Climate Variations of Heat Waves on the Croatian Adriatic Coast for the Period 1961–2018

Climate Variations of Heat Waves on the Croatian Adriatic Coast for the Period 1961–2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate Variations of Heat Waves on the Croatian Adriatic Coast for the Period 1961–2018 Darko Koračin, Krešo Pandžić, Katarina Veljović Koračin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4655203/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Oct, 2024 Read the published version in Theoretical and Applied Climatology → Version 1 posted 7 You are reading this latest preprint version Abstract An analysis of characteristics of the boreal summer season June-July-August (JJA) measured daily maximum 2-m air temperatures (BSSDMATs) and associated heat waves (HWs) for 1961–2018 was conducted for three locations on the Croatian Adriatic coast representing its northern (Rijeka), middle (Split) and southern extents (Dubrovnik). Larger values occurred in the second part of the period (1990–2018) compared to the first part (1961–1989), including significant (α = 0.01) trends in mean seasonal averages (0.44 to 0.69°C per decade), extremes, frequencies, duration, and intensity. Exceedances and HWs spanning from 10 June to 24 August were determined in 53 years (out of 58 years) by the 95th and in 9 years by the 99th percentile criteria. Since heat stress frequently affects health at the beginning of a HW, exceedances of one or more days were all considered irrespective of any minimum length or separation. In 30 years, the exceedances appeared at all three locations in the same year. There were 122–147 (30–36) HW cases lasting 245–259 (51–54) days for the 95th (99th) percentiles. The maximum event duration ranged from 9 to 12 (5) days for the 95th (99th) thresholds. Weather conditions for the longest-duration events were characterized by propagation of a strong and wide ridge from the Azores High extending to southern Europe and blocking lows from the north. Based on these results, the Croatian coast is part of a Mediterranean hot spot that has been experiencing significant increasing warming trends and associated frequency of HWs that will likely continue in the future. Climate variations daily maximum temperature heat waves heat wave intensity very hot days global warming regional warming autocorrelation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction Heat waves (HWs) represent severe weather events when maximum or average daily air temperature exceeds a certain threshold for a number of consecutive or near- consecutive days (Keelings and Waylen 2012). They can have a significant and harmful impact on humans and the environment. Occurrence of HWs appears to be a consequence of both natural and very probably anthropogenic drivers (IPCC 2021). In addition to weather and climate impacts on human beings and the environment, socioeconomic impacts are also reaching devastating levels. Some prominent cases analyzed in the literature occurred in Chicago in 1996 (Karl et al. 1997) and in Paris in 2003 (Schär et al. 2004 ). After a devastating HW across Europe, and France in particular, Stott et al. ( 2004 ) analyzed this type of phenomena from a climate perspective. They find that a long-term threshold for mean summer temperature was exceeded in 2003 for the first time since the start of the instrumental record in 1851 and estimate that it is very likely that human influence has at least doubled the risk of a HW exceeding this threshold magnitude. Severe heat waves frequently cause considerable damage to human health and mortalities worldwide (Frost et al. 1992; Kunkel et al. 1996; Guest et al. 1999; Kysely and Huth 2004; Larsen 2006; Tan et al. 2007; Tobias et al. 2010; Zaninović and Matzarakis 2014 ). In August 2003, a HW in France caused almost 15,000 deaths (Poumadère et al. 2005 ). Based on observations results, Meehl and Tebaldi ( 2004 ) indicated that HWs over North America and Europe will be more frequent, of longer duration, and will be intensified in the future due to ongoing global warming. There are various definitions of heat waves and their severity. Many countries have their own definitions of HWs, mainly based on summer maximum temperatures exceeding high percentiles or pre-determined threshold values. Della-Marta et al. ( 2007 ) define a hot day as when the maximum temperature is greater than the long-term 95th percentile of daily maximum temperature. They identify a heat wave as the maximum number of consecutive days in which the daily maximum temperature is greater than the 95th percentile. There are also studies defining a heat wave based on both maximum and minimum daily temperatures. Kuglitsch et al. ( 2010 ) specify a hot day and hot nigh as one where the daily maximum temperature and daily minimum temperature exceed the long-term daily 95th percentile, respectively. In this case, a heat wave event is defined as a period of three or more consecutive hot days and nights not interrupted by more than one non-hot day or night. According to Giorgi ( 2006 ) and Diffenbaugh and Giorgi ( 2012 ), the Mediterranean can be assumed to be a hot spot vulnerable to climate change. It is definitely of interest to investigate regional properties of this vulnerability including heat-wave phenomena. One Mediterranean region of interest is the eastern coast of the Adriatic, which is characterized by high complexity of coastal hinterland, including coastline, a spectrum of wind regime characteristics, variations in bathymetry and sea currents, as well as interaction of the Adriatic and the Ionian Sea through the Otranto Strait. Artegiani et al. ( 1997 ) discuss both atmospheric and oceanic properties over the Adriatic. Their analysis based on data from 1980–1988 shows mean patterns of air temperature over the Adriatic with values increasing from the northwest to the southeast. Although HWs can occur in all seasons, the most intensive ones are in boreal summer with stronger air temperature gradients over the northern Adriatic. Maximum average air temperatures are about 23°C and 26°C on the most northern and most southern sides, respectively; bora and sirocco winds are prevailing, especially during the colder part of the year, while coastal circulations (land and sea breezes) develop during a warm season. The significant difference in bathymetry, between the shallow northern and deep southern parts of the Adriatic and river inflow in the northern Adriatic Sea, causes differences in the seasonal characteristics of the sea and air temperatures and is strongly influences the ecosystem of the Adriatic (Zavatarelli, et al. 2000 ; Spillman et al. 2007 ). Zaninović and Matzarakis ( 2014 ) study impact of heat waves on mortality in the hinterland and coastal Croatia regions. They assess the thermal state in terms of the physiologically equivalent temperature (PET) and determine PET values for Croatian cities including Rijeka and Split. The mortality increase appeared to be highest during the first 3–5 days of a heat wave event. Previous studies have shown positive trends of the air temperature in the Adriatic region. For 1951–2010, Branković et al. ( 2013 ) found trends in the air temperature up to 0.22°C per decade and for the more recent period (1981–2010) up to 0.71°C per decade. Radilović et al. (2020) used modeling results from the EURO-CORDEX project (Kotlarski et al. 2014 ) and estimated simulated air temperature trends (1951–2005) up to 0.40°C per decade, while the station observations show trends up to 0.24°C per decade. Results from this and other similar publications suggest a need for more observational studies providing a base for evaluation of hindcast climate simulations. Since the ocean and atmosphere are in a coupled climate system, it is valuable to examine the characteristics of sea surface temperature in the Adriatic, which exhibit prominent spatial and seasonal variability (Russo et al. 2015 ). An analysis of satellite data for the period 1982–2012 indicates that the surface temperature of the Mediterranean Sea has been significantly increasing by 0.35°C per decade, with values of 0.3°C per decade in the Adriatic in summer (Shaltout and Omstedt 2014 ) when most HWs occur. Using observed data in the eastern Adriatic from 1959 to 2015, Grbec et al. ( 2018 ) also find increasing trends in the SST with increases ranging from 0.22 to 0.32°C per decade in the period 1979–2015. Regarding climate projections of the SST, Shaltout and Omstedt ( 2014 ) used results from the CMIP5 ensembles (Taylor et al. 2012 ). They show that for the most severe emission scenario RCP85 within the 2000–2100 period, climate models predict an SST increase of 0.22°C per decade in the Adriatic with 0.25°C per decade increase in summer. Consequently, previous observational studies are evidence of increasing air and sea-surface temperature trends and it is of interest to study how these effects are affecting observed exceedances of the maximum temperatures and HWs. The main objectives of this study are to investigate characteristics of the Boreal Summer Season JJA measured Daily Maximum 2-m Air Temperatures (BSSDMATs) and associated HWs for 1961–2018 over the Croatian Adriatic coast. Results will document the frequency, climate trends, intensity in terms of a heat-wave index, peak temperatures, and regional similarities and differences of the exceedances and HWs. An analyzed span of 58 years offers insight into the main statistics of exceedances and HWs, but also the extent to which characteristics are altered within global and regional climate change. The study is organized as follows. Data and methods are introduced in Section 2. Section 3 presents results on statistics of the boreal summer season June-July-August measured daily maximum 2-m air temperatures and consequent characteristics of heat waves over the Croatian Adriatic coast in the period 1961–2018. Section 4 discusses synoptic conditions conducive to occurrence of longest duration HW events. Section 5 focuses on the BSSDMATs and heat-wave climate characteristics between the first (1961–1989) and the second (1990–2018) parts of the period. Summary and conclusion are given in Section 6. 2. Data and climate characteristics of the area To investigate the properties of maximum daily surface air temperatures and HW events for the period 1961–2018 along the Croatian Adriatic coast, three weather stations operated by the Croatian Meteorological and Hydrological Service were selected: Rijeka, Split, and Dubrovnik in the northern, middle, and southern parts, respectively. An upper-air station Zadar in the middle Adriatic is also included in the analysis. Measured daily maximum air temperatures were available for the period 1961–2018. A map of the entire region is shown in Fig. 1 and coordinates of the locations are in Table 1 . Table 1 Geographical coordinates and elevations of the selected coastal surface weather stations and upper-air station at Zadar (WMO code 14430) in the Croatian Adriatic. Station Latitude Longitude Elevation Rijeka 45° 20’ N 14° 27’ E 120 m Split 43° 31’ N 16° 26’ E 122 m Dubrovnik 42° 39’ N 18° 05’ E 52 m Zadar 44° 10’ N 15° 34’ E 79 m According to the Köppen climate classification, Rijeka is characterized by a relatively wet and mild climate (Cfa), while Split and Dubrovnik are warmer and drier because they are on the borderline between the humid subtropical and Mediterranean climate (Csa) (Filipčić 1998 ). Cfa is a climate characterized by the coldest month averaging above 0°C (or − 3°C), at least one month's average temperature above 22°C, and at least four months averaging above 10°C (Filipčić 1998 ). For further reading we recommend: Penzar et al. ( 2001 ); Zaninović et al. ( 2008 ), Pandžić et al. (2022). 3. Results 3.1 Time series of the JJA maximum air temperature at three coastal sites along the Croatian Adriatic coast in the period 1961–2018 Current analysis shows that the daily maximum temperatures during the summer months (JJA; 1961–2018) exhibit significant differences in the maximum and air temperature range at the three locations (Fig. 2). Thresholds for the 95 th and 99 th percentile criteria are indicated in the figure. Figure 2 indicates that there are different amplitude ranges in the daily maximum temperature among the stations but similar variability during the period. The maximum temperatures are well spatially correlated among these stations with the correlation coefficients between 0.77 and 0.88 (figure not shown). Greater maximum values appear to be in the second part of the period at all stations. An increasing trend of the daily maximum temperature can be visually inferred and will be tested in latter discussion. Time series of BSSDMAT show that the southern location has lower air temperature values and smaller amplitude of variation compared to the other two sites, which could be due to cooling effects of the deep southern Adriatic water and blocking of inland effects on temperature by coastal mountains (Fig. 1). 3.2 Basic statistics of the BSSDMATs Basic statistics of the BSSDMATs at these three weather stations (Table 2) generally follow the results from the climatologically average temperatures as mentioned in the previous text. The results show that Rijeka and Split belong to a similar region of the maximum temperature regime with the mean of the maximum temperature greater in July than in August for both locations, while for Dubrovnik the maximum in July is lower than in August. The maximum temperature distributions approximately follow a normal distribution evidenced by small differences between the mean and median values (figures not shown). Although at a lower latitude, maximum temperatures at Dubrovnik are lower than in Rijeka and Split. Consequently, the standard deviation is also smallest for Dubrovnik. The mean maximum temperature in August for Dubrovnik is lower than the mean temperatures for June in Rijeka and Split. Although an isolated maximum of 40 °C is in Rijeka, the greatest mean of JJA maximum temperatures is in Split. Table 2. Statistics of the daily maximum temperature (MxT) (°C) in June, July, and August, for the whole period 1961–2018 and separately for 1961–1989 and 1990–2018 for Rijeka, Split, and Dubrovnik. MxT 1961–2018 Rijeka Split Dubrovnik Mean MxT June 25.51 27.26 22.55 Mean MxT July 28.52 30.38 25.13 Mean MxT August 28.47 30.02 25.20 Mean MxT 1961–2018 27.52 29.24 24.31 Standard deviation 3.93 3.50 2.78 Abs. max MxT 40.0 38.5 33.4 Abs. min MxT 14.4 15.9 12.7 Med. MxT 1961–2018 27.6 29.5 24.5 MxT 61_89 vs.91_18 Mean MxT 1961–1989 26.37 28.53 23.57 Mean MxT 1990–2018 28.67 29.95 25.05 Diff. MxT (91_18-61_89) 2.30 1.42 1.48 Note that the average maximum temperature increased by 1.42-2.30 °C in the second part of the period (1990–2018) compared to the first period (1961–1989). It appears that the location in the northern Adriatic with shallow bathymetry has the most pronounced increase in the later period. The contrast in the maximum temperature distribution can be clearly seen when comparing data for the first (1961–1989) and the last (1990–2018) period (Fig. 3). Every calendar day in the first half of the period is compared with the same calendar day after 29 years – for example, 1 June 1961 is plotted against 1 June 1990, 2 June 1961 against 2 June 1990, etc. An increase in the maximum temperature for all stations is clearly present in the second part of the period. The number of cases when the temperatures are greater in the second period (1990–2018) compared to the first period (1961–1989) is 2.1 times larger for Rijeka and Dubrovnik and 1.7 times larger for Split. Considering the first decade (1961–1970) compared to the last one (2009–2018), the ratios are even more significant: 2.8, 2.1, and 2.8 for Rijeka, Split, and Dubrovnik, respectively. 3.3 Very hot days (VHDs) The analysis also includes a determination of Very Hot Days (VHDs) when a daily maximum temperature equals or exceeds 35 °C (Hoy et al. 2016). A distribution of VHDs by years is shown in Fig. 4. There is a definite increase in frequency and peak temperatures of VHDs in the second part of the period after 1990 compared to the first part (113 vs. 2 for Rijeka and 145 vs. 42 for Split). This is significant evidence of broader regional warming and generally of global warming. Maximum daily temperatures in Dubrovnik never reached the threshold of 35 °C (Table 2). Hoy et al. (2016) mentioned that there are only a few VHDs annually over the central Europe, but they are more frequent in southeast Europe. For example, they reported 18 VHDs in Vienna in 2015 which corresponds well with 17 and 15 VHDs in Rijeka and Split, respectively. In addition, high temperatures and associated heat stress cause degradation of health and eventual increase in mortality rates. One of the parameters for probability estimation of increased mortality rate is the Physiologically Equivalent Temperature (PET) (Mayer and Höppe 1987; Zaninović and Matzarakis 2014). Zaninović and Matzarakis (2014) estimated that the PET temperature thresholds for Rijeka and Split are 36.5 and 36 °C, respectively. There were 71 such days (more than one per year on average) in Split and 32 days in Rijeka during 1961–2018. 3.4 Trends of mean annual summer temperatures 1961–2018 Since BSSDMAT trends can be visually inferred (Fig. 2), it is important to examine time series of average summer JJA maximum temperatures for actual trends. The time series is discontinued (since only JJA are considered for each year), so averages of each warm season were calculated and then tested for trends (Fig. 5). There is a definite increasing temperature trend during these 58 years at these three stations (Fig. 5). Since the seasonal maximum temperatures show distinct differences comparing 1961–1989 vs. 1990–2018 periods (see e.g., Figs. 3 and 4), separate trends were calculated for these two periods (Fig. 5). The positive trend coefficients are all statistically significant at the 0.01 level and they are quite large for all three locations for the second part of the period, while there are small insignificant and slightly negative trends in the first part of the period (1961–1989). Average seasonal maximum temperatures for Rijeka are characterized by larger variability than for Dubrovnik and Split and the largest trend coefficient is 0.68 °C per decade (R 2 =0.55). The bathymetry is shallower in the northern Adriatic compared to the middle and southern regions and wind patterns exhibit pronounced variability (Orlić et al. 1994). Although average seasonal maximum temperatures for Dubrovnik are lower with a narrower amplitude, the trend coefficient (0.45 °C per decade; R 2 =0.50) is similar to the trend coefficient for Split (0.44 °C per decade; R 2 =0.41). This signifies that the effects of global and regional warming are increasing in time and consequences on the frequency and intensity of HWs will be further discussed in the later text (Section 5). Since the air and sea are in the coupled climate system, these very high trend coefficients in the seasonal averages of the maximum air temperature suggest examining SST trends in this region and relating them to the maximum air temperature trends. Shaltout and Omsted (2014) estimate SST trends in the Mediterranean using the 0.25° AVHRR daily measurements for 1982–2012. On average, they obtained increases of 0.38 °C per decade for the Adriatic. Summer trends were somehow lower (0.30 °C per decade) compared to spring trends (0.48 °C per decade). Although there is a difference in the period considered, both the SST and BSSDMAT data show significant regional warming of the sea and air in the Adriatic. 3.5 Analysis of heat wave events 3.5.1 Autocorrelation analysis of BSSDMATs While considering HWs, it is important to estimate both the length of each event and separation between consecutive events. To assure statistical independence of consecutive HWs which can allow the use of probability density functions and modeling, most studies consider that HW events can be assumed to be independent if the separation between consecutive events is greater than some specified number of days, for example, five days or more (Curriero et al. 2002; Keellings and Wylen 2012). However, in our study, an additional analysis of one- and two-day exceedances was also included to provide more insight into the general structure of the BSSDMAT extremes. Note that heat stress and eventual mortality rates might be significant in the first part of a HW (Zaninović and Matzarakis 2014) when the human body is still not well adapted to the new and severe heat stress conditions. To provide more insight into length of events, an autocorrelation analysis was conducted for all years and all locations. Autocorrelation function shapes can be useful for stochastic modeling of time series of climate variables (Pandžić 1984; Willks 2006). The average autocorrelation coefficients as functions of a time lag (in days) are shown in Fig. 6. Autocorrelation function properties are similar among all locations, especially for lags up to 4 days or so. They compare well with the autocorrelation function for the Markov process with r 1 =0.8 for temperature (Wilks 2006; Eq. 8.6). Note that the correlation coefficients are equal to about 0.5-0.3 for usual lags of 3-5 days, which is usually considered sufficient separation for taking HW events as independent and can be applied to a HW analysis. To further test the behavior of the autocorrelation function, an analysis was conducted for all years and all locations (Fig. 7). It appears that the properties of the autocorrelation functions are quite complex, as shown by examining individual years. In contrast to the average values, there is a large scatter of autocorrelation functions for each location and year. The spread is very large comparing individual years and locations. Even for one-day lag the correlation coefficient varies from 0.9 to 0.6 considering a spectrum of results for every year and every location. For the usual 5 days, the spread of the coefficients covers values from 0.7 to zero. There is some tendency of the autocorrelation coefficient in the first part of the period to drop at a faster rate compared to the second part of the period, but the variability of the coefficients in the second part of the period is quite large. A separation of 3-5 days reported in the literature can be taken as a general value, considering the usual periodicity of synoptic systems (which, of course, can be longer in the summer months and changing under global and regional warming). However, for detailed analysis, the autocorrelation functions could be considered separately for individual years. 3.5.2 95 th and 99 th percentile criteria for determining HWs Two approaches for determining HW events were applied: a) a maximum daily temperature in excess of the 95 th percentile will be estimated as a basis for determining heat waves; b) same as a) but for more severe conditions when the maximum daily temperature exceeds the 99 th percentile. 95th percentile criterion – main characteristics The determined number of HWs varies by location and threshold selected (Tables 3 and 4). Table 3. Number of HWs for each duration in days (Dur) and their total duration in days for Rijeka (RI), Split (ST), and Dubrovnik (DU) determined by the 95 th percentile criterion for JJA 1961–2018. No criterion on minimum separation between HWs was applied. p95 RI 33.8°C ST 34.5°C DU 28.6°C Cases Days Dur HWs Days Dur HWs Days Dur HWs Days total total 0 5077 0 5091 0 5080 15248 1 75 75 1 75 75 1 103 103 253 253 2 14 28 2 24 48 2 16 32 54 108 3 11 33 3 10 30 3 13 39 34 102 4 8 32 4 8 32 4 7 28 23 92 5 4 20 5 3 15 5 3 15 10 50 6 5 30 6 2 12 6 1 6 8 48 7 1 7 7 2 14 7 3 21 6 42 8 2 16 8 1 8 8 0 0 3 24 9 2 18 9 0 0 9 0 0 2 18 10 0 0 10 0 0 10 0 0 0 0 11 0 0 11 1 11 11 0 0 1 11 12 0 0 12 0 0 12 1 12 1 12 122 259 126 245 147 256 395 760 In 53 of the 58 years, HWs occurred at least at one location and in 30 years they were shown at all locations in the same year. There were a total of 4.7% of HW days in the whole period. The number of total HWs among the locations ranged from 122 to 147 with 245-259 total event days. Note that for the period of 58 years, the number of cases and especially the total number of days with HWs are similar for all locations indicating regional-scale characteristics. The maximum duration of determined HWs were 9, 11, and 12 days for Rijeka, Split, and Dubrovnik, respectively. The most frequent one-day exceedances are for Dubrovnik, possibly due to a narrow range of values where small changes in the temperature could become greater than the threshold and vice versa. Most likely are event durations of 3-4 days. Events of more than 7 days duration are quite rare. 99 th percentile criterion – main characteristics Table 4 shows the basic statistics of the HWs according to the 99 th percentile criterion. The maximum duration length for all locations was 5 days. Table 4. Number of heat waves (HWs) for each duration (Dur) and their total duration in days (Days) for Rijeka (RI), Split (ST), and Dubrovnik (DU) determined by the 99 th percentile criterion for JJA 1961–2018. No restriction on minimum separation between the HWs was applied (as discussed in the beginning of Section 4 – autocorrelation analysis). p99 RI 35.8°C ST 36.1°C DU 30.1°C Cases Days Dur Cases Days Dur Cases Days Dur Cases Days total total 0 5282 0 5284 0 5285 15851 1 17 17 1 19 19 1 27 27 63 63 2 7 14 2 10 20 2 5 10 22 44 3 3 9 3 1 3 3 3 9 7 21 4 1 4 4 0 0 4 0 0 1 4 5 2 10 5 2 10 5 1 5 5 25 30 54 32 52 36 51 98 157 The 99 th percentile limit indicates a total of »1% extreme conditions (days) within the whole period. Note that this stricter criterion provides numbers of events and associated days among the locations similar to the 95 th percentile case. Consequently, the results for the 99 th percentile criterion indicates similar uniform regional characteristics of HWs as for the 95 th percentile criterion over the coastal Adriatic. 3.5.3 Simultaneous occurrence of HWs on an annual basis Heat waves at at least one location were determined in 53 out of 58 years considering the 95 th percentile criterion (Fig. 8). Annual variations in terms of an index (yes/no) of HW occurrence in each year are shown in Fig. 8. Indices 0, 1, 2, and 3 represent numbers indicating how many simultaneous locations had HWs present for a particular year. For example, index 3 means that HWs occurred for all three locations in a particular year, while index 2 means that HWs were determined for two locations. Regarding the 95 th percentile criterion, the extreme temperatures and HWs occurred in 34, 45, and 44 out of 58 years for Rijeka, Split, and Dubrovnik, respectively. Considering all stations, there were only 5 years (within the period 1976–1997) in which there were no HWs. In 30 years (24 years in and after 1990), HW occurred at all three locations. After 2000, for almost all years (except 2014) HWs occurred at all locations. Regarding the 99 th percentile criterion, HWs were predominantly absent before 1990. The extreme temperatures and HWs occurred in 17, 15, and 19 out of 58 years for Rijeka, Split, and Dubrovnik, respectively. In 31 years, there were no HWs at any station, while in 9 years there were HWs at all locations. Two or more events per year occur only on and after 1998. All these results further confirm previous conclusions that the second part of the period is characterized by much larger occurrence of HWs indicating the effects of global and regional warming processes. 3.5.4 Frequency and duration The total number of HW events and duration of each event per year in the period 1961–2018 for Rijeka, Split, and Dubrovnik using the 95 th and 99 th percentile methods is shown in Fig. 9. The total number of HW days significantly increased over time (Fig. 9, right panels). For both parameters and both criteria, the values generally fall below the 1:1 line (higher values correspond to later years). For later years the intervals between the highest and lowest values (frequency; total event days) are increasing, i.e., higher values are more likely in the later years. The total number of HW days significantly increased in time. This is especially pronounced for Rijeka and Split and to a lesser extent for Dubrovnik. Only after 1989 do HW lengths of 8 days or more (3 or more) occur for the 95 th (99 th ) percentile criteria. The annual frequency of HW days follows increasing BSSDMAT trends over time as shown in Figs. 2 and 5. Figure 9 shows a significant increase in events per year and corresponding duration days after 1989. More than 10 (4) days annual duration of HWs for the 95 th (99 th ) percentile criterion, respectively, occurred in and after 1994. 3.5.5 Heat wave intensity – Heat-wave index (HWI) Heat wave intensity can be examined in terms of ratios between the peak vs. threshold temperatures (95 th and 99 th percentile criteria) during the length of a HW. Distributions of peak maximum temperatures for each event show distinct differences in amplitudes between the northern Adriatic with shallow bathymetry and the southern region with the cooler influence of the deeper Adriatic Sea (Fig. 10). The peak temperatures within HWs increase over time with larger amplitudes compared to the average values. There is clear clustering, with increasing values after the 1990s. Note that the extreme values occurred at all locations and for both criteria in the last decade (2009-2018). The ratios between the peak and threshold temperatures can be assumed as a heat-wave index (HWI) for both criteria (Fig. 11). Besides an increasing number of events in time, the indices are increasing with greater variability in time. Note that the majority of greater indices are in the second part of the period. In accordance with the previous results, this figure also confirms that a significant number of HW events occurred in the second part of the period and that the HWI becomes greater in time (7 to 18% in all cases using both thresholds). The greatest HWI values are in the last decades of the period. The maximum HWIs with respect to the threshold temperatures using the 95 th (99 th ) percentiles are 1.18 (1.12) for Rijeka, 1.12 (1.07) for Split, and 1.17 (1.11) for Dubrovnik. The smaller ratios of the maximum compared to the threshold temperature for the 99 th criterion are caused by the higher threshold and smaller range of the BSSDMAT values. Note that the ratio between the peak and threshold temperatures can be used as an intensity index (HWI) for future comparison studies for other locations and other times. 4 Longest duration events Maximum HW durations in the considered period are 9, 11, and 12 for Rijeka, Split, and Dubrovnik, respectively (Table 5). Note that an event in early August 2017 occurred at all locations, although not with the same length. Table 5. The longest HW events based on the 95 th and 99 th percentile thresholds for Rijeka, Split, and Dubrovnik, 1961–2018. An event in 2017 for Rijeka is added since the timing partially coincides with the longest events in Dubrovnik and Split. p95 Duration (days) From To Year Av Tmx (°C) Peak Tmx (°C) Rijeka 9 20.Jul 28.Jul 2006 36,4 37,2 Rijeka 6 01.Aug 06.Aug 2017 37,15 39,5 Split 11 01.Aug 11.Aug 2017 36.49 37,9 Dubrovnik 12 31.Jul 11.Aug 2017 29,65 32 p99 Rijeka 5 20.Jul 24.Jul 2006 36,66 37,2 Rijeka 5 04.Aug 08.Aug 2013 37,54 39,2 Split 5 18.Jul 22.Jul 2015 37,26 38,1 Dubrovnik 5 06.Aug 10.Aug 2012 31,06 33,4 All these extreme cases occurred in and after 2006. This further confirms the increasing severity over time of the HWs in the Croatian Adriatic coast. A significant HW episode was from 1 to 6 August 2017 for all three stations. Note that the average event temperatures at Rijeka and Split were above the VHD starting limit. 4.1 Weather analysis of the extreme HW event in July/August 2017 Synoptic conditions The longest HW occurred from 31 July to 11 August 2017 in Dubrovnik. It was a regional event since HWs were also recorded in Rijeka (1-6 August) and Split (1-11 August). The weather in the Adriatic area was influenced by a strong and wide ridge from the Azores High extending to southern and southeastern Europe (Fig. 12). Figure 12 shows the development of the surface pressure field and 500 hPa geopotential prior to (27 July), during (4 August), and after the event (13 August). The HW was at a maximum when the ridge developed from the surface all the way up to 500 hPa and beyond. The ridge was gradually strengthening and blocking a low centered at the northern UK and Scandinavia. During the event, heat propagated from Northwestern Africa via the Iberian Peninsula and the western Mediterranean toward Eastern Europe. Warm subtropical air masses were steered to the Adriatic area and further to the east. The end of the event occurred during the beginning of the strengthening of the lows with frontal systems The Azores High kept the circulation unchangeable until the end of the HW episode. Back-trajectory analysis also confirms that advection from the west-southwest within the high ridge dominated during the event (figure not shown). 500-1000 hPa thickness Characteristics of the HWs can be also seen in the 500-1000 hPa thickness (hereafter RT) (Table 6) over Europe examining days before, during, and after the event. Ten days prior to the event there was a minimum RT on 27 July – a cold low connected to the northwest cold region. The maximum RT values were in the middle of the event on 4 August – a broad and warm ridge over the southern Europe extending from the southwest. The disappearance of the event was characterized again by a minimum RT – a cold low propagating from the northwest after the event on 13 August. Temperature values aloft and RT for times prior to, during, and after the event at the Zadar station (see Fig.1 and Table 1) are listed in Table 6. Table 6. Ambient temperatures (°C) at the 850, 700, 500, and 300 hPa levels, and 500-1000 hPa thickness (RT500/1000) (gpm) prior to (27 July), during (4 August), and after (13 August 2017) the HW event from radiosonde measurements at Zadar (Fig. 1 and Table 1). Date Temperature ( °C ) RT 500/1000 (gpm) 850 hPa 700 hPa 500 hPa 300 hPa 27/7/2017 14.0 -0.1 -16.1 -42.3 5598 4/8/2017 22.0 9.8 -7.7 -33.5 5820 13/8/2017 11.0 0.2 -14.9 -36.1 5600 Note that warming (8-10 °C) at all levels is significant during the event compared to the prior date (27 July) and after date (13 August) at all levels. RT during the event is about 200 gpm higher compared to both the prior and after dates. Consequently, the properties of this extreme-duration HW propagated aloft and event warming was present up to 500 hPa. Radiosonde measurements Development of this extreme event can be further examined using radiosonde data from the Zadar station (44.10°N; 15.34°E; 79.0 MSL) which is in the middle of the Croatian Adriatic coast (Fig. 1). During the event, significant warming and drying occurred in the lowest 5 km or so as compared to the time prior to and after the event (Fig. 13; Table 6). In summary, this longest HW event recorded at all three stations occurred because of deep warming perturbations throughout the atmosphere caused by the propagation of a broad ridge (all the way up to 500 hPa and beyond) from the southwest over southern and southeastern Europe. Prior to the event and after the event, cold lows from the northwest encroached on the area limiting development of the HW. The upper-air analysis suggests that an HW analysis should generally also include upper-air conditions. 5 Heat wave differences between the first (1961–1989) and the second (1990–2018) parts of the period According to the results, there are distinct differences in the properties of the BSSDMAT between the first (1961–1989) and the second (1990–2018) parts of the analyzed period (Table 2). The observed increasing seasonal temperature trends for 1990–2018 are significant (a=0.01) for all three stations with large coefficients from 0.44 to 0.68 °C per decade, while there are no significant trends for 1961–1989. Although overall trends for the whole period show significant trends, separation into the first and second parts indicate that trends in the second part are much higher than for the overall period. This confirms that the effects of global and regional warming are increasing in time and consequences on the frequency and intensity of HWs considering 1961–1989 vs. 1990–2018 periods (Table 7). Table 7. A number of HW cases based on the daily maximum temperature and their duration (all lengths ranging 1-12 days) estimated by the 95 th percentile and 99 th percentile criteria for JJA 1961–1989 and 1990–2018 periods for Rijeka, Split, and Dubrovnik. No minimum separation between the HWs was applied. P95 1961–1989 1990–2018 HW cases Dubrovnik 31 116 Rijeka 12 110 Split 35 75 Total 78 301 Durat. Days Dubrovnik 39 216 Rijeka 20 239 Split 56 189 Total 115 644 P99 1961–1989 1990–2018 HW cases Dubrovnik 3 33 Rijeka 0 30 Split 6 32 Total 9 95 Durat. Days Dubrovnik 3 48 Rijeka 0 54 Split 8 44 Total 11 146 There is a large increase in the number and duration of HWs in the second period compared to the first period as determined by both criteria and for all locations. The HWs cases increased more than three times and duration days increased by more than five times in the second period compared to the first period (95 th percentile criterion). For the 99 th percentile criterion, the number of cases and total days is about ten times greater in the second part of the period. Moreover, previous figures (e.g., Figs. 8-11) show that the increases in frequency and intensity are even larger comparing the first and last decades. Some insight into the effects of regional warming on the air temperatures between the first and second periods can be seen from a broader perspective, i.e., time series of the average air temperature for 1901-2021 for whole Croatia (Fig. 14). Despite inter-annual variations, the smoothing average clearly separates the 1960–1990 period from the 1990–2021 period. After 1990 the average almost uniformly increases. This is further confirmed by an increase of the JJA annual average of the maximum temperature for all of Croatia for 1991-2020 (26.97 °C) compared to 1961–1990 (25.17 °C) (World Bank Group 2023). Although the results also depend on the resolution of the grid (0.5° x 0.5°), their analysis indicates that the largest increase was recorded in the southern Adriatic coast. Our analysis (Table 2) shows that the largest increase of the BSSDMAT is for Rijeka (northern Adriatic), but the grid resolution of the World Bank Group data might not be able to resolve the complex orographic setups of the coast, Istrian Peninsula, and islands (Fig. 1). However, Table 2 shows that the increase of the BSSDMAT is greater for Dubrovnik (southern Adriatic) than for Split (middle Adriatic). 6 Summary and conclusions Properties of the Boreal Summer Season JJA Daily Maximum 2-m Air Temperatures (BSSDMATs) and associated heat waves (HWs) on the Croatian Adriatic coast based on the 95th and 99th percentiles for 1961–2018 were analyzed. Three locations were selected: Rijeka (northern), Split (middle), and Dubrovnik (southern region). Before 10 June and after 24 August there were no HWs determined for any of the locations. Since the intensity of heat stress in general stronger affects health at the beginning of a HW when the human body has not adapted to new conditions yet, exceedances of one or more days were all considered irrespective of any minimum length or separation. Brief summary conclusions for climate variations of each of the main BSSDMAT and HW characteristics follow. Frequency . Exceedances and associated HW events occurred in 53 (27) out of 58 years and in 30 (9) years they were present at all three locations in the same year for the 95th (99th ) percentile. At all three locations, there were 122–147 (30–36) cases lasting 245–259 (51–54) days for 95th (99th ) limits. Considering JJA for all 58 years, the HWs occurred in 4.7% (1%) of total days for the 95th (99th ) criterion. Generally, all three distant locations show similar numbers of events and days duration for both criteria. For Dubrovnik, the BSSDMAT range is lower compared to Rijeka and Split due to smoothing effects on temperature of the deep southern Adriatic and blocking of inland effects on temperature by coastal mountains. However, statistics of HWs for Dubrovnik are similar to Rijeka and Split, indicating regional signature in this type of events, especially using the 99th percentile criterion. When restricting the analysis to HWs with only 3 or more days length and separation of one or more days between the events, there were 27–33 (3–6) cases lasting 121–156 (13–23) days for the 95th (99th ) percentile limits. The number of cases when the BSSDMAT is 2.1 times greater in the second half of the period (1990–2018) compared to the first part of the period (1961–1989) for Rijeka and Dubrovnik, and 1.7 times greater for Split. Comparing the first decade (1961–1970) to the last one (2009–2018), the numbers are even greater: 2.8 for Rijeka and Dubrovnik, and 2.1 for Split. BSSDMAT and HW trends . There were significant (α = 0.01) warm-season average BSSDMAT trends in the second part of the period (1990–2018) for all three locations, ranging from 0.44 to 0.68°C per decade. Small insignificant and even slightly negative trends were observed in the first part of the period. There is an increase of more than three times the number of HW events in the second part of the period (1990–2018) compared to the first part of the period (1961–1989) with an increase in duration days of more than five times. Regarding the 99th percentile criterion, the increase was even ten times more for both the number of cases and the total number of HW days. Intensity of HWs and VHDs . Similar HW intensity was found for all stations. Intensity in terms of the heat-wave index (HWI) as a ratio between the peak temperature vs. threshold temperature grew in time with maxima for all locations of 1.12–1.18 (1.07–1.12) for the 95th (99th ) percentile threshold. Other parameters such as the number of very hot days (VHD) and physiologically equivalent temperature (PET) clearly occurred more in the second half of the period compared to the first half of the period. In most of the plots, after the year 1990 there is a pronounced increase in frequency and intensity of the HWs. Event duration and synoptic conditions conducive to exceedances . According to the medians of the beginning date (16 July) of the first HW and the end date of the last HW (15 August) in a year, the most likely span between the beginning and end of the events is 30 days. The maximum duration of the events was 12 (5) days for the 95th (99th) thresholds. The most intensive HW event occurred from 31 July to 11 August 2017, when HWs were estimated for all locations covering at least part of the period. Weather conditions for the longest duration event, and similarly for other extreme events, were characterized by a strong and wide ridge from the Azores High extending to southern and southeastern Europe and propagating all the way up to 500 hPa and beyond. Prior to and after the event, a cold low from the north was obstructing the ridge. The development of the HW was associated with significant upper-level warming and consequent stability aloft with a maximum of 500–1000 hPa thickness during the event. The analysis results emphasize that the effects of global and regional warming are increasing in time and consequently impact the frequency and intensity of HWs considering differences between1961–1989 and 1990–2018 periods. This and similar observational studies represent a basis for investing capabilities of hindcast climate modeling results, especially in coastal regions. Climate modeling projections will provide possible future characteristics of the heat waves within global and regional warming and their severity as indicated by the estimated historical temperature trends. Declarations Author Contribution D.K. conceptualization, data curation, wrote the main manuscript text, prepared figures. K.P. contributed to analysis and text.K.V.K. contributed to analysis and text, prepared figures.All authors reviewed the manuscript. Acknowledgments We thank the Croatian Meteorological and Hydrological Service for supplying the data. DK was supported under project STIM—REI, Contract Number: KK.01.1.1.01.0003, a project funded by the European Union through the European Regional Development Fund—Operational Programme Competitiveness and Cohesion 2014-2020 (KK.01.1.1.01). DK also acknowledges significant support from the University of Notre Dame, USA (ONR Grant: N00014-21-1- 2296). The study was also partially funded by the project CAAT (Coastal Auto-purification Assessment Technology) funded by the European Union from European Structural and Investment Funds 2014—2020, Contract Number: KK.01.1.1.04.0064. KVK was supported under the Science Fund of the Republic of Serbia, Program PRIZMA, project Extreme weather events in Serbia - analysis, modelling and impacts (EXTREMES), grant No. 7389. We thank Mr. Dragomir Bulatović for preparation of Figure 1. Data Availability Data can be obtained from https://meteo.hr/proizvodi_e.php?section=proizvodi_usluge¶m=services References Artegiani A, Bregant D, Paschini E, Pinardi N, Raicich F, and Russo A (1997) The Adriatic Sea general circulation. Part I: Air–sea interactions and water mass structure. J Phys Oceanogr 27:1492–1514 Branković Č, Güttler I, Gajić-Čapka M (2013) Evaluating climate change at the Croatian Adriatic from observations and regional climate models' simulations. Clim Dyn 41:2353-2373 https://doi.org/10.1007/s00382-012-1646-z Curriero FC, Heiner KS, Samet JM, Zeger SL, Strug L, Patz JA (2002) Temperature and mortality in 11 cities of the eastern United States. 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Cite Share Download PDF Status: Published Journal Publication published 19 Oct, 2024 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 22 Aug, 2024 Reviews received at journal 15 Aug, 2024 Reviewers agreed at journal 31 Jul, 2024 Reviewers invited by journal 05 Jul, 2024 Editor assigned by journal 30 Jun, 2024 Submission checks completed at journal 30 Jun, 2024 First submitted to journal 28 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4655203","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325064998,"identity":"acc9b8ba-8b64-4395-bd16-adddc41dcde9","order_by":0,"name":"Darko Koračin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDCCM2BSgoEfSH4A4gTitUg2MDDOAGogWgsDg8EBYrXwnTl87MOPGos849vdiY0/fzDkmTcQ0CJ5ti15Zs8xiWKzO2c3NvMkMBTLHCCgxeA8jzEDb4NE4rYbudsfA52VOIOQw0BaGP8CtWyekbux8QdRWs72GDODbNkgkbuxgYcYLZJnjiUzyxyTSJwB9kuaBGEtfGeSDzO+qalL7J/dC3SYjQ1hLQgggUSSpGUUjIJRMApGASYAAHY8RLg7jeyVAAAAAElFTkSuQmCC","orcid":"","institution":"University of Split","correspondingAuthor":true,"prefix":"","firstName":"Darko","middleName":"","lastName":"Koračin","suffix":""},{"id":325065001,"identity":"b28070fd-d16c-4070-91e3-5e02e97ac0f3","order_by":1,"name":"Krešo Pandžić","email":"","orcid":"","institution":"Retired from Croatian Meteorological and Hydrological Service","correspondingAuthor":false,"prefix":"","firstName":"Krešo","middleName":"","lastName":"Pandžić","suffix":""},{"id":325065002,"identity":"dfc7f258-1716-4eb9-b9d6-a01cbd4c861b","order_by":2,"name":"Katarina Veljović Koračin","email":"","orcid":"","institution":"University of Belgrade","correspondingAuthor":false,"prefix":"","firstName":"Katarina","middleName":"Veljović","lastName":"Koračin","suffix":""}],"badges":[],"createdAt":"2024-06-28 13:50:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4655203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4655203/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-024-05206-z","type":"published","date":"2024-10-19T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60912590,"identity":"473b587e-4019-4bee-bbf3-19b9e319c05e","added_by":"auto","created_at":"2024-07-23 13:04:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":291998,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Croatian Adriatic coast with indicated locations of weather stations whose data were used in the analysis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/bc91ee4c7d3f5fa370c223cb.png"},{"id":60912591,"identity":"bbdfd8f2-fca1-45ff-8065-f190a583105d","added_by":"auto","created_at":"2024-07-23 13:04:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":169926,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the boreal summer maximum daily temperatures for Dubrovnik (a), Rijeka (b), and Split (c) for JJA 1961-2018. The 95\u003csup\u003eth\u003c/sup\u003e and 99\u003csup\u003eth\u003c/sup\u003e percentiles are indicated by dashed and solid lines, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/cf6697616ed4f70271b31e02.png"},{"id":60913522,"identity":"bff2d927-168d-4583-b1ac-0a2d70388c2f","added_by":"auto","created_at":"2024-07-23 13:12:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110047,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot between maximum temperatures for Rijeka (o), Split (*), and Dubrovnik (x) for JJA calendar days 1961–1989 vs. calendar days 1990–2018 data. Red color denotes values above the 1:1 line and black below the line.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/c905eb9835f561aa4cdfc11c.png"},{"id":60912595,"identity":"28fdd8d9-95a5-4123-b3bb-c6640c625994","added_by":"auto","created_at":"2024-07-23 13:04:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":616039,"visible":true,"origin":"","legend":"\u003cp\u003eVery hot days (maximum daily temperature equal or greater than 35°C) by years for Rijeka (o) and Split (*) for the period JJA 1961–2018.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/7884477350b1b29582bc5ad6.png"},{"id":60912594,"identity":"72f329db-50bd-461e-bb75-1f0ca14ef919","added_by":"auto","created_at":"2024-07-23 13:04:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111493,"visible":true,"origin":"","legend":"\u003cp\u003eMean seasonal JJA average of the daily maximum temperature for each year JJA 1961–2018 for Rijeka (blue o), Split (red *), and Dubrovnik (black x). Linear trends are also indicated separately for 1961–1989 and 1990–2018.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/0323747f7db841eb74daf8bd.png"},{"id":60912593,"identity":"2b8234a8-3398-4f68-8f95-4c5df813a854","added_by":"auto","created_at":"2024-07-23 13:04:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70790,"visible":true,"origin":"","legend":"\u003cp\u003eMean autocorrelation functions for the BSSDMATs over 58 years for RI (o), ST(*), and DU (x) for the period JJA 1961–2018. The theoretical autocorrelation function for the Markov process is marked by blue squares, based on empirical autocorrelation coefficients for a time lag of one day (r\u003csub\u003e1\u003c/sub\u003e = 0.8).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/e7fc78fef645afb1ee9a6f69.png"},{"id":60912597,"identity":"62447139-7761-4718-8bfd-46ea20b90fdf","added_by":"auto","created_at":"2024-07-23 13:04:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":209206,"visible":true,"origin":"","legend":"\u003cp\u003eAutocorrelation coefficient BSSDMATs for each of 58 years vs. lag in days for Rijeka, Split, and Dubrovnik. Period JJA 1961–1989 (black), 1990–2018 (red). The theoretical autocorrelation function for the Markov process is marked by blue circles. A vertical dashed line indicates results for a lag of 3 days.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/ca029ba3d8a80bc76fac368c.png"},{"id":60912601,"identity":"6fecf35b-9dd8-4cac-af5a-f77db5cc91cf","added_by":"auto","created_at":"2024-07-23 13:04:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":389694,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of an index specifying the number of HWs occurring at one (index=1), two (index=2), or three (index=3) locations in each year. 95\u003csup\u003eth\u003c/sup\u003e percentile criterion (o), 99\u003csup\u003eth\u003c/sup\u003e percentile criterion (*). The period is JJA 1961–2018.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/403c871ffed8ac5bf53c1455.png"},{"id":60913523,"identity":"e05721bb-e67a-4a9c-a49d-dfe8974431ba","added_by":"auto","created_at":"2024-07-23 13:12:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":247479,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of total HW cases (a,c) and total event days (b,d) for JJA in each year (1961–2018) for Rijeka (o), Split (*), and Dubrovnik (x) determined by the 95\u003csup\u003eth\u003c/sup\u003e percentile (a,b) and the 99\u003csup\u003eth\u003c/sup\u003e percentile (c,d). The period is JJA 1961–2018.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/bffb76579eb74fdfc9e4a06e.png"},{"id":60912605,"identity":"3d29b0dd-0283-42e0-91d2-97d95fb64f34","added_by":"auto","created_at":"2024-07-23 13:04:22","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":195380,"visible":true,"origin":"","legend":"\u003cp\u003ePeak event temperatures for Rijeka (o), Split (*), and Dubrovnik (x) determined by the 95\u003csup\u003eth\u003c/sup\u003e (a) and 99\u003csup\u003eth\u003c/sup\u003e (b) percentile criteria. The period is JJA 1961–2018.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/72236a53f2184938a8f02eb0.png"},{"id":60912603,"identity":"74ea9862-e7d0-4a7d-bf4d-be4aa49c4ed9","added_by":"auto","created_at":"2024-07-23 13:04:19","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":211673,"visible":true,"origin":"","legend":"\u003cp\u003eHeat-wave index (HWI) vs. year in each event for the 95\u003csup\u003eth\u003c/sup\u003e (a) and the 99\u003csup\u003eth\u003c/sup\u003e (b) thresholds for Rijeka (o), Split (*), and Dubrovnik (x). The period is JJA 1961–2018.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/65b61bdd197b8e09f5ed5557.png"},{"id":60914146,"identity":"4fddc977-7f67-4711-818d-1bde8c68aaa7","added_by":"auto","created_at":"2024-07-23 13:20:19","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":836769,"visible":true,"origin":"","legend":"\u003cp\u003eECMWF analysis of the sea level pressure (hPa) [a,b,c], and geopotential heights (solid lines, dam) and temperature (dashed lines, °C) at 500 hPa [d,e,f]; prior to (27 July 00 UTC) [a,d], during (4 August 00 UTC) [b,e], and after the event (13 August) [c,f] over Europe.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/91075682da4827ef459db2a9.png"},{"id":60913525,"identity":"b0be8823-29ae-4f44-b79b-e7247016bf6e","added_by":"auto","created_at":"2024-07-23 13:12:19","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":525837,"visible":true,"origin":"","legend":"\u003cp\u003eSkew-T diagrams from radiosonde profiles at the Zadar station prior to (00 UTC 27 July) [a], during (00 UTC 4 August) [b], and after the event (00 UTC 13 August 2017) [c].\u003c/p\u003e\n\u003cp\u003e(From: \u003ca href=\"https://weather.uwyo.edu/upperair/sounding.html\"\u003ehttps://weather.uwyo.edu/upperair/sounding.html\u003c/a\u003e)\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/e4972510bb9b145589320756.png"},{"id":60913526,"identity":"392a6a0f-856c-4476-99c2-039156b03d01","added_by":"auto","created_at":"2024-07-23 13:12:19","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":88783,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the annual air temperature (°C) with a 5-year smoothing average for Croatia for 1901-2021. From the World Bank Group - Climate Change Knowledge Portal. The data are produced based on a 0.5° x 0.5° grid made by the Climatic Research Unit (CRU) of the University of East Anglia.\u003c/p\u003e\n\u003cp\u003eSource: \u003ca href=\"https://climateknowledgeportal.worldbank.org/country/croatia/climate-data-historical\"\u003ehttps://climateknowledgeportal.worldbank.org/country/croatia/climate-data-historical\u003c/a\u003e\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/a8e0610efae44ebfac1196a8.png"},{"id":67148701,"identity":"a3eb4850-349e-4ba0-8d82-99e8d46be253","added_by":"auto","created_at":"2024-10-21 16:06:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5556323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4655203/v1/1aee2636-1b19-43c9-bbe9-c70331a6a4dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate Variations of Heat Waves on the Croatian Adriatic Coast for the Period 1961–2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHeat waves (HWs) represent severe weather events when maximum or average daily air temperature exceeds a certain threshold for a number of consecutive or near- consecutive days (Keelings and Waylen 2012). They can have a significant and harmful impact on humans and the environment. Occurrence of HWs appears to be a consequence of both natural and very probably anthropogenic drivers (IPCC 2021). In addition to weather and climate impacts on human beings and the environment, socioeconomic impacts are also reaching devastating levels. Some prominent cases analyzed in the literature occurred in Chicago in 1996 (Karl et al. 1997) and in Paris in 2003 (Sch\u0026auml;r et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). After a devastating HW across Europe, and France in particular, Stott et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) analyzed this type of phenomena from a climate perspective. They find that a long-term threshold for mean summer temperature was exceeded in 2003 for the first time since the start of the instrumental record in 1851 and estimate that it is very likely that human influence has at least doubled the risk of a HW exceeding this threshold magnitude. Severe heat waves frequently cause considerable damage to human health and mortalities worldwide (Frost et al. 1992; Kunkel et al. 1996; Guest et al. 1999; Kysely and Huth 2004; Larsen 2006; Tan et al. 2007; Tobias et al. 2010; Zaninović and Matzarakis \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In August 2003, a HW in France caused almost 15,000 deaths (Poumad\u0026egrave;re et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Based on observations results, Meehl and Tebaldi (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) indicated that HWs over North America and Europe will be more frequent, of longer duration, and will be intensified in the future due to ongoing global warming.\u003c/p\u003e \u003cp\u003eThere are various definitions of heat waves and their severity. Many countries have their own definitions of HWs, mainly based on summer maximum temperatures exceeding high percentiles or pre-determined threshold values. Della-Marta et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) define a \u003cem\u003ehot day\u003c/em\u003e as when the maximum temperature is greater than the long-term 95th percentile of daily maximum temperature. They identify a heat wave as the maximum number of consecutive days in which the daily maximum temperature is greater than the 95th percentile.\u003c/p\u003e \u003cp\u003eThere are also studies defining a heat wave based on both maximum and minimum daily temperatures. Kuglitsch et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) specify a \u003cem\u003ehot day\u003c/em\u003e and \u003cem\u003ehot nigh\u003c/em\u003e as one where the daily maximum temperature and daily minimum temperature exceed the long-term daily 95th percentile, respectively. In this case, a heat wave event is defined as a period of three or more consecutive hot days and nights not interrupted by more than one non-hot day or night.\u003c/p\u003e \u003cp\u003eAccording to Giorgi (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Diffenbaugh and Giorgi (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the Mediterranean can be assumed to be a \u003cem\u003ehot spot\u003c/em\u003e vulnerable to climate change. It is definitely of interest to investigate regional properties of this vulnerability including heat-wave phenomena. One Mediterranean region of interest is the eastern coast of the Adriatic, which is characterized by high complexity of coastal hinterland, including coastline, a spectrum of wind regime characteristics, variations in bathymetry and sea currents, as well as interaction of the Adriatic and the Ionian Sea through the Otranto Strait. Artegiani et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) discuss both atmospheric and oceanic properties over the Adriatic. Their analysis based on data from 1980\u0026ndash;1988 shows mean patterns of air temperature over the Adriatic with values increasing from the northwest to the southeast. Although HWs can occur in all seasons, the most intensive ones are in boreal summer with stronger air temperature gradients over the northern Adriatic. Maximum average air temperatures are about 23\u0026deg;C and 26\u0026deg;C on the most northern and most southern sides, respectively; bora and sirocco winds are prevailing, especially during the colder part of the year, while coastal circulations (land and sea breezes) develop during a warm season. The significant difference in bathymetry, between the shallow northern and deep southern parts of the Adriatic and river inflow in the northern Adriatic Sea, causes differences in the seasonal characteristics of the sea and air temperatures and is strongly influences the ecosystem of the Adriatic (Zavatarelli, et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Spillman et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Zaninović and Matzarakis (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) study impact of heat waves on mortality in the hinterland and coastal Croatia regions. They assess the thermal state in terms of the physiologically equivalent temperature (PET) and determine PET values for Croatian cities including Rijeka and Split. The mortality increase appeared to be highest during the first 3\u0026ndash;5 days of a heat wave event.\u003c/p\u003e \u003cp\u003ePrevious studies have shown positive trends of the air temperature in the Adriatic region. For 1951\u0026ndash;2010, Branković et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found trends in the air temperature up to 0.22\u0026deg;C per decade and for the more recent period (1981\u0026ndash;2010) up to 0.71\u0026deg;C per decade. Radilović et al. (2020) used modeling results from the EURO-CORDEX project (Kotlarski et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and estimated simulated air temperature trends (1951\u0026ndash;2005) up to 0.40\u0026deg;C per decade, while the station observations show trends up to 0.24\u0026deg;C per decade. Results from this and other similar publications suggest a need for more observational studies providing a base for evaluation of hindcast climate simulations.\u003c/p\u003e \u003cp\u003eSince the ocean and atmosphere are in a coupled climate system, it is valuable to examine the characteristics of sea surface temperature in the Adriatic, which exhibit prominent spatial and seasonal variability (Russo et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). An analysis of satellite data for the period 1982\u0026ndash;2012 indicates that the surface temperature of the Mediterranean Sea has been significantly increasing by 0.35\u0026deg;C per decade, with values of 0.3\u0026deg;C per decade in the Adriatic in summer (Shaltout and Omstedt \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) when most HWs occur. Using observed data in the eastern Adriatic from 1959 to 2015, Grbec et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also find increasing trends in the SST with increases ranging from 0.22 to 0.32\u0026deg;C per decade in the period 1979\u0026ndash;2015. Regarding climate projections of the SST, Shaltout and Omstedt (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) used results from the CMIP5 ensembles (Taylor et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). They show that for the most severe emission scenario RCP85 within the 2000\u0026ndash;2100 period, climate models predict an SST increase of 0.22\u0026deg;C per decade in the Adriatic with 0.25\u0026deg;C per decade increase in summer. Consequently, previous observational studies are evidence of increasing air and sea-surface temperature trends and it is of interest to study how these effects are affecting observed exceedances of the maximum temperatures and HWs.\u003c/p\u003e \u003cp\u003eThe main objectives of this study are to investigate characteristics of the Boreal Summer Season JJA measured Daily Maximum 2-m Air Temperatures (BSSDMATs) and associated HWs for 1961\u0026ndash;2018 over the Croatian Adriatic coast. Results will document the frequency, climate trends, intensity in terms of a heat-wave index, peak temperatures, and regional similarities and differences of the exceedances and HWs. An analyzed span of 58 years offers insight into the main statistics of exceedances and HWs, but also the extent to which characteristics are altered within global and regional climate change.\u003c/p\u003e \u003cp\u003eThe study is organized as follows. Data and methods are introduced in Section 2. Section 3 presents results on statistics of the boreal summer season June-July-August measured daily maximum 2-m air temperatures and consequent characteristics of heat waves over the Croatian Adriatic coast in the period 1961\u0026ndash;2018. Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses synoptic conditions conducive to occurrence of longest duration HW events. Section 5 focuses on the BSSDMATs and heat-wave climate characteristics between the first (1961\u0026ndash;1989) and the second (1990\u0026ndash;2018) parts of the period. Summary and conclusion are given in Section 6.\u003c/p\u003e"},{"header":"2. Data and climate characteristics of the area","content":"\u003cp\u003eTo investigate the properties of maximum daily surface air temperatures and HW events for the period 1961\u0026ndash;2018 along the Croatian Adriatic coast, three weather stations operated by the Croatian Meteorological and Hydrological Service were selected: Rijeka, Split, and Dubrovnik in the northern, middle, and southern parts, respectively. An upper-air station Zadar in the middle Adriatic is also included in the analysis. Measured daily maximum air temperatures were available for the period 1961\u0026ndash;2018.\u003c/p\u003e \u003cp\u003eA map of the entire region is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and coordinates of the locations are in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeographical coordinates and elevations of the selected coastal surface weather stations and upper-air station at Zadar (WMO code 14430) in the Croatian Adriatic.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRijeka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026deg; 20\u0026rsquo; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u0026deg; 27\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSplit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u0026deg; 31\u0026rsquo; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u0026deg; 26\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDubrovnik\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u0026deg; 39\u0026rsquo; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026deg; 05\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZadar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u0026deg; 10\u0026rsquo; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u0026deg; 34\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the K\u0026ouml;ppen climate classification, Rijeka is characterized by a relatively wet and mild climate (Cfa), while Split and Dubrovnik are warmer and drier because they are on the borderline between the humid subtropical and Mediterranean climate (Csa) (Filipčić \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Cfa is a climate characterized by the coldest month averaging above 0\u0026deg;C (or \u0026minus;\u0026thinsp;3\u0026deg;C), at least one month's average temperature above 22\u0026deg;C, and at least four months averaging above 10\u0026deg;C (Filipčić \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). For further reading we recommend: Penzar et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); Zaninović et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Pandžić et al. (2022).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Time series of the JJA maximum air temperature at three coastal sites along the Croatian Adriatic coast in the period 1961\u0026ndash;2018\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrent analysis shows that the daily maximum temperatures during the summer months (JJA; 1961\u0026ndash;2018) exhibit significant differences in the maximum and air temperature range at the three locations (Fig. 2). Thresholds for the 95\u003csup\u003eth\u003c/sup\u003e and 99\u003csup\u003eth\u003c/sup\u003e percentile criteria are indicated in the figure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 indicates that there are different amplitude ranges in the daily maximum temperature among the stations but similar variability during the period. The maximum temperatures are well spatially correlated among these stations with the correlation coefficients between 0.77 and 0.88 (figure not shown). Greater maximum values appear to be in the second part of the period at all stations. An increasing trend of the daily maximum temperature can be visually inferred and will be tested in latter discussion.\u003c/p\u003e\n\u003cp\u003eTime series of BSSDMAT show that the southern location has lower air temperature values and smaller amplitude of variation compared to the other two sites, which could be due to cooling effects of the deep southern Adriatic water and blocking of inland effects on temperature by coastal mountains (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Basic statistics of the BSSDMATs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBasic statistics of the BSSDMATs at these three weather stations (Table 2) generally follow the results from the climatologically average temperatures as mentioned in the previous text.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results show that Rijeka and Split belong to a similar region of the maximum temperature regime with the mean of the maximum temperature greater in July than in August for both locations, while for Dubrovnik the maximum in July is lower than in August. \u0026nbsp;The maximum temperature distributions approximately follow a normal distribution evidenced by small differences between the mean and median values (figures not shown). Although at a lower latitude, maximum temperatures at Dubrovnik are lower than in Rijeka and Split. Consequently, the standard deviation is also smallest for Dubrovnik. The mean maximum temperature in August for Dubrovnik is lower than the mean temperatures for June in Rijeka and Split. Although an isolated maximum of 40 \u0026deg;C is in Rijeka, the greatest mean of JJA maximum temperatures is in Split.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Statistics of the daily maximum temperature (MxT) (\u0026deg;C) in June, July, and August, for the whole period 1961\u0026ndash;2018 and separately for 1961\u0026ndash;1989 and 1990\u0026ndash;2018 for Rijeka, Split, and Dubrovnik.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"456\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMxT 1961\u0026ndash;2018\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRijeka\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSplit\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDubrovnik\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean MxT June\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean MxT July\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean MxT August\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean MxT 1961\u0026ndash;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eAbs. max MxT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eAbs. min MxT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eMed. MxT 1961\u0026ndash;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMxT 61_89 vs.91_18\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean MxT 1961\u0026ndash;1989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean MxT 1990\u0026ndash;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.68131868131868%\" valign=\"bottom\"\u003e\n \u003cp\u003eDiff. MxT (91_18-61_89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.802197802197803%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.75824175824176%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that the average maximum temperature increased by 1.42-2.30 \u0026deg;C in the second part of the period (1990\u0026ndash;2018) compared to the first period (1961\u0026ndash;1989). \u0026nbsp;It appears that the location in the northern Adriatic with shallow bathymetry has the most pronounced increase in the later period. The contrast in the maximum temperature distribution can be clearly seen when comparing data for the first (1961\u0026ndash;1989) and the last (1990\u0026ndash;2018) period (Fig. 3). Every calendar day in the first half of the period is compared with the same calendar day after 29 years \u0026ndash; for example, 1 June 1961 is plotted against 1 June 1990, 2 June 1961 against 2 June 1990, etc.\u003c/p\u003e\n\u003cp\u003eAn increase in the maximum temperature for all stations is clearly present in the second part of the period. \u0026nbsp;The number of cases when the temperatures are greater in the second period (1990\u0026ndash;2018) compared to the first period (1961\u0026ndash;1989) is 2.1 times larger for Rijeka and Dubrovnik and 1.7 times larger for Split. Considering the first decade (1961\u0026ndash;1970) compared to the last one (2009\u0026ndash;2018), the ratios are even more significant: 2.8, 2.1, and 2.8 for Rijeka, Split, and Dubrovnik, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Very hot days (VHDs)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis also includes a determination of Very Hot Days (VHDs) when a daily maximum temperature equals or exceeds 35 \u0026deg;C (Hoy et al. 2016). \u0026nbsp;A distribution of VHDs by years is shown in Fig. 4.\u003c/p\u003e\n\u003cp\u003eThere is a definite increase in frequency and peak temperatures of VHDs in the second part of the period after 1990 compared to the first part (113 vs. 2 for Rijeka and 145 vs. 42 for Split). \u0026nbsp;This is significant evidence of broader regional warming and generally of global warming. Maximum daily temperatures in Dubrovnik never reached the threshold of 35 \u0026deg;C (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHoy et al. (2016) mentioned that there are only a few VHDs annually over the central Europe, but they are more frequent in southeast Europe. For example, they reported 18 VHDs in Vienna in 2015 which corresponds well with 17 and 15 VHDs in Rijeka and Split, respectively.\u003c/p\u003e\n\u003cp\u003eIn addition, high temperatures and associated heat stress cause degradation of health and eventual increase in mortality rates. One of the parameters for probability estimation of increased mortality rate is the Physiologically Equivalent Temperature (PET) (Mayer and H\u0026ouml;ppe 1987; Zaninović and Matzarakis 2014). Zaninović and Matzarakis (2014) estimated that the PET temperature thresholds for Rijeka and Split are 36.5 and 36 \u0026deg;C, respectively. There were 71 such days (more than one per year on average) in Split and 32 days in Rijeka during 1961\u0026ndash;2018. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Trends of mean annual summer temperatures 1961\u0026ndash;2018\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince BSSDMAT trends can be visually inferred (Fig. 2), it is important to examine time series of average summer JJA maximum temperatures for actual trends. The time series is discontinued (since only JJA are considered for each year), so averages of each warm season were calculated and then tested for trends (Fig. 5). There is a definite increasing temperature trend during these 58 years at these three stations (Fig. 5). Since the seasonal maximum temperatures show distinct differences comparing 1961\u0026ndash;1989 vs. 1990\u0026ndash;2018 periods (see e.g., Figs. 3 and 4), separate trends were calculated for these two periods (Fig. 5).\u003c/p\u003e\n\u003cp\u003eThe positive trend coefficients are all statistically significant at the 0.01 level and they are quite large for all three locations for the second part of the period, while there are small insignificant and slightly negative trends in the first part of the period (1961\u0026ndash;1989). \u0026nbsp; Average seasonal maximum temperatures for Rijeka are characterized by larger variability than for Dubrovnik and Split and the largest trend coefficient is 0.68 \u0026deg;C per decade (R\u003csup\u003e2\u003c/sup\u003e=0.55). The bathymetry is shallower in the northern Adriatic compared to the middle and southern regions and wind patterns exhibit pronounced variability (Orlić et al. 1994). Although average seasonal maximum temperatures for Dubrovnik are lower with a narrower amplitude, the trend coefficient (0.45 \u0026deg;C per decade; R\u003csup\u003e2\u003c/sup\u003e=0.50) is similar to the trend coefficient for Split (0.44 \u0026deg;C per decade; R\u003csup\u003e2\u003c/sup\u003e=0.41). \u0026nbsp;This signifies that the effects of global and regional warming are increasing in time and consequences on the frequency and intensity of HWs will be further discussed in the later text (Section 5).\u003c/p\u003e\n\u003cp\u003eSince the air and sea are in the coupled climate system, these very high trend coefficients in the seasonal averages of the maximum air temperature suggest examining SST trends in this region and relating them to the maximum air temperature trends. Shaltout and Omsted (2014) estimate SST trends in the Mediterranean using the 0.25\u0026deg; AVHRR daily measurements for 1982\u0026ndash;2012. On average, they obtained increases of 0.38 \u0026deg;C per decade for the Adriatic. Summer trends were somehow lower (0.30 \u0026deg;C per decade) compared to spring trends (0.48 \u0026deg;C per decade). \u0026nbsp;Although there is a difference in the period considered, both the SST and BSSDMAT data show significant regional warming of the sea and air in the Adriatic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis of heat wave events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1 Autocorrelation analysis of BSSDMATs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile considering HWs, it is important to estimate both the length of each event and separation between consecutive events.\u003c/p\u003e\n\u003cp\u003eTo assure statistical independence of consecutive HWs which can allow the use of probability density functions and modeling, most studies consider that HW events can be assumed to be independent if the separation between consecutive events is greater than some specified number of days, for example, five days or more (Curriero et al. 2002; Keellings and Wylen 2012).\u003c/p\u003e\n\u003cp\u003eHowever, in our study, an additional analysis of one- and two-day exceedances was also included to provide more insight into the general structure of the BSSDMAT extremes. Note that heat stress and eventual mortality rates might be significant in the first part of a HW (Zaninović and Matzarakis 2014) when the human body is still not well adapted to the new and severe heat stress conditions. To provide more insight into length of events, an autocorrelation analysis was conducted for all years and all locations. \u0026nbsp;Autocorrelation function shapes can be useful for stochastic modeling of time series of climate variables (Pandžić 1984; Willks 2006).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average autocorrelation coefficients as functions of a time lag (in days) are shown in Fig. 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAutocorrelation function properties are similar among all locations, especially for lags up to 4 days or so. They compare well with the autocorrelation function for the Markov process with r\u003csub\u003e1\u003c/sub\u003e=0.8 for temperature (Wilks 2006; Eq. 8.6). Note that the correlation coefficients are equal to about 0.5-0.3 for usual lags of 3-5 days, which is usually considered sufficient separation for taking HW events as independent and can be applied to a HW analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further test the behavior of the autocorrelation function, an analysis was conducted for all years and all locations (Fig. 7). \u0026nbsp;It appears that the properties of the autocorrelation functions are quite complex, as shown by examining individual years.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to the average values, there is a large scatter of autocorrelation functions for each location and year. The spread is very large comparing individual years and locations. Even for one-day lag the correlation coefficient varies from 0.9 to 0.6 considering a spectrum of results for every year and every location. For the usual 5 days, the spread of the coefficients covers values from 0.7 to zero. There is some tendency of the autocorrelation coefficient in the first part of the period to drop at a faster rate compared to the second part of the period, but the variability of the coefficients in the second part of the period is quite large. A separation of 3-5 days reported in the literature can be taken as a general value, considering the usual periodicity of synoptic systems (which, of course, can be longer in the summer months and changing under global and regional warming). However, for detailed analysis, the autocorrelation functions could be considered separately for individual years. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2 95\u003csup\u003eth\u003c/sup\u003e and 99\u003csup\u003eth\u003c/sup\u003e percentile criteria for determining HWs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo approaches for determining HW events were applied: a) a maximum daily temperature in excess of the 95\u003csup\u003eth\u003c/sup\u003e percentile will be estimated as a basis for determining heat waves; b) same as a) but for more severe conditions when the maximum daily temperature exceeds the 99\u003csup\u003eth\u003c/sup\u003e percentile.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e95th percentile criterion \u0026ndash; main characteristics\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe determined number of HWs varies by location and threshold selected (Tables 3 and 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Number of HWs for each duration in days (Dur) and their total duration in days for Rijeka (RI), Split (ST), and Dubrovnik (DU) determined by the 95\u003csup\u003eth\u003c/sup\u003e percentile criterion for JJA 1961\u0026ndash;2018. No criterion on minimum separation between HWs was applied.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" valign=\"bottom\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" valign=\"bottom\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" valign=\"bottom\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e33.8\u0026deg;C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" valign=\"bottom\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" valign=\"bottom\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" valign=\"bottom\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e34.5\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" valign=\"bottom\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" valign=\"bottom\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" valign=\"bottom\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e28.6\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" valign=\"bottom\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" valign=\"bottom\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.954887218045113%\" valign=\"bottom\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003eDur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.834586466165414%\" valign=\"bottom\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003eHWs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"bottom\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.458646616541353%\" valign=\"bottom\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003eDur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.834586466165414%\" valign=\"bottom\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003eHWs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"bottom\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"bottom\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003eDur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.458646616541353%\" valign=\"bottom\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003eHWs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"bottom\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"bottom\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.774436090225564%\" valign=\"bottom\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e5077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e5091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e5080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15248\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e253\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e253\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e108\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e102\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e92\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e\u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e\u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.260575296108291%\" style=\"width: 6.9808%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e\u003cem\u003e122\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 11.1693%;\"\u003e\n \u003cp\u003e\u003cem\u003e259\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.377%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.952622673434856%\" style=\"width: 9.075%;\"\u003e\n \u003cp\u003e\u003cem\u003e126\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e\u003cem\u003e245\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.106598984771574%\" style=\"width: 7.8534%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.614213197969543%\" style=\"width: 8.726%;\"\u003e\n \u003cp\u003e\u003cem\u003e147\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.475465313028765%\" style=\"width: 10.9948%;\"\u003e\n \u003cp\u003e\u003cem\u003e256\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.29103214890017%\" style=\"width: 9.2496%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e395\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.798646362098138%\" style=\"width: 7.5044%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e760\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn 53 of the 58 years, HWs occurred at least at one location and in 30 years they were shown at all locations in the same year. There were a total of 4.7% of HW days in the whole period. The number of total HWs among the locations ranged from 122 to 147 with 245-259 total event days. Note that for the period of 58 years, the number of cases and especially the total number of days with HWs are similar for all locations indicating regional-scale characteristics. The maximum duration of determined HWs were 9, 11, and 12 days for Rijeka, Split, and Dubrovnik, respectively. The most frequent one-day exceedances are for Dubrovnik, possibly due to a narrow range of values where small changes in the temperature could become greater than the threshold and vice versa. Most likely are event durations of 3-4 days. Events of more than 7 days duration are quite rare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e99\u003csup\u003eth\u003c/sup\u003e percentile criterion \u0026ndash; main characteristics\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 shows the basic statistics of the HWs according to the 99\u003csup\u003eth\u003c/sup\u003e percentile criterion. The maximum duration length for all locations was 5 days. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Number of heat waves (HWs) for each duration (Dur) and their total duration in days (Days) for Rijeka (RI), Split (ST), and Dubrovnik (DU) determined by the 99\u003csup\u003eth\u003c/sup\u003e percentile criterion for JJA 1961\u0026ndash;2018. No restriction on minimum separation between the HWs was applied (as discussed in the beginning of Section 4 \u0026ndash; autocorrelation analysis).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"bottom\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" valign=\"bottom\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" valign=\"bottom\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003e35.8\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"bottom\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" valign=\"bottom\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" valign=\"bottom\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e36.1\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"bottom\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" valign=\"bottom\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" valign=\"bottom\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e30.1\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" valign=\"bottom\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"bottom\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"bottom\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003eDur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" valign=\"bottom\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" valign=\"bottom\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"bottom\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003eDur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" valign=\"bottom\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" valign=\"bottom\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"bottom\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003eDur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" valign=\"bottom\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" valign=\"bottom\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003eDays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" valign=\"bottom\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"bottom\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003e5282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e5284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e5285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15851\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80281690140845%\" style=\"width: 9.8901%;\"\u003e\n \u003cp\u003e\u003cem\u003e30\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.97887323943662%\" style=\"width: 10.0733%;\"\u003e\n \u003cp\u003e\u003cem\u003e54\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cem\u003e32\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e\u003cem\u003e52\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" style=\"width: 7.1429%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274647887323944%\" style=\"width: 9.3407%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.626760563380282%\" style=\"width: 9.707%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e98\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" style=\"width: 10.8059%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e157\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe 99\u003csup\u003eth\u003c/sup\u003e percentile limit indicates a total of \u0026raquo;1% extreme conditions (days) within the whole period. Note that this stricter criterion provides numbers of events and associated days among the locations similar to the 95\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile case. Consequently, the results for the 99\u003csup\u003eth\u003c/sup\u003e percentile criterion indicates similar uniform regional characteristics of HWs as for the 95\u003csup\u003eth\u003c/sup\u003e percentile criterion over the coastal Adriatic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.3 Simultaneous occurrence of HWs on an annual basis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHeat waves at at least one location were determined in 53 out of 58 years considering the 95\u003csup\u003eth\u003c/sup\u003e percentile criterion (Fig. 8). Annual variations in terms of an index (yes/no) of HW occurrence in each year are shown in Fig. 8. Indices 0, 1, 2, and 3 represent numbers indicating how many simultaneous locations had HWs present for a particular year. For example, index 3 means that HWs occurred for all three locations in a particular year, while index 2 means that HWs were determined for two locations.\u003c/p\u003e\n\u003cp\u003eRegarding the 95\u003csup\u003eth\u003c/sup\u003e percentile criterion, the extreme temperatures and HWs occurred in 34, 45, and 44 out of 58 years for Rijeka, Split, and Dubrovnik, respectively. Considering all stations, there were only 5 years (within the period 1976\u0026ndash;1997) in which there were no HWs. In 30 years (24 years in and after 1990), HW occurred at all three locations. After 2000, for almost all years (except 2014) HWs occurred at all locations.\u003c/p\u003e\n\u003cp\u003eRegarding the 99\u003csup\u003eth\u003c/sup\u003e percentile criterion, HWs were predominantly absent before 1990. The extreme temperatures and HWs occurred in 17, 15, and 19 out of 58 years for Rijeka, Split, and Dubrovnik, respectively. In 31 years, there were no HWs at any station, while in 9 years there were HWs at all locations. Two or more events per year occur only on and after 1998. \u0026nbsp;All these results further confirm previous conclusions that the second part of the period is characterized by much larger occurrence of HWs indicating the effects of global and regional warming processes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.4 Frequency and duration\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe total number of HW events and duration of each event per year in the period 1961\u0026ndash;2018 for Rijeka, Split, and Dubrovnik using the 95\u003csup\u003eth\u003c/sup\u003e and 99\u003csup\u003eth\u003c/sup\u003e percentile methods is shown in Fig. 9.\u003c/p\u003e\n\u003cp\u003eThe total number of HW days significantly increased over time (Fig. 9, right panels). For both parameters and both criteria, the values generally fall below the 1:1 line (higher values correspond to later years). For later years the intervals between the highest and lowest values (frequency; total event days) are increasing, i.e., higher values are more likely in the later years. The total number of HW days significantly increased in time. This is especially pronounced for Rijeka and Split and to a lesser extent for Dubrovnik. Only after 1989 do HW lengths of 8 days or more (3 or more) occur for the 95\u003csup\u003eth\u003c/sup\u003e (99\u003csup\u003eth\u003c/sup\u003e) percentile criteria. The annual frequency of HW days follows increasing BSSDMAT trends over time as shown in Figs. 2 and 5. Figure 9 shows a significant increase in events per year and corresponding duration days after 1989. More than 10 (4) days annual duration of HWs for the 95\u003csup\u003eth\u003c/sup\u003e (99\u003csup\u003eth\u003c/sup\u003e) percentile criterion, respectively, occurred in and after 1994.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.5 Heat wave intensity \u0026ndash; Heat-wave index (HWI)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHeat wave intensity can be examined in terms of ratios between the peak vs. threshold temperatures (95\u003csup\u003eth\u003c/sup\u003e and 99\u003csup\u003eth\u003c/sup\u003e percentile criteria) during the length of a HW. \u0026nbsp;Distributions of peak maximum temperatures for each event show distinct differences in amplitudes between the northern Adriatic with shallow bathymetry and the southern region with the cooler influence of the deeper Adriatic Sea (Fig. 10).\u003c/p\u003e\n\u003cp\u003eThe peak temperatures within HWs increase over time with larger amplitudes compared to the average values. There is clear clustering, with increasing values after the 1990s. Note that the extreme values occurred at all locations and for both criteria in the last decade (2009-2018). \u0026nbsp;The ratios between the peak and threshold temperatures can be assumed as a heat-wave index (HWI) for both criteria (Fig. 11). Besides an increasing number of events in time, the indices are increasing with greater variability in time. Note that the majority of greater indices are in the second part of the period.\u003c/p\u003e\n\u003cp\u003eIn accordance with the previous results, this figure also confirms that a significant number of HW events occurred in the second part of the period and that the HWI becomes greater in time (7 to 18% in all cases using both thresholds). The greatest HWI values are in the last decades of the period. The maximum HWIs with respect to the threshold temperatures using the 95\u003csup\u003eth\u003c/sup\u003e (99\u003csup\u003eth\u003c/sup\u003e) percentiles are 1.18 (1.12) for Rijeka, 1.12 (1.07) for Split, and 1.17 (1.11) for Dubrovnik. The smaller ratios of the maximum compared to the threshold temperature for the 99\u003csup\u003eth\u003c/sup\u003e criterion are caused by the higher threshold and smaller range of the BSSDMAT values.\u003c/p\u003e\n\u003cp\u003eNote that the ratio between the peak and threshold temperatures can be used as an intensity index (HWI) for future comparison studies for other locations and other times. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"4 Longest duration events","content":"\u003cp\u003eMaximum HW durations in the considered period are 9, 11, and 12 for Rijeka, Split, and Dubrovnik, respectively (Table 5). \u0026nbsp;Note that an event in early August 2017 occurred at all locations, although not with the same length. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. The longest HW events based on the 95\u003csup\u003eth\u003c/sup\u003e and 99\u003csup\u003eth\u003c/sup\u003e percentile thresholds for Rijeka, Split, and Dubrovnik, 1961\u0026ndash;2018. An event in 2017 for Rijeka is added since the timing partially coincides with the longest events in Dubrovnik and Split.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep95\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003cp\u003e(days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrom\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003eTo\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003eAv Tmx\u003c/p\u003e\n \u003cp\u003e(\u0026deg;C)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003ePeak Tmx\u003c/p\u003e\n \u003cp\u003e(\u0026deg;C)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003eRijeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e36,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e37,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003eRijeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e01.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e06.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e37,15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e39,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003eSplit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e01.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e36.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e37,9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003eDubrovnik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e29,65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep99\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003eRijeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e20.Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e36,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e37,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003eRijeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e04.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e08.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e37,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e39,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003eSplit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e37,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e38,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.224080267558527%\" valign=\"bottom\"\u003e\n \u003cp\u003eDubrovnik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.217391304347826%\" valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e06.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.210702341137123%\" valign=\"bottom\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.876254180602007%\" valign=\"bottom\"\u003e\n \u003cp\u003e31,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\" valign=\"bottom\"\u003e\n \u003cp\u003e33,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAll these extreme cases occurred in and after 2006. This further confirms the increasing severity over time of the HWs in the Croatian Adriatic coast. A significant HW episode was from 1 to 6 August 2017 for all three stations. \u0026nbsp;Note that the average event temperatures at Rijeka and Split were above the VHD starting limit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Weather analysis of the extreme HW event in July/August 2017\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSynoptic conditions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe longest HW occurred from 31 July to 11 August 2017 in Dubrovnik. It was a regional event since HWs were also recorded in Rijeka (1-6 August) and Split (1-11 August). \u0026nbsp; The weather in the Adriatic area was influenced by a strong and wide ridge from the Azores High extending to southern and southeastern Europe (Fig. 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 12 shows the development of the surface pressure field and 500 hPa geopotential prior to (27 July), during (4 August), and after the event (13 August). The HW was at a maximum when the ridge developed from the surface all the way up to 500 hPa and beyond. The ridge was gradually strengthening and blocking a low centered at the northern UK and Scandinavia. \u0026nbsp;During the event, heat propagated from Northwestern Africa via the Iberian Peninsula and the western Mediterranean toward Eastern Europe. Warm subtropical air masses were steered to the Adriatic area and further to the east. The end of the event occurred during the beginning of the strengthening of the lows with frontal systems The Azores High kept the circulation unchangeable until the end of the HW episode. Back-trajectory analysis also confirms that advection from the west-southwest within the high ridge dominated during the event (figure not shown).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e500-1000 hPa thickness\u0026nbsp;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCharacteristics of the HWs can be also seen in the 500-1000 hPa thickness (hereafter RT) (Table 6) over Europe examining days before, during, and after the event. \u0026nbsp;Ten days prior to the event there was a minimum RT on 27 July \u0026ndash; a cold low connected to the northwest cold region. The maximum RT values were in the middle of the event on 4 August \u0026ndash; a broad and warm ridge over the southern Europe extending from the southwest. The disappearance of the event was characterized again by a minimum RT \u0026ndash; a cold low propagating from the northwest after the event on 13 August.\u003c/p\u003e\n\u003cp\u003eTemperature values aloft and RT for times prior to, during, and after the event at the Zadar station (see Fig.1 and Table 1) are listed in Table 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. Ambient temperatures (\u0026deg;C) at the 850, 700, 500, and 300 hPa levels, and 500-1000 hPa thickness (RT500/1000) (gpm) prior to (27 July), during (4 August), and after (13 August 2017) the HW event from radiosonde measurements at Zadar (Fig. 1 and Table 1).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.695652173913043%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64.17391304347827%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTemperature (\u003cem\u003e\u0026deg;C\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.130434782608695%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRT 500/1000\u003c/p\u003e\n \u003cp\u003e(gpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"top\"\u003e\n \u003cp\u003e850 hPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"top\"\u003e\n \u003cp\u003e700 hPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.390243902439025%\" valign=\"top\"\u003e\n \u003cp\u003e500 hPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.829268292682926%\" valign=\"top\"\u003e\n \u003cp\u003e300 hPa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"top\"\u003e\n \u003cp\u003e27/7/2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e-16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.217391304347824%\" valign=\"top\"\u003e\n \u003cp\u003e-42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e5598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"top\"\u003e\n \u003cp\u003e4/8/2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e-7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.217391304347824%\" valign=\"top\"\u003e\n \u003cp\u003e-33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e5820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.695652173913043%\" valign=\"top\"\u003e\n \u003cp\u003e13/8/2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.652173913043478%\" valign=\"top\"\u003e\n \u003cp\u003e-14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.217391304347824%\" valign=\"top\"\u003e\n \u003cp\u003e-36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e5600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote that warming (8-10 \u0026deg;C) at all levels is significant during the event compared to the prior date (27 July) and after date (13 August) at all levels. RT during the event is about 200 gpm higher compared to both the prior and after dates. Consequently, the properties of this extreme-duration HW propagated aloft and event warming was present up to 500 hPa.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRadiosonde measurements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDevelopment of this extreme event can be further examined using radiosonde data from the Zadar station (44.10\u0026deg;N; 15.34\u0026deg;E; 79.0 MSL) which is in the middle of the Croatian Adriatic coast (Fig. 1). \u0026nbsp;During the event, significant warming and drying occurred in the lowest 5 km or so as compared to the time prior to and after the event (Fig. 13; Table 6).\u003c/p\u003e\n\u003cp\u003eIn summary, this longest HW event recorded at all three stations occurred because of deep warming perturbations throughout the atmosphere caused by the propagation of a broad ridge (all the way up to 500 hPa and beyond) from the southwest over southern and southeastern Europe. \u0026nbsp;Prior to the event and after the event, cold lows from the northwest encroached on the area limiting development of the HW. The upper-air analysis suggests that an HW analysis should generally also include upper-air conditions.\u003c/p\u003e"},{"header":"5 Heat wave differences between the first (1961–1989) and the second (1990–2018) parts of the period","content":"\u003cp\u003eAccording to the results, there are distinct differences in the properties of the BSSDMAT between the first (1961\u0026ndash;1989) and the second (1990\u0026ndash;2018) parts of the analyzed period (Table 2). The observed increasing seasonal temperature trends for 1990\u0026ndash;2018 are significant (a=0.01) for all three stations with large coefficients from 0.44 to 0.68 \u0026deg;C per decade, while there are no significant trends for 1961\u0026ndash;1989. Although overall trends for the whole period show significant trends, separation into the first and second parts indicate that trends in the second part are much higher than for the overall period. This confirms that the effects of global and regional warming are increasing in time and consequences on the frequency and intensity of HWs considering 1961\u0026ndash;1989 vs. 1990\u0026ndash;2018 periods (Table 7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7. A number of HW cases based on the daily maximum temperature and their duration (all lengths ranging 1-12 days) estimated by the 95\u003csup\u003eth\u003c/sup\u003e percentile and 99\u003csup\u003eth\u003c/sup\u003e percentile criteria for JJA 1961\u0026ndash;1989 and 1990\u0026ndash;2018 periods for Rijeka, Split, and Dubrovnik. No minimum separation between the HWs was applied.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP95\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1961\u0026ndash;1989\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u0026ndash;2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eHW cases\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eDubrovnik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eRijeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eSplit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eDurat. Days\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eDubrovnik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eRijeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eSplit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP99\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1961\u0026ndash;1989\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u0026ndash;2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eHW cases\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eDubrovnik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eRijeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eSplit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eDurat. Days\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eDubrovnik\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eRijeka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003eSplit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThere is a large increase in the number and duration of HWs in the second period compared to the first period as determined by both criteria and for all locations. \u0026nbsp; The HWs cases increased more than three times and duration days increased by more than five times in the second period compared to the first period (95\u003csup\u003eth\u003c/sup\u003e percentile criterion). For the 99\u003csup\u003eth\u003c/sup\u003e percentile criterion, the number of cases and total days is about ten times greater in the second part of the period. Moreover, previous figures (e.g., Figs. 8-11) show that the increases in frequency and intensity are even larger comparing the first and last decades.\u003c/p\u003e\n\u003cp\u003eSome insight into the effects of regional warming on the air temperatures between the first and second periods can be seen from a broader perspective, i.e., time series of the average air temperature for 1901-2021 for whole Croatia (Fig. 14).\u003c/p\u003e\n\u003cp\u003eDespite inter-annual variations, the smoothing average clearly separates the 1960\u0026ndash;1990 period from the 1990\u0026ndash;2021 period. After 1990 the average almost uniformly increases. This is further confirmed by an increase of the JJA annual average of the maximum temperature for all of Croatia for 1991-2020 (26.97 \u0026deg;C) compared to 1961\u0026ndash;1990 (25.17 \u0026deg;C) (World Bank Group 2023). \u0026nbsp;Although the results also depend on the resolution of the grid (0.5\u0026deg; x 0.5\u0026deg;), their analysis indicates that the largest increase was recorded in the southern Adriatic coast. Our analysis (Table 2) shows that the largest increase of the BSSDMAT is for Rijeka (northern Adriatic), but the grid resolution of the World Bank Group data might not be able to resolve the complex orographic setups of the coast, Istrian Peninsula, and islands (Fig. 1). However, Table 2 shows that the increase of the BSSDMAT is greater for Dubrovnik (southern Adriatic) than for Split (middle Adriatic).\u003c/p\u003e"},{"header":"6 Summary and conclusions","content":"\u003cp\u003eProperties of the Boreal Summer Season JJA Daily Maximum 2-m Air Temperatures (BSSDMATs) and associated heat waves (HWs) on the Croatian Adriatic coast based on the 95th and 99th percentiles for 1961\u0026ndash;2018 were analyzed. Three locations were selected: Rijeka (northern), Split (middle), and Dubrovnik (southern region). Before 10 June and after 24 August there were no HWs determined for any of the locations.\u003c/p\u003e \u003cp\u003eSince the intensity of heat stress in general stronger affects health at the beginning of a HW when the human body has not adapted to new conditions yet, exceedances of one or more days were all considered irrespective of any minimum length or separation. Brief summary conclusions for climate variations of each of the main BSSDMAT and HW characteristics follow.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eFrequency\u003c/em\u003e. Exceedances and associated HW events occurred in 53 (27) out of 58 years and in 30 (9) years they were present at all three locations in the same year for the 95th (99th ) percentile. At all three locations, there were 122\u0026ndash;147 (30\u0026ndash;36) cases lasting 245\u0026ndash;259 (51\u0026ndash;54) days for 95th (99th ) limits. Considering JJA for all 58 years, the HWs occurred in 4.7% (1%) of total days for the 95th (99th ) criterion. Generally, all three distant locations show similar numbers of events and days duration for both criteria. For Dubrovnik, the BSSDMAT range is lower compared to Rijeka and Split due to smoothing effects on temperature of the deep southern Adriatic and blocking of inland effects on temperature by coastal mountains. However, statistics of HWs for Dubrovnik are similar to Rijeka and Split, indicating regional signature in this type of events, especially using the 99th percentile criterion. When restricting the analysis to HWs with only 3 or more days length and separation of one or more days between the events, there were 27\u0026ndash;33 (3\u0026ndash;6) cases lasting 121\u0026ndash;156 (13\u0026ndash;23) days for the 95th (99th ) percentile limits. The number of cases when the BSSDMAT is 2.1 times greater in the second half of the period (1990\u0026ndash;2018) compared to the first part of the period (1961\u0026ndash;1989) for Rijeka and Dubrovnik, and 1.7 times greater for Split. Comparing the first decade (1961\u0026ndash;1970) to the last one (2009\u0026ndash;2018), the numbers are even greater: 2.8 for Rijeka and Dubrovnik, and 2.1 for Split.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eBSSDMAT and HW trends\u003c/em\u003e. There were significant (α\u0026thinsp;=\u0026thinsp;0.01) warm-season average BSSDMAT trends in the second part of the period (1990\u0026ndash;2018) for all three locations, ranging from 0.44 to 0.68\u0026deg;C per decade. Small insignificant and even slightly negative trends were observed in the first part of the period. There is an increase of more than three times the number of HW events in the second part of the period (1990\u0026ndash;2018) compared to the first part of the period (1961\u0026ndash;1989) with an increase in duration days of more than five times. Regarding the 99th percentile criterion, the increase was even ten times more for both the number of cases and the total number of HW days.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eIntensity of HWs and VHDs\u003c/em\u003e. Similar HW intensity was found for all stations. Intensity in terms of the heat-wave index (HWI) as a ratio between the peak temperature vs. threshold temperature grew in time with maxima for all locations of 1.12\u0026ndash;1.18 (1.07\u0026ndash;1.12) for the 95th (99th ) percentile threshold. Other parameters such as the number of very hot days (VHD) and physiologically equivalent temperature (PET) clearly occurred more in the second half of the period compared to the first half of the period. In most of the plots, after the year 1990 there is a pronounced increase in frequency and intensity of the HWs.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eEvent duration and synoptic conditions conducive to exceedances\u003c/em\u003e. According to the medians of the beginning date (16 July) of the first HW and the end date of the last HW (15 August) in a year, the most likely span between the beginning and end of the events is 30 days. The maximum duration of the events was 12 (5) days for the 95th (99th) thresholds. The most intensive HW event occurred from 31 July to 11 August 2017, when HWs were estimated for all locations covering at least part of the period. Weather conditions for the longest duration event, and similarly for other extreme events, were characterized by a strong and wide ridge from the Azores High extending to southern and southeastern Europe and propagating all the way up to 500 hPa and beyond. Prior to and after the event, a cold low from the north was obstructing the ridge. The development of the HW was associated with significant upper-level warming and consequent stability aloft with a maximum of 500\u0026ndash;1000 hPa thickness during the event.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe analysis results emphasize that the effects of global and regional warming are increasing in time and consequently impact the frequency and intensity of HWs considering differences between1961\u0026ndash;1989 and 1990\u0026ndash;2018 periods.\u003c/p\u003e \u003cp\u003eThis and similar observational studies represent a basis for investing capabilities of hindcast climate modeling results, especially in coastal regions. Climate modeling projections will provide possible future characteristics of the heat waves within global and regional warming and their severity as indicated by the estimated historical temperature trends.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.K. conceptualization, data curation, wrote the main manuscript text, prepared figures. K.P. contributed to analysis and text.K.V.K. contributed to analysis and text, prepared figures.All authors reviewed the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank the Croatian Meteorological and Hydrological Service for supplying the data. DK was supported under project STIM\u0026mdash;REI, Contract Number: KK.01.1.1.01.0003, a project funded by the European Union through the European Regional Development Fund\u0026mdash;Operational Programme Competitiveness and Cohesion 2014-2020 (KK.01.1.1.01). DK also acknowledges significant support from the University of Notre Dame, USA (ONR Grant: N00014-21-1- 2296). The study was also partially funded by the project CAAT (Coastal Auto-purification Assessment Technology) funded by the European Union from European Structural and Investment Funds 2014\u0026mdash;2020, Contract Number: KK.01.1.1.04.0064. KVK was supported under the Science Fund of the Republic of Serbia, Program PRIZMA, project \u003cem\u003eExtreme weather events in Serbia - analysis, modelling and impacts\u003c/em\u003e (EXTREMES), grant No. 7389. We thank Mr. Dragomir Bulatović for preparation of Figure 1.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData can be obtained from https://meteo.hr/proizvodi_e.php?section=proizvodi_usluge\u0026amp;param=services\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArtegiani A, Bregant D, Paschini E, Pinardi N, Raicich F, and Russo A (1997) The Adriatic Sea general circulation. Part I: Air\u0026ndash;sea interactions and water mass structure. J Phys Oceanogr 27:1492\u0026ndash;1514\u003c/li\u003e\n \u003cli\u003eBranković Č, G\u0026uuml;ttler I, Gajić-Čapka M (2013) Evaluating climate change at the Croatian Adriatic from observations and regional climate models\u0026apos; simulations. Clim Dyn 41:2353-2373 https://doi.org/10.1007/s00382-012-1646-z\u003c/li\u003e\n \u003cli\u003eCurriero FC, Heiner KS, Samet JM, Zeger SL, Strug L, Patz JA (2002) Temperature and mortality in 11 cities of the eastern United States. 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Larger values occurred in the second part of the period (1990\u0026ndash;2018) compared to the first part (1961\u0026ndash;1989), including significant (α\u0026thinsp;=\u0026thinsp;0.01) trends in mean seasonal averages (0.44 to 0.69\u0026deg;C per decade), extremes, frequencies, duration, and intensity. Exceedances and HWs spanning from 10 June to 24 August were determined in 53 years (out of 58 years) by the 95th and in 9 years by the 99th percentile criteria. Since heat stress frequently affects health at the beginning of a HW, exceedances of one or more days were all considered irrespective of any minimum length or separation. In 30 years, the exceedances appeared at all three locations in the same year. There were 122\u0026ndash;147 (30\u0026ndash;36) HW cases lasting 245\u0026ndash;259 (51\u0026ndash;54) days for the 95th (99th) percentiles. The maximum event duration ranged from 9 to 12 (5) days for the 95th (99th) thresholds. Weather conditions for the longest-duration events were characterized by propagation of a strong and wide ridge from the Azores High extending to southern Europe and blocking lows from the north. Based on these results, the Croatian coast is part of a Mediterranean \u003cem\u003ehot spot\u003c/em\u003e that has been experiencing significant increasing warming trends and associated frequency of HWs that will likely continue in the future.\u003c/p\u003e","manuscriptTitle":"Climate Variations of Heat Waves on the Croatian Adriatic Coast for the Period 1961–2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 13:04:13","doi":"10.21203/rs.3.rs-4655203/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-22T12:34:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-15T14:06:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269054163058035150724659832621858131729","date":"2024-07-31T17:43:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-05T06:54:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-30T21:46:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-30T21:44:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2024-06-28T13:49:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1ee89638-e189-4fd2-86ef-d0e0e0786b11","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T15:58:43+00:00","versionOfRecord":{"articleIdentity":"rs-4655203","link":"https://doi.org/10.1007/s00704-024-05206-z","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2024-10-19 15:56:54","publishedOnDateReadable":"October 19th, 2024"},"versionCreatedAt":"2024-07-23 13:04:13","video":"","vorDoi":"10.1007/s00704-024-05206-z","vorDoiUrl":"https://doi.org/10.1007/s00704-024-05206-z","workflowStages":[]},"version":"v1","identity":"rs-4655203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4655203","identity":"rs-4655203","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}