ALBERT

Description: "Es gibt wissenschaftlich keine begründeten Hindernisse, die Biodiversität in ihrer Schönheit und Vielfalt zu schützen. Es bleiben nur noch sechs Jahre, um die Biodiversitätsziele bis 2030 zu erreichen. Dafür müssen wir jetzt gemeinsam anpacken." In den 10 Must-Knows aus der Biodiversitätsforschung 2024 haben 64 Wissenschaftlerinnen und Wissenschaftler ihre fundierten und vielseitigen Erkenntnisse und Empfehlungen aus den 10MustKnows22 weiterentwickelt. Die zehn ausgewählten Schlüsselbereiche des Erde-Mensch-Systems werden inhaltlich durch relevante Publikationen von 2022 und 2023 ergänzt und mit den im Dezember 2022 verabschiedeten 23 globalen Zielen des Kunming-Montreal Global Biodiversity Framework (GBF) verknüpft. Den Autorinnen und Autoren ist bewusst, dass die kommenden sechs Jahre bis 2030 wesentlich sind, um mittel- und langfristig ein ökologisch nachhaltiges und sozial gerechtes Leben auf unserer Erde zu erreichen. Mit den 10MustKnows24 möchten sie durch wissenschaftlich gesicherte Empfehlungen für Politik und Gesellschaft ihren aktiven Beitrag leisten, um die sozial-ökologische Transformation zu beschleunigen.

Description: The term “polycrisis” appears with growing frequently to capture the interconnections between global crises, but the word lacks substantive content. In this article, we convert it from an empty buzzword into a conceptual framework and research program that enables us to better understand the causal linkages between contemporary crises. We draw upon the intersection of climate change, the covid-19 pandemic, and Russia’s war in Ukraine to illustrate these causal interconnections and explore key features of the world’s present polycrisis.

Description: Climate policy increasingly requires carbon dioxide removal (CDR). We describe its role, characterize optimal flows for non-permanent removals and describe optimal pricing regimes under different information and liability conditions. Non-permanent removal – though to a certain extent optimal – creates liabilities that warrant careful risk management. Thus, seemingly cheap land-based technologies can become expensive. We discuss possibilities for integrating CDR in the EU policy architecture and define four tasks: managing the emission cap; R&D support; quality certification of removals; management of liabilities from non-permanent CDR. We propose three institutions for these tasks: a European Carbon Central Bank, a Carbon Removal Certification Authority and a Green Leap Innovation Authority.

Description: We analyze if exposure to weather risk affects the tenure security of smallholder farmers in rural Tanzania. Drawing on a household panel survey with three waves and high-resolution weather data, our identification strategy exploits exogenous variation in precipitation across time and space. Results from household fixed effects estimations show that exposure to weather risk significantly lowers farmers’ perceived tenure security, while it increases land conflicts. Moreover, weather risk influences the likelihood that farmers acquire land certificates. These findings suggest that both land formalization and land dispute resolution mechanisms are needed to cushion the impacts of weather risk.

Description: Measuring complexity allows to characterize complex systems. Existing techniques are limited to simultaneously measure complexity from short length data sets, detect transitions and periodic dynamics. This paper presents an approach based on ordinal pattern positioned slopes (OPPS). It considers exclusively OPPS group occurrences to compute the complexity from OPPS (COPPS) as the average number of patterns and applies to short data series. The COPPS measure was successfully applied to simulation data for measuring complexity, detecting transition phases and regular dynamics, distinguishing between chaotic and stochastic dynamics; and to real-world data for detecting arrhythmia ECG beats.

Description: We investigated the influence of the El Niño-Southern Oscillation (ENSO) on inter-annual precipitation variability in the far-eastern Pacific (FEP) and northern South America (NSA) using an approach based on phase synchronization (PS). First, we carried out a detailed analysis of observational data to define the inter-annual variability, eliminate the seasonal residual frequencies in hydro-climatic anomalies, and assess the statistical significance of PS. Additionally, we characterized the seasonality of regional patterns of sea surface temperature, surface pressure levels, low-level winds and precipitation anomalies associated with the ENSO states. We found that the positive (negative) precipitation anomalies experienced in the FEP and NSA differ from those previously reported in the literature. In particular, the Guianas (northeastern Amazon) and the Caribbean constitute two regions with negative (positive) rainfall anomalies during El Niño (La Niña), separated by a zone of non-significant anomalies along the Orinoco Low-level Jet corridor. Moreover, we showed that the ENSO signal is phase-locked with inter-annual rainfall and low-level wind variability in most of the study regions. Furthermore, we found consistency in the PS between the Central and Eastern Pacific El Niño indices and hydroclimatic anomalies over the Pacific. However, some areas exhibited PS, although they did not show significant precipitation anomalies, suggesting that the influence of ENSO on tropical climatology manifests not only in terms of the magnitude of anomalies but also in terms of the phases only. Our approach advances the understanding of climatic anomalies in tropical regions and provides new insights into the non-linear interactions between ENSO and hydroclimatic processes in tropical Americas.

Description: This analysis assesses the financial viability of legally investing in native Cerrado vegetation deforestation for crop production, considering climate change. The study uses data from twelve different crop models based on three different climate models to evaluate potential future crop yields in cleared land for growing soy and maize. The outcomes show that in many micro-regions, investments in clearing land for crop production would destroy economic value, that is, generate a negative net present value because of low/negative and volatile cashflows driven primarily by future yields as affected by climate. Our analysis was carried out based on present agricultural practices and technology. As climate changes, farmers may adapt their practices, which can lead to more resilient and productive crops, or grow different crops, which could provide better returns on investment in clearing land than the ones resulting from our analysis. Despite various uncertainties, farmers, policy makers and financial institutions should be aware of the climatic and financial risks associated with land clearing in Brazil, mainly in micro-regions in which all scenarios resulted in negative outcomes in the investment analysis. This study indicates that land expansion opportunities on degraded land should be prioritized over additional land clearing.

Description: Das Einzugsgebiet der Spree ist hydrologisch und wasserwirtschaftlich stark von der Entwicklung von Braunkohleförderung und –verstromung, verbunden mit Grundwasserabsenkungen, Versickerungsverlusten, Sümpfungswassereinleitungen, Kühlwassernutzung in thermischen Kraftwerken sowie Flutung ehemaliger Tagebaue, einem komplexen wasserwirtschaftlichen System und einem kontinentalen Klima geprägt. Im Projekt GLOWA Elbe wurden für das Gebiet Szenarien des globalen Wandels für den Zeitraum 2003 bis 2052 analysiert. Im Jahre 2004 war es das Ergebnis, dass das gegenwärtige System der Wasserbewirtschaftung die Folgen des angenommenen Klimawandels nicht ausgleichen kann. Nach Ablauf der ersten 20 Jahre des Simulationszeitraumes 2003 bis 2022 werden in diesem Artikel die in GLOWA Elbe und weiteren Projekten erzeugten Ergebnisse sowie die genutzten Eingangsdaten, Szenarien und Modelle hinsichtlich ihrer Übereinstimmung mit beobachteten Abflüssen bzw. Witterungsverlauf analysiert. Es zeigt sich, dass die in den Projekten genutzten Bergbauszenarien der Entwicklung bis zum jetzigen Zeitpunkt entsprechen, während die Szenarien zur klimatischen Entwicklung zum Teil deutlich von den Beobachtungen abweichen. Letzteres hat erhebliche Konsequenzen für die simulierten Abflüsse. Weiterhin kann gezeigt werden, dass auch in stark anthropogen überprägten Flusseinzugsgebieten die Auswirkungen des Klimawandels unter Nutzung hydrologischer und wasserwirtschaftlicher Modelle zuverlässig abgeschätzt werden können, wobei insbesondere die genutzten Klimaszenarien/-modelle eine erhebliche Unsicherheit hervorrufen. Deswegen ist es unabdingbar, in entsprechenden Klimafolgenstudien Eingangsdaten diverser Klimaszenarien/-modelle zu nutzen und die gesamte Bandbreite der hydrologischen/wasserwirtschaftlichen Ergebnisse darzustellen.

Description: The rapid growth of clean energy technologies is driving a rising demand for critical minerals. In 2022 at the 15th Conference of the Parties to the Convention on Biological Diversity (COP15), seven major economies formed an alliance to enhance the sustainability of mining these essential decarbonization minerals. However, there is a scarcity of studies assessing the threat of mining to global biodiversity. By integrating a global mining dataset with great ape density distribution, we estimated the number of African great apes that spatially coincided with industrial mining projects. We show that up to one-third of Africa’s great ape population faces mining-related risks. In West Africa in particular, numerous mining areas overlap with fragmented ape habitats, often in high-density ape regions. For 97% of mining areas, no ape survey data are available, underscoring the importance of increased accessibility to environmental data within the mining sector to facilitate research into the complex interactions between mining, climate, biodiversity, and sustainability.

Description: Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing. Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe.

Description: Disruptive events and calamities can have major consequences for households in the predominantly agrarian communities of Eastern Africa. Here, we analyze the impacts of environmental and non-environmental shocks on migration in Tanzania using panel models and longitudinal data from the Tanzania National Panel Survey between 2008 and 2013. Shocks are defined as events that lead to losses in income, assets, or both. We find shocks resulting from changes in environmental conditions to be positively related to migration over time with more recent shocks exerting the strongest impact. According to our estimates, the probability of having a household member absent increases by 0.81% with each additional environmental shock encountered in the past 12 months. Different types of shocks have differential effects on migration with the strongest effects being observed for shocks with an immediate impact on household livelihoods, including through livestock losses and crop damage. Households in the sample are differently affected with rural, agriculturally dependent, and poor households without alternative income sources showing the strongest changes in their migration behavior in response to shocks. Our study adds important insights into the relationship between disruptive events and migration in Eastern Africa considering a broad time window and the compounding influence of different shock types. Our findings have a range of policy implications highlighting the need for a comprehensive perspective on household responses in times of distress that considers the interplay of different shock types as well as the role of context in shaping mobility patterns.

Description: Climate stabilization is crucial for restabilizing the Earth system but should not undermine biosphere integrity, a second pillar of Earth system functioning. This is of particular con- cern if it is to be achieved through biomass-based negative emission (NE) technologies that compete for land with food production and ecosystem protection. We assess the NE con- tribution of land- and calorie-neutral pyrogenic carbon capture and storage (LCN-PyCCS) facilitated by biochar-based fertilization, which sequesters carbon and reduces land demand by increasing crop yields. Applying the global biosphere model LPJmL with an enhanced representation of fast-growing species for PyCCS feedstock production, we calculated a land-neutral global NE potential of 0.20–1.10 GtCO2 year−1 assuming 74% of the biochar carbon remaining in the soil after 100 years (for + 10% yield increase; no potential for + 5%; 0.61–1.88 GtCO 2 year−1 for + 15%). The potential is primarily driven by the achiev- able yield increase and the management intensity of the biomass producing systems. NE production is estimated to be enhanced by + 200–270% if management intensity increases from a marginal to a moderate level. Furthermore, our results show sensitivity to process- specific biochar yields and carbon contents, producing a difference of + 40–75% between conservative assumptions and an optimized setting. Despite these challenges for making world-wide assumptions on LCN-PyCCS systems in modeling, our findings point to dis- crepancies between the large NE volumes calculated in demand-driven and economically optimized mitigation scenarios and the potentials from analyses focusing on supply-driven approaches that meet environmental and socioeconomic preconditions as delivered by LCN-PyCCS.

Description: Given growing concerns about climate tipping points and their risks, it is important to investigate the capability of identifying robust precursor signals for the associated transitions. In general, the variance and short-lag autocorrelations of the fluctuations increase in a stochastically forced system approaching a critical or bifurcation-induced transition, making them theoretically suitable indicators to warn of such transitions. Paleoclimate records provide useful test beds if such a warning of a forthcoming transition could work in practice. The Dansgaard–Oeschger (DO) events are characterized by millennial-scale abrupt climate changes during the glacial period, manifesting most clearly as abrupt temperature shifts in the North Atlantic region. Some previous studies have found such statistical precursor signals for the DO warming transitions. On the other hand, statistical precursor signals for the abrupt DO cooling transitions have not been identified. Analyzing Greenland ice core records, we find robust and statistically significant precursor signals of DO cooling transitions in most of the interstadials longer than roughly 1500 years but not in the shorter interstadials. The origin of the statistical precursor signals is mainly related to so-called rebound events, humps in the temperature observed at the end of interstadial, some decades to centuries prior to the actual transition. We discuss several dynamical mechanisms that give rise to such rebound events and statistical precursor signals.

Description: Eastern Africa and Arabia were major hominin hotspots and critical crossroads for migrating towards Asia during the late Pleistocene. To decipher the role of spatiotemporal environmental change on human occupation and migration patterns, we remeasured the marine core from Meteor Site KL 15 in the Gulf of Aden and reanalyzed its data together with the aridity index from ICDP Site Chew Bahir in eastern Africa and the wet-dry index from ODP Site 967 in the eastern Mediterranean Sea using linear and nonlinear time series analysis. These analyses show major changes in the spatiotemporal paleoclimate dynamics at 400 and 150 ka BP (thousand years before 1950), presumably driven by changes in the amplitude of the orbital eccentricity. From 400 to 150 ka BP, eastern Africa and Arabia show synchronized wet-dry shifts, which changed drastically at 150 ka BP. After 150 ka BP, an overall trend to dry climate states is observable, and the hydroclimate dynamics between eastern Africa and Arabia are negatively correlated. Those spatio-temporal variations and interrelationships of climate potentially influenced the availability of spatial links for human expansion along those vertices. We observe positively correlated network links during the supposed out-of-Africa migration phases of H. sapiens. Furthermore, our data do not suggest hominin occupation phases during specific time intervals of humid or stable climates but provide evidence of the so far underestimated potential role of climate predictability as an important factor of hominin ecological competitiveness.

Description: While it is widely assumed that poor countries will suffer more from climate change, and that climate change will exacerbate inequalities within countries, systematic and large-scale evidence on this issue has been limited. In this systematic literature review, we examine and synthesize the evidence from the literature. Drawing from 127 individual papers, we find robust evidence that climate change impacts indeed increase economic inequality and disproportionately affect the poor, both globally and within countries on all continents. This result is valid across a wide range of physical impacts, types of economic inequality, economic sectors, and assessment methods. Furthermore, we highlight the channels through which climate change increases economic inequality. While the diversity of different approaches and metrics in the existing literature base precludes extracting a universal quantitative relation between climate change and economic inequality for use in future modelling, our systematic analysis provides an important stepping stone in that direction.

Description: Climate change–induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (Nw). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in Nw, ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme Nw. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme Nw with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

Description: This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).

Description: The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. These models’ performances are limited as landslide databases used in developing them often miss crucial information, e.g., underlying movement types. This study introduces a method of discerning landslide movements, such as slides, flows, and falls, by analyzing landslides’ 3D shapes. By examining landslide topological properties, we discover distinct patterns in their morphology, indicating different movements including complex ones with multiple coupled movements. We achieve 80-94% accuracy by applying topological properties in identifying landslide movements across diverse geographical and climatic regions, including Italy, the US Pacific Northwest, Denmark, Turkey, and Wenchuan in China. Furthermore, we demonstrate a real-world application on undocumented datasets from Wenchuan. Our work introduces a paradigm for studying landslide shapes to understand their underlying movements through the lens of landslide topology, which could aid landslide predictive models and risk evaluations.

Description: Anthropogenic climate change drives extreme weather events, leading to significant consequences for both society and the environment. This includes damage to road infrastructure, causing disruptions in transportation, obstructing access to emergency services, and hindering humanitarian organizations after natural disasters. In this study, we develop a novel method for analyzing the impacts of natural hazards on transportation networks rooted in the gravity model of travel, offering a fresh perspective to assess the repercussions of natural hazards on transportation network stability. Applying this approach to the Ahr valley flood of 2021, we discovered that the destruction of bridges and roads caused major bottlenecks, affecting areas considerably distant from the flood’s epicenter. Furthermore, the flood-induced damage to the infrastructure also increased the response time of emergency vehicles, severely impeding the accessibility of emergency services. Our findings highlight the need for targeted road repair and reinforcement, with a focus on maintaining traffic flow for emergency responses. This research provides a new perspective that can aid in prioritizing transportation network resilience measures to reduce the economic and social costs of future extreme weather events.

Description: We propose to explore the sustainability of climate policies based on a novel commitment metric. This metric allows to quantify how future generations’ scope of action is affected by short-term climate policy. In an example application, we show that following a moderate emission scenario like SSP2-4.5 will commit future generations to heavily rely on carbon dioxide removal or/and solar radiation modification to avoid unmanageable sea level rise.

Description: Dataset of recurrence plot bibliography. Scripts to retrieve additional data (paper citations and authors' affiliations) and to perform some statistical bibliometric and bibliographic analysis of the recurrence plot bibliography. Scripts are forPython and MATLAB.

Description: MATLAB scripts and functions used to create spatial recurrence plots to study circular structures in higherdimensional data fields, and to reproduce the figures of M. Riedl, N. Marwan, J. Kurths: Extended generalized recurrence plot quantification of complex circular patterns, European Physical Journal B, 90(58), 1–9 (2017). DOI:10.1140/epjb/e2017-70560-7

Description: A transition to healthy diets like the EAT-Lancet Planetary Health Diet could considerably reduce GHG emissions. However, the specific contributions of dietary shifts for the feasibility of 1.5°C pathways remain unclear. Here, we use the open-source Integrated Assessment Modeling (IAM) framework REMIND-MAgPIE to compare 1.5°C pathways with and without dietary shifts. We find that a flexitarian diet increases the feasibility of the Paris Agreement climate goals in different ways: The reduction of GHG emissions related to dietary shifts, especially methane from ruminant enteric fermentation, increases the 1.5°C-compatible carbon budget. Therefore, dietary shifts allow to achieve the same climate outcome with less carbon dioxide removal (CDR) and less stringent CO2 emission reductions in the energy system, which reduces pressure on GHG prices, energy prices and food expenditures.

Description: Photovoltaics (PV) and wind are the most important energy-conversion technologies for cost-efficient climate change mitigation. To reach international climate goals, the annual PV module production must be expanded to multi-terawatt (TW) scale. Economic and resource restraints demand the implementation of cost-efficient multi-junction technologies, for which perovskite-based tandem technologies are highly promising. In this work, the resource demand of the emerging perovskite PV technology is investigated, considering two factors of supply criticality, namely, mining capacity for minerals and the production capacity for synthetic materials. Overall, the expansion of perovskite PV to a multi-TW scale may not be limited by material supply if certain materials, especially indium, can be replaced. Moreover, organic charge-transport materials face currently unresolved scalability challenges. This study demonstrates that, besides the improvement of efficiency and stability, perovskite PV research and development also need to be guided by sustainable materials choices and design-for-recycling considerations.

Description: Forests, critical components of global ecosystems, face unprecedented challenges due to climate change. This study investigates the influence of functional diversity—as a component of biodiversity—to enhance long-term biomass of European forests in the context of changing climatic conditions. Using the next-generation flexible trait-based vegetation model, LPJmL-FIT, we explored the impact of functional diversity on long-term forest biomass under three different climate change scenarios (video abstract: https://www.pik-potsdam.de/~billing/video/2023/video_abstract_billing_et_al_LPJmLFIT.mp4). Four model set-ups were tested with varying degrees of functional diversity and best-suited functional traits. Our results show that functional diversity positively influences long-term forest biomass, particularly when climate warming is low (RCP2.6). Under these conditions, high-diversity simulations led to an approximately 18.2% increase in biomass compared to low-diversity experiments. However, as climate change intensity increased, the benefits of functional diversity diminished (RCP8.5). A Bayesian multilevel analysis revealed that both full leaf trait diversity and diversity of plant functional types contributed significantly to biomass enhancement under low warming scenarios in our model simulations. Under strong climate change, the presence of a mixture of different functional groups (e.g. summergreen and evergreen broad-leaved trees) was found more beneficial than the diversity of leaf traits within a functional group (e.g. broad-leaved summergreen trees). Ultimately, this research challenges the notion that planting only the most productive and climate-suited trees guarantees the highest future biomass and carbon sequestration. We underscore the importance of high functional diversity and the potential benefits of fostering a mixture of tree functional types to enhance long-term forest biomass in the face of climate change.

Description: Where annual layers are found in stalagmites, these can be used to provide insights into palaeoclimate. Sebastian F.M. Breitenbach and Norbert Marwan present a low-cost and high-resolution method for acquisition and analysis of greyscale data from speleothems by means of the free open-source ImageJ software.

Description: Lack of nitrogen limits food production in poor countries while excessive nitrogen use in industrial countries has led to transgression of the planetary boundary. However, the potential of spatial redistribution of nitrogen input for food security when returning to the safe boundary has not been quantified in a robust manner. Using an emulator of a global gridded crop model ensemble, we found that redistribution of current nitrogen input to major cereals among countries can double production in the most food insecure countries, while increasing global production of these crops by 12% with no notable regional loss or reducing the nitrogen input to the current production by one third. Redistribution of the input within the boundary increased production by 6–8% compared to the current relative distribution, increasing production in the food insecure countries by two thirds. Our findings provide georeferenced guidelines for redistributing nitrogen use to enhance food security while safeguarding the planet.

Description: On the path to climate neutrality, global production locations and trade patterns of basic materials might change due to the heterogeneous availability of renewable electricity. Here we estimate the ‘renewables pull’, which is the energy-cost savings, for varying depths of relocation for three key tradable energy-intensive industrial commodities: steel, urea and ethylene. For an electricity-price difference of €40 MWh−1, we find respective relocation savings of 18%, 32% and 38%, which might, despite soft factors in the private sector, lead to green relocation. Conserving today’s production patterns by shipping hydrogen is substantially costlier, whereas trading intermediate products could save costs while keeping substantial value creation in renewable-scarce importing regions. In renewable-scarce regions, a societal debate on macroeconomic, industrial and geopolitical implications is needed, potentially resulting in selective policies of green-relocation protection.

Description: Climate models are vital for understanding and projecting global climate change and its associated impacts. However, these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections. Addressing these challenges requires addressing internal variability, hindering the direct alignment between model simulations and observations, and thwarting conventional supervised learning methods. Here, we employ an unsupervised Cycle-consistent Generative Adversarial Network (CycleGAN), to correct daily Sea Surface Temperature (SST) simulations from the Community Earth System Model 2 (CESM2). Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole mode, as well as SST extremes. Notably, it substantially corrects climatological SST biases, decreasing the globally averaged Root-Mean-Square Error (RMSE) by 58%. Intriguingly, the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies, a common issue in climate models that traditional methods, like quantile mapping, struggle to rectify. Additionally, it substantially improves the simulation of SST extremes, raising the pattern correlation coefficient (PCC) from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32. This enhancement is attributed to better representations of interannual, intraseasonal, and synoptic scales variabilities. Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

Description: The planetary boundaries framework defines a safe operating space for humanity. To date, these boundaries have mostly been investigated separately, and it is unclear whether breaching one boundary can lead to the transgression of another. By employing a dynamic global vegetation model, we systematically simulate the strength and direction of the effects of different transgression levels of the climate change boundary (using climate output from ten CMIP6 models for CO2 levels ranging from 350 ppm to 1000 ppm). We focus on climate change-induced shifts of Earth’s major forest biomes, the control variable for the land-system change boundary, both by the end of this century and, to account for the long-term legacy effect, by the end of the millennium. Our simulations show that while staying within the 350 ppm climate change boundary co-stabilizes the land-system change boundary, breaching it (〉450 ppm) leads to its critical transgression with greater severity, the higher the ppm level rises and the more time passes. Specifically, this involves a poleward treeline shift, boreal forest dieback (nearly completely within its current area under extreme climate scenarios), competitive expansion of temperate forest into today’s boreal zone, and a slight tropical forest extension. These interacting changes also affect other planetary boundaries (freshwater change and biosphere integrity) and provide feedback to the climate change boundary itself. Our quantitative process-based study highlights the need for interactions to be studied for a systemic operationalization of the framework.

Description: This paper looks into the crucial macroeconomic feedback mechanisms emerging from the interplay among the goods market, the labor market, the financial sector, and monetary policy, particularly in the context of transitioning towards a climate-neutral economy. The investment decisions of firms, pivotal in this interaction, can trigger feedback loops with potentially destabilizing effects, underscoring the critical role of investment within the complex interplay of market and sector dynamics in the macroeconomy. Governmental intervention is highlighted as a key factor in steering the green transition while preserving economic stability. A carbon tax on fossil fuel consumption is proposed as a primary tool for facilitating this green transition. Our investigation employs a disequilibrium model of monetary growth, a la Keynes-Metzler-Goodwin (KMG), incorporating a portfolio perspective across three asset markets - money, bonds, and stocks. This framework allows for an in-depth analysis of how a carbon tax influences real production, inflation, and inequality during the transition. Our findings indicate that imposing a carbon tax on production does not markedly disrupt economic stability, as long as the carbon pricing and its growth rate remain within low bounds.

Description: We utilize a global warming level (GWL) lens to evaluate global and regional patterns of agricultural impacts as global surface temperature increases, providing a unique perspective on the experience of stakeholders with continued warming in the 21st century. We analyze crop productivity outputs from 11 crop models simulating 5 climate models under 3 emissions scenarios across 4 crops within the AgMIP/ISIMIP Phase 3 ensemble. We categorize regional productivity changes (without adaptation) into 9 characteristic climate change response patterns, identifying consistent increases and decreases as well as non-linear (peak or dip) responses indicative of inflection points reversing trends as GWLs increase. Many maize regions and pockets of wheat, rice and soybean show peak decrease patterns where initial increases may lull stakeholders into complacency or maladaptation before productivity shifts to losses at higher GWLs. Although the GWL perspective has proven useful in connecting diverse climate models and emissions scenarios, we identify multiple pitfalls that recommend proceeding with caution when applying this approach to climate impacts. Chief among these is that carbon dioxide (CO2) concentrations at any GWL depend on a climate model's transient climate response (TCR). Higher CO2 concentrations generally benefit crop productivity, so this leads to more pessimistic agricultural projections for so-called “hot” models and can skew multi-model ensemble results as models with high TCR are disproportionately likely to reach higher GWLs. While there are strong connections between many climatic impact-drivers and GWLs, vulnerability and exposure components of food system risk are strongly dependent on development pathways.

Description: Chapter 6 presents an introduction and sections on: earth system analysis from a nonlinear physics perspective; physics fields with relevance for energy technologies; towards green cities: the role of transport electrification; environmental safety; understanding and predicting space weather

Description: Wohngebäude sind für 11 % der Treibhausgasemissionen in Deutschland direkt verantwortlich und für bis zu 40 %, wenn Stromverbrauch mit einbezogen wird. Um die Klimaziele gemäß dem Bundes-Klimaschutzgesetz zu erreichen, ist ein höheres Ambitionsniveau der klimapolitischen Maßnahmen in diesem Sektor erforderlich. Dieser Bericht analysiert die Governance der Evaluierungsprozesse von Maßnahmen im deutschen Gebäudesektor. Wir analysieren die Rollen der beteiligten Akteure, das institutionelle Gefüge sowie die angewandten Metriken und Methoden der Evaluierungen.

Description: Residential buildings directly contribute 11% to local greenhouse gas emissions and up to 40% of total emissions when accounting for energy use for electricity generation. In order to achieve the climate targets in line with the Federal Climate Protection Act, increased ambition level of climate policy instruments is required in this sector. In this research, we are interested in the governance of this sector and the role of evaluation: the government-mandated processes used to evaluate policy in terms of the actors, organisations and ministries involved in executing and coordinating these processes; and the metrics and methods as well as the scope and granularity of evaluations.

Description: This paper discusses severe risks to food security and nutrition that are linked to ongoing and projected climate change, particularly climate and weather extremes in global warming, drought, flooding, and precipitation. We specifically consider the impacts on populations vulnerable to food insecurity and malnutrition due to lower income, lower access to nutritious food, or social discrimination. The paper defines climate-related “severe risk” in the context of food security and nutrition, using a combination of criteria, including the magnitude and likelihood of adverse consequences, the timing of the risk and the ability to reduce the risk. Severe climate change risks to food security and nutrition are those which result, with high likelihood, in pervasive and persistent food insecurity and malnutrition for millions of people, have the potential for cascading effects beyond the food systems, and against which we have limited ability to prevent or fully respond. The paper uses internationally agreed definitions of risks to food security and nutrition to describe the magnitude of adverse consequences. Moreover, the paper assesses the conditions under which climate change-induced risks to food security and nutrition could become severe based on findings in the literature using different climate change scenarios and shared socioeconomic pathways. Finally, the paper proposes adaptation options, including institutional management and governance actions, that could be taken now to prevent or reduce the severe climate risks to future human food security and nutrition.

Description: This article focuses on the neural-network (NN)-based adaptive tracking control issue for a class of high-order nonlinear multiagent systems both subjected to the immeasurable state variables and unknown external disturbance. Combining with the radial basis function NNs (RBF NNs), the composite disturbance observer and state observer for each follower are established, respectively. The purpose of this work is to develop NN-based adaptive tracking control schemes such that the output of each follower ultimately tracks that of the leader and all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded by utilizing the backstepping technique. Furthermore, so as to cope with the sparsity of the control resources, the proposed method is extended to the event-triggered case and the adaptive event-triggered tracking control protocol is formulated for nonlinear multiagent systems. Finally, the numerical example is performed to verify the efficacy of the proposed approach.

Description: In this paper we discuss PyBanshee, which is a Python-based open-source implementation of the MATLAB toolbox BANSHEE. PyBanshee constitutes the first fully open-source package to quantify, visualize and validate Non-Parametric Bayesian Networks (NPBNs). The architecture of PyBanshee is heavily based on its MATLAB predecessor. It presents the full implementation of existing tools and introduces new modules. Specifically, PyBanshee allows for: (i) choosing fully parametric one-dimensional margins, (ii) choosing different sample sizes for the model-validation tests based on the Hellinger distance, (iii) drawing user-defined sample sizes of the NPBN, (iv) sample-based conditioning sampling (similarly to the closed-source proprietary package UNINET by LightTwist Software) and (v) visualizing the comparison between the histograms of the unconditional and conditional marginal distributions. New detailed examples demonstrating new features are provided.

Description: The aim of this paper is to review quantitative large-N studies that investigate the effects of climate change on migration flows. Recent meta-analyses have shown that most studies find that climate change influences migration flows. There are however also many studies that find no effects or show that effects are dependent on specific contexts. To better understand this complexity, we argue that we need to discuss in more detail how to measure climate change and migration, how these measurements relate to each other and how we can conceptualise the relationship between these two phenomena. After a presentation of current approaches to measuring climate change, international and internal migration and their strengths and weaknesses we discuss ways to overcome the limitations of existing analytical frameworks.

Description: 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.

Description: Achieving sustainable development requires understanding how human behavior and the environment interact across spatial scales. In particular, knowing how to manage tradeoffs between the environment and the economy, or between one spatial scale and another, necessitates a modeling approach that allows these different components to interact. Existing integrated local and global analyses provide key insights, but often fail to capture 'meso-scale' phenomena that operate at scales between the local and the global, leading to erroneous predictions and a constrained scope of analysis. Meso-scale phenomena are difficult to model because of their complexity and computational challenges, where adding additional scales can increase model run-time exponentially. These additions, however, are necessary to make models that include sufficient detail for policy-makers to assess tradeoffs. Here, we synthesize research that explicitly includes meso-scale phenomena and assess where further efforts might be fruitful in improving our predictions and expanding the scope of questions that sustainability science can answer. We emphasize five categories of models relevant to sustainability science, including biophysical models, integrated assessment models, land-use change models, earth-economy models and spatial downscaling models. We outline the technical and methodological challenges present in these areas of research and discuss seven directions for future research that will improve coverage of meso-scale effects. Additionally, we provide a specific worked example that shows the challenges present, and possible solutions, for modeling meso-scale phenomena in integrated earth-economy models.

Description: Beaufils, T.; Berthet, E.; Ward, H.; & Wenz, L. (2023). Beyond production and consumption: Using throughflows to untangle the virtual trade of externalities. Economic Systems Research. https://doi.org/10.1080/09535314.2023.2174003 - Supporting code and data

Description: Over the last decades, the natural disturbance is increasingly putting pressure on European forests. Shifts in disturbance regimes may compromise forest functioning and the continuous provisioning of ecosystem services to society, including their climate change mitigation potential. Although forests are central to many European policies, we lack the long-term empirical data needed for thoroughly understanding disturbance dynamics, modeling them, and developing adaptive management strategies. Here, we present a unique database of 〉170,000 records of ground-based natural disturbance observations in European forests from 1950 to 2019. Reported data confirm a significant increase in forest disturbance in 34 European countries, causing on an average of 43.8 million m3 of disturbed timber volume per year over the 70-year study period. This value is likely a conservative estimate due to under-reporting, especially of small-scale disturbances. We used machine learning techniques for assessing the magnitude of unreported disturbances, which are estimated to be between 8.6 and 18.3 million m3/year. In the last 20 years, disturbances on average accounted for 16% of the mean annual harvest in Europe. Wind was the most important disturbance agent over the study period (46% of total damage), followed by fire (24%) and bark beetles (17%). Bark beetle disturbance doubled its share of the total damage in the last 20 years. Forest disturbances can profoundly impact ecosystem services (e.g., climate change mitigation), affect regional forest resource provisioning and consequently disrupt long-term management planning objectives and timber markets. We conclude that adaptation to changing disturbance regimes must be placed at the core of the European forest management and policy debate. Furthermore, a coherent and homogeneous monitoring system of natural disturbances is urgently needed in Europe, to better observe and respond to the ongoing changes in forest disturbance regimes.

Description: Smallholder farmers have adopted diverse adaptation practices to lessen the effect of climate change. However, context-specific information about why particular adaptation strategies are adopted remains limited. This study examined the factors that facilitate the choice of farm-level adaptation strategies to climate change (CC) using data collected from 269 African indigenous vegetable (AIV) farmers in Kenya. A multivariate probit (MVP) regression model was used to evaluate the determinants of adaptation choices. The most frequently adopted strategies considered for analysis were manure application, increased pesticide use, crop rotation, irrigation, change of planting dates and terracing. The results reveal that land ownership, group membership, access to extension services and education level were some of the key drivers of adoption. This implies that policies and programmes that are designed to build the ability of smallholder AIV farmers to adapt to climate change should focus on organising farmers into groups, disseminating timely weather information, improving land tenure security, increasing off-farm employment and providing greater access to extension services.

Description: That climate variability and change can potentially force multiple simultaneous breadbasket crop yield shocks has been established. But research quantifying the mechanisms behind such simultaneous shocks has been constrained by short records of crop yields. Here we compile a dataset of subnational crop yields in 25 countries dating back to 1900 to study the frequency and trends in multiple breadbasket yield shocks and how large-scale climate anomalies on interannual timescales have affected multiple breadbasket yield shocks over the last century. We find that major simultaneous breadbasket yield shocks have occurred in at least three, four, or five of nine breadbaskets 10.3%, 2.3% and 1.1% of the time for maize and 18.4%, 4.6% and 2.3% of the time for wheat. Furthermore, we find that multiple breadbasket yield shocks decreased in frequency even as those breadbaskets experience increasingly frequent climate-related shocks. For both maize and wheat breadbaskets, there were fewer simultaneous yield shocks during the 1975–2017 time period as compared to 1931–1975. Finally, we find that interannual modes of climate variability - such as the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the North Atlantic Oscillation (NAO) - have all affected the relative probability of simultaneous yield shocks in pairs of breadbaskets by up to 20–40% in both maize and wheat breadbaskets. While past literature has focused on the effects of ENSO, we find that at the global scale the NAO affects the overall number of wheat yield shocks most strongly despite only affecting northern hemisphere breadbaskets.

Description: Background: Diarrhoeal disease is a leading cause of childhood illness and death globally, and Shigella is a major aetiological contributor for which a vaccine might soon be available. The primary objective of this study was to model the spatiotemporal variation in paediatric Shigella infection and map its predicted prevalence across low-income and middle-income countries (LMICs). - Methods: Individual participant data for Shigella positivity in stool samples were sourced from multiple LMIC-based studies of children aged 59 months or younger. Covariates included household-level and participant-level factors ascertained by study investigators and environmental and hydrometeorological variables extracted from various data products at georeferenced child locations. Multivariate models were fitted and prevalence predictions obtained by syndrome and age stratum. - Findings: 20 studies from 23 countries (including locations in Central America and South America, sub-Saharan Africa, and south and southeast Asia) contributed 66 563 sample results. Age, symptom status, and study design contributed most to model performance followed by temperature, wind speed, relative humidity, and soil moisture. Probability of Shigella infection exceeded 20% when both precipitation and soil moisture were above average and had a 43% peak in uncomplicated diarrhoea cases at 33°C temperatures, above which it decreased. Compared with unimproved sanitation, improved sanitation decreased the odds of Shigella infection by 19% (odds ratio [OR]=0·81 [95% CI 0·76–0·86]) and open defecation decreased them by 18% (OR=0·82 [0·76–0·88]). - Interpretation: The distribution of Shigella is more sensitive to climatological factors, such as temperature, than previously recognised. Conditions in much of sub-Saharan Africa are particularly propitious for Shigella transmission, although hotspots also occur in South America and Central America, the Ganges–Brahmaputra Delta, and the island of New Guinea. These findings can inform prioritisation of populations for future vaccine trials and campaigns.

Description: The planetary crises require health professionals to understand the interlinkages between health and environmental changes, and how to reduce ecological harm (ie, ecological footprint) and promote positive change (ie, ecological handprint). However, health professions’ education and training are mostly lacking these aspects. In this Viewpoint, we report findings from the evaluation of the Planetary Health Academy, the first open online lecture series for transformative planetary health education in Germany. In a retrospective online survey, 458 of 3656 Planetary Health Academy participants reported on their emotions towards climate change, attitudes towards health professionals’ responsibilities, self-efficacy, and the contribution of the Planetary Health Academy to their knowledge and actions. Additionally, motivators and barriers to acting were assessed. Our findings provide insights that can inform future efforts for transformative education. Combined with network and movement building, education could act as a social tipping element toward actions to mitigate global environmental changes.

Description: To feed the growing population, achieve the Sustainable Development Goals, and fulfil the commitments of the Paris Agreement, West African countries need to invest in agricultural development and renewable energy, among other sectors. Irrigated agriculture, feeding millions of people, and hydropower, generating clean electricity, depend on water availability and compete for the resource. In the Volta basin, the planned 105 000 ha of irrigated land in Burkina Faso and Ghana could feed hundreds of thousands of people. However, irrigation in the dry season depends on upstream dams that change the river’s flow regime from intermittent to permanent, and at the same time irrigation water is no longer available for hydropower generation. Using an integrated eco-hydrological and water management model, we investigated the water demand and supply of three planned irrigation projects and the impacts of the planned Pwalugu multi-purpose dam on the hydropower potentials and water availability in the entire Volta basin. We found that future irrigation withdrawals would reduce the hydropower potential in the Volta basin by 79 GWh a−1 and the operation of Pwalugu by another 86 GWh a−1. Hence, Pwalugu contributes only about 101 GWh a−1 of its potential of 187 GWh a−1. Under climate change simulations, using an ensemble of eight bias-adjusted and downscaled GCMs, irrigation demand surprisingly did not increase. The higher evaporation losses due to higher temperatures were compensated by increasing precipitation while favouring hydropower generation. However, water availability at the irrigation site in Burkina Faso is clearly at its limit, while capacity in Ghana is not yet exhausted. Due to hydro-climatic differences in the Volta basin, the cost of irrigating one hectare of land in terms of lost hydropower potential follows a north-south gradient from the hot and dry north to the humid south. Nevertheless, food production should have priority over hydropower, which can be compensated by other renewables energies.

Description: The observed temperature increase due to anthropogenic carbon emissions has impacted economies worldwide. National income levels in origin and destination countries influence international migration. Emigration is relatively low not only from high income countries but also from very poor regions, which is explained in current migration theory by credit constraints and lower average education levels, among other reasons. These relationships suggest a potential non-linear, indirect effect of climate change on migration through this indirect channel. Here we explore this effect through a counterfactual analysis using observational data and a simple model of migration. We show that a world without climate change would have seen less migration during the past 30 years, but that this effect is strongly reduced due to inhibited mobility. Our framework suggests that migration within the Global South has been strongly reduced because these countries have seen less economic growth than they would have experienced without climate change. Importantly, climate change has impacted international migration in the richer and poorer parts of the world very differently. In the future, climate change may keep in- creasing global migration as it slows down countries' transition across the middle-income range associated with the highest emigration rates.

Description: Um die ambitionierten Klimaschutzziele zu erreichen, braucht Deutschland effektive Instrumente und Maßnahmen auf nationaler, bundesstaatlicher und kommunaler Ebene. Der Erfolg dieser Politik wird maßgeblich davon abhängen, ob es gelingt, eine breite gesellschaftliche Akzeptanz und Unterstützung für diese Maßnahmen auf den entsprechenden Ebenen zu erreichen. Die Haltung der Bevölkerung wird jedoch in der Regel auf nationaler Ebene über Umfragen gemessen, die geographische Unterschiede dabei auf den subnationalen Ebenen außer Acht lassen. Im Rahmen dieser Analyse schätzen wir die durchschnittliche Bevölkerungszustimmung zu 26 Klimaschutzmaßnahmen in den Sektoren Wärme, Transport und Energie auf Bundesland-, Landkreis- und kommunaler Ebene zwischen 2017 und 2021 mittels eines mehrstufigen Regressions- und Poststratifizierungsmodells. Die Schätzungen basieren auf zwei bundesweit repräsentativen Panel-Umfragen, dem Sozialen Nachhaltigkeitsbarometer und dem Ariadne Wärme-& Wohnen-Panel. Durch die Analyse werden erhebliche regionale Unterschiede in der Zustimmung von Klimaschutzmaßnahmen in der deutschen Bevölkerung sichtbar. Die Befürwortung einzelner Klimaschutzmaßnahmen variiert teilweise um bis zu 60 Prozentpunkte zwischen den untersuchten geographischen Einheiten. In der Gesamtbetrachtung der räumlichen Disparitäten zeichnen sich bedeutsame Unterschiede zwischen Stadt- und Landbevölkerung sowie West- und Ostdeutschland ab. Im zeitlichen Verlauf haben sich dabei die Einstellungen gegenüber einzelnen Maßnahmen, wie beispielsweise dem Ausbau von Wind- und Solarkraftanlagen, angenähert, während die öffentliche Meinung zu anderen energiepolitischen Instrumenten, wie dem Kohleausstieg, im Laufe der Jahre polarisieren. Mittels einer zusätzlich durchgeführten räumlichen Panelanalyse können wir zudem zeigen, dass sich die Veränderungen von bestimmten Kontextfaktoren auf die Zustimmung von Klimaschutzmaßnahmen auf kommunaler Ebene auswirken. So finden wir einen positiven Zusammenhang zwischen der Befürwortung des Ausbaus von Wind- und Solarkraftanlagen und dem tatsächlichen Zubau an Solar- und Windkapazitäten in diesen Regionen. Ferner wird die Haltung gegenüber klimapolitischen Maßnahmen stark von räumlichen Diffusionseffekten, d.h. der Ausbreitung von Einstellungen im sozialen Umfeld, bestimmt, wie der Einfluss von Meinungsänderungen in einer Region auf deren Nachbarregionen veranschaulicht. Die in diesem Bericht und auf dem interaktiven Online-Dashboard zur Verfügung gestellten Schätzungen der Zustimmung zu Klimaschutzmaßnahmen, stellen eine wichtige Informationsgrundlage für politische Entscheidungsträger:innen dar, um den gesellschaftlichen Herausforderungen bei der Umsetzung von Klimaschutzmaßnahmen effektiv zu begegnen. Alle generierten Daten sind im Online-Dashboard „Lokale Klimaschutzeinstellungen in Deutschland“ unter https://hertie-school-ariadne.shinyapps.io/LocalAttitudesDashboard/ einsehbar.

Description: Phase transitions in equilibrium and nonequilibrium systems play a major role in the natural sciences. In dynamical networks, phase transitions organize qualitative changes in the collective behavior of coupled dynamical units. Adaptive dynamical networks feature a connectivity structure that changes over time, coevolving with the nodes’ dynamical state. In this Letter, we show the emergence of two distinct first-order nonequilibrium phase transitions in a finite-size adaptive network of heterogeneous phase oscillators. Depending on the nature of defects in the internal frequency distribution, we observe either an abrupt single-step transition to full synchronization or a more gradual multistep transition. This observation has a striking resemblance to heterogeneous nucleation. We develop a mean-field approach to study the interplay between adaptivity and nodal heterogeneity and describe the dynamics of multicluster states and their role in determining the character of the phase transition. Our work provides a theoretical framework for studying the interplay between adaptivity and nodal heterogeneity.

Description: Agriculture is a major sector responsible for greenhouse gas emissions. Local food production can contribute to reducing transport-related emissions. Since most of the worldwide population lives in cities, locally producing food implies practicing agriculture in urban and peri-urban areas. Exemplary, we analyze the potential to produce fresh vegetables within Berlin, Germany. We investigate the spatial extent of five different urban spaces for soil-based agriculture or gardening, i.e., non-built residential areas, allotment gardens, rooftops, supermarket parking lots, and cemeteries. We also quantify inputs required for such food production in terms of water, human resources, and investment. Our findings highlight that up to 82% of Berlin’s vegetable demand could be produced within the city, based on a reasonable validation of existing areas. Meeting this potential requires 42 km of urban spaces for cultivation, a considerable amount of irrigation water, around 17 thousand gardeners, and over 750 million EUR of initial investments. The final vegetable cost would be around 2 EUR to 10 EUR per kg without any profit margin. We conclude that it is realistic to produce a significant amount of Berlin’s vegetable demand within the city, even if it comes with great challenges.

Description: Study Region: The Naryn River Basin, Kyrgyzstan. - Study Focus: We investigate the impacts of climate change in the basin based on two families of General Circulation Models (GCMs) using the hydrological model SWAT. The forcing datasets are the widely used ISIMIP2 (I2) and the newly derived ISIMIP3 (I3) data which refer to the 5th and 6th stage of the Coupled Model Intercomparison Project (CMIP). Due to notable differences in the forcing we evaluate their impacts on various hydrological components of the basin, such as discharge, evapotranspiration (ETA) and soil moisture (SM). Besides, a partial correlation (PC) analysis is used to assess the meteorological controls of the basin with special emphasize on the SM-ETA coupling. - New Hydrological Insights for the Region: Agreement in the basin’s projections is found, such as discharge shifts towards an earlier peak flow of one month, significant SM reductions and ETA increases. I3 temperature projections exceed their previous estimates and show an increase in precipitation, which differs from I2. However, the mitigating effects do not lead to an improvement in the region’s susceptibility to soil moisture deficits. The PC study reveals enhanced water-limited conditions expressed as positive SM-ETA feedback under I2 and I3, albeit slightly weaker under I3.

Description: Many modern extinction drivers are shared with past mass extinction events, such as rapid climate warming, habitat loss, pollution and invasive species. This commonality presents a key question: can the extinction risk of species during past mass extinction events inform our predictions for a modern biodiversity crisis? To investigate if it is possible to establish which species were more likely to go extinct during mass extinctions, we applied a functional trait-based model of extinction risk using a machine learning algorithm to datasets of marine fossils for the end-Permian, end-Triassic and end-Cretaceous mass extinctions. Extinction selectivity was inferred across each individual mass extinction event, before testing whether the selectivity patterns obtained could be used to ‘predict’ the extinction selectivity exhibited during the other mass extinctions. Our analyses show that, despite some similarities in extinction selectivity patterns between ancient crises, the selectivity of mass extinction events is inconsistent, which leads to a poor predictive performance. This lack of predictability is attributed to evolution in marine ecosystems, particularly during the Mesozoic Marine Revolution, associated with shifts in community structure alongside coincident Earth system changes. Our results suggest that past extinctions are unlikely to be informative for predicting extinction risk during a projected mass extinction.

Description: This article investigates the event-triggered adaptive containment control problem for a class of stochastic nonlinear multiagent systems with unmeasurable states. A stochastic system with unknown heterogeneous dynamics is established to describe the agents in a random vibration environment. Besides, the uncertain nonlinear dynamics are approximated by radial basis function neural networks (NNs), and the unmeasured states are estimated by constructing the NN-based observer. In addition, the switching-threshold-based event-triggered control method is adopted with the hope of reducing communication consumption and balancing system performance and network constraints. Moreover, we develop the novel distributed containment controller by utilizing the adaptive backstepping control strategy and the dynamic surface control (DSC) approach such that the output of each follower converges to the convex hull spanned by multiple leaders, and all signals of the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in mean square. Finally, we verify the efficiency of the proposed controller by the simulation examples.

Description: In many specific scenarios, accurateand practical cooperative learning is a commonly encountered challenge in multi-agent systems. Thus, the current investigation focuses on cooperative learning algorithms for multi-agent systems and underpins an alternate data-based neural network reinforcement learning framework. To achieve the data-based learning optimization, the proposed cooperative learning framework, which comprises two layers, introduces a virtual learning objective. The followers learn the behaviors of the virtual objects in the first layer based on the adaptive neural networks (NNs). Specifically, the actor and critic NNs are applied to acquire cooperative behaviors and assess this layer's long-term utility function. Then another layer realizes the tracking performance between the virtual objects and the leader by introducing the local data-based performance index. Then, we formulate a resulting deterministic optimization problem and resolve it effectively with the policy iteration algorithm. This intuitive cooperative learning algorithm also preserves good robustness properties and eliminates the dependence on the prior knowledge of the multi-agent system model in the solution process. Finally, a multi-robot formation system demonstrates this promising development's practical appeal and highly effective outcome.

Description: Correlation analysis serves as an easy-to-implement estimation approach for the quantification of the interaction or connectivity between different units. Often, pairwise correlations estimated by sliding windows are time-varying (on different window segments) and window size-dependent (on different window sizes). Still, how to choose an appropriate window size remains unclear. This paper offers a framework for studying this fundamental question by observing a critical transition from a chaotic-like state to a nonchaotic state. Specifically, given two time series and a fixed window size, we create a correlation-based series based on nonlinear correlation measurement and sliding windows as an approximation of the time-varying correlations between the original time series. We find that the varying correlations yield a state transition from a chaotic-like state to a nonchaotic state with increasing window size. This window size-dependent transition is analyzed as a universal phenomenon in both model and real-world systems (e.g., climate, financial, and neural systems). More importantly, the transition point provides a quantitative rule for the selection of window sizes. That is, the nonchaotic correlation better allows for many regression-based predictions. Complex connections between different units can be simply approximated by correlation analysis between corresponding time series. When the complete information (the entire time series) is considered for analysis, dynamic connections are aggregated into a single value, reflecting the overall macro linkage. When segmented information (a sliced time series) is combined with sliding windows, the underlying dynamic connections can be approximated by time-varying correlations. Intuitively, the longer the segments are, the more likely to capture cyclic behavior. A typical example is that in climate science, large-scale climate phenomena, such as seasonal changes induced by the annual cycle of solar radiation, are not observable on the timescale of diurnal cycles. Similarly, for correlation analysis, choosing a suitable window scale to capture the necessary patterns hidden in the time series is fundamental; yet, how to do so is unclear. We intend to address this issue in our work.

Description: The timescales of the flow and retreat of Greenland’s and Antarctica’s outlet glaciers and their potential instabilities are arguably the largest uncertainty in future sea-level projections. Here we derive a scaling relation that allows the comparison of the timescales of observed complex ice flow fields with geometric similarity. The scaling relation is derived under the assumption of fast, laterally confined, geometrically similar outlet-glacier flow over a slippery bed, i.e., with negligible basal friction. According to the relation, the time scaling of the outlet flow is determined by the product of the inverse of 1) the fourth power of the width-to-length ratio of its confinement, 2) the third power of the confinement depth and 3) the temperature- dependent ice softness. For the outflow at the grounding line of streams with negligible basal friction this means that the volume flux is proportional to the ice softness and the bed depth, but goes with the fourth power of the gradient of the bed and with the fifth power of the width of the stream. We show that the theoretically derived scaling relation is supported by the observed velocity scaling of outlet glaciers across Greenland as well as by idealized numerical simulations of marine ice-sheet instabilities (MISIs) as found in Antarctica. Assuming that changes in the ice-flow velocity due to ice-dynamic imbalance are proportional to the equilibrium velocity, we combine the scaling relation with a statistical analysis of the topography of 13 MISI-prone Antarctic outlets. Under these assumptions the timescales in response to a potential destabilization are fastest for Thwaites Glacier in West Antarctica and Mellor, Ninnis and Cook Glaciers in East Antarctica; between 16 and 67 times faster than for Pine Island Glacier. While the applicability of our results is limited by several strong assumptions, the utilization and potential further development of the presented scaling approach may help to constrain time-scale estimates of outlet glacier- flow, augmenting the commonly exploited and comparatively computationally expensive approach of numerical modeling.

Description: A waste-to-protein system that integrates a range of waste-to-protein upgrading technologies has the potential to converge innovations on zero-waste and protein security to ensure a sustainable protein future. We present a global overview of food-safe and feed-safe waste resource potential and technologies to sort and transform such waste streams with compositional quality characteristics into food-grade or feed-grade protein. The identified streams are rich in carbon and nutrients and absent of pathogens and hazardous contaminants, including food waste streams, lignocellulosic waste from agricultural residues and forestry, and contaminant-free waste from the food and drink industry. A wide range of chemical, physical, and biological treatments can be applied to extract nutrients and convert waste-carbon to fermentable sugars or other platform chemicals for subsequent conversion to protein. Our quantitative analyses suggest that the waste-to-protein system has the potential to maximise recovery of various low-value resources and catalyse the transformative solutions toward a sustainable protein future. However, novel protein regulation processes remain expensive and resource intensive in many countries, with protracted timelines for approval. This poses a significant barrier to market expansion, despite accelerated research and development in waste-to-protein technologies and novel protein sources. Thus, the waste-to-protein system is an important initiative to promote metabolic health across lifespans and tackle the global hunger crisis.

Description: African paleoanthropological studies typically focus on regions of the continent such asEastern, Southern and Northern Africa, which hold the highest density of Pleistocenearchaeological sites. Nevertheless, lesser known areas such as West Africa also feature a highnumber of sites. Here, we present a high-resolution map synthesising all well contextualisedPleistocene archaeological sites present in Sub-Saharan West Africa. A detailed elevation andecoregional map was developed and correlated with palaeoanthropological sites. This mapis supplemented with 1,000- and 2000-year interval climate reconstructions over the last120,000 years for three subregions of high archaeological interest. The presentedarchaeological sites were compiled by reviewing published literature, and selected basedon: (1) documented archaeological stratification or 〉10 characteristic artefacts, (2) publishedcoordinates, and (3) published chronometric ages or relative dating. The data presentedhere elucidates the current state of knowledge of Pleistocene West Africa, highlighting theregional potential for human evolutionary studies.

Description: Wie schon 2021 hat der Gebäudesektor auch im vergangenen Jahr die erlaubten Emissionen gemäß Bundes-Klimaschutzgesetz überschritten. Die Fernwärme kann einen Beitrag leisten, um die Trendwende hin zur angestrebten Klimaneutralität zu schaffen. Klar ist aber auch, dass die Wärmenetze der Zukunft deutlich flexibler und effizienter werden müssen, etwa um aus erneuerbaren Quellen oder Abwärme von Rechenzentren gewonnene Wärme nutzen zu können. Angesichts der hohen Lebensdauer der Wärmenetze von etwa 50 bis 60 Jahren ist ein Umbau dieser Netze auf die Erfordernisse der Wärmewende besonders kritisch. Allerding müssen für eine erfolgreiche Anpassung verschiedene Ebenen ineinandergreifen: Die kommunale Wärmewende, die Transformation der Netze sowie die Digitalisierung der Verbrauchsmessungen. Ergebnis der Ariadne-Analyse ist, dass für die erforderlichen Umrüstungen der Netze ein massives Finanzierungsproblem besteht. So bleiben Fernwärmenetzbetreiber auf etwa 90% der Investitionskosten sitzen. Durch eine Nahverdichtung der Fernwärmeanschlüsse könnten die Kosten verringert werden. Weiterhin bietet die Nachrüstung der Wärmezähler für die Fernauslesbarkeit bis Ende 2026 die Möglichkeit, Maßnahmen zu kombinieren, etwa den Austausch der Hausstation und den hydraulischen Abgleich. Eine koordinierte Einbeziehung der handelnden Akteure könnte daher zur Trendwende im Wärmesektor beitragen.

Description: Trends in flood magnitudes vary across the conterminous USA (CONUS). There have been attempts to identify what controls these regionally varying trends, but these attempts were limited to certain—for example, climatic—variables or to smaller regions, using different methods and datasets each time. Here we attribute the trends in annual maximum streamflow for 4,390 gauging stations across the CONUS in the period 1960–2010, while using a novel combination of methods and an unprecedented variety of potential controlling variables to allow large-scale comparisons and minimize biases. Using process-based flood classification and complex networks, we find 10 distinct clusters of catchments with similar flood behavior. We compile a set of 31 hydro-climatological and land use variables as predictors for 10 separate Random Forest models, allowing us to find the main controls the flood magnitude trends for each cluster. By using Accumulated Local Effect plots, we can understand how these controls influence the trends in the flood magnitude. We show that hydro-climatologic changes and land use are of similar importance for flood magnitude trends across the CONUS. Static land use variables are more important than their trends, suggesting that land use is able to attenuate (forested areas) or amplify (urbanized areas) the effects of climatic changes on flood magnitudes. For some variables, we find opposing effects in different regions, showing that flood trend controls are highly dependent on regional characteristics and that our novel approach is necessary to attribute flood magnitude trends reliably at the continental scale while maintaining sensitivity to regional controls.

Description: The analysis of event time series is in general challenging. Most time series analysis tools are limited for the analysis of this kind of data. Recurrence analysis, a powerful concept from nonlinear time series analysis, provides several opportunities to work with event data and even for the most challenging task of comparing event time series with continuous time series. Here, the basic concept is introduced, the challenges are discussed, and the future perspectives are summarized.

Description: This paper addresses the issue of dynamic mean-square consensus for second-order hybrid multi-agent systems. Time-varying delays and multiplicative noises are considered. New distributed control protocols are designed based on data-sampled information of neighbor agents. Equivalently using the error system based on Laplacian matrix, the method could make a dynamic consensus both under the fixed and switching topologies. By adopting stochastic system theory, Lyapunov stability method and linear matrix inequality theory, several sufficient conditions for the dynamic mean-square consensus are obtained. The upper bound of time delay and the discrete-time sampling period of hybrid multi-agent systems under a stochastic noises environment are inferred. Several simulations are presented to demonstrate the effectiveness of the proposed methods.

Description: Given two dynamical systems, we quantify how similar they are with respect to their interaction with the outside world. We focus on the case where simpler systems act as a specification for a more complex one. Combining a behavioral and probabilistic perspective we define several useful notions of the distance of a system to a specification. We show that these distances can be used to tune a complex system. We demonstrate that our approach can successfully make non-linear networked systems behave like much smaller networks, allowing us to aggregate large sub-networks into one or two effective nodes. Finally, we discuss similarities and differences between our approach and H∞ model reduction.

Description: Background: Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya. - Methods: Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019. Data pertaining to bed net use and socio-economic status (SES) were obtained from household surveys. Climatic proxy variables obtained from remote sensing were included as covariates in the models. Bayesian variable selection was used to determine the elapsing time between climate suitability and malaria incidence. - Results: Malaria incidence increased by 50% from 2008 to 2010, then declined by 73% until 2015. There was a resurgence of cases after 2016, despite high bed net use. Increase in daytime land surface temperature was associated with a decline in malaria incidence (incidence rate ratio [IRR] = 0.70, 95% Bayesian credible interval [BCI]: 0.59–0.82), while rainfall was associated with increased incidence (IRR = 1.27, 95% BCI: 1.10–1.44). Bed net use was associated with a decline in malaria incidence in children aged 6–59 months (IRR = 0.78, 95% BCI: 0.70–0.87) but not in older age groups, whereas SES was not associated with malaria incidence in this population. - Conclusions: Variability in climatic factors showed a stronger effect on malaria incidence than bed net use. Bed net use was, however, associated with a reduction in malaria incidence, especially among children aged 6–59 months after adjusting for climate effects. To sustain the downward trend in malaria incidence, this study recommends continued distribution and use of bed nets and consideration of climate-based malaria early warning systems when planning for future control interventions.

Description: To satisfy the increasing global demand for agricultural products, the expansion of irrigation is an important intensification measure. At the same time, unsustainable water abstractions and cropland expansion pose a threat to biodiversity and ecosystem functioning. Irrigation potentials are influenced by local biophysical irrigation water availability and competition of different water users. Using a novel hydro-economic data processing routine that considers economic criteria of water allocation via a productivity ranking of grid cells and both land and water sustainability criteria, we estimate global irrigation potentials at a 0.5 ° spatial resolution. We show that there is considerable technical potential to expand irrigation within local water and land boundaries. In terms of potentially irrigated areas on all global land suitable for crop production, 2144 Mha could be irrigated within land and water environmental boundaries when only considering biophysical criteria. However, not all of these areas would actually be irrigated under consideration of irrigation costs. Of these, only 698 Mha (330 Mha) have a yield gain of more than 300 (600) USD ha-1 under the current crop mix valued at their current commodity price (economic irrigation potential).

Description: The instability with respect to global glaciation is a fundamental property of the climate system caused by the positive ice-albedo feedback. The atmospheric concentration of carbon dioxide (CO2) at which this Snowball bifurcation occurs changes through Earth's history, most notably because of the slowly increasing solar luminosity. Quantifying this critical CO2 concentration is not only interesting from a climate dynamics perspective but also constitutes an important prerequisite for understanding past Snowball Earth episodes, as well as the conditions for habitability on Earth and other planets. Earlier studies are limited to investigations with very simple climate models for Earth's entire history or studies of individual time slices carried out with a variety of more complex models and for different boundary conditions, making comparisons and the identification of secular changes difficult. Here, we use a coupled climate model of intermediate complexity to trace the Snowball bifurcation of an aquaplanet through Earth's history in one consistent model framework. We find that the critical CO2 concentration decreased more or less logarithmically with increasing solar luminosity until about 1 billion years ago but dropped faster in more recent times. Furthermore, there was a fundamental shift in the dynamics of the critical state about 1.2 billion years ago (unrelated to the downturn in critical CO2 values), driven by the interplay of wind-driven sea-ice dynamics and the surface energy balance: for critical states at low solar luminosities, the ice line lies in the Ferrel cell, stabilised by the poleward winds despite moderate meridional temperature gradients under strong greenhouse warming. For critical states at high solar luminosities, on the other hand, the ice line rests at the Hadley cell boundary, stabilised against the equatorward winds by steep meridional temperature gradients resulting from the increased solar energy input at lower latitudes and stronger Ekman transport in the ocean.

Description: An ensemble of bias adjusted regional climate model simulations based on EURO-CORDEX (CORDEX-EUR11). The data set covers daily temperature (minimum, average and maximum) and precipitation for historical, rcp26, rcp45 and rcp85 experiments covering a period from 1971 to 2100. In total 8 different RCMs from 8 institutes are included in the data set. ISIMIP3BASD v2.4.1 (https://doi.org/10.5281/zenodo.4686991) method was used for bias adjustment. The method is based on a parametric quantile mapping, including trend preservation of each quantile. Bias adjustment was performed for each variable separately. We used E-OBS v19.0e (https://doi.org/10.1029/2017JD028200) data to calibrate the bias adjustment transfer functions for the period 1971 to 2005. The data was developed and utilized within the Clim4Vitis project - Climate change impact mitigation for European viticulture (https://clim4vitis.eu). We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups (CLMcom, DMI, GERICS, IPSL-INERIS, KNMI, MPI-CSC, SMHI and UHOH) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP).

Description: The atmospheric concentration of CO2 at which global glaciation (snowball) bifurcation occurs, changes throughout Earth's history, most notably because of the slowly increasing solar luminosity. Quantifying this critical CO2 concentration is not only interesting from a climate dynamics perspective, but also an important prerequisite for understanding past Snowball Earth episodes as well as the conditions for habitability on Earth and other planets. Here we use the coupled climate model CLIMBER-3α in an Aquaplanet configuration to scan for the Snowball bifurcation point for time slices spanning the last 4 billion years, thus quantifying the time evolution of the bifurcation and identifying a qualitative shift in critical state dynamics.

Description: Die Dekarbonisierung des Verkehrs- und Stromsystems ist ein weitreichender Transformationsprozess, der erhebliche Veränderungen für die Menschen mit sich bringt. Er kann nur als gesamtgesellschaftliche Aufgabe gelingen, die aus Betroffenen Beteiligte macht und Interessen sowie Vorstellungen von Bürgerinnen und Bürgern berücksichtigt. Wirksame Beteiligung im Forschungs- und Politikprozess sind bei der Verkehrs- und Stromwende deshalb unabdingbar. Unterschiedliche Formate, wie Befragungen und Deliberation erlauben es, die Einstellungen aus einem Querschnitt der Gesellschaft zu Politikoptionen zu untersuchen und Betroffene somit an der Entwicklung dieser partizipieren zu lassen. In dieser Analyse untersuchen wir in zwei parallel durchgeführten Befragungen im Rahmen der Ariadne Bürgerdeliberation und einer repräsentativen Panelbefragung die Haltung der Bürger:innen gegenüber verschiedenen Politikoptionen in den Bereichen Verkehr und Strom. Das Ziel war hierbei durch die vergleichende Betrachtung besser zu verstehen, welche Optionen gesellschaftlich mitgetragen werden (und welche nicht) und aus welchen Gründen. Die Verbindung beider Zugänge bietet eine umfassende Perspektive auf die gesellschaftliche Trägerschaft der Energie- und Verkehrswende. Die Bürgerinnen und Bürger sprachen sich in beiden Bereichen – Strom und Verkehr – für möglichst gerechte und effektive politische Optionen aus. Die Mehrheit der Befragten ist bereit, Veränderungen mitzutragen, wenn gewährleistet wird, dass alle Menschen und vulnerable Gruppen Zugang zu bezahlbarer Mobilität und Energieversorgung haben. Dies spiegelt sich in der Wahrnehmung von Stärken der verschiedenen Optionen und dem Wunsch, das Beste aus den Welten und Pfaden zu kombinieren. Für die Energie- und Verkehrswende insgesamt zeigen die Bürger:innensichten die Wichtigkeit einer Politik auf, die sich sowohl an sozialen als auch ökologischen Auswirkungen orientiert. Der klimafreundliche Umbau des Energiesystems wird von einer breiten gesellschaftlichen Mehrheit in Deutschland getragen und die Menschen sind bereit, sich bei der Umsetzung aktiv zu engagieren. Bei der Frage der Art der Gestaltung wird von den 2 Bürger:innen mehrheitlich eine dezentrale Energieversorgungsstruktur bevorzugt. Damit sind jedoch sowohl Hoffnungen als auch Sorgen verbunden. In der Verkehrswende ist den Menschen eine faire Teilhabe an Mobilität wichtig, dass also die verschiedenen Mobilitätsbedürfnisse diverser gesellschaftlicher Gruppen berücksichtigt werden. Neben der sozialen Gerechtigkeit spielt in der Verkehrswende auch die Klimawirkung – also die Effektivität von Maßnahmen – eine zentrale Rolle für die gesellschaftliche Trägerschaft. Trotz verbleibendem Diskussionsbedarf der beteiligten Bürger:innen zu konkreten Umsetzungsfragen zeigen die Ergebnisse die Bereitschaft sich mit den Auswirkungen politischer Optionen auseinanderzusetzen und dadurch Wandel nicht primär als Bedrohung und Sorge, sondern als Chance auf ein inklusiveres und attraktiveres Zusammenleben zu sehen. Eine Voraussetzung für einen wissenschaftlich informierten Lernprozess ist, das Wissen zu Politikoptionen und deren Auswirkungen für die breite Gesellschaft zugänglich und erfahrbar zu machen. Sowohl wissenschaftlich fundierte Informationsangebote als auch verständigungsorientierte Diskussionen sind effektive Formate, um dies zu ermöglichen. Letztlich lebt auch dieser Lernprozess von der Bereitschaft eigene Annahmen zu hinterfragen und sich für neue bzw. andere Argumente zu öffnen, die einem bisher als wenig relevant oder kaum nachvollziehbar erschienen. Zudem unterstreicht diese Analyse, dass in wissenschaftlich gut informierten Prozessen Bürger:innensichten eine wertvolle Ressource und Chance für eine besser gesellschaftlich abgestimmte und damit tragfähigere Klimapolitik darstellen. Aus Bürger:innensicht sollten diverse Wertvorstellungen, Bedürfnisse und Interessen bei der Umsetzung der Transformationen Eingang finden. Deswegen braucht es nach Meinung der Autorinnen und Autoren dieser Analyse einen Weg, wie die Politik über diese informiert wird – eine vielversprechende Möglichkeit hierfür ist die Befähigung von Bürger:innen zur politischen Mitbestimmung durch ein möglichst heterogenes Spektrum an Beteiligungsformen. Aus dieser Sicht stellt der Einbezug von Bürger:innensichten eine Chance für eine tragfähigere, an den Bedürfnissen der Bürger:innen orientierte

Description: Parameter table for the corrigendum of the paper "All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal" by Warszawski et al. (2021) published in Environmental Research Letters. The 50 emissions scenarios considered for analysis in this paper, including the numerical value for the 8 parameters (5 levers and 3 milestones) used in this analysis. For the individual levers, cells shaded blue stay within the high upper bounds, and cells shaded green stay within the medium upper bounds. Scenarios that stay within all high upper bounds, i.e. the filtered ensemble, are shaded blue. The SR1.5 scenarios P1-P4 are flagged on the left of the table. The P4, Shell and IEA scenarios appear at the end of the table.

Description: Klimaschutzpolitik ist eine Wette: Eine ehrgeizige Verminderung klimaschädlicher Treibhausgasemissionen kann auch dann gerechtfertigt werden, wenn die Wahrscheinlichkeit katastrophaler Folgen des Klimawandels gering ist. Je niedriger die Kosten der Vermeidung und je höher die möglichen materiellen und immateriellen Schäden eines ungebremsten Klimawandels sind, umso sinnvoller ist es, eine ambitionierte Klimapolitik umzusetzen (Edenhofer/Jakob 2017). Die Klimapolitik beschränkt dabei die zuvor freie Nutzung der Atmosphäre als Treibhausgasdeponie und macht sie zu einem regulierten globalen Gemeinschaftsgut.

Description: GDP scenarios are major drivers of climate change and climate change mitigation assessment studies. In this paper, a major update of the SSP GDP projections is presented. By using the most recent economic data and short-term projections by the World Bank and International Monetary Fund, the update captures changes in the system of national accounting and purchasing power parities, as well as the impact of the Covid 19 pandemic. Harmonization between the data and the original end-of-the century SSP projections was carried out in terms of GDP per capita in order to preserve the underlying narrative of income convergence. The result is a set of projections compatible with the most recent data and the SSP narratives. A comparison of DICE models calibrated to the original and updated SSP2 GDP per capita projections illustrates how significant the impact of an update of income data on integrated assessment results can be. The estimated global social costs of carbon in 2015 and 2030 rose by almost 30%.

Description: Given concerns about the ambition and effectiveness of current climate policies, a case has been made for the combination of demand side policies such as carbon pricing with supply side bans on fossil fuel extraction. However, little is known about their interplay in the context of climate stabilization strategies. Here, we present a multi-model assessment quantifying the effectiveness of supply side policies and their interactions with demand-side ones. We explore a variety of fossil fuel bans with four integrated assessment models and find that international supply side policies reduce carbon emissions but not at sufficient levels to stabilize temperature increase to well below 2°C. When combined with demand side policies, supply side policies reduce the required carbon price, dampen reliance on CO2 removal technologies, and increase investment in renewable energy. The results indicate the opportunity to integrate fossil fuel bans alongside price-based policies when exploring pathways to reach ambitious mitigation targets.

Description: Climate change and variability threaten the sustainability of future food production, especially in semi-arid regions where water resources are limited and irrigated agriculture is widespread. Increasing temperatures will exacerbate evaporative losses and increase plant water needs. In this regard, higher irrigation intensities have been posited as a solution to mitigate climate change impacts in these regions. Here, using the agro-hydrological model SWAT and the biophysical crop model APSIM, we show that this mitigation measure is oversimplified. We find that heat stress, driven by strong temperature increases, might be the dominating factor in controlling future crop yields and plant water needs. Our analysis encompasses agricultural areas of the Lower Chenab Canal System in Punjab, Pakistan (15,000 km2), which is part of the Indus River irrigation system, the largest irrigation system in the world, covering major cotton, rice and maize cropping zones. Climate models project a strong increase in temperature over the study region of up to 1.8 °C (±0.5 °C) until the mid-century. Both models predict a decline in future crop yields for maize and rice crops, while cotton yields are less effected by rising temperatures and strongly benefit from elevated atmospheric CO2 concentrations. For a high carbon emission scenario, the models simulate yield declines for maize of up to −10% (APSIM) and −19% (SWAT); for rice yields of up to −4% (APSIM) to −26% (SWAT), and for cotton yields of −1% (APSIM) to +11% (SWAT), until 2050, relative to the baseline scenario 1996–2005. Our modeling results further suggest that irrigation demands do not align with increasing temperature trends. Average irrigation demands increase less under higher temperatures. Overall, our study emphasizes the role of elevated heat stress, its effects on agricultural productivity as well as water demand, and its implications for climate change adaption strategies to mitigate adverse impacts in an intensively irrigated region.

Description: The achievement of several sustainable development goals and the Paris Climate Agreement depends on rapid progress towards sustainable food and land systems in all countries. We have built a flexible, collaborative modeling framework to foster the development of national pathways by local research teams and their integration up to global scale. Local researchers independently customize national models to explore mid-century pathways of the food and land use system transformation in collaboration with stakeholders. An online platform connects the national models, iteratively balances global exports and imports, and aggregates results to the global level. Our results show that actions toward greater sustainability in countries could sum up to 1 Mha net forest gain per year, 950 Mha net gain in the land where natural processes predominate, and an increased CO2 sink of 3.7 GtCO2e yr−1 over the period 2020–2050 compared to current trends, while average food consumption per capita remains above the adequate food requirements in all countries. We show examples of how the global linkage impacts national results and how different assumptions in national pathways impact global results. This modeling setup acknowledges the broad heterogeneity of socio-ecological contexts and the fact that people who live in these different contexts should be empowered to design the future they want. But it also demonstrates to local decision-makers the interconnectedness of our food and land use system and the urgent need for more collaboration to converge local and global priorities.

Description: Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.

Description: This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an example of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard; extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.

Description: The increasing impacts of climate change require strategies for climate adaptation. Dynamic Global Vegetation Models (DGVMs) are one type of multi-sectorial impact models with which the effects of multiple interacting processes in the terrestrial biosphere under climate change can be studied. The complexity of DGVMs is increasing as more and more processes, especially for plant physiology, are implemented. Therefore, there is a growing demand for increasing the computational performance of the underlying algorithms as well as ensuring their numerical accuracy. One way to approach this issue is to analyse the routines which have the potential for improved computational efficiency and/or increased accuracy when applying sophisticated mathematical methods. In this paper, the Farquhar-Collatz photosynthesis model under water stress as implemented in the Lund-Potsdam-Jena managed Land DGVM (4.0.002) was examined. We found that the numerical solution of a nonlinear equation, so far solved with the Bisection method, could be significantly improved by using Newton's method instead. The latter requires the computation of the derivative of the underlying function which is presented. Model simulations show a significant lower number of iterations to solve the equation numerically and an overall run time reduction of the model of about 16 % depending on the chosen accuracy. The Farquhar-Collatz photosynthesis model forms the core component in many DGVMs and land-surface models. An update in the numerical solution of the nonlinear equation can therefore be applied to similar photosynthesis models. Furthermore, this exercise can serve as an example for improving computationally costly routines while improving their mathematical accuracy.

Description: This profile provides an overview of projected climate parameters and related impacts on different sectors in Eastern Africa until 2080 under different climate change scenarios (called Representative Concentration Pathways, RCPs). RCP2.6 represents the low emissions scenario that aims to keep global warming likely below 2 °C above pre-industrial temperatures. RCP6.0 repre- sents a medium to high emissions scenario that is likely to exceed 2 °C. Model projections do not account for effects of future socio-economic impacts, unless indicated otherwise.

Description: This paper tests whether need or political economy factors determine the allocation of humanitarian aid in the wake of the 2015/16 winter disaster in Mongolia. The identification strategy exploits the exogenous nature of the extremely cold, snowy winter and its spatial variation across Mongolia as well as the fact that the Government defined clear criteria of need across districts based on meteorological risk projections. Using rich district-level data, we distinguish between humanitarian aid delivered by the Mongolian Government and by international donors at the extensive margin (whether a district received any aid) and intensive margin (targeted households per district). Results show that projected need is the strongest predictor for the allocation of international humanitarian aid across districts. Projected need is less relevant for the allocation of governmental humanitarian aid. We do not find evidence that political alignment or core voter considerations matter for either governmental or international humanitarian aid in this young democracy.

Description: Shared socio-economic pathway (SSP) scenarios represent a consistent set of socioeconomic assumptions and a major input of Integrated Assessment Models on climate change. This study added a driver that is missing so far in the SSP framework - the evolution of the sectoral structure of economies. A newly constructed set of structural change scenarios is presented. These structural change scenarios represent a well-known characteristic that accompanies the process of economic growth and development - the reallocation of economic activity between the three major sectors agriculture, manufacturing and services. While we construct scenarios for the sectoral shares of labor, value-added and energy based on historical data and an econometric approach, which comes with some limitation, these scenarios are linked to the SSP GDP scenarios and hence implicitly capture properties of the narratives underlying them. We find that the pattern and speed of structural change differ under different SSPs. Moreover, while the scenarios for developing countries reproduce structural change patterns (e.g., hump-shape of manufacturing labor share), observed for developed countries in the past, the projected transformation, in particular the reduction of labor shares in the agricultural sector, represents a tremendous challenge.

Description: We introduce a new model consisting of globally coupled high-dimensional generalized limit-cycle oscillators, which explicitly incorporates the role of amplitude dynamics of individual units in the collective dynamics. In the limit of weak coupling, our model reduces to the D-dimensional Kuramoto phase model, akin to a similar classic construction of the well-known Kuramoto phase model from weakly coupled two-dimensional limit-cycle oscillators. For the practically important case of D=3, the incoherence of the model is rigorously proved to be stable for negative coupling (K〈0) but unstable for positive coupling (K〉0); the locked states are shown to exist if K〉0; in particular, the onset of amplitude death is theoretically predicted. For D≥2, the discrete and continuous spectra for both locked states and amplitude death are governed by two general formulas. Our proposed D-dimensional model is physically more reasonable, because it is no longer constrained by fixed amplitude dynamics, which puts the recent studies of the D-dimensional Kuramoto phase model on a stronger footing by providing a more general framework for D-dimensional limit-cycle oscillators.

Description: Background: Growing evidence suggests low and high maternal hemoglobin (Hb) concentrations may have adverse consequences for maternal and child health. There remain questions on specific Hb thresholds to define anemia and high Hb as well as how cutoffs may vary by anemia etiology and timing of assessment. - Methods: We conducted an updated systematic review (using PubMed and Cochrane Review) on low (〈 110 g/L) and high (≥ 130 g/L) maternal Hb concentrations and associations with a range of maternal and infant health outcomes. We examined associations by timing of Hb assessment (preconception; first, second, and third trimesters, as well as at any time point in pregnancy), varying cutoffs used for defining low and high hemoglobin concentrations and performed stratified analyses by iron-deficiency anemia. We conducted meta-analyses to obtain odds ratios (OR) and 95% confidence intervals. Results: The updated systematic review included 148 studies. Low maternal Hb at any time point in pregnancy was associated with: low birthweight, LBW (OR (95% CI) 1.28 (1.22–1.35)), very low birthweight, VLBW (2.15 (1.47–3.13)), preterm birth, PTB (1.35 (1.29–1.42)), small-for-gestational age, SGA (1.11 (1.02–1.19)), stillbirth 1.43 (1.24–1.65)), perinatal mortality (1.75 (1.28–2.39)), neonatal mortality (1.25 (1.16–1.34), postpartum hemorrhage (1.69 (1.45–1.97)), transfusion (3.68 (2.58–5.26)), pre-eclampsia (1.57 (1.23–2.01)), and prenatal depression (1.44 (1.24–1.68)). For maternal mortality, the OR was higher for Hb 〈 90 (4.83 (2.17–10.74)) than for Hb 〈 100 (2.87 (1.08–7.67)). High maternal Hb was associated with: VLBW (1.35 (1.16–1.57)), PTB (1.12 (1.00-1.25)), SGA (1.17 (1.09–1.25)), stillbirth (1.32 (1.09–1.60)), maternal mortality (2.01 (1.12–3.61)), gestational diabetes (1.71 (1.19–2.46)), and pre-eclampsia (1.34 (1.16–1.56)). Stronger associations were noted earlier in pregnancy for low Hb and adverse birth outcomes while the role of timing of high Hb was inconsistent. Lower Hb cutoffs were associated with greater odds of poor outcomes; for high Hb, data were too limited to identify patterns. Information on anemia etiology was limited; relationships did not vary by iron-deficiency anemia. - Conclusion: Both low and high maternal Hb concentrations during pregnancy are strong predictors of adverse maternal and infant health outcomes. Additional research is needed to establish healthy reference ranges and design effective interventions to optimize maternal Hb during pregnancy.

Description: This experimental study investigates the dynamical transition from stable operation to thermoacoustic instability in a turbulent bluff-body stabilised dump combustor. We conduct experiments to acquire acoustic pressure and local heat release rate fluctuations and use them to characterise this transition as we decrease the equivalence ratio towards a fuel-lean setting. More importantly, we observe a significant increase in local heat release rate fluctuations at critical locations well before thermoacoustic instability occurs. One of these critical locations is the stagnation zone in front of the bluff-body. By strategically positioning slots (perforations) on the bluff-body, we ensure the reduction of the growth of local heat release rate fluctuations at the stagnation zone near the bluff-body well before the onset of thermoacoustic instability. We show that this reduction in local heat release rate fluctuations inhibits the transition to thermoacoustic instability. We find that modified configurations of the bluff-body that do not quench the local heat release rate fluctuations at the stagnation zone result in the transition to thermoacoustic instability. We also reveal that an effective suppression strategy based on the growth of local heat release rate fluctuations requires an optimisation of the slots' area-ratio for a given bluff-body position. Further, the suppression strategy also depends on the spatial distribution of perforations on the bluff-body. Notably, an inappropriate distribution of the slots, which does not quench the local heat release rate fluctuations at the stagnation zone but creates new critical regions, may even result in a dramatic increase in the amplitudes of pressure oscillations.

Description: It is widely reported that the functional connectivity estimated by statistical correlations is often varying within nonlinear systems. Generally, these varying correlations between time series are detected by sliding windows. Still, it is unclear how these correlations evolve within a chaotic system. This work intends to give a quantitative framework to identify the dynamics of correlations within chaotic systems. To this end, we embed the pairwise statistical correlations (from time series within a system) into a correlation-based system by sliding windows. This allows for detecting the dynamics of correlations within a complex system through the embedded correlation-based system. Three chaotic systems (i.e., the Lorenz, the Rossler, and the Chen systems) are employed as benchmark examples. We find that both linear and nonlinear correlations within three chaotic systems show chaotic behaviors on some short window sizes, then transit to non-chaotic states with window size increasing. Moreover, the chaotic dynamics of nonlinear correlations exhibit higher uncertainty than the linear one and the original chaotic systems. The chaotic behaviors of correlations within chaotic systems give another evidence of the difficulty of prediction for chaotic systems. Meanwhile, the identified state transitions (concerning the window size) of correlations may provide a quantitative rule to select an appropriate window size for sliding windows.

Description: Hydropower is the world’s largest and most widely used renewable energy source. It is expected that climate and land use changes, as well as hydraulic engineering measures, will have profound impacts on future hydropower potential. In this study, the hydropower potential of the Bafing watershed was estimated for the near future (P1: 2035–2065) and the far future (P2: 2065–2095). For this purpose, the moderate scenario ssp 126 and the medium–high scenario ssp 370 were used to explore possible climate impacts. In three management scenarios, we tested the interaction of the existing Manantali Dam with two planned dams (Koukoutamba and Boureya) using an ecohydrological water management model. The results show that, under ssp 126, a 6% increase in annual river flow would result in a 3% increase in hydropower potential in the near future compared with the historical period of 1984–2014. In the far future, the annual river flow would decrease by 6%, resulting in an 8% decrease in hydropower potential. Under ssp 370, the hydropower potential would decrease by 0.7% and 14% in the near and far future, respectively. The investment in the planned dams has benefits, such as an increase in hydropower potential and improved flood protection. However, the dams will be negatively affected by climate change in the future (except in the near future (P1) under ssp 126), and their operation will result in hydropower potential losses of about 11% at the Manantali Dam. Therefore, to mitigate the effects of climate change and adjust the operation of the three dams, it is essential to develop new adaptation measures through an optimization program or an energy mix combining hydro, solar, and wind power.

Description: Earth system models (ESMs) are the primary tools for investigating future Earth system states at timescales from decades to centuries, especially in response to anthropogenic greenhouse gas release. State-of-the-art ESMs can reproduce the observational global mean temperature anomalies of the last 150 years. Nevertheless, ESMs need further improvements, most importantly regarding (i) the large spread in their estimates of climate sensitivity, i.e., the temperature response to increases in atmospheric greenhouse gases; (ii) the modeled spatial patterns of key variables such as temperature and precipitation; (iii) their representation of extreme weather events; and (iv) their representation of multistable Earth system components and the ability to predict associated abrupt transitions. Here, we argue that making ESMs automatically differentiable has a huge potential to advance ESMs, especially with respect to these key shortcomings. First, automatic differentiability would allow objective calibration of ESMs, i.e., the selection of optimal values with respect to a cost function for a large number of free parameters, which are currently tuned mostly manually. Second, recent advances in machine learning (ML) and in the number, accuracy, and resolution of observational data promise to be helpful with at least some of the above aspects because ML may be used to incorporate additional information from observations into ESMs. Automatic differentiability is an essential ingredient in the construction of such hybrid models, combining process-based ESMs with ML components. We document recent work showcasing the potential of automatic differentiation for a new generation of substantially improved, data-informed ESMs.

Description: During the last glacial interval, the Northern Hemisphere climate was punctuated by a series of abrupt changes between two characteristic climate regimes. The existence of stadial (cold) and interstadial (milder) periods is typically attributed to a hypothesised bistability in the glacial North Atlantic climate system, allowing for rapid transitions from the stadial to the interstadial state – the so-called Dansgaard–Oeschger (DO) events – and more gradual yet still fairly abrupt reverse shifts. The physical mechanisms driving these regime transitions remain debated. DO events are characterised by substantial warming over Greenland and a reorganisation of the Northern Hemisphere atmospheric circulation, which are evident from concomitant shifts in the δ18O ratios and dust concentration records from Greenland ice cores. Treating the combined δ18O and dust record obtained by the North Greenland Ice Core Project (NGRIP) as a realisation of a two-dimensional, time-homogeneous, and Markovian stochastic process, we present a reconstruction of its underlying deterministic drift based on the leading-order terms of the Kramers–Moyal equation. The analysis reveals two basins of attraction in the two-dimensional state space that can be identified with the stadial and interstadial regimes. The drift term of the dust exhibits a double-fold bifurcation structure, while – in contrast to prevailing assumptions – the δ18O component of the drift is clearly mono-stable. This suggests that the last glacial's Greenland temperatures should not be regarded as an intrinsically bistable climate variable. Instead, the two-regime nature of the δ18O record is apparently inherited from a coupling to another bistable climate process. In contrast, the bistability evidenced in the dust drift points to the presence of two stable circulation regimes of the last glacial's Northern Hemisphere atmosphere.

Description: Women and children in Bangladesh face high levels of micronutrient deficiencies from inadequate diets. We evaluated the impact of a Homestead Food Production (HFP) intervention on poultry production, as a pathway outcome, and women's and children's egg consumption, as secondary outcomes, as part of the Food and Agricultural Approaches to Reducing Malnutrition cluster-randomized trial in Sylhet division, Bangladesh. The 3-year intervention (2015−2018) promoted home gardening, poultry rearing, and nutrition counseling. We randomly allocated 96 clusters to intervention (48 clusters; 1337 women) or control (48 clusters; 1368 women). Children 〈 3 years old born to participants were enrolled during the trial. We analyzed poultry production indicators, measured annually, and any egg consumption (24-h recall), measured every 2−6 months for women and their children. We conducted intention-to-treat analyses using mixed-effects logistic regression models with repeat measures, with minimal adjustment to increase precision. Poultry ownership increased by 16% points (pp) and egg production by 13 pp in the final intervention year. The intervention doubled women's odds of egg consumption in the final year (Odds Ratio [OR]: 2.31, 95% CI: 1.68−3.18), with positive effects sustained 1-year post-intervention (OR: 1.58, 95% CI: 1.16−2.15). Children's odds of egg consumption were increased in the final year (OR: 3.04, 95% CI: 1.87−4.95). Poultry ownership was associated with women's egg consumption, accounting for 12% of the total intervention effect, but not with children's egg consumption. Our findings demonstrate that an HFP program can have longer-term positive effects on poultry production and women's and children's diets.