Recent  

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.

This study introduces a new method to reduce uncertainty in climate model projections of future precipitation patterns over land. By using advanced causal discovery techniques, our approach improves the reliability of precipitation projections under different global warming scenarios, supporting the development of more effective strategies to address the impacts of climate change.

Stratospheric Aerosol Injections (SAI) could be used alongside mitigation to reduce global warming. Previous studies suggest that more atmospheric CO2 is taken up when SAI is deployed. Here we look at the entire trajectory of SAI deployment from initialization to after termination and show how the initial carbon uptake benefit and therefore lower negative emission burden is reduced in later stages of SAI where it turns into an additional burden to compensate for reduced natural carbon uptake.

Drivers of hot extremes are not well known on a global scale. Here, we show the global distribution and relevance of these drivers. Our results show that atmospheric circulation is the most critical driver of hot extremes, particularly in the mid-latitudes. Land surface factors, such as vegetation and radiation, are important in tropical and semi-arid regions. Understanding the relative contributions of atmospheric and land surface drivers of hot extremes can help improve predictions.

A new model (ORCHIDEE-NLAT) was developed to estimate lateral nitrogen (N) transport from land to oceans through rivers at daily step. The model includes transport of water and N, as well as the decomposition of dissolved organic N and particulate organic, and denitrification of dissolved inorganic N. Model evaluations indicate that ORCHIDEE-NLAT reproduce observed rates and seasonal variations of water discharge and total N flow well.

Our study explored the impact of anthropogenic land-use change (LUC) on climate dynamics, focusing on biogeophysical (BGP) and biogeochemical (BGC) effects using data from the CMIP6-LUMIP project. We found that LUC-induced carbon emissions contribute to a BGC warming of 0.20 °C, with BGC effects dominating globally over BGP effects, which show regional variability. Our findings highlight discrepancies in model simulations and emphasise the need for improved representations of LUC processes.

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 on the global climate, including the deep ocean. We find that most of the climate response is driven by Greenland’s lower elevation and enhanced by changed surface-properties, although regional differences exist. The altered climate conditions constrain a potential ice-sheet regrowth to high-bedrock elevations.

Land-use change projections are vital for impact studies. This study compares updated land-use model projections, including CO₂ fertilization among other upgrades, from the MAgPIE and IMAGE models under three scenarios, highlighting differences, uncertainty hotspots, and harmonization effects. Key findings include reduced bioenergy crop demand projections and differences in grassland area allocation and sizes, with socioeconomic-climate scenarios' largest effect on variance starting in 2030.

Current trends on CO₂ emissions increase the probability of an overshoot scenario, in which temperatures exceed the targets of the Paris Agreement and are brought back afterwards with a net-negative emission strategy. This work analyses how the climate after the overshoot would differ from the climate before, linking large scale non-reversibility mechanisms to changes in regional climates, and identifying those regions more impacted by changes on temperature and precipitation extremes.

Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere, 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, 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.

The ocean is forced at the surface by a heat flux and freshwater flux. This noise can influence long-term ocean variability and the large scale circulation. Here we study noise characteristics in reanalysis data for these fluxes. We try to capture the noise characteristics by using several noise models and compare these to state-of-the-art climate models. A point wise noise model performs better than the climate models and can be used as forcing in ocean-only models to study.

Land-cover and land management changes (LCLMCs) can alter climate even in intact areas, causing carbon changes in remote areas. This study is the first to assess these effects, finding they substantially alter global carbon dynamics, changing terrestrial stocks by up to dozens of gigatons. These results are vital for scientific and policy assessments, given the expected role of LCLMCs in achieving the Paris Agreement’s goal to limit global warming below 1.5 °C.

Flows in the ocean are driven either by atmospheric forces or by small-scale internal disturbances that are inherently chaotic. We use computer simulation results to show that these chaotic oceanic disturbances can attain spatial scales large enough to alter the motion of Earth’s pole of rotation. Given their size and unpredictable nature, the chaotic signals are a source of uncertainty when interpreting observed year-to-year polar motion changes in terms of other processes in the Earth system.

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.

In recent decades, we have witnessed abnormally hot summers and frequent weather extremes globally. These are clear signs of global warming and climate change. A constant increase in atmospheric carbon dioxide (CO₂) is a major driver of these changes. We propose an algorithm for near-real-time detection of terrestrial areas with CO₂ sources and sinks. This algorithm could aid in developing new methods of natural CO₂ reduction and exploring ecosystem responses to disturbances.