Max Planck Institute for the Science of Human History

We present TOAD v1.0 (Tipping and Other Abrupt events Detector), an open-source Python framework for the systematic detection and analysis of abrupt shifts in gridded Earth system data. TOAD provides a user-oriented workflow that combines grid-level shift detection with spatio-temporal clustering to identify domains where abrupt change co-occurs. An optional ensemble consensus step then identifies spatial patterns that are robust across ensemble members, models, variables, or method configurations, and quantifies associated statistics. The framework is method-agnostic, allowing different detection and clustering algorithms to be compared within a reproducible analysis pipeline. TOAD serves as an introspection tool for exploratory analysis of abrupt change across scales and addresses key questions in tipping-point research by identifying where such changes occur and providing first-order information on when they emerge along a time or forcing trajectory. The framework supports coordinated analyses in large model ensembles and intercomparison projects, such as TIPMIP and CMIP.

Ostracods from sediment cores of the ICDP NamCore drilling project were analysed to document their distribution, abundance, and preservation, and to explore their potential for reconstructing past lacustrine conditions on the central Tibetan Plateau. Selected core catcher samples covering sediment depths from ~8 to 470 m were investigated for their microfossil content. Sediment samples (10–15 g each) were wet-sieved at 63 µm and 200 µm, and the >200 µm fraction was examined under a stereomicroscope. Ostracods were assessed semi-quantitatively and assigned to five abundance categories (absent, 1-10, >10, >100, >1000 valves per sample). Preservation states were evaluated qualitatively, and taxonomic identifications were based on established regional faunal keys.Ostracods represent the only fossils observed in the sand-sized fraction of the analysed samples. Their abundance varies strongly, ranging from complete absence to more than 1000 valves per sample, with approximately half of the samples containing more than 100 valves. Preservation is generally good, although weakly etched, fragmented, compacted, or deformed valves occur, particularly below ~180 m core depth. Assemblages are of low diversity, with a maximum of five species per sample. At least six ostracod taxa were identified, including Leucocytherella sinensis, ?Leucocythere dorsotuberosa, ?Leucocythere postilirata, Ilyocypris ?bradyi, a smooth Ilyocypris species of uncertain taxonomic status, and juvenile Candona spp. The taxonomic assignment of ?Leucocythere dorsotuberosa and the smooth Ilyocypris species is the subject of ongoing investigations.Variations in ostracod abundance, species level assemblage composition, and variable preservation suggest changes in depositional and post-depositional conditions through the core. While the presence of ostracods throughout most sections is consistent with predominantly lacustrine settings, intervals with low abundances or poor preservation may reflect a range of factors, including lake-level changes, sedimentation dynamics, or taphonomic overprinting. Further quantitative analyses, improved taxonomic resolution, and integration with independent proxies are required to refine palaeoenvironmental interpretations.

Abstract Time series forecasting plays a vital role across scientific, industrial, and environmental domains, especially when dealing with high-dimensional and nonlinear systems. While Transformer models have recently achieved state-of-the-art performance in long-range forecasting, they often suffer from interpretability issues and instability in the presence of noise or dynamical uncertainty. We propose , a forecasting framework that combines Transformer-based sequence modeling with Koopman-inspired latent linear dynamics to improve stability, interpretability, and long-horizon robustness. Our model features a modular encoder–propagator–decoder structure, where temporal dynamics are learned via a spectrally constrained, linear Koopman-inspired propagator in a latent space. We impose structural guarantees—i.e., bounded spectral radius, Lyapunov-based energy regularization, and orthogonal parameterization—to ensure stability and interpretability. Comprehensive evaluations are conducted on dynamical systems, climate dataset, financial time series, and electricity generation dataset using the package that is prepared for this purpose. Across all experiments, consistently outperforms standard LSTM and baseline Transformer models in terms of accuracy, robustness to noise, and long-term forecasting stability. These results establish as a flexible, interpretable, and robust framework for forecasting in high-dimensional and dynamical settings.

Abstract Social norms play a pivotal role in sustainability transitions by shaping human behavior and influencing responses to challenges such as climate change and natural resource management. They can facilitate or impede collective action. Although simulation models help to explore norm dynamics, modeling approaches remain scattered and lack synthesis specific to social-ecological systems. This systematic review aims to map existing computational approaches to modeling social norms within social-ecological systems, identifying trends, gaps and implications for research and policy. We conducted a systematic search of Web of Science and Scopus (search date: 15 November 2024), screened abstracts using validated large language models, and selected 153 relevant studies for analysis. Key aspects analyzed included publication trends, thematic focus, modeling methods, spatial scale, empirical data used behavioral theory incorporation and norm representation. Publications increased over 25 years, with most studies originating from Europe and North America. Two dominant themes emerged: common-pool resource management and individual consumption behavior. Agent-based and evolutionary game-theoretic models prevail, but many omit explicit behavioral theories beyond rational choice theory. Models are mostly local or regional in scale and theoretical in nature and often lack empirical data for the quantification of the social norm modeled. Norms are generally modeled on an observation base, meaning that the majority of models include norms based on what others do. Interactions between norm types and dynamic norm signaling remain under-explored. To strengthen social-ecological modeling and its policy relevance, future work should enhance geographic diversity, integrate behavioral theories more deeply and develop models that capture dynamically signaled norms in order to advance our understanding of human–environment interactions and sustainability transitions.

Abstract Ancient DNA is a valuable data source for the understanding of our past. However, to effectively interpret this data, it is essential to know the age of the samples from which the DNA is obtained. Although the field of palaeogenomics has been recognised for its robust open data sharing practices, dating information associated with analysed samples is not reported consistently across palaeogenomic studies, nor is it included as metadata in most genetic data repositories. Here, we describe the addition of standardised precise dating information for ancient microbial genomes into the AncientMetagenomeDir metadata repository of published ancient metagenomic samples. This extension currently includes dating information for over 700 ancient microbial genomic datasets, of which 333 are dated using historical, contextual, or stratigraphic methods, and 405 are radiocarbon dated. We quantitatively assess the quality of radiocarbon date reporting and find that, despite established reporting conventions, radiocarbon dating information is often reported inconsistently across ancient metagenomic studies. This new resource provides ancient microbial researchers with standardised dating information that facilitates more accurate and consistent analysis of metagenomic sequencing data. The dataset also highlights the need for greater standardisation of radiocarbon date reporting in original publications in order to allow effective reuse of this and future ancient microbial data.

Abstract. Large-scale, nonlinear, abrupt, and potentially irreversible transitions in major Earth-system components are becoming increasingly likely under human pressures, with far-reaching consequences for ecosystems, climate stability, and human societies. Yet detecting and comparing such transitions across Earth System Model ensembles remains fragmented and inconsistent, hindering systematic assessment of tipping-point risks. Here we present the first release of the Tipping and Other Abrupt events Detector (TOAD v1.0), an open-source, user-oriented Python framework for detecting abrupt changes in gridded Earth-system data. TOAD implements a modular three-stage pipeline consisting of 1) grid-level abrupt shift detection, 2) spatio-temporal clustering of co-occurring changes, and 3) consensus synthesis to identify statistically robust regions across ensemble members, variables, models, or methodological configurations and quantifies agreement in transition timing. The framework addresses key practical challenges of large-scale spatio-temporal clustering on geographic grids and provides diagnostic statistics and visualisation tools. Detection, clustering, and synthesis algorithms can be flexibly exchanged, supporting systematic method comparison and extensibility. TOAD functions as a data-introspection tool that reveals potentially tipping-relevant dynamics across spatial and temporal scales for subsequent, process-based analysis. We apply TOAD to a synthetic benchmark, domain models of the Antarctic Ice Sheet and the global terrestrial biosphere, and a global Earth System Model ensemble of the North Atlantic Subpolar Gyre. Together, these demonstrations illustrate TOAD's applicability across diverse systems and establish a structured foundation for investigating where and when potentially tipping-relevant changes occur and for quantifying associated uncertainties, supporting coordinated assessment efforts such as the Tipping Points Modelling Intercomparison Project (TIPMIP).

Abstract. Rapid societal transformations are necessary to mitigate risky anthropogenic climate change and maintain human systems within Earth's planetary boundaries. Societal tipping processes have gained attention as mechanisms towards sustainability, yet generalisable solutions for intentionally transforming societal systems remain unclear. Here, we emphasise the significance of uncovering the systemic societal conditions enabling tipping from a complex adaptive systems perspective. Building upon research on societal tipping, transitions, and transformations, we develop the concepts of criticality – the likelihood of societal tipping – and critical agency – the human capacity to shape conditions that increase or reduce systemic criticality. We identify the transformation stages of criticality as the key analytical focus, and explore the phases of enacting critical agency within the societal tipping process. Criticality can serve as a crucial compass for policymakers, entrepreneurs, and activists, and other change-makers illuminating how critical agency can be used to instigate desired 'positive' societal tipping.

Characterised by heterogeneous microclimatic conditions, a sharp vegetation gradient and a rich archaeological record, Olkhon Island offers a unique setting to investigate past climate change and its potential influence on cultural trajectories in the Lake Baikal Region (LBR). Palynological and geochemical analyses of the uppermost 556 cm section of the 750 cm long, radiocarbon-dated sediment core (Nur-II) from Lake Nurskoye (53°3'N, 106°58'E; 456 m a.s.l.) at the western end of Olkhon Island reveal several long- and short-term climate shifts over the last 8800 years. Forest expansion beginning in the Early Holocene peaked during the second half of the 7th millennium cal BP, followed by a continuous decline culminating around 1500 cal yr BP, when open steppe vegetation reached its maximum extent. This pattern supports the long-debated hypothesis that landscape openness and the associated availability of herbivorous game were key drivers of population size, social practices and subsistence strategies among Mesolithic, Neolithic and Bronze Age hunter-gatherer communities in the region. The maximum expansion of steppe vegetation (ca. 3400–800 cal yr BP) likely attracted Bronze Age pastoral groups who migrated into the area around 3300 cal yr BP, as corroborated by the Nur-II palynological record and archaeozoological data from the LBR. However, the pollen data show no evidence that the mixed foraging-pastoral subsistence was supplemented by crop cultivation prior to the Middle Ages (i.e. 1360 cal yr BP). The pollen-inferred moisture trends on millennial to centennial scales, including the 8.2 and 4.2 ka BP cooling events, show a positive correlation with the intensity of the Asian summer monsoon circulation throughout the study period. This suggests that the region’s hydrology was primarily controlled by a south-easterly circulation regime during the Early–Middle Holocene, while westerly-derived moisture became increasingly important after ca. 7000 cal yr BP. • Detailed palynological and geochemical records from Lake Nurskoye (Olkhon Island). • Major changes in vegetation and lake sediment geochemistry occurred synchronously. • The collapse of the Kitoi culture 6660 cal yr BP followed the maximum expansion of the forests. • The expansion of steppe 3400–800 cal yr BP attracted Bronze and Iron Age pastoralists. • Moisture trends show a positive correlation with easterly and westerly circulation patterns.