A new configuration of the Australian Community Climate and Earth System Simulator coupled model, ACCESS-CM2, with a higher resolution ocean-sea ice component is introduced. The new version of the coupled climate model was designed to better capture smaller-scale ocean motions. While this configuration improves the representation of many aspects of the climate system, some biases from the existing lower-resolution version persist.

We present FRISIA version 1.0, a model for emulating sea level rise (SLR) and representing SLR impacts and adaptation in integrated assessment models (IAMs). FRISIA includes previously uncaptured coastal socio-economic feedback and a diverse set of impact strains, thereby improving the represenation of SLR impacts in IAMs. Here we describe the baseline behaviour of FRISIA, explore the effects of the additional feedback and showcase the coupling of FRISIA to an IAM.

This study developed a kilometre-scale sea-ice model in OpenFOAM that couples dynamic and thermodynamic processes for two types of ice, solid-like ice floes and fluid-like grease ice, under wave forcing. This model can help to improve data input for large-scale sea-ice models. Results show a linear relationship between the proportion of ice floes in the field and the overall viscosity. Additionally, we found that viscosity responds nonlinearly to the inclusion of thermodynamic sea-ice growth.

We set out to improve the accuracy of near-surface wind simulations in areas where buildings and tall vegetation have made the ground surface very rough. Through a clever use of differences between weather station measurements and reanalysis data, we estimated more realistic surface roughness values and created a new high-resolution map for China. This map greatly improves wind speed simulations and supports better decisions in wind-related fields.

We propose an innovative climate modelling framework that combines statistical methods with climate simulations to study Earth's environmental systems. The model captures how orbital changes and carbon dioxide levels influence climate atmospheric dynamics, offering a detailed and efficient way to explore long-term processes. This tool provides new opportunities to investigate Earth's climate history and its implications for future changes.

We studied how pollution from cars and trucks contributes to tiny airborne particles that affect air quality and climate. These particles, called secondary organic aerosols, were often underestimated in global models. By improving how certain overlooked emissions from fuel use are represented in our model, we found that their impact is much larger than previously thought. Our results suggest that road traffic plays a far greater role in global air pollution than earlier estimates showed.

Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.

This study examines how radionuclides released from nuclear power plants are transported and transformed in the global ocean over time. Using an advanced ocean simulation model, it focuses on radionuclides released during the Fukushima accident and from planned wastewater discharges. The findings show that some radionuclides can travel across the Pacific within a few years and gradually spread to other ocean basins by mid-century, emphasizing potential long-term environmental impacts.

Machine learning is increasingly used in environmental sciences for spatial predictions, but its effectiveness is challenged when models are applied beyond the areas they were trained on. We propose a Local Training Data Point Density (LPD) approach that considers how well a model's environment is represented by training data. This method provides a valuable tool for evaluating model applicability and uncertainties, crucial for broader scientific and practical applications.

Crystal growth and diffusion are common processes in geology. Our software MovingBoundaryMinerals.jl calculates compositional profiles in diffusion couples by simulating diffusion-growth processes for geometries with planar/cylindrical/spherical symmetries. Our software has been tested versus various benchmark cases and is provided as an open access software package. This package allows the further use of diffusion/growth phenomena in the calculation of the thermal histories of rocks.

Can we be sure that different computing environments, that should not change the model climate, indeed leave the climate unaltered? In this article, we present a novel methodology that answers whether two model climates are statistically the same. Besides a new methodology, able to detect significant differences between two model climates 60 % more accurately than a similar recent state-of-the-art method, we also provide an analysis on what actually constitutes a different climate.