Variation in the Extreme Temperatures and Related Climate Indices for the Marche Region, Italy

Abstract

This paper presents a study on the evolution of extreme temperatures in the Marche region, Central Italy. To this end, a complete dataset compiled using data collected from available thermometric stations over the years 1957–2019 based on minimum and maximum daily temperatures was selected. The yearly mean values of extreme temperature and relative climate indices defined by the Expert Team on Climate Change Detection and Indices were calculated, and a trend analysis was performed. The spatial distribution of the trends was assessed, and the variations in extreme temperatures in the medium–long term were considered by calculating mean values with respect to different climatological standard normals and decades. The analyzed parameters show that extreme heat events characterized by increasing intensity and frequency have occurred over the years, while cold weather events have decreased. A high percentage of stations recorded an increase in all indices related to daily maximum temperatures, and a simultaneous decline of those related to daily minimum values, under both nighttime and daytime conditions. This phenomenon characterizes the entire Marche region. A detailed analysis of the heat wave indices confirms an increasing trend, with a notable increase beginning in the early 1980s.

1. Introduction

Climate change is an unequivocal phenomenon with widespread and rapid global changes in the atmosphere, oceans, cryosphere, and biosphere. These climate changes have already influenced the global weather and climate, with marked increases in the frequency and intensity of heat extremes, marine heat waves, heavy rainfall, agricultural, and ecological droughts in some regions, and tropical cyclones, as well as reductions in the Arctic Sea ice, snowpack, and permafrost [1]. In recent decades, climate change has attracted considerable research attention, resulting in the analysis of temperature and precipitation time series through the definition of specific indices that can help identify increasing or decreasing trends in extreme events [2,3,4]. Globally, the last seven years have been the warmest years ever recorded by a considerable margin. Recently, the Copernicus Climate Change Service [5] confirmed that 2023 was the warmest calendar year as per the global temperature data records, which go back to 1850. For the European territory, the ten warmest years have occurred since 2000, and in 2021, the European record for the highest temperature was broken in Sicily, where 48.8 °C was recorded [6]. The Mediterranean region is considered a hotspot for climate change, with warming exceeding the global average increase especially in summer. This increase in temperature extremes occurred throughout the region [7,8,9].

Numerous studies that investigated temperature changes on the Italian Peninsula have confirmed these trends. Brunetti et al. [10] analyzed an Italian monthly temperature secular dataset with data from 1763 to 2003 and observed a positive trend in the average temperature throughout Italy, which was more pronounced for the minimum temperature than that for the maximum temperature over the entire observation period. Further, they showed that the trends were strictly dependent on the period selected for both temperature and precipitation. In fact, the data for the last 50 years indicate an opposite behavior; that is, the trend for the maximum temperature is stronger than that for the minimum temperature. Fioravanti et al. [11] studied recent changes in the frequency and intensity of extreme temperatures in Italy by selecting a collection of daily minimum and maximum temperature time series to calculate a series of indices. They studied the trend of each index and evaluated the average trends on a national scale at the annual and seasonal levels. The authors stated that the average annual series showed a general warming trend from 1961 to 2011, with significant trends for summer days, tropical nights, heat waves, and percentile indices at most stations. Further, warming trends were found to be more pronounced in summer and spring and weaker in winter and autumn. Non-significant “cooling” trends characterized the sub-period 1961–1977, while significant “warming” trends were identified for the period 1978–2011. These results confirmed Toreti et al.’s [12] results on the trends in extreme temperature indices.

With reference to the Marche Region, Gentilucci et al. [13] investigated temperature changes over the past 50 years by evaluating three normal climatological standards. Their analysis indicated an increase in temperature from the past to the present. For example, the comparison of the period 1971–1990 with the period 2001–2020 revealed an increase of more than 0.5 °C on average; however, a considerably weaker increase of only 0.1 °C was shown comparing the years 1981–1990 and 2011–2020. No temperature indices were considered in their study. Scorzini et al. [14] assessed recent temperature variations by focusing on extreme events at annual and seasonal scales. They investigated trends in the selected indices calculated from daily temperature data recorded from 1980 to 2012 at 34 meteorological stations distributed over the Marche and Abruzzo regions. The results revealed recent general warming trends, which were particularly pronounced in spring and summer, and cold-related extremes showed significant reductions, confirming the warming process.

This work aims to assess the existence of trends in the series of extreme temperatures and relative indices for the Marche region in the medium–long term, which are yet to be analyzed in similar studies. In fact, to the best of our knowledge, no studies have been conducted about temperature indices calculated over the 63 years from 1957 to 2019 in this regional area. This study is important for investigating the reasons behind the water scarcity in recent years in Marche [15] and comparing temperature variations in the Marche region with human stress to temperature variations. Initially, all thermometric stations available for the Marche region are analyzed to select a dataset that meets all requirements of homogeneity, completeness, and continuity of measurements. Subsequently, the minimum and maximum temperatures are analyzed, and then, extreme temperature climate indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) are calculated. The temporal trends are evaluated considering a series of maximum and minimum temperatures and related climate indices, which are calculated by performing the non-parametric Mann–Kendall (MK) test [16,17]. Further, the spatial distribution of the trends was evaluated to estimate the evolution of climatic conditions within the Marche region.

The remainder of this manuscript is organized as follows: Section 2 reports a detailed description of the Marche region and the methods of analysis and selection of the time series of the maximum and minimum temperatures at each measuring station, which constituted the investigated data sample. Section 3 provides a thorough description of the analysis methodology and criteria for analyzing the thermometric time series, that is, the indices calculated from the daily temperature data and the trend tests applied to them. Section 4 presents the results of statistical and trend analysis applied to extreme temperatures and climate indices. Finally, the conclusions are presented in Section 5.

2. Area of Interest and Dataset

2.1. Study Area

This study focused on analyzing the data series measured within the Marche region in central Italy (42° N and 44° N and between the 12° E and 14° E) with a surface area of 9694 km² (Figure 1). This region faces the Adriatic Sea and is bound to the north by the Conca River and to the south by the Tronto River. Further, it is delineated as a mountainous–hilly region, and within its borders, there is a complete absence of plains. However, there are thin alluvial zones near the mouths of the rivers or coastal strip areas. The region admits the terminal offshoots of the Apennine chain within its borders, with the highest altitudes in the region concentrated in the south (highest peaks are located at an altitude of no higher than 2500 m). Wide hilly territories extend at the end of the mountainous areas, representing the most consistent orographic type, and they slope gently down towards the coast of the Adriatic Sea [18].

The climate presents mediterranean characteristics along the coasts and is progressively continental toward the Apennine areas. However, even on the coast, the influence of the sea decreases when proceeding northward because of the shallowness of the central-northern Adriatic Sea and the exposure of the territory, which is closed with respect to the western and southern winds and is open instead to those from the east or north. Therefore, the coast of the northern Marche presents climatic characteristics similar to those of the Po Valley.

Temperatures show greater seasonal and daily ranges than those of the Tyrrhenian and Southern Adriatic coasts. South of the Marche region, the behavior is similar to that of the Mediterranean region, i.e., not too cold winters and warm summers due to the presence of breezes. North of the region, the effect of the Adriatic Sea is reduced, and the thermal behavior is more similar to that of the Po Valley. It has hot and sultry summers and cold and foggy winters with periods of intense cold capable of causing frost even on the coasts. Inland areas show typical characteristics of a continental climate with hot summers, wherein temperatures often exceed 30 °C, and winters, in which temperatures often fall below the 0 °C threshold. The temperature drops progressively with altitude, and therefore, in the Apennine Mountains, winters are very cold, with temperatures that can drop as low as −20 °C during Arctic irruptions and cool values even during summer. Libeccio winds can cause sudden temperature rises at any time of the year, causing thawing phases even in the middle of winter.

Rainfall over Marche is conditioned by the arrangement of the Apennine elevations relative to the prevailing westerly circulation in the Mediterranean area. In general, rainfall is not abundant because Atlantic disturbances tend to release their moisture content in the form of precipitation on the western Apennine side and arrive dry on the coast. The average value of the total annual precipitation varies from a minimum value of 600 mm along the coast to a maximum value of 1700 mm in mountainous areas.

2.2. Dataset

The proposed data analysis is aimed at estimating the quality of historical temperature series collected by thermometric stations located in the catchment areas of the Marche region. The objective is to analyze the characteristics of the historical series of thermometric data in terms of quality, completeness, and continuity to conduct long-term statistical analyses, such as those related to the identification of possible climatic variations. The fulfillment of these prerequisites delivers sufficiently reliable data suitable for an appropriate statistical investigation.

The Civil Protection of the Marche region is responsible for the management, collection, and sampling of hydrological data, including maximum and minimum daily temperatures. Because of the reorganization of the thermometric network of the Marche region by the regional Civil Protection, which began in the 2000s, analyzing the reliability of the time series is crucial. Only 24 thermometric stations began collecting temperature data before 2000, with measurements beginning in 1957. Other stations were subsequently added, with the total number of active stations being 111.

Among these 111 stations, 17 were activated in 1957, 7 were activated later but before 2000, and 97 were activated after 2000. Subsequently, two stations were decommissioned in 2008 and 2009, and seven additional stations have been decommissioned since 2014. Consequently, only 22 stations were found to be active in the period of interest 1957–2019.

Owing to the change and/or relocation of measuring instruments, the Civil Protection checked the homogeneity of this dataset using the Standard Normal Homogeneity test [19], along with the Craddock and Vincent tests [20,21]. According to Pavan et al. [22], these tests were applied to each time series by building a reference series that was a combination of data from four stations surrounding the tested location. The data provider declared 20 homogeneous and 2 nonhomogeneous time series. Further, the daily maximum and minimum data were discarded if there were less than 92% of the expected records based on the sampling frequency. These data were used in the subsequent analyses.

For the declared homogeneous series, the coordinates and altitudes of the two stations (mechanical RM and telemetry RT) are listed in

Table 1. List of thermometric stations in the Marche Region declared homogeneous.

Basin	Mechanical Station RM	Long. °E	Lat. °N	Altitude (m a.s.l.)	Telemetry Station RT	Long. °E	Lat. °N	Altitude (m a.s.l.)
Aso	Montemonaco	13°19′	42°53′	987.00	Montemonaco	13°19′	42°53′	995.00
Cesano	Fonte Avellana	12°43′	43°28′	689.00	Fonte Avellana	12°43′	43°28′	690.00
Cesano	Pergola	12°50′	43°33′	306.00	Pergola	12°50′	43°34′	242.48
Chienti	Lornano	13°25′	43°17′	232.00	Macerata Montalbano	13°25′	43°17′	294.00
Esino	Fabriano	12°54′	43°20′	357.00	Fabriano Centro	12°54′	43°19′	354.00
Esino	Jesi	13°14′	43°31′	96.00	Jesi	13°13′	43°31′	100.00
Foglia	Pesaro	12°54′	43°54′	11.00	Villa Fastiggi	12°52′	43°53′	22.00
Coastline between Esino and Musone	Ancona Torrette	13°27′	43°36′	6.00	Ancona Torrette	13°26′	43°36′	5.00
Metauro	Bargni	12°51′	43°44′	273.00	Piagge	12°58′	43°44′	189.00
Metauro	Fano	13°1′	43°50′	4.00	Metaurilia	13°3′	43°49′	7.12
Metauro	Fossombrone	12°48′	43°41′	116	Fossombrone	12°47′	43°41′	96
Metauro	Mercatello	12°20′	43°38′	429	-	-	-	-
Metauro	Sant’angelo in Vado	12°24′	43°39′	359	S. Angelo in Vado	12°24′	43°40′	352.4
Metauro	Urbino	12°38′	43°43′	451	Urbino	12°38′	43°43′	471
Misa	Arcevia	12°56′	43°29′	535	Arcevia	12°56′	43°29′	535
Musone	Cingoli	13°12′	43°22′	631	Poggio San Vicino	13°4′	43°22′	580
Potenza	Camerino	13°4′	43°8′	664	Camerino	13°4′	43°8′	581
Tenna	Servigliano	13°29′	43°4′	215	Servigliano	13°29′	43°5′	197
Tronto	Amatrice	13°17′	42°37′	955	Amatrice	13°17′	43°37′	954
Tronto	Ascoli Piceno	13°35′	42°51′	136	Brecciarolo	13°39′	42°51′	78.23

. The only station remaining in the same place and at the same altitude is in Arcevia, whereas the other stations all underwent a shift. The greatest difference in altitude was that of the 112 m Poggio Cancelli station. The Montemonaco, Fonte Avellana, Urbino, and Camerino stations were found to have the same latitude and longitude for both types of measurements, but at different altitudes. Further, it is important to note that only 6 of the 20 homogeneous time series were located in the southern area of the Marche region (Table 1 and Figure 1).

After identifying a homogeneous time series, only those that met the following criteria of completeness and continuity were selected. According to the World Meteorological Organization (WMO) [23], a minimum series length of 30 years is required for eliminating variations between two different years. The minimum series length specified by the WMO automatically eliminates all stations introduced after the 2000s by the regional Civil Protection because of insufficient data. In addition, the historical series were considered reliable if (i) the number of missing data was less than 20% of the total of the series, (ii) stations with more than three consecutive missing years were not considered, and (iii) monthly values obtained with more than four missing data points were discarded.

A greater number of missing years was observed after 2008, which in most cases, corresponds to the years of change between mechanical RM and telemetry RT instrumentation. Consequently, the time series considered valid for evaluating the indices in this study were related to 14 stations for thermometric data (

Table 2. Dataset selected for analysis.

Basin	Mechanical Station RM	Telemetry Station RT	First Year	Last Year	Years Tot.	Data
Aso	Montemonaco	Montemonaco	1957	2019	63	Accepted
Cesano	Fonte Avellana	Fonte Avellana	1957	2019	63	Accepted
Cesano	Pergola	Pergola	1957	2019	63	Accepted
Chienti	Lornano	Macerata Montalbano	1957	2019	63	Accepted
Esino	Fabriano	Fabriano Centro	1957	2019	63	Accepted
Esino	Jesi	Jesi	1957	2019	63	Accepted
Foglia	Pesaro	Villa Fastiggi	1957	2019	63	Not Accepted
Coastline between Esino and Musone	Ancona Torrette	Ancona Torrette	1957	2019	63	Accepted
Metauro	Bargni	Piagge	1957	2019	63	Not Accepted
Metauro	Fano	Metaurilia	1957	2019	63	Accepted
Metauro	Fossombrone	Fossombrone	1968	2019	52	Not Accepted
Metauro	Mercatello	-	1957	2011	55	Not Accepted
Metauro	Sant’angelo in Vado	S. Angelo in Vado	1968	2019	52	Not Accepted
Metauro	Urbino	Urbino	1957	2019	63	Accepted
Misa	Arcevia	Arcevia	1959	2019	61	Accepted
Musone	Cingoli	Poggio San Vicino	1957	2019	63	Accepted
Potenza	Camerino	Camerino	1957	2019	63	Not Accepted
Tenna	Servigliano	Servigliano	1957	2019	63	Accepted
Tronto	Amatrice	Amatrice	1957	2019	63	Accepted
Tronto	Ascoli Piceno	Brecciarolo	1957	2019	63	Accepted

), with a total of 63 years of observations belonging to the period 1957–2019. The Arcevia station was activated in 1959, and it consequently had a dataset of 61 years; however, it was considered for the analysis because the amount of missing data was sufficiently small.

For each of the 14 selected time series, the measured daily maximum and minimum values and their respective averages from 1957 to 2019 were available. The maximum and minimum temperature data transmitted by the mechanical thermometer station RM were recorded at 9 A.M. (referring to solar time), whereas the data transmitted by the telemetry thermometer station RT were recorded every half hour. For the latter stations, the daily maximum and minimum values were calculated over 24 h (referred to as solar time) and assigned to the day of measurement.

3. Methodology

3.1. Extreme Temperature Analysis and Spatial Interpolation

The temporal evolution of extreme temperatures was evaluated by considering the daily maximum and minimum values. The mean temperature values were calculated for each station and year. Further, the data were averaged considering different intervals: the entire observation period and three different 30-year periods used for calculating the climatological standard normal (1961–1990, 1971–2000, and 1981–2010). The temporal fluctuations in measured values with respect to the average were calculated using the temperature variance.

The averaged values were interpolated using a co-kriging methodology to evaluate the spatial distribution of extreme temperatures with altitude as an independent variable among other predictors such as the distance from the sea and latitude. Ordinary co-kriging was executed using the Geostatistical Analyst tool of ESRI ArcGIS software [24].

3.2. Extreme Temperature ETCCDI Indices

The study of climate change based on extreme events can be conducted using a set of appropriately constructed indices. The ETCCDI promoted by the WMO developed a set of globally recognized and unambiguous climate indices that represent a common guideline for studying climate at the regional scale. The ETCCDI [25] identifies 27 climate indices defined derived from daily temperature and daily precipitation values.

In this study, we considered all the 16 indices calculated from the daily maximum (TX) and minimum (TN) temperatures. Several categories of indices belong to this set: indices with fixed threshold values (frost days, FD; tropical nights, TR; summer days, SU; icing days, ID; and growing season length, GSL); indices with absolute values (maximum of maximum temperatures, TXx, minimum of maximum temperatures, TXn; maximum of minimum temperatures, TNx; minimum of minimum temperatures, TNn; and daily temperature range, DTR); percentile-based indices calculated on the distribution of events over the 1961–1990 climatological reference period, as defined by WMO [26] (cold nights, TN10p, cold days, TX10p, warm nights, TN90p, and warm days, TX90p); and event duration indicators (warm spell duration index, WSDI, and cold spell duration index, CSDI). For long-term climate monitoring, WMO [27] recommends the 1961–1990 period for the calculation of climate normals. This period is kept as reference until a convincing scientific reason for change is presented. In terms of short-term monitoring, the WMO indicates the period 1981–2010 as the new current 30-year reference period given the need for a calculation of climate normals in a changing climate. Desiato et al. [28] in their 2017 annual climate report on the Marche region recommend continuing to use the “main” thirty-year period 1961–1990 for two reasons. The first follows the indications of the WMO regarding the estimation of climate variations in the medium and long term, and the second concerns the greater availability of data for that thirty-year period compared with those of the more recent ones.

The indices were calculated using an ad hoc MATLAB R2021b routine, and the results were compared with the R routine RClimDex [29]. For the percentile-based indices TN10p, TX10p, TN90p, and TX90p, a bootstrap procedure was performed to estimate the threshold within the reference period 1961–1990 as suggested by Zhang et al. [30]. This procedure removed artificial discontinuities at the beginning and end of the period used to calculate the percentiles (base period). This problem occurs because the threshold calculated in the base period is affected by the sampling error, which determines the overestimated exceedance rates outside the reference period.

The indices are summarized in

Table 3. Definition of the ETCCDI extreme temperature indices.

Index	Definition	Description	Unit
FD	Frost days	Annual count of days when TN (daily minimum temperature) < 0 °C.	(days)
SU	Summer days	Annual count of days when TX (daily maximum temperature) > 25 °C.	(days)
ID	Icing days	Annual count of days when TX (daily maximum temperature) < 0 °C.	(days)
TR	Tropical nights	Annual count of days when TN (daily minimum temperature) > 20 °C.	(days)
GSL	Growing season length	Annual (1 January) count between first span of at least 6 days with daily mean temperature TG > 5 °C and first span after 1 July of 6 days with TG < 5 °C.	(days)
TXx	Maximum of maximum temperatures	Monthly maximum value of daily maximum temperature.	(°C)
TNx	Maximum of minimum temperatures	Monthly maximum value of daily minimum temperature.	(°C)
TXn	Minimum of maximum temperatures	Monthly minimum value of daily maximum temperature.	(°C)
TNn	Minimum of minimum temperatures	Monthly minimum value of daily minimum temperature.	(°C)
TN10p	Cold nights	Percentage of days when TN < 10th percentile.	(%)
TX10p	Cold days	Percentage of days when TX < 10th percentile.	(%)
TN90p	Warm nights	Percentage of days when TN > 90th percentile.	(%)
TX90p	Warm days	Percentage of days when TX > 90th percentile.	(%)
WSDI	Warm spell duration index	Annual count of days with at least 6 consecutive days when TX > 90th percentile.	(days)
CSDI	Cold spell duration index	Annual count of days with at least 6 consecutive days when TN < 10th percentile.	(days)
DTR	Daily temperature range	Monthly mean difference between TX and TN.	(°C)

and have been defined mostly on an annual basis, with the exception of the absolute value indices TXx, TXn, TNx, TNn, and DTR, which are based on a monthly scale caused by extreme variability during the year.

3.3. Trend Detection

To detect any trend, the time series of temperatures were processed by using the non-parametric MK test. This test does not require the data to be normally distributed, and it is less influenced by the presence of outliers in the data. The test was applied directly to (i) the yearly average maximum and minimum temperatures and (ii) ETCCDI indices listed in Table 3 for each thermometer station using a MATLAB routine [31] to assess the existence of a warming or cooling trend in climatic conditions. The trend magnitude for the different indices was quantified using the Theil–Sen approach [32,33] and the statistical significance of the trends was assessed at the 5% level.

4. Results

4.1. Extreme Temperatures

4.1.1. Statistical Analysis

Figure 2a–d show the spatial representation of the mean value of the maximum temperature with reference to the observation period 1957–2019 and the climatological 30-year periods 1961–1990, 1971–2000, and 1981–2010 of the entire Marche region. A similar representation relative to the minimum temperature is plotted in Figure 3a–d. The data were spatially interpolated using the ordinary co-kriging methodology correlated with the altitude of the meteorological stations.

The maximum averaged values range in the interval 16–20 °C, and the highest ones are distributed along the Adriatic coast, whereas the lowest are concentrated in the south-west part of the region in correspondence with the Sibillini and Monti della Laga massifs. Further, the north-west part of the region is a colder area because of the presence of the Appenine mountains. Considering the minimum values, the averaged temperatures fall within the 5–11.5 °C interval, and the northern coastal area of the Marche region shows the highest values. The coolest values are concentrated in the southern part of the region, near Amatrice. Further, the minimum values have a greater variation (6.5 °C) compared to the maximum ones (4 °C).

Over the entire historical series 1957–2019, the average maximum temperature value varies from a minimum value of 15.88 °C evaluated near the Amatrice station to a maximum value of 20.01 °C evaluated near the Ascoli Piceno station (Figure 2a). The minimum temperatures averaged in the historical series tend to have values between 4.81 °C near the Amatrice station and 11.58 °C near the Ancona Torrette station (Figure 3a). The maximum temperature occurred at the Ascoli Piceno station, where a value of 44 °C was measured in July 2000, whereas the minimum temperature was recorded as −18.2 °C at the Amatrice station in January 1963. The effect of altitude on spatial temperature variation was confirmed by the results of both the minimum and maximum values measured at the Cingoli and Fabriano stations, which have different elevations compared to the surrounding area. In fact, Cingoli is located at 580 m a.s.l., while Jesi, Lornano, and Servigliano have elevations between 96 and 294 m a.s.l. A similar and opposite condition was observed for the Fabriano station, which is located in the mountainous area but has a lower elevation (354 m a.s.l.) compared to the nearest stations of Arcevia (535 m a.s.l.) and Fonte Avellana (690 m a.s.l.). The maps in Figure 2 show this behavior, although the spatial interpolation is affected by the limited number of available stations over the entire period.

Considering the temporal evolution of maximum temperatures averaged over the normals, the general spatial distribution was maintained, and a global increase in the mean values was observed. The hottest area expanded along the coast from south to north, whereas the coolest regions were reduced to the highest part of the mountain area. The minimum value of the maximum temperature pass from 15.16 °C in the period 1961–1990 to 16.19 °C in the period 1981–2010, with a global increase of 1 °C. A slightly severe variation was observed for the maximum of the maximum temperatures that increase from 19.15 °C to 20.34 °C. A similar trend was observed for the temporal variation in the minimum temperatures with reference to the 30–year periods. The mean values increased globally, indicating an expansion of the higher values along the coast and a reduction in the cold area in the northern part of the region. No significant variation in the minimum value of the minimum temperatures is evaluated, whereas a variation of 1 °C is observed for the maximum of the minimum temperatures that pass from 11.1 °C in the period 1961–1990 to 12 °C in the period 1981–2010. Globally, the analysis showed that the increase in the maximum temperature values was greater than that of the minimum values.

Data relative to the variance of the mean temperatures showed a temporal increase for the maximum, whereas a clear tendency of temporal evolution was not observed for the minimum temperatures. The values relative to the temperatures averaged in the period 1957–2019 vary in the interval 0.56–2.51 °C for the maximum and in the interval 0.47–1.46 °C for the minimum.

4.1.2. Trend Analysis

Figure 4 shows the spatial distribution of extreme temperature trends obtained using the MK test. In this figure, the triangles indicate the stations where a significant trend was observed, and red and blue triangles indicate increasing and decreasing trends, respectively. The size of the triangles is proportional to the value of the Theil–Sen (TS) estimator. Figure 4a shows the results for the maximum values, whereas Figure 4b shows the results for the minimum values. The trend, if it exists, is a positive tendency that considers 86 and 64% of the time series for the maximum and minimum temperature values, respectively. The maximum temperature time series for Cingoli and Jesi in the central part of the Marche region showed a non-significant trend, whereas the same statistical results were obtained for the minimum temperature time series for Amatrice, Fabriano, Fonte Avellana, Pergola, and Servigliano.

Further, the values of the TS slope were greater for the maximum temperatures compared to the minimum temperatures, which revealed more evidence of an increasing trend for the former data. These results confirmed the evidence of recent studies covering the same area [13].

4.2. Analysis of ETCCDI Temperature Indices

4.2.1. Statistical Analysis

The analysis was performed for all annual indices, whereas for the monthly indices, the data for January, April, July, and October, which are representative of the four seasons, were considered.

Figure 5, Figure 6 and Figure 7 show the distribution of the extreme temperature indices calculated for each station and for different temporal intervals: five decades (1961–1970, 1971–1980, 1981–1990, 1991–2000, and 2001–2010) and the entire observation period (1957–2019).

The temporal evolution of SU (Figure 5b), TR (Figure 5c), and TX90p (Figure 5j) shows an increasing trend for all stations, with particular evidence for the last two decades. This behavior is noteworthy for the warm spell duration index (Figure 5e), which more than doubles when passing from 1991–2000 to 2001–2010. In the Ancona Torrette time series, the increase is equal to 566%. For these indices, the mean value for the last decade was greater than the average value for the entire period. Finally, the mean value of TN90p (Figure 5k) increased over the decades for 50% of the analyzed time series.

This trend can be interpreted as a global indicator of an increase in hot days during the observation period, with more evidence obtained in recent years. Considering the indices relative to the cold conditions, it is observed that CSDI (Figure 5f), TX10p (Figure 5h), and TN10p (Figure 5i) have their maximum values in the first two decades (1961–1980) and show a general reduction moving from 1961–1970 to 1981–1990, while in recent years, some time series have increased the index mean values. The number of frost days (Figure 5a) did not show an evident evolution for any time series over the entire observation period. An exception can be observed in the 2001–2010 decade, when the mean value of FD increased compared to the previous value for more than 50% of the time series. The ID index has a similar behavior (Figure 5d) with particular relevance in the mountainous area of the region.

These results indicate that the minimum temperatures decreased compared to the first decades; however, the variation over the entire period was less evident than the maximum temperature increase. The GLS index (Figure 5g) has a considerably stable value during the various considered decades for the analyzed time series; however, a reduction in the mean value was observed in recent decades for 79% of the series.

Figure 6 shows a temporal evolution of monthly indices based on the maximum temperature TX. The absolute maximum values (TXx) clearly register cooler data in mountainous areas (Amatrice, Montemonaco, and Fonte Avellana time series) and increase during the year from winter to summer. Data relative to TXx in July (Figure 6e) increased over the decades for all analyzed time series, and the mean value in 2001–2010 was greater than the average of the entire period of 1957–2019. A similar but less evident evolution was observed for TXx in January and October (Figure 6a,g). The spring data for the absolute maximum temperature (Figure 6c) did not reveal a clear temporal evolution for all time series.

Figure 6b,d,f,h show the temporal evolution of the minimum value of the maximum temperature (TXn) for the time series. The data are negative in January and in mountainous areas (Figure 6b) and increase seasonally. The TXn index shows increasing values over time in July (Figure 6f) over the entire Marche region, whereas in April and October, there is an alternation of increasing and decreasing values over time (Figure 6d,h). In January, the TXn data (Figure 6b) did not exhibit well-defined behavior for different decades.

The temporal evolution of the monthly indices based on the minimum temperature TN is shown in Figure 7. The maximum of minimum temperature (TNx) observed in July (Figure 7g) increased over time for all time series; however, the same was not observed in January (Figure 7a), April (Figure 7d), or October (Figure 7j). The last decade (2001–2010) had higher TNx values than the average of the entire period (1957–2019) in all four months considered.

The absolute minimum temperature (TNn) was negative for all time series in January (Figure 7b) and similarly positive in July (Figure 7h). A total of 29% of the time series had negative values of TNn in April (Figure 7e). In general, no clear temporal evolution of the index was observed for different time series in the months under consideration, excluding July, in which the growth of the mean value of TNn over time for the different decades was noted. The mean values of TNn calculated for January in 1961–1970 were the coolest during the observation period.

An interesting result was related to the time series of Ancona Torrette, which registered the highest mean values in the last decade for TXn, TNx, and TNn in January, April, July, and October. The increase in both the maximum and minimum temperatures resulted in a coherent evolution of the DTR index, which was influenced by the tendency of those values. A general positive temporal evolution was observed for the index in July (Figure 7i), whereas no common evolution was detected in the remaining months for the time series (Figure 7c,f,l).

4.2.2. Trend Analysis

The results obtained by applying the trend analysis methodology based on the MK test are summarized in Figure 8. The histograms show, for each index, the percentage of stations with increasing, decreasing, and without significant trends, called “positive trend” (red), “negative trend” (blue), and “non-significant trend” (white), respectively.

In Figure 8a, a reduction in the number of frost days (FDs) was present at 42% of the stations, and a corresponding negative trend for the duration of the cold wave (CSDI) was evident at 58% of the stations. The reduction in FD was confirmed by the trend in the cold nights index (TN10p), which showed a decrease at 65% of the analyzed stations. These indices enabled us to understand the evolution of the minimum temperature during the observation period. From 1957 to 2019, the behavior of these parameters identified a progressive increase in minimum temperature values, with a consequent reduction in extreme cold events, which occurred more often with a shorter duration. This phenomenon was further validated by the TN90p index; in fact, a corresponding increase in the number of days per year with a minimum temperature above the 90th percentile (TN90p) was observed at the same stations that have a decrease in the number of days per year with a minimum temperature below the 10th percentile (TN10p). Therefore, the number of warm nights gradually increased although the number of cold nights decreased. Based on the TR index, which has an increasing trend at 79% of the stations, there are more and more events in which the minimum temperature exceeded 20 °C within a day.

For the maximum temperature in the Marche region, SU showed an increasing trend at 79% of the stations. In addition, there was a positive trend for extreme daytime heat events (TX90p) at the same thermometer stations. In addition, the duration of the heat waves, marked by the WSDI, showed a positive trend. Therefore, the maximum temperature increased in terms of both intensity and duration from 1957 to 2019. Over the years, heat events have become increasingly frequent and prolonged. The number of days with a maximum temperature below the 10th percentile TX10p, which describes the occurrence of extremely cold diurnal phenomena, significantly decreased. The ID index, which quantifies the number of days with a maximum temperature below 0 °C, shows a non-significant trend for any of the considered stations. The growing season length (GSL) showed an increasing trend at only four stations, whereas no trend was detected at the other stations. When comparing the trends of the indices calculated with minimum and maximum temperatures, it was observed that both parameters had increasing values. For example, both TR and SU had a positive trend at the same percentage of stations, which indicates that an increase in temperature affects both minimum and maximum temperatures. In addition, although the CSDI has a decreasing trend, the WSDI has a positive trend. Therefore, the shorter the duration of cold waves, the longer the heat waves.

This trend in the maximum and minimum temperatures was confirmed by the results of the trend tests for the monthly climate indices, which are shown in Figure 8b–f.

Figure 8b,c for the TXx and TXn indices, respectively, show that if a trend exists, it is a positive trend, and this behavior is more evident for the TXx index. Thus, increases in maximum temperature values were most evident in the spring and summer seasons because they were present at ~70% of the stations, whereas in the winter months, a positive trend was found at less than 35% of the stations.

The minimum of the maximum temperature values described by the TXn climate index had high percentages of positive trends in June, July, and August, which exceeded those of the TXx parameter for the same months. These percentages decreased for March, April, and May and are lower than those obtained in the TXx index. For the remaining months, the positive trend was observed at ~10% of the stations, except in November and February, which showed non-significant trends.

Thus, the maximum temperature values during the observation period (1957–2019) showed more evident increases during the spring and summer months. For daytime temperatures during the summer season, the increasing trend is perceived more in the minimum values (TXn) than in the maximum values (TXx). Conversely, in the winter season, the positive trend has higher percentages in the index compared to the maximum values TXx.

An increasing trend prevailed for many months of the year for TNx and TNn climate indices, as shown in Figure 8d,e. The maximum of the minimum temperature (TNx) showed a positive trend, which was evident at more than 64% of the stations in the months of April, June, July, and August, with a maximum value of 78% reached in June and July. The percentage of stations with an increasing trend decreased in March, May, September, and November, whereas in the remaining months, stations with a non-significant trend prevailed. The increasing trend of the TNx index was more evident in the summer season (June, July, and August), as was the climatic index TXn.

The TNn climate index related to the minimum temperature values had a similar behavior to the TNx index with regard to the positive trend in various months of the year; however, the percentages identifying it were slightly lower. The highest percentages of the positive trends of TNn were in June, July, and August, and they were lower than those of TNx in the same months as well as in all other months. In February and December, there were no stations with positive trends.

The Pergola station is the only station that shows a negative trend in the TNx index in August and September, whereas the Sevigliano station shows a negative trend in the TNn index in February and December. The DTR index (Figure 8f) was calculated as the difference between the monthly average of the maximum and minimum temperatures. As described by the other climate indices, the minimum and maximum temperatures increased in terms of both maximum (TXx, TNx) and minimum (TXn, TNn) values during the observation period. Consequently, if the maximum temperature values, on average, increase proportionally with the minimum temperature values, the gap between the two quantities tends to be comparable over time. Consequently, it was difficult to perceive a clear decreasing or increasing trend in the DTR index. For each month, the negative trend percentages were lower than the positive ones; however, the latter was lower or comparable to the no-trend rate.

Figure 9 shows the spatial distribution of the annual index trends obtained using the MK test. Triangles indicate stations where a trend was observed; red and blue triangles indicate increasing and decreasing trends, respectively; and the size of the triangles is proportional to the value of Sen’s slope estimator β.

The climatic index ID is not represented because it does not present any significant trend. The FD index (Figure 9a) presents a negative trend that is more pronounced at stations located in the northern area of the Marche region. The CSDI (Figure 9j), TN10p (Figure 9g), and TX10p (Figure 9e) climate indices have a negative trend uniformly distributed in stations located in the regional territory with lower values of the β estimator. Therefore, the increase in minimum temperatures is more evident in stations located in the north, whereas the reduction in the magnitude and duration of extreme cold events is equal in all stations with a trend. The climate indices SU (Figure 9b) and TR (Figure 9c) have a positive trend in many stations in the region with appreciable values of the β estimator. The increasing trend in extreme heat events TX90p and TN90p is slightly less marked (Figure 9f,h). The positive trend of TN90p seems to be more evident in the northern area. The WSDI index (Figure 9i) has an even weaker increasing trend. The GSL index (Figure 9d) has many stations where a non-significant trend was found, and a few stations showing an increasing trend are located in the northern-central zone. Warm-related indices show a greater variation rate than those of cold-related indices. This finding confirmed those of previous studies in the same area obtained with shorter and fewer time series [19].

4.3. Analysis of Heat Wave Indices

Among the ETCCDI climate indices, TR, SU, and WSDI are indicators used to identify heat waves [34,35,36]. These are important values for assessing the effect of climate change on physical well-being. For example, if the minimum temperature remains above the value of 20 °C, the human body has no chance to cool down after a day of intense heat. There are sections of the population, such as the elderly and the sick, who are vulnerable to cooling difficulties. Therefore, several studies correlated increased temperatures with increased mortality or morbidity [37,38]. Further, this indicator suggests a potential effect on the energy sector. Higher temperatures could lead to a greater use of air conditioning, which influences electricity demand and consumption.

Figure 10 shows the values of the TR, SU, and WSDI parameters over the period 1957–2019 using linear regression. Model parameters and relative statistics are shown in Figure 10. The data refer to the value of the index averaged for the total number of analyzed stations.

A general increasing trend in the indices is evident, with more pronounced growth since the 1980s. The number of TR increased in the last 60 years with a growth rate of 0.36 days/year with an initial value of 6.6 days and a final value of 28.7 days over the entire period of 1957–2019 (Figure 10a). The time series indicate that there is an initial period from 1957 to 1981, wherein the data reduced the oscillation between 5 and 15 days of TR. Subsequently, in 1982–2019, the values increased progressively with greater oscillations, and the mean value passed from 9.6 to 22.9 days, increasing by more than double.

The SU index shows a greater trend toward increasing over the period of observation with a growth rate of 0.48 days/year (Figure 10b), which starts from 71.3 days in 1957 and finishes at 101.1 days in 2019. The comparison between the mean value of SU in the interval 1957–1981 and that in the 1982–2019 reveals an increase of 23% when passing from 75.6 to 93.2 days. The increasing trend is less regular with respect to the TR index, and the number of SU appears to have decreased in the last period after the well-known peak year of 2003.

The growth rate of the WSDI is 0.22 days/year, and the trend is influenced by the changes occurring since 1984 (Figure 10c). In fact, larger values and more pronounced oscillations were registered, determining a global increasing trend. However, recent WSDI data decrease in agreement with what has been observed for SU. The mean value of the heat wave index in 1957–1984 was 2.1 days, whereas in the interval 1985–2019, it was 9.8 days, with an increase of 370%. The index indicates the annual count of days with at least six consecutive days when the maximum temperature is greater than the 90th percentile, and it can be considered directly equivalent to the number of heat waves. Therefore, an increase of more than threefold in the mean value was considered significant.

The heat wave indices were strictly related to the values of both the maximum and minimum temperatures observed in the Marche region.

The data of monthly extreme temperatures can be analyzed to check the contemporary occurrence of tropical nights and hot days. The European Environmental Agency (EEA) defines the hot days index as the total number of days in a year in which maximum daily temperatures above a fixed threshold are registered. EEA considers 30 °C a suitable threshold for the pan-European level, but higher thresholds can be used depending on regional climate conditions [39]. Here, values of TXx and TNx indices were considered to verify threshold exceedance for both tropical night and hot day conditions. Figure 11 shows the TXx and TNx data pairs measured in June, July, and August at the Ancona Torrette, Ascoli Piceno, and Urbino stations over the entire observation period of 1957–2019. The stations were selected to represent different geographic areas: coastal, inland, and hilly area, respectively. The points that fall above the solid black lines represent the years in which the maximum of both the monthly minimum temperature and the monthly maximum temperature were greater than 20 °C and 30 °C, respectively.

The percentage of years that exceeded both thresholds is very high, exceeding 70 percent at the three stations with a maximum value of 92 percent for the Ancona Torrette station. The highest values of the TXx and TNx indices above the thresholds were recorded in July, with percentages ranging from 84% in Urbino to 95% in Ancona and Ascoli Piceno. If the TXx threshold is raised to 35 °C, the values of the percentages of years with tropical nights and hot days in the summer months decrease significantly and are 14% for Urbino, 34% for Ancona Torrette, and 57% for Ascoli Piceno. Two particular results can be observed in the data shown in Figure 11: (i) Ascoli Piceno is the station with the highest maximum temperatures, having observed 10% of years in which the summer TXx index is greater than 40 °C, and (ii) 47% of TNx measured at Ancona in July and August is greater than 25 °C. These results confirm that, given the geographical location of the three stations, the most stressful conditions for physical well-being occur in lowland areas distant from the sea, which are characterized by a continental-type climate.

5. Conclusions

The historical series of maximum and minimum temperatures collected by the thermometric stations in the Marche region were analyzed in depth to assess past and current climate changes.

The spatial distribution of the temporal evolution of the maximum temperatures averaged over normals shows a global increase in the mean values. The hottest area expanded along the coast from south to north, whereas the coolest regions were reduced to the highest part of the mountain area. A similar behavior was observed for the temporal variation in the minimum temperatures with reference to the climatological normal. Globally, the analysis showed that the temporal increase in the maximum temperature values was greater than that of the minimum values.

The ETCCDI indices confirmed the increase in hot days during the observation period with more evidence in recent years, and they revealed that the minimum temperatures reduced compared to those in the first decades. However, the variation over the entire period was smaller than the maximum increase in temperature.

Extreme heat events in the Marche region occurred over the years with increasing intensity and frequency, and accordingly, cold weather events decreased. A high percentage of stations showed an increase in SU, TR, TX90p, TN90p, and WSDI, together with a reduction in TX10p, TN10p, and CSDI. The analysis of the trends of indices based on monthly data confirms that the number of stations with positive trends was greater during summer for TXn and TNn, whereas a comparable number of trends was observed between spring and summer for TXx and TNx. This phenomenon characterized the entire Marche region. The analyzed data showed an increase in the heat wave indices in the observed interval of 1957−2019, which was particularly evident since the early 1980s for TR and WSDI.

There was a lack of thermometric stations suitably distributed within the Marche region and with a consistent historical dataset that satisfied the criteria of completeness and continuity. The modest number of thermometric stations allowed for limited spatial analysis, which tended to provide uniform results within the territory. The availability of a greater number of thermometric stations would have provided a more detailed investigation of trends, especially in the case of climate indices that showed greater trend variability.