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Implementing subgrid-scale cloudiness into the Model for Prediction Across Scales-Atmosphere (MPAS-A) for next generation global air quality modeling
Citation:
Herwehe, J., R. Bullock, R. Gilliam, Jon Pleim, AND H. Foroutan. Implementing subgrid-scale cloudiness into the Model for Prediction Across Scales-Atmosphere (MPAS-A) for next generation global air quality modeling. 16th Annual CMAS Conference, Chapel Hill, North Carolina, October 23 - 25, 2017.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
A next generation air quality modeling system is being developed at the U.S. EPA to enable seamless modeling of air quality from global to regional to (eventually) local scales. State of the science chemistry and aerosol modules from the Community Multiscale Air Quality (CMAQ) model will be online-coupled to the Model for Prediction Across Scales – Atmosphere (MPASA), a global meteorological model developed at the National Center for Atmospheric Research (NCAR). To prepare the next generation air quality model for conducting retrospective simulations, several additional preferred physics schemes and options from the Weather Research and Forecasting (WRF) model have been implemented by our team into MPAS-A: the Pleim surface layer (PSL), the Pleim-Xiu (PX) land surface model with fractional land use for a 40-class National Land Cover Database (NLCD40), the Asymmetric Convective Model 2 (ACM2) planetary boundary layer scheme, and analysis nudging four-dimensional data assimilation (FDDA). As released, MPAS-A includes the 2004 version of the Kain-Fritsch (KF) convective parameterization and only allows the resolved, grid scale clouds to affect the radiation budget. This presentation discusses results from updating the KF scheme in MPAS-A to the latest version which adds subgrid-scale cumulus cloud feedback to the radiation schemes, multiple convection triggers, and a scale-aware convective time scale. Test simulations of a Northern Hemisphere summer month (July 2013) were conducted on a global variable resolution mesh with the higher resolution cells centered over the contiguous United States. Initial conditions and driving fields for the FDDA and soil nudging were provided by NOAA/NCEP’s GDAS/FNL, GFS, and RUC analyses. Results from the MPAS-A simulations utilizing these added physics schemes and subgrid-scale cloud-radiation interactions were evaluated against observational data [such as those available from NCEP’s Meteorological Assimilation Data Ingest System (MADIS)] to ascertain the impact of these MPAS-A enhancements on air quality-relevant meteorological parameters.
