Current Development System

The GEOS-5 Atmosphere-Ocean General Circulation Model (AOGCM) has been developed to simulate climate variability on a wide range of time scales, from synoptic time scales to multi-century climate change, and have been tested in coupled simulations and data assimilation mode. Its main components are the GEOS-5 atmospheric model, the catchment land surface model and MOM5, the ocean model developed by the Geophysical Fluid Dynamics Laboratory. The ocean and atmosphere exchange fluxes of momentum, heat and fresh water through a ''skin layer'' interface which includes parameterization of the diurnal cycle and a sea ice model, LANL CICE. All components are coupled together using the Earth System Modeling Framework (ESMF). The goal in having a multi-scale modeling system with unified physics is to be able to propagate improvements made to a physical process in one component into the other components smoothly and efficiently. The GEOS-5 AOGCM was configured to participate in the Coupled Model Intercomparison Project phase 5 (CMIP-5), which provides a standard protocol for evaluation of coupled GCMs. To evaluate the model's ability to simulate the Earth's climate, it was validated against observational data and against the reanalysis products.

Atmospheric component: The generation of the GEOS-5 Atmospheric General Circulation Model (GCM) used in the forecast system is the same that was used as part of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and is described in Rienecker, et al. (2008), and most of the subsequent development of the physical parameterizations for the current, MERRA2 version is described in Molod et al. (2012). The horizontal discretization of the MERRA2 AGCM is computed on the cubed sphere grid of Putman and Lin (2007), although it still retains the option to use the latitude/longitude discretization.

The GEOS-5 AGCM physics includes parameterization schemes for atmospheric convection, large scale precipitation and cloud cover, longwave and shortwave radiation, turbulence, gravity wave drag, a land surface model, a thermodynamic sea ice model, and a simple glacier model. Convection is parameterized using the Relaxed Arakawa-Schubert (RAS) scheme of Moorthi and Suarez (1992) and includes a scheme for the generation and re-evaporation of falling rain (Bacmeister et al., 2006). A "stochastic Tokioka trigger" function (Bacmeister and Stephens, 2011) governs the upper limits on the allowable entrainment by sampling from a probability distribution function with specified parameters. The prognostic cloud cover and cloud water and ice scheme is from Bacmeister et al. (2006). The scheme includes large scale condensation governed by the probability distribution function described in Molod (2012), evaporation, autoconversion and accretion of cloud water and ice, sedimentation of cloud ice and re-evaporation of falling precipitation.

The turbulence parameterization is based on the non-local scheme of Lock (2000), acting together with the Richardson-number based scheme of Louis and Geleyn (1982). The original Lock scheme was extended in GEOS-5 to include moist heating and entrainment in the unstable surface parcel calculations. The Monin-Obukhov similarity theory based parameterization of surface layer turbulence is described in Helfand and Schubert (1995), and includes the effects of a viscous sublayer for heat and moisture transport over all surfaces except land. The ocean roughness is determined by a blend of the algorithms of Large and Pond (1981) and Kondo (1975), modified in the mid-range wind regime based on recent observations in the southern ocean according to Garfinkel et al. (2011) and in the high wind regime according to Molod et al. (2013).

The longwave radiative processes are described by Chou and Suarez (1994), and the shortwave radiative processes are from Chou (1990) and Chou (1992). The gravity wave parameterization computes the momentum and heat deposition into the grid-scale flow due to orographic (McFarlane, 1987) and nonorographic (after Garcia and Boville, 1994) gravity wave breaking.

Land surface component: The Land Surface Model from Koster et. al (2000) is a catchment-based scheme that treats subgrid scale heterogeneity in surface moisture statistically. Glacial thermodynamic process are parameterized using an adaptation of the Stieglitz et al. (2001) snow model to glacial ice (Cullather et al., 2014), and the catchment and glacier models are each coupled to the multi-layer snow model of Stieglitz et al. (2001). Sea ice albedos in the northern hemisphere are from the monthly mean observations of Duynkerke and de Roode (2001).

Ocean component: The ocean component of the GEOS-5 AOGCM is the Modular Ocean Model version 5 (MOM5) developed at Geophysical Fluid Dynamics Laboratory (Griffies et al. 2005, Griffies 2012). It is a hydrostatic primitive equations model with a staggered Arakawa B-grid or C-grid (Messinger and Arakawa 1976) and vertical coordinate based on depth or pressure. A tripolar grid is used to resolve the Arctic Ocean without polar filtering (Murray 1996). The model uses three level or two level time stepping scheme. The ocean surface boundary is computed as an explicit free surface with real fresh water forcing. The topography is represented as a partial bottom step to better represent topographically influenced advective and wave processes. Vertical mixing follows non-local K-profile parametrization of Large et al. (1994) and includes parametrization of tidal mixing on continental shelves. Horizontal mixing uses the isoneutral method developed by Gent and Mc Williams (1990). The horizontal viscosity can use anisotropic scheme of Large et al. (2001) for better representation of equatorial currents. The exchange with marginal sea is parametrized under coarse resolution as discussed in Griffies (2012).

Sea ice component:
The sea ice component of the GEOS-5 AOGCM is the CICE 4.1 developed by the Los Alamos National Laboratory (Hunke and Lipscomb 2008). The model includes several interacting components: a thermodynamic model that computes local growth rates of snow and ice due to vertical conductive, radiative and turbulent fluxes, along with snowfall; a model of ice dynamics, which predicts the velocity field of the ice pack based on a model of the material strength of the ice; a transport model that describes advection of the areal concentration, ice volumes and other state variables; and a ridging parameterization that transfers ice among thickness categories based on energetic balances and rates of strain.