3D CEMBS

CCSM/CESM (Community Climate System Model/Community Earth System Model) is a coupled climate model that consists of five separate components with an additional central coupler (CPL7) which controls time, exchange forces, domains, grids and other model data. The central part of this model is based on MCT (The Model Coupling Toolkit) - fully parallel coupling tools which provides a number of coupling services such as a component model registry, domain decomposition descriptors, communication, data redistribution and other very helpful tools. Here CESM has been adapted for the Baltic Sea and is called 3D CEMBS. In this case we have taken into account ocean model (POP) with ecosystem module and ice model (CICE) which are forced by atmospheric data model (DATM7). In addition, river inflow of freshwater and nutrient deposition is processed by land model (DLND). Other models are turned off in this configuration. The main task of the DATM7 is to interpolate forcing data on model domain. Currently the 48-hour atmospheric forcing data from ICM-UM model (University of Warsaw) are used.


Figure 1: 3D CEMBS configuration.

POP

The ocean model is based on Los Alamos National Laboratory (LANL) Parallel Ocean Program (Smith & Gent, 2004), evolved from the global ocean model (Semptner, 1974) with added free surface formulation (Killworth et al., 1991). It is a z-level coordinates, general circulation ocean model that solves the 3-dimentional primitive equations for stratified fluid using the hydrostatic and Boussinesq approximations. Numerically the model computes spatial derivatives in the spherical coordinates using finite difference technique. The placement of model variables in the horizontal direction is Arakawa B-grid (Arakawa & Lamb, 1977). Barotropic equation is solved using preconditioned conjugate gradient solver (PCG), centered differencing represents advection scheme. Biharmonic operator has been chosen as a horizontal mixing parameterization and K-Profile Parametrization (KPP) to cover vertical mixing. We also use equation of state introduced by McDougall, Wright, Jackett and Feistel.

CICE

CICE uses an elastic-viscous-plastic ice rheology (Hunke & Dukowicz, 1997). The Los Alamos CICE model is the result of an effort to develop a computationally efficient sea ice component for a fully coupled atmosphere-ice-ocean-land global climate model. It is designed to be compatible with the POP for use on massive parallel computers. CICE has 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 ice concentration, ice volume and other state variables; and a ridging parameterization that transfers ice among thickness categories based on energetic balances and rates of strain. The CICE has also multiple thickness categories and ice thickness distribution evolves over time.

Ecosystem

Ecosystem model (Fig. 2) is based on an intermediate complexity marine ecosystem model for the global domain (Moore et al., 2002) and consists of 11 main components: zooplankton, small phytoplankton, large phytoplankton (mainly diatoms), summer species (mainly cyanobacteria), one detrital class, dissolved oxygen and nutrients: NO3, NH4, PO4 and SiO4. The small phytoplankton size class is meant to represent nano- and pico-sized phytoplankton, and may limited by nitrogen (both nitrate and ammonia), phosphate, temperature and light. The larger phytoplankton class is explicitly modeled as diatoms and may be limited by the above factors as well as silicate. Growth rates of the cyanobacteria may be limited by phosphate, temperature and light. Many of the biotic and detrital compartments contain multiple elemental pools as we track carbon, nitrogen, phosphorus and silicon through the ecosystem. Small pelagic detritus is represented by dissolved organic carbon as well as very small particulates. There are plans to include large detrital pool (represented by particulalte organic carbon) in the model.


Figure 2: Chart of 3D CEMBS ecosystem model.

Model configuration

3D CEMBS model is currently configured at approximately 2km horizontal resolution (1/48o). The model bathymetry is represented as 21 vertical levels and the thickness of the four surface levels is five metres. The bottom topography is based on ETOPO1 Global Relief Model. The bathymetry data was interpolated to the model grid using kriging method. The initial state of the ocean model was prepared using temperature and salinity climatological data (Janssen et al., 1999). 3D CEMBS model domain is based on stereographic coordinates, but equator of these coordinates is in the center of the Baltic Sea (so we actually use rotated stereographic coordinates) and we can assume that shape of the cells is square and they are almost identical.


Figure 3: 3D CEMBS bottom topography (left) and layer depths in meters (right).

References

  • Amante, C., Eakins, B.W., 2009. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24
  • Arakawa, A., Lamb, V.R., 1977. Computational design of the basic dynamical process of the ucla general circulation model. Methods of Computational Physics, Vol. 17, pp. 173–265.
  • Hunke, E.C., Dukowicz, J.K., 1997. An Elastic-Viscous-Plastic Model for Sea Ice Dynamics. Journal of Physical Oceanography, Vol. 27, No. 9, pp. 1849–1867.
  • Janssen, F., Schrum, C., Backhaus, J.O., 1999. A climatological data set of temperature and salinity for the Baltic Sea and the North Sea. Deutsche Hydrographische Zeitschrift, 51(Suppl 9):5
  • Moore, J.K., Doney, S.C., Kleypas, J.A., Glover, D.M., Fung, I.Y., 2002. An intermediate complexity marine ecosystem model for the global domain. Deep-Sea Res. II,49, pp. 403-462.
  • Seifert, T., Tauber, F., Kayser, B., 2001. A high resolution spherical grid topography of the Baltic Sea – 2nd edition. Baltic Sea Science Congress, Stockholm 25-29. November 2001, Poster #147
  • Semptner, A.J., 1974. A general circulation model for the World Ocean. UCLA Dept. of Meteorology Tech.Rep., No. 8.
  • Smith, R., Gent, P., 2004. Reference manual for the Parallel Ocean Program (POP). Los Alamos National Lab. New Mexico.
  • Stroeve, J., Holland, M.M., Meier, W., Scambos, T., Serreze, M., 2007. Arctic sea ice decline: Faster than forecast. Geophys. Res. Lett., 34

Acknowledgements

  • This work was carried out in support of grant No NN305 111636 - Ministry of Science and Higher Education.

  • Calculations were carried out at the Academic Computer Center in Gdansk.