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As part of the Earth4All project, collaborators have submitted this paper to delve further into the steps to be taken to widely transform our conventional agricultural system to provide food security and improve ecological resilience in a rapidly changing global climate. This article analyses the potential positive effects on soil ecology and crop yield of a global-scale transition to regenerative agriculture, while also considering social spreading dynamics that determine the adoption of such practices by farmers. The authors argue that the transition to a global regenerative agricultural system cannot be achieved without considering the deeper societal processes driving the effective dissemination and adoption of the change. Furthermore, the surrounding factors and conditions such as farmers’ political and institutional embeddedness, public opinion, the economic situation and the climate conditions they face within their region or community, as potential barriers hindering the transition, have to be taken into account. Therefore, it is not only the farmers’ responsibility to drive the change but also the politicians, institutions, companies and individual actors’ one which, all together, will support such transition processes.

Social tipping processes have been identified as potential mechanisms for triggering necessary socio-technical transformations to avoid or mitigate disastrous climate futures. But, the coupling of social and climate systems remains comparatively under-defined. Here, we explore how (anticipated) changes to climate systems can instigate necessary positive transformations within social systems --- the wanted social tipping points. Exemplarily adopting novel survey data from the United States, we investigate how sea-level rise (SLR) risk perceptions shape climate change relevant behavioral intentions and policies. We find that SLR risk perceptions are strongly positively related to a diverse set of behaviors and support for policy measures under analysis in this study. Furthermore, we find that SLR perceptions are one of the strongest drivers toward climate change behavioral intentions, comparatively stronger than many other common individual-level determinants (e.g., political attitudes or different forms of trust). Our findings emphasize the potential role of SLR perceptions as instigators of social tipping dynamics. Heightened risk perceptions could be influential in transcending the temporal barriers presented by the necessity for contemporary socio-technical transformations to mitigate future climate impacts --- serving as a normatively desirable trigger of positive (wanted) social tipping dynamics., OSF Preprints

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.

Effective climate change mitigation necessitates swift societal transformations. Social tipping processes, where small triggers initiate qualitative systemic shifts, are potential key mechanisms instigating societal change. With large shares of the world's population coastally concentrated, sea-level rise is among the most severe impacts of climate change. Here we combine future sea-level rise estimates, social survey data, and a social activation model to exemplify a transformative pathway where climate change concern increases the social tipping potential, and extended anticipation time horizons shift the system towards an alternative sustainable state of climate action. We find that in many countries, climate change concern is sufficient, such that opportunities for social activation towards this tipped state already exist. Further, drawing upon the interrelation between climate change concern and anticipation of SLR, we find evidence of three qualitative classes of tipping potential that are regionally clustered, with greatest potential for tipping in Western Pacific rim and East Asian countries. These findings propose a transformative pathway, where increased climate change concern shifts tipping potential upwards and extended anticipation time horizons lowers the required size for critical interventions necessary to kick a social system into a more sustainable state., OSF Preprints

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

This is the fifth instalmentfrom the annual series10 New Insights in Climate Science, whichaims to synthesise and communicate the latest and most essential scientific findings on climate change. It is the result of a collaboration between Future Earth,Earth League and the World Climate Research Program (WCRP). Thisreport constitutes a climate science year-in-review for journalists, policy makers, and the general public. A peer-reviewed academic article published in parallel to this policy report, providesanin-depth explanationof the ten insights,as well as a complete account of the reviewing process. These are the 2021ten New Insights in Climate Science: Stabilizing at 1.5°C warming is still possible, but immediate and drastic global action is required. Rapid growth in methane and nitrous oxide emissions put us on track for 2.7°C warming Megafires – climate change forces fire extremes to reach new dimensions with extreme impacts Climate tipping elements incur high-impact risks Global climate action must be just Supporting household behaviour changes is a crucial but often overlooked opportunity for climate action Political challenges impede effectiveness of carbon pricing Nature-based solutions are critical for the pathway to Paris – but look at the fine print Building resilience of marine ecosystems is achievable by climate-adapted conservation and management, and global stewardship Costs of climate change mitigation can be justified by the multiple immediate benefits to the health of humans and nature

The term 'resilience' is increasingly being used in social-technical-environmental systems sciences and particularly also in the Earth system sciences. However, the diversity of resilience concepts and a certain (sometimes intended) openness of proposed definitions can lead to misunderstandings and may impede their application to complex systems modelling. We propose a guideline that aims to ease communication as well as to support systematic development of research questions and models in the context of resilience. It can be applied independently of the modelling framework or underlying theory of choice. At the heart of this guideline is a checklist consisting of four questions to be answered: (i) Resilience of what? (ii) Resilience regarding what? (iii) Resilience against what? (iv) Resilience how? We refer to the answers to these resilience questions as the "system", the "sustainant", the "adverse influence", and the "response options". The term 'sustainant' is a neologism describing the feature of the system (state, structure, function, pathway, ...) that should be maintained (or restored quickly enough) in order to call the system resilient. The use of this proposed guideline in the field of Earth system resilience is demonstrated for the application example of a potential climate tipping element: the Amazon rainforest. The example illustrates the diversity of possible answers to the checklist's questions as well as their benefits in structuring the modelling process. The guideline supports the modeller in communicating precisely what is actually meant by 'resilience' in a specific context. This combination of freedom and precision could help to advance the resilience discourse by building a bridge between those demanding unambiguous definitions and those stressing the benefits of generality and flexibility of the resilience concept., Comment: 21 pages, 2 figures, 1 table

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

To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestment from fossil fuels could play an important role in putting pressure on national governments on the road to decarbonization. Using a stochastic agent-based model of co-evolving financial market and investors' beliefs about future climate policy on an adaptive social network, here we find that the dynamics of divestment from fossil fuels shows potential for social tipping away from a fossil-fuel based economy. Our results further suggest that socially responsible investors have leverage: a small share of 10--20\,\% of such moral investors is sufficient to initiate the burst of the carbon bubble, consistent with the Pareto Principle. These findings demonstrate that divestment has potential for contributing to decarbonization alongside other social movements and policy instruments, particularly given the credible imminence of strong international climate policy. Our analysis also indicates the possible existence of a carbon bubble with potentially destabilizing effects to the economy., 22 pages, 4 figures

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.

Chaos, 28 (8), ISSN:1054-1500, ISSN:1089-7682

Transformations to create more sustainable social-ecological systems are urgently needed. Structural change is a feature of transformations of social-ecological systems that is of critical importance but is little understood. Here, we propose a framework for conceptualising and modelling sustainability transformations based on adaptive networks. Adaptive networks focus attention on the interplay between the structure of a social-ecological system and the dynamics of individual entities. Adaptive networks could progress transformations research by: 1) focusing research on changes in structure; 2) providing a conceptual framework that clarifies the temporal dynamics of social-ecological transformations compared to the most commonly used heuristic in resilience studies, the ball-and-cup diagram; 3) providing quantitative modelling tools in an area of study dominated by qualitative methods. We illustrate the potential application of adaptive networks to social-ecological transformations using a case study of illegal fishing in the Southern Ocean and a theoretical model of socially networked resource users.

Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway or airline infrastructure over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (as arising if the background flow is perturbed itself). Our results demonstrate that in all three cases, changes to the steady state solution can be analytically expressed in terms of the eigensystem of the unperturbed flow and the perturbation itself. These results are potentially relevant for developing more efficient strategies for coping with contaminations of fluid or gaseous media such as ocean and atmosphere by oil spills, radioactive substances, non-reactive chemicals or volcanic aerosols., 9 pages, 3 figures

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalised clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking., Comment: 16 pages, 5 figures

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.

In recent years extensive studies on the Earth's climate system have been carried out by means of advanced complex network statistics. The great majority of these studies, however, have been focusing on investigating correlation structures within single climatic fields directly on or parallel to the Earth's surface. Here, we develop a novel approach of node weighted coupled network measures to study correlations between ocean and atmosphere in the Northern Hemisphere extratropics and construct 18 coupled climate networks, each consisting of two subnetworks. In all cases, one subnetwork represents monthly sea-surface temperature (SST) anomalies, while the other is based on the monthly geopotential height (HGT) of isobaric surfaces at different pressure levels covering the troposphere as well as the lower stratosphere. The weighted cross-degree density proves to be consistent with the leading coupled pattern obtained from maximum covariance analysis. Network measures of higher order allow for a further analysis of the correlation structure between the two fields and consistently indicate that in the Northern Hemisphere extratropics the ocean is correlated with the atmosphere in a hierarchical fashion such that large areas of the ocean surface correlate with multiple statistically dissimilar regions in the atmosphere. Ultimately we show that, this observed hierarchy is linked to large-scale atmospheric variability patterns, such as the Pacific North American pattern, forcing the ocean on monthly time scales.

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing amounts of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis, relating the multiple visualisation challenges with a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

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

Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the eight largest (typically very large) extreme events; but only a ≥ 50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere., Biogeosciences, 14 (18), ISSN:1726-4170

Climate extremes can trigger exceptional responses in terrestrial ecosystems, for instance by altering growth or mortality rates. Such effects are often manifested in reductions in net primary productivity (NPP). Investigating a Europe-wide network of annual radial tree growth records confirms this pattern: we find that 28% of tree ring width (TRW) indices are below two standard deviations in years in which extremely low precipitation, high temperatures or the combination of both noticeably affect tree growth. Based on these findings, we investigate possibilities for detecting climate-driven patterns in long-term TRW data to evaluate state-of-the-art dynamic vegetation models such as the Lund-Potsdam-Jena dynamic global vegetation model for managed land (LPJmL). The major problem in this context is that LPJmL simulates NPP but not explicitly the radial tree growth, and we need to develop a generic method to allow for a comparison between simulated and observed response patterns. We propose an analysis scheme that quantifies the coincidence rate of climate extremes with some biotic responses (here TRW or simulated NPP). We find a relative reduction of 34% in simulated NPP during precipitation, temperature and combined extremes. This reduction is comparable to the TRW response patterns, but the model responds much more sensitively to drought stress. We identify 10 extreme years during the 20th century during which both model and measurements indicate high coincidence rates across Europe. However, we detect substantial regional differences in simulated and observed responses to climatic extreme events. One explanation for this discrepancy could be the tendency of tree ring data to originate from climatically stressed sites. The difference between model and observed data is amplified by the fact that dynamic vegetation models are designed to simulate mean ecosystem responses on landscape or regional scales. We find that both simulation results and measurements display carry-over effects from climate anomalies during the previous year. We conclude that radial tree growth chronologies provide a suitable basis for generic model benchmarks. The broad application of coincidence analysis in generic model benchmarks along with an increased availability of representative long-term measurements and improved process-based models will refine projections of the long-term carbon balance in terrestrial ecosystems., Biogeosciences, 12 (2), ISSN:1726-4170

The Asian monsoon system is an important tipping element in Earth's climate with a large impact on human societies in the past and present. In light of the potentially severe impacts of present and future anthropogenic climate change on Asian hydrology, it is vital to understand the forcing mechanisms of past climatic regime shifts in the Asian monsoon domain. Here we use novel recurrence network analysis techniques for detecting episodes with pronounced non-linear changes in Holocene Asian monsoon dynamics recorded in speleothems from caves distributed throughout the major branches of the Asian monsoon system. A newly developed multi-proxy methodology explicitly considers dating uncertainties with the COPRA (COnstructing Proxy Records from Age models) approach and allows for detection of continental-scale regime shifts in the complexity of monsoon dynamics. Several epochs are characterised by non-linear regime shifts in Asian monsoon variability, including the periods around 8.5–7.9, 5.7–5.0, 4.1–3.7, and 3.0–2.4 ka BP. The timing of these regime shifts is consistent with known episodes of Holocene rapid climate change (RCC) and high-latitude Bond events. Additionally, we observe a previously rarely reported non-linear regime shift around 7.3 ka BP, a timing that matches the typical 1.0–1.5 ky return intervals of Bond events. A detailed review of previously suggested links between Holocene climatic changes in the Asian monsoon domain and the archaeological record indicates that, in addition to previously considered longer-term changes in mean monsoon intensity and other climatic parameters, regime shifts in monsoon complexity might have played an important role as drivers of migration, pronounced cultural changes, and the collapse of ancient human societies., Climate of the Past, 11 (5), ISSN:1814-9324, ISSN:1814-9332