Predicting future changes to the global carbon cycle (and therefore climate) and quantifying anthropogenic emissions of greenhouse gases (GHGs) both require an understanding of net GHGs emissions and uptake across a variety of spatial and temporal scales. At the SIAM Conference on Computational Science and Engineering in March (CSE15) in Salt Lake City, Anna Michalak of Carnegie Institution for Science and Stanford University explored some of the core scientific questions related to understanding GHG budgets through the lens of the statistical and computational challenges that arise. she focused on the use of atmospheric observations, and applications including the natural and anthropogenic components of the methane and carbon dioxide budgets. The discussion included issues related to the solution of spatiotemporal inverse problems, uncertainty quantification, data fusion, gap filling, and issues of “big data” arising from the use of satellite observations.