Editorial Type:
Article
Article Type:
Research Article

Mechanisms Determining Diversity of ENSO-Driven Equatorial Precipitation Anomalies

Zixiang Yan aKey Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
bState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Search for other papers by Zixiang Yan in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0002-9099-3725
,
Bo Wu bState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
cCAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China

Search for other papers by Bo Wu in
Current site
Google Scholar
PubMed Close
,
Tim Li aKey Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
dInternational Pacific Research Center, University of Hawai‘i at Mānoa, Honolulu, Hawaii
eDepartment of Atmospheric Sciences, SOEST, University of Hawai‘i at Mānoa, Honolulu, Hawaii

Search for other papers by Tim Li in
Current site
Google Scholar
PubMed Close
, and
Guirong Tan aKey Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

Search for other papers by Guirong Tan in
Current site
Google Scholar
PubMed Close
Online Publication:
08 Jan 2022
Print Publication:
01 Feb 2022
DOI:
https://doi.org/10.1175/JCLI-D-21-0363.1
Page(s):
923–939
Open access
View license

Abstract

The longitudinal location of precipitation anomalies over the equatorial Pacific shows a distinctive feature with the westernmost location for La Niña, the easternmost location for eastern Pacific (EP) El Niño, and somewhere between for central Pacific (CP) El Niño, even though the center of the sea surface temperature anomaly (SSTA) for La Niña is located slightly east of that of CP El Niño. The mechanisms for such a precipitation diversity were investigated through idealized model simulations and moisture and moist static energy budget analyses. It is revealed that the boundary layer convergence anomalies associated with the precipitation diversity are mainly induced by underlying SSTA through the Lindzen–Nigam mechanism, that is, their longitudinal locations are mainly controlled by the meridional and zonal distributions of the ENSO SSTA. The westward shift of the precipitation anomaly center during La Niña relative to that during CP El Niño is primarily caused by the combined effects of nonlinear zonal moist enthalpy advection anomalies and the Lindzen–Nigam mechanism mentioned above. Such a zonal diversity is further enhanced by the “convection–cloud–longwave radiation” feedback, the SST-induced latent heat flux anomalies, and the advection of mean moist enthalpy by anomalous winds. This diversity in the longitudinal location of precipitation anomalies has contributions to the diversities in the longitudinal locations of anomalous Walker circulation and western North Pacific anomalous anticyclone/cyclone among the three types of ENSO.

© 2022 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: Bo Wu, wubo@mail.iap.ac.cn

Abstract

The longitudinal location of precipitation anomalies over the equatorial Pacific shows a distinctive feature with the westernmost location for La Niña, the easternmost location for eastern Pacific (EP) El Niño, and somewhere between for central Pacific (CP) El Niño, even though the center of the sea surface temperature anomaly (SSTA) for La Niña is located slightly east of that of CP El Niño. The mechanisms for such a precipitation diversity were investigated through idealized model simulations and moisture and moist static energy budget analyses. It is revealed that the boundary layer convergence anomalies associated with the precipitation diversity are mainly induced by underlying SSTA through the Lindzen–Nigam mechanism, that is, their longitudinal locations are mainly controlled by the meridional and zonal distributions of the ENSO SSTA. The westward shift of the precipitation anomaly center during La Niña relative to that during CP El Niño is primarily caused by the combined effects of nonlinear zonal moist enthalpy advection anomalies and the Lindzen–Nigam mechanism mentioned above. Such a zonal diversity is further enhanced by the “convection–cloud–longwave radiation” feedback, the SST-induced latent heat flux anomalies, and the advection of mean moist enthalpy by anomalous winds. This diversity in the longitudinal location of precipitation anomalies has contributions to the diversities in the longitudinal locations of anomalous Walker circulation and western North Pacific anomalous anticyclone/cyclone among the three types of ENSO.

© 2022 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: Bo Wu, wubo@mail.iap.ac.cn
Keywords: Asymmetry; ENSO; El Nino; La Nina; Precipitation

1. Introduction

El Niño–Southern Oscillation (ENSO) is the strongest interannual variability mode in the climate system, generated by air–sea interactions in the tropical Pacific (Bjerknes 1969; Neelin et al. 1998; Deser et al. 2010; Clarke 2014; Capotondi et al. 2015; Wang 2018; Timmermann et al. 2018; Okumura 2019; Fang and Xie 2020).

ENSO exhibits striking diversity in amplitude, zonal position and duration (Kug et al. 2009; Okumura and Deser 2010; Capotondi et al. 2015; Timmermann et al. 2018; Okumura 2019). First, spatial distribution and amplitude are asymmetric between El Niño and La Niña. During ENSO peak winter, the maximum positive sea surface temperature anomalies (SSTAs) during El Niño are located to the east of the minimum negative SSTAs during La Niña. The magnitude of SSTAs in the equatorial eastern Pacific during El Niño is remarkably larger than that during La Niña. There are many mechanisms responsible for this amplitude asymmetry, which have been reviewed by Capotondi et al. (2015), Okumura (2019), An et al. (2020) and Fang and Xie (2020).

Second, temporal evolution is asymmetric between El Niño and La Niña. After the mature phase, most El Niño events decay rapidly and evolve to a neutral or opposite state in the following winter, while La Niña events tend to decay slowly and persist in the following winter (McPhaden and Zhang 2009; Wu et al. 2010; Okumura and Deser 2010; Chen et al. 2016; Okumura 2019).

Third, there exist two types of El Niño events with different spatial distributions of SSTAs (Larkin and Harrison 2005a; Ashok et al. 2007; Weng et al. 2007; Kao and Yu 2009; Kug et al. 2009; Capotondi et al. 2015; Okumura 2019). In contrast to conventional El Niño [or referred to as eastern Pacific (EP) El Niño] with the strongest warm SSTAs in the equatorial far eastern Pacific, a new type of El Niño having the maximum warm SSTAs near the date line was found. This type of El Niño is referred to as “El Niño Modoki” (Ashok et al. 2007), “central Pacific (CP) El Niño” (Kao and Yu 2009), “date line El Niño” (Larkin and Harrison 2005a,b), or “warm pool El Niño” (Kug et al. 2009). In this study, we use EP and CP El Niños to represent the two types of El Niño.

ENSO-driven precipitation anomalies also show pronounced diversity. The positive precipitation anomalies over the equatorial central-eastern Pacific during El Niño are located to the east of the negative precipitation anomalies during La Niña (Hoerling et al. 1997; Kang and Kug 2002; Wu et al. 2010). The asymmetry in the precipitation anomalies have been proposed to be associated with the asymmetry in the SSTAs between El Niño and La Niña (Hoerling et al. 1997; Wu et al. 2010; Wang et al. 2019) and nonlinear responses of deep convection to underlying SST (Harrison and Vecchi 1999; Ohba and Ueda 2009; Okumura et al. 2011; McGregor et al. 2013; Takahashi and Dewitte 2016; Okumura 2019). Numerical experiments by coupled general circulation models indicate that the nonlinear responses of deep convection to underlying SST is associated with both the position of the rising branch of the mean Walker circulation, and the ENSO-induced zonal shift of the rising branch (Bayr et al. 2014, 2018). Okumura (2019) defined a positive SST deviation from the convective threshold [ΔT=(T−T*)H(T−T*), where T* denotes SST threshold for atmospheric convection closely following tropical mean SST, H is the Heaviside function], and use its distribution to explain the diversity of the ENSO-driven precipitation anomalies. This mechanism does not take into account the boundary layer thermal responses to underlying SSTAs in terms of the Lindzen and Nigam (1987) model. In fact, it has been noted that ENSO-related meridional wind anomalies in the boundary layer and associated convergence anomalies are mainly driven by the underlying SSTA gradient through the Lindzen–Nigam mechanism (air temperature in the boundary layer is constrained by underlying SST through turbulence and shallow cumulus convection) (Chiang et al. 2001; Adames and Wallace 2017).

Considering of the central roles of the ENSO-driven precipitation anomalies in linking SSTAs, surface wind and ocean thermocline variations (e.g., Bjerknes 1969; Li 1997; Boutle et al. 2007; Kug et al. 2009) and in exciting atmospheric teleconnections (e.g., Horel and Wallace 1981; Trenberth et al. 1998; Wang et al. 2008; Kim and Kug 2018; Feng et al. 2017; Yeh et al. 2018), it is important to further explore the formation mechanisms of the ENSO-driven precipitation anomalies and their diversity among different types of ENSO. In this study, we investigate these issues from the perspective of combining the Lindzen–Nigam model and the moist static energy budget.

The remainder of this paper is organized as follows. Section 2 introduces the observational and reanalysis datasets and analysis methods used in this study. In section 3, we describe the diversity of ENSO-driven precipitation anomalies during ENSO mature winter. The physical mechanisms for the precipitation diversity are investigated in sections 4 and 5. Section 6 illustrates the impacts of the precipitation diversity on the Walker circulation and the western North Pacific anomalous anticyclone. Finally, conclusions are given in section 7.

2. Data and methods

a. Data

The observational and reanalysis datasets used in this study include: 1) monthly SST data from the Met Office Hadley Centre’s sea ice and SST dataset (HadISST) with a grid resolution of 1° × 1° (Rayner et al. 2003); 2) monthly precipitation data from the Global Precipitation Climatology Project (GPCP) with a resolution of 2.5° × 2.5° (Adler et al. 2003); and 3) monthly atmospheric reanalysis data, ERA-interim, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) with a resolution of 1.5° × 1.5° (Dee et al. 2011). All the datasets cover the period of 1980–2017.

b. Methods

1) Composite analysis

We select EP and CP El Niño events and La Niña events based on following criteria. 1) The selected EP El Niño events satisfy the conditions that Niño-3 index (SSTAs averaged over 5°S–5°N, 90°–150°W) in November–December(0)–January(1) [ND(0)J(1)] is greater than both one standard deviation and the simultaneous Niño-4 index (SSTAs averaged over 5°S–5°N, 160°E–150°W) (Kug et al. 2009; Yeh et al. 2014). 2) Four “pure” CP El Niño events are selected, which have been selected in all the five classical early studies about CP El Niño (Ashok et al. 2007; Kug et al. 2009; Yeh et al. 2009; Kim et al. 2009; McPhaden et al. 2011), following Xiang et al. (2013) and Chung and Li (2013). 3) La Niña events are selected when either the normalized ND(0)J(1)-mean Niño-3 index or the normalized Niño-4 index is less than −1.0. All the selected ENSO events are listed in Table 1. Composite analyses are performed on the EP, CP El Niño and La Niña events, respectively. Statistical significances of the composites are tested using the one-tailed t-test method. In this study, we are interested with the diversity of ENSO-driven precipitation anomalies not associated with the ENSO diversity in amplitude. Hence, all composite figures are normalized by dividing corresponding Niño-3.4 index (SSTAs averaged over 5°S–5°N, 170°–120°W); that is, all variable anomalies shown corresponds to 1°C variation of SSTAs in the Niño-3.4 region.

Table 1.

Selected ENSO events in the period of 1980–2017.
Table 1.

2) Moisture and moist static energy budget analysis

Moisture budget is diagnosed to understand mechanisms responsible for ENSO-driven precipitation anomalies. Neglecting the time tendency term, the linearized vertically integrated anomalous moisture equation can be written as (Wu et al. 2017a)
P′=E′−〈u¯∇hq′〉−〈u′∇hq¯〉−〈ω¯∂pq′〉−〈ω′∂pq¯〉+NL,
in which the prime denotes monthly anomalies with climatological annual cycle removed, the bar denotes the climatological mean state, and the angle brackets denote a mass integral from the surface to 100 hPa. Here, P is precipitation, E is evaporation, and u and ω are horizontal wind and vertical p velocity, respectively; q is specific humidity and NL represents the sum of all nonlinear and transient terms.
The moist static energy (MSE) budget is diagnosed to understand the cause of tropical vertical motions (Neelin and Held 1987). According to the previous studies (Neelin 2007; Wu et al. 2017a,b), neglecting the time tendency of anomalous MSE term, the MSE equation can be written as
〈ω′∂ph¯〉=F′net−〈ω¯∂ph′〉−〈u¯∇hk′〉−〈u′∇hk¯〉+NL,
in which MSE is h = cpT + Lυq + Φ and moist enthalpy is k = cpT + Lυq. The terms cp and Lυ denote the specific heat at constant pressure and the latent heat of vaporization, respectively; T is air temperature, Φ is geopotential, and Fnet is the net MSE flux coming into the atmospheric column from the surface and the top of atmosphere.
Following Back and Bretherton (2009a), tropical vertical motions can be decomposed into two vertical modes: deep and shallow modes. Through this approach, the atmospheric boundary layer and free-troposphere processes can be isolated. The decomposition of tropical vertical motions includes following three steps. First, the two dominant modes are obtained by performing a principal component analysis (PCA) on the horizontal convergence profiles over tropical oceans (20°S–20°N) (Fig. 1a). They account for 56% and 19% of the total variance, respectively. Second, the two dominant modes are linearly combined to construct two new orthogonal convergence profiles with only one mode having projection on the near-surface (below 925 hPa) convergence (Fig. 1b). The mode with surface convergence is referred to as the shallow mode. The other mode is referred to as the deep mode. The deep and shallow modes account for 32% and 45% of the total variance of the vertical profiles of convergence, respectively. Third, the vertical profiles of deep- and shallow-mode vertical motions are computed from the corresponding vertical profiles of the horizontal convergence, respectively (Fig. 1c). They account for 45% and 46% of the total variance of the vertical profiles of vertical motion, respectively. Detailed decomposition processes can be found in Back and Bretherton (2009a) and Yan et al. (2020). The deep and shallow modes account for 78% of the variance of the vertical motions over the tropical central-eastern Pacific (10°S–10°N, 150°E–80°W) during the ENSO period. The deep mode shows a top-heavy bow structure and has almost no boundary layer convergence (Fig. 1c, red line), while the shallow mode shows a bottom-heavy bow structure and has a strong boundary layer convergence (Fig. 1c, black line). Based on the two modes, anomalous vertical motion can be decomposed:
ω′(x,y,p)≈σ′s(x,y)Ωs(p)+σ′d(x,y)Ωd(p),
in which σs and σd represent the amplitudes of the shallow and deep modes, respectively. The terms Ωs and Ωd represent the vertical motion profiles associated with shallow and deep modes. Then the vertical advections of mean moisture and MSE by anomalous vertical motions in Eqs. (1) and (2) can be approximately separated into two parts associated with the deep and shallow modes, respectively:
−〈ω′∂pq¯〉≈−σ′d〈Ωd∂pq¯〉−σ′s〈Ωs∂pq¯〉,
〈ω′∂ph¯〉≈σ′d〈Ωd∂ph¯〉+σ′s〈Ωs∂ph¯〉.
Fig. 1.
Fig. 1.

(a) The first two PCA modes of vertical profiles of horizontal convergence over the tropical oceans (20°S–20°N). (b) Deep and shallow modes of vertical profiles of horizontal convergences obtained through linear combinations of two PCA modes shown in (a). (c) Corresponding vertical profiles of deep and shallow mode of vertical motions computed from (b).

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

Then Eqs. (1) and (2) are converted to
P′=E′−〈u¯∇hq′〉−〈u′∇hq¯〉−〈ω¯∂pq′〉−σ′d〈Ωd∂pq¯〉 −σ′s〈Ωs∂pq¯〉+NL,
σ′d〈Ωd∂ph¯〉=−σ′s〈Ωs∂ph¯〉+F′net−〈ω¯∂ph′〉−〈u¯∇hk′〉−〈u′∇hk¯〉+NL.

3. Diversity of ENSO-driven precipitation anomalies in zonal position

The composites of SST and precipitation anomalies during ENSO peak phase [November–December–January (NDJ)] for EP El Niño, CP El Niño, and La Niña events are shown in Fig. 2. As noted in previous studies, ENSO SSTAs exhibit diversity in amplitude and spatial pattern (Kug et al. 2009; Kao and Yu 2009; Capotondi et al. 2015; Okumura 2019). For the EP El Niño composite, the strongest warm SSTAs are located in the equatorial eastern Pacific and extended westward to the date line (Fig. 2a). The CP El Niño composite has the strongest SSTAs in the equatorial central Pacific, and very weak SSTAs in the equatorial eastern Pacific (Fig. 2b). The pattern of reversed SSTAs in the La Niña composite more closely resembles the CP El Niño composite than the EP El Niño composite, with its center being located in the central Pacific (Fig. 2c).

Fig. 2.
Fig. 2.

(a)–(c) Composite NDJ-mean SSTAs (°C) for EP El Niño, CP El Niño, and La Niña, respectively. (d)–(f) As in (a)–(c), but for precipitation anomalies (mm day−1). Green (brown) lines in (d)–(f) denote the area where precipitation anomalies are greater than 1.0 mm day−1 (less than −1.0 mm day−1) in El Niño (La Niña). The blue triangles denote the longitudes of maximum (minimum) of the positive (negative) SSTAs over the equatorial Pacific (5°N–5°S) during El Niño (La Niña). The red triangles denote the longitudes of centers of the positive (negative) precipitation anomalies over the equatorial Pacific (5°N–5°S) during El Niño (La Niña). Values reaching 5% significance level are dotted in white. Bars along the x axis denote the ranges of one standard deviation of the longitudinal centers of the SST and precipitation anomalies.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

ENSO-driven precipitation anomalies over the tropical Pacific also present diversity in zonal distributions. For the EP El Niño composite, the center of positive precipitation anomalies is at 150°W with a tail extending to the eastern Pacific (Fig. 2d). Here, the longitude of the precipitation center is defined as ∫x1x2Pr′×lon(x)dx/∫x1x2Pr′dx, where x1, x2 are the longitudes of the bounds of the precipitation anomalies averaged over 5°N–5°S, following the definition of the mass center. In contrast, both the positive center of the CP El Niño composite and negative center of the La Niña composite are shifted westward by about 30° longitude to the date line (Figs. 2e,f). In addition, although both the center of negative precipitation anomalies and the minimum of negative SSTAs in the La Niña composite are close to their counterparts in the CP El Niño composite, the minimum of the negative precipitation anomalies in the central Pacific during La Niña tends to be located to the west of the maximum of the positive precipitation anomalies during CP El Niño (Figs. 2b,c,e,f). We also checked the composites of 6 EP El Niño, 6 CP El Niño, and 11 La Niña events over the period of 1958–2017 derived from the JAR-55 reanalysis, respectively (figure not shown). The differences in the zonal positions of precipitation anomalies among the three types of ENSO events are very similar with the results from the GPCP.

From the perspective of moisture budget, ENSO-driven tropical precipitation anomalies (Pr′) are dominated by anomalous vertical advection of climatological moisture by vertical motion anomalies (−〈ω′∂pq¯〉, Table 2 and Figs. 3a–c) for all the three types of ENSO.

Fig. 3.
Fig. 3.

(a)–(c) Vertically integrated vertical advection of climatological specific humidity by anomalous vertical motions (−〈ω′∂pq¯〉; mm day−1) for the EP El Niño, CP El Niño, and La Niña composites, respectively. (d)–(f),(g)–(i) As in (a)–(c), but for the contributions of shallow- and deep-mode vertical motion component to −〈ω′∂pq¯〉. The green and brown lines are as in Fig. 2. The black triangles denote the longitudes of the centers of the positive (negative) −σ′s〈Ωs∂pq¯〉 and −σ′d〈Ωd∂pq¯〉 over the equatorial Pacific (5°N–5°S) during El Niño (La Niña). The red triangles denote the longitudes of the centers of the ENSO-driven precipitation anomalies shown in Figs. 2d–f.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

Table 2.

Budget analyses of the moisture equation (mm day−1) averaged over the equatorial CEP (area circled by the line in Figs. 2d–f) for the EP El Niño, CP El Niño, and La Niña composites, respectively. Subcomponents of term −〈ω′∂pq¯〉 are also given.
Table 2.

The −〈ω′∂pq¯〉 term can be decomposed into two components −σ′d〈Ωd∂pq¯〉 and −σ′s〈Ωs∂pq¯〉, according to Eq. (4), which are associated with deep- and shallow-mode vertical motion anomalies, respectively (Figs. 3d–i). The magnitude of −σ′d〈Ωd∂pq¯〉 component is comparable to that of the −σ′s〈Ωs∂pq¯〉 component for all the three types of ENSO events (Table 2).

The centers of the −σ′s〈Ωs∂pq¯〉 component are at about 145°W, 175°W, and 180° for the EP, CP El Niño, and La Niña composites, respectively, all located to the east of or just at the corresponding centers of the precipitation anomalies (Figs. 3d–f). In contrast, the centers of the −σ′d〈Ωd∂pq¯〉 component are at about 165°W, 175°E, and 170°E, respectively, all located to the west of the corresponding centers of the precipitation anomalies (Figs. 3g–i). These results indicate that the zonal positions of the ENSO-driven precipitation anomalies are a combination of the shallow- and deep-mode vertical motion anomalies. However, the zonal central position of the shallow-mode vertical motion anomalies is obviously closer to the precipitation center than the deep-mode anomalies for both the EP El Niño and the La Niña composite (the difference for the CP El Niño composite is small) (Figs. 3d–i), indicating that the shallow-mode component has more weight in determining the zonal position of ENSO-driven precipitation anomalies. Furthermore, the maximum of zonal differences in the centers of the shallow mode (about 35°) is much larger than that of the deep mode (about 20°), indicating that the diversity in the zonal position of ENSO-driven precipitation anomalies is to a large part caused by the diversity of shallow-mode vertical motion anomalies (Figs. 3d–i).

4. Mechanism for setting-up of shallow-mode vertical motion anomalies

The shallow mode is dominated by divergence/convergence in the planetary boundary layer below the trade cumulus inversion. In the boundary layer, the air temperature is mainly constrained by the surface temperature through turbulence and shallow cumulus convection. Hence, the boundary layer divergence/convergence and the associated vertical motions largely driven by the gradient of underlying SST (Lindzen and Nigam 1987, Back and Bretherton 2009b). Following Back and Bretherton (2009b), a theoretical model similar to the Lindzen–Nigam model (LN-like model) is used to connect boundary layer convergence anomalies to underlying SSTAs.

Assuming that the air temperature varies linearly from surface to the top of boundary layer [mean height of 850 hPa over the tropical oceans (20°S–20°N)], a hydrostatical approximation to describe boundary layer contribution to surface pressure (PBL) can be written as
PBL=ρ0g[Φ¯850]2[4−n(Ts+T850)]⋅
Here, ρ0 = 1.225 kg m−3, [Φ¯850] denotes the mean height of the 850-hPa pressure level averaged over the tropical ocean 20°S–20°N, n=(1/T0),T0=288K, and Ts and T850 denote SST and 850-hPa air temperature, respectively. If we neglect the 850-hPa air temperature gradient for the weak temperature gradient of tropical free troposphere (Sobel et al. 2001), the boundary layer contribution to surface pressure gradient driven by SST gradient (PBL_SST) can be written as
∂xPBL_SST=−nρ0g[Φ¯850]2∂xTs,∂yPBL_SST=−nρ0g[Φ¯850]2∂yTs⋅
Based on the balance of Coriolis acceleration, pressure gradient, downward momentum mixing and friction in the planetary boundary layer, a linear mixed layer model (MLM) is used to understand the distribution of surface wind and associated convergence (Stevens et al. 2002; Back and Bretherton 2009b), which can be written as
fk×U+1ρ0∇Ps−we(UT−U)h+wd(U)h=0,
in which f denotes the Coriolis parameter, U is the horizontal surface wind, ρ0 is the constant density, Ps is the surface pressure, UT is the 850-hPa wind, we is the entrainment velocity that represents the amount of downward momentum mixing, and wd is the linearized friction coefficient.
Thus, surface wind can be calculated as
U=UTεiεe+VTfεeεi2+f2−ρ0−1(f∂yPs+εi∂xPs)εi2+f2,V=VTεiεe−UTfεeεi2+f2+ρ0−1(f∂xPs−εi∂yPs)εi2+f2,
where εe = 2 × 10−5 s−1, εi = 3.5 × 10−5 s−1 (Stevens et al. 2002).
Supposing that the surface pressure is dominated by PBL_SST, the boundary layer wind induced by the gradient of underlying SSTAs (UBL_SST and VBL_SST) can be written as
UBL_SST=ng[Φ¯850]2(f∂yTs+εi∂xTs)εi2+f2,VBL_SST=−ng[Φ¯850]2(f∂xTs−εi∂yTs)εi2+f2⋅

Figure 4 shows the zonal distributions of surface convergence anomalies averaged over 5°S–5°N calculated by using surface wind in the reanalysis data and using SSTAs in terms of Eq. (12), respectively. The zonal distributions of surface convergence anomalies can be reproduced by the LN-like model for all the three types of ENSO. Considering that the meridional gradients of ENSO SSTAs are stronger than the zonal gradients by more than one order of magnitude (figure not shown), equatorial boundary layer convergence anomalies is determined by the divergence of the meridional gradient of underlying SSTAs [(∂2/∂y2)(T′s), Fig. 4]. It is worth noting that zonal positions of the extrema of (∂2/∂y2)(T′s) are generally to the west of those of corresponding SSTAs (Fig. 5), especially for EP El Niño and La Niña, indicating that the zonal distribution of (∂2/∂y2)(T′s) is not determined by that of the ENSO SSTAs itself. This also explains why the ENSO-driven precipitation anomalies are always located to the west of ENSO SSTAs. The diversity in the zonal distributions of the (∂2/∂y2)(T′s) gives rise to the diversity in the zonal distributions of surface convergence anomalies and corresponding precipitation anomalies among the three types of ENSO.

Fig. 4.
Fig. 4.

Zonal distribution of meridional averaged composite NDJ-mean surface convergence anomalies (black lines; 10−6 s−1), LN-like model convergence anomalies driven by SSTAs only (blue lines), and its component associated with (∂2/∂y2)(T′s) (red lines) over 5°S–5°N for the (a) EP El Niño, (b) CP El Niño, and (c) La Niña composites.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

Fig. 5.
Fig. 5.

Scatter diagram for the location of minimum (maximum) in meridional averaged divergence of meridional gradient of SSTAs [(∂2/∂y2)(T′s); abscissa axis] vs maximum (minimum) in SSTAs (ordinate axis) over 5°S–5°N. The red dots denote four EP El Niño events, black dots are four CP EP El Niño events, and blue dots are seven La Niña events. The red triangle denotes the composite EP El Niño events, and the black and blue triangles denote the composite CP El Niño events and La Niña events, respectively.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

Of particular note, the main distribution of the boundary layer convergence anomalies driven by (∂2/∂y2)(T′s) over the equatorial central Pacific (155°E–160°W) during La Niña extends more west than that during CP El Niño (Figs. 4b,c). This has a contribution to the asymmetry in the main body of the precipitation anomalies over the central Pacific between La Niña and CP El Niño, with former extremum being located to the west of the latter (Figs. 2e,f), although their extrema of the SSTAs are very close (Figs. 2b,c). In addition, terms (∂f/∂y)(∂T′s/∂x) and (∂2/∂x2)(T′s), which are associated with the zonal distribution of the SSTAs, tend to shift the extremum of boundary layer convergence anomalies further westward during La Niña compared to CP El Niño, even though their magnitudes are relatively small (figure not shown).

5. Processes associated with deep-mode vertical motion anomalies

Deep-mode vertical motion is constrained by the MSE budget (Neelin and Held 1987); that is, deep-mode ascending (descending) motions export (import) MSE out of (into) atmospheric columns, which is balanced by inputs (outputs) of the MSE by horizontal advections and boundary fluxes. The zonal positions of ENSO-related σ′d〈Ωd∂ph¯〉 centers are located at about 155°W, 180°, and 175°E for the EP El Niño, CP El Niño, and La Niña composites, respectively (Fig. 6). The center longitudes of σ′d〈Ωd∂ph¯〉 are close to those of −σ′d〈Ωd∂pq¯〉 for all the three types of ENSO (Figs. 3g–i and 6), suggesting that the zonal difference in the precipitation anomalies induced by the deep mode vertical motion anomalies is determined by the zonal distributions of σ′d〈Ωd∂ph¯〉.

Fig. 6.
Fig. 6.

Spatial distribution of vertically integrated vertical advection of mean MSE by anomalous vertical motions associated with deep-mode amplitude (σ′d〈Ωd∂ph¯〉; W m−2) for the (a) EP El Niño, (b) CP El Niño, and (c) La Niña composites. The green and brown lines are as in Fig. 2. The black triangles denote the longitudes of the centers of the positive (negative) σ′d〈Ωd∂ph¯〉 over the equatorial Pacific (5°N–5°S) during El Niño (La Niña).

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

Diagnosis of Eq. (7) indicates that ENSO-related σ′d〈Ωd∂ph¯〉 is mainly balanced by net MSE flux anomalies coming into the atmospheric column from the surface and the top of atmosphere (F′net), horizontal advection of mean moist enthalpy by ENSO-related wind anomalies (−〈u′∇hk¯〉) and the vertical advection of mean MSE by shallow-mode vertical motion anomalies (−σ′s〈Ωs∂ph¯〉) (Table 3). The positive (negative) −σ′s〈Ωs∂ph¯〉 term represents that the MSE is imported (exported) into atmospheric columns by shallow-mode ascending (descending) anomalies, an effect opposite to that of deep-mode vertical motion anomalies (Back and Bretherton 2006, 2009a). Hence, the shallow-mode ascending (descending) anomalies driven by underlying SSTA gradient (section 4) tend to drive deep-mode ascending (descending) anomalies for the balance of the MSE budget. Physical processes associated with the other two terms will be investigated below, respectively.

Table 3.

Budget analyses of the moist static energy (W m−2) averaged over the equatorial CEP (area circled by the lines in Figs. 2d–f) for the EP El Niño, CP El Niño, and La Niña composites, respectively.
Table 3.

a. Net MSE flux F′net

The net MSE flux F′net can be decomposed as follows:
F′net=R′cloud+R′clear+S′net+LH′+SH′,
in which R′cloud and R′clear denote the cloud and clear-sky longwave radiative flux anomalies, S′net denotes the net shortwave radiative flux anomalies, LH′ denotes the latent heat flux anomalies and SH′ is the sensible heat flux anomalies.

The F′net over the target areas is dominated by R′cloud, with contributions reaching 74%, 44%, and 73% for the EP El Niño, CP El Niño, and La Niña composites, respectively (Figs. 7a–f). The spatial patterns of R′cloud highly resemble those of the precipitation anomalies (Figs. 2d–f and 7d–f), because R′cloud is associated with a positive feedback between deep convection and cloud–longwave radiation process in the tropics. Enhanced convections increase deep convective clouds and associated cirrostratus and cirrocumulus, and thus amplify the positive radiative forcing of high clouds. The corresponding positive MSE flux further enhances the convections in situ (Su and Neelin 2002; Bretherton and Sobel 2002; Neelin and Su 2005; Wu et al. 2017a).

Fig. 7.
Fig. 7.

(a)–(c) Spatial distribution of anomalous net MSE flux (F′net; W m−2) for the EP El Niño, CP El Niño, and La Niña composites, respectively. (d)–(f) As in (a)–(c), but for cloud longwave radiative flux (R′cloud). The green and brown lines are as in Fig. 2. The black triangles denote the longitudes of the centers of the positive (negative) F′net and R′cloud over the equatorial Pacific (5°N–5°S) during El Niño (La Niña).

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

The latent heat flux term is decomposed into Newtonian cooling (LH′O; LH′O=αLH¯T′s) and atmospheric forcing components (LH′A=LH′−LH′O), which are mainly induced by SSTAs and surface wind speed anomalies, respectively (Figs. 8a–i) (Xie et al. 2010). The LH′O term has positive contributions to the ENSO-driven deep-mode vertical motion anomalies and the precipitation anomalies for all the three types of ENSO. Its center in EP El Niño is greatly shifted westward relative to the underlying SSTAs for the stronger climatological latent heat flux over the equatorial central Pacific than over the eastern Pacific (Figs. 2a–c, 8d–f). As a result, the differences in zonal distributions of the LH′O among EP El Niño, CP El Niño, and La Niña are much smaller than their differences in the zonal distributions of the SSTAs (Figs. 2a–c and 8d–f).

Fig. 8.
Fig. 8.

(a)–(c) Spatial distribution of anomalous latent heat flux (LH′; W m−2) for the EP El Niño, CP El Niño, and La Niña composites, respectively. (d)–(f),(g)–(i) As in (a)–(c), but for the contributions of anomalous Newtonian cooling component (LH′O) and atmospheric forcing component (LH′A) to latent heat flux (Xie et al. 2010). The green and brown lines are as in Fig. 2. The dashed lines in (d)–(f) are NDJ-mean climatological latent heat flux (values greater than 200 W m−2 are shown as blue dashed lines; the small values less than 160 W m−2 are shown as brown dashed lines). The black vectors in (g)–(i) denote surface wind anomalies.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

The LH′A shows a zonal dipole pattern in the equatorial central-eastern Pacific and has negative contributions to the precipitation anomalies for all the three types of ENSO (Figs. 8g–i). The dipole pattern is caused by opposite wind anomalies associated with Rossby wave and Kelvin wave responses to the precipitation anomalies. For example, for El Niño, the positive convective heating anomalies excite cyclonic Rossby wave to the west and easterly Kelvin wave to the east. The equatorial westerly (easterly) anomalies associated with the Rossby (Kelvin) wave response tend to weaken (strengthen) the climatological easterly trade wind and thus generate negative (positive) latent heat flux anomalies. The situation is opposite for La Niña.

b. Horizontal advection of mean moist enthalpy by wind anomalies

The −〈u′∇hk¯〉 term is associated with the “wind-induced moist enthalpy advection” mechanism (Su and Neelin 2002; Ham et al. 2007; Wu et al. 2017a,b). The −〈u′∇hk¯〉 is dominated by the −〈u′∇hLυq¯〉 component for the weak gradient of temperature in the tropics. Further calculation indicates that the −〈u′∇hLυq¯〉 over the equatorial central Pacific is dominated by its zonal component. We show the NDJ-mean low-level climatological specific humidity and anomalous horizontal winds in Figs. 9a–c. There is strong zonal gradient of moisture over the equatorial central Pacific for the contrast of the warm pool and the cold tongue and off-equatorial shift of the ITCZ and SPCZ in the central-eastern Pacific. For EP and CP El Niño (La Niña), the anomalous westerly (easterly) winds transport wet (dry) air into the area of positive (negative) precipitation anomalies, which strengthens the deep-mode ascending (descending) anomalies and thus the positive (negative) Pr′ there.

Fig. 9.
Fig. 9.

Spatial distribution of NDJ-mean low-level climatological specific humidity (shading; g kg−1) and anomalous wind (vectors; m s−1) averaged over 700–1000 hPa for the (a) EP El Niño, (b) CP El Niño, and (c) La Niña composites. The thick black lines are the area where precipitation anomalies are greater than 1.0 mm day−1 (less than −1.0 mm day−1) in El Niño (La Niña) composites. Red, black, and blue lines attached to the right of (a)–(c) are the zonal distribution of vertically integrated horizontal advection of mean moist enthalpy by anomalous wind anomalies (−〈u′∇hk¯〉; W m−2), low-level zonal wind anomalies, and zonal gradient of climatological specific humidity (∂xq¯; 10−6 g kg−1 m−1) meridionally averaged over 5°S–5°N, respectively.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

Rossby wave response to the west of the convective heating anomalies and Kelvin wave response to the east cause opposite equatorial zonal wind anomalies and thus opposite −〈u′∇hk¯〉 between the central and eastern Pacific for all the three types of ENSO (Fig. 9). This indicates that −〈u′∇hk¯〉 tends to pull the precipitation anomalies westward and thus has contributions to the west shift of the ENSO-driven precipitation anomalies relative to the ENSO SSTAs. It is worth noting that the relative contribution of the −〈u′∇hk¯〉 term to the deep-mode vertical motion anomalies in EP El Niño is much larger than that in the CP El Niño and La Niña (Table 3), for the zonal wind anomalies in EP El Niño is just in phase with the reversed climatological zonal gradient of moisture (Fig. 9a).

The zonal differences in the extrema of the −〈u′∇hk¯〉 term between CP El Niño (La Niña) and EP El Niño is about 20° longitude (Figs. 9a–c), much smaller than their zonal differences in the centers of the precipitation anomalies (Figs. 2d–f and red lines in Figs. 9a–c). This is associated with the constraint of the zonal distribution of the climatological moisture gradient (blue lines in Figs. 9a–c).

c. Nonlinear advection of anomalous moist enthalpy by wind anomalies

As noted in many previous studies, nonlinear processes play important roles in ENSO diversities, which have been reviewed by An et al. (2020). In this subsection, we investigate physical processes associated with nonlinear terms in the MSE equation. Although the sum of all nonlinear terms is small in the MSE budget for ENSO-driven precipitation anomalies (Table 3), the nonlinear advection of anomalous moist enthalpy by wind anomalies (−〈u′∇hk′〉) has a contribution to the zonal asymmetry in the extremum of precipitation anomalies over the central Pacific between La Niña and CP El Niño (Wang et al. 2019; Chen et al. 2019). The −〈u′∇hk′〉 term shows the distribution of west negative and east positive over the equatorial central Pacific for both La Niña and CP El Niño (solid red lines in Fig. 10). The −〈u′∇hk′〉 term is dominated by the −〈u′∇hLυq′〉 component in the lower troposphere. For the CP El Niño composite, the zonal gradients of the positive specific humidity anomalies are positive (negative) to the west (east) of their maximum, corresponding negative (positive) zonal advection by the westerly anomalies (Fig. 10a). For the La Niña composite, both the zonal gradients of the negative specific humidity anomalies and the direction of wind anomalies show inverse sign relative to the CP El Niño composite, which leads to the similar nonlinear zonal advection distribution of west-negative and east-positive (Fig. 10b). Thus, the nonlinear zonal advections tend to displace the positive (negative) precipitation anomalies during CP El Niño (La Niña) eastward (westward) and contribute to their zonal asymmetric distributions. It worth noting that the −〈u′∇hk′〉 term has a projection on the climatology (−〈u′∇hk′〉¯, red dotted lines in the right panels of Fig. 10). When this part is removed, its net effect on ENSO is obtained (shading in the right panels of Fig. 10), which also shows the west-negative, east-positive distribution for both the CP El Niño and La Niña.

Fig. 10.
Fig. 10.

Spatial distribution of NDJ-mean low-level anomalous specific humidity (shading; g kg−1) and wind (vectors; m s−1) averaged over 700–1000 hPa for the (a) CP El Niño and (b) La Niña composites. Green (brown) line denotes the area where precipitation anomalies are greater than 1.0 mm day−1 (less than −1.0 mm day−1) in the CP El Niño (La Niña) composite. Red solid and dotted lines attached to the right of (a) and (b) are the zonal distribution of vertically integrated horizontal advection of anomalous moist enthalpy by anomalous wind anomalies (−〈u′∇hk′〉; W m−2) meridionally averaged over 5°S–5°N, and its climatology (−〈u′∇hk′〉¯). The shading is the difference between −〈u′∇hk′〉 and −〈u′∇hk′〉¯, which represents the net effect of −〈u′∇hk′〉 on ENSO. The brown and green colors represent positive contributions to negative and positive precipitation anomalies, respectively. Black and blue dashed lines denote corresponding zonal wind anomalies and zonal gradient of anomalous specific humidity (∂xq′; 10−6 g kg−1 m−1), respectively.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

6. Impacts of the ENSO-driven precipitation diversity

ENSO modulates global climate through atmospheric teleconnections excited by equatorial convective heating anomalies (Horel and Wallace 1981; Trenberth et al. 1998). Here, we investigate the impacts of the diversity of ENSO-driven precipitation anomalies on the Walker circulation and low-level circulation anomalies over the western North Pacific during ENSO mature winter.

ENSO-driven anomalous Walker circulation shows a zonal dipole pattern in the 200-hPa velocity potential plots for all the three types of ENSO (Fig. 11). The eastern poles of the anomalous Walker circulations, the ascending (descending) branch of El Niño (La Niña), are centered around the extrema of corresponding ENSO-driven precipitation anomalies (Figs. 2d–f and 11). The eastern pole of EP El Niño composite is located to the east of those of the CP El Niño and the La Niña composites by about 25° longitude. Correspondingly, the western pole of the EP El Niño composite is also located to the east of the latter only by about 10° longitude.

Fig. 11.
Fig. 11.

Spatial distribution of NDJ-mean 200-hPa velocity potential anomalies (shading; 106 m2 s−1) and NDJ-mean climatological velocity potential (contours) for the (a) EP El Niño, (b) CP El Niño, and (c) La Niña composites. Yellow dots denote the position of their extrema. Values reaching 5% significance level are dotted in white.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

During El Niño mature winter, the western North Pacific is dominated by an anomalous anticyclone, which is referred to as WNPAC (Zhang et al. 1996; Wang et al. 2000; Li et al. 2017). The zonal position of the WNPAC is associated with the extent of westward extension of the cyclonic anomalies excited by the positive precipitation anomalies over the equatorial central-eastern Pacific (Figs. 9a,b and 12) (Wu et al. 2010, 2017b; Wang et al. 2019). During CP El Niño, because of the westward shift of the positive precipitation anomalies relative to EP El Niño, the cyclonic anomalies are shifted westward remarkably, which push the WNPAC westward from the Philippine Sea to the South China Sea (Figs. 9b and 12b). Similarly, the western North Pacific anomalous cyclone (WNPC) during La Niña is shifted westward relative to the WNPAC during El Niño (Fig. 12), which has a contribution to the asymmetric decaying rate between El Niño and La Niña (Wu et al. 2010).

Fig. 12.
Fig. 12.

Spatial distribution of NDJ-mean 925-hPa streamfunction anomalies (shading; 106 m2 s−1) for the (a) EP El Niño, (b) CP El Niño, and (c) La Niña composites. Yellow dots denote the position of their extrema. Values reaching the 5% significance level are dotted in white.

Citation: Journal of Climate 35, 3; 10.1175/JCLI-D-21-0363.1

7. Summary

This study explores the mechanisms for the diversity of zonal positions of precipitation anomalies over the equatorial central-eastern Pacific in boreal winter among EP El Niño, CP El Niño and La Niña. The ENSO-driven precipitation anomalies are dominated by the vertical advection of the climatological moisture by vertical motion anomalies in terms of the moisture budget analysis. The vertical motion anomalies associated with the precipitation anomalies are decomposed into deep and shallow modes, which are understood through the MSE budget analysis and the LN-like model, respectively. The major conclusions are summarized as follows.

First, the center of positive precipitation anomalies over the equatorial central-eastern Pacific in the EP El Niño composite is located 30° longitude east of the negative center of the La Niña composite and the positive center of the CP El Niño composite. The minimum of negative precipitation anomalies over the equatorial central Pacific during La Niña located to the west of the maximum of positive precipitation anomalies during CP El Niño. The magnitudes of the precipitation anomalies are contributed by both the deep- and shallow-mode vertical motion anomalies, while their longitudinal positions are more determined by the latter.

Second, the shallow-mode vertical motion anomalies and corresponding equatorial boundary layer convergence anomalies are forced by the meridional gradients of underlying ENSO SSTAs antisymmetric about the equator. For all the three types of ENSO, the boundary layer convergence anomalies over the equatorial central-eastern Pacific can be reproduced by a simple theoretical model similar with the classical Lindzen–Nigam model, in which only observed ENSO SSTAs are specified. The diversity of the ENSO-driven precipitation anomalies arises primarily from the diversity in the meridional as well as zonal distribution of ENSO SSTAs.

Third, the ENSO-driven deep-mode vertical motion anomalies are associated with four physical processes in terms of their contributions to the MSE budget, including: 1) “convection–cloud–longwave radiation” feedback, 2) vertical advection of the climatological MSE by the shallow-mode vertical motion anomalies, 3) “wind-induced moist enthalpy advection” feedback, and 4) SST-induced latent heat flux anomalies. The convection–cloud–longwave radiation feedback is an inherent internal positive feedback in the tropical atmosphere with deep convection, and thus is just an amplifier of the precipitation anomalies induced by other forcing factors. The wind-induced moist enthalpy advection feedback represents that the equatorial zonal wind anomalies excited by the ENSO-driven convective heating anomalies cause anomalous advections of the climatological moist enthalpy, for the remarkable zonal gradient of the mean moisture over the equatorial central Pacific, transition zone between the warm pool and the cold tongue. The anomalous moist enthalpy advection tends to amplify the precipitation anomalies and pull them westward. However, because of the constraint of the distribution of the climatological moisture, the anomalous advections show less diversity in the zonal positions. The latent heat flux anomalies induced by the ENSO SSTAs through the Newtonian damping effect is another forcing factor for the ENSO-driven precipitation anomalies in addition to the boundary layer convergence anomalies induced by the SSTA gradients. The SSTAs-induced latent heat flux anomalies also show less diversity because of the constraint of the zonal distribution of the climatological latent heat flux.

Fourth, the nonlinear zonal advection of anomalous moist enthalpy by wind anomalies amplifies the asymmetry in zonal positions of the extrema of precipitation anomalies between the La Niña and CP El Niño. This term shows a west-negative east-positive distribution over the equatorial central-eastern Pacific for both La Niña and CP El Niño, and thus tends to displace former negative precipitation anomalies westward and latter positive precipitation anomalies eastward.

Finally, the diversity of ENSO-driven precipitation anomalies causes the differences in the variation of the Walker circulation and low-level anomalous cyclone/anticyclone (WNPAC/WNPC) over the western North Pacific among the different types of ENSO. The anomalous Walker circulation in EP El Niño is located to the east of that in CP El Niño and the La Niña. The center of the WNPAC in EP El Niño is located over the Philippine Sea, while the centers of the WNPAC in CP El Niño and the WNPC in La Niña are shifted westward to the South China Sea.

Acknowledgments.

This study is supported by National Key Research and Development Program of China (Grant 2018YFA0606300), the NSFC (Grants 42088101 and 42075163), and the NSF (Grant AGS-20-06553). This is IPRC Publication Number 1544 and SOEST Publication Number 11427.

REFERENCES

  • Adames, Á. F., and J. M. Wallace, 2017: On the tropical atmospheric signature of El Niño. J. Atmos. Sci., 74, 1923–1939, https://doi.org/10.1175/JAS-D-16-0309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Adler, R. F., and Coauthors, 2003: The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • An, S., E. Tziperman, Y. M. Okumura, and T. Li, 2020: ENSO irregularity and asymmetry. El Niño–Southern Oscillation in a Changing Climate, Geophys. Monogr., Vol. 253, Amer. Geophys. Union, 153–172, https://doi.org/10.1002/9781119548164.ch7.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, https://doi.org/10.1029/2006JC003798.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Back, L. E., and C. S. Bretherton, 2006: Geographic variability in the export of moist static energy and vertical motion profiles in the tropical Pacific. Geophys. Res. Lett., 33, L17810, https://doi.org/10.1029/2006GL026672.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Back, L. E., and C. S. Bretherton, 2009a: A simple model of climatological rainfall and vertical motion patterns over the tropical oceans. J. Climate, 22, 6477–6497, https://doi.org/10.1175/2009JCLI2393.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Back, L. E., and C. S. Bretherton, 2009b: On the relationship between SST gradients, boundary layer winds, and convergence over the tropical oceans. J. Climate, 22, 4182–4196, https://doi.org/10.1175/2009JCLI2392.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bayr, T., D. Dommenget, T. Martin, and S. B. Power, 2014: The eastward shift of the Walker Circulation in response to global warming and its relationship to ENSO variability. Climate Dyn., 43, 2747–2763, https://doi.org/10.1007/s00382-014-2091-y.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bayr, T., M. Latif, D. Dommenget, C. Wengel, J. Harlaß, and W. Park, 2018: Mean-state dependence of ENSO atmospheric feedbacks in climate models. Climate Dyn., 50, 3171–3194, https://doi.org/10.1007/s00382-017-3799-2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163–172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Boutle, I., R. H. S. Taylor, and R. A. Römer, 2007: El Niño and the delayed action oscillator. Amer. J. Phys., 75, 15–24, https://doi.org/10.1119/1.2358155.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bretherton, C. S., and A. H. Sobel, 2002: A simple model of a convectively coupled Walker Circulation using the weak temperature gradient approximation. J. Climate, 15, 2907–2920, https://doi.org/10.1175/1520-0442(2002)015<2907:ASMOAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Capotondi, A., and Coauthors, 2015: Understanding ENSO diversity. Bull. Amer. Meteor. Soc., 96, 921–938, https://doi.org/10.1175/BAMS-D-13-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chen, M., T. Li, X. Shen, and B. Wu, 2016: Relative roles of dynamic and thermodynamic processes in causing evolution asymmetry between El Niño and La Niña. J. Climate, 29, 2201–2220, https://doi.org/10.1175/JCLI-D-15-0547.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chen, M., T. Li, X. Shen, and X. Wang, 2019: Asymmetry of atmospheric responses to two-type El Niño and La Niña over Northwest Pacific. J. Meteor. Res., 33, 826–836, https://doi.org/10.1007/s13351-019-9022-0.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chiang, J. C. H., S. E. Zebiak, and M. A. Cane, 2001: Relative roles of elevated heating and surface temperature gradients in driving anomalous surface winds over tropical oceans. J. Atmos. Sci., 58, 1371–1394, https://doi.org/10.1175/1520-0469(2001)058<1371:RROEHA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chung, P. H., and T. Li, 2013: Interdecadal relationship between the mean state and El Niño types. J. Climate, 26, 361–379, https://doi.org/10.1175/JCLI-D-12-00106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Clarke, A. J., 2014: El Niño physics and El Niño predictability. Annu. Rev. Mar. Sci., 6, 79–99, https://doi.org/10.1146/annurev-marine-010213-135026.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Deser, C., M. A. Alexander, S.-P. Xie, and A. S. Phillips, 2010: Sea surface temperature variability: Patterns and mechanisms. Annu. Rev. Mar. Sci., 2, 115–143, https://doi.org/10.1146/annurev-marine-120408-151453.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Fang, X., and R. Xie, 2020: A brief review of ENSO theories and prediction. Sci. China Earth Sci., 63, 476–491, https://doi.org/10.1007/s11430-019-9539-0.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Feng, J., W. Chen, and Y. Li, 2017: Asymmetry of the winter extra-tropical teleconnections in the Northern Hemisphere associated with two types of ENSO. Climate Dyn., 48, 2135–2151, https://doi.org/10.1007/s00382-016-3196-2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ham, Y. G., J. S. Kug, and I. S. Kang, 2007: Role of moist energy advection in formulating anomalous Walker Circulation associated with El Niño. J. Geophys. Res., 112, D24105, https://doi.org/10.1029/2007JD008744.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Harrison, D. E., and G. A. Vecchi, 1999: On the termination of El Niño. Geophys. Res. Lett., 26, 1593–1596, https://doi.org/10.1029/1999GL900316.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hoerling, M. P., A. Kumar, and M. Zhong, 1997: El Niño, La Niña, and the nonlinearity of their teleconnections. J. Climate, 10, 1769–1786, https://doi.org/10.1175/1520-0442(1997)010<1769:ENOLNA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev., 109, 813–829, https://doi.org/10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kang, I. S., and J. S. Kug, 2002: EI Niño and La Niña sea surface temperature anomalies: Asymmetry characteristics associated with their wind stress anomalies. J. Geophys. Res., 107, 4372, https://doi.org/10.1029/2001JD000393.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kao, H. Y., and J. Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Climate, 22, 615–632, https://doi.org/10.1175/2008JCLI2309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kim, H. M., P. J. Webster, and J. A. Curry, 2009: Impact of shifting patterns of Pacific Ocean warming on North Atlantic tropical cyclones. Science, 325, 77–80, https://doi.org/10.1126/science.1174062.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kim, S., and J. S. Kug, 2018: What controls ENSO teleconnection to East Asia? Role of western North Pacific precipitation in ENSO teleconnection to East Asia. J. Geophys. Res. Atmos., 123, 10 406–10 422, https://doi.org/10.1029/2018JD028935.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kug, J. S., F. F. Jin, and S. Il An, 2009: Two types of El Niño events: Cold tongue El Niño and warm pool El Niño. J. Climate, 22, 1499–1515, https://doi.org/10.1175/2008JCLI2624.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Larkin, N. K., and D. E. Harrison, 2005a: On the definition of El Niño and associated seasonal average U.S. weather anomalies. Geophys. Res. Lett., 32, L13705, https://doi.org/10.1029/2005GL022738.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Larkin, N. K., and D. E. Harrison, 2005b: Global seasonal temperature and precipitation anomalies during El Niño autumn and winter. Geophys. Res. Lett., 32, L16705, https://doi.org/10.1029/2005GL022860.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Li, T., 1997: Phase transition of the El Niño–Southern Oscillation: A stationary SST mode. J. Atmos. Sci., 54, 2872–2887, https://doi.org/10.1175/1520-0469(1997)054<2872:PTOTEN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Li, T., B. Wang, B. Wu, T. Zhou, C. P. Chang, and R. Zhang, 2017: Theories on formation of an anomalous anticyclone in western North Pacific during El Niño: A review. J. Meteor. Res., 31, 987–1006, https://doi.org/10.1007/s13351-017-7147-6.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lindzen, R. S., and S. Nigam, 1987: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 2418–2436, https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McGregor, S., N. Ramesh, P. Spence, M. H. England, M. J. McPhaden, and A. Santoso, 2013: Meridional movement of wind anomalies during ENSO events and their role in event termination. Geophys. Res. Lett., 40, 749–754, https://doi.org/10.1002/grl.50136.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McPhaden, M. J., and X. Zhang, 2009: Asymmetry in zonal phase propagation of ENSO sea surface temperature anomalies. Geophys. Res. Lett., 36, L13703, https://doi.org/10.1029/2009GL038774.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McPhaden, M. J., T. Lee, and D. McClurg, 2011: El Niño and its relationship to changing background conditions in the tropical Pacific Ocean. Geophys. Res. Lett., 38, L15709, https://doi.org/10.1029/2011GL048275.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Neelin, J. D., 2007: Moist dynamics of tropical convection zones in monsoons, teleconnections, and global warming. The Global Circulation of the Atmosphere, T. Schneider and A. H. Sobel, Eds., Princeton University Press, 267–301.

    • Search Google Scholar
    • Export Citation

  • Neelin, J. D., and I. M. Held, 1987: Modeling tropical convergence based on the moist static energy budget. Mon. Wea. Rev., 115, 3–12, https://doi.org/10.1175/1520-0493(1987)115<0003:MTCBOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Neelin, J. D., and H. Su, 2005: Moist teleconnection mechanisms for the tropical South American and Atlantic sector. J. Climate, 18, 3928–3950, https://doi.org/10.1175/JCLI3517.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Neelin, J. D., D. S. Battisti, A. C. Hirst, F.-F. Jin, Y. Wakata, T. Yamagata, and S. E. Zebiak, 1998: ENSO theory. J. Geophys. Res., 103, 14 261–14 290, https://doi.org/10.1029/97JC03424.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ohba, M., and H. Ueda, 2009: Role of nonlinear atmospheric response to SST on the asymmetric transition process of ENSO. J. Climate, 22, 177–192, https://doi.org/10.1175/2008JCLI2334.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Okumura, Y. M., 2019: ENSO diversity from an atmospheric perspective. Curr. Climate Change Rep., 5, 245–257, https://doi.org/10.1007/s40641-019-00138-7.

    • Search Google Scholar
    • Export Citation

  • Okumura, Y. M., and C. Deser, 2010: Asymmetry in the duration of El Niño and la Niña. J. Climate, 23, 5826–5843, https://doi.org/10.1175/2010JCLI3592.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Okumura, Y. M., M. Ohba, C. Deser, and H. Ueda, 2011: A proposed mechanism for the asymmetric duration of El Niño and La Niña. J. Climate, 24, 3822–3829, https://doi.org/10.1175/2011JCLI3999.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sobel, A. H., J. Nilsson, and L. M. Polvani, 2001: The weak temperature gradient approximation and balanced tropical moisture waves. J. Atmos. Sci., 58, 3650–3665, https://doi.org/10.1175/1520-0469(2001)058<3650:TWTGAA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stevens, B., J. Duan, J. C. McWilliams, M. Münnich, and J. D. Neelin, 2002: Entrainment, Rayleigh friction, and boundary layer winds over the tropical Pacific. J. Climate, 15, 30–44, https://doi.org/10.1175/1520-0442(2002)015<0030:ERFABL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Su, H., and J. D. Neelin, 2002: Teleconnection mechanisms for tropical Pacific descent anomalies during El Niño. J. Atmos. Sci., 59, 2694–2712, https://doi.org/10.1175/1520-0469(2002)059<2694:TMFTPD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Takahashi, K., and B. Dewitte, 2016: Strong and moderate nonlinear El Niño regimes. Climate Dyn., 46, 1627–1645, https://doi.org/10.1007/s00382-015-2665-3.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Timmermann, A., and Coauthors, 2018: El Niño–Southern Oscillation complexity. Nature, 559, 535–545, https://doi.org/10.1038/s41586-018-0252-6.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., G. W. Branstator, D. Karoly, A. Kumar, N.-C. Lau, and C. Ropelewski, 1998: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J. Geophys. Res., 103, 14 291–14 324, https://doi.org/10.1029/97JC01444.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, B., R. Wu, and X. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 1517–1536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, C., 2018: A review of ENSO theories. Natl. Sci. Rev., 5, 813–825, https://doi.org/10.1093/nsr/nwy104.

  • Wang, L., W. Chen, and R. Huang, 2008: Interdecadal modulation of PDO on the impact of ENSO on the East Asian winter monsoon. Geophys. Res. Lett., 35, L20702, https://doi.org/10.1029/2008GL035287.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, X., T. Li, and M. Chen, 2019: Mechanism for asymmetric atmospheric responses in the western North Pacific to El Niño and La Niña. Climate Dyn., 53, 3957–3969, https://doi.org/10.1007/s00382-019-04767-4.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Weng, H., K. Ashok, S. K. Behera, S. A. Rao, and T. Yamagata, 2007: Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific rim during boreal summer. Climate Dyn., 29, 113–129, https://doi.org/10.1007/s00382-007-0234-0.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, B., T. Li, and T. Zhou, 2010: Asymmetry of atmospheric circulation anomalies over the western North Pacific between El Niño and La Niña. J. Climate, 23, 4807–4822, https://doi.org/10.1175/2010JCLI3222.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, B., T. Zhou, and T. Li, 2017a: Atmospheric dynamic and thermodynamic processes driving the western North Pacific anomalous anticyclone during El Niño. Part I: Maintenance mechanisms. J. Climate, 30, 9621–9635, https://doi.org/10.1175/JCLI-D-16-0489.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, B., T. Zhou, and T. Li, 2017b: Atmospheric dynamic and thermodynamic processes driving the western North Pacific anomalous anticyclone during El Niño. Part II: Formation processes. J. Climate, 30, 9637–9650, https://doi.org/10.1175/JCLI-D-16-0495.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xiang, B., B. Wang, and T. Li, 2013: A new paradigm for the predominance of standing central Pacific warming after the late 1990s. Climate Dyn., 41, 327–340, https://doi.org/10.1007/s00382-012-1427-8.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, S. P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global warming pattern formation: Sea surface temperature and rainfall. J. Climate, 23, 966–986, https://doi.org/10.1175/2009JCLI3329.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Yan, Z., B. Wu, T. Li, M. Collins, R. Clark, T. Zhou, J. Murphy, and G. Tan, 2020: Eastward shift and extension of ENSO-induced tropical precipitation anomalies under global warming. Sci. Adv., 6, eaax4177, https://doi.org/10.1126/sciadv.aax4177.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Yeh, S. W., J. S. Kug, B. Dewitte, M. H. Kwon, B. P. Kirtman, and F. F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511–514, https://doi.org/10.1038/nature08316.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Yeh, S. W., J. S. Kug, and S. Il An, 2014: Recent progress on two types of El Niño: Observations, dynamics, and future changes. Asia-Pac. J. Atmos. Sci., 50, 69–81, https://doi.org/10.1007/s13143-014-0028-3.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Yeh, S. W., and Coauthors, 2018: ENSO atmospheric teleconnections and their response to greenhouse gas forcing. Rev. Geophys., 56, 185–206, https://doi.org/10.1002/2017RG000568.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, R., A. Sumi, and M. Kimoto, 1996: Impact of El Niño on the East Asian monsoon: A diagnostic study of the ‘86/87 and ‘91/92 events. J. Meteor. Soc. Japan, 74, 49–62, https://doi.org/10.2151/jmsj1965.74.1_49.

    • Crossref
    • Search Google Scholar
    • Export Citation
Save
Cover Journal of Climate
Journal of Climate