Autumn Cold Surge Paths Over North China and the Associated Atmospheric Circulation

atmosphere

Article Autumn Cold Surge Paths over North China and the Associated Atmospheric Circulation

Bo Cai, Gang Zeng *, Guwei Zhang and Zhongxian Li

Key Laboratory of Meteorological Disaster of Ministry of Education (KLME), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Nanjing University of Information Science and Technology, Nanjing 210044, China; [email protected] (B.C.); [email protected] (G.Z.); [email protected] (Z.L.) * Correspondence: [email protected]

Received: 23 January 2019; Accepted: 7 March 2019; Published: 12 March 2019

Abstract: Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, we tracked the paths of 46 autumn cold surges affecting North China from 1961 to 2014, and classiﬁed them by clustering analysis, thereby investigating their changes and associated atmospheric circulation evolution. Our results indicate that autumn cold surges affecting North China can be classiﬁed into three types according to their paths: the north type, west type, and northwest type, with occurrences of 12, 16, and 18 respectively. Different types of cold surges have different atmospheric circulation characteristics. The north type is associated with a blocking type of atmospheric circulation pattern, with an enhanced stretching northeast ridge over the Ural Mountains and a transverse trough over Lake Baikal. However, the northwest type is characterized by a ridge–trough–ridge wave-train pattern that is located over the Barents Sea, West Siberian Plain, and Sakhalin Island, respectively. The west-type cold surge is related to a conversion type: a blocking system over the Ural Mountains forms four days before the cold surge occurrence, after which it becomes a wave-train type. The atmospheric signals detected prior to the occurrences of the three types of cold surges are also explored. The main signal of the north-path cold surges is that the energy propagates eastward from the Azores Islands to the Ural Mountains, and then forms a blocking high over the Urals. However, for the northwest-path cold surges, there is a weak trough over the Ural Mountains that gradually strengthens because the blocking high collapses over the Norwegian Sea. The key signal of the formation of the west-path cold surges is a blocking high over the Norwegian Sea’s continuous enhancement and extension to Novaya Zemlya, which results in a transmission of energy to the Ural Mountains and leads to the formation of a blocking system over here. When the above-mentioned different types of atmospheric circulation characteristics appear, the type of cold surge path and its impact area can be potentially forecasted in advance, which may reduce the losses that result from cold surges.

Keywords: North China; autumn cold surge path; prior signal; blocking; wave-train

1. Introduction North China is a political, economic, and cultural center of China. The wide-ranging abrupt temperature drops, strong winds, and sleety weather caused by cold surges usually lead to a variety of serious meteorological disasters. Most of the previous studies have focused on cold surges in winter (from December to February) or extended winter (from November to March) [1,2], because these cold surges are the strongest and most inﬂuential. However, many studies have found that autumn is the season with the highest frequency of cold surges in Northern China [3,4]. The frequent cold surges in autumn will exert serious effects on agricultural production, transportation, electricity, and people’s

Atmosphere 2019, 10, 134; doi:10.3390/atmos10030134 www.mdpi.com/journal/atmosphere Atmosphere 2019, 10, 134 2 of 15 health, as well as lead to dramatic economic losses [5–7]. Thus, study on the cold surges in autumn may provide more important information for disaster prevention and relief. In recent years, research on cold surges has mostly focused on their interannual or interdecadal changes in frequency [3,8–10]. It is generally believed that the occurrences of cold surges in northern China have declined significantly as a result of global warming. Some studies have indicated that the Siberian High (SH) has a direct effect on the activities of East Asian winter monsoons and cold surges across China [3,11–13]. Furthermore, Arctic Oscillation (AO) is also an important factor affecting cold surges in East Asia. The negative phase of AO is capable of providing circulation conditions that are favorable to the cold surge occurrences [2,14–20]. According to the results of Park et al. [14,15], extended winter (November–March) cold surges in East Asia can be divided into two types, the blocking type and wave-train type, on the basis of their dynamic origins. It has been found that blocking-type cold surges mostly occur during the AO negative phase, but the wave-train type cold surge shows no preference for positive or negative AO [20]. Therefore, a study to investigate the classification of cold surges is needed. Considering that the paths of cold surges depend on their atmospheric circulation characteristics, classifying cold surges by their paths can provide a better understanding of their changes. Meanwhile, there are regional differences in the frequency and intensity of cold surges; these differences are not only associated with the cold air intensity, but also closely related to the cold air paths. However, there are few studies on cold surge paths in North China. Hence, a further study on it is necessary and helpful to better understand the processes linked to cold surges and thus improve their forecasting. Tao [21] and Ding [22] found that the cold air invading China usually originated from three regions along four paths. In the last century, paths of the Siberian High have been often selected as a means to investigate the cold surge paths in many case studies [22,23]. However, it is hard to rely on the path for a detailed classification scheme, because it is a single and huge atmospheric system. Thus, applying more objective methods to track cold surge paths is necessary for us. In recent years, three-dimensional (3D) trajectory methods, such as the Flexible Particle (FLEXPART) and Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) models, have been developed [20,24–27]. The FLEXPART model was utilized by Park et al. [20] and Tang and Zeng [28] to investigate cold air paths over East Asia. Walsh et al. [29] employed a back-tracking method to track severe cold air paths that affected the eastern United States and Northwest Europe. Sun et al. [30] and Wang et al. [31] used a 3D wind speed trajectory inverse method to investigate the severe cold air paths invading northwest and northeast China. The 3D trajectory method has been proved to be a good tool for tracking cold air paths, because these paths contain not only information on the horizontal position, but also on the vertical position, and with higher accuracy. Therefore, this method can be used to acquire knowledge of the complete motion track of a cold air mass. Few analyses have been conducted on the changes in autumn cold surge paths and their associated atmospheric circulations. This study investigates both of these topics. Autumn ranges from October to November in our study; we found that the North China cold surge rarely happens in September: it only twice occurred in this month from 1961 to 2014, so we excluded September. The data and methods are introduced in Section2 of this paper. In Section3, autumn cold surge events are defined. Then, details are provided for the HYSPLIT model, which was adopted in this study to track the paths of autumn cold surges in North China from 1961 to 2014. Then, the classification of cold surges using cluster analysis is reported, and the evolution of their atmospheric circulations is explored in the end. Conclusions and discussions are provided in Section4 of this paper.

2. Data and Methods

2.1. Data The data used in this analysis are as follows: (1) the daily minimum air temperature data from 72 stations in North China, including Beijing, Tianjin, Shandong, Shanxi, Henan, and Hebei provinces Atmosphere 2019, 10, 134 3 of 15

(Figure1), provided by the National Meteorological Center of China Meteorological Administration (CMA); (2) daily and monthly reanalysis datasets from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR), including sea level pressure (SLP), surface temperature, zonal and meridional winds, geopotential height, and air temperature, were analyzed to provide a synoptic and dynamical characterization of cold surges with a horizontal resolution of 2.5◦ longitude by 2.5◦ latitude [32]; (3) six hourly reanalyses from NCEP/NCAR were selected for cold surge path tracking, including 3D winds, temperatures, geopotential heights, and surface pressure [32], with a horizontal resolution of 2.5◦ longitude by 2.5◦ latitude. All of the data are from the period between 1961–2014. The climatological mean used in the study is the average of the values from 1981 to 2010.

North China

Figure 1. Map of study area (North China) is surrounded by blue solid line (shading indicates the topography; unit: m; dots are 72 stations in North China). 2.2. Definition of Cold Surges According to the Chinese national standard for cold surge grades (GB/T 21987-2008) [33], a single-station cold surge event is identified by the following thresholds: (1) the minimum daily air temperature is below 4 ◦C; and (2) the minimum daily air temperature drops by at least 8 ◦C within 24 h, at least 10 ◦C within 48 h, or at least 12 ◦C within 72 h. A regional cold surge event is defined when more than 40% of the stations in the region meet the threshold of a single-station cold surge.

2.3. Stationery Wave Action Flux In order to clearly understand the development and propagation of the stationary wave before the occurrence of cold surge, the 3D wave activity flux proposed by Plumb [26] was utilized in this study to figure out the stationary wave activity flux Fs on a 300-hPa isobaric surface:

0 0  02 1 ∂(v Φ )  v − 2Ωa sin 2φ ∂λ 0 0 p  0 0 1 ∂(u Φ )  (1) Fs = cos φ −u v +  p0 2Ωa sin 2φ ∂λ  0 0  2Ω sin φ h 0 0 1 ∂(T Φ ) i S v T − 2Ωa sin 2φ ∂λ

In Equation (1), p refers to the air pressure value; p0 = 1000 hPa; (φ, λ) are latitude and altitude, respectively; (u0, v0, Φ0, T0) are the stationary disturbances’ zonal wind, meridional wind, geopotential height, and temperature, respectively; (Ω, a) are the angular velocity of rotation and the average radius ∂Tˆ κTˆ of the earth; where S = ∂z + H as the static stability, and the caret indicates the areal average over the Atmosphere 2019, 10, 134 4 of 15 average north of 20◦ N. H (= 8000 m) is the constant scale height, and κ (= 287/1004 JK−1 kg−1) is the ratio of the gas constant to the speciﬁc heat at constant pressure.

2.4. Tracking Method for Cold Surge Path The HYSPLIT model [27,34], based on the Lagrangian algorithm, was adopted to track cold surge paths in this study. Starting from a station at which the daily minimum temperature dropped the most in the study region within one day, the path was backtracked for several days. This model assumes that a particle moving along with the wind field and its trajectory is equal to an integral of the particle spatially and temporally. Moreover, the velocity vectors are linearly interpolated in both space and time. As assumed, a cold air mass can be substituted by a particle; therefore, the particle trajectory can replace the path of a cold air mass [20,35]. The advection of a particle is computed from the average of the three-dimensional velocity vector V(P, t) for the initial position P(t) and the first-guessed position P0(t + ∆t) [36]. The first-guessed position is:

P0(t + ∆t) = P(t) + V(P, t)∆t (2) and the ﬁnal position is:

P(t + ∆t) = P(t) + 0.5[V(P, t) + V(P0, t + ∆t)]∆t (3)

Furthermore, the integration time step (∆t) can vary during the simulation. It is computed from the requirement that the advection distance per time step should be less than the grid spacing. The maximum transport velocity Umax is determined by the maximum particle transport speed during the previous hour. The time step can vary from 1 min to 1 h, and is computed using the following equation:

−1 Umax(grid_units · min )∆t(min) < 0.75(grid_units) (4)

The performance of the HYSPLIT model for tracking the cold surge path should be evaluated before it is used to track all paths of cold surge events. Figure2 shows the horizontal view of eight cold air paths (random selection) tracked by the HYSPLIT model. In this ﬁgure, the dashed lines track the center points of surface temperature drops as determined every 24 h for the six days preceding autumn cold surges in North China. The times of occurrence of these eight cold waves are 16 October 1964; 30 November 1966; 27 November 1968; 13 November 1970; 10 November 1979; 20 November 1990; 17 November 1997; and 6 November 2000. It was found that these cold surge paths tracked by the HYSPLIT model coincide with the moving tracks of surface temperature drop centrodes, indicating that the HYSPLIT model has a good performance in tracking cold surge paths. Thus, the HYSPLIT model was used to track cold surge paths in this study. Atmosphere 2019, 10, 134 5 of 15 Atmosphere 2019, 10, x FOR PEER REVIEW 5 of 15

Figure 2. Comparison between surface temperature drop centrode paths (dashed lines) and cold air paths trackedtracked with with the the Hybrid Hybrid Single-Particle Single‐Particle Lagrangian Lagrangian Integrated Integrated Trajectory Trajectory (HYSPLIT) (HYSPLIT) model model (solid lines)(solid everylines) 24every h in 24 the h precedingin the preceding six days six of days eight of cold eight surges cold surges that occurred that occurred in North in China.North China. 2.5. Clustering Method 2.5. Clustering Method Here we use Ward variance minimization algorithm and K-means clustering algorithm to cluster the trajectories.Here we use Ward variance minimization algorithm and K‐means clustering algorithm to clusterWard the variancetrajectories. minimization algorithm [37]: First, divide the N samples into n categories, and thenWard narrow variance down oneminimization class at a time; algorithm the sum [37]: of First, the squares divide the of the N samples dispersions into will n categories, increase, and then narrow down one class at a time; the sum of the squares of the dispersions will increase, and choose the two types of combinations that minimize the variance until all the samples are classified choose the two types of combinations that minimize the variance until all the samples are classified into one class. into one class. K-means clustering algorithm [38]: (1) Place new K trajectories into the space represented by the K‐means clustering algorithm [38]: (1) Place new K trajectories into the space represented by the objects that are being clustered. These trajectories represent initial group centroid tracks; (2) Assign objects that are being clustered. These trajectories represent initial group centroid tracks; (2) Assign each object to the group that has the closest centroid track; (3) When all the objects have been assigned, each object to the group that has the closest centroid track; (3) When all the objects have been recalculate the positions of the K centroid tracks; (4) Repeat steps 2 and 3 until the centroids no longer assigned, recalculate the positions of the K centroid tracks; (4) Repeat steps 2 and 3 until the move. After finishing these steps, it will produce a separation of the objects into groups from which centroids no longer move. After finishing these steps, it will produce a separation of the objects into the metric to be minimized can be calculated. groups from which the metric to be minimized can be calculated. In our study, as the first step, we used the Ward variance minimization algorithm to cluster the In our study, as the first step, we used the Ward variance minimization algorithm to cluster the trajectories. This method divides the trajectories into G categories, and G mean trajectories are obtained trajectories. This method divides the trajectories into G categories, and G mean trajectories are by synthetic analysis. We let these G trajectories be the K-means’ initial group centroid tracks. Then, obtained by synthetic analysis. We let these G trajectories be the K‐means’ initial group centroid we used the K-means clustering algorithm to cluster once again. tracks. Then, we used the K‐means clustering algorithm to cluster once again. 3. Results 3. Results 3.1. Spatial–Temporal Variation in Autumn Cold Surge Frequency in North China 3.1. Spatial–Temporal Variation in Autumn Cold Surge Frequency in North China According to the definition of a single-station cold surge, the climatological mean occurrences of autumnAccording cold surges to the in Northdefinition China of area single shown‐station in Figure cold3 .surge, It can bethe observed climatological that autumn mean occurrences cold surges haveof autumn occurred cold frequently surges in in North the north China of theare Taihangshown in and Figure Yanshan 3. It Mountains,can be observed about that twice autumn or more cold per year,surges especially have occurred in the Zhangbei frequently and in Youyuthe north stations, of the withTaihang occurrences and Yanshan of up toMountains, 3.8 times perabout year. twice In the or Northmore per China year, Plain, especially cold surge in the occurrences Zhangbei are and typically Youyu less stations, than once with every occurrences year because of up of to the 3.8 barrier times formedper year. by In these the mountains.North China An Plain, exception cold surge is found occurrences in the middle are typically and western less regionsthan once of Shandong. every year because of the barrier formed by these mountains. An exception is found in the middle and western regions of Shandong.

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Figure 3. The climatological mean frequency of autumn cold surges in 72 stations of North China from 1961 to 2014 (black dots indicate the stations). Figure 3.3. TheThe climatological climatological mean mean frequency frequency of of autumn autumn cold cold surges surges in 72 in stations 72 stations of North of North China China from 1961 to 2014 (black dots indicate the stations). fromFrom 1961 54 yearsto 2014 of (black data, dots we indicate calculated the stations). 1.28 events to be the annual occurrence of autumn cold surgesFrom in 54North years China. of data, It we was calculated also found 1.28 that events there to be was the a annual significant occurrence decrease of autumn in the coldcold surges surge From 54 years of data, we calculated 1.28 events to be the annual occurrence of autumn cold inoccurrences, North China. and It an was interdecadal also found thatshift thereoccurred was ain significant the time series decrease in the in themid–late cold surge 1990s occurrences, (Figure 4). surges in North China. It was also found that there was a significant decrease in the cold surge andThe anresult interdecadal of the Mann–Kendall shift occurred test in the (figure time seriesnot shown) in the mid–late shows that 1990s this (Figure interdecadal4). The result decrease of the is occurrences, and an interdecadal shift occurred in the time series in the mid–late 1990s (Figure 4). Mann–Kendallstatistically significant test (figure at notthe shown)0.05 confidence shows that level. this interdecadalThese findings decrease are different is statistically from significant previous The result of the Mann–Kendall test (figure not shown) shows that this interdecadal decrease is atstudies’ the 0.05 conclusions confidence that level. winter These cold findings surges are in different northern from China previous have significantly studies’ conclusions decreased that since winter the statistically significant at the 0.05 confidence level. These findings are different from previous cold1980s surges [3,39], in indicating northern China seasonable have significantly discrepancies decreased between since the the autumn 1980s [3 ,and39], indicatingwinter cold seasonable surges’ studies’ conclusions that winter cold surges in northern China have significantly decreased since the discrepanciesoccurrences. between the autumn and winter cold surges’ occurrences. 1980s [3,39], indicating seasonable discrepancies between the autumn and winter cold surges’ occurrences.

Figure 4.4. Variation inin thethe occurrenceoccurrence ofof autumnautumn coldcold surgessurges inin NorthNorth ChinaChina fromfrom 19611961 toto 2014.2014. TheThe black solidsolid lineline indicates indicates the the average average occurrence occurrence for for the the study study period. period. The The gray gray solid solid line line indicates indicates the Figure 4. Variation in the occurrence of autumn cold surges in North China from 1961 to 2014. The nine-yearthe nine‐year running running average. average. black solid line indicates the average occurrence for the study period. The gray solid line indicates 3.2. Autumn Cold Surge Paths in North China 3.2. Autumnthe nine ‐Coldyear runningSurge Paths average. in North China According to the deﬁnition of a regional cold surge, 46 regional cold surges occurred in autumn 3.2. AutumnAccording Cold to Surge the definition Paths in North of a regional China cold surge, 46 regional cold surges occurred in autumn in North China from 1961 to 2014. For all of these cold surges, their six-day paths leading up to their in North China from 1961 to 2014. For all of these cold surges, their six‐day paths leading up to their occurrencesAccording were to tracked the definition by the HYSPLITof a regional model cold (Figure surge,5 46). Whenregional the cold cold surges air passes occurred by the in key autumn cold in North China from 1961 to 2014. For all of these cold surges, their six‐day paths leading up to their

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occurrences were tracked by the HYSPLIT model (Figure 5). When the cold air passes by the key surgecold surge region region (70–90 (70–90°◦ E, 43–65 E,◦ 43–65°N) in central N) in Siberia,central asSiberia, defined as bydefined National by MeteorologicalNational Meteorological Center of CMACenter [40 of], CMA it has accumulated[40], it has accumulated intensively because intensively of the because topographic of the barrier. topographic Then, itbarrier. invades Then, China it alonginvades different China paths.along different Typically, paths. it takes Typically, three to it four takes days three for theto four cold days air to for move the southwardcold air to move from thesouthward key area from to North the key China. area In to this North regard, China. clustering In this regard, analysis clustering was performed analysis in was this performed study for the in paththis study of the for period the path within of the three period days within prior three to the days cold prior surge’s to the occurrence, cold surge’s and occurrence, three types and of paths three weretypes discovered of paths were (Figure discovered5). First, (Figure there is 5). the First, west there path (frequencyis the west =path 16), (frequency whereby the = 16), cold whereby air reaches the Chinacold air after reaches passing China by after North passing Xinjiang by North (north Xinjiang of the Tianshan (north of Mountains) the Tianshan and Mountains) moves eastward and moves and eventuallyeastward and invades eventually North Chinainvades via North Qinghai, China Gansu, via Qinghai, Ningxia, Gansu, and Shanxi Ningxia, provinces. and Shanxi The second provinces. is the northwestThe second path is the (frequency northwest = path 18). In (frequency this case, = the 18). cold In this air reaches case, the Mongolia cold air reaches via the Sayan Mongolia Mountains via the fromSayan the Mountains east to the from Altai the Mountains, east to the and Altai then Mountains, it moves in and a southeastern then it moves direction in a southeastern to Central Mongolia direction beforeto Central entering Mongolia China before and finally entering invading China North and finally China invading from North North Shanxi China and from Northwest North Shanxi Hebei. Theand thirdNorthwest one is Hebei. the north The path third (frequency one is the = north 12): the path cold (frequency air moves = southeastward12): the cold air via moves Lake southeastward Baikal, passes byvia the Lake east Baikal, of the passes Mongolian by the Plateau, east of and the thenMongolian moves Plateau, southward and to then reach moves East Innersouthward Mongolia to reach and subsequentlyEast Inner Mongolia invade and North subsequently China. invade North China. AsAs shownshown inin FigureFigure5 5,, the the cold cold air air of of autumn autumn cold cold surges surges invading invading North North China China are are primarily primarily fromfrom threethree regions, regions, namely, namely, the the Kara Kara Sea, Sea, the the Barents Barents Sea, Sea, and and the the area area near near the the Norwegian Norwegian Sea Sea and and the Scandinavianthe Scandinavian Peninsula. Peninsula. The westThe west path ofpath cold of air cold mainly air mainly originates originates from the from Norwegian the Norwegian Sea and Sea the Scandinavianand the Scandinavian Peninsula, Peninsula, with a small with part a small sourced part from sourced the Barents from the Sea Barents and a tiny Sea minorityand a tiny intensified minority afterintensified meeting after the meeting cold air moving the cold southward air moving from southward the Kara Sea,from thus the furtherKara Sea, invading thus further China. invading The cold airChina. of the The northwestern cold air of paththe northwestern is mainly derived path fromis mainly the Barents derived Sea from and the KaraBarents Sea. Sea Finally, and the the Kara cold airSea. of Finally, the north the path cold is air from of the the north Kara path Sea in is most from cases. the Kara Sea in most cases. FigureFigure6 6 depicts depicts the the variability variability in in autumn autumn cold cold surgessurges inin North North ChinaChina forfor thethe threethree pathpath types.types. TheThe occurrencesoccurrences ofof autumnautumn coldcold surgessurges havehave decreaseddecreased since the 2000s, and the occurrencesoccurrences ofof northwest-pathnorthwest‐path coldcold surgessurges reducedreduced fromfrom fivefive inin thethe 1990s1990s toto oneone inin thethe 2000s,2000s, whilewhile thethe occurrencesoccurrences ofof northnorth andand westwest pathpath coldcold surgessurges remainedremained unchangedunchanged betweenbetween thethe twotwo decades.decades. WhatWhat causescauses thethe northwest-pathnorthwest‐path cold cold surges surges to to decrease decrease significantly? significantly? This This question question needs needs to to be be further further studied, studied, and and as aas first a first step, step, it is it necessary is necessary to investigateto investigate the the formation formation of cold of cold surges surges in different in different paths. paths.

FigureFigure 5.5. Six-daySix‐day horizontalhorizontal pathspaths ofof autumnautumn coldcold surgessurges inin NorthNorth ChinaChina fromfrom 19611961 toto 20142014 andand thethe correspondingcorresponding classiﬁcation:classification: westwest pathpath (blue(blue lines),lines), northwestnorthwest pathpath (green(green lines),lines), andand northnorth pathpath (red(red lines);lines); gray,gray, yellow,yellow, and and black black bold bold lineslines representrepresent thethe synthesizedsynthesized trajectoriestrajectories formedformed 7272 hh beforebefore thethe ◦ ◦ onsetonset ofof thethe coldcold surges.surges. BlackBlack rectanglesrectangles representrepresent thethe keykey regionregion (70–90 (70–90° E,E, 43–6543–65° N).N).

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Figure 6. Variability in autumn cold surge occurrences in North China from the north (N), northwest (NW), and west (W) paths. 3.3. Evolution of Atmospheric Circulation for Three Types of Autumn Cold Surges 3.3. Evolution of Atmospheric Circulation for Three Types of Autumn Cold Surges The cold air of the autumn cold surges that have affected North China is from three different The cold air of the autumn cold surges that have affected North China is from three different types of paths. What was the evolution of their atmospheric circulation before they invaded North types of paths. What was the evolution of their atmospheric circulation before they invaded North China? We used a composite analysis to investigate their evolution. China? We used a composite analysis to investigate their evolution. The 500-hPa geopotential heights (GPH) and SLP anomalies of the three types of cold surges The 500‐hPa geopotential heights (GPH) and SLP anomalies of the three types of cold surges within four days prior to the occurrence (day −4 to day 0) are shown in Figures7 and8. For the north within four days prior to the occurrence (day −4 to day 0) are shown in figures 7 and 8. For the north type of cold surge, the associated circulation is a blocking type. A blocking high extends from the type of cold surge, the associated circulation is a blocking type. A blocking high extends from the Ural Mountains to the northeast, and a distinct transverse trough is formed over northern Lake Baikal Ural Mountains to the northeast, and a distinct transverse trough is formed over northern Lake (Figure7a). The SH starts to establish in the North Siberian Plain (Figure8a), and prefrontal warming Baikal (Figure 7a). The SH starts to establish in the North Siberian Plain (Figure 8a), and prefrontal causes cyclonic circulation over Lake Baikal. This condition is conducive to the movement of a cold air warming causes cyclonic circulation over Lake Baikal. This condition is conducive to the movement mass from the nearby Kara Sea to the Eurasia continent, and then its accumulation at Lake Baikal. At of a cold air mass from the nearby Kara Sea to the Eurasia continent, and then its accumulation at day −2 (Figure7b), the blocking high gradually collapses, the transverse trough becomes longitudinal Lake Baikal. At day −2 (Figure 7b), the blocking high gradually collapses, the transverse trough at day 0 (Figure7c), and the SH expands southward (Figure8c), causing cold air to erupt southward becomes longitudinal at day 0 (Figure 7c), and the SH expands southward (Figure 8c), causing cold into the Mongolian plateau via Lake Baikal, after which it ﬁnally invades North China. air to erupt southward into the Mongolian plateau via Lake Baikal, after which it finally invades For the northwest type, a weak trough appears north of the Altai Mountains and the North China. ridge–trough–ridge wave-train pattern can be found over the Barents Sea, West Siberian Plain, and For the northwest type, a weak trough appears north of the Altai Mountains and the ridge– Sakhalin Island, followed by the cold air from the Barents Sea propagating toward the southeast to the trough–ridge wave‐train pattern can be found over the Barents Sea, West Siberian Plain, and West Siberian Plain (Figure7d). At day −2 (Figure7e), the cold trough over the West Siberian Plain is Sakhalin Island, followed by the cold air from the Barents Sea propagating toward the southeast to enhanced and moves to Lake Baikal. Warm air carried by the southerly air in front of the trough forms the West Siberian Plain (Figure 7d). At day −2 (Figure 7e), the cold trough over the West Siberian a cyclonic ﬂow on the Mongolian Plateau, thereby causing the SH to extend into this area and forcing Plain is enhanced and moves to Lake Baikal. Warm air carried by the southerly air in front of the cold air to move from the Barents Sea to the Mongolian Plateau via the Altai Mountains (Figure8e). trough forms a cyclonic flow on the Mongolian Plateau, thereby causing the SH to extend into this As the entire system moves eastward, the cold air invades North China. area and forcing cold air to move from the Barents Sea to the Mongolian Plateau via the Altai For the west type of cold surge (Figure7g), a blocking high over the Ural Mountains is Mountains (Figure 8e). As the entire system moves eastward, the cold air invades North China. anomalously oriented northward, while a tilted trough extending to Lake Balkhash is established. For the west type of cold surge (Figure 7g), a blocking high over the Ural Mountains is Thus, a blocking system is established in the southeast of the Ural Mountains. The SH is strengthened anomalously oriented northward, while a tilted trough extending to Lake Balkhash is established. above the Ural Mountains, and the cyclonic circulation in front of the tilted trough is formed over Lake Thus, a blocking system is established in the southeast of the Ural Mountains. The SH is Balkhash (Figure8g). Cold air moves from the Barents Sea and the Scandinavian Peninsula to Lake strengthened above the Ural Mountains, and the cyclonic circulation in front of the tilted trough is Balkhash, and then accumulates. At day −2 (Figure7h), the tilted trough becomes longitudinal and formed over Lake Balkhash (Figure 8g). Cold air moves from the Barents Sea and the Scandinavian Peninsula to Lake Balkhash, and then accumulates. At day −2 (Figure 7h), the tilted trough becomes

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AtmosphereAtmosphere 2019 2019, ,10 10, ,x x FOR FOR PEER PEER REVIEW REVIEW 99 ofof 1515 moves to the Altai Mountains. The blocking system turns into a zonal ridge–trough–ridge wave-train typelongitudinallongitudinal over northwestern andand movesmoves Lake toto Balkhash, thethe AltaiAltai the Mountains.Mountains. Altai Mountains, TheThe blockingblocking and the systemsystem Korean turnsturns Peninsula. intointo aa zonalzonal This system ridge–ridge– is locatedtrough–ridgetrough–ridge more southward wavewave‐‐traintrain than typetype the over northwest-typeover northwesternnorthwestern system, LakeLake which Balkhash,Balkhash, is conducive thethe AltaiAltai to Mountains,Mountains, cold air moving andand the fromthe KoreanKorean Peninsula.Peninsula. ThisThis systemsystem isis locatedlocated moremore southwardsouthward thanthan thethe northwestnorthwest‐‐typetype system,system, whichwhich isis Lake Balkhash to North China. conduciveconducive toto coldcold airair movingmoving fromfrom LakeLake BalkhashBalkhash toto NorthNorth China.China.

FigureFigureFigure 7. 7.Composite7. CompositeComposite of ofof geopotential geopotentialgeopotential height heightheight (GPH) (GPH)(GPH) (contours;(contours; intervals intervals ofof of 100100 100 m)m) m) andand and itsits its anomaliesanomalies anomalies (shaded(shaded(shaded areas; areas;areas; unit: unit:unit: m; m;m; signiﬁcant significantsignificant values valuesvalues at atat the thethe 0.05 0.050.05 conﬁdence confidenceconfidence level level areare presentedpresented presented byby by greengreen green dots)dots) dots) atat at 500500500 hPa hPahPa from fromfrom day dayday−4 − − to44 to dayto dayday 0 relative 00 relativerelative to to coldto coldcold surge surgesurge occurrences occurrencesoccurrences of of theof thethe north northnorth (a – (c(aa),––c northwestc),), northwestnorthwest (d – ((dfd),––ff and),), westandand (g west –westi) types. ( (gg––ii)) types. Redtypes. arrows Red Red arrows arrows indicate indicate indicate the wave-guided the the wave wave‐‐guidedguided line. line. line.

FigureFigureFigure 8. Sea 8.8. SeaSea level levellevel pressure pressurepressure (shaded (shaded(shaded areas; areas;areas; unit: unit:unit: hPa) hPa)hPa) and andand 850-hPa 850850‐‐hPahPa wind windwind anomalies anomaliesanomalies (vectors; (vectors;(vectors; unit: unit:unit: m/s) fromm/s)m/s) day from from−4 day today day − −44 to −to 2day day relative − −22 relative relative to cold to to surgecold cold surge surge occurrences occurrences occurrences of the of of norththe the north north (a–c ( ),(aa– northwest–cc),), northwest northwest (d– f( (d),d–– andff),), and and west (g–westiwest) types. ( (gg––ii) Red) types. types. arrows Red Red arrows arrows indicate indicate indicate the expansion the the expansion expansion of the of of Siberian the the Siberian Siberian High. High. High.

Atmosphere 2019, 10, 134 10 of 15

In order to explore the formation processes of the cold surges in the different paths, we analyzed the dynamic signals that appeared prior to the occurrences of the three types of cold surges. Furthermore, this procedure and the resulting information can improve cold surge path forecasting. Considering that the characteristics of atmospheric circulation in the four days preceding the occurrence of a cold surge are not enough to determine the formation processes, wave action ﬂux analysis was performed in this study to explore the kinetic features over the two weeks leading up to cold surge occurrence. Figures9–11 show the early formation processes of the three types of cold surges. For the north type, our focus is on how the blocking system in northern Lake Baikal is formed. From day −14 to day −10 (Figure9a–c), GPH anomalies of the upper troposphere lead to the formation of a trough–ridge–trough wave train from the Azores Islands to the west of the Ural Mountains; the perturbation grows, while its position is basically stationary. The energy propagates eastward to the downstream, and results in positive GPH anomalies over the east of the Ural Mountains at day −10. Then, the positive perturbation moves eastward to Lake Baikal, the wave energy propagates to the northeast, and this strengthens the Arctic polar vortex, which gradually moves southward. At day −6, as a result of the continuous transmission of energy, a high ridge over the Ural Mountains strengthens rapidly, extends to the northeast, and forms a transverse trough coordinate with the Arctic polar vortex. Thus, the blocking system forms over northern Lake Baikal. For the northwest type, the emphasis is on the formation of Eurasian wave trains. From day −14 to day −8 (Figure 11a–d), a blocking high is established over the Norwegian Sea. Additionally, it can be observed from the wave action ﬂux that the wave propagates from the blocking high downstream to the Ural Mountains at day −10 (Figure 10c). For this reason, the negative anomaly perturbation on the Ural Mountains becomes strong and arrives at the east side of the Ural Mountains at day −8 (Figure 10d). At day −6 (Figure 10e), the blocking high over the Norwegian Sea collapses, leading to cold air southward and causing the perturbation to propagate toward the Ural Mountains. In this case, the positive anomaly perturbation moves downward to the south and reaches Eurasia, where the ridge–trough–ridge wave train forms. Compared with the north type, the blocking system of the west type is located above the Ural Mountains, and its formation process is completely different. From day −14 to day −10 (Figure 11a–c), a weak high ridge is maintained in the Norwegian Sea, and transmits energy to the Ural Mountains, which deepens the trough. At day −8 (Figure 11d), as a result of the continuous transmission of energy from the mid-latitude Atlantic Ocean to the Norwegian Sea, the high ridge strengthens to form a blocking high over the Barents Sea, leading to the cold air moving toward the Ural Mountains. Then, the cold trough strengthens. The blocking system forms at day −6 (Figure 11e). The energy source is interrupted, thereby causing the blocking high to gradually collapse, and the cold air bursts into Lake Balkhash. As a result of the eastward shift of the tilted trough in the north of Balkhash, the blocking type is converted to the wave-train type. In summary, two weeks before the occurrence of a north cold surge, a trough–ridge–trough wave train forms from the Azores Islands to the west of the Ural Mountains. Then, the wave train stimulates two high ridges, one after another, over the Urals, forcing the polar vortex to move southward. Meanwhile, the latter high ridge is continuously strengthened, extends to the northeast, and forms a blocking high. However, the northwest cold surge develops because the blocking high collapses over the Norwegian Sea. There is a weak trough in the Ural Mountains that is gradually strengthened by the collapse of the blocking high. The atmospheric circulation presents a wave-train pattern with an orientation from the Barents Sea to Sakhalin Island. The key to the formation of the west cold surge lies in the emergence of a blocking situation in the Ural Mountains. The blocking high over the Norwegian Sea is constantly enhanced and moves to the Barents Sea and Novaya Zemlya, and it continues to transmit energy to the Ural Mountains to deepen the cold trough. A blocking situation is formed in the Ural Mountains. With the collapse of the blocking high, the cold air bursts into Lake Balkhash. As a Atmosphere 2019, 10, 134 11 of 15 result of the eastward shift of the tilted trough in the north part of Lake Balkhash, the blocking type is converted into a wave-train type, and the cold air moves eastward from Lake Balkhash to North China. Atmosphere 20192019,, 1010,, xx FORFOR PEER REVIEW 1111 ofof 1515

FigureFigure 9. 9.Composite9. Composite GPH GPH anomalies anomaliesanomalies at atat 300 300300 hPa hPa (shaded(shaded areas;areas; unit: unit: m; m; significantsignificant significant values values atat at thethe the 0.050.05 0.05 confidenceconfidenceconfidence level levellevel are areare presented presented by by green green dots) dots) andandand PlumbPlumb wave wave actionaction action fluxflux flux anomaliesanomalies anomalies (vectors,(vectors, (vectors, unit: unit: m2m/s22/s2/s)22) from) fromfrom day day− − −1414 toto day day − −−44 4((a–fa–f (a–)) f relativerelative) relative toto to thethe the north north coldcold cold surgesurge surge occurrences. occurrences.

FigureFigure 10. 10.10.Same SameSame as asas Figure Figure9 9,9,, but but for forfor the thethe northwestnorthwest typetype (( (a–fa–fa–).).f ).

Atmosphere 2019, 10, 134 12 of 15 Atmosphere 2019, 10, x FOR PEER REVIEW 12 of 15

FigureFigure 11. 11. SameSame as as Figure Figure 9,9 but, but for for the the west west type type (a–f ( a–).f ).

4.4. Conclusions Conclusions and and Discussions Discussions InIn this this study, study, we we investigated investigated the the autumn autumn cold cold surge surge paths paths and and their their associated associated atmospheric atmospheric circulationcirculation evolution. evolution. Using Using the the HYSPLIT HYSPLIT model model to to track track the the paths paths of of 46 46 autumn autumn cold cold surge surge events events affectingaffecting North North China China from from 1961 1961 to to 2014, 2014, we we classified classiﬁed the the cold cold surges surges into into three three types, types, i.e., i.e., the the north north type,type, west west type, type, and and northwest northwest type, type, with with occurrences occurrences of of 12, 12, 16, 16, and and 18, 18, respectively. respectively. OurOur results results indicate indicate that that the the north north-path‐path cold cold surge surge is is associated associated with with a ablocking blocking type type of of atmosphericatmospheric circulation, circulation, so itsits main main characteristic characteristic is theis formationthe formation of an of abnormally an abnormally enhanced enhanced blocking blockinghigh and high a transverse and a transverse trough over trough the over north the of Lakenorth Baikal of Lake four Baikal days four before days the before cold surge the cold occurrence. surge occurrence.The northwest-path The northwest cold‐path surge cold is surge linked is to linked a wave-train to a wave‐ type,train type, for which for which a ridge–trough–ridge a ridge–trough– ridgewave-train wave‐train pattern pattern appears appears over over the Barentsthe Barents Sea, Sea, West West Siberian Siberian Plain, Plain, and and Sakhalin Sakhalin Island Island at dayat day−4. −4. The The west-path west‐path cold cold surge surge is is connected connected to to a conversiona conversion type, type, whereby whereby a blockinga blocking system system over over the theUral Ural Mountains Mountains forms forms at at day day− −4,4, and and then then transforms transforms into into a a wave-train wave‐train type. type. TheThe atmospheric atmospheric signals signals that that appear appear prior prior to to the the occurrences occurrences of of the the three three types types of of cold cold surges surges werewere also also explored explored in this study.study. The The main main signal signal of of the the north-path north‐path cold cold surge surge is a trough–ridge–troughis a trough–ridge– troughwave trainwave that train forms that overforms the over Azores the Islands,Azores Islands, Ireland Island,Ireland and Island, the westand the of the west Ural of Mountainsthe Ural Mountainstwo weeks two before weeks the occurrence before the of occurrence the cold surge. of Then,the cold this surge. wave-train Then, energy this propagateswave‐train eastward,energy propagateswhere it forms eastward, two high where ridges, it forms one two after high another, ridges, over one the after Urals. another, However, over for the the Urals. northwest-path However, forcold the surge, northwest there‐path is a weakcold surge, trough there over is the a Uralweak Mountains trough over that the gradually Ural Mountains strengthens that becausegradually the strengthensblocking high because collapses the blocking over the Norwegianhigh collapses Sea. over The the key Norwegian signal to the Sea. formation The key of signal the west-path to the formationcold surge of isthe that west the‐path blocking cold surge high overis that the the Norwegian blocking high Sea over is constantly the Norwegian enhanced Sea and is constantly extends to enhancedNovaya Zemlya,and extends transmits to Novaya energy Zemlya, to the Ural transmits Mountains, energy and to the deepens Ural Mountains, the cold trough, and leadingdeepens to the the coldformation trough, of leading a blocking to the system formation over of the a Uralblocking Mountains. system over the Ural Mountains. TheThe exploration exploration of of atmospheric atmospheric circulation circulation evolution evolution and and pre pre-signaling‐signaling of of cold cold surges surges is is very very importantimportant for for theirtheir prediction. prediction. When When the above-mentionedthe above‐mentioned different different types oftypes atmospheric of atmospheric circulation circulationcharacteristics characteristics appear, the appear, type of the cold type surge of cold path surge and its path impact and area its impact can be area potentially can be forecastedpotentially in forecastedadvance, in which advance, may reducewhich may the losses reduce that the result losses from that coldresult surges. from cold surges.

Atmosphere 2019, 10, 134 13 of 15

The winter cold surges’ formation processes that were described for the blocking type and wave-train type by Park et al. [14,15] are the same as those for the north type and northwest type in our study, but the west type is a new type that is entirely different from the above two, indicating that the autumn cold surge is different from that of winter. Whether the new type of cold surge is unique to autumn is also worthy of further study. Although the atmospheric circulation evolution for the three paths of autumn cold surges in North China are discussed, and their key atmospheric signals are explored, the reasons underlying the appearance of these signals are still unclear. As mentioned above, there is always a process for maintaining and enhancing the signals preceding cold surges in the three different paths, but the mechanism for their development and maintenance should be further investigated. Arctic sea ice is an important underlying surface factor that can affect cold surge activity [41]. Previous studies have found that Arctic sea ice shows a linearly decreasing trend [42–44]. Especially since 1998, sea ice concentration has signiﬁcantly decreased [45]. Our results show that there has been an interdecadal decrease in the autumn cold surge frequency in North China since the mid–late 1990s, and northwest-path cold surges decreased signiﬁcantly in the 2000s. Is there a relationship between Arctic sea ice and cold surge paths? This issue needs to be further investigated. In addition, our research on the formation process of three-path cold surges will provide an important basis for future study on the mechanism of the cold surge occurrences in different paths.

Author Contributions: B.C. and G.Z. (Gang Zeng) initiated and designed the study, and then collected the data. B.C. analyzed the data and wrote the paper. G.Z. (Gang Zeng), G.Z. (Guwei Zhang) and Z.L. made suggestions and revised the paper. Funding: This research was funded by the National Natural Science Foundation of China grant numbers (41575085 and 41430528) and the National Key Research and Development Program of China grant number (2017YFA0603804). Acknowledgments: We thank the three anonymous reviewers for their helpful comments and suggestions, which greatly improved the manuscript. Conﬂicts of Interest: Authors express no conﬂicts of interest.

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