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Tipping elements are nonlinear subsystems of the Earth system that have the potential to abruptly shift to another state if environmental change occurs close to a critical threshold with large consequences for human societies and ecosystems. Among these tipping elements may be the Amazon rainforest, which has been undergoing intensive anthropogenic activities and increasingly frequent droughts. Here, we assess how extreme deviations from climatological rainfall regimes may cause local forest collapse that cascades through the coupled forest–climate system. We develop a conceptual dynamic network model to isolate and uncover the role of atmospheric moisture recycling in such tipping cascades. We account for heterogeneity in critical thresholds of the forest caused by adaptation to local climatic conditions. Our results reveal that, despite this adaptation, a future climate characterized by permanent drought conditions could trigger a transition to an open canopy state particularly in the southern Amazon. The loss of atmospheric moisture recycling contributes to one-third of the tipping events. Thus, by exceeding local thresholds in forest adaptive capacity, local climate change impacts may propagate to other regions of the Amazon basin, causing a risk of forest shifts even in regions where critical thresholds have not been crossed locally.

Comparable to the traditional notion of stability in system dynamics, resilience is typically measured in a way that assesses the quality of a system's response, for example the speed of its recovery. We present a broadly applicable complementary measurement framework that quantifies resilience similarly to basin stability by estimating a "resilience basin" which reflects the extent of adverse influences that the system can recover from in a sufficient manner. In contrast to basin stability, the adverse influences considered here are not necessarily displacements in state space, but arbitrarily complex impacts to the system, quantified by adequate parameters. As a proof of concept, we present two applications: (i) the well-studied single-node power system as an easy-to-follow example and (ii) a stochastic model of a low-voltage DC power grid undergoing an unregulated energy transition consisting in the random appearance of prosumers. These act as decentral suppliers of photovoltaic power and alter the flow patterns while the grid topology remains unchanged. The resilience measurement framework is applied to evaluate the effect and efficiency of two response options: (i) upgrading the capacity of existing power lines and (ii) installing batteries in the prosumer households. The framework demonstrates that line upgrades can provide potentially unlimited resilience against energy decentralization, while household batteries are inherently limited (achieving, 22 pages, 11 figures; added plain language summary after abstract, added recent references, added section with simple application example, moved details of second application into Appendix, improved grammar

Further results of numerical simulations complementing those shown in the paper.

Vital parts of the climate system, such as the West Antarctic Ice Sheet, are at risk even within the aspired aims of the Paris Agreement to limit global temperature rise to 1.5 -- 2{\deg}C. These so-called natural tipping elements are characterized by rapid qualitative shifts in their states once a critical threshold, e.g., of global mean temperature, is transgressed. We argue that the prevention of such unwanted tipping through effective climate policies may critically depend on cascading Domino effects from (anticipated) climate impacts to emission reductions via wanted social tipping in attitudes, behaviors and policies. Specifically, the latter has recently been noted as a key aspect in addressing contemporary global challenges, such as climate change, via self-amplifying positive feedbacks similar to the processes observable in climate tipping elements. Our discussion is supported with data on commonly observed linkages between (subjective) climate change knowledge, concern and consequential potential action and we argue that the presence of such interrelations serves as a necessary condition for the possibility of rapid climate action.

El Ni��o exhibits distinct Eastern Pacific (EP) and Central Pacific (CP) types which are commonly, but not always consistently, distinguished from each other by different signatures in equatorial climate variability. Here, we propose an index based on evolving climate networks to objectively discriminate between both flavors by utilizing a scalar-valued evolving climate network measure that quantifies spatial localization and dispersion in El Ni��o's associated teleconnections. Our index displays a sharp peak (high localization) during EP events, whereas during CP events (larger dispersion) it remains close to the baseline observed during normal periods. In contrast to previous classification schemes, our approach specifically account for El Ni��o's global impacts. We confirm recent El Ni��o classifications for the years 1951 to 2014 and assign types to those cases were former works yielded ambiguous results. Ultimately, we study La Ni��a episodes and demonstrate that our index provides a similar discrimination into two types.

Complex network theory provides a powerful toolbox for studying the structure of statistical interrelationships between multiple time series in various scientific disciplines. In this work, we apply the recently proposed climate network approach for characterizing the evolving correlation structure of the Earth's climate system based on reanalysis data of surface air temperatures. We provide a detailed study on the temporal variability of several global climate network characteristics. Based on a simple conceptual view on red climate networks (i.e., networks with a comparably low number of edges), we give a thorough interpretation of our evolving climate network characteristics, which allows a functional discrimination between recently recognized different types of El Ni��o episodes. Our analysis provides deep insights into the Earth's climate system, particularly its global response to strong volcanic eruptions and large-scale impacts of different phases of the El Ni��o Southern Oscillation (ENSO)., 20 pages, 12 figures