ALBERT

Description: In this paper, the existence (invariance) and stability (locally and globally) of relay interlayer synchronisation (RIS) are investigated in a chain of multiplex networks. The local dynamics of the nodes in the symmetric positions layers on both sides of the non-identical middlemost layer(s) are identical. The local and global stability conditions for this synchronisation state are analytically derived based on the master stability function approach and by constructing a suitable Lyapunov function, respectively. We propose an appropriate demultiplexing process for the existence of the RIS state. Then the variational equation transverse to the RIS manifold for demultiplexed networks is derived. In numerical simulations, the impact of interlayer and intralayer coupling strengths, variations of the system parameter in the relay layers and demultiplexing on the emergence of RIS in triplex and pentaplex networks are explored. Interestingly, in this multiplex network, enhancement of RIS is observed when a type of impurity via parameter mismatch in the local dynamics of the nodes is introduced in the middlemost layer. A common time-lag with small amplitude shift between the symmetric positions and central layers plays an important role for the enhancing of relay interlayer synchrony. This analysis improves our understanding of synchronisation states in multiplex networks with nonidentical layers.

Description: We investigate the response characteristics of a two-dimensional neuron model exposed to an externally applied extremely low frequency (ELF) sinusoidal electric field and the synchronization of neurons weakly coupled with gap junction. We find, by numerical simulations, that neurons can exhibit different spiking patterns, which are well observed in the structure of the recurrence plot (RP). We further study the synchronization between weakly coupled neurons in chaotic regimes under the influence of a weak ELF electric field. In general, detecting the phases of chaotic spiky signals is not easy by using standard methods. Recurrence analysis provides a reliable tool for defining phases even for noncoherent regimes or spiky signals. Recurrence-based synchronization analysis reveals that, even in the range of weak coupling, phase synchronization of the coupled neurons occurs and, by adding an ELF electric field, this synchronization increases depending on the amplitude of the externally applied ELF electric field. We further suggest a novel measure for RP-based phase synchronization analysis, which better takes into account the probabilities of recurrences.

Description: We investigate the response characteristics of a two-dimensional neuron model exposed to an externally applied extremely low frequency (ELF) sinusoidal electric field and the synchronization of neurons weakly coupled with gap junction. We find, by numerical simulations, that neurons can exhibit different spiking patterns, which are well observed in the structure of the recurrence plot (RP). We further study the synchronization between weakly coupled neurons in chaotic regimes under the influence of a weak ELF electric field. In general, detecting the phases of chaotic spiky signals is not easy by using standard methods. Recurrence analysis provides a reliable tool for defining phases even for noncoherent regimes or spiky signals. Recurrence-based synchronization analysis reveals that, even in the range of weak coupling, phase synchronization of the coupled neurons occurs and, by adding an ELF electric field, this synchronization increases depending on the amplitude of the externally applied ELF electric field. We further suggest a novel measure for RP-based phase synchronization analysis, which better takes into account the probabilities of recurrences

Description: The data file `CENOGRID_Loess_20.txt` contains the astronomically tuned deep-sea benthic foraminifer carbon (δ¹³C) and oxygen (δ¹⁸O) isotope reference records uniformly covering the entire Cenozoic. The first column is the tuned age in Ma, the second column the δ¹³C, and the third column the δ¹⁸O record. The original calculations were performed using the CRP Toolbox for MATLAB. In order to avoid installing the toolbox and for better performance, the functions for calculating RP and RQA were here reimplemented, providing identical result. To reproduce the RPs in Fig. 2, use the script `perform_rp.m`, for reproducing the determinism values and upper confidence bounds, use the script `perform_rqa.m`.

Description: This article examines the effects of extreme weather events on internal migration in Mongolia. Our focus is on dzuds , extremely harsh winters characterized by very cold temperature, snowfall anomalies, and/or storms causing very high livestock mortality. We exploit exogenous variation in the intensity of extreme winter events across time and space to identify their causal impacts on permanent domestic migration. Our database is a time series of migration and population data at provincial and district level from official population registries, spanning the 1992-2018 period. Results obtained with a two-way fixed effects panel estimator show that extreme winter events cause significant and sizeable permanent out-migration from affected provinces for up to two years after an event. These effects are confirmed when considering net change rates in the overall population at the district level. The occurrence of extreme winter events is also a strong predictor for declines in the local population of pastoralist households, the socio-economic group most affected by those events. This suggests that the abandonment of pastoralist livelihoods is an important channel through which climate affects within-country migration.

Description: Background: Women in rural Bangladesh face multiple, inter-related challenges including food insecurity, malnutrition, and low levels of empowerment. We aimed to investigate the pathway towards empowerment experienced by women participating in a three-year nutrition-sensitive homestead food production (HFP) program, which was evaluated through the Food and Agricultural Approaches to Reducing Malnutrition (FAARM) cluster-randomized controlled trial. Methods: We conducted 44 in-depth interviews and 12 focus group discussions with men and women in both intervention and control communities of the FAARM study site in rural, north-eastern Bangladesh. Using a modified grounded theory approach to data collection and analysis, we developed a framework to explain the pathway towards empowerment among HFP program participants. Results: The analysis and resulting framework identified seven steps towards empowerment: 1) receiving training and materials; 2) establishing home gardens and rearing poultry; 3) experiencing initial success with food production; 4) generating social or financial resources; 5) expanding agency in household decision-making; 6) producing renewable resources (e.g. farm produce) and social resources; and 7) sustaining empowerment. The most meaningful improvements in empowerment occurred among participants who were able to produce food beyond what was needed for household consumption and were able to successfully leverage these surplus resources to gain higher bargaining power in their household. Additionally, women used negotiation skills with their husbands, fostered social support networks with other women, and developed increased self-efficacy and motivation. Meanwhile, the least empowered participants lacked support in critical areas, such as support from their spouses, social support networks, or sufficient space or time to produce enough food to meaningfully increase their contribution and therefore bargaining power within their household. Conclusions: This study developed a novel framework to describe a pathway to empowerment among female participants in an HFP intervention, as implemented in the FAARM trial. These results have implications for the design of future nutrition-sensitive agriculture interventions, which should prioritize opportunities to increase empowerment and mitigate the barriers identified in our study. Trial registration: FAARM is registered with ClinicalTrials.gov (NCT02505711).

Description: The ongoing transition to renewable energy supply comes with a restructuring of power grids, changing their effective interaction topologies, more and more strongly decentralizing them and substantially modifying their input, output, and response characteristics. All of these changes imply that power grids become increasingly affected by collective, nonlinear dynamic phenomena, structurally and dynamically more distributed and less predictable in space and time, more heterogeneous in its building blocks, and as a consequence less centrally controllable. Here cornerstone aspects of data-driven and mathematical modeling of collective dynamical phenomena emerging in real and model power grid networks by combining theories from nonlinear dynamics, stochastic processes and statistical physics, anomalous statistics, optimization, and graph theory are reviewed. The mathematical background required for adequate modeling and analysis approaches is introduced, an overview of power system models is given, and a range of collective dynamical phenomena are focused on, including synchronization and phase locking, flow (re)routing, Braess’s paradox, geometric frustration, and spreading and localization of perturbations and cascading failures, as well as the nonequilibrium dynamics of power grids, where fluctuations play a pivotal role.

Description: Partial synchronization patterns play an important role in the functioning of neuronal networks, both in pathological and in healthy states. They include chimera states, which consist of spatially coexisting domains of coherent (synchronized) and incoherent (desynchronized) dynam- ics, and other complex patterns. In this perspective article we show that partial synchronization scenarios are governed by a delicate interplay of local dynamics and network topology. Our focus is in particular on applications of brain dynamics like unihemispheric sleep and epileptic seizure.

Description: For light-duty vehicles (LDVs), alternative powertrains and liquid fuels based on renewable electricity are competing options considered by policymakers and stakeholders for achieving necessary CO2 emission reductions in the transport sector. While the urgency of climate change and the need to reach mitigation targets are well understood, system-wide implications along other sustainability dimensions need further exploration. We integrate a detailed transport system model into an integrated assessment framework and couple it with prospective life cycle impact analysis. This allows to assess different technological pathways of the European LDV fleet until 2050 for a comprehensive set of environmental and resource depletion indicators. Results indicate that greenhouse gas emissions drop significantly in all mitigation scenarios. However, impacts increase in several non-climate change impact categories even with fully renewable electricity supply. Additional impacts arise from the production of battery and fuel-cell components, and from a significant rise in electricity demand, most prominently for synthetic fuels. We consequently find that changes in mobility life-styles and in the relevant industrial processes are paramount to reduce environmental impacts from a climate-friendly LDV fleet across all categories.

Description: This repository contains the source files for processing the datasets, running the analysis, and producing the plots for the following publication: Niklas H. Kitzmann, Pawel Romanczuk, Nico Wunderling and Jonathan F. Donges (2022). Detecting contagious spreading of urban innovations on the global city network. European Physical Journal Special Topics. DOI:10.1140/epjs/s11734-022-00470-4

Description: Climate change increasingly affects agricultural systems, making it necessary for farmers to adapt to changing climatic conditions. An important element shaping farmers’ adaptation decisions and their vulnerability is their respective land tenure system. Especially land tenure security can strongly influence farmers’ incentives for adapting to climate change. We review the literature to understand to what extent tenure security and other land tenure characteristics affect farmers’ ability to withstand climate change and how climate change is operationalised. 106 mostly peer-reviewed studies are examined using thematic network analysis and a network of interactions between land tenure and climate change in farming contexts is devised. The results show that three main interactions link land tenure systems and climate change with regard to agricultural livelihoods: (i) land tenure characteristics influence farmers’ adaptation uptake, type and intensity, (ii) certain tenure settings contribute to vulnerability of different socio-demographic groups in agricultural systems, e.g. women, migrants and indigenous communities, and (iii) the perception of tenure security itself is affected by climate change. Yet, the concept of tenure security is poorly defined in most studies, at times resulting in misleading conclusions and leaving important research gaps with regard to optimal land tenure incentives for farmers’ adaptation response. Climate change is often simplistically integrated into the assessments, without validation and analysis of longer term trends. None of the studies reviewed provides a comprehensive and systematic treatment of the multiple dimensions linking climate change and land tenure. Further exploration and empirical validation of the connection between land tenure and climate change in agricultural systems is thus warranted. This should include a more critical engagement with the exigencies of climate change response for agricultural systems, such as the need for flexible approaches to deal with climatic uncertainty. The results of this review are relevant for informing adaptation policy, where sustainable land governance has an integral role to play. Designing smart land tenure interventions based on improved understanding of (local) interactions between land tenure and climate can support farmers in effectively addressing the adverse effects of climate change.

Description: An equilibrium of a delay differential equation (DDE) is absolutely stable, if it is locally asymptotically stable for all delays. We present criteria for absolute stability of DDEs with discrete time-delays. In the case of a single delay, the absolute stability is shown to be equivalent to asymptotic stability for sufficiently large delays. Similarly, for multiple delays, the absolute stability is equivalent to asymptotic stability for hierarchically large delays. Additionally, we give necessary and sufficient conditions for a linear DDE to be hyperbolic for all delays. The latter conditions are crucial for determining whether a system can have stabilizing or destabilizing bifurcations by varying time delays.

Description: Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year’s data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill.

Description: COVID-19 has revealed how challenging it is to manage global, systemic and compounding crises. Like COVID-19, climate change impacts, and maladaptive responses to them, have potential to disrupt societies at multiple scales via networks of trade, finance, mobility and communication, and to impact hardest on the most vulnerable. However, these complex systems can also facilitate resilience if managed effectively. This review aims to distil lessons related to the transboundary management of systemic risks from the COVID-19 experience, to inform climate change policy and resilience building. Evidence from diverse fields is synthesised to illustrate the nature of systemic risks and our evolving understanding of resilience. We describe research methods that aim to capture systemic complexity to inform better management practices and increase resilience to crises. Finally, we recommend specific, practical actions for improving transboundary climate risk management and resilience building. These include mapping the direct, cross-border and cross-sectoral impacts of potential climate extremes, adopting adaptive risk management strategies that embrace heterogenous decision-making and uncertainty, and taking a broader approach to resilience which elevates human wellbeing, including societal and ecological resilience.

Description: This paper designs an event-triggering based communication strategy for the global attitude synchronization of a network of rigid bodies. To overcome the topological constraint on the manifold SO(3), the quaternion-based hybrid control strategy is designed using a binary logic variable, relying on the relative measurements of adjacent rigid bodies, to determine the torque orientation. The Zeno-free distributed event-triggering strategies (ETSs) are designed combining with the reset of the binary logic variable to generate discrete communication instants, where only the corresponding parts of the control inputs are updated at those discrete instants. By assuming perfect knowledge of the rigid bodies' dynamics and considering uncertainties and/or exogenous disturbances simultaneously, nominal and robust cases are analyzed to ensure the global attitude synchronization, respectively. The effectiveness of the main results is demonstrated by considering the attitude synchronization of six miniature quadrotor prototypes.

Description: This collection provides a contemporary excerpt of “Cities as complex systems”. The contributions have been submitted between April and October 2020. We briefly discuss example papers addressing the themes “urban scaling”, “urban mobility”, “flows in cities”, “spatial analysis”, “information technology and cities”, and “cities in time”. After motivating the intersection of cities and complexity, we provide an introduction and additional thoughts on urban scaling.

Description: With the prevalence of COVID-19, the modeling of epidemic propagation and its analyses have played a significant role in controlling epidemics. However, individual behaviors, in particular the self-protection and migration, which have a strong influence on epidemic propagation, were always neglected in previous studies. In this paper, we mainly propose two models from the individual and population perspectives. In the first individual model, we introduce the individual protection degree that effectively suppresses the epidemic level as a stochastic variable to the SIRS model. In the alternative population model, an open Markov queueing network is constructed to investigate the individual number of each epidemic state, and we present an evolving population network via the migration of people. Besides, stochastic methods are applied to analyze both models. In various simulations, the infected probability, the number of individuals in each state and its limited distribution are demonstrated.

Description: In industrial control systems, industrial infrastructure is often attacked by hackers. Due to the serious sample imbalance in industrial control data, the traditional machine learning method has poor performance in anomaly detection. In this paper, TrAdaboost algorithm is applied to industrial control anomaly detection. The samples that are easy to classify are taken as the source domain data, and the samples with poor classification effect are taken as the target domain. The source domain data is used to guide the target domain data training. Then, we improve the traditional TrAdaboost algorithm from two aspects of initial weight and final classifier, and apply it to industrial control anomaly detection. Finally, the performance of the algorithm on two different industrial control data sets is verified. And the improved algorithm is compared with other traditional algorithms. The experimental results show that the improved TrAdaboost algorithm has a significant advantage in predicting categories with a small sample size. This algorithm can accurately identify a few abnormal samples. Moreover, the F1 value, recall and precision value of the improved TrAdaboost algorithm on the two data sets have been significantly improved. This indicates that the improved TrAdaboost algorithm greatly improves the overall prediction accuracy of the model.

Description: To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). The SWAT+ model represents agricultural land by default in a generic way where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for Northeast Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural Hydrologic Response Units (HRUs) which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in regional to global hydrological models.

Description: Land management practices can reduce the environmental impact of agricultural land use and production, improve productivity, and transform cropland into carbon sinks. In our study we assessed the biophysical and biogeochemical impacts and the potential contribution of cover crop practices to sustainable land use. We applied the process-based, global dynamic vegetation model LPJmL (Lund–Potsdam–Jena managed Land) V. 5.0-tillage-cc with a modified representation of cover crops to simulate the growth of grasses on cropland in periods between two consecutive main crops' growing seasons for near-past climate and land use conditions. We quantified simulated responses of agroecosystem components to cover crop cultivation in comparison to bare-soil fallowing practices on global cropland for a period of 50 years. For cover crops with tillage, we obtained annual global median soil carbon sequestration rates of 0.52 and 0.48 t C ha−1 yr−1 for the first and last decades of the entire simulation period, respectively. We found that cover crops with tillage reduced annual nitrogen leaching rates from cropland soils by medians of 39 % and 54 % but also the productivity of the following main crop by an average of 1.6 % and 2 % for the 2 analyzed decades. The largest reductions in productivity were found for rice and modestly lowered ones for maize and wheat, whereas the soybean yield revealed an almost homogenously positive response to cover crop practices replacing bare-soil fallow periods. The obtained simulation results of cover crop with tillage practices exhibit a good ability of the model version to reproduce observed effects reported in other studies. Further, the results suggest that having no tillage is a suitable complementary practice to cover crops, enhancing soil carbon sequestration and the reduction in nitrogen leaching, while reducing potential trade-offs with the main-crop productivity due to their impacts on soil nitrogen and water dynamics. The spatial heterogeneity of simulated impacts of cover crops on the variables assessed here was related to the time period since the introduction of the management practice as well as to environmental and agronomic conditions of the cropland. This study supports findings of other studies, highlighting the substantial potential contribution of cover crop practices to the sustainable development of arable production.

Description: The Mid-Brunhes Transition (MBT) refers to the change in the amplitude of glacial-interglacial cycles around 430 ka BP, with more pronounced, warmer interglacials after ca. 430 ka BP. Despite the advances in the understanding of glacial cycles, the cause and mechanism of the MBT are still not entirely clear. In this study we examine (i) whether the MBT is caused by a change in the intrinsic dynamics of glacial cycles and (ii) how important the systematic changes of the orbital elements across the MBT are for the occurrence of the MBT. In order to address these questions, we take a pure machine-learning approach. We develop an artificial neural network model which provides a skilful 21-ka ahead prediction of glacial-interglacial changes in the LR04 benthic δ18O stack record as well as a sea level reconstruction obtained by an inverse model. This allows us to predict the interglacial levels from glacial conditions. Although the neural network model is trained over a pre-MBT period of 900–450 ka BP, it exhibits the intensification of interglacials after 450 ka BP. This suggests that the dynamical characteristics generating the stronger post-MBT interglacials is inherent already before the MBT. When the neural network model is forced by a hypothetical insolation for which the amplitude of the obliquity cycles is kept at pre-MBT level, the MBT-like phenomenon does not appear in our simulations. In line with earlier suggestions, our results thus give quantitative evidence that the MBT is caused by amplitude changes of the obliquity forcing. For comparison, our results suggest that the change in the mean eccentricity level across the MBT has a smaller impact on the appearance of the MBT.

Description: In this study, we provide a dynamical systems perspective to the modelling of pathological states induced by tumors or infection. A unified disease model is established using the innate immune system as the reference point. We propose a two-layer network model for carcinogenesis and sepsis based upon the interaction of parenchymal cells and immune cells via cytokines, and the co-evolutionary dynamics of parenchymal, immune cells, and cytokines. Our aim is to show that the complex cellular cooperation between parenchyma and stroma (immune layer) in the physiological and pathological case can be qualitatively and functionally described by a simple paradigmatic model of phase oscillators. By this, we explain carcinogenesis, tumor progression, and sepsis by destabilization of the healthy homeostatic state (frequency synchronized), and emergence of a pathological state (desynchronized or multifrequency cluster). The coupled dynamics of parenchymal cells (metabolism) and nonspecific immune cells (reaction of innate immune system) are represented by nodes of a duplex layer. The cytokine interaction is modeled by adaptive coupling weights between the nodes representing the immune cells (with fast adaptation time scale) and the parenchymal cells (slow adaptation time scale) and between the pairs of parenchymal and immune cells in the duplex network (fixed bidirectional coupling). Thereby, carcinogenesis, organ dysfunction in sepsis, and recurrence risk can be described in a correct functional context.

Description: The files contain MAgPIE 4.3.5 results of reduced wood harvest and forest protection scenarios. MAgPIE requires GAMS (https://www.gams.com/) including licenses for the solvers CONOPT and (optionally) CPLEX for its core calculations. As the model benefits significantly from recent improvements in GAMS and CONOPT4 it is recommended to work with the most recent versions of both. The results of the model run here have been cleaned up to avoid bulky uploads. The fulldata.gdx is the technical output of the GAMS optimization and contains all quantities that were used during the optimization in unchanged form. The mif-file is a CSV file of a specific format and is synthetized from the fulldata.gdx by post-processing scripts. It can be read in any text editor or spreadsheet program and is well suited for a brief look at the results and for further analysis.

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: 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: 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: Joint Forest Management (JFM) is a form of participatory forest governance that aims for protection, conservation and sustainable use of forest resources by involving local communities. The JFM reforms have been promoted to address forest and land degradation, as vital in reducing institutional uncertainty in complex environments and strengthening cooperation among resource users. We draw on theories of collective action and transaction costs assuming that the overharvesting problem can be reduced by efficient and effective rules that support cooperation between forest users in using common pool resources at the group level and explore how forest users respond to policies that aim to reduce overharvesting in Tajikistan. To this end, we used a framed field experiment involving actual forest resource users. We find a strong impact of rules and the associated transaction costs in dealing with environmental and institutional uncertainties. The experiment results indicate that the harvesting rate is likely to decrease when institutionalized mechanisms are introduced to coordinate the interdependence among resource users. The overall results suggest that the rule determining harvest on a rotational basis is effective in reducing harvesting under environmental uncertainty regardless of the existence of communication and under institutional uncertainty when communication is permitted.

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: 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: 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: 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: Non-continuous flooding is an effective practice for reducing greenhouse gas (GHG) emissions and irrigation water use (IRR) in rice fields. However, advancing global implementation is hampered by the lack of comprehensive understanding of GHGs and IRR reduction benefits without compromising rice yield. Here, we present the largest observational data set for such effects as of yet. By using Random Forest regression models based on 636 field trials at 105 globally georeferenced sites, we identified the key drivers of effects of non-continuous flooding practices and mapped maximum GHGs or IRR reduction benefits under optimal non-continuous flooding strategies. The results show that variation in effects of non-continuous flooding practices are primarily explained by the UnFlooded days Ratio (UFR, that is the ratio of the number of days without standing water in the field to total days of the growing period). Non-continuous flooding practices could be feasible to be adopted in 76% of global rice harvested areas. This would reduce the global warming potential (GWP) of CH4 and N2O combined from rice production by 47% or the total GWP by 7% and alleviate IRR by 25%, while maintaining yield levels. The identified UFR targets far exceed currently observed levels particularly in South and Southeast Asia, suggesting large opportunities for climate mitigation and water use conservation, associated with the rigorous implementation of non-continuous flooding practices in global rice cultivation.

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: 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: During the past decades, convective rainfall in the Echaz catchment, which is characterized by steep topography and a high degree of urbanization, led to recurring flash floods. The high spatial and temporal variability of precipitation, in combination with the small drainage basin, contribute to low predictability and to the considerable damage potential of such events. The aim of this study is the development of a simple model to predict flood severity in the Echaz catchment. This model is based on open-access precipitation data from a personal weather station (PWS) network and water level measurements from a low-cost ultrasonic sensor. Machine learning classification methods (logistic regression and decision tree) are trained with observational data to determine maximum rainfall thresholds for different accumulation periods, ranging from 5 to 60 min. Hence, the proposed model uses multiple triggers to predict the exceedance of critical water levels. As a result, severe floods can be recognized earlier and with higher reliability, providing more response time for local authorities. Although the limited data availability increases the risk of overfitting and lower performance for the first upcoming events, the model quality will increase with the incorporation of new measurement data in the future. The reduced complexity and high interpretability of the model allow for a fast decision-making process. Additionally, the model has high potential and can easily be adapted to similar small catchments.

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: 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: Natural gas based hydrogen production with carbon capture and storage is referred to as blue hydrogen. If substantial amounts of CO2 from natural gas reforming are captured and permanently stored, such hydrogen could be a low-carbon energy carrier. However, recent research raises questions about the effective climate impacts of blue hydrogen from a life cycle perspective. Our analysis sheds light on the relevant issues and provides a balanced perspective on the impacts on climate change associated with blue hydrogen. We show that such impacts may indeed vary over large ranges and depend on only a few key parameters: the methane emission rate of the natural gas supply chain, the CO2 removal rate at the hydrogen production plant, and the global warming metric applied. State-of-the-art reforming with high CO2 capture rates combined with natural gas supply featuring low methane emissions does indeed allow for substantial reduction of greenhouse gas emissions compared to both conventional natural gas reforming and direct combustion of natural gas. Under such conditions, blue hydrogen is compatible with low-carbon economies and exhibits climate change impacts at the upper end of the range of those caused by hydrogen production from renewable-based electricity. However, neither current blue nor green hydrogen production pathways render fully “net-zero” hydrogen without additional CO2 removal.

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 den 10 Must-Knows aus der Biodiversitätsforschung legen 45 Wissenschaftlerinnen und Wissenschaftler fundiert und allgemein verständlich Fakten zur biologischen Vielfalt dar. Sie analysieren die komplexen Systeme der Erde, indem sie zehn Schlüsselbereiche hervorheben, von denen wiederum jeder untrennbar mit allen anderen verbunden ist. Sie zeigen Wege auf, um einen weiteren Verlust an Artenvielfalt und Ökosystemen zu stoppen und die biologische Vielfalt zu fördern. Ihr Ziel ist, für Politik und Gesellschaft wissenschaftlich gesicherte Bewertungen der aktuellen Erkenntnisse für bessere politische Entscheidungen und Maßnahmen auf lokaler, regionaler, nationaler und globaler Ebene zur Verfügung zu stellen, um die Vielfalt des Lebens – die Biodiversität – zu erhalten. Dies sind die 10MustKnows 2022: 1. Klima- und Biodiversitätsschutz zusammen verwirklichen 2. Planetare Gesundheit stärken 3. Unsichtbare Biodiversität beachten 4. Biokulturelle Lebensräume fördern 5. Wald nachhaltig nutzen 6. Landwirtschaft umbauen 7. Land und Ressourcen schützen 8. Transnationale Infrastrukturen und Bildung für Nachhaltigkeit 9. Zugang und offene Nutzung von Forschungsdaten sichern 10. Biodiversitätsfreundliche Anreize setzen

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: 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: 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: 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: 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: 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.

Description: In this paper, we have investigated the collective dynamical behaviors of a network of identical Hindmarsh–Rose neurons that are coupled under small-world schemes upon the addition of α-stable Lévy noise. According to the firing patterns of each neuron, we distinguish the neuronal network into spike state, burst state and spike-burst state coexistence of the neuron with both a spike firing pattern and a burst firing pattern. Moreover, the strength of the burst is proposed to identify the firing states of the system. Furthermore, an interesting phenomenon is observed that the system presents coherence resonance in time and chimera states in space, namely coherence-resonance chimeras (CRC). In addition, we show the influences of α-stable Lévy noise (noise intensity and stable parameter) and the small-world network (the rewiring probability) on the spike-burst state and CRC. We find that the stable parameter and noise intensity of the α-stable noise play a crucial role in determining the CRC and spike-burst state of the system.

Description: This paper is intended to solve the fully distributed and observer-based consensus control problem of multi-agent systems with general linear dynamics under event-triggered communication (ETC). Two novel event-triggered strategies named adaptive dynamic event-triggered (ADET) schemes for one-to-all and one-to-one ETC are well developed, in which on-line triggering parameters associated with each node or edge and dynamic thresholds with updating laws are introduced, respectively. Firstly, an ADET consensus control protocol under one-to-all ETC is proposed and a sufficient condition for consensus is derived. Compared with most existing triggering rules, the on-line triggering parameter associated with each node makes the controller be designed in a fully distributed way. On the other hand, the proposed dynamic threshold has the potential of excluding Zeno behavior without sacrificing the system performance. Secondly, an ADET consensus control protocol under one-to-one ETC is studied, in which each agent asynchronously transmits its observer states to its neighbors depending on edge-based triggering functions. The one-to-one ETC provides a flexible self-regulated transmission mode. An effective fully distributed and observer-based consensus protocol is developed appropriately with available local measurement outputs, under which the agents are not required to have a priori knowledge of any global information. Further discussion on the efficiency of ADET strategies and comparison between state-based and observer-based ADET control are provided to enrich the distributed resources-aware control framework. Finally, one simulation example is provided to illustrate the theoretical results.

Description: Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation optimization. In a multi-agent system, agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination, which is manifested as cooperative/competitive behavior. This survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative games. Starting from cooperative optimization, the studies on distributed optimization and federated optimization are summarized. The survey mainly focuses on distributed online optimization and its application in privacy protection, and overviews federated optimization from the perspective of privacy protection mechanisms. Then, cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs, respectively. Multi-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects, according to whether each player can make decisions based on the information of other players. Finally, future directions for cooperative optimization, cooperative/non-cooperative games, and their applications are discussed.

Description: The Market Stability Reserve (MSR) was introduced into the European Union Emissions Trading System to address a historical surplus of emission allowances and to improve the system’s resilience to major shocks through automatic adjustments to the supply of allowances. We summarize the main strengths and weaknesses of the MSR and identify when it stabilizes the market as intended, as well as when it is destabilizing. We argue that recently proposed design changes strengthen both its stabilizing and destabilizing effects. We conclude that a price-based supply adjustment mechanism would help to address the main shortcomings rooted in the banking-based approach of the current MSR design.

Description: A steep decline in the quality and quantity of available climate proxy records before medieval times challenges any comparison of reconstructed temperature and hydroclimate trends and extremes between the first and second half of the Common Era. Understanding of the physical causes, ecological responses and societal con- sequences of past climatic changes, however, demands highly-resolved, spatially-explicit, seasonally-defined and absolutely-dated archives over the entire period in question. Continuous efforts to improve existing proxy records and reconstruction methods and to develop new ones, as well as clear communication of all uncertainties (within and beyond academia) must be central tasks for the paleoclimate community.

Description: Ambient Forcing is a novel method to sample random states from manifolds of differential-algebraic equations (DAE). These states can represent local perturbations of nodes in power systems with loads, which introduces constraints into the system. These states must be valid initial conditions to the DAE, meaning that they fulfill the algebraic equations. Additionally, these states should represent perturbations of individual variables in the power grid, such as a perturbation of the voltage at a load. These initial states enable the calculation of probabilistic stability measures of power systems with loads, which was not yet possible, but is important as these measures have become a crucial tool in studying power systems. To verify that these perturbations are network local, i.e. that the initial perturbation only targets a single node in the power grid, a new measure, the spreadability, related to the closeness centrality [1], is presented. The spreadability is evaluated for an ensemble of typical power grids. The ensemble depicts a set of future power grids where consumers, as well as producers, are connected to the grid via inverters. For this power grid ensemble, we additionally calculate the basin stability [2] as well as the survivability [3], two probabilistic measures which provide statements about asymptotic and transient stability. We also revisit the topological classes, introduced in [4], that have been shown to predict the basin stability of grids and explore if they still hold for grids with constraints and voltage dynamics. We find that the degree of the nodes is a better predictor than the topological classes for our ensemble. Finally, ambient forcing is applied to calculate probabilistic stability measures of the IEEE 96 test case [5].

Description: Flooding is the most costly natural hazard in Europe. Climatic and socioeconomic change are expected to further increase the amount of loss in the future. To counteract this development, policymaking, and adaptation planning need reliable large-scale risk assessments and an improved understanding of potential risk drivers. In this study, recent datasets for hazard and flood protection standards are combined with high resolution exposure projections and attributes of vulnerability derived from open data sources. The independent and combined influence of exposure change and climate scenarios rcp45 and rcp85 on fluvial flood risk are evaluated for three future periods centered around 2025, 2055 and 2085. Scenarios with improved and neglected private precaution are examined for their influence on flood risk using a probabilistic, multivariable flood loss model — BN-FLEMOps — to estimate fluvial flood losses for residential buildings in Europe. The results on NUTS-3 level reveal that urban centers and their surrounding regions are the hotspots of flood risk in Europe. Flood risk is projected to increase in the British Isles and Central Europe throughout the 21st century, while risk in many regions of Scandinavia and the Mediterranean will stagnate or decline. Under the combined effects of exposure change and climate scenarios rcp45, rcp85, fluvial flood risk in Europe is estimated to increase seven-fold and ten-fold respectively until the end of the century. Our results confirm the dominance of socioeconomic change over climate change on increasing risk. Improved private precautionary measures would reduce flood risk in Europe on an average by 15%. The quantification of future flood risk in Europe by integrating climate, socioeconomic and private precaution scenarios provides an overview of risk drivers, trends, and hotspots. This large-scale comprehensive assessment at a regional level resolution is valuable for multi-scale risk-based adaptation planning.

Description: The Sustainable Development Goals (SDGs) were adopted by the United Nations in 2015 as part of the “2030 Agenda for Sustainable Development” and aim to address issues ranging from poverty and economic growth to climate change. Efforts to tackle one issue can support or hinder progress towards others, often with complex systemic interactions. Thus, each of the SDGs and their corresponding targets may contribute as levers or hurdles towards achieving other SDGs and targets. Based on SDG indicator data, we create a systems model considering influence among the SDGs and their targets. Once assessed within a system, we find that more SDGs and their corresponding targets act as levers towards achieving other goals and targets rather than as hurdles. In particular, efforts towards SDGs 5 (Gender Equality) and 17 (Partnerships for the Goals) may accelerate progress, while SDGs 10 (Reduced Inequalities) and 16 (Peace, Justice and Strong Institutions) are shown to create potential hurdles. The model results can be used to help promote supportive interactions and overcome hindering ones in the long term.

Description: Electricity generation based on renewable energy (RE) sources such as wind and solar replace the most expensive generators that often rely on fossil fuels. In response to RE promotion, wholesale electricity prices and carbon emissions are therefore expected to decrease. In interconnected electricity systems, this so-called merit-order effect stimulates a change in electricity trade flows. Therefore, conventional generation and prices in neighboring countries are also likely to decrease. The impact of these trade reactions on carbon offsets is ambiguous and depends on installed generation and interconnector capacities. Moreover, the cross-border merit-order effect causes opposing effects on consumers and producers: generators’ profits decline, while consumers benefit from lower electricity costs and an increase in the consumer surplus. Using a rich data set of hourly technology-specific generation and wholesale market price data for ten central European countries, we estimate the domestic and cross-border impacts of German RE for the years 2015–2020. We find that German RE generation offset 79–113 MtCO per year. The major emission effect took place in Germany (64–99 MtCO). The average cost of emission offset of 212–321€/t were almost entirely borne by German market participants. Neighboring countries do not bear costs, but a significant shift from producer to consumer rents is observed.

Description: Interglacials and glacials represent low and high ice volume end-members of ice age cycles. While progress has been made in our understanding of how and when transitions between these states occur, their relative intensity has been lacking an explanatory framework. With a simple quantitative model, we show that over the last 800 000 years interglacial intensity can be described as a function of the strength of the previous glacial and the summer insolation at high latitudes in both hemispheres during the deglaciation. Since the precession components in the boreal and austral insolations counteract each other, the amplitude increase in obliquity cycles after 430 000 years ago is imprinted in interglacial intensities, contributing to the manifestation of the so-called Mid-Brunhes Event. Glacial intensity is also linked to the strength of the previous interglacial, the time elapsed from it, and the evolution of boreal summer insolation. Our results suggest that the memory of previous climate states and the time course of the insolation are crucial for understanding interglacial and glacial intensities.

Description: This dataset contains PISM simulation results of the Antarctic Ice Sheet based on code release v1.0-paleo-ensemble (https://doi.org/10.5281/zenodo.3574033). PISM is the open-source Parallel Ice Sheet Model developed mainly at UAF, USA and PIK, Germany. See documentation in https://www.pism.io. These are additional netCDF data from the same ensemble simulations already stored in doi:10.1594/PANGAEA.909728. 1) 1000-year snapshots since 125000 years before present, of ice thickness, bed topography, change in bed topography, floating/grounded mask, surface elevation, basal melt rate and vertically averaged velocity magnitude (SIA+SSA) (16GB) 2) 5000-year snapshots since 125000 years before present, SSA velocity components in x and y direction (8GB)

Description: Permafrost temperatures are increasing globally with the potential of adverse environmental and socio-economic impacts. Nonetheless, the attribution of observed permafrost warming to anthropogenic climate change has relied mostly on qualitative evidence. Here, we compare long permafrost temperature records from 15 boreholes in the northern hemisphere to simulated ground temperatures from Earth System models contributing to CMIP6 using a climate change detection and attribution approach. We show that neither pre-industrial climate variability nor natural drivers of climate change suffice to explain the observed warming in permafrost temperature averaged over all boreholes. However, simulations are consistent with observations if the effects of human emissions on the global climate system are considered. Moreover, our analysis reveals that the effect of anthropogenic climate change on permafrost temperature is detectable at some of the boreholes. Thus, the presented evidence supports the conclusion that anthropogenic climate change is the key driver of northern hemisphere permafrost warming.

Description: Southeastern South America is subject to considerable precipitation variability on seasonal to decadal timescales and has undergone very heavy land-cover changes since the middle of the past century. The influence of local land-cover change and precipitation as drivers of regional evapotranspiration long-term trends and variability remains largely unknown in the region. Here, ensembles of stand-alone Dynamic Global Vegetation Models with different atmospheric forcings are used to disentangle the influence of those two drivers on austral summer evapotranspiration from 1950 to 2010. This paper examines the influence of both the ENSO and the dipole-like first-mode of southeastern South American precipitation variability (EOF1) on regional evapotranspiration. We found that in the lower La Plata Basin, evapotranspiration was driven by precipitation variability and showed a positive summer trend. Moreover, the region showed marked seasonal anomalies during El Niño and La Niña summers but mainly during EOF1 phases. On the contrary, in the upper La Plata Basin, land-cover changes forced the negative summer evapotranspiration trend and particularly reduced the summer anomalies of the late 1990s, a period of ENSO and EOF1-positive phases. In the South Atlantic Convergence Zone region, the high evapotranspiration uncertainty across ensemble members impeded finding robust results, which highlights the importance of using multiple DGVMs and atmospheric forcings instead of relying on single model/forcing results.

Description: A simple two-layer model, the moist-convective rotating shallow water, which allows for low-cost high-resolution numerical simulations of the dynamics of the moist atmosphere in the presence of topography, is used to identify and understand dynamical processes governing the evolution of easterly waves propagating on the background of a low-latitude easterly jet crossing a land-sea boundary, a setup crudely representing the African Easterly Jet over the West-African plateau and the Atlantic ocean. We perform a thorough linear stability analysis and identify the unstable modes of the jet, which we use then for initialisation of fully nonlinear numerical simulations. In this way, we determine nonlinear evolution of unstable perturbations of the jet, both in the “dry” and moist-convective environments and highlight essential differences between the two cases. We identify a mechanism of formation of intense lower-layer cyclonic vortices at the northern flank of the jet and determine the influence of the land-sea contrast upon this process.

Description: Evolving networks have always been studied a lot featuring the dynamic properties of real-life networks. Studying the mechanism of the growth and death of a network is of great significance to network modeling. Identical to many models focused on the growing process, in this article, we study the decreasing process thoroughly. A novel evolving network model considering the growing and decreasing process is established based on the queueing system. Focused on the degreasing process, we originally investigate two strategies of vertex deleting that are the brutal strategy and the heritable strategy which characterizes the heritable behavior of ``dying'' vertices in real networks. On the basis of our model, stochastic properties of the proposed network are analyzed, e.g., the distribution and the expectation of the stationary scale of the network are theoretically obtained. In addition to that, degree distributions with different strategies are demonstrated in simulations, which manifests the power-low distribution. The reliability of the network is also studied by different attacks, sharing the same characteristic with the scale-free network.

Description: Creating a platform for a relaxed exchange with international like-minded people on the topic of Open Science is what the Barcamp Open Science stands for. Every year, the participants create a special flair, among other things through active participation in sessions that, as always, cover a wide range of topics. We try to summarise the event and, together with some of the session moderators, give an insight into the discussions in their sessions.

Description: The Working Group II contribution to the IPCC Sixth Assessment Report assesses the impacts of climate change, looking at ecosystems, biodiversity, and human communities at global and regional levels. It also reviews vulnerabilities and the capacities and limits of the natural world and human societies to adapt to climate change.

Description: The Paris Agreement, adopted in 2015, paved the way for a new hybrid global climate governance architecture with both bottom-up and top-down elements. While governments can choose individual climate goals and actions, a global stocktake and a ratcheting-up mechanism have been put in place with the overall aim to ensure that collective efforts will prevent increasing adverse impacts of climate change. Integrated assessment models show that current combined climate commitments and policies of national governments fall short of keeping global warming to 1.5 °C or 2 °C above preindustrial levels. Although major greenhouse gas emitters, such as China, the European Union, India, the United States under the Biden administration, and several other countries, have made new pledges to take more ambitious climate action, it is highly uncertain where global climate policy is heading. Scenarios in line with long-term temperature targets typically assume a simplistic and hardly realistic level of harmonization of climate policies across countries. Against this backdrop, this article develops four archetypes for the further evolution of the global climate governance architecture and matches them with existing sets of scenarios developed by integrated assessment models. By these means, the article identifies knowledge gaps in the current scenario literature and discusses possible research avenues to explore the pre-conditions for successful coordination of national policies towards achieving the long-term target stipulated in the Paris Agreement.

Description: Nutrition-sensitive agricultural programs have the potential to improve women's and children's nutrition, along with women's empowerment. The project-level Women's Empowerment in Agriculture Index (pro-WEAI) aims to standardize the measurement of women's agency and enable the assessment of impact over typical project timelines. Within the Food and Agricultural Approaches to Reducing Malnutrition (FAARM) cluster-randomized controlled trial in rural Habiganj, Sylhet, Bangladesh, we examined quantitative pro-WEAI data collected from a subsample of trial participants and their husbands (n = 885) approximately four months after the end of the intervention. We evaluated the impact of a three-year homestead food production program on men’s and women's agency separately by pro-WEAI domain and indicator, using multilevel logistic and linear regression. We show that women in the FAARM intervention group had levels of agency similar to men and much higher than women in the control group (Odds Ratio [OR] 7.7, p 〈 0.001), corresponding to better gender equity in intervention areas (OR 3.5, p 〈 0.001). The higher levels of agency among intervention women were driven by greater intrinsic and collective agency but not by instrumental agency. Compared to controls, more women in the intervention group found intimate partner violence unacceptable (OR 3.5, p 〈 0.001), had greater ownership of assets (OR 2.6, p = 0.001), better control of income (OR 1.8, p = 0.042), higher levels of group membership (OR 14.0, p 〈 0.001), and membership in groups they considered influential (OR 166.8, p 〈 0.001). Self-efficacy was greater in intervention areas for both women (OR 3.2, p 〈 0.001) and men (OR 2.3, p = 0.002). Our results contribute to the development of benchmarks for interpreting pro-WEAI scores across programs. Our assessment of the impact of a homestead food production program on women's agency provides additional rationale for women-led agricultural projects. We plan to build on these findings by examining the role of improved women's agency on the pathway from the intervention to nutritional impacts.

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

Description: This profile provides an overview of projected climate parameters and related impacts on different sectors in Somalia until 2080 under different climate change scenarios provided by the IPCC1 (called Representative Concentration Pathways, RCPs). RCP2.6 represents the low emissions scenario in line with the Paris Agreement; RCP6.0 represents a medium to high emissions scenario. Model projections do not account for effects of future socioeconomic impacts

Description: This profile provides an overview of projected climate parameters and related impacts on different sectors in Senegal until 2080 under different climate change scenarios (called Representative Concentration Pathways, RCPs). RCP2.6 represents the low emissions scenario in line with the Paris Agreement; RCP6.0 represents a medium to high emissions scenario. Model projections do not account for effects of future socio-economic impacts.

Description: Many children in India face the double burden of high exposure to ambient (AAP) and household air pollution, both of which can affect their linear growth. Although climate change mitigation is expected to decrease AAP, climate policies could increase the cost of clean cooking fuels. Here, we develop a static microsimulation model to project the air pollution-related burden of child stunting in India up to 2050 under four scenarios combining climate change mitigation (2 °C target) with national policies for AAP control and subsidised access to clean cooking. We link data from a nationally representative household survey, satellite-based estimates of fine particulate matter (PM2.5), a multi-dimensional demographic projection and PM2.5 and clean cooking access projections from an integrated assessment model. We find that the positive effects on child linear growth from reductions in AAP under the 2 °C Paris Agreement target could be fully offset by the negative effects of climate change mitigation through reduced clean cooking access. Targeted AAP control or subsidised access to clean cooking could shift this trade-off to result in net benefits of 2.8 (95% uncertainty interval [UI]: 1.4, 4.2) or 6.5 (UI: 6.3, 6.9) million cumulative prevented cases of child stunting between 2020–50 compared to business-as-usual. Implementation of integrated climate, air quality, and energy access interventions has a synergistic impact, reducing cumulative number of stunted children by 12.1 (UI: 10.7, 13.7) million compared to business-as-usual, with the largest health benefits experienced by the most disadvantaged children and geographic regions. Findings underscore the importance of complementing climate change mitigation efforts with targeted air quality and energy access policies to concurrently deliver on carbon mitigation, health and air pollution and energy poverty reduction goals in India.

Description: Livestock is known to contribute significantly to climate change and to negatively impact global nitrogen cycles and biodiversity. However, there has been little research on economically efficient policies for regulating meat production and consumption. In the absence of first-best policy instruments for the livestock sector, second-best consumption taxes on meat can address multiple environmental externalities simultaneously as well as improve diet-related public health. In this article, we review the empirical evidence on the social costs of meat and examine the rationales for taxing meat consumption in high-income countries. We approach these issues from the perspective of public, behavioral, and welfare economics, focusing in particular on (1) the interaction of multiple environmental externalities of meat production and consumption, (2) “alternative protein” technologies, (3) adverse effects on human health, (4) animal welfare, and (5) distributional effects of meat taxation. We present preliminary estimates of the environmental social costs associated with meat consumption and find that meat is significantly underpriced. We conclude by identifying several directions for future research on optimal meat taxation.

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

Description: The 2020s are an essential decade for achieving the 2030 Agenda and its Sustainable Development Goals (SDGs). For this, SDG research needs to provide evidence that can be translated into concrete actions. However, studies use different SDG data, resulting in incomparable findings. Researchers primarily use SDG databases provided by the United Nations (UN), the World Bank Group (WBG), and the Bertelsmann Stiftung & Sustainable Development Solutions Network (BE-SDSN). We compile these databases into one unified SDG database and examine the effects of the data selection on our understanding of SDG interactions. Among the databases, we observed more different than similar SDG interactions. Differences in synergies and trade-offs mainly occur for SDGs that are environmentally oriented. Due to the increased data availability, the unified SDG database offers a more nuanced and reliable view of SDG interactions. Thus, the SDG data selection may lead to diverse findings, fostering actions that might neglect or exacerbate trade-offs.