Utilizing Cloud Computing to Improve Climate Modeling and Studies

Climate studies have become increasingly important due to the global climate change, one of the biggest challenges for the human in the 21st century. Climate data, not only observations data collected from various sensors but also simulated data generated from diverse climate models, are essential for scientists to explore the potential climate change patterns and analyze the complex climate dynamics. Climate modeling and simulation, a critical methodology for simulating the past and predicting the future climate conditions, can produce huge amount of data that contains potentially valuable information for climate studies. However, using modeling method in climate studies poses at least two challenges for scientists. First, running climate models is a computing intensive process, which requires large amounts of computation resources. Second, running climate models is also a data intensive process generating Big geospatial Data (model output), which demands large storage for managing the data and large computing power to process and analyze these data. This presentation introduces a novel framework to tackle the two challenges by 1) running climate models in a cloud environment in an automated fashion, and 2) managing and parallel processing Big model output Data by leveraging cloud computing technologies. A prototype system is developed based on the framework using ModelE as the climate model. Experiment results show that this framework can improve climate modeling in the research cycle by accelerating big data generation (model simulation), big data management (storage and processing) and on demand big data analytics.