It is well-known that the upper ocean heat content (OHC) variability in the tropical Pacific
contains valuable information about dynamics of El Niño–Southern Oscillation (ENSO). Here …

We suggest a new nonlinear expansion of space-distributed observational time series. The
expansion allows constructing principal nonlinear manifolds holding essential part of …

Quantitative characterization of causal couplings from time series is crucial in studies of
complex systems of different origin. Various statistical tools for that exist and new ones are …

A new data-driven model for analysis and prediction of spatially distributed time series is
proposed. The model is based on a linear dynamical mode (LDM) decomposition of the …

The concept of dynamical causal effect (DCE) is generalized and equipped with a formalism
which allows one to formulate in a unified manner and interrelate a variety of causality …

Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models in:
Journal of Climate Volume 28 Issue 5 (2015) Jump to Content Jump to Main Navigation …

We present a detailed description of a new approach for the extraction of principal nonlinear
dynamical modes (NDMs) from high-dimensional data. The method of NDMs allows the joint …

Currently, causes of the middle Pleistocene transition (MPT)–the onset of large-amplitude
glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before–are a …

Data-driven, model-free analytics are natural choices for discovery and forecasting of
complex, nonlinear systems. Methods that operate in the system state-space require either …

In estimation of causal couplings between observed processes, it is important to
characterize coupling roles at various time scales. The widely used Granger causality …