The aim of this study is to identify the most effective thermal predictor of heat-related very-elderly mortality in two cities located in different geographical contexts of central Italy. We tested the hypothesis that use of the state-of-the-art rational thermal indices, the Universal Thermal Climate Index (UTCI), might provide an improvement in predicting heat-related mortality with respect to other predictors. Data regarding very elderly people (≥75 years) who died in inland and coastal cities from 2006 to 2008 (May–October) and meteorological and air pollution were obtained from the regional mortality and environmental archives. Rational (UTCI) and direct thermal indices represented by a set of bivariate/multivariate apparent temperature indices were assessed. Correlation analyses and generalized additive models were applied. The Akaike weights were used for the best model selection. Direct multivariate indices showed the highest correlations with UTCI and were also selected as the best thermal predictors of heat-related mortality for both inland and coastal cities. Conversely, the UTCI was never identified as the best thermal predictor. The use of direct multivariate indices, which also account for the extra effect of wind speed and/or solar radiation, revealed the best fitting with all-cause, very-elderly mortality attributable to heat stress.

A 29-year wind/wave hindcast is produced over the Mediterranean Sea for the period 1990–2018. The dataset is obtained by downscaling the ERA5 global atmospheric reanalyses, which provide the initial and boundary conditions for a numerical chain based on limited-area weather and wave models: the BOLAM, MOLOCH and WaveWatch III (WW3) models. In the WW3 computational domain, an unstructured mesh is used. The variable resolutions reach up to 500 m along the coasts of the Ligurian and Tyrrhenian seas (Italy), the main objects of the study. The wind/wave hindcast is validated using observations from coastal weather stations and buoys. The wind validation provides velocity correlations between 0.45 and 0.76, while significant wave height correlations are much higher—between 0.89 and 0.96. The results are also compared to the original low-resolution ERA5 dataset, based on assimilated models. The comparison shows that the downscaling improves the hindcast reliability, particularly in the coastal regions, and especially with regard to wind and wave directions.

Objective To investigate the short-term effect of air temperature on outdoor occupational injuries (out_OI) in Central Italy, also by taking different geographical factors and employment sectors of workers into account. Methods Out_OI for all of Tuscany (Central Italy), from 2003 to 2010 (n=162 399), were provided by the National Institute of Insurance for Occupational Illness and Injury. Representative daily meteorological data of the geographical area under study were obtained from the European Reanalysis-interim climatological reanalysis archive. Relationships between short-term changes in air temperature and out_OI were studied through Generalised Additive Models. Results The exposure-response curves of out_OI and short-term changes in air temperature generally showed significant out_OI increases when cold conditions occurred. The air temperature breakpoint corresponded to the 10th centile (−0.8°C) of the air temperature time series used in this study: a 1°C decrease in temperature below the 10th centile corresponded to a 2.3% (CI 1.3% to 3.3%) increase of out_OI throughout all of Tuscany. The cold effect was strongest in plain areas, especially when out_OI occurred in vehicles other than cars. No relationships of injuries with temperature extremes were observed in workers who generally spend half or most of their time outdoors, such as construction, land and forestry workers. However, these latter outdoor workers showed significant linear associations of injuries with typical (far-from-extreme) temperatures. Conclusions This large population-based study highlights the significant and independent effects of short-term air temperature changes (especially cold) in triggering out_OI. These findings represent the first step towards developing a geographically differentiated, operative outdoor-temperature-occupational-health warning system aimed at preventing outdoor work injuries.

This study describes the implementation and performances of a weather hindcast obtained by dynamically downscaling the ERA5 data across the period 1979–2019. The limited-area models used to perform the hindcast are BOLAM (with a grid spacing of 7 km over the Mediterranean domain) and MOLOCH (with a grid spacing of 2.5 km over Italy). BOLAM is used to provide initial and boundary conditions to the inner grid of the MOLOCH model, which is set in a convection-permitting configuration. The performances of such limited-area, high-resolution and long-term hindcast are evaluated comparing modelled precipitation data against two high-resolution gridded observational datasets. Any potential added-value of the BOLAM/MOLOCH hindcast is assessed with respect to ERA5-Land data, which are used as benchmark. Results demonstrate that the MOLOCH hindcast, compared to observations, provides a lower bias than ERA5-Land as regards both the mean annual rainfall ( $$-$$- 1.3% vs 8.7%) and the 90th percentile of summer daily precipitation, although a wet bias is found in southern Italy (bias $$\simeq$$≃ 17.1%). Improvements are also gained in the simulation of the 90th percentile of hourly precipitations both in winter and, to a minor extent, in summer. The diurnal cycle of summer precipitations is found to be better reconstructed in the Alps than in the hilly areas of southern Italy. This study also presents the rainfall peaks obtained in the simulation of two well-known severe precipitation events that caused floods and damages in north-western Italy in 1994 and 2011. Finally, it is discussed how the demonstrated reliability of the BOLAM and MOLOCH models associated to the documented low computational cost, promote their use as a valuable tool for downscaling not only reanalyses but also climate projections.

The first objective of the current work was to confirm the existence of consistent NDVI trends in the Sahelian region by applying per-pixel linear regression analyses to an 8-km dataset ranging from 1986 to 2000. These analyses ascertained that evident NDVI increases took place at the peak of the rainy season (August) in most of the Sahelian belt. Three sample areas were then selected in order to investigate the main origin of these increases, which was identified in corresponding rainfall variations during the previous periods, and particularly July. On this basis, per-pixel correlation analyses were conducted to identify zones where July rainfall and August NDVI values were concurrently increasing during the study period. A rainfall/NDVI correlation map was thus obtained that highlighted areas where vegetation productivity was particularly variable from year-to-year depending on the development of the rainy season. Such ‘hot spot’ areas were demonstrated to correspond to those where the predictive power of NDVI data on crop (millet) yield was highest. The paper finally discusses the possible developments of the analytical methodology applied, as well as the environmental relevance of the thematic maps produced.