Great ape abundance and per capita carbon storage in their habitats.

The ecological importance of great apes is widely recognised, yet few studies have highlighted the role of protecting great apes' habitats in mitigating climate change, particularly through carbon sequestration. This study used GIS tools to extract data from various sources, including the International Union for Conservation of Nature database, to examine carbon quantity and great ape abundance in African great ape habitats. Subsequently, we employed a generalised linear model to assess the relationship between locally measured great ape populations abundance and carbon storage across areas with different levels of protection.

Data-sampled time-varying formation for singular multi-agent systems with multiple leaders.

The time-varying formation problem of singular multi-agent systems under sampled data with multiple leaders is investigated in this paper. Firstly, a data-sampled time-varying formation control protocol is proposed in the current study where the communication among followers merely occurred at sampling instants, which can save the controller communication energy significantly. Secondly, necessary and sufficient conditions for the feasibility of the formation function are provided.

Observed carbon decoupling of subnational production insufficient for net-zero goal by 2050.

Historically, economic growth has been closely coupled to carbon emissions responsible for climate change, but to stabilize global mean temperature, net-zero carbon emissions are necessary. Some economies have begun to reduce emissions while continuing to grow, but this decoupling is not fast enough to achieve global climate targets. Subnational climate actions seem to be crucial for the achievement of these targets.

Recent achievements in nonlinear dynamics, synchronization, and networks.

This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior in dynamical networks. It targets current progress on issues such as time series analysis and data-driven modeling from real data such as climate, brain, and social dynamics. Predicting and detecting early warning signals of extreme climate conditions, epileptic seizures, or other catastrophic conditions are the primary tasks from real or experimental data.

Impacts of land-use and land-cover changes on temperature-related mortality.

Background: Land-use and land-cover change (LULCC) can substantially affect climate through biogeochemical and biogeophysical effects. Here, we examine the future temperature-mortality impact for two contrasting LULCC scenarios in a background climate of low greenhouse gas concentrations. The first LULCC scenario implies a globally sustainable land use and socioeconomic development (sustainability).

Local hydroclimate alters interpretation of speleothem δO records.

Oxygen isotopes (δO) are the most commonly utilized speleothem proxy and have provided many foundational records of paleoclimate. Thus, understanding processes affecting speleothem δO is crucial. Yet, prior calcite precipitation (PCP), a process driven by local hydrology, is a widely ignored control of speleothem δO.

Exploring the origins of switching dynamics in a multifunctional reservoir computer.

The concept of multifunctionality has enabled reservoir computers (RCs), a type of dynamical system that is typically realized as an artificial neural network, to reconstruct multiple attractors simultaneously using the same set of trained weights. However, there are many additional phenomena that arise when training a RC to reconstruct more than one attractor. Previous studies have found that in certain cases, if the RC fails to reconstruct a coexistence of attractors, then it exhibits a form of metastability, whereby, without any external input, the state of the RC switches between different modes of behavior that resemble the properties of the attractors it failed to reconstruct.

The key role of sufficiency for low demand-based carbon neutrality and energy security across Europe.

A detailed assessment of a low energy demand, 1.5 C compatible pathway is provided for Europe from a bottom-up, country scale modelling perspective. The level of detail enables a clear representation of the potential of sufficiency measures.

Community structure of tropics emerging from spatio-temporal variations in the Intertropical Convergence Zone dynamics.

The Intertropical Convergence Zone (ITCZ) is a narrow tropical belt of deep convective clouds, intense precipitation, and monsoon circulations encircling the Earth. Complex interactions between the ITCZ and local geophysical dynamics result in high climate variability, making weather forecasting and prediction of extreme rainfall or drought events challenging. We unravel the complex spatio-temporal dynamics of the ITCZ and the resulting teleconnection patterns via a novel tropical climate classification achieved using complex network analysis and community detection.

Shrimp-shaped structure and period-bubbling route to chaos in a one-dimensional economic model.

A beautiful feature of nature is its complexity. The chaos theory has proved useful in a variety of fields, including physics, chemistry, biology, and economics. In the present article, we explore the complex dynamics of a rather simple one-dimensional economic model in a parameter plane.

Technology availability, sector policies and behavioral change are complementary strategies for achieving net-zero emissions.

In this study, we analyze the effects of technology availability, political coordination, and behavioral change on transformation pathways toward net-zero greenhouse gas emissions in the European Union by 2050. We implemented an iterative stakeholder dialogue to co-design the scenarios that were calculated using a global multi-regional energy-economy-land-climate model. We find that in scenarios without behavioral change and with restriction of technologies, the target of greenhouse gas neutrality in the European Union cannot be reached.

Commentary: A road map for future data-driven urban planning and environmental health research.

Recent advances in data science and urban environmental health research utilise large-scale databases (100s-1000s of cities) to explore the complex interplay of urban characteristics such as city form and size, climate, mobility, exposure, and environmental health impacts. Cities are still hotspots of air pollution and noise, suffer urban heat island effects and lack of green space, which leads to disease and mortality burdens preventable with better knowledge. Better understanding through harmonising and analysing data in large numbers of cities is essential to identifying the most effective means of disease prevention and understanding context dependencies important for policy.

Urban agriculture matters for sustainable development.

Urban agriculture can contribute to sustainable development. However, a holistic investigation is lacking to comprehend its positive and negative impacts on the Sustainable Development Goals (SDGs). Our systematic analysis of around 1,450 relevant publications on urban agriculture, screened from 76,000 records, fills this gap.

Involution symmetry quantification using recurrences.

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot.

Livestock sector can threaten planetary boundaries without regionally differentiated strategies.

The livestock sector represents major challenges to safeguarding environmental integrity. This study comprehensively analyzes ten environmental footprints of the livestock sector from 1995 to 2022, with projections until 2030, and juxtaposes them with the planetary boundaries. We quantify greenhouse gas emissions, land use, water use, particulate matter formation, and biochemical flows associated with the livestock sector.

Sketching the spatial disparities in heatwave trends by changing atmospheric teleconnections in the Northern Hemisphere.

Pronounced spatial disparities in heatwave trends are bound up with a diversity of atmospheric signals with complex variations, including different phases and wavenumbers. However, assessing their relationships quantitatively remains a challenging problem. Here, we use a network-searching approach to identify the strengths of heatwave-related atmospheric teleconnections (AT) with ERA5 reanalysis data.