This review assesses the interaction of solar radiation modification (SRM), a technology to reduce the impacts of climate change by reflecting sunlight and earth system tipping elements. We find that SRM at least partially reduces the risk of hitting most (9 out of 15) of the tipping points we studied relative to the same emission pathway and did not overall worsen the risk for any. Uncertainties for all tipping elements studied were high, so we also lay out suggestions for future research.

The technosphere – including buildings, infrastructure, and all other non-living human creations – is a major part of our planet, but it is not often considered as an integrated part of Earth system processes. Here we propose a refined definition of the technosphere, intended to help with integration. We also characterize the functional end uses, map the global distribution, and discuss the catalytic properties that underlie the exponential growth of the trillion tonne technosphere.

Climate models can be used to skilfully predict decadal changes in North Atlantic ocean heat content. However, significant regional differences among these models reveal large uncertainties in the influence of external forcings. This study examines eight climate models to understand the differences in their predictive capacity for the North Atlantic, investigating the importance of external forcings and key model characteristics such as ocean stratification and the local atmospheric forcing.

Spatially compounding wind and precipitation (CWP) extremes can lead to severe impacts on society. We find that concurrent climate variability modes favor the occurrence of such wintertime spatially compounding events in the Northern Hemisphere and can even amplify the number of regions and population exposed. Our analysis highlights the importance of considering the interplay between variability modes to improve risk management of such spatially compounding events.

This study presents the first systematic analysis of the global catastrophic risk and existential risk literature, examining 3437 documents from OpenAlex. Using bibliographic coupling, we identify and describe 10 research clusters aligned with major risks. The field shows geographic concentration in the US/UK, gender imbalance, and a small number of prolific authors. We recommend improving diversity, fostering cross-cluster collaboration, and building connections with adjacent disciplines.

We introduce a novel approach to compare Earth system model output using a causality-based approach. The analysis of interactions between atmospheric, oceanic and biogeochemical variables in the North Atlantic subpolar gyre highlights the dynamics of each model. This method reveals potential underlying causes of model differences, offering a tool for enhanced model evaluation and improved understanding of complex Earth system dynamics under past and future climates.

Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere and 4–25 km ocean resolution, we present new projections of regional climate change, modes of climate variability, and extreme events. The 10-year-long high-resolution simulations for the 2000s, 2030s, 2060s, and 2090s were initialized from a coarser-resolution transient run (31 km atmosphere) which follows the SSP5-8.5 greenhouse gas emission scenario from 1950–2100 CE.

This study evaluated the performance of CMIP6 (Coupled Model Intercomparison Project Phase 6) climate models in simulating East Asian summer precipitation and projected its future changes using regional inter-monthly pattern metrics. CMIP6 models better captured observations with reduced biases compared to CMIP5 models. Future projections indicate an overall intensified monsoon band due to the increased moisture availability. The relative importance of atmospheric circulation and moisture change is identified for inter-model and scenario uncertainty, respectively.

For tipping points at low levels of global warming, overshoots of the threshold are becoming increasingly likely. Importantly, for some systems, tipping can still be avoided provided the forcing is reversed sufficiently quickly. Conditions for a mitigation window, which would avoid tipping, depend on system uncertainties. We highlight the need to account for uncertainty in the threshold location and other system features when designing climate mitigation strategies that avoid tipping.

By compositing trends in multiple climate variables, this study presents emerging regimes that are relevant for solar energy applications. It is shown that the favourable conditions for exploiting solar energy are emerging during spring and early summer. The study also underscores the increasingly important role of clouds in regulating surface solar radiation as the aerosol concentrations are decreasing over Europe and the societal value of satellite-based climate monitoring.

We used field empirical data worldwide to calculate plant nitrogen uptake (Nup) and nitrogen use efficiency (NUE) in woodlands and grasslands to determine their drivers which can be used as empirical validation for models. Even though some regions of the world have decreased their N deposition, N deposition is still the most important driver explaining plant nitrogen uptake, aside from climatic variables. NUE is mainly driven by soil factors.

To study tipping cascades in the climate–conflict nexus, a bi-stable tipping model analyses transitions between conflict and cooperation. The Lake Chad case demonstrates climate change as a risk multiplier combined with poor governance, lowering resilience and barriers of communities to conflict. Adaptive and anticipative governance can prevent tipping to violent conflict and induce positive tipping towards cooperation through civil conflict transformation and environmental peacebuilding.

The Atlantic Meridional Overturning Circulation (AMOC) is a tipping element in the Earth system that affects the global climate. We often use models to understand how the AMOC tips. However, these models are flawed. Here we study the effect of these flaws on the AMOC multistable regime in a climate model. We artificially add additional flaws to the model. We find that AMOC stability can be affected by such flaws, and a reduction in such flaws in climate models is therefore thought to be essential.

We present a novel data-driven approach to understand how pollution and weather processes interact to influence snowmelt in Asian glaciers and how these interactions are represented in three climate models. Our findings show where models need improvement in predicting snowmelt, particularly dust and its transport. This method can support future model development for reliable predictions in climate-vulnerable regions.