ALBERT

Description: The global road network is expanding at an unprecedented rate, threatening the persistence of many species. Yet, even for the most endangered wildlife, crucial information on the distance up to which roads impact species abundance is lacking. Here we use ecological threshold analysis to quantify the road-effect zone (REZ) for the critically endangered western chimpanzee (Pan troglodytes verus). We found: (1) the REZ extends 5.4 km (95% CI [4.9–5.8 km]) from minor roads and 17.2 km (95% CI [15.8–18.6]) from major roads, the latter being more than three times wider than a previous estimate of the average REZ for mammals; and (2) only 4.3% of the chimpanzees’ range is not impacted by existing roads. These findings reveal the high sensitivity and susceptibility of nonhuman primates to roads across West Africa, a region undergoing rapid development, and can inform the implementation of more effective guidelines to mitigate road impacts.

Description: Should economic growth continue in a world threatened by the prospect of catastrophic climate change? The scientific and public debate has brought forth a broad spectrum of views and narratives on this question, ranging from neoclassical economics to degrowth. We argue that different positions can be attributed to underlying differences in views on (a) factors that determine human well‐being, (b) the feasibility and desirability of economic growth, (c) appropriate intervention points, and (d) preferences about governance and policy options. For each of these dimensions, we propose points of agreement on which a consensus between conflicting positions might be achieved. From this basis, we distill a sustainability transition perspective that could act as a basis for a renewed debate on how to align human well‐being with environmental sustainability.

Description: In the Anthropocene, social processes have become critical to understanding planetary-scale Earth system dynamics. The conceptual foundations of Earth system modelling have externalised social processes in ways that now hinder progress in understanding Earth resilience and informing governance of global environmental change. New approaches to global modelling are needed to address these challenges, but the current modelling landscape is highly diverse and heterogeneous, ranging from purely biophysical Earth System Models, to hybrid macro-economic Integrated Assessments Models, to a plethora of models of socio-cultural dynamics. World-Earth models, currently not yet available, will need to integrate all these elements, so future World-Earth modellers require a structured approach to identify, classify, select, and combine model components. Here, we develop taxonomies for ordering the multitude of societal and biophysical subsystems and their interactions. We suggest three taxa for modelled subsystems: (i) biophysical, where dynamics is usually represented by "natural laws" of physics, chemistry or ecology (i.e., the usual components of Earth system models), (ii) socio-cultural, dominated by processes of human behaviour, decision making and collective social dynamics (e.g., politics, institutions, social networks), and (iii) socio-metabolic, dealing with the material interactions of social and biophysical subsystems (e.g., human bodies, natural resource and agriculture). We show how higher-order taxonomies for interactions between two or more subsystems can be derived, highlighting the kinds of social-ecological feedback loops where new modelling efforts need to be directed. As an example, we apply the taxonomy to a stylised World-Earth system model of socially transmitted discount rates in a greenhouse gas emissions game to illustrate the effects of social-ecological feedback loops that are usually not considered in current modelling efforts. The proposed taxonomy can contribute to guiding the design and operational development of more comprehensive World-Earth models for understanding Earth resilience and charting sustainability transitions within planetary boundaries and other future trajectories in the Anthropocene.

Description: Climate change and socioeconomic developments will have a decisive impact on people exposed to hunger. This study analyses climate change impacts on agriculture and potential implications for the occurrence of hunger under different socioeconomic scenarios for 2030, focusing on the world regions most affected by poverty today: the Middle East and North Africa, South Asia, and Sub-Saharan Africa. We use a spatially explicit, agroeconomic land-use model to assess agricultural vulnerability to climate change. The aims of our study are to provide spatially explicit projections of climate change impacts on Costs of Food, and to combine them with spatially explicit hunger projections for the year 2030, both under a poverty, as well as a prosperity scenario. Our model results indicate that while average yields decrease with climate change in all focus regions, the impact on the Costs of Food is very diverse. Costs of Food increase most in the Middle East and North Africa, where available agricultural land is already fully utilized and options to import food are limited. The increase is least in Sub-Saharan Africa, since production there can be shifted to areas which are only marginally affected by climate change and imports from other regions increase. South Asia and Sub-Saharan Africa can partly adapt to climate change, in our model, by modifying trade and expanding agricultural land. In the Middle East and North Africa, almost the entire population is affected by increasing Costs of Food, but the share of people vulnerable to hunger is relatively low, due to relatively strong economic development in these projections. In Sub-Saharan Africa, the Vulnerability to Hunger will persist, but increases in Costs of Food are moderate. While in South Asia a high share of the population suffers from increases in Costs of Food and is exposed to hunger, only a negligible number of people will be exposed at extreme levels. Independent of the region, the impacts of climate change are less severe in a richer and more globalized world. Adverse climate impacts on the Costs of Food could be moderated by promoting technological progress in agriculture. Improving market access would be advantageous for farmers, providing the opportunity to profitably increase production in the Middle East and North Africa as well as in South Asia, but may lead to increasing Costs of Food for consumers. In the long-term perspective until 2080, the consequences of climate change will become even more severe: while in 2030 56% of the global population may face increasing Costs of Food in a poor and fragmented world, in 2080 the proportion will rise to 73%.

Description: This paper explores the consequences of different policy assumptions and the derivation of globally consistent, national low-carbon development pathways for the seven largest greenhouse gas (GHG)–emitting countries (EU28 as a bloc) in the world, covering approximately 70% of global CO2 emissions, in line with their contributions to limiting global average temperature increase to well below 2 °C as compared with pre-industrial levels. We introduce the methodology for developing these pathways by initially discussing the process by which global integrated assessment model (IAM) teams interacted and derived boundary conditions in the form of carbon budgets for the different countries. Carbon budgets so derived for the 2011–2050 period were then used in eleven different national energy-economy models and IAMs for producing low-carbon pathways for the seven countries in line with a well below 2 °C world up to 2050. We present a comparative assessment of the resulting pathways and of the challenges and opportunities associated with them. Our results indicate quite different mitigation pathways for the different countries, shown by the way emission reductions are split between different sectors of their economies and technological alternatives.

Description: Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.

Description: Growing evidence suggests that climate adaptation responses that do not incorporate equity considerations may worsen inequality and increase vulnerability. Using data from a systematic review of peer-reviewed empirical research on adaptation responses to climate change (n = 1,682), we present an assessment of how social equity is considered in adaptation across regions, sectors, and social groups. Roughly 60% of peer-reviewed literature on adaptation responses considers social equity by reporting on which marginalized groups were involved in planning or implementation. Articles on responses in Africa and Asia and those focusing on poverty reduction most frequently considered social equity. Equity was less likely to be considered in adaptation responses in Europe, Australasia, and North America, as well as in literature focused on cities. Income-based inequity was more frequently considered than gender, age, or Indigenous status. Ethnic and racial minorities, migrants, and people with disabilities were rarely considered. Attention to the levels and forms in which equity is integrated into adaptation research and practice is needed to ensure just adaptation.

Description: This policy brief explores and provides a holistic view of the existing practices, issues, and opportunities for a sustainable coastal management initiative among the G20 member countries. Specifically, it highlights coastal disaster risk reduction and resilience strategies, along with the financial implications of public investment on the need of a proper assessment of the social return of adaptation projects, and on the avoided fiscal deadweight loss. The brief also discusses the importance of funding for development projects to reach mitigation targets and the need for an evaluation framework to improve the efficiency of these adaptation efforts. The policy brief suggests establishing an initiative within the G20 to address disaster risk reduction (G20- DRR) by providing a decision support system as well as a centralized dissemination platform for quality-controlled, transparent national climate impact assessments by fostering global collaboration between institutions, researchers, and experts.

Description: We know perfectly well that the combined effects of anthropogenic climate change and natural var-iability are very likely to lead to major, undesira-ble climate impacts for most citizens around the world in the not-so-distant future. To limit cli-mate impacts is essentially about weaning the world economy off greenhouse gases, adapting to those climate impacts that were not avoided, and compensating for what we cannot adapt to. All of these three components of climate policy are pursued under the auspices of the 1992 UN Frame-work Convention on Climate Change (UNFCCC), yet also by many countries, social movements, and forward-looking businesses alike (Luterbacher and Sprinz 2018).

Description: Chamberlin and Salisbury's assessment of the Permian a century ago captured the essence of the period: it is an interval of extremes yet one sufficiently recent to have affected a biosphere with near-modern complexity. The events of the Permian – the orogenic episodes, massive biospheric turnovers, both icehouse and greenhouse antitheses, and Mars-analog lithofacies – boggle the imagination and present us with great opportunities to explore Earth system behavior. The ICDP-funded workshops dubbed “Deep Dust,” held in Oklahoma (USA) in March 2019 (67 participants from nine countries) and Paris (France) in January 2020 (33 participants from eight countries), focused on clarifying the scientific drivers and key sites for coring continuous sections of Permian continental (loess, lacustrine, and associated) strata that preserve high-resolution records. Combined, the two workshops hosted a total of 91 participants representing 14 countries, with broad expertise. Discussions at Deep Dust 1.0 (USA) focused on the primary research questions of paleoclimate, paleoenvironments, and paleoecology of icehouse collapse and the run-up to the Great Dying and both the modern and Permian deep microbial biosphere. Auxiliary science topics included tectonics, induced seismicity, geothermal energy, and planetary science. Deep Dust 1.0 also addressed site selection as well as scientific approaches, logistical challenges, and broader impacts and included a mid-workshop field trip to view the Permian of Oklahoma. Deep Dust 2.0 focused specifically on honing the European target. The Anadarko Basin (Oklahoma) and Paris Basin (France) represent the most promising initial targets to capture complete or near-complete stratigraphic coverage through continental successions that serve as reference points for western and eastern equatorial Pangaea.

Description: Many biological systems possess confined structures, which produce novel influences on the dynamics. Here, stochastic resonance (SR) in a triple cavity that consists of three units and is subjected to noise, periodic force and vertical constance force is studied, by calculating the spectral amplification η numerically. Meanwhile, SR in the given triple cavity and differences from other structures are explored. First, it is found that the cavity parameters can eliminate or regulate the maximum of η and the noise intensity that induces this maximum. Second, compared to a double cavity with similar maximum/minimum widths and distances between two maximum widths as the triple cavity, η in the triple one shows a larger maximum. Next, the conversion of the natural boundary in the pure potential to the reflection boundary in the triple cavity will create the necessity of a vertical force to induce SR and lead to a decrease in the maximum of η. In addition, η monotonically decreases with the increase of the vertical force and frequency of the periodic force, while it presents several trends when increasing the periodic force’s amplitude for different noise intensities. Finally, our studies are extended to the impact of fractional Gaussian noise excitations. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 2)’.

Description: Spreading dynamics and complex contagion processes on networks are important mechanisms underlying the emergence of critical transitions, tipping points and other non-linear phenomena in complex human and natural systems. Increasing amounts of temporal network data are now becoming available to study such spreading processes of behaviours, opinions, ideas, diseases and innovations to test hypotheses regarding their specific properties. To this end, we here present a methodology based on dose–response functions and hypothesis testing using surrogate data models that randomise most aspects of the empirical data while conserving certain structures relevant to contagion, group or homophily dynamics. We demonstrate this methodology for synthetic temporal network data of spreading processes generated by the adaptive voter model. Furthermore, we apply it to empirical temporal network data from the Copenhagen Networks Study. This data set provides a physically-close-contact network between several hundreds of university students participating in the study over the course of 3 months. We study the potential spreading dynamics of the health-related behaviour “regularly going to the fitness studio” on this network. Based on a hierarchy of surrogate data models, we find that our method neither provides significant evidence for an influence of a dose–response-type network spreading process in this data set, nor significant evidence for homophily. The empirical dynamics in exercise behaviour are likely better described by individual features such as the disposition towards the behaviour, and the persistence to maintain it, as well as external influences affecting the whole group, and the non-trivial network structure. The proposed methodology is generic and promising also for applications to other temporal network data sets and traits of interest.

Description: While deforestation represents an obvious ecosystem change, forest degradation is often more difficult to discern or quantify, but it impacts anumber of ecosystem functions which are vital for biodiversity and climate feedbacks. In the Brazilian Amazon, land-use changes increasefire occurrence, especially in fragmented forests close to managed land. We used remote sensing imagery to estimate the extent and impact of forest fires in degraded tropical rain-forest in the Brazilian Legal Amazon between 2007 and 2010and examinedland-use establishing in degraded areas. The trends in degraded area vs. burned area were different. Even though degradation increased one year after a high fire year, there wasnospatialoverlap, which pointsto other causes for degradation. Up to 11% of the degraded area was burned in the same year, playing escaping fires from managed and deforested lands a significant role in degradation by fire. Eighty-fourpercent of 2007s degraded area remained forest one year later, whereas the rest was identified as deforestation, secondary vegetation or pasture.Three years after degradation, 80% remained forest, the proportion of deforested area decreased and areas in regeneration after being deforested increased. Monitoring of forest degradation across tropical forests is critical for developing land management policies and for carbon stocks/emissions estimation.

Description: Many scenarios of future climate evolution and its anthropogenic drivers include considerable amounts of bioenergy as a fuel source, as a negative emission technology, and for providing electricity. The associated freshwater abstractions for irrigation of dedicated biomass plantations might be substantial and therefore potentially increase water limitation and stress in affected regions; however, assumptions and quantities of water use provided in the literature vary strongly. This paper reviews existing global assessments of freshwater abstractions for bioenergy production and puts these estimates into the context of scenarios of other water-use sectors. We scanned the available literature and (out of 430 initial hits) found 16 publications (some of which include several bioenergy-water-use scenarios) with reported values on global irrigation water abstractions for biomass plantations, suggesting water withdrawals in the range of 128.4 to 9000 km3 yr−1, which would come on top of (or compete with) agricultural, industrial, and domestic water withdrawals. To provide an understanding of the origins of this large range, we present the diverse underlying assumptions, discuss major study differences, and calculate an inverse water-use efficiency (iwue), which facilitates comparison of the required freshwater amounts per produced biomass harvest. We conclude that due to the potentially high water demands and the tradeoffs that might go along with them, bioenergy should be an integral part of global assessments of freshwater demand and use. For interpreting and comparing reported estimates of possible future bioenergy water abstractions, full disclosure of parameters and assumptions is crucial. A minimum set should include the complete water balances of bioenergy production systems (including partitioning of blue and green water), bioenergy crop species and associated water-use efficiencies, rainfed and irrigated bioenergy plantation locations (including total area and meteorological conditions), and total biomass harvest amounts. In the future, a model intercomparison project with standardized parameters and scenarios would be helpful.

Description: Previous studies have projected a significant role for bioenergy in decarbonizing the global economy and helping realize international climate goals such as limiting global average warming to 2 ˚C or 1.5 ˚C. However, with substantial variability in bioenergy results and significant concerns about potential environmental and social implications, greater transparency and dedicated assessment of the underlying modeling and results and more detailed understanding of the potential role of bioenergy are needed. Stanford University’s Energy Modeling Forum (EMF) initiated a 33rd study (EMF-33) to explore the viability of large-scale bioenergy as part of a comprehensive climate management strategy. This special issue presents the papers of the EMF-33 study—a multi-year inter-model comparison project designed to understand and assess global, long-run biomass supply and bioenergy deployment potentials and related uncertainties. Using a novel scenario design with independent biomass supply and bioenergy demand protocols, EMF-33 separately elucidates and explores the modeling of biomass feedstock supplies and bioenergy technologies and their deployment—revealing, comparing, and assessing the modeling that is suggesting that bioenergy could be a key climate containment strategy. This introduction provides an overview of the EMF-33 study design and the overview, thematic, and individual modeling team papers and types of insights that make up this special issue. By providing enhanced transparency and new detailed insights, we hope to inform policy dialogue about the potential role of bioenergy and facilitate new research.

Description: Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (CRF data) as published by the UNFCCC secretariat at 2020-10-25

Description: The terrestrial biosphere is exposed to land-use and climate change, which not only affects vegetation dynamics, but also changes land-atmosphere feedbacks. Specifically, changes in land-cover affect biophysical feedbacks of water and energy, therefore contributing to climate change. In this study, we couple the well established and comprehensively validated Dynamic Global Vegetation Model LPJmL5 to the coupled climate model CM2Mc, which is based on the atmosphere model AM2 and the ocean model MOM5 (CM2Mc-LPJmL). In CM2Mc, we replace the simple land surface model LaD (where vegetation is static and prescribed) with LPJmL5 and fully couple the water and energy cycles using the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. These include a sub-daily cycle for calculating energy and water fluxes, a conductance of the soil evaporation and plant interception, a canopy-layer humidity, and the surface energy balance in order to calculate the surface and canopy layer temperature within LPJmL5. Exchanging LaD by LPJmL5, and therefore switching from a static and prescribed vegetation to a dynamic vegetation, allows us to model important biosphere processes, including fire, mortality, permafrost, hydrological cycling, and the impacts of managed land (crop growth and irrigation). Our results show that CM2Mc-LPJmL has similar temperature and precipitation biases as the original CM2Mc model with LaD. Performance of LPJmL5 in the coupled system compared to Earth observation data and to LPJmL offline simulation results is within acceptable error margins. The historic global mean temperature evolution of our model setup is within the range of CMIP5 models. The comparison of model runs with and without land-use change shows a partially warmer and drier climate state across the global land surface. CM2Mc-LPJmL opens new opportunities to investigate important biophysical vegetation-climate feedbacks with a state-of-the-art and process-based dynamic vegetation model.

Description: Climate change is one of the most pressing political issues of our time. Science is uncovering the unprecedented nature and scale of its impacts on people, economies and ecosystems worldwide. One critical dimension of these impacts is their effect on international peace and security.This report summarises the state of knowledge regarding security risks related to climate change. To this end, it synthesises and contextualises the existing scientific evidence. It does not reflect all aspects of the debate that have emerged across social science but focuses on those that are particularly relevant at the political level.Climate change itself is rarely a direct cause of conflict. Yet, there is ample evidence that its effects exacerbate important drivers and contextual factors of conflict and fragility, thereby challenging the stability of states and societies.

Description: The social cost of carbon is a central metric for optimal carbon prices. Previous literatureshows that inequality significantly influences the social cost of carbon, but mostly omits het-erogeneity below the national level. We present an optimal taxation model of the social cost ofcarbon that accounts for inequality betweenand within countries. We find that climate anddistributional policy can generally not be separated. If only one country does not compen-sate low-income households for disproportionate damages, the social cost of carbon tendsto increase globally. Optimal carbon prices remain roughly unchanged if national redistribu-tion leaves inequality between households unaffected by climate change and if the utility ofhouseholds is approximately logarithmic in consumption.

Description: Taking a comparative case study approach between Canada and Germany, this book investigates the contrasting response of governments to anti-wind movements. Environmental social movements have been critical players for encouraging the shift towards increased use of renewable energy. However, social movements mobilizing against the installation of wind turbines have now become a major obstacle to their increased deployment. Andrea Bues draws on a cross-Atlantic comparative analysis to investigate the different contexts of contentious energy policy. Focusing on two sub-national forerunner regions in installed wind power capacity – Brandenburg and Ontario – Bues draws on social movement theory to explore the concept of discursive energy space and propose explanations as to why governments respond differently to social movements. Overall, Social Movements against Wind Power in Canada and Germany offers a novel conceptualization of discursive-institutional contexts of contentious energy politics and helps better understand protest against renewable energy policy. This book will be of great interest to students and scholars of renewable energy policy, sustainability and climate change politics, social movement studies and environmental sociology.

Description: 1. Introduction: Renewable Energy Politics and Protest 2. Contentious Wind Energy and Context 3. Renewable Energy Policy and Politics in Canada and Germany 4. Larger Setbacks, Saving the Forests: The Anti-Wind Movement in Germany, Case Study Brandenburg 5. Preserving Health, Curbing Costs: The Anti-Wind Movement in Canada, Case Study Ontario 6. Contention in Context: Governmental Response to Social Movements 7. Conclusion: The Changing Winds of Discourses on Decarbonization

Description: In the past years, there has been an increasing number of applications of functional climate networks to studying the spatiotemporal organization of heavy rainfall events or similar types of extreme behavior in some climate variable of interest. Nearly all existing studies have employed the concept of event synchronization (ES) to statistically measure similarity in the timing of events at different grid points. Recently, it has been pointed out that this measure can however lead to biases in the presence of events that are heavily clustered in time. Here, we present an analysis of the effects of event declustering on the resulting functional climate network properties describing spatio- temporal patterns of heavy rainfall events during the South American monsoon season based on ES and a conceptually similar method, event coincidence analysis (ECA). As examples for widely employed local (per-node) network characteristics of different type, we study the degree, local clustering coefficient and average link distance patterns, as well as their mutual interdependency, for three different values of the link density. Our results demonstrate that the link density can markedly affect the resulting spatial patterns. Specifically, we find the qualitative inversion of the degree pattern with rising link density in one of the studied settings. To our best knowledge, such crossover behavior has not been described before in event synchrony based networks. In addition, declustering relieves differences between ES and ECA based network properties in some measures while not in others. This underlines the need for a careful choice of the methodological settings in functional climate network studies of extreme events and associated interpretation of the obtained results, especially when higher-order network properties are considered.

Description: Germany wishes to cut its greenhouse gas emissions by 80 to 95 per cent by 2050. However, despite the success to date, the measures which have already been planned and implemented are not sufficient for achieving this ambitious goal. In addition to the energy sector, the largest source of greenhouse gas emissions, German industry is also responsible for releasing considerable volumes of global warming gases. In its Climate Action Plan 2050, the Federal Government has for the first time set a sector target for industry. The present acatech POSITION PAPER analyses the options for (re)utilising and storing CO2 (Carbon Capture and Utilisation (CCU) and Carbon Capture and Storage (CCS)) which come into consideration for reducing greenhouse gas emissions from industrial processes. It is recommended that a wide-ranging public debate about the use of CCU and CCS be conducted in the near future. Only then will it be possible to take account of reservations about CCU and CCS, further develop suitable technology in good time and bring it to market maturity so that the necessary infrastructure can be planned, approved, funded and constructed.

Description: Fires are a fundamental part of the Earth System. In the last decades, they have been altering ecosystem structure, biogeochemical cycles and atmospheric composition with unprecedented rapidity. In this study, we implement a complex networks-based methodology to track individual fires over space and time. We focus on extreme fires—the 5% most intense fires—in the tropical forests of the Brazilian Legal Amazon over the period 2002–2019. We analyse the interannual variability in the number and spatial patterns of extreme forest fires in years with diverse climatic conditions and anthropogenic pressure to examine potential synergies between climate and anthropogenic drivers. We observe that major droughts, that increase forest flammability, co-occur with high extreme fire years but also that it is fundamental to consider anthropogenic activities to understand the distribution of extreme fires. Deforestation fires, fires escaping from managed lands, and other types of forest degradation and fragmentation provide the ignition sources for fires to ignite in the forests. We find that all extreme forest fires identified are located within a 0.5-km distance from forest edges, and up to 56% of them are within a 1-km distance from roads (which increases to 73% within 5 km), showing a strong correlation that defines spatial patterns of extreme fires.

Description: Along Peru’s rainforest rivers, rising flood extremes are increasingly exceeding coping capacities of vulnerable households. Peru 14 has detailed legislation that embraces planned relocation as a strategic solution to such situations and various relocation projects 15 are underway across the country. This research brief analyzes well-being consequences for two communities requesting reloca- 16 tion, using qualitative data collected from experts and 30 affected people. Initial results emphasize that weak governance, 17 poverty, third-party involvement, and community action have influenced relocation outcomes. Delays and fragmented imple- 18 mentation have threatened people’s well-being. One community, waiting for land to relocate since 2015, has suffered from 19 continued hazard exposure, deteriorated material conditions, and reduced subjective well-being. The second community 20 achieved relocation only after a decade in detrimental limbo. Although livelihood challenges persist, its inhabitants now benefit 21 from better market access and decreased exposure, leading to higher subjective well-being. With rising needs for relocation 22 worldwide, the cases highlight that detailed legislation is not sufficient to safeguard people’s well-being. Advancing from well- 23 meant legislation to good practice requires adequate institutional capacity, effectivemechanisms for oversight and accountability, 24 better engagement of third parties, and dedicated efforts to strengthen community agency

Description: Open quantum systems with Markovian dynamics can be described by the Lindblad equation. The quantity governing the dynamics is the Lindblad superoperator. We apply random-matrix theory to this superoperator to elucidate its spectral properties. The distribution of eigenvalues and the correlations of neighboring eigenvalues are obtained for the cases of purely unitary dynamics, pure dissipation, and the physically realistic combination of unitary and dissipative dynamics. The theory of ensembles of random matrices has proved useful in understanding the energy spectra of complex closed quantum systems, such as heavy atomic nuclei and classically chaotic billiards. In these cases, the Hamiltonian describing the system is drawn from a suitable random-matrix ensemble. More recently, it has been realized that random-matrix theory can also shed light on open quantum systems. Their dynamics is not described by a Hamiltonian but by a so-called Lindblad generator. Using random-matrix ensembles suitable for the Lindblad generator, we study its spectral properties, which are important for the dynamics of open quantum systems.

Description: Background: Fever and hypothermia have been observed in septic patients. Their influence on prognosis is subject to ongoing debates. Methods: We did a secondary analysis of a large clinical dataset from a quality improvement trial. A binary logistic regression model was calculated to assess the association of the thermal response with outcome and a multinomial regression model to assess factors associated with fever or hypothermia. Results: With 6542 analyzable cases we observed a bimodal temperature response characterized by fever or hypothermia, normothermia was rare. Hypothermia and high fever were both associated with higher lactate values. Hypothermia was associated with higher mortality, but this association was reduced after adjustment for other risk factors. Age, community-acquired sepsis, lower BMI and lower outside temperatures were associated with hypothermia while bacteremia and higher procalcitonin values were associated with high fever. Conclusions: Septic patients show either a hypothermic or a fever response. Whether hypothermia is a maladaptive response, as indicated by the higher mortality in hypothermic patients, or an adaptive response in patients with limited metabolic reserves under colder environmental conditions, remains an open question. Trial registration The original trial whose dataset was analyzed was registered at ClinicalTrials.gov (NCT01187134) on August 23, 2010, the first patient was included on July 1, 2011.

Description: We consider trends in the m seasonal subrecords of a record. To determine the statistical significance of the m trends, one usually determines the p value of each season either numerically or analytically and compares it with a significance level α~. We show in great detail for short- and long-term persistent records that this procedure, which is standard in climate science, is inadequate since it produces too many false positives (false discoveries). We specify, on the basis of the family wise error rate and by adapting ideas from multiple testing correction approaches, how the procedure must be changed to obtain more suitable significance criteria for the m trends. Our analysis is valid for data with all kinds of persistence. Specifically for long-term persistent data, we derive simple analytical expressions for the quantities of interest, which allow to determine easily the statistical significance of a trend in a seasonal record. As an application, we focus on 17 Antarctic station data. We show that only four trends in the seasonal temperature data are outside the bounds of natural variability, in marked contrast to earlier conclusions.

Description: beliebiger Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system's behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge because the counterfactual baseline, which characterizes the system behavior in the hypothetical absence of climate change, cannot be observed. Process-based and empirical impact models can fill this gap as they allow us to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.

Description: Precursor signals for bifurcation-induced critical transitions have recently gained interest across many research fields. Common indicators, including variance and autocorrelation increases, rely on the dynamical system being driven by white noise. Here, we show that these metrics raise false alarms for systems driven by time-correlated noise, if the autocorrelation of the noise process increases with time. We introduce a new indicator for systems driven by non-stationary short-term memory noise, and show that this indicator performs well in situations where the classical methods fail.

Description: We investigate dynamical properties of a quantum generalization of classical reversible Boolean networks. The state of each node is encoded as a single qubit, and classical Boolean logic operations are supplemented by controlled bit-flip and Hadamard operations. We consider synchronous updating schemes in which each qubit is updated at each step based on stored values of the qubits from the previous step. We investigate the periodic or quasiperiodic behavior of quantum networks, and we analyze the propagation of single site perturbations through the quantum networks with input degree one. A non-classical mechanism for perturbation propagation leads to substantially different evolution of the Hamming distance between the original and perturbed states.

Description: While carbon taxes are generally seen as a rational policy response to climate change, knowledge about their performance from an ex-post perspective is still limited. This paper analyzes the emissions and cost impacts of the UK CPS, a carbon tax levied on all fossil-fired power plants. To overcome the problem of a missing control group, we propose a policy evaluation approach which leverages economic theory and machine learning for counterfactual prediction. Our results indicate that in the period 2013–2016 the CPS lowered emissions by 6.2 percent at an average cost of €18 per ton. We find substantial temporal heterogeneity in tax-induced impacts which stems from variation in relative fuel prices. An important implication for climate policy is that whether a higher carbon tax leads to higher emissions reductions and higher costs depends on relative fuel prices.

Description: The employment implications of decarbonizing the energy sector have received far less attention than the technology dimension of the transition, although being of critical importance to policymakers. In this work, we adapt a methodology based on employment factors to project future changes in quantity and composition of direct energy supply jobs for two scenarios - (1) relatively weak emissions reductions as pledged in the nationally determined contributions (NDC) and (2) stringent reductions compatible with the 1.5 °C target. We find that in the near-term the 1.5°C-compatible scenario results in a net increase in jobs through gains in solar and wind jobs in construction, installation, and manufacturing, despite significant losses in coal fuel supply; eventually leading to a peak in total direct energy jobs in 2025. In the long run, improvements in labour productivity lead to a decrease of total direct energy employment compared to today, however, total jobs are still higher in a 1.5 °C than in an NDC scenario. Operation and maintenance jobs dominate future jobs, replacing fuel supply jobs. The results point to the need for active policies aimed at retraining, both inside and outside the renewable energy sector, to complement climate policies within the concept of a “just transition”.

Description: In 2012, Hurricane Sandy hit the East Coast of the United States, creating widespread coastal flooding and over $60 billion in reported economic damage. The potential influence of climate change on the storm itself has been debated, but sea level rise driven by anthropogenic climate change more clearly contributed to damages. To quantify this effect, here we simulate water levels and damage both as they occurred and as they would have occurred across a range of lower sea levels corresponding to different estimates of attributable sea level rise. We find that approximately $8.1B ($4.7B–$14.0B, 5th–95th percentiles) of Sandy’s damages are attributable to climate-mediated anthropogenic sea level rise, as is extension of the flood area to affect 71 (40–131) thousand additional people. The same general approach demonstrated here may be applied to impact assessments for other past and future coastal storms.

Description: Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a “crisis discipline” just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.

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

Description: Investigating the synchrony and interdependency of heavy rainfall occurrences is crucial to understand the underlying physical mechanisms and reduce physical and economic damages by improved forecasting strategies. In this context, studies utilizing functional network representations have recently contributed to significant advances in the understanding and prediction of extreme weather events. To thoroughly expand on previous works employing the latter framework to the East Asian Summer Monsoon (EASM) system, we focus here on changes in the spatial organization of synchronous heavy precipitation events across the monsoon season (April to August) by studying the temporal evolution of corresponding network characteristics in terms of a sliding window approach. Specifically, we utilize functional climate networks together with event coincidence analysis for identifying and characterizing synchronous activity from daily rainfall estimates between 1998 and 2018. Our results demonstrate that the formation of the Baiu front as a main feature of the EASM is reflected by a double-band structure of synchronous heavy rainfall with two centers north and south of the front. Although the two separated bands are strongly related to either low- or high-level winds which are commonly assumed to be independent, we provide evidence that it is rather their mutual interconnectivity that changes during the different phases of the EASM season in a characteristic way. Our findings shed some new light on the interplay between tropical and extratropical factors controlling the EASM intraseasonal evolution, which could potentially help to improve future forecasts of the Baiu onset in different regions of East Asia.

Description: Understanding the influence of climate change and population pressure on human conflict remains a critically important topic in the social sciences. Long-term records that evaluate these dynamics across multiple centuries and outside the range of modern climatic variation are especially capable of elucidating the relative effect of—and the interaction between—climate and demography. This is crucial given that climate change may structure population growth and carrying capacity, while both climate and population influence per capita resource availability. This study couples paleoclimatic and demographic data with osteological evaluations of lethal trauma from 149 directly accelerator mass spectrometry 14C-dated individuals from the Nasca highland region of Peru. Multiple local and supraregional precipitation proxies are combined with a summed probability distribution of 149 14C dates to estimate population dynamics during a 700-y study window. Counter to previous findings, our analysis reveals a precipitous increase in violent deaths associated with a period of productive and stable climate, but volatile population dynamics. We conclude that favorable local climate conditions fostered population growth that put pressure on the marginal and highly circumscribed resource base, resulting in violent resource competition that manifested in over 450 y of internecine warfare. These findings help support a general theory of intergroup violence, indicating that relative resource scarcity—whether driven by reduced resource abundance or increased competition—can lead to violence in subsistence societies when the outcome is lower per capita resource availability.

Description: Notoriety of the Marine Isotope Stage (MIS) 11c interglacial arises from its long duration, extending over two precessional cycles, high sea level, and persistence of high atmospheric CO2 concentrations. The strong climatic response is often considered paradoxical because it was attained under weak boreal summer insolation forcing, a function of an extended eccentricity minimum and of precession and obliquity being almost opposite in phase. Here, we trace the characteristics of MIS 11c and explore their most likely causes. MIS 11c was preceded by the largest Quaternary ice volume expansion of MIS 12, which ended with a long period of ice rafting and interhemispheric heat transfer. We suggest that the duration of MIS 12 and the size of ice sheets exceeded a critical threshold that triggered a deglaciation despite the weak insolation forcing. The weak forcing led to a slow but steady loss of ice volume, that was sufficient to allow ocean outgassing of CO2, but insufficient to raise sea level within a single precessional cycle. This gave rise to a prolonged interval with large residual ice sheets and high CO2 concentrations that is unique in the last 800,000 years. The obliquity-precession antiphasing produced a weak boreal summer insolation minimum, skipping a glacial inception and leading to continued sea-level rise and high CO2 concentrations, sustained by carbonate compensation. Full interglacial conditions were ach- ieved in the second precessional cycle, and the combined strength and length of the interglacial probably led to loss of some Greenland and Antarctic ice compared to other interglacials. While MIS 11c is highly unusual in many respects, these appear to be linked to each other through the very weak insolation forcing, which led to its extended duration, slow sea-level rise and stable CO2 concentrations through a cocktail of counteracting carbon cycle processes. Although some of these features are also encountered in other interglacials, their combination with strong interglacial intensity is unique to MIS 11c and this appears to be a function of the large MIS 12 ice sheets and the high CO2 concentrations from the beginning of the interglacial.

Description: We provide a quantitative assessment of policy options to inform the 2021 review of the EU Emissions Trading System (ETS) and raise climate ambition. We use a permit trading model in which firms utilize rolling finite planning horizons, which replicates historical price and banking developments well compared to an infinite horizon. When firms have bounded foresight, indirectly raising ambition through the Market Stability Reserve (MSR) is not equivalent to directly raising ambition through the emissions cap trajectory. Leveraging the MSR turns out to be efficiency improving as it compensates for firms’ bounded foresight by frontloading abatement efforts. We analyze the MSR interaction with the cap trajectory to exploit synergies and minimize the cost of raising ambition. We also provide a comparative assessment of a complete suite of changes in the MSR parameters. Whatever its parameters, MSR-induced resilience to demand shocks remains limited by design: the MSR acts more as an unconditional price support provider than as a responsive price stabilizer.

Description: In 2016, northern France experienced an unprecedented wheat crop loss. The cause of this event is not yet fully understood, and none of the most used crop forecast models were able to predict the event (Ben-Ari et al., 2018). However, this extreme event was likely due to a sequence of particular meteorological conditions, i.e. too few cold days in late autumn–winter and abnormally high precipitation during the spring season. Here we focus on a compound meteorological hazard (warm winter and wet spring) that could lead to a crop loss. This work is motivated by the question of whether the 2016 meteorological conditions were the most extreme possible conditions under current climate, and what the worst-case meteorological scenario would be with respect to warm winters followed by wet springs. To answer these questions, instead of relying on computationally intensive climate model simulations, we use an analogue-based importance sampling algorithm that was recently introduced into this field of research (Yiou and Jézéquel, 2020). This algorithm is a modification of a stochastic weather generator (SWG) that gives more weight to trajectories with more extreme meteorological conditions (here temperature and precipitation). This approach is inspired by importance sampling of complex systems (Ragone et al., 2017). This data-driven technique constructs artificial weather events by combining daily observations in a dynamically realistic manner and in a relatively fast way. This paper explains how an SWG for extreme winter temperature and spring precipitation can be constructed in order to generate large samples of such extremes. We show that with some adjustments both types of weather events can be adequately simulated with SWGs, highlighting the wide applicability of the method. We find that the number of cold days in late autumn 2015 was close to the plausible minimum. However, our simulations of extreme spring precipitation show that considerably wetter springs than what was observed in 2016 are possible. Although the relation of crop loss in 2016 to climate variability is not yet fully understood, these results indicate that similar events with higher impacts could be possible in present-day climate conditions.

Description: Deep learning-based methods have achieved remarkable performance in 3-D sensing since they perceive environments in a biologically inspired manner. Nevertheless, the existing approaches trained by monocular sequences are still prone to fail in dynamic environments. In this work, we mitigate the negative influence of dynamic environments on the joint estimation of depth and visual odometry (VO) through hybrid masks. Since both the VO estimation and view reconstruction process in the joint estimation framework is vulnerable to dynamic environments, we propose the cover mask and the filter mask to alleviate the adverse effects, respectively. As the depth and VO estimation are tightly coupled during training, the improved VO estimation promotes depth estimation as well. Besides, a depth-pose consistency loss is proposed to overcome the scale inconsistency between different training samples of monocular sequences. Experimental results show that both our depth prediction and globally consistent VO estimation are state of the art when evaluated on the KITTI benchmark. We evaluate our depth prediction model on the Make3D dataset to prove the transferability of our method as well.

Description: Capturing the complex spatiotemporal flame dynamics inside a rocket combustor is essential to validate high-fidelity simulations for developing high-performance rocket engines. Utilizing tools from a complex network theory, we construct positively and negatively correlated weighted networks from methylidyne (CH*) chemiluminescence intensity oscillations for different dynamical states observed during the transition to thermoacoustic instability (TAI) in a subscale multi-element rocket combustor. We find that the distribution of network measures quantitatively captures the extent of coherence in the flame dynamics. We discover that regions with highly correlated flame intensity oscillations tend to connect with other regions exhibiting highly correlated flame intensity oscillations. This phenomenon, known as assortative mixing, leads to a core group (a cluster) in the flow-field that acts as a “reservoir” for coherent flame intensity oscillations. Spatiotemporal features described in this study can be used to understand the self-excited flame response during the transition to TAI and validate high-fidelity simulations essential for developing high-performance rocket engines.

Description: The relative role of external forcing and of intrinsic variability is a key question of climate variability in general and of our planet's paleoclimatic past in particular. Over the last 100 years since Milankovic's contributions, the importance of orbital forcing has been established for the period covering the last 2.6 Myr and the Quaternary glaciation cycles that took place during that time. A convincing case has also been made for the role of several internal mechanisms that are active on timescales both shorter and longer than the orbital ones. Such mechanisms clearly have a causal role in Dansgaard–Oeschger and Heinrich events, as well as in the mid-Pleistocene transition. We introduce herein a unified framework for the understanding of the orbital forcing's effects on the climate system's internal variability on timescales from thousands to millions of years. This framework relies on the fairly recent theory of non-autonomous and random dynamical systems, and it has so far been successfully applied in the climate sciences for problems like the El Niño–Southern Oscillation, the oceans' wind-driven circulation, and other problems on interannual to interdecadal timescales. Finally, we provide further examples of climate applications and present preliminary results of interest for the Quaternary glaciation cycles in general and the mid-Pleistocene transition in particular.

Description: The classic partitioning between slow-moving, low-wavenumber planetary waves and fast-moving, high-wavenumber synoptic waves is systematically extended by means of a wavenumber/phase speed spectral decomposition to characterize the day-to-day evolution of Rossby wave activity in the upper troposphere. This technique is employed to study the origin and the propagation of circumglobal Rossby wave patterns (CRWPs), amplified Rossby waves stretching across the Northern Hemisphere in the zonal direction and characterized by few, dominant wavenumbers. Principal component analysis of daily anomalies in spectral power allows for two CRWPs to emerge as dominant variability modes in the spectral domain during boreal winter. These modes correspond to the baroclinic propagation of amplified Rossby waves from the Pacific to the Atlantic storm track in a hemispheric flow configuration displaying enhanced meridional gradients of geopotential height over midlatitudes. The first CRWP is forced by tropical convection anomalies over the Indian Ocean and features the propagation of amplified Rossby wave packets over northern midlatitudes, while the second one propagates rapidly over latitudes between 35° N and 55° N and appears to have extratropical origin. Propagation of Rossby waves from the Atlantic eddy-driven jet to the African subtropical jet occurs for both CRWPs following anticyclonic wave breaking.

Description: We study the collective dynamics in a population of excitable units (neurons) adaptively interacting with a pool of resources. The resource pool is influenced by the average activity of the population, whereas the feedback from the resources to the population is comprised of components acting homogeneously or inhomogeneously on individual units of the population. Moreover, the resource pool dynamics is assumed to be slow and has an oscillatory degree of freedom. We show that the feedback loop between the population and the resources can give rise to collective activity bursting in the population. To explain the mechanisms behind this emergent phenomenon, we combine the Ott-Antonsen reduction for the collective dynamics of the population and singular perturbation theory to obtain a reduced system describing the interaction between the population mean field and the resources.

Description: The most complex but potentially most severe impacts of climate change are caused by extreme weather events. In a globally connected economy, damages can cause remote perturbations and cascading consequences—a ripple effect along supply chains. Here we show an economic ripple resonance that amplifies losses when consecutive or overlapping weather extremes and their repercussions interact. This amounts to an average amplification of 21% for climate-induced heat stress, river floods, and tropical cyclones. Modeling the temporal evolution of 1.8 million trade relations between $\gt$7000 regional economic sectors, we find that the regional responses to future extremes are strongly heterogeneous also in their resonance behavior. The induced effect on welfare varies between gains due to increased demand in some regions and losses due to demand or supply shortages in others. Within the current global supply network, the ripple resonance effect of extreme weather is strongest in high-income economies—an important effect to consider when evaluating past and future economic climate impacts.

Description: In the present paper, we investigate both the finite-time and fixed-time synchronization of retarded shunting inhibitory cellular neural networks. By constructing suitable Lyapunov functions and feedback control schemes we derive several sufficient conditions to guarantee finite-time and fixed-time synchronization of such networks. Finally, to illustrate the effectiveness of our theoretical results we consider examples with numerical simulations.

Description: Noise-induced tipping from a low-amplitude oscillation state to a high-amplitude one is widespread in airfoil systems. Its occurrence may cause fatigue damage to the wing structure of an aircraft, which directly threatens its flight safety. Therefore, it is of utmost importance to predict the occurrence of noise-induced high-amplitude oscillations as the system parameters vary in airfoil systems. Taking a two-degrees-of-freedom airfoil model with random loadings as a prototype class of real systems, the prediction of noise-induced tipping from low-amplitude to high-amplitude oscillations is carried out in the present study. First, we analyze the effects of random fluctuations on the system response. The results show that noise-induced catastrophic high-amplitude oscillations take place before the bifurcation point of the corresponding deterministic airfoil model. Subsequently, the possibility that the low-amplitude oscillation state of the given noisy model jumps to the high-amplitude one is analyzed based on the escape probability. Then, the new concept of the high-risk region is defined. This is an efficient early warning indicator to approximately quantify the ranges of the system parameters where noise-induced high-amplitude oscillations may occur. Compared with the existing early warning indicators, this method is a non-local universal concept of stability. More importantly, it may provide theoretical guidance for aircraft designers to take some measures to avoid such catastrophic critical jump phenomena in practical engineering applications. Random fluctuations in a flight environment can induce tipping from a low-amplitude oscillation state to a high-amplitude one of an airfoil system. These typically undesirable high-amplitude oscillations often lead to airfoil structural damage, thereby increasing the risk of flight safety issues such as the aircraft breaking up in mid-air. Therefore, early warning of high-amplitude oscillations under random fluctuations has been a major problem faced during the safe flight of the aircraft. Many studies, in recent years, have been devoted to exploring early warning indicators to predict and characterize the onset of high-amplitude oscillations. However, these existing indicators can only warn of high-amplitude oscillations that are impending, which leaves operators not having enough time to avoid the occurrence of these catastrophic events. To overcome these problems, in this paper, we introduce a new and non-local concept: the high-risk region. It can provide early warning signals for the airfoil structure by quantifying the ranges of the system parameters where noise-induced high-amplitude oscillations may occur in advance.

Description: Multifractal detrended fluctuation analysis (MFDFA) has become a central method to characterise the variability and uncertainty in empiric time series. Extracting the fluctuations on different temporal scales allows quantifying the strength and correlations in the underlying stochastic properties, their scaling behaviour, as well as the level of fractality. Several extensions to the fundamental method have been developed over the years, vastly enhancing the applicability of MFDFA, e.g. empirical mode decomposition for the study of long-range correlations and persistence. In this article we introduce an efficient, easy-to-use python library for MFDFA, incorporating the most common extensions and harnessing the most of multi-threaded processing for very fast calculations.

Description: Numerous social problems can be directly related to poverty, and its elimination is thus often declared a grand challenge in modern human societies. Nevertheless, it is difficult to shake the belief that certain fractions of the population would like to see it maintained to ensure the availability of cheap workforce and its readiness to do the hardest jobs, as well as to keep the prices of natural resources in the afflicted countries as low as possible. Here we show, however, that by allowing low-income individuals to escape poverty, either by means of mobility to pursue potential opportunities in remote areas or by ending dilemmas through social learning in local areas, greatly increases cooperation and thus has the potential to raise the social capital. In particular, we find that mobility of low-income individuals can promote cooperation when the per capita mobility rate is as low as in the order of magnitude as long as network reciprocity is still active. This synergy between network reciprocity and mobility is due to the emergence of large cooperative clusters that are in this size impossible without mobility. Moreover, we find that the mobility of defectors undermines cooperation, but only a few defectors actually move as they are typically well off when surrounded by cooperators. On the contrary, the higher the cooperation level, the greater the proportion of low-income cooperator that move. Our research thus shows that by providing ways out of poverty for individuals can raise whole societies out of economic gridlocks by elevating cooperation levels.

Description: Background: The development of new methods of drug brain delivery is a crucial step for the effective therapy of the brain diseases. Pharma- and acupuncture are the forms of alternative therapy of the brain pathology, including an increase in the permeability of blood-brain barrier. However, the mechanisms of pharma- and acupuncture-mediated effects on the brain physiology remain not fully understood. Results: This pilot study on healthy mice clearly demonstrates the Evans Blue spreading in the mouse head and in the brain via the perivascular spaces (PVSs) of the trigeminal structure and the cribriform plate after the dye injection into the Feng Chi point (Galbladder 20, GB20). Conclusion: These results suggest that pharmacopuncture at GB20 can be a perspective method for brain drug delivery via PVSs.

Description: In recent years, the Indian Ocean Dipole (IOD) has received much attention in light of its substantial impacts on both the climate system and humanity. Due to its complexity, however, a reliable prediction of the IOD is still a great challenge. In this study, climate network analysis was employed to investigate whether there are early warning signals prior to the start of IOD events. An enhanced seesaw tendency in sea surface temperature (SST) among a large number of grid points between the dipole regions in the tropical Indian Ocean was revealed in boreal winter, which can be used to forewarn the potential occurrence of the IOD in the coming year. We combined this insight with the indicator of the December equatorial zonal wind in the tropical Indian Ocean to propose a network-based predictor that clearly outperforms the current dynamic models. Of the 15 IOD events over the past 37 y (1984 to 2020), 11 events were correctly predicted from December of the previous year, i.e., a hit rate of higher than 70%, and the false alarm rate was around 35%. This network-based approach suggests a perspective for better understanding and predicting the IOD.

Description: Evolutionary game on complex networks provides a new research framework for analyzing and predicting group decision-making behavior in an interactive environment, in which most researchers assumed players as profiteers. However, current studies have shown that players are sometimes conformists rather than profit-seeking in society, but most research has been discussed on a simple game without considering the impact of multiple games. In this paper, we study the influence of conformists and profiteers on the evolution of cooperation in multiple games and illustrate two different strategy-updating rules based on these conformists and profiteers. Different from previous studies, we introduce a similarity between players into strategy-updating rules and explore the evolutionary game process, including the strategy updating, the transformation of players’ type, and the dynamic evolution of the network structure. In the simulation, we implement our model on scale-free and regular networks and provide some explanations from the perspective of strategy transition, type transition, and network topology properties to prove the validity of our model. The study of network evolutionary games can provide a new perspective for explaining cooperation in society. Our task is to incorporate conformists and multigames into the traditional evolutionary game, which are more consistent with reality. Based on this model, this paper proposes two different strategy-updating rules and investigates their impact on the evolution of cooperation in the network. In addition, we make an interpretation of the simulation results in terms of strategy transition, type transition, and network topology properties. Our work may shed some new light on the study of network evolutionary games with conformists and multigames.

Description: In this paper, we propose and analyse a compartmental model of COVID-19 to predict and control the outbreak. We first formulate a comprehensive mathematical model for the dynamical transmission of COVID-19 in the context of sub-Saharan Africa. We provide the basic properties of the model and compute the basic reproduction number R0 when the parameter values are constant. After, assuming continuous measurement of the weekly number of newly COVID-19 detected cases, newly deceased individuals and newly recovered individuals, the Ensemble of Kalman filter (EnKf) approach is used to estimate the unmeasured variables and unknown parameters, which are assumed to be time-dependent using real data of COVID-19. We calibrated the proposed model to fit the weekly data in Cameroon and Gabon before, during and after the lockdown. We present the forecasts of the current pandemic in these countries using the estimated parameter values and the estimated variables as initial conditions. During the estimation period, our findings suggest that R0≈1.8377 in Cameroon, while R0≈1.0379 in Gabon meaning that the disease will not die out without any control measures in theses countries. Also, the number of undetected cases remains high in both countries, which could be the source of the new wave of COVID-19 pandemic. Short-term predictions firstly show that one can use the EnKf to predict the COVID-19 in Sub-Saharan Africa and that the second vague of the COVID-19 pandemic will still increase in the future in Gabon and in Cameroon. A comparison between the basic reproduction number from human individuals R0h and from the SARS-CoV-2 in the environment R0v has been done in Cameroon and Gabon. A comparative study during the estimation period shows that the transmissions from the free SARS-CoV-2 in the environment is greater than that from the infected individuals in Cameroon with R0h = 0.05721 and R0v = 1.78051. This imply that Cameroonian apply distancing measures between individual more than with the free SARS-CoV-2 in the environment. But, the opposite is observed in Gabon with R0h = 0.63899 and R0v = 0.39894. So, it is important to increase the awareness campaigns to reduce contacts from individual to individual in Gabon. However, long-term predictions reveal that the COVID-19 detected cases will play an important role in the spread of the disease. Further, we found that there is a necessity to increase timely the surveillance by using an awareness program and a detection process, and the eradication of the pandemic is highly dependent on the control measures taken by each government.

Description: The scientific evidence assembled in this Focus Collection on 'Reactive nitrogen and the UN sustainable development goals' emphasizes the relevance of agriculture as a key sector for nitrogen application as well as its release to the environment and the observed impacts. Published work proves the multiple connections and their causality, and presents pathways to mitigate negative effects while maintaining the benefits, foremost the production of food to sustain humanity. Providing intersections from field to laboratory studies and to modelling approaches, across multiple scales and for all continents, the Collection displays an overview of the state of nitrogen science in the early 21st century. Extending science to allow for policy-relevant messages renders the evidence provided a valuable basis for a global assessment of reactive nitrogen.

Description: The deposition of amyloid-β (Aβ) in the brain is a risk factor for Alzheimer’s disease (AD). Therefore, new strategies for the stimulation of Aβ clearance from the brain can be useful in preventing AD. Transcranial photostimulation (PS) is considered a promising method for AD therapy. In our previous studies, we clearly demonstrated the PS-mediated stimulation of lymphatic clearing functions, including Aβ removal from the brain. There is increasing evidence that sleep plays an important role in Aβ clearance. Here, we tested our hypothesis that PS at night can stimulate Aβ clearance from the brain more effectively than PS during the day. Our results on healthy mice show that Aβ clearance from the brain occurs faster at night than during wakefulness. The PS course at night improves memory and reduces Aβ accumulation in the brain of AD mice more effectively than the PS course during the day. Our results suggest that night PS is a more promising candidate as an effective method in preventing AD than daytime PS. These data are an important informative platform for the development of new noninvasive and nonpharmacological technologies for AD therapy as well as for preventing Aβ accumulation in the brain of people with disorder of Aβ metabolism, sleep deficit, elderly age, and jet lag.

Description: Prospective Life Cycle Assessment (pLCA) is useful to evaluate the environmental performance of current and emerging technologies in the future. Yet, as energy systems and industries are rapidly shifting towards cleaner means of production, pLCA requires an inventory database that encapsulates the expected changes in technologies and the environment at a given point in time, following specific socio-techno-economic pathways. To this end, this study introduces premise, a tool to streamline the generation of prospective inventory databases for pLCA by integrating scenarios generated by Integrated Assessment Models (IAM). More precisely, premise applies a number of transformations on energy-intensive activities found in the inventory database ecoinvent according to projections provided by the IAM. Unsurprisingly, the study shows that, within a given socio-economic narrative, the climate change mitigation target chosen affects the performance of nearly all activities in the database. This is illustrated by focusing on the effects observed on a few activities, such as systems for direct air capture of CO2, lithium-ion batteries, electricity and clinker production as well as freight transport by road, in relation to the applied sector-based transformation and the chosen climate change mitigation target. This work also discusses the limitations and challenges faced when coupling IAM and LCA databases and what improvements are to be brought in to further facilitate the development of pLCA.

Description: Experimental and clinical studies have shown that the technique of deep brain stimulation (DBS) plays a potential role in the regulation of Alzheimer’s disease (AD), yet it still desires for ongoing studies including clinical trials, theoretical approach and action mechanism. In this work, we develop a modified thalamo-cortico-thalamic (TCT) model associated with AD to explore the therapeutic effects of DBS on AD from the perspective of neurocomputation. First, the neuropathological state of AD resulting from synapse loss is mimicked by decreasing the synaptic connectivity strength from the Inter-Neurons (IN) neuron population to the Thalamic Relay Cells (TRC) neuron population. Under such AD condition, a specific deep brain stimulation voltage is then implanted into the neural nucleus of TRC in this TCT model. The symptom of AD is found significantly relieved by means of power spectrum analysis and nonlinear dynamical analysis. Furthermore, the therapeutic effects of DBS on AD are systematically examined in different parameter space of DBS. The results demonstrate that the controlling effect of DBS on AD can be efficient by appropriately tuning the key parameters of DBS including amplitude A, period P and duration D. This work highlights the critical role of thalamus stimulation for brain disease, and provides a theoretical basis for future experimental and clinical studies in treating AD.

Description: The atmosphere is a thermo-hydrodynamical complex system and provides oxygen to most animal life at the Earth's surface. However, the detection of complexity for the atmosphere remains elusive and debated. Here we develop a percolation-based framework to explore its structure by using the global air temperature field. We find that the percolation threshold is much delayed compared with the prototypical percolation model and the giant cluster eventually emerges explosively. A finite-size-scaling analysis reveals that the observed transition in each atmosphere layer is genuine discontinuous. Furthermore, at the percolation threshold, we uncover that the boundary of the giant cluster is self-affine, with fractal dimension df, and can be utilized to quantify the atmospheric complexity. Specifically, our results indicate that the complexity of the atmosphere decreases superlinearly with height, i.e., the complexity is higher at the surface than at the top layer and vice versa, due to the atmospheric boundary forcings. The proposed methodology may evaluate and improve our understanding regarding the critical phenomena of the complex Earth system and can be used as a benchmark tool to test the performance of Earth system models.

Description: Past attempts to reconstruct the Southern Annular Mode (SAM) using paleo archives have resulted in records which can differ significantly from one another prior to the window over which the proxies are calibrated. This study attempts to quantify not only the skill with which we may expect to reconstruct the SAM, but also assess the contribution of regional bias in proxy selection and the impact of non-stationary proxy-SAM teleconnections on a resulting reconstruction. This is achieved using a pseudo-proxy framework with output from the CM2.1 global climate model. Reconstructions derived from precipitation fields perform better, with 89 % of reconstructions calibrated over a 61 year window able to reproduce at least 50 % of inter-annual variance in the SAM, as opposed to just 25 % for surface temperature (SAT) derived reconstructions. Non-stationarity of proxy-SAM teleconnections, as defined here, plays a negligible role in reconstructions, however the range in reconstruction skill is not negligible. Reconstructions were most likely to be skilful when proxies are sourced from a geographically broad region, with a network size of 70 proxies.

Description: The transformation toward a healthy, just, and environmentally friendly food system needs to be reinforced—and not abandoned—in the face of the Russia-Ukraine war. We need comprehensive solutions that bring short-term relief and also avert the existential threat our food system poses to the health of people and the planet.

Description: Central banks can play an important role in the transition towards a climate-neutral economy. This paper discusses different green monetary policy instruments along the dimensions of feasibility of implementation and impact on the transition process. We identify the inclusion of ‘brown’ collateral haircuts into a central bank’s collateralized lending framework as the most promising conduit of green monetary policy. The impact of such interventions on the real economy is then formally explored by extending a general equilibrium transition model to include a simple banking sector with central bank lending facilities and collateral adjustments. We find that both ‘brown’ collateral haircuts and ‘green hairgrowth’ increase carbon neutral investment while decreasing carbon intensive investment and emissions. Consequently, in addition to decreasing the exposure of the central bank balance sheet to climate-related risks, climate-based collateral adjustments have the potential of increasing the political feasibility of a timely transition to a carbon neutral economy by affecting emission levels. Despite ‘green hairgrowth’ having a stronger effect on investment and emissions, ‘brown’ collateral haircuts remain the recommended policy as the former is not necessarily ‘market neutral’ and thus cannot be broadly applied across central banks.

Description: Few studies have used empirical evidence of past adaptation to project temperature-related excess mortality under climate change. Here, we assess adaptation in future projections of temperature-related excess mortality by employing evidence of shifting minimum mortality temperatures (MMTs) concurrent with climate warming of recent decades. The study is based on daily non-external mortality and daily mean temperature time-series from 11 Spanish cities covering four decades (1978–2017). It employs distributed lag non-linear models (DLNMs) to describe temperature-mortality associations, and multivariate mixed-effect meta-regression models to derive city- and subperiod-specific MMTs, and subsequently MMT associations with climatic indicators. We use temperature projections for one low- and one high-emission scenario (ssp126, ssp370) derived from five global climate models. Our results show that MMTs have closely tracked mean summer temperatures (MSTs) over time and space, with meta-regression models suggesting that the MMTs increased by 0.73 °C (95%CI: 0.65, 0.80) per 1 °C rise in MST over time, and by 0.84 °C (95%CI: 0.76, 0.92) per 1 °C rise in MST across cities. Future projections, which include adaptation by shifting MMTs according to observed temporal changes, result in 63.5% (95%CI: 50.0, 81.2) lower heat-related excess mortality, 63.7% (95%CI: 30.2, 166.7) higher cold-related excess mortality, and 11.2% (95%CI: −5.5, 39.5) lower total temperature-related excess mortality in the 2090s for ssp370 compared to estimates that do not account for adaptation. For ssp126, assumptions on adaptation have a comparatively small impact on excess mortality estimates. Elucidating the adaptive capacities of societies can motivate strengthened efforts to implement specific adaptation measures directed at reducing heat stress under climate change.

Description: Here, we demonstrate the therapeutic effects of transcranial photobiomodulation (tPBM, 1267 nm, 32 J/cm2, a 9-day course) in mice with the injected model of Alzheimer’s disease (AD) associated with accumulation of beta-amyloid (Aβ) in the brain resulting in neurocognitive deficit vs. the control group (CG) (the neurological severity score (NNS), AD 3.67 ± 0.58 vs. CG 1.00 ± 0.26%, p 〈 0.05) and mild cerebral hypoxia (AD 72 ± 6% vs. CG 97 ± 2%, p 〈 0.001). The course of tPBM improved neurocognitive status of mice with AD (NNS, AD 2.03 ± 0.14 vs. CG 1.00 ± 0.26, vs. 2.03 ± 0.14, p 〈 0.05) due to stimulation of clearance of Aβ from the brain via the meningeal lymphatic vessels (the immunohistochemical and confocal data) and an increase in blood oxygen saturation of the brain tissues (the pulse oximetry data) till 85 ± 2%, p 〈 0.05. These results open breakthrough strategies for non-pharmacological therapy of AD and clearly demonstrate that tPBM might be a promising therapeutic target for preventing or delaying AD based on stimulation of oxygenation of the brain tissues and activation of clearance of toxic molecules via the cerebral lymphatics.

Description: Continued investment in coal embroils regions in coal lock-ins, creating dependence and vested interests around coal and thereby limiting the speed and potential to switch to cleaner energy. In India, four states contribute 70% of coal production, with regions surrounding mines also housing significant operating and under-construction coal power stations. On the other hand, states in the west and south of India dominate current and near-term renewable energy capacity growth, broadly following patterns of highest resource potentials. We show that following current policies, by the end of the decade, coal-bearing states will likely sink deeper into carbon lock-ins, while the rest of the country, especially western and southern states could become increasingly decarbonised. Even in decarbonisation scenarios, gains from job and value creation in the clean energy sector might primarily take place away from existing coal regions, raising equity concerns, and ultimately putting the political feasibility of such a scenario in question. We suggest that policies aiming at higher renewable installations (mostly solar due to better potentials) in coal-bearing states, although not a one-to-one panacea, could provide an early break from lock-ins and into a just transition. This may, however, require a dedicated program and imply a small mark-up in power system costs. They would, however, help for medium-term diversification and job creation in all regions which will be key for assuring political support for the transition.

Description: The year 2020 marks the centennial of the publication of Arthur Cecil Pigou’s magnum opus The Economics of Welfare. Pigou’s pricing principles have had an enduring influence on the academic debate, with a widespread consensus having emerged among economists that Pigouvian taxes or subsidies are theoretically desirable, but politically infeasible. In this article, we revisit Pigou’s contribution and argue that this consensus is somewhat spurious, particularly in two ways: (1) Economists are too quick to ignore the theoretical problems and subtleties that Pigouvian pricing still faces; (2) The wholesale skepticism concerning the political viability of Pigouvian pricing is at odds with its recent practical achievements. These two points are made by, first, outlining the theoretical and political challenges that include uncertainty about the social cost of carbon, the unclear relationship between the cost–benefit and cost-effectiveness approaches, distributional concerns, fragmented ministerial responsibilities, an unstable tax base, commitment problems, lack of acceptance and trust between government and citizens as well as incomplete international cooperation. Secondly, we discuss the recent political success of Pigouvian pricing, as evidenced by the German government’s 2019 climate policy reform and the EU’s Green Deal. We conclude by presenting a research agenda for addressing the remaining barriers that need to be overcome to make Pigouvian pricing a common political practice.

Description: The Nobel Prize in Physics 2021 was awarded to Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi for their “groundbreaking contributions to our understanding of complex systems,” including major advances in the understanding of our climate and climate change. In this Perspective article, we review their key contributions and discuss their relevance in relation to the present understanding of our climate. We conclude by outlining some promising research directions and open questions in climate science. Classic complex systems, as coupled pendula, nonlinear circuits, or lasers, are typically constituted by a few elements or subsystems whose dynamical behavior and interactions are nonlinear and may involve memory effects. Due to these properties, they are able to generate rich and even chaotic dynamics, i.e., long-term predictions fail. In contrast, “complicated” systems can be very large, but their governing equations are linear. Thus, their system’s behavior can be understood by using a “reductionist” approach, and it can be well predicted from the behavior of the individual subsystems. However, many real systems are complex and they consist of many components, such as power grids or the human brain. Our Earth's climate system as a whole is another outstanding example of such a large complex system. Additionally, it covers a broad range of scales in space and time. Hence, it cannot be appropriately described and understood by using the reductionist approach but requires advanced techniques from complex systems science. Here, we discuss how the pioneering works of Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi (Physics Nobel Prize 2021) have given us crucial insights for understanding Earth's climate and basic underlying mechanisms of climate change and what are recent directions in this very active field of research.

Description: Global hydro-climatic trends are ambiguous, challenging the management of water resources. This challenge is addressed in the current study by investigating the impacts of hydro-climatic trends and upstream water management on hydropower generation at the Bagré dam. Modified Mann–Kendall trend test, Standard Normal Homogeneity Test and Pettit test were applied to some selected hydro-climatic variables for the trend and the change year detection, whereas the relationship between upstream dam management, hydro-climatic variables and hydropower were assessed through the Spearman correlation. The results revealed an annual positive trend for all hydro-climatic variables except for water level, lake evaporation and outflow. The break years observed in hydropower generation (2002) and inflow (2006) were mainly due to the construction of the Ziga dam in 2000 and its management change in 2005, respectively. The study also showed that hydropower generation declines each May (−30.36 MWh) and June (−16.82 MWh) due to the significant increase in irrigation withdrawals (1.94 hm3 in May and 0.67 hm3 in June). The results of this study highlighted the non-linearity in the relationship between hydropower generation and hydro-climatic variables as none of the correlation coefficients (apart from turbine) are very strong (〉0.8). As many human activities occurred in the basin, further research should be focused on the use of semi-distributed models to assess the impacts of water-use and land-use change on hydropower generation.

Description: In the 10 Must Knows from Biodiversity Science 45 scientists present facts about biodiversity in a well-founded and generally intelligible way. They analyse the complex systems of the earth by highlighting ten key areas, each of which, in turn, is inextricably linked to all the others. And they show ways to stop the continued loss of species diversity and ecosystems, and to promote biodiversity. The underlying aim is to provide policy-makers and society with scientifically validated assessments of the latest knowledge to facilitate improved policy decisions and action at local, regional, national and global levels, in order to conserve the diversity of life – biodiversity. These are the 10MustKnows 2022: 1. Achieving climate and biodiversity protection together 2. Strengthening planetary health 3. Considering hidden biodiversity 4. Promoting biocultural habitats 5. Using forests sustainably 6. Transforming agriculture 7. Protecting land and resources 8. Expanding transnational infrastructure and education for sustainability 9. Ensuring access and open use of research data 10. Setting biodiversity-friendly incentives

Description: Grasslands are subject to degradation in arid and semi-arid areas as the result of various pressures, including climate change. Therefore, spatial and temporal monitoring of vegetation cover fluctuation is needed for better understanding and management of natural resources to sustain livelihoods that depend on these grasslands. In this study, changes in vegetation cover were monitored over 1985-2015, using vegetation indices derived from the Landsat images in Chaharmahal-va-Bakhtiyari province, Iran. The percentage of vegetation cover was recorded from sampling plots in the study areas and vegetation indices were calculated from Landsat data over the study period. The relationship between vegetation cover and climate parameters (temperature (T) and precipitation (P)) was determined from precipitation maps, produced from spatial interpolation of weather stations data and temperature maps, produced by regression equation established between DEM-derived elevation and temperature. Then, a regression equation between climate variables and vegetation cover over a period of 30 years’ period was developed and applied on projected climatic variables for the 2050 under RCP2.6 and RCP8.5 scenarios. The Green Vegetation Index (GVI) was found to be the best index for estimation of vegetation cover from the correlation coefficient (r=0.79) and model RMSE (1.98). The GVI fluctuation was associated with the fluctuation in climate variables, and this was especially evident in especially dry years (1999, 2001 and 2012). From the difference between current and future vegetation cover maps, future vegetation cover change maps were produced and vulnerable areas most likely to be impacted by climate change were identified. The results predict that vegetation cover will be reduced under both scenarios, mostly in the eastern parts of the study region, while 5-25% of vegetation cover increases are projected for some areas in the western parts of the region. Further analysis showed that currently, oak forest covers large swaths of the western parts adjacent to grasslands, but forest degradation will lead to increase of grassland areas at the expense of oak forests. Overall, substantial loss of vegetation cover is predicted in the central parts and extends to the southern parts of the province. Loss of vegetation cover would pave the way to conversion of grassland to other non-productive cover types for pastoral systems. Appropriate management programs and adaptation strategies should be designed and implemented to reduce climate change impacts on grasslands in the identified vulnerable areas.

Description: It is a fundamental challenge to understand how brain function is related to its functional and structural organization, i.e., what shapes the neuronal activity patterns observed across scales that define cognitive and behavioral processes, as well as their breakdown in mental health disorders. The dynamics of neuronal networks strongly depends on intrinsic properties of the neuro-anatomical connectome and the functional relationships among neurons, and this goes beyond the connectivity matrix. In particular, the adaptation of the strengths of the synaptic connections through synaptic plasticity, the evolution of the functional connectivity in time, the inevitable time-delays resulting from both neurophysiological time constants and finite propagation velocity, noise, and inherent inhomogeneities play key roles in the emergent behavior of neuronal systems across spatial and temporal scales. A detailed characterization of these effects on the collective dynamics of neuronal networks may thus provide the means for studying the link between functional and structural connectivity and brain function. This Research Topic focuses on the structure-function relationship in neuronal networks at different temporal and spatial scales. The latter can range from fast-spiking and bursting dynamics of individual neurons, mean collective activity of neuronal populations to slow and ultra-slow fluctuations of neuronal and metabolic activity at the whole-brain scale. Special attention will be paid to the modeling of the neuronal plasticity (or adaptivity), impacts of time delays in coupling and intrinsic activity, and effects of noise or stochastic perturbations on individual and collective neuronal dynamics. The goal of this Research Topic is to collect a wide spectrum of theoretical, computational, and experimental articles, which introduce recent advances in the modeling and analysis of the interplay between the parameters that define the network structure and the repertoire of dynamical regimes of neuronal networks. The close comparison of theoretical/simulation results to empirical brain recordings may contribute to elucidate the observed phenomena from the perspective of complex networks and nonlinear dynamics. Such a collection might contribute to a better understanding of how the brain connectome structure can shape the neuronal activity in space and time, ultimately leading to cognition and behavior.

Description: Voluntary participation can improve multilateral environmental governance. We model voluntary participation of states in unanimously approved federal environmental policy. A Pareto-improving federal emission price coexists with state-level emission pricing. Federal revenues are distributed equally per capita (egalitarian), in proportion to states' historical emission levels (sovereignty), or states’ actual payments (juste retour). We find that the existence of Pareto-improving uniform federal prices depends on wealth differences, transfer rules, and on whether or not states anticipate transfers. Sovereignty transfers work in all cases. Differences in wealth can undermine egalitarian transfers. Juste retour transfers render federal policy ineffective if states anticipate them. The richest state prefers the lowest Pareto-optimal federal price (“minimum price”) as it becomes the largest net-donor. Adding different population sizes, the richest and largest (smallest) state prefers the minimum price with sovereignty and juste retour transfers (egalitarian transfers). Therefore, rich states brake and simultaneously make possible passing unanimous federal policy.

Description: A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (for ‘Rain for Peru and Ecuador’), at 0.1° spatial resolution for the period 1981-2015 covering Peru and Ecuador. It is based on the application of a) the random forest method to merge multi-source precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and b) observed and modeled streamflow data to firstly detect biases and secondly further adjust gridded precipitation by inversely applying the simulated results of the eco-hydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gauge-corrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model. For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Paċific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low-, high- and peak-flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.

Description: The role of seasonality is indisputable in climate and ecosystem dynamics. Seasonal temperature and precipitation variability are of vital importance for the availability of food, water, shelter, migration routes, and raw materials. Thus, understanding past climatic and environmental changes at seasonal scale is equally important for unearthing the history and for predicting the future of human societies under global warming scenarios. Alas, in palaeoenvironmental research, the term ‘seasonality change’ is often used liberally without scrutiny or explanation as to which seasonal parameter has changed and how. Here we provide fundamentals of climate seasonality and break it down into external (insolation changes) and internal (atmospheric CO2 concentration) forcing, and regional and local and modulating factors (continentality, altitude, large-scale atmospheric circulation patterns). Further, we present a brief overview of the archives with potentially annual/seasonal resolution (historical and instrumental records, marine invertebrate growth increments, stalagmites, tree rings, lake sediments, permafrost, cave ice, and ice cores) and discuss archive-specific challenges and opportunities, and how these limit or foster the use of specific archives in archaeological research. Next, we address the need for adequate data-quality checks, involving both archive-specific nature (e.g., limited sampling resolution or seasonal sampling bias) and analytical uncertainties. To this end, we present a broad spectrum of carefully selected statistical methods which can be applied to analyze annually- and seasonally-resolved time series. We close the manuscript by proposing a framework for transparent communication of seasonality-related research across different communities.

Description: Changes in atmospheric circulation under increasing greenhouse gas concentrations are important because of their implications for weather extremes and associated societal risks. However, uncertainties in models and future projections are still large and drivers behind circulation changes are not well understood. Particularly for Europe, a potential weakening of the Atlantic Meridional Overturning Circulation (AMOC) is considered important as it affects SST patterns and ocean-atmosphere heat fluxes and, subsequently, European climate. Here we detect and characterize changes in atmospheric circulation patterns over the North Atlantic under increasing CO2 concentrations in simulations of a very high-resolution, fully-coupled, climate model (CM2.6) with a realistic representation of the AMOC. We use an objective clustering technique (Self-Organizing Maps) and validate the model’s clusters against reanalysis data. We compare the frequency of those patterns in a CO2 doubling experiment, characterized by an AMOC decline, with those in a pre-industrial run, and find statistically significant changes. The most robust findings are (1) a ~30% increase in zonal flow regimes in February, relevant for flood risk in northwestern Europe, and (2) a ~60% increase in anticyclonic (high-pressure) circulation directly west of the UK in August, relevant for western and central European drought. A robust decrease in the frequency of Scandinavian Blocking is also seen across most months and seasons. Despite the uncertainties regarding atmospheric circulation response to climate change, our findings contribute to the increasing evidence for the emergence of robust high-impact changes over Europe.

Description: The analysis of irregularly sampled time series remains a challenging task requiring methods that account for continuous and abrupt changes of sampling resolution without introducing additional biases. The edit distance is an effective metric to quantitatively compare time series segments of unequal length by computing the cost of transforming one segment into the other. We show that transformation costs generally exhibit a nontrivial relationship with local sampling rate. If the sampling resolution undergoes strong variations, this effect impedes unbiased comparison between different time episodes. We study the impact of this effect on recurrence quantification analysis, a framework that is well suited for identifying regime shifts in nonlinear time series. A constrained randomization approach is put forward to correct for the biased recurrence quantification measures. This strategy involves the generation of a type of time series and time axis surrogates which we call sampling-rate-constrained (SRC) surrogates. We demonstrate the effectiveness of the proposed approach with a synthetic example and an irregularly sampled speleothem proxy record from Niue island in the central tropical Pacific. Application of the proposed correction scheme identifies a spurious transition that is solely imposed by an abrupt shift in sampling rate and uncovers periods of reduced seasonal rainfall predictability associated with enhanced ENSO and tropical cyclone activity.

Description: The study of the maximum number of consecutive dry days (MCDDs) is one approach to analyze precipitation behavior in arid and semi-arid regions of Iran. This study is a first attempt to investigate the MCDDs and their relationship with the El Niño/Southern Oscillation (ENSO) in winter months over Iran. The study was carried out using Tropical Rainfall Measuring Mission (TRMM) satellite data on a daily basis at 1° latitude × 1° longitude spatial resolution and reanalysis data for the period 1998–2019. Results showed that the highest values of MCDDs are observed in southeastern Iran and the lowest in northwestern Iran. Based on the coefficients of the linear trend of the MCDDs, the significant increasing trends are remarkably more abundant than declining trends, especially in the northern half of the country in December and January. The results regarding the relationship between ENSO and MCDDs indicated a non-stationary behavior, with a significant negative correlation for December (especially in the southwest) and positive correlation for January and February (especially in the southeast). The largest differences in the correlation coefficients were observed between December and January. In general, during El Niño (La Niña) phases, the length of MCDDs decreases (increases) in December and increases (decreases) in January especially in the southern half. By comparing different large-scale climate parameters for the 2 months, we found that during El Niño (La Niña) phases, a negative (positive) anomaly of geopotential height and a positive (negative) anomaly of zonal wind and specific humidity are observed over the region in December, while the opposite situation occurs in January. The innovation of this study is the use of satellite data that provide a continuous spatial coverage of the region and the consideration of the ENSO teleconnection pattern in regards to dry spells. We find that El Niño (La Niña) has contradictory effects on MCDDs in different winter months in the southern half of the country. These findings are of great importance for a country like Iran that lies in arid and semi-arid regions, as they can be useful for water resources management.

Description: Globally, mountain systems are unevenly exposed to risks of extreme precipitation. Within the Himalayan region, precipitation extremes are a rising concern, but their current understanding is limited. In this study, we use 113 years of precipitation data to rank and characterize precipitation extremes in the Indian Western Himalayas (IWH). Our statistical ranking method integrates precipitation spatial extent and its intensity across different durations for determining the severity of extreme events. The proposed ranking method accounts for multi-day duration ranking method to capture persistent precipitation episodes. Results show that the method accurately detects and ranks the most extreme precipitation events that occurred in the IWH and indicate locations of these events. Our results highlight that critical long duration events in the region (e.g., 10 days) are missed at ranks at shorter duration (e.g., 2–3 days), thereby highlighting the importance to multi-day precipitation extremes ranking. In addition, the proposed ranking method provides information about the event duration that will be associated with the highest impact on society, carrying high significance. Our findings are valuable for flood risk management and disaster risk reduction.

Description: Recent adverse weather events have questioned the stability of crop production systems. Here, we assessed the vulnerability of eleven major crops in France between 1959 and 2018 as a function of climate, crafting a novel hazard framework that combines exposure and sensitivity to weather-related hazards. Exposure was defined as the frequency of hazardous climate conditions. Sensitivity of crops was estimated by the yield response to single and compound hazards, using observed yields available at département (county) level. Vulnerability was computed as the exposure-weighted average of crop sensitivities. Our results do not reveal any evidence for historically increased vulnerability of French crop production. Sensitivity to adverse weather events, and thus the overall vulnerability, has significantly decreased for six of the eleven crops between 1959 and 2018, and shown no significant decline or remained stable for the other five. Yet compound hazards can induce yield losses of 30% or more for several crops. Moreover, as heat-related hazards are projected to become more frequent with climate change, crop vulnerability may rise again in the future.

Description: Changes in mean climatic conditions will affect natural and societal systems profoundly under continued anthropogenic global warming. Changes in the high-frequency variability of temperature exert additional pressures, yet the effect of greenhouse forcing thereon has not been fully assessed or identified in observational data. Here, we show that the intramonthly variability of daily surface temperature changes with distinct global patterns as greenhouse gas concentrations rise. In both reanalyses of historical observations and state-of-the-art projections, variability increases at low to mid latitudes and decreases at northern mid to high latitudes with enhanced greenhouse forcing. These latitudinally polarized daily variability changes are identified from internal climate variability using a recently developed signal-to-noise-maximizing pattern-filtering technique. Analysis of a multimodel ensemble from the Coupled Model Intercomparison Project Phase 6 shows that these changes are attributable to enhanced greenhouse forcing. By the end of the century under a business-as-usual emissions scenario, daily temperature variability would continue to increase by up to a further 100% at low latitudes and decrease by 40% at northern high latitudes. Alternative scenarios demonstrate that these changes would be limited by mitigation of greenhouse gases. Moreover, global changes in daily variability exhibit strong covariation with warming across climate models, suggesting that the equilibrium climate sensitivity will also play a role in determining the extent of future variability changes. This global response of the high-frequency climate system to enhanced greenhouse forcing is likely to have strong and unequal effects on societies, economies, and ecosystems if mitigation and protection measures are not taken.

Description: Anemia remains a pervasive public health problem among preschool-age children in Ghana. Recent analyses have found that anemia in Ghanaian children, particularly in Southern regions, is largely attributable to infectious causes, rather than nutritional factors. Infections with enteropathogens can reduce iron absorption and increase systemic inflammation, but few studies have examined direct links between enteropathogens and anemia. This study investigated associations between detection of individual bacterial enteropathogens and systemic inflammation, iron deficiency, and anemia among 6- to 59-month-old children in Greater Accra, Ghana. Serum samples were analyzed from a cross-sectional sample of 262 children for concentrations of hemoglobin (Hb), biomarkers of systemic inflammation [C-reactive protein (CRP) and α-1-acid glycoprotein (AGP)], and biomarkers of iron status [serum ferritin (SF) and serum transferrin receptor (sTfR)]. Stool samples were analyzed for ten bacterial enteropathogens using qPCR. We estimated associations between presence of each enteropathogen and elevated systemic inflammation (CRP 〉 5 mg/L and AGP 〉 1 g/L), iron deficiency (SF 〈 12 μg/L and sTfR 〉 8.3 mg/L) and anemia (Hb 〈 110 g/L). Enteropathogens were detected in 87% of children’s stool despite a low prevalence of diarrhea (6.5%). Almost half (46%) of children had anemia while one-quarter (24%) had iron deficiency (low SF). Despite finding no associations with illness symptoms, Campylobacter jejuni/coli detection was strongly associated with elevated CRP [Odds Ratio (95% CI): 3.49 (1.45, 8.41)] and elevated AGP [4.27 (1.85, 9.84)]. Of the pathogens examined, only enteroinvasive Escherichia coli/Shigella spp. (EIEC/Shigella) was associated with iron deficiency, and enteroaggregative Escherichia coli (EAEC) [1.69 (1.01, 2.84)] and EIEC/Shigella [2.34 (1.15, 4.76)] were associated with anemia. These results suggest that certain enteroinvasive pathogenic bacteria may contribute to child anemia. Reducing exposure to enteropathogens through improved water, sanitation, and hygiene practices may help reduce the burden of anemia in young Ghanaian children.

Description: Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various Representative Greenhouse Gas Concentration Pathways, all consistently bias-corrected on a 0.5° × 0.5° global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and for nearly 17,500 lakes using uncalibrated models and forcing data from the global grid where lakes are present. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.

Description: The tightened climate mitigation targets of the EU green deal raise an important question: Which strategy should be used to achieve carbon emissions net neutrality? This study explores stakeholder-designed narratives of the future energy system development within the deep decarbonization context. European carbon net-neutrality goals are put under test in a model comparison exercise using state of the art Energy-Environment-Economy (E3) models: ETM-UCL, PRIMES and REMIND. Results show that while achieving the transition to carbon neutrality by mid-century is feasible under quite different future energy systems, some robust commonalities emerge. Electrification of end use sectors combined with large-scale expansion of renewable energy is a no-regret decision for all strategies; Carbon Dioxide Removal (CDR) plays an important role for achieving net-neutral targets under all scenarios, but is most relevant when demand-side changes are limited; hydrogen and synthetic fuels can be a relevant mitigation option for mid-century mitigation in hard-to-abate sectors; energy efficiency can reduce the supply system strain. Finally, high carbon prices (300–900€/tCO2) are needed under all strategies in order to achieve carbon net neutrality in 2050.

Description: The EU Green Deal calls for climate neutrality by 2050 and emission reductions of 50–55% in 2030 in comparison to 1990. Achieving these reductions requires a substantial tightening of the regulations of the EU emissions trading system (EU ETS). This paper explores how the power sector would have to change in reaction to a tighter EU ETS target, and analyses the technological and economic implications. To cover the major ETS sectors, we combine a detailed power sector model with a marginal-abatement cost curve representation of industry emission abatement. We find that tightening the target would speed up the transformation by 3–17 years for different parts of the electricity system, with renewables contributing 74% of the electricity in 2030, EU-wide coal use almost completely phased-out by 2030 instead of 2045, and zero electricity generation emissions reached by 2040. Carbon prices within the EU ETS would more than triple to 129€/tCO2 in 2030, reducing cumulated power sector emissions from 2017 to 2057 by 54% compared to a scenario with the current target. This transformation would come at limited costs: total discounted power system costs would only increase by 5%. We test our findings against a number of sensitivities: an increased electricity demand, which might arise from sector coupling, increases deployment of wind and solar and prolongs gas usage. Not allowing transmission expansion beyond 2020 levels shifts investments from wind to PV, hydrogen and batteries, and increases total system costs by 3%. Finally, the unavailability of fossil carbon capture and storage (CCS) or further nuclear investments does not impact results. Unavailability of bioenergy-based CCS (BECCS) has a visible impact (18% increase) on cumulated power sector emissions, thus shifting more of the mitigation burden to the industry sector, but does not increase electricity prices or total system costs (〈1% increase).

Description: With widespread application of voltage source converter (VSC) as a key energy-conversion power electronic device by using the phase-locked loop (PLL) technique for synchronization, the system dynamics has become much complicated. In this paper, the nonlinear dynamics and transient stability of the PLL-based VSC system are investigated, within a unified framework of the (normalized) generalized swing equation. It is found that there are three different types of bifurcation, including the generalized saddle-node, Hopf, and homoclinic bifurcations. Within the coexistence parameter region, the basin boundary of the stable equilibrium point shows either a closed-loop or a fish-like pattern. With the help of the equal area criterion (EAC), the transient stabilities under different transient disturbances including short circuit, voltage dip, and power rise are analyzed. Because the equivalent damping of the VSC is state-dependent, the theoretical results based on the EAC are examined. Furthermore, based on the analytical results from the generalized swing equation, the principle for all major transient stability enhancement methods is uncovered. All these findings are well verified by extensive electromagnetic transient simulations. Therefore, the generalized swing equation provides a deeper physical insight and plays a crucial role in transient stability problems in power-electronic-dominated power systems, similar to the swing equation in traditional power systems.