Bibliography - Ventakramani Balaji

1. Griffies, Stephen, Alistair Adcroft, Ventakramani Balaji, Robert W Hallberg, Sonya Legg, T Martin, and A Pirani, et al., February 2009: Sampling Physical Ocean Field in WCRP CMIP5 Simulations: CLIVAR Working Group on Ocean Model Development (WGOMD) Committee on CMIP5 Ocean Model Output, International CLIVAR Project Office, CLIVAR Publication Series No. 137, 56pp.
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2. Balaji, Ventakramani, Thomas L Delworth, Robert W Hallberg, Hiram Levy II, Ronald J Stouffer, and Michael Winton, 2007: Toward a new generation of ice sheet models. EOS, 88(52), 578-579.
3. Delworth, Thomas L., Anthony J Broccoli, Anthony Rosati, Ronald J Stouffer, Ventakramani Balaji, J A Beesley, W F Cooke, Keith W Dixon, John Dunne, Krista A Dunne, J W Durachta, Kirsten L Findell, Paul Ginoux, Anand Gnanadesikan, C Tony Gordon, Stephen Griffies, Rich Gudgel, Matthew J Harrison, Isaac Held, Richard S Hemler, Larry Horowitz, Stephen A Klein, Thomas R Knutson, P J Kushner, A R Langenhorst, H C Lee, Shian-Jiann Lin, Jian Lu, S Malyshev, P C D Milly, V Ramaswamy, J L Russell, M Daniel Schwarzkopf, Elena Shevliakova, Joseph J Sirutis, Michael J Spelman, William F Stern, Michael Winton, Andrew T Wittenberg, Bruce Wyman, Fanrong Zeng, and Rong Zhang, 2006: GFDL's CM2 Global Coupled Climate Models. Part I: Formulation and Simulation Characteristics. Journal of Climate, 19(5), doi:10.1175/JCLI3629.1.
   [ Abstract ]

   The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved. Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments. The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic. Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/). Manuscript received 8 December 2004, in final form 18 March 2005

4. Gnanadesikan, Anand, Keith W Dixon, Stephen Griffies, Ventakramani Balaji, M Barreiro, J A Beesley, W F Cooke, Thomas L Delworth, R Gerdes, Matthew J Harrison, Isaac Held, William J Hurlin, H C Lee, Z Liang, G Nong, Ronald C Pacanowski, Anthony Rosati, J L Russell, Bonita L Samuels, Qian Song, Michael J Spelman, Ronald J Stouffer, C Sweeney, G A Vecchi, Michael Winton, Andrew T Wittenberg, Fanrong Zeng, Rong Zhang, and John Dunne, 2006: GFDL's CM2 Global Coupled Climate Models. Part II: The baseline ocean simulation. Journal of Climate, 19(5), doi:10.1175/JCLI3630.1.
   [ Abstract ]

   The current generation of coupled climate models run at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Climate Change Science Program contains ocean components that differ in almost every respect from those contained in previous generations of GFDL climate models. This paper summarizes the new physical features of the models and examines the simulations that they produce. Of the two new coupled climate model versions 2.1 (CM2.1) and 2.0 (CM2.0), the CM2.1 model represents a major improvement over CM2.0 in most of the major oceanic features examined, with strikingly lower drifts in hydrographic fields such as temperature and salinity, more realistic ventilation of the deep ocean, and currents that are closer to their observed values. Regional analysis of the differences between the models highlights the importance of wind stress in determining the circulation, particularly in the Southern Ocean. At present, major errors in both models are associated with Northern Hemisphere Mode Waters and outflows from overflows, particularly the Mediterranean Sea and Red Sea.

5. Zhang, Shoaqing, Matthew J Harrison, Andrew T Wittenberg, Anthony Rosati, Jeffrey L Anderson, and Ventakramani Balaji, 2005: Initialization of an ENSO Forecast System using a parallelized ensemble filter. Monthly Weather Review, 133(11), doi:10.1175/MWR3024.1.
   [ Abstract ]

   As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing. A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980-2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF's utilization of anisotropic background error covariances that may vary in time.

6. Hill, C, C DeLuca, Ventakramani Balaji, M J Suarez, and A Da Silva, 2004: The architecture of the Earth System Modeling Framework. Computing in Science and Engineering, 6(1), 18-28.
7. Griffies, Stephen, Ronald C Pacanowski, M Schmidt, and Ventakramani Balaji, 2001: Tracer conservation with an explicit free surface method for z-coordinate ocean models. Monthly Weather Review, 129(5), 1081-1098.
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   This paper details a free surface method using an explicit time stepping scheme for use in z-coordinate ocean models. One key property that makes the method especially suitable for climate simulations is its very stable numerical time stepping scheme, which allows for the use of a long density time step, as commonly employed with coarse-resolution rigid-lid models. Additionally, the effects of the undulating free surface height are directly incorporated into the baroclinic momentum and tracer equations. The novel issues related to local and global tracer conservation when allowing for the top cell to undulate are the focus of this work. The method presented here is quasi-conservative locally and globally of tracer when the baroclinic and tracer time steps are equal. Important issues relevant for using this method in regional as well as large-scale climate models are discussed and illustrated, and examples of scaling achieved on parallel computers provided.

8. Pauluis, O M., Ventakramani Balaji, and Isaac Held, 2001: Reply. Journal of the Atmospheric Sciences, 58(9), 1178-1179.
9. Pauluis, O M., Ventakramani Balaji, and Isaac Held, 2000: Frictional dissipation in a precipitating atmosphere. Journal of the Atmospheric Sciences, 57(7), 989-994.
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   The frictional dissipation in the shear zone surrounding falling hydrometeors is estimated to be 2-4 W m-2 in the Tropics. A numerical model of radiative-convective equilibrium with resolved three-dimensional moist convection confirms this estimate and shows that the precipitation-related dissipation is much larger than the dissipation associated with the turbulent energy cascade from the convective scale. Equivalently, the work performed by moist convection is used primarily to lift water rather than generate kinetic energy of the convective airflow. This fact complicates attempts to use the entropy budget to derive convective velocity scales.

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