SCIENCE CHINA Earth Scienceszhoutj.lasg.ac.cn/group/files/201332910849897.pdfGAMIL2.0 employs a hybrid horizontal grid, with a horizontal resolution of 2.5 (longitude) × 2.5 (latitude),

SCIENCE CHINA Earth Sciences

© Science China Press and Springer-Verlag Berlin Heidelberg 2012 earth.scichina.com www.springerlink.com

*Corresponding authors (email: [email protected])

• RESEARCH PAPER • April 2013 Vol.56 No.4: 647–661

doi: 10.1007/s11430-012-4497-x

Observational analysis and numerical simulation of the interannual variability of the boreal winter Hadley circulation

over the recent 30 years

SUN Yong1,2, ZHOU TianJun1,3* & ZHANG LiXia1

1 State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;

2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China; 3 Climate Change Research Center, Chinese Academy of Sciences, Beijing 100029, China

Received February 20, 2012; accepted August 7, 2012; published online September 21, 2012

The interannual variability of the boreal winter (DJF) Hadley Cell strength during 1979–2008 is investigated using NCEP/NCAR reanalysis data. The results of AMIP simulation of LASG/IAP AGCM GAMIL2.0 are compared against the re-analysis data. Both the reanalysis data and the simulation show that the interannual variability of the Hadley Cell strength has a non-uniform spatial distribution, as evidenced by the 1st Empirical Orthogonal Function (EOF) mode. The change of Hadley cell strength in the tropics is opposite to that in the subtropical regions. Our analysis indicates that a positive phase of EOF1 is associated with an El Niño-like warmer equatorial central and eastern Pacific and a warmer southern Indian Ocean. Above features are also seen in the results of GAMIL2.0 simulation, indicating that the interannual variability of the Hadley Cell strength is driven by the tropical ocean variability. Our analysis also demonstrates that the contribution of the warmer cen-tral-eastern Pacific to the 1st EOF mode is larger than that of the South Indian Ocean. The SST forcing enhances the local Hadley circulation strength in the central Pacific and Africa (30°S–30°N, 150°E–90°W), while it weakens the local Hadley circulation in other regions (30°S–30°N, 90°–10°W). The western Pacific anticyclone remotely driven by the El Niño forcing leads to a weakened local Hadley cell in the Northern Hemisphere, while the South Indian Ocean anticyclone driven by the remote El Niño forcing and the local warmer SST anomalies in the southern Indian Ocean results in a weakened local Hadley Cell in the Southern Hemisphere. The enhancement of the Pacific local Hadley Cell is stronger (weaker) than that of the Atlan-tic, the western Pacific, and the southern Indian Ocean in the tropical (subtropical) part, thus for the zonal mean condition the strength of the total Hadley Cell is stronger (weaker) in the tropical (subtropical) limb. The amplitude of the Hadley Cell change in the Northern Hemisphere is stronger than that in the Southern Hemisphere. Hence the leading interannual variability mode of boreal winter Hadley Cell exhibits a non-uniform spatial pattern.

Hadley circulation, interannual variability, GAMIL2.0

Citation: Sun Y, Zhou T J, Zhang L X. Observational analysis and numerical simulation of the interannual variability of the boreal winter Hadley circulation over the recent 30 years. Science China: Earth Sciences, 2013, 56: 647–661, doi: 10.1007/s11430-012-4497-x

The Hadley circulation is regarded as a large-scale over-turning of the atmosphere driven by meridional heating gra-dients, extending roughly from 30°S to 30°N. The Hadley

circulation (hereafter HC) plays a key role in earth’s climate by transporting energy and angular momentum poleward [1]. Previous studies found that the change of HC is closely linked to both regional and global climate [2–5]. Under-standing the mechanisms of HC change is of essential im-

648 Sun Y, et al. Sci China Earth Sci April (2013) Vol.56 No.4

portance in climate variability and climate change studies. Previous studies found that the HC has exhibited long-

term variations in the past decades. For example, the HC has been expanding poleward over the past 30 years [6–8]. Much evidence revealed that the boreal winter Hadley cir-culation showed a trend toward intensification in the past 50 years. It may be ascribed to the Indo-western Pacific warm-ing and the interdecadal change of ENSO [9–11]. The strengthening trend of the boreal winter HC in the past dec-ades is due to the enhancement of an asymmetric mode of ENSO [12]. The boreal summer HC in the southern hemi-sphere has undergone an inter-decadal transition in the end of the 1970s, and changed from a stronger-northern-limb and weaker-southern-limb to a weaker-northern-limb and stronger-southern-limb. This inter-decadal transition is sig-nificantly correlated with the non-uniform warming trend in the Indo-western Pacific Ocean and the Atlantic Ocean [13].

All these previous analyses were based on the reanalysis data. However, recent studies found that the quality of rea-nalysis data is questionable in revealing inter-decadal cli-mate change signals [14–18]. Whether the long-term change of the HC is data-dependent deserves further study.

The HC also exhibits a robust interannual variability. Analysis on the 26-year daily upper-air wind radiosonde data found significantly positive correlations between the strength of HC and the El Niño-Southern Oscillation [19]. With the motivation to reveal the 3-dimensional structure of HC change, interannual variability of the HC and the Walk-er Circulation was examined using NCEP/NCAR reanalysis data in previous studies, but the mechanism remains un-known [20]. By performing EOF analysis on boreal winter zonal mean stream function, two interannual modes of HC were revealed. One is equatorial symmetric, and the other is equatorial asymmetric. The symmetric mode is closely re-lated to ENSO [21]. In addition, the eddy transport process also contributes to the interannual variability of the HC [22].

Most previous studies focused on the long term or inter-decadal variability of the HC. The interannual variability of the HC is less well known. The main motivation of the cur-rent study is to identify the interannual variability mode of the boreal winter HC in the period of 1979–2008, and to present the associated 3-dimensional structure of atmos-pheric circulation changes. Whether and how SST forces the interannual variability of HC will be examined by using the atmospheric general circulation model driven by histor-ical sea surface temperature (SST).

1 Data, methods and model description

1.1 Observational data description

The following datasets are used in this paper: (1) CPC Merged Analysis of Precipitation (CMAP) from 1979 to 2008, with 2.5°×2.5° horizontal resolution [23]; (2) NCEP/

NCAR reanalysis data from 1979 to 2008, with 2.5°×2.5° horizontal resolution [24]; and (3) monthly mean sea sur-face temperature data from 1979 to 2008 derived by Met Office Hadley Centre, with 1°×1° horizontal resolution [25].

1.2 Model and experiment description

The model used here is from the atmospheric general circu-lation model GAMIL2.0 developed by LASG/IAP/CAS

[26–28]. GAMIL2.0 employs a hybrid horizontal grid, with a horizontal resolution of 2.5° (longitude) × 2.5° (latitude), and 26 vertical levels in a -p coordinates. Compared with the former version GAMIL1.0, it has a better performance on model physics: (1) convective parameterizations modifi-cations to Zhang and Mu scheme [29, 30]; and (2) micro-physics changed from Rash-Kristjansson and Zhang et al. [31–33] scheme into Morrison and Gettelman scheme. The model has been extensively used in international climate variability and climate change studies and shows reasonable performances [34–40].

The model data used here are from AMIP simulation by GAMIL2.0. The AMIP simulation is forced by observed historical SST. The simulation period spans from January 1975 to April 2009. The period of 1979–2008 is used in this study.

1.3 Method description

The mass stream function is a conventional way to depict the Hadley circulation [41]. Using pressure as the vertical coordinate, conservation of mass requires

1 1 ( cos )

0,cos cos

u v

a a p

(1)

where u is zonal velocity, v the meridional velocity, the vertical p-velocity, a the earth’s radius, longitude, and latitude. If eq. (1) is averaged over longitude, around the entire globe, then the first term on the left-hand side of eq. (1) is zero. Using square brackets to denote this zonal aver-age, the continuity equation is

1 ([ ]cos ) [ ]

0.cos

v

a p

(2)

Eq. (2) states that if one component in eq. (2) ([v] or []) is known, the other one can be identified. In other words, one variable can be used to fully define the two dimensional flow. One can use the stokes stream function ψ to charac-terize the Hadley circulation, defined by

[ ] ,2 cos

gv

a p

(3)

2

[ ] .2 cos

g

a

(4)

Sun Y, et al. Sci China Earth Sci April (2013) Vol.56 No.4 649

Theoretically, stream function can be calculated from observation of either [v] or [], but [v] is used for practical reasons because meridional velocities are more frequently and accurately observed. Solving for and integrating from the top of the atmosphere yields

p

P0

2 a cos( , p) [v(f,p)]d .p

g

(5)

To ensure vertical-mean mass balance, the meridional wind fields were corrected by removing their mass- weighted vertical mean value [42].

Mass stream function (hereafter MSF) is used to reveal the climatology and interannual variability of the Hadley circulation. In order to clarify the interannual changes of the HC, high-pass-filter is applied to mass stream function be-fore conducting EOF analysis, which removes the time scale longer than 8 years.

2 Results

2.1 Climatology of the Hadley circulation

The climatological MSF in the NCEP/NCAR reanalysis

data and the simulation is shown in Figure 1. Hadley circu-lation is a thermal circulation driven by the solar heating. In the climatological DJF mean, there are two Hadley cells in the tropics, characterized by a rising motion near the equa-tor, poleward flow at 10–15 km above the surface, a de-scending motion in the subtropics, and an equatorward flow near the surface. In both the strength and the spatial cover-age, the tropical Hadley cell in the Northern Hemisphere is stronger than that in the Southern Hemisphere (Figure 1(a)).

The simulated mean state of Hadley circulation is shown in Figure 1(b). The observed features of the HC are reason-ably reproduced in the model, which provides a solid basis to discuss the interannual variability of the Hadley circula-tion in the following section.

Although the simulation generally resembles the reanaly-sis data in reproducing the mean state of the HC, some bi-ases are still evident in the simulation. Compared to the reanalysis data, the simulated MSF is stronger in the surface and weaker in the upper level (Figure 1(c)). In order to clar-ify the reason of model bias, we examine the meridional wind and corresponding bias in Figure 1(d)–(f), respectively. As shown in Figure 1(f), the meridional wind of the model is stronger than the reanalysis in the surface but weaker in the upper level. Thus the deficiency of the model in simu-

Figure 1 The spatial patterns of climatological mean Mass Stream Function (MSF) on boreal winter from 1979 to 2008 and meridional velocity (m s1). (a) The MSF from reanalysis; (b) the simulated MSF; (c) the simulation bias of MSF; (d) the meridional velocity from reanalysis; (e) the simulated meridional velocity; (f) the simulation bias of meridional velocity.


lating the meridional wind partly explains the bias of HC simulation.

2.2 Dominant mode of interannual variability

In order to illustrate the variability of the MSF fields pre-sented in Figure 1(a) and (b), we show the standard devia-tion of the zonal mean MSF in Figure 2. The tropical Had-ley circulation shows a distinct interannual variability, which is associated with the maximum standard deviation in the Northern Hemisphere (Figure 2(a)). The corresponding result of the simulation is shown in Figure 2(b). The maxi-mal standard deviation shifts southward in comparison to the reanalysis results shown in Figure 2(a).

To further find out the simulation bias of the mass stream function, we examine the model’s performance on meridio-nal wind in Figure 3. The simulated 850 hPa meridional wind anomalies shift slightly southward associated with maximum bias in the South Pacific Convergence Zone (SPCZ) and over the southern Indian Ocean compared with the reanalysis data. As discussed above, the bias of interan-nual standard deviation is attributed to the simulated 850 hPa meridional wind bias.

To extract a major mode of interannual variability of HC, EOF analysis is performed on the DJF mean MSF in the period of 1979–2008. The first leading mode (EOF1) and corresponding principal components (PC1) are shown in

Figures 4 and 5, respectively. The EOF1 mode is obtained by regression of MSF onto the observed PC1. The EOF1 mode accounts for 57.4% of the total variance. Its spatial pattern shows two prominent characteristics featuring the MSF changes (Figure 4(a)), i.e. the HC is stronger in the tropics associated with the positive anomalies confined in 10°S–16°N and negative anomalies in 10°-26°S, while HC is weaker in the subtropics associated with positive anoma-lies confined in 30°–26°S and negative anomalies in 16°– 30°N. The simulated EOF1 mode (Figure 4(b)) is obtained by regressing the simulated MSF anomalies onto the PC1 derived from reanalysis. The simulated spatial pattern of the first EOF mode agrees well with that derived from the rea-nalysis data. However, some biases remain evident in the simulation. The positive anomalies are narrower in extent and weaker in magnitude in comparison with the reanalysis results shown in Figure 4(a).

As shown in Figure 5(a), the HC exhibits a significant interannual variability and its power spectrum (Figure 5(c)) reveals one dominant period at 4–5 years, which are within the ENSO period. The Niño-3.4 index widely used to depict ENSO variability exhibits a high correlation coefficient with PC1 (R=0.68), indicating the HC variability corre-sponds well to ENSO activity. The simulated PC1 is con-structed by regressing the simulated MSF onto the EOF1 mode derived from reanalysis (Figure 5(b)). Similar features are evident in model simulation (Figure 5(b), (d)). The

Figure 2 Latitude-height cross section of interannual standard deviation of the zonal-mean mass stream function for DJF (1010 kg s1). (a) Reanalysis; (b) simulation.


Figure 3 Latitude-longitude cross section of interannual standard deviation of the 850 hPa meridional velocity for DJF (m s1). (a) Reanalysis; (b) simula-tion.

correlation coefficient of the PC1 time series between the observation and the simulation is 0.69, which is statistically significant at the 5% level.

In summary, the data diagnosis shows that the HC has a strong interannual variability and the corresponding spatial pattern exhibits a non-uniform distribution, which is associ-ated with enhancement in the tropics and weakness in the subtropical region. These pronounced features are well re-produced in the AMIP simulation of GAMIL model. Therefore, the AMIP simulation is helpful to understand the physical mechanism of HC interannual variability.

2.3 The feature of anomalous Hadley circulation

In above analysis, the dominant mode of the interannual

variability of the HC is revealed by EOF analysis. The dominant mode is only for the zonal mean condition. One interesting and important question is: what is the 3-dimensional structure of the atmospheric circulation changes as-sociated with the HC variability mode? To answer the ques-tion, a composite analysis method is applied. We choose four positive phase years and three negative phase years. The selection of positive and negative phase years is based on the threshold of one standard deviation of the normalized PC1. The years selected for composite analysis are listed in Table 1.

2.3.1 Vertical structure of anomalous Hadley circulation

As shown in Figure 6(a), the composite MSF anomalous derived from reanalysis also shows a non-uniform spatial


Figure 4 The spatial pattern of the EOF leading mode of the MSF on DJF (accounting for 58% of the total variance). The period is 1979–2008, and the contour interval is 0.2×1010 kg s1. (a) Reanalysis; (b) simulation. The simulated mode is based on the simulated MSF regression to the time series derived from reanalysis.

Figure 5 The time series of the EOF leading mode of the MSF on DJF (PC1) and corresponding result of power spectrum analysis. (a) The PC1 of reanal-ysis and (b) the power spectrum analysis of PC1. (c), (d) Same as in (a) and (b), but for the simulation respectively. The dashed curve above denotes a sig-nificant level of 5% and 10% in (b) and (d), respectively.


Table 1 The years selected for composite analysisa)

Positive phase Negative phase

1983 1982

1987 1996

1998 1999

2003

a) Based on standardization of the time series of the leading mode, if the values >1, then we define them as positive phase years, if the values <1, then they are negative phase years.

distribution in comparison with the climatological mean MSF (shading). The composite filed is similar to the EOF1 mode. The change of Hadley cell strength in the tropics is opposite to that in the subtropical regions, resulting in an enhancement in the tropics but a weakening tendency in the subtropics. The amplitude of the Hadley cell changes in the Northern Hemisphere is stronger than that in the southern hemisphere. The result of the simulation shown in Figure 6(b) is similar to the observation shown in Figure 6(a). The model reasonably reproduces the non-uniform spatial dis-tribution of the HC interannual variability, but the simulated positive anomalies are narrower in meridional extent.

2.3.2 Horizontal distribution of anomalous Hadley circu-lation

As shown in Figure 1, Hadley circulation is usually revealed by the zonal mean MSF. However, there is an evident defi-ciency using MSF to depict HC, since the variability of lo-cal Hadley circulation cannot be depicted. To illustrate the horizontal distribution of interannual variability of HC, by following Quan et al. [9], another HC index is defined as v150v850. First, the climatological mean meridional wind (v150 minus v850) is analyzed to demonstrate whether this index is able to depict the climate features of HC. As shown in Figure 7(a), the positive values control the region be-tween 15°S and 30°N, while the negative values dominate the region south of 15°S of the Southern Hemisphere, which coincide well with the HC characteristics resulting from MSF shown in Figure 1(a). Therefore, vertical shear of me-ridional wind is a useful metric to gauging the interannual variability of HC. Second, to further examine the robustness of EOF1 mode based on MSF, a composite analysis is ap-plied to v150v850 (Figure 7(b)). Changes in the wind shear also exhibit distinct regional characteristics, associat-ed with positive anomalies confined in 10°S–12°N and south of 24°S, while negative anomalies in the 24°S–10°S

Figure 6 Zonal mean climatological meridional stream function on boreal winter based on NCEP/NCAR reanalysis (1010 kg s1) (shading). Difference the composite anomalous pattern of the zonal mean meridional stream function between the positive phase years and negative phase years. (a) Reanalysis; (b) simulation.


and north of 12°N. As a result, the HC shows a non-uniform distribution with enhancement in the tropics and a weaken-ing tendency in the subtropics, as revealed by EOF1 mode. Finally, vertical shear of meridional wind in horizontal dis-tribution is displayed in Figure 7(c). The anomalies over the tropical Pacific ocean and Africa strengthen the Hadley cir-culation, whereas anomalies located in (30°S–30°N, 60°– 120°E) and (30°S–30°N, 130°W–0°) weaken the Hadley circulation.

We also examine the simulated mean state, composite anomalies, and corresponding horizontal distribution of the vertical shear of meridional wind. As shown in Figure 7(d), the simulated mean state of v150v850 closely resembles the observation, with a pattern correlation coefficient of 0.962, which is statistically significant at the 1% level. As shown in Figure 7(e)–(f), the model also does well in re-producing the observed non-uniform distribution character-istics and the spatial pattern. The correlation coefficient of the composite v150v850 between the reanalysis and the simulation is 0.957, which is statistically significant at the 1% level.

Following the interannual variability of the HC, the ver-tical shear of meridional wind shows a non-uniform change,

which is associated with enhancement in the tropics and weakening tendency in the subtropics. The changes of me-ridional wind are consistent with the features reflected in the MSF. The AMIP simulation successfully reproduces the interannual variability of HC. Note that in the AMIP-type simulation, the only interannual external forcing changes come from the sea surface temperature (SST). Thus the HC changes over the past 30 years should be driven by SST anomalies.

3 Linkage between Hadley circulation variation and SST changes

As discussed earlier, our analysis demonstrates that the in-terannual variability of the HC is linked to the SST changes. To further exhibit how the SST anomaly affects the HC, the observed SST is regressed onto the PC1 (Figure 8(a)). The evolution of EOF1 mode is highly correlated with SST anomalies in the central and eastern equatorial Pacific and the South Indian Ocean. The corresponding results of the simulation are in good agreement with the reanalysis (Fig-ure 8(b)). Thus the El Niño-related SST anomalies in the

Figure 7 Vertical shear of meridional velocity between 150 and 850 hPa in the period of 1979–2008. (a), (b) Climatological DJF mean analysis; (c) hori-zontal distributions of the composite analysis; (d)–(f) same as in (a)–(c), but for the simulation.


Figure 8 Horizontal distributions of the regression coefficient between the time series of the leading mode and the simultaneously SST during boreal winter. (a) Reanalysis; (b) simulation.

central and eastern equatorial Pacific and the warmer SST anomalies over the South Indian Ocean should drive the interannual variation of HC.

3.1 Role of SSTA forcing from the tropical central and eastern Pacific Ocean

El Niño is a robust interannual variability signal in the cli-mate system. Previous studies suggested that following the development of El Niño, the meridional component of the trade wind would enhance, and thereby lead to an enhance-ment of the local Hadley Cell in the Pacific [43]. Later studies found that the enhancement of the meridional com-ponent of the trade wind is resulted from the intensified convergence of the meridional wind as a response to the heating in the central Pacific [44]. However, according to the Gill model, a pair of anomalous cyclones response to a heating source forms in the equatorial central and eastern Pacific Ocean, having some degree of symmetry about the equator [45]. The meridional wind anomalies east/west to the cyclone would weaken/enhance the meridional compo-nent of the trade wind. Thus the local Hadley Cell changes associated with the development El Niño should not exhibit

coherent changes across the Pacific. The relationship be-tween the local HC over the Pacific Ocean and El Niño needs to be investigated.

The HC strength is usually measured by the magnitude of released latent heating, although it is driven by direct ther-mal forcing rather than by latent heating [46]. The precipi-tation changes can be seen as a measure of the strength of latent heating. The anomalous precipitation response to SST anomaly over the equatorial central-eastern Pacific Ocean is shown in Figure 9(a). Warmer SST anomalies in the central and eastern equatorial Pacific result in excessive rainfall, whereas less rainfall with cold SST anomalies appears in the Philippine Ocean. As shown in Figure 9(b), the simu-lated precipitation agrees well with the reanalysis, suggest-ing that AMIP simulation can be used to analyze the at-mospheric circulation response to latent heating.

To illustrate the structure of the atmospheric circulation response to warmer SST anomalies, the anomalous hori-zontal winds at 850 and 200 hPa are shown in Figure 10. In the mean state (Figure 10(a), (b)), an anticyclone circulation covers both the subtropical North and South Atlantic Ocean in the lower. The superposition of an westward flow on the southern (northern) flank of the North (South) Atlantic high


Figure 9 Difference in the composite anomalous pattern of the precipitation between the positive phase years and negative phase years. (a) Reanalysis; (b) simulation.

would lead to an increase in wind speed and the corre-sponding cross-equatorial flow from the Northern Hemi-sphere reaches the northern edge of anticyclone circulation over the southern Pacific Ocean, resulting in low-level con-vergence limited around 10°S and upper-level divergence. In addition, cross-equatorial flow from the Northern Hemi-sphere extends to 10°S and encounters with a northward flow on the eastern flank of Mascarene high, resulting in low-level convergence and upper-level divergence. This means that the horizontal wind in the region (30°S–30°N, 90°W–150°E) is contributed to the climatological HC, while the horizontal wind over the Pacific Ocean contrib-utes less to the climatological HC, where the zonal winds are same in the low-level and upper-level (Figure 10(a), (b)). The simulated horizontal winds in 850 and 200 hPa are also examined (not shown) and the simulation is comparable to the reanalysis.

During boreal winter of El Niño years (Figure 10(c), (d)), a pair of anomalous cyclones (anticyclones) is seen at the surface (upper troposphere) over the equatorial central and eastern Pacific, while a pair of anomalous low-level (up-per-level) anticyclones (cyclones) are found in the Philip-pine Ocean, which is consistent with Gill model [45] (Fig-ure 10(c), (d)). The western flank (30°S–30°N, 150°E– 130°W) of anomalous cyclone (anticyclone) at 850 hPa (200 hPa) over the central and eastern Pacific flows into the equator (poleward), enhancing the climatological HC. The eastern flank (30°S–30°N, 130°–90°W) of the above- mentioned anomalous circulation has an opposite impact on climatological HC in comparison to that of the western flank, and thus weakens the local HC.

The observational analysis shows that the response of the local HC to El Niño is non-uniform over the Pacific Ocean. Due to the warmer SSTA over the central and eastern Pacific


Figure 10 Horizontal distribution of wind circulation (vector, m s1). (a) The climatological mean 850 hPa from reanalysis; (b) same as above, but for 200 hPa. The composite anomalous horizontal circulation between the positive and negative phase years: (c) 850 hPa derived from reanalysis; (d) same as in (c), but for 200 hPa; (e) and (f) same as in (c) and (d), but for the simulation.

Ocean, an anomalous cyclone at lower troposphere and an-ticyclone at upper troposphere are seen, which strengthen (weaken) the local HC to its left (right) side.

The simulated winds at 850 and 200 hPa are shown in Figure 10(e) and (f), respectively. The 850-hPa wind fea-tures a pair of anomalous cyclones, and a pair of anomalous upper-level anticyclones dominates over the tropical cen-tral-eastern Pacific Ocean. Therefore, the prominent fea-tures of atmospheric circulation response to El Niño-like SST anomaly are reasonably simulated, indicating that the interannual variability of the HC over the Pacific Ocean is driven mainly by the local forcing of El Niño.

Many studies also investigated the Atlantic Ocean Had-ley circulation variability during El Niño. The Hadley cir-culation can serve as a “tropospheric bridge” for transfer-ring the Pacific El Niño SST anomalies to the Atlantic Ocean. Previous studies emphasized in the atmospheric cir-culation response to El Niño forcing, which is associated with vertical motions and corresponding horizontal diver-gence wind filed [47]. Further study documented that during warm phase of ENSO, anomalous Walker circulation and

Hadley circulation share the same descending branch over the Atlantic Ocean, and are opposite to the climatological HC, weakening the local HC [48].

During boreal winter of El Niño year, positive heating anomaly is seen over the North Atlantic subtropics (Figure 9(a)), resulting in weakening of the Atlantic subtropical high, which is associated with an anomalous low-level cy-clone (20°–40°N, 90°–20°W) and leads to anomalous up-ward motion. In the meantime, the decreased rainfall domi-nates the South America and leads to an anomalous anticy-clone over (20°–30°N, 70°–40°W) in the upper troposphere and accompanying descending motion (Figure 10(c)). The combination of southerly wind in the eastern flank of anomalous cyclone and southerly wind in the northwestern flank of anomalous anticyclone leads to weakening of northeasterly trade wind. The eastern flank of low-level anticyclone (0°–20°N, 90°W–0°) is northerly, enhancing the climatological HC. In the upper level, the north/south wind anomalies west/east to the cyclone over the northern Atlan-tic Ocean would enhance/weaken the climatological HC. Thus the meridional component of both the eastern and


southern flank of low-level cyclone (20°–40°N, 90°–20°W) and western flank of the upper-level cyclone (0°–30°N, 30°W–30°E) leads to weakening the Northern Hemisphere Hadley cell, while the meridional component of both southeastern flank of the above mentioned low-level anticy-clone and eastern flank of the above mentioned upper-level cyclone would enhance the Hadley cell in the Northern Hemisphere. As discussed above, local Hadley circulation does not exhibit coherent changes over the Northern Atlan-tic Ocean during El Niño, although strength of the Northern Atlantic Ocean Hadley cell is weakened for the zonal mean condition.

Following the same analysis method, we examine the HC changes over the Southern Atlantic Ocean. An anomalous anticyclone (30°S–0°, 90°W–20°E) occurs in the lower troposphere and the corresponding anomalous cyclone can be seen in the upper troposphere, which weakens (strength-ens) the local HC to their left sides (right sides). Both the low-level anticyclone and upper-level cyclone over the Southern Atlantic Ocean appear weaker than that over the Northern Atlantic Ocean. The meridional wind anomalies on two sides of both the anticyclone and cyclone have an opposite impact on the local Hadley circulation over the Southern Atlantic Ocean, which is associated with en-hancement in the right and weakening in the left. The am-plitude of the left weakness is stronger than that of the right enhancement, resulting in weakening of the southern Atlan-tic Ocean Hadley circulation for the zonal mean condition during boreal winter of El Niño year.

In the simulation, changes of the local Hadley circulation over the Atlantic Ocean are well reproduced except that low-level cyclone in the north subtropical Atlantic shifts eastward and is narrower slightly in extent, while the simu-lated upper-level cyclone over the equator Atlantic Ocean also shifts eastward and is weaker in magnitude.

3.2 Remote forcing of El Niño

Many previous studies documented the anomalous North-western Pacific anticyclone during El Niño. The anticyclone is established in the late fall of the El Niño developing years and persists until the following spring or early summer. The development and maintenance of the anticyclone is at-tributed primarily to the “Wind-Evaporation-SST” feedback [49, 50]. Many other studies pointed out that SST anomalies over the Indian Ocean and Northwestern Pacific Ocean are both contributed to sustain the anticyclone. The former plays a crucial role in early summer, whereas the latter dominates in late summer [51–53].

The persistence of the anomalous anticyclone over the western North Pacific plays a crucial role in regional and global atmospheric circulation [54]. Most previous studies focus on the weakening influence of the Northwestern Pa-cific anticyclone on the local HC during the El Niño years, but the corresponding mechanism deserves further study

[55]. One interesting and important question is how the above mentioned anticyclone affects the local Hadley cir-culation. As suggested in Figure 10(c) and (d), a low-level anticyclone anomaly dominates the Philippine Ocean, while an anomalous cyclone occurs in the upper East Asian. In the lower-troposphere, the southwestern flank of the anomalous anticyclone (prevailed by southerly) weakens the climato-logical HC. In the upper-troposphere, the combination of the northerly in the western flank of anomalous cyclone over the East Asian and the northerly in the northeastern flank of the anomalous anticyclone over the Indian Ocean would increase northerly wind anomaly, weakening the climatological upper-tropospheric branch of HC. Thus the anomalous Northwestern Pacific anticyclone driven by the remote forcing of El Niño leads to a weakened Hadley cell in the Northern Hemisphere.

As shown in Figure 10(e) and (f), the AMIP simulation successfully reproduces the atmospheric circulation re-sponse to the warmer SST anomalies over the equatorial central and eastern Pacific Ocean, suggesting that the inter-annual variability of the Hadley circulation over the north-western Pacific is related mainly to SST forcing in the equatorial Pacific Ocean.

3.3 Role forcing of warm SST anomalies (SSTA) over the South Indian Ocean

As to the interannual variability of the HC, previous studies focused mainly on the linkage with ENSO. The role of In-dian Ocean SSTA is unclear. As shown in Figure8, warm SSTA is shown evidently over the tropical Indian Ocean, especially over the South Indian Ocean. In the following section, we will discuss how the Indian Ocean warmer SSTA affects the interannual variability of the HC.

As shown in Figure 10(c), an anomalous cyclone (30°– 10°S, 40°–70°E) occurs over the southwest of the Indian Ocean. An anomalous strong anticyclone (30°S–0°, 70°– 130°E) is to the right of this cyclone (30°–10°S, 40°–70°E). Therefore, the combination of the northwest anomalies of the eastern flank of the anomalous cyclone and the north-west anomalies of the western flank of the anomalous anti-cyclone leads to an increased northerly, weakening the local HC. In the upper-troposphere, the south anomalies of the western flank of the cyclonic anomaly (30°S–0°, 80°–140°E) flow into the equator, which also weaken the local HC (Figure 10(d)).

Previous studies argued that the remote forcing of El Niño plays a crucial role in development and persistence of the anticyclone over the South Indian Ocean from the late summer to boreal winter of the developing El Niño years [56]. The subsequent studies suggested that the anticyclone is resulted from the Indian Ocean air-sea interaction and independent of ENSO [57]. Based on the comparison be-tween the AMIP simulation and the observation, Zhou et al. [58] found that the seasonal evolution of the anticyclone


over the South Indian Ocean is not in phase with ENSO forcing in observations, but strictly matches with ENSO in AMIP-type simulation. It indicates that this establishment of anticyclone anomalies is linked to warmer SSTA over both the equatorial central and eastern Pacific Ocean and South Indian Ocean, which further induces a weaker local HC.

The simulated atmospheric response to SSTA over the Southern Indian Ocean is shown in Figure 10(e) and (f). Because SST is the only external forcing in the AMIP sim-ulation, the reasonable simulation of AMIP for the anticy-clonic anomaly over the Indian Ocean is resulted from at-mospheric circulation response to the SST forcing.

The above analysis shows that the interannual variability of the HC over the recent 30 years has a non-uniform dis-tribution, which is regarded as a response to local SST forcing as well as remote forcing. Due to the ocean forcing, anomalous low-level cyclone (anticyclone) associated with upper anticyclone (cyclone) occurs in the different oceans. Different sides of the anomalous circulation exhibit differ-ent impacts on the local HC. In summary, the amplitude of the Hadley cell in the Northern Hemisphere is stronger than that in the southern hemisphere. Therefore the leading in-terannual variability mode of boreal winter Hadley Cell exhibits a non-uniform spatial pattern.

4 Summary and concluding remarks

The distinct mode of the interannual variability of the Had-ley circulation in boreal winter is revealed using NCEP re-analysis data and AMIP simulation of GAMIL2.0. By comparing the AIMP simulation with the reanalysis, the 3-dimensional structure of the atmospheric circulation changes associated with the HC variability is revealed. The corresponding mechanism is also discussed. The main re-sults are as follows:

The Hadley circulation shows a distinct interannual var-iability. The maximum standard deviation is seen over the

tropics. By performing EOF analysis on the mass stream function, it shows that the first mode exhibits a non-uniform spatial distribution of Hadley circulation. The change of Hadley cell strength in the tropics is opposite to that in the subtropical regions, which can also be reflected by its me-ridional wind anomalies. The power spectral analysis of Hadley circulation reveals one dominant period at 4–5 years. The above features can be reasonably reproduced by the AMIP simulation of GAMIL2.0, suggesting that the inter-annual variability of the Hadley circulation on boreal winter should be driven by the SST forcing.

The regressions of DJF mean SST anomalies onto the PC1 in the observation and simulation suggest the interan-nual variability of the Hadley circulation is driven mainly by the tropical SST anomalies. The contribution of the warmer SST anomalies over the central-eastern Pacific Ocean is larger than that over the Southern Indian Ocean.

Changes of the zonal averaged Hadley circulation are closely related to the local Hadley circulation variability. To better understand the horizontal structure of Hadley circula-tion, the schematic diagram is shown in Figure 11. In the El Niño winter, the local Hadley Cells over the Pacific Ocean and the Atlantic Ocean do not exhibit coherent changes. However, for the zonal mean condition, they result in an intensified and weakened Hadley circulation, respectively. The Northwestern Pacific anticyclonic anomaly remotely driven by the El Niño forcing leads to a weakened local Hadley circulation in the Northern Hemisphere. The anom-alous anticyclone over the Southern Indian Ocean, driven by the combination of remote El Niño forcing and local warmer SSTA over the Southern Indian Ocean, results in a weakened local Hadley cell in the southern hemisphere. The zonal mean enhancement of the tropical (subtropical) part of the Pacific HC is stronger (weaker) than that of the Atlantic, the western Pacific and the southern Indian Ocean, Thus the strength of the zonal mean HC is stronger (weaker) in the tropical (subtropical) (Figure 11). The amplitude of the Hadley cell change in the Northern Hemisphere is stronger

Figure 11 Schematic diagram showing the linkage of the pacific El Niño and southern Indian Ocean positive SST anomalies with local Hadley circulation.


than that in the southern hemisphere. Therefore the leading interannual variability mode of boreal winter Hadley Cell exhibits a non-uniform spatial pattern.

In this study, we examine the relationship between the interannual variability of the Hadley circulation and SST changes in the tropics. It shows that the Hadley circulation changes are closely related to the SST anomalies over the central-eastern Pacific and Southern Indian Ocean. Relative role of Indian Ocean and central-eastern Pacific SST forcing in the Hadley circulation anomalies is unclear; therefore, it is necessary to analyze the AMIP simulation to confirm our finding and perform a suite of numerical experiments to investigate the relative contribution of SST forcing over the central-eastern Pacific and Southern Indian Ocean. It re-mains to be confirmed in the future study whether the ex-tra-tropical climate system (such as the East Asian Winter Monsoon) has any impact on the interannual variability of Hadley circulation.

This work was supported by National High-tech R&D Program of China (Grant No. 2010AA012304), National Basic Research Program of China (Grant No. 2010CB951904), and National Natural Science Foundation of China (Grant No. 40890054).

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SCIENCE CHINA Earth Scienceszhoutj.lasg.ac.cn/group/files/201332910849897.pdfGAMIL2.0 employs a hybrid horizontal grid, with a horizontal resolution of 2.5 (longitude) × 2.5 (latitude),