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This research presents a case study of the biases and discontinuities that were introduced in observed long-term mean wind-speed and gust data-series due to anemometer changes in a meteorological station in northern Spain, operated by the Spanish State Meteorological Agency: San Sebastian-Igueldo. Field and wind-tunnel experiments with predefined conditions have been presented in the literature, however this research uses a real case study to assess the impact of anemometer changes on wind speed measurements due to three factors being: (i) the 3-cup anemometer model (SEAC vs. THIES companies); (ii) sensor height (∼19.95 m vs. ∼20.45 m) and (iii) sensor age (20-years old vs. new). Our results show (a) substantial biases in the measured wind speed and daily peak wind gusts, with the new THIES anemometer reporting stronger surface winds than the old SEAC anemometer; (b) opposing biases under weak (negative) and moderate-strong (positive) winds; and (c) significant breakpoints in the long-term wind data-series, which highlight the importance of data homogenization. National Weather Services and climate assessment groups will benefit from these findings since errors in wind speed and gust measurements can be minimized by implementing systematic observation protocols. Robust anemometer observations provide a basis for accurate quantification of the magnitude of changes and the variability of surface winds.

Climate variability can be the source of several multiple hazards and damaging phenomena, such as flash floods, debris flows, landslides, forest fires, etc. In this study the response in the frequency of landslides, floods and forest fires to a set of climate indices is studied, referring to a region of southern Italy (Calabria) located in the center of the Mediterranean basin, a hot-spot for climate change. For these comparisons, 5022 landslides and 1584 flood occurrences for a 29-year period (1990–2018) have been selected for the whole Calabria; the burnt areas have been analyzed for the same territory from 2008 to 2018. The climate indices have been calculated by means of daily rainfall and temperature data registered in 93 stations. The results showed that landslide occurrences are more linked with climate indices describing not very intense rainfall. Conversely, floods show best matches with climate indices representative of more extreme precipitation. Regarding the burnt areas, the results confirmed that very dry climate conditions, modifying the moisture content of the soil, can change the intensity and the extension of fires.

© 2020, The Author(s). Monitoring and management of several environmental and socioeconomic sectors require climate data that can be summarized using a set of standard and meaningful climate metrics. This study describes a newly developed gridded dataset for the whole of Europe, which employed a set of 125 climate indices spanning different periods based on data availability, but mainly 1950–2017 and 1979–2017. This dataset comprehensively summarizes climate variability in Europe for a wide range of climate variables and conditions, including air temperature, precipitation, biometeorology, aridity, continentality, drought, amongst others. Climate indices were computed at different temporal scales (i.e. monthly, seasonal and annual) and mapped at a grid interval of 0.25°. We intend to update these indices on an annual basis. This dataset is freely available to research and end-user communities.