1. Introduction
The Tibetan Plateau (TP), with an average elevation around 4000 m above sea level, is one of the most elevated plateaus of the world and has the largest ice reservoir outside the polar regions, known as “the Third Pole” and “the roof of the world” (Qiu 2008; Yao et al. 2019). The TP has substantial impacts on global ecosystems in its role of the “Asian water tower” (Xu et al. 2008), supplying water resources to more than a quarter of the global population and feeding 10 great rivers such as the Yangtze, Indus, Mekong, and Ganges Rivers, and thus significantly affects water availability and food security in the surrounding regions (Immerzeel et al. 2010). Due to its geography, the TP also plays an important role in the formation and variation of Asian monsoon and even global climate, via interactions with large-scale atmospheric circulations in ways of dynamical blocking effects and thermal effects (Wu et al. 2007; Duan and Wu 2008; Boos and Kuang 2010; G. Wu et al. 2012; Hu and Duan 2015; Z. B. Wang et al. 2018; He et al. 2019; Son et al. 2020).
The rainy season of the TP is mainly in summer [June–August (JJA)], contributing more than 60% of the annual precipitation (Feng and Zhou 2012). The TP summer climate variability is closely tied to the global climate system. On the one hand, condensation heating released from the precipitation along with the surface sensible heating make the TP an atmospheric heat source in summer (Duan and Wu 2008; G. Wu et al. 2012; Jiang et al. 2016), which subsequently influences regional to global climate. On the other hand, the TP summer climate variability is also modulated by teleconnections originated from tropical air–sea interactions (Chen and You 2017; Ren et al. 2017; Yue et al. 2020), land and sea ice anomalies (Xu and Lu. 1992; Li et al. 2020), convective heating of the Asian monsoon precipitation (Jiang et al. 2016; Jiang and Ting 2017), and internal variability in the mid- and high-latitude atmosphere (Liu and Yin 2001; Liu et al. 2015; Wang et al. 2017; Z. Q. Wang et al. 2018).
El Niño–Southern Oscillation (ENSO), the dominant interannual variability mode of the climate system, is one of the most important forcing factors for the TP summer climate (Yang et al. 2000; Gao et al. 2018; Wang and Ma 2018; Yang et al. 2018). ENSO impacts on the TP climate are asymmetric between its developing and decaying summer.
During its developing phase, ENSO modulates TP summer climate through three atmospheric bridges. The first is the variation of the Indian summer monsoon (ISM) rainfall (Kumar et al. 1999). The ISM rainfall tends to be suppressed by descending branch of the anomalous Walker circulation excited by ENSO (R. Wu et al. 2012). The suppressed monsoon precipitation stimulates an ISM–East Asian summer monsoon (EASM) teleconnection (Krishnan and Sugi 2001; Wang et al. 2001), serving as a part of the circumglobal teleconnection (CGT) pattern in the upper troposphere (Ding and Wang 2005). The upper-level anomalous cyclone to the west of the TP in the ISM–EASM teleconnection causes the negative anomalous zonal moist enthalpy advection over the southwestern TP, and thus significantly suppresses precipitation there (Hu et al. 2021). The second is the variation of the tropospheric thermal status over the tropical Indian Ocean excited by the ENSO-induced convective heating anomalies over the equatorial central-eastern Pacific through atmospheric Kelvin waves (Chiang and Sobel 2002; Trenberth and Smith 2009; Zhou and Zhang 2011). The tropospheric temperature anomalies can cause upper-level zonal wind anomalies over the TP (Sun et al. 2013). The third is related to the India–Burma monsoon trough. The western North Pacific, the South China Sea, and the Bay of Bengal are dominated by cyclonic anomalies excited by El Niño–related heating anomalies over the tropical central to eastern Pacific (Wu et al. 2003, 2017). The cyclonic anomalies strengthen the India–Burma monsoon trough, and thus reduce water vapor transported into the TP from its southern lateral boundary (Zhang et al. 2016; Gao et al. 2018). In addition to the atmospheric bridges, local land–atmosphere interactions such as snow cover variations also modulate summertime large-scale circulations over the TP (Z. Wu et al. 2012, 2015). For example, positive snow cover anomalies are generated over the western TP during El Niño developing spring, which tend to maintain to the following summer through the snow-albedo feedback and contribute to the upper-level anomalous cyclone to the west of the TP during that time (Jin et al. 2017).
During ENSO decaying summer, the TP climate is mainly modulated by Indian Ocean basin mode (IOBM). The IOBM, basinwide warming or cooling of sea surface temperature (SST) in the tropical Indian Ocean, generally forms in ENSO mature winter and persists to the following summer when ENSO has decayed (Klein et al. 1999; Hu et al. 2019). The South Asian summer monsoon tends to onset late during positive IOBM (warm anomalies), which can reduce precipitation over the southeastern TP in early summer (Chen and You 2017; Zhao et al. 2018). The western North Pacific anomalous anticyclone excited by the IOBM (Wu et al. 2009; Xie et al. 2009; Wu et al. 2010; Xie et al. 2016) tends to strengthen water vapor transports into the southeastern TP and thus increase water vapor content there in late summer (Ren et al. 2017). Moreover, the IOBM also has impacts on the meridional position of the South Asian high (SAH) (Xue et al. 2015; Xue and Chen 2019), which also influences the TP summer climate.
In addition to the tropical air–sea interactions, previous studies also noted the roles of the atmospheric teleconnection originated from the middle and high latitudes in modulating the TP summer climate (Liu and Yin 2001; Gao et al. 2013; Liu et al. 2015; Wang et al. 2017, Z. Q. Wang et al. 2018). The summer North Atlantic Oscillation (SNAO), a large-scale atmospheric oscillation between the subtropical high and the subpolar low over the North Atlantic (Rogers 1984; Folland et al. 2009), can excite the north–south-dipole seesaw structure of precipitation anomalies over the eastern TP, via interactions with the midlatitude westerlies (Liu and Yin 2001; Liu et al. 2015; Wang et al. 2017).
The circumglobal teleconnection (CGT) pattern is another atmospheric teleconnection affecting the TP summer climate (Ding and Wang 2005; Bothe et al. 2009; Zhou et al. 2019). Its nodes over the North Atlantic and Eurasian continent are called the Silk Road pattern (Enomoto et al. 2003; Enomoto 2004). The summer CGT or the Silk Road pattern can modulate the zonal position of the SAH (Cen et al. 2020) and affect the TP summer climate at multiple time scales (Wei et al. 2017; Gao et al. 2020; Hu and Zhou 2021; Ma et al. 2021).
The last three large-scale signals associated with the interannual variations of the TP climate, the IOBM, NAO, and CGT, are all to some extent associated with ENSO (Moron and Gouirand 2003; Du et al. 2009; Ding et al. 2011; Takaya et al. 2020). Whether they are forcing factors to the TP climate independent of ENSO is still unclear. The key to solve this question is to separate the TP climate variability into ENSO-forced and ENSO-independent components. However, it is difficult to perform the separation in the observation for the diversities of ENSO (Capotondi et al. 2015; Timmermann et al. 2018) and complicated interactions between the TP climate and ENSO (Wang and Ma 2018).
In this study, we try to separate ENSO-forced and ENSO-independent variability of the TP summer climate based on idealized pacemaker experiments of a coupled GCM. Then, we demonstrate that the TP climate is dominated by four distinct teleconnections, which are associated with ENSO in developing and decaying phases, and ENSO-independent SNAO and CGT, respectively.
The remainder of this paper is organized as follows. Observational datasets, model experiments, and methods are introduced in section 2. In section 3, we show the dominant ENSO-forced variability modes of TP summer climate and investigate related mechanisms. In section 4, we focus on the dominant ENSO-independent variability modes and related mechanisms. Finally, major conclusions are summarized in section 5.
2. Data, model, and method
a. Datasets
The following observational and reanalysis datasets are used: 1) precipitation from the Global Precipitation Climatology Centre (GPCC) at a horizontal resolution of 2.5° × 2.5° (Schneider et al. 2013); 2) the Global Precipitation Climatology Project (GPCP) V2.3 at a horizontal resolution of 2.5° × 2.5° (Adler et al. 2003); 3) atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) with a horizontal resolution of 0.25° × 0.25° (Hersbach et al. 2020); 4) sea level pressure (SLP) from the Hadley Centre Sea Level Pressure dataset (HadSLP2) at a horizontal resolution of 5° × 5° (Allan and Ansell 2006); 5) surface air temperature (SAT) from the Global Historical Climatology Network (GHCN) Monthly, version 3, with a horizontal resolution of 0.5° × 0.5° (Peterson et al. 1998); and 6) SST from the Met Office Hadley Centre Sea Ice and SST dataset (HadISST version 1.1) with a horizontal resolution of 1° × 1° (Rayner et al. 2003). All these data are monthly and cover the period 1950–2014, except for GPCP, which only available after the year 1979.
b. Model description
In this study, we use the low-resolution version of the Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System model, finite-volume version 3 (CAS FGOALS-f3-L) coupled general circulation model (hereafter FGOALS-f3-L) developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), CAS (Guo et al. 2020; He et al. 2020).
The FGOLAS-f3-L model has four components. Its atmospheric component is version 2.2 of the Finite-Volume Atmospheric Model (FAMIL) (Zhou et al. 2015; Bao et al. 2019; Li et al. 2019), with the horizontal resolution proximately equal to a 1° × 1°; Its ocean component is version 3 of the LASG/IAP Climate System Ocean Model (LICOM3) (Liu et al. 2012; Yu et al. 2018; Lin et al. 2020), with horizontal resolution of 1° × 1°. Its land and sea ice components are version 4.0 of the Community Land Model (CLM4) (Oleson et al. 2010) and version 4 of the Los Alamos sea ice model (CICE4) (Hunke and Lipscomb 2010). The four components are coupled through a coupler developed by the National Center for Atmospheric Research (NCAR; http://www.cesm.ucar.edu/models/cesm1.0/cpl7/).
The FGOALS-f3-L reproduces the major features of the climatological atmospheric circulations and precipitation over the Asian and Indo-Pacific regions in summer, although the simulated subtropical monsoon precipitation and the westerly jet are weaker than those in the observation (Fig. 1).
Spatial distribution of boreal summer climatological mean precipitation (shading; unit: mm day−1), 850-hPa winds (vectors; unit: m s−1), and 200-hPa zonal wind (contours; unit: m s−1, interval: 5 m s−1) derived from the (a) ERA5 and GPCP and (b) FGOALS-f3-L historical simulations. The summer climatological means of all variables are calculated for the period of 1950–2014, except for the observational precipitation from GPCP in (a), which is calculated for the period of 1979–2014.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
c. Pacemaker experiments
We conducted a 10-member ensemble of tropical Pacific pacemaker experiments following the design of the GMMIP Tier-2 experiments (Zhou et al. 2016). In the pacemaker experiments, the model is initialized from the year 1870 in historical simulations and integrated up to the year 2014 using the external forcing identical to the CMIP6 historical experiment, but the simulated SST is restored to the model climatology with annual cycle plus observed monthly anomaly in the tropical central-eastern Pacific (20°S–20°N, 175°E–75°W; Fig. 2c) (hereafter referred to as PM-CEP experiments). The simulations reasonably reproduce the observed ENSO-related SST anomalies (SSTAs) in the tropical Pacific (Figs. 2a,b). The correlation coefficient between the observed and simulated winter Niño-3.4 index (area-averaged SSTAs in 5°S–5°N, 170°–120°W) reaches 0.95 (Fig. 2d).
The winter SSTA (shading; unit: °C) regressed onto the simultaneous Niño-3.4 index for (a) observation and (b) the ensemble mean of FGOALS-f3-L PM-CEP experiments. (c) The restoring region for the FGOALS-f3-L PM-CEP experiments. The restoring is applied with weight = 1 in the inner box and linearly reduced to zero in the buffer zone (zonal and meridional ranges are both 5°) from the inner to outer box. The weight coefficients are shown with shading. (d) Time series of the observed (black line) and simulated (red line) interannual variability of the winter Niño-3.4 index. The interannual variability is calculated by applying a 9-yr high-pass Lanczos filter to the detrended time series. The thick and thin red lines represent the ensemble mean and individual members, respectively.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
d. Method
In this study, we focus on interannual variability of the atmospheric circulations associated with the TP summer climate, which is obtained by performing a 9-yr high-pass Lanczos filtering (Duchon 1979) on the observed and modeled detrended variables, including SAT, precipitation, SLP, wind vectors, geopotential, and streamfunction.
We try to isolate ENSO-forced and ENSO-independent variability of the TP climate for both the PM-CEP runs and the observation, which is key to this study. For the PM-CEP experiments, the ocean–atmosphere coupled system is forced by the observed SSTAs in the tropical central-eastern Pacific, the key area of ENSO. Hence, we use the ensemble mean to represent variability excited by the remote forcing from SSTAs in the tropical central-eastern Pacific (hereafter ENSO-forced variability), while we use the intermember spread, the deviation of each member to the ensemble mean, to represent ENSO-independent variability, as done by Deser et al. (2017) and Yang et al. (2015). For the ENSO-independent variability, all 10 members with the ensemble mean removed are connected together to obtain a sample size as large as possible. The multivariate EOF analysis (MV-EOF) is performed on the ENSO-forced and ENSO-independent variability of 350-hPa wind vectors over the TP and adjoining regions to extract their dominant modes, respectively.
For the observations, we assume that the time series of the ENSO-forced modes in the observation are consistent with those derived from the ensemble mean of the PM-CEP runs. Observational variables are regressed onto the first two simulated PC time series of ENSO-forced modes to obtain spatial patterns of ENSO-forced modes in the observation. The remainder of the regression is treated as ENSO-independent variability in the observation, whose dominant modes are then obtained through the MV-EOF analysis. The observational analyses are based on the atmospheric circulation data from ERA5. It should be noted that we get similar results when using the Japanese 55-year Reanalysis (JRA-55) dataset (Kobayashi et al. 2015).
3. ENSO-forced variability
To highlight large-scale teleconnections but understate local regional climate effects, we use 350-hPa circulation over and surrounding the TP as an indicator of the atmospheric circulation that influence the TP. It also has been noted that 350-hPa circulation anomalies has largest impacts on the moist static energy budget over the TP (Hu et al. 2021). For the simulated 350-hPa geopotential, the ratio of the ensemble-mean variance and total variance averaged over the TP and adjoining regions (10°–45°N, 60°–110°E) is 41.6%, indicating that ENSO-forced variabilities play an important role in modulating the atmospheric circulations over the TP.
To identify the leading modes of the ENSO-forced variability, the MV-EOF analysis is applied to JJA-mean 350-hPa wind vectors from the ensemble mean of the PM-CEP runs over the TP and adjoining regions (blue box in Fig. 3). In addition to the TP itself, this box also includes the tropical ISM domain and the midlatitude westerly domain, which have direct impacts on the TP summer climate (Vellore et al. 2015). The first and second MV-EOF modes account for 34.0% and 19.5% of the ensemble-mean variance, respectively. The sampling errors of the eigenvalues of the two modes are larger than their neighboring eigenvalues. Hence, the two leading modes can be separated from each other and from other modes significantly (North et al. 1982). The PC1 is significantly correlated with the simultaneous Niño-3.4 index (area-averaged SSTAs in 5°S–5°N, 170°–120°W) (r = 0.67, exceeding the 95% confidence level), indicating that this mode corresponds to the ENSO developing summer. In contrast, the PC2 is significantly correlated with Niño-3.4 index in preceding winter (r = 0.63, exceeding the 95% confidence level), but their simultaneous correlation is weak (r = 0.09), indicating that this mode corresponds to ENSO decaying summer. Hence, the two modes are referred to as developing-ENSO-related mode and decaying-ENSO-related mode, repressively. We investigate their formation mechanisms below.
(a) The observed summer 350-hPa wind anomalies (vectors; unit: m s−1) and surface air temperature (SAT) anomalies (shading; unit: °C) regressed onto the ENSO-forced EOF1 derived from the ensemble mean of FGOALS-f3-L PM-CEP experiments. (b) Spatial distributions of the ENSO-forced EOF1 derived from the ensemble mean of FGOALS-f3-L PM-CEP experiments. Variance contribution is noted in the upper-left corner. Regressions of summer surface air temperature anomalies (shading; unit: °C) onto the ENSO-forced PC1 are also shown. The blue boxes represent the regions where the EOF analysis was performed. The white dots denote values passing the 5% significance level. The letter C indicates the center of the cyclonic anomalies.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
a. The developing-ENSO-related mode
In the observation, the developing-ENSO-related mode has two pronounced anomalous cyclones, with centers located in the west TP and northeastern China, respectively. The western anomalous cyclone causes local surface cold anomalies in the Pamirs and the Iranian Plateau. The eastern one has less impact on the TP climate (Fig. 3a). The western anomalous cyclone is a Rossby wave response to negative convective heating associated with the suppressed Indian summer monsoon precipitation (Ding and Wang 2005; Fig. 4c). During El Niño developing summer, the Walker circulation is shifted eastward due to warm SSTAs and associated positive precipitation anomalies over the equatorial central-eastern Pacific. The descending branch of the anomalous Walker circulation suppresses the Indian monsoon precipitation (Fig. 4a). It is worth noting that the negative correlation between the Indian summer monsoon rainfall and simultaneous Niño-3.4 index is not stable, but has interdecadal variations. This causes the decadal variation of the amplitude of the developing-ENSO-related mode. The correlation coefficient between the summer all-India monsoon rainfall index and contemporaneous Niño-3.4 index is significant during 1960–90 (r = −0.63), while not significant thereafter. Correspondingly, the amplitude of the developing-ENSO-related mode during 1960–90 is 19.1% greater than that during 1991–2014 (figure not shown). In addition to the El Niño–related warm SSTAs in the central-eastern Pacific, significant SSTAs are also seen in the tropical western Indian Ocean (Fig. 4a). However, the warm SSTAs only are a passive response to the southward cross-equatorial surface wind anomalies excited by the negative Indian monsoon precipitation anomalies (Behera et al. 2006; Zhang et al. 2019) and thus have no contribution to the TP climate.
(a) The summer 200-hPa velocity potential anomalies (contours; units: m2 s−1; interval value: 2 × 105 m2 s−1) and SSTA (shading; unit: °C) regressed onto the ENSO-forced PC1 for (a) the observations and (b) the ensemble mean of FGOALS-f3-L PM-CEP experiments. (c),(d) As in (a) and (b), but for the regressions of the summer 850-hPa wind anomalies (vectors; unit: m s−1) and precipitation anomalies (shaded; unit: mm day−1). The white dots denote values passing the 5% significance level. The observational circulations and precipitation in (a) and (b) are derived from ERA5.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
The two anomalous cyclones are reproduced by the ensemble mean of the PM-CEP runs (Fig. 3b). The simulated center of the western cyclone is highly consistent with that in the observation (Figs. 3a,b). Correspondingly, the cold anomalies in the Pamirs and the Iranian Plateau are also reproduced. In contrast, the simulated surface air temperature anomalies over the central and eastern TP are opposite to the observations. This may be associated with the relatively weak intensity of the simulated cyclone, which causes that the strong meridional wind anomalies in the observation are not reproduced. As in the observation, the anomalous cyclone is a Rossby wave response to the El Niño–induced negative Indian monsoon precipitation anomalies (Figs. 4b,d). The similarities in the large-scale circulation between the observation and the simulations indicate that the ensemble mean of the PM-CEP runs captures the fundamental physical processes responsible for the developing-ENSO-related mode, which justifies the reliability of our analysis strategy.
b. The decaying-ENSO-related mode
In the observation, the EOF2 of the ENSO-forced variability is dominated by an anomalous anticyclone centered over the Iranian Plateau and a cyclone to the northeast of the TP (Fig. 5a). The anomalous anticyclone (cyclone) corresponds to local surface warm (cold) anomalies. The SSTAs associated with this mode show a basinwide warming in the tropical Indian Ocean and the South China Sea with very weak cold anomalies in the equatorial eastern Pacific (Fig. 6a). There is a low-level anomalous anticyclone over the western North Pacific (WNPAC), indicating of the intensification and westward extension of the western North Pacific subtropical high (Fig. 6c). Both the SSTAs and the WNPAC are typical features of El Niño decaying summer (Yang et al. 2007).
As in Fig. 3, but for the ENSO-forced EOF2. The designation “C” (“AC”) in (a) and (b) indicates the center of the cyclonic (anticyclonic) anomalies.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
As in Fig. 4, but for the regressions onto the ENSO-forced PC2.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
In the ensemble mean of the PM-CEP runs, the anomalous anticyclone centered over the Iranian Plateau and the cyclone to the northeast of the TP in the observation are reproduced (Fig. 5b), except that the cyclone is shifted westward to the north of the TP (Figs. 5a,b). The cold SAT anomalies corresponding to the cyclone are also shifted westward and cause unrealistic cold anomalies over the northern TP (Fig. 5b). The simulated SSTAs also show a typical pattern of El Niño decaying summer, with basinwide SSTAs in the tropical Indian Ocean and South China Sea and weak cold anomalies in the equatorial eastern Pacific (Figs. 6b,d). These similarities between the observation and the simulations indicate that the simulations capture the fundamental physical processes responsible for the decaying-ENSO-related mode.
The anomalous anticyclone centered over the Iranian Plateau is driven by the Indian Ocean basinwide warming. The warm SSTAs in the tropical Indian Ocean enhance local convections (Yang et al. 2009; Park et al. 2010; Chowdary et al. 2016). The enhanced convective heating excites a Matsuno–Gill-type response pattern (Matsuno 1966; Gill 1980) in the upper troposphere (Fig. 7a). The anomalous anticyclone over the Iranian Plateau is the Rossby wave response to the west of the tropical heating. It should be noted that this process is more obvious in the observations (Figs. 7a,b). Yet the anomalous cyclone to the northeast of the TP is generated by the eastward propagation of atmospheric wave train originated from the anomalous anticyclone over the Iranian Plateau (Yang et al. 2009).
Regressions of the 200-hPa summer geopotential (shading; unit: m2 s−2) and the wave activity flux (vectors; unit: m2 s−2) onto the ENSO-forced PC2 in (a) the observation and (b) the PM-CEP. The white dots in denote values passing the 5% significance level.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
The anomalous anticyclone centered over the Iranian Plateau in this mode is generally antisymmetric with the anomalous cyclone in the developing-ENSO-related mode, especially for the observation (Figs. 3 and 5). However, their formation mechanisms are distinct. For the decaying-ENSO-related mode, the anomalous anticyclone is a direct response to the basinwide warming in the tropical Indian Ocean, which forms in the El Niño mature winter and maintains to the decaying summer due to the remote forcing from the equatorial central-eastern Pacific (Klein et al. 1999). In the El Niño mature winter and following spring, the Indian Ocean is a slave of El Niño forcing. In the El Niño decaying summer, the SSTAs in the equatorial central-eastern Pacific have transformed from strong positive to weak negative. During this time, the warm SSTAs in the Indian Ocean can freely force local convections, and thus cause strong upward motion anomalies over the tropical Indian Ocean (Figs. 6a,b; Wu et al. 2009). In contrast, for the developing-ENSO-related mode, the anomalous cyclone is a response to the suppressed Indian summer monsoon precipitation, which is caused by the descending branch of the anomalous Walker circulation driven by El Niño–related warm SSTAs in the central-eastern Pacific (Figs. 4a,b). During this time, the SSTAs in the western Indian Ocean are a passive response to the southward equatorial surface wind anomalies due to the suppressed Indian summer monsoon precipitation and thus have no contributions to the anomalous cyclone (Figs. 4c,d; Behera et al. 2006; Zhang et al. 2019).
4. ENSO-independent variability
As noted in section 2, for the pacemaker experiments, ENSO-independent variability is defined as member spreads deviated from the ensemble mean of the simulation, which accounts for 58.4% of the total variance of the 350-hPa geopotential anomalies over the TP in summer. For the observation, the ENSO-independent variability is calculated as the remainder of the regressions onto the PC time series of the two dominant ENSO-forced modes, which account for 76.6% of the total variance of the 350-hPa geopotential anomalies in the observation.
The MV-EOF analysis is applied to the ENSO-independent variability in the observation and the simulations, respectively. It is found that the obtained dominant modes from the observation are identical with those from the simulations, that is, the first (second) mode corresponds to the summer NAO (the CGT pattern). Hence, the two modes are referred to as the SNAO-related mode and CGT-related mode. Below, we investigate their formation mechanisms.
a. SNAO-related mode
In the observation, the ENSO-independent EOF1 has two pronounced anomalous anticyclones, with centers located in the west of TP and central China, respectively (Fig. 8a). The corresponding 200-hPa geopotential shows a typical wave train–like atmospheric teleconnection at the middle and high latitudes, with positive (negative) geopotential anomalies over northwestern Europe, the Iranian Plateau, and central China (the Black Sea and Mongolia), respectively (Fig. 8c). The wave activity fluxes at 200 hPa indicate that the wave train propagates from the high-latitude North Atlantic to downstream East Asian regions, and influence regional climate along the path (Fig. 8c).
(left) The summer 350-hPa wind anomalies (vectors; unit: m s−1) and SAT anomalies (shading; unit: °C) regressed onto the ENSO-independent PC1 derived from (a) the observations and (b) the intermember spread of FGOALS-f3-L PM-CEP experiments. Variance contribution is noted in the parentheses. (right) As in the left column, but for the summer SST and SAT (shading; unit: °C), 200-hPa geopotential (contours; unit: m2 s−2), and the wave activity flux (vectors; unit: m2 s−2). The blue boxes in (a) and (b) represent the regions where the MV-EOF analysis was performed. The white dots denote values passing the 5% significance level. “AC” in (a) and (b) indicates the center of the anticyclonic anomalies.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
In the simulation, the general characteristics of the 350-hPa atmospheric circulation anomalies associated with the ENSO-independent EOF1 in the member spreads also show two pronounced anomalous anticyclones over the west of TP and eastern China, respectively (Fig. 8b). The associated teleconnection pattern at 200 hPa exhibits a wave train–like atmospheric teleconnection, consistent with the counterparts in the observations (Fig. 8d).
To justify that the atmospheric teleconnection is associated with the ENSO-independent SNAO, we extract large-scale circulation excited by the ENSO-independent SNAO in the observation through performing the maximum covariance analysis (MCA) (Bretherton et al. 1992; Wallace et al. 1992; Czaja and Frankignoul 1999, 2002) on ENSO-independent SLP anomalies over the extratropical North Atlantic–European sector (30°–70°N, 70°W–50°E; left field) and ENSO-independent 200-hPa geopotential anomalies over the downstream regions (25°–60°N, 30°–130°E; right field). The squared covariance fraction of the first MCA mode is 41%. The correlation coefficient between two expansion coefficients of the first MCA mode reaches 0.69 (exceeding the 95% confidence level). The homogeneous map of SLP anomalies over the North Atlantic shows a meridional dipole pattern, a typical spatial pattern of the SNAO (Fig. 9b), indicating that the MCA mode represents the ENSO-independent SNAO. The expansion coefficients of SLP are defined as the SNAO index. The homogeneous map of 200-hPa geopotential height anomalies shows a southeastward-propagating wave train (Fig. 9c). Both the patterns of the SLP and the upper-level circulation highly resemble those associated with the ENSO-independent EOF1 of the TP circulation (Figs. 8c,d). The SNAO index and the PC time series of the ENSO-independent EOF1 of the TP climate reach 0.58 (exceeding the 95% confidence level). All these results support that the ENSO-independent EOF1 mode is associated with the SNAO.
(a),(b) The summer mean SLP anomalies (shading; unit: hPa) associated with (a) the ENSO-independent EOF1 and (b) the SNAO extracted from the MCA in the observations. (c) The 200-hPa geopotential (shading; unit: m2 s−2) and wave activity flux (vectors; unit: m2 s−2) anomalies associated with the SNAO extracted from the MCA in the observations. (d)–(f) As in (a)–(c), but for the results derived from the intermember spread of the PM-CEP experiments. The blue boxes in (b), (c), (e), and (f) are the regions chosen for MCA. The white dots denote values passing the 5% significance level.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
An identical MCA is performed for the simulations. Both the SNAO-related SLP anomalies with the meridional dipole pattern in the North Atlantic and the upper-level eastward-propagating wave train are reproduced (Figs. 9e,f). The correlation coefficient between two expansion coefficients of the first MCA mode reaches 0.58 (exceeding the 95% confidence level). The correlation coefficient between the PC time series of the ENSO-independent EOF1 and the expansion coefficients of SLP (simulated SNAO index) reaches to 0.76 (exceeding the 95% confidence level). These results indicate that the ENSO-independent EOF1 can also be attributed to the SNAO for the ensemble simulations, consistent with the observation.
b. CGT-related mode
The ENSO-independent EOF2 accounts for 17.1% (14.5%) of the total variance of ENSO-independent variability in the observation (intermember spread of the model simulations), respectively. In both the observation and the simulations, the most prominent feature of EOF2 is a zonal dipole pattern with an anomalous anticyclone over the TP and an anomalous cyclone located to the west of the TP. (Fig. 10a). The 200-hPa circulation anomalies associated with the ENSO-independent EOF2 exhibit a wave train from the North Atlantic to the East Asia, with its nodes located over the North Atlantic Ocean, central Europe, and the Caspian Sea (Figs. 10c,d). The upper-level anomalous anticyclones (cyclones) embedded in the wave train correspond to local surface warming (cooling) (Figs. 10c,d).
As in Fig. 6, but for the ENSO-independent EOF2. The letter “C” (“AC”) in (a) and (b) indicates the center of the cyclonic (anticyclonic) anomalies.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
We further verify that the atmospheric teleconnection pattern associated with the EOF2 is the ENSO-independent CGT pattern. The ENSO-independent CGT is defined as the leading EOF mode of the ENSO-independent summer 200-hPa meridional wind variability over the region 20°–80°N, 100°W–100°E, following Saeed et al. (2013). For both the observation and the simulations, 200-hPa geopotential anomalies associated with the mode show an eastward-propagating wave train pattern of zonal wavenumber 5, a typical pattern of the CGT pattern (Figs. 11b,d). The wave train pattern highly resembles that in the ENSO-independent EOF2 of the TP climate (Figs. 11a,c). The PC time series of the mode is defined as the CGT index. The correlation coefficient between the PC time series of the ENSO-independent EOF2 of the TP climate and the CGT index is 0.63 (0.42) for the observation (the PM-CEP experiments), respectively, both significant at the 5% level. These results support that the ENSO-independent EOF2 of the TP climate is the CGT-related mode.
The 200-hPa geopotential (contours; unit: m2 s−2) and wave activity flux (vectors; unit: m2 s−2) anomalies associated with (a) the ENSO-independent EOF2 and (b) the CGT for the observations. (c),(d) As in (a) and (b), but for the results derived from the intermember spread of the PM-CEP experiments. The gray shading denotes the summer subtropical westerly jet (unit: m s−1).
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
It has been noted that the CGT pattern can be excited by the tropical convection anomalies independent of ENSO (Ding and Wang 2005; Wang et al. 2012) and/or be generated by the energy propagation and barotropic instability of the basic-state flow (Kosaka et al. 2009; Ding et al. 2011; Chen et al. 2013). What mechanisms are responsible for the CGT-related mode of the TP climate deserves further study.
5. Conclusions and discussion
a. Conclusions
In this study, we extract four leading modes of the interannual variability of the TP summer climate and investigate their formation mechanisms. The ENSO-forced and ENSO-independent variabilities of the TP climate are explicitly isolated via the 10-member pacemaker experiments using the coupled climate model CAS FGOALS-f3-L. In the pacemaker experiments, the simulated SSTAs in the tropical central-eastern Pacific are restored to the observed anomaly. The ensemble mean and ensemble spread of the pacemaker experiments represent the ENSO-forced and the ENSO-independent variability, respectively. The main conclusions are listed below.
First, for both the observation and the pacemaker experiments, interannual variability of the upper-level circulations over and surrounding the TP in summer is dominated by four leading modes, two of which are associated with ENSO developing and decaying phases, with the other two associated with SNAO and CGT independent of ENSO. Their relative importance is compared according to their contributions to the interannual variability of the 350-hPa streamfunction. It is shown that the variance contributions of the four modes are 15.96%, 3.01%, 6.61%, and 5.91% in the observation, respectively (Fig. 12). Their ranking of the relative importance is the same for the pacemaker experiments (figure not shown).
The fractional variance (shading; unit: %) of interannual variability of summer 350-hPa streamfunction in observations explained by (a) developing-ENSO-related mode, (b) decaying-ENSO-related mode, (c) summer NAO-related mode, and (d) summer CGT-related mode. The gray dashed boxes represent the regions where the MV-EOF analyses are performed. Variance contributions averaged in the box are noted on the upper-right corners.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
Second, the positive phase of the developing- (decaying-) ENSO-related mode is characterized by an anomalous baroclinic cyclone (anticyclone) over the western TP during El Niño developing (decaying) summer. The anomalous cyclone in developing-ENSO-related mode is driven by negative Indian summer monsoon rainfall anomalies. In contrast, the anomalous anticyclone in decaying-ENSO-related mode is driven by Indian Ocean basinwide warming. These features are consistent between the observation and the pacemaker experiments.
Third, the SNAO-related mode is characterized by two anomalous anticyclones centered over the west of TP and central China, and the CGT-related mode is dominated by a zonal dipole pattern with an anomalous anticyclone over the TP and an anomalous cyclone to the west of the TP. Both the SNAO- and CGT-related modes arise from stationary Rossby wave trains. The wave train pattern associated with the SNAO-related mode forms an arch along a great-circle route, while that associated with the CGT-related mode exhibits a zonally oriented pattern.
b. Discussion
In this study, we focus on the large-scale atmospheric teleconnections over the TP and surrounding areas from both the observation and simulations by FGOLAS-f3-L, which is generated by the air–sea interactions in the tropical regions or the atmospheric dynamic processes in the mid- and high-latitude regions.
The four dominant large-scale circulation modes have great impacts on the regional climate on the TP. The summer SAT and precipitation anomalies associated with these modes in observations are shown in Fig. 13. For the SAT, the anomalous cyclone (anticyclone) generally corresponds to underlying cold (warm) anomalies (Figs. 13a–d). The impacts of the developing- and decaying-ENSO-related modes and the SNAO-related modes are mainly confined to the western TP, while the SAT anomalies associated with the CGT-related modes show a zonal dipole pattern, covering the entire TP. From this perspective, the CGT-related mode plays a more important role in modulating the TP summer climate. In addition, the anomalous anticyclone (cyclone) circulations embedded in the track of the variability modes coincide well with the surface warming (cooling) for both the observation and model simulation (Figs. 8 and 10). The surface temperature anomalies may be associated with overlying variation of cloud coverage and shortwave radiation induced by circulation anomalies (Ding and Wang 2005; Wu et al. 2016). The detained mechanism reserve further studies in future.
The summer SAT anomalies (shading; unit: °C) regressed onto the (a) developing-ENSO-related mode, (b) decaying-ENSO-related mode, (c) summer NAO-related mode, and (d) summer CGT-related mode. The white dots denote values passing the 5% significance level. The solid and dashed circles represent the cyclonic (anticyclonic) anomalies, and “C” (“AC”) indicates the center of the cyclonic (anticyclonic) anomalies. (e)–(h) As in (a)–(d), but for the regressed summer precipitation anomalies (unit: mm day−1). The observational precipitation in (e)–(h) values are derived from GPCC.
Citation: Journal of Climate 35, 5; 10.1175/JCLI-D-21-0207.1
Compared with the SAT, the precipitation anomalies associated with the four modes show more regional-scale features (Figs. 13e–h). The developing-ENSO-related mode has striking impacts over the southwestern TP, and the SNAO-related mode can induce west–east dipole precipitation anomalies over the TP (Figs. 13e,g). In comparison, the impacts of the decaying-ENSO-related and the CGT-related modes on the TP summer precipitation are limited, with the former being confined to the southern slope of the TP, and the latter to the northeastern TP (Figs. 13f,h).
This study has potential applications in the seasonal prediction of the TP climate. Wang et al. (2007) proposed a statistical–dynamic prediction approach, “predictable mode analysis” (PMA), which relies on the identification of the predictable leading modes of the predictand. This approach has been applied to the predictions of the Asian winter surface temperature (Lee et al. 2012), the Northern Hemisphere summer upper-tropospheric circulation (Lee et al. 2010), and the East Asian summer monsoon rainfall (Wang et al. 2014). In this study, we identify the leading modes of interannual variability of TP summer climate. Based on the obtained leading modes, we may construct a statistical–dynamic seasonal prediction system for the TP climate using the PMA approach in future.
As a final note, this study is based on the 10-member pacemaker experiments using a single model. Possible model dependence of the results and incomplete isolation between ENSO-forced and ENSO-independent variability due to limited ensemble members deserve further studies using large-ensemble pacemaker experiments of multiple models.
Acknowledgments.
We thank the three anonymous reviewers for their constructive comments that helped greatly to improve the original manuscript. This work is jointly supported by National Key Research and Development Program of China (Grant 2017YFA0604201), the NSFC (Grant 42075163), and the NSFC BSCTPES project (Grant 41988101). This work is also supported by the Jiangsu Collaborative Innovation Center for Climate Change.
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