In this study, a new framework of cloud condensation nuclei (CCN) prediction in polluted region has been developed and it achieves well prediction of hourly-to-yearly scale across North China Plain. The study reveals the machine learning model can largely reduce the uncertainty in simulating cloud radiative forcing, illustrating the high sensitivity of climate forcing to changes in CCN. This improvement of our new model would be helpful to aerosols climate effect assessment in models.

Cities need better tools to understand where carbon dioxide comes from and how it moves through streets and green spaces. We developed a computer model that simulates carbon dioxide in cities at fine detail, including emissions from human activities and exchanges with vegetation. Tests in London and against independent observations showed that the model reproduces daily and seasonal patterns well. The results can help improve estimates of urban emissions and support climate action in cities.

We introduce version 1.1 of the JAX Circulation Model (JCM), an open-source atmosphere model. JCM is written in JAX, a framework for high-performance Python code that supports automatic differentiation (automated calculation of how sensitive any program output is to any input). JCM's differentiability and modular design make it easier to train, test, and combine physical-theory-based and data-driven model components, thus providing a flexible and modern platform to facilitate climate research.

This paper introduces a new version of the neXtSIM sea-ice model. NeXtSIM is unique among sea-ice models in how it represents sea-ice dynamics, focusing on features such as cracks and ridges and how these impact interactions between the atmosphere and ocean where sea ice is present. The new version introduces some physical parameterisations and model options detailed and explained in the paper. Following the paper's publication, the neXtSIM code will be released publicly for the first time.