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

Abstract

AbstractReliable 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.