Is the comparative analysis of temperature trends at the Geophysical Observatory of Modena different from that at the Monte Simone Observatory, Italy? Which analysis is better and more appropriate? Can a favorable conclusion be drawn for the climate future?

Global warming has become a critical environmental, social and economic threat, with the increasing frequency and intensity of extreme weather events. This study analyses temperature trends and climate indices in the Po Valley, an important economic and agricultural region in Italy, by examining data from two historical stations: the Modena Urban Observatory and the Monte Simone Rural Observatory. The analysis extends previous studies up to 2018, assessing the magnitude of climate change since the 1950s and isolating the urban heat island (UHI) effect in Modena. Significant warming trends were confirmed in both locations, with maximum (TX) and minimum (TN) temperature trends almost doubling from 1981 to 2018 compared to 1951-2018. For example, the Texas temperature trend at Modena was 0.84 Cdecade−1 and at Mont Simon was 0.62 Cdecade−1, while the Northeast temperature trend was 0.77 and 0.80 Cdecade−1, respectively. Extreme weather indices showed a significant increase in warm days and nights (TX90p and TN90p, respectively). Specifically, we found TX90p at Modena to be 27.5 days decade−1... Specifically, frost days decreased by 1.88 days decade−1 (37% of urban participation, UC), tropical nights increased by 5.16 days decade−1 (57% UC), warm nights increased by 12.7 days decade−1 (65% UC), and cold nights decreased by 3.19 days decade−1 (39% UC). Overall, this study highlights the importance of considering both global and local factors in the analysis of regional climate trends. Keywords :Apennines, climate change, climate extremes, global warming, Po Valley, UHI, urban health Island. INTRODUCTION Global warming has become increasingly apparent since the last decades of the previous century, posing an environmental, social, and economic threat to the planet due to its widespread and intensifying effects. Several studies in scientific literature have found that global warming is increasing the frequency of extreme weather events, including heatwaves, intense precipitation, floods, and droughts (Planton et al., 2008; Stott, 2016; Xie et al., 2015). Regarding Europe, heat waves in recent decades, especially in 2003, have had unprecedented impacts, causing significant social, economic, and environmental damage (Della-Marta et al., 2007a, 2007b; Klein Tank et al., 2005; Luterbacher et al., 2004). In Italy, temperature trends analysed over various regions show a consistent upward trend, with notable increases in recent decades (Scorzini & Leopardi, 2019; Toreti et al., 2010; Ventura et al., 2002). Researchers have also used extreme indices like the Diurnal Temperature Range (DTR) to study climate patterns, highlighting a reduction in temperature ranges due to more significant increases in minimum temperatures (Bartolini et al., 2008; Brunetti et al., 2000). The Expert Team on Climate Change Detection and Indices (ETCCDI) (Klein Tank et al., 2009) has identified a set of 27 climate indices crucial for analysing temperature and precipitation, with a specific focus on extreme characteristics (Chervenkov & Slavov, 2021; Kang et al., 2014; Klein Tank et al., 2005; Meehl et al., 2000; Sillmann et al., 2013a, 2013b). The present study fits within this context, aiming to analyse temperature trends and ETCCDI indices to estimate local climate change. The area considered is the Po Valley, which hosts a third of Italy's population. This territory has been experiencing increased economic and agricultural vulnerability due to the rise in annual temperatures from the mid-20th century to the present with significant impacts on crop production and in the industrial/civil sector (i.e., increased cooling demands for systems) (Tomozeiu et al., 2006; Zullo et al., 2019). Thus, the paper aims to assess both the magnitude of the climate changes experienced by the territory since the 1950s and to isolate the signal of urbanization in the temperature series to understand its contribution to global warming, as done by Zhang et al. (2021). For this purpose, we consider two historical stations with a long-standing tradition of measuring and recording meteorological parameters located in the same geographic region (Emilia Romagna region, northern Italy) but with distinct characteristics. The first station is the Geophysical Observatory of the University of Modena and Reggio Emilia (hereafter Modena Observatory), established in 1826 within the city centre of Modena in the Po Valley, an area that has experienced considerable urban development. The second station is the Mount Cimone Observatory (hereafter Cimone Observatory), an historical station of the Italian Air Force Meteorological Service. This observatory, located at an elevated position of 2165 m a.s.l. on Mount Cimone, has been collecting data since 1947. It resides in the free troposphere, free from urban influences (Carbone et al., 2014; Ramponi, 2023). This study starts evaluating climate changes in the two locations extending the analysis of Boccolari and Malmusi (2013) to 2018 for Modena, and also includes an assessment of climate changes at Mount Cimone. It also incorporates insights from Cundari and Colombo (1992), who correlated temperature data from the Cimone Observatory with a large area in the Po Valley, demonstrating its utility for long-term temperature representation. Despite extensive use of the Cimone Observatory for CO2 and pollution studies (Alemanno et al., 2014; Bonasoni et al., 1995, 2002; Cristofanelli et al., 2018; Fratticioli et al., 2023), there is a gap in the literature on its meteorological data since Colombo's study Finally, we analysed the potential contribution of urbanization to the temperature trends at the Modena Observatory station. The expansion of built-up areas near the station over time could have caused temperature increases attributable not to global warming but to the Urban Heat Island (UHI) effect (Barbieri et al., 2018; Costanzini et al., 2022). To assess the impact of the UHI, auxiliary data from the ERACLITO databases were utilized (Antolini et al., 2016). This comprehensive approach ensures that the observed temperature trends can be accurately attributed, distinguishing between global climate change and local urban influences. 2 | STUDY AREA AND DATASETS This section outlines the data sources employed in the study, focusing on the two distinct weather stations of Modena Observatory and Cimone Observatory shown in Figure 1. Both stations play crucial roles in assessing climate changes and offer unique insights due to their disparate locations. 2.1 | Meteorological stations 2.1.1 | Modena Observatory The Modena Observatory is housed in the East Tower of the Ducal Palace in Modena's city centre (44380 52.5900N, 10550 47.2200E WGS84, at 34.6 m a.s.l.). The meteorological instruments are located on a historic balcony, positioned at a barometric height of 64.2 m a.s.l. Established in 1826 by Francesco IV, Duke of Modena, the observatory began collecting meteorological data in 1827, encompassing parameters such as temperature, humidity, precipitation, pressure, wind speed and direction, and cloud cover. Past temperature records are comparable with present ones since 1861, marking the construction of the first meteorological window, later replaced by the historical balcony. Recognized as a Centennial Observing Station by the World Meteorological Organization (WMO), Modena Observatory attests to its longstanding commitment to meteorological observations (Lombroso et al., 2020; World Meteorological Organization, 2022). The city of Modena resides in the Po Valley, marked by extensive urban areas, agricultural fields, intense breeding, and wide manufacturing districts. The region exhibits a warm temperate subcontinental climate (Köppen–Geiger classification Cfa) (Peel et al., 2007), characterized by hot summers and frequent temperature inversions, particularly in the cold period (Caserini et al., 2017). Prevailing winds, generally weak, follow two main directions: WNW and ESE, aligning with the longitudinal axis of the Po Valley. Anthropogenic emissions, combined with topographic and meteorological conditions, contribute to poor air quality (Costanzini et al., 2018; Johnstone & Dawson, 2010). The Northern Apennines act as a climatological divide between continental Europe to the north and the Mediterranean Basin to the south (Cristofanelli et al., 2018). Modena Observatory characterizes an urban context due to its location in the city centre. Modena features a hot summer climate with intense droughts in July and August, occasionally interrupted by severe thunderstorms, along with cold and wet winters. The transitional seasons are typically rainy, with peaks in fall (October) and spring (April and May). 2.1.2 | Mount Cimone Observatory The Cimone Observatory, situated at 2165 m a.s.l. on the highest peak in the Northern Apennines (44110 370 N, 10420 000 E, WGS84), serves as a strategic location for the Italian Air Force Meteorological Service. Operational since 1937, this observatory has played and currently plays a crucial role in telecommunications, meteorological forecasting, climatology, and air navigation assistance. Positioned above the Planetary Boundary Layer, in the lower free troposphere, and isolated from anthropogenic activities,the Cimone Observatory holds significance for studying atmospheric composition, especially in measuring background levels of greenhouse gases (Carbone et al., 2014). It is an active participant in the Global Atmosphere Watch program (Cristofanelli et al., 2019; Galli et al., 2019) of the WMO and is part of the Italian Meteorological Network managed by the Air Force Meteorological Service (SYNOP 16134-Metar LIVC). The climate of Mt. Cimone is classified as alpine, with minimum temperatures ranging from −22C in winter to 18C in summer (Colombo et al., 2000; Tositti et al., 2014). Prevailing wind directions include SW and NE, with speeds reaching intensities of 216 kmh−1 , resulting in a perceived wind chill temperature as low as −45C (Ciattaglia et al., 2010). Mt. Cimone experiences two rainfall peaks, with the maximum occurring in November and a secondary peak in April. 2.1.3 | Synoptic patterns of the two stations From a meteorological standpoint, the synoptic weather patterns in Modena and Mt. Cimone exhibit significant similarities, but with some notable exceptions. Instances of heavy rainfall in both locations can occur when a lowpressure system is situated above the Tyrrhenian Sea. Winter cold air flows lead to snowy precipitation in both locations. Strong winds, particularly from the southwest, affect Mt. Cimone. Under these conditions, the Tyrrhenian side of the Apennines experiences cloudiness due to the Stau effect, while Modena may experience the Föhn phenomenon. During intense southwest flows, heavy precipitation has been observed as a result of the spillover effect even at distances 10–20 km downwind from the ridge (Lombroso & Fazlagic, 2000). In both locations, the influence of large highpressure fields fosters stable weather conditions. Summer heat waves are driven by the presence of a strong African anticyclone, which causes very high temperatures in both locations, that is, in June, July, and August 2003, August 2017, and June 2019. However, summer in Mt. Cimone is also characterized by convective systems, in some cases with orographic storms (Frontero & Lombroso, 1988). Stable conditions lead to robust thermal inversions during winter in Modena, resulting in lower temperatures compared to Mt. Cimone. During winter, the phenomenon of thermal inversion is frequently observed, resulting in notable differences between the two stations. This aspect will be further explored in this paper to enhance our understanding of the temperature series and trends calculations. 2.2 | Dataset For this study, the primary datasets comprise daily historical temperature records, encompassing TX and TN values from 1951 to 2018. The DTR series was also derived to provide a comprehensive perspective. The Modena Observatory has daily temperature data available from 1861 to the present. To ensure data consistency, the homogenization process was applied to daily TX and TN, following a method by Boccolari and Malmusi (2013). This method underwent some revisions to minimize undocumented corrections made to the original series. In the original study covering 1861 to 2010, homogenization was performed using statistical tests (SNH, Pettitt, Buishand, and Von Neumann) on mDTR (annual average of DTR) and vDTR (annual average of absolute day-to-day differences of the DTR) series (Wijngaard et al., 2003). These tests assess the statistical significance of temperature series homogeneity and identify breakpoints (apart from the Von Neumann test). Corrections, whether documented or not, aim to adjust the series for homogeneity. Notably, more corrections were needed after 1900 compared to the initial 40 years. Evaluation of the first 40 years is challenging due to extensive documented interventions on measuring instruments. Although mathematically homogeneous, corrections in this period may not convincingly show consistent trends. Subsequent corrections for the 1901–2020 period, using the same methodology, yielded a satisfactory outcome. Only two corrections were needed over the 120-year period, passing all four statistical tests at a 0.01 signifi cance level, specifically for the mDTR series. The rejection of vDTR is not surprising, given its relation to the daily variability of temperature. The corrections made symmetrically on TN and TX were as follows: • Until July 31, 1967 [TX + 0.3C] and [TN − 0.3C]; a documented correction corresponding to the calibration of the maxima and minima thermometer. • Until December 31, 1953 [TX + 0.15C] and [TN − 0.15C]; an undocumented correction. Comparing such new TX and TN trends with those obtained with the previous homogenization by Boccolari and Malmusi (2013), differences of only a few hundredths of a Celsius degree per decade were recorded. Therefore, the results presented in that paper can still be considered valid. As for the Cimone Observatory, meteorological observations began in 1947 but became systematic only since 1951. The TX and TN series for this observatory were extracted from the European Climate Assessment and Dataset (ECA&D) platform (Klein Tank et al., 2002). This dataset includes homogenized series derived from data collected by climatological divisions of the National Meteorological and Hydrological Services, observatories, and research centres across Europe and the Mediterranean. The available TX and TN series include data from January 1, 1951 to December 31, 2020. Notably, there are missing data in the two series, accounting for 352 days for TX and 333 for TN. To address this, a control procedure was applied (Desiato et al., 2012; Dufek et al., 2008) to discard years with more than 30% missing data (i.e., 2019 and 2020 with 63% and 68%, respectively). Uniformly spread gaps over various years were filled through linear interpolation with the other available data. Additionally, the homogeneity of these series was checked using the same method mentioned above, and no corrections were found to be necessary. The datasets from Modena Observatory and Cimone Observatory, including the computation of seasonal and annual anomalies for TX, TN, and DTR, are presented in section 4.2. 3 | METHODOLOGY The methodology applied in this study starts from the examination of TX and TN series recorded by both stations spanning from 1951 to 2018. Initially, computations were made for seasonal and annual temperature anomalies, utilizing cross-correlation techniques to identify shared tendencies between the two stations. An analysis about thermal inversions frequency for Modena Observatory has been also included in this section to help the assessment of the differences achieved in winter between the two stations. Subsequently, an iterative procedure that systematically computed trends and their statistical significance across various periods was employed to partition the time series into meaningful subperiods. Then trends over TX and TN are evaluated, and extreme climate indices from ETCCDI are calculated. The tendencies of these indices and their significance were assessed to elucidate the nuanced changes observed in both stations. Finally, the UHI contribution has been evaluated using the Urban Minus Rural method (Bian et al., 2014; Manalo et al., 2022; Park et al., 2017; Wu et al., 2019; Zhong et al., 2023), to assess the contribution of urbanization on temperatures and ETCCDI indices trends. 3.1 | Occurrence of daytime temperature inversions in Modena The Po Valley, where the Modena Observatory is situated, experiences limited ventilation due to its morphology. During the winter, also a weak radiative forcing occurs, contributing to the persistence of stagnant events characterized by the phenomenon of thermal inversions (Caserini et al., 2017) in these situations, lower temperatures can be recorded in Modena compared to Mt. Cimone. To gain deeper insights into these conditions, an analysis of the seasonal frequency of thermal inversions in Modena was conducted, utilizing data from both observatories. For each day, the lapse rate inC/100 m was calculated, representing the difference between the temperatures at Mt. Cimone and Modena. This calculation included minimum, maximum, and mean temperatures for every day from 1951 to 2018. We started examining periods where strong stagnant events have been documented, as during January 1989 and 1990, February 1993, November 1994, January 2012, December 2013, 2015 and 2016, etc. Although thermal inversions typically cease at 1000–1500 m, during these events minimal temperature differences between Modena and Cimone have been recorded. In these periods, the presence of thermal inversions has also been assessed observing radiosonde data. Based on these findings, two thresholds have been selected to identify the presence of thermal inversion in Modena. Aggregating data from all available days across all 12 months spanning from 1951 to 2018, the frequencies of different lapse rate were computed, taking into account the seasonal distribution. Fre quency are reported grouped for meteorological seasons, as: • Winter (DJF): months of December, January, and February. • Spring (MAM): months of March, April, and May. • Summer (JJA): months of June, July, and August. • Fall (SON): months of September, October, and November. Note that winter season includes the months of December, January, and February. We consider the month of December of 1 year and the January–February of the following year, comprising one winter season. The results of this analysis, useful for the assessment of differences in temperature anomalies between the two sites (and subsequent trends), are reported in section 4.1. 3.2 | TX and TN anomalies and DTR Daily series of TX and TN temperatures from 1951 to 2018 for both stations were used to compute daily anomalies. This was done by subtracting the climatological mean for each specific date from the daily values of each series. The climatological mean was based on the Climatic Normal (CLINO) for the period from 1961 to 1990, as defined by the World Meteorological Organization (WMO) as the standard reference period for long-term climate change assessments. To smooth out large variations due to insufficient samples, a 5-day moving window centred on each specific date was used to average the values. The daily anomalies were subsequently grouped by meteorological seasons to better understand temporal variations and differences between the two stations. A cross-correlation was computed between the series of seasonal and annual TX and TN anomalies of Modena Observatory and Cimone Observatory. Furthermore, the DTR was computed as the difference between daily TX and the TN at both the Modena Observatory and Cimone Observatory. This calculation provides insights into the variation between the highest and lowest temperatures recorded in a single day, offering a more comprehensive perspective on the temperature dynamics at the two stations. The DTR is particularly valuable in discerning differences in temperature increases between minimum and maximum values. The results are presented in section 4.2. 3.3 | Temperature trends The analysis of trends in time series is essential in the context of climate change as it provides insights into the evolution of a phenomenon over a specific period. Trends TX, TN, and DTR were evaluated within the time series of both Modena and Mt. Cimone. This analysis firstly covered the entire period from 1951 to 2018, and then specific significant periods were selected based on the subsequent analysis. The goal is to pinpoint statistically significant trends, typically at a confidence level of 99%, by dividing the entire observation period in time intervals covering at least 10 years within this range. This exploration was carried out through an automated procedure that systematically computed trends and their statistical significance across various sequentially periods. These periods include, for example: 1951–2018, 1952– 2018, 1953–2018, …, 2009–2018, 1951–2017, 1952–2017, …, 2008–2017, …, 1951–1961, 1952–1961, 1951–1960. The procedure reports the results on a diagram called a “shifting trend,” indicating the subperiods to consider for trend calculations. To evaluate the presence and significance of trends on temperatures on the periods identified above, the modified Mann–Kendall test (Kendall, 1957; Pohlert, 2023; Wilks, 2019) was applied, while the Theil– Sen method (Sen, 1968) was used to estimate the slope (section 4.3). These methods are widely used in literature to pursue this aim (Alexander et al., 2006; Mallick et al., 2022; Patra & Satpati, 2022). Indices from the ETCCDI were selected and applied to the temperature series of both stations. These indices including fixed-value thresholds, absolute measures, percentiles, and duration parameters (Alexander et al., 2006; Klein Tank et al., 2009) were employed to analyse the shifts in extreme climate trends. Fixed threshold indices indicate the number of days per year when a certain condition occurred, whereas percentile indices tally the annual days surpassing or falling below a specified percentile threshold. To compute these indices MATLAB® scripts were developed and applied. For the Modified Mann–Kendall test MATLAB® pre-existing software was retrieved (Aalok, 2023), developed, and applied. Table 1 lists the selected indices. The extreme climate indices were calculated based on the TX and TN time series for both stations, spanning the entire observational period as well as other significant periods identified by the shifting trend analysis. The trends are expressed in days per decade, and their statistical significance at the 99% and 95% confidence levels was determined using the modified Mann– Kendall test. Comprehensive results are presented in section .

CONCLUSIONS:

In this study, the analysis focused on the maximum and minimum temperature series of two historical stations in the Po Valley. The first station is located in an urban area, in the centre of Modena, while the second is a station in the lower free atmosphere, the Monte Cimone Observatory. The purpose was to assess the magnitude of climate changes experienced by both stations since the 1950s and to isolate the urban effect due to the presence of extensive residential and industrial areas in the territory by evaluating the UHI effect in Modena. From the study, we found positive anomalies in both TX and TN series for both stations, with TX displaying slightly higher hotspots. Subsequently, the analysis explores into the trends of maximum and minimum temperature series over different time periods: 1951–2018 and 1981–2018. TX and TN trends in the period 1981– 2018 are almost double those computed on the period 1951–2018, confirming the anomalous rise in temperatures in recent years in both sites. We retrieved TX annual temperature trends (1981–2018) reaching 0.84Cdecade−1 for Modena and 0.62Cdecade−1 for Mt. Cimone while for TN values of 0.77Cdecade−1 for Modena and 0.80Cdecade−1 for Mt. Cimone. This general warming trend is reflected in the calculation of extreme climate indices. For instance, warm days (TX90p) in Modena increased by 27.5 daysdecade−1 in the period 1981–2018, almost twice the increase observed at the Cimone Observatory (15 daysdecade−1 ). Similarly, the rise in warm nights (TN90p) is more pronounced in Modena, with an increase of 29.5 daysdecade−1 in the period 1981–2018, compared to 22 daysdecade−1 at the Cimone Observatory. These results have been confirmed also by the comparison between the results of the present paper (in the period 1981–2018) with the findings of a past study focused on Modena in the period 1981–2010. Building on the findings of Cundari and Colombo (1992), which established that a single station (Cimone Observatory) can serve as a representative for studying long-term temperature changes in the Po Valley, it can be affirmed that this holds true in broad terms. However, when examining temperature trends in Modena, it becomes apparent that retrieving the urban influence reveals notable differences. This station, located in the city centre, has been subjected to increasing urbanization over the years. The attempt to quantify the UHI effect in terms of Urbanization Effect and Urbanization Contribution using the reference period 1961–2018, as earlier data were not available, reveals that the UHI effect is significant concerning the rise in minimum temperatures, with notably annual UE and UC, respectively: FD −1.88 no. daysdecade−1 and 37%, TR 5.16 no. daysdecade−1 and 57%, TN90 12.7 no. daysdecade−1 and 65%, and TN10 – 3.19 no. daysdecade−1 and 39%. These results align with studies found in the literature. In conclusion, this research has contributed to characterizing the Po Valley in terms of urban and free atmosphere stations, highlighting how the impact of climate change affects the two sites. During the last period, the increase in trends for minimum temperatures (particularly notable for Mt. Cimone), the negative diurnal temperature range (DTR) trend observed on Mt. Cimone, and the overall assessment of trends on extreme indices (although some are higher in absolute terms for Modena, they require adjustment for Urban Effects) might even indicate a more pronounced warming trend for Mt. Cimone. These findings underscore the importance of considering diverse local influences in the analysis of regional climate trends and provide a significant foundation for formulating targeted adaptation and mitigation strategies. Future outlooks should include analysing changes in precipitation trends, with a focus on northern Italy's climatic divide, to gain a nuanced understanding of the climatic changes the territory is experiencing.

DATA AVAILABILITY STATEMENT The Modena data were provided by the Geophysical Observatory of Modena, University of Modena and Reggio Emilia, Italy (www.ossgeo.unimore.it), and are available upon request from the corresponding author. The Cimone Observatory data are available at https://www. ecad.eu. ORCID Sofia Costanzini https://orcid.org/0000-0003-2227-622X Mauro Boccolari https://orcid.org/0000-0003-1837-5081 Stephanie Vega Parra https://orcid.org/0009-0009- 3850-5817 Francesca Despini https://orcid.org/0000-0002-6813- 131X Luca Lombroso https://orcid.org/0009-0006-3547-8508 Sergio Teggi https://orcid.org/0000-0001-7375-0599 REFERENCES Aalok, A. (2023) Modified Mann–Kendall test. GitHub. Available from: https://github.com/atharvaaalok/Modified-MannKendallTest/releases/tag/v2.0 [Accessed on 10th January 2024] Acquaotta, F., Fratianni, S. & Garzena, D. (2015) Temperature changes in the North-Western Italian Alps from 1961 to 2010. Theoretical and Applied Climatology, 122(3), 619–634. 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