We use the NEMO (Nucleus for European Modelling of the Ocean), an OGCM based on primitive equations [Madec et al., 1999; Madec, 2008] ------------------------------------------------------------ Ocean model NEMO is an ocean modelling framework which is composed of three "engines." The "engines" provide numerical solutions of ocean, sea-ice, tracers and biochemistry equations and their related physics (http://www.nemo-ocean.eu/About-NEMO). It is intended to be a flexible tool for studying the ocean and its interactions with the others components of the earth climate system such as atmosphere, sea-ice, and biogeochemical tracers over a wide range of space and time scales. The ocean engine of NEMO, ORCA2, is a primitive equation model adapted to regional and global ocean circulation problems. Prognostic variables are the three-dimensional velocity field, the sea surface height, the temperature and the salinity. In the horizontal direction, the model uses a global orthogonal curvilinear ocean grid mesh which has no singularity point inside the computational domain since the mesh poles are moved to land points [Madec and Imbard, 1996]. The ORCA2_LIM_PISCES configuration we intend to use at the beginning consists of 3 major engines, the ocean model, ORCA2, coupled to an ice model, LIM, and a biogeochemichal model, PISCES. It is based on the ORCA tripolar grid with 2 deg horizontal resolution with climatological or interannual forcing. In the ORCA2_LIM_PISCES configuration, the model domain extends from 78S to 90N. The model uses a global tripolar orthogonal curvilinear grid with 2 deg zonal resolution, and the meridional resolution is enhanced to 0.5 deg at the equator and at high latitudes to 2 cos(lat) deg south of 20S. The horizontal grid features two points of convergence in the Northern Hemisphere, both situated on continents to overcome the North Pole singularity found for geographical meshes, thus avoids singularity point inside the computational domain [Madec and Imbard, 1996], and the ratio of anisotropy is nearly one everywhere. Minimum resolution in high latitudes is about 65 km in the Arctic and 50 km in the Antarctic [Timmermann et al, 2005]. Other configurations with a higher resolution (1 deg, 0.5 deg, and 0.25 deg) have also been developed [DRAKKAR group, 2007]. #The mean grid spacing is about 2/3 of the nominal value: for example it is 1.3 degrees for #ORCA2. In the coarse resolution version such as ORCA2 the meridional grid spacing is #increased near the equator to improve the equatorial dynamics. In the vertical direction, the model uses a full or partial step z-coordinate, or s-coordinate, or a mixture of the two. There are 31 levels, with 10 levels in the top 100m and partial steps in the lowest level. The vertical mesh is deduced from a mathematical function of z [Madec and Imbard 1996]. The distribution of variables is a three-dimensional Arakawa C-type grid. Various physical choices are available to describe ocean physics, including TKE and KPP vertical physics. Lateral tracer mixing is done along isopycnals. Eddy induced tracer advection is parameterized following Gent and McWilliams (1990). Horizontal momentum is mixed along model level surfaces using the eddy viscosity coefficients varying with latitude, longitude and depth. The diffusion on tracers acts along the isopycnal surfaces (neutral surface). Vertical eddy diffusivity and viscosity coefficients are calculated using a 1.5 order turbulent kinetic energy model [Gaspar et al., 1990]. Zero fluxes of heat and salt and no-slip conditions are applied through lateral solid boundaries. At the bottom boundary, zero fluxes of heat and salt are applied through the ocean bottom. The ocean is subject to the external forcings of heat, freshwater, and momentum fluxes from the atmosphere and/or the sea-ice. The solar radiation penetrates the top meters of the ocean. The downward irradiance I(z) is formulated with two extinction coefficients [Paulson and Simpson, 1977], whose values correspond to a Type I water, the most transparent water, in Jerlov's classification [Jerlov, xxxx]. We intend to adopt in the model the parameterization proposed by Morel and Antoine [1994] to improve the upper ocean solar heating rate affected by the abundance of phytoplankton. #[Bougeault and Lacarrere, 1989, Gaspar et al. 1990; Blanke and Delecluse, 1993]. -------------------------------------------------- Sea-ice model Within NEMO, the ocean is interfaced with a sea-ice model LIM (Louvain-la-Neuve sea ice model) [Fichefet and Morales-Maqueda,1999], which is a dynamic‚ thermodynamic model specifically designed for climate studies. -------------------------------------------------- Biogeochemical model The NEMO engines include the Pelagic Interactions Scheme for Carbon and Ecosystem Studies (PISCES) ocean biogeochemistry model. PISCES is based on the Hamburg Ocean Carbon Cycle 5 (HAMOCC5) model [Aumont et al., 2003]. ----------------------------------------------------------------- http://www.meece.eu/documents/deliverables/WP2/D2.4.pdf PISCES is a biogeochemical model which simulates the marine biological productivity that describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe). This model can be categorized as one of the many Monod models [Monod, 1942] since it assumes a constant Redfield ratio and phytoplankton growth depends on external concentration of nutrients. However, when modeling silicate, iron and/or chlorophyll, assuming constant ratios is not justified anymore as these ratios can vary a lot (for instance, one order of magnitude for the Fe/C ratio). Thus, the elemental ratios of Fe, Si and Chl are prognostically predicted based on external concentrations of the limiting nutrients like in the quota approach [McCarthy, 1980; Droop, 1983]. PISCES has currently twenty-four compartments. These are five modeled limiting nutrients for phytoplankton growth: Nitrate and Ammonium, Phosphate, Silicate and Iron. Phosphate and Nitrate+Ammonium are not really independent nutrients in PISCES since they are linked by constant Redfield ratios but the nitrogen pool undergoes nitrogen fixation and denitrification. Four living compartments are represented: two phytoplankton size-classes/groups corresponding to nanophytplankton and diatoms, and two zooplankton size classes which are microzooplankton and mesozooplankton. For phytoplankton, prognostic variables are total biomass, the iron, chlorophyll and silicon contents. This means that the Fe/C, Chl/C and Si/C ratios of both phytoplankton groups are fully predicted by the model. For zooplankton, only the total biomass is modeled. For all species, the O/C/N/P ratios are supposed constant (172/122/16/1) and are not allowed to vary. -------------------------------------------------- Biogeochemical data: http://dodsp.idris.fr/reee224/ -------------------------------------------------- Atmospheric forcing fields The DFS4 dataset, a new set of 47-year-long coarse-resolution (2 deg x 2 deg) forcing variables for OGCMs based on ERA40 will be used as atmospheric forcing fields. The dataset has been extensively tested and proved that the refined ERA40 fields contributed to improve the representation of central features of the global ocean circulation [Laurent, et al, 2010]. The dataset consists of 8 variables used for interannual forcing: 6-hourly wind velocity components at 10 m above the surface, 6-hourly temperature and specific humidity at 2 m above the surface, daily solar and infra-red downwelling radiation, and monthly precipitation and snow. The dataset is currently extended to year 2007 and is available from the Drakkar group [The DRAKKAR Group, 2007; Brodeau, et al, 2006]. The hybrid forcing using CORE radiation fluxes and precipitation fields with ERA40 turbulent variables (wind, air temperature and specific humidity), DFS3, produces an Atlantic overturning circulation with the most realistic strength in ~50-year hindcasts using higher resolution versions of ORCA models [The DRAKKAR Group, 2007]. We intend to also use the CORE v.1 dataset, available from NCAR [Large and Yeager, 2004]. -------------------------------------------------- Nesting over the Arctic region; We intend to perform the experiments using a two-way nesting via the AGRIF software over the Arctic region [Debreu, et al, 2007]. AGRIF is a package for the integration of adaptive mesh refinement (AMR) features within a multidimensional model written in Fortran and discretized on a structured grid. This nesting capability that allows resolution to be focused over a region of interest by introducing an additional grid has been added to NEMO. In the current implementation only horizontal refinement is available. #The way to run an embedded model by using the NEMO/ #AGRIF framework in a pre-defined configuration is described at the website: http:// #www.nemo-ocean.eu/Using-NEMO/]. ---------------------------------------------------