In the 1980s, most researchers approached empirical analysis of low-dimensional dynamical systems through the famous Takens embedding theorem, which guarantees that the attractor of any dynamical system can be reconstructed from samples of the values of key variables.
Will climate change make weather forecasting harder than it currently is? Or, could a warmer planet make prediction easier? The answer isn’t obvious. Because atmospheric dynamics are chaotic, there are practical limits to predictability, regardless of data precision and available computing power.
The climate system involves a complex interplay between the ocean and atmosphere. Studies of this interplay typically rely on model simulations in comparison with time series data for some feature of oceanic and/or atmospheric circulation on a regional or larger scale.
Coupled Map Lattices (CML) are discrete time and space dynamical systems often used as simplified models for the study of spatially-extended non-linear systems.
In the analysis of dynamical systems, the correlation dimension is a useful indicator describing the fractal structure of invariant sets. Other measures, such as the Lyapunov exponents and the entropy, provide complementary information on the time scale of predictability of the system.
Computational models employed for simulations of weather and climate have limited spatial resolution, currently around 2 km for regional weather models and 100 km for global climate models.
Davide was given the award for “his exceptional contributions to developing the theory of extreme values for dynamical systems and its use for analysing the underlying properties of the dynamics of the atmosphere and the climate system.”
Solar radiation and the earth’s rotation are the primary drivers of large-scale atmospheric motions, while turbulence adds a layer of complexity at small scales. This picture is further complicated by ocean–land interactions, vegetation, anthropocentric forcing, and the hydrological cycle.
Extreme, high-impact weather events have serious social and economic consequences, and have garnered widespread scientific and media attention. Understanding their drivers and improving their prediction is therefore a topic of crucial societal and economic value, where the interests of academia, policy makers and private stakeholders converge.
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