Recent  

The atmospheres of Venus, Mars and Titan are similar to the atmosphere of Earth. We wanted to find out the value of the residence time of energy in these three atmospheres. We estimated the energy contained in a column of air. From experimental data, we deduced the value of the energy inflow and outflow. From these data, the time of residence of the energy is calculated. This parameter gives a time scale of the response of the planetary atmosphere against global perturbations.

In geophysics large samples of extended fields need to be analysed and we therefore often work with high-dimensional data. Fortunately, high dimensionality can be a blessing and we show how this often allows analytical results to be derived. These results include estimates of correlations between sample members and how the sample mean depends on the sample size. We show that the properties of high dimensionality with success can be applied to climate fields, e.g., those from ensemble modelling.

Turbulent mixing in the ocean interior is mainly contributed by internal wave breaking. Here, the spatially inhomogeneous and seasonally variable diapycnal diffusivities in the upper Philippine Sea were estimated from ARGO float data. The driving processes mainly included the near-inertial waves and internal tides. The mesoscale environment in the Philippine Sea played a significant role in regulating the intensity and shaping the spatial inhomogeneity of the internal tidal mixing.

The skewness of a time series is commonly used to quantify the extent to which positive (negative) deviations from the mean are larger than negative (positive) ones. However, in some cases, traditional skewness may not provide reliable information about time series skewness, motivating the development of a waveform skewness index in this paper. The waveform skewness index is used to show that changes in the relationship strength between climate time series could arise from changes in skewness.

We used regional climate networks (RCNs) to identify heat waves and droughts in Germany. RCNs provide information for whole areas and provide many details of extreme events. The RCNs were constructed on the grid of the E-Obs data set. Time series correlation was used to construct the networks. Network metrics were compared to standard extreme indices. The results show that RCNs have skill to identify severe and moderate extremes. In average years, distributions resemble Poisson distributions.