In this paper, we study solar magnetic activity by means of a complex network approach. A complex network was built based on information on the space and time evolution of sunspots provided by image recognition algorithms on solar magnetograms taken during the complete 23rd solar cycle. Both directed and undirected networks were built, and various measures such as degree distributions, clustering coefficient, average shortest path, various centrality measures, and Gini coefficients calculated for all them. We find that certain measures are correlated with solar activity and others are anticorrelated, while several measures are essentially constant along the solar cycle. Thus, we show that complex network analysis can yield useful information on the evolution of solar activity and reveal universal features valid at any stage of the solar cycle; the implications of this research for the prediction of solar maxima are discussed as well.

Glaciers and ice caps are thinning and retreating along the entire Andes ridge, and drivers of this mass loss vary between the different climate zones. The southern part of the Andes (Wet Andes) has the highest abundance of glaciers in number and size, and a proper understanding of ice dynamics is important to assess their evolution. In this contribution, we apply the ice-sheet model SICOPOLIS (SImulation COde for POLythermal Ice Sheets) to the Mocho-Choshuenco ice cap in the Chilean Lake District (40^∘ S, 72^∘ W; Wet Andes) to reproduce its current state and to project its evolution until the end of the 21st century under different global warming scenarios. First, we create a model spin-up using observed surface mass balance data on the south-eastern catchment, extrapolating them to the whole ice cap using an aspect-dependent parameterization. This spin-up is able to reproduce the most important present-day glacier features. Based on the spin-up, we then run the model 80 years into the future, forced by projected surface temperature anomalies from different global climate models under different radiative pathway scenarios to obtain estimates of the ice cap's state by the end of the 21st century. The mean projected ice volume losses are 56±16 % (RCP2.6), 81±6 % (RCP4.5), and 97±2 % (RCP8.5) with respect to the ice volume estimated by radio-echo sounding data from 2013. We estimate the uncertainty of our projections based on the spread of the results when forcing with different global climate models and on the uncertainty associated with the variation of the equilibrium line altitude with temperature change. Considering our results, we project a considerable deglaciation of the Chilean Lake District by the end of the 21st century.

We apply a complex network approach to analyze the time series of five solar parameters, and propose a strategy to predict the number of sunspots for the next solar maximum, and when this maximum will occur. The approach is based on the visibility graph (VG) algorithm, and a slightly modified version of it, the horizontal VG, which maps a time series into a complex network. Various network metrics exhibit either an exponential or a scale-free behavior, and we find that the evolution of the characteristic decay exponents is consistent with variations of the sunspot number along solar cycles. During solar minimum, the sunspot number and the F10.7 cm radio flux time series have characteristic decay exponents that correlate well with the next maximum sunspot number, suggesting that they may be good precursors of the intensity of the next solar maximum.

In this paper, solar cycles 21–24 were compared using complex network analysis. A network was constructed for these four solar cycles to facilitate the comparison. In these networks, the nodes represent the active regions of the Sun that emit flares, and the connections correspond to the sequence of solar flares over time. This resulted in a directed network with self-connections allowed. The model proposed by Abe and Suzuki for earthquake networks was followed. The incoming degree for each node was calculated, and the degree distribution was analyzed. It was found that for each solar cycle, the degree distribution follows a power law, indicating that solar flares tend to appear in correlated active zones rather than being evenly distributed. Additionally, a variation in the characteristic exponent γ for each cycle was observed, with higher values in even cycles compared to odd cycles. A more detailed analysis was performed by constructing 11 yr networks and shifting them in 1 yr intervals. This revealed that the characteristic exponent shows a period of approximately 22 yr coincident with the Hale cycle, suggesting that the complex networks provide information about the solar magnetic activity.

Dataset for the paper "The 21st-century fate of the Mocho-Choshuenco ice cap in southern Chile", published in The Cryosphere. For more information, please refer to the readme file, the metadata of the nc-files, and the paper.

We apply a complex network approach to analyse the time series of five solar parameters, and propose an strategy to predict the number of sunspots for the next solar maximum, and when will this maximum will occur. The approach is based on the Visibility Graph (VG) algorithm, and a slightly modified version of it, the Horizontal Visibility Graph (HVG), which map a time series into a complex network. Various network metrics exhibit either an exponential or a scale-free behavior, and we find that the evolution of the characteristic decay exponents is consistent with variations of the sunspots number along solar cycles. During solar minimum, the sunspots number and the solar index time series have characteristic decay exponents that correlate well with the next maximum sunspots number, suggesting that they may be good precursors of the intensity of the next solar maximum. Based on this observation, we find that, based on current data, the algorithm predicts a number of 179 sunspots for cycle 25. Combining this with the Hathaway function, adjusted to yield such maximum sunspots number, we find that the maximum for solar cycle 25 will occur in December 2024/January 2025.