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MOM6-CICE 25km Global Configurations

The sections that follow explain why we selected each model parameter for the global MOM6-CICE 25km global model configurations and how they work together across the coupled system. We start with the MOM6 ocean settings, then step through the CICE sea‑ice namelist. For every group of parameters you’ll find a description, links to the relevant code or literature, and practical guidance on when you might wish to adjust the defaults. Use this as both a quick reference and a roadmap for deeper dives into the individual configuration files such as MOM_parameter_doc.all, the list of all MOM6 parameters, and cice_in, the CICE namelist file.

MOM6 parameter choices

Code-formatted text in the following sections gives the parameter values set in the MOM_input configuration file.

Grid description

The 25km configuration uses a tripolar grid to avoid a singularity at the North Pole. The domain is zonally periodic REENTRANT_X = True and open at the north via a tripolar fold TRIPOLAR_N = True while closed in the south REENTRANT_Y = False. The horizontal grid has 1440x1152 tracer points. This is closely aligned with prior models, such as ACCESS-OM2-025 and GFDL OM4/OM5 (1440x1080) and provides eddy-permitting detail in the ocean while maintaining numerical stability. See Grids for more information.

There are 75 vertical layers (NK = 75). MOM6 supports an arbitrary Lagrangian Euler (ALE) vertical coordinate scheme (Griffies et al., 2020)1 that allows for completely general vertical coordinates. A number of coordinates are supported "out-of-the-box", including isopycnal, geopotential, terrain-following, HYCOM1 etc. This configuration uses ALE with a stretched geopotential (z-star) vertical coordinate (USE_REGRIDDING = True, REGRIDDING_COORDINATE_MODE = "ZSTAR"). The layer spacing specified via an input file (ALE_COORDINATE_CONFIG = "FILE:ocean_vgrid.nc,interfaces=zeta"). The deepest ocean depth is set to MAXIMUM_DEPTH = 6000.0. Further details of the vertical grid can be found here.

The Boussinesq approximation is used (BOUSSINESQ = True), meaning density variations only affect buoyancy, with other terms using a reference density RHO_0 = 1035 \(kg/m^3\).

Thermodynamics and Equation of State (TEOS-10)

The configuration uses the "ROQUET_RHO" equation of state for seawater thermodynamic properties (EQN_OF_STATE = "ROQUET_RHO"). This scheme is based on TEOS-10, but uses a 75-term polynomial to compute in-situ density as a function of conservative temperature and absolute salinity, closely approximating the full TEOS-10 results (Roquet et al., 2015)2. The _RHO variant specifically fits density rather than specific volume and is well-suited for layered models. Prognostic temperature and salinity are conservative temperature and absolute salinity (USE_CONTEMP_ABSSAL = True), consistent with the equation of state.

The freezing conservative temperature is calculated from absolute salinity and pressure using a 23-term polynomial fit refactored from the TEOS-10 package (TFREEZE_FORM = "TEOS_POLY"). More relevant discussion can be found in the COSIMA TWG 23-July-2025 meeting minutes.

Frazil formation

Frazil formation in the ocean is turned on (FRAZIL = TRUE). This scheme works upwards through each water column, transferring heat downwards from the layer above as needed to prevent the in-situ temperature falling below the local freezing point in each layer in turn. If the top layer is below freezing, heat is extracted from the sea ice model, which grows frazil ice in response. More details are here.

In regions where there is no frazil formation, sea-ice melt/freeze potential is calculated over the smaller of the top 10m of the ocean and the boundary layer depth (HFREEZE = 10.0).

Surface salinity restoring

Sea surface salinity is restored toward a monthly climatological dataset calculated from the World Ocean Atlas 2023 available at NOAA NCEI (RESTORE_SALINITY = True, SALT_RESTORE_FILE = "salt_sfc_restore.nc"). A piston velocity of 0.11 \(m/day\) is applied to control the strength of the salinity relaxation (FLUXCONST = 0.11). The restoring is implemented as a virtual salt flux (SRESTORE_AS_SFLUX = True). This approach conserves salt overall, balanced globally by subtracting the mean flux to avoid altering global salinity (ADJUST_NET_SRESTORE_TO_ZERO = True). No effective limit is applied to the salinity restoring flux (MAX_DELTA_SRESTORE = 999). More discussion can be found in issues/350, issues/325, issues/257.

Salinity is limited to be positive to prevent the sea-ice model from asking for more salt than is available and driving the salinity negative (BOUND_SALINITY = True, MIN_SALINITY = 0.0).

Diagnostics

Three-dimensional ocean diagnostics are output on either \(z*\)- or density-coordinates, depending on the diagnostic, rather than on the model's native coordinate. Specifically, NUM_DIAG_COORDS = 2 with DIAG_COORDS = "z Z ZSTAR", "rho2 RHO2 RHO" and the vertical coordinate levels for each are defined by DIAG_COORD_DEF_Z = "FILE:ocean_vgrid.nc,interfaces=zeta" and DIAG_COORD_DEF_RHO2 = "RFNC1:76,999.5,1020.,1034.1,3.1,1041.,0.002" (relevant info can be found at PR/622).

An ideal age tracer is configured (USE_IDEAL_AGE_TRACER = True). This tracer ages at a rate of 1/year once it is isolated from the surface and is useful for understanding water mass ventilation and residence times.

Vertical mixing parameterisations

Energetic planetary boundary layer (ePBL)

The configuration handles the vertical mixing in the ocean surface boundary layer with the ePBL scheme rather the the traditional KPP. The ePBL scheme is an energy-based 1D turbulence closure approach that integrates a boundary layer energy budget to determine mixing coefficients. It was developed by (Reichl & Hallberg, 2018)3 to improve upon KPP for climate simulations by including the effect of turbulent kinetic energy input and wind-driven mixing in a more physically constrained way. Relevant discussion can be found in issues/465, issues/426, issues/373.

The ePBL scheme parameters in the configuration are based on the GFDL OM5 configuration, including:

  • Additional mixing due to Langmuir (wave-driven) turbulence (EPBL_LANGMUIR_SCHEME = “ADDITIVE”). Since we do not explicitly couple to a wave model in this configuration (USE_WAVES = False), the Langmuir effect is parameterised via a predetermined enhancement factor using MOM6 default values. The inclusion of wave effects is expected to reduce warm SST biases by enhancing mixing under strong winds, as found in studies of Langmuir turbulence (e.g., USE_LA_LI2016 = True from (Li et al., 2016)4).
  • Using the "OM4" scheme for calculating \(m*\) (EPBL_MSTAR_SCHEME = “OM4” MSTAR_CAP = 1.25, MSTAR2_COEF1 = 0.29, MSTAR2_COEF2 = 0.152)
  • Iteratively solving for a self-consistent mixed layer depth rather than using a single-pass estimate (USE_MLD_ITERATION = True)
  • Replacing shear-induced diffusivities with ePBL diffusivities when the latter is larger than the former (EPBL_IS_ADDITIVE = False)

Interior shear-driven mixing

Shear-driven mixing is parameterised use the Jackson-Hallberg-Legg shear mixing scheme (Jackson et al., 2008)5 using the MOM6 default critical Richardson number (Ri) and shear mixing rate (USE_JACKSON_PARAM = True, RINO_CRIT = 0.25, SHEARMIX_RATE = 0.089). This scheme targets mixing in stratified shear zones, effectively adding interior vertical diffusivity when the local Ri is less than the critical value. The shear is computed at cell vertices to better capture shear between adjacent grid cells (VERTEX_SHEAR = True). The Jackson-Hallberg-Legg parameterisation is energetically constrained: it iteratively finds a diffusivity such that the energy extracted from the mean flow equals the energy used in mixing plus that lost to dissipation (MAX_RINO_IT = 25, inherited from the GFDL OM5 configuration).

Internal tidal mixing

Internal tidal mixing is parameterised using the vertical profile of energy dissipation from (Polzin, 2009)6 rather than the default St. Laurent profile, following (Melet et al., 2013)7 (INT_TIDE_DISSIPATION = True, INT_TIDE_PROFILE = "POLZIN_09"). Tidal mixing is informed by spatially-varying tidal amplitudes and roughness data that are read from files that were generated using tidal velocities from TPXO10 and bathymetry from SYNBATH (READ_TIDEAMP = True, TIDEAMP_FILE = "tideamp.nc", H2_FILE = "bottom_roughness.nc"). The maximum energy per area that can be injected as mixing is limited in the same way as in the GFDL OM5 configuration (TKE_ITIDE_MAX = 0.1).

Interior background mixing

Ocean interior background mixing is also configured based on the GFDL OM5 configuration. A weak constant background diapycnal diffusivity is set for diapycnal mixing (KD = 1.5E-05), with a floor for numerical stability (KD_MIN = 2.0e-6). Vertical mixing is enhanced in the salt-fingering regime (DOUBLE_DIFFUSION = True) and Henyey-type internal wave scaling is configured with default MOM6 parameters (HENYEY_IGW_BACKGROUND = True, HENYEY_N0_2OMEGA = 20.0, HENYEY_MAX_LAT = 73.0). The total diffusivity increment from TKE-based schemes is capped to prevent unbounded growth of shear-based or convective mixing (KD_MAX = 0.1). This is a large upper bound that would only take effect in extremely unstable cases.

Bottom boundary layer

The bottom boundary layer viscosity and thickness are calculated such that the bottom stress is quadratic and depends on the average of the velocities over the bottom 10m (BOTTOMDRAGLAW = True, LINEAR_DRAG = False, HBBL = 10.0, CDRAG = 0.003). See here for more details.

An additional Rayleigh drag is applied to layers within the bottom boundary layer to account for curvature of the bottom (CHANNEL_DRAG = True, SMAG_CONST_CHANNEL = 0.15). More details can be found here.

Horizontal viscosity and subgrid momentum mixing

A hybrid Laplacian-biharmonic viscosity scheme is used to parameterise unresolved horizontal turbulent mixing of momentum (LAPLACIAN = True, BIHARMONIC = True). The scheme helps remove small-scale kinetic energy, while preserving large-scale eddy structures, targetting the smaller scales more selectively than just using a Laplacian scheme. See the MOM6 documentation for details of how the horizontal viscosity is calculated. The biharmonic viscosity includes:

  • no constant background viscosity (AH = 0.0)
  • a grid-dependent background viscosity (AH_VEL_SCALE = 0.01, AH_TIME_SCALE = 0.0)
  • a dynamic Smagorinsky nonlinear eddy viscosity (SMAGORINSKY_AH = True, SMAG_BI_CONST = 0.06, LEITH_AH = False)

The Lapacian viscosity includes:

  • no constant background viscosity (KH = 0.0)
  • a grid-dependent background viscosity (KH_VEL_SCALE = 0.01)
  • a latitudinally-dependent background viscosity (KH_SIN_LAT = 2000.0, KH_PWR_OF_SINE = 4.0)
  • no file-base background viscosity (USE_KH_BG_2D = False)
  • no dynamic viscosity component (SMAGORINSKY_KH = False, LEITH_KH = False)
  • reduction scaling in well-resolved regions (RESOLN_SCALED_KH = True)
  • the two coefficient anisotropic viscosity scheme proposed by (Smith & McWilliams, 2003)8 is not used (ANISOTROPIC_VISCOSITY = False) The Laplacian and biharmonic coefficients are both limited locally to guarantee stability (BOUND_KH = True, BETTER_BOUND_KH = True, BOUND_AH = True, BETTER_BOUND_AH = True).

Isopycnal mixing

Baroclinic instability converts available potential energy (APE) stored in sloping isopycnals into eddy kinetic energy (EKE). In an eddy-permitting 25km configuration, this conversion is only partly resolved hence the model does not fully capture the eddy processes that naturally flatten isopycnals and release APE. As a result, isopycnal slopes remain steeper than they should be unless the unresolved eddy effects are parameterised. More details can be found in (MITgcm Documentation Team, n.d.)9.

Mesoscale eddies also induce irreversible mixing of tracers but primarily along neutral density surfaces rather than vertically. This diabatic component needs to be represented through an isopycnal diffusion parameterisation that diffuses tracers along neutral surfaces while avoiding spurious diapycnal mixing.

To represent both the flattening of isopycnals and the along-isopycnal tracer mixing, the configuration applies a hybrid mesoscale parameterisation that combines neutral diffusion (Redi, 1982)10 with the Gent-McWilliams (GM) eddy-induced advection scheme (Gent & Mcwilliams, 1990)11. Both require an eddy diffusivity,

  • thickness diffusivity (\(KHTH\) in MOM6)
  • isopycnal tracer diffusivity (\(KHTR\) in MOM6)

In MOM5, these coefficients were prescribed constants or latitude-dependent maps, but these choices are ad-hoc and not dynamically constrained. Hence MOM6 also offers the Mesoscale Eddy Kinetic Energy (MEKE) scheme (MOM6 Documentation Team, n.d.)12 which provides a flow-dependent, scale-aware GM diffusivity. MEKE prognostically computes an eddy kinetic energy field \(E(x,y,z,t)\) from which it derives an eddy velocity scale,

\[ U_{eddy}=\sqrt{2E} \]

and an eddy length scale (\(L\)) based on a configurable combination of multiple length scales. The GM thickness diffusivity is then computed as,

$$ KHTH = CU_{eddy}L $$,

Hence when EKE is large, GM diffusivity increases with stronger flattening of isopycnals and similarly when EKE is small, GM diffusivity decreases and let the resolved eddies do the work. Hence the GM flattening of isopycnals becomes energetically consistent and scale-aware, adapting automatically to the local eddy field.

Isopycnal thickness diffusion (Gent McWilliams bolus transport)

In MOM6, GM is implemented in a thickness diffusion form. MOM6 adds a diffusive flux of layer thickness to the thickness (mass continuity) equation. This height diffusion formulation is mathematically equivalent to the original scheme, which was expressed as an eddy-induced (bolus) velocity that advects tracers and layer thickness (Adcroft et al., 2019)13. GM is turned on via THICKNESSDIFFUSE = True.

This configuration uses MEKE (USE_MEKE = True) to provide the GM diffusivity. Because we do not supply an external EKE field (EKE_SOURCE = "prog"), EKE is generated internally through instability growth. MEKE_BGSRC = 1.0E-13 prevents EKE from decaying to zero in very quiet regions. It serves as a floor to aid numerical stability and is analogous to a background diffusivity but in energy form. MEKE_GMCOEFF = 1.0 means the scheme converts eddy potential energy to eddy kinetic energy with 100% efficiency for the GM effect. MEKE_KHTR_FAC = 0.5 and MEKE_KHTH_FAC = 0.5 map some of the eddy energy to tracer diffusivity and thickness diffusivity, respectively. In practice, MEKE supplies the dynamically varying GM coefficient that flattens isopycnals and removes APE in a physically informed way. We use KHTH_USE_FGNV_STREAMFUNCTION = True, which solves a 1-D boundary-value problem to ensure the GM streamfunction is smooth in the vertical and vanishes at the surface and bottom (Ferrari et al., 2010)14. FGNV_FILTER_SCALE = 0.1 applies light spatial filtering to reduce noise in the diagnosed streamfunction. We set RES_SCALE_MEKE_VISC = False, meaning viscosity is not explicitly scaled by MEKE.

With MEKE, MOM6 becomes explicitly resolution-aware: as horizontal resolution increases and more eddy processes are partially resolved, the diagnosed GM coefficient naturally decreases; conversely, GM strengthens where eddies remain under-resolved (e.g. high latitudes), removing the need for ad-hoc spatial maps of GM coefficients.

Isopycnal tracer mixing (Redi)

Neutral tracer diffusion is enabled with USE_NEUTRAL_DIFFUSION = True, allowing tracers to mix primarily along neutral density surfaces, thus reducing spurious diapycnal mixing in stratified regions. The along-isopycnal diffusivity is set to KHTR = 50.0, following GFDL OM4_05 configuration. We also use USE_STORED_SLOPES = True and keep NDIFF_CONTINUOUS = True to ensure smooth neutral-direction slopes and numerical stability.

Shortwave penetration

Shortwave penetration into the ocean is calculated using the (Manizza et al., 2005)15 chlorophyll-based opacity scheme with three shortwave radiation bands (VAR_PEN_SW = True, PEN_SW_NBANDS = 3). The monthly climatology of surface chlorophyll concentration is calculated from the Copernicus-GlobColour product using Laplace interpolation to fill missing regions.

CICE namelist

The CICE sea ice model is configured using a Fortran namelist file called ice_in. This file contains a series of named blocks, each starting with &groupname and ending with /. Each block represents a different component of the sea ice model, for example:

  1. grid configuration
  2. thermodynamics
  3. radiation and albedo
  4. dynamics and advection
  5. diagnostics and output settings

This document walks through each of these namelist groups and provides a short explanation of what each group controls and some configuration options set in this ACCESS-OM3 configurations. In general, the ice_in file only includes changes from defaults. For a complete list of runtime configuration settings (including defaults) For detailed explanations of parameters, refer to CICE Documentation and Icepack Documentation(for vertical sea ice processes only).

setup_nml

This group defines time-stepping, run length, output frequencies, initial conditions, and I/O settings.

  • Time-stepping and run length
    • The timestep dt is not defined in ice_in directly; it is overwritten in the CICE NUOPC cap to match the driver timestep (coupling timestep). See NUOPC driver for more information.

  • Initialisation:

    • When there is no existing restart file to set the initial state, initialisation is set by ice_ic
      • We use "none" and the model starts with no sea ice.
      • We don't use "default", as CICE initialises sea ice concentration and thickness based on latitude and this leads to very large areas of sea ice.

  • Output frequencies for history:

    • Up to five output streams are available: 

       histfreq = "d", "m", "x", "x", "x"
 hist_suffix = ".1day.mean", ".1mon.mean", "x", "x", "x"

    • Daily averaged output: .1day.mean

    • Monthly averaged output: .1mon.mean
    • Streams marked "x" are unused.

  • History files use hist_time_axis = "middle" to centre timestamps in the averaging interval.

grid_nml

This group defines the spatial grid, land mask, and ice thickness category structure.

  • Horizontal Grid
    • Tripolar grid at 25 km nominal resolution: grid_type = "tripole"
    • Grid files:
      • The grid is defined by grid_file = "./INPUT/ocean_hgrid.nc" and grid_format = "mom_nc". We use the MOM grid file in CICE for best consistency between model components.
      • Land mask file kmt_file = "./INPUT/kmt.nc",
      • Bathymetry file bathymetry_file = "./INPUT/topog.nc". (not currently used)
  • Grid staggering
    • Atmosphere and ocean coupling grids use A-grid: grid_atm = "A", grid_ocn = "A",
    • Sea ice uses B-grid: grid_ice = "B".
  • Ice Thickness Categories:
    • Five ice thickness categories: ncat = 5,
    • Four vertical layers in sea ice: nilyr = 4,
    • One snow layer: nslyr = 1.
  • Grid output:
    • grid_outfile = .true. writes the cice grid into a seperate NetCDF (eg, access-om3.cice.static.nc).

thermo_nml

Controls thermodynamic processes in sea ice.

  • Uses the multi-layer thermodynamics of (Bitz & Lipscomb, 1999)16.
  • All parameters are left as default, except:
    • dsdt_slow_mode = -5e-08: tunes brine drainage (slows down salt removal from ice).

dynamics_nml

Configures sea ice motion and advection.

  • Dynamics:
    • Uses the default elastic-viscous-plastic (EVP) rheology (Hunke & Dukowicz, 1997)17,
    • Default EVP subcycling count ndte = 120.
  • Advection:
    • advection = "remap": Uses incremental remapping for ice and tracer transport (Dukowicz & Baumgardner, 2000)18.
  • SSH:
    • ssh_stress = "coupled": ice feels drag from ocean surface slopes (important for coupling).

shortwave_nml

This group deals with how solar radiation is treated in the ice model and the surface albedo parameters for ice and snow.

  • Radiation scheme:
    • shortwave = "ccsm3", albedo_type = "ccsm3": NCAR CCSM3 scheme.
  • Albedo settings:
    • albicev = 0.86 and albicei = 0.44 for bare ice albedo (visible (v) and near infrared (i) respectively). These two values are for thick, cold ice. An albicev of 0.86 means snow-free ice reflects ~86% of visible light when cold, and albicei of 0.44 means ~44% of near-IR is reflected. These values are relatively high to ensure the ice does not absorb too much sunlight when snow is absent.
    • albsnowv = 0.98, albsnowi = 0.70 are for cold snow albedo (v and IRrespectively). By using these two values, we assumes fresh dry snow is bright in visible (98%) and also high in near-IR (70%).
  • Albedo thickness dependence:
    • ahmax = 0.1 is the thickness parameter for albedo, which is constant above this thickness. In our configuration, it means once ice is ~10cm thick, it is treated optically like thick ice and there will be no further albedo increase. Thinner ice, which is less than 10cm, will have a lower effective albedo. This value is set for consistency with ACCESS-OM2.
  • Pond/algae effects:
    • kalg = 0.0 means no additional algae-related absorption,
    • r_snw = 0.0 is a tuning parameter for snow (broadband albedo) from Delta-Eddingon shortwave, here it is 0, which means not using additional boradband albedo tuning.
    • sw_redist = .true. - if penetrating shortwave radiation is greater than the amount which can be absorbed, then redistribute it to the top surface

forcing_nml

The forcing namelist governs how external forcing (atm andocn) is applied to the ice, including coupling flux adjustments.

  • Atmosphere
    • highfreq = .true.: Uses the relative atmosphere-ice velocity instead of the only atmospheric velocity for boundary layer fluxes
  • Ocean
    • update_ocn_f = .true.: uses coupled frazil water/salt fluxes from ocean,
    • ustar_min = 0.0005: Minimum ocean friction velocity to ensure stability.
  • Freezing temperature
    • tfrz_option = "linear_salt": Freezing point depends on salinity. This is inconsistent with the Thermodynamics and Equation of State (TEOS-10) freezing point calculated in the ocean model. See issue 235 and CICE-Icepack issue
    • ice_ref_salinity = 5: sets the reference salinity of newly formed ice and the baseline for salt flux calculations. It means when sea water freezes, the ice is assumed to trap salt at 5 psu and the remainder is rejected to the ocean. This field is set for consistency with the constants assumed by MOM6.

domain_nml

This group namelist controls how the computational domain is divided among processors.

  • Global grid size
    • nx_global = 1440, ny_global = 1152 define the total grid points (same as MOM6 ocean grid),
  • Block size
    • we use a two-level decomposition - first into blocks of size 30x27 (block_size_x = 30, block_size_y = 27), then these blocks are distributed to MPI tasks. Each MPI task may get multiple blocks to better balance computational load. The chosen block size is a tuning for performance. Smaller blocks improve load balance but can increase halo communication overhead.
  • Distribution type
    • distribution_type = "roundrobin": Assigns blocks cyclically to spread out computational load. See CICE Documentation for more information.
  • Processor shape
    • processor_shape = "square-ice" indicates the model guess on how to arrange MPI tasks in X vs Y dimension. “square-ice” is a pre-set suggesting a slightly X-dominated partition for sea ice. It means the decomposition of blocks to processors will result in more processor domains along x-direction (longitude) than y (latitude), roughly balancing to a square domain per proc.
  • Computational Blocks
    • max_blocks = -1: Internally calculated number of blocks per processor,
    • maskhalo_bound, maskhalo_dyn, maskhalo_remap = .true.: Mask unused halo cells for boundary handling.

Output variables and diagnostics (icefields_nml and others)

  • In the namelist, each output field is listed as f_<var> = <code> or as logical .false.. The codes are single or double letters, where,

    1. d = daily history files (every histfreq_n days, which is 1 here)
    2. m = monthly files
    3. md = both monthly and daily files
    4. x = do not write this field (disabled)
    5. .false. field disabled

  • Our output diagnostics are configured to focus on:

    1. Sea ice state

      • f_aice = "md": concentration (ie, fractional area of ice cover),
      • f_hi = "md": sea ice volume divided by grid cell area,
      • f_hs = "md": snow (on sea ice) volume divided by grid cell area,
      • f_aicen = "m": ice fraction in each thickness category,
      • f_vicen = "m": ice volume (divided by grid cell area) in each category,
      • f_snoice = "md": snow-ice formation,
      • f_congel = "md": congelation ice growth,
      • f_frazil = "md": frazil ice formation due to frazil heat flux from ocean,
      • f_frzmlt = "md": freeze/melt potential,
      • f_dvidtd = "md": ice volume tendency due to dynamics/transport,
      • f_dvidtt = "md": ice volume tendency due to thermodynamics,

    2. Energy fluxes:

      • f_fsens_ai = "m": sensible heat flux,
      • f_flatn_ai = "m": latent heat flux,
      • f_fsensn_ai = "m": sensible heat flux, category,
      • f_fsurfn_ai = "m": net surface heat flux, categories,
      • f_fcondtopn_ai = "m": top sfc conductive heat flux, cat,

    3. Momentum:

      • f_uvel = "md", f_vvel = "md": sea ice velocity components (u,v) ,

  • Some diagnostics are on by default in CICE, and others are configured on in the ice_in file. For the complete list, it's best to refer to the history output files or the ice.log file in model output.

  • Most diagnostics are averaged over grid-cell areas (not sea ice area). Where a variable name ends in _ai then it is averaged over grid cell area, if there is another variable of the same name without the _ai, then it is an ice area average. If turning on CMIP style diagnostics (those starting with si), then refer to the metadata to confirm if it is an ice area or grid cell area average.
  • For time-invarient grid information, its best to use the areas and latitutes/longitudes stored in the access-om3.cice.static.nc output file.

References

  1. Griffies, S. M., Adcroft, A., & Hallberg, R. W. (2020). A primer on the vertical lagrangian-remap method in ocean models based on finite volume generalized vertical coordinates. Journal of Advances in Modeling Earth Systems, 12(10), e2019MS001954. ↩

  2. Roquet, F., Madec, G., McDougall, T. J., & Barker, P. M. (2015). Accurate polynomial expressions for the density and specific volume of seawater using the TEOS-10 standard. Ocean Modelling, 90, 29--43. ↩

  3. Reichl, B. G., & Hallberg, R. (2018). A simplified energetics based planetary boundary layer (ePBL) approach for ocean climate simulations. Ocean Modelling, 132, 112--129. ↩

  4. Li, Q., Webb, A., Fox-Kemper, B., Craig, A., Danabasoglu, G., Large, W. G., & Vertenstein, M. (2016). Langmuir mixing effects on global climate: WAVEWATCH III in CESM. Ocean Modelling, 103, 145--160. ↩

  5. Jackson, L., Hallberg, R., & Legg, S. (2008). A parameterization of shear-driven turbulence for ocean climate models. Journal of Physical Oceanography, 38(5), 1033--1053. ↩

  6. Polzin, K. L. (2009). An abyssal recipe. Ocean Modelling, 30(4), 298--309. ↩

  7. Melet, A., Hallberg, R., Legg, S., & Polzin, K. (2013). Sensitivity of the ocean state to the vertical distribution of internal-tide-driven mixing. Journal of Physical Oceanography, 43(3), 602--615. https://doi.org/10.1175/jpo-d-12-055.1 ↩

  8. Smith, R. D., & McWilliams, J. C. (2003). Anisotropic horizontal viscosity for ocean models. Ocean Modelling, 5(2), 129--156. http://dx.doi.org/10.1016/s1463-5003(02)00016-1 ↩

  9. MITgcm Documentation Team. (n.d.). GM/redi parameterisation. https://mitgcm.readthedocs.io/en/latest/phys_pkgs/gmredi.html. ↩

  10. Redi, M. H. (1982). Oceanic isopycnal mixing by coordinate rotation. Journal of Physical Oceanography, 12(10), 1154--1158. ↩

  11. Gent, P. R., & Mcwilliams, J. C. (1990). Isopycnal mixing in ocean circulation models. Journal of Physical Oceanography, 20(1), 150--155. ↩

  12. MOM6 Documentation Team. (n.d.). Mom_meke module reference. https://mom6.readthedocs.io/en/main/api/generated/modules/mom_meke.html#detamom-meke. ↩

  13. Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O., Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., et al. (2019). The GFDL global ocean and sea ice model OM4. 0: Model description and simulation features. Journal of Advances in Modeling Earth Systems, 11(10), 3167--3211. ↩

  14. Ferrari, R., Griffies, S. M., Nurser, A. G., & Vallis, G. K. (2010). A boundary-value problem for the parameterized mesoscale eddy transport. Ocean Modelling, 32(3-4), 143--156. ↩

  15. Manizza, M., Le Quéré, C., Watson, A. J., & Buitenhuis, E. T. (2005). Bio-optical feedbacks among phytoplankton, upper ocean physics and sea-ice in a global model. Geophysical Research Letters, 32(5). https://doi.org/10.1029/2004GL020778 ↩

  16. Bitz, C. M., & Lipscomb, W. H. (1999). An energy-conserving thermodynamic model of sea ice. Journal of Geophysical Research: Oceans, 104(C7), 15669--15677. ↩

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