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Citation: Hou, J.; Fang, Z.; Geng, X.
Recent Strengthening of the ENSO
Influence on the Early Winter East
Atlantic Pattern. Atmosphere 2023,14,
1809. https://doi.org/10.3390/
atmos14121809
Academic Editor: Muhammad
Azhar Ehsan
Received: 9 October 2023
Revised: 8 December 2023
Accepted: 9 December 2023
Published: 11 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
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4.0/).
atmosphere
Article
Recent Strengthening of the ENSO Influence on the Early
Winter East Atlantic Pattern
Jiayi Hou 1,2, Zheng Fang 1,2 and Xin Geng 1,2,3,*
1CIC-FEMD/ILCEC, Key Laboratory of Meteorological Disaster of Ministry of Education (KLME),
Nanjing University of Information Science and Technology, Nanjing 210044, China
2School of Atmospheric Science, Nanjing University of Information Science and Technology,
Nanjing 210044, China
3Division of Environmental Science and Engineering, Pohang University of Science and Technology,
Pohang 37673, Republic of Korea
*Correspondence: gengxin@nuist.edu.cn
Abstract:
Previous studies have demonstrated that the influence of the El Niño–Southern Oscillation
(ENSO) on the Euro-Atlantic atmospheric circulation varies considerably during the boreal winter.
Compared to the late winter (January–March) relationship, the early winter (November–December)
teleconnection is more uncertain and less understood. In this paper, we revisited this early winter
regional ENSO teleconnection using the Hadley Centre Global Sea Ice and Sea Surface Temperature
(HadISST) and the European Centre for Medium-Range Weather Forecasting (ECMWF) fifth genera-
tion reanalysis (ERA5) datasets for the period 1979–2022. It was found that the signal projected well
onto the second dominant mode of Euro-Atlantic atmospheric variability, the East Atlantic Pattern
(EAP), rather than the previously mentioned North Atlantic Oscillation (NAO). This influence is
associated with ENSO-induced dipolar convection anomalies in the Gulf of Mexico and Caribbean
Sea (GMCA), which leads to an EAP via exciting Rossby waves propagating northward into the
North Atlantic. We further revealed that this ENSO–EAP teleconnection underwent a pronounced
interdecadal strengthening around the late 1990s. Prior to the late 1990s, the convective response to
ENSO in the GMCA was weak. The atmospheric responses over the Euro-Atlantic were mainly driven
by the ENSO-induced convective forcing in the tropical Indian Ocean, which favors an NAO-like
pattern. In contrast, since the late 1990s, ENSO has induced stronger precipitation anomalies in the
GMCA, which exert a dominant influence on the Euro-Atlantic atmospheric circulation and produce
an EAP. These results have useful implications for the further understanding of ENSO-related early
winter atmospheric and climate variability in the Euro-Atlantic region.
Keywords:
ENSO; East Atlantic Pattern; North Atlantic Oscillation; ENSO teleconnection; early winter
1. Introduction
The climate impacts of the El Niño–Southern Oscillation (ENSO) have been demon-
strated almost everywhere on the globe [
1
–
3
], including remote areas outside the Pacific,
such as the Euro-Atlantic sector [
4
]. The canonical atmospheric response over this region is
first detected in the boreal late winter (January–March) and resembles a negative (positive)
phase of the North Atlantic Oscillation (NAO) during El Niño (La Niña) conditions [
4
].
However, unlike the climate response in the North Pacific and North America, which is rel-
atively robust and well understood, the ENSO effect in this region is subject to considerable
uncertainty [5–7].
It is demonstrated that the Euro-Atlantic ENSO signal varies nonlinearly with ENSO
strength [
8
–
10
], is sensitive to ENSO flavors [
11
,
12
], and exhibits robust subseasonal nonsta-
tionarity from November to March [
13
–
15
]. During the early winter (November–December)
of El Niño events, the Euro-Atlantic atmospheric response is characterized by a negative
Atmosphere 2023,14, 1809. https://doi.org/10.3390/atmos14121809 https://www.mdpi.com/journal/atmosphere
Atmosphere 2023,14, 1809 2 of 14
sea level pressure (SLP) anomaly in the midlatitudes and a positive anomaly in the subtrop-
ics [
14
–
17
]. But the response abruptly reverses its sign in early January [
18
], and a negative
NAO pattern is present thereafter [
15
,
19
,
20
]. The mechanisms responsible for the ENSO
teleconnection also differ between early and late winter. While the former is mostly due to
the Rossby waves excited by the ENSO-induced tropical convection anomalies [
14
,
15
,
17
],
the latter is generally considered as a consequence of a combination of ENSO-related
tropical North Atlantic sea surface temperature (SST) modulation [
21
,
22
], tropospheric
Rossby waves or transient eddies [
20
,
23
], and stratospheric processes involving a perturbed
polar vortex [
19
,
24
,
25
]. Although ENSO can be satisfactorily predicted by the models
in dynamical prediction systems [
26
,
27
], the spatio-temporal complexity of this ENSO
teleconnection has posed a major challenge for climate models to skillfully capture the
Euro-Atlantic wintertime atmospheric circulation anomalies associated with ENSO [6].
The low fidelity of the ENSO influence on the Euro-Atlantic climate in global climate
models is suggested to be more severe in early winter than that in late winter [
28
,
29
],
indicating a need for further understanding. Although the model biases of the ENSO SST
or convection feature may be one of the reasons [
14
], we would like to point out here that
even for observations, the climate research community has not reached a consensus on the
question about what the ENSO influence on the early winter Euro-Atlantic atmospheric
circulation is and how this influence is generated. While some studies claim an ENSO–
NAO teleconnection in early winter [15,17], other studies suggested that the ENSO signal
corresponds to an East Atlantic pattern (EAP) [
30
,
31
]. As we know, the NAO and the
EAP are two dominant modes of the atmospheric circulation variability over the Euro-
Atlantic region [
32
–
35
]. They are theoretically orthogonal and have different climate
effects on the region [
34
,
36
]. These divergent views about the ENSO footprint in the early
winter atmospheric circulation make it difficult to assess the ability of climate models to
capture the true ENSO influence. ENSO-induced tropical convection anomalies, which
are evident in almost all the tropical ocean basins, have been suggested to be the key
factors for the early winter ENSO influence [
14
,
15
,
17
]. However, precipitation anomalies in
different tropical ocean basins tend to have different extratropical effects [
37
,
38
]. In this
case, this inconsistency may be related to an insufficient consideration of the respective
roles of different tropical convection anomalies. The role of the monopolar anomalies
in the western-central Indian Ocean (WCIO) [
38
,
39
], the western Pacific [
16
], and the
central-eastern Pacific [
18
,
38
], as well as the dipolar convection anomalies over the tropical
western–eastern Indian Ocean (TWEIO) [
15
,
17
] and over the Gulf of Mexico–Caribbean Sea
(GMCA) [
14
,
40
], needs to be quantified and compared. In addition, the late winter ENSO–
NAO teleconnection has been reported to show prominent interdecadal variations [
5
,
41
].
How the early winter ENSO teleconnection has changed in the recent decades is also an
important scientific question to be addressed.
Considering the above scientific issues, we revisited the ENSO influence on the early
winter Euro-Atlantic atmospheric circulation in this study. Based on the latest reanalysis
datasets from 1979 to 2022, we show that the early winter ENSO signal projects better
onto the EAP rather than onto the NAO, which is frequently mentioned previously. We
further suggest that this ENSO–EAP relationship is evidently intensified during the past
two decades. Possible mechanisms responsible for this ENSO influence and its interdecadal
change are also proposed. In the remainder of the article, Section 2describes the data and
methodology, Section 3provides the results and possible mechanisms, and the conclusions
and discussion are summarized in Section 4.
2. Data and Methodology
In this study, the monthly datasets (1979–2022) used include global SST from the
Hadley Centre sea ice and SST dataset (HadISST) version 1.1 with the horizontal reso-
lution of 1
◦
longitude
×
1
◦
latitude [
42
]. The precipitation and atmospheric circulation
datasets are derived from the fifth generation of the European Centre for Medium-Range
Weather Forecasts (ECMWF) Reanalysis (ERA5) [
43
] with the horizontal resolution of 1
◦
Atmosphere 2023,14, 1809 3 of 14
longitude
×
1
◦
latitude. To confirm our results, the atmospheric circulation was also ex-
amined based on the National Centers for Environmental Prediction/National Center for
Atmospheric Research (NCEP/NCAR) Reanalysis dataset with a horizontal resolution of
2.5◦×2.5◦[44]
and the Japanese 55 year (JRA-55) Reanalysis dataset with a horizontal
resolution of
1.25◦×1.25◦[45].
We also utilized the monthly CMAP precipitation dataset
(2.5◦×2.5◦) [46], which is a combination of various satellite estimates and gauge data.
We focused the analysis mainly on the boreal winter season (November–March, ND-
JFM), and unless explicitly stated, the winter of 2000 represents the average from Novem-
ber 2000 to March 2001. The early winter in this study denotes the two-month means of
November–December. Anomalies were calculated relative to the monthly mean climatol-
ogy over the entire period we used (1979–2022). To exclude the possible effects associated
with global warming or long-term trends, the linear trends in all variables were removed.
Statistical significance tests were performed using the two-tailed Student’s t-test.
Several climatic indices were employed to facilitate our analyses. The Niño-3.4 index,
which is defined as the area-averaged SST anomalies in the Niño-3.4 region (5
◦
S–5
◦
N,
120
◦
–170
◦
W), was used to measure the amplitude of ENSO. A threshold of
±
0.5 standard
deviations of the December to February (DJF) average Niño-3.4 index defines ENSO winters.
This method identifies 14 El Niño winters, i.e., 1982, 1986, 1987, 1991, 1994, 1997, 2002, 2004,
2006, 2009, 2014, 2015, 2018, and 2019, and 15 La Niña winters, i.e., 1983, 1984, 1985, 1988,
1995, 1998, 1999, 2000, 2005, 2007, 2008, 2010, 2011, 2017, and 2020. The horizontal wave
activity flux (WAF) developed by Takaya and Nakamura [
47
] was applied to analyze the
source and direction of Rossby wave energy propagation. It is defined as
F=pcos ϕ
2|U|
U
a2cos2ϕ∂ψ0
∂λ 2−ψ0∂2ψ0
∂λ2+V
a2cos φ∂ψ0
∂λ
∂ψ0
∂ϕ −ψ0∂2ψ0
∂λ∂ ϕ
U
a2cos ϕ∂ψ0
∂λ
∂ψ0
∂ϕ −ψ0∂2ψ0
∂λ∂ ϕ +V
a2∂ψ0
∂ϕ 2−ψ0∂2ψ0
∂ϕ2(1)
where Fis the WAF; pis the pressure normalized to 1000 hPa;
ϕ
is the latitude;
λ
is the
longitude; and ais the radius of the Earth. The geostrophic stream function
ψ
is defined as
z/f, where zis the geopotential, and f(=2
Ω
sin
ϕ
) is the Coriolis parameter with the Earth’s
rotation rate (
Ω
). Also, |U|, U, and Vrepresent the basic states of wind speed, zonal, and
meridional wind, respectively, while
ψ0
denotes the perturbed stream function. Since the
Coriolis parameter approaches zero near the equator, the WAF is not calculated or plotted
within the latitudes of 10◦S–10◦N.
3. Results
3.1. Early Winter ENSO Teleconnection over the Euro-Atlantic Sector
Considering that both the NAO [
15
,
17
] and EAP [
30
,
31
] have been proposed to be
related to the ENSO forcing in early winter, we first performed an empirical orthogonal
function (EOF) analysis of the SLP anomalies over the Euro-Atlantic region (25–80
◦
N,
70
◦
W–40
◦
E) to describe these two modes in Figure 1. The first mode was consisted
of a north–south dipole of the SLP anomalies near Iceland and the Azores, showing a
distinct NAO-like pattern. The second mode also showed a north-south dipole of anomaly
centers spanning the North Atlantic from east to west, but its anomaly centers were shifted
southeast to the approximate nodal lines of the NAO pattern, referred to as the EAP mode.
These results are consistent with previous studies [
33
,
35
,
48
] and remained unchanged when
using different reanalysis datasets such as the NECP/NCAR (Figure S1) and the JRA-55
(Figure S2). The NAO and EAP indices are thus defined as the standardized first and second
time series of these two modes, respectively. Note that we define the positive EAP pattern
here as characterized by the center south of Iceland showing low pressure anomalies.
Atmosphere 2023,14, 1809 4 of 14
Atmosphere 2023, 14, x FOR PEER REVIEW 4 of 14
time series of these two modes, respectively. Note that we define the positive EAP paern
here as characterized by the center south of Iceland showing low pressure anomalies.
Figure 1. The (a) first and (c) second EOF spatial paerns (shading in hPa) and the corresponding
(b) first and (d) second normalized time series (representing the NAO and EAP indices, respec-
tively) of the early winter SLP anomalies in the North Atlantic region during 1979–2022. The per-
centages in (a,b) are the variability explained by the corresponding EOF. The dots in (b) indicate the
anomalies exceeding the 95% confidence level.
To reassess the influence of ENSO, Figure 2a illustrates the spatial paern of the early
winter SLP anomalies regressed onto the Niño-3.4 index. It shows a dipolar SLP paern
over the North Atlantic, with the negative anomaly located to the south of Iceland and
west of Ireland, and the positive anomaly located in the subtropics. When comparing this
paern (Figure 2a) with the NAO and EAP (Figure 1a,c), it becomes evident that it aligns
more closely with the EAP than with the NAO. The paern correlation coefficients of the
ENSO-regressed SLP spatial distribution with the NAO and EAP paerns over the Euro-
Atlantic region were 0.57 and 0.77, respectively. And the temporal correlation coefficients
of the Niño-3.4 index with the NAO and EAP indices were, respectively, 0.18 and 0.45
(Figure 2b). While the ENSO-NAO relationship was weak, the laer was strong and sig-
nificant at the 95% confidence level, suggesting that ENSO could exert a robust in-phase
influence on the early winter EAP. The close relationship between the ENSO and EAP was
also manifested in the high paern similarity of their associated tropical precipitation
anomalies (Figure 2c,d). A warm ENSO event can induce a strong positive precipitation
anomaly in the central-eastern tropical Pacific, the western-central Indian Ocean, and the
Gulf of Mexico. Meanwhile, negative precipitation anomaly was detected in the western
Pacific and the Caribbean Sea. These precipitation anomalies were also accompanied by a
positive EAP paern. As suggested by previous studies [14,15,17], during early winter,
ENSO-induced tropical convection anomalies play a key role in transmiing the ENSO
Figure 1.
The (
a
) first and (
c
) second EOF spatial patterns (shading in hPa) and the corresponding (
b
)
first and (
d
) second normalized time series (representing the NAO and EAP indices, respectively) of
the early winter SLP anomalies in the North Atlantic region during 1979–2022. The percentages in
(
a
,
b
) are the variability explained by the corresponding EOF. The dots in (
b
) indicate the anomalies
exceeding the 95% confidence level.
To reassess the influence of ENSO, Figure 2a illustrates the spatial pattern of the early
winter SLP anomalies regressed onto the Niño-3.4 index. It shows a dipolar SLP pattern
over the North Atlantic, with the negative anomaly located to the south of Iceland and
west of Ireland, and the positive anomaly located in the subtropics. When comparing
this pattern (Figure 2a) with the NAO and EAP (Figure 1a,c), it becomes evident that it
aligns more closely with the EAP than with the NAO. The pattern correlation coefficients
of the ENSO-regressed SLP spatial distribution with the NAO and EAP patterns over
the Euro-Atlantic region were 0.57 and 0.77, respectively. And the temporal correlation
coefficients of the Niño-3.4 index with the NAO and EAP indices were, respectively, 0.18
and 0.45 (Figure 2b). While the ENSO-NAO relationship was weak, the latter was strong
and significant at the 95% confidence level, suggesting that ENSO could exert a robust in-
phase influence on the early winter EAP. The close relationship between the ENSO and EAP
was also manifested in the high pattern similarity of their associated tropical precipitation
anomalies (Figure 2c,d). A warm ENSO event can induce a strong positive precipitation
anomaly in the central-eastern tropical Pacific, the western-central Indian Ocean, and the
Gulf of Mexico. Meanwhile, negative precipitation anomaly was detected in the western
Pacific and the Caribbean Sea. These precipitation anomalies were also accompanied by
a positive EAP pattern. As suggested by previous studies [
14
,
15
,
17
], during early winter,
ENSO-induced tropical convection anomalies play a key role in transmitting the ENSO
signal to the North Atlantic. We can therefore infer that the precipitation anomalies marked
in Figure 2c,d may be important for the ENSO–EAP teleconnection.
Atmosphere 2023,14, 1809 5 of 14
Atmosphere 2023, 14, x FOR PEER REVIEW 5 of 14
signal to the North Atlantic. We can therefore infer that the precipitation anomalies
marked in Figure 2c,d may be important for the ENSO–EAP teleconnection.
Figure 2. (a) Regression coefficients of the early winter North Atlantic SLP anomalies (shading in
hPa) with respect to the DJF Niño-3.4 index. (b) Scaerplot of the early winter NAO (red quadrate)
and EAP (blue circle) indices with the DJF Niño-3.4 index with the corresponding linear regression
lines. The correlation coefficients (R) of the NAO and EAP indices with the Niño-3.4 index are also
displayed. (c) Regression coefficients of the early winter tropical precipitation anomalies (shading
in mmday−1) with respect to the DJF Niño-3.4 index. (d) is the same as (c) but for the regression
coefficients with respect to the early winter EAP index. The dots in (a,c,d) indicate the anomaliesex-
ceeding the 95% confidence level. The navy, green, and red boxes in (c,d) mark the domains used to
define the Pr_TWEIO, Pr_CP, and Pr_GMCA indices, respectively.
To understand the relative role of these ENSO-related tropical precipitation anoma-
lies in the early winter ENSO teleconnection, we define three precipitation indices accord-
ing to Figure 2c,d. The tropical western-eastern Indian Ocean dipolar precipitation index
(Pr_TWEIO) is defined as the difference between the area-averaged precipitation anomaly
over the tropical western (10° S–10° N, 40°–80° E) and eastern (10° S–15° N, 90°–140° E)
Indian Oceans. The tropical central Pacific index (Pr_CP) is represented by the area-aver-
aged precipitation anomaly in the tropical central Pacific (5° S–5° N, 160° E–180°–120° W).
And the dipolar GMCA precipitation index (Pr_GMCA) is defined as the difference be-
tween the area-averaged precipitation anomaly over the Gulf of Mexico (22°–35° N, 75°–
100° W) and the Caribbean Sea (5°–18° N, 65°–85° W). The definitions of these three indices
basically follow those used in previous studies [14,15,18]. All of these three indices
showed a significant relationship with the EAP (Table 1). In order to examine their indi-
vidual effect, we performed a multiple regression analysis. It was found that the inde-
pendent effects of the Pr_CP and Pr_TWEIO indices were no longer significant at the 95%
confidence level. Only the Pr_GMCA index, with a regression coefficient of 0.50, was still
able to have an independent significant influence on the EAP. This Pr_GMCA index alone
can explain 35% of the total variance of the EAP, suggesting that the precipitation
Figure 2.
(
a
) Regression coefficients of the early winter North Atlantic SLP anomalies (shading in hPa)
with respect to the DJF Niño-3.4 index. (
b
) Scatterplot of the early winter NAO (red quadrate) and
EAP (blue circle) indices with the DJF Niño-3.4 index with the corresponding linear regression lines.
The correlation coefficients (R) of the NAO and EAP indices with the Niño-3.4 index are also displayed.
(
c
) Regression coefficients of the early winter tropical precipitation anomalies (shading in mmday
−1
)
with respect to the DJF Niño-3.4 index. (
d
) is the same as (
c
) but for the regression coefficients
with respect to the early winter EAP index. The dots in (
a
,
c
,
d
) indicate the anomaliesexceeding the
95% confidence level. The navy, green, and red boxes in (
c
,
d
) mark the domains used to define the
Pr_TWEIO, Pr_CP, and Pr_GMCA indices, respectively.
To understand the relative role of these ENSO-related tropical precipitation anomalies
in the early winter ENSO teleconnection, we define three precipitation indices according
to Figure 2c,d. The tropical western-eastern Indian Ocean dipolar precipitation index
(Pr_TWEIO) is defined as the difference between the area-averaged precipitation anomaly
over the tropical western (10
◦
S–10
◦
N, 40
◦
–80
◦
E) and eastern (10
◦
S–15
◦
N,
90◦–140◦E
) In-
dian Oceans. The tropical central Pacific index (Pr_CP) is represented by the area-averaged
precipitation anomaly in the tropical central Pacific (5
◦
S–5
◦
N,
160◦E–180◦–120◦W
). And
the dipolar GMCA precipitation index (Pr_GMCA) is defined as the difference between
the area-averaged precipitation anomaly over the Gulf of Mexico (22
◦
–35
◦
N,
75◦–100◦W
)
and the Caribbean Sea (5
◦
–18
◦
N, 65
◦
–85
◦
W). The definitions of these three indices basi-
cally follow those used in previous studies [
14
,
15
,
18
]. All of these three indices showed a
significant relationship with the EAP (Table 1). In order to examine their individual effect,
we performed a multiple regression analysis. It was found that the independent effects
of the Pr_CP and Pr_TWEIO indices were no longer significant at the 95% confidence
level. Only the Pr_GMCA index, with a regression coefficient of 0.50, was still able to
have an independent significant influence on the EAP. This Pr_GMCA index alone can
explain 35% of the total variance of the EAP, suggesting that the precipitation anomalies in
the GMCA may be the key bridge that transmits ENSO effects to the North Atlantic. To
uncover the physical processes by which the Pr_GMCA influences the EAP, in Figure 3, we
Atmosphere 2023,14, 1809 6 of 14
show the regressed early winter 200-hPa geopotential height anomalies and the associated
WAF with respect to the Niño-3.4 and Pr_GMCA indices, respectively. For both cases, an
evident positive geopotential height anomaly existed to the east of the Gulf of Mexico.
Over there, a Rossby wave train formed and propagated northward, creating a negative
anomaly to the south of Iceland and west of Ireland, resulting in a positive EAP. This strong
similarity between the ENSO-regressed and Pr_GMCA-regressed patterns across the North
Atlantic indicated that the PR_GMCA played an important role in creating the ENSO–EAP
teleconnection in early winter by exciting a Rossby wave train. These results remained
essentially unchanged when the CMAP precipitation dataset was utilized (Figure S3), and
were generally in good agreement with previous studies that also underscore the role of
the Pr_GMCA [14,41], although they do not mention the ENSO–EAP relationship.
Table 1.
Regressed and partially regressed EAP index onto the tropical precipitation indices. The
value marked with an asterisk indicates that it is significant at the 95% confidence level.
Pr_CP Pr_TWEIO Pr_GMCA
Regressed EAP index 0.46 * 0.51 * 0.59 *
Partially regressed EAP index −0.12 0.35 0.50 *
Atmosphere 2023, 14, x FOR PEER REVIEW 6 of 14
anomalies in the GMCA may be the key bridge that transmits ENSO effects to the North
Atlantic. To uncover the physical processes by which the Pr_GMCA influences the EAP,
in Figure 3, we show the regressed early winter 200-hPa geopotential height anomalies
and the associated WAF with respect to the Niño-3.4 and Pr_GMCA indices, respectively.
For both cases, an evident positive geopotential height anomaly existed to the east of the
Gulf of Mexico. Over there, a Rossby wave train formed and propagated northward, cre-
ating a negative anomaly to the south of Iceland and west of Ireland, resulting in a positive
EAP. This strong similarity between the ENSO-regressed and Pr_GMCA-regressed pat-
terns across the North Atlantic indicated that the PR_GMCA played an important role in
creating the ENSO–EAP teleconnection in early winter by exciting a Rossby wave train.
These results remained essentially unchanged when the CMAP precipitation dataset was
utilized (Figure S3), and were generally in good agreement with previous studies that also
underscore the role of the Pr_GMCA [14,41], although they do not mention the ENSO–
EAP relationship.
Table 1. Regressed and partially regressed EAP index onto the tropical precipitation indices. The
value marked with an asterisk indicates that it is significant at the 95% confidence level.
Pr_CP Pr_TWEIO Pr_GMCA
Regressed EAP index 0.46 * 0.51 * 0.59 *
Partially regressed EAP index −0.12 0.35 0.50 *
Figure 3. Regression coefficients of the early winter 200 hPa geopotential height anomalies (shading
in m) and the associated anomalous wave activity flux (WAF, vectors in m2s−2) with respect to the
(a) DJF Niño-3.4 and (b) early winter Pr_GMCA indices. The dots denote the geopotential height
anomalies exceeding the 95% confidence level. The anomalous WAF flux is shown only when its
magnitude is larger than 0.1 m2s−2.
3.2. Interdecadal Strengthening of the ENSO–EAP Relationship
To inspect the possible interdecadal change of the early winter ENSO–EAP telecon-
nection, Figure 4a shows the time evolution of the 21 year running correlation between
the Niño-3.4 and EAP indices. We can see that the ENSO–EAP relationship underwent a
pronounced interdecadal strengthening during the late 1990s. ENSO and EAP were sig-
nificantly correlated after the late 1990s at the 95% confidence level (R = 0.51, Table 2),
while a non-significant relationship was found before the late 1990s (R = 0.28, Table 2). The
conclusions remained unchanged when we used other reanalysis datasets to calculate the
EAP index (Figure 4a), or when we used different running time windows, such as 19 and
23 years (Figure S4).
Figure 3.
Regression coefficients of the early winter 200 hPa geopotential height anomalies (shading
in m) and the associated anomalous wave activity flux (WAF, vectors in m
2
s
−2
) with respect to the
(
a
) DJF Niño-3.4 and (
b
) early winter Pr_GMCA indices. The dots denote the geopotential height
anomalies exceeding the 95% confidence level. The anomalous WAF flux is shown only when its
magnitude is larger than 0.1 m2s−2.
3.2. Interdecadal Strengthening of the ENSO–EAP Relationship
To inspect the possible interdecadal change of the early winter ENSO–EAP telecon-
nection, Figure 4a shows the time evolution of the 21 year running correlation between
the Niño-3.4 and EAP indices. We can see that the ENSO–EAP relationship underwent
a pronounced interdecadal strengthening during the late 1990s. ENSO and EAP were
significantly correlated after the late 1990s at the 95% confidence level (R = 0.51, Table 2),
while a non-significant relationship was found before the late 1990s (R = 0.28, Table 2). The
conclusions remained unchanged when we used other reanalysis datasets to calculate the
EAP index (Figure 4a), or when we used different running time windows, such as 19 and
23 years (Figure S4).
Atmosphere 2023,14, 1809 7 of 14
Atmosphere 2023, 14, x FOR PEER REVIEW 7 of 14
Figure 4. (a) The 21 year running correlation coefficients between the DJF Niño-3.4 index and early
winter EAP index during 1979–2022 based on the JRA-55 (red curve), NCEP/NCAR (green curve),
and ERA5 (blue curve) reanalysis datasets. The horizontal gray dashed line indicates the 95% con-
fidence level for the correlation. Regression coefficients of the early winter North Atlantic SLP anom-
alies (shading in hPa) with respect to the DJF Niño-3.4 index during (b) 1979–1996 (denoted as P1)
and (c) 1997–2022 (denoted as P2). The dots in (b,c) indicate the anomalies exceeding the 95% con-
fidence level.
Table 2. Correlation coefficients between the Niño-3.4 index and the early winter Euro-Atlantic at-
mospheric modes. The value marked with an asterisk indicates that it is significant at the 95% con-
fidence level.
P1 (1979–1996) P2 (1997–2022)
Cor. (Niño-3.4, EAP) 0.28 0.51 *
Cor. (Niño-3.4, NAO) 0.57 * 0.10
According to Figure 4a, we then refer to the period from 1979 to 1996 as P1 and the
period from 1999 to 2022 as P2 for further investigation. The spatial paerns of the ENSO-
regressed early winter SLP anomalies during these two periods are shown in Figure 4b,c,
respectively. It is interesting to note that while the paern in P2 resembles the positive
EAP paern, the paern in P1 projects well onto the NAO. The paern correlation coeffi-
cients between the P1 paern and the NAO paern and between the P2 paern and the
EAP paern over the Euro-Atlantic region were 0.92 and 0.86, respectively. These results
suggest that the early winter ENSO teleconnection to the Euro-Atlantic changed its paern
from NAO to EAP around the late 1990s (Table 2).
Figure 4.
(
a
) The 21 year running correlation coefficients between the DJF Niño-3.4 index and
early winter EAP index during 1979–2022 based on the JRA-55 (red curve), NCEP/NCAR (green
curve), and ERA5 (blue curve) reanalysis datasets. The horizontal gray dashed line indicates the 95%
confidence level for the correlation. Regression coefficients of the early winter North Atlantic SLP
anomalies (shading in hPa) with respect to the DJF Niño-3.4 index during (
b
) 1979–1996 (denoted as
P1) and (
c
) 1997–2022 (denoted as P2). The dots in (
b
,
c
) indicate the anomalies exceeding the 95%
confidence level.
Table 2.
Correlation coefficients between the Niño-3.4 index and the early winter Euro-Atlantic
atmospheric modes. The value marked with an asterisk indicates that it is significant at the 95%
confidence level.
P1 (1979–1996) P2 (1997–2022)
Cor. (Niño-3.4, EAP) 0.28 0.51 *
Cor. (Niño-3.4, NAO) 0.57 * 0.10
According to Figure 4a, we then refer to the period from 1979 to 1996 as P1 and the
period from 1999 to 2022 as P2 for further investigation. The spatial patterns of the ENSO-
regressed early winter SLP anomalies during these two periods are shown in Figure 4b,c,
respectively. It is interesting to note that while the pattern in P2 resembles the positive EAP
pattern, the pattern in P1 projects well onto the NAO. The pattern correlation coefficients
between the P1 pattern and the NAO pattern and between the P2 pattern and the EAP
pattern over the Euro-Atlantic region were 0.92 and 0.86, respectively. These results suggest
that the early winter ENSO teleconnection to the Euro-Atlantic changed its pattern from
NAO to EAP around the late 1990s (Table 2).
Atmosphere 2023,14, 1809 8 of 14
3.3. Possible Mechanism
We now turn to an analysis of the possible reasons that are responsible for this in-
terdecadal change. In Figure 5, the spatial patterns of the ENSO-regressed tropical SST,
850 hPa wind, and precipitation anomalies during P1 and P2 are examined. While the
typical ENSO-related SST, low-level wind, and precipitation anomalies were present in the
Pacific on a broad scale, differences can be found in the details. Compared to the SST in
P1, there were significant warm SST anomalies in the subtropical and tropical northeastern
Pacific in P2. Correspondingly, the precipitation responses in the tropical eastern Pacific
were apparently stronger than that in P1. As a direct response to the atmospheric anomaly
generated by the warm SST anomaly in the eastern Pacific, the precipitation was reduced
in the Caribbean Sea [
49
,
50
], also with a stronger magnitude in P2. In response to this
reduced convection, a stronger anticyclonic circulation developed over the subtropical
western North Atlantic (Figure 5b), leading to a stronger precipitation surplus in the Gulf
of Mexico in P2. We then compared the ENSO-induced tropical precipitation anomalies
during P1 and P2 in Table 3. Although the precipitation response in the CP and TWEIO
remained almost unchanged from P1 to P2, a significant enhancement of the precipitation
response in P2 was found in the GMCA, which was the key region for creating an effective
ENSO–EAP teleconnection as we demonstrated in the previous section. Therefore, we
suggest that the intensification of the Pr_GMCA response to ENSO in P2 was responsible
for the interdecadal strengthening of the ENSO–EAP relationship.
Atmosphere 2023, 14, x FOR PEER REVIEW 8 of 14
3.3. Possible Mechanism
We now turn to an analysis of the possible reasons that are responsible for this inter-
decadal change. In Figure 5, the spatial paerns of the ENSO-regressed tropical SST, 850
hPa wind, and precipitation anomalies during P1 and P2 are examined. While the typical
ENSO-related SST, low-level wind, and precipitation anomalies were present in the Pacific
on a broad scale, differences can be found in the details. Compared to the SST in P1, there
were significant warm SST anomalies in the subtropical and tropical northeastern Pacific
in P2. Correspondingly, the precipitation responses in the tropical eastern Pacific were
apparently stronger than that in P1. As a direct response to the atmospheric anomaly gen-
erated by the warm SST anomaly in the eastern Pacific, the precipitation was reduced in
the Caribbean Sea [49,50], also with a stronger magnitude in P2. In response to this re-
duced convection, a stronger anticyclonic circulation developed over the subtropical west-
ern North Atlantic (Figure 5b), leading to a stronger precipitation surplus in the Gulf of
Mexico in P2. We then compared the ENSO-induced tropical precipitation anomalies dur-
ing P1 and P2 in Table 3. Although the precipitation response in the CP and TWEIO re-
mained almost unchanged from P1 to P2, a significant enhancement of the precipitation
response in P2 was found in the GMCA, which was the key region for creating an effective
ENSO–EAP teleconnection as we demonstrated in the previous section. Therefore, we
suggest that the intensification of the Pr_GMCA response to ENSO in P2 was responsible
for the interdecadal strengthening of the ENSO–EAP relationship.
Figure 5. Regression coefficients of the early winter (a,b) SST (shading in K), 850 hPa wind (vectors
in m/s), and (c,d) precipitation (shading in mmday−1) anomalies with respect to the DJF Niño-3.4
index during (a,c) P1 and (b,d) P2. The dots indicate the anomalies exceeding the 95% confidence
level. The 850 hPa wind anomaly is shown only when its zonal or meridional component is signifi-
cant at the 95% confidence level.
Table 3. ENSO-regressed tropical precipitation indices (units: mmday−1) during P1 and P2. The
value marked with an asterisk indicates that it was significant at the 95% confidence level.
Pr_CP Pr_TWEIO Pr_GMCA
P1 (1979–1996) 1.90 * 1.52 * 0.69 *
P2 (1997–2022) 1.84 * 1.52 * 1.29 *
Previous studies have demonstrated that the tropical Indian Ocean dipolar precipi-
tation is also important for establishing the early winter ENSO teleconnection to the Euro-
Figure 5.
Regression coefficients of the early winter (
a
,
b
) SST (shading in K), 850 hPa wind (vectors in
m/s), and (
c
,
d
) precipitation (shading in mmday
−1
) anomalies with respect to the DJF Niño-3.4 index
during (
a
,
c
) P1 and (
b
,
d
) P2. The dots indicate the anomalies exceeding the 95% confidence level. The
850 hPa wind anomaly is shown only when its zonal or meridional component is significant at the
95% confidence level.
Table 3.
ENSO-regressed tropical precipitation indices (units: mmday
−1
) during P1 and P2. The
value marked with an asterisk indicates that it was significant at the 95% confidence level.
Pr_CP Pr_TWEIO Pr_GMCA
P1 (1979–1996) 1.90 * 1.52 * 0.69 *
P2 (1997–2022) 1.84 * 1.52 * 1.29 *
Atmosphere 2023,14, 1809 9 of 14
Previous studies have demonstrated that the tropical Indian Ocean dipolar precip-
itation is also important for establishing the early winter ENSO teleconnection to the
Euro-Atlantic [
15
,
17
]. To examine the role of Pr_GMCA and Pr_TWEIO, we show the
partial regression patterns of the Euro-Atlantic early winter SLP anomalies with respect
to the Pr_TWEIO and Pr_GMCA indices during P1 and P2 in Figure 6. During P1, due
to the weak response of the Pr_GMCA to ENSO, its effect on the Euro-Atlantic SLP was
relatively weak. The ENSO influence was mainly contributed by the tropical precipitation
forcing in the Indian Ocean, which favored a positive NAO pattern. This is in agreement
with previous studies [
15
,
17
]. However, during P2, we can clearly see that the effect of the
Pr_GMCA significantly strengthened, which was well projected onto the EAP. Although
the Pr_TWEIO was still at work and favored an NAO response, the ENSO influence was
dominated by the Pr_GMCA, and finally a positive EAP was generated. These results
suggest that the magnitude of the ENSO-induced precipitation anomaly in the GMCA is
crucial for the ENSO–EAP teleconnection.
Atmosphere 2023, 14, x FOR PEER REVIEW 9 of 14
Atlantic [15,17]. To examine the role of Pr_GMCA and Pr_TWEIO, we show the partial
regression paerns of the Euro-Atlantic early winter SLP anomalies with respect to the
Pr_TWEIO and Pr_GMCA indices during P1 and P2 in Figure 6. During P1, due to the
weak response of the Pr_GMCA to ENSO, its effect on the Euro-Atlantic SLP was relatively
weak. The ENSO influence was mainly contributed by the tropical precipitation forcing in
the Indian Ocean, which favored a positive NAO paern. This is in agreement with pre-
vious studies [15,17]. However, during P2, we can clearly see that the effect of the
Pr_GMCA significantly strengthened, which was well projected onto the EAP. Although
the Pr_TWEIO was still at work and favored an NAO response, the ENSO influence was
dominated by the Pr_GMCA, and finally a positive EAP was generated. These results sug-
gest that the magnitude of the ENSO-induced precipitation anomaly in the GMCA is cru-
cial for the ENSO–EAP teleconnection.
Figure 6. Partial regression coefficients of the Euro-Atlantic SLP anomalies (shading in hPa) with
respect to the (a) Pr_TWEIO and (b) Pr_GMCA indices during P1. (c,d) are the same as (a,b) but
during P2. The dots indicate the SLP anomalies exceeding the 95% confidence level.
To further understand the role of the precipitation anomaly in the GMCA, Figure 7
displays the relationships between the Niño-3.4 and Pr_GMCA indices, and between the
Pr_GMCA and EAP indices during the ENSO early winter months for P1 and P2. We can
clearly see that the ENSO events in P2 were able to generate stronger precipitation anom-
alies in the GMCA in P2 compared to those in P1 (Figure 7a). This stronger Pr_GMCA
response, in turn, was able to effectively establish a Rossby wave train that led to an EAP
anomaly. The stronger the ENSO-induced precipitation anomaly in the GMCA, the more
robust the EAP response, and also the ENSO–EAP teleconnection can be produced during
the ENSO early winter (Figure 7b). However, in P1, the precipitation response was too
weak to excite a clear Rossby wave bridging ENSO and the Euro-Atlantic atmospheric
Figure 6.
Partial regression coefficients of the Euro-Atlantic SLP anomalies (shading in hPa) with
respect to the (
a
) Pr_TWEIO and (
b
) Pr_GMCA indices during P1. (
c
,
d
) are the same as (
a
,
b
) but
during P2. The dots indicate the SLP anomalies exceeding the 95% confidence level.
To further understand the role of the precipitation anomaly in the GMCA, Figure 7
displays the relationships between the Niño-3.4 and Pr_GMCA indices, and between the
Pr_GMCA and EAP indices during the ENSO early winter months for P1 and P2. We
can clearly see that the ENSO events in P2 were able to generate stronger precipitation
anomalies in the GMCA in P2 compared to those in P1 (Figure 7a). This stronger Pr_GMCA
response, in turn, was able to effectively establish a Rossby wave train that led to an EAP
anomaly. The stronger the ENSO-induced precipitation anomaly in the GMCA, the more
Atmosphere 2023,14, 1809 10 of 14
robust the EAP response, and also the ENSO–EAP teleconnection can be produced during
the ENSO early winter (Figure 7b). However, in P1, the precipitation response was too weak
to excite a clear Rossby wave bridging ENSO and the Euro-Atlantic atmospheric pattern.
The EAP variability may be influenced by other climate drivers, so we were unable to
observe a significant relationship between the Pr_GMCA and the EAP. These findings were
qualitatively consistent when using the CMAP precipitation dataset (Figures S5 and S6). In
summary, we conclude that the Pr_GMCA played a key role in shaping the ENSO–EAP
teleconnection during early winter.
Atmosphere 2023, 14, x FOR PEER REVIEW 10 of 14
paern. The EAP variability may be influenced by other climate drivers, so we were una-
ble to observe a significant relationship between the Pr_GMCA and the EAP. These find-
ings were qualitatively consistent when using the CMAP precipitation dataset (Figures S5
and S6). In summary, we conclude that the Pr_GMCA played a key role in shaping the
ENSO–EAP teleconnection during early winter.
Figure 7. (a) Scaerplot of the early winter monthly Pr_GMCA and Niño-3.4 indices for the ENSO
winters during P1 (red quadrate) and P2 (blue circle) with the corresponding linear regression lines.
(b) Scaerplot the early winter monthly EAP and Pr_GMCA indices for the ENSO winters during
P1 (red quadrate) and P2 (blue circle). The correlation coefficients (R) are also displayed.
4. Conclusions and Discussion
Previous studies have shown that the ENSO teleconnection to the Euro-Atlantic sec-
tor is subject to considerable subseasonal transitions [13–15]. Compared with the late win-
ter ENSO impacts that display a negative (positive) NAO during El Niño (La Niña), the
early winter ENSO teleconnection is less understood. In this study, based on the multiple
reanalysis datasets, we revisited the ENSO influence on the early winter Euro-Atlantic
atmospheric circulation during the 1979–2022 period. It was found that the ENSO foot-
print fied the EAP much beer than the frequently mentioned NAO paern. This influ-
ence was associated with ENSO-induced dipolar convection anomalies over the GMCA
region, which can set up a Rossby wave train propagating northward into the North At-
lantic, leading to a low-pressure anomaly south of Iceland and west of Ireland in El Niño
cases, and thus a positive EAP.
We then examined the possible interdecadal change of this early winter ENSO tele-
connection. It was revealed that the ENSO–EAP relationship underwent a pronounced
interdecadal strengthening around the late 1990s. While a clear EAP response was de-
tected during ENSO early winter after the late 1990s, the ENSO-regressed paern resem-
bled a NAO paern before the late 1990s, suggesting that there was a shift of the early
winter ENSO teleconnection over the Euro-Atlantic sector around the late 1990s. We as-
sume that the changing response of the GMCA precipitation to ENSO played a key role
in this interdecadal shift. Prior to the late 1990s, the ENSO-related GMCA precipitation
anomaly was weak, unable to exert an effective influence on the Euro-Atlantic atmos-
pheric circulation. The ENSO teleconnection to this region was mainly contributed to by
the precipitation anomaly in the TWEIO, which favored a NAO anomaly. In contrast, the
GMCA precipitation response to ENSO has been strongly enhanced since the late 1990s.
Although the TWEIO precipitation forcing is still at work, the role of the GMCA
Figure 7.
(
a
) Scatterplot of the early winter monthly Pr_GMCA and Niño-3.4 indices for the ENSO
winters during P1 (red quadrate) and P2 (blue circle) with the corresponding linear regression lines.
(
b
) Scatterplot the early winter monthly EAP and Pr_GMCA indices for the ENSO winters during P1
(red quadrate) and P2 (blue circle). The correlation coefficients (R) are also displayed.
4. Conclusions and Discussion
Previous studies have shown that the ENSO teleconnection to the Euro-Atlantic sector
is subject to considerable subseasonal transitions [
13
–
15
]. Compared with the late winter
ENSO impacts that display a negative (positive) NAO during El Niño (La Niña), the
early winter ENSO teleconnection is less understood. In this study, based on the multiple
reanalysis datasets, we revisited the ENSO influence on the early winter Euro-Atlantic
atmospheric circulation during the 1979–2022 period. It was found that the ENSO footprint
fitted the EAP much better than the frequently mentioned NAO pattern. This influence
was associated with ENSO-induced dipolar convection anomalies over the GMCA region,
which can set up a Rossby wave train propagating northward into the North Atlantic,
leading to a low-pressure anomaly south of Iceland and west of Ireland in El Niño cases,
and thus a positive EAP.
We then examined the possible interdecadal change of this early winter ENSO tele-
connection. It was revealed that the ENSO–EAP relationship underwent a pronounced
interdecadal strengthening around the late 1990s. While a clear EAP response was detected
during ENSO early winter after the late 1990s, the ENSO-regressed pattern resembled a
NAO pattern before the late 1990s, suggesting that there was a shift of the early winter
ENSO teleconnection over the Euro-Atlantic sector around the late 1990s. We assume that
the changing response of the GMCA precipitation to ENSO played a key role in this inter-
decadal shift. Prior to the late 1990s, the ENSO-related GMCA precipitation anomaly was
weak, unable to exert an effective influence on the Euro-Atlantic atmospheric circulation.
The ENSO teleconnection to this region was mainly contributed to by the precipitation
Atmosphere 2023,14, 1809 11 of 14
anomaly in the TWEIO, which favored a NAO anomaly. In contrast, the GMCA precipi-
tation response to ENSO has been strongly enhanced since the late 1990s. Although the
TWEIO precipitation forcing is still at work, the role of the GMCA precipitation overwhelms
and eventually produces an EAP by exciting a north-propagating Rossby wave train.
Our results are consistent with those of Thornton et al. [
48
], who demonstrated a
visible but weak seasonal predictability of the ENSO-related EAP and surface climate
over the Euro-Atlantic in early winter. This weaker signal is suggested to be caused by
the weaker model simulation of tropical–extratropical teleconnections [
48
], which in turn
seems to be related to the underestimated convection response to ENSO in the GMCA
region [
14
,
40
]. Our results highlight the role of the GMCA precipitation in the ENSO–EAP
teleconnection and thus confirm these arguments. In addition, the conclusion of the recent
ENSO–EAP teleconnection strengthening indicates a pattern shift of the ENSO fingerprint
on the Euro-Atlantic early winter atmospheric circulation. These results are important for
understanding the ENSO teleconnection in early winter and also important for improving
the seasonal climate prediction over the Euro-Atlantic region.
We note that our results are mostly based on the observational analysis, and that
evidence from modeling and CMIP6 simulations need to be presented in the future. In
this study, we mainly focused on the role of ENSO-induced low-frequency Rossby waves.
However, the North Atlantic is a region of abundant atmospheric eddy-low frequency flow
feedbacks [
51
]. Therefore, their possible effects may also need to be investigated [
52
,
53
].
In addition, this study mainly concentrated on the linear ENSO effects, and whether this
teleconnection is sensitive to the type or diversity of ENSO needs to be clarified. Since
the ENSO teleconnections are subject to significant changes under the future warming
climate [
54
,
55
], the way in which they respond to greenhouse warming is also an interesting
topic worthy of future study.
Supplementary Materials:
The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/atmos14121809/s1, Figure S1: Two leading modes of the early winter
SLP anomaly in the Euro-Atlantic region based on the NCEP/NCAR reanalysis dataset. Figure S2:
Same as Figure S1 but based on the JRA-55 reanalysis dataset. Figure S3: The important role of the
ENSO-induced convection anomalies in the GMCA in the ENSO-NAO linkage suggested by the
CMAP precipitation dataset. Figure S4: Time evolution of the ENSO-EAP 19-year and 23-year running
correlation coefficients. Figure S5: The independent influences of the Pr_TWEIO and Pr_GMCA on
the Euro-Atlantic early winter SLP anomaly during P1 and P2 based on the CMAP precipitation
dataset. Figure S6: The relationships between the Niño-3.4 and Pr_GMCA indices, and between the
Pr_GMCA and EAP indices during the ENSO early winter months for P1 and P2 based on the CMAP
precipitation dataset.
Author Contributions:
X.G. initiated the idea and designed the research, J.H. and Z.F. performed the
analyses and wrote the initial manuscript of the paper. X.G. supervised the whole work, helped to
interpret the results, and developed the manuscript content. All the authors reviewed the paper. All
authors have read and agreed to the published version of the manuscript.
Funding:
This work was supported by the National Natural Science Foundation of China (42125501
and 41905073) and the China Scholarship Council (202008320174).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
All reanalysis datasets used in this study are publicly available and
can be downloaded from the corresponding websites. The HadISST dataset: https://www.metoffice.
gov.uk/hadobs/hadisst/data/download.html, accessed on 23 November 2023; The ERA5 reanalysis
datasets: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5, accessed on 23
November 2023; The NCEP/NCAR reanalysis datasets: https://psl.noaa.gov/data/gridded/data.
ncep.reanalysis.html, accessed on 23 November 2023; The CMAP precipitation dataset: https://psl.
noaa.gov/data/gridded/data.cmap.html, accessed on 23 November 2023; The JRA-55 reanalysis
datasets: https://jra.kishou.go.jp/JRA-55/index_en.html, accessed on 23 November 2023.
Atmosphere 2023,14, 1809 12 of 14
Conflicts of Interest: The authors declare no conflict of interest.
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