Building a local climate reference dataset: Application to the Abruzzo region (Central Italy), 1930–2019

Abstract

Reliable secular time series of essential climatic variables are a fundamental element for the assessment of vulnerability, impact and adaptation to climate change. Here, we implement a readily portable procedure for building an upgradable long-term homogeneous climate dataset using monthly and daily observations of temperature and precipitation over a given area of interest, exemplified here with Abruzzo, a region in Central Italy characterized by complex orography. We process the dataset according to a preliminary ranking of stations based on data quantity and quality, and we exploit the Climatol algorithm for inhomogeneity correction. The corrected time series show trends in broad agreement with external databases (CRU, Berkeley Earth, E-OBS), and highlight the importance of relying on a local network for a better representation of gradients and variability over the territory. We estimate that maximum (TX) and minimum temperature (TN) increased by~1.6 and~2.2 C/century, respectively, over the period 1930-2019, while in the recent decades 1980-2019 we found an accelerated trend of~5.7 and~3.9 C/century. Precipitation (RR) decreased by~10%/century in 1930-2019, while it has been increasing at a rate of~26%/century in 1980-2019. The Köppen-Geiger climate classification is sensitive to the increase of precipitation in the recent decades, which is attributable to decreased summer precipitation overcompensated by more rain in late spring and early autumn. The cold climate types are retreating upwards along the slopes of the mountain ranges. Over the period 1980-2019, extreme values are also displaying significant trends. Every 2 years, there is one less frost day (TN <0 C) and one more summer day (TX >25 C) in the Apennines area, while there is one more tropical night (TN >20 C) in the Adriatic coastal area. Precipitation extremes are increasing, especially along the coast, with rain accumulated in the rainiest days increasing at a rate of 1-2%/year.

Climatic Suitability of Different Areas in Abruzzo, Central Italy, for the Cultivation of Hazelnut

“…According to this criterion, in the next 10 years the suitability classification of Vasto could shift from suitable to moderately suitable; on the contrary, Isola del Gran Sasso, Penne, Guardiagrele, Pescara, and Nereto could shift from moderate to not very suitable; and, finally, Catignano could shift from not very suitable to unsuitable. An increase in warm-season precipitation was also reported by Curci [22] as a combined effect of increased precipitation in summer shoulder months (April-June and September) and a decrease in summer months (July-August).…”

“…The area east of the Apennines has a prevalent temperate climate without a dry season and hot summer, while the western part is more variable, from temperate, in the valleys to cold in the mountains. Large parts of the valleys are wet in summer [22]. Climatic historical series from the Hydrographic and Mareographic Office were checked for quality and homogenization, as detailed in Curci [22], according to international standards [23][24][25].…”

“…Large parts of the valleys are wet in summer [22]. Climatic historical series from the Hydrographic and Mareographic Office were checked for quality and homogenization, as detailed in Curci [22], according to international standards [23][24][25]. The homogenization procedure is based on the Climatologic algorithm.…”

Analysis of two-decade meteorological and air quality trends in Rome (Italy)

“…In Italy, the climatological features and the effects of local phenomena, such as the UHI, have been extensively examined in recent years (Mariani et al 2016 ; Curci et al 2021 ). Rome, the most populous and extended Italian city and one of the most densely populated in Europe (ISTAT 2021 ), has been studied since the 1980s, to evaluate the intensity and temporal variability of the UHI (Colacino and Lavagnini 1982 ; Bonacquisti et al 2006 ).…”

Opposite trends of sea-breeze speeds and gusts in Eastern Spain, 1961-2019

“…missing data in lling, reconstruction and homogenization can be applied to entire datasets (i.e., essential climate variables for SB detection) on a sub-daily basis(Guijarro 2018;Curci et al 2021), this implies huge computational cost and effort. To address the need to improve the processing time and accuracy in SB selection methods for long-term studies, we propose a robust automated method to identify potential SB days using alternative variables (e.g.,Laird et al 2001; Azorin-Molina and Lopez-Bustins 2008), which can be used worldwide at any coastal region with few adjustments.…”