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Uncertainty in Climate Change Projections of the Hadley Circulation: The Roleof Internal Variability
SARAH M. KANG
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea

CLARA DESER
Climate and Global Dynamics Division, National Center for Atmospheric Research,* Boulder, Colorado

LORENZO M. POLVANI
Department of Applied Physics and Applied Mathematics, and Department of Earth and Environmental Sciences,
Columbia University, New York, New York
(Manuscript received 6 November 2012, in final form 17 March 2013)
ABSTRACTThe uncertainty arising from internal climate variability in climate change projections of the Hadley circulation (HC) is presently unknown. In this paper it is quantified by analyzing a 40-member ensemble ofintegrations of the Community Climate System Model, version 3 (CCSM3), under the Special Report on
Emissions Scenarios (SRES) A1B scenario over the period 2000–60. An additional set of 100-yr-long timeslice integrations with the atmospheric component of the same model [Community Atmosphere Model,
version 3.0 (CAM3)] is also analyzed.
Focusing on simple metrics of the HC—its strength, width, and height—three key results emerge from theanalysis of the CCSM3 ensemble. First, the projected weakening of the HC is almost entirely confined to theNorthern Hemisphere, and is stronger in winter than in summer. Second, the projected widening of the HC
occurs only in the winter season but in both hemispheres. Third, the projected rise of the tropical tropopause
occurs in both hemispheres and in all seasons and is, by far, the most robust of the three metrics.
This paper shows further that uncertainty in future trends of the HC width is largely controlled by extratropical variability, while those of HC strength and height are associated primarily with tropical dynamics.
Comparison of the CCSM3 and CAM3 integrations reveals that ocean–atmosphere coupling is the dominantsource of uncertainty in future trends of HC strength and height and of the tropical mean meridional circulation in general. Finally, uncertainty in future trends of the hydrological cycle is largely captured by theuncertainty in future trends of the mean meridional circulation.

1. Introduction
The mean meridional atmospheric circulation at
low latitudes is commonly referred to as the Hadleycirculation (HC). It plays a central role in the earth’s* The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: Sarah M. Kang, School of Urban
and Environmental Engineering, Ulsan National Institute of Science and Technology, 100 Banyeon-ri, Eonyang-eup, Ulsan 689798, South Korea.
E-mail: skang@unist.ac.kr
DOI: 10.1175/JCLI-D-12-00788.1
Ó 2013 American Meteorological Societyhydrological cycle by determining the locations of theintertropical convergence zone (ITCZ), associated with
regions with the largest precipitation, as well as thelarge-scale subtropical dry zones, where most deserts arefound. There are indications that the HC has been widening in recent decades (see, e.g., Seidel et al. 2008), andthis would have substantial societal impacts. It is thusof great importance to accurately project changes in the
HC in the coming decades.
To do so, it is crucial to understand the uncertainties
that arise in model projections. As recently reviewed in
Deser et al. (2012, hereafter DEA12), three sourcesof uncertainty need to be distinguished. The first is theuncertainty arising from our ignorance of the future

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forcings of the climate system. The second is the uncertainty associated with the fact that different climate
models respond in different ways to identical climate
forcings. The third is the uncertainty that arises from the‘‘internal variability’’ of the climate system.
This last uncertainty is, in many ways, a more fundamental one, because it would persist even if the forcings
were precisely known and the models were highly accurate: it is an uncertainty intrinsic to the climate systemitself. The first type of uncertainty is usually estimatedby carrying out projections with a number of different
future scenarios. The second type is estimated by usinga large number of different climate models all subject
to the identical forcing scenarios. The Coupled Model
Intercomparison Project (CMIP) is one such exercise(Meehl et al. 2007). The third type of uncertainty requires a large ensemble of identically forced integrations
with the same model, and is only now starting to beinvestigated.
DEA12, one of the first studies to focus on projection
uncertainties associated with internal climate variability, used a 40-member ensemble of integrations of the
National Center for Atmospheric Research Community
Climate System Model, version 3 (CCSM3). Each integration was forced with an identical A1B greenhousegas (GHG) and ozone recovery scenario over the period
from 2000 to 2060. DEA12 documented the projection uncertainties associated with internal variability as
reflected in three key variables: surface temperature,
precipitation, and sea level pressure. In a nutshell, theyfound that circulation changes are considerably more
uncertain than surface temperature changes, notably at
mid- and high latitudes, because of the variability associated with the annular modes (Thompson and Wallace2000).
The goal of this paper is to extend the DEA12 study
and explore the uncertainties arising from internal variability, as they relate to future changes in the HC. A
number of previous papers have computed future HC
trends from phase 3 of the CMIP (CMIP3) multimodel
dataset, and have reported a general weakening andwidening of the HC (e.g., Lu et al. 2007, 2008; Gastineau
et al. 2008). Our work differs from those, in that we here
seek to document which aspects of the HC changes arelikely to be more (or less) uncertain as a consequence of
the internal variability of the climate system alone. To
this end, we revisit the same 40 integrations analyzed in
DEA12, but here we focus on a few simple aspects ofthe HC.
As recently summarized in Davis and Rosenlof (2012),
part of the confusion in the recent literature regarding
the discrepancies between observed and modeled trends
in tropical expansion, stems from the wide variety of

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metrics that have been used across several different
studies, some of which have been found to be unreliable(Birner 2010). For simplicity, therefore, we will here
limit ourselves to three key metrics of the HC: its
strength, its width, and its height.
The strength of the HC is an important metric, as it
determines the intensity of the tropical hydrologicalcycle (for a given moisture amount), which accounts forthe bulk of the global-mean precipitation and evaporation. In a warming climate, the tropical circulation is
expected to weaken based on simple thermodynamic
constraints (Held and Soden 2006), although the weakening occurs preferentially in the Walker cell, the zonally asymmetric component (Vecchi and Soden 2007).
In fact, the CMIP3 models exhibit a very large spreadin projections of HC weakening, with a significant HC
trend appearing only at the 60% confidence level(Gastineau et al. 2008). How much of this uncertainty
is related to internal climate variability is an open
question.
The width of the HC—that is, its latitudinal extent ineach hemisphere—is also an important feature of the
HC because it controls the position of the subtropicaldry zones. It also exerts a strong influence on the extratropical climate, by affecting Rossby wave propagation (Held and Phillips 1990; Esler et al. 2000). In recent
decades, a poleward expansion of the HC has been reported in several studies, although much uncertainty
remains about the amplitude of this expansion (Davisand Rosenlof 2012). Moreover, the CMIP3 models appear unable to capture the observed trends (Johanson
and Fu 2009). How projections of tropical expansion
might be affected by internal climate variability is presently unknown.
Finally, the height of the HC—characterized, for instance, by the mean tropopause height in the deep
tropics—has been suggested as an important indicatorof climate change (Sausen and Santer 2003). Beyond
this, of course, it is well known that important flux exchanges occur (between the troposphere and the stratosphere) at the tropical tropopause, notably of water vapor
and chemical constituents. In the coming decades anincrease in tropopause height (i.e., a vertical expansionof the HC) is expected in response to warming of thetroposphere and cooling of the stratosphere (Santer
et al. 2003). The robustness of this result, as it might beaffected by internal climate variability, remains largely
untested.
Hence, the goal of this paper is to establish which ofthese three metrics, each characterizing a distinct andimportant aspect of future changes in the HC, is most or
least uncertain, and to understand the sources of that
uncertainty. For brevity the term uncertainty, here and1 OCTOBER 2013

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elsewhere in the paper, will be used as a shortcut for‘‘uncertainty in future trends due to internal climate
variability.’’ In the next section, we describe the model
data we use and define the HC metrics precisely. In
section 3, we document the uncertainty in each metric,
and show that projection of the vertical HC expansion is,
by far, the least uncertain. In section 4, we analyze therelative contributions to uncertainty stemming from thesea surface temperature changes and direct atmosphericradiative forcings. More importantly, we explore theorigin of uncertainty for each metric in section 5, andshow that the dominant source of uncertainty is ocean–
atmosphere coupling in the tropics. A brief discussion
closes the paper.

2. Models and methods
a. ModelsThe primary model output used in this study is the
40-member ensemble of CCSM3 integrations describedin DEA12, to which the reader is referred for more complete details. CCSM3 is a coupled ocean–atmosphere–land–cryosphere general circulation model. For this40-member ensemble, CCSM3 is run at spectral T42
horizontal truncation (corresponding, roughly, to 2.88
latitude 3 2.88 longitude) for the atmosphere, land,and cryosphere components. The ocean model resolution is uniform in longitude (1.1258) and variable inlatitude (from 0.278 at the equator to about 0.648 in thewestern North Pacific). The atmosphere is verticallydiscretized by 26 levels, 8 of which are located above
100 hPa.
Each of the 40 ensemble members is integrated for theperiod from 2000 to 2060, using identical external forcings: an A1B GHG scenario, stratospheric ozone recovery, and smaller changes in sulfate aerosol and black
carbon, as detailed in Meehl et al. (2006). Only the atmospheric initial conditions differ from one ensemble
member to the next. They are taken from different days
during December 1999 and January 2000 from a single
twentieth-century CCSM3 integration. Since there isno significant memory in the ocean–land–sea-ice initialconditions that last beyond about five years (Branstator
and Teng 2010), they are identical for all members of
the ensemble, and are taken from the conditions on 1
January 2000 from the same twentieth-century CCSM3
integration.
In addition to the above-mentioned 40-member CCSM3
ensemble, we make use of several 100-yr-long integrations of the atmospheric component of CCSM3, theCommunity Atmospheric Model version 3 (CAM3).
These CAM3 integrations, carried out using identicalhorizontal and vertical resolutions as the CCSM3 integrations, are used to investigate the relative contributions of the direct effects of atmospheric radiative
forcing versus the indirect effects via changes in sea
surface temperature (SST) to the uncertainty in future
projections. Specifically, four 100-yr-long CAM3 integrations were performed in time-slice mode, thatis, such that all forcings have no time dependence ortrends other than a seasonal cycle.
The first CAM3 ensemble, labeled REF was forced
using the 40-member CCSM3 ensemble-mean, monthlymean SST and sea ice concentrations (SSTs) averaged
over the period 2000–09, and with atmospheric chemicalcomposition (mainly, GHG, and tropospheric and stratospheric ozone) also set at year 2000 levels. This is thereference integration. To examine the impact of thedirect atmospheric radiative forcing on the future HC
trend uncertainty, a second ensemble labeled ATM wasanalyzed. It is identical to REF, except for the atmospheric chemical composition, which was set to the2051–60 average value. Analogously, the role of the indirect effect via SST forcing is made clear with a third
ensemble, labeled SST—again, identical to REF, except
for the prescribed SSTs, which were set to the 2051–60
mean. A final ensemble, labeled SST1ATM, was forced
with both SSTs and atmospheric chemical composition
at 2051–60 mean levels. This labeling scheme is identicalto the one used in Deser and Phillips (2009), where
similar forcing combinations were used. The characteristics of internal variability in CCSM3 and CAM3 havebeen extensively documented in a special issue of the
Journal of Climate (2006, Vol. 19, No. 11) devoted to
CCSM3. In general, CCSM3 realistically simulates the
major patterns of internal climate variability, although
the ENSO period is shorter than observed (Deser et al.
2006).

b. Methods
As already mentioned, we focus our study on three
key metrics that describe the HC in simple terms: thestrength, the width, and the height. The first two arequantified from the mean meridional streamfunction
C, defined by
C(f, p) 5

2pa cosfgð0y(f, p0 ) dp0 ,(1)

pwhere f is latitude, p is pressure, y is the zonally averaged meridional wind, a is the radius of the earth, andg is the gravitational acceleration.
The strength Cmax of the HC is defined as the maximum value of C at 500 hPa, in each hemisphere. The
width fC50 of the HC is defined as the latitude of its

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poleward edge, in each hemisphere. More precisely:
fC50 is here computed as the latitude where jCj falls to
10% of Cmax at 500 hPa. We use the 10% threshold,instead of the zero crossing, because the summer HC isso weak (especially in the Northern Hemisphere) that insome models and years the zero crossing of C at 500 hPa
is ill defined. The height Pt is defined as the averaged
tropopause pressure, centered at the latitude of Cmax
with a latitudinal width of 108, in each hemisphere;
the tropopause is computed following the algorithm ofReichler et al. (2003), which uses the thermal definitionof the tropopause.
To compute the climate response, we calculate theepoch differences between the last 10 yr (2051–60) andthe first 10 yr (2005–14) of each model integration. As
shown in DEA12, using the epoch difference yields
similar results to computing linear trends. We will
therefore refer to the epoch differences as the ‘‘trends’’in the text below. For the CAM3 integrations, to enabledirect comparison to CCSM3, we first construct 40 setsof 10 arbitrarily chosen years from the 100-yr CAM3
time-slice integrations, thereby building 40 ensemble
members. Then, the response is the 10-yr mean difference between the REF integration and any of the forced
integrations.
To evaluate the uncertainty of the climate response
we compute Nmin, the minimum number of ensemble
members needed to detect the response with 95% statistical confidence. Again, following DEA12, we define
Nmin asNmin 5 8/(X/s)2 ,(2)where X is the ensemble-mean trend of a given quantity(e.g., the tropopause height), and s is the standard deviation, computed from the 40 individual trends, ofthe same quantity. It should be clear that large valuesof Nmin reflect high uncertainty for a given quantity, andvice versa.
Finally, as in DEA12, we characterize the dominantpatterns in the uncertainty of the climate response by
conducting an EOF analysis on the set of 40 trend maps.
First, the uncertain component of mean meridional circulation trend for each ensemble member is computedby removing the ensemble mean of C trends from the C
trend of that ensemble member: this quantity is denoted
as DC0 . Then, the singular vector decomposition (SVD)
is performed on DC0 —that is, DC0 5 USVT—where thecolumns of U are the EOFs. We note that for zonallyaveraged quantities (e.g., zonal-mean precipitation minus evaporation in Fig. 8), the square root of cosf ismultiplied before applying the SVD to account for thearea-weighted covariance matrix. To distinguish the

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dominant patterns in the extratropics and in the tropics,
a separate EOF analysis is computed for each hemisphere poleward of 308 and for the tropics (308S–308N).
The leading principal component (PC1) is obtained as
the first column of a 5 VST; and the variance explainedby the leading mode is obtained as L 5 S(1, 1)2/N, where
N is the size of an ensemble (540). To illustrate theentire global pattern of C trend uncertainty, we plot0
regressions
pﬃﬃﬃﬃ of DC onto the standardized PC record0(a 5 a/ L): this quantity will be referred to as ‘‘theleading EOF of C trend uncertainty’’ (and is shown in
Fig. 7, to be discussed below). Similarly, regressions ofprecipitation minus evaporation (P 2 E) trends onto
a0 will be discussed (in conjunction with Fig. 8) insection 5.

3. Uncertainties in future Hadley cell trends
We start by considering the ensemble-mean trends ofthe zonal-mean meridional stream function C, shownby the colors in Figs. 1a and 1b, for December–February(DJF) and June–August (JJA) (left and right, respectively). In those panels we also plot its ensemble-mean
climatology (2005–14), shown by the black contours. To
guide the eye, we draw an 3 symbol at the latitude of theclimatological Cmax in each hemisphere (which we denote as fmax); we also mark the poleward edges of theclimatological cells in each hemisphere with a 1 symbol;
and, finally, we draw a horizontal line segment where
the model’s climatological, zonal mean, thermal tropopause averaged over the latitudes with a center at fmax
and a width of 108 is found in each season.
Several points can be gathered from Figs. 1a and 1b.
First, as the winter cells are climatologically stronger
than the summer cells, the trends are found to be stronger
in the winter hemispheres: this hemispheric asymmetryis particularly clear for DJF. Also, in that season, we see
a clear HC weakening of the winter cell (see how thedark blue region overlaps much of the winter cell andthe 3 of northern fmax). In JJA, in contrast, the C trends
happen to change sign just around the southern fmax,
indicating a HC weakening in the northern tropics but
a strengthening in the southern tropics. Similarly, in thesummer hemispheres, the edges of the climatologicalcells (1) fall in latitudes with no C trends, suggestingthat only the winter hemisphere will show statisticallysignificant expansion. The bottom line is that C trends
show a surprisingly complex structure, suggesting thatwidely used metrics (such as Cmax and fC50) may not beadequate to capture changes in the HC.
This conclusion is reinforced in Figs. 1c and 1d, where
we show Nmin, the minimum size of an ensemble needed
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FIG. 1. (a),(c) CCSM3 40-member ensemble-mean C climatology (black contours) and trends (colors). Positive values (red shading andsolid contours) indicate clockwise circulation; negative values (blue shading and dashed contours) counterclockwise circulation. Black
contour interval: 5 3 1010 kg s21. (b),(d) The Nmin, needed to detect significant trends. Gray areas indicate locations where trends are notsignificant at the 95% confidence level. In all panels, the climatological latitudes fmax are marked with an 3, fC50 with a 1, and Pt witha horizontal line segment in each hemisphere; (left) DJF and (right) JJA.

in Eq. (2). These panels can be contrasted directly withthe corresponding plots for surface temperature, precipitation, and sea level pressure (SLP) shown in the left
column of Fig. 1 in DEA12. Note the highly complex
latitudinal structure of Nmin for C trends, in contrast to
the much simpler structure for Nmin of surface temperature in DEA12. This confirms and extends a result already reported in DEA12, namely, that circulation
trends in some locations can be more uncertain thansurface temperature changes. Furthermore, the 3 and 1
symbols fall, in many cases, where no statistically significant trends are found, or where a large number ofmodel integrations is required to establish trends, again
suggesting the lack of robustness of many HC trends.

To bring out the relative uncertainty of the individual
HC metrics, we plot in the top row of Fig. 2 the computed trends for each of the three metrics (the individual
ensemble members with crosses, the ensemble mean
with a bar); in the bottom row, the corresponding Nminvalues are shown. For each panel, both the DJF (left)
and JJA (right) results are given, and the light and darkbars show the Southern and Northern Hemispheres (SHand NH), respectively.
Consider first the HC strength as quantified by Cmax,
shown in Figs. 2a and 2b. Robust weakening trends areclear in the NH (dark gray bars), in both seasons, with
only a handful of ensemble members needed to establish
a statistically significant result (Nmin # 4). The SH in

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FIG. 2. (a) Trends of HC strength (Cmax in 1010 kg s21) and (b) corresponding Nmin, from 40-member CCSM3 ensemble. (c),(d) As in(a),(b), but for the HC edge (fC50 in 8). (e),(f) As in (a),(b), but for the HC height (2Pt in hPa). Light (dark) gray shows the SH (NH).
(top) Bars denote ensemble-mean trends, and 3 symbols denote individual ensemble member trends. (bottom) The initials N.S. indicatethat the ensemble-mean response is not significant at the 95% confidence level.

contrast, shows highly uncertain trends in HC strength,
actually insignificant in DJF. A slight strengthening in
JJA is misleading, as noted above in reference to Fig. 1b,
since C trends in that season show a dipole pattern, withstrengthening and weakening to the south and north ofthe center of the southern cell. The lesson here is that
although the HC has been reported to weaken with
global warming (e.g., Lu et al. 2007), one needs to qualifythat statement, insofar as the weakening appears to berobust only in the NH, at least in CCSM3.
As for the width of the HC, Figs. 2c and 2d show that
it is not the hemisphere that matters but the season. In
summer, the HC width trends are highly uncertain: in
the SH this is due to the cancellation between ozone
recovery and increasing GHG (Polvani et al. 2011), andin the NH the huge uncertainty arises from the fact thatthe HC is exceedingly weak (see Fig. 1b) and hence
the edge is barely detectable. In contrast, the winterHC widens robustly in both hemispheres, with only
a few ensemble members needed to establish the result(Nmin # 3). This seasonality in the detectability of HC
widening has been discussed in Kang and Lu (2012).
Again, therefore, the widening statement needs to bequalified, as the HC expansion appears to occur robustly
only in the winter season.
Finally, the trends for the HC height are shown in
Figs. 2e and 2f. These trends are remarkably robust, witha single model integration sufficient to detect the trends,

irrespective of season and hemisphere (in fact, Nmin ;0.1). This result is particularly surprising, in that ourmodel is not a stratosphere-resolving model, and thus
the resolution around the tropical tropopause is relatively coarse. The robustness of the future vertical expansion of the tropical mean meridional circulationsuggests that this metric might be as reliable as surface
temperature as a possible fingerprint of global warming,as suggested in Sausen and Santer (2003).

4. Relative contributions of SST forcing and direct
atmospheric radiative forcing
We now turn to analyzing the CAM3 integrations withsingle forcings, that is, the atmospheric model integrations with SSTs and atmospheric constituents alteredindependently. The 40-member ensemble-mean C trends
for these integrations are shown in Fig. 3, with DJF in
the left column and JJA in the right one. The top row
shows the trends for the SST1ATM case, the middle
row for SST, and the bottom row for ATM. The trends
are shown in color, and the black contour shows the‘‘climatology’’ (i.e., the 40-member mean of the REF
integration).
The first thing to note, comparing Figs. 3a and 3dwith Figs. 1a and 1c, is the close similarity between the
CAM3 SST1ATM trends and the CCSM3 trends. Thisconfirms that the atmospheric model alone is able to1 OCTOBER 2013

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FIG. 3. As in Figs. 1a and 1c, but for the CAM3 integrations: (a),(d): SST1ATM, (b),(e) SST, and (c),(f) ATM. Black
contours show the ensemble mean for the unforced REF case.

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FIG. 4. CCSM3, CAM3 SST1ATM, the sum of CAM3 SST and CAM3 ATM, CAM3 SST, and CAM3 ATM for (a),(c) trends in HC
strength and (b),(d) corresponding Nmin, for (left) DJF and (right) JJA.

accurately reproduce the trends of the coupled model
once the SSTs and atmospheric constituents are specified (Deser and Phillips 2009). The uncertainties, however, are not always the same between the two but are
dependent on the HC metrics, as discussed below. Second, contrasting the top and middle rows in Fig. 3, one
can see that the tropical C trends result primarily fromchanges in SSTs in both seasons. Third, note the nearly
equal and opposite DJF trends in the latitude band 308–
608S (Figs. 3b and 3c), showing the nearly total cancellation between increasing GHG (SST case) and ozone
recovery (ATM case), already documented in Polvani
et al. (2011).
We now consider, one by one, the three HC metrics,
and how they are affected by the different forcings,
starting with the HC strength. Figure 4 summarizes,
for Cmax, the trends and Nmin values. The NH trends
are quite robust, showing a clear weakening response,
as we have already noted, irrespective of season and forcing. The cause for this weakening, however, appears to
depend on the season. The left panels in Fig. 4 clearlysuggest that the SSTs are responsible for the NH weakening
in DJF; the right panels, in contrast, indicate that SSTs
are not the immediate cause for the NH weakening in JJA.
Whether this behavior is peculiar to CAM3 we cannot tellat this point, and rather than speculate we await confirmation of this result with a different model before attempting an explanation. In contrast, the SH trends exhibit
high uncertainty, which stems from direct atmosphericradiative forcing in DJF and from SST forcing in JJA.

Turning next to the widening of the HC, the trends
and Nmin for fC50 are shown in Fig. 5. The key result forthe coupled CCSM3 integrations—that is, that widening
is robust only in the winter hemisphere—is also seen in
the CAM3 integrations (contrast the two leftmost pairsof bars in each panel, showing the coupled and uncoupled SST1ATM results, respectively). However, inthe uncoupled integrations, the widening trend in the
NH also appears to be robust in summer (Fig. 5d). The
widening of the winter hemisphere HC results fromthe indirect effect of the atmospheric radiative forcing(e.g., via SST changes). The same conclusion can be drawn
from Fig. 6 in the case of the HC height metric. Note thevery low values of Nmin for all ensembles of integrations,
except for the ATM one. The SSTs, therefore, appear tobe the key players in nearly all robust trends associatedwith the tropical mean meridional circulation.

5. Characterization of uncertainties in future trends
We now characterize the dominant patterns of uncertainty in future trends, along the lines of DEA12,
with an EOF analysis as described at the end of section
2. The top, middle, and bottom rows of Fig. 7 show the
global distribution of C trend uncertainty (DC0 ) regressed upon the leading PC of tropical, southern extratropical, and northern extratropical DC0 , respectively, forboth DJF (left column) and JJA (right column).
In both seasons, the leading tropical EOF (EOF1;
Figs. 7a and 7d) is characterized by a modulation of HC

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FIG. 5. As in Fig. 4, but for the HC edge (fC50 in 8).

strength centered around the equator. It explains 47%of the variance in tropical C trends in both seasons. Note
that, although the EOF analysis is restricted to thetropics, nonnegligible regression coefficient amplitudes
are found in the extratropics. The leading extratropicalEOF (middle and bottom panels) is characterized by
the Ferrel cell (FC) shift, associated with an annularmode structure in both seasons and hemispheres. It isinteresting to note that in DJF, the extratropical EOF1of one hemisphere is linked to the other hemisphere: apoleward shift of the southern FC accompanies a poleward shift of the northern FC and vice versa. However,these hemispheric modes occur independently of one
another, as indicated by the near-zero correlation between the PC records in the NH and SH (in both CCSM3
and CAM3 ensembles); a similar result for the PCs of
the sea level pressure was reported in DEA12. The extratropical EOF1 in general explains a larger fraction

FIG. 6. As in Fig. 4, but for the HC height (2Pt in hPa).

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FIG. 7. Global distribution of C trend uncertainty (DC0 ) regressed upon PC1 of DC0 (J kg21) in the (a),(d) tropics(308S–308N), (b),(e) southern extratropics (908–308S), and (c),(f) northern extratropics (308–908N), from CCSM3
40-member ensemble, for (left) DJF and (right) JJA. Percent variance explained by each EOF is given in the topright corner of each panel.

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TABLE 1. Pattern correlation between the EOF1 of uncertainty
in C trends from 40-member CCSM3 and CAM3 SST1ATM.
Values in the parentheses are the correlation coefficients within thelatitudinal bands where the EOF is computed [308S–308N for
tropics (trop) and 308–908S/N for extratropics (ExT)].

DJF
JJA

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Trop

SH ExTNH ExT

0.25 (0.29)
0.39 (0.51)

0.64 (0.99)
0.84 (0.97)

0.87 (0.97)
0.72 (0.94)

of the variance than the tropical EOF1, and the largest
variance (59%) is explained by the southern extratropical EOF1 in DJF.
For clarity, we only show in Fig. 7 results for the
CCSM3 ensemble: the extent to which the leading EOF
of C trend uncertainty in CAM3 resembles that in
CCSM3 is quantified by computing the correlation coefficients between the two models (Table 1). The firstnumber indicates the pattern correlation coefficient using
the global map in Fig. 7, and the number in the parenthesis
indicates the correlation coefficient within the latitudinalbands where the EOF is computed (308S–308N for tropical
EOF and 30–908S–N for southern and northern extratropical EOFs) and hence the greater values in the parenthesis. For the extratropical EOF1, a strong correlation
between the CCSM3 and CAM3 exists: it reaches up to 0.87
in NH DJF when the global pattern is used and is nearly
1 (for both seasons and hemispheres) when the specified
latitudinal band is used. This implies the extratropical
pattern, which largely characterizes variability associatedwith the annular modes, is a result of internal atmospheric variability alone. However, the tropical EOF1
exhibits a much weaker correlation between the coupled and uncoupled models, indicating that coupled
ocean–atmosphere variability is important in the tropics.
The question is then how the dominant patterns of
C trend uncertainty in Fig. 7 are related to the trend
uncertainty of each of the simple HC metrics shown in
Fig. 2. In Table 2, we show the correlation coefficient
between the leading PC records of C trend uncertainty
and the trend of each HC metric (Cmax, fC50, and 2Pt),
for the CCSM3 ensemble. The first number denotes theDJF value and the second number JJA. Values significant at 1% according to the two-tailed Student’s t test
and sufficiently large (.0.5) are displayed in bold. As
shown in Table 2, the tropical C EOF1 is well correlatedwith the HC strength in the winter hemisphere, whereas
the extratropical EOF1 is well correlated with the HCedge in the summer hemisphere. This is consistent withthe understanding that the weak summer HC is subject
to the influence of eddy momentum fluxes originating from the midlatitudes, whereas the strong winterHC is more constrained by the angular momentum
conservation and is shielded from extratropical eddies

TABLE 2. Correlation coefficient between PC1 of uncertainty in
C trend and the uncertainty in the trend of the maximum HC
strength in the SH (first row) and the NH (second row), the HC edgein the SH (third row) and the NH (fourth row), and the HC heightin the SH (fifth row) and the NH (sixth row). First value is for DJF
and the second value is for JJA. Results based on CCSM3. Values
significant at 1% according to the two-tailed Student’s t test andlarger than 0.5 are displayed in bold.

SH Cmax
NH Cmax
SH fC500
NH fC500
SH 2PtNH 2PtTrop PC1SH ExT PC1

NH ExT PC1

20.05/0.71
20.91/0.25
0.01/0.57
20.20/0.49
20.01/0.34
20.05/0.47

0.33/20.33
0.19/0.08
0.91/20.59
20.47/20.27
0.68/20.16
0.55/20.22

20.14/0.31
0.09/0.30
20.36/0.33
0.35/0.58
20.24/0.19
20.34/0.24(Schneider and Bordoni 2008; Bordoni and Schneider2010). Moreover, Table 2 indicates that the northern
extratropical C EOF1 in JJA is less correlated with the
NH HC edge (0.58) compared to its SH counterpart inDJF (0.91). This may be because the NH HC edge is notwell defined because of the very weak northern summer
HC and large zonal asymmetries in the NH, as noted inKang and Polvani (2011). Thus, the HC strength is moreassociated with tropical dynamics, and the HC edge ismore controlled by extratropical dynamics. It is, however, noted that the correlation between the HC edges
with the tropical C EOF1 in JJA is also fairly large
in both hemispheres, so that it is feasible that tropicalsources of uncertainty can also influence the extent of
the HC. In contrast to HC strength and width, uncertainties in HC height trends are not consistently related
to any of the leading patterns of uncertainty in DC0 ,except for the southern extratropics (Table 2). Thus,
there appears to be a decoupling in the trend uncertainties between the thermally based HC height metricand the dynamically based C EOF1 patterns.
Last, we take a look at the hydrological cycle by considering how the leading pattern of C trend uncertainty
is associated with the trend uncertainty in the zonalmean hydrological cycle (P 2 E) in CCSM3 (Fig. 8). In
both seasons, the leading P 2 E trend EOF (dashed) isvery similar to the P 2 E trend regression patterns associated with C trend EOF1 (solid), with a patterncorrelation (within the specified latitudinal band used
for EOF analysis) ranging from 0.90 to 0.99, except for
the NH JJA, which exhibits a lower pattern correlationof 0.35 due to differences at high latitudes. Similarly,
high values are found when the pattern correlations
are not restricted to the specified latitudinal band but
computed globally, as evidenced by the similarity of thesolid and dashed curves in Fig. 8. The only exception tothis is for the northern extratropical C EOF1 in DJF,
which shows large differences in P 2 E values in the7552

JOURNAL OF CLIMATEVOLUME 26

FIG. 8. Zonal-mean P 2 E trend uncertainty regressed onto EOF1 of C trend uncertainty (solid) and EOF1 of uncertainty in zonalmean P 2 E trends (dashed) in the (a) tropics, (b) southern extratropics, and (c) northern extratropics (mm day21), from CCSM3
40-member ensemble; DJF in blue and JJA in red. Percent variance explained by EOF1 of P 2 E trend uncertainty is given in the top-rightcorner of each panel, with the first value in DJF and the second in JJA.

tropics for unknown reasons. Thus, the leading patternsof P 2 E trend uncertainty are largely explained bythose of the mean meridional circulation trend uncertainty. In particular, tropical C EOF1 in both seasons(Fig. 8a), characterized by a modulation of HC strength,is accompanied by a meridional shift of the ITCZ. The
extratropical C EOF1 in both seasons (Figs. 8b and 8c),
associated with the annular modes, is accompanied bya tripole pattern of P 2 E: a positive (negative) annularmode is associated with high-latitude moistening (drying), midlatitude drying (moistening), and subtropicalmoistening (drying), as reported in Kang et al. (2011).
This linkage between the trend uncertainties in extratropical C and P 2 E is stronger in the SH, possibly
because the zonal-mean diagnostics are more representative of the SH climate system.

6. Summary
By means of an ensemble of 40 integrations of the
CCSM3 coupled model forced with the A1B GHG

scenario and ozone recovery from 2000 to 2060, we haveinvestigated future trends and associated uncertainties
in the tropical mean meridional circulation arising frominternal climate variability. We have focused on three
simple metrics: the strength, width, and height of the
Hadley circulation.
Three features emerge robustly from our large ensembleof model integrations. First, weakening of the HC occurs
only in the NH, with SH trends being largely insignificant.
Second, the widening of the HC occurs only in the winterseason, irrespective of hemisphere. Third, and perhaps
most surprisingly, only a single integration is needed torobustly establish the rising of the tropical tropopause withclimate change, and this is irrespective of season.
Also, a careful analysis of the trends in mean meridional stream function reveals a highly complex latitude–
altitude structure, dependent on the season and thehemisphere under consideration. This suggests thattrends for many common metrics used to analyze theexpansion of the tropics are likely very uncertain, andought to be used with caution.

1 OCTOBER 2013

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KANG ET AL.

We have taken advantage of several 100-yr-long timeslice integrations with the atmospheric model component to determine the relative roles of direct and indirect(via SST changes) radiative effects of changes in atmospheric constituents that are responsible for the modeled trends in HC metrics. We have found that SSTchanges are largely responsible for the HC trends, withthe direct atmospheric radiative effect playing only avery minor role. Our finding that SST changes are theprimary driver of increases in HC width differs from theresults of Lu et al. (2009), who found that direct atmospheric radiative forcing changes were responsible for
HC widening during the period 1950–2000. Differencesin the relative amplitudes and patterns of SST changes
in the two studies due to different time periods underconsideration, as well as differences in the strength ofatmospheric radiative forcing, may account for the discrepancy. Note also that SST changes in Lu et al. (2009)
are largely internal as opposed to forced by GHG (seeDeser and Phillips 2009).
We have also examined the source of the uncertainty
in future HC trends. The leading pattern of uncertainty
in the mean meridional circulation trends, as determined from an EOF analysis of the 40 individual ensemble members, was found to be associated with themodulation of the HC strength in the tropics and theannular mode of atmospheric circulation variability inthe extratropics, in both seasons and hemispheres. Furthermore, correlations between the leading modes of
uncertainty in the CCSM3 and CAM3 ensembles indicate that much of the spread in the future tropicalcirculation trends owes its existence to ocean–atmosphere
coupling. In particular, the correlation coefficient between the leading PC records of uncertainty in the mean
meridional circulation trends and the trend of each HC
metric reveals that HC strength uncertainty is controlled
primarily by tropical variability resulting from ocean–
atmosphere coupling, whereas HC edge uncertainty is
associated mostly with extratropical variability internal
to the atmosphere. Finally, we have shown that theleading pattern of uncertainty in the trends of the mean
meridional circulation is able to explain most of theleading pattern of uncertainty in the trends of the hydrological (P 2 E) cycle, in the tropics and the extratropics, and in both seasons. A similar strong linkage
between projected changes in precipitation and changes
in the atmospheric circulation has recently been reported by Scheff and Frierson (2012).
Acknowledgments. We thank Haiyan Teng for her
help with data uploading. The work of SMK and LMP
was supported, in part, by a grant from the U.S. National
Science Foundation to Columbia University. SMK is also

supported by the 2013 Creativity & Innovation Research
Fund 1.130033 of UNIST (Ulsan National Institute of
Science and Technology). We thank the two anonymous
reviewers for their helpful comments and suggestions.
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