We present a new approach to estimating sedimentary proxy records along with the proxy uncertainty. We provide analytical expressions for the proxy record, while transparently propagating uncertainties from the ages to the proxy record. We represent proxies on an error-free, precise timescale. Our approach provides insight into the interrelations between proxy variability and the various uncertainties. We demonstrate our method with synthetic examples and proxy data from the Lonar lake in India.

We use visibility graphs to characterize asymmetries in the dynamics of sunspot areas in both solar hemispheres. Our analysis provides deep insights into the potential and limitations of this method, revealing a complex interplay between effects due to statistical versus dynamical properties of the observed data. Temporal changes in the hemispheric predominance of the graph connectivity are found to lag those directly associated with the total hemispheric sunspot areas themselves.

In the past few years, complex networks have been extensively applied to climate sciences, yielding the new field of climate networks. Here, we generalize climate network analysis by investigating the influence of altitudes in network topology. More precisely, we verified that nodes group into different communities corresponding to geographical areas with similar relief properties. This new approach may contribute to obtaining more complete climate network models.

It is rarely ensured that the parameters of statistical distributions are stable through the entire duration of a data collection process. A failure of stability leads to nonsmoothness and nonlinearity in the physical processes. We propose testing for stability of parameters of exponential family distributions and generalized linear models. We study Atlantic tropical storms using the techniques developed here.

This paper presents an improvement on the conventional ARIMA model for precipitation time-series forecast. The precipitation time series of 12 months is first classified into several clusters. The maxima, minima, and truncation means of each cluster are then predicted using the improved ARIMA models, which are further used to predict the monthly precipitation through a set of regression models. A case study demonstrates that the present approach could increase the forecast accuracy by 21%.

We implemented sandpile cellular automata to examine the effects of the heterogeneous toppling threshold on the global avalanche statistics. A systematic scaling exponent increase is revealed by increasing the coverage extent and decreasing the compactness rather than varying the range of contrast or the diversity. The distinct extent and compactness of the heterogeneous failure threshold are thus likely critical factors that manifest in the reported dynamic variations of seismicity scaling.