ALBERT

Description: We used transition path theory (TPT) to infer “reactive” pathways of floating marine debris trajectories. The TPT analysis was applied on a pollution-aware time-homogeneous Markov chain model constructed from trajectories produced by satellite-tracked undrogued buoys from the National Oceanic and Atmospheric Administration's Global Drifter Program. The latter involved coping with the openness of the system in physical space, which further required an adaptation of the standard TPT setting. Directly connecting pollution sources along coastlines with garbage patches of varied strengths, the unveiled reactive pollution routes represent alternative targets for ocean cleanup efforts. Among our specific findings we highlight: constraining a highly probable pollution source for the Great Pacific garbage patch; characterizing the weakness of the Indian Ocean gyre as a trap for plastic waste; and unveiling a tendency of the subtropical gyres to export garbage toward the coastlines rather than to other gyres in the event of anomalously intense winds. Given a Markov chain, namely, a model describing the stochastic state transitions in which the transition probability of each state depends only on the state attained in the previous event, transition path theory (TPT) provides a rigorous approach to study the statistics of transitions from a set of states to another, possibly disconnected set of states. Envisioning the motion of floating debris as described by a Markov chain that accounts for the ability of coastal states to “pollute the oceans,” TPT is employed to unveil “reactive” pathways representing direct transitions from potential release locations along the shorelines to accumulation sites across the world ocean. These include the subtropical gyres, whose strength in this context is investigated

Description: Complex network theory provides an important tool for the analysis of complex systems such as the Earth’s climate. In this context, functional climate networks can be constructed using a spatiotemporal climate dataset and a suitable time series distance function. The resulting coarse-grained view on climate variability consists of representing distinct areas on the globe (i.e., grid cells) by nodes and connecting pairs of nodes that present similar time series. One fundamental concern when constructing such a functional climate network is the definition of a metric that captures the mutual similarity between time series. Here we study systematically the effect of 29 time series distance functions on functional climate network construction based on global temperature data. We observe that the distance functions previously used in the literature commonly generate very similar networks while alternative ones result in rather distinct network structures and reveal different long-distance connection patterns. These patterns are highly important for the study of climate dynamics since they generally represent pathways for the long-distance transportation of energy and can be used to forecast climate variability on subseasonal to interannual or even decadal scales. Therefore, we propose the measures studied here as alternatives for the analysis of climate variability and to further exploit their complementary capability of capturing different aspects of the underlying dynamics that may help gaining a more holistic empirical understanding of the global climate system.

Description: Julia code to calculate recurrence plots of the Rössler system: - calculated from the original continuous data (regular recurrence plot) and - from the events series representing the maxima of the x-component (edit distance recurrence plot).

Description: Deep-time paleoclimatic records document large-scale shifts and perturbations in Earth's climate; during the Cenozoic in particular transitions have been recorded on time scales of 10 thousand to 1 million years. Bifurcations in the leading dynamical modes could be a key element driving these events. Such bifurcation-induced critical transitions are typically preceded by characteristic early-warning signals, for example in terms of rising standard deviation and lag-one autocorrelation. These early-warning signals are generated by a widening of the underlying basin of attraction when approaching the bifurcation, a phenomenon dubbed critical slowing down. The associated dynamical transitions should therefore be preceded by characteristic signals that can be detected by statistical methods. Here, we reveal the presence of significant early-warning signals prior to several climate events within a paleoclimate record spanning the last 66 million years - the Cenozoic Era. We computed standard deviation and lag-one autocorrelation of the CENOzoic Global Reference benthic foraminifer carbon and oxygen Isotope Dataset (CENOGRID), comprising two time series of deep sea carbonate isotope variations of 18O and 13C. We find significant early-warning signals for five out of nine previously identified Cenozoic paleoclimatic events in at least one of the two records, which can be considered as viable candidates for bifurcation-induced transitions to be analysed in follow-up studies. Our results suggest that some of the major climate events of the last 66 Ma were triggered by bifurcations in leading modes of variability, indicating bifurcations could be a key component of Earth's climate system deep-time evolution.

Description: Complex network approaches have been recently emerging as novel and complementary concepts of nonlinear time series analysis that are able to unveil many features that are hidden to more traditional analysis methods. In this work, we focus on one particular approach: the application of ordinal pattern transition networks for characterizing time series data. More specifically, we generalize a traditional statistical complexity measure (SCM) based on permutation entropy by explicitly disclosing heterogeneous frequencies of ordinal pattern transitions. To demonstrate the usefulness of these generalized SCMs, we employ them to characterize different dynamical transitions in the logistic map as a paradigmatic model system, as well as real-world time series of fluid experiments and electrocardiogram recordings. The obtained results for both artificial and experimental data demonstrate that the consideration of transition frequencies between different ordinal patterns leads to dynamically meaningful estimates of SCMs, which provide prospective tools for the analysis of observational time series. In the past decade, the field of nonlinear time series analysis has been undergoing fast developments benefiting from concepts from complex network theory. Along this line of research, ordinal pattern transition networks have been expanding the established concept of ordinal time series analysis and provide new insights into the dynamical organization underlying time series data that complement existing methods like permutation entropy. Permutation based on ordinal patterns is a simple and easy to implement concept that naturally provides statistical complexity measures (SCMs), which in the case of permutation entropy relies on pattern frequencies only. Yet, much additional information can be exploited by including ordinal pattern transition frequencies into the definitions of SCMs—an idea that, however, has not been widely developed and applied so far. In this work, we generalize existing permutation based SCMs by means of ordinal pattern transition networks that take into account the pattern transition properties explicitly. The usefulness of our generalizations is demonstrated by using time series of both model and experimental data

Description: Little is known about the distribution of ice in the Antarctic Ice Sheet (AIS) during the Last Glacial Maximum (LGM). Whereas marine and terrestrial geological data indicate that the grounded ice advanced to a position close to the continental-shelf break, the total ice volume is unclear. Glacial boundary conditions are potentially important sources of uncertainty, in particular basal friction and climatic boundary conditions. Basal friction exerts a strong control on the large-scale dynamics of the ice sheet and thus affects its size and is not well constrained. Glacial climatic boundary conditions determine the net accumulation and ice temperature and are also poorly known. Here we explore the effect of the uncertainty in both features on the total simulated ice storage of the AIS at the LGM. For this purpose we use a hybrid ice sheet shelf model that is forced with different basal drag choices and glacial background climatic conditions obtained from the LGM ensemble climate simulations of the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3). Overall, we find that the spread in the simulated ice volume for the tested basal drag parameterizations is about the same range as for the different general circulation model (GCM) forcings (4 to 6 m sea level equivalent). For a wide range of plausible basal friction configurations, the simulated ice dynamics vary widely but all simulations produce fully extended ice sheets towards the continental-shelf break. More dynamically active ice sheets correspond to lower ice volumes, while they remain consistent with the available constraints on ice extent. Thus, this work points to the possibility of an AIS with very active ice streams during the LGM. In addition, we find that the surface boundary temperature field plays a crucial role in determining the ice extent through its effect on viscosity. For ice sheets of a similar extent and comparable dynamics, we find that the precipitation field determines the total AIS volume. However, precipitation is highly uncertain. Climatic fields simulated by climate models show more precipitation in coastal regions than a spatially uniform anomaly, which can lead to larger ice volumes. Our results strongly support using these paleoclimatic fields to simulate and study the LGM and potentially other time periods like the last interglacial. However, their accuracy must be assessed as well, as differences between climate model forcing lead to a large spread in the simulated ice volume and extension.

Description: A prominent feature of earthquakes is their empirical laws, including memory (clustering) in time and space. Several earthquake forecasting models, such as the epidemic-type aftershock sequence (ETAS) model, were developed based on these empirical laws. Yet, a recent study [1] showed that the ETAS model fails to reproduce the significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) discovered in the data. Our generalized ETAS model accurately reproduces the short- and long-term/distance memory observed in the Italian and Southern Californian earthquake catalogs. The revised ETAS model is also found to improve earthquake forecasting after large shocks.

Description: Coupling between networks is widely prevalent in real systems and has dramatic effects on their resilience and functional properties. However, current theoretical models tend to assume homogeneous coupling where all the various subcomponents interact with one another, whereas real-world systems tend to have various different coupling patterns. We develop two frameworks to explore the resilience of such modular networks, including specific deterministic coupling patterns and coupling patterns where specific subnetworks are connected randomly. We find both analytically and numerically that the location of the percolation phase transition varies nonmonotonically with the fraction of interconnected nodes when the total number of interconnecting links remains fixed. Furthermore, there exists an optimal fraction r∗ of interconnected nodes where the system becomes optimally resilient and is able to withstand more damage. Our results suggest that, although the exact location of the optimal r∗ varies based on the coupling patterns, for all coupling patterns, there exists such an optimal point. Our findings provide a deeper understanding of network resilience and show how networks can be optimized based on their specific coupling patterns.

Description: Rapid and continuous analysis of radiocarbon (14C) concentration in carbonate samples at spatial resolution down to 100 µm has been made possible with the new LA-AMS (laser ablation accelerator mass spectrometry) technique. This novel approach can provide radiocarbon data at a spatial resolution similar to that of stable carbon (C) isotope measurements by isotope ratio mass spectrometry of micromilled samples and, thus, can help to interpret δ13C signatures, which otherwise are difficult to understand due to numerous processes contributing to changes in the C-isotope ratio. In this work, we analyzed δ13C and 14C on the Holocene stalagmite SPA 127 from the high-alpine Spannagel Cave (Austria). Both proxies respond in a complex manner to climate variability. Combined stable carbon and radiocarbon profiles allow three growth periods characterized by different δ13C signatures to be identified: (i) the period 8.5 to 8.0 ka is characterized by relatively low δ13C values with small variability combined with a comparably high radiocarbon reservoir effect (expressed as dead carbon fraction, dcf) of around 60 %. This points towards C contributions of host rock dissolution and/or from an “old” organic matter (OM) reservoir in the karst potentially mobilized due to the warm climatic conditions of the early Holocene. (ii) Between 8 and 3.8 ka there was a strong variability in δ13C with values ranging from −8 ‰ to +1 ‰ and a generally lower dcf. The δ13C variability is most likely caused by changes in C exchange between cave air CO2 and dissolved inorganic carbon in drip water in the cave, which are induced by reduced drip rates as derived from reduced stalagmite growth rates. Additionally, the lower dcf indicates that the OM reservoir contributed less to stalagmite growth in this period possibly as a result of reduced meteoric precipitation or because it was exhausted. (iii) In the youngest section between 3.8 and 2.4 ka, comparably stable and low δ13C values, combined with an increasing dcf reaching up to 50 % again, hint towards a contribution of an aged OM reservoir in the karst. This study reveals the potential of combining high-resolution 14C profiles in speleothems with δ13C records in order to disentangle climate-related C dynamics in karst systems.Rapid and continuous analysis of radiocarbon (14C) concentration in carbonate samples at spatial resolution down to 100 µm has been made possible with the new LA-AMS (laser ablation accelerator mass spectrometry) technique. This novel approach can provide radiocarbon data at a spatial resolution similar to that of stable carbon (C) isotope measurements by isotope ratio mass spectrometry of micromilled samples and, thus, can help to interpret δ13C signatures, which otherwise are difficult to understand due to numerous processes contributing to changes in the C-isotope ratio. In this work, we analyzed δ13C and 14C on the Holocene stalagmite SPA 127 from the high-alpine Spannagel Cave (Austria). Both proxies respond in a complex manner to climate variability. Combined stable carbon and radiocarbon profiles allow three growth periods characterized by different δ13C signatures to be identified: (i) the period 8.5 to 8.0 ka is characterized by relatively low δ13C values with small variability combined with a comparably high radiocarbon reservoir effect (expressed as dead carbon fraction, dcf) of around 60 %. This points towards C contributions of host rock dissolution and/or from an “old” organic matter (OM) reservoir in the karst potentially mobilized due to the warm climatic conditions of the early Holocene. (ii) Between 8 and 3.8 ka there was a strong variability in δ13C with values ranging from −8 ‰ to +1 ‰ and a generally lower dcf. The δ13C variability is most likely caused by changes in C exchange between cave air CO2 and dissolved inorganic carbon in drip water in the cave, which are induced by reduced drip rates as derived from reduced stalagmite growth rates. Additionally, the lower dcf indicates that the OM reservoir contributed less to stalagmite growth in this period possibly as a result of reduced meteoric precipitation or because it was exhausted. (iii) In the youngest section between 3.8 and 2.4 ka, comparably stable and low δ13C values, combined with an increasing dcf reaching up to 50 % again, hint towards a contribution of an aged OM reservoir in the karst. This study reveals the potential of combining high-resolution 14C profiles in speleothems with δ13C records in order to disentangle climate-related C dynamics in karst systems.Rapid and continuous analysis of radiocarbon (14C) concentration in carbonate samples at spatial resolution down to 100 µm has been made possible with the new LA-AMS (laser ablation accelerator mass spectrometry) technique. This novel approach can provide radiocarbon data at a spatial resolution similar to that of stable carbon (C) isotope measurements by isotope ratio mass spectrometry of micromilled samples and, thus, can help to interpret δ13C signatures, which otherwise are difficult to understand due to numerous processes contributing to changes in the C-isotope ratio. In this work, we analyzed δ13C and 14C on the Holocene stalagmite SPA 127 from the high-alpine Spannagel Cave (Austria). Both proxies respond in a complex manner to climate variability. Combined stable carbon and radiocarbon profiles allow three growth periods characterized by different δ13C signatures to be identified: (i) the period 8.5 to 8.0 ka is characterized by relatively low δ13C values with small variability combined with a comparably high radiocarbon reservoir effect (expressed as dead carbon fraction, dcf) of around 60 %. This points towards C contributions of host rock dissolution and/or from an “old” organic matter (OM) reservoir in the karst potentially mobilized due to the warm climatic conditions of the early Holocene. (ii) Between 8 and 3.8 ka there was a strong variability in δ13C with values ranging from −8 ‰ to +1 ‰ and a generally lower dcf. The δ13C variability is most likely caused by changes in C exchange between cave air CO2 and dissolved inorganic carbon in drip water in the cave, which are induced by reduced drip rates as derived from reduced stalagmite growth rates. Additionally, the lower dcf indicates that the OM reservoir contributed less to stalagmite growth in this period possibly as a result of reduced meteoric precipitation or because it was exhausted. (iii) In the youngest section between 3.8 and 2.4 ka, comparably stable and low δ13C values, combined with an increasing dcf reaching up to 50 % again, hint towards a contribution of an aged OM reservoir in the karst. This study reveals the potential of combining high-resolution 14C profiles in speleothems with δ13C records in order to disentangle climate-related C dynamics in karst systems.

Description: In the past few decades, boreal summers have been characterized by an increasing number of extreme weather events in the Northern Hemisphere extratropics, including persistent heat waves, droughts and heavy rainfall events with significant social, economic, and environmental impacts. Many of these events have been associated with the presence of anomalous large-scale atmospheric circulation patterns, in particular, persistent blocking situations, i.e., nearly stationary spatial patterns of air pressure. To contribute to a better understanding of the emergence and dynamical properties of such situations, we construct complex networks representing the atmospheric circulation based on Lagrangian trajectory data of passive tracers advected within the atmospheric flow. For these Lagrangian flow networks, we study the spatial patterns of selected node properties prior to, during, and after different atmospheric blocking events in Northern Hemisphere summer. We highlight the specific network characteristics associated with the sequence of strong blocking episodes over Europe during summer 2010 as an illustrative example. Our results demonstrate the ability of the node degree, entropy, and harmonic closeness centrality based on outgoing links to trace important spatiotemporal characteristics of atmospheric blocking events. In particular, all three measures capture the effective separation of the stationary pressure cell forming the blocking high from the normal westerly flow and the deviation of the main atmospheric currents around it. Our results suggest the utility of further exploiting the Lagrangian flow network approach to atmospheric circulation in future targeted diagnostic and prognostic studies. As the frequency and severity of mid-latitude extreme weather events such as heat waves, droughts, and heavy rainfall events are projected to further increase with ongoing climate change, developing reliable forecasts of such events is becoming a gradually more pressing issue. However, while the quality of predictions has been improving considerably on short-term (up to 2 weeks lead time) and seasonal time scales (beyond 3 months), sub-seasonal forecasting (from 2 weeks to about 3 months) remains a challenging task. This results in part from a limited understanding and representation of phenomena that could potentially increase the predictability at these sub-seasonal scales. One type of such phenomena is atmospheric blocking events. These large-scale, nearly stationary, atmospheric pressure patterns can remain in place for several days or even weeks, disturbing the usual westerly driven circulation and the resulting succession of weather regimes over the mid-latitudes. Despite numerous studies, a comprehensive theory explaining the emergence of blocking-related circulation anomalies and allowing an early forecasting of incipient blocking situations remains to be found. In this work, we utilize a network-based approach, so-called Lagrangian flow networks, for studying the atmospheric circulation associated with blocking situations during Northern hemisphere summer. We discuss the ability of different network measures to detect and track important spatial characteristics of blocking events, suggesting the potential of complex network approaches to provide key elements for future diagnostic and prognostic studies of atmospheric blocking events

Description: The pilgrimages of Muslims to Makkah (Hajj and Umrah) is one of the largest religious gatherings in the world which draws millions of people from around 180 countries each year. Heat stress during summer has led to health impacts including morbidity and mortality in the past, which is likely to worsen due to global warming. Here we investigate the impacts of increasing heat stress during the peak summer months over Makkah at present levels of warming as well as under Paris Agreement's targets of 1.5 °C and 2 °C global mean temperature increase above pre-industrial levels. This is achieved by using multi member ensemble projections from the half a degree additional warming, prognosis and projected impacts project. We find a substantial increase in the exceedance probabilities of dangerous thresholds (wet-bulb temperature 〉24.6 °C) in 1.5 °C and 2 °C warmer worlds over the summer months. For the 3 hottest months, August, September and October, even thresholds of extremely dangerous (wet-bulb temperature 〉29.1 °C) health risks may be surpassed. An increase in exceedance probability of dangerous threshold is projected by two and three times in 1.5 °C and 2 °C warmer worlds respectively for May as compared to the reference climate. September shows the highest increase in the exceedance probability of extremely dangerous threshold which is increased to 4 and 13 times in 1.5 °C and 2 °C warmer worlds respectively. Based on the indicators of hazard, exposure and vulnerability, we carried out probabilistic risk analysis of life-threatening heat stroke over Makkah. A ten time increase in the heat stroke risk at higher wet-bulb temperatures for each month is projected in 2 °C warmer world. If warming was limited to 1.5 °C world, the risk would only increase by about five times, or half the risk of 2 °C. Our results indicate that substantial heat related risks during Hajj and Umrah happening over peak summer months, as it is the case for Hajj during this decade, will require substantial adaptation measures and would negatively affect the performance of the rite. Stringent mitigation actions to keep the global temperature to 1.5 °C can reduce the risks of heat related illnesses and thereby reduce the non-economic loss and damage related to one of the central pillars of a world religion.

Description: Persistent hot–dry or cold–wet summer weather can have significant impacts on agriculture, health, and the environment. For northwestern Europe, these weather regimes are typically linked to, respectively, blocked or zonal jet stream states. The fundamental dynamics underlying these circulation states are still poorly understood. Edward Lorenz postulated that summer circulation may be either fully or almost intransitive, implying that part of the phase space (capturing circulation variability) cannot be reached within one specific summer. If true, this would have major implications for the predictability of summer weather and our understanding of the drivers of interannual variability of summer weather. Here, we test the two Lorenz hypotheses (i.e., fully or almost intransitive) for European summer circulation, capitalizing on a newly available very large ensemble (2000 years) of present-day climate data in the fully coupled global climate model EC-Earth. Using self-organizing maps, we quantify the phase space of summer circulation and the trajectories through phase space in unprecedented detail. We show that, based on Markov assumptions, the summer circulation is strongly dependent on its initial state in early summer with the atmospheric memory ranging from 28 days up to ~45 days. The memory is particularly long if the initial state is either a blocked or a zonal flow state. Furthermore, we identify two groups of summers that are characterized by distinctly different trajectories through phase space, and that prefer either a blocked or zonal circulation state, respectively. These results suggest that intransitivity is indeed a fundamental property of the atmosphere and an important driver of interannual variability.

Description: Identifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data.

Description: Atmosphere and ocean dynamics display many complex features and are characterized by a wide variety of processes and couplings across different timescales. Here we demonstrate the application of multivariate empirical mode decomposition (MEMD) to investigate the multivariate and multiscale properties of a reduced order model of the ocean–atmosphere coupled dynamics. MEMD provides a decomposition of the original multivariate time series into a series of oscillating patterns with time-dependent amplitude and phase by exploiting the local features of the data and without any a priori assumptions on the decomposition basis. Moreover, each oscillating pattern, usually named multivariate intrinsic mode function (MIMF), represents a local source of information that can be used to explore the behavior of fractal features at different scales by defining a sort of multiscale and multivariate generalized fractal dimensions. With these two complementary approaches, we show that the ocean–atmosphere dynamics presents a rich variety of features, with different multifractal properties for the ocean and the atmosphere at different timescales. For weak ocean–atmosphere coupling, the resulting dimensions of the two model components are very different, while for strong coupling for which coupled modes develop, the scaling properties are more similar especially at longer timescales. The latter result reflects the presence of a coherent coupled dynamics. Finally, we also compare our model results with those obtained from reanalysis data demonstrating that the latter exhibit a similar qualitative behavior in terms of multiscale dimensions and the existence of a scale dependency of the statistics of the phase-space density of points for different regions, which is related to the different drivers and processes occurring at different timescales in the coupled atmosphere–ocean system. Our approach can therefore be used to diagnose the strength of coupling in real applications.

Description: We investigate the role of small-scale dynamics in inducing large-scale transitions in the solar wind magnetic field by means of dynamical system metrics based on instantaneous fractal dimensions. By looking at the corresponding multiscale features, we observe a break in the average attractor dimension occurring at the crossover between the inertial and the kinetic/dissipative regime. Our analysis suggests that large-scale transitions are induced by small-scale dynamics through an inverse cascade mechanism driven by local correlations, while electron contributions (if any) are hidden by instrumental noise.

Description: The Earth’s climate is a complex system characterized by multi-scale nonlinear interrelationships between different subsystems like atmosphere and ocean. Among others, the mutual interdependence between sea surface temperatures (SST) and precipitation (PCP) has important implications for ecosystems and societies in vast parts of the globe but is still far from being completely understood. In this context, the globally most relevant coupled ocean–atmosphere phenomenon is the El Niño–Southern Oscillation (ENSO), which strongly affects large-scale SST variability as well as PCP patterns all around the globe. Although significant achievements have been made to foster our understanding of ENSO’s global teleconnections and climate impacts, there are many processes associated with ocean–atmosphere interactions in the tropics and extratropics, as well as remote effects of SST changes on PCP patterns that have not yet been unveiled or fully understood. In this work, we employ coupled climate network analysis for characterizing dominating global co-variability patterns between SST and PCP at monthly timescales. Our analysis uncovers characteristic seasonal patterns associated with both local and remote statistical linkages and demonstrates their dependence on the type of the current ENSO phase (El Niño, La Niña or neutral phase). Thereby, our results allow identifying local interactions as well as teleconnections between SST variations and global precipitation patterns.

Description: This climate risk profile provides an overview of projected climate parameters and related impacts on different sectors in Jordan until 2080, under different climate change scenarios provided (called Represent- ative Concentration Pathways, RCPs). RCP2.6 repre- sents a low emissions scenario that aims to keep global warming below 2 °C above pre-industrial temperatures; RCP6.0 represents a medium to high emissions scenario. Model projections do not account for effects of future socioeconomic impacts.

Description: Radar reflections from the interior of the Greenland ice sheet contain a comprehensive archive of past accumulation rates, ice dynamics, and basal melting. Combining these data with dynamic ice sheet models may greatly aid model calibration, improve past and future sea level estimates, and enable insights into past ice sheet dynamics that neither models nor data could achieve alone. Unfortunately, simulating the continental-scale ice sheet stratigraphy represents a major challenge for current ice sheet models. In this study, we present the first three-dimensional ice sheet model that explicitly simulates the Greenland englacial stratigraphy. Individual layers of accumulation are represented on a grid whose vertical axis is time so that they do not exchange mass with each other as the flow of ice deforms them. This isochronal advection scheme does not influence the ice dynamics and only requires modest input data from a host thermomechanical ice sheet model, making it easy to transfer to a range of models. Using an ensemble of simulations, we show that direct comparison with the dated radiostratigraphy data yields notably more accurate results than calibrating simulations based on total ice thickness. We show that the isochronal scheme produces a more reliable simulation of the englacial age profile than traditional age tracers. The interpretation of ice dynamics at different times is possible but limited by uncertainties in the upper and lower boundary conditions, namely temporal variations in surface mass balance and basal friction.

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

Description: We examine how optimal renewable energy (RE) support policies need to be adjusted to account for carbon prices. We show theoretically and empirically that changing carbon prices requires adjusting RE subsidies due to two motives. First, RE premiums need to be reduced to reflect the carbon value embedded in the market price. Second, once a coal to gas switch occurs, RE premiums and feed-in tariffs need to be adjusted to account for changes in the marginal external benefit of RE. We use empirical estimations and numerical simulation models to quantify these effects for the United Kingdom. We show that the second effect is empirically small, whereas the first effect requires to completely phase-out RE premiums with increasing carbon prices. Finally, a fuel switch increases solar-induced abatement, whereas wind-induced abatement is rather invariant to a fuel switch. Yet, the differentiation of optimal subsidies between wind and solar power is modest.

Description: PowerDynamics.jl is a Julia package for time-domain modeling of power grids that is specifically designed for the stability analysis of systems with high shares of renewable energies. It makes use of Julia’s state-of-the-art differential equation solvers and is highly performant even for systems with a large number of components. Further, it is compatible with Julia’s machine learning libraries and allows for the utilization of these methods for dynam- ical optimization and parameter fitting. The package comes with a number of predefined models for synchronous machines, transmission lines and in- verter systems. However, the strict open-source approach and a macro-based user-interface also allows for an easy implementation of custom-built mod- els which makes it especially interesting for the design and testing of new control strategies for distributed generation units. This paper presents how the modeling concept, implemented component models and fault scenarios have been experimentally tested against measurements in the microgrid lab of TECNALIA.

Description: Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2022-12-13.

Description: Prudent risk management requires consideration of bad-to-worst-case scenarios. Yet, for climate change, such potential futures are poorly understood. Could anthropogenic climate change result in worldwide societal collapse or even eventual human extinction? At present, this is a dangerously underexplored topic. Yet there are ample reasons to suspect that climate change could result in a global catastrophe. Analyzing the mechanisms for these extreme consequences could help galvanize action, improve resilience, and inform policy, including emergency responses. We outline current knowledge about the likelihood of extreme climate change, discuss why understanding bad-to-worst cases is vital, articulate reasons for concern about catastrophic outcomes, define key terms, and put forward a research agenda. The proposed agenda covers four main questions: 1) What is the potential for climate change to drive mass extinction events? 2) What are the mechanisms that could result in human mass mortality and morbidity? 3) What are human societies' vulnerabilities to climate-triggered risk cascades, such as from conflict, political instability, and systemic financial risk? 4) How can these multiple strands of evidence—together with other global dangers—be usefully synthesized into an “integrated catastrophe assessment”? It is time for the scientific community to grapple with the challenge of better understanding catastrophic climate change.

Description: Swarm intelligence occurs when the collective behavior of low-level individuals and their local interactions form an overall pattern of uniform function. Incorporating swarm intelligence allows us to disregard global models when we explore collective cooperation systems that lack any central control. Blockchain is a key technology in the functioning of Bitcoin and combines network and cryptographic algorithms. A group of agents agrees on a particular status and records the protocol without controlling it. Blockchain and other distributed systems, such as ant colony systems, allow the building of “ants” that are more secure, flexible, and successful. We use the principle of blockchain technology and carry out ant colony research to solve three urgent problems. We use new security protocols, system implementations, and business models to generate ant swarm system scenarios. Finally we combine these two technologies to solve the problems of limitation and reduced future potential. Our work opens the door to new business models and approaches that allow ant colony technologies to be applied to a wide range of market applications.

Description: Good leaders can greatly influence the effective governance of water resources, however, how their attributes relate to group cooperation in Water User Associations (WUAs) remains an open question. Using the case of Chile, we explore the factors of three non-cooperative behaviors in WUAs by performing a two-stage cluster analysis. The results describe four clusters that differ in structural and human characteristics, where highly cooperative WUAs are characterized by having presidents who dedicate more time to their duties, are more active in applying for governmental subsidies, are embedded in social organizations, have high levels of bridging social capital, and have a positive attitude toward the presidency. Our results add to the limited empirical knowledge about the role of leadership in fostering cooperation in the use of common-pool resources. This article sheds light on this matter as the results suggest that policy interventions should aim at strengthening social capital and providing incentives to increase the time dedication of presidents to the WUAs duties.

Description: Carbon dioxide removal from the atmosphere is becoming an important option to achieve net zero climate targets. This paper develops a welfare and public economics perspective on optimal policies for carbon removal and storage in non-permanent sinks like forests, soil, oceans, wood products or chemical products. We derive a new metric for the valuation of non-permanent carbon storage, the social cost of carbon removal (SCC-R), which embeds also the conventional social cost of carbon emissions. We show that the contribution of CDR is to create new carbon sinks that should be used to reduce transition costs, even if the stored carbon is released to the atmosphere eventually. Importantly, CDR does not raise the ambition of optimal temperature levels unless initial atmospheric carbon stocks are excessively high. For high initial atmospheric carbon stocks, CDR allows to reduce the optimal temperature below initial levels. Finally, we characterize three different policy regimes that ensure an optimal deployment of carbon removal: downstream carbon pricing, upstream carbon pricing, and carbon storage pricing. The policy regimes differ in their informational and institutional requirements regarding monitoring, liability and financing.

Description: Degassing of CO2 and precipitation of calcite to the surface of stalagmites can strongly impact isotope signals imprinted into the calcite of these speleothems. Here, we show that in all the variety of conditions occurring in nature only two distinct types of degassing exist. First, when a thin film of calcareous solution comes in contact to cave air, which has a lower pCO2 value than that of the aqueous CO2 in the water, molecular CO2 escapes by physical diffusion in several seconds. In a next step lasting several ten seconds, pH and DIC in the solution achieve chemical equilibrium with respect to the CO2 in the cave atmosphere. This solution becomes supersaturated with respect to calcite. During precipitation for each unit CaCO3 deposited one molecule of CO2 is generated and escapes from the solution. This precipitation driven degassing is active during precipitation only. We show that all variations of out gassing proposed in the literature are either diffusive outgassing or precipitation driven degassing and that diffusive outgassing has no influence on the isotope composition of the HCO3− pool and consequently on that of calcite. Its isotope imprint is determined solely by precipitation driven degassing in contrast to most explanations in the literature. We present a theoretical model of δ13C and δ18O that explains the contributions of various parameters such as changes in temperature, changes of pCO2 in the cave atmosphere, and changes in the drip intervals to the isotope composition of calcite precipitated to the apex of the stalagmite. We use this model to calculate quantitatively changes of δ13C and δ18O observed in field experiments (Carlson et al., 2020) in agreement to their experimental data. We also apply our model to prior calcite precipitation (PCP) in the field as reported by Mickler et al. (2019). We discuss how PCP influences isotope composition signals. In summary, we present a transparent method based on few commonly accepted equations that allows calculation of the isotope composition δ13C and δ18O of CaCO3 under various temperatures, pCO2 in the cave air, degrees of PCP, and concentrations of the water entering the cave.

Description: Buildings energy consumption is one of the most important contributors to GHG emissions worldwide, responsible for 23% of energy-related CO2 emissions. Decarbonising buildings energy demand will pass through two types of strategies: first through an overall reduction of energy demand, that could to some extent be reaped at negative costs; and second through a reduction of the carbon content of energy via fuel switching and supply side decarbonisation. This study assesses the contributions of each of these strategies for the decarbonisation of the buildings sector in line with a 1.5°C global warming. We show that in a 1.5°C scenario combining mitigation policies and a reduction of market failures in efficiency markets, 81% of the reductions in buildings emissions are achieved through the reduction of the carbon content of energy, while the remaining 19% are due to efficiency improvements which reduce energy demand by 31%. Without supply side decarbonisation, efficiency improvements almost entirely suppress the doubling of emissions that would otherwise be expected, but fail to induce an absolute decline in emissions. Our modelling and scenarios show the impact of both climate change mitigation policies and of the alleviation of market failures pervading through energy efficiency markets. The results show that the reduction of the carbon content of energy through fuel switching and supply-side decarbonisation is of paramount importance for the decarbonisation of buildings.

Description: The global public sphere has changed dramatically over the past decades: A significant part of public discourse now takes place on algorithmically driven platforms. Despite its growing importance, there is scant large-scale academic research on the long-term evolution of user behaviour on these platforms. Here, we evaluate the behaviour of 600,000 individual Twitter users between 2012 and 2019 and find empirical evidence for a cohort-level acceleration of the way Twitter is used. Across time, we observe changing user-level behaviours: more tweets per time, denser interactions with others via retweets, and shorter content horizons, expressed as an individual's decaying autocorrelation of topics over time. We show that the change in usage patterns is not simply caused by a growing user base. While behaviour remains remarkably stable within each cohort over time, we relate these observations to changing compositions of new users with each new cohort containing increasingly active individuals. Our findings complement recent empirical work on social acceleration by tracking cohorts over time, controlling for cohort size, and analyzing their behavioural composition.

Description: This article studies the fixed-time tracking consensus of second-order nonlinear multi-agent systems with disturbance. To make the fixed-time tracking consensus, a consensus protocol based on the integral sliding mode surface is proposed, which can ensure the adjacent agents remain within a limited communication range in the communication process. By adopting Lyapunov stability theory and matrix theory, sufficient conditions for the fixed-time tracking consensus are given, and a bound of the settling time is obtained. Finally, a simulation example is presented to verify the potential correctness of the obtained results.

Description: The decarbonisation of industry is a bottleneck for EU's 2050 target of climate neutrality. Replacing fossil fuels with low-carbon electricity is at the core of this challenge; however, the aggregate electrification potential and resulting system-wide CO2 reductions for diverse industrial processes are unknown. Here, we present the results from a comprehensive bottom-up analysis of the energy use in eleven industrial sectors (accounting for 92% of Europe's industry CO2 emissions), and estimate the technological potential for industry electrification in three stages. 78% of the energy demand is electrifiable with technologies that are already established, while 99% electrification can be achieved with the addition of technologies currently under development. Such a deep electrification reduces CO2 emissions already based on the carbon intensity of today's electricity (~300 gCO2/kWhel). With an increasing decarbonisation of the power sector (IEA: 12 gCO2/kWhel in 2050), electrification could cut CO2 emissions by 78%, and almost entirely abate the energy-related CO2 emissions, reducing the industry bottleneck to only residual process emissions. Despite its decarbonisation potential, the extent to which direct electrification will be deployed in industry remains uncertain and depends on the relative cost of electric technologies compared to other low-carbon options.

Description: By means of a new multilayer pseudo-spectral moist-convective thermal rotating shallow-water (mcTRSW) model in a full sphere, we present a possible equatorial adjustment beyond Gill's mechanism for the genesis and dynamics of the Madden–Julian oscillation (MJO). According to this theory, an eastward-propagating MJO-like structure can be generated in a self-sustained and self-propelled manner due to nonlinear relaxation (adjustment) of a large-scale positive buoyancy anomaly, depressed anomaly, or a combination of these, as soon as this anomaly reaches a critical threshold in the presence of moist convection at the Equator. This MJO-like episode possesses a convectively coupled “hybrid structure” that consists of a “quasi-equatorial modon” with an enhanced vortex pair and a convectively coupled baroclinic Kelvin wave (BKW), with greater phase speed than that of dipolar structure on an intraseasonal time-scale. Interaction of the BKW, after circumnavigating the entire Equator, with a new large-scale buoyancy anomaly may contribute to excitation of a recurrent generation of the next cycle of MJO-like structure. Overall, the generated “hybrid structure” captures a few of the crudest features of the MJO, including its quadrupolar structure, convective activity, condensation patterns, vorticity field, phase speed, and westerly and easterly inflows in the lower and upper troposphere. Although moisture-fed convection is a necessary condition for the “hybrid structure” to be excited and maintained in the proposed theory in this study, it is fundamentally different from moisture-mode theories, because the barotropic equatorial modon and BKW also exist in “dry” environments, while there are no similar “dry” dynamical basic structures in moisture-mode theories. The proposed theory can therefore be a possible mechanism to explain the genesis and backbone structure of the MJO and to converge some theories that previously seemed divergent.

Description: Over the past decade, Mali has experienced both rising insecurity and significant climate change, leading to growing attention to the ways climate and conflict interact. However, in the absence of clear data and analysis, many discussions on this topic happen without evidence, creating the risk of instrumentalising discourse and continuing the current focus on militarisation. Guided by the Weathering Risk methodology and analytical framework, 2 this paper contributes to filling this gap, presenting qualitative and quantitative data analysis of climate security risks in the country

Description: Dieses Papier beschreibt Empfehlungen zur Weiterentwicklung des nationalen Brennstoff-Emissionshandelsgesetzes (BEHG) – und wie ein reibungsfreier Übergang zur europäischen Ebene gestaltet werden kann. Bei der Einführung und Ausgestaltung eines EU-Emissionshandelssystem für Gebäude und Straßenverkehr (EU ETS-II) bestehen derzeit noch diverse Unsicherheiten. Unabhängig von den Entscheidungen auf europäischer Ebene identifizieren wir jedoch vier No-Regret Maßnahmen zur Weiterentwicklung des BEHG, die in allen Fällen förderlich sind: Zeitliches Vorziehen und Anhebung des Preiskorridors plus Versteigerung: Die Einführung eines Preiskorridors und die Versteigerung von Zertifikaten sollte auf das Jahr 2023 vorgezogen werden. Zudem sollte der Preiskorridor angehoben und verbreitert werden, um den neuen nationalen Klimazielen Rechnung zu tragen. BEHG Emissionsmengen analog zu Sektorzielen: Die EU-Kommission hat im Fit-for-55 Paket eine Erhöhung des deutschen ESR-Ziels auf 50% vorgeschlagen, was auch ungefähr den nationalen Zielen im Rahmen des Klimaschutzgesetzes (KSG) von 2021 entspricht. Um zu einem früheren Zeitpunkt Verbindlichkeit zu schaffen, sollten daher die BEHG-Emissionsmengen aus den KSG Sektorzielen abgeleitet werden. Direkte Pro-Kopf-Rückerstattung: Für den zu erwartenden Fall deutlich steigender CO2-Preise sollte die Bundesregierung schon vor 2023 die institutionellen Voraussetzungen für die Umsetzung des Klimagelds wie im Koalitionsvertrag beschrieben schaffen. Nationaler CO2-Mindestpreis: Bis spätestens 2025 sollte ein Mindestpreis zur eventuellen Ergänzung eines EU ETS-II vorbereitet und ggf. implementiert werden. Dadurch kann im Fall anfänglich niedriger Preise im EU ETS-II garantiert werden, dass der CO2-Preis in Deutschland weiterhin kontinuierlich ansteigt. Neben diesen Maßnahmen auf nationaler Ebene, sollte sich die Bundesregierung in den Fit-for-55 Verhandlungen einsetzen (1) für die Flexibilität zwischen EU ETS und ESR sowie (2) für ein graduelles Linking zwischen ETS-I und ETS-II. Mit beiden Maßnahmen können die sehr hohen Preisunterscheide reduziert und die Effizienz der Klimapolitik erhöht werden.

Description: Zusammenfassung 1. Einleitung 2. Zwei Unsicherheiten 3. Eckpunkte und No-Regret Optionen 4. Implikationen für die Fit-for-55 Verhandlungen

Description: This profile provides an overview of projected climate parameters and related impacts on different sectors in Iraq until 2080 under different climate change scenarios provided by the IPCC¹ (called Representative Concentra- tion Pathways, RCPs). RCP2.6 represents a low emissions scenario that aims to keep global warming below 2 °C above pre-industrial temperatures; RCP6.0 represents a medium to high emissions scenario. Model projections do not account for effects of future socioeconomic impacts.

Description: While the Intergovernmental Panel on Climate Change (IPCC) physical science reports usually assess a handful of future scenarios, the Working Group III contribution on climate mitigation to the IPCC's Sixth Assessment Report (AR6 WGIII) assesses hundreds to thousands of future emissions scenarios. A key task in WGIII is to assess the global mean temperature outcomes of these scenarios in a consistent manner, given the challenge that the emissions scenarios from different integrated assessment models (IAMs) come with different sectoral and gas-to-gas coverage and cannot all be assessed consistently by complex Earth system models. In this work, we describe the “climate-assessment” workflow and its methods, including infilling of missing emissions and emissions harmonisation as applied to 1202 mitigation scenarios in AR6 WGIII. We evaluate the global mean temperature projections and effective radiative forcing (ERF) characteristics of climate emulators FaIRv1.6.2 and MAGICCv7.5.3 and use the CICERO simple climate model (CICERO-SCM) for sensitivity analysis. We discuss the implied overshoot severity of the mitigation pathways using overshoot degree years and look at emissions and temperature characteristics of scenarios compatible with one possible interpretation of the Paris Agreement. We find that the lowest class of emissions scenarios that limit global warming to “1.5 ∘C (with a probability of greater than 50 %) with no or limited overshoot” includes 97 scenarios for MAGICCv7.5.3 and 203 for FaIRv1.6.2. For the MAGICCv7.5.3 results, “limited overshoot” typically implies exceedance of median temperature projections of up to about 0.1 ∘C for up to a few decades before returning to below 1.5 ∘C by or before the year 2100. For more than half of the scenarios in this category that comply with three criteria for being “Paris-compatible”, including net-zero or net-negative greenhouse gas (GHG) emissions, median temperatures decline by about 0.3–0.4 ∘C after peaking at 1.5–1.6 ∘C in 2035–2055. We compare the methods applied in AR6 with the methods used for SR1.5 and discuss their implications. This article also introduces a “climate-assessment” Python package which allows for fully reproducing the IPCC AR6 WGIII temperature assessment. This work provides a community tool for assessing the temperature outcomes of emissions pathways and provides a basis for further work such as extending the workflow to include downscaling of climate characteristics to a regional level and calculating impacts.

Description: This document provides technical information on the two datasets behind the NGFS scenarios. It is intended to answer technical questions for those who want to perform analyses on the datasets themselves. It is an update of the Technical Documentations published in June 2020 and 2021 alongside the first two sets of NGFS Scenarios. It is therefore aligned with the third set of NGFS Scenarios, released in September 2022.

Description: PRIMAP2 is the next generation of the PRIMAP climate policy analysis suite.

Description: The NGFS scenarios have been developed to provide a common starting point for analysing climate risks to the economy and financial system. They have been created as a tool to shed light on potential future risks, and to prepare the financial system for the shocks that may arise. Importantly, the NGFS scenarios are not forecasts: instead, they aim at exploring the bookends of plausible futures (neither the most probable nor desirable) for financial risk assessment.

Description: Using openscm-runner, you can run different simple climate models using a unified API. It supports emissions-driven runs only. rOpenscmRunner is a wrapper to easily use openscm-runner from R.

Description: PRIMAP2 is the next generation of the PRIMAP climate policy analysis suite.

Description: The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2021, and all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC. For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "common reporting format" (CRF). For developing countries, non-AnnexI in terms of the UNFCCC, this is the data available through the UNFCCC DI interface (di.unfccc.int) with additional country submissions read from pdf and where available xls files. For a list of these submissions please see below. For South Korea the latest official GHG inventory has not yet been submitted to the UNFCCC but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC. Gaps in the country reported data are filled using third party data such as CDIAC, BP (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR v6.0 (all sectors). Lower priority data are harmonized to higher priority data in the gap-filling process. For the third party priority time series gaps in the third party data are filled from country reprted data sources. Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions. The v2.4 release of PRIMAP-hist reduces the time-lag from 2 to 1 years. Thus we include data for 2021 while the last version (2.3.1) included data for 2019 only. For energy CO$_2$ growth rates from the BP statistical review of world energy are used to extend the country reported (CR) or CDIAC (TP) data to 2021. For CO$_2$ from cement production Andrew cement data are used. For all other sectors and gases no emission estimates exist. Thus PRIMAP-hist relies on numerical methods and uses a linear extrapolation based on the last 15 years. COVID-19 has primarily impacted energy related emissions and in tests with CRF data no impact of COVID in the performance of linear extrapolation of emissions data in the other sectors has been detected. Version 2.4 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized.

Description: Global coastal flood exposure (population and assets) has been growing since the beginning of the industrial age and is likely to continue to grow through 21st century. Three main drivers are responsible: (a) climate-related mean sea-level change, (b) vertical land movement contributing to relative sea-level rise, and (c) socio-economic development. This paper attributes growing coastal exposure and flood risk from 1860 to 2100 to these three drivers. For historic flood exposure (1860?2005) we find that the roughly six-fold increase in population exposure and 53-fold increase in asset exposure are almost completely explained by socio-economic development (〉97% for population and 〉99% for assets). For future exposure (2005?2100), assuming a middle-of-the-road regionalized socio-economic scenario (SSP2) without coastal migration and sea-level rise according to RCP2.6 and RCP6.0, climate-change induced sea-level rise will become the most important driver for the growth in population exposure, while growth in asset exposure will still be mainly determined by socio-economic development.

Description: The development of new methods for modulation of drug distribution across to the brain is a crucial step in the effective therapies for glioblastoma (GBM). In our previous work, we discovered the phenomenon of music-induced opening of the blood-brain barrier (OBBB) in healthy rodents. In this pilot study on rats, we clearly demonstrate that music-induced BBB opening improves the therapeutic effects of bevacizumab (BZM) in rats with GBM via increasing BZM distribution to the brain along the cerebral vessels.

Description: Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.

Description: Was macht es mit der Wirtschaft, wenn das Wetter immer wechselhafter und unberechenbarer wird? Der Klimaexperte Anders Levermann forscht dazu.

Description: Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, especially below national scales, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys of 3,008 households and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries (Indonesia, Madagascar, Papua New Guinea, Philippines, and Tanzania). Our study reveals three key findings: First, overall potential losses to fisheries are higher than potential losses to agriculture. Second, while most locations (〉 2/3) will experience potential losses to both fisheries and agriculture simultaneously, climate change mitigation could reduce the proportion of places facing that double burden. Third, potential impacts are more likely in communities with lower socioeconomic status.

Description: Countries’ reliance on global food trade networks implies that regionally different climate change impacts on crop yields will be transmitted across borders. This redistribution constitutes a significant challenge for climate adaptation planning and may affect how countries engage in cooperative action. This paper investigates the long-term (2070–2099) potential impacts of climate change on global food trade networks of three key crops: wheat, rice and maize. We propose a simple network model to project how climate change impacts on crop yields may be translated into changes in trade. Combining trade and climate impact data, our analysis proceeds in three steps. First, we use network community detection to analyse how the concentration of global production in present-day trade communities may become disrupted with climate change impacts. Second, we study how countries may change their network position following climate change impacts. Third, we study the total climate-induced change in production plus import within trade communities. Results indicate that the stability of food trade network structures compared to today differs between crops, and that countries’ maize trade is least stable under climate change impacts. Results also project that threats to global food security may depend on production change in a few major global producers, and whether trade communities can balance production and import loss in some vulnerable countries. Overall, our model contributes a baseline analysis of cross-border climate impacts on food trade networks.

Description: We present MUSE spectroscopy, Megacam imaging, and Chandra X-ray emission for SPT-CL J0307-6225, a z=0.58 major merging galaxy cluster with a large BCG-SZ centroid separation and a highly disturbed X-ray morphology. The galaxy density distribution shows two main overdensities with separations of 0.144 and 0.017 arcmin to their respective BCGs. We characterize the central regions of the two colliding structures, namely 0307-6225N and 0307-6225S, finding velocity derived masses of M200, N = 2.44 ± 1.41 × 1014M⊙ and M200, S = 3.16 ± 1.88 × 1014M⊙, with a line-of-sight velocity difference of |Δv| = 342 km s−1. The total dynamically derived mass is consistent with the SZ derived mass of 7.63 h−170 ± 1.36 × 1014M⊙. We model the merger using the Monte Carlo Merger Analysis Code, estimating a merging angle of 36+14−12 ° with respect to the plane of the sky. Comparing with simulations of a merging system with a mass ratio of 1:3, we find that the best scenario is that of an ongoing merger that began 0.96+0.31−0.18 Gyr ago. We also characterize the galaxy population using Hδ and [O ii] λ3727 Å lines. We find that most of the emission-line galaxies belong to 0307-6225S, close to the X-ray peak position with a third of them corresponding to red-cluster sequence galaxies, and the rest to blue galaxies with velocities consistent with recent periods of accretion. Moreover, we suggest that 0307-6225S suffered a previous merger, evidenced through the two equally bright BCGs at the centre with a velocity difference of ∼674 km s−1.

Description: In 2015, the international community committed to limiting global warming well below 2°C. Since 2015, however, and before the coronavirus pandemic stroke, GHG emissions have continued on their growing track and the achievement of ambitious climate targets has become even more arduous. In order to rein in global warming well below 2°C, energy systems must reach net-zero emissions by mid-century. The energy supply, in particular the electricity sector, offers a great potential for reducing emissions. But in the absence of large transformations on the energy demand side, achieving the Paris Agreement’s target would necessitate an extensive recourse to debated negative emission technologies. The interest in demand-side solutions has therefore risen over the last few years. Today, buildings account for 28% of CO2 emissions in the energy system. This sector is therefore an essential building block of any successful mitigation strategy. The aim of this thesis is to investigate the contribution of buildings to limit climate change. The widespread view on the role of buildings is that there is a large and cost-effective potential for energy demand reductions, and that this potential remains unexploited due to some barriers, which policies should remove. This thesis relies on energy modeling to shed a new light on that widespread view. It uses the strengths of both an energy simulation model and of an integrated assessment model representing the energy, economy and climate systems. In order to assess the role of buildings in climate policies, the thesis addresses the following complementary questions: How will buildings energy consumption evolve in the future? What is the technological and behavioral potential for demand reductions? What are optimal climate change mitigation pathways for the buildings sector in the context of the overall energy system, and when the energy efficiency gap is taken into account? This thesis shows that the landscape of buildings energy demand will undergo major changes in the 21st century: while cooking and other heating purposes account for the bulk of the demand today; space cooling, appliances and lighting will represent the lion’s share tomorrow. Similarly, despite its current weight in demand, traditional biomass will gradually leave the stage. Against this background, radical changes in technologies and behaviors could lead to a halving of energy demand. The decarbonization of the sector however does not only pass through energy demand reductions. In the scenarios presented in this thesis, most of the decarbonization is attributed to the decline in the emissions per unit of energy consumed — a topic under-represented in the literature dealing with build- ings energy demand. In light of the thesis’ results, and supported by the literature, we challenge the widespread view on the role of buildings in climate change mitigation. Indeed, the widespread narrative focuses mostly on energy demand reductions and does not embrace the strategy consisting in decreasing the amount of emissions per unit of energy — in particular via electrification and fuel switching. This strategy accounts however for a substantial part of the sector’s decarbonization. We therefore propose an alternative narrative: Two complementary and interacting strategies can lead to a deep decarbonization of buildings energy demand: reducing energy demand and decreasing the carbon content of energy demand through energy supply decarbonization and fuel switching. Virtually all energy services in buildings could be provided by carbon-free energy carriers. How- ever market incentives as well as barriers do not allow for a widespread uptake of clean energy carriers and efficient technologies. Policies should remove barriers to the uptake of efficient and low-carbon technologies, and design markets to give the right incentives in favor of these options.

Description: Weather extremes pose a persistent threat to society on multiple layers. Besides an average of ~37,000 deaths per year, climate-related disasters cause destroyed properties and impaired economic activities, eroding people's livelihoods and prosperity. While global temperature rises – caused by anthropogenic greenhouse gas emissions – the direct impacts of climatic extreme events increase and will further intensify without proper adaptation measures. Additionally, weather extremes do not only have local direct effects. Resulting economic repercussions can propagate either upstream or downstream along trade chains causing indirect effects. One approach to analyze these indirect effects within the complex global supply network is the agent-based model Acclimate. Using and extending this loss-propagation model, I focus in this thesis on three aspects of the relation between weather extremes and economic repercussions. First, extreme weather events cause direct impacts on local economic performance. I compute daily local direct output loss time series of heat stress, river floods, tropical cyclones, and their consecutive occurrence using (near-future) climate projection ensembles. These regional impacts are estimated based on physical drivers and local productivity distribution. Direct effects of the aforementioned disaster categories are widely heterogeneous concerning regional and temporal distribution. As well, their intensity changes differently under future warming. Focusing on the hurricane-impacted capital, I find that long-term growth losses increase with higher heterogeneity of a shock ensemble. Second, repercussions are sectorally and regionally distributed via economic ripples within the trading network, causing higher-order effects. I use Acclimate to identify three phases of those economic ripples. Furthermore, I compute indirect impacts and analyze overall regional and global production and consumption changes. Regarding heat stress, global consumer losses double while direct output losses increase by a factor 1.5 between 2000 – 2039. In my research I identify the effect of economic ripple resonance and introduce it to climate impact research. This effect occurs if economic ripples of consecutive disasters overlap, which increases economic responses such as an enhancement of consumption losses. These loss enhancements can even be more amplified with increasing direct output losses, e.g. caused by climate crises. Transport disruptions can cause economic repercussions as well. For this, I extend the model Acclimate with a geographical transportation route and expand the decision horizon of economic agents. Using this, I show that policy-induced sudden trade restrictions (e.g. a no-deal Brexit) can significantly reduce the longer-term economic prosperity of affected regions. Analyses of transportation disruptions in typhoon seasons indicate that severely affected regions must reduce production as demand falls during a storm. Substituting suppliers may compensate for fluctuations at the beginning of the storm, which fails for prolonged disruptions. Third, possible coping mechanisms and adaptation strategies arise from direct and indirect economic responses to weather extremes. Analyzing annual trade changes due to typhoon-induced transport disruptions depict that overall exports rise. This trade resilience increases with higher network node diversification. Further, my research shows that a basic insurance scheme may diminish hurricane-induced long-term growth losses due to faster reconstruction in disasters aftermaths. I find that insurance coverage could be an economically reasonable coping scheme towards higher losses caused by the climate crisis. Indirect effects within the global economic network from weather extremes indicate further adaptation possibilities. For one, diversifying linkages reduce the hazard of sharp price increases. Next to this, close economic interconnections with regions that do not share the same extreme weather season can be economically beneficial in the medium run. Furthermore, economic ripple resonance effects should be considered while computing costs. Overall, an increase in local adaptation measures reduces economic ripples within the trade network and possible losses elsewhere. In conclusion, adaptation measures are necessary and potential present, but it seems rather not possible to avoid all direct or indirect losses. As I show in this thesis, dynamical modeling gives valuable insights into how direct and indirect economic impacts arise from different categories of weather extremes. Further, it highlights the importance of resolving individual extremes and reflecting amplifying effects caused by incomplete recovery or consecutive disasters.

Description: Freshwater ecosystems have been degraded due to intensive freshwater abstraction. Therefore, environmental flow requirements (EFRs) methods have been proposed to maintain healthy rivers and/or restore river flows. In this study, we used the Variable Monthly Flow (VMF) method to calculate the transgression of freshwater planetary boundaries: (1) natural deficits in which flow does not meet EFRs due to climate variability, and (2) anthropogenic deficits caused by water abstractions. The novelty is that we calculated spatially and cumulative monthly water deficits by river types including the frequency, magnitude and causes of environmental flow (EF) deficits (climatic and/or anthropogenic). Water deficit was found to be a regional rather than a global concern (〈5% of total discharge). The results show, that, from 1960 to 2000, perennial rivers with low flow alteration, such as the Amazon, had a EF deficit of 2-12% of the total discharge, and that the natural deficit was responsible for up to 75% of the total deficit. In rivers with high seasonality and high water abstractions such as the Indus, the total deficit represents up to 130% of its total discharge, 85% of which is due to withdrawals. We highlight the need to allocate water to humans and ecosystems sustainably.

Description: Human actions and climate change have drastically altered river flows across the world, resulting in adverse effects on riverine ecosystems. Environmental flows (EFs) have emerged as a prominent tool for safeguarding the riverine ecosystems, but at the global scale, the assessment of EFs is associated with high uncertainty related to the hydrological data and EF methods employed. Here, we present a novel, in-depth global EF assessment using environmental flow envelopes (EFEs). Sub-basin-specific EFEs are determined for approximately 4400 sub-basins at a monthly time resolution, and their derivation considers the methodological uncertainties related to global-scale EF studies. In addition to a lower bound of discharge based on existing EF methods, we introduce an upper bound of discharge in the EFE. This upper bound enables areas to be identified where streamflow has substantially increased above natural levels. Further, instead of only showing whether EFs are violated over a time period, we quantify, for the first time, the frequency, severity, and trends of EFE violations during the recent historical period. Discharge was derived from global hydrological model outputs from the ISIMIP 2b ensemble. We use pre-industrial (1801–1860) quasi-natural discharge together with a suite of hydrological EF methods to estimate the EFEs. We then compare the EFEs with recent historical (1976–2005) discharge to assess the violations of the EFE. These violations most commonly manifest as insufficient streamflow during the low-flow season, with fewer violations during the intermediate-flow season, and only a few violations during the high-flow season. The EFE violations are widespread and occur in half of the sub-basins of the world during more than 5 % of the months between 1976 and 2005, which is double compared with the pre-industrial period. The trends in EFE violations have mainly been increasing, which will likely continue in the future with the projected hydroclimatic changes and increases in anthropogenic water use. Indications of increased upper extreme streamflow through EFE upper bound violations are relatively scarce and dispersed. Although local fine-tuning is necessary for practical applications, and further research on the coupling between quantitative discharge and riverine ecosystem responses at the global scale is required, the EFEs provide a quick and globally robust way of determining environmental flow allocations at the sub-basin scale to inform global research and policies on water resources management.

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

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

Description: Emissionsneutrale Transformationspfade effizient und sozial gerecht gestalten: Zur Bewältigung der Klimakrise und zur Umsetzung des Pariser Klimaschutzabkommens hat sich die Europäische Union zum Ziel gesetzt, die Treibhausgasemissionen (THG-Emissionen) bis 2050 auf Netto-Null zu senken. Mit dem nationalen Klimaschutzplan sucht auch Deutschland den Regulierungsrahmen so zu setzen, dass im Jahr 2045 Netto-Null-THG-Emissionen erreicht werden. Während über das Ziel weitgehende Einigkeit besteht, ist der Weg dorthin noch umstritten. Szenarien gemeinsam mit zentralen gesellschaftlichen Stakeholdern entwickeln: Im Rahmen des Projekts „Deep Transformation Scenarios for Informing the Climate Policy Discourse“ (DIPOL) haben das PIK, RWI und adelphi mit Akteuren aus Wirtschaft, Wissenschaft und Zivilgesellschaft zusammengearbeitet, um eine Reihe von Szenario-Narrativen zu entwickeln, die die Perspektiven dieser Stakeholder zur Erreichung der deutschen und europäischen Klimaziele verbinden und so eine Reihe möglicher Entwicklungspfade für Europa und Deutschland aufzeigen. Ausgewählte Stakeholder wurden hierzu über drei Jahre kontinuierlich in die Szenario-Entwicklung einbezogen und auf diese Weise ein Co-Produktionsprozess etabliert.

Description: Estimates of climate change’s economic impacts vary widely, depending on the applied methodology. This uncertainty is a barrier for policymakers seeking to quantify the benefits of mitigation. In this Perspective, we provide a comprehensive overview and categorization of the pathways and methods translating biophysical impacts into economic damages. We highlight the open question of the persistence of impacts as well as key methodological gaps, in particular the effect of including inequality and adaptation in the assessments. We discuss the need for intensifying interdisciplinary research, focusing on the uncertainty of econometric estimates of damages as well as identification of the most socioeconomically relevant types of impact. A structured model intercomparison related to economic impacts is noted as a crucial next step.

Description: Peatlands cover only about 3% the global land area, but store about twice as much carbon as global forest biomass. If intact peatlands are drained for agriculture or other human uses, peat oxidation can result in considerable CO2 emissions and other greenhouse gases (GHG) for decades or even centuries. Despite their importance, emissions from degraded peatlands have so far not been included explicitly in mitigation pathways compatible with the Paris Agreement. Such pathways include land-demanding mitigation options like bioenergy or afforestation with substantial consequences for the land system. Therefore, besides GHG emissions owing to the historic conversion of intact peatlands, the increased demand for land in current mitigation pathways could result in drainage of presently intact peatlands, e.g. for bioenergy production. Here, we present the first quantitative model-based projections of future peatland dynamics and associated GHG emissions in the context of a 2°C mitigation pathway. Our spatially explicit land-use modelling approach with global coverage simultaneously accounts for future food demand, based on population and income projections, and land-based mitigation measures. Without dedicated peatland policy and even in the case of peatland protection, our results indicate that the land system would remain a net source of CO2 throughout the 21st century. This result is in contrast to the outcome of current mitigation pathways, in which the land system turns into a net carbon sink by 2100. However, our results indicate that it is possible to reconcile land use and GHG emissions in mitigation pathways through a peatland protection and restoration policy. According to our results, the land system would turn into a global net carbon sink by 2100, as projected by current mitigation pathways, if about 60% of present-day degraded peatlands would be rewetted in the coming decades, next to the protection of intact peatlands.

Description: Forests and wood products play a major role in climate change mitigation strategies and the transition from a fossil-based economy to a circular bioeconomy. Accurate estimates of future forest productivity are crucial to predict the carbon sequestration and wood provision potential of forests. Since long, forest managers have used empirical yield tables as a cost-effective and reliable way to predict forest growth. However, recent climate change-induced growth shifts raised doubts about the long-term validity of these yield tables. In this study, we propose a methodology to improve available yield tables of 11 tree species in the Netherlands and Flanders, Belgium. The methodology uses scaling functions derived from climate-sensitive process-based modelling (PBM) that reflect state-of-the-art projections of future growth trends. Combining PBM and stand information from the empirical yield tables for the region of Flanders, we found that for the period 1987–2016 stand productivity has on average increased by 13% compared to 1961–1990. Furthermore, simulations indicate that this positive growth trend is most likely to persist in the coming decades, for all considered species, climate or site conditions. Nonetheless, results showed that local site variability is equally important to consider as the in- or exclusion of the CO2 fertilization effect or different climate projections, when assessing the magnitude of forests' response to climate change. Our projections suggest that incorporating these climate change-related productivity changes lead to a 7% increase in standing stock and a 22% increase in sustainably potentially harvestable woody biomass by 2050. The proposed methodology and resulting estimates of climate-sensitive projections of future woody biomass stocks will facilitate the further incorporation of forests and their products in global and regional strategies for the transition to a climate-smart circular bioeconomy.

Description: Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain 〉 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

Description: International migration patterns, at the global level, can to a large extent be explained through economic factors in origin and destination countries. On the other hand, it has been shown that global climate change is likely to affect economic development over the coming decades. Here, we demonstrate how these future climate impacts on national income levels could alter the global migration landscape. Using an empirically calibrated global migration model, we investigate two separate mechanisms. The first is through destination-country income, which has been shown consistently to have a positive effect on immigration. As countries’ income levels relative to each other are projected to change in the future both due to different rates of economic growth and due to different levels of climate change impacts, the relative distribution of immigration across destination countries also changes as a result, all else being equal. Second, emigration rates have been found to have a complex, inverted U-shaped dependence on origin-country income. Given the available migration flow data, it is unclear whether this dependence—found in spatio-temporal panel data—also pertains to changes in a given migration flow over time. If it does, then climate change will additionally affect migration patterns through origin countries’ emigration rates, as the relative and absolute positions of countries on the migration “hump” change. We illustrate these different possibilities, and the corresponding effects of 3°C global warming (above pre-industrial) on global migration patterns, using climate model projections and two different methods for estimating climate change effects on macroeconomic development.

Description: Impedance/admittance models (IM/AMs) have been widely used to analyze the small-signal stability of grid-tied power electronic devices, such as the voltage source converter (VSC). They can be either derived from theoretical analysis under white-box conditions, where all parameters and control structures are fully known, or measured based on experiments under black-box conditions, where the topology and parameters of the controllers are completely unknown. As the IM/AMs depend on specific operating conditions, it is highly desirable to develop fast algorithms for IM/AM prediction (or estimation) under the black-box and variable-operating-point conditions. This article extends the nearly-decoupled AM method for sequence AMs proposed recently by Liu et al to fit any unknown control structure, including not only grid-following VSC, but also grid-forming VSC. It is, therefore, referred to as the fully-decoupled IM (FDIM) method. Furthermore, a curve fitting method for the transfer function is proposed to expedite the algorithm, based on the information of a few disturbance frequencies only. Finally, the algorithm is verified by wide simulations and experiments under different situations, including the direct-drive wind turbine generator. The whole approach is expected to be broadly applicable to the stability analysis of power-electronic-based power systems under variable operating conditions.

Description: Understanding the interactions (synergies and trade-offs) among the Sustainable Development Goals (SDGs) is crucial for enhancing policy coherence between different sectors. However, spatial differences in the SDG interactions and their temporal variations at the sub-national scale are still critical gaps that need to be urgently filled. Here, we assess the spatial and temporal variation of the SDG interactions in China based on the systematic classification framework of SDGs. The framework groups the seventeen SDGs into three categories, namely “Essential Needs,” “Objectives,” and “Governance.” Spatially, we found that the SDGs in “Essential Needs” & “Objectives” and “Essential Needs” & “Governance” generally show trade-offs in the eastern provinces of China. Synergies among all three SDG categories are observed in some central and western China provinces, which implies that these regions conform to sustainable development patterns. In addition, temporally, the synergies of the three SDG categories have shown a weakening trend in the last decade, mainly due to the regional differences in the progress of SDG7 (Affordable and Clean Energy). Overall, our results identify the necessity for provinces to enhance the synergies between SDG12 (Responsible Production and Consumption) and other SDGs to tackle the trade-offs between the “Essential Needs” and “Objectives.” Meanwhile, promoting the progress of SDG7 will also contribute to balanced development across provinces.

Description: This work assesses land cover changes on the Upper Runde sub-catchment, Zimbabwe, and associated effects on sedimentation rates and risks. The model was implemented using the common Geographic Information Systems tools. To achieve this objective, mean annual and monthly rainfall, as well as sediment data, were used (December 2016 and April 2017). Land use and land cover changes were assessed using time-series Landsat data acquired between the years 2000 and 2016. The Revised Universal Soil Loss (RUSLE) model was used to model sedimentation rates in the catchment. Land cover results showed that the catchment experienced significant (α = 0.05) changes during the period of monitoring. For example, forests and woodlands decreased by 39% and 23% between 2000 and 2016, respectively. Sedimentation results indicated that the catchment had an average sediment load of 6272 mg/l as compared to the expected maximum of 3000 mg/l. RUSLE soil loss simulation results showed an increase in average soil loss from 1.2 ton/ha/yr. in 2000 to 1.7 ton/ha/yr. in 2016 and an increase in sediment yield by 19.2% from 3476 mg/l in 2000–4144 mg/l in 2016. Overall, the findings of this study demonstrate that the catchment experiences high sedimentation. Therefore, catchment sediment monitoring and soil conservation actions should be a priority.

Description: Das globale Ernährungssystem ist in der Krise. Der russische Angriffskrieg auf die Ukraine hat einen starken Preisanstieg für Lebensmittel, Düngemittel und Treibstoff ausgelöst und damit eine Verschlechterung der Ernährungssicherheit insbesondere in Ländern mit niedrigem und mittlerem Einkommen. Gleichzeitig bedrohen Dürren und Extremwetterereignisse die Ernten weltweit. Mangel- und Fehlernährung existieren in zunehmendem Maße nebeneinander und kosten ebenso wie die Folgen der globalen Umweltzerstörung viele Menschenleben. Derzeit fokussiert sich die politische Diskussion vor allem auf den Anpassungsbedarf der Produktionsseite. Eine Kombination mit Anpassungen der Konsumseite sind jedoch unerlässlich, um Produzent*innen zu entlasten und die diversen Krisen gemeinsam anzugehen. Insbesondere die deutliche Reduktion von Konsum und Produktion tierischer Lebensmittel ist ein zentraler Hebel, um das globale Ernährungssystem resilienter, fairer und nachhaltiger zu gestalten. In diesem Policy Brief weisen wir auf die Dringlichkeit entsprechender politischer Maßnahmen hin und machen drei konkrete Umsetzungsvorschläge: Einrichtung eines Transformationsfonds für die Umstellung, Etablierung wirkungsvoller Lenkungsabgaben und Aufbau zukunftsweisender Institutionen zur Transformation des Ernährungssystems.

Description: As sustainable metropolitan regions require more densely built-up areas, a comprehensive understanding of the urban acoustic environment (AE) is needed. However, comprehensive datasets of the urban AE and well-established research methods for the AE are scarce. Datasets of audio recordings tend to be large and require a lot of storage space as well as computationally expensive analyses. Thus, knowledge about the long-term urban AE is limited. In recent years, however, these limitations have been steadily overcome, allowing a more comprehensive analysis of the urban AE. In this respect, the objective of this work is to contribute to a better understanding of the time–frequency domain of the urban AE, analysing automatic audio recordings from nine urban settings over ten months. We compute median power spectra as well as normalised spectrograms for all settings. Additionally, we demonstrate the use of frequency correlation matrices (FCMs) as a novel approach to access large audio datasets. Our results show site-dependent patterns in frequency dynamics. Normalised spectrograms reveal that frequency bins with low power hold relevant information and that the AE changes considerably over a year. We demonstrate that this information can be captured by using FCMs, which also unravel communities of interlinked frequency dynamics for all settings.

Description: Deep decarbonization of energy systems poses considerable challenges to electricity markets and there is a growing consensus that an energy-only design based on short-term marginal cost pricing cannot deliver adequate levels of investment and long-term coordination across actors and sectors. Based on the instructive example of the evolution of European electricity market designs, we discuss several shortcomings of energy-only markets and illustrate how ad-hoc policies that intend to address them have limitations of their own, notably a lack of systemwide coordination. Second, we describe how the sheer scale and nature of deep decarbonization targets requiring massive investment in capital-intensive low-carbon technologies exacerbate these issues. Ambitious emission reduction targets thus require an evolution of market design towards hybrid regimes. Hybrid markets separate long-term investment decisions from short-term operations through a balanced and differentiated use of competitive and regulatory design elements to coordinate and de-risk investment. Finally, a historical analysis of the evolution of different electricity market designs shows how hybrid markets constitute contemporary forms of long-run marginal cost pricing that are appropriate for meeting deep decarbonization targets with reduced uncertainty and hence lower private and social costs.

Description: Globally, freshwater systems are degrading due to excessive water withdrawals. We estimate that if rivers’ environmental flow requirements were protected, the associated decrease in irrigation water availability would reduce global yields by ~5%. As one option to increase food supply within limited water resources, we show that dietary changes towards less livestock products could compensate for this effect. If all currently grown edible feed was directly consumed by humans, we estimate that global food supply would even increase by 19%. We thus provide evidence that dietary changes are an important strategy to harmonize river flow protection with sustained food supply.

Description: Forests and forestry play a key role in policy targets to achieve climate neutrality. In a comprehensive new European Forest Institute study, a multidisciplinary team of 12 authors from 7 countries have analysed how much forests and wood use can contribute to climate change mitigation, and how that contribution can be maximised.

Description: Recent decades have seen a rise in the use of physics methods to study different societal phenomena. This development has been due to physicists venturing outside of their traditional domains of interest, but also due to scientists from other disciplines taking from physics the methods that have proven so successful throughout the 19th and the 20th century. Here we characterise the field with the term ‘social physics’ and pay our respect to intellectual mavericks who nurtured it to maturity. We do so by reviewing the current state of the art. Starting with a set of topics that are at the heart of modern human societies, we review research dedicated to urban development and traffic, the functioning of financial markets, cooperation as the basis for our evolutionary success, the structure of social networks, and the integration of intelligent machines into these networks. We then shift our attention to a set of topics that explore potential threats to society. These include criminal behaviour, large-scale migration, epidemics, environmental challenges, and climate change. We end the coverage of each topic with promising directions for future research. Based on this, we conclude that the future for social physics is bright. Physicists studying societal phenomena are no longer a curiosity, but rather a force to be reckoned with. Notwithstanding, it remains of the utmost importance that we continue to foster constructive dialogue and mutual respect at the interfaces of different scientific disciplines.

Description: Even in the early days of COVID-19, the European Union signalled its commitment to make its post-pandemic recovery green. The disruption caused by the pandemic would require significant rebuilding of the economy, offering an opportunity to accelerate green investment in the context of the European Green Deal. But pursuing a green recovery is not as straightforward as it might seem. Complex trade-offs must be negotiated between the need for short-term stimulus and the need to address the long-term challenge posed by global warming. To support policymakers in this difficult endeavour, Bruegel launched in 2020 the Bruegel Green Recovery Group, an initiative supported by the European Climate Foundation. Its aim was to be a platform for dialogue between high-level EU policymakers and academics on green recovery, in Europe and beyond. This Blueprint compiles some of the work of prominent voices of the Group, on issues that will touch the lives of all Europeans.

Description: We present results from the FAOSTAT emissions shares database, covering emissions from agri-food systems and their shares to total anthropogenic emissions for 196 countries and 40 territories for the period 1990–2019. We find that in 2019, global agri-food system emissions were 16.5 (95 %; CI range: 11–22) billion metric tonnes (Gt CO2 eq. yr−1), corresponding to 31 % (range: 19 %–43 %) of total anthropogenic emissions. Of the agri-food system total, global emissions within the farm gate – from crop and livestock production processes including on-farm energy use – were 7.2 Gt CO2 eq. yr−1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2 eq. yr−1; and emissions from pre- and post-production processes – manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal – were 5.8 Gt CO2 eq. yr−1. Over the study period 1990–2019, agri-food system emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAOSTAT data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased 9 %. In 2019, in terms of individual greenhouse gases (GHGs), pre- and post-production processes emitted the most CO2 (3.9 Gt CO2 yr−1), preceding land use change (3.3 Gt CO2 yr−1) and farm gate (1.2 Gt CO2 yr−1) emissions. Conversely, farm gate activities were by far the major emitter of methane (140 Mt CH4 yr−1) and of nitrous oxide (7.8 Mt N2O yr−1). Pre- and post-production processes were also significant emitters of methane (49 Mt CH4 yr−1), mostly generated from the decay of solid food waste in landfills and open dumps. One key trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along the agri-food system, at global, regional and national scales. In fact, our data show that by 2019, pre- and post-production processes had overtaken farm gate processes to become the largest GHG component of agri-food system emissions in Annex I parties (2.2 Gt CO2 eq. yr−1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2 eq. yr−1), becoming larger than emissions from land use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2 eq. yr−1), the USA (700 Mt CO2 eq. yr−1) and the EU-27 (600 Mt CO2 eq. yr−1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (https://www.fao.org/faostat/en/#data/EM; FAO, 2021a), with annual updates.

Description: This paper studies the synchronization of a network with linear diffusive coupling, which blinks between the variables periodically. The synchronization of the blinking network in the case of sufficiently fast blinking is analyzed by showing that the stability of the synchronous solution depends only on the averaged coupling and not on the instantaneous coupling. To illustrate the effect of the blinking period on the network synchronization, the Hindmarsh-Rose model is used as the dynamics of nodes. The synchronization is investigated by considering constant single-variable coupling, averaged coupling, and blinking coupling through a linear stability analysis. It is observed that by decreasing the blinking period, the required coupling strength for synchrony is reduced. It equals that of the averaged coupling model times the number of variables. However, in the averaged coupling, all variables participate in the coupling, while in the blinking model only one variable is coupled at any time. Therefore, the blinking coupling leads to an enhanced synchronization in comparison with the single-variable coupling. Numerical simulations of the average synchronization error of the network confirm the results obtained from the linear stability analysis.

Description: We consider an adaptive network, whose connection weights coevolve in congruence with the dynamical states of the local nodes that are under the influence of an external stimulus. The adaptive dynamical system mimics the adaptive synaptic connections common in neuronal networks. The adaptive network under external forcing displays exotic dynamical states such as itinerant chimeras whose population density of coherent and incoherent domains coevolves with the synaptic connection, bump states, and bump frequency cluster states, which do not exist in adaptive networks without forcing. In addition, the adaptive network also exhibits partial synchronization patterns such as phase and frequency clusters, forced entrained, and incoherent states. We introduce two measures for the strength of incoherence based on the standard deviation of the temporally averaged (mean) frequency and on the mean frequency to classify the emergent dynamical states as well as their transitions. We provide a two-parameter phase diagram showing the wealth of dynamical states. We additionally deduce the stability condition for the frequency-entrained state. We use the paradigmatic Kuramoto model of phase oscillators, which is a simple generic model that has been widely employed in unraveling a plethora of cooperative phenomena in natural and man-made systems.

Description: Studying the stability of synchronization of coupled oscillators is one of the prominent topics in network science. However, in most cases, the computational cost of complex network analysis is challenging because they consist of a large number of nodes. This study includes overcoming this obstacle by presenting a method for reducing the dimension of a large-scale network, while keeping the complete region of stable synchronization unchanged. To this aim, the first and last non-zero eigenvalues of the Laplacian matrix of a large network are preserved using the eigen-decomposition method and Gram-Schmidt orthogonalization. The method is only applicable to undirected networks and the result is a weighted undirected network with smaller size. The reduction method is studied in a large-scale a small-world network of Sprott-B oscillators. The results show that the trend of the synchronization error is well maintained after node reduction for different coupling schemes.

Description: Jerk systems are some of the simplest dynamical systems that can exhibit chaotic dynamics. This paper investigates the synchronization of coupled jerk systems with coupling in single variables. We apply the well-known approach for synchronization analysis, the master stability function, which determines the stability of the synchronization manifold. It is shown that a jerk system in which the jerk equation is not dependent on the acceleration has similar master stability functions when coupled in velocity or acceleration variables. Therefore, the system has the same synchronization behavior in these two coupling configurations. Such an equivalence has not been reported in the literature.

Description: We show that unavoidable stochastic fluctuations are not only affecting information processing in a destructive or constructive way, but may even induce conditions necessary for the artificial intelligence itself. In this proof-of-principle paper we consider a model of a neuron-astrocyte network under the influence of multiplicative noise and show that information encoding (loading, storage, and retrieval of information patterns), one of the paradigmatic signatures of intelligent systems, can be induced by stochastic influence and astrocytes. Hence, astrocytes, recently proved to play an important role in memory and cognitive processing in mammalian brains, may play also an important role in the generation of a system's features providing artificial intelligence functions. Hence, one could conclude that intrinsic stochasticity is probably positively utilized by brains, not only to optimize the signal response but also to induce intelligence itself, and one of the key roles, played by astrocytes in information processing, could be dealing with noises.

Description: Photodynamic therapy (PDT) is a promising add-on therapy to the current standard of care for patients with glioblastoma (GBM). The traditional explanation of the anti-cancer PDT effects involves the PDT-induced generation of a singlet oxygen in the GBM cells, which causes tumor cell death and microvasculature collapse. Recently, new vascular mechanisms of PDT associated with opening of the blood–brain barrier (OBBB) and the activation of functions of the meningeal lymphatic vessels have been discovered. In this review, we highlight the emerging trends and future promises of immunotherapy for brain tumors and discuss PDT-OBBB as a new niche and an important informative platform for the development of innovative pharmacological strategies for the modulation of brain tumor immunity and the improvement of immunotherapy for GBM.

Description: This article establishes a multitask graph filter model based on the recursive least square (RLS) method and proposes an online distributed alternating direction method of multipliers (ODADMM) algorithm. We are interested in the time-varying graph signal, i.e., the graph filter is estimated from streaming data. Considering that current popular graph shift operators' energy can not be preserved, which will lead to slow estimation speed, so the RLS method is adopted in graph filters (GFs) to improve the convergence rate. Besides, a multitask GFs model is proposed for node-variant GFs, where each vertex cooperates with neighbours to improve the estimation performance by utilizing the correlation of tasks. Then, according to our model, a distributed alternating direction method of multipliers (DADMM) algorithm is designed, while it has enormous computational complexity. To address this drawback, an ODADMM algorithm is further developed, and the algorithm can converge to an optimal point that is validated. Numerical results verify that the proposed algorithm is more competitive in convergence speed and performance than other related algorithms, and two real scenes are tested to verify the effectiveness of the algorithm.

Description: Networks of identical coupled oscillators display a remarkable spatiotemporal pattern, the chimera state, where coherent oscillations coexist with incoherent ones. In this paper we show quantitatively in terms of basin stability that stable and breathing chimera states in the original two coupled networks typically have very small basins of attraction. In fact, the original system is dominated by periodic and quasi-periodic chimera states, in strong contrast to the model after reduction, which can not be uncovered by the Ott-Antonsen ansatz. Moreover, we demonstrate that the curve of the basin stability behaves bimodally after the system being subjected to even large perturbations. Finally, we investigate the emergence of chimera states in brain network, through inducing perturbations by stimulating brain regions. The emerged chimera states are quantified by Kuramoto order parameter and chimera index, and results show a weak and negative correlation between these two metrics.

Description: This article investigates the pinning asymptotic stabilization of probabilistic Boolean networks (PBNs) by a digraph approach. In order to stabilize the PBN asymptotically, a mode-independent pinning control (MIPC) is designed to make the target state a fixed point, and transform the state transition digraph into one that has a spanning branching rooted at the target vertex. It is shown that if there is a mode-dependent pinning control that can asymptotically stabilize the PBN, then there must exist an MIPC that can do the same with fewer pinned nodes and control inputs. A necessary and sufficient condition is given to verify the feasibility of a set of pinned nodes based on an auxiliary digraph. Three algorithms are proposed to find a feasible set of pinned nodes with the minimum cardinality. The main results are extended to the case where the target is a limit cycle. Compared with the existing results, the total computational complexity of these algorithms is strongly reduced. The obtained results are applied to a numerical example and a cell apoptosis network.

Description: In this article, event-triggered attitude consensus is considered for multiagent systems with guaranteed fixed-time convergence. Due to the non-Euclidean property of the attitude configuration space, the attitude consensus is more challenging to achieve under the sampled-data setting. An event-triggered attitude consensus protocol and event-triggered condition are proposed based on the axis–angle attitude representation. The fixed-time attitude consensus is reached if the initial attitudes lie in local regions on the attitude configuration space. The theoretical results reveal that the settling time is related to the interevent interval and the algebraic connectivity of the topology graph. We further consider the consensus protocol under a jointly connected graph, and establish the settling time estimation that depends on the switching instants. Numerical simulations are conducted to verify the validity of the theoretical results finally.

Description: Extreme events are defined as events that largely deviate from the nominal state of the system as observed in a time series. Due to the rarity and uncertainty of their occurrence, predicting extreme events has been challenging. In real life, some variables (passive variables) often encode significant information about the occurrence of extreme events manifested in another variable (active variable). For example, observables such as temperature, pressure, etc., act as passive variables in case of extreme precipitation events. These passive variables do not show any large excursion from the nominal condition yet carry the fingerprint of the extreme events. In this study, we propose a reservoir computation-based framework that can predict the preceding structure or pattern in the time evolution of the active variable that leads to an extreme event using information from the passive variable. An appropriate threshold height of events is a prerequisite for detecting extreme events and improving the skill of their prediction. We demonstrate that the magnitude of extreme events and the appearance of a coherent pattern before the arrival of the extreme event in a time series affect the prediction skill. Quantitatively, we confirm this using a metric describing the mean phase difference between the input time signals, which decreases when the magnitude of the extreme event is relatively higher, thereby increasing the predictability skill.

Description: The blood-brain barrier (BBB) poses a significant challenge for drug delivery to the brain. Therefore, the development of safe methods for an effective delivery of medications to the brain can be a revolutionary step in overcoming this limitation. Using a quantum-dot-based 1267 nm laser (photosensitiser-free generation of singlet oxygen), we clearly show the photostimulation of lymphatic delivery of bevacizumab (BMZ) to the brain tissues and the meninges. These pilot findings open promising perspectives for photomodulation of a lymphatic brain drug delivery bypassing the BBB, and potentially enabling a breakthrough strategy in therapy of glioma using BMZ and other chemotherapy drugs.

Description: Krisenzeiten werfen Fragen auf, zwin- gen zum Umdenken. Im Oktober ver- gangenen Jahres machte Papst Franzis- kus in einem TED Talk darauf aufmerk- sam, dass die Covid-19-Pandemie Licht auf eine andere Herausforderung von globaler Dimension wirft: die sozial- ökologische Krise. Alles auf dieser Welt sei miteinander verbunden, die Pande- mie erinnere uns daran. Er lud konfes- sionsübergreifend dazu ein, sich ge- meinsam auf eine Reise zu begeben. Ei- ne Reise, die zum Umdenken und, wich- tiger noch, Umlenken führen soll. In de- ren Mittelpunkt sollen konkrete Maß- nahmen für eine Transformation ste- hen. Ziel sei es, innerhalb der nächsten Dekade eine Welt zu schaffen, in der man sowohl den Bedürfnissen gegen- wärtiger als auch denen zukünftiger Generationen gerecht werden könne.

Description: This article is concerned with the security issue in the state estimation problem for a networked control system (NCS). A new model of joint false data injection (FDI) attack is established wherein attacks are injected to both the remote estimator and the communication channels. Such a model is general that includes most existing FDI attack models as special cases. The joint FDI attacks are subjected to limited access and/or resource constraints, and this gives rise to a few attack scenarios to be examined one by one. Our objective is to establish the so-called insecurity conditions under which there exists an attack sequence capable of driving the estimation bias to infinity while bypassing the anomaly detector. By resorting to the generalized inverse theory, necessary and sufficient conditions are derived for the insecurity under different attack scenarios. Subsequently, easy-to-implement algorithms are proposed to generate attack sequences on insecure NCSs with respect to different attack scenarios. In particular, by using a matrix splitting technique, the constraint-induced sparsity of the attack vectors is dedicatedly investigated. Finally, several numerical examples are presented to verify the effectiveness of the proposed FDI attacks.

Description: Volcanoes can be extremely damaging to the environment, human society, and also impact climate change. During volcanic eruption, massive amounts of gases and dust particles are thrown into the atmosphere and propagated instantaneously by the stratospheric circulation, resulting in a huge impact on the interactive pattern of the atmosphere. Here, we develop a climate network-based framework to study the temporal evolution of lower stratospheric atmosphere conditions in relation to a volcanic eruption, the Hunga Tonga-Hunga Ha’apai (HTHH) volcano, which erupted on 20 December 2021. Various spatial-temporal topological features of the climate network are introduced to analyze the nature of the HTHH. We show that our framework has the potential to identify the dominant eruption events of the HTHH and reveal the impact of the HTHH eruption. We find that during the eruption periods of the HTHH, the correlation behaviors in the lower stratosphere became much stronger than during normal periods. Both the degree and clustering coefficients increased significantly during the dominant eruption periods, and could be used as indications for the eruption of HTHH. The underlying mechanism for the observed cooperative mode is related to the impact of a volcanic eruption on global mass circulations. The study on the network topology of the atmospheric structure during a volcanic eruption provides a fresh perspective to investigate the impact of volcanic eruptions. It can also reveal how the interactive patterns of the atmosphere respond to volcanic eruptions and improve our understanding regarding the global impacts of volcanic eruptions.

Description: An effective computational singular perturbation (CSP) method for solving the non-standard FitzHugh-Nagumo system is constructed and evaluated by detailed algebra factorization. Furthermore, our studies illustrate a geometric CSP framework with the layer and reduced problems for the non-standard FitzHugh-Nagumo system. Finally, the first two CSP manifolds and two CSP fast fibers are also presented for the FitzHugh-Nagumo system by one-step and two-step CSP updates in this context. The CSP method can further describe Fenichel manifolds and facilitate a better understanding of the dynamic properties of complex neuron models or circuit neural models.

Description: A formalized and quantifiable responsibility score is a crucial component in many aspects of the development and application of multi-agent systems and autonomous agents. We can employ it to inform decision making processes based on ethical considerations, as a measure to ensure redundancy that helps us in avoiding system failure, as well as for verifying that autonomous systems remain trustworthy by testing for unwanted responsibility voids in advance. We follow recent proposals to use probabilities as the basis for responsibility ascription in uncertain environments rather than the deterministic causal views employed in much of the previous formal philosophical literature. Using an axiomatic approach we formally evaluate the qualities of (classes of) proposed responsibility functions. To this end, we decompose the computation of the responsibility a group carries for an outcome into the computation of values that we assign to its members for individual decisions leading to that outcome, paired with an appropriate aggregation function. Next, we discuss a number of intuitively desirable properties for each of these contributing functions. We find an incompatibility between axioms determining upper and lower bounds for the values assigned at the member level. Regarding the aggregation from member-level values to group-level responsibility we are able to axiomatically characterise one promising aggregation function. Finally, we present two maximally axiom compliant group-level responsibility measures – one respecting the lower bound axioms at the member level and one respecting the corresponding upper bound axioms.