It is paramount to determine the critical forcing levels (and the associated uncertainties) beyond which the systems in question will abruptly change their state, with potentially devastating climatic, ecological, and societal impacts. For this purpose, we need to substantially enhance our understanding of the dynamics of the Tipping Elements and their interactions, on the basis of paleoclimatic evidence, present-day observations, and models spanning the entire hierarchy of complexity. Moreover, to be able to mitigate - or prepare for - potential future transitions, early warning signals have to be identified and monitored in both observations and models.
This multidisciplinary session invites contributions that address Tipping Points in the Earth system from the different perspectives of all relevant disciplines, including
- the mathematical theory of abrupt transitions in (random) dynamical systems,
- paleoclimatic studies of past abrupt transitions,
- data-driven and process-based modelling of past and future transitions,
- methods to anticipate critical transitions from data
- the implications of abrupt transitions for climate sensitivity and response,
- ecological and socioeconomic impacts
- decision theory in the presence of uncertain Tipping Point estimates and uncertain impacts
We are transitioning towards a climate state on Earth featuring rapid changes in response to anthropogenic greenhouse gas emissions and land-use change, with severe observable and projected impacts on the occurrence of extreme weather events and increasing risk of crossing large-scale tipping points. Neither the transition nor the long-term climate state has been observed by (human-made) measurements before, making information on past climatic states increasingly more important to help anticipate future Earth System change. Paleoclimate records have enormously expanded over the past decades, and provide extremely rich information about physical, cryospheric, biological, and ecological processes on many spatial and temporal scales. Yet, it has been difficult so far to directly transform this knowledge on past processes into a more confident evaluation of future projections for the Earth system. In this contribution, I will summarise lessons learned from past climate change on our understanding of climate variability, abrupt changes and climate response to greenhouse gas changes and other forcing. For example, generalizations of classical measures such as equilibrium climate sensitivity can be useful in the palaeoclimate and future context for understanding the response of a climate state to radiative forcing beyond the linear regime, i.e. when (part of) the climate system is close to a tipping point. Finally, this contribution will present the ambition and programme of the starting EU-HORIZON project Past-to-Future (P2F) aiming at developing, expanding and using the wealth of paleoclimate data to improve existing Earth System Models in terms of their ability to describe possibly exotic, out of sample, climate states and the transition pathways towards them from current conditions.
How to cite: von der Heydt, A.: Past to future: Towards fully paleo-informed future climate projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19085, https://doi.org/10.5194/egusphere-egu25-19085, 2025.
The Tipping Point Modelling Intercomparison Project (TIPMIP) is an international intercomparison project that aims to systematically advance our understanding of tipping dynamics in various Earth system components, and assess the associated uncertainties. By connecting and evaluating various models, TIPMIP will fill critical knowledge gaps in Earth system and climate modelling by improving the assessment of overall anthropogenic forcing and long-term commitments (irreversibilities). In this contribution, we report on the status of the project, highlighting recent advances including the finalisation of experimental protocols and first results. Moreover, we provide an overview on the established scientific infrastructure and next steps, inviting the tipping points modelling community for contributions.
How to cite: Loriani, S., Dennis, D., Donges, J. F., and Winkelmann, R.: News from TIPMIP, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18552, https://doi.org/10.5194/egusphere-egu25-18552, 2025.
The climate system is prone to various tipping mechanisms at the global scale, such as the abrupt changes induced by the potential shutdown of the Atlantic meridional overturning circulation. Thus, it is essential to develop robust Early Warning Signals (EWSs) to assess the risk of crossing tipping points. Classically, EWSs are statistical measures based on time series of climate state variables, and their spatial distribution is not exploited. However, spatial information is crucial to identifying the starting location and development of a transition process. Methods that use spatial information become particularly relevant in the current era, when satellite observations with high spatiotemporal coverage produce huge amounts of data.
We use complex networks constructed from several climate variables (like surface air temperature, specific humidity and cloud cover) on the numerical grid of climate simulations. Using the pyUnicorn Python package [1], we construct networks based on linear and nonlinear spatial correlations of time series at each grid point. We seek for network properties that can serve as EWS when approaching a state transition at the planetary scale, as obtained by the MIT general circulation model in a coupled-aquaplanet configuration for CO2 concentration-driven simulations.
We show that network indicators such as the normalized degree, the average length distance and the betweenness centrality are capable of detecting tipping points at the global scale [2]. We assess and compare the applicability as EWS of these indicators to traditional methods. Moreover, we analyse climate networks’ ability to identify nonlinear dynamical patterns. Finally, we discuss the generalisation to network indicators that include causal relationships.
References
[1] J. Donges et al., Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015)
[2] L. Moinat, J. Kasparian, M. Brunetti, Tipping detection using climate networks, Chaos 34, 123161 (2024)
How to cite: Brunetti, M., Kasparian, J., and Moinat, L.: Detection of global-scale tipping using climate networks , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7369, https://doi.org/10.5194/egusphere-egu25-7369, 2025.
In Paris 2015, the global community agreed to keep global warming well below 2.0°C aiming to limit it to 1.5°C above pre-industrial levels. However, recent research has shown that overshooting this temperature guardrail is becoming increasingly likely and several climate data teams across the world recorded 2024 as the first individual year with a global warming level above 1.5°C.
Such temperature levels endanger critical components of the Earth system, the so-called climate tipping elements such as the Greenland and West Antarctic Ice Sheet, The Atlantic Meridional Overturning Circulation, or the Amazon rainforest. In this presentation, we will show the latest evidence on how overshooting temperature targets increases tipping risks. In particular, we will discuss the role of overshooting the 1.5°C and 2.0°C for the stability of critical Earth system components, and also assess the likelihood for climate tipping cascades beyond these global warming levels.
How to cite: Wunderling, N., Högner, A., Möller, T., Ritchie, P., Rockström, J., Steinert, N., and Donges, J. F.: Increased climate tipping risks from temperature overshoots, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15540, https://doi.org/10.5194/egusphere-egu25-15540, 2025.
A tipping element is a system that may pass a tipping point, that is, a threshold value of an environmental stressing condition at which a small disturbance can cause an abrupt shift of the tipping element from one state to another, accelerated by positive feedbacks. For example, under rising temperatures and increasing deforestation, the Amazon rainforest could tip from a forest state to a savanna state; one feedback involved is that fewer trees means less evapotranspiration, thus less rainfall and, finally, less trees. Therefore, the fewer trees, the harder it is for the remaining forest to adapt and survive. This phenomenon is called critical slowing down: approaching a bifurcation, a tipping system's resilience decreases, resulting in increasing autocorrelation and variance. The latter indicators are thus often measured to detect bifurcation-induced tipping and are called early-warning signals (EWS).
Let us describe the dynamics of a tipping element Y with the following equation: dY/dt = f(Y, r) + η, with r the environmental stressing condition involved in the tipping behavior and η some noise (e.g., climate variability). Deriving EWS directly from Y's time series relies on the assumption that the noise η is not correlated (white-noise), otherwise any trend in η's autocorrelation would be incorporated in Y's autocorrelation, even if not related to the tipping behavior contained in f(Y, r). In the Amazon rainforest example, increasing deforestation due to human activity is a part of r with a long-term effect, while e.g. ENSO also influences the forest but on short time scales and with sometimes opposite effects depending on its phase (El Niño/La Niña/neutral), and would be part of η. If for example El-Niño's autocorrelation increases with time, the rainforest autocorrelation might also increase regardless of whether the forest is approaching the bifurcation point or not, therefore the autocorrelation would no longer reflect changes in the forest resilience.
To know how far the system is from the bifurcation point, we want to measure Y's internal autocorrelation (excluding noisy influences η, considering only f(Y, r)), and thus to answer the question: "If we intervene in the system and set the value of Y at time t-1, how does Y evolve at time t?" This defines the direct causal effect of Yt-1 on Yt and comes under the heading of causal inference: we look at the influence of setting Yt-1=yt-1 on Yt, whatever the values of the other variables causing Y, which is fundamentally different from a direct measure where the value of Y at time t-1 is, in the general case, dependent on the state of the other variables. To measure how the direct causal effect of Yt-1 on Yt evolves with time (with changing r), we use causal effect estimation, which quantifies the causal effect of hypothetical interventions in a system from observational data --the interventional distribution being rarely available in the majority of systems--- and an assumed causal graphical model that allows us to derive an adjustment expression that controls for confounders. We demonstrate the method on an ideally forced simulated system and discuss potential applications.
How to cite: Lanson, A. and Runge, J.: Causal effect estimation for robust detection of critical slowing down, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10947, https://doi.org/10.5194/egusphere-egu25-10947, 2025.
The Atlantic Meridional Overturning Circulation (AMOC) is a key tipping element of the climate system due to its influence in regulating the meridional transport of heat and freshwater. Its stability is influenced by the interplay between external forcings (such as greenhouse gasses increase) and internal climate variability. Due to limitations on the deterministic predictability of the AMOC asymptotic state, the concept of a “probabilistic safe operating space” has been proposed. For this purpose, rare-event techniques, specifically the Giardina–Kurchan–Tailleur–Lecomte (GKTL) and Trajectory-Adaptive Multilevel Splitting (TAMS) algorithms, offer promising tools for testing the multistability of the system and assessing this probability at lower computational costs than traditional Monte Carlo methods. Here, using the intermediate complexity model (PlaSIM-LSG), we estimate the probability of AMOC collapse in sensitivity experiments at different CO2concentrations and under RCPs scenarios. In particular, TAMS has been applied in order to assess the probability of reaching a low circulation state of the AMOC associated with a 1°C temperature anomaly over central and western Europe. Our findings from sensitivity experiments, consistently with previous studies, indicate that for a wide range of CO2 concentrations (500-600 ppm), the probability of an AMOC collapse is significantly different from zero (1-10% within 150 years). While such a collapse is unlikely to happen within the 21st century, it becomes likely to happen by 2150 in higher emission scenarios. It is important to note that PlaSIM-LSG does not account for the North Atlantic freshwater flux from Greenland melting which introduces a stabilizing bias for the AMOC-on state. Accounting for this mechanism would likely increase the probability of an AMOC collapse. These results underscore the importance of probabilistic assessments in understanding AMOC stability and highlight the potential for rare-event algorithms to provide insights into the statistical properties of tipping point.
How to cite: Cini, M., Jacques-Dumas, V., and Dijkstra, H. A.: Assessing the Probability of CO2-Driven AMOC Collapses Using Rare Event Algorithms in PlaSIM-LSG, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10648, https://doi.org/10.5194/egusphere-egu25-10648, 2025.
The Atlantic Meridional Overturning Circulation (AMOC), as recorded in paleoclimate proxies, is one of the climate systems with a potential abrupt transition. Increasing identification of statistical signals—critical slowing down—in observational fingerprints empirically raises concerns that the system may be approaching a tipping point. However, state-of-the-art Earth System Models (ESMs) rarely project an abrupt collapse of AMOC, and its loss of stability has yet to be thoroughly investigated, leaving it unclear whether warning signals of AMOC tipping is overlooked in ESMs or exaggerated in fingerprints. Here, a warning signal over the deep convection site of AMOC is consistently identified in both observations and ESM, and we present that the currently observed signal is reconciled with the modeled one, with warming exceeding the Paris Agreement goal. This warning signal is in accordance with physical stability of the AMOC, the AMOC-induced freshwater convergence into the Atlantic basin, is overestimated in the ESM, so that it projects a delayed tipping point. These results suggest that the observed AMOC is approaching a tipping point akin to the projections of models simulating a much warmer Earth, underscoring potentially overlooked risks in ESMs assessments.
How to cite: Shin, Y., Oh, J.-H., Boers, N., Bathiany, S., Årthun, M., Lee, H., Iwakiri, T., Kim, G.-I., Kim, H., and Kug, J.-S.: Reconciled warning signals in observations and models indicate a nearing AMOC tipping point , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-859, https://doi.org/10.5194/egusphere-egu25-859, 2025.
There is growing concern that the Atlantic Meridional Overturning Circulation (AMOC), a vital Earth system component, could weaken or even collapse under climate change. Despite the severe potential impacts associated with such a transition, it remains extremely challenging to reliably estimate the proximity to a critical threshold and to predict the AMOC's fate under future anthropogenic forcing. We argue that a global viewpoint on the dynamics beyond the detection of early-warning signals is needed for a robust risk assessment. Here we explore the phase space of an intermediate-complexity earth system model, PlaSim-LSG, featuring a multistable AMOC. For two different atmospheric carbon dioxide (CO2) levels, we explicitly compute the Melancholia (M) state that separates the strong and weak AMOC attractors found in the model. The M state is a chaotic saddle embedded in the basin boundary between the competing states (an edge state). We show that, while being unstable, the M state can govern the transient climate for centuries. The M state exhibits strong AMOC oscillations on centennial timescales driven by sea ice and oceanic convection in the North Atlantic. Combining these insights with simulations under future CO2 forcing scenarios (SSPs), we demonstrate that in our model the AMOC undergoes a boundary crisis at CO2 levels projected to be reached in the next decade. Near the crisis, the AMOC behavior becomes highly unpredictable. Founded in dynamical systems theory, our results offer an interpretation of the so-called stochastic bifurcation recently observed in a CMIP6 earth system model under the same time-dependent forcing scenario.
How to cite: Börner, R., Mehling, O., von Hardenberg, J., and Lucarini, V.: Global stability of the AMOC under CO2 forcing: Boundary crisis, long transients and oscillatory edge states, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19783, https://doi.org/10.5194/egusphere-egu25-19783, 2025.
How to cite: Ghasemi, B., Gayen, B., Vreugdenhil, C., and Sohail, T.: Slowing Down of the Atlantic Meridional Overturning Circulation Due to Excess Freshwater: Insights from Turbulence-Resolving Simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21133, https://doi.org/10.5194/egusphere-egu25-21133, 2025.
The subpolar gyre (SPG) of the North Atlantic plays a pivotal role in the Atlantic Meridional Overturning Circulation (AMOC) and climate through various teleconnections. This study examines the tipping point in thisregion within CMIP6 projections using three models: CESM2-WACCM, MRI-ESM2-0, and NorESM2-LM. These models, selected for exhibiting tipping patterns in at least one emission scenario, reveal distinct yet converging patterns of change, suggesting a destabilization of the subpolar region driven by shifts in salinity, temperature, and density profiles. A consistent feature across the models is pronounced freshening in the upper 150 meters of the water column. This results in a strong stratification, accompanied by cooling in the top 250 meters and warming between 150 and 1500 meters. The resulting enhancement in water column stability leads to a marked reduction in mixed layer depth (MLD). These changes disrupt vertical mixing, weaken nutrient transport, and alter regional circulation dynamics, with cascading effects on marine ecosystems and climate feedback mechanisms. We employed a density-based approach that accounts for the combined effects of temperature and salinity on water density to identify the critical surface salinity leading to the tipping of the SPG. This critical salinity represents a threshold for the salinity level beyond which density-driven stratification results in a stable water column. For stability to break, surface salinity must exceed this critical salinity. All three models consistently identify a critical salinity threshold of approximately 33.8 g·kg⁻¹. When surface salinity drops below this threshold, the subpolar region experiences rapid cooling, reduced convection, and potentially irreversible transitions. The tipping point of the SPG is preceded by an expansion of areas in the SPG where surface salinity falls below this critical threshold, accompanied by a decrease in MLD. To complement our analyses, we used the ISAS dataset to assess how far the system is from an SPG tipping point. Our next step is to establish an observable spatial pattern of early warning. Our findings underscore the vulnerability of the North Atlantic subpolar region to salinity-driven tipping points, which may lead to potentially irreversible transitions. This highlights the critical need for precise monitoring and advanced modeling of salinity dynamics to enhance predictability in future climate scenarios.
How to cite: Almeida, L. and Swingedouw, D.: Critical Salinity as an early warning of Tipping Point in the North Atlantic Subpolar Gyre, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3514, https://doi.org/10.5194/egusphere-egu25-3514, 2025.
The Atlantic Meridional Overturning Circulation (AMOC) is considered one of the Earth’s climate tipping elements. Concerns have been raised that global warming could increase the freshwater input into the North Atlantic at high northern latitudes and thereby abruptly interrupt the deep water formation that fuels the AMOC’s lower limb and is necessary to maintain the overturning. To assess the risks such an AMOC tipping scenario poses to societies, it is essential to understand how an AMOC collapse feeds back into the climate system as a whole. It is of particular interest whether an AMOC tipping would have a stabilizing or destabilizing effect on other climate tipping elements. In this context, we studied the impact of an AMOC shutdown on the Amazon Rainforest, which is itself thought to be at risk of undergoing a transition to a savanna state. We forced a km-scale atmosphere-only model with sea surface temperatures from a second lower-resolution coupled climate model simulation that features a collapsed AMOC state. Previous studies indicate that land-atmosphere interactions are different in such convection-resolving models compared to CMIP-type models, possibly affecting the response of precipitation to large-scale perturbations. In general, our simulation confirms the global AMOC-collapse induced precipitation and temperature anomaly patterns also seen in coupled climate model hosing experiments. Most prominently these comprise a cooling and drying of the North Atlantic region and a corresponding southward shift of the tropical rainbelt. However, upon closer examination, we find that over land the signal is attenuated, and in particular precipitation patterns over the Amazon Rainforest appear to be remarkably robust against an AMOC shutdown. This, in turn, means that a tipping of the AMOC would to a first degree neither have a stabilizing nor destabilizing effect on the Amazon Rainforest.
How to cite: Riechers, K., Hohenegger, C., Schmidt, H., Esch, M., and Stevens, B.: Amazon precipitation response to an AMOC shutdown in a km-scale atmospheric model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17856, https://doi.org/10.5194/egusphere-egu25-17856, 2025.
Populations in mountain ecosystems face the risk of extinction due to climate change. Yet, how these risks will materialise remains unclear because many ecological parameters are unknown. We show that progress can be made by examining how habitats get fragmented and isolated as populations shift to higher elevations. When this shift is slow relative to dispersal, the amount of aggregation and connectivity between habitat fragments determine the warming threshold beyond which populations cannot sustain themselves. If the shift is rapid compared to dispersal, there is also a critical warming rate beyond which populations cannot track their preferred range and go extinct. Through simulations and analyses of stochastic spreading processes on real and artificial landscapes, we investigate how mountain topography, warming rates, and demographic mechanisms affect extinction thresholds. Understanding the link between mountain topography and extinction risks may enable targeted interventions to mitigate these risks, especially in areas with fragmentation bottlenecks.
How to cite: Wuyts, B., Karger, D., Sieber, J., and Boussange, V.: The link between topography and climate extinction risks in mountain ecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16872, https://doi.org/10.5194/egusphere-egu25-16872, 2025.
In this paper, I explore how to assess technologies’ potential to aid a sustainable transformation via societal tipping points. I do this by providing a definition of sustainability that combines justice, well-being, and the value of nature with insights from value-sensitive design and the technology assessment literature. This exploration serves as an additional consideration for the developing societal tipping point scholarship. I argue that research surrounding societal tipping points can be meaningfully bolstered through philosophical reflection on the inherent ethical implications of sustainability, and the value-ladenness of technological development. There is a salient push within various strands of climate adaptation and sustainable transition scholarship towards systems thinking. In order to reorient society within the Anthropocene, and to adapt to a destabilized climate, such scholarship argues that the underlying subsystems society currently relies on need to change. Conceiving societal structures, such as institutional, political, and financial arrangements, the various planetary spheres (bio-, cryo-, hydro-, atmo-, and geosphere), and the techno-scientific infrastructure as interdependent systems has heuristic and practical allure. As a heuristic, it allows researchers and policymakers to account for the numerous interrelated systems that affect climate change and environmental degradation. Practically, this heuristic should enable the identification of impactful and sustainable action. Knowing how the subsystems interoperate, what drives them, and what function they provide, accordingly serves as a baseline to identify possible leverage points to change them. Conceptually mirroring climate tipping points, there is growing interest in societal tipping points as possible catalysts for decisive climate action. This interest is premised on the idea that societal tipping points within a currently unsustainable global societal-ecological-technical system can be identified and operationalized in order to tip the system (or subsystems) into a sustainable direction This premise raises at least two critical issues that have so far received little attention. First, the question arises what tipping towards a more sustainable system would look like. The concept of sustainability is arguably vague, especially when it comes to its aptness in describing climate action. Answering this question requires a reflective and ethically thick conception of sustainability, which in turn, needs to represent a future-oriented conception of justice, well-being, and nature. Second, it is crucial to reflect on the interdependent ways in which technological development and the implementation of new technologies affect the societal values and norms that drive them, since technology plays a central role for achieving societal tipping points. If technology is seen as a an accelerator and facilitator for a sustainable transition, the value-ladenness that technological innovation comes with needs to be addressed. Importantly, some technologies that seem sustainable on the surface, may actually entrench and enforce existing unsustainable modes of behavior and policies. Accordingly, this paper expands on the societal tipping points literature by proposing a concept of sustainability that serves as a means to assess the potential of technologies to facilitate sustainable tipping.
How to cite: Hofbauer, B.: Can We Tip Sustainably? Ethical Considerations on the Role of Sustainable Technology in Societal Tipping Points, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2923, https://doi.org/10.5194/egusphere-egu25-2923, 2025.
Social norms are a key socio-cultural driver of human behaviour and have been identified as a central process in potential social tipping dynamics. They play a central role in governance and thus represent a possible intervention point for collective action problems in the Anthropocene, such as natural resource management.
A detailed modelling framework for social norm change is needed to capture the dynamics of human societies and their feedback interactions with the natural environment. To date, resource use models often incorporate social norms in an oversimplified manner, as a robust and detailed coupled social-ecological model, scaling from the local to the global World-Earth scale, is lacking.
Here we present a multi-level network framework with a complex contagion process for modelling the dynamics of descriptive and injunctive social norms. The framework is complemented by social groups and their attitudes, which can significantly influence the adoption of social norms. We integrate the modelling concept of norms together with an additional individual social learning component into a model of coupled social-ecological dynamics with a closed feedback loop, implemented in the copan:CORE framework for World--Earth modelling.
We find that norms generally bifurcate the behaviour space into two extreme states (sustainable vs. unsustainable) divided by regime shifts. Reaching a sustainable (i.e. safe) state becomes more likely with low thresholds of conforming to sustainable norms, as well as lower consideration rates of own resource harvesting success. The success of a generic social norm intervention is also found to be highly dependent on the group topology and exhibit a phase-transition like shape under certain conditions. The regime shifts in thresholds, individual learning and norm intervention hint at exploitable underlying tipping processes.
Our findings suggest that explicitly modelling social norm processes together with social groups enriches the dynamics of social-ecological models and determines safe operating spaces. Consequently, both should be taken into account when representing human behaviour in coupled World--Earth models.
How to cite: Bechthold, M., Barfuss, W., Butz, A., Breier, J., Constantino, S., Heitzig, J., Schwarz, L., Vardag, S., and Donges, J.: Social norms and groups structure safe operating spaces and exhibit regime shifts in renewable resource use in a social-ecological multi-layer network model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3532, https://doi.org/10.5194/egusphere-egu25-3532, 2025.
We propose a framework that allows to integrate social (tipping) dynamics into a DICE-style IAM model, in which a policy maker determines abatement effort and savings. The policy maker maximizes the sum of social welfare and a political penalty/reward, depending on whether the majority of the population opposes (penalty) or supports (reward) more ambitious abatement policies. The social process itself depends inter alia on observable climate impacts. We provide numerical simulations that illustrate the impact of the tipping process on policy choices which in turn are built around a total cost of carbon that embraces both the "classical" social cost of carbon and a political cost of carbon. Our initial findings illustrate (i) the considerable scope for political penalties (rewards) to stifle (boost) abatement policies; (ii) an incentive for the policy maker to distort policies in a way that boosts political support; and (iii) a considerable deviation between the total cost of carbon and the social cost of carbon. We argue how the model can be used for the purpose of understanding climate policy making from a "social dynamics" perspective.
How to cite: Kuhn, M., Wagner, G., and Wrzaczek, S.: Optimal climate policies under the shadow of social tipping, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10019, https://doi.org/10.5194/egusphere-egu25-10019, 2025.
The relationship between nature and industry has been constantly contested for decades, regardless of the warning on the Earth as transformed by human action (Turner, B. L., et al., 1993) which address the unprecedented changes in the biosphere that have taken place over the last 300 years. Accumulation of the human impacts has also led to the degradation of the atmosphere resulting in anthropogenic warming that have brought tremendous threats to societies. While standing at the tipping point, international societies have made substantive advancement to push industrial and financial sectors taking responsibility in carbon accounting and climate risk assessment (i.e., TCFD). The relationship between climate and industry is complex, but the threat from both of them on biodiversity and natural capital (NC) loss is even devastating. In 2021, the initiative of Taskforce on Nature-related Financial Disclosures (TNFD) was launched and the TNFD Recommendations and Guidance is released in 2023. With a broader focus on the industrial and financial sector dependence and impact on the NC, TNFD contains a big range of ambiguity in developing methodology. Meanwhile, climate risk is regarded as a driving force of ecosystem change in the assessment. How to access relevant data, in suitable spatial and temporal resolution, to quantify the NC related dependency and risk becomes the most fundamental challenge before tipping elements and tipping interactions can be identified to facilitate a social and industrial transformation.
This study collaborates with a listed high-tech company in Taiwan to assess the relationship between its value chain and NC following the LEAP approach. We have developed a high-spatial-resolution database to identify the dependencies and impacts on NC across different operational locations. Meanwhile, we conduct materiality assessments through internal questionnaires, examining the significance of different types of NC to business operations from the perspectives of Consequences rating and Likelihood rating. Finally, we aim to establish TNFD risk matrices by integrating the assessment results from spatial and materiality assessments, with the hope of helping enterprises to identify NC requiring immediate attention and action.
Overall, the integrated TNFD assessment method combining spatial and materiality analyses serve as tipping elements between enterprises and NC; it may help enterprises systematically quantify their dependencies and impacts on NC, thereby identifying operational locations and types of NC that require priority action. Meanwhile, high-resolution spatial databases can support enterprises in defining the locations, scope, and even causes of NC issues, which in turn can help identify key external stakeholders and initiate new engagements. This integrated assessment approach has the potential to address the methodological gaps in TNFD development and to provide a concrete empirical foundation for business operational transformation, helping enterprises to develop early adaptation and response strategies when facing global ecosystem changes.
How to cite: Chung, M.-K., Lin, K. E., and Tseng, W.-L.: Advancing the pathway towards natural capital based assessment for industrial and financial sectors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18362, https://doi.org/10.5194/egusphere-egu25-18362, 2025.
Achieving ambitious climate change targets, such as limiting global warming to 1.5°C, requires both political and social determination. Bottom-up pro-environmentalist behaviours can facilitate crossing social tipping points (STPs), resulting in new social norms with lower impact on global warming. While the passing of STPs has been described qualitatively, it remains poorly understood how the climate benefits of this phenomenon can be quantified.
Here, we introduce a stylised system dynamics model that couples socio-ecological contagion with global warming via greenhouse gas emission pathways to estimate the impact of crossing social tipping points on greenhouse gas mitigation and global warming. . This is explored through two examples of bottom-up and top-down mitigation interventions.
Results indicate that a STP could be crossed before 2050. While neither bottom-up nor top-down interventions alone are likely to achieve the 1.5°C target, their combined effect significantly reduces overshoot. This represents a significant step towards understanding how both bottom-up and top-down interventions can be harnessed to mitigate global warming. Our research underscores the importance of bottom-up pro-environmental movements, emphasizing their crucial role in not only reducing personal carbon footprints but also alleviating the burden on technological top-down interventions. This evidence of the benefits of promoting socio-ecological contagion should bolster the determination of individuals and community grassroots groups. Additionally, it should encourage top-down interventions to acknowledge and support the complementary role of collective action.
How to cite: Elliot, T., Donges, J., Pizzol, M., and Otto, I.: Manifesting tipping points in pro-environmental behaviour for climate change mitigation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10788, https://doi.org/10.5194/egusphere-egu25-10788, 2025.
Mounting evidence that human activities are driving Earth’s climate toward dangerous tipping points has raised the question of whether these may be averted by quickly reaching tipping points in human societies. Prior work on social tipping dynamics has focused mainly on defining its key features, identifying and characterising important social tipping elements, and operationalizing interventions for triggering individual elements. However, the success of climate-stabilizing interventions will depend on their timing and coordination across multiple tipping elements that operate on different characteristic time scales, and these coupled dynamics are currently not understood. In this work we explore the challenges of intervention timing and the potential to coordinate subsystem tipping cascades in a multiscale system to achieve a timely whole-system transition. We develop a stylized model in which the changing climate is coupled to a network of social tipping elements such as public support for climate action, political policymaking, financial investment in energy technologies, and energy infrastructure substitution, each with its characteristic dynamics and time scales. We study how intervention timing interacts with tipping cascades between subsystems and derive principles for navigating the system to the desired state. Additionally, we analyze the effects of `windows of opportunity’ - unpredictable system shocks that are likely to become more frequent as climate change intensifies - on our model transformation pathways, and ascertain how these may be exploited to disrupt system-stabilizing feedbacks and synchronize subsystem changes. Our findings emphasise the importance of a complexity-based understanding of human agency and governance of the world-Earth system.
How to cite: Ringsmuth, A., Tilman, A., Everall, J., Campiglio, E., Pieler, M., Constantino, S., and Otto, I.: Navigating multiscale social tipping dynamics to stabilise Earth’s climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19773, https://doi.org/10.5194/egusphere-egu25-19773, 2025.
As part of a project exploring the relation between the Greenland ice sheet stability and the AMOC, we present coupled climate and ice sheet simulations of Greenland with the Earth System Model (ESM) UKESM, a state-of-the-art ESM capable of representing the interactions between ice sheets and the atmosphere and their co-evolution (UKESM-ice; Smith et al., 2021).
Recent large ensemble exercises indicate that there is no sign of non-linear volume change or irreversibility at the scale of the Greenland ice sheet in UKESM-ice, even at high warming levels and despite large ice losses.
We present new simulations exploring Greenland's potential for tipping with modified (snow and ice sheet) parameters and including a recently developed scheme for the marine forcing of outlet glaciers, which was previously omitted from UKESM-ice and prevented the representation of the direct influence of the ocean on the Greenland ice sheet. Results show linear trends of (large) ice volume change at the scale of the ice sheet but local evidence of accelerating melt along the South West margin.
Next steps in the project include providing fresh water from UKESM-ice surface runoff and solid discharge of icebergs to investigate their effect on the strength of the AMOC in NEMO simulations and using a new high resolution NEMO dataset as ocean forcing of the ice sheet in UKESM-ice.
How to cite: Lang, C., Smith, R., George, S., Marsh, R., and Sinha, B.: ISOTIPIC: Greenland ice sheet potential for tipping with the Earth System Model UKESM-ice, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19501, https://doi.org/10.5194/egusphere-egu25-19501, 2025.
Despite its potential future collapse and profound impacts, assessing the tipping dynamics of the Atlantic Meridional Overturning Circulation (AMOC) remains a significant challenge. Complex models such as Earth System Models (ESMs) and Earth System Models of Intermediate Complexity (EMICs) introduce substantial uncertainties in identifying tipping points. To address this, recent research has focused on developing conceptual models based on non-linear dynamics to capture the tipping behavior of the system. However, existing conceptual models typically simulate the AMOC response to a single temperature forcing, whereas it is well established that the AMOC is also influenced by freshwater flux.
In this study, we develop and validate an AMOC Tipping Calibration module that incorporates two forcing parameters: global mean temperature and freshwater flux. This module is designed as an emulator for the AMOC response within cGenie, an EMIC. Following validation, the emulator is integrated into SURFER, a simplified climate model that enables rapid and efficient simulations of AMOC trajectories under various scenario-based pathways. Our results show that incorporating both forcing parameters improves the accuracy of AMOC trajectory predictions. The methodology used to develop the two-parameter emulator is generalizable and can be applied to other tipping elements. By facilitating a greater number of simulations than complex models while maintaining calibration to them, this tool represents a significant advancement in exploring and understanding the potential future behaviour of the AMOC and other tipping elements.
How to cite: Laridon, A., Couplet, V., Thiery, W., and Crucifix, M.: Development and Validation of a Tipping Element Emulator Integrated into a Simplified Climate Model to Simulate the AMOC Collapse, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-821, https://doi.org/10.5194/egusphere-egu25-821, 2025.
The Atlantic Meridional Overturning Circulation (AMOC) is one of the most critical tipping elements in Earth’s climate system, with its collapse posing far-reaching implications for weather dynamics and extremes, sea level rise, and Northern Hemisphere cooling. Although it is considered a low-probability but high-impact scenario, recent studies suggest that the AMOC may already be on a trajectory towards collapse. Moreover, current climate models struggle to fully capture the complex interactions between Greenland ice sheet melting and the AMOC slowdown, adding further uncertainty to climate projections. Artificial hosing experiments in the North Atlantic, project the weakening of the AMOC to increase sea surface temperature in the Southern Hemisphere and the tropics, particularly in ocean basins where tropical cyclones form. This warming, combined with rising sea levels and changes in vertical wind shear, could create conditions that promote the development of tropical cyclones and amplify their impacts. Although several studies have explored the relationship between tropical cyclone activity and AMOC weakening, the associated socioeconomic impacts remain uncertain. The goal is to investigate the direct and indirect socioeconomic impacts of tropical cyclones under future climate scenarios characterized by a weakened and fully collapsed AMOC. Will tropical cyclones affect areas that were previously unaffected? Will tropical cyclones' activity intensify, leading to greater societal impacts? To answer these questions, two sets of five-member ensemble simulations were performed for a 2°C stabilization emission scenario using the GFDL ESM2M, with and without induced AMOC collapse. These simulations were then coupled with the MIT coupled statistical-dynamical tropical cyclone model to simulate tropical cyclone activity under these conditions, and the probabilistic climate risk modeling platform CLIMADA was used to analyze the socioeconomic impacts. We anticipate this study to be a stepping stone in a broader ongoing effort to assess the socioeconomic impacts of extreme weather events triggered by tipping points.
How to cite: Colombi, N., Kropf, C. M., Burger, F. A., Meiler, S., Emanuel, K., Frölicher, T. L., and Bresch, D. N.: Tipping the AMOC: Impacts of Tropical Cyclones in a Changing Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11175, https://doi.org/10.5194/egusphere-egu25-11175, 2025.
The global climate system continues to change under the influence of human activities. Of particular concern is the difficulty of continuing human activities due to irreversible and long-term abrupt changes caused by the global climate system exceeding its tipping point. Climate systems that have the potential to exceed the tipping point are called tipping elements and are being studied. Among them, the Atlantic Meridional Overturning Circulation (AMOC) plays a central role in the movement of materials through the ocean and is connected to many other tipping elements. While there is concern that the AMOC may decrease in strength due to rising temperatures in the Atlantic Ocean, freshwater inflow due to melting ice in the Arctic region has been investigated as a stabilizing factor. Therefore, it is important to comprehensively consider these influences when evaluating the AMOC tipping point.
In the AMOC modelling, Stommel’s two box model describes its nature well. Although, it does not treat freshwater input from multiple estuaries. We have applied three box model (TBM) [1] which divide the Atlantic Ocean into three elements with double estuaries. Freshwater inflows to the Arctic Ocean due to ice sheet thawing in Greenland and permafrost thawing in Siberia were calculated using AWI-ESM [2] and CMIP6 [3] data, respectively. In addition, temperature differences between the southern and northern Atlantic regions were calculated by MRI-ESM2.0 [4].
We also adopted the method of analyzing the time-series behavior of the AMOC as a stochastic process, as in Ditlevsen et al. (2010) [5]. Finally, we estimated the age of AMOC decay based on the analytical AMOC behavior by TBM and by identifying the parameters of the Langevin equation.
[1] E. Lambert, T. Eldevik, P.M. Haugan “How northern freshwater input can stabilise thermohaline circulation”, Tellus A: Dynamic Meteorology and Oceanography, 68 (1) (2016), p. 31051
[2] Ackermann, L., Danek, C., Gierz, P., and Lohmann, G. “AMOC Recovery in a multicentennial scenario using a coupled atmosphere-ocean-ice sheet model”, Geophys. Res. Lett., 47, 2020.
[3] Wang, S., Wang, Q., Wang, M., Lohmann, G., & Qiao, F. (2022). ”Arctic Ocean freshwater in CMIP6 coupled models” Earth’s Future, 10(9)
[4] Yukimoto, Seiji; Koshiro, Tsuyoshi; Kawai, Hideaki; et al. (2019) “MRI-ESM2.0 model output prepared for CMIP6 ScenarioMIP ssp585”
[5] Ditlevsen, P. D. and Johnsen, S. J.: Tipping points: Early warning and wishful thinking, Geophys. Res. Lett., 37, L19703, 2010
How to cite: Kono, K. and Fukuda, T.: Instability analysis of the AMOC with varying freshwater input and sea water temperature in the Atlantic Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14805, https://doi.org/10.5194/egusphere-egu25-14805, 2025.
Resilience is typically defined as the ability of vegetation to recover from external perturbations such as fires or droughts, and it can be quantitatively measured by the rate of recovery following such events. Resilience can also be assessed indirectly, even in the absence of large perturbations. One key metric for this is autocorrelation. A loss of resilience over time, often referred to as "slowing down," can be detected as an increase in autocorrelation. In simple one-dimensional dynamical systems, a reduction in resilience is also associated with increased sensitivity of the system's stable state to external conditions.
Recent studies, using indicators such as the Normalized Difference Vegetation Index (NDVI) and Vegetation Optical Depth (VOD), have found that resilience tends to be higher in wetter regions of tropical forests compared to drier regions, and that resilience has been decreasing across large parts of the Amazon rainforest. Additionally, empirical recovery rates after disturbances have been found to correlate with autocorrelation, supporting the practical relevance of theoretical expectations. However, it remains unclear which specific vegetation properties and processes determine the observed patterns.
Here we use idealized simulations with the state-of-the-art dynamic vegetation model LPJmL and explore how the resilience of natural forests and its indicators depend on (i) climate, (ii) vegetation composition (i.e., the mix of plant functional types), (iii) the vegetation property (variable) being considered, and (iv) the nature of the perturbation(s). We find that autocorrelation qualitatively aligns with the recovery time from large, negative perturbations that affect all tree types similarly.
However, there are exceptions where the factors listed above can influence the relationship in unexpected ways. Specifically, for some tree types and climate regimes, recovery rates and autocorrelation do not align with each other, nor with the forest's sensitivity to climate change. For example, perturbations that alter the relative abundance of tree types can lead to different recovery rates compared to those affecting all tree types uniformly. Moreover, vegetation variables that recover quickly when perturbed in isolation (e.g., fluxes like net primary productivity) may still co-evolve with slower variables they depend on (e.g., carbon stored in trees). We identify key mechanisms behind these features in the model and test their relevance by simulating a more realistic setup, using observed climate data within a geographically realistic domain. We also discuss the relevance of these mechanisms in the real world.
Our findings highlight the need to better understand the nature of disturbances and trends in ecosystems, as well as the mechanisms captured by satellite-derived indicators. This knowledge, along with improved resilience monitoring, will be crucial for making reliable predictions about how ecosystems will respond to human-induced changes.
How to cite: Bathiany, S., Blaschke, L., Morr, A., and Boers, N.: Vegetation resilience and sensitivity in complex dynamic vegetation models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13560, https://doi.org/10.5194/egusphere-egu25-13560, 2025.
Forests have considerable potential to influence the stability of the Earth system and mitigate climate change by influencing biogeochemical and biophysical processes. Tree cover, as the primary layer of exchange for carbon, energy and water cycles, play a critical role in such dynamics. However, the persistence and functionality of forests are highly dependent on their resilience to the ongoing rapid changes in natural and anthropogenic pressures. Experimental evidence of a sudden increase in tree mortality across different biomes is rising concerns about the ongoing changes in forest resilience and the associated risks to the climate mitigation potential of forests. Previous global-scale assessments of forest resilience have focused on the use of critical slowing down indicators, such as temporal autocorrelation and variance. These studies have provided important insights, but they can only partially capture the effects of stochastic disturbances and forest management.
In this study, we explore the potential of spatial statistical indicators (SSI), such as spatial variance and skewness, as early warning signals of regime shifts in global forests. To this aim, we first derive tree cover values for the 2000-2023 period at 0.05-degree spatial resolution for the whole globe by combining multiple satellite observations. We then, develop a machine learning model to disentangle the climate effects on tree cover distributions and elucidate the underlying mechanisms. SSI are ultimately computed on the residuals of the machine learning model and their spatial and temporal variations analysed.
Results show, along with a widespread erosion of tree cover, an increase in both SSI prominently in tropical and boreal forests over the observational period. According to the stability theory, the simultaneous increase in these metrics indicates a rising instability of the system by reflecting an alteration of the shape of the basin of attraction. Such patterns appear largely driven by the increase in stochastic perturbations and human pressures which are not detected using traditional critical slowing down indicators. Overall, this study contributes to better understand the recent dynamics in forest resilience and its underlying mechanisms that can lead to critical transitions. Considering the expected intensification of natural pressures in view of climate change, it is becoming urgent to identify adaptation measures to preserve the long-term stability of global forests and the provision of their ecosystem services.
How to cite: Mura, M., Somasundaram, D., Migliavacca, M., Dakos, V., Cescatti, A., and Forzieri, G.: Spatial variance and spatial skewness as leading indicators of regime shifts in global forests, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6613, https://doi.org/10.5194/egusphere-egu25-6613, 2025.
Tipping points have been identified in several components of the Earth system, raising concerns about abrupt and potentially irreversible changes under climate change. A recent analysis of GRACE satellite data reveals a sudden and unprecedented decline in terrestrial water storage (TWS) during 2015–2016, coinciding with a major El Niño–Southern Oscillation (ENSO) event (Rodell et al. 2024). This decline suggests a recent net drying of the land and raises the hypothesis of a regime shift in the global terrestrial water system. Potential mechanisms include enhanced evapotranspiration, intensifying drought frequency and severity, and land–atmosphere feedbacks. Early warning signals, such as increased autocorrelation and variance observed prior to the decline, support this hypothesis.
To evaluate the significance and rarity of the observed transition, we develop a detection methodology and apply it to both observational estimates and climate model simulations. By analysing fully coupled pre-industrial control simulations, historical simulations, and AMIP-style experiments with prescribed sea surface temperatures, we aim to disentangle the roles of anthropogenic climate change and specific modes of climate variability (e.g., ENSO) in driving this transition. Furthermore, we explore the potential for transitions to alternative states in global TWS. Our work establishes a framework for understanding abrupt changes in TWS and their implications for the terrestrial water cycle in a warming climate.
References
Rodell, M., Barnoud, A., Robertson, F.R. et al. An Abrupt Decline in Global Terrestrial Water Storage and Its Relationship with Sea Level Change. Surv Geophys 45, 1875–1902 (2024). https://doi.org/10.1007/s10712-024-09860-w
How to cite: Fisch, C., Gudmundsson, L., Schumacher, D. L., and Seneviratne, S. I.: Assessment of a potential regime shift in global terrestrial water storage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17534, https://doi.org/10.5194/egusphere-egu25-17534, 2025.
The Anthropocene epoch is characterized by an excessive use of natural resources and energy that drives the environmental destruction of the planet. However, large inequalities exist among different social groups that benefit to various degrees from the use of resources and energy, as well as among those suffering from the negative impacts of environmental destruction. In this paper, we systematically analyze these differences and discuss a social stratification theory based not only on differences in terms of possessions or social status, but also on differences in how these groups can control and benefit from the planetary material cycles and energy flows or suffer the consequences of environmental degradation. Referring to consumption data, we propose six global socio-metabolic classes and show distinctive patterns in the energy use of these classes. More research is needed to reveal differences in the use of natural resources essential for maintaining the biosphere integrity, such as land, water, nitrogen, and phosphorus. Targeted policy measures that address excessive appropriation of energy and natural resources are needed, as are expansions in infrastructure and institutional change that supports the wellbeing of humankind, and especially of the most marginalized classes.
How to cite: Otto, I. M.: Socio-metabolic class conflicts in the Anthropocene, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17704, https://doi.org/10.5194/egusphere-egu25-17704, 2025.
The rapid growth of the electronics market, driven by high demand for new technologies, has shortened the lifespan of electronic products, leading to a surge in electronic waste (E-waste). Comprising 25% plastics, E-waste contains unrecovered critical and toxic materials, necessitating advanced recycling strategies. HeliosLab, an infrastructure combining imaging sensors and robotic chemical analyses, was developed at the Helmholtz Institute Freiberg. HeliosLab integrates spectroscopy-based modalities such as RGB and hyperspectral imaging (HSI) across multiple wavelength ranges that can be used to optimize E-waste sorting. The complexity of the hyperspectral data, compounded by multisensory integration, requires sophisticated automated algorithms to efficiently process large volumes of data and extract critical material features. These advancements ensure scalable, fast, and automated detection solutions for industrial-scale E-waste recycling operations.
We are developing smart and novel processing methodologies utilizing state-of-the-art (SOTA) machine learning hyperspectral imaging (HSI) classification models. In this study, we focus on Transformer-based architectures, known for their self-attention mechanisms that effectively capture contextual relationships between their input tokens, which enables unique spatial-spectral feature detection, relevant to remote sensing and HSI applications. Such an approach significantly advances automated polymer identification.
To test the model’s performance on unseen data and evaluate the generalization performance of those SOTA models in industrial-like environments, multiscene datasets are required. We acquired a new multiscene HSI polymer dataset in the near visible (NIR) to the short-wave infrared (SWIR) (400-2500 nm) using hyperspectral cameras available at HeliosLab. The initial deployment highlighted the challenges related to both, the data quality and quantity, as well as regarding methodological frameworks. This led us to develop a tailored Transformer-based topology capable of detecting polymer fingerprints using novel refined extractions of the spatial and spectral features. Our research and advancements contribute to the automation and optimization of polymer detection in E-waste recycling, paving the way for improved resource recovery and environmental sustainability.
How to cite: Arbash, E., de Lima Ribeiro, A., Fuchs, M., Ghamisi, P., Scheunders, P., and Gloaguen, R.: Optimizing Plastic Identification in E-Waste Recycling through Hyperspectral Imaging and Transformer-Based Machine Learning Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13620, https://doi.org/10.5194/egusphere-egu25-13620, 2025.
Plastics represent, in volume, up to 50% of materials present in modern vehicles with most of them being black. Consequently, black plastics are a key material stream to be managed in end-of-life vehicles (ELV) waste. The EU directive on ELV, updated in 2023, introduces new rules covering all aspects of a vehicle life cycle, from its design and market placement until its final treatment as ELV. These new specific criteria now put pressure on car manufacturers and black plastic recyclers to boost circularity in the production and recycling chains, including:
- Improving circular design of vehicles to facilitate removal of materials, parts and components for reuse and recycling;
- Ensuring that at least 25% of the plastics used to build a new vehicle comes from recycling (of which 25% from recycled ELVs).
The first step required to improve the circularity of ELV polymers is to identify the main polymer types present in the stream with optical sensing. Current identification workflows are successfully employed by the plastic-waste recycling industry, based on material-specific signals present in the visible-to-near infrared (VNIR) and short-wave infrared (SWIR) ranges (400–2500 nm). Nevertheless, VNIR/SWIR sensors are unsuitable for identification of black plastics due to the strong signal absorption by dark pigments in this spectral region. In recent years, novel hyperspectral sensors operating in mid-wave infrared (MWIR, 2700–5300 nm) have been successfully employed for identification of black plastics. Yet, the automotive industry requires high-performance materials which led to the development and use of very specific polymer variants, including multi-polymer blends (e.g. ABS/PC), polymer subtypes (e.g. PA6 and PA6.6), and functional additives (e.g. glass fiber, talc, carbon black). Consequently, the identification of the usual polymer classes is not adapted to meet the minimum quality requirements for recycling and, hence, not adequate for future use for car material streams (closed loop).
Such complexity is justified by the need for high performance and functionality of materials in automotive applications, but impacts recyclability and ultimately leads to downcycling. In order to ensure that high-purity black plastics are obtained at the end of the recycling operation, at the standards needed by the automotive industry, it is necessary to go beyond the identification of main polymer types.
In this contribution, we address the current challenges and propose solutions to identify the important high-performance polymers used by the automotive industry that could be recycled. Further, we evaluate the suitability of current industrial optical sensing techniques for identification of black plastics originated from ELV waste. We also propose solutions for the identification of plastics with highly-complex composition present in ELVs such as multi-polymer blends (e.g. ABS/PC), polymer subtypes (e.g. PA6 and PA6.6), and functional additives (e.g. glass fiber, talc, carbon black).
How to cite: de Lima Ribeiro, A., Fuchs, M., Madriz, Y., and Gloaguen, R.: Challenges and solutions on identification of high-performance black plastics for closed-loop car recycling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12221, https://doi.org/10.5194/egusphere-egu25-12221, 2025.
As the world enters a period of accelerated climate change, we require the rapid development of an early warning system (EWS) that identifies whether climatic conditions will result in reaching a tipping point. Tipping points represent critical thresholds where a small disturbance can cause a significant, qualitative shift in a system's state that can have crucial effects on human livelihoods. The impacts of political developments on future emission pathways, highlights the need for warning systems focused on climate risk communication that can be deployed and updated easily by policy teams with data pertaining to representative emission profiles. We are developing an early warning system to detect tipping points using a combination of observational and model data. In this abstract, we introduce the Tipsy-API platform; a dynamics-informed deep learning model to forecast relevant thresholds of the Greenland ice sheet and Atlantic Meridional Ocean Circulation. Following the objective of a “real time” warning system, our framework iteratively updates forecasts with new observations to adjust the tipping point prediction accordingly. Finally, the framework will be deployed online and be available as an API, which we aim to be interactive and iteratively updated once new information about future warming becomes available. This ongoing work attempts to understand and address the requirements of a UK Government R&D funding agency, with the remit of engaging in high risk and high reward climate research. Thus, our project aims to both reduce uncertainty about tipping points and to allow for necessary open communication with policy makers and other relevant stakeholders.
How to cite: Roesch, C. and Last, C.: Dynamics-informed deep learning for tipping point forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20396, https://doi.org/10.5194/egusphere-egu25-20396, 2025.
