OPA ocean dynamics
LIM2 mono-category sea-ice model with visco-plastic rheology
LIM3 multi-category sea-ice model with elasto-visco-plastic rheology
and prognostic ice salinity
TOP biogeochemical tracers:
- TRP: passive tracer transport module
- passive tracers (CFC-11, C14 Helium,...)
- PISCES: 24 variable biogeochemical model
- LOBSTER: 8 variable biogeochemical model
------------------------------------------------------------------------
The DRAKKAR Group
All configurations are based on the NEMO Ocean/
Sea-Ice GCM numerical code and use the quasi-isotropic global
ORCA grid (Madec, 2007).
Global ORCAii configurations
Global DRAKKAR configurations span resolutions of 2°
(ORCA2), 1° (ORCA1), 1/2° (ORCA05) and 1/4° (ORCA025,
Fig. 1 page 14).
The targeted configuration for the ensemble of hindcasts is the
eddy permitting ORCA025, extensively described in Barnier et
al. (2006).
The ORCA grid becomes
finer with increasing latitude, so the effective 1/4° resolution
is 27.75 km at the equator and 13.8 km at 60°S or 60°N. It is ~7
km in the center of the Weddell and Ross Seas and ~10 km in
the Arctic.
In the vertical, there are 46 levels with partial steps
in the lowest level.
The AGRIF refinement package (Debreu et al., 2007) allows
local grid refinements as shown in the Agulhas Retroflection
region (Fig. 1, Biastoch et al., 2007)
A coordinated series of simulations were
conducted in 2006 at LEGI (G70), IFM-GEOMAR (KAB0012,
KAB002) and KNMI (KNM01) (Table 1), which compare
the ability of the Coordinated Ocean adn sea-ice Reference
Experiment (CORE) (Large & Yeager, 2004, LY04) and ERA40
atmospheric forcing data sets, and of different T,S restoring
scenarios to control the strength of the Atlantic meridional
overturning cell (AMOC) and global T,S drifts.
All experiments use the downward shortwave and longwave
radiation forcing from CORE (derived from satellite ISCCP
products), these variables being significantly biased in ERA40
(Brodeau et al., 2006). Turbulent fluxes are calculated using
LY04 bulk formulas, input variables being wind components, air
temperature and air humidity. Restoring of varying strengths to
climatological sea surface salinity (SSS) is also used. In addition,
for the rather uncertain precipation, two different versions were
used: the original CORE fields and a modified version, CORE*,
in which original CORE precipation is reduced northward of
30°N by 15-20%.
3.3. Sea-ice
ORCA025 hindcasts show a decrease of the Arctic sea-ice area
since the early 1980’s, as seen in satellite data. Arctic sea-ice area
and concentration generally compare well with observations,
in spatial patterns as well as integral values (Fig. 6, page 14).
Sea-ice volume (not shown) is larger (and more realistic) in
experiments using CORE turbulent fluxes (ice is too thin with
ERA40). The simulation of Antarctic sea-ice is less satisfactory,
with too little ice remaining in summer, and an overly large
winter ice extent.
The hybrid forcing using CORE radiation fluxes and precipitation
fields with ERA40 turbulent variables (wind, air temperature
and air humidity), referred to as the DRAKKAR Forcing Set #3
(DFS3) is currently our best choice to obtain an AMOC of realistic
strength with the ORCAii configurations.
------------------------------------------------------------------------
NEMO_bkv3.2
The ocean engine of NEMO (Nucleus for European Modelling of the Ocean) is a primitive
equation model adapted to regional and global ocean circulation problems. It is
intended to be a flexible tool for studying the ocean and its interactions with the others
components of the earth climate system (atmosphere, sea-ice, biogeochemical tracers, ...)
over a wide range of space and time scales. Prognostic variables are the three-dimensional
velocity field, a linear or non-linear sea surface height, the temperature and the salinity.
In the horizontal direction, the model uses a curvilinear orthogonal grid and in the vertical
direction, a full or partial step z-coordinate, or s-coordinate, or a mixture of the two.
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. Within NEMO, the ocean is interfaced with a sea-ice model (LIM v2 and v3),
passive tracer and biogeochemical models (TOP) and, via the OASIS coupler, with several
atmospheric general circulation models.
The Nucleus for European Modelling of the Ocean (NEMO ) is a framework of ocean
related engines, namely OPA1 for the ocean dynamics and thermodynamics, LIM2 for
the sea-ice dynamics and thermodynamics, TOP 3 for the biogeochemistry (both transport
(TRP) and sources minus sinks (LOBSTER, PISCES) 4. It is intended to be a flexible
tool for studying the ocean and its interactions with the other components of the earth
climate system (atmosphere, sea-ice, biogeochemical tracers, ...) over a wide range of
space and time scales.
The ocean component of NEMO has been developed from the OPA model, release
8.2, described in Madec et al. [1998]. This model has been used for a wide range of
applications, both regional or global, as a forced ocean model and as a model coupled
with the atmosphere. A complete list of references is found on the NEMO web site.
The following chapters deal with the discrete equations. Chapter 3 presents the space
and time domain. The model is discretised on a staggered grid (Arakawa C grid) with masking
of land areas and uses a Leap-frog environment for time-stepping. Vertical discretisation
used depends on both how the bottom topography is represented and whether the
free surface is linear or not. Full step or partial step z-coordinate or s- (terrain-following)
coordinate is used with linear free surface (level position are then fixed in time). In nonlinear
free surface, the corresponding rescaled height coordinate formulation (z* or s*)
is used (the level position then vary in time as a function of the sea surface heigh). The
following two chapters (4 and 5) describe the discretisation of the prognostic equations
for the active tracers and the momentum. Explicit, split-explicit and implicit free surface
formulations are implemented as well as rigid-lid case. A number of numerical schemes
are available for momentum advection, for the computation of the pressure gradients, as
well as for the advection of tracers (second or higher order advection schemes, including
positive ones).
Surface boundary conditions (chapter 6) can be implemented as prescribed fluxes, or
bulk formulations for the surface fluxes (wind stress, heat, freshwater). The model allows
penetration of solar radiation There is an optional geothermal heating at the ocean bottom.
Within the NEMO system the ocean model is interactively coupled with a sea ice model
(LIM) and with biogeochemistry models (PISCES, LOBSTER). Interactive coupling to
Atmospheric models is possible via the OASIS coupler [Valcke 2006].
Other model characteristics are the lateral boundary conditions (chapter 7). Global
configurations of the model make use of the ORCA tripolar grid, with special north fold
boundary condition. Free-slip or no-slip boundary conditions are allowed at land boundaries.
Closed basin geometries as well as periodic domains and open boundary conditions
are possible.
Physical parameterisations are described in chapters 8 and 9. The model includes an
implicit treatment of vertical viscosity and diffusivity. The lateral Laplacian and biharmonic
viscosity and diffusion can be rotated following a geopotential or neutral direction.
There is an optional eddy induced velocity [Gent and Mcwilliams 1990] with a space and
time variable coefficient Tr´eguier et al. [1997]. The model has vertical harmonic viscosity
and diffusion with a space and time variable coefficient, with options to compute the
coefficients with Blanke and Delecluse [1993], Large et al. [1994], or Pacanowski and
Philander [1981] mixing schemes.
Specific online diagnostics (not documented yet) are available in the model : output
of all the tendencies of the momentum and tracers equations, output of tracers tendencies
averaged over the time evolving mixed layer.
The model is implemented in FORTRAN 90, with preprocessing (C-pre-processor). It
runs under UNIX. It is optimized for vector computers and parallelised by domain decomposition
with MPI. All input and output is done in NetCDF (Network Common Data
Format) with a optional direct access format for output.
------------------------------------------------------------
R. Timmermann et al
Abstract
The dynamic–thermodynamic Louvain-la-Neuve sea ice model (LIM) has been coupled to the OPA
primitive equation ocean general circulation model. In the ORCA2-LIM configuration, the model is run on
a global domain with 2 mean resolution. Model runs are forced with a combined dataset consisting of daily
NCEP/NCAR reanalysis data and various climatologies. The models performance is evaluated with respect
to the representation of sea ice and the high latitude oceans. The annual cycle of sea ice growth and
decay is realistically captured in both hemispheres, with ice extent, thickness and drift in close
agreement with observations. The location of the main sites of deep convection (Labrador and Greenland
Seas; continental shelves of marginal seas of the Southern Ocean) is well reproduced. Model deficiencies
include a slight overestimation of summer ice extent in the Arctic, and a significant underestimation of
multi-year ice in the Weddell Sea. Furthermore, the width of the Arctic Ocean Boundary Current and the
Antarctic Circumpolar Current is overestimated. Sensitivity studies indicate that the use of the combined
forcing dataset is crucial to achieve a reasonable summer sea ice coverage and that the direct use of
the NCEP/NCAR wind stress data leads to an overestimation of sea ice drift velocities. A restoring of
sea surface salinity is necessary to avoid spurious open ocean convection in the Weddell Sea.
2.1. Ocean model
In the ORCA2-LIM configuration, lateral tracer mixing is done along isopycnals. Eddyinduced
tracer advection is parameterized following Gent and McWilliams (1990) with the
coefficients decreased in the tropics, i.e. between 20N and 20S. Momentum is mixed along model
level surfaces using coefficients varying with latitude, longitude and depth.
Vertical eddy diffusivity and viscosity coefficients are computed from a level-1.5 turbulent
closure scheme based on a prognostic equation for the turbulent kinetic energy. This parameterization
has been developed for the atmosphere by Bougeault and Lacarrere (1989), adapted for
the ocean by Gaspar et al. (1990) and embedded in OPA by Blanke and Delecluse (1993). Double
diffusive mixing (i.e. salt fingering and diffusive layering) is computed following Merryfield et al.
(1999). In locations with a statically unstable stratification, a value of 100 m2 s1 is assigned to the
vertical eddy coefficients for momentum and tracers.
The Beckmann and D€oscher (1997) bottom boundary layer scheme ensures an improved
representation of dense water spreading over topography in this geopotential-coordinate model.
2.2. Sea ice model
2.3. Ice–ocean coupling
2.4. Configuration on a global grid
In the ORCA2-LIM configuration, the model domain extends from 78S to 90N. The model
uses a tripolar grid with 2 zonal resolution, and a meridional resolution varying from 0.5 at the
equator to 2 cos(phi) south of 20S (Fig. 1). 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. Local mesh refinements are applied to the
Mediterranean, Red, Black and Caspian Seas. None of them appears to be of particular
importance for the study of high latitude climate, but the fine resolution is needed in order to have
their local circulation and their role in the World Ocean's circulation considered correctly.
Bottom topography and coastlines are derived from the study of Smith and Sandwell (1997),
complemented by the ETOPO5 dataset. Vertical discretization uses 30 z-levels in the water column,
with 10 levels in the top 100 m.
2.5. Initialization and forcing
Model runs are initialized using the January data from the Polar Science Center Hydrographic
Climatology (PHC; Steele et al., 2001). An initial sea ice thickness of 3 m in the Arctic and 1 m in
the Antarctic is prescribed in regions with a sea surface temperature below 0C. An initial snow
cover with a thickness of 0.5 m in the Arctic and 0.1 m in the Antarctic is assumed.
Atmospheric forcing fields are a combined dataset consisting of the NCEP/NCAR daily
reanalysis data of 10 m wind speed and 2 m temperature (Kalnay et al., 1996), and monthly
climatologies of relative humidity (Trenberth et al., 1989), total cloudiness (Berliand and Strokina,
1980) and precipitation (Xie and Arkin, 1996). A quadratic bulk formula with a drag
coefficient of 2.25x10^-3 is used to compute the surface wind stress. Surface fluxes of sensible and
latent heat are computed using empirical parameterizations described by Goosse (1997). Evaporation/
sublimation is derived from the latent heat flux. River runoff rates are prescribed using the
climatological Baumgartner and Reichel (1975) dataset combined with a mean seasonal cycle
derived from the Global Runoff Data Centre (GRDC, 2000) data. To avoid spurious model drift,
a restoring of sea surface salinity (SSS) towards the seasonal PHC dataset is applied with a time
scale of 60 d.
Given that reliable sea ice observations are scarce before the late seventies, forcing data from
the period 1977–1999 are used for this study. The first pass of this dataset, already run in fully
coupled mode, is used to obtain a quasi-stationary seasonal cycle; results from the second pass are
used to assess the model's performance.
Of course, with only 23 years of spinup, the model is not run ‘‘to equilibrium’’ in a strict sense.
However, in contrast to the low latitude ocean, in which deep water properties change on time
scales of decades to centuries, communication between the surface and the deep ocean in polar
regions occurs as part of the seasonal signal or an interannual variability. So, in order to investigate
ice–ocean interaction, the model does not need to be integrated for hundreds of years––
provided that areas that feature a much slower variability are initialized close to reality.
--------------------------------------------------------------
Manfredi Manizza,1,2 Corinne Le Quere,1,3,4 Andrew J. Watson,5 and Erik T. Buitenhuis1
´´
[5] We use the OPA model, an OGCM based on primitive
equations [Madec and Imbard, 1996; Madec et al., 1999]
which has an horizontal irregular grid with a resolution of
about 2°. The latitudinal resolution is enhanced to $0.5° at
the equator and at high latitudes and the vertical resolution
is 10 m in the top 100 meter. In OPA the vertical eddy
diffusivity and viscosity coefficients are calculated by a 1.5
order turbulent kinetic energy model [Gaspar et al., 1990].
Sub-grid eddy induced mixing is parameterized according
to Gent and McWilliams [1990]. OPA is also coupled to
LIM, a sea-ice model [Fichefet and Morales-Maqueda,1999].
[6] We use three different model versions: (1) In the
simulation labeled Dead Ocean (OPADO), the penetration
of solar radiation in the water column depends on the
physical properties of seawater for mean open ocean
condition and is computed by splitting the total surface
irradiance Io in two wavelength bands [Paulson and
Simpson, 1977]:
[9] (3) In the simulation labeled Green Ocean (OPAGO),
we consider the influence of phytoplankton on light penetration
but in this case the [Chl] is computed by an ocean
biogeochemistry model, the Dynamic Green Ocean Model
(DGOM). In this third version, we also use the entire
vertical profile of [Chl], taking into account the self
shading effect caused by the presence of phytoplankton.
The visible light is computed at every vertical level of the
model (z) as a function of the irradiance at the vertical level
just above (z-1), as follows:
where Dz is the thickness of each layer between two vertical
levels.
[10] The DGOM is a modified version of the PISCES
model [Aumont et al., 2003]. It includes Phosphorous,
Silicate, Iron and light co-limitation and represents five
Plankton Functional Types (Nanophytoplankton, Diatoms
and Coccolithophores for phytoplankton and meso
and micro size classes for zooplankton) [Le Que´re´ et al.,
2005]. In OPAGO the total [Chl] is the sum of the chlorophyll
of all three phytoplankton types.
[11] The DGOM reproduces the spatial gradients observed
by SeaWiFS, although the model underestimates the surface
[Chl] in the North Atlantic (>50N). The model is initialized
with observations both for the physics and the biogeochemistry.
The model was forced by NCEP re-analyzed fields
[Kalnay et al., 1996]. Simulations are run for 10 years. We
present results for the year 2000
------------------------------------------------------------------
AGRIF nesting tool
the nesting of models via AGRIF (NST)
Table of contents
* Downloads :
* 1. Preparing the child model
o 1.1 Refinement ratio
o 1.2 Child Grid Position
o 1.3 One-way vs Ywo-way nesting
* 2. Getting the lateral boundary conditions, winds and surface fluxes
* 3. Running the model
o Development Team
o Support
o Kown Users
* References :
o NEMO / AGRIF
o Nesting Tool
* Acknowledgement :
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. This page aims to explain the way to run an embedded model by using the NEMO/AGRIF framework in a pre-defined
configuration. In this way we are going to describe here how to provide the grid coordinates, the surface forcing and the
initial conditions required by each child model. In order to become familiar with NEMO/AGRIF, you should read it even if you
are going to create your own configuration.
-----------------------------------------------------------------------------------------------------------
ORCA1 configurations page
Links to downloadable versions of the configuration and input files for active versions of ORCA1 are provided at the bottom of this page but please browse the following paragraphs to ensure that you collect the correct configuration files to match your needs.
The source files files are independent of the vertical grid. The ORCA1 configuration can be enabled using the new key: key_orca_r1. By default, a 42 level grid with depths closely matching those used in the Hadley Centre's HadGOM1 model will be configured by adding the following preprocessor key to the P_P setting in the Makefile prior to compilation:
key_orca_r1
However, other vertical grids may be defined and these can be accommodated within the ORCA1 configuration by assigning a particular integer value to the key.
For example, following discussions with Anne-Marie Treguier in August 2006, we agreed a 64 level vertical grid which matches requirements for projects at IFREMER and is very similar to the OCCAM vertical resolution. This configuration is supported without the introduction of new keys or modules with some preprocessing directives in par_ORCA_R1.h90. The relevant code snippets can be viewed here. This alternative vertical grid is enabled by setting the preprocessor key to 64 with the following setting in the Makefile.:
key_orca_r1=64
Furthermore, a 46 level configuration has been introduced which is compatible with the DRAKKAR projects ORCA025 and ORCA05 configurations. This is enabled by:
key_orca_r1=46
Any other value will currently default to the 42-level grid but more alternatives could be supported by using the #elif preprocessor construct. Please use the email address provided on the contact page (under "Other resources") to request the introduction of other vertical grids.
DFS4 Surface Forcing page
This page contains links to the DFS4 surface forcing datasets which have been compiled from NCEP and ECMWF reanalysis products by the Drakkar group. Details will soon be available in "An ERA40-based atmospheric forcing for global ocean circulation models" by Laurent Brodeau, Bernard Barnier, Anne-Marie Treguier, Thierry Penduff and Sergei Gulev, 2009 which has been accepted for publication in Ocean Modelling. The data available here are all on their respective source grids ready for use with the interpolation on the file option. NOCS is only supporting the use of these data with the sbc_core option:
ln_blk_core = .true.
The namelist.v3.2 file contains the setting for this option which will require the following weights files:
------------------------------------------------------
manuel_pisces.pdf Olivier AUMONT PISCES biogeochemical model
PISCES is a biogeochemical model which simulates the marine biological productivity and
that describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).
Historically, this model can be seen as one of the many Monod models (Monod, 1942) by
opposition to the quota models (McCarthy, 1980; Droop, 1983), the other big family of ocean
biogeochemical model. Thus, 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. And PISCES was supposed to be suited for a wide range of spatial and temporal
scales, including quasi-steady state simulations on the global scale.
However, when modeling silicate, iron and/or chlorophyll, assuming constant ratios is not
justified anymore as these ratios can vary a lot. For instance, the Fe/C ratio can vary by
at least an order of magnitude to be compared to the N/C ratio which varies by \only" two
times. Thus, in PISCES, a compromise between the two classical families of ocean model
was chosen. 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. On the other hand,
the phytoplankton growth rates also depends on these external concentrations as in the Monod
approach.
Model description
PISCES has currently twenty-four compartments (see gure 1). There are ve modeled
limiting nutrients for phytoplankton growth: Nitrate and Ammonium, Phosphate, Silicate and
Iron. It should be mentioned that phosphate and nitrate+ammonium are not really independent
nutrients in PISCES. They are linked by constant Red eld ratios but the nitrogen pool
undergoes nitrogen xation and denitri cation. This means that if the latter two processes are
set to zero and if the sizes of the nitrogen and phosphorus pools are identical, the distributions
of both nutrients should be exactly the same.
Four living compartments are represented: two phytoplankton size-classes/groups corresponding
to nanophytoplankton 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 C/N/P/O2 ratios are supposed constant and are not
allowed to vary. In PISCES, the Red eld ratio -O/C/N/P is set to 172/122/16/1 (Takahashi
et al., 1985). In addition, the Fe/C ratio of both zooplankton groups is kept constant. No
silici ed zooplankton is assumed. The bacterial pool is not yet explicitly modeled.
There are three non-living compartments: semi-labile dissolved organic matter, small and
big sinking particles. As for the living compartments, the C, N and P pools are not distinctly
modeled. Thus, constant Red eld ratios are imposed for C/N/P. However, the iron, silicon
and calcite pools of the particles are explicitly modeled. As a consequence, their ratios are
allowed to vary. The sinking speed of the particles is not altered by their content in calcite
and biogenic silicate ("The ballast e ect", (Honjo, 1996; Armstrong et al., 2002)). The latter
particles are assumed to sink at the same speed than big organic matter particles. All the
non-living compartments experience aggregation due to turbulence and di erential settling.
In addition to the ecosystem model, PISCES also simulates dissolved inorganic carbon, total
alkalinity and dissolved oxygen. The latter tracer is also used to de ne the regions where oxic
or anoxic remineralization takes place.
3 Model equations
The reader should be aware that in the following equations, the conversion ratios between
the di erent elements (Re eld ratios) have been often omitted except when particular parameterizations
are de ned. All phytoplankton and zooplankton biomasses are in carbon units
except for the silicon, chlorophyll and iron content of phytoplankton. Finally, all parameters
and their standard values in PISCES are listed in Table 1 at the end of this section.
2