Abstract

The snow-cover days over the middle and lower reaches of the Yangtze River (MLRYR) were extracted from Chinese historical documents for the winter of 1620, which includes the beginning of the tenth month of 1620 through the middle ten days of the second month of 1621 in Chinese lunar calendar. By using these records, the winter temperature anomalies compared with the 1961–1990 mean at nine stations were estimated. The results show an average of 50 snow-cover days over the MLRYR ranging from 30 d in Shanghai to 100 d in Jingzhou. The average snow-cover duration was approximately 70 d in Hefei, Huoshan, Nanjing, and Chaohu, and 40–60 d in Anqing, Wuhan, Changde, Changsha, and Jingdezhen. However, Shanghai and southern Jiangsu province had the lowest number of snow-cover days at 30 d. The regional mean winter temperature in 1620 was estimated to be 4.4 °C lower than the 1961–1990 mean. The maximum negative anomaly of −5.7 °C occurred in Jingdezhen, and the minimum anomaly of −3.6 °C was detected in Changsha. Both anomalies were significantly lower than those of the coldest winter during the instrumental observation period.

Keywords

Climate extremes ; Snow-cover days ; Chinese historical documents ; Middle and lower reaches of the Yangtze River ; Winter of 1620

1. Introduction

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) stated that the globally averaged combined land and ocean surface temperature has increased by 0.85 °C during 1880–2012 (IPCC, 2013 ). This warming trend has also been observed particularly in China (ECSCNARCC, 2011 ). Under this warming background, there is increasing concern that extreme climate events may be changing in frequency and intensity as a result of human influences (Trenberth et al., 2007 ). Climate extremes have gathered recent attention in climate change research because such extremes have more severe effects than normal climatic conditions on nature and mankind (Pfister and Brázdil, 1999  ;  Brázdil et al., 2010 ). Anomalous cold winters are important climate extremes that can cause severe hazardous conditions. In 2008, for example, the most severe cold winter during the previous 30 years occurred in southern China. This extremely cold winter, which was characterized by low temperatures, deep snow accumulation, and lengthy frozen days, caused direct economic losses of more than CN¥150 billion (Wang et al., 2008 ; Ding et al., 2008  ;  Chen and Fan, 2009 ). Similar cold winters occurred many times in history (Zheng et al., 2012 ; Zheng et al., 2014 ; Hao et al., 2011a  ;  Hao et al., 2011b ).

During recent decades, an increasing number of studies have used instrumental data to investigate climate extremes (Zhai et al., 1999  ;  Meehl et al., 2000 ). However, due to the limited instrumental observations, climate extremes in historical periods have not been fully studied. In China, large amounts of historical documents are available that contain abundant records of extreme events such as snow and ice storms, frozen rivers and lakes, and the durations of frost and snow (Ge et al., 2003 ). Although several severe meteorological disasters and extreme climate events have been described qualitatively on the basis of these records (Gong et al., 1987 ; Zhang, 1997  ;  Wang et al., 2004 ), quantitative reconstructions of temperature anomalies during these extreme events are very scarce. In the present study, the instrumental temperature and snow-cover days extracted from Chinese historical documents are used to reconstruct temperatures in the winter of 1620 for the middle and lower reaches of the Yangtze River (MLRYR). In addition to revealing the strength of historical climate extremes, this study also provides a background for understanding the recent instrumentally observed climate extremes.

2. Data and methods

2.1. Modern meteorological data

The modern instrumental records of monthly mean temperatures covering 27 sites (Fig. 1 ) from 1961 to 2010 were derived from the China Meteorological Administration (http://cdc.cma.gov.cn/home.do ). The mean of individual site measurement was calculated to represent the regional mean temperature of the MLRYR. The daily snow depth measurements were available in 9 of 27 sites, which were derived from China Monthly Meteorological Report. To maintain consistency with historical records, a snow-cover day for each site was defined as a day in which when half of the observation field area was covered with snow, even at depths less than 0.5 cm (An et al., 2009  ;  Cui et al., 2005 ).


Study area and stations used to reconstruct temperatures.


Fig. 1.

Study area and stations used to reconstruct temperatures.

2.2. Snow-cover records from historical documents

Snow-cover records for the winter of 1620 were compiled mainly from local gazettes, which are comprehensive books that record both natural and social conditions of an administrative unit (Ge et al., 2008 ). The government paid significant attention to editing local gazettes, predefining a fixed format and setting up a professional department to supervise and review the quality of each gazette. Therefore, the derived snow-cover days were homogeneous and reliable. In this study, a total of 54 records on the extremely cold winter of 1620 were extracted from that reported by Zhang (2004) . The original sources of these records, which are historical documents stored in 75 libraries and archives in 37 cities across China, have been carefully checked, and the errors of records regarding time, place, and descriptions were corrected as much as possible (Zhang, 2004 ). Of these 54 records, data from 9 stations with snow-cover records both in 1620 and modern times were used to reconstruct winter temperatures.

The duration for recording historical snow-cover descriptions in the winter of 1620 was from the beginning of the tenth Chinese lunar month of 1620 to the middle ten days of the second Chinese lunar month of 1621, which corresponds to the Gregorian calendar of October 25, 1620, to March 12, 1621, covering 139 d. Three types of snow-cover descriptions appear in the historical documents: (1) Directly recorded snow-cover days, such as the following description “It was bitter cold in the spring of 1621, with snow covering on the ground for more than 50 d. Many birds and beasts were frozen to death.”1 Such descriptions totaled 29, accounting for 53.7%. By using these records, snow-cover days can be extracted directly. (2) Records of the starting and ending dates of snow-cover, such as the following description “It snowed heavily in the winter. The heavy snow lasted from the eleventh month of 1620 to the second month of 1621 in Chinese lunar calendar.”2 Such records totaled 14, accounting for 25.9%. On the basis of these records, the snow-cover days can be calculated. (3) General descriptions of snow-cover events, such as the following description “Heavy snow started from the winter of 1620 and continued until the next spring …”3 Records of this type totaled 11, accounting for 20.4%. Although the exact number of snow-cover days could not be obtained from these records, the general conditions of snowfall and snow cover can be estimated.

2.3. Winter temperature reconstruction

The snow in the MLRYR, located in the middle latitudes, occurs mostly in winter, and the snow-cover days are closely related to temperature (Qin et al., 2006  ;  Pang et al., 2006 ). To reconstruct the regional mean winter temperature in 1620, we first performed a regression analysis between winter temperature and the number of snow-cover days by using modern instrumental data from the nine stations in which snow-cover data for 1961–2010 were available. Subsequently, winter temperature anomalies at these stations were estimated on the basis of the derived regression equations. Finally, by applying a stepwise regression model between the regional winter temperature and the mean winter temperature of these stations, the regional mean winter temperature in 1620 over the MLRYR was calculated.

3. Results

3.1. Spatial pattern of snow-cover days

Fig. 2 shows the spatial pattern of snow-cover days across the MLRYR in the winter of 1620, in which large spatial variabilities are observed with regional mean of 50 d. Generally, more snow-cover days occurred in the northern part of the study area than those in the south. The maximum snow-cover days, 100 d, occurred in Jingzhou county, Hubei province. As recorded in historical documents, the heavy snow event in Jingzhou began in the beginning of the tenth Chinese lunar month and continued until the twentieth day of the first Chinese lunar month of the following year. The second-highest number of heavy snow days, 70 d, occurred in Hefei, Huoshan, Nanjing, and Chaohu counties, followed by 40–60 d in Anqing, Wuhan, Changde, Changsha, and Jingdezhen, and 30 d in Shanghai and southern Jiangsu province.


Snow-cover days for the middle and lower reaches of the Yangtze River in the ...


Fig. 2.

Snow-cover days for the middle and lower reaches of the Yangtze River in the winter of 1620.

3.2. Estimates of winter temperatures for the nine stations

The winter temperatures correlated strongly with the number of snow-cover days (Table 1 ). The correlation coefficients ranged from 0.42 to 0.68 among the nine stations. The regression coefficients (k ) ranged from −0.17 to −0.08, which suggests that the winter temperatures decreased by 0.8–1.7 °C if the number of snow-cover days increased by 10 d.

Table 1. Regressions between winter temperatures and snow-cover days in the middle and lower reaches of the Yangtze River (1961–2010).
Station k b r R2 Significance
Shanghai −0.170 0.974 −0.424 0.180 P  = 0.002
Nanjing −0.078 1.008 −0.466 0.217 P  = 0.001
Anqing −0.115 1.184 −0.648 0.420 P  < 0.001
Hefei −0.084 1.228 −0.626 0.392 P  < 0.001
Huoshan −0.086 1.341 −0.679 0.460 P  < 0.001
Wuhan −0.125 1.440 −0.645 0.416 P  < 0.001
Changde −0.133 1.331 −0.660 0.435 P  < 0.001
Changsha −0.120 1.216 −0.609 0.371 P  < 0.001
Jingdezhen −0.172 1.164 −0.606 0.367 P  < 0.001

Note: The linear regression is set as y  = kx  + b , where x is the number of snow-cover days, and y is the winter temperature anomaly (reference period 1961–1990). r is the correlation coefficient, and R2 is the explained variance, measuring the proportion of variation for which the regression compensates.

Hence, we were able to reconstruct the winter temperatures for each station by using the regression equations listed in Table 1 . For example, the following gazette entry was used: “In the 48th year of Emperor Wanli (1620), it snowed heavily in winter at Huanggang county, with snow covering on the ground for totally 48 d from the eleventh Chinese lunar month to the next spring. Few people stayed outside and birds were frozen to death”4 . Huanggang is very close to Wuhan. Therefore, on the basis of the regression equation between winter temperature and snow-cover days in Wuhan (Table 1 ), the winter temperature of Wuhan was estimated to be 4.6 °C lower than the 1961–1990 mean. Table 2 shows the reconstructed winter 1620 temperature anomalies recorded by the nine stations. The winter of 1620 was extremely cold, with temperature anomalies ranging from −6 °C to −4 °C with respect to the 1961–1990 mean. The maximum anomaly value of −5.7 °C occurred in Jingdezhen, Jiangxi province, followed by Changde, Wuhan, Huoshan, Hefei, and Shanghai with winter temperature anomalies ranging between −5 °C and −4 °C. In comparison, Nanjing, Anqing, and Changsha showed smaller anomalies ranging from −4 °C to −3 °C. The negative anomalies during the historical periods were more severe than the lowest value during the instrumental observation period.

Table 2. Reconstructed temperatures of winter 1620 in the middle and lower reaches of the Yangtze River.
Station Snow-cover days (d) Temperature anomaly (°C, α  = 0.05) Number of records Minimum temperature in 1961–2010 (°C)
Shanghai 30 −4.1 ± 1.0 1 −2.9
Nanjing 60 −3.7 ± 1.0 4 −2.2
Anqing 45 −4.0 ± 1.5 14 −1.8
Hefei 70 −4.7 ± 1.4 8 −2.4
Huoshan 70 −4.7 ± 1.5 3 −1.9
Wuhan 48 −4.6 ± 1.6 14 −1.9
Changde 45 −4.7 ± 1.4 8 −2.2
Changsha 40 −3.6 ± 1.4 3 −2.0
Jingdezhen 40 −5.7 ± 1.5 3 −2.1

3.3. Regional mean winter temperature reconstruction

The regional mean winter temperature correlated strongly with the individual site-based temperatures, and the correlation coefficients were more than 0.9, corresponding to the explained variance of more than 80% (P  < 0.001). Thus, it was reasonable to estimate the regional mean temperature by using site-based temperatures. Through stepwise regression, only four representative sites were retained. As shown by Equation (1) , winter temperatures at these four sites explained 99% of the variance of the regional mean winter temperature.

( 1)

Because the winter temperature anomalies in Shanghai, Changsha, Jingdezhen, and Hefei were −4.1 °C, −3.6 °C, −5.7 °C, and −4.7 °C, respectively, the regional mean winter temperature anomaly in 1620 was estimated to be 4.4 °C lower than the 1961–1990 mean. In the modern instrumental period from 1951 to 2010, the coldest winter, with a temperature anomaly of −1.9 °C, was in 1967. Obviously, the regional mean winter temperature in 1620 was significantly lower than the coldest value in the modern instrumental period.

4. Conclusions and discussion

In this study, 54 records of snow-cover days from historical documents were collected. By using these records as well as modern meteorological observations, the spatial characteristics of snow-cover days and cold temperatures in the winter of 1620 were analyzed over the MLRYR. The main conclusions are summarized in the following points:

  • In the winter of 1620, the regional mean number of snow-cover days was 50 d in the MLRYR, ranging from 30 d in Shanghai to 100 d in Jingzhou. The number of snow-cover days was approximately 70 d in Hefei, Huoshan, Nanjing, and Chaohu and 40–60 d in Anqing, Wuhan, Changde, Changsha, and Jingdezhen. The lowest number of snow-cover days, 30 d, was reconstructed in Shanghai and southern Jiangsu province.
  • The winter of 1620 was extremely cold, and the regional mean winter temperature was approximately 4.4 °C lower than the 1961–1990 mean. The maximum anomaly of −5.7 °C occurred in Jingdezhen, and the minimum anomaly of −3.6 °C was found in Changsha. The cold temperatures in the winter of 1620 were significantly more severe than those recorded in any other year of the modern instrumental period (1951–2010).

It should be noted that the number of snow-cover days at some stations was estimated by using the starting and ending dates of snow-cover days rather than the direct records of snow-cover days. It is possible that several snow-free days occurred during the study period. Therefore, it is valuable to assess the uncertainties caused by this data processing. We used daily snow depth data over the MLRYR to select years with more than 10 snow-cover days. The proportion of snow-cover days in the snow-cover duration (days from the beginning to the end of snow cover) was then calculated after removing the outliers. It was determined that the snow-cover days accounted for 65.2%, 79.4%, and 93.3% of snow-cover duration in the years with more than 10 d, 20 d, and 25 d of snow cover, respectively. Given that the snow-cover days in most stations reached up to 40 d in the MYRYR in 1620, there would be very few snow-free days during the snow-cover duration. Therefore, the approach that uses the beginning and ending dates of snow cover to calculate the snow-cover days caused limited uncertainties in the results.

In addition, because the duration for recording historical snow-cover descriptions was from October 25, 1620, to March 12, 1621, the period included the winter (December to February) generally used in instrumental observation periods in addition to days prior to and following the winter. Thus, the uncertainties of temperature anomaly estimations may also be induced from differences in the definition of winter in historical times and instrumental observation period, which was used to establish the regression equation for reconstructing historical temperature anomalies. However, in 87% of the snow-cover records in 1620, events occurred in December 1620 through February 1621, which is the same as the winter of the instrumental observation period. Therefore, the uncertainties resulted from such definition differences should be acceptable.

Acknowledgements

Deepest gratitude goes to the anonymous reviewers for constructive comments and suggestions that helped to improve the quality of this paper. This research was supported by grants (to IGSNRR) from the Chinese Academy of Sciences (XDA05080100 ), the Ministry of Science and Technology of the Peoples Republic of China (2010CB950101 ), the Basic Research Project of the Ministry of Science and Technology (2011FY120300 ), and the National Natural Science Foundation of China (41271124 ).

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Notes

1. Quoted from Gazettes of Changde Prefecture, vol. 17.

2. Quoted from Gazettes of Chao County, vol. 4.

3. Quoted from Gazettes of Shucheng County, vol. 29.

4. Quoted from Gazettes of Huanggang County, vol. 19.

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