Special Issue: Climate Change and Its Regional Response

Mechanism and effects of the increase in winter temperatures in the Arctic region on cold winters in Heilongjiang Province, Northeast China for the period 1961-2018

  • WANG Xiaodi , 1 ,
  • LI Yongsheng , 2, 3, * ,
  • ZHANG Lijuan 4 ,
  • SONG Shuaifeng 5 ,
  • PAN Tao 4 ,
  • REN Chong 4 ,
  • TAN Yulong 6
  • 1. School of Geography and Tourism, Harbin University, Heilongjiang Province Key Laboratory of Cold Region Wetland Ecology and Environment Research, Harbin 150086, China
  • 2. Climate Center of Heilongjiang Province, Harbin 150030, China
  • 3. Key Opening Laboratory for Northeast China Cold Vortex Research, Shenyang 110166, China
  • 4. Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China
  • 5. The State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
  • 6. Hulin Meteorological Bureau of Heilongjiang Province, Hulin 158499, Heilongjiang, China
*Li Yongsheng (1984-), E-mail:

Wang Xiaodi (1983-), PhD, E-mail:

Received date: 2021-03-01

  Accepted date: 2021-11-19

  Online published: 2022-04-25

Supported by

National Natural Science Foundation of China(41771067)

National Natural Science Foundation of China(U20A2082)

Key Project of Natural Science Foundation of Heilongjiang Province(ZD2020D002)


With the advent of climate change, winter temperatures have been steadily increasing in the middle-to-high latitudes of the world. However, we have not found a corresponding decrease in the number of extremely cold winters. This paper, based on Climatic Research Unit (CRU) re-analysis data, and methods of trend analysis, mutation analysis, correlation analysis, reports on the effects of Arctic warming on winter temperatures in Heilongjiang Province, Northeast China. The results show that: (1) during the period 1961- 2018, winter temperatures in the Arctic increased considerably, that is, 3.5 times those of the Equator, which has led to an increasing temperature gradient between the Arctic and the Equator. An abrupt change in winter temperatures in the Arctic was observed in 2000. (2) Due to the global warming, an extremely significant warming occurred in Heilongjiang in winter, in particular, after the Arctic mutation in 2000, although there were two warm winters, more cold winters were observed and the interannual variability of winter temperature also increased. (3) Affected by the warming trend in the Arctic, the Siberian High has intensified, and both the Arctic Vortex and the Eurasian Zonal Circulation Index has weakened. This explains the decrease in winter temperatures in Heilongjiang, and why cold winters still dominate. Moreover, the increase in temperature difference between the Arctic and the Equator is another reason for the decrease in winter temperatures in Heilongjiang.

Cite this article

WANG Xiaodi , LI Yongsheng , ZHANG Lijuan , SONG Shuaifeng , PAN Tao , REN Chong , TAN Yulong . Mechanism and effects of the increase in winter temperatures in the Arctic region on cold winters in Heilongjiang Province, Northeast China for the period 1961-2018[J]. Journal of Geographical Sciences, 2022 , 32(2) : 225 -240 . DOI: 10.1007/s11442-022-1944-2

1 Introduction

Due to the global warming, frequent extreme cold events have happened in the northern hemisphere, characterized by large affected scope and long duration. For instance, in January 2011, an extremely low temperature event hit a large portion of China. The long-lasting event with large affected regions became the most serious event since 1977 (Hu et al., 2012). In February 2021, many regions in southern U.S. saw their lowest temperatures in decades, which led to the death of more than 70 people. As frequent occurrences of regional extreme cold events often bring disasters and losses, its formation mechanism has attracted great concern.
It is widely accepted that the average surface temperature of the Earth has been gradually increasing globally since the start of the Industrial Revolution in the 19th century (Folland et al., 2001). However, significant spatial and temporal variations in magnitude have also been confirmed at different latitudes and during different seasons. For example, the most dramatic warming has been found in the middle-to-high latitudes of the northern hemisphere. Winter has been shown to be the fastest-warming season in terms of the global average, followed by spring (Easterling et al., 2000; Kim et al., 2013; Screen et al., 2012). The Arctic is warming up at near-record speed, twice as fast as the global average, due to climate change, a phenomenon known as “Arctic amplification” (Serreze and Barry, 2011). As a result of climate warming, a new pattern of more frequent and more intense weather events has unfolded around the world (Miller et al., 2019; Tong et al., 2019; Zhang et al., 2019; An et al., 2020; Hu and Sun, 2020; Jiao et al., 2020). Some researchers speculate that northeastern China is one of the regions that are most likely to experience considerable changes in climate in the context of the continuous and perilous trend in global warming. Extreme warming events are expected to be more frequent, while extreme cooling events will tend to disappear (Libanda, 2020; Liu and Xu, 2020). The reality, however, is that cold winters at middle-to-high latitudes in the northern hemisphere have frequently been observed since the beginning of the 21st century (Qiao et al., 2014). In the recent decade, although affected by global warming, Heilongjiang has more cold winters. For instance, there was a 5-year succession of cold winters from 2009-2013 (Li et al., 2010; Song et al., 2011). In 2017-2018, cold waves frequently hit the province, with many record low temperatures observed according to the data of meteorological stations (Ban et al., 2019). Studies have suggested that rapid warming in the Arctic will also affect the rest of the world, which is called a “spillover effect” (Qin et al., 2017). The changes occurring in the Arctic, the heat sink of the northern hemisphere, are of equal importance to variations in atmospheric circulation and climate. To some extent, the effects of changes in the Arctic on climate even exceed those on atmospheric circulation (Zhang et al., 2008). Li et al. (2014) discovered that the difference in temperature between the Arctic and the Equator in winter is highly connected to winter temperatures in East Asia. An abnormal decrease in temperature as a result of temperature advection is one reason for the extremely low winter temperatures at the middle latitudes of East Asia. The above-mentioned studies indicate that frequent cold winters at middle-to-high latitudes in the northern hemisphere are necessarily related to the abrupt and accelerating Arctic warming. During the three days from December 29, 2020 to January 1, 2021, a cold wave swept across China. Experts at the China Meteorological Administration pointed out that the strong cold wave was also a result of the warming in the Arctic, which leads to variations in both the Polar Vortex and the Siberian High, and also to changes in winter temperatures in China (Li et al., 2021). It is another example of the direct relationship between the rapid warming of the Arctic and cold winters in China. However, so far, there have been few studies of the effects of Arctic warming and the mechanism by which it affects climate and weather patterns in other regions of the world.
Some researchers have explored the atmospheric circulation that causes extreme temperatures, which has been of great assistance to this study. The forms of atmospheric circulation that lead to extreme temperatures in China include the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and their synergistic interaction (Chan and Zhou, 2005; Suo et al., 2008; Wan et al., 2010; Zhou and Wu, 2010; Renom et al., 2011; Wu and Qian, 2015). The AO has a stronger effect on extreme temperatures than the NAO, especially on cold events from the standpoint of both frequency and intensity, also known as the cold index (Jiao et al., 2020). Zhang et al. (2020) confirmed the significant correlation between extreme temperatures and the El Niño-Southern Oscillation at varying scales. Heilongjiang Province, at the northeastern-most tip of China, is regarded as the region most sensitive to climate warming due to its unique geographical location. It is located in the northern part of the East Asian monsoon zone, toward the northeast of Eurasia, and south of the circum-Arctic regions. The above results provide reference for revealing the effects of Arctic climate warming on cold winters. However, there was little research on the effects of Arctic warming on general atmospheric circulation, and further on cold winters in Northeast China. Thus, Heilongjiang is affected by both temperature variations in the Arctic and the temperature difference between Eurasia and the Pacific Ocean. Hence, this paper reveals the effect and mechanism of Arctic warming on extreme winter temperatures in the study area, Heilongjiang, which is expected to be beneficial to further similar studies focused on other regions of East Asia.

2 Data sources and methods

2.1 Study area

Heilongjiang Province, located in the east of the Eurasian continent near the western shore of the Pacific Ocean, lies between 43°25′N-53°33′N and 121°11′E-135°05′E. It covers an area of 454,000 km2, with a temperate continental monsoon climate, characterized by long cold winters and short warm summers. The province spans a region from the medium temperate zone in the south of China to the cold temperate zone in the north, and from the humid zone in the east, semi-humid zone in the middle to the semi-arid zone in the west. The annual mean temperature here ranges from -4° to 5℃, while annual precipitation is 400-650 mm.

2.2 Data processing

2.2.1 Annual average temperatures of the Equator and the Arctic

The annual average temperatures of the Equator and the Arctic were obtained from the database of the Climatic Research Unit (CRU) of the University of East Anglia, UK. The CRU data are a comprehensive dataset, with high spatial resolution and continuity, including the elements of temperature, precipitation, wind speed, evaporation, and frost days. The dataset begins from the year 1901 and includes monthly datasets. This study selected CRU-TS4.04, a high-resolution gridded spatial climate variable dataset, and downloaded the global average temperatures in winter (from December to the following February) for the period 1961-2019 from http://www.cru.uea.ac.uk/data, with a spatial resolution (grid basis) of 0.5° × 0.5°. The Arctic ranges from 70°N to 90°N and the Equator from 10°S to 10°N. Subsequently, we calculated the mean values of grid points within the above scopes.

2.2.2 Winter temperatures for Heilongjiang

The daily winter temperatures for Heilongjiang during 1961-2019 were provided by the Heilongjiang Meteorological Data Center, a department of the Heilongjiang Meteorological Bureau. Depending on the continuity of the data, this study examined daily data in winter from 62 stations (Figure 1) from January 1961 to February 2019. Winter refers to the period from November to the following February.
Figure 1 Location of meteorological stations in Heilongjiang Province, China

2.2.3 Atmospheric circulation factors

The data for the atmospheric circulation factors were provided by the National Center for Environment Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), also known as NCEP/NCAR. The spatial resolution was based on a 1.875° × 1.875° grid. These data were obtained from http://www.ncep.noaa.gov, covering the following.
(1) Siberian High intensity (SHI)
The Siberian High (SH) intensity (SHI) was represented by the average pressure value of the center of the SH. The center of the area ranges between 40°N-60°N and 70°E-120°E (Gong et al., 2002).
(2) Arctic Polar Vortex intensity (APVI)
This meteorological term for the Arctic Polar Vortex (APV) describes the large mass of cold air that constantly hovers around the Earth's north and south poles. It is a whirlpool of low-altitude cold air that swirls around a high-pressure area or a whirlpool of high-altitude cold air around a low-pressure area. The low-pressure area is called the APV. In the northern hemisphere, there is an area formed by closed contour lines at 500 hPa barometric surface pressure, taking the Arctic as its center. The APVI is represented by the value of the geopotential height at the center.
(3) East Asian Trough (EAT) intensity
The EAT, also known as the Far East Trough, is formed by the westerlies of the troposphere at middle-to-high latitudes, which allows cold air to move from high to middle latitudes in winter. China is at the rear of the EAT, and thus its winter climate is affected by the strength of the EAT. The latter is obtained by adding the potential heights of each point on the EAT in the height field of 500 hPa (30°N-55°N and 110°E-170°E) and subtracting the differences between the maximum and minimum potential heights.
(4) Arctic Oscillation (AO)
The AO, also known as the Northern Annular Mode (NAM) or the Northern Hemisphere Annular Mode (NHAM), is a natural form of atmospheric circulation at middle to high latitudes in the northern hemisphere. It reflects the antiphase variation in atmospheric pressure in both Arctic and middle latitudes. When the AO is in its negative mode, the surface temperature at middle altitudes is reduced, and vice versa. Adopting the empirical orthogonal function (EOF) to analyze the unusual field at 1000 hPa, ranging from 20°N-90°N and 0°-360°, we can calculate the time coefficient of the first-order mode. The AO is the standardized time coefficient.
(5) Eurasian Zonal Circulation Index (EZCI)
The ranges 45°N-65°N and 0°-150°E at 500 hPa are divided into five zones at intervals of 30°. EZCI (IZi) is calculated using Eq. (1) below.
${{I}_{zi}}=-\frac{\overline{{{\Delta }_{z}}}}{{{\Delta }_{\varphi }}}=\frac{\overline{{{z}_{1}}-{{z}_{2}}}}{{{\varphi }_{1}}-{{\varphi }_{2}}}=\frac{\mathop{\sum }_{i=1}^{\iota }{{Z}_{1i}}-\mathop{\sum }_{i=1}^{\iota }{{Z}_{2i}}}{\iota \left( {{\varphi }_{2}}-{{\varphi }_{1}} \right)}$
where φ1 and φ2 stand for the latitudes of IZi, Z1i and Z2i are the height values of the two parallel latitude circles of φ1 and φ2, respectively, and i is the number of height values in the uniform distribution. EZCI equals the average index of the five zones.
(6) East Asian Winter Monsoon (EAWM) index
The EAWM, as the name implies, is the major factor affecting East Asia in winter. Many indexes can represent EAWM. This study used the index proposed by Wang and Jiang (2004), which is the average value of 25°N-45°N and 110°E-145°E at 500 hPa.

2.3 Research methods

2.3.1 Rate of change of climate warming

This study adopted the linear propensity estimation method to analyze the long-term trends of the various meteorological elements. Eq. (2) for one-dimensional linear regression model was used to established the relationship between the meteorological elements and time (Jia, 2018).
y=ax + b
where a is the linear regression coefficient representing the rate of change of the meteorological elements. Taking temperature change as an example, a positive value of a means that the temperature increases with time, and vice versa. The absolute value of a represents the rate of change; b is known as the intercept; a×10 is called the rate of temperature change, with ℃/10a as its unit.

2.3.2 Correlation analysis

Correlation analysis is a statistical method used to discover if there is a relationship between two variables/datasets, as well as the relatedness and negative/positive correlation of the variables/datasets if such a relationship exists. The Pearson correlation method was adopted in this study to quantitatively analyze the relationship between temperature and atmospheric circulation (Jia, 2018).

2.3.3 Mann-Kendall test

The nonparametric Mann-Kendall test is commonly employed to detect trends in climate data, etc., with less interference from abnormal values. Besides it being a simple and convenient calculation, another advantage is that the sample points do not need to follow a certain distribution. Using the Mann-Kendall test, we were able to know the exact time of an abrupt change, and also the areas involved (Jia, 2018).
The Mann-Kendall test was carried out using the formulas given in Eqs. (3) and (4) below, where n and x stand for the number of samples and the time sequence, respectively:
${{r}_{i}}=\left\{ \begin{matrix} +1\ \ \ \ \ \ \ \ \text{if}\ {{x}_{i}}>{{x}_{j}} \\ ~~ \\ 0\ \ \ \ \ \ \ \ \ \ \text{if}\ {{x}_{i}}\le {{x}_{j}} \\\end{matrix} \right.\ \ \ \ \ (j=1,2,\ldots,i)$
The order list Sk is the number of values at time i greater than those at time j.
The statistics are defined by Eq. (5), where the time series is assumed to be randomly independent.
$U{{F}_{k}}=\frac{\left[ {{S}_{k}}-E({{S}_{k}}) \right]}{\sqrt{Var({{S}_{k}})}}~~~~(k=1,2,\ldots,n)$
where UF1=0, E(Sk) and Var(Sk) are the average value and variance of Sk. Sk is random variable.
If ${{x}_{1}},{{x}_{2}},\ldots,{{x}_{n}}$ are independent items with the same continuous distribution, their values are obtained from Eq. (6):
where UFi is standard normal distribution, which is calculated at a time sequence x of ${{x}_{1}},{{x}_{2}},\ldots,{{x}_{n}}$. Given a significant level α, we can examine the normal distribution table. If $U{{F}_{i}}$ > ${{U}_{\alpha }}$, there exists an obvious change in the sequence.
On the basis of an inverted time sequence α of ${{x}_{n}},{{x}_{n-1}},\ldots,{{x}_{1}}$, the above process was repeated and the conditions of $U{{B}_{k}}=-U{{F}_{k}},\ k=n,n-1,\ldots,1,\ U{{B}_{1}}=0$ were followed.
Positive values of UF or UB indicate an increasing trend, and vice versa. If UF or UB is beyond the critical curve, there exists an obvious trend. The range stands for the period with an abrupt change. If UB or UF has a point of intersection with the critical curve, the corresponding year for that point is the start year of the abrupt change.

2.3.4 The standard deviation method

There are currently three main types of detection and diagnosis of extreme temperature events, namely, the absolute critical value method, the percentile method, and the standard deviation method. This paper adopts the standard deviation method to judge the year of extreme temperature. The standard deviation method uses the standard deviation of the mean value of regional air temperature ± n times to determine the threshold, and the value of n is determined according to the distribution of specific values. According to the actual data distribution in this article, choose n=1.

3 Results and analysis

3.1 Winter temperature changes in the Arctic and the Equator since the 1960s

This study used the downloaded CRU data to calculate the temperatures of the Equator (10°S-10°N) and the Arctic (70°N-90°N), as well as their differences in winter for the period 1961-2018 (Figure 2). The results showed that the temperatures of both the Arctic and the Equator have shown continuous and significant increasing trends during that time, with rates of 0.53℃/10a (P < 0.01) and 0.15℃/10a (P < 0.01), and their differences have shown continuous and significant decreasing trends, with rates of 0.38℃/10a (P < 0.01), and the winter temperature rise rate in the Arctic is 3.5 times that of the equator.
Figure 2 Temperatures of the Arctic, the Equator, and the differences between them, as well as the anomalies and abrupt changes during the period 1961-2018
Compared with the 1960s, the temperatures of the Equator and Arctic rose by 0.83℃ and 2.92℃, respectively. The difference between the two continuous decreases was 2.1℃, with a rate of 0.37℃/10a (P < 0.01), characterized by an extremely significant reduction.
As can be seen from Figure 2, the temperatures of the Equator and Arctic, and the difference between the two in winter showed an abrupt change around the year 2000. The results of the Mann-Kendall test suggest that the years when these abrupt changes took place in the Arctic and the Equator were 2001, 1996 and 2003, respectively. Hence, this paper takes the year 2001 as the boundary marker in order to study the effect of Arctic warming on years with extreme winter temperatures in Heilongjiang.

3.2 Winter temperature changes in Heilongjiang

The average temperature of Heilongjiang in winter during the period 1961-2018 was -16.77℃. During that time, an extremely significant warming trend can be observed, with a rate of 0.43℃/10a (P < 0.01) (Figure 3a). The abrupt change occurred in the mid-1980s (Figure 3b), which was also the start of a warm period. This warm period showed an increasing temperature trend, compared with the cold period. The standard deviation of the mean temperature during 1961-1985 was ±1.41℃, while that during 1986-2018 was ±1.77℃, which means that the winter temperature fluctuated around 1985, the number of extreme warm years increased thereafter, and this phenomenon became more obvious after 2000 (Figure 3a).
Figure 3 Winter temperature: (a) variation, (b) anomalies, and (c) abrupt changes in Heilongjiang during 1961-2018
The standard deviation of the average winter temperature was ±1.972℃ during the study period. Taking $\bar{T}\pm \sigma $ as the criterion, we obtained 11 cold winters and two warm winters. That is to say, while climate warming was continuing and increasing, there were more cold winters than warm ones in Heilongjiang. Specifically, against the background of the abrupt warming change in the Arctic, there have been four cold winters and two warm winters since 2000 (Figure 3c).

3.3 The effects of Arctic warming on general atmospheric circulation

3.3.1 The selection of atmospheric circulation factors

The calculated correlation coefficients between the SH, AO, APV, EAT, EZCI, EAWM, and the winter temperatures of Heilongjiang are presented in Table 1. It can be seen that the above-mentioned atmospheric circulation factors are all significantly correlated with the winter temperatures of Heilongjiang. Based on these coefficients, we further analyzed the variation in circulation with the winter temperatures of the Arctic, the Equator, and the difference between them.
Table 1 Correlation coefficients between winter temperatures of Heilongjiang and atmospheric circulation
Correlation coefficient -0.313* -0.406** 0.482** 0.604** 0.595** 0.525**

Note: * and **: significance at the 0.05 and 0.01 levels, respectively.

3.3.2 The relationship of Arctic warming with each atmospheric circulation factor

According to the results presented in section 3.3.1, we analyzed the relation of Arctic warming with each selected atmospheric circulation factor in the order of the value of correlation coefficients.
(1) Relationship with Arctic Oscillation (AO)
The AO showed a slight increase during 1961-2018, with a rate of 0.163/10a (Figure 4a), which means that the AO tended to move toward a positive phase. When AO is at this positive phase, the Arctic surface atmospheric pressure is at a low level, with strong zonal circulation but a weak meridional one, which leads to an increase in surface temperature at middle latitudes. There were obvious interdecadal variations in terms of the AO during the study period (Figure 4b). The Mann-Kendall test result indicated that the abrupt change year for the ANO was 1988 (Figure 4c). The average values of AO were -0.286 before 2001 and -0.168 after 2002. The correlation coefficients of AO with Arctic winter temperature and of the difference between the Arctic and Equator temperatures were 0.053 and -0.096, respectively, suggesting no significant relationship (P > 0.05).
Figure 4 Arctic Oscillation: (a) variation, (b) anomalies, and (c) abrupt changes during 1961-2018
(2) Relationship with Eurasian Zonal Circulation Index (EZCI)
We can see a slight increasing trend for EZCI during 1961-2018, with a rate of 0.11/10a (P > 0.05) (Figure 5a). The anomalies in the distribution show that EZCI was negatively dominated after 2000, which means that the index was relatively weak (Figure 5b). Mann-Kendall analysis showed that the year with an abrupt change was 1974, and no abrupt change was observed after 2000 (Figure 5c). The average values of EZCI were 0.229 before 2001 and -0.549 after 2002.
Figure 5 Eurasian zonal circulation: (a) variation, (b) anomalies, and (c) abrupt changes during 1961-2018
The correlation coefficients of EZCI with winter temperatures in the Arctic and of the difference between the Equator and the Arctic temperatures were -0.263 and -0.326, respectively, showing significant correlations (P < 0.05). The EZCI, which represents the intensity of the circulation, was negatively correlated with winter temperatures in the Arctic and of the difference between the Equator and Arctic temperatures.
(3) Relationship with East Asian winter monsoon index
The EAWMI rose significantly during 1961-2018 (P < 0.01), increasing by 3.1 hPa (Figure 6a), with a rate of 6.477 hPa/10a. As shown in the anomalies in the distribution, the EAWMI was mainly positive, indicating that the winter monsoon was relatively strong (Figure 6b). The year of abrupt change was 1985, and there was no abrupt change after 2000 on the basis of Mann-Kendall analysis (Figure 6c). The average values of EAWMI were 554.014 hPa before 2001 and 555.494 hPa after 2002. The correlation coefficients of EAWMI with Arctic temperatures and the difference between the Arctic and Equator temperatures in winter were 0.131 and -0.010, respectively, with no correlation (P > 0.05). This means that the higher the winter temperature at the Equator, the stronger the EAWMI.
Figure 6 Winter monsoon index: (a) variation, (b) anomalies, and (c) abrupt changes during 1961-2018
(4) Relationship with East Asian Trough (EAT) intensity
No obvious increasing trend was found in the intensity of the EAT during 1961-2018 (P > 0.05), with a rate of 1.82 hPa/10a (Figure 7a). Figure 7b shows that there were more years with a positive anomaly than years with a negative one, indicating that the intensity of the EAT was relatively weak. The Mann-Kendall test result showed that the abrupt change occurred in 2002 (Figure 7c) due to the abrupt change in winter temperature in the Arctic. The average atmospheric pressure of the EAT was 585.9 hPa during 1961-2001 and 590.0 hPa during 2002-2018. The intensity of the EAT reduced with the increase in atmospheric pressure. The correlation coefficients of EAT intensity with Arctic temperatures and the difference between the Arctic and Equator temperatures in winter were 0.143 and -0.140, respectively, with no obvious correlation (P > 0.05).
Figure 7 East Asian Trough intensity: (a) variation, (b) anomalies, and (c) abrupt changes during 1961-2018
(5) Relationship with Arctic Polar Vortex intensity (APVI)
The atmospheric pressure of the APV reduced significantly during 1961-2018 (P < 0.05), with a rate of 5.60 hPa/10a (Figure 8a). We can see from Figure 8b that a positive anomaly dominated after 2000, at a relatively weak level. The Mann-Kendall data in Figure 8c showed that an abrupt change occurred in 2002, as the Arctic winter temperature changed abruptly in the same year. The average values of atmospheric pressure were 499.8 hPa before 2001 and 502.6 hPa after 2002. The increase in atmospheric pressure resulted in a decrease in the APV intensity. The correlation coefficients for APV with Arctic temperatures and the difference between the Arctic and Equator temperatures in winter were 0.358 and 0.301, respectively, both with an extremely significant correlation (P < 0.01). The higher the Arctic temperatures and the greater the difference between the Arctic and Equator temperatures, the higher the pressure of the APV and the weaker the APV intensity, and vice versa.
Figure 8 Arctic Polar Vortex intensity: (a) variation, (b) anomalies, and (c) abrupt changes during 1961-2018
(6) Relationship with Siberian High intensity (SHI)
The average SHI was 1028.94 hPa during 1961-2018, with no significant increase (P > 0.05) and a rate of 0.062 hPa/10a (Figure 9a). We can see an obvious periodic change in terms of the SH anomaly (Figure 9b). An abrupt change was observed in 2007, after which the SH became stronger (Figure 9c). The average values of SHI were 1028.722 hPa before 2001 and 1029.605 hPa after 2002. The correlation coefficients of SHI with Arctic winter temperatures and the difference between the Arctic and Equator temperatures were 0.362 and 0.403, respectively, at a relatively high level, which implies an extremely significant correlation (P < 0.01). That is, the higher the Arctic temperature, the smaller the difference between the temperatures of the Arctic and the Equator and the stronger the SHI.
Figure 9 Siberian High: (a) variation, (b) anomalies, and (c) abrupt changes during 1961-2018

3.4 Analysis of the effect on years with extreme winter temperatures in Heilongjiang

The above analysis can be concluded as Table 2. In the six atmospheric calculation factors that are closely connected with winter temperatures in Heilongjiang, SH, APV, and EZCI have significant correlation with temperature in the Arctic region, as well as temperature difference between the Arctic and the Equator. Hence, it is clear that, temperatures in the Arctic region, and the difference between the Arctic and the Equator, first affect the SHI, APV and EZCI, which further influences winter temperatures in Heilongjiang. Specifically, due to the increase of temperature in the Arctic region, the temperature difference between the Arctic and the Equator decreases, which further reduces the EZCI (significant negative correlation), intensifies the SH (significant positive correlation), and strengthens the atmospheric pressure of the APV (significant positive correlation).
Table 2 Relationship between variation in circulations and Arctic temperatures and with the difference between the Arctic and Equator temperatures
Circulation Rate of change Arctic Difference between Arctic and Equator
AO 0.163/10a 0.053 -0.096
EZCI 0.11/10a -0.263* -0.326*
EAWMI 6.477** hPa/10a 0.131 -0.010
EAT 1.82 hPa/10a 0.143 -0.140
APV 5.60* hPa/10a 0.358** 0.301**
SHI 0.062 hPa/10a 0.362** 0.403**

4 Discussion

The Arctic is the region on the globe that is undergoing the most significant climate warming, especially in winter. Scientists are speculating as to whether this warming of the Arctic, as well as the significant decrease in the difference between the temperatures of the Arctic and the Equator in winter, might have an effect on global temperatures at middle-to-high latitudes in winter. This study took Heilongjiang in China as the study area in order to explore how climate warming in the Arctic affects winter temperatures in northeast Asia, which lays a foundation for further studies of the effects of increasing temperatures in the Arctic on winter temperatures at middle-to-high latitudes. The following points are highlighted.
(1) Many researchers have shown that high latitudes, especially the Arctic, are the areas on the globe experiencing the most significant warming. Xu et al. (2017) used the China Meteorological Administration global Land Surface Air Temperature (CMA-LAST) dataset to analyze the variations in spatial temperatures globally during 1979-2014 and pointed out that the high latitudes witnessed the greatest rate of warming. Kim et al. (2013) studied the same spatial temperature variations since 1979 with the assistance of the ERA re-analysis dataset, and came up with a consistent conclusion, namely, that various surface warming events have occurred in the Arctic. Screen et al. (2012) also found that temperatures in the northern hemisphere at high altitudes (70°N-80°N) increased faster than those at middle altitudes (30°N-40°N) during 1979-2013, with a rate of 0.86℃/10a. This paper additionally explains that the surface temperature of the Arctic rose at a rate of 0.53℃/10a during 1961-2018. The temperatures at the Equator and the differences in the temperatures of the Arctic and the Equator were also calculated.
(2) Li et al. (2006) have studied the effects of temperature change in the Arctic region on the temperature in China. They believed that Arctic temperature change influenced the PV, EAT and SH, which leads to the temperature change in China. This paper suggests that besides PV and SH, EZCI plays an important role in the temperature change in the Arctic in winter. Different from the previous conclusions, this paper shows that there is no significant relationship between Arctic temperatures in winter and EAT. Regarding to the indicators, we can see another difference between Li's research and this paper. Li et al. indicated that the area of PV has great effects on the temperature in the Arctic region, while APVI has little effect on it. However, this paper believes that the temperature change in the region first affects PVI, which means there is a close relationship between the two. Because of the increase of surface temperature, APVI weakens and cold air masses diffuse outward, leading to an increase in the PV area. The geopotential height field outside the Arctic reduces, thus the cold air mass moves southward, causing a relatively low temperature in China. In addition, the weakened PVI, the cold air, the decreased EZCI and intensified surface SH further affect and alter the paths and intensity of the cold air. This explains the changing characteristics of extremely low temperature events in China.
(3) The year 2000 with an abrupt change in the Arctic in winter was ahead of those of the APV and SH, which means that PV and SH are closely related with the temperature of the Arctic. Based on current theories, the AO index, the EAT intensity, and the EAWMI are regarded as the most vital factors affecting winter temperatures in Heilongjiang. However, the authors suggest that these three circulations have no obvious correlations with the temperatures of the Arctic and of the difference between those of the Arctic and Equator.

5 Conclusions

With the global warming, Heilongjiang has witnessed an increase in winter temperature. However, the number of cold winters was still larger than that of warm winters, and the interannual amplitude of the average temperature in winter also increased. This paper believed that, there is a significant correlation between the winter temperatures in Heilongjiang with the high pressure of the Arctic's surface, SH, AO, PV, EAT, EZCI and WMI. The temperature rise in the Arctic region, together with the decrease of the temperature difference between the Arctic and the Equator, affected SH, PV and EZCI, and further led to the change of winter temperatures in Heilongjiang. To be specific, the temperature rise in the Arctic, and the decrease in the temperature difference between the Arctic and the Equator, weakened the intensity of PV and EZCI, and intensified the SH.
The temperatures of the Equator and the Arctic, and their difference in winter, all showed extremely significant increasing trends, with rates of 0.15℃/10a, 0.53℃/10a, and 0.38℃/ 10a, respectively, with an abrupt change around the year 2000. Heilongjiang Province in China experienced the same trend in the winters of the study period, with a rate of 0.43℃/10a. There were still extremely cold years after 2000. The correlation coefficients of winter temperature in Heilongjiang with those of the Arctic and the Equator and the temperature difference between them were 0.241, 0.149, and -0.091, respectively, with no obvious correlation between them. Heilongjiang's winter temperatures are affected by atmospheric circulation. The extremely significant increases in temperature in the Arctic and the difference in temperature between the Arctic and the Equator in winter were caused by the SH, APV, and the circulation index based on current analysis, which is also regarded as the main reason for the cold winters in Heilongjiang. Warm winters occurred after 2000, which were due mainly to the current global warming trend.
An D, Du Y, Berndtsson R et al., 2020. Evidence of climate shift for temperature and precipitation extremes across Gansu province in China. Theoretical and Applied Climatology, 139(5): 1137-1149.


Ban J, Li Y, Zhao J et al., 2019. Cause analysis of temperature anomaly in Heilongjiang province in winter of 2017-2018. Heilongjiang Meteorology, 29(1): 14-23. (in Chinese)

Chan J C L, Zhou W, 2005. PDO, ENSO and the early summer monsoon rainfall over South China. Geophysical Research Letters, 32(8): L08810.

Easterling D R, Karl T R, Gallo K P et al., 2000. Observed climate variability and change of relevance to the biosphere. Journal of Geophysical Research: Atmospheres, 105(D15): 20101-20114.


Folland C K, Karl T R, Christy J R et al., 2001. Observed climate variability and change. Climate Change 2001:The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 99-192.

Gong D, Zhu J, Wang S, 2002. The influence of Siberian High on large-scale climate over continental Asia. Plateau Meteorology, 21(1): 8-14. (in Chinese)

Gong D Y, Wang S W, 2003. Influence of Arctic Oscillation on winter climate over China. Acta Geographica Sinica, 58(4): 559-568. (in Chinese)

He C, He J H, 2003. Relation between Arctic Oscillation and North China air temperature in winter. Journal of Nanjing Institute of Meteorology, (1): 1-7. ( in Chinese)

Hu T, Sun Y, 2020. Projected changes in extreme warm and cold temperatures in China from 1.5 to 5°C global warming. International Journal of Climatology, 40(8): 3942-3953. (in Chinese)


Hu Y C, Dong W J, He Y, 2007. Progress of the study of extreme weather and climate events at the beginning of the twenty first century. Advances in Earth Science, 22(10): 1066-1075. (in Chinese)

Jia J, 2018. Statistics. Beijing: China Renmin University Press.

Jiao W, Zhang B, Ma B et al., 2020. Temporal and spatial changes of extreme temperature and its influencing factors in northern China in recent 58 years. Arid Land Geography, 43(5): 1220-1230.

Kim S J, Choi H S, Kim B M et al., 2013. Analysis of recent climate change over the Arctic using ERA-Interim reanalysis data. Advances in Polar Science, 24(4): 326-338.


Li Fuyu, Collins William D, Wehner Michael F et al., 2011. Impact of horizontal resolution on simulation of precipitation extremes in an aqua-planet version of Community Atmospheric Model (CAM3). Tellus A: Dynamic Meteorology and Oceanography, 63(5): 884-892.


Li H, Jian J F, Wu P et al., 2021. Hot thinking behind frequent cold events. China Meteorological News, 5041(4): 114.

Li L, Guan Z, Cai J, 2014. Interannual variation of winter temperature difference between polar and equatorial regions associated with East Asian climate anomalies. Science Bulletin, 59(27): 2720-2727. (in Chinese)

Li Y, Yu M, Zhang J, 2010. Diagnostic analysis of temperature anomaly in Heilongjiang province in winter 2009. Heilongjiang Meteorology, 27(3): 4-7. (in Chinese)

Libanda B, 2020. Multi-model synthesis of future extreme temperature indices over Zambia. Modeling Earth Systems and Environment, 6: 743-757.


Liu X, Xu Z, 2020. Spatial and temporal pattern of extreme temperature during 1961-2018 in China. Journal of Water and Climate Change, 11(4): 1633-1644.


Miller D E, Wang Z, 2019. Skillful seasonal prediction of Eurasian winter blocking and extreme temperature frequency. Geophysical Research Letters, 46(20): 11530-11538.


Qian W, Lin X, Zhu Y et al., 2007. Climatic regime shift and decadal anomalous events in China. Climatic Change, 84(2): 167-189.


Qiao S B, Shen B Z, Wang X J et al., 2014. Feature analysis and preliminary causes study of the frequent cooling winter in northern Eurasia since 2004. Acta Meteorologica Sinica, 72(6): 1143-1154. (in Chinese)

Qin D H, Yao T, Ding Y et al., 2017. An Introduction to Cryosphere Science. Beijing: Science Press. (in Chinese)

Renom M, Rusticucci M, Barreiro M, 2011. Multidecadal changes in the relationship between extreme temperature events in Uruguay and the general atmospheric circulation. Climate Dynamics, 37(11): 2471-2480.


Screen J A, Deser C, Simmonds I, 2012. Local and remote controls on observed Arctic warming. Geophysical Research Letters, 39(10): L10709.

Serreze M C, Barry R G, 2011, Processes and impacts of Arctic Amplification: A research synthesis. Global and Planetary Change, 77(1/2): 85-96.


Sone W, Wu Z W, Li Y F et al., 2018. Interannual association of the near-surface temperature between eastern China and the Arctic in winter. Climatic and Environmental Research, 23(4): 463-478.

Song L, Li Y, Zhang J, 2011. Analysis of continuous low temperature anomaly from winter of 2009 to spring of 2010 in Heilongjiang province. Journal of Meteorology and Environment, 27(2): 14-18. (in Chinese)

Sun Wenyi, Mu Xingmin, Song Xiaoyan et al., 2016. Changes in extreme temperature and precipitation events in the Loess Plateau (China) during 1960-2013 under global warming. Atmospheric Research, 168: 33-48.


Wei Ting, Ding Minghu, Wu Bingyi et al., 2016. Variations in temperature-related extreme events (1975-2014) in Ny-Ålesund, Svalbard. Atmospheric Science Letters, 17(1): 102-108.


Suo L L, Huang J Y, Tan B K, 2008. The influence of winter Arctic Oscillation on maximum and minimum air temperature over China in winter. Journal of Tropical Meteorology, 24(2): 163-168.

Tong S, Li X, Zhang J et al., 2019. Spatial and temporal variability in extreme temperature and precipitation events in Inner Mongolia (China) during 1960-2017. Science of the Total Environment, 649: 75-89.


Wan S Q, Gu C H, Kang J P et al., 2010. Monthly extreme high-temperature response to atmospheric oscillation in China. Acta Physica Sinica, 59(1): 676-682. (in Chinese)


Wang H, Jiang D, 2004. A new East Asian winter monsoon intensity index and atmospheric circulation comparison between strong and weak composite. Quaternary Sciences, 24(1): 19-27.

Wu B Y, 2019. Two extremely cold events in East Asia in January of 2012 and 2016 and their possible associations with Arctic warming. Transactions of Atmospheric Sciences, 42(1): 14-27. (in Chinese)

Wu K, Qian W, 2015. Secular non-linear trends and multi-timescale oscillations of regional surface air temperature in eastern China. Climate Research, 63(1): 19-30.


Xu W H, Li Q X, Jones P et al., 2017. A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900. Climate Dynamics, 50(15): 2513-2536.


Zhang D, Zheng J, Fan J et al., 2019. Spatiotemporal variations of extreme temperature indices in different climatic zones of China over the past 60 years. Chinese Journal of Agrometeorology, 40(7): 422-434. (in Chinese)

Zhang K, Dong X, Liao K et al., 2020. Characteristics of seasonal changes in extreme temperature and their relativity with ENSO in the Yellow River Basin from 1960 to 2017. Research of Soil and Water Conservation, 27(2): 185-192. (in Chinese)

Zhang X, Sorteberg A, Zhang J et al., 2008. Recent radical shifts of atmospheric circulations and rapid changes in Arctic climate system. Geophysical Research Letters, 35(22): L22701.


Zhou L T, Wu R, 2010. Respective impacts of the East Asian winter monsoon and ENSO on winter rainfall in China. Journal of Geophysical Research: Atmospheres, 115(D2): 1-11.

Zhou Yaqing, Ren Guoyu, 2011. Change in extreme temperature event frequency over mainland of China, 1961- 2008. Climate Research, 50(2/3): 125-139.