1. Introduction
As a result of recent climate changes, anomalous climate conditions have more frequently occurred in the Korean Peninsula (KMA 2019). Thus, there is a growing demand for 1-month or subseasonal-to-seasonal (S2S) forecasts and an improvement in weekly predictability to minimize socioeconomic damage by the abnormal climate. The S2S forecasting falls in between medium-range forecasting (up to 7–10 days) and seasonal forecasting. It is considered a challenging time range because the time scale is long enough to lose much of the initial condition data, and it is sufficiently short for capturing ocean variability (White et al. 2017; Vitart 2014).
The ongoing S2S prediction research project in the World Meteorological Organization (WMO), World Weather Research Programme (WWRP), and World Climate Research Programme (WCRP), aims to improve our forecasting ability and understanding of the S2S time scale (Vitart et al. 2012; Robertson et al. 2015). A sizable project database has been established; currently, the archives of near-real-time forecasts and hindcasts of up to 60 days are available (Vitart et al. 2017). One of the subprojects involves identifying and simulating key sources of S2S predictability. There is a great potential advantage in subseasonal forecasting if the model can capture the atmospheric teleconnections—for example, El Niño–Southern Oscillation (ENSO) is one of the leading sources of predictability for S2S models (Mariotti et al. 2020). Once active, its effects are perceived on the S2S to multiannual time scale (DiNezio et al. 2017), and its conditions tend to be predictable (e.g., Zheng et al. 2016). The influence of the ENSO on subseasonal variability not only affects the midtropospheric flow, as illustrated in many studies, but also potentially modulates surface air temperature (SAT) and the occurrence of weather extremes (Chen and van den Dool 1999, 1997; Compo et al. 2001; Tam and Lau 2005).
Of the vital circulatory systems that affect East Asia, the ENSO and Arctic Oscillation (AO) play an important role in predicting the East Asian winter climate. It is generally accepted that the AO could impact the East Asia winter monsoon (EAWM) in association with a stronger East Asia trough or an anomalous anticyclonic flow over the Urals at the midtroposphere (Luo et al. 2017; Yao et al. 2017; Luo et al. 2016a,b; Overland et al. 2015; Cohen et al. 2014; Takaya and Nakamura 2013; Tang et al. 2013; Cheung et al. 2012; Lee and Jhun 2006; Takaya and Nakamura 2005; Chen et al. 2005, 2003; Wu and Wang 2002; Gong and Ho 2002; Gong et al. 2001; Yihui 1990). In addition, results from a number of studies have suggested that the anomalous SAT over East Asia may be caused by a Siberian high under the influence of the winter AO (Park et al. 2010; Yang and Li 2008; Jeong and Ho 2005; Gong and Wang 2003; Ding et al. 1991). Furthermore, anomalous stratospheric processes, such as stratospheric polar vortices, could induce the negative phase of AO, leading to a weaker circulation of westerlies in the polar region and causing an increase in the possibility of cold surges over Eurasia (Song et al. 2018; Wei et al. 2015; Park et al. 2014, 2011; Mitchell et al. 2013; Wen et al. 2013; Yoshida and Yamazaki 2011; Wang and Chen 2010a; Nakagawa and Yamazaki 2006; Chen et al. 2005; Baldwin et al. 2003; Baldwin and Dunkerton 2001; Baldwin and Dunkerton 1999).
The EAWM typically weakens when there are positive sea surface temperature (SST) anomalies in the tropical eastern Pacific (e.g., El Niño), and it strengthens when there are negative SST anomalies (e.g., La Niña) (Chongyin 1990; Zhang et al. 1996; Wang et al. 2000; Sakai and Kawamura 2009; Wen et al. 2000; Tomita and Yasunari 1996; Liren et al. 1997, Hamada et al. 2002; Chang et al. 2004). In addition, studies have demonstrated that the ENSO significantly influences the South Korean climate variability (Kang 1998; Lee 1998; Yeo et al. 2018). For example, Lee and Julien (2016) revealed that temperature anomalies in South Korea are below (above) normal for the summer through fall of an El Niño (La Niña) year and above (below) normal for the winter through spring of the following year. El Niño also contributes to fluctuations in the EAWM by inducing abnormally high pressure in the western North Pacific (Zhang et al. 1996; Wang and Chen 2010b). Hence, the ENSO results in SAT variation in East Asia and North America due to its large-scale circulation changes.
Li et al. (2021) showed that the blocking anticyclone over the Ural region can be more persistent and quasi-stationary because of the sea ice loss in the Barents–Kara Sea. Cheung et al. (2012) revealed that less (more) Ural–Siberia blocking and a weak (strong) Siberian high cause warmer (colder) winters in East Asia when the ENSO and AO are in phase (out of phase). When they are out of phase, the linkage between the Siberian high and Ural–Siberia blocking weakens. Chen et al. (2013) demonstrated that when the winter ENSO and AO are out of phase, the AO affects the temperature and precipitation over northern China, and the ENSO affects the temperature and precipitation over southern China. Previous studies have demonstrated the combined effect of ENSO and AO, predominantly at a monthly or seasonal time scale (Park and Ahn 2016; Huang et al. 2017). Therefore, this study aims to analyze the combined effects of the ENSO and AO at the weekly time scale to improve the temperature predictability of a 1-month forecast during wintertime in South Korea.
The remainder of this paper is organized as follows: The datasets and methods are described in section 2. Section 3 presents the features of the combined effect of ENSO and AO and the model evaluation. Section 4 summarizes the findings.
2. Data and method
a. Data
To examine large-scale circulation patterns, 500-hPa geopotential height (GPH), mean sea level pressure (MSLP), SAT, and 850-hPa zonal and meridional wind components from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) are used (Hersbach et al. 2020). The target analysis period based on the reanalysis data was boreal winter (December–February) from 1983 to 2018, and anomalies for all variables were defined as deviations from the climatological mean (1983–2012). All datasets were interpolated into a horizontal resolution of 1.5° × 1.5° and averaged weekly. Additionally, mean temperature anomalies from 45 automated surface observing systems (ASOS) from the Korea Meteorological Administration (KMA) stations were used to evaluate the spatial average of mean temperatures from model simulations over South Korea. Temperature anomalies, defined as deviations from the climatological mean (1983–2012), were averaged weekly, from Monday to Sunday, during the boreal winter.
S2S hindcast data from the ECMWF model was used. The ECMWF S2S hindcast has a horizontal resolution of T639/319 and a vertical resolution of L91 for its on-the-fly production cycle. The hindcast period used in this study was from 1999 to 2010, and data from the 11 ensemble members were averaged. We obtained the sets of daily hindcasts (reforecasts) from the database on the ECMWF website (http://apps.ecmwf.int/datasets/data/s2s/). More detailed information about this model can be found in Vitart et al. (2017).
b. Method
A time series of daily AO index since January 1983 from the Climate Prediction Center (CPC) of the National Oceanic and Atmospheric Administration (NOAA) can be found online (
Bootstrap tests were applied to determine significant differences. This is a statistical method that uses data resampling with replacements of 5000 samples to estimate robustness and can be used across almost all statistics. Most commonly, these include standard errors as well as confidence intervals of population parameters, such as the mean, median, correlation coefficient, or regression coefficient. Bootstrapping statistics are useful when dealing with small sample sizes and infer the characteristics of a population from the statistical characteristics of the sample, without assuming the probability distribution of the sample (Mudelsee 2013; Hennemuth et al. 2013; Hesterberg et al. 2003; Efron and Tibshirani 1993; Diaconis and Efron 1983).
Schematic diagram for considering the roles of ENSO and AO. The number of weeks in the observation for each case is represented as N.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
3. Results
a. Responses of temperature and circulation patterns to combined effect of ENSO and AO
Before investigating the combined effect of the ENSO and AO, the influence of each climate factor was identified at the weekly time scale. During a positive ENSO, positive GPH anomalies at 500 hPa were found over the northwest Pacific Ocean, including South Korea and Japan, and negative GPH anomalies were centered over the Gulf of Alaska. In contrast, during a negative ENSO, weak positive GPH anomalies were found over the central North Pacific (30°–60°N, 160°E–150°W), and negative GPH anomalies were found over northeast Asia and Alaska. Negative (positive) GPH anomalies were found over the North Pole, and positive (negative) GPH anomalies occurred from Lake Baikal to the North Pacific during a positive (negative) AO. These characteristic patterns agree with a monthly or seasonal time scale as in previous studies (Zhou et al. 2001).
Figures 2–7 present composite analyses of the combined effect of ENSO and AO from reanalysis data. In the EP case, positive GPH anomalies expanded from Lake Baikal to the east of Japan, while negative GPH anomalies appeared over the Arctic, to the south of Alaska, and to the south of the Caspian Sea (Fig. 2a). In the lower troposphere, negative MSLP anomalies extended from the Arctic to the Ural region, while positive MSLP anomalies occurred over the western North Pacific (WNP) and to the east of the Philippines. SAT anomalies were warmer than average over the majority of the Eurasian continent, including South Korea (Fig. 2b). A strong and warm temperature advection was noticeable over South Korea (Fig. 2e), while both advection of the mean temperature by anomalous winds and the advection of the anomalous temperature by mean winds indicated the presence of warm advection (Figs. 2c,d).
Composite maps on the week of EP cases (N = 56): (a) GPH anomalies at 500 hPa (m), (b) SAT anomaly (°C; shading) with mean sea level pressure anomaly (hPa; blue and red lines denote positive and negative anomaly, respectively, and the black line denotes zero), (c) advection of mean temperature by anomalous wind, (d) advection of anomalous temperature by mean wind, and (e) total thermal advection (°C day−1) during boreal winter 1983–2018. The stippled area denotes the 95% confidence level.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 2, but for EN cases (N = 57).
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 2, but for LP cases (N = 74).
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 2, but for LN cases (N = 68).
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 2, but for PA cases (N = 57).
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 2, but for NA cases (N = 47).
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
In the EN case, strong negative GPH anomalies were recorded over the south of Alaska through to Lake Baikal, while positive GPH anomalies occurred significantly over the tropical regions and central Asia (Fig. 3a). Positive MSLP anomalies and cold temperature anomalies occurred over the northern parts of the Eurasian continent, with negative MSLP anomalies recorded over the south of Alaska. Furthermore, warm temperature anomalies were captured in the southern part of the Eurasian continent and Alaska (Fig. 3b). Cold advection was prominent to the north of 32°N including South Korea, while warm advection occurred over southern China and the northwestern Pacific (Fig. 3e). Notably, the warm advection was primarily controlled by the mean temperature advection of anomalous winds (Fig. 3c), while the cold advection occurred as a result of anomalous temperature advection by mean wind (Fig. 3d).
The composite circulation patterns of the LP case contrasted those of the EN case, with strong positive GPH anomalies centered over the south of Alaska through to Japan, and a further positive GPH anomaly centered over Siberia. Weak negative GPH anomalies occurred over the tropics and Tibetan Plateau (Fig. 4a), while strong negative MSLP anomalies were recorded over the Arctic region, and strong positive MSLP anomalies centered over the south of Alaska. Warm temperature anomalies occurred over the Arctic, Siberia, and South Asia, while cold temperature anomalies arose over the Middle East, southeastern China, and far eastern regions of Russia (Fig. 4b). Warm temperature advection was predominant over South Korea, primarily due to the mean temperature advection by anomalous wind (Figs. 4c–e).
Notably, circulation patterns of the LN case were almost in direct contrast to those related to the EP case. Strong positive GPH anomalies were prominent over the Arctic region, while negative GPH anomalies occurred from the south of Lake Baikal to the south of Alaska (Fig. 5a). These negative GPH anomalies also originated from the ENSO and AO, and were aggregated, as in the EP case, but opposite in sign. In the lower troposphere, strong positive MSLP anomalies extended from the Arctic to the Eurasian continent, while strong negative MSLP anomalies appeared over the south of the Kamchatka Peninsula. Furthermore, strong negative temperature anomalies occurred over the Eurasian continent including South Korea (Fig. 5b). Cold advection was significant over this area (Fig. 5e), owing to the contribution of both advection terms to the cold advection.
In the PA case, strong positive GPH anomalies over the south of Alaska extended westward to Lake Baikal, while negative GPH anomalies were prominent over the Arctic region (Fig. 6a). In the lower troposphere, MSLP anomalies appeared in a similar pattern to that of the GPH anomalies. Strong positive MSLP anomalies occurred over the south of Alaska, with strong negative MSLP anomalies over the Ural area. Temperature anomalies were above average over the Eurasian continent, but below average for the Arctic coast (Fig. 6b). In addition, warm temperature advection was significant over South Korea, as both terms of advection contributed to the warm advection (Fig. 6e).
In the NA case, strong positive GPH anomalies persisted over the North Pole, with negative GPH anomalies over Siberia and the northwestern Pacific (Fig. 7a). Similarly, strong positive MSLP anomalies appeared over the North Pole, with negative MSLP anomalies over the Pacific and Lake Baikal. Temperature anomalies were below average in Siberia and above average in the Middle East, eastern China, and over the East Siberian Sea (Fig. 7b). Warm advection was prominent over South Korea, primarily explained by the anomalous temperature advection driven by mean wind (Figs. 7d,e)
The station observed temperature anomalies over South Korea for each of the selected cases are shown in the boxplot of Fig. 8. Temperature anomalies of both the EP and PA cases were predominately positive, while the temperature anomalies of the LN case were predominantly negative. These results are in agreement with those of previous studies, indicating that the EAWM grows weaker during a positive ENSO and stronger during a negative ENSO (Wang et al. 2000; Liren et al. 1997; Tomita and Yasunari 1996; Zhang et al. 1996). Additionally, our results indicate that the in phase of ENSO and AO enhances its impact on EAWM. However, results related to the EN, LP, and NA cases were not significantly above or below average.
Boxplot of temperature anomalies from 45 stations in South Korea (°C) for each case. Green triangles denote the mean, orange lines denote the median of the cases, and black circles denote outliers.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
In general, it is well known that a negative AO phase can lead to more frequent cold weather events over East Asia. Therefore, we divided the NA cases into quartiles and compared the warm cases (upper quartiles) with the cold cases (lower quartiles). The AO index with mean temperature anomalies over South Korea is plotted in Fig. 9. This scatterplot demonstrates that a strongly negative AO does not imply that temperature anomalies will always be far below average (Fig. 9a). During cold cases, positive GPH anomalies over the Barents–Kara Sea expanded eastward, while negative GPH anomalies appeared over South Korea (Fig. 9b). However, strong negative GPH anomalies occurred over Siberia and positive GPH anomalies over South Korea during warm cases (Fig. 9c).
(a) Scatterplot between negative AO index and mean temperature anomalies (°C) for 45 stations in South Korea. Also shown are composited 500-hPa GPH mean and anomalies (m) for (b) lower-quartile and (c) upper-quartile cases. Red and blue dots represent upper- and lower-quartile cases, respectively, and fitted lines for each category are displayed.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
b. ECMWF S2S predictability for combined effect of ENSO and AO
We investigated the performance of the ECMWF S2S model in simulating the spatial patterns depending on the combined effect of ENSO and AO. Previous studies have asserted that the model exhibits an acceptable capacity to predict the wintertime AO index for lead times of up to 9–14 days in the S2S forecast (Black et al. 2017; Zuo et al. 2016; Wang et al. 2010; Wang and Robertson 2019). We presented the AO index predictability of ECMWF S2S hindcast as a function of lead time during winter for the period of 1999–2010 in Fig. 10. The correlation coefficients between the AO index from ECMWF S2S and the CPC were 0.81 at lead 1, 0.47 at lead 2, and less than 0.30 after lead 3. The correlation was significant (significance level p < 0.05) at all lead times. The model shows reliable predictability of AO index up to two weeks of lead time, which is consistent with the results of previous studies (Black et al. 2017; Zuo et al. 2016). Mariotti et al. (2020) noted that the ECMWF S2S hindcast displayed acceptable skill in predicting SST anomalies for the Niño-3.4 region and it can provide skillful predictions of certain large-scale patterns at least two weeks in advance.
Correlation coefficient between the CPC AO index and ECMWF S2S forecast AO index as a function of lead time during the winter for the period of 1999–2010 (N = 163). The correlation was significant (p < 0.05) at all weeks.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
Composite maps of geopotential height anomalies at 500 hPa over 20°–90°N, 40°E–120°W as well as a boxplot of mean temperature anomalies over South Korea with lead times are displayed in Figs. 11–16. Mean temperature anomalies were obtained from five grid points of the model and 45 stations over South Korea, respectively.
Composite maps of GPH anomalies at 500 hPa (m) and boxplot of temperature anomalies (°C) over South Korea for EP case; −W1 is at lead 1, −W2 is at lead 2, and so forth. The PCC of the presented area (20°–90°N, 40°E–120°W) is presented on the top-right corner of each panel, and the dotted area denotes the 95% confidence level. The boxplot shows the station observed temperature anomalies and ECMWF SAT anomalies averaged over South Korea (5 grid points) with up to 4 weeks of lead time. Green triangles denote the mean, orange lines denote the median of the cases, and black circles denote outliers.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 11, but for EN cases.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 11, but for LP cases
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 11, but for LN cases
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 11, but for PA cases.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
As in Fig. 11, but for NA cases.
Citation: Journal of Climate 34, 18; 10.1175/JCLI-D-20-0772.1
Figure 11 refers to the EP case. The positive GPH anomalies were widely spread from Siberia to the east of Japan and over the Gulf of Alaska, while the strongly negative GPH anomalies appeared over the Arctic region and south of the Caspian Sea. Positive GPH anomalies over South Korea were well reproduced by the model up to lead 3. At lead 4, the positive GPH anomalies were significant over Siberia and over the east of Japan, respectively, however, they did not combine over South Korea as in the other lead times. Additionally, the amplitude of the pattern predicted by the model appeared to be stronger with a decrease in lead time. The Pearson correlation coefficient (PCC) values over the showing area were 0.89 at lead 1, 0.62 at lead 2, and 0.20 at lead 3. Therefore, the model provides a skillful prediction of the combined anomaly patterns up to 2 weeks of lead times. The simulated mean temperature anomalies exhibited a consensus on above average temperature across all lead times.
Strongly negative GPH anomalies over the North Pacific together with weak negative GPH anomalies over the west of Lake Baikal, and positive GPH anomalies over the Arctic were significant in the EN case (Fig. 12). The negative GPH anomalies over the North Pacific were continuously well reproduced by the model, although the weak negative GPH anomalies over the west of Lake Baikal were only shown up to lead 2. The PCC values were 0.87 at lead 1, 0.52 at lead 2, and 0.35 at lead 3. No significant signal over South Korea and the simulated mean temperature anomalies were predicted near average at all lead times.
In the LP case (Fig. 13), positive GPH anomalies occurred over the northeastern Pacific with negative GPH anomalies over the Arctic. In addition, positive and negative GPH anomalies occurred over the east and west of South Korea, respectively. Although the anomalous pattern over East Asia was not well reproduced by the model after lead 1, the strong positive GPH anomalies over the North Pacific were significant and continuous at all lead times. The PCC values were 0.85 at lead 1, 0.53 at lead 2, and 0.25 at lead 3, and the simulated mean temperature anomalies over South Korea were predicted near average at all lead weeks.
Results from the LN case demonstrated the opposite pattern to that of the EP case (Fig. 14). Negative GPH anomalies appeared significantly over the south of Alaska, from Lake Baikal to South Korea, while positive GPH anomalies occurred over the polar region. In the model simulation, the negative GPH anomalies over East Asia were sufficiently reproduced at all lead times. Specifically, the PCC values were 0.89 at lead 1, 0.59 at lead 2, and 0.39 at lead 3. The simulated mean temperature anomalies exhibited a consensus on below average temperature for all lead times.
Figure 15 presents results related to the PA case. Strong negative GPH anomalies appeared over the Arctic region, with strong positive GPH anomalies widely spread over the North Pacific. The model reproduced the positive GPH anomalies up to lead 3, but the negative GPH anomalies over the Arctic were only significant up to lead 2. The PCC values were 0.87 at lead 1, 0.50 at lead 2, and less than 0.2 after lead 3. The simulated mean temperature anomalies were mostly above average in this case, and the model positively predicted the above average temperature anomalies for all lead times.
Results of the NA case are presented in Fig. 16. Positive GPH anomalies appeared over the Arctic, Ural region, Middle East, and North Pacific, while negative GPH anomalies occurred over the Bering Sea and extended to the south of Lake Baikal. In the model simulation, GPH anomalies over the Pacific were reproduced up to lead 2, but the anomalous pattern over the continent varied depending on lead time. The PCC values were 0.91 at lead 1, 0.59 at lead 2, and approximately 0.3 at lead 3. The simulated mean temperature anomalies were predicted near average at all lead times.
4. Conclusions
In this study, we investigated how the ENSO and AO affect the Korean Peninsula mean temperature during boreal winter at the subseasonal time scale, and we verified the model reproducibility.
Results from reanalysis data demonstrated that positive ENSO and AO phases can cause an increase in mean temperature anomalies through positive GPH anomalies and warm advection in the EP and PA cases. Additionally, negative ENSO and AO phases resulted in a decrease in mean temperature anomalies through negative GPH anomalies and cold advection in the LN case. In these three cases, the characteristic circulation patterns induced by the climate factors were significant for South Korea. However, no significant circulation patterns were recorded over South Korea during the EN, LP, and NA cases. In addition, the majority of predicted mean temperatures in EN, LP, and NA cases were near average across all lead times. Therefore, it can be assumed that temperature anomalies over South Korea are predictable in EP, LN, and PA cases, based on the status of these climate factors.
Results from several previous studies indicate that more frequent cold (warm) events can be expected over East Asia during a negative (positive) AO phase (Wen et al. 2013; Park et al. 2010; Wei and Lin 2009; Wen et al. 2009; Chen et al. 2005; Jeong and Ho 2005; Thompson and Wallace 2001). In contrast, our results demonstrate that temperature anomalies over South Korea during the NA case were near average. This result can be related to an exclusion of the ENSO effect when selecting cases on weekly AO variability, unlike previous studies, which used monthly AO variability. Rudeva and Simmonds (2021) also reveals that the teleconnection patterns affecting China during wintertime are significantly different when the mean field is taken as daily averages rather than monthly averages.
The ECMWF S2S model is able to predict the AO with a lead time of approximately two weeks. The model reproduced circulation patterns over East Asia based on the status of climate factors reasonably well and sufficiently simulated mean temperature anomalies over South Korea during EP, LN, and PA cases. The circulation patterns related to the climatic factors affecting mean temperatures of South Korea were sufficiently calculated by the model three weeks in advance. In addition, the model successfully predicted weekly mean temperature anomalies for South Korea. Therefore, the use of the ENSO and AO as predictors for 1-month predictions allows for the reliable forecasting of mean temperature anomalies over South Korea.
Acknowledgments
This research was supported by the APEC Climate Center.
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