Recent  

Paleoclimate proxy data is essential to broaden our understanding of climate variability. There remains however challenges for traditional methods of variability analysis to be applied to such data which is usually irregular. We perform a comparative analysis of different methods of scaling analysis, which provide variability estimates as a function of timescales, applied to irregular paleoclimate proxy data.

In weather forecasting, observations are incorporated into a model of the atmosphere through a process called data assimilation. Sometimes observations in one location may impact the weather forecast in another faraway location in undesirable ways. The impact of distant observations on the forecast is mitigated through a process called localization. We propose a new method for localization when a model has multiple length scales, as in a model spanning both the ocean and the atmosphere.

The same power-law we used to quantify growth of tree roots in time describes equally the assemblage of river networks in time. Even the basic length scale of both networks is the same. The one difference is that the basic time scale is 10 times shorter for drainage networks than for tree roots, since the relevant flow rate is ten times faster. This result overturns the understanding of drainage networks and forms a basis to organize thoughts about surface and subsurface hydrology.

This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspect of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.

A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.

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.