This session has four subthemes:
1) Nonlinear Dynamics of the Atmosphere, Ocean and the Coupled Climate System
Recent years have seen a substantial progress in the understanding of the nonlinear processes responsible for important dynamical aspects of the coupled atmosphere-ocean-climate system. In particular, the low-frequency variability (LFV) on seasonal to decadal time scales is now known to arise from irregular transitions between distinct atmospheric and oceanic regimes, as well as from the interaction between low- and high-frequency modes. Moreover, the application of the methods of dynamical systems theory (DST) has shown that a significant part of the LFV is governed by low-order nonlinear dynamics.
2) Multifractals and singularity analysis in mineral exploration and environmental assessment
In recent years there has been a significant increase in nonlinear modeling studies applied to geological, geophysical, geochemical and remote sensing data for mineral exploration and environmental assessment. Various forms of multifractal modeling including the application of universal multifractals to geophysical and geochemical survey data have been proven to provide useful new types of information. Singularity analysis is an offshoot of multifractal modeling. It allows the delineation of local anomalies which are superimposed on broader regional map patterns commonly constructed by well-known contouring methods such as moving averaging, inverse distance weighted interpolation or various Kriging methods. Local singularity analysis can target centers of local enrichment or depletion of chemical elements in the Earth’s crust.
3) Scaling fluctuations in the ocean and atmosphere
Oceanic and atmospheric fields show deterministic and stochastic fluctuations over a very large range of scales. Due to the influence of turbulence and other forcing, such fluctuations often possess fluctuations over some given range of scales. This session focuses on methods, observations, and data analyses aiming to identify such scaling ranges and characterize them.