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    Coherent response of the Indian Monsoon Rainfall to Atlantic Multi-decadal Variability over the last 2000 years
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Naidu, Pothuri Divakar; Ganeshram, Raja; Bollasina, Massimo A.; Panmei, Champoungam; Nürnberg, Dirk; Donges, Jonathan F.
    Indian Summer Monsoon (ISM) rainfall has a direct effect on the livelihoods of two billion people in the Indian-subcontinent. Yet, our understanding of the drivers of multi-decadal variability of the ISM is far from being complete. In this context, large-scale forcing of ISM rainfall variability with multi-decadal resolution over the last two millennia is investigated using new records of sea surface salinity (δ18Ow) and sea surface temperatures (SSTs) from the Bay of Bengal (BoB). Higher δ18Ow values during the Dark Age Cold Period (1550 to 1250 years BP) and the Little Ice Age (700 to 200 years BP) are suggestive of reduced ISM rainfall, whereas lower δ18Ow values during the Medieval Warm Period (1200 to 800 years BP) and the major portion of the Roman Warm Period (1950 to 1550 years BP) indicate a wetter ISM. This variability in ISM rainfall appears to be modulated by the Atlantic Multi-decadal Oscillation (AMO) via changes in large-scale thermal contrast between the Asian land mass and the Indian Ocean, a relationship that is also identifiable in the observational data of the last century. Therefore, we suggest that inter-hemispheric scale interactions between such extra tropical forcing mechanisms and global warming are likely to be influential in determining future trends in ISM rainfall.
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    A network-based microfoundation of Granovetter’s threshold model for social tipping
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Wiedermann, Marc; Smith, E. Keith; Heitzig, Jobst; Donges, Jonathan F.
    Social tipping, where minorities trigger larger populations to engage in collective action, has been suggested as one key aspect in addressing contemporary global challenges. Here, we refine Granovetter’s widely acknowledged theoretical threshold model of collective behavior as a numerical modelling tool for understanding social tipping processes and resolve issues that so far have hindered such applications. Based on real-world observations and social movement theory, we group the population into certain or potential actors, such that – in contrast to its original formulation – the model predicts non-trivial final shares of acting individuals. Then, we use a network cascade model to explain and analytically derive that previously hypothesized broad threshold distributions emerge if individuals become active via social interaction. Thus, through intuitive parameters and low dimensionality our refined model is adaptable to explain the likelihood of engaging in collective behavior where social-tipping-like processes emerge as saddle-node bifurcations and hysteresis. © 2020, The Author(s).