Convener:
Antonello Pasini, IIA – Istituto sull’Inquinamento Atmosferico (CNR)
Observations and modelling represent the basis for understanding climate change and planning future actions. Up to now, the general attention has been devoted to time series of standard variables, such as temperature and precipitation, for historical climatology, and to the results of GCMs and RCMs for modelling activity.
More recently, also new data and data analyses have been considered. This allows us to investigate the characteristic features of the complex climate system in greater detail (e.g., considering multi-sensors systems in the analysis of extremes), eventually supplying us with new climatologies for non-standard variables.
From the modelling point of view, the recent big amount of data available and some unavoidable uncertainties in dynamical modelling led to consider even some data-driven models (e.g., neural networks and autoregressive models) as useful tools for investigating climate characteristic features and impacts. These models showed their ability to handle with climate variability, especially on time horizons and spatial scales in which it is crucial for understanding the climate behaviour.
In this framework, the aim of this session is to provide an opportunity to discuss innovative data analyses and modelling techniques which could help us to better identify climate characteristic features and their impacts in present and future situations.