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  • View in gallery

    Seasonal teleconnectivity (shading) for filtered daily anomalies of the 250-hPa streamfunction deviation (from the zonal mean) and 250-hPa zonal winds (contours). A 10–30-day bandpass Lanczos filter is applied. The values of teleconnectivity are multiplied by 100 in the plots.

  • View in gallery

    November–April Hovmöller diagram of lagged correlations between filtered daily anomalies of the 250-hPa streamfunction deviation at the reference site (25°N, 60°E) and those of the averaged 250-hPa streamfunction deviation over 20°–35°N. The slope of the solid lines is the reciprocal of the mean 250-hPa November–April zonal wind speed (° day−1) at 60°E averaged over 20°–35°N.

  • View in gallery

    (a) November–January and (b) February–April variance of 10–30-day filtered daily anomalies of the 250-hPa streamfunction deviation (contours) and the ratio of the variance to the 10-day low-pass filtered variance (shading).

  • View in gallery

    Morlet wavelet for daily anomalies of the (a) 250-hPa streamfunction deviation, (b) 250-hPa zonal wind, (c) 250-hPa geopotential height deviation, and (d) surface air temperature at the reference site (25°N, 95°E). The value presented is the power spectrum of the wavelet divided by that at the 95% confidence level.

  • View in gallery

    (a) First and (c) second EOF modes of filtered daily anomalies of the 250-hPa streamfunction deviation averaged over 10°–40°N and (b),(d) their normalized PCs in 1980. Variance accounted for by each mode is shown at the right in (a) and (c). (e) Lagged correlations of the first two EOFs. (f) Time series of the teleconnection index defined as the squared sum of the first two PCs in 1980 (black curve) and time series of the daily climatology of the index (red).

  • View in gallery

    Power spectra (thick curves) for (a) time series of the daily teleconnection index and (b) time series of the monthly teleconnection index. Markov red noise spectra (solid curve) and their 95% (lower dashed curve) and 99% (upper dashed curve) confidence bounds are also shown.

  • View in gallery

    Composite patterns of daily anomalies for OLR (shading; W m−2) and the filtered 250-hPa streamfunction deviation (contours; scale is 106) as a function of phase. The phase and number of days used to create the composite values are shown at the middle above each panel. Only the days with a teleconnection index greater than 1.0 are included. Zero contours are not drawn, and the negative values are shown as dashed contours.

  • View in gallery

    As in Fig. 7, but for surface air temperature (shading; °C).

  • View in gallery

    November–April lagged correlations between monthly anomalies of the teleconnection index and monthly anomalies of SST (shading) and between monthly anomalies of the teleconnection index and monthly anomalies of the 250-hPa streamfunction deviation (contours). Lag time (months) is displayed at the middle above each panel. Zero contours are not drawn, and the negative values are shown as dashed contours. The critical value for the Pearson correlation coefficient at the 90% confidence level is 0.11 based on the Student’s t test.

  • View in gallery

    (a) Lagged correlations of the two indexes. The dashed line represents the 90% confidence level according to the Student’s t test. (b) Scatter diagram of monthly anomalies of the teleconnection index and monthly anomalies of the Niño-3.4 index, which leads the teleconnection index by 2 months. The solid line represents the least squared regression line. Six points with a teleconnection index greater than 4.5 are not shown.

  • View in gallery

    Composite filtered daily anomalies of the 250-hPa streamfunction deviation for the El Niño years (contours; scale is 106) and the differences in the composite values between El Niño and La Niña years (shading) as a function of phase. Above each panel, we present the phase and number of days used to create the composite patterns for the El Niño and La Niña years sequentially. Only the days with a teleconnection index greater than 1.0 are included. Zero contours are not drawn, and the negative values are shown as dashed contours.

  • View in gallery

    November–April teleconnectivity during the El Niño years (contours) and the difference between the teleconnectivity during the El Niño years and that during the La Niña years (shading). The teleconnectivity index is multiplied by 100.

  • View in gallery

    As in Fig. 12, but for the variance of filtered daily anomalies of the 250-hPa streamfunction deviation.

  • View in gallery

    As in Fig. 12, but for (a) 250-hPa zonal winds and (b) the total wavenumber of 250-hPa stationary waves. The stippled area denotes the region where the change significantly exceeds the 95% confidence level (Student’s t test).

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Afro-Eurasian Intermediate-Frequency Teleconnection and Modulation by ENSO

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  • 1 Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, and Institute of Earth Climate and Environment System, Sun Yat-sen University, Guangzhou, Guangdong, China
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Abstract

The Afro-Eurasian intermediate-frequency atmospheric teleconnection conveys meteorological signals zonally, leads to various atmospheric variations, and causes extreme events along its path. This study, aimed at demonstrating the characteristics of the teleconnection, reveals that the teleconnection accounts for nearly half of the atmospheric variability and significantly influences different meteorological fields. With the propagation of signals associated with the teleconnection, local weather varies from prolonged dry and warm days to extended wet and cold days. El Niño–Southern Oscillation (ENSO) modulates the interannual variation of the teleconnection: it becomes more active and its downstream pattern shifts southward during El Niño events. Two responsible mechanisms are proposed for the ENSO modulation: the eddy-to-eddy interaction that leads to the change in the activeness of the teleconnection and the waveguide effect that accounts for the shift of the teleconnection. First, the El Niño–related Atlantic anomalies of the Rossby wave train and storm track amplify the Atlantic disturbances of the intermediate frequency and thus the activeness of the teleconnection. Second, during El Niño years, the East Asian jet stream shifts southward, resulting in the southward shifts of the downstream waveguide effect and thus the downstream pattern. This study also demonstrates that when investigating an atmospheric mode or its impacts, the signals of different time scales should be separated and the cross-frequency interactive systems necessitate examinations.

Denotes content that is immediately available upon publication as open access.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Prof. Song Yang, yangsong3@mail.sysu.edu.cn

Abstract

The Afro-Eurasian intermediate-frequency atmospheric teleconnection conveys meteorological signals zonally, leads to various atmospheric variations, and causes extreme events along its path. This study, aimed at demonstrating the characteristics of the teleconnection, reveals that the teleconnection accounts for nearly half of the atmospheric variability and significantly influences different meteorological fields. With the propagation of signals associated with the teleconnection, local weather varies from prolonged dry and warm days to extended wet and cold days. El Niño–Southern Oscillation (ENSO) modulates the interannual variation of the teleconnection: it becomes more active and its downstream pattern shifts southward during El Niño events. Two responsible mechanisms are proposed for the ENSO modulation: the eddy-to-eddy interaction that leads to the change in the activeness of the teleconnection and the waveguide effect that accounts for the shift of the teleconnection. First, the El Niño–related Atlantic anomalies of the Rossby wave train and storm track amplify the Atlantic disturbances of the intermediate frequency and thus the activeness of the teleconnection. Second, during El Niño years, the East Asian jet stream shifts southward, resulting in the southward shifts of the downstream waveguide effect and thus the downstream pattern. This study also demonstrates that when investigating an atmospheric mode or its impacts, the signals of different time scales should be separated and the cross-frequency interactive systems necessitate examinations.

Denotes content that is immediately available upon publication as open access.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Prof. Song Yang, yangsong3@mail.sysu.edu.cn

1. Introduction

Atmospheric teleconnection patterns have been explored and recognized for decades (e.g., Wallace and Gutzler 1981), and these patterns link tropical and extratropical climates over different ocean basins and different continents (Trenberth et al. 1998; Alexander et al. 2002; Wang et al. 2004; Zhao et al. 2011). Among the various teleconnections, the intermediate-frequency teleconnection (IFT; defined for the period of 10–30 days) originates from the entrance of westerly jet streams, which serve as the Rossby waveguide and trap the Rossby wave by confining its extension to the zonal direction, and is oriented along the waveguide, with structure accounted for by Rossby wave dispersion (Blackmon et al. 1984a,b). In a much broader sense, intermediate-frequency disturbances, such as the quasi-biweekly oscillation, are connected with equatorial Rossby waves or upstream extratropical Rossby wave trains (Kikuchi and Wang 2009). Details of theoretical interpretations including the waveguide effect have been put forward for the propagation of the Rossby waves, and the wide spatial span of the waveguide has also been revealed and explained (Hoskins and Ambrizzi 1993; Ambrizzi et al. 1995; Branstator 2002). The theoretical interpretation implies that the IFT is closely related to the waveguide and must be linked to external forcing from outside of the atmosphere as well as atmospheric patterns with a wider spatial span.

Meanwhile, there has been an explosion of knowledge about the effects of intermediate-frequency disturbances on weather and climate. For instance, the downstream disturbances of intermediate frequency excited by the East Asian cold surge can enhance the daily precipitation over the western United States (Jiang and Deng 2011; Jiang et al. 2014). Weekly variations of intermediate frequency, coupled with those on the lower-frequency and synoptic scales, are also known to contribute to the variability of atmospheric blocking (Rennert and Wallace 2009; Jiang et al. 2013). Furthermore, the quasi-biweekly oscillation revealed in tropical–subtropical convection (Wang and Duan 2015) is found to be closely related to other intraseasonal variations, such as the 30–60-day oscillation of the summer monsoon systems (Mao and Wu 2005; Ding et al. 2006). In brief, the IFT extends the aforementioned effects not only locally but also remotely, and exerts wide-ranging influences on atmospheric systems at different time scales. These influences suggest the importance of the IFT for atmospheric systems and the need to investigate the variation of the IFT so as to further understand its impacts.

From a global perspective, the climate variability over Afro-Eurasia serves as an excellent test bed for exploring the IFT. On the one hand, many high-impact weather extremes such as the Russian heat wave in the summer of 2010 (Trenberth and Fasullo 2012) occur in Afro-Eurasia. On the other hand, the Arctic Oscillation is one of the most prominent modes of the winter climate and is strongly coupled with the variations of surface air temperature over the Eurasian continent (Thompson and Wallace 1998, 2000). Therefore, the contribution of the IFT over Afro-Eurasia to these prominent high-impact atmospheric phenomena is worthy of assessment. Meanwhile, the variability of Afro-Eurasian climate also manifests sensitive and conspicuous responses, and the Afro-Eurasian IFT (AEIFT) responds to external factors sensitively. For example, the weather extremes in Afro-Eurasia are associated with worldwide climate variability such as El Niño–Southern Oscillation (ENSO), the Asian monsoon system, and the strong convection over the tropical Atlantic (Trenberth and Fasullo 2012). Nevertheless, previous studies have emphasized that natural atmospheric variability might play a large role in weather extremes, with the European cold spells in February 2012 as an example, although global warming explains a fraction of the variability as well (Peterson et al. 2013). Moreover, the responses of Eurasian surface climate stand out with changes in the external forcing such as oceanic, solar, and volcanic forcing (Shindell et al. 2001). These results suggest that the responses over Afro-Eurasia to the external forcing, more specifically the responses of the AEIFT, are prominent in the global atmospheric system. Note that the atmospheric phenomena within Afro-Eurasia interact with one another, displaying features of spatial changes similar to those of the AEIFT. In winter, there exists a connection between the Arctic Oscillation and the variability of the East Asian winter monsoon (Gong et al. 2001). In summer, the descent over the Mediterranean or the Sahel rainfall is closely linked to the Asian monsoon heating via the monsoon–desert mechanism (Rodwell and Hoskins 1996, 2001; He et al. 2017). It is therefore feasible and important to investigate the atmospheric teleconnection over Afro-Eurasia and its responses to the variability from outside of the atmosphere to further improve our understanding of the climate variability over Afro-Eurasia and the IFT-associated weather and climate features in other regions.

Since atmospheric teleconnections are a source of climate variability and important for atmospheric predictability over many places in the world including Afro-Eurasia, many teleconnection patterns have been distinguished on various time scales. For example, the Asian–Bering–North American teleconnection modulates the surface temperature and temperature extremes in North America all year round, especially in winter (Yu et al. 2016). This teleconnection is influenced by the snow cover over the Siberian plain and is maintained by synoptic eddies (Yu et al. 2018). In summer the Silk Road pattern transmits energy from the eastern Mediterranean Sea and the Aral Sea to northeastern Asia (Enomoto et al. 2003). It is modulated by the Asian waveguide and influences the temperature variability around Japan (Enomoto 2004). Moreover, a Eurasian wave train originates over the Atlantic (Lu et al. 2002) and imposes the influence of the Atlantic multidecadal oscillation on the climate over Eurasia and even the Pacific region (Gao et al. 2017). The Eurasian wave train also contributes to the variability of the Indian summer monsoon, whose heating induces a downstream wave train propagation that affects the East Asian monsoon (Wu 2002; Ding and Wang 2005, 2007), revealing that the monsoon is a wave train conductor in summer. In winter, the waveguide moves equatorward and strengthens, and thus the primary teleconnection patterns reveal a larger zonal wavenumber and equatorward displacement (Ambrizzi et al. 1995). According to Sun et al. (2017), the Africa–Asia multidecadal teleconnection pattern, due to Rossby wave dynamics, may also extend the influence of the Atlantic multidecadal oscillation on the downstream climate in the absence of abundant monsoon heating. It is the Tibetan Plateau, rather than the monsoon, that plays a leading role in the teleconnection in winter or spring and serves as an eddy activity insulator. Upstream signals can be blocked by the Tibetan Plateau, and thus prolonged with increased persistence from winter to spring. These blocked signals are able to maintain low-temperature anomalies and finally delay and weaken the Asian summer monsoon (Yang et al. 2004). In brief, these extensive studies have demonstrated that external signals, more specifically the variations of sea surface temperature (SST), are important in modulating the variability of the teleconnection pattern, whereas local factors, such as the waveguide, monsoon systems, and topography, interact with the external signals. Nevertheless, the modulation of local atmospheric factors such as the waveguide is comparatively less predicted than that of the tropical SST, because the local factors are both attributed to internal dynamic processes and induced by the external forcing from the tropical SST (Lau 1997; Yang et al. 2002). Hence, the variability of the AEIFT, which is closely related to the waveguide, must derive from variations of tropical SST.

It is equally interesting to note that most of the distinguished teleconnection patterns manifest features of both south–north dipole and eastward propagation (Branstator 2002; Ding and Wang 2007). In other words, the patterns include both low-frequency signals (longer than 30 days) and intermediate-frequency signals with a cross-frequency coupling (Rennert and Wallace 2009). Thus, it is worth examining the physical processes from the SST to the teleconnection focusing on certain frequencies, such as the intermediate frequencies in this study.

A more comprehensive understanding of the modulation of IFT by SST forcing can improve the prediction of the IFT, and therefore favor weather and climate predictions, since the IFT influences both weather and climate. Among all SST forcings, ENSO is the principal variability of the climate system, at least on the interannual time scale. On the one hand, during El Niño events, intermediate-frequency eddy kinetic energy decreases over the North Pacific but increases over the North Atlantic (Jiang et al. 2013), indicating that more intermediate-frequency activities occur over the North Atlantic. On the other hand, ENSO changes the location and strength of jet streams, which plays a dominant role in IFT (L’Heureux and Thompson 2006; Lu et al. 2008; Yu and Zou 2013). Hence, it can be argued that ENSO may influence the IFT, which originates in the Atlantic (Blackmon et al. 1984b; Hsu and Lin 1992), over the southern Eurasian continent in winter or spring.

In this study, we focus on the interannual variability of the AEIFT and its modulation by ENSO. We first reveal the features of the AEIFT with a goal of measuring the AEIFT and understanding its impacts on atmospheric systems. We will depict the features of the AEIFT modulated by ENSO and focus on the mechanisms of the modulation, especially the mechanisms on different time scales. The study is focused on the AEIFT of cold-season months, since the AEIFT is active from November to the following April.

This paper is organized as follows. In section 2, we describe the datasets and methods employed. The characteristics of the AEIFT and the associated dynamics are discussed in section 3. In section 4, correlation and composite analyses are conducted to show the modulation of the AEIFT by ENSO. Conclusions and discussion are given in section 5, stressing the importance of cross-frequency interaction.

2. Datasets and methods

The datasets employed in this study include the daily and monthly mean zonal wind, meridional wind, geopotential height, and surface air temperature on a 2.5° × 2.5° grid from 1979 to 2015 from the NCEP–DOE reanalysis by the NOAA/OAR/ESRL PSD, Boulder, Colorado, from their website (http://www.esrl.noaa.gov/psd/) (Kanamitsu et al. 2002). The data used also include 1) the daily NOAA Outgoing Longwave Radiation (OLR) Climate Data Record on a 1° × 1° grid from 1979 to 2012 from the interpolated OLR data provided by the NOAA/OAR/ESRL PSD (http://www.esrl.noaa.gov/psd/) and 2) the monthly mean SST on a 1° × 1° grid from 1979 to 2015 from the Hadley Centre Sea Ice and Sea Surface Temperature dataset provided by the UK Met Office (www.metoffice.gov.uk/hadobs) (Rayner et al. 2003).

We concentrate on the teleconnection that is characterized as wave patterns by using the streamfunction deviations from their zonal mean. To remove the annual climatological cycle, daily anomalies of the deviations are employed throughout this study. The Lanczos filter weights are applied to obtain signals in certain frequencies (Duchon 1979). Two indexes are adopted in this study. The first is the teleconnectivity defined by Wallace and Gutzler (1981), which is used to distinguish the intensity of teleconnection pattern at every grid. The teleconnectivity at grid i is the absolute value of the strongest negative correlation in the map of the correlation onto i:
e1
where refers to the correlation between i and every j. An EOF analysis is conducted to define the second index with the goal of quantitatively describing the magnitude of the AEIFT. Four different phases of the AEIFT are defined based on a two-dimensional phase diagram of the corresponding principal components (PCs) 1 and 2. Specifically, phase 1 is defined as the combination of negative PC1s and PC2s, phase 2 as the combination of positive PC1s and negative PC2s, phase 3 as the combination of positive PC1s and PC2s, and phase 4 as the combination of negative PC1s and positive PC2s. The second index is a teleconnection index defined as the sum of the squares of PC1 and PC2. To describe the magnitude of the waveguide effect, stationary wavenumbers are calculated in consideration of the effect of spherical geometry as follows:
e2
where ϕ is latitude, Ω is the angular speed of Earth, and is the relative rotational speed of the atmosphere (Hoskins and Ambrizzi 1993). A Morlet wavelet analysis is employed to distinguish dominant signals, in which a red noise background is used for a regular chi-squared test. A spectral analysis is used in this study with 10% of the series to be tapered, and periodogram estimates are averaged utilizing modified Daniell smoothing. The ENSO events analyzed are obtained from the NOAA/NWS/CPC (http://origin.cpc.ncep.noaa.gov/) and defined from July to the following June. El Niño events are recorded in 1979/80, 1982/83, 1986/87, 1987/88, 1991/92, 1994/95, 1997/98, 2002/03, 2004/05, 2006/07, and 2009/10, and La Niña events in 1983/84, 1984/85, 1988/89, 1995/96, 1998/99, 1999/2000, 2000/01, 2007/08, 2010/11, and 2011/12.

3. Characteristics of teleconnection

We focus on the 250-hPa atmospheric circulation in the subtropical to midlatitude regions of Afro-Eurasia in this study. While midlatitude features are more conspicuously represented at the lower levels, tropical features often emerge from the higher levels (Hoskins and Ambrizzi 1993). As shown in Fig. 1, the seasonal teleconnectivity for the intermediate-frequency daily streamfunction anomalies takes on a couple of zonal patterns. It exhibits three main paths in the Northern Hemisphere: one crossing North America from the Pacific to the Atlantic and the other two crossing the Eurasian continent. Moreover, the paths of the seasonal teleconnectivity bear resemblance to those of the 250-hPa jet streams. Because of the enhancement of westerly jet streams and the southward shift of the easterlies, the features in winter and spring are characterized by stronger and wavier IFT compared with summer and autumn (Hoskins and Karoly 1981). Interestingly, these patterns all originate over the eastern ocean basins (also see Fig. 2), in which the eastern edges of the storm tracks are located. This feature is related to the interaction between high-frequency (or synoptic scale; shorter than 10 days) and intermediate-frequency eddies, which is a main source of the intermediate-frequency phenomena over the eastern ocean basins, such as atmospheric blocking (Jiang et al. 2013). This feature also implies a relationship between the IFT and high-frequency eddies, such as the storm track. The role of the interaction between various eddies in the modulation of the AEIFT by ENSO will be discussed in section 4.

Fig. 1.
Fig. 1.

Seasonal teleconnectivity (shading) for filtered daily anomalies of the 250-hPa streamfunction deviation (from the zonal mean) and 250-hPa zonal winds (contours). A 10–30-day bandpass Lanczos filter is applied. The values of teleconnectivity are multiplied by 100 in the plots.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

Fig. 2.
Fig. 2.

November–April Hovmöller diagram of lagged correlations between filtered daily anomalies of the 250-hPa streamfunction deviation at the reference site (25°N, 60°E) and those of the averaged 250-hPa streamfunction deviation over 20°–35°N. The slope of the solid lines is the reciprocal of the mean 250-hPa November–April zonal wind speed (° day−1) at 60°E averaged over 20°–35°N.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

We now explore the features of the southern path of the AEIFT originating over the Atlantic from November to April. Figure 2 is the Hovmöller diagram of the lagged correlation of filtered daily streamfunction anomalies onto a reference site, which is located over the northern Arabian Sea and is the strongest teleconnectivity core of the AEIFT. The correlation coefficients around the reference point reach their local minima on day −8 and day 8. The zonal phase speed of the AEIFT is derived from the propagation of the same phase of the AEIFT or the slope of the phase propagation path in the figure, and the zonal group velocity from the propagation of the same amplitude of the AEIFT or the slope of the group propagation path in the figure. The slope of the phase propagation path is larger than that of the solid lines representing the reciprocal of the zonal wind speed, while the slope of the group propagation path is slightly smaller than that of the solid lines. These features indicate that the phase speed relative to the ground is smaller than the zonal wind speed, while the group velocity is slightly larger than the wind. Although the phase speed is westward relative to the wind, both group and phase velocities are eastward relative to Earth’s surface when the subtropical westerly jets dominate along the AEIFT. Therefore, Fig. 2 exhibits the Rossby wave dispersion: the wave group propagates eastward more rapidly than the wave phase, and the shape of the wave group changes as the group propagates (Holton and Hakim 2013a). The figure also indicates that the AEITF exhibits a 16-day period, which a waveform takes to return to its initial state. Note also that the AEIFT originates over the eastern Atlantic from a Rossby wave source (Sardeshmukh and Hoskins 1988) and extends into the Middle East jet stream and across the East Asian jet stream shown in Figs. 1 and 2. These features are in agreement with the relationship between the AEIFT and the disturbances over the eastern Atlantic, as well as the relationship between the AEIFT and the waveguide mentioned earlier.

A question arises then: Does the AEIFT stand out among the climate variability of various time scales? Figure 3 shows the variance of the intermediate-frequency daily anomalies and the ratio of the variance to that longer than 10 days. The magnitude of intermediate-frequency variance peaks between 20° and 30°N, where the AEIFT and the subtropical jets are located. Compared to the magnitude cores along the AEIFT during November–January (Fig. 3a), the cores during February–April (Fig. 3b) extend westward along North Africa and weaken over East Asia. Similar changes are found in the Middle East jet stream and the East Asian jet stream during November and the following April (Pan et al. 2011; Ni et al. 2014). Although this similarity does not necessarily depict a causal relationship between the intermediate-frequency disturbances and the waveguide, it implies a linkage between the two. Meanwhile, the intermediate-frequency variance accounts for nearly 40% of the variance longer than 10 days along the AEIFT. The ratio around the Middle East jet stream is high during November–January (Fig. 3a), whereas that around the East Asian jet stream is high during February–April (Fig. 3b). These features reveal that the intermediate-frequency signals are of great importance for intermediate-to-low-frequency climate variability, especially around the jet streams.

Fig. 3.
Fig. 3.

(a) November–January and (b) February–April variance of 10–30-day filtered daily anomalies of the 250-hPa streamfunction deviation (contours) and the ratio of the variance to the 10-day low-pass filtered variance (shading).

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

The impacts of intermediate-frequency variability on other meteorological elements are presented in Fig. 4. The power spectra of the Morlet wavelet of the 250-hPa streamfunction, 250-hPa zonal wind, 250-hPa geopotential height, and surface air temperature at 25°N, 95°E, where one of the teleconnectivity cores of the AEIFT is located, consistently exceed the 95% confidence level in the period of 8–32 days during every cold season. These features confirm significant intermediate-frequency signals of the meteorological elements during the cold season, without any exception. Additionally, the power at the 2–8-day frequencies exceeds the 95% confidence level during every cold season. Therefore, synoptic signals can also be discovered. Along the AEIFT path, there exist similar characteristics in the wavelets mentioned above (not shown). The impacts of the variability of meteorological elements along the AEIFT on the ecosystem have been shown in previous studies (Yu et al. 2010; Shen et al. 2011), suggesting that the intermediate-frequency variability plays a role in the meteorological elements along the AEIFT and thus exerts an impact on the local ecosystem.

Fig. 4.
Fig. 4.

Morlet wavelet for daily anomalies of the (a) 250-hPa streamfunction deviation, (b) 250-hPa zonal wind, (c) 250-hPa geopotential height deviation, and (d) surface air temperature at the reference site (25°N, 95°E). The value presented is the power spectrum of the wavelet divided by that at the 95% confidence level.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

Since the intermediate-frequency variability plays a role in the climate variability along the AEIFT path, we further depict the magnitude of the AEIFT quantitatively using an EOF analysis. For streamfunction anomalies averaged from 10° to 40°N, the EOF analysis is conducted for the meridional average over the region from 20°W to 110°E. Figure 5 reveals that the first two EOF modes explain nearly half of the total variance. EOF1 is characterized by peaks and troughs with the preferred locations of large-value centers in the teleconnectivity map (Fig. 5a), while the peaks and troughs in EOF2 are more eastward than those in EOF1 (Fig. 5c). The first two modes together show an eastward propagation of the AEIFT. The lagged correlation of these two dominant EOF modes reaches its maximum and minimum on day 4 and day −4, respectively, showing a 16-day period (Fig. 5e), which is the same as the period shown in Fig. 2. Both the patterns and the temporal evolution match the earlier-mentioned characteristics of the AEIFT, indicating that the results from the EOF analysis can satisfactorily describe the AEIFT quantitatively. With the two dominant EOF modes revealed, a teleconnection index is then defined as the squared sum of the corresponding PCs (Figs. 5b,d) to describe the magnitude of the AEIFT. Although the magnitude of a specific event was measured by the sum of absolute values of the PCs in previous studies (Maloney and Hartmann 1998; Zhao and Moore 2004), the squared sum can highlight strong events and their important features. Thus, only the results from the squared sum are presented next. Climatologically, the teleconnection index is greater than 1.0 in the cold-season months, when there is an active period of the AEIFT (Fig. 5f), which is the reason why the current study is focused on the cold-season months. The spectral analysis shown in Fig. 6 indicates that the teleconnection index exhibits an 8-day period for the daily mean data and a 12-month period for the monthly mean data. To remove the 8-day cycle, monthly means will be adopted in section 4.

Fig. 5.
Fig. 5.

(a) First and (c) second EOF modes of filtered daily anomalies of the 250-hPa streamfunction deviation averaged over 10°–40°N and (b),(d) their normalized PCs in 1980. Variance accounted for by each mode is shown at the right in (a) and (c). (e) Lagged correlations of the first two EOFs. (f) Time series of the teleconnection index defined as the squared sum of the first two PCs in 1980 (black curve) and time series of the daily climatology of the index (red).

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

Fig. 6.
Fig. 6.

Power spectra (thick curves) for (a) time series of the daily teleconnection index and (b) time series of the monthly teleconnection index. Markov red noise spectra (solid curve) and their 95% (lower dashed curve) and 99% (upper dashed curve) confidence bounds are also shown.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

The impacts of the AEIFT on regional weather during the cold-season months are shown in Figs. 7 and 8, with a focus on OLR and surface air temperature. The composite patterns, in which data samples are chosen when the teleconnection index is greater than 1.0, are constructed based on the four different phases of the AEIFT. In the Northern Hemisphere, the positive streamfunction is associated with an anomalous ridge. Negative OLR anomalies, indicators of enhanced convection, are found in and to the west of the upper intermediate-frequency ridge anomalies (Fig. 7). Because of the eastward propagation of the ridge, the figure indicates that the ridge is followed by enhanced convection. In other words, the ridge can promote precipitation, in agreement with the idealized model of a baroclinic disturbance (Holton and Hakim 2013c). In addition, negative surface air temperature anomalies are found in the negative streamfunction, an anomalous trough (Fig. 8), implying that negative temperature anomalies follow negative OLR anomalies. Since the convective weather in and to the east of an upper trough cools the surface air, this feature also suggests that the negative temperature anomalies in the trough probably result from the enhanced convection to their east. Therefore, Figs. 7 and 8 indicate that the upper ridge anomalies along the AEIFT are followed by enhanced convection, resulting in negative surface air temperature anomalies within the following trough anomalies. Meanwhile, positive OLR anomalies are found in and to the west of negative streamfunction (Fig. 7), demonstrating that an anomalous trough can dampen any tendency of convection. Furthermore, positive temperature anomalies in the ridges follow the positive OLR anomalies to their east (Fig. 8). This feature suggests that the trough anomalies along the AEIFT are followed by the suppressed convection, resulting in positive surface air temperature anomalies within the following ridge anomalies. In brief, these features are responsible for the prolonged convective and cold weather as well as the clear and warm weather over land and for the variation of local weather as the AEIFT signals pass by. It can be inferred that large weather variations along the AEIFT path may emerge when the AEIFT is strong (not shown). Hence, a year with strong weather variations may accompany a strong AEIFT. To understand the interannual AEIFT-driven weather variation, it is therefore of great importance to identify the modulation factors of the interannual AEIFT variation.

Fig. 7.
Fig. 7.

Composite patterns of daily anomalies for OLR (shading; W m−2) and the filtered 250-hPa streamfunction deviation (contours; scale is 106) as a function of phase. The phase and number of days used to create the composite values are shown at the middle above each panel. Only the days with a teleconnection index greater than 1.0 are included. Zero contours are not drawn, and the negative values are shown as dashed contours.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for surface air temperature (shading; °C).

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

4. Modulation by ENSO

After revealing the relationships of the AEIFT with intermediate-frequency disturbances and waveguide and depicting the significant role of the AEIFT in atmospheric systems, we further explore the tropical SST variations that modulate the AEIFT. Although the disturbances and waveguide are mostly attributed to internal dynamic processes of the atmosphere, SST forcing can be regarded as a potential regulator of the disturbances and the waveguide, and thus the AEIFT.

To identify the factors that modulate the interannual variation of the AEIFT, we conduct an analysis of lagged correlations between the teleconnection index and SST anomalies and between the teleconnection index and unfiltered streamfunction anomalies, from November to the following April. In our computations, the annual cycle of the index is removed, and monthly data are used to exclude the signals of the synoptic scale. Figure 9a shows that positive SST anomalies emerge from the eastern and central Pacific when SST leads the teleconnection index by 5 months. The anomalies then begin to stand out with a lead of 3–4 months (Figs. 9b,c) and peak with a lead of 2 months (Fig. 9d). Meanwhile, slightly negative SST anomalies persist around the Maritime Continent. The pattern of such SST anomalies is characterized by an El Niño SST anomaly distribution. Moreover, a dipole of upper-level tropical anomalous anticyclones related to El Niño is confined over the central Pacific when the streamfunction leads the teleconnection index by 1–2 months (Figs. 9d,e). In other words, the dipole lags the SST anomalies. Afterward, a Rossby wave train significantly emerges downstream across North America and approaches the Atlantic (Figs. 9e,f). Thus, Fig. 9 indicates that the anomalous circulation is associated with the ENSO SST anomalies and emerges from the central Pacific across North America toward the Atlantic. We assert that this ENSO-related circulation modulates the AEIFT originating over the Atlantic, and ENSO plays a role in the interannual variability of the AEIFT, as discussed later. It is interesting to note that the wave train resembles the features of the Pacific–North American pattern and the tropical Northern Hemisphere pattern, which partly originate from ENSO (Alexander et al. 2002; Hoerling and Kumar 2002; Straus and Shukla 2002; Mo 2010; Yu et al. 2015).

Fig. 9.
Fig. 9.

November–April lagged correlations between monthly anomalies of the teleconnection index and monthly anomalies of SST (shading) and between monthly anomalies of the teleconnection index and monthly anomalies of the 250-hPa streamfunction deviation (contours). Lag time (months) is displayed at the middle above each panel. Zero contours are not drawn, and the negative values are shown as dashed contours. The critical value for the Pearson correlation coefficient at the 90% confidence level is 0.11 based on the Student’s t test.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

To further test the hypothesis that ENSO modulates the AEIFT by changing the low-frequency circulation, the lagged correlation of the teleconnection index onto the Niño-3.4 index (Trenberth and Stepaniak 2001) is calculated (Fig. 10a). The correlation peaks when the Niño-3.4 index leads by 2 months and then decreases as the lead time turns into lag time, showing that the simultaneous correlation and the ENSO-lead correlation are stronger than the teleconnection–lead correlation. This feature indicates that the SST anomalies emerge prior to the AEIFT anomalies, and thus can be regarded as a potential modulator for the AEIFT. We further disclose the relationship between the AEIFT and Niño-3.4 SST anomalies, which lead the AEIFT by 2 months. Figure 10b, which shows the scatters of both indexes, displays a least squared regression line with a significantly positive slope. The magnitude of anomalies along the AEIFT becomes stronger with a larger Niño-3.4 index, although the samples are somehow dispersed. In other words, warmer SST anomalies in the eastern and central Pacific are more favorable for active AEIFT. For the central Pacific (CP) index (Kao and Yu 2009), the correlation also peaks when the CP index leads by 2 months, and the AEIFT is more closely related to CP ENSO than to the eastern Pacific (EP) ENSO (not shown). Figure 9 supports this hypothesis, as it is mainly characterized by a pattern of subtropical North Pacific SST anomalies, which leads to the CP ENSO events rather than the EP ENSO events (Yu et al. 2012). In short, Figs. 9 and 10 depict a link between the AEIFT and ENSO, that is, ENSO leads the AEIFT by 2 months.

Fig. 10.
Fig. 10.

(a) Lagged correlations of the two indexes. The dashed line represents the 90% confidence level according to the Student’s t test. (b) Scatter diagram of monthly anomalies of the teleconnection index and monthly anomalies of the Niño-3.4 index, which leads the teleconnection index by 2 months. The solid line represents the least squared regression line. Six points with a teleconnection index greater than 4.5 are not shown.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

To verify the modulation of the interannual variation of AEIFT by ENSO and to understand the physical mechanisms responsible for this modulation, we first depict the changes in the detailed pattern of the AEIFT based on a composite analysis of ENSO events. Changes in the patterns of AEIFT’s filtered daily streamfunction anomalies between El Niño and La Niña, and the composite anomalies for the El Niño years (see Fig. 11; samples are chosen when the teleconnection index is greater than 1.0). The positive (negative) changes in the composite anomalies around the Middle East jet stream are at the same longitude as the positive (negative) anomalies for the El Niño years, while positive (negative) changes around the East Asian jet stream are at the same longitude as the southern parts of the positive (negative) anomalies and the northern parts of the negative (positive) anomalies for the El Niño years. In other words, both upstream cyclonic and anticyclonic circulation anomalies are strengthened throughout the life cycle of the AEIFT during the El Niño years. Simultaneously, downstream features shift southward and are enhanced slightly. These results reveal that the AEIFT is strengthened upstream, concurrently shifts southward, and is slightly strengthened downstream during the El Niño years, or more specifically during the CP El Niño years, as opposed to the features during the La Niña years. As local weather variation is accompanied by the propagation of the AEIFT (Figs. 7 and 8), the change in the AEIFT pattern is associated with changes in OLR and surface air temperature along the AEIFT. Specifically, the AEIFT-driven OLR and temperature anomalies are enhanced upstream and shift southward downstream (not shown). Thus, ENSO may further influence the AEIFT-driven weather variation. Note also that the AEIFT’s phase-related anomalies emerge over the Pacific throughout the life cycle of the AEIFT as shown in Fig. 11.

Fig. 11.
Fig. 11.

Composite filtered daily anomalies of the 250-hPa streamfunction deviation for the El Niño years (contours; scale is 106) and the differences in the composite values between El Niño and La Niña years (shading) as a function of phase. Above each panel, we present the phase and number of days used to create the composite patterns for the El Niño and La Niña years sequentially. Only the days with a teleconnection index greater than 1.0 are included. Zero contours are not drawn, and the negative values are shown as dashed contours.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

Figure 12 reveals the composite difference in the intermediate-frequency teleconnectivity between El Niño and La Niña. During the El Niño years, an increase in the teleconnectivity is found around the Middle East jet stream, while the teleconnectivity slightly decreases north of the teleconnectivity core around the East Asian jet stream. Since the teleconnectivity derives from correlation, it represents the capability or the effectiveness of atmospheric signal propagation. The changes in teleconnectivity imply an increased upstream propagation and a slightly reduced downstream propagation during the El Niño years. Thus, this increased upstream teleconnectivity may enhance the upstream anomalies of the AEIFT shown in Fig. 11. An increase in teleconnectivity is also found in another AEIFT path around the East Asian jet stream at 130°E: the northern path of the AEIFT in Figs. 1a and 1c, implying increased effectiveness of signal propagation from Siberia to the North Pacific. This feature may explain the source of the anomalies over the Pacific in Fig. 11, but a further analysis of the characteristics is beyond the scope of this study.

Fig. 12.
Fig. 12.

November–April teleconnectivity during the El Niño years (contours) and the difference between the teleconnectivity during the El Niño years and that during the La Niña years (shading). The teleconnectivity index is multiplied by 100.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

The composite difference in the variance of intermediate-frequency streamfunction anomalies between El Niño and La Niña is shown in Fig. 13. The variance increases along the AEIFT path with a southward shift downstream during the El Niño years. As the variance represents the amplitude of the anomalies, its increase means more intermediate-frequency disturbances or enhancement of intermediate-frequency anomalies with a southward shift downstream, consistent with the changes in the pattern of the AEIFT displayed in Fig. 11. The increase in the teleconnectivity in the Middle East jet stream can account for the increase in the variance, as the increased propagation capability in the jet stream can convey more signals from the Atlantic to the Afro-Eurasia region. The increase in variance must also be related to changes over the Atlantic, the source region of the AEIFT, and may partly result from the Rossby wave train closely related to ENSO over the Atlantic shown in Fig. 9 (discussed also in section 5a) due to eddy–eddy interaction (Jiang et al. 2013).

Fig. 13.
Fig. 13.

As in Fig. 12, but for the variance of filtered daily anomalies of the 250-hPa streamfunction deviation.

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

The change in teleconnectivity may result from the change in the waveguide. Furthermore, during El Niño years, zonal wind anomalies exhibit two north–south dipoles, one in the entrance of the Middle East jet stream with a negative center over the north and a positive one over the south, and the other with a negative center around the East Asian jet stream and a positive one over the south (Fig. 14a). This feature indicates that the upstream jet stream shifts southward, while the downstream jet stream weakens with an enhancing new jet core to its south during the El Niño years, in agreement with previous studies (L’Heureux and Thompson 2006; Lu et al. 2008). Since the change in the jet stream can induce a change in the total wavenumber of stationary waves, which reveals the waveguide effect directly, we further investigate this feature by evaluating the composite of the difference in the total wavenumber of stationary waves between El Niño and La Niña. As shown in Fig. 14b, although the upstream waveguide effect does not display any significant change, an enhancement to the south of the maximum downstream waveguide effect and a weakening to the north of the maximum are apparent during the El Niño years. This change in the total wavenumber enables the signal in the East Asian jet stream to propagate more easily in the southern part than in the northern one, and thus leads to the southward shift of AEIFT’s downstream propagation during the El Niño years (Figs. 11 and 13). The feature in Fig. 14 further verifies the relationship between the waveguide and the AEIFT mentioned in section 3.

Fig. 14.
Fig. 14.

As in Fig. 12, but for (a) 250-hPa zonal winds and (b) the total wavenumber of 250-hPa stationary waves. The stippled area denotes the region where the change significantly exceeds the 95% confidence level (Student’s t test).

Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0130.1

5. Discussion and conclusions

a. Discussion

In this study, we have demonstrated that eddy–eddy interaction plays a certain role in the modulation of the AEIFT by ENSO. First, we show that the features in Figs. 1 and 2 imply a relationship between the AEIFT and high-frequency eddies. During the El Niño events, increased storm activity over Europe and the southward shift of the Atlantic storm track emerge from January to March, but cross the Atlantic from October to December (Brönnimann 2007; Shaman 2014). Thus, the active AEIFT is related to the increased storm activity in the Atlantic and Europe. Second, stationary wave train anomalies are found from the Pacific to the Atlantic, indicating increased low-frequency disturbances (Fig. 9). In consequence, intermediate-frequency disturbances increase over the eastern Atlantic during El Niño events, which mainly result from eddy–eddy interaction, including the interaction between high-frequency and intermediate-frequency eddies as well as that between intermediate-frequency and low-frequency eddies (Jiang et al. 2013). As more intermediate-frequency signals are induced in the eastern Atlantic, there is an increase in downward signal propagation. Thus, the atmospheric teleconnectivity in the Middle East jet stream increases (Fig. 12), and more intermediate-frequency disturbances or enhancements of intermediate-frequency anomalies emerge along the AEIFT path (Fig. 13).

Meanwhile, a dipole of the downstream waveguide effect anomaly emerges (Fig. 14b), which weakens the downstream teleconnectivity north of the maximum (Fig. 12) and induces a southward shift of the downstream AEIFT pattern (Fig. 11) (Hoskins and Ambrizzi 1993; Chen et al. 2005). Evidence supports the hypothesis that ENSO induces the zonal wind anomalies and thus the waveguide effect. For instance, ENSO can generate the dipole of zonally averaged zonal wind anomalies during the boreal winter (Lu et al. 2008). The thermal forcing mechanism and eddy momentum flux anomalies play dominant roles in generating the zonal wind anomalies (L’Heureux and Thompson 2006). Specifically, the anomalous warming in the tropical troposphere as a result of El Niño is responsible for the subtropical westerly anomalies (Yulaeva and Wallace 1994), in agreement with the thermal wind balance (Holton and Hakim 2013b). Also, the westerly anomalies intensify the equatorward wave dissipation, and thus the poleward eddy momentum flux (Hoskins and Karoly 1981; Webster and Holton 1982). Then the flux converges in the poleward flank of the westerly anomalies, intensifying the westerly anomalies (L’Heureux and Thompson 2006). The anomalous convergence further induces anomalous eddy momentum flux divergence and thus easterly anomalies, poleward by several dynamic mechanisms as in Robinson (2000) and Seager et al. (2003). Then, a question arises: In addition to the effect of ENSO, how are the East Asian jet and Middle East jet related to each other on the interannual time scale? Although a negative correlation is found between the East Asian jet and the Middle East jet in winter (Yang et al. 2004), there is no evidence regarding a causal relationship between the two jets. Conversely, the two jets are influenced by common factors, for instance, the circumpolar jet in the stratosphere (Kidston et al. 2015). Meanwhile, the changes in the location and intensity of the two jets result respectively from their adjacent atmospheric heating and thermal gradients (Yang et al. 2002; Kuang and Zhang 2005; Zhang and Huang 2011; Zhao et al. 2014). Consequently, the relationship between the two jets can be spurious as a result of confounding elements, and ENSO remains as a major common driver of the jets on the interannual time scale. It is equally noteworthy that the waveguide can influence the propagation of intermediate-frequency disturbances, but the intermediate-frequency disturbances can also modulate the jet streams and their waveguide effect in return (Edmon et al. 1980; Randel and Held 1991). This makes low-frequency jet streams and intermediate-frequency activity an interactive system (Ren et al. 2012).

In addition to ENSO, the interannual variation of the North Atlantic Oscillation (NAO) or its homologue, the Arctic Oscillation, also influences the weather and climate over the Eurasian continent in terms of the storm activity and jet streams (Hurrell et al. 2003; Hurrell and Deser 2009). Therefore, the NAO may affect the AEIFT together with ENSO or under the effect of ENSO. First, Figs. 9e and 9f exhibit negative streamfunction anomalies over Greenland and positive ones over the Azores. These anomalies over the Atlantic resemble the feature of the positive phase of the NAO. Furthermore, the positive NAO pattern can enhance 300-hPa storm activity over the Atlantic around 50°N (Hurrell and Deser 2009; Ren et al. 2009), which can contribute to the eddy–eddy interaction mentioned earlier in this section. However, the NAO mostly features changes in the North Atlantic jet instead of those in the AEIFT-related Middle East jet (Woollings et al. 2010), and its signal is prone to being confined locally rather than extending along the Asian jet in the absence of the Mediterranean convergence anomaly (Watanabe 2004). Consequently, the NAO-like pattern is a less distinct indicator than the ENSO pattern in Fig. 9, and the correlation between the NAO-like pattern and the AEIFT is lower than that between the ENSO pattern and the AEIFT. Second, positive NAO-like patterns are more likely to accompany La Niña events in all seasons than in early winter, when the positive NAO-like patterns tend to coexist with El Niño events (Brönnimann 2007; Fereday et al. 2008). Evidence can also be found in Figs. 9e and 9f that the positive NAO-like pattern coincides with El Niño in agreement with their relationship in early winter mentioned above (Brönnimann 2007; Fereday et al. 2008). However, the NAO does not have a robust and stationary connection to ENSO, and its variation on the interannual time scale, rather than its interdecadal variation, cannot be consistently and aptly accounted for by ENSO (Wanner et al. 2001; Greatbatch and Jung 2007; Shaman 2014). Consequently, it is not sufficient to infer that ENSO consistently modulates the AEIFT on the interannual time scale in terms of the NAO. To sum up, we believe that the two mechanisms provided in this study are sufficient to describe the way that ENSO modulates the AEIFT. Nonetheless, there is evidence that there exist nonstationary influences of ENSO on the NAO depending on the magnitude, phase, and type of ENSO as well as those of the NAO (Pozo-Vázquez et al. 2001; Sutton and Hodson 2003; Graf and Zanchettin 2012). As to the question of how different NAO events and ENSO events on different time scales combine to modulate the AEIFT, further investigations would be needed.

We have shown that low-frequency signals can influence the intermediate-frequency signals, being mediated by different time scales. Besides, there is a close relationship between ENSO and AEIFT when the intraseasonal variation of the AEIFT is not removed (figure not shown), indicating that intraseasonal signals may strengthen the effect of ENSO with cross-frequency interaction (Rennert and Wallace 2009). Among the intraseasonal signals, the Madden–Julian oscillation may be involved when the interannual variation of the AEIFT is affected by intraseasonal variations (Deng and Jiang 2011). This interaction can be found in a number of previous distinguished teleconnection patterns (Branstator 2002; Ding and Wang 2007), which manifest features of both a south–north dipole and eastward propagation. In other words, the low-frequency signals include the intraseasonal variations and intermediate-frequency signals. Nevertheless, although we have emphasized the need to separate signals of different time scales for a better understanding of the impacts of atmosphere–ocean modes in this study, such as the ENSO–AEIFT mode, we do not understate the significance of investigating cross-frequency interactive signals as a whole. In short, the atmospheric system is a weather–climate continuum and a cross-frequency interactive system (Rennert and Wallace 2009; Jiang et al. 2013).

Our analysis has also revealed that the variability of the AEIFT is more closely related to CP ENSO than to EP ENSO events. Interestingly, the atmospheric responses to the heating induced by ENSO display patterns with both stationary and distant positions, whereas the heating varies in location (Barsugli and Sardeshmukh 2002; DeWeaver and Nigam 2004; Liu and Alexander 2007). This feature indicates that one mechanism may remain effective while the other may vary with different types of ENSO events, resulting in different efficiencies of the modulation. In addition, the efficiencies of modulation by ENSO may vary with the changing mean state specifically under global warming (Webster and Yang 1992; Kinter et al. 2002). Moreover, although there is a close relationship between ENSO and the AEIFT, the internal factors of the AEIFT such as atmospheric internal variability are also important for the variability of the AEIFT (Hassanzadeh and Kuang 2015).

b. Conclusions

This study has discussed the characteristics of the AEIFT and its influences on various meteorological fields, indicating the importance of the AEIFT on atmospheric systems, the link between the AEIFT and waveguide, and the link between the AEIFT and high-frequency eddies. The physical mechanisms for the modulation of interannual variation of the AEIFT by ENSO are also investigated. Particularly, we highlight the contribution of ENSO-related eddy–eddy interaction among different time scales to the interannual variation of the AEIFT.

The main impact of the AEIFT on atmosphere systems manifests through local weather, which varies from prolonged anomalous dry and warm days to prolonged anomalous wet and cold days. The variability of the AEIFT is modulated by ENSO, and two possible mechanisms for this are put forward: one is the eddy–eddy interaction, and the other is the change in the waveguide. Compared to the La Niña years, the El Niño–related SST anomalies favor both stationary Rossby wave anomalies and positive storm track anomalies over the Atlantic. Consequently, the intermediate-frequency eddies are enhanced due to the eddy–eddy interaction, resulting in an enhancement in the pattern of AEIFT anomalies and variations of the AEIFT-driven weather during the El Niño years. The El Niño–related SST anomalies are also associated with southward shifts of the East Asian jet stream and its waveguide effect. As a result, the downstream features of the AEIFT shift southward as well.

Another equally significant finding of this study is that ENSO and AEIFT display a closer relationship when the intraseasonal variation of the AEIFT is included. Therefore, to better distinguish the impact of a particular atmosphere–ocean mode, signals of different time scales need to be separated, and the cross-frequency interaction also requires a careful exploration.

Acknowledgments

The authors thank Drs. Jin-Yi Yu, Yi Deng, Wen Zhou, Bin Yu, Bian He, William Lau, and Xiuzhen Li for their invaluable suggestions. The authors also thank the three anonymous reviewers for their careful reading, stimulating suggestions, and constructive comments. This study was jointly supported by the National Natural Science Foundation of China (Grants 41690123, 41690120, 41705050, and 91637208), the “111-Plan” Project of China (Grant B17049), the Jiangsu Collaborative Innovation Center for Climate Change, and the Zhuhai Joint Innovative Center for Climate, Environment and Ecosystem. We thank the Computational and Information Systems Laboratory at the National Center for Atmospheric Research for providing the NCAR Command Language software.

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