Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.

Simple Climate Models (SCMs) are widely used tools to explore how Earth's climate may change in the future. In recent decades, the number and types of SCMs have increased significantly, hindering efforts to understand cross-model differences. In this study, we provide an overview of the main principles guiding climate simulation by SCMs, as well as a description of most high-profile SCMs. This work offers a clear reference to support the informed use of these important tools.

Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.

Studies on plastic pollution have emerged as a rapidly growing field of research. Modelling microplastic transport in estuaries stems from their complex hydrodynamics and diverse particle behaviours affecting the dispersion and retention of microplastics. We review key modelling approaches applied in estuaries analysing their set-ups and parameterizations. We provide recommendations and future directions to improve the accuracy and modelling strategies for estuarine microplastic research.