ALBERT

Description: This article investigates the event-triggered adaptive containment control problem for a class of stochastic nonlinear multiagent systems with unmeasurable states. A stochastic system with unknown heterogeneous dynamics is established to describe the agents in a random vibration environment. Besides, the uncertain nonlinear dynamics are approximated by radial basis function neural networks (NNs), and the unmeasured states are estimated by constructing the NN-based observer. In addition, the switching-threshold-based event-triggered control method is adopted with the hope of reducing communication consumption and balancing system performance and network constraints. Moreover, we develop the novel distributed containment controller by utilizing the adaptive backstepping control strategy and the dynamic surface control (DSC) approach such that the output of each follower converges to the convex hull spanned by multiple leaders, and all signals of the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in mean square. Finally, we verify the efficiency of the proposed controller by the simulation examples.

Description: This paper presents a study on the predefined-time (PdT) and practical PdT synchronization of competitive neural networks (CNN) in the presence of different time scales and external disturbances. Two types of external disturbances, which satisfy Lipschitz or bounded conditions, are investigated respectively. The new PdT and practical PdT stability theorems are derived in singularly perturbed systems, where the final residual set is given in detail. By employing the newly derived stability theorems, novel autonomous controllers are designed without relying on a continuous linear term and time scale parameters, while enabling PdT or practical PdT synchronization for drive-response CNNs. Additionally, upper bounds for the settling time are estimated, allowing for adjusting the predefined synchronization times regardless of the initial conditions. Finally, numerical simulations are conducted to demonstrate the effectiveness of the main results.

Description: The evolution of the Antarctic Ice Sheet is of vital importance given the coastal and societal implications of ice loss, with a potential to raise sea level by up to 58 m if melted entirely. However, future ice-sheet trajectories remain highly uncertain. One of the main sources of uncertainty is related to nonlinear processes and feedbacks of the ice sheet with the Earth System on different timescales. Due to these feedbacks and the ice-sheet inertia, ice loss may already be triggered in the next decades and then unfolds delayed on multi-centennial to millennial timescales. This committed Antarctic sea-level contribution is not reflected in typical sea-level projections based on mass balance changes of Antarctica, which often cover decadal-to-centennial timescales. Here, using two ice-sheet models, we systematically assess the multi-millennial sea-level commitment from Antarctica in response to warming projected over the next centuries under low- and high-emission pathways. This allows bringing together the time horizon of stakeholder planning with the much longer response times of the Antarctic Ice Sheet. Our results show that warming levels representative of the lower-emission pathway SSP1-2.6 may already result in an Antarctic mass loss of up to 6 m sea-level equivalent on multi-millennial timescales. This committed mass loss is due to a strong grounding-line retreat in the Amundsen Sea Embayment as well as a potential drainage from the Ross Ice Shelf catchment and onset of ice loss in Wilkes subglacial basin. Beyond warming levels reached by the end of this century under the higher-emission trajectory SSP5-8.5, a collapse of the West Antarctic Ice Sheet is triggered in the entire ensemble of simulations from both ice-sheet models. Under enhanced warming, next to the marine parts, we also find a substantial decline in ice volume of regions grounded above sea level in East Antarctica. Over the next millennia, this gives rise to a sea-level increase of up to 40 m in our experiments, stressing the importance of including the committed Antarctic sea-level contribution in future projections.

Description: Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicators will only produce reliable results if the noise driving the system is white and stationary. In the more realistic case of time-correlated red noise, increasing (decreasing) the correlation of the noise will lead to spurious (masked) alarms for both variance and AC(1). Here, we propose two new methods that can discriminate true CSD from possible changes in the driving noise characteristics. We focus on estimating changes in the linear restoring rate based on Langevin-type dynamics driven by either white or red noise. We assess the capacity of our new estimators to anticipate critical transitions and show that they perform significantly better than other existing methods both for continuous-time and discrete-time models. In addition to conceptual models, we apply our methods to climate model simulations of the termination of the African Humid Period. The estimations rule out spurious signals stemming from nonstationary noise characteristics and reveal a destabilization of the African climate system as the dynamical mechanism underlying this archetype of abrupt climate change in the past.

Description: We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey–Glass or Stuart–Landau-like systems, but also to reservoirs whose dynamical model is not available.

Description: Central Asia (CA) is among the world's most vulnerable regions to climate change. Increasing anthropogenic greenhouse gas concentrations (GHGs) are the primary forcing of the current and future climate system for the time scale of a century. By analysing observation datasets, we show that a warming of 1.2°C led to a decrease of 20% in snow-depth CA during the last 70 years, especially over the mountains. In recent decades, longer summer times and fewer icing days (more than 20 days·year−1) have exposed unprecedented shock to CA's climate system's components. Furthermore, we analyse 442 model simulations from Coupled Model Inter-comparison Project Phase 5 and 6 (CMIP5, CMIP6) and show that CMIP6 simulations are generally warmer and wetter than the CMIP5 ones in CA. For instance, under the highest emission scenarios (RCP8.5 and SSP5-8.5), CMIP6 projects a 6.1°C increase, while CMIP5 projects a 5.3°C increase, suggesting CMIP6 anticipates greater warming with high emissions. In contrast to CMIP6, the CMIP5 precipitation trends suggest a potential nonlinear relationship between increased greenhouse gas emissions and changes in precipitation, though the impact is much less pronounced than the temperature changes. Our analysis shows that CMIP6 models are more sensitive to temperature rise than CMIP5 ones. Both simulation sets' ensemble means capture well the observed warming trend. The imposed snow-melting leads to an increase in the run-off in the vicinity of glaciers. Such climatic shifts lead to more flooding events in CA. Given the projected warming range of 2–6°C in CA at the end of the century in various scenarios and models, such warming trends might be catastrophic in this region. The seasonal cycle of the temperature change indicates an extension of the glacier's melting period under future scenarios with fossil-fueled development. The models' uncertainty increases for the far-future time-slice, and warming larger than 4°C in CA is very likely among all the models and during all the seasons if no sustainable action is taken. This study also incorporates a detailed Köppen climate classification analysis, revealing significant shifts towards warmer climate categories in Central Asia, which may have profound implications for regional hydrological cycles and water resource management, particularly in the Amu Darya and Syr Darya river basins under warmer scenario by the end of the century. The Tundra and ice cap climate categories will lose more than 60% of their coverage at the end of the century compared to the historical period in the Amu Darya and Syr Darya river basins.

Description: Media inform the public, thereby influencing societal debates and political decisions. Despite climate change’s importance, drivers of media attention to climate change remain differently understood. Here we assess how different sociopolitical and extreme weather events affect climate change media coverage, both immediately and in the weeks following the event. To this end, we construct a data set of over 90,000 climate change articles published in nine major German newspapers over the past three decades and apply fixed effects panel regressions to control for confounders. We find that United Nations Climate Change Conferences affect coverage most strongly and most persistently. Climate protests incite climate coverage that extends well beyond the reporting on the event itself, whereas many articles on weather extremes do not mention climate change. The influence of all events has risen over time, increasing the media prominence of climate change.

Description: Understanding the ongoing investments in coal-fired power plants requires an analysis of the political economy. Here, we conduct a computational analysis of 212 interviews from 12 countries on the political economy of coal using topic modelling (TM). Our study highlights relevant topics by actor group and country. While most topics are similarly distributed across all actor groups, we find distinct clusters of countries in which similar topics play important roles. For example, in Indonesia and India, sustaining low electricity tariffs is brought forward as a reason to invest in coal, whereas in South Africa and Kenya the civil society is considered instrumental in the choice of coal or alternatives. To validate our findings, we compare them to outcomes of qualitative case studies and to papers grouping countries based on quantifiable factors. As this study is among the first to apply TM to interview data, we thereby highlight strengths and challenges for such application and the interpretability of results. We argue that topic models are effective supplements to qualitative case studies, particularly when analysing large amounts of text.

Description: The twenty-eighth Conference of the Parties (COP28) to the United Nations Framework Convention on Climate Change (UNFCCC) in Dubai among many other items concluded the first Global Stocktake (GST) under the Paris Agreement. This article discusses the conference's outcomes in the areas of mitigation, loss and damage, adaptation, climate finance, and cooperation under Article 6 of the Paris Agreement. The conference arguably made history by for the first time ever recognising the need to "transition away" from fossil fuels, adopting specific targets for the scale-up of renewable energy and energy efficiency, and by operationalising a fund to support developing countries in dealing with loss and damage caused by climate change. However, the legal language in the call for an energy transition is relatively non-committal and the conference failed to underpin the new global objectives with adequate resources. Actual implementation of the Dubai outcomes will therefore to a large extent depend on whether COP29 in Baku, Azerbaijan - already billed as "finance COP" - will be able to cut the Gordian knot of finance.

Description: North Rhine-Westphalia (NRW) is the industrial center of Germany and one of the most important industrial locations in Europe. It is a key location for the energy-intensive basic materials industry like the production of steel and non-ferrous metals, (petro)chemicals, cement and lime, bricks, glass and ceramics, and paper. Around 20 % of NRW's total greenhouse emissions derive from industrial processes. By 2045, industry must achieve climate-neutrality, which requires a massive transformation effort. Technologically, this needs large-scale utilization of green hydrogen, carbon management, consequent circular economy, and climate-neutral production of process heat. Furthermore, various adjustments to the policy framework are essential.