1. Introduction
The western North Pacific anomalous anticyclone (WNPAC), which is also referred to as the Philippine Sea anomalous anticyclone or anomalous northwest Pacific anticyclone, is a core research object in studies of the relationship between El Niño–Southern Oscillation (ENSO) and the East Asian–western North Pacific (EA–WNP) monsoon [see reviews by Wang and Li (2004), Li and Wang (2005), and Zhou et al. (2014a)]. The WNPAC not only influences the EA–WNP winter (Zhang et al. 1996, 1999; Wang et al. 2000; Lau and Nath 2000) and summer (Chang et al. 2000a,b; Yang et al. 2007; Li et al. 2008; Xie et al. 2009; Wu et al. 2009, 2010a; Wang et al. 2013) monsoon, but also contributes to the decay of El Niño (Weisberg and Wang 1997a,b; Wang et al. 1999; Wang et al. 2001; Kug et al. 2006; Li et al. 2007; Ohba and Ueda 2009; Wu et al. 2010b; Okumura et al. 2011; Chen et al. 2016).
The life cycle of the WNPAC is tightly linked with both the phase of El Niño and the annual cycle of the tropical climate. It forms during El Niño–developing fall, fully establishes during El Niño mature winter, maintains during the following spring and summer, and decays after that (e.g., Wang et al. 2000; Wang and Zhang 2002; Wang et al. 2003). At present, it is widely accepted that the maintenance mechanisms responsible for the WNPAC during El Niño mature winter and the following spring are distinct from those for the WNPAC during El Niño decaying summer (Yang et al. 2007; Xie et al. 2009; Wu et al. 2009, 2010b; Wang et al. 2013). However, at present, the WNPAC maintenance mechanisms during winter and spring remain inconclusive.
The temporal evolutions of El Niño–related sea surface temperature anomalies (SSTAs) and the corresponding precipitation and low-level circulation anomalies, which were obtained through regressions against the December (year 0)–February (year 1) [D(0)JF(1)] mean Niño-3.4 index (area-averaged SSTAs over 5°S–5°N, 120°–170°W), are shown in Fig. 1. We used years 0 and 1 to represent El Niño developing and decaying years, respectively. As noted in previous studies (e.g., Rasmusson and Carpenter 1982), typical El Niño events are fully established during boreal summer (Fig. 1b), with warm SSTAs dominating the equatorial central-eastern Pacific (CEP). The warm SSTAs further strengthen in fall, reach a maximum in winter, and then gradually weaken in spring (Figs. 1b,d,f,h). The cold SSTAs in the western North Pacific (WNP) establish in June–August (year 0) [JJA(0)] and are maintained in the following three seasons (Figs. 1b,d,f,h).
Seasonal mean precipitation (shading; mm day−1) and 925-hPa streamfunction anomalies (contours; 106 m2 s−1) regressed against the D(0)JF(1)-mean Niño-3.4 index for (a) JJA(0), (c) SON(0), (e) D(0)JF(1), and (g) MAM(1). The interval of the contours is 0.3. (b),(d),(f),(h) As in (a),(c),(e),(g), but for SST anomalies (K). For the precipitation and SST anomalies, only values reaching the 5% significance level are shown. Red dashed box in (e) [(g)] denotes the position of negative precipitation anomalies over the tropical WNP during El Niño mature winter (decaying spring). The location of the red dashed boxes is 1°–14°N, 125°–160°E.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Seasonal mean precipitation (shading; mm day−1) and 925-hPa streamfunction anomalies (contours; 106 m2 s−1) regressed against the D(0)JF(1)-mean Niño-3.4 index for (a) JJA(0), (c) SON(0), (e) D(0)JF(1), and (g) MAM(1). The interval of the contours is 0.3. (b),(d),(f),(h) As in (a),(c),(e),(g), but for SST anomalies (K). For the precipitation and SST anomalies, only values reaching the 5% significance level are shown. Red dashed box in (e) [(g)] denotes the position of negative precipitation anomalies over the tropical WNP during El Niño mature winter (decaying spring). The location of the red dashed boxes is 1°–14°N, 125°–160°E.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Seasonal mean precipitation (shading; mm day−1) and 925-hPa streamfunction anomalies (contours; 106 m2 s−1) regressed against the D(0)JF(1)-mean Niño-3.4 index for (a) JJA(0), (c) SON(0), (e) D(0)JF(1), and (g) MAM(1). The interval of the contours is 0.3. (b),(d),(f),(h) As in (a),(c),(e),(g), but for SST anomalies (K). For the precipitation and SST anomalies, only values reaching the 5% significance level are shown. Red dashed box in (e) [(g)] denotes the position of negative precipitation anomalies over the tropical WNP during El Niño mature winter (decaying spring). The location of the red dashed boxes is 1°–14°N, 125°–160°E.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
From JJA(0) to D(0)JF(1), although the cold SSTAs in the tropical WNP generally tend to intensify, local precipitation and circulation anomalies experience remarkable changes in their spatial patterns. In JJA(0), the tropical WNP is dominated by positive precipitation and cyclonic anomalies (Fig. 1a). In September–November (year 0) [SON(0)], the positive precipitation anomalies and the center of the cyclonic anomalies shift eastward and negative precipitation anomalies are seen over the east of the Philippine Sea (Fig. 1c). In D(0)JF(1), the negative precipitation anomalies dominate the entire tropical WNP and the WNPAC fully establishes (Fig. 1e). The negative precipitation anomalies and WNPAC are maintained during the following spring (Fig. 1g).
Wang et al. (2000, 2003) proposed that the persistence of the WNPAC relies on the local wind–evaporation–SST feedback. The northeasterly anomalies to the southeastern flank of the WNPAC strengthen the climatological northeasterly trade wind and thus enhance evaporation and cool the SST in situ. Lau and Nath (2003) conducted numerical experiments to support this mechanism. On the contrary, Stuecker et al. (2015) proposed that the life cycle of the WNPAC does not rely on the local air–sea interactions in the WNP, but instead on interactions between El Niño and the annual cycle. They designed idealized SST boundary conditions to drive an atmospheric general circulation model (AGCM), in which the SSTAs were only specified in the equatorial CEP. The model reproduced the major life cycle of the WNPAC. However, the detailed dynamic processes causing the formation and maintenance of the WNPAC were not studied in Stuecker et al. (2015).
As the spatial pattern of the SSTAs from JJA(0) to MAM(1) does not change greatly, it is conceivable that the tight seasonal phase locking of the WNPAC is caused by the change in the background climatological state. However, what seasonal change in the climatological state plays a fundamental role and through what dynamic or thermodynamic processes the background state modulates the precipitation and circulation anomalies remain unknown.
This study consists of two parts. In this paper (Part I), we aim to answer the following question: what are the mechanisms responsible for the persistence of the WNPAC during El Niño mature winter and the following spring?
The remaining sections of this paper are arranged as follows. Section 2 introduces the observational and reanalysis datasets, analysis methods, and numerical models used in the study. Section 3 explores the WNPAC maintenance mechanisms during El Niño mature winter. Section 4 investigates whether the mechanisms proposed in section 3 work in the following spring. Section 5 discusses some uncertain issues and summarizes the major conclusions.
2. Datasets, methods, and models
a. Datasets
The datasets used in this study were 1) monthly precipitation data from the Global Precipitation Climatology Project (GPCP; Adler et al. 2003); 2) monthly SST data from the Met Office Hadley Centre Sea Ice and SST dataset (HadISST; Rayner et al. 2003); and 3) 6-hourly wind, geopotential height, temperature, specific humidity, and surface heat fluxes from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011). The horizontal resolutions of the GPCP, HadISST, and ERA-Interim datasets were 2.5° × 2.5°, 1° × 1°, and 1.5° × 1.5°, respectively. All the datasets cover the period 1979–2012.
b. Methods
Vertical profiles of December–May-mean ω′ (blue line; 10−2 Pa s−1) and climatological MSE (red line; 103 J kg−1) averaged over 1°–14°N, 125°–160°E (red dashed box in Figs. 1e,g).
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Vertical profiles of December–May-mean ω′ (blue line; 10−2 Pa s−1) and climatological MSE (red line; 103 J kg−1) averaged over 1°–14°N, 125°–160°E (red dashed box in Figs. 1e,g).
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Vertical profiles of December–May-mean ω′ (blue line; 10−2 Pa s−1) and climatological MSE (red line; 103 J kg−1) averaged over 1°–14°N, 125°–160°E (red dashed box in Figs. 1e,g).
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
c. Models
A state-of-the-art coupled global climate model (CGCM) FGOALS-s2 was used in this study. The model was developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS) (Bao et al. 2013; Zhou et al. 2014b). Its four components—atmosphere, land, ocean and sea ice—are coupled together by the CCSM3.0 coupler, version 6.0, developed in the National Center for Atmospheric Research (NCAR) (Collins et al. 2006). The atmospheric component is Spectral Atmosphere Model of IAP LASG, version 2 (SAMIL2), with the horizontal resolution of about 2.81° longitude × 1.66° latitude and 26 levels in the vertical direction (Bao et al. 2013). The ocean component is LASG IAP Climate System Ocean Model, version 2 (LICOM2), with a horizontal resolution of about 1° × 1° in the extratropical zone and 0.5° × 0.5° in the tropics and 30 levels in the vertical direction (Liu et al. 2012). Evaluation on the performances of CMIP5 models shows that the FGOALS-s2 is one of the best models in simulating the WNPAC, with amplitude of the simulated WNPAC comparable with that in the observation (Wu and Zhou 2016).
3. WNPAC during El Niño mature winter
a. Observational analysis
In the observations, the WNPAC is tightly linked to the local negative precipitation anomalies (Fig. 1e). In terms of the Gill model, tropical circulation anomalies can be understood as passive responses to specified diabatic heating anomalies (Gill 1980). Hence, we focused on the processes causing the negative precipitation anomalies over the tropical WNP.
Moisture budget analysis indicated that the negative precipitation anomalies over the tropical WNP are caused by negative anomalous advection of the climatological vertical moisture by descending anomalies 
Budget analysis of the (a) moisture (mm day−1) and (b) MSE (W m−2) equations for the area 1°–14°N, 125°–160°E (red dashed box in Fig. 1e). Each term is regressed against the D(0)JF(1)-mean Niño-3.4 index. Detailed introductions of Eqs. (2) and (5) are given in section 2b.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Budget analysis of the (a) moisture (mm day−1) and (b) MSE (W m−2) equations for the area 1°–14°N, 125°–160°E (red dashed box in Fig. 1e). Each term is regressed against the D(0)JF(1)-mean Niño-3.4 index. Detailed introductions of Eqs. (2) and (5) are given in section 2b.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Budget analysis of the (a) moisture (mm day−1) and (b) MSE (W m−2) equations for the area 1°–14°N, 125°–160°E (red dashed box in Fig. 1e). Each term is regressed against the D(0)JF(1)-mean Niño-3.4 index. Detailed introductions of Eqs. (2) and (5) are given in section 2b.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
1) Net MSE flux
In terms of the MSE budget analysis, the negative net MSE flux Fnet had the largest contribution to the anomalous descending motion over the tropical WNP (Fig. 3b). We show below that the negative Fnet is generated by a local atmospheric internal positive feedback and thus cannot be the mechanistic root of the negative precipitation anomalies over the tropical WNP and the WNPAC.
Figure 4 shows the spatial patterns of 
(a) D(0)JF(1)-mean net energy flux into the atmosphere regressed against the D(0)JF(1)-mean Niño-3.4 index. (b),(c) As in (a), but for the cloud longwave radiation and latent heat flux, respectively (W m−2). Values reaching the 5% significance level are stippled in white. The location of the black dashed box is the same as that of the red dashed box in Figs. 1e,g.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
(a) D(0)JF(1)-mean net energy flux into the atmosphere regressed against the D(0)JF(1)-mean Niño-3.4 index. (b),(c) As in (a), but for the cloud longwave radiation and latent heat flux, respectively (W m−2). Values reaching the 5% significance level are stippled in white. The location of the black dashed box is the same as that of the red dashed box in Figs. 1e,g.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
(a) D(0)JF(1)-mean net energy flux into the atmosphere regressed against the D(0)JF(1)-mean Niño-3.4 index. (b),(c) As in (a), but for the cloud longwave radiation and latent heat flux, respectively (W m−2). Values reaching the 5% significance level are stippled in white. The location of the black dashed box is the same as that of the red dashed box in Figs. 1e,g.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
The above analysis indicates that in the tropical WNP, the negative net MSE flux anomalies are primarily contributed by the weakened longwave cloud radiative forcing anomalies induced by the internal positive feedback with the suppressed convection, while the latent heat flux anomalies have no contribution. The situation is distinct from the equatorial CEP, where El Niño–related warm SSTAs drive the atmosphere by increasing the upward latent heat flux, although internal positive feedback due to longwave cloud radiative forcing is still very important (Su and Neelin 2002; also seen in Figs. 4b,c).
2) Horizontal advection of climatological moist enthalpy by anomalous wind
The 
(a) D(0)JF(1)-mean 925-hPa climatological specific humidity (shading; g kg−1) and anomalous wind regressed against the D(0)JF(1)-mean Niño-3.4 index (vectors; m s−1). (b) Horizontal advection of the climatological specific humidity by the anomalous wind, which is converted to the energy unit (W m−2). Values reaching the 5% significance level are stippled in white. The location of the black box is same as that of the red dashed box in Figs. 1e,g.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
(a) D(0)JF(1)-mean 925-hPa climatological specific humidity (shading; g kg−1) and anomalous wind regressed against the D(0)JF(1)-mean Niño-3.4 index (vectors; m s−1). (b) Horizontal advection of the climatological specific humidity by the anomalous wind, which is converted to the energy unit (W m−2). Values reaching the 5% significance level are stippled in white. The location of the black box is same as that of the red dashed box in Figs. 1e,g.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
(a) D(0)JF(1)-mean 925-hPa climatological specific humidity (shading; g kg−1) and anomalous wind regressed against the D(0)JF(1)-mean Niño-3.4 index (vectors; m s−1). (b) Horizontal advection of the climatological specific humidity by the anomalous wind, which is converted to the energy unit (W m−2). Values reaching the 5% significance level are stippled in white. The location of the black box is same as that of the red dashed box in Figs. 1e,g.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
The anomalous northerly wind component is part of both the cyclonic anomalies generated by the positive precipitation anomalies over the equatorial CEP and the WNPAC itself (Fig. 5a), which indicates that negative 
A similar mechanism associated with the 
3) Vertical advection of anomalous MSE by climatological vertical motion
Compared with the horizontal moist enthalpy advection, the 
Vertical profiles of DJF-mean climatological ω (blue line; 10−2 Pa s−1) and D(0)JF(1)-mean h′ (red solid line), s′ (=cpT′ + ϕ′; red dashed line), and Lυq′ (red dotted line) (103 J kg−1) averaged over 1°–14°N, 125°–160°E (red dashed box in Figs. 1e,g).
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Vertical profiles of DJF-mean climatological ω (blue line; 10−2 Pa s−1) and D(0)JF(1)-mean h′ (red solid line), s′ (=cpT′ + ϕ′; red dashed line), and Lυq′ (red dotted line) (103 J kg−1) averaged over 1°–14°N, 125°–160°E (red dashed box in Figs. 1e,g).
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Vertical profiles of DJF-mean climatological ω (blue line; 10−2 Pa s−1) and D(0)JF(1)-mean h′ (red solid line), s′ (=cpT′ + ϕ′; red dashed line), and Lυq′ (red dotted line) (103 J kg−1) averaged over 1°–14°N, 125°–160°E (red dashed box in Figs. 1e,g).
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
The vertical structure of h′ is dominated by the Lυq′ component, while the contribution from the anomalous dry static energy s′ is relatively small (Fig. 6). The moisture content changes in tropical atmospheric columns are associated with the following two processes: 1) In the boundary layer, q′ is primarily modulated by underlying SSTAs. The spatial pattern of the 925-hPa specific humidity anomalies resembles that of the SSTAs, with their pattern correlation reaching 0.81 (Figs. 7c,d). 2) In the lower free troposphere, q′ is constrained by the local anomalous vertical motions. The pattern correlation between 750-hPa specific humidity and 750-hPa ω for the entire tropics reaches −0.7 (Figs. 7a,b). This process is associated with another atmospheric internal positive feedback, known as the moisture–convection feedback [see review by Emanuel (2007)]. For the tropical WNP, large-scale descending motion anomalies associated with the negative precipitation anomalies cause decreases in the midlevel moisture, which in turn suppresses local convection through increasing gross moist stability. For the entire atmospheric column, the contribution of the SSTA-related negative q′ in the boundary layer to the 
(a) D(0)JF(1)-mean 750-hPa specific humidity anomalies regressed against the D(0)JF(1)-mean Niño-3.4 index (g kg−1). (b)–(d) As in (a), but for 750-hPa ω (10−2 Pa s−1), 925-hPa specific humidity, and SST anomalies (K), respectively. Values reaching the 5% significance level are stippled in white.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
(a) D(0)JF(1)-mean 750-hPa specific humidity anomalies regressed against the D(0)JF(1)-mean Niño-3.4 index (g kg−1). (b)–(d) As in (a), but for 750-hPa ω (10−2 Pa s−1), 925-hPa specific humidity, and SST anomalies (K), respectively. Values reaching the 5% significance level are stippled in white.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
(a) D(0)JF(1)-mean 750-hPa specific humidity anomalies regressed against the D(0)JF(1)-mean Niño-3.4 index (g kg−1). (b)–(d) As in (a), but for 750-hPa ω (10−2 Pa s−1), 925-hPa specific humidity, and SST anomalies (K), respectively. Values reaching the 5% significance level are stippled in white.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
The above analyses for individual terms on the right-hand side of Eq. (5) suggest that the negative precipitation anomalies over the tropical WNP are driven by local forcing of the cold SSTAs and remote forcing from the equatorial CEP. The remote forcing is the negative anomalous enthalpy advection by teleconnected northerly anomalies. For the local forcing, the cold SSTAs in the tropical WNP tend to suppress local convection through decreasing overlying moisture and thus increasing atmospheric gross moist stability. The negative precipitation anomalies are further amplified by the three positive feedbacks; that is, the convection–cloud radiative forcing, wind–moist enthalpy advection–convection, and moisture–convection feedbacks.
b. Numerical experiments
Observational analysis indicated that both the remote forcing from the equatorial CEP and local forcing are favorable for the negative precipitation anomalies over the tropical WNP and thus the WNPAC. However, it is impossible to separate the local and remote forcing cleanly in the observational analysis. Hence, we conducted a series of numerical experiments using the CGCM FGOALS-s2 to resolve this issue.
The three sets of designed experiments are listed in Table 1. The first experiment was a pacemaker-coupled simulation. The upper-700-m ocean temperature in the equatorial CEP was restored to the observational anomalies plus model climatology (black dashed triangles in Figs. 8b,d,f), while in the other ocean areas the oceanic and atmospheric components of the model were freely coupled (hereafter resCEP run). In the second experiment, the upper-700-m ocean temperature in the equatorial CEP was also restored to the observational anomalies plus model climatology, while in the other ocean areas, the ocean temperature was restored to the model climatology (hereafter resCEP_clmGLB run). The third experiment was similar to the resCEP_clmGLB run, but only ocean temperatures in the tropical WNP (green dashed quadrilateral in Fig. 8f) are restored to the model climatology. The atmospheric and oceanic components are freely coupled in other ocean areas, as in the resCEP run. This experiment is referred to as resCEP_clmWNP run. All the experiments covered the period of 1979–2012 and had three ensemble members. Three-member ensemble means were analyzed.
Experiments using the FGOALS-s2 model.
WNPAC and associated precipitation and SST anomalies during El Niño mature winter [D(0)JF(1)] simulated by the FGOALS-s2. (a),(b) As in Figs. 1e,f, but for the resCEP run. (c),(d) As in (a),(b), but for the resCEP_clmGLB run. (e),(f) As in (a),(b), but for the resCEP_clmWNP run. (g) The differences between (a) and (e).(h) The differences between (b) and (f). Red dashed box in (a) is used for the MSE budget analysis. The location of the red dashed box is 5°–15°N, 125°–165°E. Black dashed triangles in (b),(d),(f) denote the ocean areas, in which modeled upper-700-m ocean temperatures are restored to the observational anomalies plus the model climatology. Green dashed quadrilateral in (f) denotes the ocean areas in which upper-700-m ocean temperatures are restored to the model climatology.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
WNPAC and associated precipitation and SST anomalies during El Niño mature winter [D(0)JF(1)] simulated by the FGOALS-s2. (a),(b) As in Figs. 1e,f, but for the resCEP run. (c),(d) As in (a),(b), but for the resCEP_clmGLB run. (e),(f) As in (a),(b), but for the resCEP_clmWNP run. (g) The differences between (a) and (e).(h) The differences between (b) and (f). Red dashed box in (a) is used for the MSE budget analysis. The location of the red dashed box is 5°–15°N, 125°–165°E. Black dashed triangles in (b),(d),(f) denote the ocean areas, in which modeled upper-700-m ocean temperatures are restored to the observational anomalies plus the model climatology. Green dashed quadrilateral in (f) denotes the ocean areas in which upper-700-m ocean temperatures are restored to the model climatology.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
WNPAC and associated precipitation and SST anomalies during El Niño mature winter [D(0)JF(1)] simulated by the FGOALS-s2. (a),(b) As in Figs. 1e,f, but for the resCEP run. (c),(d) As in (a),(b), but for the resCEP_clmGLB run. (e),(f) As in (a),(b), but for the resCEP_clmWNP run. (g) The differences between (a) and (e).(h) The differences between (b) and (f). Red dashed box in (a) is used for the MSE budget analysis. The location of the red dashed box is 5°–15°N, 125°–165°E. Black dashed triangles in (b),(d),(f) denote the ocean areas, in which modeled upper-700-m ocean temperatures are restored to the observational anomalies plus the model climatology. Green dashed quadrilateral in (f) denotes the ocean areas in which upper-700-m ocean temperatures are restored to the model climatology.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
In the resCEP run, the atmospheric circulation anomalies over the tropical WNP during El Niño mature winter and the following spring were modulated by both the direct remote forcing from the equatorial CEP via the atmospheric bridge and local air–sea interactions. In the resCEP_clmGLB run, only the direct remote forcing from the equatorial CEP works. In the resCEP_clmWNP run, the direct remote forcing of El Niño still worked, while the air–sea interactions in the tropical WNP were explicitly suppressed. Hence, through the comparisons among the three runs, the relative contributions of the remote forcing and local air–sea interactions to the maintenance of the WNPAC can be separated.
The WNPAC and the negative precipitation anomalies over the tropical WNP were realistically reproduced by the resCEP run (Fig. 8a). Furthermore, the underlying cold SSTAs were also well reproduced, suggesting that the pacemaker experiment can capture the air–sea interactions in the tropical WNP (Fig. 8b). The MSE budget analysis for the resCEP run indicated that the modeled suppressed convection over the tropical WNP (5°–15°N, 125°–165°E; red dashed box in Fig. 8a) is associated with three terms: 
MSE budgets (W m−2) over the tropical WNP (5°–15°N, 125°–165°E, red dashed box in Fig. 8a) during El Niño mature winter for the resCEP and resCEP_clmGLB runs.
The resCEP_clmGLB run represents pure impacts of remote forcing from the equatorial CEP through an atmospheric bridge. The resCEP_clmGLB run reproduced the WNPAC (Fig. 8c). However, the intensity of the WNPAC in it was weaker than that in the resCEP run (Figs. 8a,c). The magnitude of area-averaged 925-hPa streamfunction anomalies over 0°–25°N, 105°–165°E in the resCEP_clmGLB run reached about 60% of that in the resCEP run (Figs. 8a,c).
On the other hand, the differences between the resCEP and resCEP_clmWNP runs represent the impacts of the cold SSTAs in the tropical WNP (Fig. 8g). The intensity of the WNPAC in Fig. 8g reaches about 43% of that in the resCEP run (Fig. 8a), indicating that the local air–sea interactions have a significant contribution to the WNPAC. In addition, the local air–sea interactions tend to stretch the center of the WNPAC eastward (Fig. 8g). It is worth noting that both the intensity and pattern of the WNPACs are very similar in the resCEP_clmGLB and resCEP_clmWNP runs (Figs. 8c,e; differences in 925-hPa streamfunction anomalies <5% over 0°–25°N, 105°–165°E), suggesting that SSTAs in other ocean areas, such as basinwide warming in the tropical Indian Ocean, have few contributions to the WNPAC, consistent with our previous studies (Wu et al. 2012; Chen et al. 2016).
Above results indicated that both the remote forcing from the equatorial CEP and the local air–sea interactions contribute to the maintenance of the WNPAC, with scale ratio of 6:4. Further comparisons among these experiments reveal some more interesting results.
First, the 
Second, cold SSTAs can contribute to the WNPAC through modulating the local latent heat flux. During El Niño mature winter, the WNP is dominated by the northeasterly trade wind. The northeasterly wind anomalies to the southeastern flank of the WNPAC strengthen the trade wind and thus tend to enhance the evaporation (Wang et al. 2000). However, this effect is greatly offset by the underlying cold SSTAs (cold SSTAs tend to suppress the evaporation). As a result, the latent heat flux anomalies are very weak in both the observation (Fig. 4c) and resCEP run (Fig. 9a). However, if the cold SSTAs were not generated, the evaporation would be greatly intensified by the enhanced wind speed. The corresponding positive latent heat flux anomalies tend to weaken the negative precipitation anomalies and WNPAC in terms of the Eq. (5). This phenomenon can be seen in the resCEP_clmWNP run (Fig. 9b). Because of the strong positive latent heat flux anomalies, even the 
(a) D(0)JF(1)-mean latent heat flux regressed against the D(0)JF(1)-mean Niño-3.4 index simulated by the resCEP run. (b) As in (a), but for the resCEP_clmWNP run. Values reaching the 5% significance level are stippled in white. The location of the black dashed box is the same as that of the red dashed box in Fig. 8a.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
(a) D(0)JF(1)-mean latent heat flux regressed against the D(0)JF(1)-mean Niño-3.4 index simulated by the resCEP run. (b) As in (a), but for the resCEP_clmWNP run. Values reaching the 5% significance level are stippled in white. The location of the black dashed box is the same as that of the red dashed box in Fig. 8a.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
(a) D(0)JF(1)-mean latent heat flux regressed against the D(0)JF(1)-mean Niño-3.4 index simulated by the resCEP run. (b) As in (a), but for the resCEP_clmWNP run. Values reaching the 5% significance level are stippled in white. The location of the black dashed box is the same as that of the red dashed box in Fig. 8a.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
4. WNPAC during El Niño decaying spring
During El Niño decaying spring, the negative precipitation anomalies over the tropical WNP and the WNPAC are still maintained, although their intensities weaken. Do the forcing mechanisms for El Niño mature winter work in the spring? We also conducted budget analyses of moisture and MSE for the spring (Fig. 10). The results were generally consistent with those for the preceding winter. The negative precipitation anomalies over the tropical WNP were caused by a negative 
The vertical structures of the area-averaged anomalous MSE and its s′ and Lυq′ subcomponents over the tropical WNP in MAM(1) resembles that in the preceding D(0)JF(1) (figure not shown). This suggests that both the surface cold SSTAs and atmospheric internal feedback between the anomalous downward motions and negative specific humidity anomalies in the lower free troposphere contribute to the negative 
As in D(0)JF(1), the magnitude of 
To further investigate the relative importance of the remote forcing from the equatorial CEP and the local air–sea interaction in the maintenance of the WNPAC in MAM(1), we compared the WNPAC in MAM(1) simulated by the resCEP, resCEP_clmGLB, and resCEP_clmWNP runs (Fig. 12). It was calculated that the magnitude of area-averaged 925-hPa streamfunction anomalies over 0°–25°N, 105°–165°E in the resCEP_clmGLB run reached about 73% of that in the resCEP run (Figs. 12a,c), indicating that the remote forcing from the equatorial CEP is a dominant factor for maintaining the WNPAC in MAM(1). On the other hand, the differences between the resCEP and resCEP_clmWNP runs represent the impacts of the local air–sea interactions. It is estimated that the relative contribution of the local air–sea interactions to the WNPAC in MAM(1) is about 42% (Fig. 12e). The sum of the contributions of the CEP and the WNP is greater than 1, suggesting that SSTAs in other ocean areas, such as the Indian Ocean, may have some negative contributions to the WNPAC.
5. Discussion and conclusions
a. Discussion
ENSO tends to reach its mature phase during boreal winter and decay in the following spring, with the Niño-3.4 index decreasing by about 45% (Figs. 1f,h). However, idealized numerical experiments indicated that the relative contribution of the remote forcing from the equatorial CEP to the WNPAC in El Niño decaying spring is larger than that in El Niño mature winter. It is speculated that the increased relative contribution is associated with stronger responses of low-level circulation anomalies to El Niño–related positive precipitation anomalies over the equatorial CEP. Though the positive precipitation anomalies over the equatorial CEP in El Niño decaying spring are weaker than that in the preceding winter (Figs. 1e,g), the intensity of the northern branch of the twin Rossby wave–like cyclonic anomalies over the central Pacific in MAM(1) is close to that in D(0)JF(1) (Figs. 1e,g). Because the magnitude of the meridional gradient of the climatological specific humidity in MAM is only slightly smaller than that in DJF, the magnitude of the 
The major bias of the resCEP run is that the magnitude of the 
The overestimation of the Fnet term is primarily caused by the underestimate of the longwave cloud radiative forcing anomalies. The magnitude of the 
b. Conclusions
The WNPAC plays a central role in linking the EA–WNP monsoon and El Niño [e.g., reviews by Wang and Li (2004) and Li and Wang (2005)]. However, the WNPAC maintenance mechanisms during El Niño mature winter and the following spring are still controversial (Li et al. 2016). In this study, we tried to resolve this issue through moisture and MSE budget analyses and idealized numerical experiments. The main conclusions are summarized as follows.
In terms of the Gill model, the WNPAC is a Rossby wave–like response to the negative precipitation anomalies over the tropical WNP. The moisture and MSE budget analyses indicated that the negative precipitation anomalies are stimulated by the combined effects of 1) the negative moist enthalpy advection anomalies of the northerly component to the western flank of the northern branch of the twin cyclonic anomalies, which are driven by the enhanced convection over the equatorial CEP (a schematic for the mechanism is shown in Fig. 13) and 2) increased gross moist stability of the dry anomalies in the planetary boundary layer driven by the underlying cold SSTAs.
Schematic of the anomalous moist enthalpy advection mechanism, which is the most essential forcing mechanism responsible for the maintenance of the WNPAC during El Niño mature winter [D(0)JF(1)] and the following spring [MAM(1)]. Warm SSTAs in the equatorial CEP (red line) enhance local convection (green shading) and thus stimulate cyclonic anomalies to the northwest (black solid line). The northerly component of the western flank of the cyclonic anomalies advects off-equatorial dry (low moist enthalpy) air into the tropical WNP and thus suppresses convection there (orange shading). The suppressed convection further stimulates the WNPAC (black dashed line) to the northwest. More detailed explanations can be found in the text.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Schematic of the anomalous moist enthalpy advection mechanism, which is the most essential forcing mechanism responsible for the maintenance of the WNPAC during El Niño mature winter [D(0)JF(1)] and the following spring [MAM(1)]. Warm SSTAs in the equatorial CEP (red line) enhance local convection (green shading) and thus stimulate cyclonic anomalies to the northwest (black solid line). The northerly component of the western flank of the cyclonic anomalies advects off-equatorial dry (low moist enthalpy) air into the tropical WNP and thus suppresses convection there (orange shading). The suppressed convection further stimulates the WNPAC (black dashed line) to the northwest. More detailed explanations can be found in the text.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Schematic of the anomalous moist enthalpy advection mechanism, which is the most essential forcing mechanism responsible for the maintenance of the WNPAC during El Niño mature winter [D(0)JF(1)] and the following spring [MAM(1)]. Warm SSTAs in the equatorial CEP (red line) enhance local convection (green shading) and thus stimulate cyclonic anomalies to the northwest (black solid line). The northerly component of the western flank of the cyclonic anomalies advects off-equatorial dry (low moist enthalpy) air into the tropical WNP and thus suppresses convection there (orange shading). The suppressed convection further stimulates the WNPAC (black dashed line) to the northwest. More detailed explanations can be found in the text.
Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-16-0489.1
Then the negative precipitation anomalies and associated WNPAC are further amplified by three positive feedbacks. The first is the convection–cloud radiative forcing feedback; that is, the suppressed convection over the tropical WNP causes a decrease in deep convective cloud and associated cirrostratus and cirrocumulus in situ, which in turn further suppresses the convection through weakening the longwave cloud radiative warming effect. The second is the wind–moist enthalpy advection–convection feedback; that is, the northeasterly anomalies to the southeastern flank of the WNPAC advect low moist enthalpy air into the tropical WNP and thus suppress convection over there, which further intensifies the WNPAC. The last is the moisture–convection feedback; that is, the downward motion anomalies associated with the suppressed convection reduce the moisture in the lower free troposphere, which increases the gross moist stability and thus further suppresses local convection.
The observational analysis indicated that both the cold SSTAs in the tropical WNP, which are generated by the local air–sea interactions, and remote forcing from the warm SSTAs in the equatorial CEP contribute to the formation of the WNPAC. To separate their relative contributions, we conducted three idealized pacemaker experiments, with the ocean temperature in the equatorial CEP restored to the observational anomaly plus model climatology. The only difference among the three experiments is that the resCEP run has free air–sea interactions outside the equatorial CEP, while the resCEP_clmGLB (resCEP_clmWNP) run restores ocean temperature outside the equatorial CEP (in the tropical WNP) to the model climatology. The intensity of the D(0)JF(1)- [MAM(1)-] mean WNPAC in the resCEP_clmGLB run reaches about 60% (70%) of that in the resCEP run, indicating that the contribution of the remote forcing is larger than that of the local air–sea interactions. Furthermore, the MSE budget analyses for the numerical experiments confirmed the findings from the observational analysis that the most important remote forcing mechanism is the negative anomalous moist enthalpy advection by teleconnected northerly anomalies.
In Part II of this study (Wu et al. 2017), we explore the formation mechanisms of the WNPAC during the late fall of the El Niño developing phase. A key issue is why the WNPAC forms in the late fall instead of the preceding El Niño–developing summer and early fall, under the condition that the spatial distributions of the SSTAs in the tropical Pacific have not changed significantly since the summer (Fig. 1).
Acknowledgments
This work is jointly supported by the National Natural Science Foundation of China (Grants 41661144009, 41675089, and 41330423), China National Key R&D Program (2017YFA0603802 and 2015CB453201), NSF AGS-1565653, and Jiangsu Collaborative Innovation Center for Climate Change. This is SOEST publication number 10213, IPRC publication number 1282 and ESMC publication number 177.
APPENDIX
Restoring Method used in the resCEP, resCEP_clmGLB, and resCEP_clmWNP Runs
The three sensitivity numerical experiments were conducted based on an ocean data assimilation system. The assimilation system was constructed on the CGCM FGOALS-s2.
The used assimilation scheme is referred to as Ensemble Optimal Interpolation–Incremental Analysis Update (EnOI-IAU) scheme. The assimilated observational records are derived from the EN4.1.1 dataset offered by the Hadley Centre, which collected all available global oceanic temperature and salinity profiles (Good et al. 2013). In the study, an anomaly-field assimilation approach was used to avoid the fact that the model drifts away from its preferred climatology.
The width of the assimilation cycle is 1 month. The EnOI-IAU scheme includes three major steps. The first step is “forecast,” which generates the first guess of the assimilation cycle. The second step is “EnOI,” which calculates analysis increment through combining the first guess and constructed observational data in the window (Oke et al. 2002). Here the constructed observational data are the sums of the model climatology and observational anomalies. The third step is “IAU,” which incorporates the analysis increments in the upper 700 m to the model as small constant forcing terms of the prognostic equations in each integration step (Bloom 1996). The outputs of the IAU were used in the study.
For the resCEP run, the assimilation domain was confined to the equatorial CEP (black dashed triangle in Fig. 8b). Strictly speaking, only analysis increments in the domain were added to the oceanic prognostic equations during the IAU step. The model was integrated freely outside the domain. In this way, the ocean temperature anomalies in the equatorial CEP are restored to the observational anomalies, while in other ocean areas, the ocean temperature anomalies are modulated by free air–sea interactions.
For the resCEP_clmGLB run, the assimilation domain was nearly global ocean (70°N–70°S). In the EnOI step, we constructed observational data through adding observational anomalies in the equatorial CEP (black dashed triangle in Fig. 8d) to the global model climatology. In this way, the ocean temperature anomalies in the equatorial CEP are restored to the observational anomalies, while in other ocean areas, the enhancement of the ocean temperature anomalies are suppressed.
For the resCEP_clmWNP run, the assimilation domains were both the equatorial CEP (black dashed triangle in Fig. 8f) and tropical WNP (green dashed quadrilateral in Fig. 8f). The constructed data for the assimilation are the observational anomalies plus model climatology in the CEP and model climatology in the WNP, respectively. Outside the tropical WNP and CEP, the atmospheric and oceanic components are coupled freely.
REFERENCES
Adler, R. F., and Coauthors, 2003: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–present). J. Hydrometeor., 4, 1147–1167, doi:10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
Back, L. E., and C. S. Bretherton, 2009: A simple model of climatological rainfall and vertical motion patterns over the tropical oceans. J. Climate, 22, 6477–6497, doi:10.1175/2009JCLI2393.1.
Bao, Q., and Coauthors, 2013: The Flexible Global Ocean–Atmosphere–Land System Model, spectral version 2: FGOALS-s2. Adv. Atmos. Sci., 30, 561–576, doi:10.1007/s00376-012-2113-9.
Bloom, S. C., 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256, doi:10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2.
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, doi:10.1175/1520-0442(2002)015<2907:ASMOAC>2.0.CO;2.
Chang, C. P., Y. S. Zhang, and T. Li, 2000a: Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs. Part I: Roles of the subtropical ridge. J. Climate, 13, 4310–4325, doi:10.1175/1520-0442(2000)013<4310:IAIVOT>2.0.CO;2.
Chang, C. P., Y. S. Zhang, and T. Li, 2000b: Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs. Part II: Meridional structure of the monsoon. J. Climate, 13, 4326–4340, doi:10.1175/1520-0442(2000)013<4326:IAIVOT>2.0.CO;2.
Chen, M.-C., T. Li, X.-Y. 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, doi:10.1175/JCLI-D-15-0547.1.
Collins, W. D., and Coauthors, 2006: The Community Climate System Model version 3 (CCSM3). J. Climate, 19, 2122–2143, doi:10.1175/JCLI3761.1.
Dee, D. P., S. M. Uppala, and A. J. Simmons, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.
Emanuel, K., 2007: Quasi-equilibrium dynamics of the tropical atmosphere. The Global Circulation of the Atmosphere, T. Schneider and A. Sobel, Eds, Princeton University Press, 186–218.
Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447–462, doi:10.1002/qj.49710644905.
Good, S. A., M. J. Martin, and N. A. Rayner, 2013: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans, 118, 6704–6716, doi:10.1002/2013JC009067.
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, doi:10.1029/2007JD008744.
Kug, J. S., B. P. Kirtman, and I. S. Kug, 2006: Interactive feedback between ENSO and Indian Ocean in an interactive ensemble coupled model. J. Climate, 19, 6371–6381, doi:10.1175/JCLI3980.1.
Lau, N.-C., and M. J. Nath, 2000: Impact of ENSO on the variability of the Asian-Australian monsoons as simulated in GCM experiments. J. Climate, 13, 4287–4309, doi:10.1175/1520-0442(2000)013<4287:IOEOTV>2.0.CO;2.
Lau, N.-C., and M. J. Nath, 2003: Atmosphere–ocean variations in the Indo-Pacific sector during ENSO episodes. J. Climate, 16, 3–20, doi:10.1175/1520-0442(2003)016<0003:AOVITI>2.0.CO;2.
Li, S. L., J. Lu, G. Huang, and K. M. Hu, 2008: Tropical Indian Ocean basin warming and East Asian summer monsoon: A multiple AGCM study. J. Climate, 21, 6080–6088, doi:10.1175/2008JCLI2433.1.
Li, T., and B. Wang, 2005: A review on the western North Pacific monsoon: Synoptic-to-interannual variabilities. Terr. Atmos. Ocean. Sci., 16, 285–314, doi:10.3319/TAO.2005.16.2.285(A).
Li, T., B. Wang, and L. Wang, 2016: Comments on “Combination mode dynamics of the anomalous northwest Pacific anticyclone.” J. Climate, 29, 4685–4693, doi:10.1175/JCLI-D-15-0385.1.
Li, Y., R. Lu, and B. Dong, 2007: The ENSO-Asian monsoon interaction in a coupled ocean–atmosphere GCM. J. Climate, 20, 5164–5177, doi:10.1175/JCLI4289.1.
Liu, H., P. Lin, Y. Yu, and X. Zhang, 2012: The baseline evaluation of LASG/IAP Climate system Ocean Model (LICOM) version 2.0. Acta Meteor. Sin., 26, 318–329, doi:10.1007/s13351-012-0305-y.
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. Sobel, Eds, Princeton University Press, 267–301.
Neelin, J. D., and I. M. Held, 1987: Modeling tropical convergence based on the moist static energy budget. Mon. Wea. Rev., 115, 3–12, doi:10.1175/1520-0493(1987)115<0003:MTCBOT>2.0.CO;2.
Neelin, J. D., and H. Su, 2005: Moist teleconnection mechanisms for the tropical South American and Atlantic sector. J. Climate, 18, 3928–3950, doi:10.1175/JCLI3517.1.
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, doi:10.1175/2008JCLI2334.1.
Oke, P. R., J. S. Allen, R. N. Miller, G. D. Egbert, P. M. Kosro, 2002: Assimilation of surface velocity data into a primitive equation coastal ocean model. J. Geophys. Res., 107, 3122, doi:10.1029/2000JC000511.
Okumura, Y. M., M. Ohba, C. Deser, and H. Ueda, 2011: A proposed mechanism for the asymmetry duration of El Niño and La Niña. J. Climate, 24, 3822–3829, doi:10.1175/2011JCLI3999.1.
Potter, G. L., and R. D. Cess, 2004: Testing the impact of clouds on the radiation budgets of 19 atmospheric general circulation models. J. Geophys. Res., 109, 111–125, doi:10.1029/2003JD004018.
Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354–384, doi:10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.
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, doi:10.1029/2002JD002670.
Stuecker, M. F., F.-F. Jin, A. Timmermann, and S. McGregor, 2015: Combination mode dynamics of the anomalous northwest Pacific anticyclone. J. Climate, 28, 1093–1111, doi:10.1175/JCLI-D-14-00225.1.
Su, H., and J. D. Neelin, 2002: Teleconnection mechanism for tropical Pacific descent anomalies during El Niño. J. Atmos. Sci., 59, 2694–2712, doi:10.1175/1520-0469(2002)059<2694:TMFTPD>2.0.CO;2.
Wang, B., and Q. Zhang, 2002: Pacific–East Asian teleconnection. Part II: How the Philippine Sea anomalous anticyclone is established during El Niño development. J. Climate, 15, 3252–3265, doi:10.1175/1520-0442(2002)015<3252:PEATPI>2.0.CO;2.
Wang, B., and T. Li, 2004: East Asian monsoon–ENSO interactions. East Asian Monsoon, C.-P. Chang, Ed., World Scientific 177–212, doi:10.1142/9789812701411_0005.
Wang, B., R. G. Wu, and X. H. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 1517–1536, doi:10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
Wang, B., R. G. Wu, R. Lukas, and S. I. An, 2001: A possible mechanism for ENSO turnabout. Dynamics of Atmospheric General Circulation and Climate, China Meteorological Press, 552–578.
Wang, B., R. G. Wu, and T. Li, 2003: Atmosphere–warm ocean interaction and its impacts on Asian–Australian monsoon variation. J. Climate, 16, 1195–1211, doi:10.1175/1520-0442(2003)16<1195:AOIAII>2.0.CO;2.
Wang, B., B. Xiang, and J.-Y. Lee, 2013: Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions. Proc. Natl. Acad. Sci. USA, 110, 2718–2722, doi:10.1073/pnas.1214626110.
Wang, C. Z., R. H. Weisberg, and J. I. Virmani, 1999: Western Pacific interannual variability associated with the El Niño-Southern Oscillation. J. Geophys. Res., 104, 5131–5149, doi:10.1029/1998JC900090.
Weisberg, R. H., and C. Z. Wang, 1997a: A western Pacific oscillator paradigm for the El Niño Southern Oscillation. Geophys. Res. Lett., 24, 779–782, doi:10.1029/97GL00689.
Weisberg, R. H., and C. Z. Wang, 1997b: Slow variability in the equatorial west-central Pacific in relation to ENSO. J. Climate, 10, 1998–2017, doi:10.1175/1520-0442(1997)010<1998:SVITEW>2.0.CO;2.
Wu, B., and T. Zhou, 2016: Relationships between ENSO and the East Asian-western North Pacific monsoon: Observations versus 18 CMIP5 models. Climate Dyn., 46, 729–743, doi:10.1007/s00382-015-2609-y.
Wu, B., T. Zhou, and T. Li, 2009: Seasonally evolving dominant interannual variability modes of East Asian climate. J. Climate, 22, 2992–3005, doi:10.1175/2008JCLI2710.1.
Wu, B., T. Li, and T. Zhou, 2010a: Asymmetry of atmospheric circulation anomalies over the western North Pacific between El Niño and La Niña. J. Climate, 23, 4807–4822, doi:10.1175/2010JCLI3222.1.
Wu, B., T. Li, and T. Zhou, 2010b: Relative contributions of the Indian Ocean and local SST anomalies to the maintenance of the western North Pacific anomalous anticyclone during El Niño decaying summer. J. Climate, 23, 2974–2986, doi:10.1175/2010JCLI3300.1.
Wu, B., T. Zhou, and T. Li, 2012: Two distinct modes of tropical Indian Ocean precipitation in boreal winter and their impacts on equatorial western Pacific. J. Climate, 25, 921–938, doi:10.1175/JCLI-D-11-00065.1.
Wu, B., T. Zhou, and T. Li, 2017: Atmospheric dynamic and thermodynamic processes driving the western North Pacific anomalous anticyclone during El Niño. Part II: Formation processes. J. Climate, 30, 9637–9650, doi:10.1175/JCLI-D-16-0495.1.
Xie, S.-P., K. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo-western Pacific climate during the summer following El Niño. J. Climate, 22, 730–747, doi:10.1175/2008JCLI2544.1.
Yang, J. L., Q. Y. Liu, S. P. Xie, Z. Y. Liu, and L. X. Wu, 2007: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys. Res. Lett., 34, L02708, doi:10.1029/2006GL028571.
Zhang, R. H., 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, doi:10.2151/jmsj1965.74.1_49.
Zhang, R. H., A. Sumi, and M. Kimoto, 1999: A diagnostic study of the impact of El Niño on the precipitation in China. Adv. Atmos. Sci., 16, 229–241, doi:10.1007/BF02973084.
Zhou, T., B. Wu, and L. Dong, 2014a: Advances in research of ENSO changes and the associated impacts on Asian-Pacific climate. Asia-Pac. J. Atmos. Sci., 50, 405–422, doi:10.1007/s13143-014-0043-4.
Zhou, T., Y. Yu, Y. Liu, and B. Wang, 2014b: Flexible Global Ocean-Atmosphere-Land System Model: A Modeling Tool for the Climate Change Research Community. Springer, 483 pp., doi:10.1007/978-3-642-41801-3.