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Winter Weather Patterns over Northern Eurasia and Arctic Sea Ice Loss
BINGYI WU
Chinese Academy of Meteorological Sciences, Beijing, China
DÖRTHE HANDORF, KLAUS DETHLOFF, AND ANNETTE RINKE
Research Unit Potsdam, Alfred Wegener Institute for Polar and Marine Research, Potsdam, Germany
AIXUE HU
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado
(Manuscript received 23 January 2013, in final form 23 April 2013)
ABSTRACT
Using NCEP–NCAR reanalysis and Japanese 25-yr Reanalysis (JRA-25) winter daily (1 December–
28 February) data for the period 1979–2012, this paper reveals the leading pattern of winter daily 850-hPa
wind variability over northern Eurasia from a dynamic perspective. The results show that the leading pattern
accounts for 18% of the total anomalous kinetic energy and consists of two subpatterns: the dipole and the
tripole wind patterns. The dipole wind pattern does not exhibit any apparent trend. The tripole wind pattern,
however, has displayed significant trends since the late 1980s. The negative phase of the tripole wind pattern
corresponds to an anomalous anticyclone over northern Eurasia during winter, as well as two anomalous
cyclones occurring over southern Europe and in the mid- to high latitudes of East Asia. These anomalous
cyclones in turn lead to enhanced winter precipitation in these two regions, as well as negative surface
temperature anomalies over the mid- to high latitudes of Asia. The intensity of the tripole wind pattern and
the frequency of its extreme negative phase are significantly correlated with autumn Arctic sea ice anomalies.
Simulation experiments further demonstrate that the winter atmospheric response to Arctic sea ice decrease
is dynamically consistent with the observed trend in the tripole wind pattern over the past 24 winters, which is
one of the causes of the observed declining winter surface air temperature trend over Central and East Asia.
The results of this study also imply that East Asia may experience more frequent and/or intense winter
extreme weather events in association with the loss of Arctic sea ice.
1. Introduction
Wind variations, especially surface wind variations,
play important roles in regulating air–sea and air–land
interactions by means of affecting heat flux and water
evaporation, thus further influencing soil moisture and
the hydrological cycle (e.g., Wever 2012; McVicar et al.
2012). As the carrier of energy, water vapor, and aerosol,
wind anomalies are directly associated with extreme
weather events and air environmental variations. During the boreal winter season, wind anomalies are usually
connected to cold-air outbreaks and heavy snowfall,
Corresponding author address: Bingyi Wu, Institute of Climate
System, Chinese Academy of Meteorological Sciences, No. 46,
Haidian District, Beijing 100081, China.
E-mail: [email protected]
DOI: 10.1175/MWR-D-13-00046.1
Ó 2013 American Meteorological Society
such as during the winter of 2011/12 in Eurasia. In late
January 2012, extreme cold polar air carried by a suddenly strengthened anticyclone (Siberian high) spreads
over nearly all of Eurasia, leading to a very rare extreme
cold event, as well as heavy snowfall in Europe. On the
other hand, cold-air outbreaks play a critical role in
improving the environmental air quality over East Asia,
particularly over central and eastern China.
Recently, countries in East Asia have experienced
frequent cold winters and extreme weather events.
China has been subjected to three successive cold winters during 2009–12. The average surface air temperature from December 2012 to the beginning of January
2013 reached its lowest record in China over the past 28
winters. Additionally, anomalous low temperatures, heavy
snowfalls, and frozen precipitation also occurred in East
Asia, including heavy snowfall over Japan in December
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WU ET AL.
2005, successive frozen precipitation events over southern
China in January–February 2008, extreme heavy snowfall
in northwestern China during the winter of 2009/10, and
anomalous low temperatures and frequent heavy snowfall
events in north and northeast China from November to
December 2012. Indeed, recent cold winters in some East
Asian countries have been directly connected to the recovery of the Siberian high intensity and its frequent
positive anomalies since 2004 (Jeong et al. 2011; Wu et al.
2011).
Although some studies have investigated the predominant patterns of winter wind variability over East
Asia and the Arctic (e.g., Wu et al. 2012), some important issues still remain unresolved. For example, what
are the dominant patterns of daily wind field variability
over northern Eurasia during the winter season? Furthermore, do these dominant patterns exhibit significant
trends in their intensity and frequency? Although the
Arctic Oscillation (AO) and its regional counterpart,
the North Atlantic Oscillation (NAO), directly affect
the synoptic regime and climate variability of northern
Eurasia, the differences between the AO (or NAO) and
the dominant patterns of wind variability are quite significant, due to the fact that they reflect different physical
mechanisms (Wu et al. 2012).
The aims of the present study are to explore the
leading pattern of winter daily 850-hPa wind variability
and its trends over northern Eurasia, and to examine
the associations between the intensity and frequency of
the leading pattern and previous autumn (September–
November) sea ice concentration (SIC) anomalies. Investigating these issues will aid in our understanding the
dominant features of winter wind variability and the
possible causes, which will be beneficial in improving
our prediction ability for extreme weather events.
2. Data and method
The data used in this study include the following: 1)
a 33-winter (1979/80–2011/12) dataset of daily sea level
pressure (SLP), surface air temperature (SAT), 850-hPa
winds, and 500-hPa geopotential heights from 1 December to 28 February of the next year (for a total of
2970 days) obtained from the National Centers for Environmental Prediction–National Center for Atmospheric
Research (NCEP–NCAR) 40-Year Reanalysis Project
and the Japanese 25-yr Reanalysis (JRA-25; Onogi et al.
2007; http://ds.data.jma.go.jp/gmd/jra/download/categorye.html/anl_p25); 2) monthly mean global land precipitation data from 1979 to 2011 (Chen et al. 2002;
http://ftp.cpc.ncep.noaa.gov/precip/50yr/gauge/2.5deg/
format_bin/); 3) the monthly Arctic SIC dataset (on a 18
latitude 3 18 longitude grid) for the period of 1979–2012
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obtained from the British Atmospheric Data Centre
(BADC; http://badc.nerc.ac.uk/data/hadisst/); and 4) the
monthly mean AO index during the period 1979–2012
(http://www.cpc.ncep.noaa.gov/products/precip/CWlink/
daily_ao_index/monthly.ao.index.b50.current.ascii).
To reveal the leading pattern of daily 850-hPa wind
variability during the boreal winter season, the complex
vector empirical orthogonal function (CVEOF) analysis
method was applied to normalized wind anomalies that
were derived from the daily 850-hPa winds subtracting
the corresponding daily climatology over the 33 winters,
and the domain of the CVEOF analysis was 408–708N,
408–1208E. It should be noted that if a larger domain was
used, for example, over 408–708N, 208–1208E, the results
would be similar to that over 408–708N, 408–1208E. The
additional motivation for selecting this particular domain
is to focus on the influence of the dominant wind patterns
over Asia. Instead of surface wind fields, we used the
850-hPa wind field in this study. This is because surface
wind fields possess strong inhomogeneities, which may
not be related to systematic climate variability (Wever
2012). For more detailed information concerning the
statistical and physical meanings of the CVEOF method
and its advantages over the traditional EOF method, the
reader is referred to Wu et al. (2012). Additionally, the
maximum covariance analysis (MCA; see von Storch
and Zwiers 1999) is also used to further detect coupled
patterns between autumn SIC over the domain 508–
908N, 08–3608 and the ensuing winter meridional winds
at 850 hPa over the domain 408–708N, 408–1208E.
The Monte Carlo method is applied to determine
statistical field significance, as described in Livezey and
Chen (1983). For an anomalous field derived from linear
regression, the percentage of grid points that are statistically significant at the 0.05 level is first identified over
a given domain. This process is then repeated 1000 times
using different series of numbers randomly selected from
a normal distribution. The anomalous field is deemed
significant if the percentage of the significant grid
points exceeds that derived from the 1000 experimental
replications.
Additionally, the ECHAM5 (Roeckner et al. 2003)
model (T63 spectral resolution and 19 pressure levels)
was applied to explore the impacts of SIC on the model
atmosphere. A 30-yr simulation with the climatological
monthly sea surface temperature (SST) and observed
Northern Hemisphere monthly SIC from 1978 to 2007
as the external forcing was performed (the climatological monthly SST and SIC observational data were
obtained online: http://www-pcmdi.llnl.gov/projects/
amip/AMIP2EXPDSN/BCS/bcsintro.php), and this experiment was repeated with 12 different atmospheric initial
conditions, which were derived from a 30-yr control run.
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TABLE 1. Frequencies for different phase ranges from 1979 to 2012.
08 phase (the positive phase of the dipole pattern)
908 phase (the positive phase of the tripole pattern)
1808 phase (the negative phase of the dipole pattern)
2708 phase (the negative phase of the tripole pattern)
471
1009
528
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3. Spatial features of the leading wind pattern and
physical significance of the phase evolution
The leading pattern of winter daily 850-hPa wind
variability (for a total of 2970 days) accounts for 18% of
the total anomalous kinetic energy. To demonstrate the
leading pattern’s spatial evolution, composite analyses
were performed for the following typical four different
leading phase ranges: the 08 phase (u , 458 or u $ 3158),
908 phase (458 # u , 1358), 1808 phase (1358 # u , 2258),
and 2708 phase (2258 # u , 3158). Table 1 shows the
respective frequencies for the typical four different
phase ranges.
For the 08 phase, the predominant feature is an
anomalous cyclone and anticyclone pair occupying, respectively, the northern Asian continent–Siberian marginal seas of the Arctic Ocean and Europe (Fig. 1a).
Meanwhile, there are weak anomalous anticyclonic
and cyclonic centers over the East Asian coast and the
Mediterranean. Correspondingly, SLP anomalies exhibit a dipole structure with opposite anomalous centers
covering, respectively, eastern Europe and the area
between Taymyr and Lake Baikal in Siberia (Fig. 2a).
Negative SAT anomalies appear in northern Asia and
the Arctic Ocean, and there are two separated centers of
positive SAT anomalies south of the Barents and Kara
Seas and around Lake Baikal (Fig. 3a). It is shown that
positive SAT anomalies cover most of East Asia. When
the leading wind pattern is in its 908 phase, wind anomalies show a tripole structure and a dominant anomalous
cyclone occupies a region from northern Eurasia and the
Barents Sea eastward to the Laptev Sea, with its center
close to south of the Kara Sea (Fig. 1b). The two weak
anomalous anticyclones are located over the southern
section of Europe and over northeastern Asia. The
spatial distribution of the wind anomalies is dynamically
consistent with a dominant monopole structure of the
SLP anomalies (Fig. 2b). Furthermore, corresponding
SAT anomalies also show a dipole pattern with opposite
anomalous centers located over the northern Asian
continent and over the Kara Sea (Fig. 3b). Cold- and
warm-air advection induced by wind anomalies is responsible for the spatial distribution of SAT anomalies.
The anomalous wind, SLP, and SAT patterns corresponding to the 1808 (2708) phase show the opposite
scenario to that in the 08 (908) phase (Figs. 1c,d, 2c,d, and
FIG. 1. (a) Composite of winter daily 850-hPa wind anomalies for
the 08 (u , 458 or u $ 3158) phase of the leading wind pattern, (b)–
(d) as in (a), but for the 908 (458 # u , 1358), 1808 (1358 # u , 2258),
and 2708 (2258 # u , 3158) phases, respectively. Units are m s21.
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FIG. 2. As in Fig. 1, but for composite of winter daily SLP anomalies
for different phase ranges; intervals are 2 hPa.
FIG. 3. As in Fig. 1, but for composite of winter daily SAT anomalies
for different phase ranges; intervals are 18C.
3c,d). It is seen that SAT anomalies in most of East Asia
are out of phase with those in the Arctic Ocean and its
marginal seas.
The above analysis indicates that the leading wind
pattern consists of two subpatterns, and the corresponding SLP and SAT anomalies also display different
spatial structures. Consequently, the evolution of the
leading phase reflects the spatial evolution of the two
subpatterns and their frequencies (Table 1). In this
study, the two subpatterns are referred to as the dipole
and the tripole wind patterns, respectively. As indicated
in our previous study (Wu et al. 2012), the real and
imaginary parts of the leading complex principal component can be regarded as two intensity indices used to
characterize the two wind patterns. Thus, the dipole
wind pattern incorporates the 08 and 1808 phases of the
leading wind pattern, and a positive (negative) phase of
the dipole wind pattern corresponds to the 08 (1808)
phase. Similarly, the tripole wind pattern incorporates
the 908 and 2708 phases of the leading wind pattern, and
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FIG. 4. (a) Composite of winter daily 500-hPa geopotential heights and corresponding geopotential height
anomalies for the 08 (u , 458 or u $ 3158) phase of the leading wind pattern, (b)–(d) as in (a), but for the 908 (458 # u ,
1358), 1808 (1358 # u , 2258), and 2708 (2258 # u , 3158) phases, respectively; contour intervals are 50 and 20 gpm,
respectively.
a positive (negative) phase of the tripole wind pattern
corresponds to the 908 (2708) phase.
In the midtroposphere, the evolution of height anomalies, from the 08 to 2708 phases, displays a coherent
westward migration process (Fig. 4). In Asia, east of 608E,
this westward migration process is concurrent with the
strengthened amplitudes of height anomalies. In Europe,
west of 608E, along with a westward shift, the amplitude
of the height anomalies is weakened. A very similar
pattern of evolution also appears in the SLP anomalies
(Fig. 2). This westward shift essentially reflects a coherent westward migration of troughs and ridges of
height fields in the midtroposphere, which is relevant to
Rossby waves (Francis and Vavrus 2012). Indeed, a
single dipole or tripole wind pattern is not able to
characterize the propagation of Rossby waves.
4. Time evolution of the two wind patterns
The intensity of the two wind patterns shows strong
interannual variability but no significant trend over the
entire data record from 1979 to 2012 (Figs. 5a,b).
However, some previous studies have shown that winter
atmospheric circulation experienced significant changes
in the late 1980s (Walsh et al. 1996; Tanaka et al. 1996;
Tachibana et al. 1996; Watanabe and Nitta 1999). It is
found that both the SLP over the central Arctic and the
polar vortex (the area-mean vorticity over 808–908N)
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FIG. 5. Normalized winter mean intensity time series of the (a) dipole and (b) tripole wind patterns. Frequencies of the (c) positive and
(e) negative phases of the dipole wind pattern. (d),(f) As in (c),(e), respectively, but for the tripole wind pattern; dashed red lines represent
linear trends since the winter of 1988/89.
throughout the troposphere have decreased noticeably
since 1988 (Walsh et al. 1996; Tanaka et al. 1996). Thus,
this study chooses the period of 1988–2012 to discuss
trends since 1988 only. The tripole wind pattern has
shown a negative trend intensity (at the 99% significance level) since the winter of 1988/89 (Fig. 5b), implying a coherent strengthening trend in the anomalous
anticyclone over northern Eurasia and two anomalous
cyclones over southern Europe and northeastern Asia.
Thus, this negative trend is dynamical consistent with the
recent recovery of the winter Siberian high intensity over
the past two decades (Jeong et al. 2011; Wu et al. 2011). If
slightly different start winters were considered, for example from 1986/87 to 1990/91, the decline trend still
reached the 99% significance level. In the past four winters, the intensity of the tripole wind pattern was 21.0 or
less, indicating that strong anomalous anticyclones prevailed over northern Eurasia during those winters. In the
winter of 2011/12, the intensity of the tripole wind pattern
was the strongest in the entire study period, consistent
with the extreme cold winter over Eurasia.
The following two aspects are noteworthy: 1) the
frequency of the tripole wind pattern was far greater
than the dipole wind pattern (Table 1), and 2) neither
the intensity nor the frequency of the dipole wind pattern exhibits any significant trend (Figs. 5c,e) since the
winter of 1988/89. Based on these findings, we decide to
focus primarily on the tripole wind pattern in this study.
Although frequencies of the positive phase of the tripole
wind pattern did not exhibit any significant trend during
the study period, its decline trend was at the 99% significance level after the winter of 1987/88 (Fig. 5d).
Frequency reached its record lowest value during the
winter of 2011/12 (,10 times). In contrast, frequencies
of the negative phase of the tripole wind pattern have
shown a positive trend at the 99% significance level after
the winter of 1987/88, and the maximum occurred during
the winter of 2011/12 (Fig. 5f). This positive trend is
dynamically consistent with the recent recovery of the
Siberian high intensity and observed negative trend in
winter land SAT anomalies over Eurasia (Fig. 6a). The
SAT over Central and East Asia exhibits significant
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FIG. 6. Spatial distributions of winter mean SAT trends over (a)
the past 24 winters (1988/89–2011/12) and (b) the past 33 winters
(1979/80–2011/12). The magenta (light blue) and red (blue) areas
denote positive (negative) trends at the 95% and 99% significance
levels, respectively; intervals are 18C (10 yr)21.
negative trends, consistent with previous studies (e.g.,
Fig. 1 of Cohen et al. 2009; Fig. 5 of Cohen et al. 2012;
Figs. 6 and 7 of Wu et al. 2011). Additionally, winter
SAT trends are also negative over much of the mid- to
high latitudes of Asia during the period 1979–2012
(Fig. 6b). It is noteworthy that significant surface
warming trends were observed over the Tibetan Plateau. The causes of winter surface warming trends
around the Tibetan Plateau and its impacts on East
Asia deserve investigations in the future. It should be
pointed out that a very similar tripole wind pattern was
also detected from the JRA-25 data (not shown).
According to the daily intensity time series of the
tripole wind pattern (for a total of 2970 days; not shown),
the extreme tripole wind pattern is defined as its standard deviation being less than 21.28 (the extreme negative phase range) or greater than 1.28 (the extreme
positive phase range), and each phase range corresponds
to the probability of the tripole wind pattern being less
than 10%. It is found that cumulative frequencies for the
extreme negative (positive) phase range are 332 (307)
times. Although the mean intensities of the two extreme
phase ranges do not display any apparent trend (not
shown), their frequency time series show distinctive
VOLUME 141
evolution trends from the winter of 1988/89 (both are
at the 99% significance level; Figs. 7a,b). The extreme
negative phase of the tripole wind pattern prevailed
during the winter of 2011/12, and cumulative frequencies
were 36 times, occurring mainly during mid-January–
mid-February of 2012 (not shown), which led to extreme
snowfall events in Europe and Japan. The correlations of
the winter AO with two time series in Fig. 7 are 0.35 and
20.35, respectively (at the 95% marginal significance
level), suggesting that the negative phase of the winter
AO tends to be favorable for the occurrence of the extreme tripole wind pattern in its negative phase.
The tripole wind pattern influences Eurasian precipitation, and its intensity and frequency of negative
phases produce similar anomalous precipitation patterns (Figs. 8a,b). Increased precipitation mainly occurs
in southern Europe and the mid- to high latitudes of East
Asia, particularly in the latter where the increase of
precipitation exceeds 20% (Fig. 8b). Comparing Fig. 1d,
increased precipitation is dynamically consistent with an
anomalous cyclone, with the sole exception being in the
midlatitude Asian continent between 808 and 1108E,
where increased precipitation is associated with anomalous convergence.
5. Possible associations with autumn SIC
Arctic sea ice loss has been evident particularly since
the rapid decline in summer Arctic sea ice starting in the
late 1990s (e.g., Comiso et al. 2008), which may affect
not only Arctic atmospheric temperature and thickness,
but also climate variability in remote regions (Francis
et al. 2009; Screen and Simmonds 2010; Overland and
Wang 2010; Deser et al. 2004, 2007; Dethloff et al. 2006;
Wu et al. 2011; Francis and Vavrus 2012; among others).
Recently, some regions of Eurasia have experienced
cold winters, such as the winters of 2007/08, 2009/10,
2010/11, and 2011/12 (Fig. 9). It appears that cold winters
have become more frequent over East Asia. Some
studies have shown that less Arctic sea ice in the
previous autumn/winter plays a role in the cold Eurasian weather and enhanced Siberian high, through
large-scale dynamic and thermal processes (Francis
et al. 2009; Honda et al. 2009; Petoukhov and Semenov
2010; Jaiser et al. 2012; Wu et al. 1999, 2011; Hopsch et al.
2012; Tang et al. 2013). On the synoptic time scale, Francis
and Vavrus (2012) have provided an original view on the
increased frequency and magnitude of weather extremes
over North America and the North Atlantic sector. They
indicated that the Arctic amplification results in decreases
of winter upper-level zonal winds, which cause more
persistent weather conditions that could increase the
likelihood of certain types of extreme weather.
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FIG. 7. Frequencies of the extreme (a) positive and (b) negative phase ranges of the tripole
wind pattern during the winter season. Dashed red lines represent linear trends since the winter
of 1988/89.
In fact, the intensity of the tripole wind pattern is
significantly correlated with the previous autumn’s
(September–November) SIC (Fig. 10a). Significant
positive SIC anomalies are observed in the Pacific sector
of the Arctic Ocean and in the area close to northern
parts of the Siberian marginal seas. The above (below)
normal autumn SIC in these areas tends to correspond
to the positive (negative) phase of the tripole wind
pattern. The frequency of the extreme negative phase of
the tripole wind pattern is also closely correlated with
the previous autumn SIC, and significantly negative SIC
anomalies appear also in the Pacific sector of the Arctic
Ocean and in the north of the Barents Sea across north
of the Kara Sea to north of the Laptev Sea (Fig. 10b).
Compared with the previous autumn, winter SIC
anomalies associated with the tripole wind pattern are
obviously weaker (Figs. 10c,d). The result of the Monte
Carlo simulations implies that anomalous SIC fields in
Figs. 10a,b are at the 95% statistical significance level.
The correlation between the tripole wind pattern and
the previous autumn SIC is further supported by the
leading coupled pattern between autumn SIC and 850-hPa
meridional winds in the ensuing winter (Fig. 11). It is seen
that decreased autumn Arctic SIC in the Siberian marginal
seas of the Arctic Ocean is associated with northerly
anomalies over the mid- to high latitudes of the Asian
continent and southerly anomalies over most of Europe.
Thus, decreasing autumn SIC may favor the more frequent occurrence of the tripole wind pattern with the
negative phase.
The evidence shows that autumn Arctic SIC decreases
and the Arctic amplification (Screen and Simmonds
2010) are associated with significant increases in the
winter mean SLP averaged over 1999–2012 in northern
Eurasia relative to that in 1988–99 (Fig. 12a). At the
midtroposphere, positive height anomalies emerge over
the Arctic and around the Ural Mountain and negative
height anomalies appear over Europe and the mid- to
high latitudes of East Asia (Fig. 12b). It is seen that over
northern Eurasia, the spatial distribution of SLP (500-hPa
height) anomalies resembles that in Fig. 2d (Fig. 4d).
To further study these relationships, we applied the
same analysis process to the simulation results of the
ECHAM5 model forced by observed Northern Hemisphere SIC during 1978–2007. Here, only the simulated
winter mean SLP, 500-hPa height, and SAT anomalies
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FIG. 8. (a) Winter mean anomalous precipitation percentages, derived from a linear regression on the inverted normalized winter mean intensity time series of the tripole wind
pattern. (b) As in (a), but for regression on the normalized frequency time series of the negative
phase of the tripole wind pattern. The magenta (light blue) and red (blue) areas denote negative (positive) precipitation anomalies at the 95% and 99% significance levels, respectively.
Units are %.
during the period of 1999–2007 relative to those of 1988–
99 are analyzed. Thus, 12 simulation experiments contain 96 (132) winter cases during the period 1999–2007
(1988–99). The simulated winter mean SLP and 500-hPa
height anomalies, however, derived from all simulation
experiments, do not hold the same relationships as in the
observations (not shown). Through inspecting differences in winter mean SLP and 500-hPa height between
two periods (1999–2007 minus 1988–99) in each 30-yr
simulation, it is found that only five of the experiments,
to a great extent, can reproduce the major features of the
observed atmospheric circulation anomalies over northern Eurasia. Thus, the five simulation experiments contain 55 (40) winter cases during 1988–99 (1999–2007),
and composite anomalies are shown in Figs. 12c–f. Over
northern Eurasia, the spatial distributions of simulated
SLP and 500-hPa height anomalies closely resemble the
observations though the amplitudes of these anomalies
are relatively weaker. At the surface, accompanying
Arctic sea ice loss, significant warming is observed over
the Arctic Ocean and its marginal seas, and concurrent
negative SAT anomalies emerge over much of the Asian
continent (Figs. 12e,f). Thus, the ECHAM5 simulations
suggest that a similar mechanism, as indicated from the
observations, may be working in the model. Due to the
differences between the observed and simulated patterns as shown in Fig. 12, however, and due to the fact
that only five simulations reproduce the major features
of the observed atmospheric circulation anomalies over
northern Eurasia, the influence of internal variability
and model uncertainties is suggested to be rather large.
Further, the initial atmospheric conditions may also
influence the atmospheric response to the SIC forcing,
and further investigations are needed.
Many previous studies have suggested that decreased
autumn Arctic sea ice would lead to an intensified heat
loss from the ocean and a stronger heating effect on the
overlying atmosphere, which strengthens the atmospheric baroclinicity and instability (Alexander et al.
2004; Jaiser et al. 2012; Porter et al. 2012). As the seasons
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FIG. 9. Spatial distributions of winter mean SAT anomalies:
(a) 2007/08, (b) 2009/10, (c) 2010/11, and (d) 2011/12. Intervals are 28C.
progress, through a negative feedback process, baroclinic atmospheric processes diminish and barotropic
interactions connected with the development of long
planetary waves become more important, resulting in
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frequent anticyclonic anomalies over northern Eurasia
and the Siberian marginal seas of the Arctic Ocean
(Alexander et al. 2004; Deser et al. 2004, 2007, 2010;
Jaiser et al. 2012; Wu et al. 2011). Thus, we summarize in
Fig. 13 the schematic of how reduced Arctic sea ice influences the winter SAT and precipitation across Eurasia. Through the negative feedback process, persistent
negative autumn/winter Arctic SIC anomalies cause the
frequent occurrence of the winter anomalous atmospheric blocking pattern over northern Eurasia and
some marginal seas of the Arctic Ocean. This atmospheric
blocking pattern induces cold-air outbreaks southward
from the polar region, leading to negative SAT anomalies in the mid- to high latitudes of Asia. Meanwhile,
through the anomalous atmospheric blocking pattern
affecting the prevailing pattern of winter atmospheric
variability, the reduced autumn/winter Arctic SIC influences weather events over the midlatitudes of Eurasia.
This feedback process indeed involves the roles of sea
surface temperature (SST) anomalies in the northern
North Atlantic and subarctic areas (Deser et al. 2007; Wu
et al. 2011). In addition, other than the Arctic SIC, other
factors, such as SST in the North Atlantic and the Atlantic
multidecadal oscillation (AMO), may also contribute to
increases in winter anomalous blocking patterns over
northern Eurasia (Li 2004; Peng et al. 2003).
On the other hand, a short-term trend in the tripole
wind pattern could reflect the nature of the interdecadal
(or multidecadal) variability of the regional climate
system, which contributes a great deal to the short-term
trend. In fact, a coherent pattern of interdecadal (or
multidecadal) variability in the atmosphere–ocean–ice
system in the Arctic and northern Eurasia requires that
Arctic sea ice anomalies have an important impact on
the atmosphere (Mysak et al. 1990; Mysak and Venegas
1998). It is possible that Central and East Asia may
experience more frequent cold winters under the
background of an increasing trend in the tripole wind
pattern and a declining trend in autumn sea ice. We are
left to ask the following: How long will the increasing
trend in the tripole wind pattern persist? What is the underlining forcing(s) necessary to maintain this increasing
trend—nature decadal–interdecadal variability, or does
the enhanced anthropogenic forcing also played a role, or
some combination of the two? These questions are relevant for the proposed dynamic feedback mechanism and
for the seasonal prediction of the East Asian winter
monsoon, and they need to be studied further in the future.
6. Summary
This paper reveals the leading pattern of daily
850-hPa wind variability during the winter over northern
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FIG. 10. (a) Autumn mean SIC anomalies, derived from a linear regression on the normalized winter mean intensity of the tripole wind pattern. The magenta (light blue) and red (blue) areas denote negative (positive) SIC
anomalies at the 95% and 99% significance levels, respectively. (b) As in (a), but regressed on the normalized
frequency time series of the extreme negative phase ranges of the tripole wind pattern. (c),(d) As in (a),(b), respectively, but for winter mean SIC anomalies. Intervals are 3%.
Eurasia from a dynamical perspective. It is found that the
leading pattern accounts for 18% of the total anomalous
kinetic energy and consists of two subpatterns: the dipole
and tripole wind patterns. The dipole wind pattern does
not exhibit any significant trend in the study period. The
tripole wind pattern, however, has displayed significant
trends in its intensity and frequency since the late 1980s.
The negative phase of the tripole wind pattern corresponds to anomalous cyclones over southern Europe and
over the mid- to high latitudes of East Asia, thereby enhancing winter precipitation in those regions. The intensity of the tripole wind pattern and the frequency of its
extreme negative phase are significantly correlated
with autumn Arctic SIC. The simulated results from
the ECHAM5 model forced by observed Northern
Hemisphere SIC during 1978–2007 suggest that the decreased Arctic SIC causes strengthening of the winter
anomalous blocking pattern over northern Eurasia,
supporting the observational association. Thus, autumn
Arctic SIC may provide a potential precursor for the
ensuing winter daily tripole wind pattern and precipitation in southern Europe and the mid- to high latitudes
of East Asia. The results of this study imply that East
Asia may experience more frequent and/or intense winter extreme weather events in association with the loss of
Arctic sea ice.
NOVEMBER 2013
WU ET AL.
FIG. 11. (a) Normalized time series of the leading coupled pattern between autumn mean SIC north of 508N (blue line) and the ensuing winter mean meridional
winds at 850 hPa (NCEP–NCAR reanalysis data) over the domain 408–708N and
408–1208E (red solid line; red dashed line represents its trend; their correlation is
0.8). The leading coupled pattern (MCA1) accounts for 59% of the covariance. (b)
Autumn mean SIC anomalies, derived from a linear regression on the normalized
inverted time series of 850-hPa meridional winds in the leading coupled pattern
(%). (c) As in (b), but for the ensuing winter mean 850-hPa meridional wind
anomalies (m s21), derived from a linear regression on the normalized inverted
time series of autumn SIC in the leading coupled pattern.
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MONTHLY WEATHER REVIEW
FIG. 12. Winter mean (a) SLP and (b) 500-hPa height anomalies during 1999–2012 (13 winters) relative
to 1988–99 (11 winters). The magenta (light blue) and red (blue) areas denote positive (negative)
anomalies at the 95% and 99% significance levels, respectively. Intervals are 1 hPa in (a) and 10 gpm in
(b). (c),(d) As in (a),(b), respectively, but for the simulated winter mean SLP and 500-hPa height
anomalies during 1999–2007 relative to 1988–99, derived from simulation experiments of the ECHAM5
model forced by observed Northern Hemisphere SIC during 1978–2007. (e) As in (d), but for the simulated winter mean SAT anomalies. (f) Significance test of winter mean SAT anomalies in (e); the
meaning for color areas is same as in (a). Intervals are 0.3 hPa in (c), 5 gpm in (d), and 0.58C in (e).
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WU ET AL.
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FIG. 13. Schematic of how reduced Arctic sea ice affects winter SAT and precipitation
tendencies across Eurasia. Arrows denote the spatial distribution of the anomalous anticyclone
and cyclone associated with the negative phase of the tripole wind pattern in the lower troposphere. The brown line represents the 500-hPa height isoline. Yellow and green areas indicate less and more precipitation, respectively. Red and purple areas, respectively, depict
positive and negative SAT anomalies.
Acknowledgments. The authors are grateful to
Dr. Jennifer A. Frances, Dr. Judah L. Cohen, and an
anonymous reviewer for their support and constructive
suggestions, which helped to significantly improve this paper. The authors thank Dr. Jingzhi Su for kindly providing
simulation experiments with the ECHAM5 model forced
by Northern Hemisphere sea ice concentration. This study
was supported by the National Key Basic Research Project
of China (2013CBA01804), the National Natural Science
Foundation of China (41221064), the Chinese Project
(GYHY200906017), and the State Oceanic Administration Project (201205007). AH was supported by the Office
of Science (BER), U.S. Department of Energy, Cooperative Agreement DE-FC02-97ER62402. The National Center for Atmospheric Research is sponsored by
the National Science Foundation.
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