ALBERT

Description: Health outcomes of in utero Ramadan exposure have been reported in a systematic literature review; however, the available literature on long-term effects were not fully covered. Our study aims to specifically review the long-term outcomes of in utero Ramadan exposure. We searched for original research articles analyzing any long-term outcome of in utero Ramadan exposure, excluding maternal and perinatal outcomes. Sixteen studies from 8304 non-duplicate search results were included. Most studies suggest negative consequences from in utero Ramadan exposure on health, as well as on economic outcomes later in adulthood. Higher under-five mortality rate, higher mortality under three months, and under one year, shorter stature, lower body mass index, increased incidence of vision, hearing and learning disabilities, lower mathematics, writing and reading scores, as well as a lower probability to own a home were associated with Ramadan exposure during conception or the first trimester of pregnancy. Furthermore, age and sex seem to play a pivotal role on the association. Existing studies suggest that in utero Ramadan exposure may adversely impact long-term health and economic well-being. However, evidence is limited. Meanwhile, increasing awareness of the potential risks of Ramadan fasting during pregnancy should be raised among pregnant women and clinicians and other antenatal care workers should promote better maternal healthcare

Description: The principle of underwater acoustic signal propagation is of vital importance to realize the “digital ocean”. However, underwater circumstances are becoming more complex and multi-factorial because of raising human activities, changing climate, to name a few. For this study, we formulate a mathematical model to describe the complex variation of underwater propagating acoustic signals, and the solving method are presented. Firstly, the perturb-coefficient nonlinear propagation equation is derived based on hydrodynamics and the adiabatic relation between pressure and density. Secondly, physical elements are divided into two types, intrinsic and extrinsic. The expression of the two types are combined with the perturb-coefficient nonlinear propagation equation by location and stochastic parameters to obtain the stochastic nonlinear differential propagation model. Thirdly, initial and boundary conditions are analyzed. The existence theorem for solutions is proved. Finally, the operator splitting procedure is proposed to obtain the solution of the model. Two simulations demonstrate that this model is effective and can be used in multiple circumstances.

Description: Worldwide bees provide an important ecosystem service of plant pollination. Climate change and land-use changes are among drivers threatening bee survival with mounting evidence of species decline and extinction. In developing countries, rural areas constitute a significant proportion of the country's land, but information is lacking on how different habitat types and weather patterns in these areas influence bee populations. This study investigated how weather variables and habitat-related factors influence the abundance, diversity, and distribution of bees across seasons in a farming rural area of Zimbabwe. Bees were systematically sampled in five habitat types (natural woodlots, pastures, homesteads, fields, and gardens) recording ground cover, grass height, flower abundance and types, tree abundance and recorded elevation, temperature, light intensity, wind speed, wind direction, and humidity. Zero-inflated models, censored regression models, and PCAs were used to understand the influence of explanatory variables on bee community composition, abundance, and diversity. Bee abundance was positively influenced by the number of plant species in flower (p 〈 .0001). Bee abundance increased with increasing temperatures up to 28.5°C, but beyond this, temperature was negatively associated with bee abundance. Increasing wind speeds marginally decreased probability of finding bees. Bee diversity was highest in fields, homesteads, and natural woodlots compared with other habitats, and the contributions of the genus Apis were disproportionately high across all habitats. The genus Megachile was mostly associated with homesteads, while Nomia was associated with grasslands. Synthesis and applications. Our study suggests that some bee species could become more proliferous in certain habitats, thus compromising diversity and consequently ecosystem services. These results highlight the importance of setting aside bee-friendly habitats that can be refuge sites for species susceptible to land-use changes.

Description: Background Although effects on labour is one of the most tangible and attributable climate impact, our quantification of these effects is insufficient and based on weak methodologies. Partly, this gap is due to the inability to resolve different impact channels, such as changes in time allocation (labour supply) and slowdown of work (labour productivity). Explicitly resolving those in a multi-model inter-comparison framework can help to improve estimates of the effects of climate change on labour effectiveness. Methods In this empirical, multi-model study, we used a large collection of micro-survey data aggregated to subnational regions across the world to estimate new, robust global and regional temperature and wet-bulb globe temperature exposure-response functions (ERFs) for labour supply. We then assessed the uncertainty in existing labour productivity response functions and derived an augmented mean function. Finally, we combined these two dimensions of labour into a single compound metric (effective labour effects). This combined measure allowed us to estimate the effect of future climate change on both the number of hours worked and on the productivity of workers during their working hours under 1·5°C, 2·0°C, and 3·0°C of global warming. We separately analysed low-exposure (indoors or outdoors in the shade) and high-exposure (outdoor in the sun) sectors. Findings We found differentiated empirical regional and sectoral ERF's for labour supply. Current climate conditions already negatively affect labour effectiveness, particularly in tropical countries. Future climate change will reduce global total labour in the low-exposure sectors by 18 percentage points (range −48·8 to 5·3) under a scenario of 3·0°C warming (24·8 percentage points in the high-exposure sectors). The reductions will be 25·9 percentage points (–48·8 to 2·7) in Africa, 18·6 percentage points (–33·6 to 5·3) in Asia, and 10·4 percentage points (–35·0 to 2·6) in the Americas in the low-exposure sectors. These regional effects are projected to be substantially higher for labour outdoors in full sunlight compared with indoors (or outdoors in the shade) with the average reductions in total labour projected to be 32·8 percentage points (–66·3 to 1·6) in Africa, 25·0 percentage points (–66·3 to 7·0) in Asia, and 16·7 percentage points (–45·5 to 4·4) in the Americas. Interpretation Both labour supply and productivity are projected to decrease under future climate change in most parts of the world, and particularly in tropical regions. Parts of sub-Saharan Africa, south Asia, and southeast Asia are at highest risk under future warming scenarios. The heterogeneous regional response functions suggest that it is necessary to move away from one-size-fits-all response functions to investigate the climate effect on labour. Our findings imply income and distributional consequences in terms of increased inequality and poverty, especially in low-income countries, where the labour effects are projected to be high.

Description: Both ice sheets in Greenland and Antarctica are discharging ice into the ocean. In many regions along the coast of the ice sheets, the icebergs calve into a bay. If the addition of icebergs through calving is faster than their transport out of the embayment, the icebergs will be frozen into a mélange with surrounding sea ice in winter. In this case, the buttressing effect of the ice mélange can be considerably stronger than any buttressing by mere sea ice would be. This in turn stabilizes the glacier terminus and leads to a reduction in calving rates. Here we propose a simple parametrization of ice mélange buttressing which leads to an upper bound on calving rates and can be used in numerical and analytical modelling.

Description: In the Paris Agreement, just about all of the world’s nations pledged to “pursue efforts to limit the temperature increase to 1.5 degrees Celsius above pre-industrial levels”. On Saturday, the top climate diplomats from the U.S. and China, John Kerry and Xie Zhenhua, reiterated in a joint statement that they want to step up their climate mitigation efforts to keep that goal “within reach”.

Description: The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). Version 2.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2019. Data sources of W5E5 are version 2.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2020), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003). Variables (with short names and units in brackets) included in the W5E5 dataset are Near Surface Relative Humidity (hurs, %), Near Surface Specific Humidity (huss, kg kg-1), Precipitation (pr, kg m-2 s-1), Snowfall Flux (prsn, kg m-2 s-1), Surface Air Pressure (ps, Pa), Sea Level Pressure (psl, Pa), Surface Downwelling Longwave Radiation (rlds, W m-2), Surface Downwelling Shortwave Radiation (rsds, W m-2), Near Surface Wind Speed (sfcWind, m s-1), Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and WFDE5-ERA5 Mask (mask, 1).

Description: Billions of people rely on groundwater as an accessible source for drinking water and irrigation, especially in times of drought. Its importance will likely increase with a changing climate. It is still unclear, however, how climate change will impact groundwater systems globally and thus the availability of this vital resource. This study investigates uncertainties in groundwater recharge projections using a multi-model ensemble of eight global hydrological models (GHMs) that are driven by the bias-adjusted output of four global circulation models (GCMs). Preindustrial and current groundwater recharge values are compared with recharge for different global warming (GW) levels as a result of three representative concentration pathways (RCPs). Results suggest that the uncertainty range is extensive, and projections with confidence can only be made for specific regions of the world. In some regions, reversals of groundwater recharge trends can be observed with global warming. On average, a consistent median increase of groundwater recharge in northern Europe of 19 % and a decrease of 10 % in the Amazon at 3 °C GW compared to preindustrial levels are simulated. In the Mediterranean, a 2 °C GW leads to a reduction of GWR of 38 %. Because most GHMs do not include CO2 driven vegetation processes, we investigate how, including the effect of evolving CO2 concentrations into the calculation of future groundwater recharge impacts the results. In some regions, the inclusion of these processes leads to differences in groundwater recharge changes of up to 100 mm year−1. Overall, models that include CO2 driven vegetation processes simulate less severe decreases of groundwater recharge and in some regions even increases instead of decreases. In regions where GCMs predict decreases in precipitation, and groundwater availability is most important, the model agreement among GHMs with dynamic vegetation is lowest in contrast to GHMs without, which show a high agreement.

Description: This paper aims to evaluate the life cycle greenhouse gas (GHG) emissions of importing electrical power into Singapore, generated from a large-scale solar photovoltaic (PV) power plant in Australia, through a long-distance subsea high-voltage direct current (HVDC) cable. A cost optimization model was developed to estimate the capacities of the system components. A comprehensive life cycle assessment model was built to estimate emissions of manufacturing and use of these components. Our evaluation shows that, for covering one fifth of Singapore’s electrical energy needs, a system with an installed capacity of 13GWPV, 17 GWh battery storage and 3.2GW subsea cable is required. The life cycle GHG emissions of such a system are estimated to be 110gCO2eq/kWh, with the majority coming from the manufacturing of solar PV panels. Cable manufacturing does not contribute largely toward GHG emissions. By varying full-load hours and cable lengths, it was assessed that sites closer to Singapore might provide the same energy at same/lower carbon footprint and reduced cost, despite the lower insolation as compared to Australia. However, these sites could cause greater emissions from land use changes than the deserts of Australia, offsetting the advantages of a shorter HVDC cable

Description: This paper is concerned with the resonance manifestation of a stochastically driven system consisting of dual Duffing oscillators with coupled active-passive hybrid control (APHC), i.e., time-delayed feedback control and viscoelastic damping. Analytic solutions are successively determined in both deterministic and stochastic modes. Based on our simulations in the deterministic mode, changes to the rules and trends of the mean square response of the system, including coupling parameters such as the viscoelastic coefficient, displacement and velocity feedback gain coefficients variation in the stochastic mode, are depicted in detail. These are accompanied by (double) jump and bifurcation phenomena, the emergence of a time-delayed isle and its convergence with other branches. Our numerical simulation results confirm the effectiveness of the theoretical analyses method as well. In addition, the diversity of phase portraits in the two modes illustrates different effects of the coupled APHC parameters, the excitation amplitude and random noise intensity and also shows that the coupling parameters are significant factors in the alteration of dynamical behaviors.

Description: Die USA ächzen unter eisiger Kälte bis hinunter in den Süden von Texas. Mindestens dreißig Menschen kamen ums Leben, Millionen sind ohne Strom. Auch bei uns in Deutschland ist gerade eine Kältewelle vorübergegangen, und im Januar gab es einen historischen Schneesturm in Madrid. Was ist da los?

Description: Different paleoclimate proxy records evidence repeated abrupt climate transitions during previous glacial intervals. These transitions are thought to comprise abrupt warming and increase in local precipitation over Greenland, sudden reorganization of the Northern Hemisphere atmospheric circulation, and retreat of sea ice in the North Atlantic. The physical mechanism underlying these so-called Dansgaard–Oeschger (DO) events remains debated. A recent analysis of Greenland ice core proxy records found that transitions in NaC concentrations and 18O values are delayed by about 1 decade with respect to corresponding transitions in Ca2C concentrations and in the annual layer thickness during DO events. These delays are interpreted as a temporal lag of sea-ice retreat and Greenland warming with respect to a synoptic- and hemispheric-scale atmospheric reorganization at the onset of DO events and may thereby help constrain possible triggering mechanisms for the DO events. However, the explanatory power of these results is limited by the uncertainty of the transition onset detection in noisy proxy records. Here, we extend previous work by testing the significance of the reported lags with respect to the null hypothesis that the proposed transition order is in fact not systematically favored. If the detection uncertainties are averaged out, the temporal delays in the 18O and NaC transitions with respect to their counterparts in Ca2C and the annual layer thickness are indeed pairwise statistically significant. In contrast, under rigorous propagation of uncertainty, three statistical tests cannot provide evidence against the null hypothesis.We thus confirm the previously reported tendency of delayed transitions in the 18O and NaC concentration records. Yet, given the uncertainties in the determination of the transition onsets, it cannot be decided whether these tendencies are truly the imprint of a prescribed transition order or whether they are due to chance. The analyzed set of DO transitions can therefore not serve as evidence for systematic lead–lag relationships between the transitions in the different proxies, which in turn limits the power of the observed tendencies to constrain possible physical causes of the DO events.

Description: Rhythmic behavior represents one of the most striking and ubiquitous manifestations of functional evolution for a wide class of natural and man-made systems. The emergence of diverse (ar)rhythmic dynamics can be well understood by models of coupled dynamical networks, where the interplay between the intrinsic dynamics of a single unit and the coupling functions plays a critical role in shaping a vast repertoire of collective behaviors. Under certain circumstances, all the individual dynamical systems may cease their oscillations totally when coupled, which results in the emergence of oscillation quenching in coupled oscillatory systems. Macroscopic oscillations of coupled dynamical networks can also be gradually weakened and even completely quenched via aging transition. Oscillation reviving, an inverse process of quenching and aging, refers to the restoration of rhythmic activity of coupled dynamical networks from the phenomena of quenching and aging. The study on quenching, aging, and reviving of rhythmic behaviors in coupled dynamical networks has developed into an active and rapidly evolving area of research with a wide variety of applications, where tremendous progresses with vital insights have been witnessed in the last decade. In this review, we endeavour to provide an exhaustive overview on the most important aspects of quenching, aging, and reviving in coupled dynamical networks ranging from theories to experiments and applications. The prevailing knowledge is integrated and pulled together to make the relevant results and methods more generally accessible for researchers in distinct communities of science and technology. Relevant open issues and challenges that deserve of special attentions are highlighted for future study. The present review should stimulate deeper investigations on the collapse and revival of macroscopic rhythmic behaviors, which will enlighten our understanding on evading irreversible failures of coupled dynamical networks and even guide us to identify the precursors of critical transitions. Our work will foster further studies on the physical principles of collective rhythms that robustly emerge in nature and real life.

Description: Alzheimer’s disease (AD), characterized by cognitive impairment, mainly affects middle-aged and elderly people. As the aging process of the world continues to intensify, AD harms people’s life, economy and society more and more seriously. Therefore, it has become an urgent problem to study the pathogenesis of AD and seek treatment on this basis. Hybrid synapse, autapse and spatial correlated noise in diverse neural activities have been investigated separately, however, theoretically understanding combination of them still has not been fully studied. Here in this paper, a neural network with multiple associative memory abilities is established from the perspective of the degeneration of associative memory ability in AD patients under the conditions of hybrid synapse, autapse and spatial correlated noise. In order to explore the pathogenesis, a synaptic loss and synaptic compensation model are established to analyze the associative memory ability of AD in different degrees of disease. The simulation results demonstrate the effectiveness of the proposed models and pave a way in the study of dynamic mechanism with higher bio-interpretability in neural networks.

Description: The blood-brain barrier (BBB) has a significant contribution to the protection of the central nervous system (CNS). However, it also limits the brain drug delivery and thereby complicates the treatment of CNS diseases. The development of safe methods for an effective delivery of medications and nanocarriers to the brain can be a revolutionary step in the overcoming this limitation. Here, we report the unique properties of the lymphatic system to deliver tracers and liposomes to the brain meninges, brain tissues, and glioma in rats. Using a quantum-dot-based 1267 nm laser (for photosensitizer-free generation of singlet oxygen), we clearly demonstrate photostimulation of lymphatic delivery of liposomes to glioma as well as lymphatic clearance of liposomes from the brain. These pilot findings open promising perspectives for photomodulation of lymphatic delivery of drugs and nanocarriers to the brain pathology bypassing the BBB. The lymphatic “smart” delivery of liposomes with antitumor drugs in the new brain tumor branches might be a breakthrough strategy for the therapy of gliomas.

Description: Every year, millions of people around the world are being displaced from their homes due to climate-related disasters. River flooding is responsible for a large part of this displacement. Previous studies have shown that river flood risk is expected to change as a result of global warming and its effects on the hydrological cycle. At the same time, future scenarios of socio-economic development imply substantial population increases in many of the areas that presently experience disaster-induced displacement. Here we show that both global warming and population change are projected to lead to substantial increases in flood-induced displacement risk over the coming decades. We use a global climate-hydrology-inundation modelling chain, including multiple alternative climate and hydrological models, to quantify the effect of global warming on displacement risk assuming either current or projected future population distributions. Keeping population fixed at present levels, we find roughly a 50% increase in global displacement risk for every degree of global warming. Adding projected population changes further exacerbates these increases globally and in most world regions, with the relative global flood displacement risk is increasing by roughly 350% at the end of the 21st century, compared to an increase of 150% without the contribution of population change. While the resolution of the global models is limited, the effect of global warming is robust across greenhouse gas concentration scenarios, climate models and hydrological models. These findings indicate a need for rapid action on both climate mitigation and adaptation agendas in order to reduce future risks to vulnerable populations.

Description: Sauerstoff ist lebenswichtig. Wälder produzieren ihn, sind aber stark bedroht. Die Auswirkungen auf das Lebenselixier von Mensch und Tier ist erstaunlich gering – ein anderes Problem umso gravierender.

Description: The European Commission Flood Risk Directive review shows that while many nations have embraced the concepts of flood risk management, there is still quite more to do in delineating risk–cost-effective measures and developing cost estimates and financing of those measures. Not mentioned are the necessary changes to existing design standards and protocols which will have to change in order to properly encompass climate change and variability, with associated uncertainties. Adjustments in engineering design standards and changes in hazards are examined, based on trend detection in observational records and projections for the future. Issues of urban and transport (motorways and railways) drainage design are also examined. Furthermore, risk reduction strategies are discussed. Finally, a way of accounting for non-stationarity in determining design precipitation and design floods is tackled. Climate change adjustments in engineering design standards, such as design precipitation and design floods, are reviewed via examples from Europe.

Description: Climate change not only impacts production and market consumption but also the relative scarcity of nonmarket goods, such as environmen-tal amenities. We study fundamental drivers of the resulting relative price changes, their potential magnitude, and their implications for climate policy in Nordhaus’s Dynamic Integrated Climate-Economy (DICE) model, thereby addressing one of its key criticisms. We pro-pose plausible ranges for these relative prices changes based on best available evidence. Our central calibration reveals that accounting for relative prices is equivalent to decreasing pure time preference by 0.6 percentage points and leads to a more than 50 percent higher social cost of carbon.

Description: A portion of human-caused carbon dioxide emissions will stay in the atmosphere for hundreds of years, raising temperatures and sea levels globally. Most nations' emissions-reduction policies and actions do not seem to reflect this long-term threat, as collectively they point toward widespread permanent inundation of many developed areas. Using state-of-the-art new global elevation and population data, we show here that, under high emissions scenarios leading to 4 ∘C warming and a median projected 8.9 m of global mean sea level rise within a roughly 200- to 2000-year envelope, at least 50 major cities, mostly in Asia, would need to defend against globally unprecedented levels of exposure, if feasible, or face partial to near-total extant area losses. Nationally, China, India, Indonesia, and Vietnam, global leaders in recent coal plant construction, have the largest contemporary populations occupying land below projected high tide lines, alongside Bangladesh. We employ this population-based metric as a rough index for the potential exposure of the largely immovable built environment embodying cultures and economies as they exist today. Based on median sea level projections, at least one large nation on every continent but Australia and Antarctica would face exceptionally high exposure: land home to at least one-tenth and up to two-thirds of current population falling below tideline. Many small island nations are threatened with near-total loss. The high tide line could encroach above land occupied by as much as 15% of the current global population (about one billion people). By contrast, meeting the most ambitious goals of the Paris Climate Agreement will likely reduce exposure by roughly half and may avoid globally unprecedented defense requirements for any coastal megacity exceeding a contemporary population of 10 million.

Description: The heart rate in humans is regulated by the autonomic nervous system, which modulates the frequency of heart contractions, resulting in heart rate variability (HRV). Therefore, to assess the activity of the autonomic nervous system, which contains important information for medical diagnostics, methods based on the analysis of interbeat interval variability are often used. This approach does not require the use of invasive methods for measuring the signals of the autonomic nervous system, but its accuracy is an open question. Using mathematical modeling, we investigate the possibility of extracting the signal of frequency modulation of the heartbeats from the electrocardiogram (ECG) signal and conduct a detailed comparison of the extracted signal with the real modulating signal. Since the quality of extraction of the signal of frequency modulation from the ECG depends on the method of demodulation, we compare two different approaches. One is based on the detection of the main oscillation rhythm and its bandpass filtering, and the other on the heterodyning technique. It is shown that low-frequency (LF) and high-frequency (HF) oscillations in HRV associated, respectively, with sympathetic and parasympathetic modulation by the autonomic nervous system, in the general case, significantly differ from the signals of frequency modulation of the heart rate in shape, but have close similarity with them in the frequency domain. We find that in model systems, the similarity of the LF component of HRV with sympathetic modulation of the heart rate is higher than the similarity of the HF component of HRV with parasympathetic modulation.

Description: This paper studies the mean‐square consensus for heterogeneous multi‐agent systems with probabilistic time delay. Each agent in the system has an objective function and only knows its own objective function. Control protocols for the system both over the fixed and the switched weighted‐balanced topologies are designed. The consensus state of agents' position can make the sum of objective functions minimum. By adopting probability statistics, stochastic process, matrix theory and some stability method, sufficient conditions for the consensus protocol are given. Several simulations are presented to illustrate the potential correctness of the results.

Description: Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation; CV) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

Description: Despite the large body of literature on ape conservation, much of the data needed for evidence‐based conservation decision‐making is still not readily accessible and standardized, rendering cross‐site comparison difficult. To support knowledge synthesis and to complement the IUCN SSC Ape Populations, Environments and Surveys database, we created the A.P.E.S. Wiki (https://apeswiki.eva.mpg.de), an open‐access platform providing site‐level information on ape conservation status and context. The aim of this Wiki is to provide information and data about geographical ape locations, to curate information on individuals and organizations active in ape research and conservation, and to act as a tool to support collaboration between conservation practitioners, scientists, and other stakeholders. To illustrate the process and benefits of knowledge synthesis, we used the momentum of the update of the conservation action plan for western chimpanzees (Pan troglodytes verus) and began with this critically endangered taxon. First, we gathered information on 59 sites in West Africa from scientific publications, reports, and online sources. Information was compiled in a standardized format and can thus be summarized using a web scraping approach. We then asked experts working at those sites to review and complement the information (20 sites have been reviewed to date). We demonstrate the utility of the information available through the Wiki, for example, for studying species distribution. Importantly, as an open‐access platform and based on the well‐known wiki layout, the A.P.E.S. Wiki can contribute to direct and interactive information sharing and promote the efforts invested by the ape research and conservation community. The Section on Great Apes and the Section on Small Apes of the IUCN SSC Primate Specialist Group will guide and support the expansion of the platform to all small and great ape taxa. Similar collaborative efforts can contribute to extending knowledge synthesis to all nonhuman primate species. Despite the large body of literature on ape conservation, much of the data needed for evidence‐based conservation decision‐making is still not readily accessible and standardized, rendering cross‐site comparison difficult. To support knowledge synthesis and to complement the IUCN SSC Ape Populations, Environments and Surveys database, we created the A.P.E.S. Wiki (https://apeswiki.eva.mpg.de), an open‐access platform providing site‐level information on ape conservation status and context. The aim of this Wiki is to provide information and data about geographical ape locations, to curate information on individuals and organizations active in ape research and conservation, and to act as a tool to support collaboration between conservation practitioners, scientists, and other stakeholders. To illustrate the process and benefits of knowledge synthesis, we used the momentum of the update of the conservation action plan for western chimpanzees (Pan troglodytes verus) and began with this critically endangered taxon. First, we gathered information on 59 sites in West Africa from scientific publications, reports, and online sources. Information was compiled in a standardized format and can thus be summarized using a web scraping approach. We then asked experts working at those sites to review and complement the information (20 sites have been reviewed to date). We demonstrate the utility of the information available through the Wiki, for example, for studying species distribution. Importantly, as an open‐access platform and based on the well‐known wiki layout, the A.P.E.S. Wiki can contribute to direct and interactive information sharing and promote the efforts invested by the ape research and conservation community. The Section on Great Apes and the Section on Small Apes of the IUCN SSC Primate Specialist Group will guide and support the expansion of the platform to all small and great ape taxa. Similar collaborative efforts can contribute to extending knowledge synthesis to all nonhuman primate species.

Description: Auf dem Weg zur Klimaneutralität ökonomisch stark zu bleiben ist keine einfache Aufgabe. Doch wenn die neue Bundesregierung die richtigen Prioritäten setzt, kann der große Wurf gelingen. Ein Gastbeitrag.

Description: Tropical rainforests are recognized as one of the terrestrialtipping elements which could have profound impacts on the global cli-mate, once their vegetation has transitioned into savanna or grasslandstates. While several studies investigated the savannization of, e.g., theAmazon rainforest, few studies considered the influence of fire. Fire isexpected to potentially shift the savanna-forest boundary and henceimpact the dynamical equilibrium between these two possible vegeta-tion states under changing climate. To investigate the climate-inducedhysteresis in pan-tropical forests and the impact of fire under future cli-mate conditions, we employed the Earth system model CM2Mc, whichis biophysically coupled to the fire-enabled state-of-the-art dynamicglobal vegetation model LPJmL. We conducted several simulation ex-periments where atmospheric CO2concentrations increased (impactphase) and decreased from the new state (recovery phase), each withand without enabling wildfires. We find a hysteresis of the biomassand vegetation cover in tropical forest systems, with a strong regionalheterogeneity. After biomass loss along increasing atmospheric CO2concentrations and accompanied mean surface temperature increase ofabout 4°C (impact phase), the system does not recover completely intoits original state on its return path, even though atmospheric CO2concentrations return to their original state. While not detecting large-scale tipping points, our results show a climate-induced hysteresis intropical forest and lagged responses in forest recovery after the climatehas returned to its original state. Wildfires slightly widen the climate-induced hysteresis in tropical forests and lead to a lagged response inforest recovery by ca. 30 years.

Description: Ein australisches Expertengremium erklärt das 1,5-Grad-Ziel des Klimaabkommens für Paris für gescheitert. Das Limit sei nicht mehr zu halten. Doch die Faktenlage ist differenzierter.

Description: Das Wetter verändert sich durch den Klimawandel auf überraschende Weise: Es wird nicht einfach nur wärmer, sondern zwischendurch auch mal deutlich kälter. So wie aktuell in Teilen Europas und Amerikas.

Description: Von der Konferenz in Glasgow ist kein Durchbruch zu erwarten – aber vielleicht von einem „Klimaklub“, analysiert Ottmar Edenhofer.

Description: There is increasing evidence linking the mass-extinction event at the Cretaceous-Paleogene boundary to an asteroid impact near Chicxulub, Mexico. Here we use model simulations to explore the combined effect of sulfate aerosols, carbon dioxide and dust from the impact on the oceans and the marine biosphere in the immediate aftermath of the impact. We find a strong temperature decrease, a brief algal bloom caused by nutrients from both the deep ocean and the projectile, and moderate surface ocean acidification. Comparing the modeled longer-term post-impact warming and changes in carbon isotopes with empirical evidence points to a substantial release of carbon from the terrestrial biosphere. Overall, our results shed light on the decades to centuries after the Chicxulub impact which are difficult to resolve with proxy data.

Description: Despite Germany’s Paris Agreement pledge and coal exit legislation, the political debate around carbon-intensive coal remains heated. Coal power and mining have played an important, yet changing role in the history of German politics. In this paper, we analyze the entire parliamentary debate on coal in the German parliament (Bundestag) from its inception in 1949 to 2019. For this purpose we extract the more than 870,000 parliamentary speeches from all protocols in the history of the Bundestag. We identify the 9167 speeches mentioning coal and apply dynamic topic modeling – an unsupervised machine learning technique that reveals the changing thematic structure of large document collections over time – to analyze changes in parliamentary debates on coal over the past 70 years. The trends in topics and their varying internal structure reflect how energy policy was discussed and legitimized over time: Initially, coal was framed as a driver of economic prosperity and guarantee of energy security. In recent years, the debate evolved towards energy transition, coal phase-out and renewable energy expansion. Germany’s smaller and younger parties, the Greens and the Left Party, debate coal more often in the context of the energy transition and climate protection than other parties. Our results reflect trends in other countries and other fields of energy policy. Methodologically, our study illustrates the potential of and need for computational methods to analyze vast corpora of text and to complement traditional social science methods.

Description: In verified generic programming, one cannot exploit the structure of concrete data types but has to rely on well chosen sets of specifications or abstract data types (ADTs). Functors and monads are at the core of many applications of functional programming. This raises the question of what useful ADTs for verified functors and monads could look like. The functorial map of many important monads preserves extensional equality. For instance, if f , g : A → B are extensionally equal, that is, ∀x ∈ A, f x = g x, then map f : List A → List B and map g are also extensionally equal. This suggests that preservation of extensional equality could be a useful principle in verified generic programming. We explore this possibility with a minimalist approach: we deal with (the lack of) extensional equality in Martin-Löf’s intensional type theories without extending the theories or using full-fledged setoids. Perhaps surprisingly, this minimal approach turns out to be extremely useful. It allows one to derive simple generic proofs of monadic laws but also verified, generic results in dynamical systems and control theory. In turn, these results avoid tedious code duplication and ad- hoc proofs. Thus, our work is a contribution towards pragmatic, verified generic programming.

Description: Building codes are an effective policy instrument to reduce energy consumption, but their impact depends on local building construction, renovation and demolition cycles, affected by economic and demographic development. In this research a unique global building stock model, with country level detail, is developed to understand the impact of building codes on global energy scenarios. The model shows that the majority of buildings standing in 2050 will be built after 2015, mostly outside of the OECD. In these regions despite growing space cooling demand due to projected economic development, insulation levels of new buildings remain low. New construction policies could thereby have a significant impact. In Africa and China the model shows that if all new buildings would be near zero-energy buildings in 2050 this would save respectively 64% and 43% of space heating and cooling energy demand. In OECD countries, on the contrary, the slower stock turn-over results in renovation policies being more effective, but also more vulnerable to delays. Delaying policy implementation by only 10 years drops global annual emission savings in 2050 by approximately 1 Gt , showing the necessity of a fast and ambitious ramp up of building codes for achieving the Paris climate agreement.

Description: Understanding of future climate change impacts and successful planning of adaptation measures are of vital importance for Central Asia given the region's economic vulnerability, dependence on scarce water resources, and observed above global average warming rates. This paper analyses how impacts of climate change on the hydrological regimes and temperature patterns could affect the irrigated agricultural production in two case study areas, the Aspara and Isfara river basins. The methodology applied is based on analysis of temperature indicators and current cropping calendars in target locations combined with hydrological simulations by the process-based Soil and Water Integrated Model (SWIM) of the two river basins. The selected climate change projections comprise the moderate and high emissions scenarios - RCP4.5 and RCP8.5. The results reveal that climate change will create unfavourable conditions for irrigated spring crops, due to decrease of discharge during the vegetation period. On the other hand, the projected shift of peak discharge to an earlier date offers benefits for irrigated winter cereals, providing more water for irrigation in spring. Results suggest that, there is an opportunity to adapt the irrigated agricultural production in the selected regions by fitting the cropping calendars to changing vegetation periods and to the timing of peak discharges.

Description: The transport sector is a crucial bottleneck in the decarbonization challenge. To study the sector’s decarbonization potential in the wider systems perspective, we couple a large-scale integrated assessment model, Regionalized Model of INvestments and Development (REMIND), to a detailed transport model, Energy Demand Generator-Transport (EDGE-T). This approach allows the analysis of mobility futures in the context of long-term and global energy sector transformations, at a high level of modal and technological granularity and internal consistency. The runtime of the coupled system increases by ~ 15–20% compared with a REMIND standalone application, and first convergence tests are promising. To illustrate the capabilities of our modeling approach, we focus on a reference pathway for Europe. Preliminary results indicate that transport service demands grow in the next decades for both passenger and freight transport. Transport system emissions are expected to decrease in the same time range, due to a shift towards electric drivetrains, advanced vehicles, more efficient modes as well as a slight increase in the share of biofuels.

Description: Output data used in the GMD publication: CM2Mc-LPJmL v1.0: Biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model, Drüke et al. (https://doi.org/10.5194/gmd-2020-436)

Description: Mathematical models for complex systems under random fluctuations often certain uncertain parameters. However, quantifying model uncertainty for a stochastic differential equation with an -stable Lévy process is still lacking. Here, we propose an approach to infer all the uncertain non-Gaussian parameters and other system parameters by minimizing the Hellinger distance over the parameter space. The Hellinger distance measures the similarity between an empirical probability density of non-Gaussian observations and a solution (as a probability density) of the associated nonlocal Fokker-Planck equation. Numerical experiments verify that our method is feasible for estimating single and multiple parameters. Meanwhile, we find an optimal estimation interval of the estimated parameters. This method is beneficial for extracting governing dynamical system models under non-Gaussian fluctuations, as in the study of abrupt climate changes in the Dansgaard-Oeschger events.

Description: While the short-term economic impacts of extreme weather events are well documented, little is known about their impacts and transmission channels on economic growth in the long run. Using panel data regressions and national shares of people exposed to tropical cyclones and fluvial floods as exogenous predictors, we find output growth losses from severe tropical cyclones and fluvial floods to accumulate to −6.5% and −5.0% over 15 years, respectively. We further observe a strongly non-linear increase of these losses with disaster intensity. To understand how the observed impacts depend on the countries’ development level, we implement a country-specific regression framework. While we find evidence that higher development can prevent economic growth losses from fluvial floods, this is not the case for tropical cyclones. Further, we systematically study the economic and non-economic transmission channels through which these events impact on economic growth in the long run. We find that rising household consumption and government expenditure are the main growth-loss mitigating channels, whereas rising investment is the main growth-loss amplifying channel in the period 1971–2010.

Description: In this work we model the dynamics of power grids in terms of a two-layer network, and use the Italian high voltage power grid as a proof-of-principle example. The first layer in our model represents the power grid con- sisting of generators and consumers, while the second layer represents a dynamic communication network that serves as a controller of the first layer. The dynamics of the power grid is modelled by the Kuramoto model with inertia, while the communication layer provides a con- trol signal Pci for each generator to improve frequency synchronization within the power grid. We propose dif- ferent realizations of the communication layer topology and of the control signal, and test the control perfor- mances in presence of generators with stochastic power output. When using a control topology that allows all generators to exchange information, we find that a con- trol scheme aimed to minimize the frequency difference between adjacent nodes operates very efficiently even against the worst scenarios with the strongest perturba- tions. On the other hand, for a control topology where the generators possess the same communication links as in the power grid layer, a control scheme aimed at restor- ing the synchronization frequency in the neighborhood of the controlled node turns out to be more efficient.

Description: The topic of finding effective strategies to restrain epidemic spreading in complex networks is of current interest. A widely used approach for epidemic containment is the fragmentation of the contact networks through immunization. However, due to the limitation of immune resources, we cannot always fragment the contact network completely. In this study, based on the size distribution of connected components for the network, we designed a risk indicator of epidemic outbreaks, the generalized Herfindahl–Hirschman index (GHI), which measures the upper bound of the expected infection's prevalence (the fraction of infected nodes) in random outbreaks. An immunization approach based on minimizing GHI is developed to reduce the infection risk for individuals in the network. Experimental results show that our immunization strategy could effectively decrease the infection's prevalence as compared to other existing strategies, especially against infectious diseases with higher infection rates or lower recovery rates. The findings provide an efficient and practicable strategy for immunization against epidemic diseases.

Description: 2020 and 2021 have been unprecedented years due to the rapid spread of the modified severe acute respiratory syndrome coronavirus around the world. The coronavirus disease 2019 (COVID-19) causes atypical infiltrated pneumonia with many neurological symptoms, and major sleep changes. The exposure of people to stress, such as social confinement and changes in daily routines, is accompanied by various sleep disturbances, known as ‘coronasomnia’ phenomenon. Sleep disorders induce neuroinflammation, which promotes the blood–brain barrier (BBB) disruption and entry of antigens and inflammatory factors into the brain. Here, we review findings and trends in sleep research in 2020–2021, demonstrating how COVID-19 and sleep disorders can induce BBB leakage via neuroinflammation, which might contribute to the ‘coronasomnia’ phenomenon. The new studies suggest that the control of sleep hygiene and quality should be incorporated into the rehabilitation of COVID-19 patients. We also discuss perspective strategies for the prevention of COVID-19-related BBB disorders. We demonstrate that sleep might be a novel biomarker of BBB leakage, and the analysis of sleep EEG patterns can be a breakthrough non-invasive technology for diagnosis of the COVID-19-caused BBB disruption.

Description: The Mesozoic era (∼252 to 66 million years ago) was a key interval in Earth's evolution toward its modern state, witnessing the breakup of the supercontinent Pangaea and significant biotic innovations like the early evolution of mammals. Plate tectonic dynamics drove a fundamental climatic transition from the early Mesozoic supercontinent toward the Late Cretaceous fragmented continental configuration. Here, key aspects of Mesozoic long-term environmental changes are assessed in a climate model ensemble framework. We analyze so far the most extended ensemble of equilibrium climate states simulated for evolving Mesozoic boundary conditions covering the period from 255 to 60 Ma in 5 Myr timesteps. Global mean temperatures are generally found to be elevated above the present and exhibit a baseline warming trend driven by rising sea levels and increasing solar luminosity. Warm (Triassic and mid-Cretaceous) and cool (Jurassic and end-Cretaceous) anomalies result from pCO2 changes indicated by different reconstructions. Seasonal and zonal temperature contrasts as well as continental aridity show an overall decrease from the Late Triassic-Early Jurassic to the Late Cretaceous. Meridional temperature gradients are reduced at higher global temperatures and less land area in the high latitudes. With systematic sensitivity experiments, the influence of paleogeography, sea level, vegetation patterns, pCO2, solar luminosity, and orbital configuration on these trends is investigated. For example, long-term seasonality trends are driven by paleogeography, but orbital cycles could have had similar-scale effects on shorter timescales. Global mean temperatures, continental humidity, and meridional temperature gradients are, however, also strongly affected by pCO2.

Description: Although the prediction of the Indian Summer Monsoon (ISM) onset is of crucial importance for water-resource management and agricultural planning on the Indian sub-continent, the long-term predictability { especially at seasonal time scales { is little explored and remains challenging. We propose a method based on artificial neural networks that provides skilful long-term forecasts (beyond 3 months) of the ISM onset, although only trained on short and noisy data. It is shown that the meridional tropospheric temperature gradient in the boreal winter season already contains the signals needed for predicting the ISM onset in the subsequent summer season. Our study demonstrates that machine-learning-based approaches can be simultaneously helpful for both data-driven prediction and enhancing the process understanding of climate phenomena.

Description: We present a fully automated method for the optimal state space reconstruction from univariate and multivariate time series. The proposed methodology generalizes the time delay embedding procedure by unifying two promising ideas in a symbiotic fashion. Using non-uniform delays allows the successful reconstruction of systems inheriting different time scales. In contrast to the established methods, the minimization of an appropriate cost function determines the embedding dimension without using a threshold parameter. Moreover, the method is capable of detecting stochastic time series and, thus, can handle noise contaminated input without adjusting parameters. The superiority of the proposed method is shown on some paradigmatic models and experimental data from chaotic chemical oscillators.

Description: DataLad is a Python-based tool for the joint management of code, data, and their relationship,built on top of a versatile system for data logistics (git-annex) and the most popular distributedversion control system (Git). It adapts principles of open-source software development anddistribution to address the technical challenges of data management, data sharing, and digitalprovenance collection across the life cycle of digital objects. DataLad aims to make datamanagement as easy as managing code. It streamlines procedures to consume, publish, andupdate data, for data of any size or type, and to link them as precisely versioned, lightweightdependencies. DataLad helps to make science more reproducible and FAIR (Wilkinson et al.,2016). It can capture complete and actionable process provenance of data transformations toenable automatic re-computation. The DataLad project (datalad.org) delivers a completelyopen, pioneering platform for flexible decentralized research data management (RDM) (Hanke,Pestilli, et al., 2021). It features a Python and a command-line interface, an extensiblearchitecture, and does not depend on any centralized services but facilitates interoperabilitywith a plurality of existing tools and services. In order to maximize its utility and target audience, DataLad is available for all major operating systems, and can be integrated intoestablished workflows and environments with minimal friction.

Description: There exists a range of subsystems in the climate system exhibiting threshold behaviour which could be triggered under global warming within this century resulting in severe consequences for biosphere and human societies. While their individual tipping thresholds are fairly well understood, it is of yet unclear how their interactions might impact the overall stability of the Earth's climate system. This cannot be studied yet with state-of-the-art Earth system models due to computational constraints as well as missing and uncertain process representations of some tipping elements. Here, we explicitly study the effects of known physical interactions between the Greenland and West Antarctic Ice Sheet, the Atlantic Meridional Overturning Circulation, the El-Nino Southern Oscillation and the Amazon rainforest using a conceptual network approach. We analyse the risk of domino effects being triggered by each of the individual tipping elements under global warming in equilibrium experiments, propagating uncertainties in critical temperature thresholds and interaction strengths via a Monte-Carlo approach. Overall, we find that the interactions tend to destabilise the network. Furthermore, our analysis reveals the qualitative role of each of the five tipping elements showing that the polar ice sheets on Greenland and West Antarctica are oftentimes the initiators of tipping cascades, while the AMOC acts as a mediator, transmitting cascades. This implies that the ice sheets, which are already at risk of transgressing their temperature thresholds within the Paris range of 1.5 to 2 °C, are of particular importance for the stability of the climate system as a whole.

Description: The COVID-19 pandemic has impacted social, economic, and environmental systems worldwide, slowing down and reversing the progress made in achieving the Sustainable Development Goals (SDGs). SDGs belong to the 2030 Agenda to transform our world by tackling humankind's challenges to ensure well-being, economic prosperity, and environmental protection. We explore the potential impacts of the pandemic on SDGs for Nepal. We followed a knowledge co-creation process with experts from various professional backgrounds, involving five steps: online survey, online workshop, assessment of expert's opinions, review and validation, and revision and synthesis. The pandemic has negatively impacted most SDGs in the short term. Particularly, the targets of SDG 1, 4, 5, 8, 9, 10, 11, and 13 have and will continue to have weakly to moderately restricting impacts. However, a few targets of SDG 2, 3, 6, and 11 could also have weakly promoting impacts. The negative impacts have resulted from impeding factors linked to the pandemic. Many of the negative impacts may subside in the medium and long terms. The key five impeding factors are lockdowns, underemployment and unemployment, closure of institutions and facilities, diluted focus and funds for non-COVID-19-related issues, and anticipated reduction in support from development partners. The pandemic has also opened a window of opportunity for sustainable transformation, which is short-lived and narrow. These opportunities are lessons learned for planning and action, socio-economic recovery plan, use of information and communication technologies and the digital economy, reverse migration and 'brain gain,' and local governments' exercising authorities.

Description: Sind die extremen Regenfälle Wetterphänomene – oder doch Klima? Forscher haben die Zunahme solcher Ereignisse schon vor 30 Jahren als Folge von Erderwärmung prognostiziert. Inzwischen belegen Messdaten den Zusammenhang.

Description: Die Wissenschaften spielen eine immer wichtigere Rolle in unserer komplexen Welt. Wie man erkennen kann, welche Experten vertrauenswürdig sind – und welche nicht.

Description: Future flood and drought risks have been predicted to transition from moderate to high levels at global warmings of 1.5 °C and 2.0 °C above pre-industrial levels, respectively. However, these results were obtained by approximating the equilibrium climate using transient simulations with steadily warming. This approach was recently criticised due to the warmer global land temperature and higher mean precipitation intensities of the transient climate in comparison with the equilibrium climate. Therefore, it is unclear whether floods and droughts projected under a transient climate can be systematically substituted for those occurring in an equilibrated climate. Here, by employing a large ensemble of global hydrological models (HMs) forced by global climate models, we assess the validity of estimating flood and drought characteristics under equilibrium climates from transient simulations. Differences in flood characteristics under transient and equilibrium climates could be largely ascribed to natural variability, indicating that the floods derived from a transient climate reasonably approximate the floods expected in an equally warm, equilibrated climate. By contrast, significant differences in drought intensity between transient and equilibrium climates were detected over a larger global land area than expected from natural variability. Despite the large differences among HMs in representing the low streamflow regime, we found that the drought intensities occurring under a transient climate may not validly represent the intensities in an equally warm equilibrated climate for approximately 6.7% of the global land area.

Description: The world's most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the total data volume will be in the order of 20PB. Here, we present a dataset of annual, monthly, global, hemispheric and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing reduced complexity climate models against the behaviour of more physically complete models. In addition to its use for reduced complexity climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global, hemispheric and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialized ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, that is identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way.

Description: The intertropical convergence zone (ITCZ) is an important component of the tropical rain belt. Climate models continue to struggle to adequately represent the ITCZ and differ substantially in its simulated response to climate change. Here we employ complex network approaches, which extract spatio-temporal variability patterns from climate data, to better understand differences in the dynamics of the ITCZ in state-of-the-art global circulation models (GCMs). For this purpose, we 5 study simulations with 14 GCMs in an idealized slab-ocean aquaplanet setup from TRACMIP – the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project. We construct network representations based on the spatial correlation pattern of monthly surface temperature anomalies and study the zonal mean patterns of different topological and spatial network characteristics. Specifically, we cluster the GCMs by means of their zonal network measure distribution utilizing hierarchical clustering. We find that in the control simulation, the zonal network measure distribution is able to pick 10 up model differences in the tropical SST contrast, the ITCZ position and the strength of the Southern Hemisphere Hadley cell. Although we do not find evidence for consistent modifications in the network structure tracing the response of the ITCZ to global warming in the considered model ensemble, our analysis demonstrates that coherent variations of the global SST field are linked with ITCZ dynamics. This suggests that climate networks can provide a new perspective on ITCZ dynamics and model differences therein.

Description: In the past decades, human activities caused global Earth system changes, e.g., climate change or biodiversity loss. Simultaneously, these associated impacts have increased environmental awareness within societies across the globe, thereby leading to dynamical feedbacks between the social and natural Earth system. Contemporary modelling attempts of Earth system dynamics rarely incorporate such co-evolutions and interactions are mostly studied unidirectionally through direct or remembered past impacts. Acknowledging that societies have the additional capability for foresight, this work proposes a conceptual feedback model of socio-ecological co-evolution with the specific construct of anticipation acting as a mediator between the social and natural system. Our model reproduces results from previous sociological threshold models with bistability if one assumes a static environment. Once the environment changes in response to societal behaviour, the system instead converges towards a globally stable, but not necessarily desired, attractor. Ultimately, we show that anticipation of future ecological states then leads to metastability of the system where desired states can persist for a long time. We thereby demonstrate that foresight and anticipation form an important mechanism which, once its time horizon becomes large enough, fosters social tipping towards behaviour that can stabilise the environment and prevents potential socio-ecological collapse.

Description: Agricultural activity replaces natural vegetation with cultivated land and it is a major cause of local and global climate change. Highly specialized agricultural production leads to exten‐sive monoculture farming with a low biodiversity that may cause low landscape resilience. This is the case on the Salento peninsula, in the Apulia Region of Italy, where the Xylella fastidiosa bacterium has caused the mass destruction of olive trees, many of them in monumental groves. The historical land cover that characterized the landscape is currently in a transition phase and can strongly affect climate conditions. This study aims to analyze how the destruction of olive groves by X. fastidiosa affects local climate change. Land surface temperature (LST) data detected by Landsat 8 and MODIS satellites are used as a proxies for microclimate mitigation ecosystem services linked to the evolu‐tion of the land cover. Moreover, recurrence quantification analysis was applied to the study of LST evolution. The results showed that olive groves are the least capable forest type for mitigating LST, but they are more capable than farmland, above all in the summer when the air temperature is the highest. The differences in the average LST from 2014 to 2020 between olive groves and farmland ranges from 2.8 °C to 0.8 °C. Furthermore, the recurrence analysis showed that X. fastidiosa was rapidly changing the LST of the olive groves into values to those of farmland, with a difference in LST reduced to less than a third from the time when the bacterium was identified in Apulia six years ago. The change generated by X. fastidiosa started in 2009 and showed more or less constant behavior after 2010 without substantial variation; therefore, this can serve as the index of a static situation, which can indicate non‐recovery or non‐transformation of the dying olive groves. Failure to restore the initial environmental conditions can be connected with the slow progress of the up‐rooting and replacing infected plants, probably due to attempts to save the historic aspect of the landscape by looking for solutions that avoid uprooting the diseased plants. This suggests that so‐cial‐ecological systems have to be more responsive to phytosanitary epidemics and adapt to ecolog‐ical processes, which cannot always be easily controlled, to produce more resilient landscapes and avoid unwanted transformations.

Description: Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several studies have been proposed to jointly train these two tasks using some small modifications, such as changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks. In this article, we propose multitask generative adversarial networks (Multitask GANs), which are not only competent in semantic segmentation and depth completion but also improve the accuracy of depth completion through generated semantic images. In addition, we improve the details of generated semantic images based on CycleGAN by introducing multiscale spatial pooling blocks and the structural similarity reconstruction loss. Furthermore, considering the inner consistency between semantic and geometric structures, we develop a semantic-guided smoothness loss to improve depth completion results. Extensive experiments on the Cityscapes data set and the KITTI depth completion benchmark show that the Multitask GANs are capable of achieving competitive performance for both semantic segmentation and depth completion tasks.

Description: Industrial control systems are the brain and central nervous system of a country’s vital infrastructure. Once the control system collapses, the consequences are unimaginable. Therefore, the safety of industrial control system has become the top priority in the field of safety. Aiming at the problem that the traditional abnormal flow detection model in the industrial control system is not accurate in identifying abnormalities, we combine the perception ability of deep learning with the decision-making ability of reinforcement learning, and propose an abnormal flow detection model based on deep reinforcement learning. The neural network is used to extract the features of the preprocessed dataset, and then the learning strategy can be adjusted according to the special advantages of strengthening the decision-making ability of learning and feedback. The experimental results show that the model based on deep reinforcement learning can achieve 98.06% accuracy in abnormal flow detection.Compared with various methods proposed by peers in current literature, this method is superior to other technologies in four evaluation indexes including accuracy rate, accuracy rate, recall rate and F1 score, among which the accuracy is increased by 2 percentage points.

Description: We analyze the influence of an external sound source in a network of FitzHugh–Nagumo oscillators with empirical structural connectivity measured in healthy human subjects. We report synchronization patterns, induced by the frequency of the sound source. We show that the level of synchrony can be enhanced by choosing the frequency of the sound source and its amplitude as control parameters for synchronization patterns. We discuss a minimum model elucidating the modalities of the influence of music on the human brain.

Description: Our current food systems not only fail to end malnourishment, but also exhibit substantial ecological impacts. Thus they are an obstacle to achieving numerous Sustainable Development Goals (SDGs). Our authors have a look at the context and explain how these negative impacts can be converted into positive ones.

Description: The brown planthopper, Nilaparvata lugens (Stål) is the most serious pest in rice across the world. N. lugens is also known to transmit stunted viral disease; the insect alone or in combination with a virus causes the breakdown of rice vascular system, leading to economic losses in commercial rice production. Despite its immense economic importance, information on its potential distribution and factors governing the present and future distribution patterns is limited. Thus, in the present study we used maximum entropy modelling with bioclimatic variables to predict the present and future potential distribution of N. lugens in India as an indicator of risk. The predictions were mapped for spatio-temporal variation and area was analysed under suitability ranges. Jackknife analysis indicated that N. lugens geographic distribution is mostly influenced by temperature-based variables that explain up to 68.7% of the distribution, with precipitation factors explaining the rest. Among individual factors, the most important for distribution of N. lugens was annual mean temperature followed by precipitation of coldest quarter and precipitation seasonality. Our results highlight that the highly suitable areas under current climate conditions are 7.3%, whereas all projections show an increase under changing climatic conditions with time up to 2090, and with emission scenarios and a corresponding decrease in low-risk areas. We conclude that climate change increases the risk of N. lugens with increased temperature as it is likely to spread to the previously unsuitable areas in India, with adaptation strategies required.

Description: The movements of animals between farms and other livestock holdings for trading activities form a complex livestock trade network. These movements play an important role in the spread of infectious diseases among premises. For studying the disease spreading among animal holdings, it is of great importance to understand the structure and dynamics of the trade system. In this paper, we propose a temporal network model for animal trade systems. Furthermore, a novel measure of node centrality important for disease spreading is introduced. The experimental results show that the model can reasonably well describe these spreading-related properties of the network and it can generate crucial data for research in the field of the livestock trade system.

Description: We develop a model of optimal carbon taxation and redistribution taking into account horizontal equity concerns by considering heterogeneous energy efficiencies. By deriving first- and second-best rules for policy instruments including carbon taxes, transfers and energy subsidies, we then investigate analytically how horizontal equity is considered in the social welfare maximizing tax structure. We calibrate the model to German household data and a 30 percent emission reduction goal. Our results show that energy-intensive households should receive more redistributive resources than energy-efficient households if and only if social inequality aversion is sufficiently high. We further find that redistribution of carbon tax revenue via household-specific transfers is the first-best policy. Equal per-capita transfers do not suffer from informational problems, but increase mitigation costs by around 15 percent compared to the first- best for unity inequality aversion. Adding renewable energy subsidies or non-linear energy subsidies, reduces mitigation costs further without relying on observability of households’ energy efficiency.

Description: We propose a mathematical model of the human cardiovascular system. The model allows one to simulate the main heart rate, its variability under the influence of the autonomic nervous system, breathing process, and oscillations of blood pressure. For the first time, the model takes into account the activity of the cerebral cortex structures that modulate the autonomic control loops of blood circulation in the awake state and in various stages of sleep. The adequacy of the model is demonstrated by comparing its time series with experimental records of healthy subjects in the SIESTA database. The proposed model can become a useful tool for studying the characteristics of the cardiovascular system dynamics during sleep.

Description: The Paris Agreement does not only stipulate to limit the global average temperature increase to well below 2°C, it also calls for "making finance flows consistent with a pathway towards low greenhouse gas emissions". Consequently, there is an urgent need to understand the implications of climate targets for energy systems and quantify the associated investment requirements in the coming decade. A meaningful analysis must however consider the near-term mitigation requirements to avoid the overshoot of a temperature goal. It must also include the recently observed fast technological progress in key mitigation options. Here, we use a new and unique scenario ensemble that limit peak warming by construction and that stems from seven up-to-date integrated assessment models (IAMs). This allows us to study the near-term implications of different limits to peak temperature increase under a consistent and up-to-date set of assumptions. We find that ambitious immediate action allows for limiting median warming outcomes to well below 2°C in all models. By contrast, current nationally determined contributions for 2030 would add around 0.2°C of peak warming, leading to an unavoidable transgression of 1.5°C in all models, and 2°C in some. In contrast to the incremental changes as foreseen by current plans, ambitious peak warming targets require decisive emission cuts until 2030, with the most substantial contribution to decarbonization coming from the power sector. Therefore, investments into low-carbon power generation need to increase beyond current levels to meet the Paris goals, especially for solar and wind technologies and related system enhancements for electricity transmission, distribution and storage. In scenarios limiting peak warming to below 2°C, while coal is phased out quickly, oil and gas are still being used significantly until 2030, albeit at lower than current levels. This requires continued investments into existing oil and gas infrastructure, but investments into new fields in such scenarios might not be needed. The results show that credible and effective policy action is essential for ensuring efficient allocation of investments aligned with medium-term climate targets.

Description: The worldwide transportation sector remains 93% dependent on the oil industry. While other economic sectors in developed nations have begun to decarbonize in recent decades, greenhouse gas emissions from transportation have continued to rise. This article examines how automobiles came to be so dependent on a finite and socially detrimental resource when electric and ethanol-powered vehicles have always offered distinct performance advantages, and how the latter vehicles fell from socio-political favour to technological lock-out.

Description: In the present study, we quantify the vorticity interactions in a bluff body stabilized turbulent combustor during the transition from combustion noise to thermoacoustic instability via intermittency using complex networks. To that end, we perform simultaneous acoustic pressure, high-speed particle image velocimetry (PIV) and high-speed chemiluminescence measurements during the occurrence of combustion noise, intermittency and thermoacoustic instability. Based on the Biot–Savart law, we construct time-varying weighted spatial networks from the flow fields during these different regimes of combustor operation. We uncover that the turbulent networks display weighted scale-free behaviour intermittently during the different regimes of combustor operation, with the strong vortical structures acting as the hubs. Further, we discover two optimal locations for injecting steady air jets to successfully suppress the thermoacoustic oscillations. The amplitude of the acoustic pressure fluctuations of the suppressed state is comparable to that during the occurrence of combustion noise. However, the weighted scale-free network topology during the suppressed state is not as dominant as compared with the state of combustion noise.

Description: In actual network transmission, data loss due to network congestion and power shortage is unavoidable. However, this phenomenon has not been considered in Boolean networks (BNs) analysis so far. The loss of data is usually random, so BNs with missing data are modeled by introducing Bernoulli distribution sequences. A novel augmented system is constructed to deal with the randomly missing data. The initial states of the BNs are supposed to be chosen from the whole space or only part of it, which leads to the global and local stable problems. Some necessary and sufficient conditions are proposed for both problems. Two algorithms are developed to check the asymptotic stability of BNs. An illustrative example is also given to show the effectiveness of the derived results.

Description: Agroforestry is a promising adaptation measure for climate change, especially for low external inputs smallholder maize farming systems. However, due to its long-term nature and heterogeneity across farms and landscapes, it is difficult to quantitatively evaluate its contribution in building the resilience of farming systems to climate change over large areas. In this study, we developed an approach to simulate and emulate the shading, micro-climate regulation and biomass effects of multi-purpose trees agroforestry system on maize yields using APSIM, taking Ethiopia as a case study. Applying the model to simulate climate change impacts showed that at national level, maize yield will increase by 7.5% and 3.1 % by 2050 under RCP2.6 and RCP8.5, respectively. This projected increase in national-level maize yield is driven by maize yield increases in six administrative zones whereas yield losses are expected in other five zones (mean of -6.8% for RCP2.6 and -11.7% for RCP8.5), with yields in the other four zones remaining stable overtime. Applying the emulated agroforestry leads to increase in maize yield under current and future climatic conditions compared to maize monocultures, particularly in regions for which yield losses under climate change are expected. A 10% agroforestry shade will reduce maize yield losses by 6.9% (RCP2.6) and 4.2 % (RCP8.5) while 20% shade will reduce maize yield losses by 11.5% (RCP2.6) and 11% (RCP8.5) for projected loss zones. Overall, our results show quantitatively that agroforestry buffers yield losses for areas projected to have yield losses under climate change in Ethiopia, and therefore should be part of building climate-resilient agricultural systems.

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 2021-06-28.

Description: Being an extensively produced natural fiber on earth, cotton is of importance for economies. Although the plant is broadly adapted to varying environments, growth and irrigation water demand of cotton may be challenged by future climate change. To study the impacts of climate change on cotton productivity in different regions across the world and the irrigation water requirements related to it, we use the process-based, spatially detailed biosphere and hydrology model LPJmL. We find our modelled cotton yield levels in good agreement with reported values and simulated water consumption of cotton production similar to published estimates. Following the ISIMIP protocol, we employ an ensemble of five General Circulation Models under four Representative Concentration Pathways (RCPs) for the 2011–2099 period to simulate future cotton yields. We find that irrigated cotton production does not suffer from climate change if CO2 effects are considered, whereas rainfed production is more sensitive to varying climate conditions. Considering the overall effect of a changing climate and CO2 fertilization, cotton production on current cropland steadily increases for most of the RCPs. Starting from ~ 65 million tonnes in 2010, cotton production for RCP4.5 and RCP6.0 equates to 83 and 92 million tonnes at the end of the century, respectively. Under RCP8.5, simulated global cotton production raises by more than 50 % by 2099. Taking only climate change into account, projected cotton production considerably shrinks in most scenarios, by up to one-third or 43 million tonnes under RCP8.5. The simulation of future virtual water content (VWC) of cotton grown under elevated CO2 results for all scenarios in less VWC compared to ambient CO2 conditions. Under RCP6.0 and RCP8.5, VWC is notably decreased by more than 2000 m3 t−1 in areas where cotton is produced under purely rainfed conditions. By 2040, the average global VWC for cotton declines in all scenarios from currently 3300 to 3000 m3 t−1 and reduction continues by up to 30 % in 2100 under RCP8.5. While the VWC decreases by the CO2 effect, elevated temperature (and thus water stress) reverse the picture. Except for RCP2.6, the global VWC of cotton increase slightly but steadily under the other RCPs until mid century. RCP8.5 results in an average global VWC of more than 5000 m3 t−1 by end of the simulation period. Given the economic relevance of cotton production, climate change poses an additional stress and deserves special attention. Changes in VWC and water demands for cotton production are of special importance, as cotton production is known for its intense water consumption that led, e.g., to the loss of most of the Aral sea. The implications of climate impacts on cotton production on the one hand, and the impact of cotton production on water resources on the other hand illustrate the need to assess how future climate change may affect cotton production and its resource requirements. The inclusion of cotton in LPJmL allows for various large-scale studies to assess impacts of climate change on hydrological factors and the implications for agricultural production and carbon sequestration.

Description: Tropical cyclones range among the costliest disasters on Earth. Their economic repercussions along the supply and trade network also affect remote economies that are not directly affected. We here simulate possible global repercussions on consumption for the example case of Hurricane Sandy in the US (2012) using the shock-propagation model Acclimate. The modeled shock yields a global three-phase ripple: an initial production demand reduction and associated consumption price decrease, followed by a supply shortage with increasing prices, and finally a recovery phase. Regions with strong trade relations to the US experience strong magnitudes of the ripple. A dominating demand reduction or supply shortage leads to overall consumption gains or losses of a region, respectively. While finding these repercussions in historic data is challenging due to strong volatility of economic interactions, numerical models like ours can help to identify them by approaching the problem from an exploratory angle, isolating the effect of interest. For this, our model simulates the economic interactions of over 7000 regional economic sectors, interlinked through about 1.8 million trade relations. Under global warming, the wave-like structures of the economic response to major hurricanes like the one simulated here are likely to intensify and potentially overlap with other weather extremes.

Description: The electricity sector will play a central role in achieving the European Green Deal target of a net-zero EU by 2050. The range of available low carbon electricity generation technologies and abundant resources, including wind and solar, mean that electricity will be a leading sector in decarbonisation, provided it can attract sufficient finance (see Finance Policy Brief). Electricity is also important for the decarbonisation of other sectors. Buildings, transport and industry will increasingl, switch to technologies that use electricity in order to achieve decarbonisation. The success of these sectors switching to electricity in order to decarbonise, depends of course on electricity itself being decarbonised first.

Description: The EU's Green Deal proposal and Farm to Fork strategy explicitly call for both demand and supply measures to reduce food system emissions. While research clearly illustrates the importance of dietary transitions, impacts of potential supply-side measures are not well understood in relation to competitiveness concerns and leakage effects. This study assesses trade and GHG emission impacts of two supply-side mitigation strategies in the EU (plus UK and Switzerland), against a 2050 baseline featuring healthy/sustainable diets adopted by European consumers. To capture potential leakage effects arising from changing external trade flows, two supply-side strategies (intensification and extensification) are assessed against three trade policy regimes, resulting in six scenarios formulated with detailed inputs from the EUCalc model and simulated with a purported-designed CGE model. Our results show that intensification, while improving the EU+2's external trade balance, does not reduce its emissions, compared to the baseline. In contrast, extensification leads to a substantial emission abatement that augments reductions from the assumed dietary transition embodied in the baseline, resulting in a combined 31.1% of agricultural emission reduction in EU+2 during 2014-2050. However, this is at the expense of worsening agrifood trade balance amounting to US$25 billion, and significant carbon leakages at 48%, implying that half of the EU+2's emission reduction are cancelled out by rising emissions elsewhere. Furthermore, implementing the EU+2's prospective regional trade agreements leads to increased EU emissions; however, a border carbon adjustment by the EU+2 can improve its trade balance and partially shifting mitigation burdens to other countries, but ultimately only marginally reduce global emissions (and carbon leakage). Finally, different trade and emission effects are identified between the crop and livestock sectors, pointing to the desirability of a mixed agriculture system with intensified livestock sector and extensified crop agriculture in EU+2 that balances emission reduction goals and competitiveness concerns.

Description: Background: Anticipating changes in international migration patterns is useful for demographic studies and for designing policies that support the well-being of those involved. Existing forecasting methods do not account for a number of stylized facts that emerge from large-scale migration observations and theories: existing migrant communities – diasporas – act to lower migration costs and thereby provide a mechanism of self-amplification; return migration and transit migration are important components of global migration flows; and poverty constrains emigration. Objective: Here we present hindcasts and future projections of international migration that explicitly account for these nonlinear features. Methods: We develop a dynamic model that simulates migration flows by origin, destination, and place of birth. We calibrate the model using recently constructed global datasets of bilateral migration. Results: We show that the model reproduces past patterns and trends well based only on initial migrant stocks and changes in national incomes. We then project migration flows under future scenarios of global socioeconomic development. Conclusions: Different assumptions about income levels and between-country inequality lead to markedly different migration trajectories, with migration flows either converging towards net zero if incomes in presently poor countries catch up with the rest of the world; or remaining high or even rising throughout the 21st century if economic development is slower and more unequal. Importantly, diasporas induce significant inertia and sizable return migration flows. Contribution: Our simulation model provides a versatile tool for assessing the impacts of different socioeconomic futures on international migration, accounting for important nonlinearities in migration drivers and flows.

Description: Sustainable intensification (SI) of agriculture is a promising strategy for boosting the capacity of the agricultural sector to meet the growing demands for food and non-food products and services in a sustainable manner. Assessing and quantifying the options for SI remains a challenge due to its multiple dimensions and potential associated trade-offs. We contribute to overcoming this challenge by proposing an approach for the ex-ante evaluation of SI options and trade-offs to facilitate decision making in relation to SI. This approach is based on the utilization of a newly developed SI metrics framework (SIMeF) combined with agricultural systems modelling. We present SIMeF and its operationalization approach with modelling and evaluate the approach’s feasibility by assessing to what extent the SIMeF metrics can be quantified by representative agricultural systems models. SIMeF is based on the integration of academic and policy indicator frameworks, expert opinions, as well as the Sustainable Development Goals. Structured along seven SI domains and consisting of 37 themes, 142 sub-themes and 1128 metrics, it offers a holistic, generic, and policy-relevant dashboard for selecting the SI metrics to be quantified for the assessment of SI options in diverse contexts. The use of SIMeF with agricultural systems modelling allows the ex-ante assessment of SI options with respect to their productivity, resource use efficiency, environmental sustainability and, to a large extent, economic sustainability. However, we identify limitations to the use of modelling to represent several SI aspects related to social sustainability, certain ecological functions, the multi-functionality of agriculture, the management of losses and waste, and security and resilience. We suggest advancements in agricultural systems models and greater interdisciplinary and transdisciplinary integration to improve the ability to quantify SI metrics and to assess trade-offs across the various dimensions of SI.

Description: One of the most fundamental questions in the field of neuroscience is the emergence of synchronous behaviour in the brain, such as phase, anti-phase, and shift-phase synchronisation. In this work, we investigate how the connectivity between brain areas can influence the phase angle and the neuronal synchronisation. To do this, we consider brain areas connected by means of excitatory and inhibitory synapses, in which the neuron dynamics is given by the adaptive exponential integrate-and-fire model. Our simulations suggest that excitatory and inhibitory connections from one area to another play a crucial role in the emergence of these types of synchronisation. Thus, in the case of unidirectional interaction, we observe that the phase angles of the neurons in the receiver area depend on the excitatory and inhibitory synapses which arrive from the sender area. Moreover, when the neurons in the sender area are synchronised, the phase angle variability of the receiver area can be reduced for some conductance values between the areas. For bidirectional interactions, we find that phase and anti-phase synchronisation can emerge due to excitatory and inhibitory connections. We also verify, for a strong inhibitory-to-excitatory interaction, the existence of silent neuronal activities, namely a large number of excitatory neurons that remain in silence for a long time.

Description: We analyze how the structure of complex networks of non-identical oscillators influences synchronization in the context of the Kuramoto model. The complex network metrics assortativity and clustering coefficient are used in order to generate network topologies of Erdös–Rényi, Watts–Strogatz, and Barabási–Albert types that present high, intermediate, and low values of these metrics. We also employ the total dissonance metric for neighborhood similarity, which generalizes to networks the standard concept of dissonance between two non-identical coupled oscillators. Based on this quantifier and using an optimization algorithm, we generate Similar, Dissimilar, and Neutral natural frequency patterns, which correspond to small, large, and intermediate values of total dissonance, respectively. The emergency of synchronization is numerically studied by considering these three types of dissonance patterns along with the network topologies generated by high, intermediate, and low values of the metrics assortativity and clustering coefficient. We find that, in general, low values of these metrics appear to favor phase locking, especially for the Similar dissonance pattern. The topology of networks of phase oscillators plays a very important role on the synchronization of the system. The individual dynamics of each oscillator, characterized by their individual frequencies, also play a very important role, which is not completely understood. What effect the emergency of cycles, the connection of nodes with close or very distinct degree have on synchronization? Furthermore, is this affected by the natural frequencies of the oscillators being connected? These questions are also important if we take into consideration the emergence of synchronization phenomena in nature that leads the involved agents from the disorder to order in a scenario in which the agent interconnections are not all-to-all. Here, we investigate these issues.

Description: We study changes in the blood–brain barrier (BBB) permeability in mice caused by a 2-h intermittent sound and discuss their reflection in the electrical activity of the brain. Using the detrended fluctuation analysis (DFA), multiresolution wavelet analysis (MWA) and their recent modifications, we compare the capabilities of reliably characterizing the transitions between closed and open BBB states. It is shown that an enhanced approach, combining MWA with DFA of the decomposition coefficients, improves the interstate separation by limiting the most appropriate level of resolution. This approach can be useful in various studies of complex systems based on experimental data.

Description: The call for a decent life for all within planetary limits poses a dual challenge: Provide all people with the essential resources needed to live well and, collectively, not exceed the source and sink capacity of the biosphere to sustain human societies. We examine the corridor of possible distributions of household energy and carbon footprints that satisfy both minimum energy use for a decent life and available energy supply compatible with the 1.5°C target in 2050. We estimated household energy and carbon footprints for expenditure deciles for 28 European countries in 2015 by combining data from national household budget surveys with the Environmentally-Extended Multi-Regional Input-Output model EXIOBASE. We found a top-to-bottom decile ratio (90:10) of 7.2 for expenditure, 3.1 for net energy and 2.6 for carbon. The lower inequality of energy and carbon footprints is largely attributable to inefficient energy and heating technologies in the lower deciles (mostly Eastern Europe). Adopting best technology across Europe would save 11 EJ of net energy annually, but increase environmental footprint inequality. With such inequality, both targets can only be met through the use of CCS, large efficiency improvements, and an extremely low minimum final energy use of 28 GJ per adult equivalent. Assuming a more realistic minimum energy use of about 55 GJ/ae and no CCS deployment, the 1.5°C target can only be achieved at near full equality. We conclude that achieving both stated goals is an immense and widely underestimated challenge, the successful management of which requires far greater room for maneuver in monetary and fiscal terms than is reflected in the current European political discourse.

Description: This paper presents a new synchronization criterion with a hybrid control approach for multidirectional associative memory neural networks based on memristor (MMAMNNs). That is, the method of impulsive and feedback control combing with the (event) self-triggered mechanism is adopted. However some projective synchronization errors based on state related parameters of MMAMNNs will be affected by the diverse initial conditions. Thus, the new criterion is supported by establishing a novel Lyapunov function combined with the features of such diverse parameters and systems. A collaborative proposed method is designed to make the error of such system converging to zero. Then, the Zeno-behavior is testified to disappear from the proposed programs. Finally, some examples demonstrate the validity of the proposed method and to show its potential application in image protection.

Description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios. The ISIMIP3b part of the third simulation round is dedicated to a quantification of climate-related risks at different levels of global warming and socio-economic change. ISIMIP3b group I simulations are based on historical climate change as simulated in CMIP6 combined with observed historical socio-economic forcing. ISIMIP3b group II simulations are based on climate change according to the CMIP6 future projections combined with socio-economic forcings fixed at 2015 levels. ISIMIP3b group III simulations additionally account for future changes in socio-economic forcing. This dataset covers the CMIP6-based and bias-adjusted atmospheric climate input data for all three groups of ISIMIP3b simulations. Such data are available for 5 CMIP6 global climate models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL), 5 CMIP6 experiments (piControl, historical, SSP126, SSP37, SSP585) and 11 CMIP6 variables (huss, hurs, pr, prsn, ps, rlds, rsds, sfcWind, tas, tasmax, tasmin).

Description: We study the first-passage problem for a process governed by a stochastic differential equation (SDE) driven simultaneously by both parametric Gaussian and Lévy white noises. We extend the path integral (PI) method to solve the SDE with this combined noise input and the corresponding fractional Fokker-Planck-Kolmogorov equations. Then, the PI solutions are modified to analyze the first-passage problem. Finally, numerical examples based on Monte Carlo simulations verify the extension of the PI method and the modification of the PI solutions. The detailed effects of the system parameters on the first-passage problem are analyzed.

Description: We propose a novel scheme to regulate noise infusion into the chaotic trajectories of uncoupled complex systems to achieve complete synchronization. So far the noise-induced synchronization utilize the uncontrolled noise that can be applied in the entire state space. Here, we consider the controlled (intermittent) noise which is infused in the restricted state space to realize enhanced synchronization. We find that the intermittent noise, which is applied only to a fraction of the state space, restricts the trajectories to evolve within the contraction region for a longer period of time. The basin stability of the synchronized states (SS) is found to be significantly enhanced compared to uncontrolled noise. Additionally, we uncover that the SS prevail for an extended range of noise intensity. We elucidate the results numerically in the Lorenz chaotic system, the Pikovski–Rabinovich circuit model and the Hindmarsh–Rose neuron model.