Abstract:
Climate simulation has significant uncertainties due to our current limited understanding of the processes and interactions between different components of the Earth. Model sensitivity analysis, which tests the sensitivity of model output to the input parameter values, is a standard practice for determining the model uncertainties and improving model accuracy. A common approach for climate model sensitivity analysis is to run a model many times by sweeping a large number of adjustable parameters. However, this approach is hampered by three computational challenges: computing intensity, data intensity, and procedure complexity. This dissertation proposes three optimization methodologies to address these challenges respectively, including 1) tackling the computing intensity challenge posed by climate simulation using Model as a Service, a new service model in the context of cloud computing; 2) managing and processing the big model output – “data intensity” – using a scalable big spatiotemporal data analytics framework; 3) solving the procedure complexity issue using a service-oriented cloud-based scientific workflow framework.
