Rui Kong
China University of Geoscience & China Academy of Meteorological Science
1 Introduction
Freezing weather(Wang, 2011; Zhao et al., 2010) is a type of comprehensive extreme weather that causes damage by freezing the surface of objects. And it has been provided to be the costliest and least irresistible natural hazard in winter all over the world, especially with significant damage to Agricultural, industry, Power, and economic security(Armenakis & Nirupama, 2014; Danz et al., 2021; Brazdil et al., 2021; Jones, 2022; Marinier et al., 2022; Swarna & Hossain, 2022; Gu et al., 2008). For example, the ice storm that occurred on 5–9 January 1998 is still one of the largest natural disasters in Canadian history, causing direct losses of $4 billion(Cortinas, 2000).
Historically, in China, extreme freezing disasters mostly occurred in some parts of the country(Wu, 1996; Wang et al., 2009; Tang et al., 2011; Ding et al., 2008; Qu et al., 2011; Yao et al., 2012; Hu et al., 2017; Wang et al., 2017; Liu et al., 2020; Zhou et al., 2021). However, in January 2008, extraordinarily frequent and long-lasting snowstorms attacked two-thirds of provinces in China, causing an economic loss of over 150 billion RMB. Following this, rare disastrous freezing weather was observed respectively in different regions of China in 2011, 2018, and 2020. Notably, the disaster freezing rain event that occurred in Jilin in 2020 broke the historical understanding of freezing types in the north of China.
On the other hand, under the background of global warming, strong warming also has happened in China with an increase of 0.26℃ per decade from 1951-2021. When every additional increment of global warming, changes in extremes continue to become larger in frequency and intensity(IPCC, 2021), which mainly works by changes in atmospheric circulation patterns (Horton et al., 2015).
Therefore, a question might be put forward: freezing weather pattern in China has changed? The objectives of this paper are to: 1) determine and document the spatiotemporal patterns of rime, glaze, and hard rime in China, with a special focus on the last decade; 2) define whether the pattern of freezing weather is changing or not. 3) analyze the changes of observations data in the typical extreme freezing event year.
2 Materials and Methods
2.1 Data Resources
The traditional method for defining local freezing weather is to analyze freezing weather data records of a single point for a specific area. This dataset is the National Freezing Dataset (V1.0) , which was obtained from the China Meteorological Data Sharing Service System of the China Meteorological Administration, including daily frost, rime, glaze, and occurring ice at each station. By 2017, there are 2479 stations that had observations,
2.1 Trend analysis
Although it is a simple statistical method, linear trends is still useful for investigating changes in climate change(Boyles & Raman, 2003). Taking the time series as x and the freezing day series as y, establish a linear regression equation of one variable as follows:
The regression coefficient a>0 indicates that y increases with time, and a<0 indicates that y decreases with time. And we always take 10 times of a as the climate inclination rate.
1.2 Barycenter model
The barycenter model comes from the concept of barycenter in physics, the forces in all directions around the center of gravity of the regional space are kept relatively balanced. This model has significant advantages in discriminating the spatial evolution of elements (Liang et al., 2022). The mathematical expression formula of the freezing days coordinate solution model is:
(3)
where, X, Y present the longitude and latitude of barycenter respectively , present the longitude and latitude of the station , presents the freezing day of the station .
1.1 Trend analysis
Although it is a simple statistical method, linear trends is still useful for investigating changes in climate change(Boyles & Raman, 2003). Taking the time series as x and the freezing day series as y, establish a linear regression equation of one variable as follows:
(1)
The regression coefficient a>0 indicates that y increases with time, and a<0 indicates that y decreases with time. And we always take 10 times of a as the climate inclination rate.
1.2 Barycenter model
The barycenter model comes from the concept of barycenter in physics, the forces in all directions around the center of gravity of the regional space are kept relatively balanced. This model has significant advantages in discriminating the spatial evolution of elements (Liang et al., 2022). The mathematical expression formula of the freezing days coordinate solution model is:
(3)
where, X, Y present
1.1 Trend analysis
Although it is a simple statistical method, linear trends is still useful for investigating changes in climate change(Boyles & Raman, 2003). Taking the time series as x and the freezing day series as y, establish a linear regression equation of one variable as follows:
(1)
The regression coefficient a>0 indicates that y increases with time, and a<0 indicates that y decreases with time. And we always take 10 times of a as the climate inclination rate.
1.2 Barycenter model
The barycenter model comes from the concept of barycenter in physics, the forces in all directions around the center of gravity of the regional space are kept relatively balanced. This model has significant advantages in discriminating the spatial evolution of elements (Liang et al., 2022). The mathematical expression formula of the freezing days coordinate solution model is:
(3)
where, X, Y present the longitude and latitude of barycenter respectively , present the longitude and latitude of the station , presents the freezing day of the station .
the longitude and latitude of barycenter respectively , present the longitude and latitude of the station , presents the freezing day of the station .
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