Emergent constraints on equilibrium climate sensitivity (ECS) have generally got statistically weaker in the latest set of state-of-the-art climate models (CMIP6) compared to past sets (CMIP5). We look at why this weakening happened for one particular study (Cox et al, 2018) and attribute it to an assumption made in the theory that when corrected for restores there is a stronger relationship between predictor and ECS.

Fast, self-propelling positive synergies between improvements in social and biophysical systems' conditions are necessary to ensure a safe and just world for possibly 10 billion people by 2050. A regenerative view of human–environmental interactions in science, policy, education, and across many individual and organisational practices can contribute to achieving such a future – a global net-positive tipping point for people and the planet.

Some components of the Earth system may irreversibly collapse under global warming. Among them, the Atlantic Meridional Overturning Circulation (AMOC), the Greenland Ice Sheet, and West Antarctica Ice Sheet are of utmost importance for maintaining the present-day climate. In a simplified model, we show that both the rate of ice melting and the natural variability linked to freshwater fluxes over the Atlantic Ocean drastically affect how an ice sheet collapse impacts the AMOC stability.

Phytoplankton are tiny oceanic algae able to absorb the light penetrating the ocean. The light absorbed by these organisms is re-emitted as heat in the surrounding environment, a process commonly called phytoplankton light absorption (PLA). As a consequence, PLA increases the oceanic temperature. We studied this mechanism with a climate model under different climate scenarios. We show that phytoplankton light absorption is reduced under strong warming scenarios, limiting oceanic warming.

This research focuses on understanding different atmospheric patterns like blocking, zonal, and transition regimes and analyzing their predictability. We used an idealized land–atmosphere coupled model to simulate Earth's atmosphere. Then we identified these blocking, zonal, and transition regimes using Gaussian mixture clustering and studied their predictability using Lyapunov exponents.

We investigate whether the Amazon rainforest has lost substantial resilience since 1990. This assertion is based on trends in the observational record of vegetation density. We calculate the same metrics in a large number of climate model simulations and find that several models behave indistinguishably from the observations, suggesting that the observed trend could be caused by internal variability and that the cause of the ongoing rapid loss of Amazon rainforest is not mainly global warming.

Scientists exploring the histories of planets and life are uniquely positioned to communicate a perspective that is fundamental to our survival: humanity is wholly embedded in Earth and its biosphere. There is no escaping our planet and its history. Only policies that build on this perspective will contribute to a flourishing future for humanity. We offer a few brief glimpses of this cosmic perspective and call on our colleagues to acknowledge the powerful stories emanating from their work.

We provide an overview of the state and future of Earth system science in Svalbard as a synthesis of the recommendations made by the scientific community active in the archipelago. This work helped identify foci for developments of the observing system and a path forward to reach the full interdisciplinarity needed to operate at Earth system science scale. Better understanding of the processes in Svalbard will benefit both process-level understanding and Earth system models.

Mathematical models that include interactions between humans and the environment can provide valuable information to further our understanding of tipping points. Many social processes such as social norms and rates of social change can affect these tipping points in ways that are often specific to the system being modeled. Higher complexity of social structure can increase the likelihood of these transitions. We discuss how data are used to predict tipping events across many coupled systems.

The calibration of Earth system model parameters is a high-dimensionality problem subject to data, time, and computational constraints. In this study, we propose a practical solution for finding diverse near-optimal solutions. We argue that the effective degrees of freedom in the model performance response to parameter input is relatively small. Comparably performing parameter configurations exist and showcase different trade-offs in model errors, providing insights for model development.

It was recently suggested that global warming can be explained by the non-anthropogenic factor of seismic activity. If that is the case, it would have profound implications. We have assessed the validity of the claim by using a statistical technique that evaluates the existence of causal connections between variables, finding no evidence for any causal relationship between seismic activity and global warming.

The response of vegetation productivity to rainfall is a crucial process linking the water and carbon cycles and influencing the evolution of the climate system. However, there are many uncertainties in its representation in Earth system models. We show that the vegetation productivity responses to short-term rainfall events are very different between models due to their differing sensitivities to water availability. We also evaluate the models against a range of observational products.

We might be able to constrain uncertainty in future climate projections by investigating variations in the climate of the past. In this study, we investigate the interactions of climate variability between the tropical Pacific (El Niño) and the North Pacific in a warm past climate – the mid-Pliocene, a period roughly 3 million years ago. Using model simulations, we find that, although the variability in El Niño was reduced, the variability in the North Pacific atmosphere was not.

Climate change mitigation strategies developed with socioeconomic models rely on the widespread (re)planting of trees to limit global warming below 2°. However, most of these models neglect climate-driven shifts in forest damage like fires. By assessing existing mitigation scenarios, we show the exposure of projected forestation areas to fire-promoting weather conditions. Our study highlights the problem of ignoring climate-driven shifts in forest damage and ways to address it.

Land surface processes are crucial for the exchange of carbon, nitrogen, and energy in the Earth system. Using meteorological and land use data, we found that higher resolution improved not only the model representation of snow cover but also plant productivity and that water returned to the atmosphere. Only by combining high-resolution models with high-quality input data can we accurately represent complex spatially heterogeneous processes and improve our understanding of the Earth system.

Tipping points are abrupt, rapid, and sometimes irreversible changes, and numerous approaches have been proposed to detect them in advance. Such approaches have been termed early warning signals and represent a set of methods for identifying changes in the underlying behaviour of a system across time or space that might indicate an approaching tipping point. Here, we review the literature to explore where, how, and which early warnings have been used in real-world case studies so far.

A fifth of the world's population lives in eastern China, whose climate is dominated by the East Asian Summer Monsoon (EASM). Therefore, it is important to know how the EASM will change under global warming. Here, we use the data of 34 climate models of the latest generation to understand how the EASM will change throughout the 21st century. The models project that the EASM will intensify and that variability between years will increase associated with an increase in extremely wet seasons.

While the most recent climate projections agree that the Arctic is warming, differences remain in how much and in other climate variables such as precipitation. This presents a challenge for stakeholders who need to develop mitigation and adaptation strategies. We tackle this problem by using the storyline approach to generate four plausible and actionable realisations of end-of-century climate change for the Arctic, spanning its most likely range of variability.