S. Keevallik and K. Vint: Temperature extremes and detection of heat and cold waves 473 Proceedings of the Estonian Academy of Sciences, 2015, 64, 4, 473–479 doi: 10.3176/proc.2015.4.02 Available online at www.eap.ee/proceedings Temperature extremes and detection of heat and cold waves at three sites in Estonia Sirje Keevallika* and Kairi Vintb a Marine Systems Institute at Tallinn University of Technology, Akadeemia tee 15a, 12618 Tallinn, Estonia b Estonian Environment Agency, Mustamäe tee 33, 10616 Tallinn, Estonia Received 7 January 2015, revised 7 May 2015, accepted 21 September 2015, available online 26 November 2015 Abstract. Daily maximum and minimum temperatures are analysed from long time series for three Estonian sites: Tallinn on the southern coast of the Gulf of Finland (1920–2013), Tartu in inland Estonia (1894–2013), and Pärnu on the northern coast of the Gulf of Riga (1878–2013). The probabilities of extreme temperatures (defined in the meteorological practice in Estonia as lower than – 30 °C in winter and higher than 30 °C in summer) and their successions (incl. cold and heat waves) are evaluated for the three sites. It is suggested that the thresholds based on upper or lower percentiles of the distributions of daily maxima/minima recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) are more suitable for the detection of cold and heat waves in Estonian climatic conditions than the current practice. Key words: temperature extremes, heat wave, cold wave, probability of occurrence, Estonia. 1. INTRODUCTION justify the determination of heat and cold waves in * Estonian climatic conditions. The climate of a certain region is characterized not only As daily temperature maxima in summer are by the average (long-term, annual, seasonal, monthly, connected with heat waves and minima in winter with daily) values of the meteorological parameters, but also cold waves, analysis of temperature extremes permits by their variability. To describe the variability of air one to estimate the occurrence probability of various temperature, several characteristics can be used on long- severe events. In this context, extreme values of term, annual, seasonal, monthly, or daily basis: standard meteorological parameters need precise definitions (e.g. deviation, average maxima and minima, absolute Wong et al., 2011). maxima and minima. The main objective of the present Analysis of daily temperature extremes is usually paper is to describe the statistics of the maximum and directed towards detection of climate change. For this minimum temperatures at three sites characteristic of purpose, different indices have been constructed. The different regions of Estonia. simplest indices – monthly mean maximum and In many aspects, however, various types of severe minimum temperatures – are used by Jaagus et al. events such as temperature extremes and heat and cold (2014) together with the diurnal temperature range to waves are of great importance. There are no universal calculate trends in the Baltic States during 1951–2010. definitions of heat wave and cold wave as both depend They chose that time interval to involve as many on the background climatic conditions of the region stations with homogeneous time series as possible. under consideration (Robinson, 2001; Meehl and Homogeneity of the time series was granted by means Tebaldi, 2004). One purpose of our paper is to better of using similar measurement devices and routines and leaving out stations that had been relocated. Statistically significant increasing trends in maximum and minimum temperatures (which were coherent with changes in the * Corresponding author, [email protected] 474 Proceedings of the Estonian Academy of Sciences, 2015, 64, 4, 473–479 mean temperature) were detected for the annual averages and several months. No trend in the annual values of the diurnal temperature range was detected. Regarding temperature, the following indicators are suggested for mid-latitude climates (Frich et al., 2002): total number of frost days during a year (minimum below 0 °C); intra-annual extreme temperature range; growing season length; heat wave duration index: maximum period for the year of at least 5 consecutive days with T at least max 5 °C above the 1961–1990 daily Tmax normal; Fig. 1. Location of the meteorological stations under con- per cent of time Tmin exceeding 90th percentile of sideration. daily climatological distribution of minimum tem- perature – a measure of the number of warm nights. 2012), they are not crucial from the viewpoint of our These indicators have been used to clarify whether study. Several changes (relocation, new instruments, the frequency and/or severity of temperature extremes different observation times) have indeed affected the changed during the second half of the 20th century on a data sets at these stations. For instance, the relocation of global scale. The changes demonstrated considerable Tallinn meteorological station in 1980 and Pärnu coherence showing an increase in the number of warm meteorological station in 1990 caused an increase in the summer nights, a decrease in the number of frost days, daily averages in the data sets from 1931–2010 and and a decrease in the intra-annual extreme temperature 1901–2010, respectively. Such an increase was also range. The most popular indices of extremes to detect detected for Tartu in 1966 (in the context of the data set climate change (e.g. Wong et al., 2011; García-Cueto for 1881–2010) when the observation routine of 4 times et al., 2014) are given by the Expert Team on Climate a day was replaced by 8 times a day (Keevallik and Change Detection and Indices (ETCCDI) (Klein Tank Vint, 2012). et al., 2009). These indices can be used also for The absolute maximum temperature in Estonia establishing suitable thresholds for heat and cold waves (35.6 °C) was recorded on 11 August 1992 and the at any site (Unkaševič and Tošič, 2015) and are used in absolute minimum of – 43.5 °C on 17 January 1940. this study. According to the homepage of the Estonian Weather Service, the situation is labelled as (very) dangerous when the daily maximal temperature is above 30 °C 2. MATERIAL AND METHODS during at least (three) two consecutive days or the daily minimal temperature is below – 30 °C during at least For the calculation of the probabilities of rare events the (three) two consecutive days. The choice of these time series must be as long as possible (Van den Brink thresholds is not documented, but it seems to be related et al., 2005). As homogeneity is not of vital importance to the comfort temperature of the human beings in this in this context, we use data from three Estonian climate zone. We use the same thresholds below in this meteorological stations with the longest data sets: study to identify the extreme events. In Estonia, daily Tallinn on the southern coast of the Gulf of Finland, maxima above 30 °C may only occur from May to Tartu representing inland Estonia, and Pärnu on the September, and daily minima below – 30 °C from northern coast of the Gulf of Riga (Fig. 1). December to February. Notice that as the temperatures The instrumental observations in Tallinn date back are given in the data files with the accuracy to the first to the end of the 18th century (Tarand, 2003), but the decimal place, actually the thresholds 29.5 ºC were time series of daily minimum and maximum tem- used. peratures starts in January 1920. Recordings of daily A simple estimate of the return period of an extreme minimum and maximum temperatures in Tartu are event within a relatively long time series is calculated as available since 1 January 1894 and in Pärnu from the ratio of the number of years in the time series to the 1 January 1878. number of recorded occurrences during this time period. For different reasons, there are gaps in the time A more complicated method to calculate values for series of daily extreme temperatures. These gaps are large return periods from short records is described by shown in Table 1 where WWI and WWII denote the Van den Brink and Können (2011). The inverse value of hectic times of the two world wars. the return period is the probability of the occurrence p Even though the employed time series contain of the event in any one year. The probability P of the several (minor) inhomogeneities (Keevallik and Vint, S. Keevallik and K. Vint: Temperature extremes and detection of heat and cold waves 475 Table 1. Missing values in the series of daily maximum and minimum temperatures Station Missing maxima Missing minima Tallinn 1–2 Aug 1938 1 Jun–30 Sep 1941 (WWII) 1 Jun–30 Sep 1941 (WWII) 1–30 Sep 1944 (WWII) 1–30 Sep 1944 (WWII) 17 Sep 1945 1–31 Mar 1946, 11 Apr 1946, 18 Apr 1946, 30 Apr 1946, 17 May 1 Feb–31 Jul 1946 1946, 21 May 1946, 24–26 May 1946, 28–31 Jul 1946 1 Sep 2003 1 Sep 2003 26–27 Oct 2005 26 Oct 2005, 27 Oct 2005 Tartu 1 Mar 1894, 15 Mar 1894, 28 Jan 1895, 5 Feb 1895 1 Aug–31 Sep 1944 (WWII) 8–10 Feb 1895, 13–14 Feb 1895, 17 Feb 1895, 23 Feb 1895 1–31 May 1951 1 Aug–30 Sep 1944 (WWII) 11–29 Feb 1952 1–31 May 1951 1 Sep 2003, 11 Oct 2003, 17 Oct 2003 1 Sep 2003, 11 Oct 2003, 17 Oct 2003 Pärnu 1 Jan 1878–31 Dec 1914 1–19 Aug 1878 20 Aug–31 Oct 1915 (WWI) 31 Aug 1880 1 Jun 1917–31 Dec 1919 (WWI) 31 May 1882 5–10 Feb 1924 1 Jan 1883–31 Dec 1886 1–31 Jan 1942 (WWII) 1 Jul–23 Aug 1893 1 Sep–31 Dec 1944 (WWII) 1–30 Jun 1903 1 Mar–31 May 1945 (WWII) 20 Aug–31 Oct 1915 (WWI) 1–31 Jul 1945 1 Jun 1917–31 Dec 1919 (WWI) 1 Mar–31 May 1945 (WWII) 1–31 Jul 1945 occurrence of this event k times in n successive years is evidently more continental than that of Tallinn.