Statistical downscaling is widely used in large operational centers around the world, using exclusively linear relations (MLR); this study uses a statistical downscaling methodology using a nonlinear technique known as ANNs with CMIP5 project data. The artificial neural network can perform tasks that a linear program cannot. The main advantages of this are its temporal processing ability and its ability to incorporate several preceding predictor values as input without any additional effort.

In this paper, a turbulent ocean is modelled in the laboratory. The rotation of the Earth around its axis is represented by the rotation of a turntable. Similar to that in the Earth's ocean, the currents in the laboratory "ocean" are created by density effects when the water is heated or made salty. The laboratory currents are measured by a system which is not unlike the satellite altimetry system used by oceanographers to create "topographic" maps of the elevation of the water surface.

This paper proposes a technique for dating an ice core. The proposed technique employs a hybrid method combining the sequential Monte Carlo method and the Markov chain Monte Carlo method, which is referred to as the particle Markov chain Monte Carlo method. The sequential Monte Carlo method, which is also known as the particle filter, is widely used for nonlinear time-series analysis. This paper demonstrates the usefulness of the approach in time-series analysis for dating an ice core.

The paper presents a new method called cumulative areawise testing that allows scientists to better extract important signals from geophysical time series. The method was found to be able to distinguish aspects of time series that are random from those of potential physical importance better than existing methods in wavelet analysis.