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Modelling local climate change using site-based data

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Abstract

In the context of the ongoing United Nations Framework Convention on Climate Change (UNFCCC) process, it seems important to focus attention not only on global mean surface air temperature (GSMT) but also on the climate of specific regions in order to gain insights into the dynamics of the changes, the timescales of the periodic components, the local trends and the relationships between climatic variables in the region of interest. This is important for scientists as well as for policymakers. This paper provides an analysis of the changes in local air temperature and precipitation depth in exceptionally long observational records and examines the relationships between these two variables. The focus is on monthly values. Temperature maximum, minimum, range, and cumulative precipitation depth are considered. The wavelet analysis shows that the scale of variation is different for temperature and precipitation and that the behavior of the temperature range values diverges from the behavior of the minimum and maximum values. The timescale of important changes in the long-term trend is, however, similar. Results also suggest that the main mode of variability is persistent through time in the series of temperature maximum, minimum, and range but not in precipitation depth. This is a clear evidence of climate change. All series show variances that change over time and are, as expected, nonstationary. The analysis of the wavelet coherence shows that the relationship between precipitation and temperature evolves through time, and its intensity varies considering different time scales. The association between these climatic variables is particularly strong in the last decade. Is it noteworthy that the analysis of the coherence suggests that temperature is leading to rain and not the other way around. This highlights the impact of global warming on the hydrologic cycle and on related human activities.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The research reported in the present paper was supported by Fondazione Cassa di Risparmio di Modena through the Grant 2018-0093, by the University of Modena and Reggio Emilia through the Grant FAR 2020 Mission Oriented, and by the European Union NextGenerationEU/NRRP, Mission 4 Component 2 Investment 1.5, Call 3277 (12/30/2021), Award 0001052 (06/23/2022), under the Project ECS00000033 “Ecosystem for Sustainable Transition in Emilia-Romagna,” Spoke 6 “Ecological Transition Based on HPC and Data Technology.” The authors thank the anonymous reviewer for comments that led to improvements in the manuscript.

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Author notes

  1. Isabella Morlini, Maria Franco-Villoria and Stefano Orlandini have contributed equally to this work.

Authors and Affiliations

  1. Department of Communication and Economics, University of Modena and Reggio Emilia, Reggio Emilia, Italy

    Isabella Morlini

  2. Department of Economics Marco Biagi, University of Modena and Reggio Emilia, Modena, Italy

    Maria Franco-Villoria

  3. Department of Engeneering Enzo Ferrari, University of Modena and Reggio Emilia, Modena, Italy

    Stefano Orlandini

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  1. Isabella Morlini
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  2. Maria Franco-Villoria
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Correspondence to Isabella Morlini.

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Morlini, I., Franco-Villoria, M. & Orlandini, S. Modelling local climate change using site-based data. Environ Ecol Stat 30, 205–232 (2023). https://doi.org/10.1007/s10651-023-00560-z

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