SO₂ from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO₂ emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.

The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.

Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform-resolution simulations.

Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian process (GP) emulators to accelerate subglacial drainage modelling by ~ 1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for > 90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.

A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.

This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.

The application of distributed graph communication topology in Earth models has rarely been studied. We tested and compared this topology with the traditional point-to-point communication method using a wave model. We found that this topology with the Intel MPI (message-passing interface) library is more efficient. Additionally, using this topology can greatly improve the performance of the wave model and could help improve the performance of other Earth models.

This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.

Phytoplankton, tiny photosynthetic organisms, produce nearly half of Earth's oxygen. To analyze their physiology, diversity, and evolution in the ocean, we developed a model that treats phytoplankton as individual particles. Moreover, our model considers phytoplankton size, temperature, and light traits and allows for mutations in phytoplankton cells. Thus, our model provides a valuable tool for advancing the study of phytoplankton physiology, diversity, and evolution.

This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.