PUBLICATIONS
Geophysical Research Letters
RESEARCH LETTER
10.1002/2013GL058842
Key Points:• South Paciﬁc shows stronger coupling totropics than North Paciﬁc via the PMMs
• The hemispheric asymmetry of thePMMs is mainly due to asymmetricmean trades• Modulation of the mean state asymmetry can impact tropical Paciﬁc variability

Correspondence to:
H. Zhang,hzhang@rsmas.miami.edu

Citation:Zhang, H., C. Deser, A. Clement, and R.
Tomas (2014), Equatorial signatures of the
Paciﬁc Meridional Modes: Dependence
on mean climate state, Geophys. Res. Lett.,
41, 568–574, doi:10.1002/2013GL058842.
Received 26 NOV 2013
Accepted 8 JAN 2014
Accepted article online 13 JAN 2014
Published online 27 JAN 2014

Equatorial signatures of the Paciﬁc Meridional
Modes: Dependence on mean climate state
Honghai Zhang1, Clara Deser2, Amy Clement1, and Robert Tomas2
1

Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA, 2Climate Analysis, Climate
and Global Dynamics, National Center for Atmospheric Research, Boulder, Colorado, USA

Abstract Extratropical atmospheric variability can impact tropical climate in the Paciﬁc sector via the
Paciﬁc Meridional Modes (PMMs). The South PMM (SPMM) has a larger equatorial signature than the
North PMM (NPMM) for the same amount of extratropical variability. Here we explain this interhemisphericasymmetry using an atmospheric general circulation model coupled to a slab ocean model. By imposing ananomalous interhemispheric heating gradient, we strengthen the northeasterly trades and weaken
the southeasterly trades, shifting the Intertropical Convergence Zone south of the equator. As a result, theSPMM no longer inﬂuences the equatorial region while the NPMM shows strengthened linkages to thecentral-western equatorial Paciﬁc. By demonstrating that background winds determine the propagation of
the wind-evaporation-sea surface temperature feedback fundamental for the PMMs, we conclude that theinterhemispheric asymmetry between the PMMs is largely attributed to the asymmetric mean trades in the
Paciﬁc. The results have implications for both paleoclimate studies and model development.

1. Introduction
The “Meridional Mode” represents a set of physical processes through which extratropical atmospheric variability inﬂuences tropical climate variability [e.g., Chiang and Vimont, 2004; Zhang et al., 2014], including
Atlantic hurricane activity [Kossin and Vimont, 2007] and El Niño–Southern Oscillation (ENSO) [Chang et al.,2007]. A comprehensive physical understanding of the Meridional Mode is thus of both scientiﬁc andpractical importance. The Meridional Mode involves the following set of processes: A weakening of thesubtropical trade winds in association with extratropical variability warms the local sea surface temperature(SST) by modifying the turbulent heat ﬂux (note that a strengthening of the winds has the opposite effect).
The anomalous SST signal then propagates along the direction of the climatological trade winds via the windevaporation-SST (WES) feedback mechanism [Xie and Philander, 1994], resulting in a corresponding warm
tropical SST anomaly. Cloud radiative feedback may amplify the SST anomaly in the eastern basin but are notrequired for the existence of the Meridional Mode [Zhang et al., 2014].
The Meridional Mode was identiﬁed ﬁrst in the Atlantic [Nobre and Shukla, 1996; Chang et al., 1997] and thenin the North Paciﬁc [Chiang and Vimont, 2004]. Operating on seasonal-to-decadal time scales, these
Meridional Modes are driven by extratropical atmospheric internal variability known as the North Atlantic
Oscillation [e.g., Xie and Tanimoto, 1998] and North Paciﬁc Oscillation [Chiang and Vimont, 2004], respectively.
With evidence from both observations and climate models of different complexity, Zhang et al. [2014,
hereafter ZCD13] have recently showed that a similar Meridional Mode also exists in the South Paciﬁc.
Despite the similarities between the North Paciﬁc Meridional Mode (NPMM) and the South Paciﬁc Meridional
Mode (SPMM), ZCD13 highlighted a key difference, namely, the interhemispheric asymmetry in their spatial
structure. The NPMM is largely conﬁned to the Northern Hemisphere with some inﬂuence on the northwestern tropical Paciﬁc, while the SPMM exhibits much larger impact on the eastern-to-central equatorial
Paciﬁc. As a result, the SPMM leads to thermodynamic ENSO-like variability [Clement et al., 2011] and may
even trigger dynamical ENSO events [ZCD13]. This difference between the NPMM and the SPMM is consistent
with the model-based ﬁndings of Matei et al. [2008] and Okumura [2013]. Matei et al. [2008] found that thesubtropical South Paciﬁc has a larger and faster impact on the tropical Paciﬁc through the atmosphere-ocean
mixed layer interaction (akin to the SPMM), while the impact from the subtropical North Paciﬁc is weaker andslower and arises via the oceanic meridional overturning circulation. Okumura [2013] argued that tropicalPaciﬁc decadal variability originates primarily from the Paciﬁc/South American pattern over the South Paciﬁc,

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Geophysical Research Letters10.1002/2013GL058842

with little contribution from the corresponding
Paciﬁc/North American pattern over the North
Paciﬁc. The physics underlying these decadal
extratropical-to-tropical linkages are similar to
the PMMs.
To explain the interhemispheric asymmetry between the two PMMs, ZCD13 hypothesized that itis due to the asymmetric mean surface winds in thetropical Paciﬁc, whose distribution determines thespatial extent of the WES feedback [Liu and Xie,
1994].
The climatological southeast trade winds in2
Figure 1. Qﬂux anomaly (W/m ) added to the SOM in the conthe eastern Paciﬁc cross the equator and converge
trol run as a function of latitude. Positive values (heat divergence)with the northeast trade winds at the Intertropicaldenote cooling of the ocean (dT/dt = Qnet  Qﬂux). See the text
Convergence Zone (ITCZ) around 7°N, favoring afor its derivation.
stronger connection between the SPMM and theeastern equatorial Paciﬁc while conﬁning the NPMM to the north of the ITCZ. This hypothesis has theoreticalsupport from two analytical studies, Liu and Xie [1994] and Wang [2010]. Liu and Xie [1994] explored theequatorward propagation of extratropical annual variability in a simpliﬁed Lindzen-Nigam atmosphericmodel coupled to an oceanic mixed layer model including partial ocean dynamics such as entrainment andadvection. They demonstrated that the wave speed of the meridional propagation induced by the entrainment-evaporation process (similar to the WES feedback) is directly proportional to the mean meridionalsurface winds, which implies that the distribution of the mean meridional winds determines the spatial extent of the meridional propagation. Wang [2010] investigated thermodynamically coupled modes of variability in the tropics by analytically solving an idealized Gill-type model coupled to a slab ocean (no ocean
dynamics). They found that cross-equatorial winds can introduce asymmetry to the analytical solutions thatwould otherwise be symmetric about the equator.
In this study, we will apply a state-of-the-art atmospheric general circulation model coupled to a slab ocean
mixed layer model (AGCM-slab model) to test the hypothesis proposed by ZCD13. Compared to the idealized
deterministic atmosphere models in Liu and Xie [1994] and Wang [2010], the AGCM includes much more sophisticated physics aimed for a more realistic climate simulation, and in particular, it allows stochastic forcing(e.g., intrinsic atmospheric variability independent of SST). The strategy is to modify the mean surface wind
distribution in the tropical Paciﬁc and examine the sensitivity of the equatorial impacts of the PMMs to theinterhemispheric asymmetries in the background climate state. A simple conceptual model is also constructed
to analytically support the conclusions drawn from the climate model experiments.

2. Experimental Design
We employ the standard slab ocean conﬁguration of the Community Climate System Model version 4 (CCSM4)[Gent et al., 2011], in which the active Community Atmosphere Model version 4 (CAM4), Community Land
Model version 4 (CLM4), and the sea ice component version 4 (CICE4) are coupled to a slab ocean model (SOM)(labeled CAM4SOM) [Bitz et al., 2012]. This AGCM-slab ocean conﬁguration has proven capable of simulating theSPMM and NPMM in ZCD13. In CAM4SOM, both the prescribed ocean heat transport convergence (termedQﬂux) that compensates for the missing ocean dynamics and the depth of the slab ocean are derived from a
20 year preindustrial control run of the fully coupled CCSM4. The Qﬂux ﬁeld has a repeating seasonal cycle and isused to force the SST climatology toward that of the CCSM4. The depth of the slab ocean varies in space but isconstant in time. In CAM4, all external forcings such as greenhouse gas concentrations and solar insolation are
set to their preindustrial values. A detailed description of this CAM4SOM conﬁguration can be found in Bitz et al.
[2012] who used it to investigate the climate sensitivity of the CCSM4.
In this study CAM4 and CLM4 are run on the same 2° ﬁnite volume grid, while SOM and CICE4 are run on thenominal 1° displaced pole grid. We perform two experiments, each of which is integrated from the same
initial condition for 100 years. The control run (cntl) uses the aforementioned default settings and has theannual mean trade winds in the central to eastern tropical Paciﬁc converging to the north of the equator as inobservations. The sensitivity run (Qa) is the same as the control run except that a Qﬂux anomaly is added at
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Figure 2. Regression (shading) of SST anomalies onto the anomalous SST time series averaged over the northeast Paciﬁc (15°N–25°N, 140°W–120°W,
NEP index indicated by blue boxes) for the NPMM (Figure 2a, cntl; Figure 2c, Qa) and over the southeast Paciﬁc (15°S–25°S, 110°W–90°W, SEP index
indicated by blue boxes) for the SPMM (Figure 2b, cntl; Figure 2d, Qa). Mean surface winds are superimposed as vectors, with the zero contour ofmean meridional wind component shown in the dashed magenta curves.

each time step to shift the annual mean ITCZ southward. The imposed Qﬂux anomaly (Figure 1), which cools
the northern ocean and warms the southern ocean, is derived from the Qﬂux prescribed in the control run in
the following manner. We take the zonal mean of the difference between winter (November to January) andsummer (May to July) mean Qﬂux from 40°S to 35°N and multiply it by a factor of 1/3. We ﬁnd that 1/3 is areasonable choice that allows the Qﬂux anomaly to change the mean climate sufﬁciently while at the sametime not to make the solution unstable. A 10° wide blending zone with the Qﬂux anomaly decreasing poleward sinusoidally to zero is further added to the northern (35°N) and southern (40°S) boundary, respectively.
A ﬁnal adjustment to the Qﬂux anomaly is applied so that its global mean value is zero (i.e., no net heat is
added to the model). In the Qa experiment, it takes about 20 years for the mean climate to equilibrate in
response to the added Qﬂux anomaly. In the next section, we will present our results on the basis of the last 80
years of integration from both the cntl and Qa experiments.

3. Results
3.1. Climate Model Results
Figure 2 shows the annual mean surface winds in the Paciﬁc for the two experiments. In the cntl experiment, thesoutheasterly trades cross the equator in the eastern basin and converge with the northeasterly trades at the
ITCZ around 7°N (Figures 2a and 2b). In contrast, in the Qa experiment, the weakened southeasterly trades arelimited to the South Paciﬁc while the strengthened northeasterly trades cross the equator throughout thetropical Paciﬁc, pushing the mean convergence zone to around 20°S (Figures 2c and 2d). This change in theposition of the mean convergence zone (and associated precipitation maximum; not shown) affects the
structure of the PMMs as discussed next.
One way to visualize the spatial pattern of the PMMs is via linear regression on their subtropical center ofaction [ZCD13]. Here we illustrate the SPMM and NPMM by regressing monthly SST anomalies (with meanseasonal cycle removed) onto the SST anomaly time series averaged over their subtropical centers of action:
the southeast Paciﬁc (SEP: 15°S–25°S, 110°W–90°W) and northeast Paciﬁc (NEP: 15°N–25°N, 140°W–120°W),
respectively (Figure 2). The regression is not sensitive to the precise area used for the SST indices. The spatial
structure of the PMMs is reproduced by the control CAM4SOM (Figures 2a and 2b), consistent with themultimodel ensemble results in ZCD13 (cf. their Figures 1a and 1b). Both PMMs extend equatorward andwestward along the direction of the mean trade winds. In particular, the interhemispheric asymmetry of the

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Geophysical Research Letters10.1002/2013GL058842

Figure 3. (a1–d1) Mean surface winds and (a2–d2) composite Hovmoller diagrams averaged along the axis of propagation of the NPMM (Figures 3a2 and 3b2) andSPMM (Figures 3c2 and 3d2). The axis of propagation is deﬁned as an 8° wide band with its central axis connecting the endpoints (24°N,120°W) and (8°S, 160°E) for
the NPMM and the endpoints (28.6°S, 81°W) and (7.1°N, 179°E) for the SPMM. Results for the cntl and Qa experiments are shown in Figures 3a2 and 3c2 andFigures 3b2 and 3d2, respectively. The zonal (green) and meridional (red) components of the mean winds are plotted along with the total wind speed (blue). In the
Hovmoller diagrams, thin black contours represent wind speed anomalies (solid for positive, dashed for negative), and thick colored contours represent latent heatﬂux anomalies (green for downward, magenta for upward). The shading denotes SST anomalies. The composites are based on the NEP index for the NPMM and the
SEP index for the SPMM. Note that the time axis goes from right to left.

mean winds appears to be in agreement with the difference between the PMMs: The SPMM has a larger
equatorial signature particularly in the eastern-to-central equatorial Paciﬁc where the mean southeasterly
trades cross the equator, while the NPMM, with little projection on the equatorial Paciﬁc near dateline, islargely conﬁned to the North Paciﬁc as are the mean northeasterly trades.
In the Qa experiment, the spatial pattern of the two PMMs changes dramatically in response to the imposedinterhemispheric heating gradient (Figures 2c and 2d). The SST signal associated with the SPMM has a muchsmaller spatial extent and is completely conﬁned to the southeast subtropical Paciﬁc together with the weakened southeasterly trades (Figure 2d), while that associated with the NPMM has a much larger spatial extent
and extends southwestward along the strengthened northeasterly trades with its farthest expansion reaching
the western Paciﬁc where the mean northeasterly trades terminate (Figure 2c). Note that the interhemisphericasymmetry between the PMMs in Qa is still in agreement with that between the changed mean trade winds, asin the cntl experiment. These consistent changes in the spatial structure of the PMMs and the climatologicalwinds suggest an important role for the mean state in regulating the equatorial linkages of the PMMs.
To show the propagation of the PMMs and their dependence on the mean state, we perform composite
analysis for NPMM and SPMM warm events based on the NEP and SEP indices, respectively [ZCD13]. Only
included in the composite are the warm events with a peak that is larger than 1 standard deviation of the SST
indices. Before conducting the composite, the mean seasonal cycle is removed and a low-pass ﬁlter with acutoff period of 1.5 years is applied to focus on the interannual and decadal time scales (as in ZCD13). Notethat the results are not sensitive to the low-pass ﬁlter used. Figure 3 shows the composite Hovmoller diagrams plotted along two 8° wide tilted bands selected for the NPMM (Figures 3a2 and 3b2) and the SPMM
(Figures 3c2 and 3d2) with their center delineated by the y axis. Also plotted in Figure 3 are the mean surface
wind speed and its zonal and meridional components along the same bands (Figures 3a1, 3b1, 3c1, and 3d1).
In the cntl experiment, the SST signals associated with both the NPMM (Figure 3a2) and SPMM (Figure 3c2)
are preceded by weaker wind speeds and positive (downward) latent heat ﬂux anomalies and exhibit clear
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Geophysical Research Letters10.1002/2013GL058842

Table 1. Parameters Used in the Conceptual Model

westward-and-equatorward propagation, conﬁrming the
WES feedback as the propagating mechanism for the PMMs.
For both PMMs, the equatorward propagation is stronglyfCoriolis parameter
constrained by the mean meridional surface winds: The sigμ
thermal coupling coefﬁcientA
atmospheric dissipation coefﬁcientnals do not propagate beyond the latitude where the mean
sea water density
ρomeridional winds switch direction (Figures 3a1 and 3c1). Thiscp
sea water speciﬁc heatis consistent with the asymmetry shown in Figure 2 (left). In
hslab ocean depth
the Qa case, the propagation associated with the PMMs
ρaair density
shows a corresponding change, but the constraint from theLvlatent heat of vaporization
bulk exchange coefﬁcientCEmean meridional surface winds still holds. The NPMM propagates to the south of the equator along with the enhanced
mean northeasterly trades that continue across the equator (Figures 3b2 and 3b1), while the SPMM exhibits avery weak propagation that stops when the meridional winds switch direction (Figures 3d2 and 3d1). These
results from the two experiments suggest that the mean surface winds, particularly their meridional component, exert a dominant constraint on the interhemispheric asymmetry between the NPMM and the SPMM.
ParameterDeﬁnition

3.2. Analytical Model Results
To further explain the constraint from the mean winds, we construct a simple model that couples theLindzen-Nigam one-layer trade wind boundary layer model used in Liu and Xie [1994] with the slab ocean
model used in Wang [2010]. The governing equations are8>< fv ¼ μT x  Au
fu ¼ μT y  Av
(1)>:
T t ¼ QLH =ρo hcp
where state variables u, v, and T are the zonal, meridional surface winds, and SST, respectively. Other parameters are listed in Table 1. As in Wang [2010], weponly
consider the latent heat ﬂux (positive upward):
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
QLH = ρa Lv CE U (qs  q), where U is the wind speed ( u2 þ v 2 ) and q(s) is the (saturated) speciﬁc humidity at
reference height. Separating the variables into mean and perturbation parts (X ¼ X þ X ′ ) and linearizing
equations (1) around the mean, one obtains the equations for perturbation (primes dropped)8>< fv ¼ μT x  Au>:

fu ¼ μT y  Av

(2)

T t ¼ aðuu þ vv Þ=U  bTv CEðqs  qÞ; b ¼ a Lv U2 and note a ≥ 0, b ≥ 0. Combing these three equations by eliminating u andwhere a ¼ ρρa Lhc
o p
R T
v

v, one obtains a single SST equation for the coupled system
T t þ C x T x þ C y T y ¼ bT(3)aμðAu  f v Þ
Cx ¼  2
U f þ A2aμ
ðf u þ Av Þ
Cy ¼  2
U f þ A2

(4)

where

Equation (3) is a ﬁrst-order partial differential equation, with its left-hand side representing nondispersive
two-dimensional waves propagating at the total wave speed (Cx, Cy) along the characteristic delineated by
dt
¼1
ds
dx
¼ Cx
ds
dy
¼ Cy
ds
The SST anomaly evolves along this characteristic following dT
ds ¼ bT. This propagation is fundamentally due
to the WES feedback. As can be seen from expression (4), the meridional wave speed Cy depends on both
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Geophysical Research Letters10.1002/2013GL058842

components of the mean winds. In the tropics where easterlies prevail (u < 0), the meridional propagation
related with u is equatorward and switches direction at the equator (C y ðuÞ ¼ U f 2aμþA2 f u e  f, note U f 2aμþA2 ≥ 0).
ðÞðÞIn contrast, the meridional propagation related with v follows the mean meridional winds (C y ðv Þ ¼ U f 2aμþA2 Av e v ),ðÞ
so that cross-equatorial mean meridional winds will lead to cross-equatorial propagation of the SST anomalies [seeLiu and Xie, 1994, Figure 3]. Therefore, it is the mean meridional winds that are responsible for the interhemisphericasymmetry between the PMMs by inﬂuencing the WES feedback.

4. Summary and Discussion
ZCD13 have recently shown that the South Paciﬁc has a stronger coupling to the equatorial Paciﬁc via the
SPMM than the North Paciﬁc via the equivalent NPMM and hypothesized that this difference is due to theasymmetric distribution of mean trade winds in the tropical Paciﬁc. In this study we test this hypothesis by
ﬁrst modifying the mean surface wind distribution in CAM4SOM and then examining the consequentchanges in the PMMs.
Speciﬁcally, we shift the ITCZ to the south of the equator by cooling the Northern Hemisphere and warming
the Southern Hemisphere. With this new climate state, the spatial pattern of the SPMM is completely conﬁned to the South Paciﬁc while that of the NPMM expands to the central-western equatorial Paciﬁc. These
spatial changes in the PMMs are in agreement with those in the mean surface winds. Further analysis
demonstrates that the propagation of the PMMs changes correspondingly, with a strong constraint fromthe mean surface winds. The equatorward propagation of both PMMs cannot go beyond the location of themean convergence zone where the mean meridional winds change direction. These results suggest that thechanges in the PMMs are attributable to the changes in the mean trade winds. The strengthened northeasterly trades which extend across the equator provide the necessary condition for the NPMM to propagate
into the Southern Hemisphere, while the suppressed mean southeasterly trades remove the same necessary
condition for the SPMM to reach the equator. In both cases, the fundamental reason is the dependence of the
WES feedback on the background surface winds. We demonstrate this dependence with a conceptual model
constructed by coupling a Lindzen-Nigam type atmosphere model to a slab ocean model, similar to the studyof Liu and Xie [1994]. Therefore, we conclude that the mean surface winds in the tropical Paciﬁc, particularly
their meridional component, impose a dominant constraint on the equatorial inﬂuence of the PMMs, andtheir asymmetric distribution largely accounts for the interhemispheric asymmetry between PMMs.
However, there are some changes of the PMMs between the cntl and Qa experiments that cannot be explainedby the mean wind constraint. For instance, in the eastern equatorial Paciﬁc, the NPMM in the Qa experimentdoes not show any response to the changes in the northeasterly trades (Figure 2a versus 2c). This may be related to the orientation of the coastline which keeps the North Paciﬁc Oscillation (the extratropical driver of theNPMM) from extending southeastward toward the eastern equatorial Paciﬁc. In the western Paciﬁc, the two
PMMs in the cntl experiment do not extend as far west as the mean surface winds (Figures 2a and 2b). This is
likely attributable to the damping from high-level clouds [ZCD13].
This study employs a slab ocean model conﬁguration that excludes active ocean dynamics and oceanatmosphere dynamical feedback. Nonetheless, ZCD13 have shown that the interhemispheric asymmetry between the PMMs is a robust feature not only in AGCM-slab models but also in fully coupled climate models (andobservations). Although not required by the physics of the PMMs, ocean dynamics and associated feedback
may contribute to the interhemispheric asymmetry between the PMMs either directly via meridional ocean
advection [Liu and Xie, 1994] or indirectly by inﬂuencing the climatological surface winds. The role of ocean
dynamics is left to future work.
This study has implications for understanding the role of mean climate in regulating internal climate variability. For example, our results suggest that the connections from the extratropics to the tropics may varyover glacial and interglacial periods when the interhemispheric contrast and cross-equatorial winds weresigniﬁcantly different from modern climate [Timmermann et al., 2007; DiNezio et al., 2011]. From the modelingdevelopment perspective, our work suggests that resolving issues in the simulation of the mean state, suchas the “cold tongue” and “double-ITCZ” biases, in the current generation of climate models (including the oneused here) may inﬂuence, and potentially improve, the simulation of climate variability associated with
the PMMs.

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Geophysical Research Letters

Acknowledgments
We would like to acknowledge the highperformance computing support fromYellowstone (ark:/85065/d7wd3xhc)
provided by NCAR’s Computational andInformation Systems Laboratory [2013],
sponsored by the National Science
Foundation. We would also like to thankthe Advanced Study Program at NCAR
for the ﬁnancial support for H.Z.’s visit at
NCAR during which this work has been
conducted. Comments from two anonymous reviewers help to improve themanuscript. NCAR is supported by the
National Science Foundation. H.Z. and
A.C.C. were supported by grants fromthe NSF Climate and Large-scale
Dynamics program, the NOAA Climate
Program Ofﬁce, and the DOE Biologicaland Environmental Research Program.
The Editor thanks two anonymous reviewers for assistance in evaluating
this paper.

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