This dataset comprises output from global atmospheric simulations performed with the Model for Prediction Across Scales - Atmosphere (MPAS-A) as part of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains as part of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison project (Phase 1). Simulations were conducted over the 40-day period 1st of August to 10th of September 2016, at horizontal mesh spacings ranging from 480 km to 3.75 km. The dataset includes a suite of sensitivity experiments designed to isolate the effects of convective parameterization and changes to the microphysics. Output consists of two-dimensional diagnostic fields (15 minutes frequency) and three-dimensional history files (3-hourly frequency) on MPAS unstructured Voronoi meshes. For comparison purposes, some output fields (mostly post-processed precipitation) from several other DYAMOND participant models (ARPEGE, FV3, GEOS, ICON, IFS, NICAM, SAM, UM, and CAM - MPAS variants) is also included. The data support research into global convective organization, tropical cyclone activity, precipitation, and the sensitivity of numerical models to physical parameterization choices.
Background and Objectives: DYAMOND (Stevens et al. 2019, PGMFD) is a community intercomparison framework for global storm-resolving models (GSRMs) that explicitly represent deep convection without convective parameterization at kilometer-scale grid spacings. This dataset supports analysis of MPAS-A across a broad range of resolutions and physics configurations, enabling study of resolution dependence and parameterization sensitivity in a global convection-permitting context.
Model and Simulations: MPAS-A was configured with the "convection_permitting" physics suite: Thompson microphysics, MYNN boundary layer and surface layer, Noah land surface model, RRTMG radiation, and scale-aware version of the Tiedtke cumulus parameterization. Simulations were initialized on 1 August 2016 from ECMWF analyses with observed SSTs updated throughout the integration.
