Impact of the winter North Atlantic Oscillation (NAO) on the Western Pacific (WP) pattern in the following winter through Arctic sea ice and ENSO: part I—observational evidence
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On the basis of a 51-year statistical analysis of reanalysis data, we propose for the first time that the positive phase of the Western Pacific (WP) pattern in the winter is linked to the negative phase of the North Atlantic Oscillation (NAO) in the previous winter, and vice versa. We show that there are two possible mechanisms responsible for this interannual remote linkage. One is an Arctic mechanism. Extensive Arctic sea ice in the summer after a negative NAO acts as a bridge to the positive phase of the WP in the next winter. The other mechanism involves the tropics. An El Niño occurrence after a negative winter NAO acts as another bridge to the positive phase of the WP in the following winter. The timescale of the Arctic route is nearly decadal, whereas that of the tropical route is about 3–5 years. The tropical mechanism indicates that the NAO remotely excites an El Niño in the second half of the following year. A process perhaps responsible for the El Niño occurrence was investigated statistically. A negative NAO in the winter increases Eurasian snow cover. This anomalous snow cover then intensifies the cold air outbreak from Asia to the western tropical Pacific. This outbreak can intensify the westerly wind burst and excite El Niño in the following year. We suggest that the phase of the NAO in the winter could be a predictor of the WP in the following year.
KeywordsArctic sea ice ENSO Long-term prediction NAO WP
This study tested the hypothesis that Asian weather and climate in a given winter can be predicted 1 year in advance. A persistent cold wave and heavy snowfall during the winter in East Asia have social, economic, and psychological impacts on Japan because of the lack of atomic power stations in the post-Fukushima accident era. The colder the winter, the more electricity Japan needs. A cold wave is associated with an anomaly in hemispheric atmospheric circulation. One of the most important components of atmospheric circulation correlated with surface air temperature (SAT) teleconnections over Eastern Asia is the Western Pacific (WP) pattern identified by Wallace and Gutzler (1981). The negative phase of the WP pattern affects the Eastern Asian monsoon and leads to abnormally cool temperatures over Eastern Asia and Eastern Siberia in the winter (Gong et al. 2001; Zhang et al. 2009). Prediction of the WP pattern in advance is thus important for Japanese society. However, knowledge of the long-term variation of the WP pattern and its prediction is much less advanced than understanding and prediction of other large-scale atmospheric modes.
Honda et al. (2009) have hypothesized that winter weather in East Asia is related to ice reduction in the Barents–Kara Seas during the previous autumn. Their atmospheric general circulation model (AGCM) results indicate that a decrease of the extent of sea ice in summer and autumn strengthens the Siberian anticyclone in the following winter and in this way brings about a cold anomaly over East Asia. Honda et al. (2009) did not specifically take note of the WP pattern, but the extent of ice cover in autumn may be a key metric for long-term forecasting of the severity of the winter in East Asia, and specifically in Japan. Takano et al. (2008) have also shown that the Siberian-Japan pattern, which is similar to the WP pattern, favors heavy snowfall in Japan, and Hori et al. (2011) have pointed out that arrival of a cold wave in East Asia is related to an anticyclonic anomaly formed in association with the decline of sea ice in the Barents–Kara Seas.
Ogi et al. (2003) have shown that the extent of sea ice during the summer in the Barents Sea and summertime weather in East Asia are related to the North Atlantic Oscillation (NAO) during the winter of the previous year. Rodwell et al. (1999) have also pointed out the impact of the NAO on Arctic Sea ice cover in the Barents Sea. Collectively, these previous studies suggest that the winter cold wave in East Asia is associated with the NAO during the previous winter. However, no previous studies have examined whether the NAO during the winter can be used to predict the WP pattern in the next winter. The first goal of the present study was to determine whether there was an association between the WP pattern during the winter and the NAO during the previous winter.
El Niño/Southern Oscillation (ENSO) is another well-known key factor needed for long-term prediction of the
WP pattern. The WP pattern is known to be one of the most influential teleconnection patterns excited by ENSO (e.g., Horel and Wallace 1981; Mo and Livezey 1986; Kodera 1998). For example, Horel and Wallace (1981) have shown that El Niño events during the winter and the negative phase of the Southern Oscillation Index (SOI) are associated with the positive phase of the winter WP.
For the prediction of East Asian weather in the winter, the influence of both the tropics (e.g., ENSO) and the Arctic (e.g., sea ice) should therefore be considered. As the previous studies have suggested, the WP pattern during the winter can be related to both the NAO and ENSO. If interannual variation of the NAO is somehow related to ENSO, the mechanism responsible for the connection among the WP, NAO, and ENSO might be complicated. For example, if the NAO influences ENSO, or if ENSO influences the NAO, these influences would need to be carefully taken into account. Some studies have demonstrated that the Arctic Oscillation (AO) during the spring influences ENSO in the following winter (Nakamura et al. 2006, 2007; Chen et al. 2014). Because the AO and NAO have similar structures in the northern hemisphere over the Atlantic Ocean, the NAO may also influence ENSO. It is important to distinguish cause and effect in the interactions between the NAO, WP, and ENSO. The second goal of the present study was therefore to test the hypothesis that the NAO influences ENSO. Finally, we discuss the physical processes responsible for the NAO-WP linkage at both low and high latitudes. The methodology of this study primarily involved statistical analyses of a reanalysis dataset from the latter half of the twentieth century.
2 Data and methods
The atmospheric dataset used in this study was the National Centers for Environmental Prediction and National Centers for Atmospheric Research (NCEP/NCAR) reanalysis dataset (Kalnay et al. 1996). The extent of sea ice cover and sea surface temperature (SST) data used in this study came from the sea ice and sea surface temperature dataset version 1 (HadISST1) of the Met Office of the Hadley Center (Rayner et al. 2003). The snow depth data from 1979 through 2010 in the Japan Meteorological Agency Climate Data Assimilation System reanalysis (JCDAS) (Onogi et al. 2007) were also used in this study. We used monthly mean data from 1960 to 2010, except for the snow data.
Definitions of indices used in this study
Region and variable
North Atlantic Z500
Western Pacific Z500
Tropical Pacific SST
Longitude and latitude
NAO + WP
Dec (+1 yr)
NAO + WP + ENSO
Dec (+1 yr)
Dec (+1 yr)
To examine the possible linkages among the NAO and one-year-lagged WP and ENSO as well as the underlying mechanisms, we carried out a lead-lag linear regression of the atmospheric variables, SST, sea ice concentration, and snow depth against EOF1 scores. Because of the time scales of SST variability associated with ENSO and sea ice variability, both indices and response variables were normalized and detrended. The indices and variables were then divided into high-, middle-, and low-frequency variations. The high-, middle-, and low-frequency data were obtained by 3-year high-pass, 3–7-year band-pass, and 7-year low-pass filters, respectively. The low-pass data were obtained by 7-year running mean, the high-pass data were obtained by subtracting a 3-year running mean, and the band-pass data were determined by subtracting the 7-year low-pass data from the 3-year running mean data.
3 Results and discussion
3.1 The winter WP and the previous winter NAO
3.2 The path from the winter NAO to the winter WP the following year
Some mechanisms that connect the winter NAO and the WP in the following winter are not apparent. The mechanisms cannot involve the atmosphere, because the atmosphere cannot retain information long enough to connect phenomena in successive winters. Candidate mechanisms may involve the ocean, land, and sea ice, because their heat capacities are much larger than that of the atmosphere. Here we point out some processes that may modulate the internal variability of the atmosphere and play roles in connecting these two atmospheric patterns.
3.2.1 Arctic sea ice variations
3.2.2 Tropical SST variations
Correlation coefficients among indices
NAO + WP
NAO + WP + ENSO
NAO (sign reversed)
WP (+1 yr)
ENSO (+1 yr)
NAO + WP
3.2.3 Possible mechanism
We have shown that the winter NAO is likely associated with the WP during the winter of the following year, and that WP is strongly connected to the temperature anomaly in Japan. In addition, we have shown that there are two processes, one involving high latitudes, and the other involving low latitudes.
The high-latitude process involves Arctic sea ice variations. The negative (positive) phase of the winter NAO changes oceanic currents in the North Atlantic and weakens (strengthens) oceanic heat transport into the Arctic (Wohlleben and Weaver 1995; Kwok and Rothrock 1999; Sandø et al. 2010; Schlichtholz 2011). This weakened (strengthened) heat transport also slows down (speeds up) the reduction of sea ice in the spring. A condition of more (less) ice than normal then persists until the season of ice freezing in autumn. In winter, all of the Arctic Ocean is covered by sea ice, regardless of the autumn ice area. Less (more) ice production during the freezing season reduces (increases) the heat released from the ocean to the atmosphere in the Arctic. An anomalously small (large) heat flux excites stationary Rossby wave propagation, which induces warm (cold) advection to Japan, as shown by Honda et al. (2009). The influence of the NAO on this mechanism is more obvious at low-frequency time scales, such as a decadal time-scale. This scenario is reasonable, because sea ice and the ocean have large heat capacities.
The low-latitude process involves variations of tropical Pacific SSTs on a timescale typical of ENSO. As mentioned above, El Niño (La Niña) induces positive (negative) WP in the winter (e.g., Horel and Wallace 1981; Mo and Livezey 1986; Kodera 1998). It is thus necessary to examine processes that connect the winter NAO and the subsequent ENSO. An El Niño outbreak tends to start in the late winter/early spring (Philander 1985; Barnett et al. 1989; Yu and Rienecker 1998; Yu et al. 2003). We thus focus on the impacts of the winter NAO on the tropics in late winter/early spring. Some studies have pointed out the relationship between an NAO-associated snow anomaly on the Eurasian continent and tropical atmospheric variations. Clark et al. (1999) and Hori and Yasunari (2003), for example, have shown that there is an anomalous increase (decrease) of snowfall in western Eurasia in association with a negative (positive) winter NAO. Barnett et al. (1989) have shown that winter snow on the Eurasian continent is associated with the summer Indian monsoon and ENSO. In addition, Nakamura et al. (2006, 2007) have shown that an outbreak of cold air from Asia to the tropics intensifies the westerly wind burst over the western tropical Pacific and triggers El Niño. The winter NAO thus has the potential to intensify a cold air outbreak through its effect on the snow anomaly in the western Eurasian continent. We therefore hypothesize that tropical atmospheric variations, which are related to El Niño outbreaks, can respond to the Eurasian snow anomaly associated with the wintertime NAO. To test this hypothesis, we examined the Eurasian snow anomaly and associated atmospheric processes by regression against the NAO + WP + ENSO index. We focused on only the 3–7-year band-pass timescale, because the regression of SST anomaly in the central-to-eastern tropical Pacific against the NAO + WP index was highest on this timescale (Fig. 6).
We have used statistical analyses to show for the first time that a positive (negative) phase of the WP pattern during the winter, which brings a warm (cold) anomaly to East Asia, including Japan, is related to a negative (positive) phase of the NAO in the previous winter. Exploiting this interannual linkage may facilitate long-term weather prediction. In this study we have proposed two mechanisms responsible for this interannual linkage. One mechanism involves the Arctic, and the other involves the tropics. The key factor in the case of the Arctic mechanism is Arctic sea ice. The Arctic mechanism is more obvious at low-frequency timescales, such as decadal or multi-decadal time-scales. The other mechanism involves variations of tropical SSTs. We found that a negative winter NAO induced an El Niño-like SST anomaly in the following winter. Because El Niño excites the WP pattern remotely, a NAO in one winter can induce a WP pattern in the following winter. A process that may link a negative NAO during the winter to El Niño is the following. Unusually extensive snow cover on the western Eurasian continent as the result of the influence of the negative phase of the NAO during the winter, which brings about cold winters in Europe, makes the air temperature in the region low. The negative heat release associated with the cold surface generates a wave, and the propagation of the wave excites an anticyclonic circulation over the Tibetan plateau. This anticyclonic circulation brings about an outbreak of cold air from Asia to the western tropical Pacific Ocean, which intensifies the western wind burst over the western tropical Pacific Ocean. This burst of westerly winds can excite El Niño from summer to winter in the following year.
Both the long-term and short-term variations of the winter NAO therefore act to induce a WP in the following winter. The phase of the NAO during the winter could therefore be a predictor of the WP the following year. We have proposed two processes to account for the connectivity of the NAO and WP. Other processes that are not apparent may also influence the WP in the following winter. For example, the winter WP might have an influence on the NAO in the following winter because the time scales of the discovered linkage between the NAO and WP is in 3–5 years or decadal. This possibility must be explored in future studies. Because this study was based on statistical analysis of the reanalysis dataset, the true cause of the relationships between the NAO, Arctic sea ice, ENSO, and WP is still uncertain. In the case of the tropics in particular, the impacts of the NAO on El Niño outbreaks should be examined in more detail. To confirm the proposed processes, we performed a multi-model ensemble analysis using the Coupled Global Climate Model (CGCM) from the Coupled Model Intercomparison Project phase 3 (CMIP3) of the World Climate Research Programme (Meehl et al. 2007; Randall et al. 2007) in Part II (Nakamura et al. 20141) as a counterpart of this study.
Nakamura et al. (2014) was submitted to Climate Dynamics as a companion paper.
We extend grateful thanks to K. Kodera; his enormous support and insightful comments were invaluable during the course of this study. We are also grateful to the reviewer’s valuable comments that improved the manuscript. This study was supported by the Green Network of Excellence Program (GRENE Program) Arctic Climate Change Research Project and the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) through a Grant-in-Aid for Scientific Research in Innovative Areas 2205.
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