Climate Services (Dec 2024)

Leveraging observations and model reanalyses to support regional climate change adaptation activities: An integrated assessment for the Marche Region (Central Italy)

  • Alice Crespi,
  • Anna Napoli,
  • Gaia Galassi,
  • Marco Lazzeri,
  • Antonio Parodi,
  • Dino Zardi,
  • Massimiliano Pittore

Journal volume & issue
Vol. 36
p. 100512

Abstract

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Acknowledging the increasing urgency of climate change, many local administrations, in Italy as well as abroad, are currently elaborating their own adaptation strategy. A key step of this process is understanding the current climate, past variability and ongoing trends. Combined with the analysis of vulnerable and exposed elements, it supports the identification of key climatic impacts and risks for the territory and the elaboration of future scenarios. Several climatic datasets are available for this purpose, ranging from station observations to interpolated products and to model reanalyses, each with its own features. The study aimed to shed light on these differences and thus help practitioners make better, more informed decisions. Three gridded datasets, offering global, European and national coverage, were compared to derive a local characterization of mean climatic features, recent trends and climate extremes for the Marche Region (Central Italy). The assessment was based on temperature and precipitation variables from the global reanalysis ERA5-Land, the European observation dataset E-OBS, and the high-resolution reanalysis dynamically downscaled for Italy VHR-REA_IT. The analysis showed that large-scale products such as E-OBS and ERA5-Land can still represent a robust complement for adaptation planning. However, important limitations in describing spatial and temporal patterns need to be properly accounted for in the decision-making process. Only an integrative approach based on a multi-source data evaluation would properly address the multi-faceted aspects of climate variability on a regional scale, derive a more comprehensive analysis of past and current conditions and better manage the underlying uncertainty.

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