The NASA Multi-scale Modeling Framework (MMF) is based on the coupling of the 2-dimensional Goddard Cumulus Ensemble Model (2DGCE) and the Goddard Earth Observing System (GEOS) GCM. The MMF, which replaces cloud parameterizations with a cloud resolving model (CRM), is a promising approach in climate modeling, because large-scale cloud parameterization is the largest uncertainties of climate models to project future climate. In other words, MMF takes a hybrid approach to couple low-resolution and high-resolution model physics in a unified framework. The embedded 2DGCEs can explicitly simulate cloud dynamics and microphysics, and provide cloud-precipitaiton properties and statistics that match the scale of high-resolution satellite observations, while climate model must assume subgrid horizontal and vertical profiles of these properties. The use of a GCM will allow the large-scale atmosphere's response to cloud, radiation and surface processes. The MMF system will improve our understanding of cloud and precipitation processes over many scales of motion ranging from cloud microphysical processes up to large-scale circulations that organize the growth and decay of precipitation systems. The Goddard MMF includes the GEOS run at 2.5 x 2.0deg (or 1.24x1.0deg) horizontal grid spacing with 32 vertical layers from the surface to 0.4 hPa and a 2D (x-z) GCE embedded at each GEOS column using 64 x 28 (or 32x28 for 1.25x1.0deg case) grid points with 4 km horizontal grid spacing and a cyclic lateral boundary. Globally, there are a total of 13,104 GCEs running at the same time. The time step for the 2D GCE is 10 seconds, and the GEOS-GCE coupling frequency is one hour (i.e. the GEOS physical time step). Because of high MPI scalability, the computational demand of the NASA MMF is far less (1~2 order) than a future global CRM. At each GEOS column, the global model provides the mean atmospheric conditions and the large-scale temperature and moisture advection forcings to the GCE, which feedbacks the tendencies of thermodynamic variables and cloud statistics of a GCM.
The GEOS4 has been constructed with the unique finite-volume dynamic core developed at Goddard (Lin 2004) and the physics package from the NCAR Community Climate Model CCM3 (Kiehl et al. 1998). The unique features of the finite-volume dynamical core include: an accurate conservative flux-form semi-Lagrangian transport algorithm (FFSL) with a monotonicity constraint on sub-grid distribution that is free of Gibbs oscillation (Lin and Rood 1996, 1997), a terrain-following Lagrangian control-volume vertical coordinate, a physically consistent integration of pressure gradient force for a terrain-following coordinate (Lin 1997, 1998), and a mass, momentum, and total energy conserving mapping algorithm for Lagrangian to Eulerian control-volume vertical coordinate transformation. The physical parameterizations in the GEOS have been upgraded with the gravity wave scheme from the NCAR Whole Atmosphere Community Model (WACCM) and the CLM version 2 (CLM-2). The GEOS also includes the ability to use passive water vapor tracers to diagnose the geographic source of water in precipitation and to provide quantitative diagnostics of precipitation recycling (Bosilovich and Schubert 2002). The embded 2D GCE model use the bulk single-moment microphysics (Lang et al. 2009). We are currently upgrading into the GEOS5, which features interactive aerosol transportation model and coupled ocean model.
To improve the representation of cloud-scale moist processes and land-atmosphere interactions, a global, Multi-scale Modeling Framework (MMF) coupled to the Land Information System (LIS) has been developed at NASA GSFC (Mohr et al. 2012). The LIS is a scalable land data assimilation system that integrates a suite of advanced Land-Surface Models (LSMs), high-resolution satellite and observational data, data assimilation and parameter optimization techniques, and high performance computing tools (Kumar et al. 2006; Peters-Lidard et al. 2007). Executing LIS generates spatially and temporally distributed estimates of land surface processes using either observed or model-derived meteorology to constrain and force the user-specified LSMs. These models include the operational LSMs: CLM, Noah, and Catchment. The data assimilation functions of LIS are not currently used in MMF-LIS. Planned future upgrades will add data assimilation to the GEOS, allowing future users to utilize the assimilation functions already in LIS.
Global maps of June–August 2007 mean latent heat flux in W m−2 for a) FLUXNET, b) CLM 2.0, c) MERRA, d) CLM 2.1. The color scale is the same for all maps. Grid resolution of each map is indicated.
Tao, W.-K., J.-D. Chern, R. Atlas, D. Randall, X. Lin, M. Khairoutdinov, J.-L. Li, D. E. Waliser, A. Hou, C. Peters-Lidard, W. Lau, and J. Simpson, 2009: A Multi-scale modeling system: Development, applications and critical issues. Bull. Amer. Meteor. Soc.,90, 515-534.
Mohr, K.I., W.-K. Tao, J.-D. Chern, S.V. Kumar, and C. Peters-Lidard, 2012: The NASA-Goddard Multi-scale Modeling Framework-Land Information System: Global land/atmosphere interaction with resolved convection. Environmental Modeling and Software, 39, 103-115.