1. Introduction

Climate change affects all parts of the Earth. However, researchers are paying particular attention to the investigation of climate change in Antarctica, which is largely covered by glaciers. Antarctic ice sheets directly affect the global climate and ocean circulation (Kuhn et al. 2010), and an increase in the area and mass of the glacier ice contributes to lowering sea levels, intensifies atmospheric circulation, and increases planetary albedo (Lurcock, Florindo 2017; Silva et al. 2020). One of the most profound consequences of climate warming is the degradation of glacial sheets, including on the Antarctic peninsula (Vaughan, Doake 1996; Turner et al. 2002; Grischenko et al. 2005; Kuhn et al. 2010; Wouters et al. 2015; Silva et al. 2020; Diener et al. 2021). Warming on the Antarctic Peninsula has also impacts on the terrestrial flora, seasonal snow cover, lake ecology, penguin distribution, ice-shelf distribution, glacier thickness, and seaice duration (Vaughan et al. 2003; Turner et al. 2014, 2016).

The rate and tendencies of warming at the Antarctic Peninsula are still subject of debate (Vaughan et al. 2003; Martazinova et al. 2010; Stastna 2010; Qu et al. 2011; Schneider et al. 2012; Bromwich et al. 2013, Ding, Steig 2013; Franzke 2013; Tymofeyev 2013; Turner et al. 2016; Gonzalez, Fortuny 2018; Sato et al. 2021), and the peninsula presents physical and geographical features that distinguish it from the rest of Antarctica (King, Tuner 1997; Vaughan et al. 2003). The rugged alpine topography, a maritime climate along the west and central costa, and a continental climate along the east coast result in higher temperatures at the west coast compared to areas with similar latitudes and elevations on the east coast (Morris, Vaughan 1994). Several studies have reported stable trends in surface air temperature increase on the Antarctic Peninsula. For example, Vaughan et al. (2003) demonstrated that the Antarctic Peninsula has warmed at 3.7 ± 1.6°C century-1, which is several times the rate of global warming and different to most of the other station records from the Antarctic continent. However, Bromwich et al. (2013) reported a linear increase in annual temperature between 1958 and 2010 by 2.4 ± 1.2°C, establishing central West Antarctica as one of the fastest-warming regions globally. Stastna (2010) reported about three distinct regions with different trends of warming on the Antarctic Peninsula. Franzke (2013) and Turner et al. (2014, 2016, 2020, 2021) reported extremely high rates of surface air temperature changes on the Antarctic Peninsula. Moreover, according to Turner et al. (2016), Gonzalez and Fortuny (2018), and Sato et al. (2021), on the Antarctic Peninsula, the surface air temperature varies significantly, which has been especially evident in recent decades. Thus, current research is aimed at understanding such variable trends and the factors that determine them (Clem et al. 2019; Bozkurt et al. 2020; Turner et al. 2020; Bozkurt et al. 2021).

This study examines the extreme air temperatures in the Ukrainian Antarctic Akademik Vernadsky station, located on the western side of the Antarctic Peninsula. Historical observations of this station have been used in various studies that investigated the trends of average and extreme temperatures. For example, Turner et al. (2005) reported that the Antarctic Peninsula has experienced a major warming over the last 50 years and that on the Akademik Vernadsky station, the surface air temperature increased at a rate of 0.56°C decade−1 over the year and 1.09°C decade−1 during the winter. Martazinova et al. (2010) reported that according to the data of the Akademik Vernadsky station, the increase in the mean annual air temperature exceeded 2℃ for the observation period 1947-2007. According to Franzke (2013), the Akademik Vernadsky station has been experiencing a significant warming trend of about 0.6°C decade−1 over the last few decades. Tymofeyev (2013) reported the greatest warming, with a linear trend coefficient of 0.53°C/10 years, at the Akademik Vernadsky station. At the same time, modern warming is separated by a period of relative cooling in the beginning and middle 1970s. Turner et al. (2020) reported that 13 of the 17 stations have experienced a positive trend in annual mean temperature over the full length of their record (until 2018), with the largest being observed at the Akademik Vernadsky station (0.46 ± 0.15°C·decade−1). One of the latest in-depth studies analyzing the formation conditions and trends of maximum and minimum air temperature in Antarctica is the paper Turner et al. (2021). However, both in this paper and in others, the climate extremes indices were not used. These indices have been developed by the expert team of the World Meteorological Organization (Zhang, Yang 2004; Tashebo et al. 2021) and contribute to a better understanding and analysis of the trends of climate change, particularly the temperature and rainfall variables (Brown et al. 2010; Costa et al. 2020; Zhou et al. 2020; Tashebo et al. 2021). In this context, the objective of this paper was to investigate the extreme temperature indices change at the Ukrainian Antarctic Akademik Vernadsky station for the period 1951-2020.

2. Study area, data, and methodology

2.1. Study area

Until 1996, the Ukrainian Antarctic Akademik Vernadsky station was a British Faraday station. The station is located on Galindez Island, Argentine Islands Archipelago, near the western coast of the Antarctic Peninsula (Fig. 1) in the middle part of the peninsula (65.25°S, 64.27°W). The island is dominated by large-scale circumpolar circulation in the atmosphere and ocean (King, Tuner 1997).

Fig. 1.

Location of the Ukrainian Antarctik Akademik Vernadsky station (background graphic from Klok, Kornus 2021).


The climate of Galindez Island is marine subarctic (King, Tuner 1997; Franzke 2013). Wind and temperatures conditions are mainly formed by the mountain system of the Antarctic Peninsula (Turner et al. 2002; King, Comiso 2003). The average plateau height is 2,000 m above sea level and the height of individual peaks reaches 2,800 m (King, Tuner 1997). This system forms the foehn wind, and the air cools over the ice cover and forms local winds. The area over the Pacific Ocean is dominated by the low-pressure systems that move eastwards towards the Antarctic Peninsula (King, Comiso 2003), causing frequent precipitation and strong winds, with frequent snowfall and snowstorms. The anticyclonic type of weather is less common. In this case, calm frosty weather is established for a long period, sometimes with fog and frost (King, Tuner 1997; King, Comiso 2003).

For the period 1951-2020, the warmest month of the year was January, with a multi-annual mean monthly temperature of +0.8°C, and the coldest month was August, with a multi-annual mean monthly temperature of –8.7°C. The highest mean monthly air temperature was +2.4°C (February) and the lowest –20.1°C (July) (Table 1).

Table 1.

Multiannual mean monthly air temperature (°C) at the Akademik Vernadsky station for the period of 1951-2020.

July–8.4–2.61989, 1998–20.11959

2.2. Data

In this study, daily air temperature data for eight terms (0, 3, 6, 9, 12, 15, 18, 21 UTC) of the Akademik Vernadsky station, provided by the National Antarctic Scientific Center of Ukraine (NASC), were used; the period was 1951-2020. Data before 1996 were kindly provided by the Meteorological Information Database of the British Antarctic Survey.

When carrying out investigations, the quality of the initial data is extremely important. During the existence of the Akademik Vernadsky station, various measuring instruments and complexes were used to measure surface air temperature. Thus, regular meteorological observations of surface air temperature with the help of mercury thermometers in a psychrometric booth were started in 1947. In 1985, the Synoptic and Climatological Automatic Weather Station (SCAWS) was installed in a psychrometric booth, which was replaced by the Modular Automatic Weather Station (MAWS) in 1992. In March 2011, the MAWS system was replaced by the Ukrainian-made Mobile Meteorological Complex “Troposphere” (Mobile AWS “Troposphere”). This complex contains the temperature sensor in its own radiation protection, with artificial ventilation. In April 2020, the Mobile AWS “Troposphere” was transferred to reserve status. The main data source currently is the automatic weather station Vaisala AWS-310, which was installed 1 year earlier. This station contains the temperature sensor in its own radiation protection, with passive ventilation.

In the period from 1947 to 1950, the measurement of surface air temperature took place at different times. However, the observation data were incomplete, and data were therefore checked for missing values, gross errors, and outliers that exceeded four standard deviations from the mean for each day. The missing values were recovered by multiple regression depending on air temperature before and after the missing value; such regression dependences were established for each month of the year. The amount of missing data was insignificant (0.1% of the total data), and there were no gross errors or significant outliers in our dataset. After quality control procedures, daily minimum and maximum air temperature were calculated.

2.3. Methodology

A core set of 27 indices has been developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) to standardize the definitions and analysis of extremes (Peterson et al. 2001; Klein Tank et al. 2006). Of these 27 indices, only 16 refer to air temperature, and the remaining ones refer to precipitation. Based on the analysis of the temperature regime at the Akademik Vernadsky station during the recent 70 years, 11 extreme temperature indices were chosen (Table 2) as they are most suitable to study the temporal characteristics of extreme air temperature events. The annual indices and their trend equations were obtained using the RStudio Software (version 1.4.1717) (R Core Team 2017). Percentile indices were calculated using the standard reference period of 1981-2010 to facilitate comparison of the results with those of other studies using the same reference period. The Mann-Kendall non-parametric trend test was employed to assess the statistical significance of the indices series (Mann 1945; Kendall 1975), using the RStudio Software. The statistical significance of trends was estimated depending on the τ value:

τ=PQnn1/2' 1

where P is the number of concordant pairs, Q is the number of discordant pairs, and n is the total amount of data.

Table 2.

Definition of extreme air temperature indices.

FD0Frost daysAnnual count when TN (daily minimum) <0°CDays
ID0Ice daysAnnual count when TX (daily maximum) <0°CDays
TXxHighest TmaxHighest annual value of daily maximum temperature°C
TNxHighest TminHighest annual value of daily minimum temperature°C
TXnLowest TmaxLowest annual value of daily maximum temperature°C
TNnLowest TminLowest annual value of daily minimum temperature°C
TN10pCool nightsPercentage of days when TN <10th percentile%
TX10pCool daysPercentage of days when TX <10th percentile%
TN90pWarm nightsPercentage of days when TN >90th percentile%
TX90pWarm daysPercentage of days when TX >90th percentile%
DTRDiurnal tempera-ture rangeAverage annual difference between TX and TN°C

3. Results

3.1. Cold extremes indices (ID0, FD0, TXn, TNn and TX10p, TN10p)

The number of frost days during the period of 1951-2020 varied from 149 to 254. The maximum number of frost days was observed in 1967 and 1969 (349 days) and the minimum number in 2001 (271 days). The ID0 and FD0 indices significantly decreased at –0.427 and –0.452 day year-1, respectively (Table 3, Fig. 2).

Table 3.

Annual trends of the extreme indices of daily air temperature for the Academik Vernadsky station, 1951– 2020.

Temperature indicesTrend equationR2τp-valueStatistical significance of trend
FD0y = –0.46x + 11640.18–0.2810.0006yes
ID0y = –0.45x + 10960.15–0.2490.0024yes
TXxy = 0.016x –
TNxy = 0.007x –
TXny = 0.16x –347.40.240.338<0.0001yes
TNny = 0.20x –425.20.320.369<0.0001yes
TN10py = –0.46x + 941.40.44–0.420<0.0001yes
TX10py = –0.28x + 578.60.34–0.407<0.0001yes
TN90py = 0.07x –
TX90py = 0.06x –
DTRy = –0.029x + 62.30.47–0.469<0.0001yes
Fig. 2.

Annual count when TX (daily maximum) < 0°C (a) and TN (daily minimum) < 0°C (b).


The indices TXn and TNn showed an upward trend. Annually, TXn and TNn increased by 0.164°C and 0.201°C, respectively (Table 3, Fig. 3). The lowest air temperature was observed in the winter period of 1958 (–42.4°C). In the winter of 1977, the air temperature also dropped below –40°C (–40.2°C). The warmest winter was that of 1989, when the temperature did not fall below –10.2°C at night and below –7.2°C during the day.

Fig. 3.

Lowest annual values of daily maximum (a) and minimum (b) air temperatures.


Annually, the TX10p and TN0p indices showed a significant decrease in cool days and cool nights by 0.46% and 0.28%, respectively (Table 3, Fig. 4). In 1959, the largest number of cold days and nights, namely 43.8% and 58.3%, respectively, was observed. In 1989, the number of cool days and nights was lowest.

Fig. 4.

Percentage of days when TX < 10th (a) and TN < 10th (b) percentile of 1981-2010.


3.2. Hot extremes indices (TXx, TNx, TX90p, and TN90p)

The TXx and TNx indices showed no statistically significant tendencies for the period of 1951-2020 (Table 3, Fig. 5). Annually, the TX90p and TN90p indices showed a small increase in warm days and nights by 0.056% and 0.067%, respectively (Table 3, Fig. 6). The highest air temperature was observed in the summer of 1985, with +10.9°C during the day and +5.1°C at night. In the summer of 1973, the daytime temperature did not exceed +4.8°C, and in the summer of 1978, the night air temperature dropped to +1.3°C. The largest number of warm days was observed in 2018 and that of warm nights in 1998, with 15.3% and 19.3%, respectively. In 2002, the number of warm days was lowest (4.2%), whereas the number of warm nights was lowest in 1958 and 1959 (2.7%).

Fig. 5.

Highest annual values of daily maximum (a) and minimum (b) air temperatures.

Fig. 6.

Percentages of days when TX > 90th (a) and TN > 90th (b) percentile of 1981-2010.


DTR index is an average annual difference between daily maximum and minimum air temperature. DTR index shows a small negative trend (–0.026°C/year) over the last 70 years (Table 3, Fig. 7). This trend is due to larger increases in average annual minimum air temperatures (0.06°C/year) than average annual maximum air temperatures (0.03°C/year) over the same period.

Fig. 7.

Average annual difference between TX and TN (a), average annual minimum (b) and maximum (c) air temperatures.


4. Discussion

Analysis of 11 extreme air temperature indices at the Ukrainian Antarctic Akademik Vernadsky station showed the indicate unequivocal signs of heating. These results are in good agreement with those of studies using other methodological approaches (Turner et al. 2005, 2014, 2020, 2021; Franzke 2013; Gonzalez, Fortuny 2018; Bozkurt et al. 2021). Turner et al. (2005) and Franzke (2010) reported the positive statistically significant trend of the mean annual air temperature at the Akademik Vernadsky station, whereas Franzke (2013) reported that for the period of February 1947 to January 2011, the Akademik Vernadsky station experienced a significant warming trend and the magnitude of extremely cold temperatures was reduced; however, the annual maximum temperature did not increase. Some authors, such as Turner et al. (2016) and Gonzalez and Fortuny (2018), reported the decrease tendencies of the annual mean temperature on the Antarctic Peninsula in recent decades, including at the Vernadsky station. This was explained by the natural internal variability of the regional atmospheric circulation. Investigation of surface air temperature trends using the latest observational data demonstrated the presence of a persistent warming trend (Turner et al. 2020, 2021; Bozkurt et al. 2021), which is also confirmed by our research. Turner et al. (2021) researched the variability and change in the frequency of extreme daily mean temperatures in Antarctica; for the Akademik Vernadsky station, the authors observed an increase in the percentage of extreme warm days and a decrease in cold days. On the Antarctic Peninsula, the warming trend will continue in the future. According to the global climate models, forecasts suggest that the Antarctic Peninsula temperatures will increase more significantly than in other parts of Antarctica and in the world (Chyhareva et al. 2019; Stiegert et al. 2019). Chyhareva et al. (2019) reported that for the Antarctic Peninsula region for RCP4.5 and RCP8.5 scenarios on average forecast to reduce the cold period; for the Akademik Vernadsky station, this process will be three times more intensive, indicating that the region is more vulnerable to climate change. Stiegert et al. (2019) reported that with a temperature increase by 1.5°C, irreversible and dramatic changes to glacial, terrestrial, ocean, and biological systems on the Antarctic Peninsula can be expected.

5. Conclusions

Our study presents an evaluation of climate extremes indices by focusing on the analysis of daily minimum and maximum air temperatures at the Ukrainian Antarctic Akademik Vernadsky station. The results show a trend of warming for the period of 1951-2020. This is indicated by the calculated extreme air temperature indices, which showed statistically significant tendencies, namely during the recent 70 years:

  • – indices of ice and frost days, cool nights and days, and the diurnal temperature range significantly decreased;

  • – indices of warm nights and days, lowest annual values of daily maximum and minimum air temperature significantly increased.

In this study, the application of the climate indices made it possible to obtain more complete information about the tendencies of the extreme air temperature at the Ukrainian Antarctic Akademik Vernadsky station. Such results are highly important in the context of understanding the temporal variability of annual and seasonal air temperature on the Antarctic Peninsula. In general, the results of this investigation support previous findings. Further research should focus on the application of the climate indices for the investigation of the extreme temperature changes at the Akademik Vernadsky station during particular months or seasons.