Climate simulations create huge amounts of data that are difficult to store and share. In this study, we developed a simple method to reduce file sizes while keeping the scientific information accurate. By carefully shortening numbers before applying compression, we tested different settings on U.S. weather simulations and found ways to save space without losing key results. This approach helps scientists work more efficiently and supports better access to climate data for the wider community.

This study evaluated the PM_2.5 simulation by the latest CRACMM mechanism coupled with CMAQ, covering different seasons and specific regions over China. Results derived by CRACMM are compared with two well-established chemical mechanisms, Saprc07 and CB6. Differences in PM_2.5 and SOA drivers between CRACMM and the two existing mechanisms are further explored. Results provide a solid foundation for the further application of CRACMM in understanding and regulating air pollution globally.

Black carbon (BC) affects climate and the environment, and its aging process alters its properties. Current models, like WRF-CMAQ, lack full accounting for it. We developed the WRF-CMAQ-BCG model to better represent BC aging by introducing bare and coated BC species and their conversion. The WRF-CMAQ-BCG model introduces the capability to simulate BC mixing states and bare and coated BC wet deposition, and it improves the accuracy of BC mass concentration and aerosol optics.

This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM_2.5.

We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.