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. ORCA2, the ocean engine of NEMO, 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 ORCA2_LIM, a coupled ocean/sea-ice configuration based on the ORCA tripolar grid with 2° horizontal resolution with climatological forcing, coupled to PISCES, a biogeochemichal model. ORCA2, a coarse resolution version of the global ocean configuration, uses the horizontal curvilinear mesh used to overcome the North Pole singularity found for geographical meshes. The horizontal resolution of ORCA2 is based on a 2 degrees Mercator mesh. In the northern hemisphere the mesh has two poles so that the ratio of anisotropy is nearly one everywhere. 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 ORCA2_LIM_PISCES configuration, the model domain extends from 78S to 90N. The model uses a tripolar 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(phi) deg south of 20S. The grid features two points of convergence in the Northern Hemisphere, both situated on continents. Minimum resolution in high latitudes is about 65 km in the Arctic and 50 km in the Antarctic [Timmermann et al, 2005]. Bottom topography and coastlines are delived from the work of Smith and Sandwell [1997] together with the ETOPO5 dataset. Other configurations with a higer resolution (1 deg, 0.5 deg, and 0.25 deg) are also available [DRAKKAR group, 2007]. 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. A restoring of sea surface salinity (SSS) towards the climatology SST is applied with a time scale of 60(?) days. 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. 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 PISCES: 24 variable passive tracer and biogeochemical models (TOP) TRP: passive tracer transport module - passive tracers (CFC-11, C14 Helium,...) 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]. PISCES simulates the marine biological productivity and that describes the biogeochemical cycles of carbon and of the main nutrients such as P, N, Si, and Fe. It assumes a constant Redfield ratio and phytoplankton growth depends on the external concentration in nutrients. This choice was dictated by the computing cost as describing the internal pools of the different elements requires many more prognostic variables. Thus, the elemental ratios of Fe, Si and Chl are prognostically predicted based on the external concentrations of the limiting nutrients like in the quota approach [Monod, 1942]. On the other hand, the phytoplankton growth rates also depends on these external concentrations as in the Monod approach. PISCES has currently twenty-four compartments. It includes 3 nutrients, 2 phytoplanktons, 2 zooplanktons, one detritus and semilabile dissolved organic matter. It explicitly represents the colimitation of phytoplankton growth by light and three distinct nutrients: phosphate, iron and silicate (see Appendix A for Fe parameterization). ----------------------------------------------------------------- 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 representaation of central features of the global ocean circulation [Laurent, et al, 2010]. 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 air humidity), DFS3, produces an Atlantic overturning circulation with the most realistic strength in ~50-year hidcasts using higher resolution vesrions 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/]. ---------------------------------------------------