ALBERT

Description: The magnitudes of river floods in Europe have been observed to change, but their alignment with changes in the spatial coverage or extent of individual floods has not been clear. We analyze flood magnitudes and extents for 3,872 hydrometric stations across Europe over the past five decades and classify each flood based on antecedent weather conditions. We find positive correlations between flood magnitudes and extents for 95% of the stations. In central Europe and the British Isles, the association of increasing trends in magnitudes and extents is due to a magnitude‐extent correlation of precipitation and soil moisture along with a shift in the flood generating processes. The alignment of trends in flood magnitudes and extents highlights the increasing importance of transnational flood risk management.

Description: Ride-sharing services may substantially contribute to future sustainable mobility. Their collective dynamics intricately depend on the topology of the underlying street network, the spatiotemporal demand distribution, and the dispatching algorithm. The efficiency of ride-sharing fleets is thus hard to quantify and compare in a unified way. Here, we derive an efficiency observable from the collective nonlinear dynamics and show that it exhibits a universal scaling law. For any given dispatcher, we find a common scaling that yields data collapse across qualitatively different topologies of model networks and empirical street networks from cities, islands, and rural areas. A mean-field analysis confirms this view and reveals a single scaling parameter that jointly captures the influence of network topology and demand distribution. These results further our conceptual understanding of the collective dynamics of ride-sharing fleets and support the evaluation of ride-sharing services and their transfer to previously unserviced regions or unprecedented demand patterns.

Description: Code for coupling the Parallel Ice Sheet Model PISM with the Modular Ocean Model MOM

Description: Time lags occur in a vast range of real-world dynamical systems due to finite reaction times or propagation speeds. Here we derive an analytical approach to determine the asymptotic stability of synchronous states in networks of coupled inertial oscillators with constant delay. Building on the master stability formalism, our technique provides necessary and sufficient delay master stability conditions. We apply it to two classes of potential future power grids, where processing delays in control dynamics will likely pose a challenge as renewable energies proliferate. Distinguishing between phase and frequency delay, our method offers an insight into how bifurcation points depend on the network topology of these system designs.

Description: The nature of interactions between ecological, physical and hydrological characteristics that determine the effects of land cover change on surface and sub-surface hydrology is not well understood in both natural and disturbed environments. The spatiotemporal dynamics of water fluxes and their relationship with land cover changes between 2009 and 2017 in the headwater Buzi sub-catchment in Zimbabwe is evaluated. To achieve this, land cover dynamics for the area under study were characterised from the 30 m Landsat data, using the eXtreme Gradient Boosting (XGBoost) algorithm. After the land cover classification, the key water balance components namely; interception, transpiration and evapotranspiration (ET) contributions for each class in 2009 and 2017 were estimated. Image classification of Landsat data achieved good overall accuracies above 80% for the two periods. Results showed that the percentage of the plantation land cover types decreased slightly between 2009 (25.4%) and 2017 (22.5%). Partitioning the annual interception, transpiration and ET according to land cover classes showed that the highest amounts of ET in the basin were from plantation where land cover types with tea had the highest interception, transpiration and ET in the catchment. Higher ET, interception and transpiration were observed in the eastern parts of the catchment. At catchment level, results show that 2017 had a higher water balance than 2009, which was partly explained by the decrease in plantation cover type.

Description: Throughout the history of complex life, Earth's climate and biogeochemical cycles have been perturbed by Large Igneous Province (LIP) volcanism, with several LIP episodes correlating with major mass extinction events. Yet many aspects of the interplay between geological, climatic and ecological processes in the Earth System during these times of global upheaval remain poorly understood. This study focuses on the Central Atlantic Magmatic Province and the associated extinction event in the latest Triassic, about 201 million years ago. Although climate and carbon cycle models successfully reproduce aspects of the end-Triassic environmental changes, many questions regarding the causal and temporal relations behind them remain unresolved. Here, we report an effort to model and quantify the dynamic response of the Earth System to short pulses of volcanogenic volatile emissions for an ensemble of emission scenarios. For the first time in the context of the end-Triassic events, this is done with a coupled climate model and under consideration of both carbon and sulfur emissions. Tested are pulses with ∼1−6 kyr duration during which 2500 - 7500 GtC are emitted and 0 - 500 GtS form stratospheric sulfate aerosols. The simultaneous emission of carbon and sulfur during one pulse of volcanic activity causes climatic fluctuations on annual to millennial timescales: A sequence of transient global cooling and subsequent sustained warming, overprinted with high interannual variability. The simulated maximum global warming ranges from +1.8 to +4.4 °C, while the amplitude of cooling is considerably higher in the upper range of the tested sulfur emission scenarios. The magnitude of temperature change varies regionally, being lowest in the Tethys realm. Changes in steric sea level (∼1−3 m) and ocean overturning strength, a surface ocean pH decrease (∼0.2−0.4) and a drop of the carbonate saturation especially in the Tethys are also obtained from the simulations during each emission pulse. By evaluating the simulated temperature changes against thermal tolerance limits of stony corals in a simplified manner, we find that these are not clearly transgressed on a global scale in the simulated warming scenarios. However, the climatic variability potentially introduced by the volcanic forcing would have represented significant stress for marine organisms. This study represents a significant step towards integrating multiple volcanic forcing mechanisms and environmental response processes in space and time to yield a more complete picture of impacts of CAMP volcanism and LIPs in general.

Description: In seinem Umweltgutachten 2020 greift der SRU umweltpolitische Themenfelder auf, in denen großer Handlungsbedarf besteht: Klimapolitik, Kreislaufwirtschaft, Gewässerschutz, Lärmschutz, städtische Mobilität und nachhaltige Quartiersentwicklung. Gleichzeitig zeigt der SRU auf, wie ein Umsteuern mit zielgerichteten Maßnahmen möglich ist. Vor dem Hintergrund der deutschen EU-Ratspräsidentschaft analysiert das Gutachten zudem anstehende Weichenstellungen in Europa. In Deutschland wie in der EU muss die Politik unter Beweis stellen, dass sie angesichts der enormen ökologischen und wirtschaftlichen Herausforderungen entschlossen handeln kann. Auf der europäischen Ebene werden ambitionierte Umweltziele, eine fokussierte und zugleich flexible Arbeitsmethodik und klare Vorgaben für Umsetzung und Monitoring benötigt. Auch bislang nicht ausreichend ökologisch ausgerichtete Wirtschaftsbereiche müssen jetzt den Umwelt- und Klimaschutz stärker in den Vordergrund stellen.

Description: Several studies have shown that the greenhouse gas reduction resulting from the current nationally determined contributions (NDCs) will not be enough to meet the overall targets of the Paris Climate Agreement. It has been suggested that more ambition mitigations of short-lived climate forcer (SLCF) emissions could potentially be a way to reduce the risk of overshooting the 1.5 or 2 °C target in a cost-effective way. In this study, we employ eight state-of-the-art integrated assessment models (IAMs) to examine the global temperature effects of ambitious reductions of methane, black and organic carbon, and hydrofluorocarbon emissions. The SLCFs measures considered are found to add significantly to the effect of the NDCs on short-term global mean temperature (GMT) (in the year 2040: − 0.03 to − 0.15 °C) and on reducing the short-term rate-of-change (by − 2 to 15%), but only a small effect on reducing the maximum temperature change before 2100. This, because later in the century under assumed ambitious climate policy, SLCF mitigation is maximized, either directly or indirectly due to changes in the energy system. All three SLCF groups can contribute to achieving GMT changes.

Description: Emission reduction from the coal-dominated power sector is vital for achieving China's carbon mitigation targets. Although the coal expansion has been slowed down due to the cancellation of and delay in new construction, coal-based power was responsible for over one third of China's energy-related CO2 emissions by 2018. Moreover, with a technical lifetime of over 30 years, current investment in coal-based power could hinder CO2 mitigation until 2050. Therefore, it is important to examine whether the current coal-based power planning aligns with the long-term climate targets. This paper introduces China's Nationally Determined Contribution (NDC) goals and an ambitious carbon budget along with global pathways well-below 2 degrees that are divided into five integrated assessment models, which are two national and three global models. We compare the models' results with bottom-up data on current capacity additions and expansion plans to examine if the NDC targets are in line with 2-degree pathways. The key findings are: 1. NDC goals alone are unlikely to lead to significant reductions in coal-based power generation. On the contrary, more plants may be built before 2030; 2. this would require an average of 187–261 TWh of annual coal-based power capacity reduction between 2030 and 2050 to achieve a 2 °C compatible trajectory, which would lead to the stranding of large-scale coal-based power plants; 3. if the reduction in coal power can be brought forward to 2020, the average annual coal-based power reduction required would be 104–155 TWh from 2020 to 2050 and the emissions could peak earlier; 4. early regulations in coal-based power would require accelerated promotion of alternatives between 2020 and 2030, with nuclear, wind and solar power expected to be the most promising alternatives. By presenting the stranding risk and viability of alternatives, we suggest that both the government and enterprises should remain cautious about making new investment in coal-based power sector.

Description: The ambitious climate policy goals of the 2015 Paris Agreement cannot be achieved without an active involvement of cities and individual actors. This chapter pays special attention to individuals in two core roles: consumers and citizens. It reports the results of a 1-year urban real-world lab experiment in the German capital Berlin, with 100 private households reducing their personal carbon footprints by using various feedbacks, including a weekly carbon tracker. The project did address the consumer and the citizen alike. We propose to differentiate the citizen with respect to moral and political aspects, respectively. While a reduction of the average carbon footprint of about 11% (33% less than the German average) could be achieved, the chapter contextualises the project results by focusing on an improved interplay between consumer and citizen and by asking how the (local) political system could profit from real-world labs.

Description: This technical report documents the REMIND-MAgPIE model and the REMIND-MAgPIE scenarios that were selected to support transition risk analysis for the Task-Force for Climate Related Financial Disclosures (TCFD) Banking Pilot Phase II. REMIND-MAgPIE is an optimisation model that integrates the macroeconomic, agriculture and land-use, energy, water and climate systems. It describes, in a forward-looking fashion, the complex and non-linear dynamics in and between these systems. In line with the TCFD recommendations, the scenarios generated with this model can be integrated into risks assessments frameworks to identify and evaluate the risks related to a transition to a low-carbon economy.

Description: Scientists have repeatedly argued that transformative, multiscale global scenarios are needed as tools in the quest to halt the decline of biodiversity and achieve sustainability goals. As a first step towards achieving this, the researchers who participated in the scenarios and models expert group of the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) entered into an iterative, participatory process that led to the development of the Nature Futures Framework (NFF). The NFF is a heuristic tool that captures diverse, positive relationships of humans with nature in the form of a triangle. It can be used both as a boundary object for continuously opening up more plural perspectives in the creation of desirable nature scenarios and as an actionable framework for developing consistent nature scenarios across multiple scales. Here we describe the methods employed to develop the NFF and how it fits into a longer term process to create transformative, multiscale scenarios for nature. We argue that the contribution of the NFF is twofold: (a) its ability to hold a plurality of perspectives on what is desirable, which enables the development of joint goals and visions and recognizes the possible convergence and synergies of measures to achieve these visions and (b), its multiscale functionality for elaborating scenarios and models that can inform decision‐making at relevant levels, making it applicable across specific places and perspectives on nature. If humanity is to achieve its goal of a more sustainable and prosperous future rooted in a flourishing nature, it is critical to open up a space for more plural perspectives of human–nature relationships. As the global community sets out to develop new goals for biodiversity, the NFF can be used as a navigation tool helping to make diverse, desirable futures possible.

Description: Atlantic hurricane activity varies substantially from year to year and so do the associated damages. Longer-term forecasting of hurricane risks is a key element to reduce damages and societal vulnerabilities by enabling targeted disaster preparedness and risk reduction measures. While the immediate synoptic drivers of tropical cyclone formation and intensification are increasingly well understood, precursors of hurricane activity on longer time-horizons are still not well established. Here we use a causal network-based algorithm to identify physically motivated late-spring precursors of seasonal 15Atlantic hurricane activity. Based on these precursors we construct seasonal forecast models with competitive skill compared to operational forecasts. We present a skillful model to forecast July to October cyclone activity at the beginning of April.Earlier seasonal hurricane forecasting provides a multi-month lead time to implement more effective disaster risk reduction measures. Our approach also highlights the potential of applying causal effects network analysis in seasonal forecasting

Description: Covid-19 ist nicht schlimmer als die normale Grippe? Bill Gates hat die Coronakrise erfunden? Die Leugner wissenschaftlicher Erkenntnisse verwenden immer wieder dieselben Methoden, um ihr Laienpublikum zu verführen.

Description: A stochastic nonlinear ray propagation model is proposed to carry out an exploration of the nonlinear ray theory in underwater signal propagation. The recurrence plot method is proposed to quantify the ray chaos and stochastics to optimize the model. Based on this method, the distribution function of the control parameter δ is derived. Experiments and simulations indicate that this stochastic nonlinear ray propagation model provides a good explanation and description on the stochastic frequency shift in underwater signal propagation.

Description: A new gridded rainfall dataset available for Peru is introduced, called PISCOp V2.1 (Peruvian Interpolated data of SENAMHI’s Climatological and Hydrological Observations). PISCOp has been developed for the period 1981 to the present, with an average latency of eight weeks at 0.1° spatial resolution. The merging algorithm is based on geostatistical and deterministic interpolation methods including three different rainfall sources: (i) the national quality-controlled and infilled raingauge dataset, (ii) radar-gauge merged precipitation climatologies and (iii) the Climate Hazards Group Infrared Precipitation (CHIRP) estimates. The validation results suggest that precipitation estimates are acceptable showing the highest performance for the Pacific coast and the western flank of the Andes. Furthermore, a meticulous quality-control and gap-infilling procedure allowed us to reduce the formation of inhomogeneities (non-climatic breaks). The dataset is publicly available at https://piscoprec.github.io/ and is intended to support hydrological studies and water management practices.

Description: Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.

Description: In this paper, a twisting-based consensus algorithm is put forward to deal with the event-triggered finite-time consensus for networked Lagrangian systems with directed graphs. First, a fully distributed event-triggered finite-time protocol is considered, for which we can show that each agent can achieve the consensus after a settling time. In order to remove the requirement of continuous monitoring, a pull-based triggering mechanism is employed. Simultaneously, the Zeno-behavior can be excluded under a finite-time dynamic condition. Then, due to the advantages of non-chattering behaviors and finite-time convergence, a twisting-based consensus algorithm based on homogeneous techniques is developed to drive the Euler-Lagrange systems to the consensus value in a settling time. By means of Pólya's theorem and Sum of Squares tools, a polynomial Lyapunov function is constructed to verify our criteria. At last, we give a numerical example for 2-DOF prototype manipulators to verify the validity of the theoretical results.

Description: Limiting global mean temperature changes to well below 2 ∘C likely requires a rapid and large-scale deployment of negative emission technologies (NETs). Assessments so far have shown a high potential of biomass-based terrestrial NETs, but only a few assessments have included effects of the commonly found nutrient-deficient soils on biomass production. Here, we investigate the deployment of enhanced weathering (EW) to supply nutrients to areas of afforestation–reforestation and naturally growing forests (AR) and bioenergy grasses (BG) that are deficient in phosphorus (P), besides the impacts on soil hydrology. Using stoichiometric ratios and biomass estimates from two established vegetation models, we calculated the nutrient demand of AR and BG. Insufficient geogenic P supply limits C storage in biomass. For a mean P demand by AR and a low-geogenic-P-supply scenario, AR would sequester 119 Gt C in biomass; for a high-geogenic-P-supply and low-AR-P-demand scenario, 187 Gt C would be sequestered in biomass; and for a low geogenic P supply and high AR P demand, only 92 Gt C would be accumulated by biomass. An average amount of ∼150 Gt basalt powder applied for EW would be needed to close global P gaps and completely sequester projected amounts of 190 Gt C during the years 2006–2099 for the mean AR P demand scenario (2–362 Gt basalt powder for the low-AR-P-demand and for the high-AR-P-demand scenarios would be necessary, respectively). The average potential of carbon sequestration by EW until 2099 is ∼12 Gt C (∼0.2–∼27 Gt C) for the specified scenarios (excluding additional carbon sequestration via alkalinity production). For BG, 8 kg basalt m−2 a−1 might, on average, replenish the exported potassium (K) and P by harvest. Using pedotransfer functions, we show that the impacts of basalt powder application on soil hydraulic conductivity and plant-available water, to close predicted P gaps, would depend on basalt and soil texture, but in general the impacts are marginal. We show that EW could potentially close the projected P gaps of an AR scenario and nutrients exported by BG harvest, which would decrease or replace the use of industrial fertilizers. Besides that, EW ameliorates the soil's capacity to retain nutrients and soil pH and replenish soil nutrient pools. Lastly, EW application could improve plant-available-water capacity depending on deployed amounts of rock powder – adding a new dimension to the coupling of land-based biomass NETs with EW.

Description: Mapping spatial and temporal variability of urban microclimate is pivotal for an accurate estimation of the ever‐increasing exposure of urbanized humanity to global warming. This particularly concerns cities in arid/semi‐arid regions which cover two fifths of the global land area and are home to more than one third of the world's population. Focusing on the desert city of Be'er Sheva Israel, we investigate the spatial and temporal patterns of urban–rural and intra‐urban temperature variability by means of satellite observation, vehicular traverse measurement, and computer simulation. Our study reveals a well‐developed nocturnal canopy layer urban heat island in Be'er Sheva, particularly in the winter, but a weak diurnal cool island in the mid‐morning. Near surface air temperature exhibits weak urban–rural and intra‐urban differences during the daytime (〈1°C), despite pronounced urban surface cool islands observed in satellite images. This phenomenon, also recorded in some other desert cities, is explained by the rapid increase in surface skin temperature of exposed desert soils (in the absence of vegetation or moisture) after sunrise, while urban surfaces are heated more slowly. The study highlights differences among the three methods used for describing urban temperature variability, each of which may have different applications in fields such as urban planning, climate change mitigation, and epidemiological research.

Description: Climate change is projected to detrimentally affect African countries’ economic development, while income inequalities across economies is among the highest on the planet. However, it is projected that income levels would converge on the continent. Hitherto there is limited evidence on how climate change could affect projected income convergence, accelerating, slowing down, or even reversing this process. Here, we analyze convergence considering climate-change damages, by employing an economic model embedding the three dimensions of risks at the country-level: exposure, vulnerability and hazards. The results show (1) with historical mean climate-induced losses between 10 and 15 percent of GDP per capita growth, the majority of African economies are poorly adapted to their current climatic conditions, (2) Western and Eastern African countries are projected to be the most affected countries on the continent and (3) As a consequence of these heightened impacts on a number of countries, inequalities between countries are projected to widen in the high warming scenario compared to inequalities in the low and without warming scenarios. To mitigate the impacts of economic development and inequalities across countries, we stress (1) the importance of mitigation ambition and Africa’s leadership in keeping global mean temperature increase below 1.5 °C, (2) the need to address the current adaptation deficit as soon as possible, (3) the necessity to integrate quantitatively climate risks in economic and development planning and finally (4) we advocate for the generalization of a special treatment for the most vulnerable countries to access climate-related finance. The analysis raises issues on the ability of African countries to reach their SDGs targets and the potential increasing risk of instability, migration across African countries, of decreased trade and economic cooperation opportunities as a consequence of climate change – exacerbating its negative consequences.

Description: This paper presents an approach for a quantitative analysis of movement patterns of nomadic households based on GPS trajectories. We distributed GPS loggers to 400 Mongolian herder households who carried them over a 9-month period, continuously recording position data every 30 minutes. A total of 142 of the resulting trajectories fulfilled our data quality criteria and were considered during the analysis. Based on this data, we derive summary indicators describing key parameters of the households' mobility including measures of distance and number of movements as well as shape characteristics of the trajectories. We conduct an explorative statistical analysis of these summary indicators to investigate patterns in the nomadic mobility. We identify three movement strategies based on the number of dierent campsite locations and the distances traveled between campsites. We also compare the results to the existing literature on the mobility of Mongolian herders. Our findings show that GPS-based studies present a suitable framework to quantitatively analyze different movement strategies of nomadic herders.

Description: Anthropogenic increases in atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The integrated assessment community has quantified anthropogenic emissions for the shared socio-economic pathway (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentrations for these SSP scenarios – using the reduced-complexity climate–carbon-cycle model MAGICC7.0. We extend historical, observationally based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios, respectively. We also provide the concentration extensions beyond 2100 based on assumptions regarding the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO2 emissions. By 2150, CO2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel-driven scenario projects CO2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from 66 % for the present day to roughly 68 % to 85 % by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterizations that reflect the Oslo Line-By-Line model results. In comparison to the representative concentration pathways (RCPs), the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are more evenly spaced and extend to lower 2100 radiative forcing and temperatures. Performing two pairs of six-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the March–April–May (MAM) season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (∼5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies' projections over the next 100 to 500 years unequivocally depict a “hockey-stick” upwards shape. The SSP concentration time series derived in this study provide a harmonized set of input assumptions for long-term climate science analysis; they also provide an indication of the wide set of futures that societal developments and policy implementations can lead to – ranging from multiple degrees of future warming on the one side to approximately 1.5 ∘C warming on the other.

Description: Two different approaches are used to assess the impacts associated with natural hazards and climate change in cities. A bottom-up approach uses high resolution data on constituent assets within the urban area. In contrast, a top-down approach uses less detailed information but is consequently more readily transferable. Here, we compare damage curves generated by each approach for coastal flooding in London. To compare them, we fit a log-logistic regression with three parameters to the calculated damage curves. We find that the functions are remarkably similar in their shape, albeit with different inflection points and a maximum damage that differs by 13%–25%. If rescaled, the curves agree almost exactly, which enables damage assessment to be undertaken following the calculation of the three parameters.

Description: India is facing a double burden of malnourishment with co-existences of under- and over-nourishment. Various socioeconomic factors play an essential role in determining dietary choices. Agriculture is one of the major emitters of greenhouse gases (GHGs) in India, contributing 18% of total emissions. It also consumes freshwater and uses land significantly. We identify eleven Indian diets by applying k-means cluster analysis on latest data from the Indian household consumer expenditure survey. The diets vary in calorie intake [2289–3218 kcal/Consumer Unit (CU)/day] and dietary composition. Estimated embodied GHG emissions in the diets range from 1.36 to 3.62 kg CO2eq./CU/day, land footprint from 4 to 5.45 m2/CU/day, whereas water footprint varies from 2.13 to 2.97 m3/CU/day. Indian diets deviate from a healthy reference diet either with too much or too little consumption of certain food groups. Overall, cereals, sugar, and dairy products intake are higher. In contrast, the consumption of fruits and vegetables, pulses, and nuts is lower than recommended. Our study contributes to deriving required polices for the sustainable transformation of food systems in India to eliminate malnourishment and to reduce the environmental implications of the food systems.

Description: In the brain, the excitation-inhibition balance prevents abnormal synchronous behavior. However, known synaptic conductance intensity can be insufficient to account for the undesired synchronization. Due to this fact, we consider time delay in excitatory and inhibitory conductances and study its effect on the neuronal synchronization. In this work, we build a neuronal network composed of adaptive integrate-and-fire neurons coupled by means of delayed conductances. We observe that the time delay in the excitatory and inhibitory conductivities can alter both the state of the collective behavior (synchronous or desynchronous) and its type (spike or burst). For the weak coupling regime, we find that synchronization appears associated with neurons behaving with extremes highest and lowest mean firing frequency, in contrast to when desynchronization is present when neurons do not exhibit extreme values for the firing frequency. Synchronization can also be characterized by neurons presenting either the highest or the lowest levels in the mean synaptic current. For the strong coupling, synchronous burst activities can occur for delays in the inhibitory conductivity. For approximately equal-length delays in the excitatory and inhibitory conductances, desynchronous spikes activities are identified for both weak and strong coupling regimes. Therefore, our results show that not only the conductance intensity, but also short delays in the inhibitory conductance are relevant to avoid abnormal neuronal synchronization.

Description: The Greenland ice sheet is one of the largest contributors to global mean sea-level rise today and is expected to continue to lose mass as the Arctic continues to warm. The two predominant mass loss mechanisms are increased surface meltwater run-off and mass loss associated with the retreat of marine-terminating outlet glaciers. In this paper we use a large ensemble of Greenland ice sheet models forced by output from a representative subset of the Coupled Model Intercomparison Project (CMIP5) global climate models to project ice sheet changes and sea-level rise contributions over the 21st century. The simulations are part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We estimate the sea-level contribution together with uncertainties due to future climate forcing, ice sheet model formulations and ocean forcing for the two greenhouse gas concentration scenarios RCP8.5 and RCP2.6. The results indicate that the Greenland ice sheet will continue to lose mass in both scenarios until 2100, with contributions of 90±50 and 32±17 mm to sea-level rise for RCP8.5 and RCP2.6, respectively. The largest mass loss is expected from the south-west of Greenland, which is governed by surface mass balance changes, continuing what is already observed today. Because the contributions are calculated against an unforced control experiment, these numbers do not include any committed mass loss, i.e. mass loss that would occur over the coming century if the climate forcing remained constant. Under RCP8.5 forcing, ice sheet model uncertainty explains an ensemble spread of 40 mm, while climate model uncertainty and ocean forcing uncertainty account for a spread of 36 and 19 mm, respectively. Apart from those formally derived uncertainty ranges, the largest gap in our knowledge is about the physical understanding and implementation of the calving process, i.e. the interaction of the ice sheet with the ocean.

Description: This study assesses Japan’s mid-century low-emission pathways using both national and global integrated assessment models in the common mitigation scenario framework, based on the carbon budgets corresponding to the global 2 °C goal. We examine high and low budgets, equal to global cumulative 1600 and 1000 Gt-CO2 (2011–2100) for global models, and 36 and 31 Gt-CO2 (2011–2050) in Japan for national models, based on the cost-effectiveness allocation performed by the global models. The impacts of near-term policy assumption, including the implementation and enhancement of the 2030 target of the nationally determined contribution (NDC), are also considered. Our estimates show that the low budget scenarios require a 75% reduction of CO2 emissions by 2050 below the 2010 level, which is nearly the same as Japan’s governmental 2050 goal of reducing greenhouse gas emissions by 80%. With regard to near-term actions, Japan’s 2030 target included in the NDC is on track to meet the high budget scenario, whereas it is falling short for the low budget scenario, which would require emission reductions immediately after 2020. Whereas models differ in the type of energy source on which they foresee Japan basing its decarbonization process (e.g., nuclear- or variable renewable energy-dependent), the large-scale deployment of low-carbon energy (nuclear, renewable, and carbon capture and storage) is shared across most models in both the high and low budget scenarios. By 2050, low-carbon energy represents 44–54% of primary energy and 86–97% of electricity supply in the high and low budget scenarios, respectively.

Description: Cave microclimate and geochemical monitoring is vitally important for correct interpretations of proxy time series from speleothems with regard to past climatic and environmental dynamics. We present results of a comprehensive cave-monitoring programme in Waipuna Cave in the North Island of New Zealand, a region that is strongly influenced by the Southern Westerlies and the El Niño–Southern Oscillation (ENSO). This study aims to characterise the response of the Waipuna Cave hydrological system to atmospheric circulation dynamics in the southwestern Pacific region in order to assure the quality of ongoing palaeo-environmental reconstructions from this cave. Drip water from 10 drip sites was collected at roughly monthly intervals for a period of ca. 3 years for isotopic (δ18O, δD, d-excess parameter, δ17O, and 17Oexcess) and elemental (Mg∕Ca and Sr∕Ca) analysis. The monitoring included spot measurements of drip rates and cave air CO2 concentration. Cave air temperature and drip rates were also continuously recorded by automatic loggers. These datasets were compared to surface air temperature, rainfall, and potential evaporation from nearby meteorological stations to test the degree of signal transfer and expression of surface environmental conditions in Waipuna Cave hydrochemistry. Based on the drip response dynamics to rainfall and other characteristics, we identified three types of discharge associated with hydrological routing in Waipuna Cave: (i) type 1 – diffuse flow, (ii) type 2 – fracture flow, and (iii) type 3 – combined flow. Drip water isotopes do not reflect seasonal variability but show higher values during severe drought. Drip water δ18O values are characterised by small variability and reflect the mean isotopic signature of precipitation, testifying to rapid and thorough homogenisation in the epikarst. Mg∕Ca and Sr∕Ca ratios in drip waters are predominantly controlled by prior calcite precipitation (PCP). Prior calcite precipitation is strongest during austral summer (December–February), reflecting drier conditions and a lack of effective infiltration, and is weakest during the wet austral winter (July–September). The Sr∕Ca ratio is particularly sensitive to ENSO conditions due to the interplay of congruent or incongruent host rock dissolution, which manifests itself in lower Sr∕Ca in above-average warmer and wetter (La Niña-like) conditions. Our microclimatic observations at Waipuna Cave provide a valuable baseline for the rigorous interpretation of speleothem proxy records aiming at reconstructing the past expression of Pacific climate modes.

Description: Dynamical effects on healthy brains and brains affected by tumor are investigated via numerical simulations. The brains are modeled as multilayer networks consisting of neuronal oscillators whose connectivities are extracted from Magnetic Resonance Imaging (MRI) data. The numerical results demonstrate that the healthy brain presents chimera-like states where regions with high white matter concentrations in the direction connecting the two hemispheres act as the coherent domain, while the rest of the brain presents incoherent oscillations. To the contrary, in brains with destructed structures, traveling waves are produced initiated at the region where the tumor is located. These areas act as the pacemaker of the waves sweeping across the brain. The numerical simulations are performed using two neuronal models: (a) the FitzHugh–Nagumo model and (b) the leaky integrate-and-fire model. Both models give consistent results regarding the chimera-like oscillations in healthy brains and the pacemaker effect in the tumorous brains. These results are considered a starting point for further investigation in the detection of tumors with small sizes before becoming discernible on MRI recordings as well as in tumor development and evolution.

Description: Two small annually laminated stalagmites from Zoolithencave (southeastern Germany) grew between CE 1821 and 1970 (Zoo‐rez‐1) and CE 1835 and 1970 (Zoo‐rez‐2), respectively. Trace element concentrations were determined by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA‐ICP‐MS). Samples for δ13C and δ18O analyses were micromilled on annual and subannual resolution. Soil and host rock samples were analyzed by X‐Ray Diffraction (XRD) and their elemental concentrations determined via inductively coupled plasma optical emission spectrometer (ICP‐OES). Trace element concentrations in the stalagmites show two groups in the principal component analyses: one with Mg, Ba, and Sr and another with Y, P, and Al, respectively. The second group reflects the content of detrital material. Increased weathering of soil minerals seems to have a strong influence on the silicate/carbonate weathering ratio controlling the variability of Mg, Ba, and Sr. Meteorological and Global Network of Isotopes in Precipitation (GNIP) station data were used to calculate the δ18O values of the drip water (infiltration‐weighted, mean annual, and the mean of the winter precipitation δ18O values) as well as the corresponding speleothem calcite. The δ18O values calculated by the infiltration‐weighted model show similar patterns and amplitudes as the measured δ18O values of the two stalagmites. This suggests that the δ18O values of speleothem calcite reflect the δ18O values of infiltration‐weighted annual precipitation, which zis related to mean annual temperature, resulting in a significant correlation between mean annual temperature and the measured δ18O values of stalagmite Zoo‐rez‐2. This relationship could potentially be used for quantitative climate reconstruction in the future by extending the time series back in time with further stalagmites from Zoolithencave.

Description: For the article “How to Tailor my Process-based Model? Dynamic Identifiability Analysis of Flexible Model Structures” (Pilz et al., submitted) a flexible simulation environment is coupled with an algorithm for dynamic identifiability analysis to form a diagnostic tool for process-based model building. This software publication provides the simulation environment ECHSE along with the new WASA engine. The latter was developed and employed for the associated research article. The original ECHSE software was introduced by Kneis (2015) and can be obtained from http://echse.github.io/

Description: In this study, the dead carbon fraction (DCF) variations in stalagmite M1-5 from Socotra Island in the western Arabian Sea were investigated through a new set of high-precision U-series and radiocarbon (14C) dates. The data reveal an extreme case of very high and also climate-dependent DCF. For M1-5, an average DCF of 56.2±3.4 % is observed between 27 and 18 kyr BP. Such high DCF values indicate a high influence of aged soil organic matter (SOM) and nearly completely closed-system carbonate dissolution conditions. Towards the end of the last glacial period, decreasing Mg∕Ca ratios suggest an increase in precipitation which caused a marked change in the soil carbon cycling as indicated by sharply decreasing DCF. This is in contrast to the relation of soil infiltration and DCF as seen in stalagmites from temperate zones. For Socotra Island, which is influenced by the East African–Indian monsoon, we propose that more humid conditions and enhanced net infiltration after the Last Glacial Maximum (LGM) led to dense vegetation and thus lowered the DCF by increasing 14CO2 input into the soil zone. At the onset of the Younger Dryas (YD) a sudden change in DCF towards much higher, and extremely variable, values is observed. Our study highlights the dramatic variability of soil carbon cycling processes and vegetation feedback on Socotra Island manifested in stalagmite DCF on both long-term trends and sub-centennial timescales, thus providing evidence for climate influence on stalagmite radiocarbon. This is of particular relevance for speleothem studies that aim to reconstruct past atmospheric 14C (e.g., for the purposes of 14C calibration), as these would rely on largely climate-independent soil carbon cycling above the cave.

Description: Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution in response to different climate scenarios and assess the mass loss that would contribute to future sea level rise. However, there is currently no consensus on estimates of the future mass balance of the ice sheet, primarily because of differences in the representation of physical processes, forcings employed and initial states of ice sheet models. This study presents results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climate model results. Simulations of the Antarctic ice sheet contribution to sea level rise in response to increased warming during this period varies between −7.8 and 30.0 cm of sea level equivalent (SLE) under Representative Concentration Pathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment with constant climate conditions and should therefore be added to the mass loss contribution under climate conditions similar to present-day conditions over the same period. The simulated evolution of the West Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica mass change varies between −6.1 and 8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighing the increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelf collapse, here assumed to be caused by large amounts of liquid water ponding at the surface of ice shelves, yields an additional simulated mass loss of 28 mm compared to simulations without ice shelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, the calibration of these melt rates based on oceanic conditions taken outside of ice shelf cavities and the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario based on two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared to simulations done under present-day conditions for the two CMIP5 forcings used and display limited mass gain in East Antarctica.

Description: To meet its rapidly growing electricity demand, Vietnam envisages ramping up its coal-fired generation capacity substantially. Realizing all coal-fired capacity additions that are planned globally would undermine international climate targets. This paper systematically analyzes the political economy shaping climate and energy policies in Vietnam, and finds that the country's coal developments are primarily motivated by political rather than economic considerations. Based on extensive data collected in semi-structured interviews, we identify the relevant actor groups and how their objectives influence energy policy formulation. This allows us to unravel the complex political channels that link Vietnam's move to coal to four overarching energy goals, which are, in turn, closely entangled with the Communist Party's strategy to legitimize its power: affordability, security of supply, promoting the domestic energy industry, and environmental sustainability. We show why Vietnam's tightly regulated electricity market, dominated by state-owned enterprises and vested interests, favors large-scale coal investments and weakens renewable energy regulations. While environmental and health concerns are becoming politically more relevant on the provincial level, silo mentalities within the administration and among international organizations result in weakly integrated environmental strategies. These in-depth insights from Vietnam have wider implications for understanding the adoption of coal in other country contexts.

Description: Importance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections.

Description: Coupling of the El Niño–Southern Oscillation (ENSO) and Indian monsoon (IM) is central to seasonal summer monsoon rainfall predictions over the Indian subcontinent, although a nonstationary relationship between the two nonlinear phenomena can limit seasonal predictability. Radiative effects of volcanic aerosols injected into the stratosphere during large volcanic eruptions (LVEs) tend to alter ENSO evolution; however, their impact on ENSO-IM coupling remains unclear. Here, we investigate how LVEs influence the nonlinear behavior of the ENSO and IM dynamical systems using historical data, 25 paleoclimate reconstructions, last-millennium climate simulations, large-ensemble targeted climate sensitivity experiments, and advanced analysis techniques. Our findings show that LVEs promote a significantly enhanced phase-synchronization of the ENSO and IM oscillations, due to an increase in the angular frequency of ENSO. The results also shed innovative insights into the physical mechanism underlying the LVE-induced enhancement of ENSO-IM coupling and strengthen the prospects for improved seasonal monsoon predictions.

Description: This paper introduces the Special Issue (SI) “How evaluation of hydrological models influences results of climate impact assessment.” The main objectives were as follows: (a) to test a comprehensive model calibration/validation procedure, consisting of five steps, for regional-scale hydrological models; (b) to evaluate performance of global-scale hydrological models; and (c) to reveal whether the calibration/validation methods and the model evaluation results influence climate impacts in terms of the magnitude of the change signal and the uncertainty range. Here, we shortly describe the river basins and large regions used as case studies; the hydrological models, data, and climate scenarios used in the studies; and the applied approaches for model evaluation and for analysis of projections for the future. After that, we summarize the main findings. The following general conclusions could be drawn. After successful comprehensive calibration and validation, the regional-scale models are more robust and their projections for the future differ from those of the model versions after the conventional calibration and validation. Therefore, climate impacts based on the former models are more trustworthy than those simulated by the latter models. Regarding the global-scale models, using only models with satisfactory or good performance on historical data and weighting them based on model evaluation results is a more reliable approach for impact assessment compared to the ensemble mean approach that is commonly used. The former method provides impact results with higher credibility and reduced spreads in comparison to the latter approach. The studies for this SI were performed in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).

Description: pycascades - a software package to simulate dynamics of tipping cascades on complex networks. This first release of pycascades goes along with the submission of the model description paper.

Description: Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

Description: In der Arktis hat der Juli Hitzerekorde, ausufernde Brände und massiven Eisschwund gebracht. Die Folgen kommen auch bei uns in Deutschland an.

Description: An accurate estimation of terrestrial water storage (TWS) is crucial for water resource management and drought monitoring. However, the uncertainties in model physics, surface parameters and meteorological data often limit the accuracy of land surface hydrological models in estimating TWS. In this study, a multi-model-based framework was developed to predict TWS in China by 2050 using a Bayesian model averaging (BMA) method and GRACE satellite observations. Compared to GRACE observations, our BMA-based TWS anomaly (TWSA) estimations reduce root mean square errors by 10–16% and increase correlation coefficients by 26–46% over semi-humid and semi-arid basins than simple arithmetical averaging for the validation period (2008–2016). At the same time, BMA shows decreasing root mean square differences (10–12%) over humid basins. The calibrated BMA weights were then applied to future projections of TWSA under two Representative Concentration Pathways (RCP): RCP 2.6 and RCP 6.0. The overall rate of TWSA for the future period (2021–2050) was detected with the same direction as that from past decades (2003–2016), but with larger decreasing values. Especially for the Haihe basin in North China, BMA-based TWSA would decrease faster by about 19% for RCP 2.6 and 26% for RCP 6.0. These results suggest a decreasing trend in future TWS over most of the basins in China due to combined effects of global warming and human activities, which suggests likely aggravated risk of water shortage and a growing need for adaptive water resources management.

Description: This repository contains code and data for the reproduction of analysis and figures from: Kotz, Wenz, Stechemesser, Kalkuhl and Levermann (2020).

Description: Understanding the complex behavior of the near-Earth electromagnetic environment is one of the main challenges of Space Weather studies. This includes both the correct characterization of the different physical mechanisms responsible for its configuration and dynamics as well as the efforts which are needed for a correct forecasting of several phenomena. By using a nonlinear multi-scale dynamical systems approach, we provide here new insights into the scale-to-scale dynamical behavior of both quiet and disturbed periods of geomagnetic activity. The results show that a scale-dependent dynamical transition occurs when moving from short to long timescales, i.e., from fast to slow dynamical processes, the latter being characterized by a more regular behavior, while more dynamical anomalies are found in the behavior of the fast component. This suggests that different physical processes are typical for both dynamical regimes: the fast component, being characterized by a more chaotic and less predictable behavior, can be related to the internal dynamical state of the near-Earth electromagnetic environment, while the slow component seems to be less chaotic and associated with the directly driven processes related to the interplanetary medium variability. Moreover, a clear difference has been found between quiet and disturbed periods, the former being more complex than the latter. These findings support the view that, for a correct forecasting in the framework of Space Weather studies, more attention needs to be devoted to the identification of proxies describing the internal dynamical state of the near-Earth electromagnetic environment.

Description: In the twenty-first century, modern bioenergy could become one of the largest sources of energy, partially replacing fossil fuels and contributing to climate change mitigation. Agricultural and forestry biomass residues form an inexpensive bioenergy feedstock with low greenhouse gas (GHG) emissions, if harvested sustainably. We analysed quantities of biomass residues supplied for energy and their sensitivities in harmonised bioenergy demand scenarios across eight integrated assessment models (IAMs) and compared them with literature-estimated residue availability. IAM results vary substantially, at both global and regional scales, but suggest that residues could meet 7–50% of bioenergy demand towards 2050, and 2–30% towards 2100, in a scenario with 300 EJ/year of exogenous bioenergy demand towards 2100. When considering mean literature-estimated availability, residues could provide around 55 EJ/year by 2050. Inter-model differences primarily arise from model structure, assumptions, and the representation of agriculture and forestry. Despite these differences, drivers of residues supplied and underlying cost dynamics are largely similar across models. Higher bioenergy demand or biomass prices increase the quantity of residues supplied for energy, though their effects level off as residues become depleted. GHG emission pricing and land protection can increase the costs of using land for lignocellulosic bioenergy crop cultivation, which increases residue use at the expense of lignocellulosic bioenergy crops. In most IAMs and scenarios, supplied residues in 2050 are within literature-estimated residue availability, but outliers and sustainability concerns warrant further exploration. We conclude that residues can cost-competitively play an important role in the twenty-first century bioenergy supply, though uncertainties remain concerning (regional) forestry and agricultural production and resulting residue supply potentials.

Description: This paper is concerned with the consensus issue for a class of multiagent systems with Markovian switching topology under aperiodic sampled data measurements. By constructing a novel piecewise stochastic Lyapunov-Krasovskii functional, some novel conditions with less conservative are established such that the consensus is achieved in the mean square sense. In contrast to some previous publications, the sample period is no longer fixed and the transition probability matrix of Markovian switching topology is uncertain. This issue which is of practical and theoretical significance is further investigated when the sampled data controller of each agent is suffered from distinct time-varying input delay. Quite different with the related studies, a maximally allowable input delay upper bound is replaced by the permissible input delay interval. Furthermore, the corresponding consensus is elegantly obtained in terms of linear matrix inequalities. Finally, the effectiveness and practicability of our consensus criteria are well illustrated by the numerical examples.

Description: Wheat production plays an important role in Morocco. Current wheat forecast systems use weather and vegetation data during the crop growing phase, thus limiting the earliest possible release date to early spring. However, Morocco's wheat production is mostly rainfed and thus strongly tied to fluctuations in rainfall, which in turn depend on slowly evolving climate dynamics. This offers a source of predictability at longer time scales. Using physically guided causal discovery algorithms, we extract climate precursors for wheat yield variability from gridded fields of geopotential height and sea surface temperatures which show potential for accurate yield forecasts already in December, with around 50% explained variance in an out‐of‐sample cross validation. The detected interactions are physically meaningful and consistent with documented ocean‐atmosphere feedbacks. Reliable yield forecasts at such long lead times could provide farmers and policy makers with necessary information for early action and strategic adaptation measurements to support food security.

Description: Understanding spatiotemporal patterns of climate extremes has gained considerable relevance in the context of ongoing climate change. With enhanced computational capacity, data driven methods such as functional climate networks have been proposed and have already contributed to significant advances in understanding and predicting extreme events, as well as identifying interrelations between the occurrences of various climatic phenomena. While the (in its basic setting) parameter free event synchronization (ES) method has been widely applied to construct functional climate networks from extreme event series, its original definition has been realized to exhibit problems in handling events occurring at subsequent time steps, which need to be accounted for. Along with the study of this conceptual limitation of the original ES approach, event coincidence analysis (ECA) has been suggested as an alternative approach that incorporates an additional parameter for selecting certain time scales of event synchrony. In this work, we compare selected features of functional climate network representations of South American heavy precipitation events obtained using ES and ECA without and with the correction for temporal event clustering. We find that both measures exhibit different types of biases, which have profound impacts on the resulting network structures. By combining the complementary information captured by ES and ECA, we revisit the spatiotemporal organization of extreme events during the South American Monsoon season. While the corrected version of ES captures multiple time scales of heavy rainfall cascades at once, ECA allows disentangling those scales and thereby tracing the spatiotemporal propagation more explicitly.

Description: Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources. View Full-Text

Description: The choice of the base period, intentionally chosen or not, as a reference for assessing future changes of any projected variable can play an important role for the resulting statement. In regional climate impact studies, well-established or arbitrarily chosen baselines are often used without being questioned. Here we investigated the effects of different baseline periods on the interpretation of discharge simulations from eight river basins in the period 1960-2099. The simulations were forced by four bias-adjusted and downscaled Global Climate Models under two radiative forcing scenarios (RCP 2.6 and RCP 8.5). To systematically evaluate how far the choice of different baselines impacts the simulation results, we developed a similarity index that compares two time series of projected changes. The results show that 25% of the analysed simulations are sensitive to the choice of the baseline period under RCP 2.6 and 32% under RCP 8.5. In extreme cases, change signals of two time series show opposite trends. This has serious consequences for key messages drawn from a basin-scale climate impact study. To address this problem, an algorithm was developed to identify flexible baseline periods for each simulation individually, which better represent the statistical properties of a given historical period.

Description: We present a novel data set of subnational economic output, Gross Regional Product (GRP), for more than 1,500 regions in 77 countries that allows us to empirically estimate historic climate impacts at different time scales. Employing annual panel models, long-difference regressions and cross-sectional regressions, we identify effects on productivity levels and productivity growth. We do not find evidence for permanent growth rate impacts but we find robust evidence that temperature affects productivity levels considerably. An increase in global mean surface temperature by about 3.5C until the end of the century would reduce global output by 7-14% in 2100, with even higher damages in tropical and poor regions. Updating the DICE damage function with our estimates suggests that the social cost of carbon from temperature-induced productivity losses is on the order of 73-142$/tCO2 in 2020, rising to 92-181$/tCO2 in 2030. These numbers exclude non-market damages and damages from extreme weather events or sea-level rise.

Description: Forest ecosystems, their products and services play an important role in achieving ambitious climate change mitigation objectives at the same time requiring profound adaptation to climate change. Forest management schemes to support climate action have to be developed within their regional context but also have to be aligned with national or EU-level climate, forest and sustainability policies.The conference on “Managing forests in the 21st century” is the final conference of the FORMASAM, REFORCEand FOREXCLIMresearch projects. The conference bringstogether scientific experts on forest management from all over Europe facing very specific management challenges. The aim isto discuss and improve the understanding therole of forests and forest management in the context of climate change. The conference addressesclimate change impacts, as well as needs for mitigation and adaptation especially with regard to the following scientific questions:1.What are the impacts of climate extremes and disturbances?2.What are the management challenges (and options) for resilient forests?3.What can we do to increase the contribution of forest management to climate change mitigation?

Description: Detrended fluctuation analysis (DFA) is widely used to characterize long-range power-law correlations in complex signals. However, it has restrictions when nonstationarity is not limited only to slow variations in the mean value. To improve the characterization of inhomogeneous datasets, we have proposed the extended DFA (EDFA), which is a modification of the conventional method that evaluates an additional scaling exponent to take into account the features of time-varying nonstationary behavior. Based on EDFA, here, we analyze rat electroencephalograms to identify specific changes in the slow-wave dynamics of brain electrical activity associated with two different conditions, such as the opening of the blood–brain barrier and sleep, which are both characterized by the activation of the brain drainage function. We show that these conditions cause a similar reduction in the scaling exponents of EDFA. Such a similarity may represent an informative marker of fluid homeostasis of the central nervous system. Natural systems often exhibit complex dynamics with long-range power-law correlations. To quantify this phenomenon, different variants of fluctuation analysis are used with detrended fluctuation analysis (DFA),1,2 representing a universal method that can be applied to both stationary and nonstationary time series. In many practical situations, however, nonstationarity cannot be eliminated by simply removing the slow variation in mean value. For such a case, we have recently proposed an extended method (EDFA), which estimates two scaling exponents: the first exponent is associated with the conventional DFA and the second exponent quantifies the impact of nonstationarity.3 Here, we use EDFA to identify informative markers of changes in electroencephalograms associated with two different factors, namely, open blood–brain barrier (BBB) and sleep, which are characterized by activated brain drainage function. By means of EDFA, we uncover that both factors, sound and sleep, cause similar changes in the low-frequency dynamics of electroencephalograms (EEG). These results offer a hint of the similarity of EEG dynamics related to distinct mechanisms of fluid drainage from the brain during sleep and after the BBB opening

Description: The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain–computer interface applications. Even though some well-known features of motor-related electroencephalograms have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here, we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of μ-rhythm in the sensorimotor cortex, i.e., tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of μ -band neuronal activity. This is due to the lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case, we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this, we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and, therefore, are able to classify the laterality of executed movements. The detection of the motor-related brain activity for noninvasive electroencephalogram (EEG)-based brain–computer interfaces (BCIs) is an actively discussed topic in many areas of research. This is of special interest in the context of neurorehabilitation and non-muscular control of remote devices using BCI-based techniques. Traditionally used methods for motor-related feature extraction, such as spatial filtering and time-frequency analysis, allow one to associate motor actions with event-related desynchronization (ERD) of μ -band oscillations (8–13 Hz) over the sensorimotor cortex. However, these features, i.e., location of brain activity sources, amplitudes of spectral components, etc., are of strong inter- and intrasubject variability. Moreover, inherent nonstationarity and a poor signal-to-noise ratio of EEG signals strongly complicate the detection and classification of motor-related patterns in single trials. To find new features of the motor-related brain activity, we explore EEG signals from the viewpoint of signal complexity. In particular, we put forward the hypothesis that μ-band ERD causes the reduction of random fluctuations of neuronal activity, resulting in a more regular behavior of EEG signals during motor task accomplishments. With this goal in mind, we apply recurrence quantification analysis (RQA), a nonlinear method that describes the recurrence structure of a system by several quantifiers, in order to examine its complexity and uncover hidden underlying phenomena. Our findings show that certain RQA measures, namely, determinism and recurrence time entropy, allow one to reveal new features associated with neuronal activity complexity reduction. These measures are not only sensitive to the transitions from background to motor-related brain activity but also very useful for distinguishing different types of motor actions (left/right limbs motion), which is valuable in the context of potential BCI applications

Description: Am Freitag starten Klimawandelleugner eine neue Offensive - sie schicken 16 unschuldig anmutende Fragen an Bundestagsabgeordnete. Bei genauem Blick entpuppt sich die Schrift als pure Propaganda.

Description: Global municipal waste production causes multiple environmental impacts, including greenhouse gas emissions, ocean plastic accumulation, and nitrogen pollution. However, estimates of both past and future development of waste and pollution are scarce. We apply compositional Bayesian regression to produce the first estimates of past and future (1965–2100) waste generation disaggregated by composition and treatment, along with resultant environmental impacts, for every country. We find that total wastes grow at declining speed with economic development, and that global waste generation has increased from 635 Mt in 1965 to 1999 Mt in 2015 and reaches 3539 Mt by 2050 (median values, middle-of-the-road scenario). From 2015 to 2050, the global share of organic waste declines from 47% to 39%, while all other waste type shares increase, especially paper. The share of waste treated in dumps declines from 28% to 18%, and more sustainable recycling, composting, and energy recovery treatments increase. Despite these increases, we estimate environmental loads to continue increasing in the future, although yearly plastic waste input into the oceans has reached a peak. Waste production does not appear to follow the environmental Kuznets curve, and current projections do not meet UN SDGs for waste reduction. Our study shows that a continuation of current trends and improvements is insufficient to reduce pressures on natural systems and achieve a circular economy. Relative to 2015, the amount of recycled waste would need to increase from 363 Mt to 740 Mt by 2030 to begin reducing unsustainable waste generation, compared to 519 Mt currently projected.

Description: Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

Description: The process-based model 4C (FORESEE) has been developed over the past twenty years. The objective of this paper is to give a comprehensive description of the main features of 4C and to present an evaluation of the model at four different forest sites across Europe. The evaluation was focused on growth parameters, carbon, water and heat fluxes. The main data source for the evaluation was the PROFOUND database. We applied different statistical metrics of evaluation and compared the inter-annual and inter-monthly variability of observed and simulated carbon and water fluxes. The ability to reproduce forest growth differs from site to site and is best for the pine stand site Peitz. The model's performance in simulating carbon and water fluxes was very satisfactory on daily and monthly time scales in contrast to the annual time scale. This underlines the conclusion that processes that are either not represented in dependence on on medium- to long-term dynamic influences such as allocation, or those that are not represented at all but may have a large impact at specific sites – such as the dynamics of non-structural carbohydrates (NSC) and ground vegetation growth – need to be elaborated for general forest growth investigations under climate change. On the other hand, 4C has shown a great potential for improvement since it emphasizes the representation of boundary conditions such as soil temperature at different depths. Therefore, more spatial differentiation of processes such as organ-specific respiration should easily be accomplished. Nonetheless, by using the PROFOUND database we were able to demonstrate the applicability and reliability of 4C.

Description: We propose an additional elaborate hydrological signature index to quantify similarity (and dissimilarity) between recurring flood dynamics and between observation and model simulation as implied by their phase space trajectories. These phase space trajectories are reconstructed from their corresponding hydrographs (i.e., event time series) using Taken’s time delay embedding method. This reconstructed phase space allows multi-dimensional relationship between observation points (i.e., at different time of the event) to be analyzed. Such approach considers the relationships of set of magnitude points in their unique time sequence that are relevant to the complex temporal cascading processes in flood. In a simpler terms, the new index considers the characteristics shape dynamics of a hydrograph and optionally the antecedent discharge conditions that may implicitly cascade to the subsequent rainfall-runoff event and cause an extreme or unusual hydrograph shape. This new similarity index can be used to comprehensively assess the recurrence of extreme event characteristics, change of flood dynamics, shift of seasonality, and as additional metric or objective function to evaluate and calibrate hydrological and hydraulics models.

Description: Global mitigation efforts remain insufficient to limit the global temperature increase to well below 2 °C. While a growing academic literature analyzes this problem, perceptions of which obstacles inhibit goal attainment and which responses might be most effective seem to differ widely. This makes prioritization and agreement on the way forward difficult. To inform prioritization in global climate policy and research agendas, we present quantitative data on how 917 experts from the IPCC and the UNFCCC perceive the importance of different obstacles and response options for achieving 2 °C. On average, respondents consider opposition from special interest groups the most important obstacle and technological R&D the most important response. Our survey also finds that the majority of experts perceives a wide range of issues as important, supporting an agenda that is inclusive in terms of coverage. Average importance ratings differ between experts from the Global North and South, suggesting that balanced representation in global fora and regionally differentiated agendas are important. In particular, opposition from special interest groups is a top priority among experts from North America, Europe and Oceania. Investigating the drivers of individual importance ratings, we find little difference between experts from the IPCC and the UNFCCC, while expert's perceptions correlate with their academic training and their national scientific, regulatory, and financial contexts.

Description: Fresh water—the bloodstream of the biosphere—is at the center of the planetary drama of the Anthropocene. Water fluxes and stores regulate the Earth's climate and are essential for thriving aquatic and terrestrial ecosystems, as well as water, food, and energy security. But the water cycle is also being modified by humans at an unprecedented scale and rate. A holistic understanding of freshwater's role for Earth system resilience and the detection and monitoring of anthropogenic water cycle modifications across scales is urgent, yet existing methods and frameworks are not well suited for this. In this paper we highlight four core Earth system functions of water (hydroclimatic regulation, hydroecological regulation, storage, and transport) and key related processes. Building on systems and resilience theory, we review the evidence of regional‐scale regime shifts and disruptions of the Earth system functions of water. We then propose a framework for detecting, monitoring, and establishing safe limits to water cycle modifications and identify four possible spatially explicit methods for their quantification. In sum, this paper presents an ambitious scientific and policy grand challenge that could substantially improve our understanding of the role of water in the Earth system and cross‐scale management of water cycle modifications that would be a complementary approach to existing water management tools.

Description: Mass loss from the Antarctic Ice Sheet constitutes the largest uncertainty in projections of future sea level rise. Ocean-driven melting underneath the floating ice shelves and subsequent acceleration of the inland ice streams is the major reason for currently observed mass loss from Antarctica and is expected to become more important in the future. Here we show that for projections of future mass loss from the Antarctic Ice Sheet, it is essential (1) to better constrain the sensitivity of sub-shelf melt rates to ocean warming and (2) to include the historic trajectory of the ice sheet. In particular, we find that while the ice sheet response in simulations using the Parallel Ice Sheet Model is comparable to the median response of models in three Antarctic Ice Sheet Intercomparison projects – initMIP, LARMIP-2 and ISMIP6 – conducted with a range of ice sheet models, the projected 21st century sea level contribution differs significantly depending on these two factors. For the highest emission scenario RCP8.5, this leads to projected ice loss ranging from 1.4 to 4.0 cm of sea level equivalent in the ISMIP6 simulations where the sub-shelf melt sensitivity is comparably low, opposed to a likely range of 9.2 to 35.9 cm using the exact same initial setup, but emulated from the LARMIP-2 experiments with a higher melt sensitivity based on oceanographic studies. Furthermore, using two initial states, one with and one without a previous historic simulation from 1850 to 2014, we show that while differences between the ice sheet configurations in 2015 are marginal, the historic simulation increases the susceptibility of the ice sheet to ocean warming, thereby increasing mass loss from 2015 to 2100 by about 50 %. Our results emphasize that the uncertainty that arises from the forcing is of the same order of magnitude as the ice dynamic response for future sea level projections.

Description: In this paper, the output feedback set stabilization problem for Boolean control networks (BCNs) is investigated with the help of the semi-tensor product (STP) tool. The concept of output feedback control invariant (OFCI) subset is introduced, and novel methods are developed to obtain the OFCI subsets. Based on the OFCI subsets, a technique, named spanning tree method, is further introduced to calculate all possible output feedback set stabilizers. An example concerning lac operon for the bacterium Escherichia coli is given to illustrate the effectiveness of the proposed method. This technique can also be used to solve the state feedback (set) stabilization problem for BCNs. Compared with the existing results, our method can dramatically reduce the computational cost when designing all possible state feedback stabilizers for BCNs.

Description: Understanding ice shelf–ocean interaction is fundamental to projecting the Antarctic ice sheet response to a warming climate. Numerical ice shelf–ocean models are a powerful tool for simulating this interaction, yet are limited by inherent model weaknesses and scarce observations, leading to parameterisations that are unverified and unvalidated below ice shelves. We explore how different models simulate ice shelf–ocean interaction using the 2nd Ice Shelf–Ocean Model Intercomparison Project (ISOMIP+) framework. Vertical discretisation and resolution of the ocean model are shown to have a significant effect on ice shelf basal melt rate, through differences in the distribution of meltwater fluxes and the calculation of thermal driving. Z-coordinate models, which generally have coarser vertical resolution in ice shelf cavities, may simulate higher melt rates compared to terrain-following coordinate models. This is due to the typically higher resolution of the ice–ocean boundary layer region in terrain following models, which allows better representation of a thin meltwater layer, increased stratification, and as a result, better insulation of the ice from water below. We show that a terrain-following model, a z-level coordinate model and a hybrid approach give similar results when the effective vertical resolution adjacent to the ice shelf base is similar, despite each model employing different paradigms for distributing meltwater fluxes and sampling tracers for melting. We provide a benchmark for thermodynamic ice shelf–ocean interaction with different model vertical coordinates and vertical resolutions, and suggest a framework for any future ice shelf–ocean thermodynamic parameterisations.

Description: Tipping elements in the climate system are large-scale subregions of the Earth that might possess threshold behavior under global warming with large potential impacts on human societies. Here, we study a subset of five tipping elements and their interactions in a conceptual and easily extendable framework: the Greenland Ice Sheets (GIS) and West Antarctic Ice Sheets, the Atlantic meridional overturning circulation (AMOC), the El–Niño Southern Oscillation and the Amazon rainforest. In this nonlinear and multistable system, we perform a basin stability analysis to detect its stable states and their associated Earth system resilience. By combining these two methodologies with a large-scale Monte Carlo approach, we are able to propagate the many uncertainties associated with the critical temperature thresholds and the interaction strengths of the tipping elements. Using this approach, we perform a system-wide and comprehensive robustness analysis with more than 3.5 billion ensemble members. Further, we investigate dynamic regimes where some of the states lose stability and oscillations appear using a newly developed basin bifurcation analysis methodology. Our results reveal that the state of four or five tipped elements has the largest basin volume for large levels of global warming beyond 4 °C above pre-industrial climate conditions, representing a highly undesired state where a majority of the tipping elements reside in the transitioned regime. For lower levels of warming, states including disintegrated ice sheets on west Antarctica and Greenland have higher basin volume than other state configurations. Therefore in our model, we find that the large ice sheets are of particular importance for Earth system resilience. We also detect the emergence of limit cycles for 0.6% of all ensemble members at rare parameter combinations. Such limit cycle oscillations mainly occur between the GIS and AMOC (86%), due to their negative feedback coupling. These limit cycles point to possibly dangerous internal modes of variability in the climate system that could have played a role in paleoclimatic dynamics such as those unfolding during the Pleistocene ice age cycles.

Description: We use Recurrence Quantification Analysis (RQA) to study features of Generalized Synchronization (GS) between El Niño-Southern Oscillation (ENSO) and monthly hydrological anomalies (HyAns) of rainfall and streamflows in Colombia. To that end, we check the sensitivity of the RQA concerning diverse HyAns estimation methods, which constitutes a fundamental procedure for any climatological analysis at inter-annual timescales. In general, the GS and its sensitivity to HyAns methods are quantified by means of time-lagged joint recurrence analysis. Then, we link the GS results with the dynamics of major physical mechanisms that modulate Colombia's hydroclimatology, including the Caribbean, the CHOCO and the Orinoco Low-Level Jets (LLJs), and the Cross-Equatorial Flow (CEF) over northwestern Amazonia (southern Colombia). Our findings show that RQA exhibits significant differences depending on the HyAns methods. GS results are similar for the HyAns methods with variable annual cycle but the time-lags seem to be sensitive. On the other hand, our results make evident that HyAns in the Pacific, Caribbean, and Andean regions of Colombia exhibit strong (weak) GS with the ENSO signal during La Niña (El Niño), when hydrological anomalies are positive (negative). Results from the GS analysis allow us to identify spatial patterns of non-linear dependence between ENSO and the Colombian's climatology. The mentioned moisture transport sources constitute the interdependence mechanism and contribute to explain hydrological anomalies in Colombia during the phases of ENSO. During La Niña (El Niño), GS is strong (weak) for the Caribbean and the CHOCO LLJs whereas GS is moderate (strong) for the Orinoco LLJ. Moreover, moisture advection by the Caribbean and CHOCO LLJs exhibit synchrony with HyAns at 0–2 (2–4) months-lags over north-western Colombia and the Orinoco LLJ moisture advection synchronizes with HyAns at similar month-lags over the Amazon region of Colombia. Furthermore, our results suggest a strong (weak) GS between negative (positive) Sea Surface Temperatures (SST) anomalies in the Eastern Pacific and rainfall anomalies in Colombia. In contrast, GS is strong (weak) for positive (negative) SST anomalies in the Central Pacific. Our GS results contribute to advance our understanding on the regional effects of both phases of ENSO in Colombia, whose socio-economical, environmental and ecological impacts cannot be overstated. This work provides a novel approach that reveals new insights into the impact of ENSO on northern South America.

Description: This software can be used to quantify emissions mitigation targets stated in the Nationally Determined Contributions (NDCs). The output includes national targets and emissions pathways and globally aggregated mitigated emissions pathways. Several quantification options are available, including, i.a., the five marker scenarios of the Shared Socioeconomic Pathways (SSPs) as baseline trajectories.

Description: Energy transitions cannot be fully grasped without appreciating their spatial implications. This paper takes up the idea of conceptualizing the socio-spatial dimensions of energy transitions and examines the respective value of the Territory, Place, Scale, and Network (TPSN) framework. The fundamental contribution of this framework is to move the focus of the debate away from whether one ontology of the socio-spatial is ‘better’ than another. By applying the TPSN framework to emblematic cases of regional energy spaces in Germany, we realized that the dynamics of different fields of action within an energy transition are characterized in each case by a specific pattern of the four socio-spatial dimensions and related strategies. The paper concludes with a discussion of the benefits and the shortcomings of the framework as it relates to understanding energy transitions. The fundamental role of place-making at a nexus with territorializations, as well as the additional importance of networking and rescaling strategies, are to be understood with the additional factors of the role of governance spaces, the upscaling of local experiments, powerful space-related discourses, and the socio-materiality of spaces.

Description: Many high-dimensional complex systems exhibit an enormously complex landscape of possible asymptotic states. Here, we present a numerical approach geared towards analyzing such systems. It is situated between the classical analysis with macroscopic order parameters and a more thorough, detailed bifurcation analysis. With our machine learning method, based on random sampling and clustering methods, we are able to characterize the different asymptotic states or classes thereof and even their basins of attraction. In order to do this, suitable, easy to compute, statistics of trajectories with randomly generated initial conditions and parameters are clustered by an algorithm such as DBSCAN. Due to its modular and flexible nature, our method has a wide range of possible applications in many disciplines. While typical applications are oscillator networks, it is not limited only to ordinary differential equation systems, every complex system yielding trajectories, such as maps or agent-based models, can be analyzed, as we show by applying it the Dodds–Watts model, a generalized SIRS-model, modeling social and biological contagion. A second order Kuramoto model, used, e.g. to investigate power grid dynamics, and a Stuart–Landau oscillator network, each exhibiting a complex multistable regime, are shown as well. The method is available to use as a package for the Julia language.

Description: This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data.

Description: Evidence shows that soil moisture (SM) anomalies (deficits or excesses) are the key factor affecting crop yield in rain-fed agriculture. Over last decades, Poland has faced several major droughts and at least one major soil moisture excess event leading to severe crop losses. This study aims to simulate the multi-annual variability of SM anomalies in Poland, using a process-based SWAT model and to assess the effect of climate change on future extreme SM conditions, potentially affecting crop yields in Poland. A crop-specific indicator based on simulated daily soil moisture content for the critical development stages of investigated crops (winter cereals, spring cereals, potato and maize) was designed, evaluated for past conditions against empirical crop-weather indices (CWIs), and applied for studying future climate conditions. The study used an ensemble of nine bias-corrected EURO-CORDEX projections for two future horizons: 2021–2050 and 2071–2100 under two Representative Concentration Pathways: RCP4.5 and 8.5. Historical simulation results showed that SWAT was capable of capturing major SM deficit and excess episodes for different crops in Poland. For spring cereals, potato and maize, despite a large model spread, projections generally showed increase of severity of soil moisture deficits, as well as of total area affected by them. Ensemble median fraction of land with extreme soil moisture deficits, occupied by each of these crops, is projected to at least double in size. The signals of change in soil moisture excesses for potato and maize were more dependent on selection of RCP and future horizon.

Description: Process‐based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach. However, accurate simulations of nitrous oxide (N2O) fluxes remain challenging. Models are limited by our understanding of soil‐plant‐microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N2O emissions under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multimodel evaluation with three commonly used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N2O fluxes on annual timescales, while APSIM was most accurate for daily N2O fluxes. The multimodel ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the Intergovernmental Panel on Climate Change (IPCC)‐derived method for the Swiss agricultural GHG inventory (IPCC‐Swiss), but individual models were not systematically more accurate than IPCC‐Swiss. The model ensemble overestimated the N2O mitigation effect of the clover‐based treatment (measured: 39–45%; ensemble: 52–57%) but was more accurate than IPCC‐Swiss (IPCC‐Swiss: 72–81%). These results suggest that multimodel ensembles are valuable for estimating the impact of climate and management on N2O emissions.

Description: Vor wenigen Tagen traten die USA aus dem Pariser Klimaabkommen aus – das könnte jetzt revidiert werden. Was Umweltökonom Ottmar Edenhofer sonst noch vom neuen Präsidenten erwartet.

Description: We present an analytically tractable scheme to solve the mean transition path shape and mean transition path time of one-dimensional stochastic systems driven by Poisson white noise. We obtain the Fokker-Planck operator satisfied by the mean transition path shape. Based on the non-Gaussian property of Poisson white noise, a perturbation technique is introduced to solve the associated Fokker-Planck equation. Moreover, the mean transition path time is derived from the mean transition path shape. We illustrate our approximative theoretical approach with the three paradigmatic potential functions: linear, harmonic ramp, and inverted parabolic potential. Finally, the Forward Fluxing Sampling scheme is applied to numerically verify our approximate theoretical results. We quantify how the Poisson white noise parameters and the potential function affect the symmetry of the mean transition path shape and the mean transition path time.

Description: The frequency constitutes a key state variable of electrical power grids. However, as the frequency is subject to several sources of fluctuations, ranging from renewable volatility to demand fluctuations and dispatch, it is strongly dynamic. Yet, the statistical and stochastic properties of the frequency fluctuation dynamics are far from fully understood. Here we analyze properties of power-grid frequency trajectories recorded from different synchronous regions. We highlight the non-Gaussian and still approximately Markovian nature of the frequency statistics. Furthermore, we find that the frequency displays significant fluctuations exactly at the time intervals of regulation and trading, confirming the need of having a regulatory and market design that respects the technical and dynamical constraints in future highly renewable power grids. Finally, employing a recently proposed synthetic model for the frequency dynamics, we combine our statistical and stochastic analysis and analyze in how far dynamically modeled frequency properties match the ones of real trajectories.

Description: Ärzte sind zentral für zukunftsverträgliches Handeln. Das muss uns für Forschung und Lehre bewusst sein.

Description: Der Meeresspiegel steigt. Und nicht nur das: Er steigt immer schneller. Wie stark der Klimawandel diesen Effekt treibt, ist inzwischen weitgehend entschlüsselt – auch dank moderner Satellitentechnik.