The use of Commercial Cloud Computing in has evolved rapidly over the past decade. Platform-as-a-Service paved the way for a wide range of services from Software-as-a-Service to new capabilities around Artificial Intelligence, Machine Learning, serverless computing, High Performance Computing (HPC), online GIS and more. SaaS applications provide secure, cost-effective services to other industries, the use of cloud computing for science and engineering has lagged behind. HPC on the cloud, coupled with other services, has emerged with performance characteristics comparable to on-premises systems that make it an attractive and potentially cost-effective option for some applications. Highly complex, full physics-based models are becoming easier and faster to distribute and execute in commercial cloud environments. This session seeks use cases and scenarios to highlight the reasons for adoption and barriers to the adoption of scientific cloud computing. Abstracts should describe use cases, benchmarks, and real-world examples of the scientific use of cloud computing.