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  • View in gallery
    Fig. 1.

    Time series of the normalized EAWMres (solid line) and EAWM (dashed line) indices. The original index, defined by the 850-hPa meridional wind anomalies over the area of 20°–40°N, 100°–140°E, was multiplied by −1.0 so that a positive (negative) value represents a strong (weak) EAWM/EAWMres winter. Gray dashed lines represent the ±0.5 standard deviations.

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    Fig. 2.

    Regression patterns of wintertime mean (DJF) surface air temperature (Ts; °C; color filling) (a) upon the EAWM index during the whole period of 1948–2015, (b) upon the Niño-3.4 index during the whole period of 1948–2015, (c) upon the EAWM index during ENSO years, and (d) upon the EAWM index during neutral ENSO years. Contour indicates the variance of Ts explained by the index in each category. Stippling indicates the 95% confidence level.

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    Fig. 3.

    Composite anomaly patterns of the wintertime surface air temperature (Ts; °C) for (a) strong EAWM@neutral ENSO years (sEAWM@neuENSO), (b) weak EAWM@neutral ENSO years (wEAWM@neuENSO), and (c) the difference between (a) and (b), and for (d) El Niño, (e) La Niña, and (f) the difference between (d) and (e). Stippling indicates areas exceeding the 95% confidence level.

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    Fig. 4.

    Regression patterns of wintertime mean (DJF) circulation anomalies with respect onto the EAWM index during neutral ENSO years. (a) Zonal wind at 200 hPa (U200; color filling; m s−1). Stippling indicates the 95% confidence level. Purple contours represent the climatological mean of the wintertime U200 [contour interval (CI) = 20 m s−1], and the zero contours are thickened. (b) Geopotential height at 500 hPa (Z500; contours; gpm). (c) Horizontal wind at 850 hPa (Wind850; vectors; m s−1) and geopotential height at 850 hPa (Z850; contours; gpm). Shading in (b) and (c) indicates the 95% confidence level.

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    Fig. 5.

    Composite anomalies for the wintertime zonal wind at 200-hPa (U200; color) in the categories of (a) sEAWM@neuENSO and (b) wEAWM@neuENSO, and (c) the difference between (a) and (b). Purple contours represent the climatological mean of the wintertime U200 (CI = 20 m s−1), and the zero contours are thickened. Stippling indicates the 95% confidence level.

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    Fig. 6.

    As in Figs. 5a and 5b, but for horizontal stationary Rossby wave activity flux (vector; m2 s−2) and geopotential height at 200 hPa (Z200; gpm). The Z200 anomalies exceeding the 95% confidence level are shaded.

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    Fig. 7.

    Variables for the sEAWM@neuENSO case at 200 hPa: (a) the basic-state streamfunction (color filling; 108 m2 s−1) and the critical layer (green lines; zonal wind U = 0), (b) the total wavenumber of the stationary Rossby waves, (c) composite streamfunction anomalies (106 m2 s−1), (d) composite relative vorticity anomalies (10−5 s−1), and (e) composite anomalies of the wave activity flux divergence (10−5 m s−2). Green lines in Figs. 7c and 7d indicate the critical layer as in Fig. 7a. Shading inside the green lines in Figs. 7a, 7c, and 7d signifies the easterly zonal wind regions of the basic state (U < 0). In Fig. 7b, negative values of Ks2 (Ks2=β*/U) are shaded.

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    Fig. 8.

    Composite sEAWM@neuENSO wintertime (DJF) anomaly patterns for (a) the barotropic energy conversion (CK) at 200 hPa (10−4 m2 s−3) and the location of climatological jet stream (contours;ms−1, interval: 20, 40, 60), (b) the storm track (shading,gpm) and its climatological mean (contours; interval: 50, 60, 70), and (c) the extended EP flux (E; vectors;m2 s−2) and its divergence (shading,10−5 m s−2). Stippling in (b) and (c) indicates the 95% confidence level for storm track activities and divergence of the E vector, respectively.

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    Fig. 9.

    As in Fig. 7, but for the wEAWM@neuENSO case.

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    Fig. 10.

    As in Fig. 8, but for wEAWM@neuENSO.

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    Fig. 11.

    (a),(c),(e) As in Fig. 4, but the regression is calculated in ENSO years only. (b),(d),(f) As in (a), (c), and (e), but replacing the EAWM index with the EAWMres index.

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    Fig. 12.

    Composite anomaly patterns of the wintertime surface air temperature (Ts) for (a) strong EAWMres@El Niño (sEAWMres@EN), (b) weak EAWMres@El Niño (wEAWMres@EN), and (c) the difference between (a) and (b), and for (d) strong EAWMres@La Niña (sEAWMres@LN), (e) weak EAWMres@La Niña (wEAWMres@LN), and (f) the difference between (d) and (e). Stippling indicates the 95% confidence level.

  • View in gallery
    Fig. 13.

    Regression patterns of anomalous surface air temperature (Ts) in (a1) ND, (b1) DJ, (c1) JF, and (d1) FM with respect to the EAWM_ND. (a2)–(d2) As in (a1)–(d1), but with respect to the EAWM_DJ. Stippling indicates the 95% confidence level.

  • View in gallery
    Fig. 14.

    The orange dashed line shows the evolution of correlation coefficients between the EAWM_ND and the 2-month running mean of Ts anomalies in the key area of NA from ND to FM. The red solid line shows the evolution of correlation coefficients between the EAWM_DJ and the 2-month running mean of Ts anomalies in the key area of NA from DJ to MA. The gray dashed line indicates the 95% confidence level.

  • View in gallery
    Fig. A1.

    Lead–lag regression patterns of the perturbation streamfunction (106 m−2 s−1) at 200 hPa with respect to the EAWM index in boreal winter. (a)–(f) The EAWM leads for −2, 2, 4, 6, and 10 days, respectively. The green lines and associated shadings indicate the basic-state critical layer (U = 0) and easterly winds regions (U < 0), respectively.

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Different Impacts of the East Asian Winter Monsoon on the Surface Air Temperature in North America during ENSO and Neutral ENSO Years

Tianjiao MaaCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Wen ChenaCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bCollege of Earth and Planetary Sciences, University of the Chinese Academy of Sciences, Beijing, China

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Hans-F. GrafaCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Shuoyi DingcInstitute of Atmospheric Sciences, Fudan University, Shanghai, China

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Peiqiang XuaCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Lei SongaCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Xiaoqing LanaCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Abstract

The present study investigates different impacts of the East Asian winter monsoon (EAWM) on surface air temperature (Ts) in North America (NA) during ENSO and neutral ENSO episodes. In neutral ENSO years, the EAWM shows a direct impact on the Ts anomalies in NA on an interannual time scale. Two Rossby wave packets appear over the Eurasian–western Pacific (upstream) and North Pacific–NA (downstream) regions associated with a strong EAWM. Further analysis suggests that the downstream wave packet is caused by reflection of the upstream wave packet over the subtropical western Pacific and amplified over the North Pacific. Also, the East Asian subtropical westerly jet stream (EAJS) is intensified in the central and downstream region over the central North Pacific. Hence, increased barotropic kinetic energy conversion and the interaction between transient eddies and the EAJS tend to maintain the circulation anomaly over the North Pacific. Therefore, a strong EAWM tends to result in warm Ts anomalies in northwestern NA via the downstream wave packet emanating from the central North Pacific toward NA. A weak EAWM tends to induce cold Ts anomalies in western-central NA with a smaller magnitude. However, in ENSO years, an anomalous EAJS is mainly confined over East Asia and does not extend into the central North Pacific. The results confirm that the EAWM has an indirect impact on the Ts anomalies in NA via a modulation of the tropical convection anomalies associated with ENSO. Our results indicate that, for seasonal prediction of Ts anomalies in NA, the influence of the EAWM should be taken into account. It produces different responses in neutral ENSO and in ENSO years.

Additional affiliation: Visiting Fellow of the Chinese Academy of Sciences.

Corresponding author: Wen Chen, cw@post.iap.ac.cn

Abstract

The present study investigates different impacts of the East Asian winter monsoon (EAWM) on surface air temperature (Ts) in North America (NA) during ENSO and neutral ENSO episodes. In neutral ENSO years, the EAWM shows a direct impact on the Ts anomalies in NA on an interannual time scale. Two Rossby wave packets appear over the Eurasian–western Pacific (upstream) and North Pacific–NA (downstream) regions associated with a strong EAWM. Further analysis suggests that the downstream wave packet is caused by reflection of the upstream wave packet over the subtropical western Pacific and amplified over the North Pacific. Also, the East Asian subtropical westerly jet stream (EAJS) is intensified in the central and downstream region over the central North Pacific. Hence, increased barotropic kinetic energy conversion and the interaction between transient eddies and the EAJS tend to maintain the circulation anomaly over the North Pacific. Therefore, a strong EAWM tends to result in warm Ts anomalies in northwestern NA via the downstream wave packet emanating from the central North Pacific toward NA. A weak EAWM tends to induce cold Ts anomalies in western-central NA with a smaller magnitude. However, in ENSO years, an anomalous EAJS is mainly confined over East Asia and does not extend into the central North Pacific. The results confirm that the EAWM has an indirect impact on the Ts anomalies in NA via a modulation of the tropical convection anomalies associated with ENSO. Our results indicate that, for seasonal prediction of Ts anomalies in NA, the influence of the EAWM should be taken into account. It produces different responses in neutral ENSO and in ENSO years.

Additional affiliation: Visiting Fellow of the Chinese Academy of Sciences.

Corresponding author: Wen Chen, cw@post.iap.ac.cn

1. Introduction

The East Asian winter monsoon (EAWM) is a vibrant climate system in mid- to high latitudes during the boreal winter. The variability of EAWM is associated with changes of the Siberian–Mongolia high and the Aleutian low at sea level, the East Asian trough in the middle troposphere, and the East Asian westerly jet stream in the upper troposphere (Jhun and Lee 2004; Chen et al. 2005; Wu et al. 2006; Wang and Chen 2010, 2014; Chen et al. 2014a). The EAWM not only induces local climate anomalies over East Asia, but also exerts remote impacts on its downstream regions of the Pacific and North America (NA) (Chang and Lau 1980; Compo et al. 1999; Chen et al. 2000; Ha et al. 2012; Jia et al. 2015; Song et al. 2016; Yu et al. 2018).

Many previous studies mainly focused on understanding the processes of external forcing and atmospheric internal variability affecting the EAWM variability, such as El Niño–Southern Oscillation (ENSO) (Zhang et al. 1996, 1999; Wang et al. 2000; Xie et al. 2009; Kim et al. 2017), the Arctic Oscillation (AO) (Gong et al. 2001; Wu and Wang 2002; Jhun and Lee 2004; Chen et al. 2005; Cheung et al. 2012), Eurasian (EU) teleconnections (Liu et al. 2014), the western Pacific (WP) pattern (Park and Ahn 2016; Oh et al. 2017), autumn Arctic sea ice concentration (Wu et al. 2011, 2015; Chen et al. 2014b), Eurasian snow cover (Watanabe and Nitta 1999), and others. The influence of the EAWM on other climate systems has received less attention. Li (1989) suggested that a strong EAWM with frequent cold surge activities weakens the trade wind in the equatorial central western Pacific, which may lead to the occurrence of El Niño. The meridional monsoon wind links the variation of atmosphere circulation in the middle and high latitudes to the tropics through southward transportation of cold air. Cold surge events induce convective activities over the tropical western Pacific around the Philippines and the South China Sea, and the associated diabatic heating enhances the local Hadley circulation over East Asia and the two Walker circulations over the Indian and Pacific Oceans (Chang and Lau 1980, 1982; Compo et al. 1999). Furthermore, with the change of tropical diabatic forcing, the associated anomalous upper-level divergence or convergence generates tropical–extratropical wave trains, which exert broad climatic impacts over the Pacific and downstream (Jia et al. 2014; Feng et al. 2017; Ding et al. 2017). So far, less effort has been devoted to exploring the impacts of the EAWM on other parts of the climate system and the respective climate anomalies.

Some studies suggested that the variability of the extratropical atmospheric circulation over East Asia shows a significant relationship with climate anomalies in NA (Yang et al. 2002; Song et al. 2016; Yu et al. 2018; Dai and Tan 2019). Yang et al. (2002) found that the variation of the East Asian jet stream (EAJS) is connected to a teleconnection pattern spanning from Asia to North America. An intensified EAJS is associated with enhanced stationary wave activity propagating from the middle to high latitudes of East Asia eastward to NA, which there leads to temperature and precipitation anomalies. Yu et al. (2018) proposed an Asian–Bering Sea–North American (ABNA) teleconnection pattern, which extends from North Asia, across the Bering Sea, and downstream to NA related to an eastward propagating Rossby wave. The variation of both average temperature and temperature extremes in NA is closely related to the ABNA teleconnection pattern, especially in the boreal wintertime. Song et al. (2016) found that a strong (weak) East Asian trough event leads to warm (cold) temperature anomalies in NA after about 6–10 days. In this case, the climatological ridge to the west of the Rocky Mountains is weakened, which reduces cold air advection from the Arctic to NA. The slack of the ridge may be attributed to an eastward propagating Rossby wave train from East Asia to NA. These studies indicate that the variations of EAWM can have significant impacts on the climate over the downstream regions of the Pacific and NA.

The impact of ENSO on North American climate has been well studied in the framework of the Pacific–North American (PNA) teleconnection (Wallace and Gutzler 1981; Hoerling et al. 1997; Kumar and Hoerling 1998). But some works showed that the relationship between North American climate anomalies and ENSO variability is not steady (Hoerling and Kumar 1997; Weng et al. 2009) and the EAWM can modulate the ENSO-related teleconnections and surface air temperature (Ts) anomaly patterns in NA (Ma et al. 2018). A warm (cold) ENSO event is associated with increased (decreased) convection or precipitation in the tropical central-eastern Pacific and decreased (increased) convection or precipitation in the tropical western Pacific. This distribution of anomalous tropical Pacific convection is critical for the generation of tropical–extratropical wave trains, and the physical processes can be described in more detail as follows. The anomalous tropical Pacific convective activities cause upper-level divergence or convergence anomalies in the tropics, and then induce upper-level convergence or divergence anomalies in the midlatitudes through modulating the local Hadley circulation. The anomalous midlatitude upper-level convergence or divergence forms a Rossby wave source anomaly over the central North Pacific, which further excites a PNA-like wave train (Simpkins et al. 2014; Feng et al. 2017). However, the variation of tropical Pacific convection is complex because not only is it a nonlinear response to ENSO-induced SST anomalies (Ding et al. 2017), but also it is modulated by the disturbance of the EAWM during wintertime through cold air outbreak activities (Ma et al. 2018). For example, when an El Niño happens together with a strong EAWM, the anomaly pattern of precipitation shows a positive center in the tropical central Pacific and feeble negative precipitation anomalies over the tropical western and eastern Pacific. This is because the strong EAWM, enhancing convection and precipitation in the tropical western Pacific and simultaneously boosting the Walker circulation, counteracts the precipitation anomalies induced by El Niño. The modulated precipitation anomalies result in a westward shift of the PNA-like wave train and associated temperature anomalies over NA. In contrast, the combined effects of El Niño and weak EAWM result in enhanced and eastward extended positive precipitation anomalies over the tropical central-eastern Pacific, which is favorable for an eastward shift of a PNA-like wave train and associated air temperature anomalies over NA. Therefore, the EAWM plays a significant modulating role on the Ts anomalies in NA during ENSO events.

The linkage between North American climate variability and the EAWM during a neutral ENSO year (i.e., when the ENSO index is neutral, or, in other words, neither El Niño nor La Niña occur) has not yet been investigated. The current study will focus on this issue for the first time. The climate anomaly over NA is associated with the variation of the subtropical westerly jet stream, which is affected by the EAWM and ENSO (Yang et al. 2002). However, the EAWM-related EAJS anomalies are different between neutral ENSO and ENSO years [please see the regression patterns of zonal wind anomaly at 200 hPa (U200) upon the EAWM index for neutral ENSO years in Fig. 4a and for the ENSO years in Fig. 11a, respectively]. In the neutral ENSO winters, a strong (weak) EAWM is associated with a strengthened (weakened) EAJS that extends to the eastern part of North Pacific. In contract, during ENSO winters, the variation of EAJS relevant to the EAWM is confined to over East Asia. These results imply that the mechanisms for the EAWM’s impact on the NA climate may be different during ENSO and neutral ENSO years. Hence, it is interesting to explore if the EAWM could influence the Ts anomalies in some regions of NA during neutral ENSO years. Therefore, the current work concentrates on investigation of the impact of EAWM on the Ts anomaly in NA during neutral ENSO years and the possible physical mechanisms; a comparison between neutral ENSO years and ENSO years is also carried out.

The structure of the paper is organized as follows: The datasets and methods used in the study are described in section 2. Section 3 presents the Ts anomaly patterns in NA related to the variation of EAWM during ENSO events and neutral ENSO years. In section 4, the possible mechanisms of how the EAWM affects the Ts in NA are elucidated. First, the large-scale teleconnection patterns as well as Rossby wave activities coupled to the EAWM during neutral ENSO years are emphasized in section 4a. Second, the linkage of EAWM and Ts anomalies in NA during ENSO events is stated in section 4b. Then, a summary and discussion are provided in section 5.

2. Data and methods

The National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) monthly reanalysis geopotential height, horizontal wind, and vertical velocity data are used in this study (Kalnay et al. 1996). The NCEP–NCAR dataset has a horizontal resolution of 2.5° latitude × 2.5° longitude and has 17 levels in the vertical direction spanning from 1000 to 10 hPa. This NCEP–NCAR reanalysis dataset is provided by the National Oceanic and Atmospheric Administration (NOAA)/Earth System Research Laboratory (ESRL) Physical Sciences Division (PSD) from their website at https://www.esrl.noaa.gov/psd. The time period of the NCEP–NCAR dataset covers 1948 to the present. For the Ts, the Climatic Research Unit (CRU) Time series (TS) data version 4.00 is employed (Harris and Jones 2017). The CRU TS4.00 data are based on observational data by National Meteorological Services, and are interpolated on to a high-resolution (0.5° × 0.5°) grid using angular-distance weighting interpolation. The monthly CRU TS4.00 Ts data are available for the period of 1901–2015 from the Centre for Environmental Data Analysis (CEDA) on their website (http://catalogue.ceda.ac.uk). In addition, the NOAA Precipitation Reconstruction (PREC) data are used in this study (Chen et al. 2002). This PREC precipitation dataset is constructed globally on a 2.5° latitude × 2.5° longitude grid for the period from 1948 to the present, and was downloaded from NOAA/OAR/ESRL PSD (https://www.esrl.noaa.gov/psd).

The time period of 1948–2015 that all the datasets cover is considered in this study. Boreal winter mean indicates the monthly average of December–February (DJF), and the winter of 1949 refers to the December 1948–February 1949. As we focus on the interannual time scale, the original data were filtered by a 7-yr high-pass Lanczos filter prior to use (Duchon 1979).

Linear regression and composite analysis are the primary methods to distinguish the influence of EAWM on Ts in NA during ENSO and neutral ENSO winters. Here, the ENSO and neutral ENSO winters are obtained from the Climate Prediction Center (CPC; http://origin.cpc.ncep.noaa.gov) based on an oceanic Niño index (ONI), which is the 3-month running mean of ERSST.v5 SST anomalies in the Niño-3.4 region (5°N–5°S, 120°–170°W). An El Niño (La Niña) event is defined when the ONI is greater (less) than +0.5 (−0.5) for a minimum of five consecutive overlapping seasons. Then the years classified as neither an El Niño nor a La Niña are defined as neutral ENSO years. ENSO (including El Niño and La Niña) and neutral ENSO years are listed in Table 1.

Table 1.

ENSO and neutral ENSO years. Superscript S (W) indicates a strong (weak) EAWM year. The selections of S/W years are based on the EAWMres index for the ENSO group, and based on the EAWM index for the neutral ENSO group; 1952 indicates the wintertime of December 1951–February 1952.

Table 1.

The strength of the EAWM is estimated by an EAWM index proposed by Yang et al. (2002), which is the average of the 850-hPa meridional wind over the area of 20°–40°N, 100°–140°E. Furthermore, a residual EAWM index, namely EAWMres, is introduced to exclude the contribution of ENSO (Chen et al. 2013). The EAWMres index is calculated by removing the linear regression part upon the Niño-3.4 index from the original EAWM index. Although the linear regression technique cannot fully remove the climate anomalies induced by ENSO, it is a common approach in many previous studies and generally works to suppress the ENSO-induced variations (Chen et al. 2013; Jia and Ge 2017). The time series of the normalized EAWM/EAWMres index are shown in Fig. 1. It should be noted that the indices have been multiplied by −1.0 so that a positive (negative) value corresponds to a strong (weak) EAWM/EAWMres year. A strong (weak) EAWM or EAWMres is defined when the EAWM or EAWMres index exceeds +0.5 (−0.5) standard deviations. The strong and weak EAWM years are superscripted as S and W, respectively, in Table 1. It should be noted that, in the ENSO years, the S and W years are selected based on the EAWMres index, which represents the variation of EAWM that is independent of ENSO; in the neutral ENSO years, the S/W years are selected based on the EAWM index as the ENSO contribution is small.

Fig. 1.
Fig. 1.

Time series of the normalized EAWMres (solid line) and EAWM (dashed line) indices. The original index, defined by the 850-hPa meridional wind anomalies over the area of 20°–40°N, 100°–140°E, was multiplied by −1.0 so that a positive (negative) value represents a strong (weak) EAWM/EAWMres winter. Gray dashed lines represent the ±0.5 standard deviations.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

Considering the limited number of samples in each case, the two-tailed nonparametric Monte Carlo bootstrap significance test (Mudelsee 2010) is adopted to examine the mean difference between two cases, or the difference between the means of selected case and the climatological mean. Details about the significance test can be found in Ding et al. (2017).

Wave activity flux (Takaya and Nakamura 2001) is used to investigate the propagation of the quasi-stationary Rossby wave activity. The wave activity flux defined by Takaya and Nakamura (hereafter, TN flux) is parallel to the local group velocity of a stationary Rossby wave train under the Wentzel–Kramers–Brillouin (WKB) approximation and is independent of wave phase. The horizontal dimension of the wave activity flux is calculated as Eq. (1) in the following:

W= p2|U¯|[u¯(ψx2ψψxx)+υ¯(ψxψyψψxy)u¯(ψxψyψψxy)+υ¯(ψy2ψψyy)].

Here ψ′, u, and υ represent the perturbation of geostrophic streamfunction, zonal wind, and meridional wind, respectively. The overbar represents the climatologic mean over the period of 1948–2015, and the subscripts x and y indicate the partial derivatives in the zonal and meridional directions, respectively.

The extended Eliassen–Palm flux (denoted as the E vector; Trenberth 1986) is employed to diagnose the interactive processes between synoptic eddies and the low-frequency flow. The horizontal flux components are described as follows:

E=[12×(υ2¯u2¯)i , υ¯j]×cosφ ,

where u′ and υ′ represent the synoptic-scale disturbance of zonal and meridional winds, respectively, the overbars denote the time average, and φ is latitude. The divergence of the E vector represents the eddy-induced accelerations of zonal wind components due to barotropic processes.

The barotropic kinetic energy conversion (denoted as CK) is calculated as in Kosaka and Nakamura (2006):

CK= υ2u22(u¯xυ¯y)uυ(u¯y+υ¯x),

where u′ and υ′ represent the perturbed zonal and meridional winds, and u¯ and υ¯ stand for the basic state of zonal and meridional winds, respectively. For more details please see Kosaka and Nakamura (2006).

The total wavenumber of stationary waves is defined as in Eq. (2.4) in Hoskins and Ambrizzi (1993):

Ks=β*U=βUyyU,

where β*=βUyy represents the meridional gradient of the absolute vorticity and U is the zonal velocity of the background flow; Ks is the largest wavenumber that can be propagated in a particular region. When the zonal wind shear Uyy becomes large, the Ks value tends to be zero or even imaginary. All wave rays must turn before this area, so reflections occur near the regions where Ks tends to be zero (Hoskins and Ambrizzi 1993). On the other hand, when U = 0, which is called a critical layer, the Ks becomes infinite. The Rossby wave ray theory is not valid close to such a critical layer (Hoskins and Ambrizzi 1993). By conducting a series of numerical experiments, Enomoto and Matsuda (1999) showed that the Rossby wave packets are reflected near the critical layer. It should be noted that the reflection mechanisms near the critical layer are involved with vorticity overturning, which is different from the refraction of wave rays near the turning latitudes.

3. EAWM-associated Ts anomalies in North America during ENSO and neutral ENSO years

The features of Ts anomalies in NA associated with the EAWM during ENSO and neutral ENSO winters are investigated in this section. Figures 2a and 2b show the Ts anomaly patterns regressed onto the EAWM index and Niño-3.4 index during the whole period of 1948–2015, and then Figs. 2c and 2d distinguish the regression patterns of Ts with respect upon the EAWM index during ENSO and neutral ENSO years. Without the separation into ENSO or neutral ENSO cases, an intensified EAWM is coupled to weak warm anomalies in the western United States and feeble cold anomalies in the eastern part of the central NA (Fig. 2a). For comparison, Ts anomalies linearly coupled to the tropical central-eastern Pacific SST anomalies are presented in Fig. 2b, which shows positive values in Canada and negative values surrounding the Gulf of Mexico, consistent with previous studies (e.g., Hoerling et al. 1997). The anomalous Ts pattern, as a response to the EAWM index in the category that contains ENSO years only (Fig. 2c), bears a close resemblance to that in the whole period (Fig. 2a), except that the positive Ts anomalies are weaker in the western United States and the negative anomalies are stronger and northwestward expanded in eastern central NA. Moreover, this anomalous Ts pattern in Fig. 2c is quite similar to that in Fig. 2b, but with opposite sign mainly in the regions of Alaska to Canada, the southern United States, and tropical South America. One should be careful, however, since the similarity between these two Ts anomaly patterns in Figs. 2b and 2c might be attributed to the variation of ENSO, which affects both the EAWM index and North American temperature simultaneously. For example, on the one hand, a cold ENSO event is related to a stronger EAWM index because the negative SST anomalies over the tropical central-eastern Pacific could give rise to anomalous northerlies along the southern East Asia coasts via the generation of anomalous lower-troposphere western North Pacific cyclone (Zhang et al. 1996; Wang et al. 2000). On the other hand, a cold ENSO event, through the PNA teleconnection, contributes to anomalous cold-north and warm-south NA, which bears great resemblance to that in Fig. 2c. Therefore, a strong EAWM index during ENSO events in Fig. 2c links with a La Niña condition over the tropical central-eastern Pacific, and the latter give rise to the cold-north and warm-south Ts distribution in NA, which is opposite with that induced by an El Niño as shown in Fig. 2b. In contrast, the response of Ts anomalies to EAWM during neutral ENSO years presents a totally different feature, exhibiting significant warm anomalies in the western NA. The EAWM can explain about 20%–40% of the total Ts variance in regions of northwest Canada and western United States during neutral ENSO years. Actually, the PNA teleconnection pattern can explain only about 15% of the North American Ts variance in boreal winter (see Fig. 8 in Yu et al. 2018). This suggests that the EAWM may be an important factor in affecting the North American Ts in a neutral ENSO year. The Ts anomaly pattern associated with the EAWM in neutral ENSO years is not similar to that induced by SST anomalies in the Niño-3.4 region (Fig. 2b), but is seemingly related to the EAJS (Yang et al. 2002), which will be discussed later. There are relevant differences between Figs. 2c and 2d, suggesting that the EAWM has different impacts on Ts in NA during ENSO and neutral ENSO years.

Fig. 2.
Fig. 2.

Regression patterns of wintertime mean (DJF) surface air temperature (Ts; °C; color filling) (a) upon the EAWM index during the whole period of 1948–2015, (b) upon the Niño-3.4 index during the whole period of 1948–2015, (c) upon the EAWM index during ENSO years, and (d) upon the EAWM index during neutral ENSO years. Contour indicates the variance of Ts explained by the index in each category. Stippling indicates the 95% confidence level.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

To confirm the regressed results and check if there are any asymmetric responses, we further depict the composite anomalies of Ts: Figs. 3a–c show the patterns of anomalous Ts for strong and weak EAWM during the neutral ENSO years (Table 1, hereafter referred to as sEAWM@neuENSO and wEAWM@neuENSO, respectively) and the difference between them, respectively. The Ts anomaly patterns during El Niño, La Niña, and their difference (Figs. 3d–f) are given for the purpose of comparison. Figures 3a and 3b show that the strong and weak EAWM have asymmetric impacts on Ts in NA during neutral ENSO years. The former features strong warm anomalies in northern North America, and the latter shows weak cold anomalies in the western United States (Figs. 3a,b). The difference of these two maps allows us to estimate the linear component of the EAWM impacts (Fig. 3c), which shows significant warm anomalies in western North America, and is consistent with the linear regressed Ts anomaly pattern with respect to the EAWM index during neutral ENSO years as presented in Fig. 2d. El Niño and La Niña are known to be associated with asymmetric Ts anomaly patterns in NA; the former is characterized by a maximum warm center over south-central Canada while the latter shows a maximum cold center over northwest Canada and Alaska (Figs. 3d,e). The linear composite difference of them exhibits a warm north and cold south pattern, divided along 40°N (Fig. 3f). The composite Ts anomaly maps during El Niño and La Niña are consistent with previous studies (Hoerling et al. 1997) but are quite different from those in the sEAWM@neuENSO and wEAWM@neuENSO cases. In addition, the amplitude of positive Ts anomalies over northern and western Canada in sEAWM@neuENSO could reach as much as 2°C, which is greater than that during El Niño or La Niña years. Therefore, the EAWM plays an important role in the variation of North American Ts during neutral ENSO years, and the possible mechanisms will be further discussed in the following.

Fig. 3.
Fig. 3.

Composite anomaly patterns of the wintertime surface air temperature (Ts; °C) for (a) strong EAWM@neutral ENSO years (sEAWM@neuENSO), (b) weak EAWM@neutral ENSO years (wEAWM@neuENSO), and (c) the difference between (a) and (b), and for (d) El Niño, (e) La Niña, and (f) the difference between (d) and (e). Stippling indicates areas exceeding the 95% confidence level.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

4. Possible mechanisms

a. EAWM-related teleconnection patterns in neutral ENSO cases

1) Large-scale circulation anomaly patterns

The large-scale circulation anomalies related to the variation of EAWM during neutral ENSO winters are discussed in this part, including major components of the EAWM system such as the EAJS in the upper troposphere, the East Asian trough in the midtroposphere, the Siberian high, the Aleutian low, and the northerly winds along the East Asian coast on the lower level. Figure 4 shows the regression patterns with respect to the EAWM index during neutral ENSO years for zonal wind at 200 hPa (U200) (Fig. 4a), geopotential height at 500 hPa (Z500) (Fig. 4b), and geopotential height and horizontal wind at 850 hPa (Z850 and Wind850) (Fig. 4c). The regression pattern of U200 shows that the intensification of the EAWM during neutral ENSO years is accompanied by an acceleration of the subtropical westerly jet stream over East Asia and the North Pacific and a reduction of westerlies in the northern extratropics around 45°–60°N and in the lower latitudes from the tropical western Pacific northeastward to Baja California (Fig. 4a). The most predominant feature is the fluctuations in the strength and location of the EAJS. The process that the EAWM induces changes of the Pacific jet stream may be related to the activity of the cold surge, which has been demonstrated in several earlier works (Chang and Lau 1980; Chang and Lum 1985; Compo et al. 1999). We further examined the lead–lag regression patterns of the zonal wind at 200 hPa against the daily EAWM index. It is seen that a stronger EAWM is associated with an intensified upper-level jet stream over the East Asian region at day 0 and then extends eastward into the Pacific region in the following 2–10 days, consistent with the above studies (figure not shown). On the interannual time scale, a stronger EAWM year, which generally has more and stronger cold surge activities, can lead to an enhanced jet stream. In addition, the jet over the Pacific region may further be reinforced by the eddy–mean flow interactions (Lau 1988). A wavy Z500 anomaly pattern resembling a positive PNA is observed with alternating anomalous cyclone, anticyclone, and cyclone spanning from central North Pacific to southeast United States. The anomalous positive geopotential height center over western NA strengthens the climatological ridge west of the Rocky Mountains (Fig. 4b). In the meantime, there is an associated cyclonic anomaly in the lower troposphere over the North Pacific. The anomalous southwest–southeast winds at the eastern flank of the cyclonic anomaly over the North Pacific lead warm advection from the Pacific Ocean to western NA and thus increase the temperature there (Fig. 4c). It has been suggested that the fluctuations of the EAJS can lead to north–south displacements of the storm track activity on the one hand (Lau 1988), and can change the barotropic instability, which then influences the low-frequency fluctuations on the other hand (Simmons et al. 1983). Hence, in the following we will further investigate the EAJS anomalies associated with EAWM during neutral ENSO years.

Fig. 4.
Fig. 4.

Regression patterns of wintertime mean (DJF) circulation anomalies with respect onto the EAWM index during neutral ENSO years. (a) Zonal wind at 200 hPa (U200; color filling; m s−1). Stippling indicates the 95% confidence level. Purple contours represent the climatological mean of the wintertime U200 [contour interval (CI) = 20 m s−1], and the zero contours are thickened. (b) Geopotential height at 500 hPa (Z500; contours; gpm). (c) Horizontal wind at 850 hPa (Wind850; vectors; m s−1) and geopotential height at 850 hPa (Z850; contours; gpm). Shading in (b) and (c) indicates the 95% confidence level.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

Figure 5 further depicts the composite patterns of the U200 anomaly for the sEAWM@neuENSO, wEAWM@neuENSO, and their difference to confirm the regressed results on the one hand and reveal possible asymmetric features on the other hand. During boreal winter, the climatological westerly wind dominates most of the upper-level Asia–Pacific–NA region, except for prevailing easterly winds in the tropics from Indonesia to the date line. The climatological maximum U200 center appears over East Asia and is zonally elongated to the central North Pacific, representing the climatological location of the East Asian subtropical westerly jet stream in the wintertime (purple contours in Fig. 5). In a sEAWM@neuENSO case, the acceleration of 200-hPa westerly wind emerges in the central and downstream regions of the climatological jet stream core. In particular the downstream increase is more evident associated with an eastward extent of the exit region of jet stream (Fig. 5a). In a wEAWM@neuENSO case, the distribution of anomalous U200 features negative values along the subtropical regions of East Asia–Pacific and positive values to its north, indicating a weakened and northward shifted EAJS (Fig. 5b). However, compared with strong EAWM, a much smaller anomaly associated with weak EAWM is clearly observed. The composite difference of U200 between sEAWM@neuENSO and wEAWM@neuENSO (Fig. 5c) shows an analogous spatial pattern to that of the regression (Fig. 4a), suggesting that the features of anomalous U200 related to the EAWM in a neutral ENSO year are robust and do not rely on the analysis methods.

Fig. 5.
Fig. 5.

Composite anomalies for the wintertime zonal wind at 200-hPa (U200; color) in the categories of (a) sEAWM@neuENSO and (b) wEAWM@neuENSO, and (c) the difference between (a) and (b). Purple contours represent the climatological mean of the wintertime U200 (CI = 20 m s−1), and the zero contours are thickened. Stippling indicates the 95% confidence level.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

2) Rossby wave activity and propagation

The above analyses demonstrate the impact of EAWM on NA climate anomaly during neutral ENSO years, which is associated with the upper-tropospheric processes of the EAWM. Consequently, we further show the composite of 200-hPa geopotential height anomalies (Z200) and horizontal wave activity flux (TN flux) in each case (Figs. 6a and 6b for sEAWM@neuENSO and wEAWM@neuENSO, respectively) to illustrate the propagation of Rossby waves and the associated teleconnection pattern. The variation of the EAWM, although considered to be active mainly in the lower troposphere, is found to be accompanied by upper-tropospheric geopotential height anomalies in the extratropics of the whole Northern Hemisphere, which will result in a modulation of planetary wave activity (Chen et al. 2003, 2005; Takaya and Nakamura 2013). For the neutral ENSO years, a strong EAWM is accompanied by a PNA-like wavy Z200 anomaly pattern with an anomalous cyclone over the central North Pacific, an anticyclone over western NA, and a third cyclone over Florida (Fig. 6a). Similar anomalies occur in a weak EAWM, but with a much weaker anomalous anticyclone and cyclone over, respectively, the central North Pacific and the western borders of Canada and the United States (Fig. 6b). Vertically, geopotential height anomalies related to the anomalous EAWM show an equivalent barotropic structure throughout the whole troposphere. The variations of upper-level stationary Rossby waves associated with the anomalous EAWM, including the wave propagation features and its possible forming and maintaining mechanisms, are depicted in Figs. 610, and analyses for sEAWM@neuENSO and wEAWM@neuENSO are given sequentially.

Fig. 6.
Fig. 6.

As in Figs. 5a and 5b, but for horizontal stationary Rossby wave activity flux (vector; m2 s−2) and geopotential height at 200 hPa (Z200; gpm). The Z200 anomalies exceeding the 95% confidence level are shaded.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

Fig. 7.
Fig. 7.

Variables for the sEAWM@neuENSO case at 200 hPa: (a) the basic-state streamfunction (color filling; 108 m2 s−1) and the critical layer (green lines; zonal wind U = 0), (b) the total wavenumber of the stationary Rossby waves, (c) composite streamfunction anomalies (106 m2 s−1), (d) composite relative vorticity anomalies (10−5 s−1), and (e) composite anomalies of the wave activity flux divergence (10−5 m s−2). Green lines in Figs. 7c and 7d indicate the critical layer as in Fig. 7a. Shading inside the green lines in Figs. 7a, 7c, and 7d signifies the easterly zonal wind regions of the basic state (U < 0). In Fig. 7b, negative values of Ks2 (Ks2=β*/U) are shaded.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

Fig. 8.
Fig. 8.

Composite sEAWM@neuENSO wintertime (DJF) anomaly patterns for (a) the barotropic energy conversion (CK) at 200 hPa (10−4 m2 s−3) and the location of climatological jet stream (contours;ms−1, interval: 20, 40, 60), (b) the storm track (shading,gpm) and its climatological mean (contours; interval: 50, 60, 70), and (c) the extended EP flux (E; vectors;m2 s−2) and its divergence (shading,10−5 m s−2). Stippling in (b) and (c) indicates the 95% confidence level for storm track activities and divergence of the E vector, respectively.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

Fig. 9.
Fig. 9.

As in Fig. 7, but for the wEAWM@neuENSO case.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

Fig. 10.
Fig. 10.

As in Fig. 8, but for wEAWM@neuENSO.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

In the sEAWM@neuENSO, enhanced eastward propagation of stationary Rossby wave activities is observed, with one horizontal TN flux emanating from the area upstream of the anticyclonic center over Siberia to East Asia and the other propagating from the cyclonic center over central-eastern North Pacific to the east coast of NA (Fig. 6a). The dynamical connection between the upstream and downstream wave packets is further examined in Fig. 7. Reflections of Rossby waves are suggested to occur over the subtropical western Pacific. On the one hand, the critical layers for stationary Rossby waves, at which the basic-state zonal wind is zero (U = 0) (Fig. 7a), can act as a reflector of the Rossby waves (Stewartson 1977; Warn and Warn 1978; Andrews et al. 1987; Killworth and McIntyre 1985; Enomoto and Matsuda 1999). On the other hand, the strong zonal wind shear (Uyy) to the south flank of the EAJS results in a sharp reduction of Ks [Ks=(βUyy/U)] toward the equator (Fig. 7b), which can reflect Rossby waves back to higher latitudes (Hoskins and Ambrizzi 1993). Hence, the upstream Rossby wave packet shown in Fig. 6a tends to propagate into the lower latitudes over the western Pacific and reflect back into North Pacific. To further confirm the reflections of the wave packet, we calculated the streamfunction anomalies in the sEAWM@neuENSO as shown in Fig. 7c. The result clearly shows that the wave train is reflected around 150°E–180° over the subtropical western Pacific, and then continues to propagate toward the North Pacific–NA regions. The horizontal distribution of relative vorticity anomalies in Fig. 7d also confirms the low-latitude reflections. As suggested by Enomoto and Matsuda (1999), vorticity anomalies near the critical layer tend to be passively advected by the basic-state anticyclonic flow so that the vorticity phase lines change their direction, resulting in a reflection. The time evolution of the perturbation streamfunction (Fig. A1) shows the reflection processes around the critical layer. The phase lines of negative vorticity anomalies to the north of the critical layer rotate from a northeast–southwest tilt to a northwest–southeast tilt (from day +2 to +6; Figs. A1c–e), which is parallel to the local basic-state streamlines, implying that the vorticity anomalies are advected by the anticyclonic basic flow (Figs. 7a,d). As a result, the upstream wave packet is reflected and then continues to propagate toward the North Pacific and NA. It is worth noting that the meridional location of reflections does not occur exactly at the center of basic-state anticyclone (~150°E) as suggested by the critical layer theory. Nevertheless, the reflections extend eastward to around 180° along the regions of small Ks (turning latitudes). This extension implies that the strong zonal wind shear on the south edge of the subtropical jet probably also contributes to the reflection of wave packet. In summary, the downstream wave packet can be considered to be caused by the reflection of the upstream wave packet over the subtropical western Pacific.

It is interesting that the downstream wave packet seems to be enhanced over the eastern North Pacific as shown in Figs. 7c and 7d. The wave activity flux divergence (Fig. 7e) confirms that there are divergent anomalies in the exit region of EASJ, suggesting an amplification of the downstream wave packet. In fact, Enomoto and Matsuda (1999) and Naoe et al. (1997) have found similar wave amplification in their numerical experiments, which they attributed to the barotropic kinetic energy conversion (CK). In addition, synoptic eddy–mean flow interactions may also contribute to developing and maintaining the circulation anomalies over North Pacific (Ding et al. 2017). We will then move on to the possible mechanisms of the maintenance and amplification of the downstream wave packet.

Figure 8a presents the composited CK in the sEAWM@neuENSO years. The intensification of EAJS in the central North Pacific results in large positive CK anomalies in this region, indicating that the barotropic process contributes crucially to the local circulation anomaly. Associated with positive CK in the central-eastern North Pacific, kinetic energy is derived from the background basic flow. And the perturbations can disperse downstream to give rise to anomalous centers in the North Pacific–NA region, which resemble the structure of PNA teleconnection. Furthermore, Cai and Mak (1990) have shown that the atmospheric low-frequency variability exhibits a symbiotic relationship with the high-frequency transients. The modulated effect on the high-frequency transients by the low-frequency circulation anomaly can be demonstrated by Fig. 8b, which presents the composited storm track activity in the upper troposphere. Here, the storm track is represented by the variance of 2–8-day bandpass 200-hPa geopotential height. Figure 8b shows a zonally elongated meridional dipole that straddles the climatological storm track axis, indicating that the North Pacific storm track tends to migrate southward in this case. In addition, the modulated high-frequency transients may further give feedback to the circulation anomaly as shown in Fig. 8c. The composited extended Eliassen–Palm flux (denoted as the E vector) in Fig. 8c reveals a significant divergence of the E vector in the exit region of the EAJS over the central-eastern Pacific, indicating an acceleration of the westerly and negative (positive) height tendencies to the north (south) as documented by Lau (1988). The induced negative geopotential height tendency coincides well with the negative action center, suggesting that the circulation anomaly is reinforced by the feedback from the high-frequency transients. Therefore, the above analysis reveals a clear picture of the formation mechanism of the circulation anomaly as follows: In the neutral ENSO episodes, the EAJS tends to be intensified significantly over the central North Pacific associated with a strong EAWM; hence, barotropic instability over the central North Pacific is increased, which further contributes to a wave train–like anomaly emanating from the central North Pacific toward NA. On the one hand, the induced circulation anomaly can affect the behavior of high-frequency transients and lead to the meridional shift of the climatological storm track; on the other hand, the modulated activity of the high-frequency transients can reinforce the original circulation anomaly by transporting the anomalous eddy fluxes. Meanwhile, the energy of the amplified disturbance tends to propagate downstream, resulting in formation of a new action center over NA, and the bridge between the variability of the EAWM and the climate anomaly in NA at this time is fully built up.

For the wEAWM@neuENSO, a weaker Rossby wave packet originating from northern Eurasia propagates equatorward into the lower latitudes of the western Pacific (Fig. 6b). A similar reflection of the Rossby wave packet occurs around 120°E near the critical layer but is much weaker compared to that in the sEAWM@neuENSO years, resulting in formation of the downstream Rossby wave packet propagating poleward (Fig. 9). Over North Pacific, the downstream wave packet tends to be reflected by the northern edge of subtropical jet, and then continues to propagate toward NA. Therefore, the wave reflections play a crucial role in linking the upstream and downstream wave packets. In this case, anomalous CK is smaller over North Pacific compared with that in the sEAWM@neuENSO, too (Fig. 10a), resulting in weaker kinetic energy conversion form the basic flow. Accompanied with a weakened and poleward shrunken EAJS (Fig. 5b), storm track axes move northward (Fig. 10b). The excessive transient eddies north of its climatology axes are accompanied by divergence of the E vector and positive height tendencies to the south (Figs. 10b,c). The interaction between transient eddies and upper-troposphere jet stream helps to maintain the positive height anomalies over the North Pacific. The anomalous wave train over the North Pacific to NA in this case is much weaker than that in the sEAWM@neuENSO. This may be attributed to smaller variation of barotropic energy conversion over the North Pacific associated with weak anomalous upper-troposphere zonal wind, and may also be related to the weakened jet stream, which cannot serve as a waveguide. Therefore, a wEAWM@neuENSO is accompanied by an eastward transport of stationary Rossby wave activity from the central North Pacific to the western United States, however with small magnitude.

The net contribution of CK to the maintenance of related anomalies can be quantified by evaluation the time scale τCK with which the observed perturbation kinetic energy (KE) could be fully replenished through CK (Kosaka and Nakamura 2006):

τCK=KECK,

where KE=( u2+υ2)/2, and the brackets basically represent a horizontal integration [e.g., the North Pacific (20°–60°N, 160°E–140°W) region in this study].

The values of τCK for the sEAWM@neuENSO and wEAWM@neuENSO cases are listed in Table 2. For both of the two cases, each of those time scales is much less than 1 month, indicating that the barotropic kinetic energy conversion can effectively act to maintain the local anomalies. In addition, the time scale τCK is much smaller in the upper troposphere than in the lower troposphere, suggesting that the upper tropospheric replenishing is more effective.

Table 2.

Time scale τCK (days) with which horizontal integrated KE could be replenished through barotropic energy conversion CK. Here CK is integrated over the domains of North Pacific (20°–60°N, 160°E–140°W). Values for 200, 500, and 850 hPa and a vertical integral of 1000–100 hPa are shown.

Table 2.

b. Linkage of EAWM and North American Ts anomalies in ENSO cases

The analysis in section 4a demonstrates that anomalous EAJS over the central North Pacific associated with the EAWM variability plays an important role in the wave train–like anomaly emanating from the central North Pacific toward NA, which results in the NA Ts anomalies in neutral ENSO years. However, in ENSO years, anomalous EAJS related to the EAWM is quite different. Figures 4a and 11a help us to make a comparison of the 200-hPa zonal wind anomaly patterns regressed on the EAWM index in neutral ENSO (Fig. 4a) and ENSO years (Fig. 11a). As mentioned above, Fig. 4a suggests a strengthened and eastward extended EAJS associated with a strong EAWM in neutral ENSO cases. In contrast, the strengthening of EAJS associated with a strong EAWM is mainly confined over the East Asian continent in ENSO cases (Fig. 11a). This is consistent with Wu and Sun (2017), who reported that the winter EAJS core location had strong zonal variability and suggested that the mid- to high-latitude circulation systems could be responsible for the east-located EAJS and the tropical circulation systems could be closely related to the west-located EAJS. The EAWM-induced U200 anomalies over the North Pacific are very weak and not significant in ENSO years (Fig. 11a). Furthermore, the related horizontal Rossby wave activity fluxes are also weak over the North Pacific–NA region (figure not shown), suggesting that it is unlikely for the EAWM to influence the NA Ts through midlatitude processes in this case. Hence, the results in this study agree well with those in Ma et al. (2018), which indicated that the EAWM could have an indirect influence on the NA Ts anomalies in ENSO episodes by modulating the tropical convection anomalies associated with ENSO. In the following, we will further compare main differences in the NA Ts anomalies related to strong/weak EAWM between the neutral ENSO and ENSO cases.

Fig. 11.
Fig. 11.

(a),(c),(e) As in Fig. 4, but the regression is calculated in ENSO years only. (b),(d),(f) As in (a), (c), and (e), but replacing the EAWM index with the EAWMres index.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

To explore the relationship between the EAWM and the NA Ts anomalies during ENSO episodes, a residual EAWM (EAWMres) index, which is obtained by removing the ENSO-related part by linear regression from the original EAWM index, is employed. This is done because the ENSO variability can lead to variations of the original EAWM index on the one hand, and contribute to the NA Ts anomalies on the other hand, which may obscure their relationship. For example, the regressed patterns with respect to the original EAWM index coherently show EAWM-related characteristics in the East Asia and western North Pacific regions, but also show ENSO-related features in the eastern Pacific and the tropics. Over East Asia, there are striking variations associated with EAWM, such as a strengthened EAJS in the upper troposphere (Fig. 11a), a deepened East Asian trough in the midtroposphere (Fig. 11b), and northerly wind anomalies along the east coast of Asia in the lower troposphere (Fig. 11c). Meanwhile, in the eastern Pacific, there are alternative positive–negative–positive U200 anomaly centers from the tropics to high latitudes (Fig. 11a) and negative Z500 anomalies along the tropics (Fig. 11b), which mimics the ENSO-related (La Niña phase) feature. These hybrid features related to the EAWM index may be interpreted such that because the index itself shows a linear response to SST anomalies in the tropical eastern Pacific (e.g., the Niño-3/3.4 region), it thereby can partly reflect the ENSO-related variations (Chen et al. 2013). In contrast, the EAWMres index may effectively suppress contributions originating from ENSO, which is consistent with previous studies (Chen et al. 2013; Jia and Ge 2017). For example, the U200 anomalies coupled with the EAWMres index are weak over the tropical and eastern Pacific (Fig. 11d), and the Z500 anomalies are feeble over the tropics (Fig. 11e). Therefore, the EAWMres index is useful for depicting the variations associated with independent EAWM variability without contributions from ENSO.

By using the EAWMres index, we can further explore the NA Ts anomalies response to the independent EAWM variability during ENSO years. It is widely known that El Niño and La Niña can induce an asymmetric PNA-like teleconnection pattern due to different locations of tropical Pacific forcing, resulting in distinct responses of Ts anomalies in NA. In addition, strong and weak EAWM might have some nonlinear influence on large-scale atmospheric circulation, as illustrated in section 4a. Thus, it is necessary to use the composite analysis method to delineate North American Ts anomalies forced by the EAWMres during El Niño and La Niña episodes, respectively. Figure 12 shows the composite Ts anomalies in NA for categories of strong EAWMres@El Niño (sEAWMres@EN; Fig. 12a) and weak EAWMres@El Niño (wEAWMres@EN; Fig. 12b), and their difference (Fig. 12c). Figures 12d–f are the same as Figs. 12a–c, but for La Niña events. The distribution of Ts anomalies in sEAWMres@EN features a northwest-warm and southeast-cold contrast (Fig. 12a), whereas in the wEAWMres@ENs the Ts anomaly pattern has an insignificant warm center in central Canada (Fig. 12b). For the La Niña episodes, when combining with a strong EAWMres, we find colder Alaska and Canada and a warmer west coast of the United States (Fig. 12d). Contrastingly, when combining with a weak EAWMres, the Ts anomaly pattern shows a cold north and warm south contrast in NA (Fig. 12e). The composite results suggest that, for the El Niño/La Niña winters, the strength of EAWMres can also modulate the distributions of Ts anomalies in NA. The mechanisms about the modulation effect of EAWM on NA Ts anomalies during an ENSO event have been demonstrated in Ma et al. (2018), which are shown to be related to changes in tropical precipitation anomalies and the associated generation of tropical–extratropical teleconnections.

Fig. 12.
Fig. 12.

Composite anomaly patterns of the wintertime surface air temperature (Ts) for (a) strong EAWMres@El Niño (sEAWMres@EN), (b) weak EAWMres@El Niño (wEAWMres@EN), and (c) the difference between (a) and (b), and for (d) strong EAWMres@La Niña (sEAWMres@LN), (e) weak EAWMres@La Niña (wEAWMres@LN), and (f) the difference between (d) and (e). Stippling indicates the 95% confidence level.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

It should be noted that the above results of Ts anomalies forced by strong/weak EAWMres are consistent with Ma et al. (2018), although the samples selected in this study are slightly different because the CPC has recently updated the historical El Niño and La Niña events based on the ERSSTv5 SST data [the previous version is based on ERSSTv4 and is employed in Table 1 of Ma et al. (2018)]. Thus, Fig. 12 further confirms that the modulation effect of EAWMres on the NA Ts anomalies during ENSO episodes is robust and does not rely on individual samples.

5. Summary and discussion

In this study, we explored the relationship between the EAWM and Ts anomalies in North America (NA) during the boreal winter. It is found that the EAWM has different impacts on North American Ts during ENSO and neutral ENSO episodes. Here neutral ENSO refers to weak SST anomalies over the tropical central-eastern Pacific—in other words, neither El Niño nor La Niña. In general, during neutral ENSO years, the EAWM variability is closely coupled to the Ts anomalies in northwestern NA through a stationary Rossby wave train from central North Pacific to NA. During ENSO years, the variation of EAWM modulates the Ts anomalies in NA via the modulation of the tropical convection anomalies associated with ENSO as demonstrated in Ma et al. (2018).

The EAWM plays an important role in affecting the North American Ts anomalies during neutral ENSO years. It can explain as much as 20%–40% of the total Ts variance in western NA and the present study emphasizes on the related mechanisms in this case. In the sEAWM@neuENSO case, two stationary Rossby wave packets appear associated with a strong EAWM, one over the upstream regions of Eurasia and the other over the downstream regions of the North Pacific and NA. The upstream wave packet propagates in a southeasterly direction and tends to be reflected into higher latitudes when it approaches the critical layer over the lower latitudes of western Pacific. Also, the strong zonal wind shear on the south edge of the EAJS might play a role in the lower-latitude wave reflection. This wave reflection is suggested to contribute to the formation of the downstream wave packet. Moreover, the strengthening and eastward extension of the EAJS lead to increased barotropic kinetic energy conversion over the central North Pacific, which further contributes to the amplification of the downstream wave packet emanating from the central North Pacific toward NA. The synoptic-scale transient eddy activities tend to move southward corresponding to the jet acceleration, and the eddies in turn feed back to the jet stream to induce a negative height tendency to the north, which is beneficial to maintain the anomalous cyclone center over North Pacific. Therefore, in sEAWM@neuENSO, the intensified EAJS favors an anomalous Rossby wave train from North Pacific to NA, which links the EAWM variability to the Ts anomaly in western NA. For the wEAWM@neuENSO, weak anomalous Rossby wave from central North Pacific to western NA is observed, which leads to negative Ts anomalies in western-central NA. However, the magnitude of the anomalous Rossby wave in a wEAWM@neuENSO is smaller than that in a sEAWM@neuENSO, possibly owing to the EAJS being weak in the former case and, hence, not serving as a waveguide. Therefore, the variation of EAWM during neutral ENSO years is closely associated to the Ts anomalies in western NA.

In ENSO years, however, anomalous EAJS is mainly confined over East Asia and does not extend into the central North Pacific. The results suggest that it is unlikely for the EAWM to influence the NA Ts through midlatitude processes in ENSO years, and further confirm that the EAWM has an indirect impact on the North American Ts anomalies via a modulation of the tropical convection anomalies associated with ENSO as demonstrated in Ma et al. (2018). The relationship between EAWM and North American Ts anomalies during ENSO events is then compared. The variations of U200, Z500, and Wind850 related to the EAWM in the eastern Pacific during ENSO years are obscured by the close relationship between ENSO and those circulation anomalies (e.g., by PNA). Therefore, an EAWMres index is introduced to investigate the independent variability of EAWM apart from ENSO. The EAWMres exerts its impact on the variation of Ts in NA during ENSO events through the modulation of the tropical convection anomalies associated with ENSO (Ma et al. 2018). A sEAWMres@EN is associated with anomalous warm center over northwest NA while a wEAWMres@EN is related to weak warm conditions over central-east NA. Similarly, strong and weak EAWMres give rise to different distributions of Ts anomalies in the wEAWMres@LN and sEAWMres@LN subgroups.

It is noted that, in the case of sEAWM@neuENSO, the turning latitudes are not completely consistent with the wave activity fluxes in the central-eastern North Pacific. The northeastward wave activity fluxes seem to propagate across the eastern edge of the turning latitudes near the Bering Strait (Figs. 6a and 7b), which may be related to the approximation of Ks by considering only the zonal component of the basic wind (U) as in Hoskins and Ambrizzi (1993). The meridional component of the basic flow (V) is also important for the meridional propagation of Rossby waves (Karoly 1983; Li et al. 2015). Since the V is relatively large over the eastern-central North Pacific (Fig. 7a), the approximation of Ks may lead to inaccurate estimate of the turning latitudes over there, and hence contribute to their inconsistency with the wave activity fluxes.

The present work focused on the analysis of simultaneous relationship between the EAWM and the Ts anomalies in NA during neutral ENSO winters. However, the seasonal prediction skill for the EAWM is not high based on current numerical climate models. It will be helpful for the seasonal forecast of the winter Ts in NA if the EAWM could lead for some time; this issue will be briefly discussed here. The lead–lag relationship between the EAWM and the Ts in NA is examined in Figs. 13 and 14, and only neutral ENSO years are considered here. All the monthly mean data are averaged for the two successive months to filter out the anomalies possibly from intraseasonal variations such as the Madden–Julian oscillation.

Fig. 13.
Fig. 13.

Regression patterns of anomalous surface air temperature (Ts) in (a1) ND, (b1) DJ, (c1) JF, and (d1) FM with respect to the EAWM_ND. (a2)–(d2) As in (a1)–(d1), but with respect to the EAWM_DJ. Stippling indicates the 95% confidence level.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

Fig. 14.
Fig. 14.

The orange dashed line shows the evolution of correlation coefficients between the EAWM_ND and the 2-month running mean of Ts anomalies in the key area of NA from ND to FM. The red solid line shows the evolution of correlation coefficients between the EAWM_DJ and the 2-month running mean of Ts anomalies in the key area of NA from DJ to MA. The gray dashed line indicates the 95% confidence level.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

Figure 13 displays the evolution of lagged regression maps of NA Ts with respect to the November–December (ND) averaged EAWM index (EAWM_ND) from ND to the following February–March (FM) and the December–January (DJ) averaged EAWM index (EAWM_DJ) from DJ to the following March–April (MA), respectively. Consistent with Fig. 2d, the EAWM couples well with positive Ts anomalies in the western-central part of NA (WC-NA hereafter) during neutral ENSO winters. Although the EAWM_ND is associated with weak and insignificant positive WC-NA Ts anomalies in ND simultaneously (Fig. 13a1), the EAWM_ND tends to induce significant warm WC-NA Ts anomalies one month later in DJ (Fig. 13b1) and to persist into the following January–February (JF; Fig. 13c1). In addition, the EAWM_DJ is associated with strong and significant positive WC-NA Ts anomalies simultaneously (Fig. 13a2). These warm anomalies become much stronger and cover a much larger area in NA one month later (Fig. 13b2) and then weaker in the following February–March (Fig. 13c2). It should be noted that the EAWM_JF has no significant impacts on the North American Ts in the following months (figure not shown). Figure 14 further shows the evolution of lead–lag correlation coefficients between the EAWM_ND (EAWM_DJ) and the two-month-running-mean Ts anomalies in the key NA area (35°–40°N, 105°–115°W) from ND (DJ) to the following FM (MA), respectively. The results indicate that there is a significant relationship between the EAWM_ND and the DJ Ts anomalies in NA. In addition, there are significant DJ Ts anomalies in NA associated with the EAWM_DJ that can persist into the following JF. Hence, the EAWM is beneficial to the seasonal forecast of the Ts anomalies in NA during neutral ENSO years. It is also very interesting that Fig. 14 presents a clear evolution for the impacts of EAWM on the Ts anomalies in NA, which seems to be linked to the evolution of EAWM (e.g., Wei et al. 2011). This result again implies that the EAJS may play an important role in the impact of EAWM on the NA climate during neutral years. Further studies about this issue are needed in the future.

Acknowledgments

We thank the three anonymous reviewers for their constructive suggestions and comments, which helped to improve the paper. This work was supported jointly by the National Natural Science Foundation of China (Grants 41721004, 41975051, and 41961144025), the Chinese Academy of Sciences Key Research Program of Frontier Sciences (QYZDY-SSW-DQC024), and the Jiangsu Collaborative Innovation Center for Climate Change. The authors declare that they have no conflict of interest.

APPENDIX

Wave Reflection Process near the Critical Layer

Figure A1 shows the time evolution of the perturbation streamfunction associated with the EAWM around the critical layer.

Fig. A1.
Fig. A1.

Lead–lag regression patterns of the perturbation streamfunction (106 m−2 s−1) at 200 hPa with respect to the EAWM index in boreal winter. (a)–(f) The EAWM leads for −2, 2, 4, 6, and 10 days, respectively. The green lines and associated shadings indicate the basic-state critical layer (U = 0) and easterly winds regions (U < 0), respectively.

Citation: Journal of Climate 33, 24; 10.1175/JCLI-D-18-0760.1

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