Towards convection-resolving, global atmospheric simulations with the Model for Prediction Across Scales (MPAS) v3.1: an extreme scaling experiment, links to sourcecode of model and grides

The Model for Prediction Across Scales (MPAS) is a novel set of Earth system simulation components and consists of an atmospheric model, an ocean model and a land-ice model. Its distinct features are the use of unstructured Voronoi meshes and C-grid discretisation to address shortcomings of global models on regular grids and the use of limited area models nested in a forcing data set, with respect to parallel scalability, numerical accuracy and physical consistency. This concept allows one to include the feedback of regional land use information on weather and climate at local and global scales in a consistent way, which is impossible to achieve with traditional limited area modelling approaches. Here, we present an in-depth evaluation of MPAS with regards to technical aspects of performing model runs and scalability for three medium-size meshes on four different high-performance computing (HPC) sites with different architectures and compilers. We uncover model limitations and identify new aspects for the model optimisation that are introduced by the use of unstructured Voronoi meshes. We further demonstrate the model performance of MPAS in terms of its capability to reproduce the dynamics of the West African monsoon (WAM) and its associated precipitation in a pilot study. Constrained by available computational resources, we compare 11-month runs for two meshes with observations and a reference simulation from the Weather Research and Forecasting (WRF) model. We show that MPAS can reproduce the atmospheric dynamics on global and local scales in this experiment, but identify a precipitation excess for the West African region. Finally, we conduct extreme scaling tests on a global 3?km mesh with more than 65 million horizontal grid cells on up to half a million cores. We discuss necessary modifications of the model code to improve its parallel performance in general and specific to the HPC environment. We confirm good scaling (70?% parallel efficiency or better) of the MPAS model and provide numbers on the computational requirements for experiments with the 3?km mesh. In doing so, we show that global, convection-resolving atmospheric simulations with MPAS are within reach of current and next generations of high-end computing facilities.

Global habitat suitability of deep-water Antipatharia

Antipatharia are a diverse group of corals with many species found in deep water. Many Antipatharia are habitat for associates, have extreme longevity and some species can occur beyond 8500 m depth. As they are major constituents of 'coral gardens', which are Vulnerable Marine Ecosystems (VMEs), knowledge of their distribution and environmental requirements is an important pre-requisite for informed conservation planning particularly where the expense and difficulty of deep-sea sampling prohibits comprehensive surveys. This study uses a global database of Antipatharia distribution data to perform habitat suitability modelling using the Maxent methodology to estimate the global extent of black coral habitat suitability. The model of habitat suitability is driven by temperature but there is notable influence from other variables of topography, surface productivity and oxygen levels. This model can be used to predict areas of suitable habitat, which can be useful for conservation planning. The global distribution of Antipatharia habitat suitability shows a marked contrast with the distribution of specimen observations, indicating that many potentially suitable areas have not been sampled, and that sampling effort has been disproportionate to shallow, accessible areas inside marine protected areas (MPAs). Although 25% of Antipatharia observations are located in MPAs, only 7-8% of predicted suitable habitat is protected, which is short of the Convention on Biological Diversity target to protect 10% of ocean habitats by 2020.