FENG Juan,LI Jianping,ZHU Jianlei,LI Fei,and SUN Cheng

1College of Global Change and Earth System Science,Beijing Normal University,Beijing 100875

2State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029

3Joint Center for Global Change Studies,Beijing 100875

4Department of Lower Atmosphere Observation Research,Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing 100029

Simulation of the Equatorially Asymmetric Mode of the Hadley Circulation in CMIP5 Models

FENG Juan1,2,3,LI Jianping?1,3,ZHU Jianlei2,LI Fei4,and SUN Cheng1,3

1College of Global Change and Earth System Science,Beijing Normal University,Beijing 100875

2State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029

3Joint Center for Global Change Studies,Beijing 100875

4Department of Lower Atmosphere Observation Research,Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing 100029

The tropical Hadley circulation(HC)plays an important role in influencing the climate in the tropics and extra-tropics. The realism of the climatological characteristics,spatial structure,and temporal evolution of the long-term variation of the principal mode of the annual mean HC(i.e.,the equatorially asymmetric mode,EAM)was examined in model simulations from the Coupled Model Intercomparison Project Phase 5(CMIP5).The results showed that all the models are moderately successful in capturing the HC’s climatological features,including the spatial pattern,meridional extent,and intensity,but not the spatial or temporal variation of the EAM.The possible reasons for the poor simulation of the long-term variability of the EAM were explored.None of the models can successfully capture the differences in the warming rate between the tropical Southern Hemisphere(SH)and Northern Hemisphere(NH),which is considered to be an important driver for the variation of the AM.Most of the models produce a faster warming in the NH than in the SH,which is the reverse of the observed trend. This leads to a reversed trend in the meridional gradient between the SH and NH,and contributes to the poor simulation of EAM variability.Thus,this aspect of the models should be improved to provide better simulations of the variability of the HC.This study suggests a possible reason for the poor simulation of the HC,which may be helpful for improving the skill of the CMIP5 models in the future.

tropical Hadley circulation,equatorially asymmetric mode,CMIP5,sea surface temperature

1.Introduction

The Hadley circulation(HC)is the largest atmospheric circulation system on the planet,and is defined as the zonalmean meridional mass circulation in the atmosphere bounded approximately by 30?S and 30?N.The HC is a thermally driven meridional circulation with poleward mass transport in the upper troposphere and equatorward mass transport in the lower troposphere(Quan et al.,2004).The HC plays an essential role in influencing the climate at low,mid,and high latitudes,and is thus of great importance to the global climate(e.g.,Lindzen,1994;Chang,1995;Hou,1998;Diaz and Bradley,2004;Feng et al.,2013).

Recently,the long-term variability of the HC has been studied intensively,with the width and intensity of the HC being the key issues(Hu and Zhou,2009).Many studies,using a variety of observational and reanalysis data,have consistently shown that the width of the HC shows an obvious poleward expansion trend,and the rate of expansion has been quantified(Fu et al.,2006;Hudson et al.,2006;Frierson et al.,2007;Lu et al.,2007;Seidel et al.,2008;Johanson and Fu,2009;Hu et al.,2011).In terms of intensity,observations show enhanced average annual HC intensity in the 1990s(Chen et al.,2002;Wielicki et al.,2002).The significant increasing trend of the Northern Hemisphere(NH)winter HC can be traced back to the 1950s(Quan et al.,2004; Ma and Li,2008;Hu and Zhou,2009).However,the intensity of the boreal summer HC shows no obvious trend(Quan et al.,2004;Feng et al.,2011).Furthermore,the intensity of the HC since 1979 has been examined(Stachnik and Schumacher,2011;Nguyen et al.,2013),revealing inconsistent trends among different datasets.

In addition to its intensity and width,the spatial struc-ture of the long-term variability is another important aspect of the HC,and has received considerable attention in recent years.The annual cycle of the HC consists of equatorially symmetric and asymmetric parts(Dima and Wallace,2003). More recently,the first principal mode of the long-term variability of the HC was found to be equatorially asymmetric in both boreal winter and summer,with variability that is considered to be closely related to the sea surface temperature (SST)over the Indo-Pacific warm pool(IPWP;Ma and Li, 2008;Feng et al.,2011).A further study by Li and Feng (2015)indicated that the faster warming of SST within the IPWP in the Southern Hemisphere(SH),as compared to the NH,is responsible for the variation of the equatorially asymmetric mode(EAM)in both boreal winter and summer.Feng et al.(2013)also studied the long-term variability of the boreal spring HC,and reported that the structure of the principal mode of the HC is also equatorially asymmetric.They found that the long-term strengthening trend in the AM contributes to frequent droughts in the extra-tropics during boreal spring. These studies raise the possibility that the spatial structure of the principal mode of the HC is independent of its climatological structure,and that the unequal warming in the tropical NH and SH may contribute to its long-term variability.This possibility is further supported by the findings of Feng and Li (2013),who investigated the influence of different types of El Ni?no–Southern Oscillation events on the HC,and revealed that the spatial structure of the SST meridional gradient is responsible for the spatial anomalies of the HC.

The above review of the present status of HC research suggests that the variability of the HC is complex.Given that the variation of the HC is closely linked to changes in global atmospheric circulation,and has major impacts on weather and climate on the global scale,it is important to understand the long-term variability of the HC as well as its future changes.Of more practical importance,if numerical models can successfully simulate the variation of the HC, this would be of great interest for identifying and understanding the changes in the HC,and would also be important for predicting future climate change.Recent work by Hu et al.(2013)discussed Coupled Model Intercomparison Project Phase 5(CMIP5)simulations of the poleward expansion of the HC,and reported that the simulated poleward expansion in CMIP5 is much weaker than in observations.However, few studies evaluating model performance have focused on the spatial structure of the principal mode of long-term HC variability.Such an approach would not only improve understanding of the variability of the HC,but would also provide some reference points for improvements to climate models.

CMIP5 has provided a comprehensive evaluation of stateof-the-art multi-model datasets of coupled general circulation models(CGCMs),and has proved to be a useful benchmark for model sensitivity and predictability experiments to SST forcing(Taylor et al.,2012).However,current climate models still possess clear deficiencies in simulating the variability of climatic modes(Guo et al.,2013;Zheng et al.,2013; Zhu et al.,2013).Although considerable advances have been made in improving the performance of CGCMs,relatively little effort has been directed toward obtaining a proper simulation of the long-term variation of climatic circulation.In the present study,the performance of CGCMs in simulating the complex long-term variation of the HC,in particular the primary mode of the annual mean HC,is examined with the aim of identifying the possible causes of unsatisfactory simulations,and thus contribute to the improvement of current CGCMs.

The remainder of the paper is organized as follows.Section 2 describes the models,observational datasets,and methods used in the study.Section 3 outlines the performance of CMIP5 models in reproducing the spatial and temporal evolution of the EAM of the HC.Section 4 discusses the possible causes of unsatisfactory simulation of the EAM.And finally, conclusions and further discussion are provided in section 5.

2.Models,observationaldatasets,and methodology

2.1.Models

CMIP5 has brought together more than 20 international climate modeling centers to conduct a comprehensive set of long-term simulations of 20th century climate and different climate change scenarios in the 21st century.CMIP5 is a standard experimental protocol for global CGCMs.It provides a community-based infrastructure in support of climate model diagnosis,intercomparison,validation,data access, and documentation.

The simulations from 10 coupled models developed at different modeling centers(see Table 1)were used in the present study.Models were selected on the basis of data availability and model diversity.Considering that the simulation periods for each model are different,the model simulations of monthly meridional wind and surface temperature from January 1961 to December 2000 were chosen to provide a common study period.Multiple simulations are available from most models,with different realizations based on different initial conditions,but only the first standard simulations were used in this study.

2.2.Observational datasets

The reanalysis data used in this study were from the National Centers for Environmental Prediction/National Center for Atmospheric Research(NCEP/NCAR)dataset,from the late 1940s to the present day(Kalnay et al.,1996).The 40-year European Centre for Medium-Range Weather Forecasts Reanalysis(ERA40)from 1958 to mid-2002(Uppala et al., 2005)was also used to verify the long-term variation of the HC.Two SST datasets were extracted:one from the Met Office Hadley Centre Sea Ice and SST dataset version 1,on a 1?×1?latitude–longitude grid(HadISST;Rayner et al., 2003),and the other from the Improved Extended Reconstruction SST dataset(ERSST;Smith and Reynolds,2004) with 2?×2?horizontal resolution,to explore and con firm the impacts of tropical SST on the long-term variability of the HC.Based on the coverage and availability of the modelsimulations and reanalysis data,the period 1961–2000 was selected to examine the long-term variability of the principal mode of the annual mean HC,and to evaluate the simulation performance of the CMIP5 models.

Table 1.List of the CMIP5 models used in this study.

2.3.Methodology

The HC was characterized by the mass stream function (MSF)of the mean meridional circulation(MMC).The MSF was obtained by vertically integrating the zonal-mean meridional winds in the conventional way(Li,2001),and was defined by

where R is the mean radius of the earth,φis the latitude,[ˉv]is the zonal mean meridional wind,g is the gravitational acceleration,and p the pressure.The operatorsˉand[]represent temporal and zonal averaging,respectively.As the annual mean HC has a two-cell structure and tends to be symmetric about the equator(Figs.1 and 2),and to avoid one cell dominating the calculated intensity,the HC intensity(HCI)was calculated separately in the NH and SH,and defined as the maximum of the absolute value of the annual mean MSF in each hemisphere.The locations of the poleward edges and ascending branch of the HC were identified as the latitudes where the MSF reached zero at 500 hPa.These were obtained using linear interpolation,and then the width of the HC could be derived from the differences between the poleward edge locations in each hemisphere.

EOF analysis was employed to determine the principal mode of year-to-year variability of the annual mean MMC. North’s rule was employed to determine whether the EOF modes could be significantly separated.That is,the adjacent significant separated modes of the EOF’s eigenvalues should satisfy the relation

whereλis the eigenvalue,and N is the valid degrees of freedom.The relationship between the principal mode of the annual mean HC and SST was investigated by correlation analysis.Linear trends were computed using least-squares linear regression.The statistical significance of the values of the correlations and linear trends was assessed by means of the two-sided Student’s t-test.

3.Performance of CMIP5 models in reproducing the EAM of the annual mean HC

3.1.Climatological HC simulated by CMIP5 models

The southern component of the HC based on ERA40 data is more intense than that based on NCEP/NCAR data,but nonetheless there is good agreement between the spatial patterns of the HC(Figs.1a and b).The northern and southern components of the HC have equivalent extent and magnitude, with descending branches around 30?latitude in each hemisphere and an ascending branch near the equator.

Table 2.Locations of the southern and northern edges and the ascending branch of the climatological Hadley circulation(HC),together with its extent and intensity(HCI).The HCIs of both the Southern Hemisphere(SH)and Northern Hemisphere(NH)are shown.The numbers in parentheses are the corresponding standard deviations;R1 is the correlation of the PCs of the long-term variability of the annual mean HC between observations and models;R2 is the spatial correlation of SST trends between observations and models within the range (20?S–20?N,0?–358?E).

3.2.Evaluation of the simulated EAM of long-term HC variability

In this section,the spatial pattern of the principal mode of the annual mean HC is analyzed.The explained variance of the first leading mode of the long-term variability of the HC is close to 50%in boreal winter(Ma and Li,2008),spring (Feng et al.,2013),and summer(Feng et al.,2011),giving us confidence that the first leading mode captures the main variation of the HC.Large differences in the amplitude and structure of the second-and higher-order modes are found in different reanalyses(Feng et al.,2013;Li and Feng,2015).In addition,in the present study,large discrepancies are apparent in the second and third modes of the annual mean MSF calculated from ERA40 and NCEP/NCAR data,and the second and third modes in the NCEP/NCAR data are not fully separable according to North’s rule(not shown).Therefore, only the first leading mode,together with its variability,will be discussed.

The first principal mode of the annual mean HC,in both the NCEP/NCAR and EAR40 data,displays an EAM dominating the variability of the annual mean HC.Note that thismode is consistently observed in the two reanalysis datasets, and explains～50%of the variance of the annual mean HC, indicating that this mode can be reliably identified.In fact, this mode is consistently observed in four reanalyses[i.e., NCEP/NCAR,ERA,JRA25(Japanese 25-year Reanalysis) and the NCEP-DOE(Department of Energy)Reanalysis] within the period 1979–2000 with an explained variance of around 50%,further establishing the robustness of our results.The stronger component of this mode is centered to the north of the equator,extending from 10?S to 30?N.The ascending branch of this component is located to the south of the equator,with a descending branch in the NH.In contrast, the counterpart in the SH is weak in both extent and magnitude,and has its descending branch at～30?S.Note that the first principal mode here is similar to those observed during boreal winter(Ma and Li,2008)and spring(Feng et al., 2013).

The principal components(PCs)of the EAM show similar significant upward trends in both the NCEP/NCAR and ERA40 dataset(Figs.1e and f),indicating a strengthening of the EAM during 1961–2000,which would intensify the northern component of the HC.The PCs determined fromthe two reanalysis datasets are highly correlated,with a correlation coefficient of 0.91.However,there are many uncertainties in the PCs from the CMIP5 model simulations(Fig. 5).Even those models that successfully simulate the spatial structure of the EAM have PCs with insignificant trends, and none of the correlation coefficients between their interannual variation and that in the reanalyses is significant(see R1 in Table 2).A similar result is seen when the low-latitude band is analyzed,except the significant downward trend in FGOALS-s2 vanishes(not shown).This result implies that none of the models can simulate the long-term trend or the interannual variation of the first leading mode of the annual mean HC variability.

4.Possible causes of the poor simulation of the EAM of the HC

The above results indicate that the CMIP5 models perform poorly in simulating the leading mode of the annual mean HC’s long-term variability.In this section,we explore the possible causes of this poor performance for the purposeof providing some reference points for improving the simulation skill of these models.The HC is a thermally driven meridional circulation,and its variation is closely linked to the underlying thermal structure(Lindzen and Nigam,1987; Hou and Lindzen,1992).Therefore,the potential contribution of tropical SST to the variation of the EAM is examined.

First,the distribution of the correlation between the PCs of the EAM and SST is considered,as well as the warming trend of SST during 1961–2000,based on ERSST and HadISST data(Fig.6).Significant positive correlation over the south of the eastern tropical Pacific,tropical Atlantic,and in the IPWP is apparent(Figs.6a and b).The areas of significant correlation overlap the regions with a significant warming trend(Figs.6c and d),indicating that the interannual variation of the PCs is associated with the variation of tropical SST.Note that the warming of tropical SST is equatorially asymmetric(i.e.a stronger signal in the SH than in the NH),and to further explore this,we next consider the temporal evolution of SST averaged over the tropical region in each hemisphere(20?S–0?and 0?–20?N)(Fig.7).Based onERSST data,both the southern and northern components of averaged SST exhibit signi ficant warming trends,with coefficients of 1.23?C(100 yr)-1and 0.79?C(100 yr)-1respectively from 1961 to 2000.Similarly,based on HadISST,the results are 1.15 and 0.82?C(100 yr)-1.That is,the warming in the tropical SH is more rapid than in the NH in both reanalyses,and this is also clear in their difference(Figs.7e and f). Their difference[i.e.SST in(20?S–0?)minus SST in(0?–20?N)]shows an obvious upward trend,with a coefficient of 0.45?C(100 yr)-1and 0.34?C(100 yr)-1based on ERSST and HadISST data respectively,both statistically significant at the 0.05 confidence level,indicating that the meridional thermal gradient of the tropics in each hemisphere reduced during 1961–2000.As shown theoretically by Feng et al. (2013),the anomalous spatial pattern of HC is closely linked to the structure of the meridional thermal gradient.Furthermore,they also established that the location of the ascending branch of the anomalous HC corresponds exactly to the position where the SST meridional gradient passes through zero from positive to negative.The possible influence on the HC of the SST difference between the southern and northern tropics can be further seen from the composite difference in the HC MSF between the years of larger and smaller SST difference(Fig.8).The variation of the tropical hemisphere gradient is associated with an anomalous vertical circulation with anomalous ascent located in the SH,similar to the EAM of the HC.This implies that the difference between the tropical SH and NH SST contributes to the intensity of the EAM.

Accordingly,we further explore the long-term trends of SST in the CMIP5 model results(Fig.9).The significant warming trends in the IPWP and tropical Atlantic are captured well by all the models,but not the warming in the south of the eastern tropical Pacific.In addition,the cooling in the north of the central Pacific is not reproduced in all the models.The quality of the simulation of the long-term trend of SST within the tropics(i.e.20?S–20?N,0?–360?E)is further seen in the spatial correlation coefficients between the observations(based on ERSST;R2 in Table 2)and the models.The correlation coefficients are all above 0.34, indicating a reasonable response of the model simulations to the underlying thermal forcing.

The discussion above indicates that most of the simulations of the underlying thermal forcing in CMIP5 models are inconsistent with observations,which may explain the poor simulation of the spatial structure and temporal evolution of the EAM.

5.Discussions and Conclusion

This study did not identify why the models cannot reproduce the warming differences between the tropical SH and NH,since the variation of the underlying SST is a complex issue that is not only linked to atmospheric processes,but is also affected by processes in the interior ocean,as well as air–sea interaction.Nevertheless,a possible cause of the poor simulation by CMIP5 models of the long-term variability of the principal mode of the annual mean HC is highlighted in this paper,and we hope the resultwillbe helpfulin improving CMIP5 model simulations.

Acknowledgements.This work was jointly supported by the National Natural Science Foundation of China(Grant Nos. 41205046 and 41475076),the 973 Program(Grant No.2013CB 430203).We thank the World Climate Research Programme’s Working Group on Coupled Modeling,which is responsible for CMIP,and the climate modeling groups(listed in Table 1 of this paper)for producing and making available their model output.

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14 July 2014;revised 26 December 2014;accepted 5 January 2015)

?Corresponding author:LI Jianping

Email:ljp@bnu.edu.cn