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  • View in gallery

    Taylor diagram of (a) the SON SAM pattern and (b) the climatological mean DJF precipitation in the NH. The red dots (numbered) refer to the 25 models. The correlation coefficients and the ratio of the standard deviation between models and observations (ERA-Interim and CMAP) are shown by the cosine of the azimuth angle and the radial distance, respectively. REF on the horizontal axis indicates the reference point (ERA-Interim and CMAP).

  • View in gallery

    Correlation coefficients between the SON SAMI and DJF zonal-mean precipitation for the (top) observations (based on SAMI, SAMI_E, and SAMI_M, respectively) and simulations in the 25 model runs from the CMIP5 historical experiment. The dashed lines indicate significance at the 90% confidence level using a two-tailed Student’s t test. The black, red, blue, and magenta lines in the observations across the top denote results based on the CMAP, GPCP, GPCC, and PREC/L datasets, respectively. The filled areas indicate the models that basically reproduce the tripole pattern of DJF precipitation anomalies associated with the SON SAM.

  • View in gallery

    Regression of the SON SAMI onto the zonal-mean DJF vertical velocity (10−5 Pa s−1) for the (a) observations and the (b) type-I and (c) type-II multimodel means. The stippled areas indicate statistical significance at the 90% confidence level using a two-tailed Student’s t test. The blue (red) shading corresponds to upward (downward) motion.

  • View in gallery

    Lead–lag regression of the SON SAMI onto (a) zonal-mean 10-m zonal wind (m s−1) and (b) SST (°C) from June to the following March in the observations; in parentheses a “0” denotes the current year and a “1” the following year. The shaded areas indicate statistical significance at the 90%, 95%, and 99% confidence levels using a two-tailed Student’s t test.

  • View in gallery

    Lead–lag regression of the SON SAMI onto the zonal-mean 10-m zonal wind (m s−1) for the (a) type-I and (b) type-II multimodel means from June to the following March; in parentheses a “0” denotes the current year and a “1” the following year. Shading indicates statistical significance at the 90%, 95%, and 99% confidence levels using a two-tailed Student’s t test.

  • View in gallery

    As in Fig. 5, but for SST (°C).

  • View in gallery

    The JJA, SON, and DJF 10-m zonal wind (m s−1) regressed onto the SON SAMI from the (a)–(c) observations and the (d)–(f) type-I and (g)–(i) type-II multimodel means. Stippled areas indicate statistical significance at the 90% confidence level using a two-tailed Student’s t test. The latitude lines are at 10° intervals starting from 30°S.

  • View in gallery

    As in Fig. 7, but for SST (°C).

  • View in gallery

    The DJF zonal-mean vertical velocity (10−5 Pa s−1) regressed onto the DJF SODI from the (a) ERA-Interim data and the (b) type-I and (c) type-II multimodel means. Stippled areas indicate regions where the regression coefficient is significant at the 90% confidence level using a two-tailed Student’s t test. The blue (red) shading corresponds to upward (downward) motion.

  • View in gallery

    Spatial pattern of the SON SAM as the leading EOF mode at the 700-hPa geopotential height south of 20°S from the observations and the 25 model runs from the CMIP5 historical experiment. The percentages in red are the explained variance. The latitude lines are at 20° intervals starting from 20°S.

  • View in gallery

    Cross-correlation coefficients of zonal-averaged SH SLP anomalies from the observations and the 25 model runs from the CMIP5 historical experiment. The red numbers at the top right of each panel are the maximum correlation coefficient. The contour interval is 0.3. Shading indicates statistical significance at the 90% confidence level using a two-tailed Student’s t test.

  • View in gallery

    The climatological mean DJF precipitation (mm) north of 20°S from the observations and the 25 model runs from the CMIP5 historical experiment for boreal winter.

  • View in gallery

    Regression of the SON SAMI onto the zonal-mean DJF vertical velocity (10−5 Pa s−1) in the 25 models runs from the CMIP5 historical experiment. The stippled areas indicate statistical significance at the 90% confidence level using a two-tailed Student’s t test. The blue (red) shading corresponds to upward (downward) motion.

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Cross-Seasonal Relationship between the Boreal Autumn SAM and Winter Precipitation in the Northern Hemisphere in CMIP5

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  • 1 State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou, and State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • | 2 College of Global Change and Earth System Sciences, Beijing Normal University, and Joint Center for Global Change Studies, Beijing, China
  • | 3 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • | 4 College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, and College of Global Change and Earth System Sciences, Beijing Normal University, Beijing, China
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Abstract

Recent work suggests that the boreal autumn Southern Hemisphere annular mode (SAM) favors a tripole pattern of winter precipitation anomalies in the Northern Hemisphere. This study focuses on the abilities of climate models that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) to reproduce the physical processes involved in this observed cross-seasonal connection. A systematic evaluation suggested that 16 out of 25 models were essentially capable of reproducing this cross-seasonal connection. Two categories of models were selected to explore the underlying reasons for these successful simulations. Models that successfully simulated the cross-seasonal relationship were placed in the type-I category, and these performed well in reproducing the related physical mechanism, known as the “coupled ocean–atmosphere bridge,” in terms of the SST variability associated with the SAM and response of the meridional circulation to these SST anomalies. In contrast, the type-II category of models showed poor performance in representing the related processes and associated feedbacks, and the model biases compromised the performance of the simulated cross-seasonal relationship. These results demonstrate that the capability of the CMIP5 models to reproduce SST variability associated with the boreal autumn SAM and related coupled ocean–atmosphere bridge process plays a decisive role in the successful simulation of the cross-seasonal relationship.

Corresponding author address: Prof. Jianping Li, College of Global Change and Earth System Sciences, Beijing Normal University, No. 19, XinJieKouWai St., Beijing 100875, China. E-mail: ljp@bnu.edu.cn

Abstract

Recent work suggests that the boreal autumn Southern Hemisphere annular mode (SAM) favors a tripole pattern of winter precipitation anomalies in the Northern Hemisphere. This study focuses on the abilities of climate models that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) to reproduce the physical processes involved in this observed cross-seasonal connection. A systematic evaluation suggested that 16 out of 25 models were essentially capable of reproducing this cross-seasonal connection. Two categories of models were selected to explore the underlying reasons for these successful simulations. Models that successfully simulated the cross-seasonal relationship were placed in the type-I category, and these performed well in reproducing the related physical mechanism, known as the “coupled ocean–atmosphere bridge,” in terms of the SST variability associated with the SAM and response of the meridional circulation to these SST anomalies. In contrast, the type-II category of models showed poor performance in representing the related processes and associated feedbacks, and the model biases compromised the performance of the simulated cross-seasonal relationship. These results demonstrate that the capability of the CMIP5 models to reproduce SST variability associated with the boreal autumn SAM and related coupled ocean–atmosphere bridge process plays a decisive role in the successful simulation of the cross-seasonal relationship.

Corresponding author address: Prof. Jianping Li, College of Global Change and Earth System Sciences, Beijing Normal University, No. 19, XinJieKouWai St., Beijing 100875, China. E-mail: ljp@bnu.edu.cn

1. Introduction

The Southern Hemisphere (SH) annular mode (SAM), also referred to as the Antarctic Oscillation (AAO), is the leading mode of large-scale climate variability in the SH extratropics. The predominant characteristic of the SAM is a hemispheric “seesaw” structure in fluctuations of air mass between the southern polar cap region and midlatitudes (Gong and Wang 1998, 1999; Limpasuvan and Hartmann 1999; Thompson and Wallace 2000; Li and Wang 2003; Li 2005). The SAM-related circumpolar wind stresses have the potential to induce anomalous surface heat exchange. These zonal wind anomalies also affect the oceanic meridional Ekman transport, which is associated with redistribution of heat near the surface. These dynamic and thermodynamic processes can imprint the SAM signature onto the sea surface temperature (SST). Both observations and model simulations indicate that the SAM events favor dipole-like SST anomalies (SSTAs) in the Southern Ocean [referred to as the Southern Ocean dipole (SOD); Liu et al. 2015; Li 2016], which mainly reflects out-of-phase latitude band variations in SSTAs between high and middle latitudes in the SH. Positive (negative) SAM events are usually associated with cold (warm) SST poleward of 45°S and warm (cold) SST equatorward of 45°S (Watterson 2000, 2001; Cai and Watterson 2002; Hall and Visbeck 2002; Carleton 2003; Lefebvre et al. 2004; Sen Gupta and England 2006; Ciasto and Thompson 2008; Wu et al. 2009a; Thompson et al. 2011; Zheng and Li 2012; Liu et al. 2015).

The SAM-related heating anomalies induce anomalous eddy activities by altering the meridional SST gradient and surface baroclinicity. Then, adjustment of the meridional circulation is driven by eddy–zonal flow interactions (Liu et al. 2015; Zheng et al. 2015a), which plays an important role in the influence of SAM on large-scale and remote climate anomalies. Via the meridional circulation, there are remarkable correlations between the SAM anomalies and cloud cover, water vapor, and precipitation, characterized by a zonally symmetric seesaw pattern between the subtropical and mid-to-high latitudes in the SH (Boer et al. 2001; Sen Gupta and England 2006).

Given that the meridional circulation in the two hemispheres shares the same upward branch, the Northern Hemisphere (NH) meridional circulation also responds to the SAM-related SSTAs. Meanwhile, by virtue of the high-inertia ocean, the SAM-related SSTAs can persist for a long period and may provide a source of predictability (Nan and Li 2005a,b; Wu et al. 2006a,b, 2009a; Zheng and Li 2012; Wu et al. 2015; Zheng et al. 2015a; Li 2016). Liu et al. (2015) reported a cross-seasonal correlation between the boreal autumn [September–November (SON)] SAM and winter [December–February (DJF)] zonal-mean precipitation in the NH. Through a combination of oceanic meridional heat advection and surface heat fluxes, the positive (negative) SON SAM signature can be imprinted onto SST and is marked by the positive (negative) SOD pattern. Such positive (negative) SON SOD anomalies can persist or be transmitted into DJF via the “memory” of SST. This then strengthens (weakens) the NH upward and downward branches and favors a positive (negative) tripole pattern of precipitation anomalies in the NH under suitable moisture conditions, with more (less) precipitation near the equator and midlatitude regions but less (more) precipitation over the subtropics. Thus, via this “coupled ocean–atmosphere bridge” process (Nan and Li 2003, 2005a,b; Wu et al. 2009a; Zheng and Li 2012; Zheng et al. 2014; Zheng et al. 2015a; Liu et al. 2015; Li 2016), the climatic impacts of the SON SAM are not only restricted to the SH or a regional scale but also extend into the NH and represent a consistent response in the same latitude zone (Liu et al. 2015). This cross-seasonal relationship between the SON SAM and NH DJF precipitation offers a new perspective for NH climate prediction.

Comprehensive coupled general circulation models are powerful tools for examining large-scale climatic behavior and dynamics. For instance, the performances of the CMIP models in representing the surface winds (Bracegirdle et al. 2013) and the SST responses to the SAM over the Southern Ocean (Screen et al. 2010) have been evaluated. In addition, the influence of the SAM on SH precipitation (Cai and Cowan 2006; Karpechko et al. 2009; Purich et al. 2013) and Antarctic surface air temperatures (Marshall and Bracegirdle 2015) has also been investigated in the CMIP models. Furthermore, the availability of new ensemble data outputs from a suite of state-of-the-art climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012), as well as recent progress in the observations and understanding of how the coupled ocean–atmosphere bridge process influences the SAM–precipitation relationship, provides a unique opportunity to more comprehensively evaluate this cross-seasonal relationship. Here, we attempt to diagnose the SAM–precipitation relationship using physically based coupled models participating in CMIP5 with the objective of assessing the abilities of the models to represent this cross-seasonal relationship and the role of the coupled ocean–atmosphere bridge mechanism.

The remainder of this paper is organized as follows. A detailed description of our datasets and methodology is given in section 2. Sections 3 and 4 evaluate the representation of the cross-seasonal relationship between the SON SAM and NH DJF precipitation in the CMIP5 models, with a focus on whether the coupled ocean–atmosphere bridge mechanism is accurately portrayed. Section 4 also explores the possible model biases that lead to unrealistic features in the simulated SAM–precipitation relationship. Finally, a discussion and our conclusions are given in section 5.

2. Data and methodology

a. Data

Monthly reanalysis datasets (hereafter referred to as observations) are obtained from the following sources. We employ two global precipitation datasets with a horizontal resolution of 2.5° × 2.5°: the Global Precipitation Climatology Project (GPCP), version 2.2 (Adler et al. 2001; Huffman and Bolvin 2012; Huffman et al. 2015), and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997; NOAA/CPC/NCEP/NWS/U.S. Department of Commerce 1995). Another two higher-resolution (0.5° × 0.5°) gridded land-only precipitation datasets, the Global Precipitation Climatology Centre (GPCC; Rudolf and Schneider 2005; WCRP 2005) and NOAA Precipitation Reconstruction over Land (PREC/L; Chen et al. 2002; NOAA 2002), are also used to further verify the results for precipitation over land. Atmospheric variables are extracted from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) globally archived dataset (Dee et al. 2011), which has a horizontal resolution of 2.5° × 2.5°. The SST data are taken from the Extended Reconstructed SST, version 3 (ERSST.v3; Smith et al. 2008; NOAA 2008).

We analyze and compare the monthly mean outputs of 25 coupled GCMs from the CMIP5 historical simulations that aim to replicate climate variations during the period 1850–2005 by imposing each modeling group’s best estimates of natural (e.g., solar irradiance and volcanic aerosols) and anthropogenic (e.g., greenhouse gases, sulfate aerosols, and ozone) climate forcing. The models chosen (Table 1) are based upon their availability when originally downloading data from the Earth System Grid (ESG). Further details regarding CMIP5 and the experimental design are available from the Earth System Grid (http://cmip-pcmdi.llnl.gov/cmip5/) data archive and Taylor et al. (2012). Variables matching those from observations and reanalysis are obtained for each member. The statistical results obtained are insensitive to the ensemble member analyzed (Deser et al. 2012; Liu et al. 2012; Purich et al. 2013; Zheng et al. 2013); therefore, the results present in the current study are based upon the first simulation (r1i1p1) for each of the models considered.

Table 1.

Details of the CMIP5 models used in this study. Values in parentheses for the horizontal resolution denote the spectral truncation of the model. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

Table 1.

With respect to the reliability of data from the SH, this study focuses on historical simulations covering approximately the same period as the observations (1979–2005). Beginning in 1979, satellite-based measurements, along with ground-based observations, substantially improved spatial and temporal sampling as well as the reliability of the reanalysis products, supporting our choice of the period 1979–2005 for model evaluation. Autumn and winter in this article refer to the boreal season and are defined as SON and DJF, respectively.

b. Methodology

To validate the CMIP5 historical simulations with respect to the observations, each model variable is linearly interpolated to the same horizontal resolution as the relevant reanalysis variable when performing multimodel ensemble means (constructed with equal weights). In the present study, the SON SAM pattern is identified as the leading empirical orthogonal function (EOF; Wilks 2005) mode of the SON 700-hPa geopotential height southward of 20°S. The gridded geopotential height data are weighted by the square root of the cosine of their latitude to ensure that equal areas are assigned equal weights. To ensure that the SAM patterns are comparable from different models, we follow Zheng et al. (2013) and normalize the EOF loading pattern using the standard deviation of the corresponding principal components.

We employ Nan and Li’s (2003) definition of the SAM index (SAMI; the difference in the normalized monthly zonal-mean SLP between 40° and 70°S) to quantitatively measure the SAM in the observations and in each model. This SAMI definition is broadly applied in studies of the SAM and its climatic impact (Nan and Li 2003, 2005a,b; Wu et al. 2006a,b, 2009a; Li and Li 2009, 2010, 2012; Feng et al. 2010a; Sun and Li 2012; Zheng and Li 2012; Feng et al. 2013) and is a modification of the AAO index defined by Gong and Wang (1999). This definition of SAMI is closely correlated with the observation-based SAMI by Marshall (2003; referred to as SAMI_M) and the SAMI defined as the leading EOF of SLP anomalies south of 20°S (referred to as SAMI_E; Feng et al. 2010b) by Thompson and Wallace (2000), with correlation coefficients of 0.89 and 0.96, respectively, for the period 1979–2005. Similar results can be derived by applying the same technique to different versions of SAMI, indicating that they are insensitive to the choice of SAMI. Hereafter, SAMI refers to the definition given by Nan and Li (2003).

We employ a Taylor diagram (Taylor 2001) to provide a concise statistical summary of how well the observed and modeled patterns are matched in terms of their correlation and the ratio of their standard deviations, which are simply indicated by a single point on the diagram. Through a comparison with the observations (marked as REF in the Taylor diagram) of both the pattern and magnitude, we are able to clearly assess the accuracy of each model simulation. We also use various statistical methods to explore the influence of the SON SAM on DJF precipitation, including correlation, singular value decomposition (SVD), and simultaneous and lead–lag regression analysis.

3. Linkage between the SON SAM and DJF precipitation in the NH

a. Simulated SON SAM and DJF precipitation in the NH

First, we test the ability of the CMIP5 models to reproduce the spatial pattern of SON SAM and the climatology of NH DJF precipitation. We employ a pair of Taylor diagrams (Fig. 1; Taylor 2001) to quantitatively compare the performance of the model with respect to the SON SAM and DJF precipitation with the observations (ERA-Interim for SAM pattern and CMAP for precipitation). For the SON SAM (Fig. 1a), the spatial correlation coefficients between the models and observations are greater than 0.8 for all models except CNRM-CM5 (0.75). This result indicates that the ability of the models to simulate the SON SAM pattern (Fig. A1), notably defined as the meridional seesaw structure between the polar cap region and midlatitudes in the SH, is generally reasonable. This finding is consistent with the results of Purich et al. (2013). The cross correlation of the zonally averaged SH SLP anomalies in the CMIP5 models (Fig. A2) shows that the models are also able to reproduce the zonally symmetrical features of the SON SAM. Pressure patterns in the middle (30°–55°S) and high latitudes (60°–90°S) are out of phase, which is an indication of hemispheric-wide fluctuations of air masses between the two atmospheric annular belts of action (Li and Wang 2003). However, there are noticeable biases in the simulated amplitude of the observed SAM pattern. Most of the models overestimate the amplitude of the SAM pattern, with the ratio of the standard deviation (RSD) of the modeled to observed SAM patterns being greater than 1.0, although some models, such as CNRM-CM5 and INM-CM4.0, generate underestimates.

Fig. 1.
Fig. 1.

Taylor diagram of (a) the SON SAM pattern and (b) the climatological mean DJF precipitation in the NH. The red dots (numbered) refer to the 25 models. The correlation coefficients and the ratio of the standard deviation between models and observations (ERA-Interim and CMAP) are shown by the cosine of the azimuth angle and the radial distance, respectively. REF on the horizontal axis indicates the reference point (ERA-Interim and CMAP).

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

Similarly, most models generally present reasonable patterns for the NH DJF precipitation climatology, with the spatial correlation coefficients exceeding the 99% confidence limit in all models, as evaluated against CMAP (Fig. 1b). The models have the capacity to capture the maximum precipitation centers over Europe, the East Asian littoral, and the eastern coast of North America (Fig. A3). The observed distribution of precipitation associated with the NH intertropical convergence zone (ITCZ) is also realistically reproduced by all models (Fig. A3). In addition, compared with the SON SAM, the magnitude of the NH DJF precipitation tends to fit the observations a little better with a smaller RSD, despite the magnitude of the NH DJF precipitation being overestimated in the majority of the models (except BNU-ESM, FGOALS-s2, IPSL-CM5A-LR, and MIROC-ESM).

In general, although there is noticeable range in intensity, all models have some skill in simulating the essential features of large-scale climate elements. The simulations of the spatial pattern of the SON SAM and the climatology of NH DJF precipitation in the CMIP5 models agree well with the reanalysis data. Moreover, the relatively reasonable simulations of the spatial structures of the above two provide the basic guarantee for assessing the performance of the CMIP5 models in simulating the relationship between the SON SAM and NH DJF precipitation.

b. Correlation between the SON SAM and DJF precipitation in the NH

Figure 2 highlights the relationship between the SON SAM and zonal-mean DJF precipitation in the NH for the observations (Fig. 2, top) and model simulations (Fig. 2, rows 2–6). The most evident feature in the observations is the tripole pattern of precipitation anomalies. And there is good agreement for various precipitation datasets based on the different versions of SAMI. This indicates that the positive (negative) boreal autumn SAM usually favors more (less) precipitation over the tropics and midlatitudes but less (more) precipitation in the subtropics (Liu et al. 2015).

Fig. 2.
Fig. 2.

Correlation coefficients between the SON SAMI and DJF zonal-mean precipitation for the (top) observations (based on SAMI, SAMI_E, and SAMI_M, respectively) and simulations in the 25 model runs from the CMIP5 historical experiment. The dashed lines indicate significance at the 90% confidence level using a two-tailed Student’s t test. The black, red, blue, and magenta lines in the observations across the top denote results based on the CMAP, GPCP, GPCC, and PREC/L datasets, respectively. The filled areas indicate the models that basically reproduce the tripole pattern of DJF precipitation anomalies associated with the SON SAM.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

The simulated correlation between the SON SAM and zonal-mean NH DJF precipitation is variable. Of the 25 models studied here, 16 (i.e., ACCESS1.0, BCC_CSM1.1, BNU-ESM, CanESM2, CSIRO Mk3.6.0, FGOALS-s2, GFDL CM3, GFDL-ESM2M, GFDL-ESM2G, GISS-E2-R, HadCM3, HadGEM2-ES, HadGEM2-AO, MIROC5, MIROC-ESM, and MPI-ESM-LR) are able to successfully reproduce the tripole pattern of precipitation anomalies, although with some biases in the locations and intensity of the precipitation anomalies. Furthermore, two models (i.e., ACCESS1.0 and BCC_CSM1.1) not only successfully simulate the tripole pattern of precipitation anomalies but also are able to reproduce regions that are significant at the 90% confidence level. In particular, BCC_CSM1.1 stands out as the best-performing model overall, with the simulated rain belt locations being highly consistent with the observations, whereas the simulation by ACCESS1.0 tends to be dominated by a southern bias (southern than the observations). Compared with ACCESS1.0 and BCC_CSM1.1, some of the other models only capture two regions that are significant at the 90% confidence level. BNU-ESM, GFDL CM3, GFDL-ESM2G, and HadGEM2-ES only perform well in the tropics and subtropics. The variability generated by GFDL-ESM2M and HadCM3 is largely consistent with the observations in the tropical and midlatitude regions, and that generated by HadGEM2-AO and MIROC-ESM is mainly in line with the observations in the subtropical and midlatitude regions. The other six models (i.e., CanESM2, CSIRO Mk3.6.0, FGOALS-s2, GISS-E2-R, MIROC5, and MPI-ESM-LR) only weakly reproduce the tripole pattern of precipitation anomalies, but the correlation coefficients are not statistically significant in most regions.

We also use the SVD to further isolate the coupled relationship between the SON SAM and NH DJF precipitation (not shown). In the observations, the leading SVD mode accounts for 36% of the total covariance. The atmospheric signal is characterized by the SAM pattern, and the NH DJF precipitation occurs as zonal belts with a tripole pattern. For the CMIP5 simulations, the leading SVD mode of the 16 models named in the previous paragraph agree well with the observations, which means that when the SON SAM is in its positive (negative) phase, positive (negative) DJF precipitation anomalies occur over the tropical and midlatitude regions, but negative (positive) anomalies occur over the subtropics in the NH. Nevertheless, the other 9 models (i.e., CMCC-CESM, CNRM-CM5, GISS-E2-H, INM-CM4.0, IPSL-CM5A-LR, MPI-ESM-MR, MPI-ESM-P, MRI-CGCM3, and NorESM1-M) of the 25 studied here fail to reproduce the coupled relationship between the SON SAM and NH DJF precipitation.

c. Correlation between the SON SAM and NH DJF atmospheric circulation

Vertical velocity plays an important role in large-scale precipitation, which has also been shown to respond to SAM anomalies via the coupled ocean–atmosphere bridge (Nan and Li 2003, 2005a,b; Wu et al. 2009a; Zheng and Li 2012; Li et al. 2013; Zheng et al. 2014, 2015a; Liu et al. 2015; Li 2016). Figure 3a shows a height–latitude section of the regression of observed DJF vertical velocity onto the SON SAMI. When the SON SAM is strong (weak), the Ferrel cell is strengthened (weakened) south of 40°S, and the meridional circulation is adjusted accordingly. Corresponding to the tripole pattern of precipitation anomalies, the response of the NH DJF vertical velocity anomalies to the SON SAM also presents a tripole pattern. When the SON SAM is positive (negative), anomalous ascending (descending) motion appears over tropical and midlatitude regions, and there is an anomalous descending (ascending) motion near the subtropics. This also accords with the general understanding that ascending (descending) motion is favorable to positive (negative) precipitation anomalies (Liu et al. 2015).

Fig. 3.
Fig. 3.

Regression of the SON SAMI onto the zonal-mean DJF vertical velocity (10−5 Pa s−1) for the (a) observations and the (b) type-I and (c) type-II multimodel means. The stippled areas indicate statistical significance at the 90% confidence level using a two-tailed Student’s t test. The blue (red) shading corresponds to upward (downward) motion.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

For illustrative purposes, two groups of models are selected based on their ability to simulate the cross-seasonal correlation between the SON SAM and NH DJF precipitation. We test the spatial pattern of the correlation coefficients and the significant regions to define 10 type-I models and 9 type-II models. The type-I models reproduce the tripole pattern of precipitation anomalies and capture at least two branches of the tripole that are significant at the 90% confidence level (i.e., ACCESS1.0, BCC_CSM1.1, BNU-ESM, GFDL CM3, GFDL-ESM2M, GFDL-ESM2G, HadCM3, HadGEM2-ES, HadGEM2-AO, and MIROC-ESM). The nine type-II models are unable to simulate the tripole pattern of precipitation anomalies (i.e., CMCC-CESM, CNRM-CM5, GISS-E2-H, INM-CM4.0, IPSL-CM5A-LR, MPI-ESM-MR, MPI-ESM-P, MRI-CGCM3, and NorESM1-M). The selection criteria allow for a distinction between model ensemble members that have distinctly different abilities to simulate the cross-seasonal relationship between the SON SAM and NH DJF precipitation. By selecting the above two groups we are able to highlight key processes and mechanisms involved in the successful simulation of the SAM–precipitation connection.

The simulated patterns of NH DJF vertical velocity obtained by regression with the SON SAMI for the multimodel ensemble means of the above two types are given in Figs. 3b and 3c, respectively. Consistent with the observations, the type-I models (Fig. 3b) can essentially simulate the associated significant tripole pattern of vertical velocity anomalies. A good correspondence between the significant vertical velocity anomalies and precipitation anomalies can be seen in the same region. This result suggests that the response of DJF vertical velocity to the SON SAM makes an important contribution to the DJF precipitation variability in these models. Unsurprisingly, the type-II models that fail to capture the SON SAM-related tripole pattern of DJF precipitation variability also inadequately reproduce the response of vertical velocity (Fig. 3c). The magnitude of the vertical velocity variability associated with the SON SAM is weaker and more discordant, especially in the NH subtropical and midlatitude. Therefore, the lower consistency between ensemble members in this category (Fig. A4) reflects the poor performance in reproducing the SAM-related tripole pattern of DJF vertical velocity variability. In brief, the response of DJF vertical velocity to the SON SAM and the match between DJF vertical velocity and precipitation, which are two essential elements of the cross-seasonal relationship between the SON SAM and NH DJF precipitation in observations, are crucial to successfully simulating the cross-seasonal relationship.

Taken together, it is those models that reproduce the tripole pattern response of the NH DJF vertical velocity to the SON SAM that have the basic capacity to simulate the tripole pattern of precipitation anomalies, despite the biases that exist in the locations of the precipitation anomalies and the significance level. Overall, BCC_CSM1.1 stands out as the best-performing model, as it produces a significant tripole pattern and reasonable latitudinal locations of precipitation anomalies. The most profound difference between the models that are able to reproduce the tripole pattern of precipitation and those that cannot is the significant response of the DJF vertical velocity to the SON SAM as well as the effective match between vertical velocity and precipitation in the former. This will be further discussed below.

4. Physical mechanisms

The difficulty in understanding the cross-seasonal influence of the SON SAM on NH DJF precipitation lies in explaining how the anomalous preceding SAM signal persists and is transmitted to the NH in the next season. It is the coupled ocean–atmosphere bridge that plays a key role in the relevant physical process (Liu et al. 2015). In this section, the anomalies of surface zonal wind, SST, and circulation anomalies related to SON SAM in the CMIP5 models are examined to evaluate the performance of state-of-the-art CGCMs in simulating the coupled ocean–atmosphere bridge.

a. SST associated with the SON SAM

In the observations, the SON SAM favors dipole-like SSTAs (Fig. 4b) in the Southern Ocean (i.e., the SOD; Liu et al. 2015; Li 2016) via dipole-like variations in circumpolar winds (Fig. 4a) and related dynamic and thermodynamic processes (Liu et al. 2015). The positive (negative) SON SAM events are usually associated with enhanced (weakened) westerly winds that induce increased (decreased) ocean heat release and equatorward (poleward) Ekman transport of cold (warm) water, which leads the cold (warm) SST poleward of 45°S. The situation is reversed equatorward of 45°S (Sen Gupta and England 2006; Wu et al. 2009a; Zheng et al. 2013; Liu et al. 2015; Zheng et al. 2015a). This SOD can persist through to DJF by virtue of the memory characteristics of SST (Fig. 4b).

Fig. 4.
Fig. 4.

Lead–lag regression of the SON SAMI onto (a) zonal-mean 10-m zonal wind (m s−1) and (b) SST (°C) from June to the following March in the observations; in parentheses a “0” denotes the current year and a “1” the following year. The shaded areas indicate statistical significance at the 90%, 95%, and 99% confidence levels using a two-tailed Student’s t test.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

In the type-I model simulations, the lead–lag regression between the SON SAM and surface zonal wind anomalies is more successfully simulated, although the intensity is larger than the observations. Corresponding to the simulated SON SAM is the dipole-like surface zonal wind anomalies, which are able to persist into the following season (Fig. 5a). This is a consistent element in 7 of the 10 type-I models (i.e., BCC_CSM1.1, BNU-ESM, GFDL-ESM2G, GFDL CM3, HadCM3, HadGEM2-ES, and HadGEM2-AO; not shown). The other three models (i.e., ACCESS1.0, GFDL-ESM2G, and MIROC-ESM) only capture the significant simultaneous correlation (not shown), but the persistence of the surface zonal wind related to the SON SAM is slightly inferior compared with the observations. Note that the patterns of precipitation anomalies in these three models are also located farther south than in the observations, especially those branches in the midlatitudes (Fig. 2). For the type-II models, the SON SAM-related zonal wind anomalies are almost dissipated in DJF (Fig. 5b). Specifically, the persistence of the surface zonal wind related to the SON SAM is generally weaker than the observations except for CRNM-CM5 (not shown). This may indicate that the positive feedback between the SAM and surface zonal wind favored by wave–mean interaction (Liu et al. 2015) is underestimated in these models.

Fig. 5.
Fig. 5.

Lead–lag regression of the SON SAMI onto the zonal-mean 10-m zonal wind (m s−1) for the (a) type-I and (b) type-II multimodel means from June to the following March; in parentheses a “0” denotes the current year and a “1” the following year. Shading indicates statistical significance at the 90%, 95%, and 99% confidence levels using a two-tailed Student’s t test.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

As with the circumpolar winds, the response of the SOD related to the SON SAM in the mean of the type-II models (Fig. 6b) is weaker than the observations (Fig. 4b) and the mean of the type-I models (Fig. 6a), especially for the northern branch. Meanwhile, in both the observations and the type-I multimodel mean, the SST anomalies of the northern branch are mainly concentrated in the SH extratropics, which corresponds to the positive feedback mechanism between the SOD and SAM. It is manifested as the SAM imprints its signature onto the SOD via the aforementioned dynamic and thermodynamic processes. The SOD, in turn, affects the SAM and the meridional circulation by altering the meridional SST gradient and surface baroclinicity, which induces the wave–mean interaction (Hartmann and Lo 1998; Limpasuvan and Hartmann 1999, 2000; Lorenz and Hartmann 2001; Cai et al. 2003; Marshall and Connolley 2006). However, in the mean of the type-II models, the SSTAs in the northern branch tend to dominate in the tropics in the previous season [June–August (JJA)]. This may have a weakening effect on the SON SAM from the perspective of the positive feedback mechanism between the SOD and SAM. The SON SOD may be further affected and have a weakening tendency. Therefore, the response of the subtropical SSTAs of the type-II multimodel mean is remarkably different from the observations and the type-I multimodel mean. For the southern branch, the simulated responses are both weaker than the observations in the type-I and type-II models. This result may indicate that the lag time of the response of SST to the SON SAM at high latitudes in the model is systematically longer than in the observations, despite the significant response in the mean of the type-I models appearing about a month before that in the type-II models.

Fig. 6.
Fig. 6.

As in Fig. 5, but for SST (°C).

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

To help visualize the development of the SOD more comprehensively, Figs. 7 and 8 present the spatial distributions of the SAM-related surface zonal wind anomalies and SSTAs, respectively. In accordance with the observations (Figs. 7a–c), dipole-like surface zonal wind anomalies are evident from JJA to DJF in the type-I multimodel mean (Figs. 7d–f). Correspondingly, the SSTAs associated with the SON SAM are also marked as the SOD pattern with a maximum response in DJF in the type-I multimodel mean (Figs. 8d–f). This result is consistent with the previous findings that the maximum response of SST to extratropical atmospheric forcing has a certain degree of lag (Wu et al. 2009b; Zheng et al. 2015b). However, the dipole-like surface zonal wind anomalies related with SON SAM are only reproduced from SON to DJF in the type-II multimodel mean (Figs. 7h,i). Meanwhile, the response of the DJF zonal wind (Fig. 7i) is weaker than the observation (Fig. 7c) and type-I multimodel mean (Fig. 7f). Because of this lack of persistence in the circumpolar westerlies and related weak dynamic and thermodynamic processes, the type-II models perform less well whether in zonal consistency or significance of the SAM-related SSTAs, with a very weak SON SOD (Fig. 8h) and correspondingly weak DJF SOD (Fig. 8i), especially in the subtropical regions.

Fig. 7.
Fig. 7.

The JJA, SON, and DJF 10-m zonal wind (m s−1) regressed onto the SON SAMI from the (a)–(c) observations and the (d)–(f) type-I and (g)–(i) type-II multimodel means. Stippled areas indicate statistical significance at the 90% confidence level using a two-tailed Student’s t test. The latitude lines are at 10° intervals starting from 30°S.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for SST (°C).

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

Although the relatively adequate representation of the dipole-like pattern of SON SAM-related circumpolar westerlies and SSTAs in the type-I multimodel mean, some model biases still remain. The northern response of the zonal-mean SSTAs (Fig. 6a) starts earlier and finishes later than the observations (Fig. 4b). This may be relevant to the overestimated response of the subtropical zonal wind anomalies in the Pacific Ocean and Atlantic Ocean in JJA (Fig. 7d). Corresponding to the zonal wind biases, the positive biases of the JJA SSTAs can be seen in the type-I multimodel mean in the Pacific Ocean (Fig. 8d). In the Atlantic Ocean, the type-I models overestimate the positive response of the subtropical zonal wind anomalies. But the response of SST is also positive (Fig. 8d), which is remarkably different from the negative SSTAs in the observations (Fig. 8a). This may indicate that the type-I models have no ability to reproduce the response mechanism of the SST to the SAM in the Atlantic Ocean. The positive biases of the SSTAs in the subtropical Pacific Ocean and Atlantic Ocean would be put onto the zonal mean result zonal average as the notable positive biases of subtropical SST anomalies in JJA (Fig. 6a). In addition, the persistence and intensity of subtropical zonal wind anomalies are also overestimated in the type-I multimodel mean (Figs. 5a and 7d–f), which is accompanied with the stronger persistence and intensity of subtropical SSTAs (Figs. 6a and 8d–f) than in the observations (Figs. 4b and 8a–c). Moreover, the simulated southern responses of SOD are weaker and start later than the observations. The models can roughly capture the zonal wind related with the SON SAM from JJA to SON in the SH high latitudes. But the response of the SSTs in this region is very weak from JJA to SON, with only significant anomalies in the southern Pacific Ocean. This may indicate that the models have finite capacity to reproduce the response mechanism of the SST to the SAM in the SH high latitudes, inducing the systematically overestimated response time of SST to the SON SAM at high latitudes.

In short, the type-I multimodel mean relatively accurately reproduces the evolution of the dipole-like circumpolar westerlies and SSTAs associated with the SON SAM. In contrast, although the type-II multimodel mean also reproduces the dipole-like circumpolar westerlies from SON to DJF, the intensity and persistence of the westerlies are weaker. This would result in weaker dynamic and thermodynamic processes as seen in the observations. Therefore, a weaker response of SSTAs and persistence of the SOD signal are portrayed in the type-II multimodel mean. This difference between the two types of models may have the potential to influence the response of the meridional circulation to the SOD in DJF in the two types of models and will be explored in the next section.

b. Response of DJF atmospheric circulation to DJF SOD

The SOD plays a bridging role in the cross-seasonal effect of the SON SAM on DJF precipitation in the NH, in that it can change the meridional SST gradient and surface baroclinicity, which induces anomalous eddy activity. Moreover, the associated convergence (divergence) of the eddy heat flux balances the diabatic cooling (heating) by the adjustment of vertical movements. Thus, the main feature of SAM in terms of climate impacts depends on its influence on the meridional circulation via the wave–mean flow interactions (Li and Wang 2003; Liu et al. 2015). As the meridional circulation in the two hemispheres is a unified whole, meridional circulation in the NH also presents a corresponding response to the SAM-related SOD. Thus, the meridional circulation variations associated with the SOD extend as far as the NH. Specifically, the positive (negative) SON SAM is followed by strengthening (weakening) of the NH meridional circulation, with strengthened (weakened) upward motion centered in tropical and middle latitudes and downward motion over the subtropics (Liu et al. 2015; Fig. 9a).

Fig. 9.
Fig. 9.

The DJF zonal-mean vertical velocity (10−5 Pa s−1) regressed onto the DJF SODI from the (a) ERA-Interim data and the (b) type-I and (c) type-II multimodel means. Stippled areas indicate regions where the regression coefficient is significant at the 90% confidence level using a two-tailed Student’s t test. The blue (red) shading corresponds to upward (downward) motion.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

For convenience, the SOD index (SODI) is defined as the difference in the normalized monthly zonal-mean SST between 40° and 60°S, which is the latitude of the maximum negative cross correlation of SST. Figure 9 presents the regression of the DJF vertical velocity onto the DJF SODI. As in the observations (Fig. 9a), the type-I multimodel mean not only reproduces the strengthening of the Ferrel cell south of 40°S but also captures the tripole pattern of vertical velocity anomalies in the NH (with anomalous upward motion centered at 10° and 50°N and anomalous downward motion near 30°N; Fig. 9b), which is consistent with the results based on the SON SAMI (with a pattern correlation between Fig. 3b and Fig. 9b of 0.96 over 0°–60°N, which is significant at the 99% confidence level). These vertical velocity anomalies also correspond to the tripole pattern of DJF precipitation in the NH. It indicates that the type-I models can roughly reproduce the bridging role of the SOD in the connection between the SON SAM and DJF vertical velocity anomalies in the NH. However, in the type-II multimodel mean, the picture is obviously different. The magnitude of the vertical velocity variability associated with the SOD is weaker and more fragmentary, with less consistency between ensemble members (not shown), especially in the NH. This result reflects the poor performance in reproducing the SOD variability patterns in this category.

Overall, the dipole-like responses of surface zonal wind and SST to the SON SAM are generally reproduced in both the type-I and type-II multimodel means. However, compared with the observations and the type-I multimodel means, the simulated amplitude and evolution of surface zonal wind and SST are weaker in the type-II category. Consequently, it follows that the type-II multimodel mean underestimates the vertical velocity variability associated with the SOD and fails to reproduce the contribution of the SOD to the zonal tripole-like precipitation pattern in the NH, which is sensitive to changes in vertical velocity. Hence, from this perspective it seems likely that the evolution of the SOD associated with SON SAM plays a key role in determining the cross-seasonal relationship between the SON SAM and NH DJF precipitation.

5. Discussion and conclusions

As an anomalous signal with a hemispheric scale, the climatic impact of the SAM is not limited solely to the SH or regional effects but also extends as far as the NH and induces a zonally consistent response via adjustment of the meridional circulation (Liu et al. 2015). In this study, we have systematically evaluated the ability of the CMIP5 models to reproduce the observed cross-seasonal relationship between the SON SAM and NH DJF precipitation.

First, we assess the SON SAM pattern and the climatology of the NH DJF precipitation simulated by the models. Generally speaking, the essential large-scale climate features tend to be accurately portrayed when compared with the observations, despite some biases in magnitude. For the SON SAM, both the meridional “seesaw” structure (Fig. A1) and zonally symmetrical features (Fig. A2) are reproduced. Regarding the NH DJF precipitation, the local maximum centers (over Europe, the East Asian littoral, and the eastern coast of North America), as well as the precipitation pattern associated with the ITCZ in the NH, is also consistent with the reanalysis data (Fig. A3). This provides certain conditions that can be used to evaluate the ability of the CMIP5 models to capture the cross-seasonal relationship between the SON SAM and DJF precipitation in the NH.

In the historical simulations, 16 of the 25 models tested (i.e., ACCESS1.0, BCC_CSM1.1, BNU-ESM, CanESM2, CSIRO Mk3.6.0, FGOALS-s2, GFDL CM3, GFDL-ESM2M, GFDL-ESM2G, GISS-E2-R, HadCM3, HadGEM2-ES, HadGEM2-AO, MIROC5, MIROC-ESM, and MPI-ESM-LR) basically reproduce the cross-seasonal correlation between the SON SAM and NH DJF precipitation but with widely varying confidence levels. In particular, all of the three branches of correlations, which are significant at the 90% confidence level, are simulated in ACCESS1.0 and BCC_CSM1.1, but a southward bias in location is evident in ACCESS1.0. The other eight models (i.e., BNU-ESM, GFDL CM3, GFDL-ESM2M, GFDL-ESM2G, HadCM3, HadGEM2-ES, HadGEM2-AO, and MIROC-ESM) only reproduce two significant branches. In contrast, 9 of the 25 models tested (i.e., CMCC-CESM, CNRM-CM5, GISS-E2-H, INM-CM4.0, IPSL-CM5A-LR, MPI-ESM-MR, MPI-ESM-P, MRI-CGCM3, and NorESM1-M) are unable to simulate a realistic cross-seasonal relationship (Fig. 2).

Furthermore, it is worth noting that the responses of the NH DJF vertical velocity to the SON SAM also present a tripole pattern (Fig. 3b) in those models that reproduce the tripole pattern of precipitation that is consistent with the observations (Fig. 3a). Moreover, the regions of significant vertical velocity anomalies and precipitation anomalies are also matched in those models. However, a totally different situation arises in the models with no ability to reproduce the cross-seasonal relationship, and they generate a messy structure (Fig. 3c). This result suggests that the significant response of vertical velocity and the perfect cooperation between vertical velocity and precipitation are the key to successful simulation of the cross-seasonal relationship.

To further explore the deep-seated reasons for the successful simulation of the cross-seasonal relationship, two categories of models are defined according to their performance in simulating the cross-seasonal correlation. In line with the observations (Figs. 4, 7a–c, and 8a–c), dipole-like patterns of circumpolar westerlies (Figs. 5a and 7d–f) and SSTAs (Figs. 6a and 8d–f) in the type-I multimodel mean are evident and can persist from SON to DJF. This has the potential to influence the meridional circulation via wave–mean interaction and an impact on DJF precipitation in the NH. Although the type-II multimodel mean also reproduced dipole-like circumpolar westerlies, the intensity and persistence of the westerlies are weaker (Figs. 5b and 7g–i), reflecting weaker dynamic and thermodynamic processes compared with the observations. A weaker SOD is depicted in the type-II multimodel mean (Figs. 6b and 8g–i), causing a weaker SST gradient and response of the vertical velocity (Fig. 9c), especially in the NH. In brief, the immediate cause of the poor performance of the type-II models is the lack of adequate representation of the persistence of SOD variability. It implies that the predictability related with the SON SAM is damping in DJF in the type-II models. Hence, the capabilities of the CMIP5 models to reproduce the variability in the SOD associated with the SON SAM play a decisive role in the successful simulation of the cross-seasonal relationship between SON SAM and DJF precipitation in the NH.

Although the type-I models prove to be relatively accurate, some issues cannot be neglected, and the corresponding improvement is needed in the future. For instance, individual models present a spatial diversity in the precise location of the peak precipitation anomalies (Fig. 2). Meanwhile, noticeable systematic model biases exist in the simulated variability of SOD, especially in the lag time of the responses of SST to the SON SAM at SH high latitudes induced by the finite-capacity CMIP models to reproduce the response mechanism of the SST to the SAM in this region. Most notable, and probably relevant for the stronger circumpolar westerlies associated with the SON SAM, is the overestimated magnitude and persistence of subtropical SSTAs compared with the observations. Furthermore, the discrepancies of the SOD propagate to the related vertical movement, especially the underestimated and expanded response near 50°N.

In summary, the type-I models are capable of representing the cross-seasonal relationship between the SON SAM and DJF precipitation in the NH for the correct reasons. Those models perform well in many aspects of the simulation, such as the tripole pattern response of vertical movement and the associated tripole-like distribution of precipitation, and the related physical mechanism known as the “coupled ocean–atmosphere bridge” (Li 2016). The model BCC_CSM1.1 is the best-performing model overall in capturing the cross-seasonal relationship, generating a realistic statistically significant tripole pattern as well as reasonable latitudinal locations for the precipitation anomalies that closely matched the observations. In contrast, because of type-II models’ poor performance in representing the related processes and associated feedbacks, their model biases compromise the performance of the simulated cross-seasonal relationship. Therefore, if they are to realistically represent the cross-seasonal relationship between the SON SAM and DJF precipitation in the NH, the challenges facing coupled models include precisely depicting the evolution of the SOD associated with the SON SAM, accurately reproducing the related wave–mean interaction that plays a dominant role in the response of the meridional circulation to the SOD, and reasonably matching between precipitation and vertical movement. Further research will be required if we wish to develop a comprehensive understanding of the various aspects of the deficiencies associated with the CMIP5 historical simulations. Moreover, recent studies have reported that the variability of the SH could be implicated as a remote driver of the tropical and NH climate at various time scales (Sun et al. 2013; Ding et al. 2015; Liu et al. 2015; Sun et al. 2015a,b; Zheng et al. 2015a). This means that the SH may yield some additional predictability source for the NH climate, especially for precipitation, which is inextricably linked with industrial and agricultural production and the hydrological cycle (Sun et al. 2015a,b). Hence, our attention to the influences of the SAM on the NH climate should not only be concentrated on the interannual scale but also extend to future projections of the NH DJF precipitation from the perspective of the interaction between the SH and NH. The performance improvement of the CMIP models may give additional confidence to explore this issue in the future.

Acknowledgments

This work was jointly supported by the 973 Program (2013CB430200) and the NSFC Project (41530424, 41405086, and 41321004). We acknowledge the WCRP’s Working Group on Coupled Modelling, which is responsible for CMIP, and the climate modeling groups listed in Table 1 for making the WCRP model output available. Comments and suggestions by three anonymous reviewers have helped us to improve the paper.

APPENDIX

Individual Performances of the 25 CMIP5 Models

Shown are the individual performances of the 25 CMIP5 models for the SON SAM pattern (Fig. A1), cross correlation of zonal-mean SH SLP (Fig. A2), the climatological mean DJF precipitation north of 20°S (Fig. A3), and the regression map of the zonal-mean DJF vertical velocity based on the SON SAMI (Fig. A4). In Figs. A1A3 the observations are given in the top panels for reference.

Fig. A1.
Fig. A1.

Spatial pattern of the SON SAM as the leading EOF mode at the 700-hPa geopotential height south of 20°S from the observations and the 25 model runs from the CMIP5 historical experiment. The percentages in red are the explained variance. The latitude lines are at 20° intervals starting from 20°S.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

Fig. A2.
Fig. A2.

Cross-correlation coefficients of zonal-averaged SH SLP anomalies from the observations and the 25 model runs from the CMIP5 historical experiment. The red numbers at the top right of each panel are the maximum correlation coefficient. The contour interval is 0.3. Shading indicates statistical significance at the 90% confidence level using a two-tailed Student’s t test.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

Fig. A3.
Fig. A3.

The climatological mean DJF precipitation (mm) north of 20°S from the observations and the 25 model runs from the CMIP5 historical experiment for boreal winter.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

Fig. A4.
Fig. A4.

Regression of the SON SAMI onto the zonal-mean DJF vertical velocity (10−5 Pa s−1) in the 25 models runs from the CMIP5 historical experiment. The stippled areas indicate statistical significance at the 90% confidence level using a two-tailed Student’s t test. The blue (red) shading corresponds to upward (downward) motion.

Citation: Journal of Climate 29, 18; 10.1175/JCLI-D-15-0708.1

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