The North Atlantic Oscillation (NAO) phenomenon has a significant impact on the global climate. In this paper, we perform the identification of optimal precursors for NAO to investigate the predictability problem of NAO events. We select proper simulation duration and find out the structure of OPRs for two types of NAO events. Besides, the method applied in this work can be generalized to numerical models that do not have an adjoint model, and its efficiency has been further enhanced.

Fluid parcels that are transported in complicated flows often contain sub-sets of particles that stay close to each other over a finite time interval. We here propose a new method to detect such finite-time coherent sets based on the density-based clustering technique OPTICS. Different from previous methods, our method has an intrinsic notion of coherent sets at different spatial scales. OPTICS is readily implemented in the SciPy sklearn package, making our method easy to use for everybody.

We propose an enhanced correlation identification (ECI) algorithm to attenuate the background noise. The cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Experiments on both synthetic and real SSIP data show that the ECI algorithm is proposed to preserves the valid information of the raw SSIP data to display the actual location and shape of adjacent high resistivity anomalies.

Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series, together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series, after deterministically subtracting geophysical signals from the observations.

To infer information of an unknown quantity that helps to understand an associated system better and to predict future outcomes, observations and a physical model that connects the data points to the unknown parameter are typically used as information sources. Yet this problem is often very challenging due to the fact that the unknown is generally high dimensional, the data is sparse and the model can be nonlinear. We propose a novel approach to address these challenges.

An unprecedented amount of rainfall data is nowadays available, such as ensemble model output, weather radar estimates and in-situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is the precipitation reconstruction through the combination of numerical model output with observations from multiple data sources.

The surface transport of heat, nutrients and plastic in the North Atlantic Ocean is organized into large scale flow structures. We propose a new and simple method to detect such features in ocean drifter data sets, by identifying groups of trajectories with similar dynamical behaviour using network theory. We successfull detect well-known regions such as the Subpolar and Subtropical Gyres, the Western Boundary Current region and the Carribean Sea.

Research on South Atlantic Anomaly (SAA) is conducted during active and normal period in certain phase of solar cycle 24. The result reveal SAA region has a tendency to be persistent during both period compare to other regions during the phase studied. Other regions in the research experience a tendency to be persistent and antipersistent. Based on the observation, the Earth magnetic field strength and the year the solar cycle phase occur may have an influence on the characteristic of a region.

This review paper covers progress in the climate sciences in the past two decades, through the perspective of ten major problems for the geosciences in the 21st century. It concentrates on the last of these ten, Can we achieve enlightened climate control of our planet by the end of the century? Coupled climate-economy modeling is described as a main tool in achieving such control, while emphasizing the role of nonlinear, intrinsic variability and of the uncertainties in the two subsystems.