Projecting future precipitation is essential for preparing for climate change, but current climate models still have large uncertainties, especially over land. This study presents a new method to improve precipitation projections by identifying which models best capture key climate patterns. By giving more weight to models that better represent these patterns, our approach leads to more reliable future precipitation projections over land.

We investigate the probabilities of triggering climate tipping points under various emission scenarios and how they are altered by additional carbon emissions from the tipping of the Amazon and permafrost. We find that there is a high risk for triggering climate tipping points under a scenario comparable to current policies. However, the additional warming and hence the additional risk of triggering other climate tipping points from the tipping of the Amazon and permafrost remain small.

We present a high-resolution paleogeographic reconstruction of the Baltic Sea for the Holocene period by combining eustatic sea-level change, glacio-isostatic movement, and sediment dynamics. In the northeastern part, morphological change is dominated by regression caused by post-glacial rebound that outpaces the eustatic sea level rise, whereas a transgression, together with active sediment erosion/deposition, constantly shapes the coastal morphology in the southeastern part.

Social tipping dynamics have received recent attention as a potential mechanism for effective climate actions – yet how such tipping dynamics could unfold remains largely unquantified. We explore how social tipping processes can develop by enabling necessary conditions (exemplified by climate change concern) and increased perceptions of localized impacts (sea level rise). The likelihood of social tipping varies regionally, mostly along areas with the highest exposure to persistent risks.

This work provides a relevant analysis of changes in extreme precipitation over the Caribbean and their link with warming in different ocean basins. It also improves our understanding of the impact of warming on extreme precipitation events, which can cause devastating damage to economic sectors such as agriculture, biodiversity, health, and energy.

This study investigated the computational benefits of using multiple models of varying cost and accuracy to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. Despite some models being incapable of capturing the local features of the ice-flow fields, using multiple models reduced the error in the estimated statistics by over an order of magnitude when compared to an approach that only used a single accurate model.

AI is impacting science, providing key data insights, but most algorithms are statistical requiring cautious "out-of-sample" extrapolation. Yet climate research concerns predicting future climatic states. We consider a new method of AI-led equation discovery. Equations offer process interpretation and more robust predictions. We recommend this method for climate analysis, suggesting illustrative application to atmospheric convection, land–atmosphere CO₂ flux, and global ocean circulation models.

Using a comprehensive set of simulations with the Max Planck Institute for Meteorology Earth System Model, we disentangle and quantify the impacts of a disintegrated Greenland Ice Sheet (GrIS) on the global climate, including the deep ocean. We find most of the climate response is driven by Greenland’s lower elevation and enhanced by changed surface properties, although regional differences exist. Despite the confinement of most responses to the Arctic, the GrIS also influences remote climates.

This study explores using large language models (LLMs) to simulate policy-making in land systems. We integrated LLMs into a land use model and simulated LLM-powered institutional agents steering meat production by taxation. The results show LLMs can generate boundedly rational policy-making behaviours that can hardly be modelled using conventional methods; LLMs can offer the reasoning behind policy actions. We also discussed LLMs’ potential and challenges in large-scale simulations.

Early warning signals used to detect tipping points are tested on a dataset of daily views of online electric vehicle (EV) adverts. The attention given to EV adverts spikes upwards after specific events before returning to normality more slowly over time. Alongside increases in autocorrelation and variance, these results are consistent with the movement towards a tipping point to an EV-dominated market, highlighting the ability of these signals to work in previously untested social systems.

Past research has shown that the North Atlantic Ocean circulation reacts strongly to global forest cover changes. Using Earth system model simulations featuring idealised forestation and deforestation of North America, this study shows that the North Atlantic Ocean is highly sensitive to upstream land cover changes. Anomalies in air temperature over land propagate downstream and modify ocean-to-atmosphere heat fluxes over the North Atlantic through altering the cold-air outbreak frequency.

Glaciers in the high-latitude Northern Hemisphere (outside Greenland) are losing mass at roughly half the Greenland ice sheet's rate. Still, this is usually not included in the freshwater input data for ocean circulation models. Also, the submarine melt of glaciers (outside the ice sheets) has not been quantified yet. We tackle both issues by using a glacier model's output as additional fresh water for the ocean model and by using the ocean model's output to model submarine melt.

Transitioning to a net-zero economy necessitates significant changes in the financial system. By promoting collective behaviour, aligning expectations, using public finance, reducing capital costs, and encouraging low-carbon investments, the financial system can play a crucial role in re-orienting economies onto a net-zero course. These mechanisms underscore the potential for positive tipping points in sustainable finance and stress the importance of policy interventions to capitalize on them.

Climate sensitivity (S) to doubled atmospheric carbon dioxide has remained stubbornly uncertain for decades. Multiple lines of evidence can be used to constrain S, but any analysis relies on unavoidable subjective decisions. Here, we present a framework for combining the subjective judgments of multiple experts in a fair and robust way.