ALBERT

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Description: In some catchments, the distribution of annual maximum streamflow shows heavy tail behavior, meaning the occurrence probability of extreme events is higher than if the upper tail decayed exponentially. Neglecting heavy tail behavior can lead to an underestimation of the likelihood of extreme floods and the associated risk. Partly contradictory results regarding the controls of heavy tail behavior exist in the literature and the knowledge is still very dispersed and limited. To better understand the drivers, we analyze the upper tail behavior and its controls for 480 catchments in Germany and Austria over a period of more than 50 years. The catchments span from quickly reacting mountain catchments to large lowland catchments, allowing for general conclusions. We compile a wide range of event and catchment characteristics and investigate their association with an indicator of the tail heaviness of flood distributions, namely the shape parameter of the GEV distribution. Following univariate analyses of these characteristics, along with an evaluation of different aggregations of event characteristics, multiple linear regression models, as well as random forests, are constructed. A novel slope indicator, which represents the relation between the return period of flood peaks and event characteristics, captures the controls of heavy tails best. Variables describing the catchment response are found to dominate the heavy tail behavior, followed by event precipitation, flood seasonality, and catchment size. The pre-event moisture state in a catchment has no relevant impact on the tail heaviness even though it does influence flood magnitudes.

Description: The accurate prediction of rainfall, and in particular of the heaviest rainfall events, remains challenging for numerical weather prediction (NWP) models. This may be due to subgrid-scale parameterizations of processes that play a crucial role in the multi-scale dynamics generating rainfall, as well as the strongly intermittent nature and the highly skewed, non-Gaussian distribution of rainfall. Here we show that a U-Net-based deep neural network can learn heavy rainfall events from a NWP ensemble. A frequency-based weighting of the loss function is proposed to enable the learning of heavy rainfall events in the distributions' tails. We apply our framework in a post-processing step to correct for errors in the model-predicted rainfall. Our method yields a much more accurate representation of relative rainfall frequencies and improves the forecast skill of heavy rainfall events by factors ranging from two to above six, depending on the event magnitude.

Description: Tropical cyclones range among the costliest of all meteorological events worldwide and planetary scale warming provides more energy and moisture to these storms. Modelling the national and global economic repercussions of 2017’s Hurricane Harvey, we find a qualitative change in the global economic response in an increasingly warmer world. While the United States were able to balance regional production failures by the original 2017 hurricane, this option becomes less viable under future warming. In our simulations of over 7000 regional economic sectors with more than 1.8 million supply chain connections, the US are not able to offset the losses by use of national efforts with intensifying hurricanes under unabated warming. At a certain warming level other countries have to step in to supply the necessary goods for production, which gives US economic sectors a competitive disadvantage. In the highly localized mining and quarrying sector—which here also comprises the oil and gas production industry—this disadvantage emerges already with the original Hurricane Harvey and intensifies under warming. Eventually, also other regions reach their limit of what they can offset. While we chose the example of a specific hurricane impacting a specific region, the mechanism is likely applicable to other climate-related events in other regions and other sectors. It is thus likely that the regional economic sectors that are best adapted to climate change gain significant advantage over their competitors under future warming.

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

Description: Phasing out coal is a prerequisite to achieving the Paris climate mitigation targets. In 2018, the German government established a multi-stakeholder commission with the mandate to negotiate a plan for the national coal phase-out, fueling a continued public debate over the future of coal. This study analyzes the German coal debate on Twitter before, during, and after the session of the so-called Coal Commission, over a period of three years. In particular, we investigate whether and how the work of the commission translated into shared perceptions and sentiments in the public debate on Twitter. We find that the sentiment of the German coal debate on Twitter becomes increasingly negative over time. In addition, the sentiment becomes more polarized over time due to an increase in the use of more negative and positive language. The analysis of retweet networks shows no increase in interactions between communities over time. These findings suggest that the Coal Commission did not further consensus in the coal debate on Twitter. While the debate on social media only represents a section of the national debate, it provides insights for policy-makers to evaluate the interaction of multi-stakeholder commissions and public debates.

Description: Future power grids will be operating a large number of heterogeneous dynamical actors. Many of these will contribute to the fundamental dynamical stability of the system, and play a central role in establishing the self-organized synchronous state that underlies energy transport through the grid. We derive a normal form for grid-forming components in power grids. This allows analyzing the grids’ systemic properties in a technology neutral manner, without detailed component models. Our approach is based on the physics of the power flow in the grid on the one hand, and on the common symmetry that is inherited from the control objectives grid-forming power grid components are trying to achieve. We provide an initial experimental validation that this normal form can capture the behavior of complex grid-forming inverters without any knowledge of the underlying technology, and show that it can be used to make technology-independent statements on the stability of future grids.

Description: Recent publications indicate that the Amazon may be acting more as a carbon source than a sink in some regions. Moreover, the Amazon is a source of moisture for other regions in the continent, and deforestation over the years may be reducing this function. In this work, we analyze the impacts of elevated CO2 (eCO2) and Land Use Change (LUC) on Gross Primary Productivity (GPP) and evaporation in the Southern Amazon (70S 140S, 660W 510W), which suffered strong anthropogenic influence in the period of 1981‒2010. We ran four Dynamic Global Vegetation Models (DGVMs), isolating historical CO2, constant CO2, LUC, and Potential Natural Vegetation (PNV) scenarios with three climate variable datasets: precipitation, temperature and shortwave radiation. We compared the outputs to five “observational” datasets obtained through eddy covariance, remote sensing, meteorological measurements, and machine learning. The results indicate that eCO2 may have offset deforestation, with GPP increasing by ∼13.5% and 9.3% (dry and rainy seasons, respectively). After isolating the LUC effect, a reduction in evaporation of ∼4% and ∼1.2% (dry and rainy seasons, respectively) was observed. The analysis of forcings in subregions under strong anthropogenic impact revealed a reduction in precipitation of ∼15 and 30 mm, and a temperature rise of 10 and 0.60 C (dry and rainy seasons, respectively). Differences in the implementation of plant physiology and Leaf area Index (LAI) in the DGVMs introduced some uncertainties in the interpretation of the results. Nevertheless, we consider that it was an important exercise given the relevance.

Description: As part of the Earth4All project, collaborators have submitted this paper to delve further into the steps to be taken to widely transform our conventional agricultural system to provide food security and improve ecological resilience in a rapidly changing global climate. This article analyses the potential positive effects on soil ecology and crop yield of a global-scale transition to regenerative agriculture, while also considering social spreading dynamics that determine the adoption of such practices by farmers. The authors argue that the transition to a global regenerative agricultural system cannot be achieved without considering the deeper societal processes driving the effective dissemination and adoption of the change. Furthermore, the surrounding factors and conditions such as farmers’ political and institutional embeddedness, public opinion, the economic situation and the climate conditions they face within their region or community, as potential barriers hindering the transition, have to be taken into account. Therefore, it is not only the farmers’ responsibility to drive the change but also the politicians, institutions, companies and individual actors’ one which, all together, will support such transition processes.

Description: Humanity faces an array of grave, long-term challenges, now often labeled “global systemic risks.” While scientific knowledge of the individual risks spawning these crises is deep, our understanding of causal links among risks remains shallow. This observation raises two key questions: What causal processes might be accelerating and amplifying risks within global natural and social systems and synchronizing risks (and their concomitant crises) across these systems? And what might humanity do to mitigate or even reverse these processes? We offer a novel analytical framework to aid identification of hitherto unrecognized, complex teleconnections and self-reinforcing feedbacks among global systems. We argue that the ultimate result of such unrecognized processes could be a global polycrisis—a single, macro-crisis of interconnected, runaway failures of Earth’s vital natural and social systems that irreversibly degrades humanity’s prospects. We therefore call for a global scientific collaboration to discern causal mechanisms that might generate a polycrisis and actionable policies to mitigate this risk.

Description: We are now at “code red” on planet Earth. Humanity is unequivocally facing a climate emergency. The scale of untold human suffering, already immense, is rapidly growing with the escalating number of climate-related disasters. Therefore, we urge scientists, citizens, and world leaders to read this Special Report and quickly take the necessary actions to avoid the worst effects of climate change. 2022 marks the 30th anniversary of the “World Scientists’ Warning to Humanity,” signed by more than 1700 scientists in 1992. Since this original warning, there has been a roughly 40% increase in global greenhouse gas emissions. This is despite numerous written warnings from the Intergovernmental Panel on Climate Change and a recent scientists’ warning of a climate emergency with nearly 15,000 signatories from 158 countries (Ripple et al. 2020). Current policies are taking the planet to around 3 degrees Celsius warming by 2100, a temperature level that Earth has not experienced over the past 3 million years (Liu and Raftery 2021). The consequences of global heating are becoming increasingly extreme, and outcomes such as global societal collapse are plausible and dangerously underexplored (Kemp et al. 2022). Motivated by the moral urgency of this global crisis, here, we track recent climate-related disasters, assess planetary vital signs, and provide sweeping policy recommendations.

Description: Chapter 3 takes a long-term perspective on climate change mitigation pathways. Its focus is on the implications of long-term targets for the required short- and medium-term system changes and associated greenhouse gas (GHG) emissions. This focus dictates a more global view and on issues related to path-dependency and up-scaling of mitigation options necessary to achieve different emissions trajectories, including particularly deep mitigation pathways that require rapid and fundamental changes.

Description: While the economic case for carbon pricing is compelling, public support for it remains low. Using a stated-choice experiment with a sample of 6000 German household heads, we examine how their fairness preferences influence support for carbon taxes under different revenue uses. We find that respondents who prefer green spending are more likely to support a carbon tax, but the support diminishes considerably for higher tax rates – in contrast to respondents who support social cushioning. When restricted to options for direct revenue redistribution, Germans prefer lump-sum payments over directing payments to the poorest or the most affected. Preferences over these options depend both on genuinely different concepts of fairness and economic circumstances. Our findings have implications for advancing carbon pricing, as its support can be increased substantially when designed according to citizens' fairness preferences.

Description: A lot of things in life need money and so does polar science: money is needed to participate in conferences, undertake fieldwork campaigns or pay for salaries, such as in PhD projects or permanent research positions. To give an overview on the general funding landscape for polar early-career scientists in Germany, APECS Germany (the German National Committee of the Association of Polar Early Career Scientists, APECS) has started to host a list of grant, fellowship and other funding opportunities at https://apecs-germany.de/funding/ (last access: 15 October 2022). This is visualized in Fig. 1. Once a suitable funding scheme has been found, grant writing requires good preparation, a well-structured and written proposal, and several back-up plans.

Description: Renewable energies, such as solar and wind energy, play a critical role in achieving rapid decarbonization to limit global warming by replacing fossil energy. However, lack of knowledge on renewable energy potentials in developing countries is a barrier in making adequate policies to promote these energies. Thus, we have carried out a spatial and economic analysis of solar and wind energy potential at the provincial level for the first time in Nepal. Our analysis is built upon the spatial energy modeling based on technical, geographical, and economic suitability criteria, utilizing open-source geographical information system platforms. A significant amount of renewable energy could be harnessed in Nepal, i.e., up to about 47,628 MW and 1,686 MW from solar and wind energy, respectively. Similarly, Nepal has a co-location potential of about 890 and 267 MW of solar and wind energy. Karnali and Gandaki provinces have the highest solar and wind energy potential due to a large share of suitable locations with good resource quality. We estimate the 10th percentile of Levelized cost of electricity generation of 91 USD/MWh for solar and 46 USD/MWh for wind. Our findings are helpful for the formulation of resource-specific policies of Nepal at a sub-national level.

Description: Scripts to reproduce the figures in Marwan & Kraemer "Trends in recurrence analysis of dynamical system", The European Physical Journal ST, 2022

Description: This repository contains the relevant information to reproduce the results of the paper "Towards dynamical stability analysis of sustainable power grids using Graph Neural Networks" published at Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022.

Description: Is German climate policy on the right path? A CO2 budget approach allows a transparent comparison between national and international climate targets. The SRU has recently updated its work on a national CO2 budget for Germany. It shows that rapid emission reductions are crucial. The short report in Q&A format has now been published in English language. In 2020, the SRU recommended aligning Germany's climate targets with a CO2 budget. This budget was transparently derived from the goals of the Paris climate agreement. The analysis was widely received in Germany and also an important scientific basis of Germany's Federal Constitutional Court's historic decision on climate policy in 2021. The current paper updates the SRU's CO2 budget calculations on the basis of the latest scientific knowledge. It also answers a number of questions that came up in public discussion. Germany's remaining fair CO2 budget for a 1.5°C path expires in 2031, that for 1.75°C in 2040 (assuming linear reduction). According to the SRU's calculation, the current German Climate Change Act corresponds to a pathway which limits global warming to less than 2°C, but significantly more than 1.5°C.

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

Description: The freshwater ecosystems around the world are degrading, such that maintaining environmental flow (EF) in river networks is critical to their preservation. The relationship between streamflow alterations and, re- spectively, EF violations, and freshwater biodiversity is well established at the scale of stream reaches or small basins (∼〈100 km2). However, it is unclear if this relationship is robust at larger scales, even though there are large-scale ini- tiatives to legalize the EF requirement. Moreover, EFs have been used in assessing a planetary boundary for freshwater. Therefore, this study intends to conduct an exploratory evalu- ation of the relationship between EF violation and freshwater biodiversity at globally aggregated scales and for freshwater ecoregions. Four EF violation indices (severity, frequency, probability of shifting to a violated state, and probability of staying violated) and seven independent freshwater biodiver- sity indicators (calculated from observed biota data) were used for correlation analysis. No statistically significant neg- ative relationship between EF violation and freshwater bio- diversity was found at global or ecoregion scales. These findings imply the need for a holistic bio-geo-hydro-physical approach in determining the environmental flows. While our results thus suggest that streamflow and EF may not be the only determinant of freshwater biodiversity at large scales, they do not preclude the existence of relationships at smaller scales or with more holistic EF methods (e.g., including water tem- perature, water quality, intermittency, connectivity, etc.) or with other biodiversity data or metrics.

Description: Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely-used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models’ performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapor pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe’s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.

Description: We describe the physics and features of the ice-sheet model Yelmo, an open-source project intended for collaborative development. Yelmo is a thermomechanical model, solving for the coupled velocity and temperature solutions of an ice sheet simultaneously. The ice dynamics are currently treated via a “hybrid” approach combining the shallow-ice and shallow-shelf/shelfy-stream approximations, which makes Yelmo an apt choice for studying a wide variety of problems. Yelmo's main innovations lie in its flexible and user-friendly infrastructure, which promotes portability and facilitates long-term development. In particular, all physics subroutines have been designed to be self-contained, so that they can be easily ported from Yelmo to other models, or easily replaced by improved or alternate methods in the future. Furthermore, hard-coded model choices are eschewed, replaced instead with convenient parameter options that allow the model to be adapted easily to different contexts. We show results for different ice-sheet benchmark tests, and we illustrate Yelmo's performance for the Antarctic ice sheet.

Description: Basal ice-shelf melting is the key driver of Antarctica’s increasing sea-level contribution. In diminishing the but- tressing force of the ice shelves that fringe the ice sheet the melting increases the solid-ice discharge into the ocean. Here we contrast the influence of basal melting in two different ice-shelf regions on the time-dependent response of an idealized, inherently buttressed ice-sheet-shelf system. Carrying out three-dimensional numerical simulations, the basal-melt perturba- tions are applied close to the grounding line in the ice-shelf’s 1) ice-stream region, where the ice shelf is fed by the fastest ice masses that stream through the upstream bed trough and 2) shear margins, where the ice flow is slower. The results show that melting below one or both of the shear margins can cause a decadal to centennial increase in ice discharge that is more than twice as large compared to a similar perturbation in the ice-stream region. We attribute this to the fact that melt-induced ice-shelf thinning in the central grounding-line region is attenuated very effectively by the fast flow of the central ice stream. In contrast, the much slower ice dynamics in the lateral shear margins of the ice shelf facilitate sustained ice-shelf thinning and thereby foster buttressing reduction. Regardless of the melt location, a higher melt concentration toward the grounding line generally goes along with a stronger response. Our results highlight the vulnerability of outlet glaciers to basal melting in stagnant, buttressing-relevant ice-shelf regions, a mechanism that may gain importance under future global warming.

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

Description: The coldest, the windiest, the driest: the continent of Antarctica is a place of extremes. Located at the South Pole, Antarctica is covered by a vast ice sheet, millions of years old and in some areas more than 4,000 m thick. If all this ice were to melt, sea levels would rise by roughly 58 m. Despite its massive size, the Antarctic ice sheet is vulnerable, losing more and more ice as the climate is warming. Most of this ice loss happens along the coast, where the ice sheet slowly flows into the ocean and forms ice shelves, which melt from below because of the comparably warmer ocean water. While the ice loss is still relatively slow right now, several processes could accelerate it and eventually even make it partly unstoppable. Wide-spread ice loss can only be prevented on the long-term if we manage to limit global warming to well below 2°C.

Description: Urban growth can take different forms, such as infill, expansion, and leapfrog development. Here we focus on leapfrogging, which is characterised as new urban development bypassing vacant land. Analysing a sample of 100 global locations, we study the probability that land cover is converted from non-urban to urban as a function of the minimum distance to existing urban cells. The probability decreases with the distance but in many of the considered real-world samples it increases again just before the maximum possible distance. Comparing these empirical findings with numerical ones from a gravitational model, we discover that the characteristic increase can be found in both. Our results indicate that the conversion probability as a function of the distance to urban land cover includes three urban growth domains. (i) Expansion of existing settlements, (ii) discontinuous development of coincidental new settlements rather close to existing ones, and (iii) leapfrogging of new settlements far away from existing ones. We conclude that gravitational effects can explain discontinuous development but leapfrogging can be attributed to a scarcity of developable land at long distances to settlements.

Description: Extreme weather conditions in the face of due to climate change often disproportionately affects the weakest members of society. Agricultural insurance programs that are specifically designed specifically for smallholders in developing countries are valuable tools that can help farmers to cope with the resulting risks. A broad range of methods including household surveys, experimental games, and agent-based models have been used to assess and improve the effectiveness of such climate insurance products. In addition Furthermore, process-based crop models have been used to derive suitable insurance indices. However, climate change raises specific socioeconomic andas well as environmental challenges that need to be considered when designing insurance schemes. We argue that, in light of these pressing challenges, some of the methodological approaches currently applied to study climate insurance reach their limits when applied independently. This has fundamental implications. On the one hand, not all undesired side effects of insurance can be detected and, on the other hand, insurance indices cannot be derived sufficiently well. We therefore advocate a sound combination of different methods, especially by linking empirical analyses and modelling, and underline the resulting potential with the help of stylized examples. Our study highlights how methodological synergies can make climate insurance products more effective in supporting the most vulnerable households, especially under changing climatic conditions.

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

Description: Public support for stringent climate policies is currently weak. We develop a model to study the dynamics of public support for climate policies. It comprises three interconnected modules: one calculates policy impacts; a second translates these into policy support mediated by social influence; and a third represents the regulator adapting policy stringency depending on public support. The model combines general-equilibrium and agent-based elements and is empirically grounded in a household survey, which allows quantifying policy support as a function of effectiveness, personal wellbeing and distributional effects. We apply our approach to compare two policy instruments, namely carbon taxation and performance standards, and identify intertemporal trajectories that meet the climate target and count on sufficient public support. Our results highlight the importance of social influence, opinion stability and income inequality for public support of climate policies. Our model predicts that carbon taxation consistently generates more public support than standards. Finally, we show that under moderate social influence and income inequality, an increasing carbon tax trajectory combined with progressive revenue redistribution receives the highest average public support over time.