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The Role of Pacific Asian Marginal Seas on North Pacific Climate Variability and ENSO
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Yu-heng Tseng1, Chun Hoe Chow2, Ruiqiang Ding3, Jianping Li3, Huang-Hsiung Hsu2
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Climate and Global Dynamics Division, NCAR, Boulder, Colorado, USA
Research Center for Environmental Changes, Academia Sinica, Taiwan
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid
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Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing,
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China
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(submitted to J. Clim.)
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Corresponding author: Dr. Yu-heng Tseng, Climate and Global Dynamics Division, National
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Center for Atmospheric Research, 1850 Table Mesa Dr., Boulder, CO80305, USA. Email:
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[email protected].
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Abstract
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We investigate the dominant coupled atmospheric and oceanic modes in the North Pacific
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climate variability and explore the impact of the Pacific Asian Marginal Sea (PAMS) on them
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using observation and the Community Earth System Model (CESM), both of which clearly
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indicate the two dominant coupled modes of surface variability. The first mode of the combined
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empirical orthogonal function (CEOF) analysis represents the Pacific Decadal Oscillation (PDO) /
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El Niño–Southern Oscillation (ENSO) variability, which expresses the zonal variability in the
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mid-latitudes and tropics. The second mode shows the North Pacific Oscillation (NPO) / Victoria
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Mode (VM) variability reflecting the footprint of the meridional variability through the tropical–
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extratropical teleconnection. Wavelet analysis for both the observation and CESM indicates that
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the first mode is dominated by interannual-scale variability, while the second mode is dominated
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by decadal-scale variability. These two leading modes can explain most of the North Pacific
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climate variability and are linked with each other. We also identified the potential origin of these
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two dominant modes resulting from atmospheric boundary layer variability in the PAMS. The
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summer zonal wind anomalies in the PAMS link directly to the consequent PDO/ENSO pattern,
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while the winter meridional wind anomalies in the PAMS acts as a pivotal driver to modulate the
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NPO/VM pattern through atmospheric teleconnection. Particularly, the upper-level eastward
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propagation strengthens the south lobe of the NPO from the subtropical pressure low anomaly. The
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spring VM consequently drives the zonal mode in the subtropical and tropical Pacific (including
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the zonal wind variability in the PAMS), thus triggering the onset of PDO/ENSO variability.
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Further analysis shows that the East Asian Winter Monsoon (EAWM) may play an important role
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in controlling low-level meridional wind variability in the PAMS but does not explain its
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completed variability. These dynamical processes are also confirmed by the CESM simulation
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with a difference in the time scale required to modulate the NPO.
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Keywords: North Pacific Oscillation, East Asian Winter Monsoon, ENSO, Victoria Mode
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1 Introduction
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The ocean–atmosphere (O–A) coupled system and its variation in the Pacific show a large
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impact on global weather and climate. For example, El Niño–Southern Oscillation (ENSO) is a
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particular mode of Pacific climate variability with strong coupling between the atmosphere and
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ocean in the tropical Pacific. The onset and evolution of an ENSO warm event is strongly related
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to atmospheric and oceanic variations in the tropical Pacific (e.g., Bjerknes, 1969; Wyrtki, 1975).
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Current understanding of ENSO evolution and its development has advanced over the last few
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decades through many different ENSO theories and classifications. In general, there are two
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groups of theoretical explanations. First, ENSO variation is a self-sustained, unstable and
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naturally oscillating mode of the O–A system (e.g., Battisti, 1988; Suarez & Schopf, 1988;
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Battisti & Hirst, 1989; Jin, 1997). Second, ENSO is a stable mode triggered by atmospheric
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random “noise” forcing (Wang & Picaut, 2013). In either case, the positive O–A feedback of
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Bjerknes (1969) is the triggering mechanism that initiates the development of El Niño, resulting
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from a rapid collapse of the easterly trade winds (i.e., westerly wind bursts, WWB). This
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unstable interaction between the trade winds and sea surface temperature (SST) is further
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enhanced through changes in the ocean thermocline depth. The accumulated warm water in the
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western Pacific surges eastward in the form of equatorial downwelling Kelvin waves to initiate
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an El Niño event that matures in December; however, the trigger of the Bjerknes feedback
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remains unclear.
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Several previous studies have found that the teleconnection between the tropics and
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mid-latitudes may drive ENSO variations through the “Seasonal Footprinting Mechanism” (SFM)
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proposed by Vimont et al. (2003). The SFM asserts that the second leading pattern of winter
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atmospheric circulation, the North Pacific Oscillation (NPO), and its variability over the North
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Pacific can significantly impact the spring sea surface temperature (SST) anomalies in the central
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North Pacific (e.g., Vimont et al., 2009; Alexander et al., 2010; Furtado et al., 2012) by
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modifying the wind-stress fields and changing the net surface heat flux over the North Pacific.
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This SST footprint can subsequently persist into summer to force the overlying atmosphere,
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resulting in zonal wind stress anomalies triggering an ENSO event in the following winter. The
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NPO is defined as the second leading mode of winter sea level pressure (SLP) anomalies over
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the North Pacific (Walker & Bliss, 1932; Rogers, 1981). Hereafter, the seasons referred to are
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those of the Northern Hemisphere.
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Pegion and Alexander (2013) noted that the existence of negative (positive) NPO in winter(0)
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does not always result in El Niño (La Niña) the following winter [winter(1)]. Hereafter, we
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denote the year in which the El Niño (La Niña) developing year 0 and the preceding and
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following years as year −1 and 1, respectively. Therefore, the NPO intensifies after
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December(−1), associated with the NPO peak during winter(0) or D(−1)JF(0), and ENSO
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matures in December(0), sometimes extending to D(0)JF(1). Throughout the manuscript, DJF
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refers to D(-1)JF(0) unless mentioned otherwise. Some studies have also shown that the impact
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of the NPO on the development of ENSO conditions through the SFM depends on the state of
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the tropical Pacific (e.g., Anderson, 2007; Alexander et al., 2010). Anderson (2007) found that
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the link between the SLP anomalies associated with winter(0) NPO and ENSO-related SST
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anomalies in winter(1) is stronger when the positive heat content anomalies in the western
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equatorial Pacific occur in autumn(−1), followed be negative SLP anomalies over the subtropical
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central North Pacific in DJF. A weak relation exists between the winter SLP anomalies and the
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ENSO state in the following year when these two patterns are of the same sign. Both ocean heat
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content and subtropical SLP states are related, but the variability of the subtropical SLP
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anomalies affects the occurrence of El Niño one year later. His results indicate that a deeper
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(shallower) thermocline in the western equatorial Pacific together with a negative (positive) NPO
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is more effective in producing warm (cold) ENSO events, consistent with the recharge–oscillator
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mechanism.
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The spring SST footprint is commonly called the “Meridional Mode” (MM), showing an
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opposite-signed meridional SST anomalies gradient in the central-eastern North Pacific, with one
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sign of the anomaly maximizing in the subtropics (10°–30°N) and the other located at the
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equator (Chiang & Vimont, 2004). These processes eventually impact the tropics and trigger the
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ENSO variability (Chang et al., 2007). The MM is closely linked to the “Victoria Mode” (VM)
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of SST anomalies, the second dominant empirical orthogonal function (EOF) mode north of
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20°N North Pacific (Bond et al., 2003), and is different from the North Pacific Gyre Circulation
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defined in Di Lorenzo et al. (2008) which is based merely on the eastern North Pacific. These
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VM/MM patterns reach a maximum in late winter and early spring, and then persist until
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summer in the subtropics, where they can subsequently force the overlying atmosphere to initiate
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the WWB of the Bjerknes feedback at the equator, which in turn triggers the development of El
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Niño the following winter. Several studies have shown that these patterns are indeed forced by
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the NPO variation (e.g., Alexander et al., 2010; Deser et al., 2012; Furtado et al., 2012; Ding et
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al., 2015a).
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Ding et al. (2015a) further suggest that the VM may act as an effective pathway for
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NPO-like atmospheric variability to drive ENSO variability via the SFM. They also find that
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there exists another similar but independent influence of extratropical atmospheric variability in
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the South Pacific on the occurrence of canonical El Niño events (Ding et al., 2014; Zhang et al.,
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2014). The differences between VM and MM are discussed in Ding et al. (2015a). In this paper,
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we focus on the possible pathway related to the origin of the NPO-like atmospheric variability
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associated with the VM/MM in the northern hemisphere (the second dominant mode), thus
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leading to ENSO (the first dominant mode). Figure 1 shows the correlation map of Niño4 index
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in January(1) and Pacific SST anomalies at different lags (only correlation coefficients
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significant at the p < 0.05 level are shown) from Extended Reconstructed SST version 3b
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(ERSST.v3b) observations (1958–2010). The Niño4 index is chosen here because it closely
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connects the VM/MM mode and central tropical warming. Similar patterns and correlations can
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be found using Niño3 or Niño3.4 indices. The pattern of evolution shows the VM/MM mode in
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different months before the mature phase of ENSO. We find a good correlation between the SST
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anomalies and Niño4 index in the western North Pacific (WNP) and eastern Indian Ocean more
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than 9 months prior (left panel). The correlation is higher than 0.6 (significant at the p < 0.05
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level) at the 12-month lead time in the WNP. Note that this high correlation region (signals
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already emerged at 15-month lead time) is located at the southern edge of the WNP index
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defined in Wang et al. (2012) and may directly trigger the winter(0) SST dipole in WNP, which
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is related to the development of ENSO in the following winter(1). This high correlation feature
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also extends northeastward into the Kuroshio Extension region. Similar patterns can also be
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found in the Hadley Centre SST data set (not shown).
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Besides the VM/MM and its footprint on sea surface height variability, several other
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regional studies have also found that SST anomalies in the marginal seas of the WNP are colder
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than normal monthly climatology in the developing years [year(0)] of El Niño. Hong et al. (2001)
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found that summer(0) SST anomalies in the East (Japan) Sea tend to be colder than during
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year(−1) prior to the developing year [year(0)]. SST anomalies during El Niño developing years
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are also opposite to those during La Niña developing years. Similar cold SST anomalies in
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spring(0) have recently been further identified based on three independent long-term
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observational stations off the east coast of South Korea (Jo et al., 2014). These signals of cold
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SST anomalies in the WNP are consistent with the correlation map in Figure 1.
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In particular, Wang et al. (2012) identified that an SST anomaly dipole in the WNP during
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winter(0) is related to the development of El Niño in the following winter [winter(1)], and used it
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to enhance the ENSO forecast. They also observed a strong correlation between ENSO and the
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preceding SST anomalies dipole in the Pacific Asian Marginal Seas (PAMS) of the WNP
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(similar to Fig. 1) a year in advance based on a robust statistical analysis. They thought that the
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spatial pattern in the WNP shares similar characteristics with the MM (Chiang & Vimont, 2004;
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Chang et al., 2007; Zhang et al., 2009), except that the meridional SST anomalies gradient and
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low-level zonal wind anomalies occur in the western tropical Pacific. Indeed, the high correlation
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band shown in Wang et al. (2012) and Figure 1 illustrates the evolution of VM/MM. These
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studies
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subtropical/extratropical North Pacific, associated with the winter NPO, are significantly related
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to El Niño 12 months later and can be used as a useful predictor for El Niño development
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(Anderson, 2007; Wang et al., 2012). However, it remains unclear as to how these regional WNP
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findings are linked to the basin-scale VM/MM and the NPO.
strongly
support
the
notion
that
large-scale
SST
anomalies
over
the
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In this paper, we seek to isolate the dominant source of extratropical NPO forcing on ENSO
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and explore how it originates from the PAMS. The driving origin and mechanism modulating the
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NPO to influence ENSO will be addressed by means of observation and verified by the
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Community Earth System Model (CESM). We further confirm that the PAMS origin of NPO
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variability on ENSO also controls the two dominant modes in the North Pacific climate
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variability. Section 2 introduces the observational data, numerical simulation, and statistical
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methods. Section 3 describes and compares the dominant surface pattern and variability in the
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North Pacific. Section 4 details the origin of NPO variability on ENSO resulting from the
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basin-scale tropical–extratropical teleconnection from the PAMS. Section 5 validates the origin
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of the ENSO precursor in the CESM simulation. Section 6 discusses the driving role of surface
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wind in the PAMS on ENSO and how this PAMS origin explains the ENSO precursor signals in
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the literature. Finally, conclusions and suggestions for further work are provided in Section 7.
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2 Observations and Numerical Simulation
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2.1 Observational data
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Three observational datasets for the period 1958–2010 are used for comparison. The
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monthly SLP, surface wind components, surface latent heat flux, air temperature and wind
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vectors are taken from the National Centers for Environmental Prediction – National Center for
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Atmospheric Research (NCEP–NCAR) reanalysis project (Kalnay et al., 1996; Kistler et al.,
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2001) on a 2.5°×2.5° horizontal grid resolution and 17 vertical pressure levels ranging from 1000
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to 10 hPa. For the surface latent heat flux, we also verify the dynamical processes discussed in
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this paper using objectively analyzed air-sea fluxes, OAFLUX (Yu et al., 2008). Some minor
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differences can be found in the amplitude; however, there is no significant difference in terms of
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the patterns comparing with the NCEP–NCAR reanalysis product. Therefore, for the sake of
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consistency, we present all observational results based on NCEP–NCAR reanalysis throughout
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the paper. The ERSST.v3b data are taken from the National Climatic Data Center on a 2°×2°
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horizontal grid (Smith et al., 2008). All relevant Niño indices are calculated from the ERSST.v3b
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data based on the standard definition. The representation of the NPO is defined according to the
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second dominant SLP anomalies mode in the combined empirical orthogonal function (CEOF)
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analysis.
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2.2 Numerical simulation using Community Earth System Model (CESM)
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Coupled model simulation is used to examine and further verify the influence of the PAMS
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origin on North Pacific variability. The CESM version 1 is used in this study (Gent et al., 2011).
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The default atmospheric model is based on the nominal 1° horizontal resolution (1.25°×0.9°),
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26-vertical-level, finite-volume dynamic core of the Community Atmospheric Model 4 (CAM4)
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described in Neale et al. (2013). The land model is the Community Land Model version 4
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(CLM4) and shares the same horizontal grid as CAM4 (Lawrence et al., 2011). The ocean
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component of CESM is the Parallel Ocean Program version 2 (POP2), which is a hydrostatic,
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free-surface, primitive-equation model formulated on a curvilinear orthogonal grid (Danabasoglu
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et al., 2012). The nominal 1° horizontal resolution version of the ocean component is used with
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60 vertical levels. The sea-ice model is the updated Los Alamos Sea Ice Model version 4
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(CICE4), and shares the same horizontal grid as POP2 (Hunke & Lipscomb, 2008).
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Our simulation is branched from a preindustrial (AD1850) control experiment and integrated
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for 150 years to ensure quasi-steady statistical results. Figure 2 shows the climatological mean
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(contours) SLP and standard deviation (colors) of monthly SLP anomalies from (a) the
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observation (1958-2010) and (b) the CESM simulation (labeled as “ctrl” in the figures). Figures
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2c and 2d are the climatological mean (contours) SST and standard deviation (colors) of monthly
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SST anomalies, respectively. The linear trends are removed. The modeled climatological mean
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SLP and SST compares reasonably well with the observation except for some minor differences
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in the strengths and locations of extreme as expected. Similar to the observation, a large modeled
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standard deviation can be observed along the equatorial Pacific cold tongue and Kuroshio
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extension region; however, the magnitudes in CESM simulation are much stronger than those in
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the observation, consistent with Deser et al. (2012).
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In order to ensure that the modeled Bjerknes feedback (Bjerknes, 1969) is compatible with
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the observation regardless of ENSO type, we show a scatter diagram of averaged zonal wind
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speed in the observation (or wind stress in the CESM) vs. Niño4 SST anomalies (160°E–150°W)
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in Figure 3. Both axes are normalized by their respective standard deviations. Surface wind
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changes can affect the thermocline structure along the equator. On the other hand, the SST
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anomalies can also modify the wind convergences. This interactive coupling strength can be
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estimated by the slope of the linear fit for the scatter plot, Δ(zonal wind stress anomalies)/Δ(SST
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anomalies). The modeled slope and R2 are similar to the observation. This confirms the
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capability and credibility of CESM to reasonably simulate the surface zonal wind and the
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coupling strength of Bjerknes feedback in the tropical Pacific.
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2.3 Statistical methods
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The CEOF is used to clarify the covariance shared by different variables. Here, we use the
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CEOF to analyze the covariability between the SLP and SST anomalies, which can be useful to
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explain the O–A dynamical links in the Pacific. The anomalies in this study is with respect to the
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monthly climatological mean. To emphasize the large-scale pattern variability, we apply the
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3-month running mean to both the SLP and SST anomalies time series. Also, the resolution is 5°
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for the SLP anomalies and 4° for the SST anomalies in the observational data so that the
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modeled resolution is interpolated to approximately 5° and 3°, respectively. Prior to the analysis
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of CEOF, the SLP and SST anomalies are normalized by the domain average standard
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deviations.
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Wavelet analysis is also used to determine the dominant modes of variability in frequency
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and how those modes vary over time (Torrence & Compo, 1998). We use the Morlet wavelet
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function. The 5% significance (or 95% confidence) level is determined based on a red-noise
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background. For comparison, the spectra shown in this study are normalized by the total data
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number divided by the data variance.
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3. The Dominant Surface Pattern and Variability in the North Pacific
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3.1 Spatial pattern
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It has been shown that the ENSO and Pacific decadal variability can be reasonably
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represented using the CESM framework (Deser et al., 2012). However, the coupled O–A modes
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in the North Pacific and their associated dynamics and connections have been not sufficiently
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addressed and clarified. Figures 4 and 5 compare the two leading CEOF modes in the
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observation (top) and model (bottom). The variances of CEOF1 and CEOF2 are 30.2% and 7.9%
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in the observation as compared with 40.9% and 7.8% in the CESM simulation, respectively.
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Figures 4a and 4c show the leading CEOF mode (CEOF1) of SLP anomalies in the North Pacific
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is the Aleutian Low (AL), the semi-permanent low-pressure winter center over the Aleutian
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Islands caused by planetary waves (as compared with the left panel in Fig. 2), in association with
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another strong pressure high near the Indo-Pacific Warm Pool region and another strong pressure
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low near the eastern tropic. The spatial distributions are all similar between the observation and
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CESM simulation, but the CESM simulation can explain higher variances than the observation,
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indicating significant modeled variability. The canonical PDO/ENSO pattern emerges in the
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CEOF1 of SST anomalies (Figs. 4b and 4d) with warm anomalies in the cold tongue from the
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central-eastern tropical Pacific and cold anomalies in the western Pacific Ocean, which extend to
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the central North Pacific in the mid-latitudes (as compared with the right panel in Fig. 2). In the
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mid-latitudes, the modeled strength is slightly weaker than the observation although the
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variability pattern of the associated SST anomalies resembles the observed PDO pattern (Mantua
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et al., 1997; Zhang et al., 1997). In the tropics, the modeled positive ENSO anomalies seem to be
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stronger and extend more zonally in the CESM simulation compared to the observation
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(consistent with the standard deviation difference in Fig. 2).
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The second CEOF mode (CEOF2) of SLP anomalies in the North Pacific (Figs. 5a and 5c)
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presents a meridional dipole structure of the NPO in the central-eastern Pacific, with positive
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anomalies in the north above 40°N (over the Aleutian Islands) and negative anomalies in the
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south between 0° and 40°N (over Hawaii). There is another weak pressure low in the subtropical
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WNP, which has drawn only minor attention previously (Anderson, 2007). This NPO pattern is
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very similar to that reported earlier (Linkin & Nigam, 2008; Furtado et al., 2012) and is a robust
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winter atmospheric feature. The models also show an NPO structure similar to the observation
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with a weaker magnitude. In addition, the CEOF2 of the SST footprint resembles the VM/MM
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mode described above (Bond et al., 2003; Di Lorenzo et al., 2008; Ding et al., 2015a), with a
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region of negative SST anomalies extending from the WNP to the Kuroshio Extension, encircled
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by warm SST anomalies around the North Pacific coast reaching the central tropical Pacific (Figs.
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5b and 5d). The modeled CEOF2 pattern also resembles to the observation, with a stronger
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footprint than the observation north of the subtropical region.
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3.2 Long-term variability associated with the spatial pattern in the North Pacific
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Within the coupled O–A modes in the North Pacific, we find that the pattern of CEOF1 SST
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shows a PDO/ENSO pattern, while the CEOF2 SST shows a VM/MM pattern, from the above
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section. Several studies have indicated that these patterns are subject to the atmospheric forcing
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of the AL and NPO, respectively (e.g., Furtado et al., 2012; Ding et al., 2015a). We further
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investigate the connection between the first two principal components (PC1/PC2) of the CEOF
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patterns and their relationships with two oceanic indices (Niño3 and the El Niño Modoki index
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(EMI) defined in Ashok et al. (2007)) using the lag correlation (Fig. 6). The positive (negative)
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x-axis means PC1/PC2 leads (lags) Niño3 or EMI index. In the observation, the temporal
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evolution of PC1 is highly correlated with Niño3 index (R=0.93) and marginally correlated with
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EMI index (R=0.36). All correlations discussed hereafter are significant at the p < 0.05 level
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unless otherwise stated (95% confidence level lines are shown). This indicates that the spatial
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pattern of CEOF1 corresponds directly to the canonical ENSO variability. The temporal
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evolution of PC2 leads the time series of PC1 by 8 months (R=0.38), showing that the
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appearance of the second mode ahead of ENSO occurrence (Furtado et al., 2012) and the spatial
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pattern of CEOF2 can potentially be seen as a precursor signal of ENSO. The correlation is not
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very high due to a large amount of high-frequency noises associated with the PC2 (only a
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3-month running mean is applied prior to the CEOF analysis). The lag correlations are also flat
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when PC2 leads by 6 to 10 months. This relation explains the lead–lag relation of PC2 and Niño3
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at R=0.34 when the PC2 leads Niño3 by 7 months. In general, the PC1 and Niño3 index occur
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almost simultaneously, but the CEOF2 pattern associated with PC2 can last several months,
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approximately 6 to 10 months before the matured phase of CEOF1. The correlation of PC1 with
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Niño3.4 is even higher at 0.90 (not shown). This result confirms that North Pacific CEOF1
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(PDO/ENSO) mode is indeed a coupled tropical and extratropical variability linked directly with
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Niño3 (or Niño3.4) variability, while the Niño3 (or Niño3.4) signal is expressed particularly in
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the tropics only (Zhang et al., 1997).
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Of particular interest is the finding that PC2 leads EMI by 2 months at R=0.68 (much shorter
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than PC2 leading Niño3 by 7 months at R=0.34), which implies that El Niño Modoki (EM) may
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be a direct footprint resulting from the PC2 patterns. The connection between the CEOF2 pattern
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and canonical ENSO (represented here by Niño3) may be the direct cause and effect evolving
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from CEOF2 to CEOF1 through the central tropical Pacific warming (represented by EMI). The
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evolution can actually be seen in Figure 1, where the CEOF1 and CEOF2 SST patterns
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resembles Figures 1f and 1c, respectively. Specifically, the warming in the central Pacific is the
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intermediate process that may result from two different dynamics: one related closely to the
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extratropical impact of the NPO associated with additional O–A interaction in the tropics and the
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other related to the recharge–oscillator mechanism (Wang & Wang, 2013; Chen et al., 2015). We
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will further address the intermediate dynamical processes in Section 4.
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The modeled CESM PC1/PC2 relationship is further evaluated to ensure that the observed
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dynamics can be well reproduced. Figure 6b shows that the modeled lag correlations compare
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reasonably well with the observation. The high correlations of approximately 0.97 between PC1
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and Niño3 index indicate that the ENSO variability can be well represented by the basin-scale
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change of PC1 in the CESM simulation (Deser et al., 2012). We need to be mindful that the
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higher correlation between PC1 and EMI in CESM simulation (0.71) than that in the observation
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may be misleading because this comes directly from the misrepresentation of the westward
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elongation of canonical ENSO in the models (see modeled CEOF1 pattern in Fig. 4). In addition,
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this causes the leading relationship of PC2 to EMI and Niño3 in the model to be less clear due to
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the zonally elongated CEOF1 pattern. Thus, the leading relation of PC2 to Niño3 at 10 months is
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actually similar to the fact that PC2 leads PC1 (or ENSO) in the models.
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The modeled ENSO precursor associated with PC2 is a robust feature in the CESM with a
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shorter response time of the NPO than the observation, consistent with the previous composite
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analysis in Deser et al. (2012), so that the lag time of PC1 is longer than that of PC2 in the
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CESM (10 months as compared with 8 months). Our results also confirm that the SFM can be
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well represented in the CESM simulation, but the timing differs from the observation (Deser et
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al., 2012). When the anomalous low pressure is strengthened around the southern lobe of the
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NPO, the anomalous westerly winds over the central and eastern subtropical Pacific reduce the
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wind speed and upward latent heat flux, thereby warming the underlying ocean from
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December(−1) to spring(0). The positive SST anomalies that extend into the tropical Pacific are
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then enhanced in summer(0) and subsequently develop into a warm event [see more discussion
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of the model processes in Deser et al. (2012)].
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Figure 7 further compares the corresponding wavelet analysis of PC1 and PC2. The left
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panel represents the wavelet power spectrum, and the right panel indicates the global power
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spectrum averaged over the two time series. High variability is represented by red, whereas blue
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indicates weak variability in the wavelet power spectrum. The observation shows that the
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nonstationary variability of PC1 and PC2 changes with time at multiple time scales. The global
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power spectrum indicates peaks at about 5 and 12 years. The dashed curves show a significance
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level of 95%, and only the 5-year peak passes the significance test for both PC1 and PC2;
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however, both peaks are consistent with the wavelet analysis of the Niño3.4 index in Tzeng et al.
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(2012). This further supports the notion that PC1 reflects the change in ENSO. In the wavelet of
336
PC2, the global power spectrum in the interannual variability range is quite similar to that in the
337
wavelet of PC1 with a weaker spectrum, which is clearly the precursor of PC1. The other peak of
16
338
12 years (11.7 years, precisely) can be seen in both PC1 and PC2, but neither on passes the
339
significance tests in the observation due to the shortness of the observation record (1958–2010).
340
The CESM provides a useful tool for investigating this further since a longer simulation can be
341
performed. The control simulation shows quite a clear peak of about 5 years in PC1 and a peak
342
of about 12 years in PC2. This supports the view of Furtado et al. (2012) that PC1 dominates at
343
the interannual scale of ENSO variability while PC2 relates more to the decadal or
344
low-frequency variability assuming that the underlying Pacific dynamics and variability can be
345
well represented in the model. However, the 12-year peak has not yet been sufficiently studied
346
and the similarity between the two modes are not well understood. Indeed, these two dominant
347
modes simulated in the CESM model are more separated from each other than those in the
348
observation in terms of the power spectrums.
349
350
4 Origin of the ENSO Precursor Resulting from the Basin-scale Tropical–Extratropical
351
Teleconnection
352
The two dominant surface patterns in the North Pacific and their associated variability are
353
documented in Section 3. Most importantly, the lead–lag relationship between the NPO/VM
354
patterns (CEOF2) and PDO/ENSO pattern (CEOF1) in both the observation and CESM
355
simulation confirmed that the NPO/VM linked with the PDO/ENSO, as suggested earlier (Di
356
Lorenzo, 2010). Here, we propose the interannual variability of NPO/VM (i.e., CEOF2) results
357
from the atmospheric changes in the PAMS (motivated by Fig. 1) and verify the processes in
358
both the observation and CESM simulation. The influence of NPO/VM can further lead to the
359
development of ENSO (i.e., CEOF1). Particularly, the two dominant surface covariability modes
360
addressed in Section 3 actually link directly to the atmospheric boundary layer wind variability
17
361
in a specific region of PAMS with different seasonal forcing. Figure 8a shows the lead–lag
362
correlation of the 3-month averaged 10 m zonal wind anomaly (hereafter referred to as “U10ps”)
363
in the PAMS (110~140°E, 5~25°N) with the PC1 associated with CEOF1 (PDO/ENSO) in the
364
observation. This region is chosen from the largest SST anomalies correlation map with the
365
Niño4 index 12 months prior to the mature phase of El Niño shown in Figure 1b. The results are
366
not sensitive if a slightly larger region is selected (e.g., 100~140°E, 5~30°N) as long as the high
367
correlation area in Figure 1b is included. There are two peaks showing the U10ps leading the PC1
368
associated with the PDO/ENSO pattern. The highest correlation is 0.45 when PC1 lags the JAS
369
zonal wind by 5 months, and the next highest is 0.35 when PC1 lags the JFM zonal wind by 3 to
370
4 months. Areas with correlation significant at the 95% confidence level are shaded. Figure 8b
371
shows a similar lead–lag correlation between the equatorward meridional 10 m wind anomaly
372
(hereafter referred to as “-V10ps”) and PC2 associated with NPO/VM pattern. There is only one
373
peak when the negative NDJ -V10ps leads PC2 by 3 months (R=0.67). The lead–lag correlation
374
supports the proposed dynamical process. The enhancement of winter(0) -V10ps (NDJ) in the
375
PAMS leads to the growth of winter(0) NPO, reaching its maximum in January or February. The
376
NPO then forces the surface SST footprint (VM) through the SFM which is matured in the
377
following March or April (Ding et al., 2015a). The 3-month leading time scale shown in Figure
378
8b matches the origin of PC2 associated with NPO/VM resulting from the variability of -V10ps.
379
The CEOF2 (NPO/VM) mode then conveys the extratropical forcing into subtropical and
380
tropical Pacific, leading to the variability of summer(0) U10ps, which is favorable to the
381
development of CEOF1 (PDO/ENSO) mode. This results in the marginal correlation of
382
summer(0) U10ps (JAS) with PC1. Particularly, the anomalous westerly wind in PAMS can trigger
383
the WWB of the Bjerknes feedback, thus leading to the mature phase of El Niño. We also note
18
384
that the other weak peak at JFM U10ps in Figure 8a may be associated with the ENSO in the
385
preceding year (Xie et al., 2009).
386
Figure 9 further shows the normalized time series of NDJ -V10ps and JAS-averaged U10ps in
387
the PAMS, where the maximum correlations are presented in Figures 8a and 8b, respectively.
388
The 3-month leading time of NDJ -V10ps is apparent when the JFM PC2 is superimposed (dashed
389
red). Similarly, the zonal surface wind anomaly (JAS averaged U10ps) in PAMS shows a close
390
leading relation with D(0)JF(1) PC1 (dashed blue), particularly after 1980. All ENSO years since
391
1964 are labeled in their corresponding December, as shown in Figure 9 (El Niño: black solid
392
lines, La Niña: black dashed lines). These results confirm again that the D(0)JF(1) PC1 reflects
393
the ENSO variability discussed in Section 3 and the inconsistency between the variability of PC1
394
and PC2.
395
In order to verify the proposed mechanism linking the two modes with their origins in the
396
PAMS, we address the spatial patterns associated with U10ps and -V10ps, respectively. Since
397
CEOF2 patterns can be viewed as the precursor of CEOF1 (Fig. 6), we first discuss the origin of
398
CEOF2. Figure 10 shows the correlation map of the NDJ -V10ps (grey solid box in top-left panel)
399
with several lags (NDJ, DJF, JFM, FMA, MAM) of SLP (left), SST (middle) and latent heat flux
400
(right) anomalies. We confirm that the JFM SLP anomalies and FMA SST anomalies patterns
401
shown in Figure 10 match well with the CEOF2 patterns (contours are the CEOF2 of SLP and
402
SST anomalies in Fig. 5). It appears that the NDJ -V10ps indeed enhances the negative phase of
403
the NPO through its evolution, represented by the dark-blue shading propagating from the
404
western edge of the NPO circulation (NDJ) to the center of NPO (JFM). During NDJ, the
405
maximum correlation of -0.6 with SLP anomalies is located at the east side of the PAMS box,
406
which is also the center of the other weak pressure low in the subtropical WNP associated with
19
407
the NPO (Fig. 4). The SLP anomalies around the southern lobe of the NPO have a low
408
correlation (0.3). When the correlation patterns further evolve in late winter, the correlation in
409
the southern lobe of the NPO increases with time associated with the development of
410
well-known NPO-related circulation in the North Pacific (surface winds in the right panel of Fig.
411
10). The maximum correlation map can be seen in JFM SLP anomalies, resembling the matured
412
phase of the NPO. Similar correlation maps of SLP anomalies persist until MAM and then decay
413
gradually over time.
414
The correlation maps of SST anomalies have an evolution pattern very similar to the left
415
panel of Figure 1. The largest correlation can be seen in FMA, which is similar to the mature
416
phase of VM (Ding et al., 2015a). The evolution of SST anomalies is consistent with the surface
417
heat flux changes discussed previously (e.g., Ding et al., 2015a; Vimont et al., 2003). The latent
418
heat flux (major source of the total heat exchange at the surface) shows a large amount of heat
419
loss to the atmosphere (positive upward) in the subtropical WNP. The pattern is similar to the
420
general latent heat release in the WNP which cools the ocean surface. The subsequent weakening
421
and shrinking of the latent heat flux after JFM is consistent with the enhancement of the negative
422
SST anomalies due to the formation of the VM/MM mode. In the central-eastern subtropical
423
Pacific, negative latent heat flux anomalies (downward to the ocean) associated with SFM can
424
also be seen from NDJ to JFM, and decay afterward. The resulting net latent heat flux exchanges
425
in the Pacific are combined with the anomalous surface westerly winds in the central-eastern
426
tropical Pacific, thereby warming the tropical SST after spring. The associated Pacific dynamics
427
are consistent with the characteristics of the evolved SFM (Vimont et al., 2009; Alexander et al.,
428
2010).
429
The evolved correlation map of the NDJ -V10ps in Figure 10 confirms the origin of CEOF2
20
430
pattern from the NDJ -V10ps (surface equatorward wind variability in PAMS). Further lead–lag
431
analysis of SST anomalies in the same region shows no such lead correlation with PC2 as in
432
Figure 8b (not shown). The maximum correlation between SST and PC2 is 0.66 in JFM at zero
433
lag, indicating that the local SST response reflects the change of CEOF2 pattern associated with
434
PC2 simultaneously. This further confirms that the SST footprint of VM is indeed the response of
435
the NPO (Ding et al., 2015a) and can extend its impacts to the whole PAMS (Jo et al., 2014) in
436
spring (0). Figure 11 shows the vertical section of the correlation along 25°N at different lags.
437
The corresponding correlation of geo-potential heights confirms that the eastward atmospheric
438
propagation strengthens the southern lobe of the NPO from NDJ to JFM. The propagation is
439
clear in the mid-troposphere (approximately 500 mb), similar to the atmospheric wave-like
440
activity.
441
In addition, similar correlation map of vertical velocity Ω (negative upward and positive
442
downward) supports the reduced downward motion (subduction) between 100°E and 140°E and
443
the enhanced upward motion (deep convection) between 180°E and 220°E throughout the whole
444
column from NDJ to JFM. After DJF, we can also see that another downward Ω emerges
445
between 140°E and 160°E in the atmospheric boundary layer and persists until MAM,
446
corresponding to the low-level latent heat flux change associated with VM. The air temperature
447
(right panel in Fig. 11) also shows the corresponding changes consistent with the change of SST
448
anomalies. In general, the vertical section sequence shows that the mid-troposphere convection
449
may play an important role in modulating the teleconnection pattern and may directly result from
450
eastward wave propagation.
451
Moreover, Figure 8a suggests that the summer(0) U10ps is potentially linked to the PC1
452
associated with the CEOF1 (PDO/ENSO) patterns. We further investigate the correlation map of
21
453
the JAS U10ps (grey box in top-left panel) with several leads (MAM, MJJ) and lags (JAS, SON,
454
NDJ) of SLP (left), SST (middle) and latent heat flux (right) anomalies in Figure 12. From the
455
correlation maps evolving from MAM to JAS, it is interesting to see that the surface patterns are
456
very similar to the spring NPO/VM impact on the tropical precipitation through the
457
wind-evaporation-SST (WES) feedback (Xie & Philander, 1994; Ding et al., 2015b). During
458
MAM (top panels of Fig. 12), the surface wind anomalies and the SST anomalies resemble the
459
NPO wind forcing and VM, respectively. Significant changes of dynamics appear in the
460
subtropical and tropical Pacific; however, the largest correlation in the SLP anomalies does not
461
collocate with the southern lobe of the NPO (shifting approximately 5° south) and the largest
462
correlation in the SST anomalies only appears in the southern branch of the VM (Fig. 10). The
463
warm SST anomalies in the subtropical band of central Pacific associated with the anomalous
464
southwesterlies emphasize the enhanced equatorward impacts. After MJJ, the northeasterly trade
465
winds weaken and subsequently reduce the upward latent heat flux, warming the tropical central
466
Pacific. The associated O–A dynamics then leads to a positive WES feedback, which
467
significantly contributes to the equatorward development of warm SST anomalies in the
468
central-eastern Pacific. This warming enhances the SST zonal gradient across the tropical Pacific,
469
thus forcing the anomalous southwesterlies in the western tropical Pacific and the anomalous
470
easterlies in the eastern North Pacific. These winds cause low-level convergence in the
471
central-eastern tropical Pacific (upward Ω in the middle panel of Fig. 13). Figure 13 shows the
472
corresponding correlation along the vertical section of 5°N. This enhanced upward motion in the
473
central-eastern Pacific is consistent with the well-known change of Walker circulation associated
474
with the development of El Niño. It is also clear that the zonal variability can be seen in the
475
vertical section of the geo-potential height and air temperature profile.
22
476
Figures 12 and 13 provide supporting evidence that the evolution of JAS U10ps links to the
477
subsequent CEOF1 pattern (bottom panels). The correlation maps of SLP and SST anomalies
478
during NDJ resemble the CEOF1 of SLP and SST anomalies (superimposed by contours);
479
however, the JAS U10ps may not be the direct cause. Its correlation (0.45) with PC1 is lower than
480
the direct impact of -V10ps on the PC2 (R=0.67) (Figs. 8a and 8b). The evolution of correlation
481
map suggests that the JAS U10ps may result from the influence of NPO/VM and is linked with the
482
change of tropical Walker circulation. The NPO/VM-related variability may be the driving force
483
that influences the summer zonal wind anomalies in the PAMS, and then triggers the tropical O–
484
A coupling mode until the mature stage of the subsequent El Niño.
485
486
5 Validation of the Origin of the ENSO Precursor in the CESM Simulation
487
488
The observational results in Section 4 support the view that the U10ps and -V10ps are closely
489
linked with the CEOF1 (PDO/ENSO) patterns (dominated by the zonal variability) and CEOF2
490
(NPO/VM) patterns (dominated by the meridional variability), respectively. The two dominant
491
modes are not independent and are closely connected through the origin in the PAMS,
492
specifically the surface meridional wind anomalies. Figures 8c and 8d verify the lead–lag
493
correlation in the CESM simulation between U10ps and PC1 and between -V10ps and PC2,
494
respectively. In the U10ps and PC1 relation, the CESM simulation shows a similar but stronger
495
lead–lag correlation (maximum R=0.75) than the observation. This could be due to a longer
496
CESM simulation (150 years) than the observation (53 years). Figure 14 shows the correlation of
497
the JAS U10ps with several leads/lags (MAM, MJJ, JAS, SON, NDJ) of SLP anomalies (left) and
498
SST anomalies (right) in the CESM simulation from year 50 to 150. We can see that the
23
499
evolution in the correlation maps is quite similar to that in the observation (Fig. 12), but the
500
correlation is much stronger in both SLP and SST anomalies patterns overall. The correlation
501
maps during NDJ closely match with the CEOF1 patterns of SLP and SST anomalies (contours).
502
This indicates that the modeled JAS U10ps and its associated subtropical-to-tropical O–A
503
interaction can correctly establish the necessary zonal gradient to generate the CEOF1 pattern in
504
the model.
505
However, the modeled correlation is much lower than the observation in terms of the
506
meridional wind anomalies and PC2 relation (R=0.41 when PC2 lags 0 to 1 month). Most
507
importantly, Figure 8d indicates the change of -V10ps and the variability of PC2 happen almost
508
simultaneously or only a little delay in the simulation, implying the response of PAMS on NPO
509
is faster than that in the observation (approximately 2 to 3 months). Figure 15 shows the
510
correlation of the DJF -V10ps with several lags (DJF, JFM, FMA, MAM, AMJ) of SLP (left) and
511
SST (right) anomalies in the CESM simulation from year 50 to 150. Note that we choose DJF in
512
the model simulation because the modeled response time is shorter than that in the observation.
513
The correlation maps still resemble the CEOF2 of SLP and SST anomalies but the correlation is
514
lower than that in the observation (Fig. 10). The observed eastward enhancement of the south
515
lobe of NPO can also be found from DJF to FMA in the CESM simulation (comparing with NDJ
516
to FMA in the observation) with a shorter response time. Nevertheless, these results confirm the
517
CESM simulation can reasonably simulate the dynamical process associated with the
518
development of CEOF1, i.e., ENSO precursor (Alexander et al., 2010; Deser et al., 2012), and
519
the linkage of the two dominant modes with PAMS origin can also be identified with a slightly
520
time difference to influence the CEOF2.
521
24
522
6 Discussion
523
Past studies on the NPO have focused mainly on the impacts of its teleconnection patterns
524
on the weather and climate of North America (Linkin & Nigam, 2008) and how the NPO
525
initiates ENSO through the SFM. Most studies looking at the NPO-induced SFM focus on the
526
consequent changes of surface and subsurface heat content in the central-eastern tropical Pacific
527
(e.g., Anderson, 2004; Anderson & Maloney, 2006; Anderson, 2007). Only few studies have
528
emphasized the origin of NPO variability. The modulation of NPO due to the -V10ps we describe
529
here mostly affects the NPO’s southern center of action (north of Hawaii) prior to the
530
NPO-induced SFM. This is similar to the finding of low-frequency forcing of EM in modulating
531
the low-frequency mode of the NPO in Di Lorenzo et al. (2010) and Furtado et al. (2012);
532
however, we point out a more direct impact of the PAMS surface wind variability in modulating
533
the NPO on the time scale of months. This explains all northern hemispheric precursors of El
534
Niño in the literature and helps us to form a potential framework of ENSO prediction and an
535
integrated Pacific climate paradigm linking the two dominant modes.
536
Our results suggest that the surface wind variability in the PAMS can be seen as the
537
trigger-forcing of the tropical–extratropical teleconnection associated with the CEOF2
538
(NPO/VM) in terms of CEOF1 evolution (i.e., ENSO/PDO). The summer(0) -U10ps in PAMS
539
associated with the VM-induced subtropical SST gradient and tropical low-level atmospheric
540
convergence triggers the O–A interaction, which is favorable for the Bjerknes feedback. This
541
connection can be easily explained in terms of the shifting of Walker circulation in the ENSO
542
cycle.
543
On the other hand, the 3-month lead of -V10ps in PAMS suggests that the variability of NPO
25
544
is controlled by the surface wind change in the PAMS. Wang et al. (2012) implied that the
545
surface winds in the WNP occur almost at the same time as the NPO from December to February
546
(Fig. 4 in Wang et al., 2012). We further clarify, however, that the NPO is intensified through the
547
change and eastward propagation of the meridional wind anomalies in WNP in both observation
548
(Section 4) and CESM simulation (Section 5). The winter(0) -V10ps plays an important role in
549
enhancing the growth of NPO (particularly the southern lobe of the NPO), thus forcing the
550
following SST footprint of spring VM in the North Pacific. This leads to the mature phase of
551
CEOF2 pattern (NPO/VM).
552
We present the 3-month averaged winter-to-spring SLP and wind anomalies for three
553
different years, indicating maximum (1981-1982), weak (1993-1994), and minimum (1997-1998)
554
NDJ -V10ps in Figure 9, from left to right in Figure 16. Note that 1981-1982 and 1997-1998
555
winters correspond to a strong El Niño and a strong La Niña one year ahead while the NPO/VM
556
may play an important role in triggering the ENSO events. The weak 1993-1994 winter is
557
followed by an El Niño without any NPO/VM impact. Comparing the first three top panels (NDJ
558
to JFM), we can clearly see the enhancement of negative (or positive) NPO phase in the
559
1981-1982 (or 1997-1998) winter. There is no apparent enhancement of NPO in the 1993-1994
560
winter. The surface wind vectors also confirm that the enhanced signals of NPO begin from the
561
PAMS in NDJ, as discussed in the last section. Note that although the 1993-1994 winter does not
562
show the growth of NPO due to the weak -V10ps in the PAMS, the surface winds south of the
563
PAMS in 1993-1994 (middle panel) show a similar westerly enhancement to those in 1981-1982
564
(opposite to the easterly wind in 1997-1998). These winds are prone to the development of El
565
Niño, indicating that the NPO/VM may not be the only contributor to the ENSO development,
566
consistent with Ding et al. (2015a). There is no direct cause-and-effect relation. The meridional
26
567
winds (-V10ps) in PAMS link directly to the variability of CEOF2 (NPO/VM), while the zonal
568
wind (U10ps) connects closely to the variability of CEOF1 (PDO/ENSO). The above discussion is
569
supported by both the observation and CESM simulation; however, what triggers the variability
570
of -V10ps, which modulates the NPO variability and its associated SST footprint of VM?
571
PAMS is a region where the thermal contrast between the East Asian continent and the
572
Pacific is important. The East Asian Winter Monsoon (EAWM) plays a dominant role in the
573
winter climate in this region. It has been previously proposed that anomalous EAWM may
574
contribute toward generating the equatorial westerly and thus possibly be responsible for the
575
initiation of El Niño events (Li, 1990). Li (1990) argued that the strong cold surges associated
576
with a strengthened EAWM penetrate into the South China Sea (SCS) and western tropical
577
Pacific, triggering strong convection over the warm tropical ocean (Chang & Lau, 1980; Lau et
578
al., 1982). The anomalous convection induces westerly wind anomalies to its west as a Rossby
579
wave response, which excites downwelling oceanic Kelvin waves and leads to the anomalous
580
warming in the eastern Pacific; however, a strengthened (or weakened) winter monsoon did not
581
always precede an El Niño (or La Niña) event (Wang & Li, 2004). Indeed, the correlations
582
between different EAWM indices and Niño3.4 index are not very significant when Niño3.4
583
index lags one year, except the EAWM indices which include the low-level East China Sea (ECS)
584
and SCS winds in their definition (e.g., Chen et al., 2000). This is not surprising because that
585
kind of definition is the area-averaged -V10ps in the PAMS discussed here. In fact, the PAMS
586
origin chosen in the current study (grey box in Fig. 1b and the top right panel of Fig. 10) is very
587
close to the southern domain that is used to define the EAWM index in Chen et al. (2000). The
588
response of -V10ps that we define here varies simultaneously with the SCS winds with a
589
correlation larger than 0.85 and lags behind the ECS winds by 1 month. These results confirm
27
590
that the along-coast wind (mostly meridional wind) associated with EAWM in the PAMS may
591
trigger the NPO/VM pattern, thus leading to El Niño in some cases.
592
The formation of -V10ps (or the along-coast wind) is partially caused by the shape of the
593
East Asian coasts and partially related to the pressure gradient between the Siberian high and the
594
Maritime Continent low (Wang & Chen, 2014). Note that the marginally high correlation region
595
of winter SLP anomalies above China shown in Figures 10 (observation) and 15 (CESM
596
simulation) further supports the view of a meridional pressure gradient. We further investigate
597
how the north-south pressure contrast correlates with the zonal wind anomalies in the PAMS and
598
thus PC2, using the North-South Index (NSI) defined in Wang and Chen (2014). The NSI is part
599
of an integrated EAWM index defined by the difference between the SLP over Siberia
600
(40°-60°N, 70°-120°E) and the Maritime Continent (20°S-10°N, 110°-160°E). It indeed has a
601
marginal correlation with PC2 (see the contours superimposed in Figs. 8b and 8d for the lead–lag
602
correlation). The correlation is higher than 0.4 when OND NSI leads PC2 by 8 months in the
603
observation. Although it is much lower than the relation between the -V10ps and PC2 (correlation
604
is 0.67), the OND NSI seems to relate to the -V10ps (maximum correlation is 0.57 at a 2-month
605
lead). The EAWM, however, may not be the only dynamic to control the -V10ps in the PAMS.
606
The variability of subtropical high in the WNP may contribute partially to -V10ps. Unfortunately,
607
we have not yet found a well-defined WNP subtropical high index to confirm this. We suspect
608
that the circulation variability near the WNP subtropical high may also connect with the
609
triggering pressure low shown in the CEOF2 pattern.
610
Nevertheless, the impact of EAWM on the PC2 variation is also supported by the CESM.
611
Similar to the observation, the high correlation of this NSI leads the -V10ps and thus the PC2;
612
however, the correlation is lower and the time scale is shorter than in the observation, consistent
28
613
with our discussion in Section 3. This lower correlation may be due to the shifting of Siberia
614
High and Maritime Continent in the CESM simulation. The low correlation in the CESM can
615
partially explain the slightly lower correlation between PC1 and PC2 in the CESM simulation
616
than in the observation (Fig. 5) and why the two dominant modes in the wavelet spectrum can be
617
clearly distinguished in the simulation (Fig. 7). The modeled PC2 associated with CEOF2 pattern
618
may mix some other influences in the CESM simulation.
619
There are some additional mechanisms that could potentially excite the thermodynamic
620
heating/cooling source in the PAMS to affect ENSO variability. For example, the East Asian
621
summer monsoon proposed in Li et al. (2007) supports our finding of zonal wind anomalies in
622
the PAMS influencing the CEOF1 pattern associated with PC1. Chang et al. (2009) also found
623
that significant negative (positive) SST anomalies in WNP appear in the strong (weak)
624
Subtropical Mode Water (STMW) case. Their statistical analysis indicated that summer(−1)
625
STMW variability can also affect ENSO events 18 months later. They suspected that summer
626
STMW(−1) variability produces subtropical atmospheric variability through long-term persistent
627
SST anomalies over its reemergence area to derive the VM pattern, and eventually modulates the
628
amplitude of ENSO events; however, the role of the STMW still needs to be clarified. We
629
suspect that it relates directly to the contribution of the Pacific subtropical cell (Chen et al., 2015,
630
submitted to J. Clim.).
631
Note that there is another pressure low anomaly (adjacent to the meridioinal wind anomalies
632
box in the PAMS) in the WNP of CEOF2 in both the observation and CESM simulation [also
633
found in Anderson (2004, 2007)]. We identified that the growth of NPO starts from this pressure
634
low and propagates eastward; however, a more detailed and careful study is required to clarify
635
the role of this pattern and its underlying ocean dynamics. It is possible that ocean dynamics may
29
636
change the local O–A interaction, thus contributing to the -V10ps. For example, the
637
preconditioned Warm Water Volume (WWV) in the recharge–oscillator mechanism reflects the
638
associated oceanic responses and the subsurface changes to precondition the Pacific WWV [as
639
do WWB, e.g., Fedorov (2002)]. The development of ENSO events may require both the
640
influence of EAWM and the ocean subsurface preconditioning (Chen et al., 2015) one year in
641
advance. In general, the local O–A interaction in the PAMS due to both the EAWM and the
642
underlying ocean precondition resulting from the Pacific Subtropical Cell circulation may be a
643
key triggering mechanism that affects -V10ps, which is the origin of the two dominant modes in
644
the North Pacific. Further investigation is needed, but that is beyond the scope of this study.
645
Moreover, our results are in fact consistent with previous evidence linking the Tsushima
646
Warm Current to the NPO reported by Hirose et al. (2009). They showed that changes in the
647
Tsushima Warm Current (a current fed by the Kuroshio, which passes between Korea and Japan)
648
during autumn were correlated with the following winter NPO / Western Pacific pattern. The
649
surface wind stress in the PAMS indeed controls the transport of Kuroshio and its branches due
650
to the Sverdrup circulation. The consequent response of atmospheric circulation to the Kuroshio
651
can also be found through a positive feedback process in the WNP (Qiu & Chen, 2010; Shen et
652
al., 2014). The intensive coupled O–A interaction in the WNP significantly modulates the North
653
Pacific climate variability.
654
655
7 Conclusion and Future Directions
656
The origin of the well-known extratropical forcing on ENSO and its associated pathway
657
originating from the PAMS has been analyzed in this study using observation and CESM
658
simulation. The two dominant modes of surface variability (PDO/ENSO and NPO/VM) can
30
659
explain most of the North Pacific climate variability in both the observation and CESM, and are
660
closely linked with each other. Both modes have similar peaks at the interannual and decadal
661
scales; however, CEOF1 (PDO/ENSO) is dominated by variability at the interannual scale, while
662
CEOF2 is dominated by decadal-scale variability. CEOF2 can be considered as the precursor
663
signal of CEOF1 through its identified origin in the PAMS.
664
These results lead us to believe that the PAMS region is a hot spot where the surface wind
665
anomalies resulting from EAWM and other potential causes can trigger ENSO events one year
666
later, thereby allowing the relevant ENSO onset mechanisms to occur stochastically. Here, we
667
further confirm its origin in the PAMS modulating the NPO/VM variability from -V10ps and thus
668
affecting El Niño, serving as useful precursors three to four seasons ahead. The schematic in
669
Figure 17 summarizes a possible explanation of dynamical processes linking the two dominant
670
modes through the PAMS origin. The variability of NPO is initiated by the change of meridional
671
wind (-V10ps) in the PAMS, and then the equatorward wind in the PAMS propagates
672
northeastward, thus enhancing the subsidence in the PAMS and deep convection in the southern
673
lobe of the NPO. This is followed by a typical NPO forced VM/MM (Ding et al., 2015a) forming
674
the combined SLP and SST pattern of CEOF2 from spring(0) to summer(0). The westerly wind
675
in the PAMS (U10ps) further extends southeastward to the tropical-subtropical WNP and changes
676
the Walker circulation, forming the CEOF1 pattern.
677
The detailed O–A interaction involved in the PAMS is still not clear. We find that the local
678
meridional wind anomalies in the PAMS link directly to the CEOF2 pattern but not the local SST
679
anomalies in the early development stage of NPO (December to February). Our entire analysis is
680
based on the lead–lag correlation and thus needs to be considered with caution; however, our
681
results are consistent with all precursor signals in the WNP in the literature and can explain them
31
682
all. We also confirm a thermodynamically coupled wind–evaporation–SST feedback (Xie &
683
Philander, 1994; Vimont et al., 2009; Alexander et al., 2010) for the SFM, and thus the VM/MM,
684
in spring may be applied in both observation and CESM simulation, consistent with many
685
previous studies. The local O–A interaction in the PAMS may involve the subsurface WWV
686
contribution and interaction with EAWM. Further investigations into the relevant O–A
687
interaction in the PAMS are planned using CESM forcing sensitivity studies.
688
Note that the origin in the PAMS and its influence on the sequential NPO/VM and
689
PDO/ENSO described here can be viewed as one dominant pathway in the northern hemisphere,
690
which can explain many northern hemispheric ENSO precursors in the literature. Our finding
691
supports the view that the summer U10ps in the PAMS links directly to the consequent
692
PDO/ENSO pattern (zonal variability) while the winter -V10ps in the PAMS acts as a pivotal
693
driver to modulate the NPO/VM pattern (meridional variability) through atmospheric
694
teleconnection. Alexander et al. (2010) tested the SFM hypothesis by imposing the NPO-related
695
surface heat flux anomaly forcing in a coupled general circulation model, and their results
696
showed that El Niño-like warming was generated in ~70% of ensemble simulations. Our results
697
confirm that the major ENSO cycle is still determined by its relevant tropical coupled O–A
698
interaction (Bjerknes feedback) in the evolution of CEOF1 and that the variability of NPO/VM
699
(CEOF2) may link to the surface pattern triggering an El Niño. But this is only one part of the
700
O–A interaction triggered in the PAMS region. The associated subsurface dynamic in PAMS
701
related directly to the ENSO cycle will be further discussed in a separate paper.
702
703
Acknowledgements
32
704
Support from NCAR, USA and the National Science Council, Taiwan, under the Consortium for
705
Climate Change Study (CCliCS) project of NSC-100-2119-M-001-029-MY5 is greatly
706
appreciated. NCAR is supported by the NSF. We would also like to acknowledge the National
707
Center for High-Performance Computing, Taiwan, for providing computing resources to
708
facilitate this research. The OAFLUX data is provided by the WHOI OAFlux project
709
(http://oaflux.whoi.edu), which is funded by the NOAA Climate Observations and Monitoring
710
(COM) program.
33
711
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Figure 1. The correlation of Pacific SST anomalies (based on 3-month running mean) and January(1)
Niño4 index at different lags from 1958–2010. The Niño4 index has lags of (a) 15 months; (b) 12 months;
(c) 9 months; (d) 6 months; (e) 3 months; and (f) 0 months (no lag). Only p<0.05 is shown. The contours
lines correspond to R=0.6 (bold black), 0.5 (thin black) and 0.4 (grey). The grey box in (b) shows the
specific PAMS region.
Figure 2. Climatological mean SLP (contour, units: +1000 mbar) and standard deviation of monthly SLP
anomalies (color) from (a) the observation (1958–2010) and (b) CESM simulation. (c) and (d) are the
same except for SST (units: °C). Linear trends are removed.
Figure 3. Scatter diagram of averaged zonal wind speed (or stress) vs. Niño4 SST anomalies (160°E–
150°W), normalized by their respective standard deviations from (a) the observation and (b) CESM
simulation.
Figure 4. The CEOF1 of (a, c) SLP anomalies (mbars) and (b, d) SST anomalies (°C) (3-month running
mean) in the North Pacific (top: observation; bottom: CESM simulation).
Figure 5. Same as Fig. 4 except for the CEOF2.
Figure 6. (a) Lag-correlation between Niño3 (and EMI index) and PC1/PC2 of the CEOF (3-month
running mean) based on observation. Positive axis means Niño3 (and EMI index) lags. (b) is the same as
(a) except for CESM simulation. Grey dashed line shows the 95% confidence level.
Figure 7. Wavelet power spectrum (left) and global power spectrum (right) of PC1 (colors) and PC2
(black contour lines) from the observation and CESM simulation (9-month smoothing is applied to
emphasize the interannual to decadal variability). The local wavelet power spectrum provides a measure
of the variance distribution of the time series according to time and periodicity; high variability is
represented by red, whereas blue indicates weak variability in the wavelet power spectrum. For the global
power spectrum, the dashed lines indicate 95% significant level and the periods of 3, 5 and 12 years,
respectively.
Figure 8. (a) Observed lead-lag correlation between the 3-month averaged zonal wind anomalies (U10ps) in
PAMS (110~140°E, 5~25°N) and PC1. (b) Same as (a) except for the meridional wind anomalies (-V10ps)
and PC2. Areas with a correlation that is significant at the 95% confidence level are shaded. (c) and (d)
are the same as (a) and (b), except for CESM simulation. Contours are the lead-lag correlation between
NSI and PC2 (see text for definition).
Figure 9. Time series of NDJ-averaged -V10ps, JAS-averaged U10ps in PAMS (superimposed by their
associated JFM-averaged PC2 and DJF-averaged PC1 with maximum correlations in Figs. 8a and 8b,
respectively). All time series are aligned with their corresponding months. Black solid (dashed) lines label
the El Niño (La Niña) years in December.
Figure 10. Correlation of the NDJ-averaged meridional wind anomalies (-V10ps) in PAMS (grey box in the
top-left panel) with several lags (NDJ, DJF, JFM, FMA, MAM) of SLP anomalies (left), SST anomalies
(middle) and latent heat flux anomalies (right) in the observation (contours in the left and middle panels
are the CEOF2 of SLP and SST anomalies in Fig. 5). Grey dashed boxes in the top-left panel show the
domain to define the EAWM index.
41
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Figure 11. Same as Fig. 10 except for the vertical section along 25°N of geo-potential height (left),
vertical velocity Ω (middle) and air temperature (right).
Figure 12. Correlation of the JAS-averaged zonal wind anomalies (U10ps) in PAMS (grey box in the
top-left panel) with several leads/lags (MAM, MJJ, JAS, SON, NDJ) of SLP anomalies (left), SST
anomalies (middle) and latent heat flux anomalies (right) in the observation (contours in the left and
middle panels are the CEOF1 of SLP and SST anomalies in Fig. 4).
Figure 13. Same as Fig. 12 except for the vertical section along 5°N of geo-potential height (left), vertical
velocity Ω (middle) and air temperature (right).
Figure 14. Correlation of the JAS-averaged zonal wind anomalies (U10ps) in PAMS (grey box in the
top-left panel) with several leads/lags (MAM, MJJ, JAS, SON, NDJ) of SLP anomalies (left) and SST
anomalies (right) in the CESM simulation from year 50 to 150 (contours in the left and middle panels are
the CEOF1 of SLP and SST anomalies in Fig. 4).
Figure 15. Correlation of the DJF-averaged meridional wind anomalies (-V10ps) in PAMS (grey box in the
top-left panel) with several lags (DJF, JFM, FMA, MAM, AMJ) of SLP anomalies (left) and SST
anomalies (right) in the CESM simulation from year 50 to 150 (contours in the left and middle panels are
the CEOF2 of SLP and SST anomalies in Fig. 5).
Figure 16. SLP (color shaded) and wind (vectors) anomalies averaged in NDJ, DJF, JFM, FMA and MAM
in 1981~1982 (left), 1993~1994 (middle) and 1997~1998 (right).
Figure 17. Schematic diagram of the dynamical processes linking the two dominant modes through the
PAMS origin. The SLP (contours) and SST (color) anomalies associated with CEOF2 patterns are
overlaid.
42