Abstract: We present a survey of physical and computational techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allow more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures, such as many-core processors and GPUs, so that these approaches that were previously prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas are poised to transform climate modeling in the coming decades.

Abstract: This paper addresses two key parallelization challenges in the unstructured mesh-based ocean modeling code, MPAS-Ocean, which uses a mesh based on Voronoi tessellations: (1) load imbalance across processes, and (2) unstructured data access patterns, that inhibit intra- and inter-node performance. Our work analyzes the load imbalance due to naive partitioning of the mesh, and develops methods to generate mesh partitioning with better load balance and reduced communication. Furthermore, we present methods that minimize both inter- and intra-node data movement and maximize data reuse. The techniques include predictive ordering of data elements for higher cache efficiency, as well as communication reduction approaches. We present detailed performance data when running on thousands of cores using the Cray XC30 supercomputer and show that our optimization strategies can exceed the original performance by over 2x. Additionally, many of these solutions and can be broadly applied to a wide variety of unstructured grid-based computations.

Abstract: We present an adaptive non-hydrostatic dynamical core based on a higher-order finite volume discretization on the cubed sphere. Adaptivity is both in space, using nested horizontal refinement; and in time, using subcycling in refined regions. The algorithm is able to maintain scalar conservation with careful flux construction at refinement boundaries, as well as conservative coarse-fine interpolation. We show results for simple tests as well as more challenging ones that highlight the benefits of refinement.