability of the sum of the ranks of the two samples being significantly different, with the ranks taken
from the pooled data. The WMW test is favorable to the
more traditional t test as it is nonparametric (like the KS
test) and less influenced by outliers. But even when the
assumptions of the t test are met, the WMW test is almost as good (Wilks 2006).
The probability in the KS test depends on the numbers
of degrees of freedom in the two datasets. The WMW
test moreover assumes that the data constituents are
statistically independent. Most of the samples considered are assumed to be independent as they include only
one data point from each cyclone trajectory (such as
lifetime or lysis latitude). However, we also evaluate
samples of feature-point latitudes. These are retrieved
at 6-h intervals, and as such the adjacent points are not
necessarily independent. Calculating the autocorrelation of the cyclone speeds along the tracks, we found
that these decay approximately exponentially, with decay times from 1–4 days in the different basins. So we
reduced the number of degrees of freedom assuming the
feature points were independent after 3 days.
3. Results
a. Changes in baroclinicity
The atmospheric response to changes in SST was explored by GL12, but it is useful to re-examine the effect
on baroclinicity, for reference with the later results.
GL12 also presented zonally averaged boreal winter
fields, but we will consider the response in the SH and in
the two separate NH basins (the NA and NP), in both
summer and winter.
Shown in Figs. 2 and 3 is the Eady parameter (Lindzen
and Farrell 1980), defined as
g ›T ,
(1)
sB1 5 0:31
NT ›y where N is the Brunt–V€
ais€
al€
a frequency (the remaining
notation is standard). This is a common measure of baroclinicity and indicates the maximum growth of baroclinic
disturbances. We consider the zonally averaged parameter
as a function of pressure. Plotted in the figures are the
differences in sB1 between each of the four sensitivity
experiments and the control run. The result for the latter is
indicated by the solid contours (with values of 4, 6, and
8 days21 for NH winter, and of 3, 5, and 7 days21 for
summer), and the former by the color shading. The results
from three regions (the SH, NA, and NP) are shown from
left to right, separated by black vertical lines.
The strongest response is in the 2K-lowlat run (in which
SSTs are increased poleward of 458), as seen in Figs. 2a
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FIG. 2. The zonally averaged Eady parameter (day21) computed separately in the (left) SH (averaged over all longitudes), (center) NA
(averaged 908W–08), and (right) NP (averaged over 1308E–1308W) for the (a),(b) 2K-lowlat run and the (c),(d) 2K run during DJF and
JJA. The color shading denotes the difference fields and the thick black contours are control run contours, for reference. The control run
contours are the 4, 6, and 8 day21 contours for (a) and (c) (DJF) and the 3, 5, and 7 day21 contours for (b) and (d) (JJA). The Eady
parameter is defined in the text.
and 2b. In DJF (Fig. 2a), the maxima in baroclinicity shift
poleward, all three regions. The shift is evident at both
the jet level and at low levels. In JJA (Fig. 2b), a poleward
shift is still seen in the SH. The changes occur over
a larger meridional extent, and subsequent results will
show that the storm track broadens similarly. This is consistent with the SH being less zonally symmetric in winter,
exhibiting a characteristic spiraling toward the pole (e.g.,
Hoskins and Hodges 2005). A poleward shift is also evident
in the NA during JJA, but the maximum shifts equatorward in the NP (Fig. 2b). This was not noted by GL12, who
only examined the zonally averaged fields in DJF.
The response in the 2K run (in which SSTs are increased uniformly over all oceans) is similar (cf. Figs. 2c,d with
Figs. 2a,b). The main difference is that the low-level
changes are weaker. Excess latent heating in the tropics
increases the temperature gradient aloft, but the surface
gradient is largely unaffected by the uniform warming.
The exception is again the NP track (Figs. 2c,d) where
the changes resemble those seen in the 2K-lowlat run at
the jet level and at low levels.
Many similar features are also seen with the 22Khighlat run, albeit with smaller amplitude and a more
barotropic structure. There is a poleward shift in baroclinicity in all three regions in the DJF period (Fig. 3a),
and similar, though less pronounced, shifts in JJA
(Fig. 3b). The 22K-highlat run lacks the large changes
near the jet level seen in the 2K-lowlat run, as there is no
excess heating in the tropics. Furthermore, there is little
change in the NP in JJA, indicating that the polar cooling
has little effect on the (relatively high latitude) baroclinicity there. The difference from the previous runs is
presumably due to the lack of low-latitude heating.
The changes in the 2K-highlat run resemble those in
the 22K-highlat run, but with the opposite sign. There is
an equatorward shift in the SH and NA baroclinicity
maxima in DJF (Fig. 3c) and in the SH in JJA (Fig. 3d).
However, the response in the NP differs, particularly in
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FIG. 3. As in Fig. 2, but for the (a),(b) 22K-highlat and (c),(d) 2K-highlat runs.
DJF. Then, there is a clear poleward shift in the 22Khighlat run and little to no response with the 2K-highlat
perturbation.
To summarize, the forcings generate two distinct
changes in baroclinicity. In the runs where the warming
includes the low latitudes, the baroclinicity increases
mostly aloft, at the jet level. In the cases where the
surface gradients are altered, the changes in baroclinicity are more barotropic, extending up to about
300 hPa. Increasing either the low-latitude warming or
the midlatitude SST gradient results in a poleward shift
in baroclinicity. The exception is the NP in summertime,
where the response is not always so clear. As shown by
GL12 for DJF, the changes in the storm tracks (in terms
of bandpass-filtered geopotential height variability and
eddy fluxes and heat and momentum) largely mirror
those seen in baroclinicity.
b. Changes in storm-track position by track density
We now consider changes in the storm tracks as seen
by tracking individual cyclones. First, we examine the
track density, indicating the concentration of cyclone
trajectories (Hodges 1996). As this is a geographical
mapping of the storm paths, it most closely resembles
the Eulerian fields discussed in GL12. We consider track
density fields from the control run, from ERA-Interim
for validation, and from the 2K-lowlat run. As will be
shown, the latter case presents the clearest response of
the sensitivity runs, and we therefore focus more on this
run. The remaining sensitivity runs are considered later.
The control run fields are shown in Fig. 4. As always,
the SH storm track is much more zonal than its NH
counterparts, particularly in summer (DJF). It is centered at about 508S and exhibits only weak longitudinal
variations (Fig. 4a). There are greater such variations in
winter though, with the largest concentration of storms
southwest of Australia (Fig. 4b). In the NH, there are three
distinct tracks in wintertime, in the NA, NP, and Mediterranean region (Fig. 4c). The first two display a characteristic southwest–northeast tilt, with the NA track for
example bending toward the United Kingdom. In summer,
(Fig. 4d) the tracks are narrower and weaker, and the
Mediterranean track is absent. In addition, the NA and NP
summer tracks are poleward of the winter positions.
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FIG. 4. The control run track density field shown for (a) SH summer, (b) SH winter, (c) NH winter, and (d) NH summer. Track density is
given as a number density per month with a unit area of about a 58 spherical cap (’106 km2).
It is useful to compare with the track densities obtained
with the ERA-Interim data (Fig. 5). The ERA-Interim
fields are very similar to those from CAM3, both in location and magnitude, but there are differences as well.
The maximum southwest of Australia in the SH winter is
much weaker in ERA-Interim than in CAM3 (Figs. 5b
and 4b); however, a corresponding inconsistency is not
evident when considering the feature-density field (not
shown). There are also more storms off the Antarctic
coast, near Wilkes Land and over the Amundsen and
Bellinghausen Seas. The latter is likely related to differences in sea ice coverage between CAM3 and ERAInterim. The structure of the SH summer track is more
similar (Figs. 4a and 5a), although the CAM3 track has
greater magnitudes at midlatitudes and somewhat smaller
magnitudes near the Antarctic coast.
In NH winter, the NP storm track and the entrance
region of the NA track are very similar between CAM3
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FIG. 5. As in Fig. 4, but for ERA-Interim.
and ERA-Interim (Figs. 4c and 5c). However, the exit
region of the NA track lies farther north in ERAInterim, being intensified off the southern tip of Greenland. Correspondingly, ERA-Interim exhibits more
storms over the Nordic Seas and into the Arctic. That the
storm tracks are too zonal is a known issue with CAM3
(Hurrell et al. 2006). Perhaps the most striking difference,
though, is that the NP track lies much farther south in
summer in ERA-Interim than in CAM3 (Figs. 5d and 4d).
The NA summer track, however, is more similar, albeit
somewhat too zonal. Beyond the mentioned differences the agreement is reasonably good, particularly
in light of the differences between the two datasets;
ERA-Interim has dramatically higher spatial resolution
(T255L60 in ERA-Interim versus T42L26 in CAM3)
and uses observed SST and sea ice data varying from
year to year, whereas CAM3 was run with a climatological dataset.
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FIG. 6. As in Fig. 4, but for the 2K-lowlat difference field.
The changes in storm-track position induced by the
2K-lowlat forcing are shown in Fig. 6. The response is
again clearest in SH summer (Fig. 6a), with a decrease
in track density equatorward of 508S and an increase
near 608S. The changes are consistent with those in the
Eady parameter at low levels (Fig. 2a), except that the
track changes occur about 108 poleward. As noted in
GL12, the changes in bandpass-filtered variability
likewise occur poleward of those in baroclinicity. The
difference field for the winter tracks (Fig. 6b) is more
spread out latitudinally, in line with the control run
(Fig. 4b), but the poleward shift is clear nevertheless.
Note too there is a decrease in storms over the Ross
Sea, but this is in a region where CAM3 differs with
reanalysis (Fig. 5b).
The changes in the NH, as expected, vary more spatially. Nevertheless, the NP and NA winter tracks clearly
shift poleward, the Mediterranean track weakens, and
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FIG. 7. Overview of the storm-track genesis regions. The black boxes show the DJF cases, and
red boxes the JJA cases. For instance, the NP winter (DJF) storm track consists of all cyclones
with genesis within the black box over the NP basin. Focusing on the extratropics, we exclude
cyclones with tropical origin. These are the systems that form equatorward of a threshold 258 in
summer and 208 in winter, except in the NA where the threshold is 208 during both seasons but
we exclude systems formed off the west coast of northern Africa. Also, to highlight the cyclones
formed in the primary storm-track regions, we do not retain systems forming in regions where
the genesis density (not shown) is less than 0.2 per month per unit area, where the unit area is
about a 58 spherical cap.
there are more storms occurring over northern Europe
(Fig. 6c). The changes in the NH summer (Fig. 6d) are
less clear. There is a poleward shift downwind of the
Rockies and in the entrance region to the NA track, but
the NP track on the other hand shifts equatorward, in
line with the Eady parameter (Fig. 2).
c. Changes in feature-point latitude
The track changes can be seen succinctly in PDFs of
feature-point latitude. This reflects the cyclone-center
concentration (and only includes feature points that
have been assigned to a trajectory). We subdivide the
results according to the genesis regions shown in Fig. 7,
corresponding to the SH, NA, and NP storm tracks.
Note that the regions differ between winter and summer, in line with the changes in the tracks described
above. In keeping with our focus on extratropical
storms, tropical cyclones are excluded (details are given
in the caption). We also exclude a small group of cyclones that form in the low subtropics (258–358S) in the
SH summer. These exhibit distinctly different behavior
than the midlatitude storms, as discussed in section 4.
The PDFs for the control run are shown in Fig. 8 (solid
lines), with the PDFs from ERA-Interim for comparison
(dotted lines). In the SH in DJF (Fig. 8a), the majority of
storms in the control run occur between 458 and 658S,
with a peak near 538S. The distribution from ERAInterim is similar, albeit slightly broader and shifted
poleward. The agreement is better in SH winter (Fig.
8b), and both PDFs cover a broader range of latitudes.
The agreement is similarly good in NH winter, in both
the Atlantic (Fig. 8c) and Pacific (Fig. 8e) tracks. It is
less convincing in NH summer (Figs. 8d,f). The CAM3
distribution is more sharply peaked, with the mode
near 558N in both basins, while there are more storms
toward the equator in ERA-Interim and the modes lie to
the south.
Also shown in the figure are the differences between
the PDFs from the 2K-lowlat and control runs, indicating latitudinal shifts in the storm concentrations
(orange lines with gray shading). The significance of the
differences is assessed using the KS test, described in
section 2. In the SH summer (Fig. 8a), the difference
PDF is positive south of 558S and negative to the north,
indicating a poleward shift. The differences are moreover significant by the KS test. In the SH winter (Fig.
8b), the changes are more spread out, like the PDFs
themselves, but the differences are significant and indicate a poleward shift. The change in the NA winter
(Fig. 8c) is likewise poleward and significant. The PDF
shifts poleward too in the NP in winter (Fig. 8d) and in
the NA in summer (Fig. 8e), but the differences in both
cases are not significant. The PDF is significantly different in the NP in summer, though (Fig. 8f), showing an
equatorward shift. The results of the WMW test (section
2) were largely consistent with those from the KS test
(Tables 1 and 2).
The changes in the mean feature-point latitudes are
listed by storm-track region and season in Tables 1 and 2
for the 2K-lowlat and the other sensitivity runs. The
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FIG. 8. Feature-point latitude PDFs for the control run (solid black line), ERA-Interim (dotted black line), and 2K-lowlat difference
with respect to the control run (solid orange line with light and dark gray shading). The three PDFs are shown for (a),(b) SH summer and
winter cyclones and for NA and NP (c),(d) winter and (e),(f) summer cyclones. When the difference between the 2K-lowlat and the
control PDFs are significant by the KS test at the 95% level, an asterisk is given in the bottom-left corner.
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TABLE 1. Overview of the mean feature-point latitude (FP), genesis latitude (gen.), lysis latitude (lys.), 5-day latitudinal and longitudinal displacements (dlat and dlon), lifetimes (Tlife), maximum intensity (z850 hPa), and total cyclone count (Ncc) for the SH summer and
winter. For the control run, we show the full values. For the sensitivity runs, we show the percent of change relative to the control run value
for the relevant region. When the change in location is significant at the 95% level according to the WMW test, values are given in
boldface. The significance of the changes in the cyclone count is not assessed.
FP (8S)
Gen. (8S)
Lys. (8S)
Dlat (8S)
Dlon (8E)
Tlife (days)
jz850 hPaj (1025 s21)
Ncc
SH DJF
Control
2K-lowlat
22K-highlat
2K
2K-highlat
54.13
1.94%
0.55%
1.44%
20.26%
49.91
0.97%
0.01%
1.04%
20.38%
55.97
2.15%
0.87%
1.55%
20.94%
8.09
19.24%
8.21%
4.11%
29.78%
70.32
14.63%
11.60%
5.66%
217.52%
4.95
23.77%
22.37%
20.71%
4.54%
5.48
2.94%
2.47%
1.60%
22.40%
4203
23.07%
21.43%
22.21%
21.57%
SH JJA
Control
2K-lowlat
22K-highlat
2K
2K-highlat
50.29
0.66%
20.56%
1.04%
20.27%
46.37
0.43%
20.80%
1.82%
20.13%
52.98
0.95%
20.34%
1.38%
20.77%
8.27
10.74%
3.96%
24.66%
27.01%
67.93
6.04%
0.53%
0.05%
25.55%
4.53
24.62%
22.74%
21.27%
1.77%
6.22
2.48%
1.31%
0.82%
22.08%
7628
22.05%
1.00%
24.50%
23.00%
changes are generally modest. For example, the mean
storm-track latitude in the 2K-lowlat run shifts poleward
by 1.94% in the SH winter, corresponding to 18. In
winter, the track shifts by 0.66% (0.48). The NA track
shifts poleward by 2.01% (1.38) in winter, while the NP
track shifts by 1.47% (0.78). In summer, the mean for the
NA track shifts barely at all, while it shifts equatorward
by 1.62% in the NP, corresponding to about 18.
Thus, in most regions the storm tracks shift poleward
under the 2K-lowlat warming. The exception is the NP,
where the track shifts equatorward. But as noted, the track
in the NP lies too far north in CAM3; the result might have
been otherwise had it been in the proper position.
The results for all four sensitivity runs are shown in
Fig. 9. Plotted are the changes in mean feature-point
latitude as well as in the mean genesis and lysis latitudes.
TABLE 2. As in Table 1, but for the NA and NP storm tracks.
Dlon (8E)
Tlife (days)
jz850 hPaj (1025 s21)
Ncc
12.06
14.68%
11.48%
16.43%
2.52%
55.24
19.04%
13.67%
6.98%
26.61%
4.54
24.14%
22.11%
22.53%
20.55%
6.72
2.30%
2.37%
20.24%
23.37%
1448
24.63%
20.90%
23.11%
4.14%
59.30
0.16%
1.02%
0.28%
20.19%
8.16
4.18%
17.63%
8.91%
211.98%
52.58
8.67%
6.25%
4.63%
28.43%
5.23
20.71%
25.22%
25.24%
2.59%
4.91
20.95%
0.39%
22.20%
23.91%
764
0.65%
5.24%
20.79%
4.97%
42.33
1.27%
20.43%
0.84%
20.23%
52.70
2.28%
0.50%
3.20%
0.49%
13.47
12.68%
14.47%
14.59%
6.90%
36.60
7.19%
13.65%
2.23%
7.87%
4.31
21.44%
21.06%
20.76%
20.06%
6.65
2.66%
2.76%
2.30%
20.39%
1716
2.10%
2.21%
4.78%
3.44%
54.29
22.03%
1.03%
21.91%
0.73%
58.84
21.50%
20.73%
20.93%
0.24%
6.27
22.22%
26.63%
21.92%
24.09%
48.64
6.76%
10.81%
6.68%
4.74%
4.99
0.55%
26.46%
2.19%
22.06%
4.35
1.63%
22.27%
20.86%
24.41%
774
12.92%
1.55%
7.75%
3.62%
FP (8N)
Gen. (8N)
Lys. (8N)
Dlat (8N)
NA DJF
Control
2K-lowlat
22K-highlat
2K
2K-highlat
52.94
2.01%
1.40%
1.53%
20.15%
45.74
1.54%
0.97%
1.79%
0.84%
56.29
1.30%
1.42%
0.34%
0.17%
NA JJA
Control
2K-lowlat
22K-highlat
2K
2K-highlat
57.44
0.06%
1.01%
1.02%
0.17%
51.91
20.07%
0.75%
1.31%
0.78%
NP DJF
Control
2K-lowlat
22K-highlat
2K
2K-highlat
49.37
1.47%
0.41%
1.81%
20.08%
NP JJA
Control
2K-lowlat
22K-highlat
2K
2K-highlat
57.61
21.62%
0.60%
21.30%
0.62%
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Encircled points indicate significant changes by the
WMW test (see also Tables 1 and 2). In the 2K-lowlat
case (left column of Fig. 9a), the mean latitude is seen to
shift poleward in all regions, except in NP summer. The
response in the 22K-highlat and 2K runs (second and
third columns) is similar, with poleward shifts of up to
0.58–1.18 in most cases. The summer tracks in the SH
and the NP shift equatorward in the 2K and 22K-highlat
runs, respectively, but the changes in both cases are
insignificant. Interestingly though there is essentially
no change in the 2K-highlat run, indicating that the
high-latitude warming has little impact on the lowlevel storm tracks.
The changes in genesis latitude (Fig. 9b) are generally consistent but less pronounced. A 0.58–18
poleward shift is seen in many basins in the 2K-lowlat,
2K, and 22K-highlat runs. The exception is again
the NP summer cyclones, which form 18 equatorward
in the two former cases. The changes in the 2K-highlat
run are again weak and inconsistent. The shifts in lysis
latitude (Fig. 9c), on the other hand, are more pronounced. In many cases, the shift is like that seen in
the feature-point latitude.
Thus the runs with stronger midlatitude SST gradients
(the 2K-lowlat and -2K-highlat runs) or uniform warming
(the 2K run) show poleward shifts in mean storm-track
and lysis latitude, while the changes in genesis latitude are
somewhat less pronounced. Next we consider how the
perturbations affect the characteristics of the cyclones
themselves.
d. Cyclone characteristics
FIG. 9. The changes in the (a) mean feature-point, (b) genesis,
and (c) lysis latitudes in response to the sensitivity run forcings are
summarized for the SH, NA, and NP summer and winter storm
tracks. In each panel, the changes in the six storm tracks are shown
separately for the five sensitivity runs in different columns. Each
storm-track case is assigned specific markers and colors. The redfilled square is associated with SH DJF, the green star with SH JJA,
the gray-filled triangle with NA DJF, the orange-filled diamond with
NP DJF, the magenta plus with NA JJA, and the purple cross with
NP JJA. When the markers are encircled, this indicates that the
change in location is significant at the 95% level by the WMW test.
Figure 10a shows the mean cyclone intensity in the
control run as a function of time (since formation),
for the three track regions during the two seasons.
Figure 10b shows the mean trajectories relative to the
cyclones’ genesis positions. Corresponding results from
the ERA-Interim data are shown in Figs. 10c and 10d.
Note that the cyclone population is decreasing in
time, so the error bars (not shown) increase in time.
Generally the first 6–7 days represent the most robust
response.
The cyclones intensify during the first 2–3 days,
weakening or maintaining strength thereafter (Fig. 10a).
The strongest growth occurs in the NP and NA tracks
in winter, whereas the weakest cyclones occur in NH
summer. The SH winter and summer cyclones lie between and are comparably strong. The general picture is
the same with the ERA-Interim cyclones (Fig. 10c), although they are somewhat stronger than in CAM3. The
largest difference is in NH summer, when the ERAInterim cyclones are nearly twice as strong. The qualitative picture is nevertheless similar.
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FIG. 10. The time evolution of the mean (a),(c) intensities and (b),(d) trajectories from the control run and ERA-Interim, respectively,
for the SH, NA, and NP winter and summer storm tracks. The storm-track cases are assigned line colors in accordance with Fig. 9. At every
time step, the intensity of a cyclone is defined as the absolute value of its relative vorticity. Note that the mean intensity is found by
averaging over the ensemble of existing cyclones at every time step. Similarly the mean trajectory is found by averaging over the relative
positions of the cyclones, with respect to their origin, at every time step. The colored bullets superimposed on the lines indicate time; each
bullet marks the passing of one additional day since cyclogenesis. All fields are shown for the first 11 days.
The cyclones move poleward and eastward in all
cases (Fig. 10b). The largest poleward displacements
occur with the NH winter cyclones. This is perhaps
unsurprising given the southwest–northeast tilt of
the NH winter tracks. The trajectories in the other
cases are similar, at least over the first week. The
result for the ERA-Interim cyclones is very similar
(Fig. 10d).
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FIG. 11. As in Fig. 8, but for the SH summer cyclones and their (a),(b) 5-day meridional and zonal displacements (dlat and dlon),
(c) lifetimes (Tlife), and (d) intensity. The 5-day meridional displacement is defined as the latitudes covered by a cyclone during the first
5 days of its lifetime. Similarly the 5-day zonal displacement is the longitudes covered. Both measures exclude cyclones living less than
5 days. The intensity is defined as the absolute value of the most extreme along-track relative vorticity.
PDFs of different characteristics for the SH DJF cyclones are shown in Fig. 11. Shown in the figure are cyclone intensity, 5-day latitudinal and longitudinal
displacements, and cyclone lifetimes. We define the intensity here as the maximum absolute along-track relative vorticity. The 5-day latitudinal and longitudinal
displacements are the degrees traveled toward the pole
and eastward, respectively, during the first 5 days of the
cyclones lifetime. The lifetime reflects the duration of
each trajectory. As in Fig. 8, each panel displays the PDF
from the control run, ERA-Interim, and the 2K-lowlat
difference.
The PDFs for CAM3 and ERA-Interim compare well.
The agreement is particularly good for the lifetimes
(Fig. 11c). The displacement PDFs (Figs. 11a,b) have
similar shapes and peak at the same latitude, but the
ERA-Interim PDFs are slightly shifted toward the left,
indicating somewhat greater translation for the CAM3
cyclones. The maximum intensity PDFs are also comparable (Fig. 11d), but there are more strong storms in
ERA-Interim.
The cyclone characteristics exhibit clear changes in
response to the 2K-lowlat forcing. The mode of the
5-day latitudinal displacement is 108 in the control run,
and most cyclones travel from 08 to 208 poleward (Fig. 11a).
The cyclones travel farther in the 2K-lowlat run. Indeed,
the mean 5-day latitudinal displacement has increased by
nearly 20%, amounting to an additional displacement of
1.68 toward the pole (Table 1). As the greater displacement
occurs over the same 5 days, the cyclones are moving faster
poleward in the 2K-lowlat run. Furthermore, most cyclones
in the control run travel about 808 eastward during the first
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FIG. 12. As in Fig. 9, but for the changes in the (a),(b) mean values of the 5-day meridional and zonal displacements (dlat and dlon),
(c) cyclone lifetimes (Tlife), and (d) intensity.
5 days (Fig. 11b). In the 2K-lowlat run the mean 5-day
longitudinal displacement increases by about 15%, corresponding to 108 of longitude (Table 1).
The SST perturbation also affects the cyclone lifetimes (Fig. 11c). Most cyclones live about 3 days in the
control run, although the extended tail of the PDF indicates that there are cyclones that last up to two weeks.
In the 2K-lowlat run, there are more short-lived and fewer
long-lived cyclones. As such, the mean lifetime decreases
by about 4% (corresponding to 0.2 days; Table 1).
The cyclones are also stronger in the 2K-lowlat run
(Fig. 11d). In the control run the maximum intensities
are up to and exceeding 1024 s21, with a mode of about
4 3 1025 s21. In the 2K-lowlat run there is a decrease in
the number of cyclones weaker than 8 3 1025 s21 and an
increase in the number of stronger cyclones, so the mean
maximum intensity increases by roughly 3%, equivalent
to 1.6 3 1026 s21 (Table 1).
The results from the other perturbation runs are
shown in Fig. 12. Once again, the changes in the 22Khighlat and 2K runs are broadly consistent with those
seen in the 2K-lowlat run: the cyclones have shorter
lifetimes, and they travel faster and are stronger. The
changes in the 2K-lowlat run are the most pronounced,
while the changes in the 2K run are the least. Evidently
the increase in the midlatitude gradient is the most important here. And in line with the relatively weak response seen previously for the 2K-highlat run, the
changes in the displacements and lifetimes are generally
inconsistent in this case; the only clear aspect is that the
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FIG. 13. The time evolution of the mean latitudinal displacements (relative to the initial position) vs the mean intensities. Results are
shown from all five runs (the control and the sensitivity runs) for the SH (a) DJF and (b) JJA storm-track regions. As in Fig. 10, the
intensity is the absolute values of z850 hPa, and the bullets superimposed on the lines indicate time with 1-day intervals.
storms are weaker. However, considering only the SH
tracks, the response to the 2K-highlat forcing is opposite
to the other runs. There are more long-lived cyclones,
they travel less, and the intensity is smaller.
Last, there is the number of cyclones in the different
runs. Previous studies suggest that the number of cyclones will decrease in a warmer climate (e.g., Bengtsson
et al. 2006; Lambert and Fyfe 2006; Pinto et al. 2007;
Ulbrich et al. 2009; Catto et al. 2011). The number of
identified cyclones is shown in the last columns of Tables
1 and 2. In the 2K-lowlat run, there is a decrease in the
number of storms of between 2% and 5% in the SH and
in NA winter. The results are largely consistent for the
2K and 22K-highlat runs. At the same time, though,
there is an increase in the number in NA summer and in
the NP in both seasons. Furthermore, the number of
storms in the 2K-highlat run decreases in the SH and
increases in the NH, during both seasons. So the results
regarding cyclone number are less conclusive than the
other characteristics.
e. Changes in translation speed
In addition, there are indications that the changes in
cyclone properties are related to one other. Shown in
Fig. 13 is the time evolution of the mean latitudinal
displacements (relative to the starting latitudes) for the
SH cyclones plotted against their mean intensities, for
all the runs. In DJF (Fig. 13a) the most rapid poleward
displacements occur in the 2K-lowlat run, and this run
also has the most intense cyclones, on average. The
poleward displacements in the 22K-highlat and 2K runs,
though somewhat less, still exceed those in the control
run, and the intensities are also greater. Furthermore,
the cyclones in the 2K-highlat run are the weakest of
the set and travel the most slowly poleward. The same
tendencies are evident in SH winter (Fig. 13b) although
the differences are less obvious than in summer.
We collate results from all the runs and all the stormtrack cases in Fig. 14a. Shown here is the mean 5-day
latitudinal displacements plotted against the mean maximum intensity (the maximum typically occurs during the
first few days; Fig. 10a). So each run yields six points in
the figure, one for each storm-track case. The 5-day latitudinal displacements are correlated with cyclone intensity, with a correlation coefficient of 0.66. When the
storms are stronger, they move faster poleward. In fact, the
correlation is even higher (0.81) if one excludes the ERAInterim data, which show larger intensities than the cyclones in CAM3. Consistent with Fig. 10, the strongest
cyclones and largest poleward displacements occur in NH
winter.
The same relation is seen in the differences in maximum intensity and 5-day latitudinal displacement between the perturbation and control runs (Fig. 14c). The
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FIG. 14. The mean 5-day displacements plotted against the mean maximum intensity. Each storm-track region in each of the runs is
represented by a unique marker symbol and color, as shown in the figure. Shown are (a),(b) the 5-day meridional and zonal displacement
(dlat and dlon), respectively, vs the intensity, and (c),(d) the corresponding plots for the difference fields. The black lines in (a),(c), and
(d) are linear regression lines; the Pearson’s correlation coefficient is given in the bottom-right corner of the panels.
correlation coefficient in this case is 0.66. On average, an
increase of 2 3 1026 s21 in vortex intensity corresponds
to an additional 18 poleward displacement in 5 days.
A number of studies have addressed how cyclones
move poleward. The phenomenon was originally studied
in the context of tropical cyclone migration, and early
studies focused on the motion of an isolated vortex on
the b plane (Adem 1956; Chan and Williams 1987;
Sutyrin and Flierl 1994). A cyclonic vortex advects fluid
to the north on its eastern side and to the south on its
western side, and this generates vorticity anomalies
(b gyres), which then act back on the vortex. The result is
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4291
a drift that is poleward and, in the absence of a mean
flow, westward. Smith (1993) proposed a scaling relation
for the drift velocity:3
z
y d 5 CzR
bR
1/2
5 Cz3/2 R1/2 b21/2 ,
where R is the vortex radius, z is its relative vorticity, and
C is a dimensionless constant. The drift scales with the
vortex intensity and size, and is inversely proportional to
the square root of b. So stronger/larger vortices drift
faster than weaker ones, but the drift is inhibited by
having a stronger potential vorticity (PV) gradient.
Recently, others have studied the poleward drift of
extratropical cyclones. These studies suggest that baroclinic effects are important. In particular, advection by
the main cyclone generates an oppositely signed vortex at
higher levels, and the resulting baroclinic dipole can selfadvect poleward (Rivi
ere et al. 2012; Oruba et al. 2013).
In these studies, the strength of the upper anomaly depends on the PV gradient, and a stronger gradient yields
a stronger dipole, resulting in more rapid translation.
The studies are thus inconsistent with regards to the
effect of the PV gradient, as a stronger gradient inhibits
self-advection for barotropic vortices but enhances it for
baroclinic vortices. We examined the zonally averaged
PV gradients in the present runs but found no consistent
relation with the changes in vortex translation. However, where the previous studies concur is with respect to
vortex intensity. In both the barotropic and baroclinic
studies, having stronger vortices implies more rapid selfadvection. This is consistent with the present results.
The relation between the 5-day longitudinal displacement and the maximum intensity is less clear (Fig. 14b).
The strong winter cyclones in the NP, for example, travel
less far eastward than do the winter cyclones in the SH,
which are substantially weaker. However, within a given
storm track the two quantities appear to be correlated.
Moreover the changes in the 5-day longitudinal displacement are correlated with the changes in maximum
intensity (Fig. 14d). Here the correlation coefficient is 0.6.
Increasing the mean vortex intensity by 2 3 1026 s21
corresponds to 58 of additional longitudinal displacement
in 5 days.
While an increase in cyclone intensity can produce
a more rapid poleward drift, as noted, it is not so obvious
that it would also produce more rapid longitudinal displacement. A stronger barotropic vortex on the b plane
3
Smith’s results pertain to the total (zonal and meridional) drift
velocity. However a similar scaling is expected to apply to the
meridional drift alone.
FIG. 15. As in Fig. 14, but for the difference in the 5-day zonal
displacement against the difference in udepth. The latter is the zonal
velocity difference field averaged in time, over pressures between
850 and 250 hPa, over longitudes pertaining to the relevant ocean
basin (over all longitudes in the SH, 708W–108E in the NA, and 1508–
308E in the NP), and taken at the latitude where the difference field
has the largest response (within the relevant storm-track region).
will actually translate less in the longitudinal direction
than a weak one, as it moves more poleward.
However, the mean zonal wind also changes in the
sensitivity runs. The increase in baroclinicity (due to
increasing either the midlatitude SST gradient or the
low-latitude heating) is dominated by an increase in the
meridional temperature gradient (e.g., Yin 2005; GL12).
Stronger temperature gradients yield stronger jets, by
thermal wind, and this in turn implies stronger steeringlevel flow.
Plotting the change in mean 5-day longitudinal displacement against the change in the depth-averaged
mean zonal wind (udepth) also yields a positive correlation (Fig. 15). Thus the cyclones travel more eastward
when udepth is stronger. An increase in udepth of 4 m s21
produces a 5-day longitudinal displacement that is about
68 larger. Of course a 4 m s21 increase corresponds to
a 158 displacement in 5 days, but the cyclones are also
crossing the mean wind and so do not experience the
maximum advection during their whole lifetimes. The
direction and magnitude of the changes are in line with
this expectation. And while the tendency with weaker
zonal flow is not as clear as with the stronger flow, the
correlation coefficient is 0.64 nevertheless.
As noted, the relation between poleward drift and intensity is clearest in SH summer (Fig. 13a). The tendencies
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are less clear in winter (Fig. 13b), and this is also the case in
similar plots for the NH (not shown). The reason is that the
near-zonality of the SH summer storm track facilitates
differentiating poleward and longitudinal translation. As
self-advection presumably occurs across the mean PV
gradient, the motion need not be poleward if the jet is
tilted. And a tilting or spiraling jet will obviously impact
poleward drift. So differentiating the two effects is less
straightforward in the NH and the SH winter.
4. Summary and discussion
We have examined changes in cyclone characteristics
induced by changes in SST in the AGCM simulations of
GL12. The latter focused on changes in storm-track
position and intensity by using bandpass-filtered statistics. Here we focused instead on properties associated
with the cyclones’ life cycle. The cyclones were identified using the objective vortex tracking routine of
Hodges (1994, 1995, 1999) applied to the z850 hPa. We
studied the statistics of the cyclones’ positions, lifetimes, intensities, and translational properties over the
20-yr period of the simulations. Whereas GL12 considered zonally averaged fields during boreal winter only, we
expanded the analysis to include the summer and winter
seasons in both hemispheres, and treat the NA and NP
tracks separately.
In the control run, the SSTs were determined by a
20-yr climatology (Reynolds et al. 2002; Collins et al.
2004, 2006). In the perturbation experiments, SSTs were
increased or decreased by 2 K equatorward and poleward of 458 (in the 2K-lowlat and 62K-highlat runs) and
uniformly over the oceans (in the 2K run). In the former
perturbation runs the midlatitude SST gradients were
increased or decreased, whereas these gradients were
unaltered in the 2K run. These forcings in turn altered
tropospheric baroclinicity (as indicated by the Eady
parameter). It turns out that increasing either the midlatitude gradient or the heating at low latitudes causes
the maximum baroclinicity to shift poleward (Fig. 2 and 3).
The strongest response is in the 2K-lowlat run, where both
effects are present.
One can deduce changes in storm-track position by
using the identified cyclone positions or ‘‘feature points’’
(Fig. 9 and Tables 1 and 2). Averaging the positions and
comparing with the control run yielded track shifts that
were consistent with the changes in baroclinicity and
also with the results of GL12. The mean positions in the
2K-lowlat, 2K, and 22K-highlat runs shift poleward by
0.58–1.18. The warming in the 2K-highlat run, on the
other hand, produces a relatively weak response.
An exception is the NP summer, where the baroclinicity
maximum, storm track, and jet (latter is not shown) shift
VOLUME 27
equatorward when the low latitudes are warmed (Fig. 9a).
The shift occurs in the 2K-lowlat and 2K runs but is absent
in the 62K-highlat runs, so the effect evidently stems from
low-latitude heating. However, this response may be peculiar to CAM3, as the NP storm track lies much farther
north in summertime than it does for example in the
ERA-Interim analysis.
Equatorward shifts in the storm tracks are also known
to occur when changing the subtropical SST gradients
(e.g., Brayshaw et al. 2008; Chen et al. 2010; GL12). In
one run of GL12, a 2-K warming was imposed equatorward of 158. This yielded an equatorward shift in the
storm tracks, with similar changes in the jets, baroclinicity,
Hadley cells, and eddy fluxes of heat and momentum.
Applying the vortex tracking routine to that run, we found
that the mean NP summer storm-track and genesis latitudes shift equatorward by more than 28 and 38, respectively (not shown). So a warming confined to the tropics/
subtropics can produce dramatic changes which differ
from the typical global warming response. This point has
been made previously in relation to El Ni~
no (e.g., Lu et al.
2008; Chen et al. 2008).
The SST alterations also affect the cyclones themselves. When the storm tracks shift poleward, the cyclones are more intense. There are also more short-lived
storms, so the mean lifetime is shorter. However, the
results regarding the number of storms were inconclusive.
Previous studies generally find the number of cyclones
to decrease in a warmer climate (e.g., Bengtsson et al.
2006; Lambert and Fyfe 2006; Pinto et al. 2007; Ulbrich
et al. 2009; Catto et al. 2011), but the results regarding
storm intensity are less consistent, with some finding an
increase (e.g., Lambert and Fyfe 2006; Mizuta et al.
2011) and others finding relatively little change (e.g.,
Bengtsson et al. 2006, 2009; Pinto et al. 2007; Catto et al.
2011). The present results regarding changes in intensity
are in most cases significant. However, it is worth emphasizing that we are only considering the effect of
changes in the SSTs. Other effects may have been at play
in the simulations cited above.
However, an increase in storm intensity may help
explain the observed increase in bandpass-filtered geopotential height variability in GL12. Consider the SH
summer track in the 2K-lowlat run. Here, the number of
storms decreased by 3% while the intensity increased by
roughly the same amount. GL12 found that the maximum bandpass-filtered height variability shifted poleward and also increased in magnitude. As the cyclones
are a major contributor to the variability, it makes sense
that the intensity should increase if the number decreases. In the other cases where GL12 observed increased variability, we also find stronger storms, and
vice versa.
1 JUNE 2014
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GRAFF AND LACASCE
We also find that the cyclones translate faster, both
poleward and eastward. The poleward translation moreover is positively correlated with cyclone intensity. That
a stronger vortex can produce larger meridional displacements is to be expected from Kelvin’s circulation theorem
(e.g., Pedlosky 1987). That stronger cyclones move poleward faster follows both from studies with barotropic
vortices on the b plane (e.g., Smith 1993; LaCasce 1998)
and in realistic models with baroclinic vortices (e.g.,
Riviere et al. 2012; Oruba et al. 2013). We have not established the exact cause of the increased translation in
these runs, merely that the drift velocity is correlated with
cyclone intensity. Changes in the mean PV gradient (not
shown here) were inconclusive, but changes in the mean
temperature gradient are clear. Thus it may be that the
self-induced translation stems from an interaction with
the temperature field. Further work, most likely with
idealized models, is required to know for certain.
However, the change in poleward translation by itself
cannot explain the shift in the storm tracks. The changes
in baroclinicity suggest storm formation shifts with latitude, and the cyclone genesis statistics support this. But
the changes in lysis were usually greater as the stronger
storms move further poleward. This likely impacts the
maximum in the bandpass-filtered geopotential variability, whose maximum lays poleward of the maximum
in baroclinicity (GL12).
The increased eastward advection, on the other hand,
stems from a strengthening of the mean zonal winds.
The stronger temperature gradients imply greater vertical shear, so the jets are intensified. The eastward
translation is accordingly strongly correlated with the
depth-integrated zonal wind. A similar effect was suggested by Catto et al. (2011) in response to a quadrupling
in CO2, as cyclones formed over the Kuroshio during
boreal winter were shown to translate farther downstream with a stronger jet.
As noted earlier, we omitted a set of cyclones which
form between 258 and 358S in the SH during summer.
The changes associated with these systems are opposite
to those of the mid- and high-latitude systems: with an
increase in the mean SST or in the midlatitude gradients,
these cyclones form and die closer to the equator and
also travel less poleward and eastward. This set was
exceptional (i.e., there is no corresponding set of storms
in the NH in summer). The reason for the different behavior is not known but is likely related to changes in the
mean circulation in the near tropics.
To isolate the effect of the SST changes, the sea ice
distribution was left unaltered in the sensitivity runs. Realistically though, a cooling or warming of the high-latitude
SSTs would also alter the sea ice cover. The storm-track
response to such changes is, however, not straightforward,
as can be seen from the body of idealized studies using
AGCMs. For instance, Bader et al. (2011) found that the
NA and NP tracks shift equatorward and poleward, respectively, in response to removing the Arctic ice sheet.
Magnusdottir et al. (2004) found a decreasing trend in the
Labrador and Greenland Sea ice to cause an equatorward
shift in the NA track. Kvamstø et al. (2004), on the other
hand, found a poleward shift in the NA when reducing the
Labrador Sea ice while Seierstad and Bader (2009) found
projected sea ice loss to cause weakening of the NA track
without a shift.
Similarly conflicting results have been found for the
SH storm tracks. Removing the Antarctic ice sheet,
Men
endez et al. (1999) found an equatorward shift in
the SH track, and supporting results were obtained by
Bader et al. (2013) using projected sea ice fields. In
contrast, Kidston et al. (2011) only found weak changes
with an ice reduction, but also a poleward shift in the
tracks with a larger ice cover. Clearly more work is required to establish the dependence on ice cover.
The present SST forcings, while facilitating identification of regional responses, are idealized. Nevertheless, the track-density changes in the SH, NA, and NP
storm tracks induced by warming the low latitudes (the
2K-lowlat run) are similar to the response to global
warming in Bengtsson et al. (2006). The latter used a
coupled climate model and Hodges’ tracking routine to
investigate storm-track response to the Intergovernmental
Panel on Climate Change (IPCC) A1B scenario, and
poleward shifts were seen in both hemispheres. As here,
the changes were clearer in the SH than in the NH, and
clearer in DJF than JJA. Bengtsson et al. (2006) furthermore observed an equatorward shift of the NP summer track. However, they did not find a consistent change
in storm intensity. It is important to understand the difference between the two studies; besides translating
faster, stronger storms have an obvious societal impact.
Acknowledgments. We thank Kevin Hodges for graciously providing and assisting with his tracking routine,
Gwendal Rivi
ere for his useful remarks, and three reviewers (Hans von Storch and two anonymous reviewers) whose comments helped improve the original
manuscript. Furthermore, we acknowledge the use of
ERA-Interim data produced by the ECMWF and obtained from the ECMWF data server.
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