Motivation

The current threat of global climate change and the public demand for confident projections of climate change pose the ultimate challenge to science: predicting the future behaviour of a system of such overwhelming complexity as the Earth's climate. The proposed Theme Issue focusses on two aspects that make prediction so challenging.

For one, even though the physical processes underlying the climate (pre-dominantly fluid dynamics) are well understood in principle, this understanding warns us that arbitrarily fine-grained details of topography and forcing can affect the large-scale features of the system after short time. As it is unrealistic to resolve the finest relevant scales in earth system models, the dynamics of the microscopic scales acts like a stochastic subsystem that couples into the macroscopic model. One aspect of the proposed Theme Issue is the modelling of these subsystems, their coupling with the large system as a stochastic subsystem.

The other aspect is how one can extract and predict large-scale nonlinear features from observations and simulations in the presence of stochasticity, for example, tipping points, multi-stability, or relaxation oscillations. The proposed Issue gives a snapshot of current research on time series analysis and statistic forecasting techniques and its application to understanding of paleo-climate and climate variability.

Both aspects are two sides of the same coin: the complexity of the Earth's climate makes certainty in the predictions impossible. Current research focusses on removing systematic biases and capturing the inherent variability.