Stratospheric aerosol injection (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 SAI deployment from start to after termination. We show how the initial CO₂ uptake benefit, and hence lower mitigation burden, is reduced in later stages of SAI, where the reduction in natural CO₂ uptake turns into an additional mitigation burden. 

An obstacle in studying climate extremes is the lack of robust statistics. We use a rare event algorithm to gather robust statistics on extreme Arctic sea ice lows with probabilities below 0.1 % and to study drivers of events with amplitudes larger than observed in 2012. The work highlights that the most extreme sea ice reductions result from the combined effects of preconditioning and weather variability, emphasizing the need for thoughtful ensemble design when turning to real applications.

Large-scale modes of variability are present in the climate system. These modes are known to have influences on each other but are usually viewed as linear influences. The nonlinear connections among a set of key climate indices are explored here using tools from information theory, which allow us to characterize the causality between indices. It was found that quadratic nonlinear dependencies between climate indices are present at low frequencies, reflecting the complex nature of their dynamics.

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.

Our study uses a global climate model to investigate how groundwater and floodplains influence today's climate. We found that these continental water sources, often overlooked in climate models, can influence precipitation, temperature, and land surface hydrology. This research contributes to a better understanding of the dynamics of the Earth system and highlights the importance of considering interactions between hydrology and the atmosphere.

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.

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 Land Use Model Intercomparison Project (LUMIP) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that LUC-induced carbon emissions contribute to a BGC warming of 0.21 °C, with BGC effects dominating globally over BGP effects, which show regional variability. Our findings highlight discrepancies in model simulations and emphasize the need for improved representations of LUC processes.

A new offline model (LSM_Nlateral_Off) was developed to simulate the lateral transfer of nitrogen from land to oceans through the river network, incorporating the decomposition of DON (dissolved organic N) and PON (particulate organic N) and denitrification of DIN (dissolved inorganic N) during fluvial transport. Evaluations using observational data indicate that the model reproduces observed rates and seasonal variations in water discharge and N flow well.

We analyze drivers of hot temperature extremes, focusing on both atmospheric and land-surface factors. Using observation-based data and an analogue-based approach, we find that geopotential height at 500 hPa is the most significant driver worldwide at daily and weekly timescales. Surface net radiation and evaporative fraction play relevant roles at the daily scale and wind at the weekly timescale. The driver influence varies by region, related to differences in climate regimes and land cover.

The Holocene Peatland Model (HPM) is a widely used peatland model to understand and predict long-term peatland dynamics. Here, we test whether the HPM can predict Sphagnum litterbag decomposition rates from oxic to anoxic conditions. Our results indicate that decomposition rates change more gradually from oxic to anoxic conditions and may be underestimated under anoxic conditions, possibly because the effect of water table fluctuations on decomposition rates is not considered.

The climate of the last 3 Myr has varied between cold and warm periods. Numerous independent mechanisms have been proposed to explain this; however, no effort has been made to study their competing effects. Here we present a simple but physically motivated model that includes these mechanisms in a modular way. We identify ice-sheet dynamics and lithosphere displacement as main triggers, but reproducing the climate records additionally requires the natural darkening of ice.