R. Tomozeiu

Statistically downscaled climate change projections of surface temperature over Northern Italy for the periods 2021–2050 and 2070–2099

Future changes of seasonal minimum and maximum temperature over Northern Italy are assessed for the periods 2021–2050 and 2070–2099 against 1961–1990. A statistical downscaling technique, applied to the ENSEMBLES-Stream1 and CIRCE global simulations (A1B scenario), is used to reach this objective. The statistical scheme consists of a multivariate regression based on Canonical Correlation Analysis. The set-up of the statistical scheme is done using large-scale fields (predictors) derived from ERA40 reanalysis and seasonal mean minimum and maximum temperature (predictands) derived from observational data at around 75 stations, distributed over Northern Italy, over the period 1960–2002. A similar technique is also applied to the number of frost days and ice days at a reduced number of stations in order to construct projections on change of the selected extreme temperature indices for the two future periods. The evaluation of future projections for these extreme indices is relevant due to its impacts on transports, health, and agriculture. The downscaling scheme constructed using observed data is then applied to large-scale fields simulated by global models (A1B scenario), in order to construct scenarios on future change of seasonal temperature, mean and extreme indices, at local scale. The significance of changes is tested from the statistical point of view. The results show that significant increases could be expected to occur under scenario conditions in both minimum and maximum temperature, associated with a decrease in the number of frost and ice days in both periods and more intense to the end of the century.

Increasing resolution of climate assessment and projection of temperature and precipitation in an alpine area

In mountain regions, important differences in the time trends of climate series can be detected even within relatively small areas, leading to uncertainty when assessing climate change. The paper deals with a structured algorithm for high-resolution downscaling of climate characterisation in a region (precipitation and temperature), leading to a twofold application: increasing spatial resolution of past climate definition for the area and attaining high-resolution downscaling for climate projections. In the first stage, multi-variate analysis (‘partial least squares’ regression) was applied to a number of time series (10) in order to obtain climate averages for a larger number of sites. Predictions made with single-site values (such as seasonal means) can in some cases be improved by applying ‘random perturbation’ of the value and averaging single predictions in the ensemble. This analysis laid the foundation for implementing the same technique to the output of statistical downscaling of multi-model climate projections. Climate shift in the study area (Trentino), located in the north-eastern Italian Alps, was simulated for two 30-year time windows: 2021–2050 and 2071–2099. Progressive warming is predicted, being stronger in the summer, along with a mixed, seasonally differentiated trend for precipitation.

Forecasting Models for Urban Warming in Climate Change

Defining UHI phenomenon required and interdisciplinar approach using both simulation models and climate data elaborations at regional and metropolitan level. In particular the WP 3 of UHI project provided a detailed survey on the main studies and practices to counteract urban heat islands in different European areas; discussed climate models at regional level; simulated the evaluation of urban warming in the different cities involved in the project, providing locally proper measuring and analysis in connection with the specific urban forms.

Assessment of Tropospheric Ozone Increase in Future Climate Change Scenarios

This work aims to assess the impact of climatological increment of temperature on the tropospheric ozone concentration in the Po Valley (Italy). Creation, destruction, and transport of ozone is not only governed by the sun, via photochemical reactions, but also by atmospheric conditions. Air quality is therefore significant, and its connection with climate change important. With a statistical downscaling of data from different General Circulation Models (GCMs), and the application of a Weather Generator (WG), it was possible to generate data series of daily temperature in the future (2021–2050). These were compared to data from the past (1961–1990, from the Agroscenari project), and the present (2000–2013, measured in six stations), showing how temperatures are bound to increase. We calibrated a simple statistical model based on a stratified sampling technique over a dataset of measured ozone and temperature, predicting the summer ozone daily maximum distribution. This allowed us to determine changes in ozone concentration over the years as a consequence of temperature increase. The results suggest that the last decade can be viewed as a projection of the future “ozone climate” in the Po Valley.

UHI in the Metropolitan Cluster of Bologna-Modena: Mitigation and Adaptation Strategies

The pilot action took place in a district of Modena, the Villaggio Artigiano, characterized by the presence of disused small industrial buildings, which is part of a wider redevelopment context and regeneration process.