Long-lead ENSO control of the boreal summer intraseasonal oscillation in the East Asian-western North Pacific region
Long-lead ENSO control of the boreal summer intraseasonal oscillation in the East Asian-western North Pacific region
Hai Lin 1
0 Recherche en Prévision Numérique Atmosphérique, Environment and Climate Change Canada , Dorval, QC , Canada
1 Published in partnership with CECCR at King Abdulaziz University
ARTICLE The boreal summer 30-60-day intraseasonal oscillation (ISO) in the East Asian-western North Pacific (EAWNP) region strongly influences persistent heavy rainfall in East Asia and tropical cyclone activities in the tropical western Pacific. In this study, we show that there exists a significant interannual south-north shift of the EAWNP-ISO activity between the equatorial eastern Indian Ocean -Maritime continent region and the subtropical South China Sea-western Pacific. This is reflected by the year-to-year changes in the occurrence frequency of individual ISO phases that correspond to different latitudinal locations of the convection anomaly. This interannual shift is largely controlled by the El Niño-Southern Oscillation (ENSO) in the preceding winter and spring. Following an El Niño event, the ISO convective activity tends to occur in the equatorial eastern Indian Ocean-Maritime continents, whereas after a La Niña, frequent ISO-related precipitation occurs in the region of south China and the subtropical South China Sea-western Pacific. This result implies long-lead predictability for the occurrence of the summertime EAWNP-ISO activity and its associated weather.
An important feature of the boreal summer intraseasonal
oscillation (ISO) is its northward propagation in the Asian summer
monsoon region,1–6 which has an important impact on the
weather in the highly populated East Asian region. Rainfall
variability of the East Asian summer monsoon system is largely
determined by the northward propagating ISO. For example, the
timing of the active and break of the Asian monsoon as well as
precipitation associated with the Mei-Yu in central China are
influenced by the ISO.7–9 The ISO in the western North Pacific
region can modulate the subtropical high variability and typhoon
activity.10,11 The EAWNP-ISO activity is closely associated with
heavy rainfall in south China,12–14 and tropical cyclone activity in
the tropical western Pacific.15–18 Besides its local impact, the East
Asian monsoon variability has a far-reaching influence on the
global atmospheric circulation through teleconnections.19–21 It is
clear that a close monitoring and a skilful forecast of the ISO in the
EAWNP region are of great societal and economical importance.
The boreal summer EAWNP-ISO undergoes a significant
interannual variability. This usually leads to year-to-year changes
in frequency of tropical cyclone,18 total precipitation,22 and the
date of Mei-Yu onset.9 The El Niño—Southern Oscillation (ENSO)
has been identified as an important driver for the ISO interannual
variability in previous studies. For example, the western Pacific ISO
intensity was found to be strong during an El Niño developing
summer, but weak in an El Niño decaying summer.23,24 The ENSO
modulation of ISO intensity depends on the ISO time scale and
location.25,26 A correlation between the northward propagating
ISO in the East Asian summer monsoon region (22.5°–45°N) and
the preceding winter ENSO was reported.27 It was found that
during El Niño summers, the western North Pacific ISO is
dominated by a higher-frequency oscillation of around 20–40 days,
whereas during La Niña summers the ISO is dominated by a
lower-frequency of around 40–70 days.25 However, questions
remain such as what is the dominant mode of EAWNP-ISO
interannual variability, how the ISO in the East Asian summer
monsoon region is associated with the northward propagation of
the tropical ISO, and what is the mechanism for the ENSO
influence of EAWNP-ISO.
In this study, we aim to address the above questions. We show
that the EAWNP-ISO activity undergoes a strong interannual
variability of its location of preferred occurrence, which is strongly
controlled by ENSO in the preceding seasons. This would provide
an important source of skill for seasonal predictions of persistent
heavy rainfall and tropical cyclone activities.
Shown in Fig. 1a is the 38-summer average and standard deviation
of the frequency of occurrence for all eight EAWNP-ISO phases
(aee the Methods section). It can be seen that phases 2–3 and 7–8
have a relatively high frequency of appearance, consistent with
previous studies.9,28 Phases 2 and 7–8 have a relatively large
interannual variability (Fig. 1a red bars). As shown in Fig. 2, in the
ISO phases 2–3 the enhanced convection occurs in the eastern
Indian Ocean—Maritime continents, while in phases 7–8 the
convection is located in the subtropical South China Sea—western
Pacific. This indicates that the EAWNP-ISO has a relatively large
year-to-year variability in these two regions.
Figure 1b illustrates the leading EOF mode (EOF1) of the
frequency of occurrence, which accounts for 39% of the total
variance and is well separated from the other EOF modes
according to the sampling error criterion.29 As can be seen, the
dominant mode is characterized by an out-of-phase relationship
between EAWNP-ISO phase 2 and phase 7. This indicates an
interannual shift of occurrence frequency of EAWNP-ISO
a) Mean and standard deviation
convection between the equatorial region of the eastern Indian
Ocean—Maritime continents and the subtropical South China Sea
Shown in Fig. 1c in red curve is the time evolution of the
principal component of EOF1 (PC1), which is normalized by its
standard deviation. Strong interannual variability can be observed.
Figure 3a compares the northward propagation feature of the
EAWNP-ISO for strong positive and strong negative PC1 summers,
where lagged regressions of zonally averaged OLR anomaly of
90°–150°E with respect to that at the equator are plotted for
summers with PC1 > 0.5 and PC1 < −0.5, respectively. The zonal
range of 90°–150°E is the same as that used to define the
EAWNPISO index.28 Both the positive and negative PC1 cases have
10 summers selected. As is seen, the behavior of the EAWNP-ISO is
different between the positive and negative PC1 summers. During
positive PC1, the northward propagation of the ISO convection is
confined to south of 20°N. This reflects the fact that there is a
strong preference of ISO for phase 2 during positive PC1 (Fig. 1c).
On the other hand, during negative PC1, the EAWNP-ISO
northward propagation is much stronger and reaches farther
north to 30°N. This indicates that in positive PC1 summers, the ISO
tends to stay in the tropical Indian Ocean and Maritime continents
area, whereas in negative PC1 summers, the EAWNP-ISO can
propagate further north and influence the weather in south China.
The frequency of occurrence of the ISO-related convection in
specific locations may also determine the distribution of
summertime mean convection and total precipitation. Shown in
Fig. 3c is the difference of composites of May–September
averaged OLR anomaly between positive PC1 and negative PC1.
In positive (negative) PC1 summers, the summertime averaged
convection activity is enhanced (reduced) in the equatorial eastern
Indian Ocean and suppressed (enhanced) in the western Pacific
region around 15°N.
ENSO is a strong interannual variability signal, which influences
the ISO by changing its background basic state. To investigate the
relationship between the interannual variability of the EAWNP-ISO
and ENSO, composites of Nino3.4 index are calculated for positive
and negative PC1 years. As the peak season of ENSO is in boreal
winter, the composite is made for the months preceding the
summer ISO season. A Student’s t-test is performed for the null
hypothesis of a zero composite. As shown in Fig. 1d, statistically
significant positive Nino3.4 index is observed for positive PC1
years in the preceding months from October to April, with the
maximum value in December. For negative PC1, significant
negative Nino3.4 index is seen from the preceding December to
March with the strongest value in January. To better visualize this
association, the Nino3.4 index averaged in January, February, and
March is plotted in Fig. 1c in black curve, which matches very well
with the PC1 time series (red curve). The correlation coefficient
between these two time series reaches 0.82. This clearly indicates
that the dominant mode of summertime ISO frequency is largely
controlled by the ENSO activity in the preceding winter and
An important question to ask is how ENSO controls the
EAWNPISO variability. Previous studies30,31 have demonstrated that El
Niño (La Niña) in boreal winter influences the East Asia climate
through a Pacific-East Asian teleconnection, which is linked by a
lower-tropospheric anticyclonic (cyclonic) anomaly in the western
North Pacific as a Rossby wave response to the equatorial western
Pacific suppressed (enhanced) convection. The western North
Pacific circulation anomaly can then persist to the following
summer. Shown in Fig. 4a is the differences of composites for
May–September averaged Z850 anomaly between years with
Nino3.4 index in the preceding January to March greater than 0.5
and smaller than −0.5. Positive Z850 anomalies are seen in the
western Pacific and Southeast Asia around 20°N, consistent with
what was observed.30 These positive Z850 anomalies result in
westward extension of the subtropical high in the western Pacific
accompanied with lower-tropospheric divergence. As a result,
collocated with these positive Z850 anomalies are negative
anomalies of 850 hPa specific humidity (Fig. 4b). As discussed in
previous studies,25,32–34 the seasonal mean moisture field is one of
the important factors for the ISO propagation. Following an El
Niño, the western Pacific subtropical anticyclonic anomaly and its
associated negative moisture anomalies hinder the northward
propagation of the ISO, causing the ISO convection to remain in
phases 2 and 3. After a La Niña, on the other hand, the cyclonic
anomaly in the subtropical western Pacific and the associated
positive moisture anomalies helps the ISO to propagate northward
to reach South China and the subtropical western Pacific, a
location corresponding to ISO phase 7. Therefore, the interannual
south-north shift of the EAWNP-ISO activity between the
equatorial eastern Indian Ocean—Maritime continent region and
the subtropical South China Sea—western Pacific is largely
controlled by the ENSO activity in the preceding spring and
summer through the Pacific-East Asian teleconnection.
This study reveals a significant interannual south-north shift of the
intraseasonal oscillation activity between the equatorial eastern
Indian Ocean—Maritime continent region and the subtropical
South China Sea—western Pacific. This interannual variability is
highly correlated with ENSO in the preceding winter and spring
(a correlation of 0.82). This implies a long-lead predictability for
the ISO and its associated persistent heavy rainfall and tropical
cyclone activities in the East Asian and western North Pacific
As shown in Fig. 3, stronger northward propagation of the ISO
occurs during summers when PC1 is negative than in summers
when PC1 is positive. Negative (positive) PC1 likely follows a La
Nina (El Nino) winter and spring, indicating that the ISO northward
propagation is possibly determined by ENSO and predictable
several seasons in advance. The ISO northward propagation likely
influences the subseasonal to seasonal (S2S) forecast skill. This
implies that the summertime S2S forecast skill in the East Asian
and western North Pacific region is also closely related to ENSO.
The data used in this study include the daily averaged values of 850 hPa
geopotential height (Z850) and specific humidity (SHUM850), and zonal
wind (U850) of the NCEP/NCAR reanalysis,35 and the outgoing longwave
radiation (OLR) from the National Oceanic and Atmospheric Administration
(NOAA) polar-orbiting series of satellites.36 The data cover the period of 38
years from 1979 to 2016. The analysis is performed for the ISO in the
extended boreal summer season of 153 days from May 1 to September 30.
The OLR anomalies that are used to produce Fig. 2, Fig. 3a, b are
obtained following the same steps as described in Lin.28 In brief, starting
with the unfiltered daily OLR data for the whole year from 1979 to 2016,
the seasonal cycle which is the time mean and first three harmonics of the
daily climatology is first removed for each grid point. Then the time
average of the 120 days immediately preceding each day is subtracted. By
subtracting the previous 120-day average most of the interannual
variability is removed. Left in the daily anomaly is variability mainly of
subseasonal time scales. Finally, we select the daily anomaly data of the
boreal summer period of 153 days from May 1 to September 30 to perform
The monthly Nino3.4 index is obtained from the NOAA ESRL Physical
Sciences Division website, which is based on the HadISST1 dataset.37 The
Nino3.4 index is the area average of SST anomaly in the tropical Pacific
(5°N–5°S, 170°–120°W) with the 1981–2010 mean removed.
Fig. 4 Differences of composites for May–September averaged
a geopotential height b specific humidity anomalies at 850 hPa
between years with Nino3.4 index in the preceding January to
March greater than 0.5 and smaller than -0.5. The contour interval is
2 m for a and 0.1 g kg−1 for b. Contours for positive values are in red,
whereas those for negative values are in blue. The zero contour is
omitted. The shaded areas indicate that the value is different from
zero at a significance level of 0.05 according to a Student’s t-test
To represent the ISO in the East Asian-western North Pacific region, the
EAWNP-ISO index28 is used, which is calculated as principal components
(PCs) of the two leading empirical orthogonal function (EOF) modes of the
EOF analysis on the combined anomaly fields of 90°–150°E zonally
averaged zonal wind at 850 and OLR from 10°S to 40°N. Eight phases are
defined for the EAWNP-ISO corresponding to different latitudinal locations
of the ISO convection (Fig. 2). This EAWNP-ISO index has been applied in
previous studies to analyze the impact of subseasonal variability on
persistent heavy rainfall in South China,12 tropical cyclone genesis over the
western North Pacific,18 and Mei-Yu Onset.9 Different boreal summer ISO
indices have been designed in the literature to represent the northward
ISO propagation.38,39 The results presented here are expected to be
independent of the ISO index definition.
To investigate the interannual variability of the EAWNP-ISO, we adopt an
approach similar to that described in a previous study40 where the
interannual variability of the Madden-Julian Oscillation41 was analyzed.
Based on the phase and amplitude data of the daily EAWNP-ISO index, we
count the number of days, i.e., frequency of occurrence, of each phase
during each extended summer, where the days are included when the
EAWNP-ISO amplitude is greater than 1.0. Different amplitude thresholds
are used and the results are found insensitive to the threshold. Therefore,
the frequency of occurrence is a function of ISO phase and year
(Supplementary Table 1 in supplemental material). To identify the
dominant phase structure of the interannual variability of the
EAWNPISO, an empirical orthogonal function (EOF) analysis is performed on the
frequency of occurrence. This study focuses on the first EOF mode. The
second EOF (EOF2) accounts for 15% of the total variance, and shows an
out-of-phase relationship between EAWNP-ISO phase 7 and phases 8/1
(Supplementary Fig. 1a). Its principal component (PC2) is illustrated in
Supplementary Fig. 1b.
Shown in Fig. 3a, b are lead-lag regressions that are calculated between
the OLR anomalies at the reference latitude (i.e., the equator) and those at
all different latitudes in the extended summers with PC1 > 0.5 and PC1 <
−0.5, respectively. A negative lag (−n) means that the OLR anomalies lead
that at the reference point by n days, while a positive lag (+n) indicates
that the OLR anomalies lag that at the reference point by n days. The
values of n from −30 to 30 are used. The regression values plotted in
Fig. 3a, b correspond to one standard deviation of the OLR variability at the
reference latitude, thus they have a unit of W m−2.
The research presented here was derived from data sets that are all publicly available.
The datasets generated during and/or analyzed during the current study are available
from the author on reasonable requests.
Fortran 77 computer code was used for data processing and analysis. Computer
codes are available from the author upon reasonable requests.
The NCEP/NCAR Reanalysis and OLR data were provided by the NOAA/OAR/ESRL
PSD, Boulder, CO, USA, from their Website at https://www.esrl.noaa.gov/psd. The
author would like to thank two anonymous reviewers for their helpful comments.
The author performed the analysis, interpretation of results and writing.
Supplementary information accompanies the paper on the npj Climate and
Atmospheric Science website (https://doi.org/10.1038/s41612-019-0088-2).
Competing interests: The author declares no competing interests.
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