Volume 42, Issue 9 p. 4808-4829
RESEARCH ARTICLE
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Temperature extremes and circulation types in the Czech Republic, 1961–2020

Pavel Zahradníček,

Corresponding Author

Pavel Zahradníček

Global Change Research Institute, Czech Academy of Sciences, Brno, the Czech Republic

Czech Hydrometeorological Institute, Brno Regional Office, Brno, the Czech Republic

Correspondence

Pavel Zahradníček, Global Change Research Institute, Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, the Czech Republic.

Email: zahradnicek.p@czechglobe.cz

Contribution: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing - original draft, Writing - review & editing

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Rudolf Brázdil,

Rudolf Brázdil

Global Change Research Institute, Czech Academy of Sciences, Brno, the Czech Republic

Institute of Geography, Masaryk University, Brno, the Czech Republic

Contribution: Conceptualization, Methodology, Supervision, Writing - original draft, Writing - review & editing

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Jan Řehoř,

Jan Řehoř

Global Change Research Institute, Czech Academy of Sciences, Brno, the Czech Republic

Institute of Geography, Masaryk University, Brno, the Czech Republic

Contribution: Data curation, Formal analysis, Methodology, Writing - review & editing

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Ondřej Lhotka,

Ondřej Lhotka

Global Change Research Institute, Czech Academy of Sciences, Brno, the Czech Republic

Institute of Atmospheric Physics, Czech Academy of Sciences, Praha, the Czech Republic

Contribution: Data curation, Formal analysis, Methodology, Resources, Writing - review & editing

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Petr Dobrovolný,

Petr Dobrovolný

Global Change Research Institute, Czech Academy of Sciences, Brno, the Czech Republic

Institute of Geography, Masaryk University, Brno, the Czech Republic

Contribution: Data curation, Formal analysis, Methodology, Resources, Validation, Writing - review & editing

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Petr Štěpánek,

Petr Štěpánek

Global Change Research Institute, Czech Academy of Sciences, Brno, the Czech Republic

Czech Hydrometeorological Institute, Brno Regional Office, Brno, the Czech Republic

Contribution: Methodology, Software

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Miroslav Trnka,

Miroslav Trnka

Global Change Research Institute, Czech Academy of Sciences, Brno, the Czech Republic

Department of Agrosystems and Bioclimatology, Mendel University in Brno, Brno, the Czech Republic

Contribution: Conceptualization, Methodology, Supervision

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First published: 20 December 2021
Citations: 2

Funding information: Czech Science Foundation, Grant/Award Number: 18-15958S; Masaryk University, Grant/Award Number: MUNI/A/1570/2020; Ministry of Education, Youth and Sports of the Czech Republic, Grant/Award Number: CZ.02.1.01/0.0/0.0/16_019/0000797; National Agency for Agricultural Research, Grant/Award Number: QK1910338

Abstract

This paper presents the characteristics of extreme temperatures in the Czech Republic, as calculated from homogenized series of daily maximum (TMAX) and daily minimum (TMIN) temperatures recorded by 133 climatological stations throughout the territory in the 1961–2020 period. In general, statistically significant increasing linear trends were recognized in series of absolute TMAX, absolute TMIN, numbers of summer days, tropical days, days with tropical nights, heat-wave, and warm-anomaly days. Significant decreasing linear trends appeared in series of numbers of frost days, ice days, cold-wave, and cold-anomaly days. Objective classification of circulation types demonstrated the importance of anticyclonic types (especially those with warm airflow from the southern quadrant) and an unclassified type in the development of summer hot extremes, while winter cold extremes were linked to cold (north-)easterly advection. Significant changes in the frequency of certain circulation types emerged, as well as an increasing number of anticyclonic types conducive to hot extremes, a trend that contributed to their more frequent occurrence in recent decades. Existing trends in temperatures were complemented by spatiotemporal analysis of extreme temperatures, the characteristics of extremes and the circulation types in the two “normal” periods of 1961–1990 and 1991–2020. These exhibited significant differences in means and variances. The results obtained are also discussed in a broader context.

1 INTRODUCTION

Temperature changes, expressed at mean values of daily, monthly, seasonal, and annual series, constitute the most immediately visible demonstration of recent climate change arising out of anthropogenic forcing and appearing at global, continental and regional scales (e.g., Hansen et al., 2010; Marotzke and Forster, 2015; IPCC, 2018; Krauskopf and Huth, 2020). However, no less important than mean temperatures are the changes that have taken place in their extremes. These may be described in terms of a range of characteristics, such as minimum and maximum temperatures (e.g., Brázdil et al., 1996; Domonkos et al., 2003; Jaagus et al., 2014; Scherrer and Begert, 2019; Sulikowska and Wypych, 2020), cold waves and heat waves (e.g., Della-Marta et al., 2007; García-Herrera et al., 2010; Kyselý, 2010; Lhotka and Kyselý, 2015; 2019; Perkins, 2015; Russo et al., 2015; Kim et al., 2018; Fenner et al., 2019; Tomczyk et al., 2019), and characteristic days (e.g., Ramos et al., 2011; Burić et al., 2014; Lapin et al., 2016; Scorzini et al., 2018; Zhao et al., 2021). Temperature extremes, both positive and negative, may contribute to significant increases in human mortality, as documented by numerous papers (e.g., Analitis et al., 2008; Výberči et al., 2015; Graczyk et al., 2019).

With reference to the Czech Republic, several papers have centred on analysis of mean maximum and mean minimum temperatures, together with the diurnal temperature range (e.g., Brázdil et al., 1995; 2009; Pokorná and Kučerová, 2018; Zahradníček et al., 2021). Considerable attention has also been devoted to heat waves and related mortalities (e.g., Kyselý and Kříž, 2008; Kyselý and Plavcová, 2012; Hanzlíková et al., 2015; Holtanová et al., 2015; Urban et al., 2017; Arsenović et al., 2019); cold waves, too, have attracted their share of investigation (Kyselý et al., 2009; Plavcová and Urban, 2020). Valeriánová et al. (2017) analysed extreme high maximum air temperature events and the synoptic scale of their circulation conditions in the 1961–2010 period, while Jůza (2017) investigated the occurrence of days with tropical nights in 2005–2015. Piskala and Huth (2020) concentrated upon day-to-day temperature changes with respect to the passing of atmospheric fronts.

The aim of the current article is to analyse extreme temperature characteristics in the entire Czech Republic (further CR), taking selected altitudinal intervals into account, in the 1961–2020 period, with particular respect to their spatiotemporal variability and their relationships to the objective classification of circulation types. Section 2 presents the temperature data utilized and the principles of the classification of circulation types. The methods deployed for the analysis of the characteristics of extreme temperatures are described in section 3. The results that appear in section 4 are presented separately for absolute maximum and minimum temperatures, characteristic days, heat waves and cold waves, warm and cold anomalies, day-to-day changes, two 30-year normal periods, and relationships to circulation types. Discussion of the results in the broader context is provided in section 5, followed by some concluding remarks.

2 DATA

2.1 Temperature data

The basic datasets used in this study consisted of the daily maximum (TMAX) and minimum (TMIN) temperatures measured at 133 climatological stations (Figure 1) run by the Czech Hydrometeorological Institute (CHMI), covering the territory of the CR during the 1961–2020 period. All of stations selected had kept at least 40 years of observations; these were extended and complemented by homogenization procedures to 60 years (Zahradníček et al., 2021).

Details are in the caption following the image
Geographical distribution of the climatological stations of the CHMI over the territory of the Czech Republic, arranged by altitude into four groups, I–IV. Places/stations mentioned in the text: 1 – Brno, 2 – Františkovy Lázně, 3 – Labská bouda, 4 – Plzeň, 5 – Prague-Kbely, 6 – Strážnice, 7 – Velké Meziříčí [Colour figure can be viewed at wileyonlinelibrary.com]
Since the CR features a range of altitudes (from 115 to 1,603 m asl), the 133 stations used were divided, in similar fashion to that employed by as in Zahradníček et al. (2021), into four altitudinal groups. Their orography was also taken into account (Demek, 1987):
  • Group I: 41 stations at altitudes of <300 m, largely consisting of lowlands and flat, hilly lands.
  • Group II: 68 stations between 301 and 600 m, consisting of flat and dissected hilly lands.
  • Group III: 21 stations between 601 and 900 m, consisting of flat and dissected highlands.
  • Group IV: 3 stations at altitudes at >900 m, consisting of flat and dissected mountain positions.

2.2 Classification of circulation types

The objective classification of circulation types used to analyse synoptic patterns contributing to temperature extremes was based on flow strengths, flow direction, and vorticity (Jenkinson and Collison, 1977; Plavcová and Kyselý, 2011). Circulation types were calculated at daily resolution, using sea-level pressure (SLP) at 16 points (Figure 2) with respect to the geographical centre of the CR, coordinates 49.74°N and 15.33°E. The SLP data from the NCEP/NCAR reanalysis (Kalnay et al., 1996) were employed for calculation of circulation indices for the 1961–2020 period. A SLP value for a given point was obtained through an inverse distance-weighted mean of the nine closest NCEP/NCAR grid points. The classification defines 27 circulation types, of which 9 are anticyclonic (A, AN, ANE, AE, ASE, AS, ASW, AW, ANW), 9 cyclonic (C, CN, CNE, CE, CSE, CS, CSW, CW, CNW), and 8 directional (N, NE, E, SE, S, SW, W, NW). Days remaining unclassified due to low flow strength (<3) and vorticity (<3) were designated type U (for more details of this classification and visualization of the individual types, see Řehoř et al., 2021).

Details are in the caption following the image
Position of the 16 SLP points as inputs for the calculation of circulation indices (the cross indicates the geographical centre of the Czech Republic) [Colour figure can be viewed at wileyonlinelibrary.com]

3 METHODS

All analyses in this contribution were based on homogenized series of daily TMAX and TMIN measurements taken in the 1961–2020 period by 133 climatological stations run by the CHMI. The quality control of daily data, break-point detection (monthly data), adjustment of the daily data by own DAP method (Distribute Adjustment by Percentile, developed from the “variable correction method” used for adjustment of regional climate models by Déqué, 2007) and filling gaps by interpolation methods from neighbour stations were applied by using ProClimDB and AnClim software (Štěpánek et al., 2011a; 2011b; 2013; Squintu et al., 2020). The homogenization procedure appears in detail in Zahradníček et al. (2021) as part of their investigation into the spatiotemporal variability of air temperatures in the CR in the 1961–2019 period.

In the current study, the characteristics of extreme temperatures were first established for each of the 133 individual stations and then averaged for the entire CR and for four altitudinal Groups I–IV (see section 2.1) to obtain mean areal series. The stations are distributed relatively evenly over the territory of the CR, so their localization has no significant influence on the calculation of such areal means for the entire CR. The following characteristics of extreme temperatures were investigated:
  1. Mean annual absolute temperature maximum (ATMAX) and absolute temperature minimum (ATMIN) and their annual ranges (Figure 4).
  2. The occurrence of absolute daily extremes of TMAX and TMIN separately for each of 365 days of the year for the entire 1961–2020 period, attributed to individual decades in this period (Figure 5).
  3. Mean annual number of tropical days (TMAX ≥30.0°C), summer days (TMAX ≥25.0°C), days with tropical nights (TMIN ≥20.0°C), frost days (TMIN <0.0°C), and ice days (TMAX <0.0°C), including the mean dates of their earliest (starts) and latest (ends) occurrences (Table 2 and Figures 6 and 7).
  4. Mean number of heat-wave (HW) days, mean maximum duration, and mean highest TMAX of HWs, including the percentage of stations with HWs in each year (Figure 8). HWs were defined as events in which days with TMAX ≥30.0°C (i.e., tropical days) occurred on least at three consecutive days.
  5. Mean number of cold-wave (CoW) days, mean maximum duration, and mean lowest TMIN of CoWs, including the percentage of stations with CoWs in each year (Figure 9). CoWs were defined as events lasting at least three consecutive days with TMAX <0.0°C (i.e., ice days).
  6. Mean annual number of days with warm or cold anomalies, selected with respect to a threshold of the 95th percentile for TMAX (warm anomaly) and the 5th percentile for TMIN (cold anomaly). For every day of the year, the 95th percentile of TMAX at a given station for the 1961–1990 reference period was calculated using a 15-day window for every day. A warm anomaly was defined as a case in which the 95th percentile threshold was exceeded by TMAX on at least at three consecutive days. The same approach was applied for calculation of cold anomalies, when TMIN for at least at three consecutive days lay below the 5th percentile of TMIN in 1961–1990 (Figure 10).
  7. Mean and mean maximum day-to-day (inter-diurnal) changes in TMAX and TMIN, calculated as the difference between two successive days.
Details are in the caption following the image
Mean relative frequencies (%) of circulation types in the summer (red) and winter (blue) half-years over the Czech Republic in the 1961–2020 period [Colour figure can be viewed at wileyonlinelibrary.com]
Details are in the caption following the image
Fluctuations in mean annual absolute maximum temperature (a), absolute minimum temperature (b) and their annual range (c), smoothed by 10-year Gaussian filter and supplemented by their linear trends for the Czech Republic during the 1961–2020 period [Colour figure can be viewed at wileyonlinelibrary.com]
TABLE 1. Mean relative annual frequencies (Freq. in %) of circulation types over the Czech Republic in the 1961–2020 period (U – unclassified type) and their linear trends (days/10 years)
Anticyclonic circulation types
Type All A AN ANE AE ASE AS ASW AW ANW
Freq. 43.4 10.0 3.6 3.2 2.7 2.3 2.7 5.1 8.0 5.8
Trend 6.6 2.2 0.6 0.0 0.1 −0.1 0.0 1.5 2.1 0.1
Cyclonic circulation types
Type All C CN CNE CE CSE CS CSW CW CNW
Freq. 16.9 3.1 1.1 1.5 2.0 2.3 2.2 1.9 1.6 1.2
Trend 4.8 −0.6 −0.2 0.8 0.8 1.2 −0.5 0.1 0.5 −0.3
Directional circulation types U
Type All N NE E SE S SW W NW
Freq. 38.1 3.4 3.7 3.6 4.0 4.4 6.4 7.8 4.8 1.6
Trend −1.8 0.2 −0.4 −0.3 −0.7 −0.3 0.1 0.2 −0.7 0.0
  • Note: Statistically significant trends (level 0.05) appear in italic type.
Details are in the caption following the image
Fluctuations in relative annual frequencies of the occurrence of extreme absolute daily maximum (b) and minimum (b) temperatures for 133 climatological stations over the Czech Republic in the 1961–2020 period. Smoothed by 10-year Gaussian filter and supplemented by linear trends [Colour figure can be viewed at wileyonlinelibrary.com]
Details are in the caption following the image
Fluctuations in mean annual numbers of summer (a) and tropical (b) days and in their mean earliest/latest occurrences (c) in the Czech Republic during the 1961–2020 period. Smoothed by 10-year Gaussian filter and supplemented by linear trends [Colour figure can be viewed at wileyonlinelibrary.com]
Details are in the caption following the image
Fluctuations in mean annual numbers of frost (a) and ice (b) days and in their mean earliest/latest occurrences (c) in the Czech Republic during the 1961–2020 period. Smoothed by 10-year Gaussian filter and supplemented by linear trends [Colour figure can be viewed at wileyonlinelibrary.com]
Details are in the caption following the image
Fluctuations in heat waves in the Czech Republic during the 1961–2020 period: (a) mean annual number of heat-wave days; (b) mean maximum duration and mean highest maximum temperature of heat waves; (c) number of stations that recorded heat wave(s) in a given year and their attribution to altitudinal Groups I–III. In parts (a) and (b), linear trends are indicated and series are smoothed by 10-year Gaussian filter [Colour figure can be viewed at wileyonlinelibrary.com]
Details are in the caption following the image
Cold waves in the Czech Republic during the 1961–2020 period: (a) mean annual numbers of cold-wave days; (b) mean maximum duration and mean lowest minimum temperature of cold waves; (c) number of stations that recorded cold wave(s) in a given year. Linear trends are indicated in parts (a) and (b) and series are smoothed by 10-year Gaussian filter [Colour figure can be viewed at wileyonlinelibrary.com]

The relationships between circulation types, objectively classified (section 2.2), and the above characteristics were analysed by adding the related circulation type to a given day, then by calculation of absolute and relative frequencies of the days classified in this way. Days featuring certain temperature characteristics occurring within the set of 133 stations were selected in such a way that those which appeared for at least one station with a related circulation characteristic were used for attribution of circulation type. In order to obtain comparative relative frequencies of individual circulation types and their groups, percentages of their mean relative occurrence in the 1961–2020 period were calculated (Table 1). For relating individual circulation types to the occurrence of defined temperature extremes, differences between the relative frequencies of the circulation types associated with extreme parameters and the mean relative frequencies of the circulation types during the 1961–2020 period were calculated. If such characteristics occurred solely in the summer half-year (April–September; SHY) or winter half-year (October–March; WHY), mean relative frequencies during the corresponding half-year were used for comparison (Figure 3).

TABLE 2. Mean number (n), date of first (df) and date of the last (dl) occurrence and their linear trends (t in days/10 years) for summer (TMAX ≥25.0°C), tropical (TMAX ≥30.0°C), frost (TMIN <0.0°C), and ice (TMAX <0.0°C) days in the Czech Republic (CR) and four altitudinal Groups (I–IV) in the 1961–2020 period
Group Summer days Tropical days
Number First date Last date Number First date Last date
n t df t dl t n t df t dl t
CR 39.8 4.4 18 May 2.2 7 Sep 0.8 7.6 1.8 27 Jun 3.2 8 Aug 2.5
I 52.8 5.2 8 May 2.2 15 Sep 0.3 11.7 2.6 19 Jun 4.2 14 Aug 3.9
II 39.9 4.4 17 May −2.1 8 Sep 0.7 7.3 1.7 28 Jun 3.5 7 Aug 2.3
III 19.3 3.1 6 Jun 3.1 24 Aug 2.6 1.9 0.7 16 Jul −2.8 28 Jul 0.4
IV 3.9 0.8 4 Jul −2.8 7 Aug −0.8 0.1 0.0
Group Frost days Ice days
Number First date Last date Number First date Last date
n t df t dl t n t df t dl t
CR 115.3 4.7 13 Oct 1.0 28 Apr 1.6 35.0 2.9 26 Nov 1.1 27 Feb 3.1
I 98.3 4.1 17 Oct 1.0 21 Apr −1.0 26.1 2.4 2 Dec 1.5 19 Feb 3.1
II 116.2 5.1 11 Oct 1.2 29 Apr 1.8 33.2 3.0 27 Nov 1.3 25 Feb 3.4
III 137.7 4.8 10 Oct 0.3 4 May 1.5 51.2 3.9 17 Nov −0.0 15 Mar 2.2
IV 170.5 4.9 27 Sep 0.4 22 May 3.0 84.0 3.1 29 Oct −0.5 16 Apr 2.6
  • Note: Statistically significant trends (at significance level 0.05) appear in italic type.
Details are in the caption following the image
Fluctuations in the mean annual number of warm anomaly days (a) and cold anomaly days (b) in the Czech Republic during the 1961–2020 period. Linear trends are indicated and series are smoothed by 10-year Gaussian filter [Colour figure can be viewed at wileyonlinelibrary.com]

The 1961–2020 period analysed comprises two 30-year sub-periods: 1961–1990 and 1991–2020. These represent two different climatic “normals” (WMO, 2017). The two subperiods were used for overall comparison of extreme temperature characteristics and circulation patterns. In order to compare temperature extremes, their values were expressed as box-plots, thus including the entire distribution of the characteristics under investigation, looking at the higher-order moments (such as asymmetry) in more than two sub-periods. Possible differences between the first and second moments were evaluated by applying the t test for means and the F test for variances (Figure 11). Testing differences between the two subperiods was complemented with the nonparametric Mann–Whitney U test, which is more robust to possible outliers and distribution skewness. To demonstrate the spatial differences between two normal periods, maps were constructed for six characteristics of extreme temperatures (Figure 12). Differences in the relative frequency of circulation types were tested by means of the two-proportion Z test (Sprinthall, 2011) (Table 3).

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Box-plots of characteristics of extreme temperatures in the Czech Republic during the 1961–1990 and 1991–2020 periods: (a) “warm” events, (b) “cold” events, (c) “extreme” temperatures. AVG (STD) denotes statistically significant differences (at a significance level of 0.05) in means (standard deviation) according to the t test (F test) [Colour figure can be viewed at wileyonlinelibrary.com]
Details are in the caption following the image
Spatial differences in temperature extremes over the territory of the Czech Republic between 1991–2020 and 1961–1990: (a) summer days, (b) tropical days, (c) frost days, (d) ice days, (e) ATMAX, (f) ATMIN [Colour figure can be viewed at wileyonlinelibrary.com]
TABLE 3. Differences in relative frequencies (%) of circulation types over the Czech Republic between the 1991–2020 and 1961–1990 periods in the summer half-year (a), winter half-year (b), and the entire year (c)
Char. Anticyclonic circulation types
All A AN ANE AE ASE AS ASW AW ANW
a 5.3 2.6 0.3 −0.1 0.2 0.5 −0.2 1.0 0.4 0.4
b 4.6 0.6 0.6 −0.1 0.0 0.3 0.1 1.3 2.2 −0.4
c 5.0 1.6 0.5 −0.1 0.1 0.4 0.0 1.1 1.3 0.1
Char. Cyclonic circulation types
All C CN CNE CE CSE CS CSW CW CNW
a 4.1 −0.3 −0.2 1.2 0.7 0.8 −0.3 0.1 0.5 −0.3
b 2.4 −0.3 0.1 0.0 0.4 1.3 −0.4 0.3 −0.4 −0.1
c 3.4 −0.3 0.0 0.6 0.6 1.1 0.3 0.2 0.5 −0.2
Char. Directional circulation types U
All N NE E SE S SW W NW
a −1.3 0.5 0.8 −0.3 0.2 0.7 −0.3 −0.4 0.9 0.1
b 2.0 −0.1 0.0 0.0 0.7 1.2 −0.3 0.9 −0.7 −0.1
c 1.6 0.2 −0.4 −0.1 −0.3 −0.2 −0.3 0.3 0.8 0.0
  • Note: Statistically significant differences (significance level 0.05) appear in italic type (Char. – characteristics).

Series of extreme temperature characteristics were smoothed by 10-year Gaussian filter and complemented by linear trends, with their significance evaluated by t test. In all cases, the significance level was set at 0.05.

4 RESULTS

4.1 Circulation patterns in 1961–2020

The occurrence of individual circulation types and their groups appear in Table 1 in terms of the mean relative annual frequencies for the CR in the 1961–2020 period. Anticyclonic types were the most frequent, at 43.4% (A 10.0%, AW 8.0%, and ANW 5.8% were the three most frequent) followed by directional types, at 38.1% (W 7.8%, SW 6.4%, and NW 4.8%). The percentage of cyclonic types reached only 16.9% (C 3.1%, CSE 2.3%, and CS 2.2%). A total of 1.6% of days remained unclassified (type U). Linear trends for individual circulation types and their groups (statistically significant at the 0.05 level) indicated rising trends for anticyclonic types (6.6 days/10 years) and four individual types (A, AN, ASW, and AW). Decreasing trends were significant for cyclonic types (−4.8 days/10 years) and four individual types (CNE, CE, CSE, and CW). Trends in directional types were insignificant, as were for individual types.

Because a number of temperature extremes are almost entirely limited to the SHY and WHY periods, Figure 3 shows the relative frequencies of individual circulation types in these two half-years separately. The relative frequencies of types AS, ASW, AW, SE, S, SW, and W in the WHY were significantly higher, as were types A, AN, ANE, C, CN, CNE, CE, CSE, N, NE, and U in the SHY. Turning to significant linear trends for circulation types in 1961–2020, differences between the two half-years were substantial (not shown). In the WHY, significant trends increased for only AW and ASW (for all anticyclonic types 2.5 days/10 years) and decreased for CSE. In the SHY, significant increases appeared for types A and ASW (for all anticyclonic types 4.1 days/10 years) and decreases for CNE, CE, CSE, CW, and CNW (for all cyclonic types −3.5 days/10 years).

4.2 Maximum and minimum temperatures

Series of mean annual ATMAX indicate a significant increasing tendency in the CR during the 1961–2020 period, with a linear trend of 0.5°C/10 years (Figure 4a). The lowest mean ATMAX figures emerged for 1977–1978, after which a sharp increase took place, to the highest value in 1983 (the difference between the lowest and highest ATMAX reached 7°C). The extreme ATMAX for 1983 was slightly surpassed in 2013 and 2015. In similar fashion, mean annual ATMIN also shows a significantly increasing linear trend of 0.7°C/10 years (Figure 4b). The difference between the two most extreme ATMINs, in 1974 (maximum) and 1985 (minimum), reached 18.1°C. The values for the annual range between ATMAX and ATMIN exhibited an insignificant decreasing trend (Figure 4c).

In the relationship between circulation types and ATMAX/ATMIN, anticyclonic circulation types predominated markedly for ATMIN, at 66.7%, while the percentages for directional and cyclonic types (28.9 and 4.3%, respectively) were deeply below their 60-year means (cf. Table 1). Further to type A (central anticyclone), at 25.1%, the most important types featured cold airflow from eastern Europe (AE 8.9%, E 7.9%, and SE 7.6). For ATMAX, the share of anticyclonic types was below the 60-year mean (37.2%), while cyclonic types (16.8%) and directional types (37.2%) were close to their means. Most frequent were types with warm airflow from the southwest and south (SW 10.6%, ASW 9.8%, and S 9.3%); this also held for unclassified type U, at 8.8%.

When only the highest ATMAX for each of the 133 climatological stations during the 1961–2020 period were selected, it became evident that they were distributed into 18 days in July–August: at 48 stations (36.1%) occurring on July 27, 1983 (type A), at 25 stations (18.8%) on August 8, 2013 (type CW), and at 11 stations (8.3%) on August 8, 2015 (type CSE), always as a parts of HWs. The lowest ATMINs at each of 133 stations occurred on 19 different days during December–February. Related extremes over three consecutive days predominated: at 67 stations (50.4%) on 7–9 January 1985 (types AE, A, A; for 31 stations, that is, 23.3%, on 7 January) and at 33 stations (24.9%) on 12–14 January 1987 (types CNE, SE, SE).

As follows from Figure 5, which shows fluctuations in relative annual frequencies of the occurrence of extreme daily ATMAX and ATMIN, absolute positive extremes concentrated around the second part of 1961–2020, while absolute negative extremes did the opposite. In terms of decadal frequencies, daily ATMAX occurrences were at their highest in 2011–2020 (28.9%; 6.1% in 2015) and lowest in 1971–1980 (9.5%). Conversely, daily ATMIN occurrences achieved a maximum in 1971–1980 (23.7%) and a minimum in 2011–2020 (7.5%). Comparing these annual frequencies with the two “normal” periods of 1961–1990 and 1991–2020, the corresponding values were 31.0 and 69.0% for daily ATMAX, and 68.6 and 31.4% for daily ATMIN.

4.3 Characteristic days

Records of annual means revealed 39.8 summer days and 7.6 tropical days in the CR during the 1961–2020 period; these values decreased with increasing altitude, as documented for the four altitudinal Groups I–IV (Table 2). Series of the mean annual numbers of such days (Figure 6) showed statistically significant increases, with linear trends of 4.4 summer days/10 years and 1.8 tropical days/10 years. These were at their highest for stations located at altitudes of less than 300 m asl (Group I) (5.2/10 years and 2.6 days/10 years, respectively), decreasing with rising altitude (from Groups I–IV); all were, however, statistically significant. Summer days, from May to September, exhibited earlier beginnings and later endings during 1961–2020, as was evident from their negative and positive trends, respectively. These changes were at their most marked for stations in group III (−3.1 days/10 years and 2.6 days/10 years, respectively). The same tendencies also appeared for tropical days occurring, on average, during June–August. Linear trends for these changes were highest for Group I (−4.2 days/10 years and 3.9 days/10 years, respectively). Thus, for both summer and tropical days, a prolongation of the mean period in which they occurred took place (by 18 and 34 days respectively) during the 60-year period.

In comparison with summer and tropical days, the occurrence of days with tropical nights was very low—an annual mean of only 0.25 days in the entire CR during the 1961–2020 period (from 0.5 days in Group I to 0.1 days in Group III), with a maximum in 2015 (2.2 days). However, the related increasing trends were all statistically significant (not shown). Despite the low frequency of these days in the country mean, their occurrence became a serious problem in the larger cities such as Prague and Brno (for the location of places in the CR, see Figure 1), where they were exacerbated by the urban heat island effect, largely in association with HWs (Skalák et al., 2015).

The mean annual numbers of frost and ice days contrasted sharply with the tendencies shown for summer and tropical days, achieving 115.3 and 35.0 days respectively in the CR during the 1961–2020 period. Their mean numbers rose with increasing altitude (Table 2). The significant linear trends for the entire CR were −4.7 frost days/10 years and −2.9 ice days/10 years (Figure 7). In terms of individual altitudinal groups, decreases were at their highest in Group II for frost days (−5.1 days/10 years) and in Group III for ice days (−3.9 days/10 years); Group I figures were lowest for both characteristics. None of the mean beginnings of frost and ice days exhibited significant trends. However, the last occurrence of such days appeared earlier in average terms, constituting a shortening of the mean period in which frost and ice days may occur (by 14 and 25 days, respectively, during the 60-year period).

Examination of circulation types showed that the occurrence of summer and tropical days corresponded to above-mean frequencies of anticyclonic types (46.8 and 47.7%, respectively) and below-mean frequencies of cyclonic (16.2 and 14.8%) and directional (32.8 and 31.7%) types during the SHY. Of the individual types, A and AW made up nearly a fifth of all such days. Also above mean were southerly and southwesterly directions. Days with tropical nights had above-mean frequencies, with cyclonic types (20.6%) and unclassified type U (6.4%) predominant, while anticyclonic (39.5%) and directional (33.4%) types were below their 60-year occurrences in the SHY. AW and SW types appeared most frequently.

Although anticyclonic circulation types remained the most frequent for frost and ice days (44.0% for both), the percentages of all groups of circulation types were close to the 60-year mean of the WHY. However, those with airflow from the northeastern quadrant exhibited above-mean percentages, while those with airflow from the southwestern quadrant lay below mean. Although type A was the most frequent, its frequency was only slightly above the long-term mean.

Notable differences between the two basic groups of days appeared for unclassified type U: for summer and tropical days these were 4.1 and 5.7%, respectively, but for frost and ice days only 0.6 and 0.3%. This is primarily related to substantially higher abundance of the U type in summer due to a tendency towards less distinct air pressure gradients over Europen-Atlantic domain in this season, that is, an unclassified pressure field.

4.4 Heat waves and cold waves

Figure 8 shows fluctuations in the characteristics of HWs over the territory of the CR in the 1961–2020 period. The mean annual number of HW days (Figure 8a) is at its highest in 2015 and in 1994, a fact also reflected in local maxima of smoothed values in the early 1990s and mid-2010s; a significant increasing trend in HW days achieved 1.1 days/10 years. Similar features also appear in the mean maximum duration of HWs (Figure 8b), as well as in mean TMAX, which were at their highest in 2015. Mean duration of HWs extended significantly over the 60 years, by 3.4 days (trend 0.56 days/10 years), while mean TMAX during HWs increased by 1.2°C. With respect to decreases in TMAX with rising altitude, the characteristics shown in Figure 8a,b exhibit decreases from Group I to Group III. As follows from Figure 8c, HWs did not occur at the stations at altitudes of over 900 m asl (Group IV). The year 1994 was exceptional, when all stations in Groups I–III recorded a heat wave. A similar situation also appeared in Groups I and II in 1992, 2007, 2013, and 2015. For the entire CR, altitudinal Groups I–III experienced statistically significant increasing trends for all three HW characteristics mentioned above. Addressing HWs at the 133 climatological stations in the 60-year period reveals that those lasting 3 days constituted nearly half of them (47.9%), followed by 4-day HWs (21.7%) and 5-day HWs (9.9%), i.e., 79.5% altogether. The longest HW, at a length of 19 days, was recorded at the Strážnice station (176 m asl) between 23 July and August 10, 2018, with a mean TMAX of 32.9°C. Anticyclonic types (52.6%) prevailed in this longest HWs, followed by directional (42.1%); of the 11 types that occurred, type E was the most frequent (21.1%), followed by A, AN, ANE, AE, and NE (10.5% each).

Figure 9 shows fluctuations in the characteristics of CoWs over the territory of the CR in the 1961–2020 period. The mean annual number of CoW days (Figure 9a) is at its highest in 1963, followed by three comparable values in 1969, 1996 and 2010. A significant decreasing trend in CoW days stood at −2.6 days/10 years (at its highest in group III, at −3.7 days/10 years). Similar trends also occurred in mean maximum duration of CoWs (Figure 9b), but 1984 is added to 1963 and 1996. This duration became shorter by 6 days for the 60-year period (mean 12 days). Mean TMIN during CoWs exhibited a significant increasing trend (0.35°C/10 years). In accord with decreases in temperatures with rising altitude, the characteristics of CoWs in Figure 9a,b show increases in their values from Group I to Group IV. As follows from Figure 9c, CoWs usually occurred at all stations in all four altitudinal Groups I–IV. The years 1974 and 2020 were exceptions, during which a relatively lower number of stations in Groups I–III recorded a CoW. Some stations located at altitudes of under 600 m asl (Groups I–II) also recorded no CoW in 1988–1989 and 2015. As was the case for the entire CR, altitudinal Groups I–IV experienced significant decreasing trends for all three CoW characteristics mentioned above. Consideration of the frequency of CoWs at the 133 climatological stations over the 60-year period reveals that durations of CoWs were most frequently 9 days (9.8%), 7 days (9.1%), and 8 days (7.8%), that is, 26.7% altogether. The longest CoW, lasting 78 days, was recorded at the Labská bouda station (1,315 m asl) between December 17, 1962 and March 4, 1963, with a mean TMIN of −14.0°C. Anticyclonic circulation types (43.6%) prevailed in this longest of CoWs, followed by directional (37.2%) and cyclonic (18.0%). The most frequent among the 22 individual circulation types that occurred were AE (14.1%), E (11.5%), A (10.3%), and SE (9.0%), that is, 44.9% altogether.

The increased occurrence of anticyclonic circulation types (a difference of 8.7% compared with the 60-year mean for SHY) and unclassified type U (4.2%) is typical of HW days, while cyclonic and directional types were less frequent (differences −6.0 and −6.9%, respectively). In terms of circulation types, with the exception of A (3.5% increase), types with airflow from the eastern sector exhibited the most notable increases (ASE 2.9%, together with ANE, AE, and AS 6.6%; E, SE, and S 4.8% altogether). Notable decreases appeared in the occurrence of types with airflow input from the northwest quadrant (NW −3.7%, N −3.5%, W −2.6%, ANW −2.6%, i.e., −12.4% altogether). Dependence on anomalous circulation types is much less marked for CoW days, a trend that is apparent in smaller differences compared with the 60-year mean for WHY (0.6% for directional, 0.9% for cyclonic, and −1.4% for anticyclonic types). Differences of ≥1% compared with the corresponding means appeared only for types with cold airflow in the winter months: E (1.5%), NE (1.5%), N (1.3%), and ANE (1.1%).

4.5 Warm and cold anomalies

The mean annual number of warm anomaly days, defined with respect to the threshold of the 95th percentile for TMAX in 1961–1990, reached 11.1 days in the CR during the 1961–2020 period (similar means appear in Groups I–III, with 9.0 days in Group IV). In annual distribution, warm anomalies were at their most frequent in summer and at their least in autumn. Annual fluctuations of warm anomaly days showed a clear and significant increasing trend of 2.9 days/10 years (Figure 10a), with a highest annual value of 34.2 days in 2015. Significant trends for the entire CR and the four altitudinal groups occurred in summer and in annual values, and, with the exception of group IV, in winter and summer as well. No significant trend was recorded in spring.

The mean annual number of cold anomaly days, defined with respect to the threshold of the 5th percentile for TMIN, in 1961–2020 in the CR, shows a decreasing linear trend (Figure 10b). This is statistically significant for annual and summer values in the entire CR and all altitudinal groups. A significant decreasing trend also appeared for autumn in Groups I and II. The 60-year mean annual number of cold anomaly days was 4.6 days in the CR, with similar values in Groups I–III; the figure for Group IV was 6.3 days. In annual distribution, the maximum number occurred in winter and the minimum in summer.

Increases in the occurrence of circulation types with warm airflow from the southwest quadrant proved the most strongly associated with warm anomaly days. These consisted of directional types SW (5.5% compared with the 60-year mean), S (2.9%), and W (2.2%), together with anticyclonic types ASW (3.7%) and AW (3.7%), representing an increase of 18.0% overall. Less frequent were circulation types with cold airflow from northern directions (directional types NE −3.1%, N −2.7%, NW −2.3% and anticyclonic types AN −2.5% and ANE −2.0%). For cold anomaly days, there was a strongly marked increase in the occurrence of anticyclonic types: 17.7% compared with the 60-year mean. Particularly involved were types A (difference 8.2%), AE (3.2%), ANE (2.4%), ASE (2.2%), and AS (2.1%), and decreases in cyclonic types by −9.9% (C −2.1%) and directional types by −7.3% (W −4.9% and SW −3.3%).

4.6 Day-to-day temperature change

Day-to-day temperature change served to investigate sudden falls or rises in daily TMAX and TMIN. Mean annual day-to-day change in TMAX for the entire CR in 1961–2020 stood at 2.5°C, with similar values for the four altitudinal groups. The maximum in annual variation appeared in April–May and the minimum in December–January, finding a reflection in related extremes in spring (and summer) and in winter respectively. The majority of the related linear trends were statistically insignificant, although an increasing trend in March for the entire CR and Groups I–III was noted. This situation changed just a little when only the maximum values of day-to-day change were considered. Mean maximum annual change was 11.4°C, with similar values in the four altitudinal groups. Except for August in Group IV, none of the linear trends proved significant. The very highest day-to-day fall in TMAX, of −23.4°C, occurred between December 31, 1978 (type C) and January 1, 1979 (type CNE) at the Prague-Kbely station (282 m asl), while the very highest rise, of 18.4°C, was recorded between January 14, 1968 and January 15, 1968 (type W on both days) for Velké Meziříčí (452 m asl).

The values of mean annual day-to-day change in TMIN remained the same for the entire CR and Groups I and II, at 2.3°C. The maximum in annual variation was achieved in January and the minimum in July, finding reflection in the same extremes in winter and summer respectively. Significant increasing linear trends appeared particularly in May–June, summer, spring, and in annual values. Turning to only maximum values of TMIN day-to-day change, the situation was similar to that for mean values. Mean maximum annual rise was 11.1°C for the entire CR. All related linear trends were insignificant, with the exception of a decrease for September in Group II. The very highest day-to-day falls and rises in TMIN occurred on the same days as for TMAX, values of −25.5°C between December 31, 1978 and January 1, 1979 at the Lysá hora station (1,322 m asl) and 25.0°C between January 14, 1968 and January 15, 1968 in Františkovy Lázně (435 m asl).

Sets of values defined by the thresholds of the 1st and 99th percentiles were created for analysis of extreme day-to-day changes in TMAX and TMIN. On the first days of extreme falls in day-to-day TMAX, there was a notably low frequency of anticyclonic types (16.5% below the 60-year mean), with an above-mean occurrence of cyclonic types (11.8%) and directional types (2.8%). On the second days, a distinctly higher frequency of anticyclonic types (35.4%) emerged, together with a lower frequency of cyclonic types (22.6%) than on the first days. Among individual circulation types, those with airflow from the western quadrant were the most common, often with a clockwise transition (e.g., SW to W, W to NW). On the first days of extreme rises in day-to-day TMAX, anticyclonic types prevailed, at 53.6%, while the frequencies of cyclonic and directional types were below mean (11.0 and 34.7%, respectively). On the second days of these changes, the frequencies of circulation types moved closer to their means, with no notable shifts.

In extreme changes in day-to-day TMIN, the differences between the first and second days were substantial. Extreme decreases started with a very high frequency of directional types (40.0%), followed by anticyclonic types, with 37.9% (5.5% below 60-year mean), and cyclonic types with 21.1% (4.2% above mean). However, on the second days, anticyclonic types took up an overwhelming majority, of 58.7% (15.3% above mean), while the frequency of cyclonic (10.9%) and directional (29.7%) types was below mean. Types with northern airflow were the most common on the first days and type A with 17.5% was the most frequent on the second day. Conversely, extreme rises in day-to-day TMIN began with a very high frequency of anticyclonic types (54.8%); on the second days this decreased to 33.6%, while the frequency of cyclonic and directional types increased. Among individual types, those with westerly and southerly airflow were the most common.

4.7 Comparison of normal periods

In order to compare two normal periods 1961–1990 and 1991–2020, the temperature characteristics may be roughly divided into three main groups, expressing “warm” events, “cold” events, and “extreme” temperatures (Figure 11a–c). Significant differences between 30-year means appeared in all characteristics, except ATMIN, CoW days, and TMAX day-to-day changes. Variance increased, largely in 1991–2020, except in ATMIN, ice days, CoW days, and cold anomaly days. However, these increases were significant only for numbers of tropical days, days with tropical nights, HW days, and warm and cold anomaly days. The same results were confirmed with the non-parametric Mann–Whitney U test with the only exception concerning of numbers of ice days (not significant difference according to Mann–Whitney test). The two normal subperiods show mostly different linear trends. However, their slopes are statistically significant only in case of the number of summer days.

In the light of recent rising temperatures, the box-plots naturally characterize quite opposite changes in the distribution of “warm” (Figure 11a) and “cold” (Figure 11b) events. Closer examination reveals that the shift towards higher (more frequent) values is more evident for “warm” events. For instance, the 1991–2020 boxes (representing the interquartile range, i.e., 50% of all values) stand clearly above the 1961–1990 boxes for most of “warm” events. The same trend appears for ATMAX. A similar shift of “cold” event distribution to lower (or less frequent) values is less marked, especially in median values (Figure 11b for ice days and CoWs). The lesser change in ATMIN distribution accords with this finding. Finally, the 1991–2020 normal exhibited clearly higher positive asymmetry in most of its “warm” event characteristics. This skewness was not so marked for the remaining temperature characteristics.

Based on data from the 133 climatological stations, differences between the two 30-year periods may be described from a spatial point of view (Figure 12). In general, they reflected a dependence of extreme temperatures on altitude and orography. Lower positions tended to return higher positive differences (i.e., in 1991–2020 compared to the previous 30-year period) for numbers of summer days and tropical days (Figure 12a,b) and lower negative differences for numbers of frost and ice days (Figure 12c,d). Higher positions, consisting largely of the mountains around the borders of the CR, showed an opposite tendency. In the spatial expression of differences in TMAX, the greatest increase appeared in the eastern part of the CR and the least in its southwestern part (Figure 12e). In particular, the least increases in TMIN during 1991–2020 appeared north and northeast of a line connecting Brno and Prague; the greatest increases occurred in a broad belt extending from southern to western Bohemia, with a core region around Plzeň (Figure 12f).

In terms of circulation, the relative frequencies of anticyclonic types increased by 5.0% in 1991–2020 compared with the previous 30 years. This was balanced out by a decrease in the occurrence of cyclonic types, by 3.4%, and directional types, by 1.6% (Table 3). All these changes were statistically significant (significance level 0.05). Among individual circulation types, the highest increases in relative frequencies were recorded for anticyclonic types A (1.6%), AW (1.3%), and ASW (1.1%), while the highest decreases occurred in types CSE (−1.1%) and NW (−0.8%). Differences in the relative occurrence of these five circulation types were significant, as were the positive shifts for types AN and ASE, and negative for CNE, CE, CS, and CW. This shows that changes in the occurrence of anticyclonic and cyclonic circulations types are important to changes in trends of temperature characteristics analysed. Since types A and ASW are associated with ATMAX, summer days and tropical days, the increased frequency of these circulation types in the SHY is apparently an important driver of the more frequent occurrence of warm and hot days.

5 DISCUSSION

As is generally known, long series of meteorological variables are biased by several nonmeteorological effects (e.g., station relocation, changes in instruments, station surroundings, observers, automation of measurements) which has to be removed during the homogenization procedure to obtain reliable results during the time series analysis. Careful homogenization supposes at least the use of appropriate methods, good metadata and well experience with homogenization, that is, conditions completely fulfilled in this study of temperature extremes in the CR. As shown by Štěpánek et al. (2011a), up to 68% of raw Czech temperature series exhibit different nonhomogeneities. Around half of detected break points can be explained in metadata. Despite the mean corrections in temperature series achieves only ~0.4°C, the applied homogenization procedure contributes undoubtedly to the improvement of results obtained.

The significant increase in air temperatures in the past decades that has been extensively documented projects into changes in their extremes. This is reflected in increasing trends among the characteristics corresponding with warm and hot weather and in decreasing trends corresponding with cold or frosty weather. This is clearly evident from the results presented herein for selected characteristics of temperature extremes over the territory of the CR in the 1961–2020 period. These are in accord with many other European papers. For example, Ramos et al. (2011), analysing temperatures at 23 Portuguese stations in the 1976–2006 period, found statistically significant positive trends in the annual number of tropical nights, summer days, warm spells, warm nights, and warm days. Burić et al. (2014) reported negative trends in cold nights and cold days, positive in indices of warm conditions, for four stations in Montenegro during 1951–2010. Lapin et al. (2016) identified increases in maximum temperatures and heat waves, particularly in the frequency of tropical and supertropical (TMAX ≥35.0°C) days and days with tropical and summer (TMIN ≥15.0°C) nights from 1991 for seven stations in Slovakia over the 1951–2015 period. Výberči et al. (2018), in an investigation of extended series of unusually warm (above normal) and cool (below normal) days for the 1951–2017 period in the same country, found a substantial increase in warm spells in terms of their frequency, duration and intensity, while cold spells exhibited a slight-to-moderate decline, or weakened. Scorzini et al. (2018), using 34 stations in the central Adriatic region of Italy covering 1980–2012, reported significant increases in the duration of warm spells, in the frequency of warm days and nights, summer days and tropical nights. In contrast, cold-related extremes, frost and ice days showed significant reductions. Popov et al. (2019) found a significant increasing tendency in indices of warm extremes and a decreasing tendency in cold-related indices at four stations in Bosnia and Herzegovina in the 1961–2016 period. Micu et al. (2021) reported a strong decline in frost and ice days and a significant increase in ATMAX, summer days and warm-spell duration over the 1961–2018 period in the area of the southern Carpathians (Romania).

Rebetez (2001) argued, on the basis of an analysis covering changes in TMAX and TMIN at Davos and Neuchatel (Switzerland) in the 1901–1999 period, that a warming climate was also accompanied by a reduction of day-to-day variability in monthly series, particularly marked for night-time temperatures. However, in the CR in 1961–2020 (section 4.6), statistically significant increasing trends were found in TMIN day-to-day change only for May–June, spring, summer, and the year, while the corresponding trends were generally insignificant for day-to-day changes in TMAX. Piskala and Huth (2020) investigated asymmetry in day-to-day changes for the Prague-Karlov station (CR) over the 1961–1998 period. They concluded that marked winter day-to-day increases in TMIN were related to the passing of warm, cold and occluded fronts, while deep drops in summer TMAX were due to cold fronts.

Increasing trends in the characteristics of temperature extremes induced by TMAX in the CR (ATMAX, summer and tropical days, HWs, warm anomalies) may also be related to a combination of different factors during the period studied: spring and summer TMAX exhibited the highest increases after 1990 (Zahradníček et al., 2021); summer precipitation totals were very low in the 2010s (Brázdil et al., 2021); a significant increase in soil-drought episodes appeared in the SHY after 1990 (Řehoř et al., 2020, 2021). This is in accord with the concept of feedback between land and atmosphere, in which drier land, influencing latent and sensible heat fluxes, contributes to additional increases in air temperatures (Seneviratne et al., 2006, 2010).

Changes in extreme temperatures may lead to a range of consequences at different altitudes in the CR. Their relative expression in the two 30-year periods (section 4.7) illustrates this clearly. For example, the number of summer days at altitudes of over 900 m asl in 1991–2020 was double that of 1961–1990, while below 300 m asl this increase was only 40.5%. This may find reflection in an impact on evapotranspiration in lower positions. The surface becomes drier earlier and plants begin to take soil water earlier (Žalud et al., 2020). This may then contribute to spring drought. Similarly, the extreme relative increase in tropical days (at 236%) may have negative impacts on human health (Nastos and Matzarakis, 2012) and, once more, on intensification of drought, in summer (Mozny et al., 2020). Changes in numbers of ice days are also worthy of consideration. These decreased by 18.5% in positions lower than 300 m and by 10% above 900 m between the two 30-year periods. Thus, reduction in snowfall, snow-cover depth and duration in the lower positions will be more marked than at greater altitudes (predominance of liquid over solid precipitation). This has already become apparent in much larger declines in snow cover over the lowlands (compared to climate normal) than in the mountains (Zahradníček et al., 2016).

Although changes in temperature extremes are often attributable to the established rise in global temperatures (Rahmstorf and Coumou, 2011), regional analyses highlight the importance of long-term circulation anomalies to the development of extreme events. The 2015–2018 period was characterized by an exceptionally high frequency of anticyclonic circulation patterns, especially in summer, also related to record-breaking characteristics of HWs and droughts in central Europe (e.g., Hoy et al., 2017; Lhotka et al., 2020). Blocking anticyclones drive extreme events (in both summer and winter) through interruption of the prevailing zonal airflows that contribute to intensified meridional airflow and radiative weather (Sillmann et al., 2011; Brunner et al., 2017; Tomczyk et al., 2019). The changes in temperature extremes presented herein are therefore partially associated with heightened frequency of anticyclonic circulation types in recent decades. However, the question remains open as to whether this arises out of an observed shift towards more anticyclonic weather as part of natural climate variability, or whether it is linked to ongoing climate change.

Projected future changes in temperature extremes, and their characteristics given various climate scenarios, are frequently addressed in a range of contributions (e.g., Lhotka et al., 2018; Cardell et al., 2020; Zhao et al., 2021). Because the observed series of selected characteristics show quite dramatic changes, accelerating particularly in the past three decades, their comparison with existing projections may provide interesting results. Twelve regional climate models (RCMs), with resolutions of 0.11° (~12.5 km) from the CORDEX project (Giorgi and Gutowski, 2015), were selected as those best representing the climate of the CR (Štěpánek et al., 2019). An ensemble of 12 RCM simulations for 2006–2100 was employed, from which median values for annual numbers of tropical and frost days were calculated for RCP4.5 and RCP8.5 (Figure 13). In the overlap period between observed data and model projections (2006–2020), the annual number of tropical days for the entire CR was higher by 40% compared to model simulations for RCP4.5 (27% for RCP8.5). The observed mean annual numbers of tropical days for 2011–2020 have even been projected as much as for 2041–2060 in RCP8.5 and for 2061–2080 in RCP4.5. This fact is probably linked to the increased frequency of anticyclonic circulation types in summer, conducive to hot extremes, that has been observed in recent decades. This phenomenon was not projected by the RCMs, which tend to have difficulties providing a robust circulation response to warming trends (Zappa, 2019) and to the capture of (multi)decadal climate variability (Cheung et al., 2017). In the projections for mean annual numbers of frost days, the observed values were 6% lower when compared to RCP4.5 and comparable with those for RCP8.5 in the common 2006–2020 period. It appears that the projected decrease in frost days to the end of the 21st century could be closer to the observed current situation.

Details are in the caption following the image
Comparison of the observed mean annual numbers of tropical (a) and frost (b) days in the Czech Republic during the 1961–2020 period in the current paper, with projections of 12 regional climate models for RCP4.5 and RCP8.5 scenarios up to the end of the 21st century (Štěpánek et al., 2019). Numbers of days are smoothed by 10-year Gaussian filter [Colour figure can be viewed at wileyonlinelibrary.com]

6 CONCLUSIONS

The main results of the analysis of extreme temperatures in the CR during the 1961–2020 period may be summarized as follows:
  1. ATMAX and ATMIN exhibit statistically significant increasing trends. While extreme ATMIN concentrated mainly within 1961–1990, extreme ATMAX tended to centre on 1991–2020.
  2. Characteristic days show opposite tendencies: the mean annual numbers of summer and tropical days and days with tropical night increases, while those of frost and ice days decrease. On average, summer and tropical days begin earlier and end later, while frost and ice days have stable beginnings, but they end sooner.
  3. HWs are characterized by a statistically significant increasing number of days, while CoWs show the opposite tendency, that is, HWs lasted longer and the length of CoWs was shorter. HWs lasting 3–5 days and CoWs lasting 7–9 days were the most frequent. HWs have not yet appeared at altitudes of over 900 m asl.
  4. Statistically significant linear trends exhibited increases in days of warm anomaly (threshold 95th percentile of TMAX) and decreases in days of cold anomaly (threshold 5th percentile of TMIN). The mean number of days taken up by warm anomalies was over double the days for cold anomalies.
  5. Day-to-day change in TMAX exhibited no statistically significant trends, in contrast to significant day-to-day changes in spring and especially in summer TMIN. The very highest positive and negative changes occurred in December–January.
  6. ATMAX, ATMIN and numbers of summer and tropical days, days with tropical night, days of HWs and warm anomalies exhibited higher means in 1991–2020 compared with 1961–1990. Numbers of frost and ice days, days of CoWs and cold anomalies showed lower means. The variability of the characteristics analysed increased in 1991–2020, with the exceptions of ATMIN and numbers of ice days, days of CoWs and cold anomalies.
  7. Statistically significant increasing trends appeared for anticyclonic circulation types (altogether, A, AN, ASW, and AW); they decreased for cyclonic types (altogether, CNE, CE, CSE and CW). Trends in directional circulation types were insignificant.
  8. This study demonstrated the importance of anticyclonic circulation types (especially those with warm airflow from the southern quadrant), and an unclassified type, in the development of hot and warm extremes in the SHY, while cold extremes of the WHY tended to be linked to cold (north-)easterly advection. Significant changes in the occurrence of some circulation types and increased abundance of anticyclonic types conducive to warm and hot extremes contributed to their more frequent occurrence in recent decades.

ACKNOWLEDGEMENTS

This paper was supported by the Ministry of Education, Youth and Sports of the Czech Republic for SustES – Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions, project, ref. CZ.02.1.01/0.0/0.0/16_019/0000797. Miroslav Trnka also received funding from the National Agency for Agricultural Research, project no. QK1910338, Jan Řehoř from Masaryk University, project MUNI/A/1570/2020 and Petr Štěpánek from the Czech Science Foundation, project no. 18-15958S. Marie Budíková (Brno) is acknowledged for recommendations on the use of the two-proportion Z-test. Tony Long (Carsphairn, Scotland) helped work up the English.

    AUTHOR CONTRIBUTIONS

    Pavel Zahradníček: Conceptualization; data curation; formal analysis; methodology; visualization; writing – original draft; writing – review and editing. Rudolf Brázdil: Conceptualization; methodology; supervision; writing – original draft; writing – review and editing. Jan Řehoř: Data curation; formal analysis; methodology; writing – review and editing. Ondřej Lhotka: Data curation; formal analysis; methodology; resources; writing – review and editing. Petr Dobrovolný: Data curation; formal analysis; methodology; resources; validation; writing – review and editing. Petr Štěpánek: Methodology; software. Miroslav Trnka: Conceptualization; methodology; supervision.