HadGEM3-GC31-MM
Name: HadGEM3-GC31-MM
Long name: HadGEM3-GC3.1-N216ORCA025
Coupled model type(s): GCM
Version: 3.1
Top level description
- aerosol: UKCA-GLOMAP-mode,
- atmos: MetUM-HadGEM3-GA7.1 (N216;432 x 324 longitude/latitude; 85 levels; top level 85km),
- atmosChem: none,
- land: JULES-HadGEM3-GL7.1,
- landIce: none,
- ocean: NEMO-HadGEM3-GO6.0 (ORCA025 tripolar primarily 0.25 deg; 1440 x 720 longitude/latitude; 75 levels; top grid cell 0-1m),
- ocnBgchem: none,
- seaIce: CICE-HadGEM3-GSI8 (ORCA025 tripolar primarily 0.25 deg; 1440 x 720 longitude/latitude)
Coupler: OASIS3-MCT
Activity Properties
Description:Describes forcings related to aerosols, greenhouse gases and other – land use forcing and solar forcing.
Overview:Radiative forcings of the model for historical and scenario (aka Table 12.1 IPCC AR5)
Name: CMIP v6.2.0
Overview: SEE INFORMATION UNDER AEROSOLS, GREENHOUSE GASES and OTHER – LAND USE FORCING AND SOLAR FORCING
Description:CO2, CH4, N2O, CFC-12 (equivalent) and HFC134 (equivalent) are prescribed as spatially constant scalars using the data of Meinshausen et al. (2017). Ozone is prescribed as a 3D field using the data of Hegglin et al. (in prep).
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Properties:
Description: Ozone-depleting and non-ozone-depleting fluorinated gases forcingProvision: Y
Equivalence concentration: Option 3
Additional information: Equivalent concentrations of CFC-12 and HFC-134a, as provided by Meinshausen et al. (2017), are used to represent the radiative forcing of all fluorinated gases. Concentration is treated as spatially constant, using the global mean data of Meinshausen et al. (2017).
Description: Methane forcingProvision: Y
Additional information: Concentration is treated as spatially constant, using the global mean data of Meinshausen et al. (2017).
Description: Carbon dioxide forcingProvision: Y
Additional information: Concentration is treated as spatially constant, using the global mean data of Meinshausen et al. (2017).
Description: Nitrous oxide forcingProvision: Y
Additional information: Concentration is treated as spatially constant, using the global mean data of Meinshausen et al. (2017).
Description: Stratospheric ozone forcingProvision: Y
Additional information: 3D concentration of ozone is prescribed throughout the troposphere and stratosphere, using the data of Hegglin et al. (in prep).
Description: Troposheric ozone forcingProvision: Y
Additional information: 3D concentration of ozone is prescribed throughout the troposphere and stratosphere, using the data of Hegglin et al. (in prep).
Description:The following aerosol species are simulated interactively: sulphate, black carbon (BC), organic carbon (OC), sea salt, mineral dust. Emissions of sea salt and dust are calculated by the model, while the other species are driven by prescribed emissions of SO2, BC, OC and monoterpene.
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Properties:
Description: SO4 aerosol forcingProvision: E
Additional information: H2SO4 is formed by the gas-phase and aqueous oxidation of precursor gases SO2 and DMS. Emissions of SO2 are taken from anthropogenic sources (Hoesly et al., 2018) and from continuously degassing volcanoes (Dentener et al., 2006). We use the biogenic DMS emission dataset of Spiro et al. (1992), and an interactive marine DMS emission calculated using the flux parameterisation of Liss & Merlivat (1986) and climatological DMS sea water concentration of Lana et al. (2011).
Description: Black carbon aerosol forcingProvision: E
Additional information: The model uses primary emissions of black carbon aerosol from biomass burning (van Marle et al., 2017) and anthropogenic (Hoesly et al., 2018) sources. Biomass burning emissions are scaled by a factor of 2 in order to achieve reasonable agreement with observed AOD in present-day simulations.
Description: Cloud albedo effect forcing (RFaci)Provision: E
Aerosol effect on ice clouds: false
Additional information: Cloud droplet number concentration is diagnosed from CCN and the variance of updraft velocity using the scheme of West et al. (2014).
Description: Cloud lifetime effect forcing (ERFaci)Provision: E
Aerosol effect on ice clouds: false
Rfaci from sulfate only: false
Additional information: nil:inapplicable
Description: Dust forcingProvision: M
Additional information: Dust emissions are calculated online using wind speed, soil moisture, bare soil fraction and soil properties, using the scheme of Woodward (2011).
Description: Nitrate forcingProvision: N/A
Additional information: nil:inapplicable
Description: Organic carbon aerosol forcingProvision: E
Additional information: The model uses primary emissions of organic carbon aerosol from biomass burning (van Marle et al., 2017) and anthropogenic (Hoesly et al., 2018) sources, and additionally simulates the formation of secondary organic aerosol (SOA). Biomass burning primary emissions are scaled by a factor of 2 in order to achieve reasonable agreement with observed AOD in present-day simulations. SOA is formed from the oxidation of terpenes from biogenic (Sindelarova, 2014) and biomass burning (van Marle et al., 2017) sources.
Description: Sea salt forcingProvision: M
Additional information: Primary emissions of sea salt aerosol are calculated online using instantaneous model wind speed with the scheme of Gong (2003).
Description: Stratospheric volcanic forcingProvision: Y
Historical explosive volcanic aerosol implementation: nil:withheld
Future explosive volcanic aerosol implementation: Type B
Additional information: Stratospheric aerosol is not simulated interactively. Volcanic forcing is imposed on the model’s radiation scheme using zonal mean fields of extinction, single scattering albedo and asymmetry parameter, using the CMIP6 dataset.
Description: Tropospheric volcanic forcingProvision: E
Historical explosive volcanic aerosol implementation: Type E
Future explosive volcanic aerosol implementation: Type E
Additional information: A constant background 3D emission of SO2 is used to represent the tropospheric source from both continuously degassing and explosive volcanoes, using the dataset of Dentener et al. (2006). This SO2 feeds into the interactive tropospheric aerosol simulation (see aerosol component description).
Description:Describes land use forcing and solar forcing. Land use change is imposed via prescribed surface tile fractions. Solar forcing is imposed via total and spectral solar irradiance.
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Properties:
Description: Land use forcingProvision: Y
Crop change only: false
Additional information: The model uses prescribed vegetation fractions, specified as five plant functional types (PFTs), including C3 grass and C4 grass. A present-day distribution of PFT fractional coverage is derived from the IGBP land cover dataset (Global Soil Data Task, 2000), and changing crop and pasture fractions are projected onto this distribution using the land use data of Hurtt et al (in prep). Rangeland is not taken into account.
Description: Solar forcingProvision: irradiance
Additional information: Total and spectral solar irradiance are specified using the CMIP6 dataset of Matthes et al. (2017). The upper and lower limits of the model spectral bands are 200 nm and 10,000 nm; the CMIP6 SSI from 10 nm to 200 nm is included in the first model spectral band (200 – 220 nm), and the CMIP6 SSI from 10,000nm to 100,000 nm is included in the last model spectral band (2,380 – 10,000 nm).
Key properties
Description: Describes summary properties, genealogy and history of the model, software properties, conservation, coupling and tuning.
Properties:
Description: Flux correction properties of the modelDetails: No flux correction is used.
Description: Genealogy and history of the modelCmip5 differences: Ocean and sea-ice component models have been completely replaced since HadGEM2, by NEMO and CICE respectively. Vertical resolution of the ocean has increased to 75 levels, with 1m resolution at the surface. The number of atmospheric vertical levels has increased to 85, and the stratosphere is now well-resolved with an 85km top. The model has a new dynamical core, and key physics developments include prognostic cloud, condensate and rain, a 2-moment aerosol scheme, and improvements to liquid and mixed-phase cloud microphysics. Key land surface developments since HadGEM2 include a multi-layer snow scheme and improved canopy-radiation interaction.
Year released: 2016
Cmip3 parent: HadGEM1
Cmip5 parent: HadGEM2-AO
Previous name: nil:inapplicable
Description: Software properties of modelRepository: Requires registration – available on request.
Code version: MetUM vn10.9, NEMO vn3.6, CICE vn5.1.2
Code languages: Fortran90, c, python
Components structure: Atmospheric and land components are compiled as a single executable, coupled to an ocean and sea-ice executable via OASIS3-MCT.
Coupler: OASIS3-MCT
Subtopics:
Description:The atmosphere uses the Unified Model and land surface uses JULES – these component models run on the same grid and as part of the same model executable so can be considered to be “tightly coupled”, passing data where necessary by sub-routine arguments or shared data arrays. Similarly the ocean (NEMO) and sea ice (CICE) models are compiled into a single executable and are “tightly coupled” on the same grid (with the caveat that CICE uses an Arakawa B grid placement of velocities in contrast to the C grid in NEMO). For further information see Appendix A of Williams et al. (2017).
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Description:SEE INDIVIDUAL SCIENCE MODELS and model documentation, e.g. Williams et al. (2017).
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Description:Water is conserved to within 10mSv (107 Kg s-1). Total energy is conserved to within 0.001 Wm-2.
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Properties:
Description: Global fresh water convervation properties of the modelGlobal: Atmospheric water is conserved globally in the dynamical core after using a corrector. Water is not conserved in some parameterizations. Global water non conservation in each of the atmosphere, ocean, soil moisture, river routing, land snow and sea ice sub-models is smaller than an acceptance criterion of 10 mSv (10,000,000 Kg s-1).
Atmos ocean interface: Water is not intrinsically conserved in the atmosphere/ocean coupling interface. Global non conservation of water is smaller than an acceptance criterion of 10 mSv (10,000,000 Kg s-1).
Atmos land interface: Water is conserved by ensuring that one of the terms in the surface water balance equation is determined as the residual of the other terms. The atmosphere and land models share a common grid, so there is no horizontal interpolation error.
Atmos sea-ice interface: Water is not intrinsically conserved in the atmosphere/sea-ice interface. The model contains an error in the implementation of the sublimation flux from sea ice to atmosphere. Global non conservation is however smaller than acceptance criterion of 10 mSv (10,000,000 Kg s-1).
Ocean seaice interface: Freshwater is conserved intrinsically between ice and ocean. Freshwater is passed from ice to ocean by means of a single flux, constructed during the sea ice mass balance calculations. It is incremented in proportion to the net decrease in combined ice and snow mass during a timestep, with the net sublimation component removed. In addition, it is incremented by the quantity of rain falling on the sea ice surface, which is assumed to drain immediately to the ocean.
Runoff: River water is routed conservatively using the total runoff integrating pathways (TRIP) model of Oki and Sud (1998) to outflow points over the ocean. The river outflow field is interpolated to the ocean grid the using 1st-order conservative remapping, and the result is added to the surface freshwater flux.
Iceberg calving: Icebergs are explicitly advected by the ocean model, supplied by a calving flux at the coast. The magnitude of this flux is set to balance snow surface mass balance (see snow accumulation property). The spatial distribution of the calving flux is taken from the dataset of Marsh et al. (2015), and is scaled independently for the northern and southern hemispheres.
Endoreic basins: For closed seas the volume of water within each basin is calculated. If the volume increases water is transported to a pre-defined runoff point in the open ocean. If the volume decreases then water is gathered from everywhere else on the globe to make up the short-fall. in this manner the water volume of closed seas lakes remains fixed.
Snow accumulation: Snow is allowed to accumulate on land and sea-ice with no removal. This accumulated snow is available as a source of melt in long climate runs. In order to avoid drift in the ocean fresh water mass, an iceberg calving flux is used, the magnitude of which is calibrated using the net surface mass balance (=snowfall – sublimation – runoff) over regions of permanent snow cover, diagnosed from the end portion of the piControl-spinup.
Description: Global heat convervation properties of the modelGlobal: Atmosphere model is not formulated to conserve atmospheric energy. It uses instead an energy correction, calculated daily as an energy residual and applied on each timestep as a uniform temperature increment. Global energy non conservation is two orders of magnitude smaller than an acceptance criterion of 0.1 W m-2.
Atmos ocean interface: Heat is not intrinsically conserved in the atmosphere/ocean coupling interface. Global energy non conservation in the interface is three orders of magnitude smaller than acceptance criterion of 0.1 W m-2.
Atmos land interface: Energy is conserved by ensuring that one of the terms in the surface energy balance equation is determined as the residual of the other terms. The atmosphere and land models share a common grid, so there is no horizontal interpolation error.
Atmos sea-ice interface: The atmosphere/sea-ice coupling interface is located immediately below the sea ice surface, as described in West et al (2016). Heat is exchanged between the models by means of three fluxes: downwards conduction, top melt and net sublimation. To enable fluxes to be roughly proportionate to underlying ice area, they are passed as local values in which the gridbox mean flux calculated in the atmosphere is divided by the ice fraction field used for the ensuing coupling period. This method has been shown to conserve energy perfectly in theory, but there is an implementation error in the sublimation flux which leads to a loss of conservation. Despite this error, heat is conserved to within an average of 10e-5 Wm-2 in practice.
Ocean seaice interface: Heat is intrinsically conserved between ice and ocean. At the beginning of the ocean timestep, freezing potential is calculated, passed to the sea ice model in order to grow new ice, and the ocean non-solar heat flux is incremented by this amount. The sea ice model constructs an ice-ocean heat flux during the thermodynamics calculations. An initial value (<=0) is calculated based on the extent to which sea surface temperature is greater than ice base temperature. This is then incremented by the energy used to melt ice. At the end of the timestep, the flux is passed to the ocean model, and is used to increment the ocean non-solar heat flux during the following timestep.
Land ocean interface: No heat is exchanged between the land and ocean.
Description: Global momentum convervation properties of the modelDetails: The momentum conservation properties of the ocean and atmosphere have not been explicitly diagnosed. Wind stress is regridded from the atmosphere to ocean grid using non-conservative bilinear interpolation.
Description: Global salt convervation properties of the modelOcean seaice interface: Salt is conserved intrinsically between ice and ocean. Salt is passed from ice to ocean by means of a single flux, constructed during the sea ice mass balance calculations. It is incremented in proportion to the net decrease in combined ice and snow mass during a timestep, with the net sublimation component removed. For the purposes of these calculations, sea ice is assumed to have a uniform salinity of 8.
Realms
Canonical name: atmos
Short name:
Description: Atmosphere Realm
Overview: Atmosphere Realm
Grid
Description: Horizontal grid is 0.54 x 0.83 degrees lat-lon, denoted N216. Horizontal grid staggering uses an Arakawa C-grid. Vertical grid uses 85 levels with a Charney-Phillips staggering and terrain-following hybrid height coordinates.
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Description:These prognostic fields are discretised horizontally onto a regular longitude/latitude grid with Arakawa C-grid staggering, whilst the vertical discretisation utilises a Charney-Phillips staggering using terrain-following hybrid height coordinates. The discretised equations are solved using a nested iterative approach centred about solving a linear Helmholtz equation.
Properties:
Description: Atmosphere discretisation in the horizontalScheme type: fixed grid
Scheme method: finite difference
Scheme order: nil:inapplicable
Horizontal pole: filter
Grid type: Latitude-Longitude
Description: Atmosphere discretisation in the verticalCoordinate type: hybrid sigma-pressure
Realm’s Key Properties
Description: A brief description of the key properties of the model, including type, basic approximations, orography, resolutions and timestepping.
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Description:The horizontal resolution is 0.83 x 0.56 degrees lat-lon everywhere, giving approximately isotropic resolution of 60 km in mid-latitudes. In the vertical 85 levels are used, with 50 levels below 18 km, 35 levels above this and a fixed model lid 85 km above sea level.
Description:With ENDGame, the UM uses a nested iterative structure for each atmospheric timestep within which processes are split into an outer loop and an inner loop. The semi-Lagrangian departure point equations are solved within the outer loop using the latest estimates for the wind variables. Appropriate fields are then interpolated to the updated departure points. Within the inner loop, the Coriolis, orographic and non-linear terms are solved along with a linear Helmholtz problem to obtain the pressure increment. Latest estimates for all variables are then obtained from the pressure increment via a back-substitution process; see Wood et al. (2014) for details.
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Description:The GLOBE dataset (GLOBE Task Team et al., 1999) is used, smoothed to avoid dynamical instabilities.
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Description:n/a
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Realm’s Processes
Description: The Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates observational datasets from model variables. Several simulators are included in COSP. The ones relevant for CMIP6 are: ISCCP cloud products, CALIPSO lidar forward model and CALIPSO/GOCCP cloud products, CloudSat radar forward model, MISR cloud products, and MODIS cloud products. COSP is a stand-alone software, documented in Bodas-Salcedo et al. (2011) and Swales et al. (2018).
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Description:ISCCP simulator 4.2, as distributed in the COSP package.
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Description:The Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates observational datasets from model variables. Several simulators are included in COSP. The ones relevant for CMIP6 are: ISCCP cloud products, CALIPSO lidar forward model and CALIPSO/GOCCP cloud products, CloudSat radar forward model, MISR cloud products, and MODIS cloud products. COSP is a stand-alone software, documented in Bodas-Salcedo et al. (2011) and Swales et al. (2018).
Description:CloudSat radar simulator that is distributed as part of the COSP package. Documented in Haynes et al. (2007).
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Description:CALIPSO lidar simulator that is distributed as part of the COSP package. Documented in Chepfer et al. (2008) and Cesana and Chepfer (2013).
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Description: Separate schemes are used for orographic and non-orographic gravity waves, namely Warner and McIntyre (2001) for non-orographic and Palmer, Shutts and Swinbank (1986) for orographic.
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Description:When Froude number is less than a critical value, a fraction of the flow is assumed to pass around the sides of the orography, and a drag is applied to the flow within this blocked layer. Mountain waves are generated by the remaining proportion of the layer, which the orography pierces through. The acceleration of the flow due to wave stress divergence is exerted at levels where wave breaking is diagnosed.
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Description:Waves on scales too small for the model to sustain explicitly are represented by a spectral sub-grid parametrisation scheme (Scaife et al., 2002), which by contributing to the deposited momentum leads to a more realistic tropical quasi-biennial oscillation. The scheme, described in more detail in Walters et al. (2011), represents processes of wave generation, conservative propagation and dissipation by critical-level filtering and wave saturation acting on a vertical wavenumber spectrum of gravity wave fluxes following Warner and McIntyre (2001). Momentum conservation is enforced at launch in the lower troposphere, where isotropic fluxes guarantee zero net momentum, and by imposing a condition of zero vertical wave flux at the model’s upper boundary. In between, momentum deposition occurs in each layer where reduced integrated flux results from erosion of the launch spectrum, after transformation by conservative propagation, to match the locally evaluated saturation spectrum. See Walters et al. (2011) for more detail.
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Description: Covers orbital parameters, solar constant, solar pathways (SW radiation only) and the treatment of volcanoes. Variations in solar insolation have no impact on model ozone.
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Description:Variations in solar insolation have no impact on model ozone.
Description:For stratospheric volcanic aerosol, the radiation scheme is given the temporally, vertically and meridionally varying optical properties provided in the CMIP6 forcing: extinction, single scattering albedo and asymmetry parameter. In the troposphere, aerosols are simulated interactively based on emissions of primary aerosol and precursor gases. Included in these emissions is a constant background 3D emission of SO2 from continuously outgassing volcanoes (Dentener et al., 2006).
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Description:For CMIP6-DECK and all CMIP6 MIPs except PMIP, orbital parameters are fixed at epoch J2000, using parameters recommended by NASA-JPL (http://ssd.jpl.nasa.gov/elem_planets.html). Year length and time of perihelion are adjusted for the model’s 360-day calendar. For PMIP experiments, the equations of Berger (1978) are used to calculate the orbital parameters.
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Description:Transient solar forcing is applied using the data of Matthes et al. (2017), including temporal variability of the solar constant and spectral distribution.
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Description:The model takes account of SW radiation only.
Description: The parametrisation used is the prognostic cloud fraction and prognostic condensate (PC2) scheme (Wilson et al., 2008a, b) along with the cloud erosion parametrisation described by Morcrette (2012) and critical relative humidity parametrisation described in Van Weverberg et al. (2016). PC2 uses three prognostic variables for water mixing ratio – vapour, liquid and ice – and a further three prognostic variables for cloud fraction: liquid, ice and mixed-phase. Cloud inhomogeneity is represented in the radiation and in warm rain microphysics using regime-dependent inhomogeneity.
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Description:Cloud ice snow is represented by a single frozen water species. Field et al. (2007) and mass-diameter relations of Cotton et al. (2013). Microphysical process rates obtained using moment-prediction equations which depend on temperature and ice water content. A consistent particle size distribution uses a sum of exponential and gamma function. Fall speed calculated according to Furtado (2016).
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Description:The parametrisation of cloud droplets is described in Edwards and Slingo (1996) using the method of “thick averaging”. Pade fits are used for the variation with effective radius, which is computed from the number of cloud droplets. The parametrisation of ice crystals is described in Baran et al. (2016). Full treatment of scattering is used in both the SW and LW. The sub-grid cloud structure is represented using the Monte Carlo Independent Column Approximation (McICA) as described in Hill et al. (2011), with the parametrisation of sub-gridscale water content variability described in Hill et al. (2015).
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Description:Cloud water is derived from saturation adjustment. Droplet number concentration is a function of aerosol number, calculated by UKCA-activate (West et al., 2014). Droplet number affects radiation, utilising the shape function of Liu et al. (2008), and autoconversion (Khairoutdinov and Kogan, 2000).
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Description: Based on Wilson and Ballard (1999) with significant additions. Calculates transfers between liquid, ice and vapour allowing for mixed-phase and with assumed particle-size distributions for the liquid and ice, and size-dependent fall speeds. Cloud inhomogeneity is taken into account when calculating autoconversion and accretion. Size distribution for rain is based on Abel and Boutle (2012) and for snow on Field et al., 2007.
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Description:Based on Wilson and Ballard (1999) with significant additions. Calculates transfers between liquid, ice and vapour allowing for mixed-phase and with assumed particle-size distributions for the liquid and ice, and size-dependent fall speeds. Cloud inhomogeneity is taken into account when calculating autoconversion and accretion. Size distribution for rain is based on Abel and Boutle (2012) and for snow on Field et al., 2007.
Description:Based on Wilson and Ballard (1999) with significant additions. Calculates transfers between liquid, ice and vapour allowing for mixed-phase and with assumed particle-size distributions for the liquid and ice, and size-dependent fall speeds. Cloud inhomogeneity is taken into account when calculating autoconversion and accretion. Size distribution for rain is based on Abel and Boutle (2012) and for snow on Field et al., 2007.
Description: Deep and shallow convection are based on the Gregory Rowntree (1990) scheme with modifications. The turbulence convection scheme is that of Lock et al. (2000) with the modifications described in Lock (2001) and Brown et al. (2008). It is a first-order turbulence closure mixing adiabatically conserved heat and moisture variables, momentum and tracers.
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Description:Shallow convection is based on the Gregory Rowntree (1990) scheme with modifications.
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Description:The scheme is that of Lock et al. (2000) with the modifications described in Lock (2001) and Brown et al. (2008). It is a first-order turbulence closure mixing adiabatically conserved heat and moisture variables, momentum and tracers. For unstable boundary layers, diffusion coefficients (K profiles) are specified functions of height within the boundary layer, related to the strength of the turbulence forcing. Two separate 15 K profiles are used, one for surface sources of turbulence (surface heating and wind shear) and one for cloud-top sources (radiative and evaporative cooling).
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Description:Deep convection is based on the Gregory Rowntree (1990) scheme with modifications.
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Description: Solar radiation is treated in six SW bands and thermal radiation in nine LW bands. Gaseous absorption uses the correlated-k method with newly derived coefficients for all gases (except where indicated below) based on the HITRAN 2012 spectroscopic database (Rothman et al., 2013). Scaling of absorption coefficients uses a look-up table of 59 pressures with five temperatures per pressure level based around a mid-latitude summer profile. The method of equivalent extinction (Edwards, 1996; Amundsen et al., 2017) is used for minor gases in each band.
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Description:Based on the HITRAN 2012 spectroscopic database. The water vapour continuum is represented using laboratory results from the CAVIAR project (Continuum Absorption at Visible and Infrared wavelengths and its Atmospheric Relevance) between 1 and 5 microns and version 2.5 of the MlawerTobin_CloughKneizysDavies (MT_CKD-2.5) model at other wavelengths.
Description:The parametrisation of ice crystals is described in Baran et al. (2016). Full treatment of scattering is used in both the SW and LW.
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Description:The parametrisation of cloud droplets is described in Edwards and Slingo (1996) using the method of thick averaging. Pad fits are used for the variation with effective radius, which is computed from the number of cloud droplets. The parameterisation of Liu et al (2008) is used to represent the effect of droplet size spectral dispersion.
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Description:The sub-grid cloud structure is represented using the Monte Carlo Independent Column Approximation (McICA) as described in Hill et al. (2011), with the parametrisation of subgrid-scale water content variability described in Hill et al. (2015).
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Description:The aerosol scattering and absorption coefficients and asymmetry parameters are precomputed for a wide range of plausible Mie parameters and stored in look-up tables for use during run-time when the atmospheric chemical composition, including mean aerosol particle radius and water content are known. As the aerosol species are internally mixed within the modal aerosol scheme the refractive indices of each mode are calculated online as a volume weighted mean of the component species contributing to that mode. The component refractive indices are documented in Bellouin et al. (2013).
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Description:The aerosol scattering and absorption coefficients and asymmetry parameters are pre-computed for a wide range of plausible Mie parameters and stored in look-up tables for use during run-time when the atmospheric chemical composition, including mean aerosol particle radius and water content, are known. As the aerosol species are internally mixed within the modal aerosol scheme, the refractive indices of each mode are calculated online as a volume weighted mean of the component species contributing to that mode. The component refractive indices are documented Bellouin et al. (2013).
Description:Based on the HITRAN 2012 spectroscopic database. The water vapour continuum is represented using laboratory results from the CAVIAR project (Continuum Absorption at Visible and Infrared wavelengths and its Atmospheric Relevance) between 1 and 5 microns and version 2.5 of the MlawerTobin_CloughKneizysDavies (MT_CKD-2.5) model at other wavelengths.
Description:Gaseous absorption uses the correlated-k method, with 81 k-terms used for the major gases in nine LW bands. See Table 1 of Walters et al. (2017) for details.
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Description:In the LW bands 81 k-terms are used for the major gases. Absorption by H2O, O3, CO2, CH4, N2O, CFC-12 (CCl2 F2) and HFC134a (CH2 FCF3) is included. The atmospheric concentrations of CFC-12 and HFC134a are adjusted to represent absorption by all the remaining trace halocarbons.
Description:Absorption by H2O, CO2, O3, O2, N2O and CH4 is included.
Description:The parametrisation of ice crystals is described in Baran et al. (2016). Full treatment of scattering is used in both the SW and LW.
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Description:The parametrisation of cloud droplets is described in Edwards and Slingo (1996) using the method of “thick averaging”. Pade fits are used for the variation with effective radius, which is computed from the number of cloud droplets. The parameterisation of Liu et al. (2008) is used to represent the effect of droplet size spectral dispersion.
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Description:The subgrid cloud structure is represented using the Monte Carlo Independent Column Approximation (McICA) as described in Hill et al. (2011), with the parametrisation of subgrid-scale water content variability described in Hill et al. (2015b).
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Description:Gaseous absorption uses the correlated-k method, with 41 k-terms for the major gases in six SW bands. See Table 1 in Waters et al. (2017) for details.
Description: Semi-implicit, semi-Lagrangian, using cubic interpolation.
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Description:Not applicable for global models.
Description:A horizontal diffusion scheme is not applicable for the global models.
Description:The UM’s ENDGame dynamical core uses a semi-implicit semi-Lagrangian formulation to solve the non-hydrostatic, fully-compressible deep-atmosphere equations of motion (Wood et al., 2014). The primary atmospheric prognostics are the three-dimensional wind components, virtual dry potential temperature, Exner pressure and dry density.
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Properties:
Description: Momentum advection schemeConservation method: nil:inapplicable
Conserved quantities: nil:inapplicable
Scheme staggering type: Arakawa C-grid
Scheme characteristics: staggered grid | 3rd order |
Scheme name: semi-Lagrangian
Description: Tracer advection schemeConservation method: Priestley algorithm
Conserved quantities: dry mass | tracer mass |
Scheme characteristics: cubic semi-Lagrangian
Scheme name: None
Description:For wind, w=0 at the boundary, with w-damping in the top few layers.
Canonical name: seaice
Short name:
Description: Sea ice realm
Overview: Sea ice realm
Grid
Description: The sea ice model CICE uses eORCA025, a tripolar grid with poles located in Canada, Russia and Antarctica. It has resolution of roughly 0.25 degree over most of the world. Ice velocities, forces and stresses are calculated at the corners of grid cells. The model has four vertical layers of ice, equally-spaced, and one vertical layer of snow which is used in thermodynamic calculations only if the snow is greater than 10 cm thick.
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Description:The sea ice is discretised on the eORCA025 grid in the horizontal (with surface calculations carried out on the N216 atmosphere grid), with four equally-spaced layers and an optional snow layer in the vertical, with equal dynamic and thermodynamic timesteps of 2700 seconds, and with five thickness categories.
Properties:
Description: Sea ice discretisation in the horizontalGrid: Ocean grid
Grid type: Structured grid
Scheme: Incremental remapping
Thermodynamics time step: 2700
Dynamics time step: 2700
Additional details: nil:inapplicable
Description: Sea ice vertical propertiesLayering: Multi-layers
Number of layers: 4
Additional details: The layers are equally-spaced.
Description:CICE uses a discretised sub gridscale thickness distribution, as described by Thorndike et al. (1975), which is evolved according to ice advection, ice growth and melt, and mechanical ridging.
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Description:Snow on sea ice is modelled per category. For thermodynamic purposes its area fraction is equal to the sea ice area fraction in its category, and it becomes thermodynamically active at a thickness of 10 cm. For radiative purposes, its area fraction is parameterised from its thickness.
Realm’s Key Properties
Description: Information on key assumptions made in the sea ice model, including conserved quantities, resolution and tuning.
Subtopics:
Description:Identifies the prognostic variables in the sea ice model.
Description:Most properties of seawater used to calculate the formation or melt of sea ice are located in the ocean model, NEMO.
Description:The eORCA025 grid, on which the ice state and subsurface variables are provided, has a resolution of approximately 0.25 degree in most places. In the Arctic the grid becomes irregular with respect to latitude and longitude, and has a resolution of approximately 10 km. The N216 grid, on which the ice surface variables are provided, has a longitudinal resolution of 0.83 degrees, reducing at the poles, and latitudinal resolution of 0.55 degrees.
Description:The objective of the tuning was to simulate seasonal cycles of ice extent and ice volume in the present-day control simulation (using forcing from the year 2000) which were entirely within 20% of HadISST.2 1990-2009 (Titchner and Rayner, 2014; Arctic and Antarctic) and PIOMAS 1990-2009 (Schweiger et al, 2011; Arctic only). The tuning was implemented by varying three parameters within observational constraints: bare ice albedo, snow albedo and basal drag. Due to differences in oceanic heat transport in the different model resolutions, snow albedo was tuned to different values in each, to enable realistic extent and volume simulations. For HadGEM3-GC31-MM visible snow albedo = 0.98 and near-IR = 0.70.
Citations:
Description:Sea ice is modelled as a continuum; salinity is uniform in the horizontal; for the purposes of thermodynamics, snow covers either all or none of the ice present in a category; snow has a constant density and conductivity; a constant floe diameter of 200 m is used in lateral melt calculations.
Description:Energy and mass of sea ice are conserved by the model. Salt is not conserved. The incremental remapping scheme conserves energy and mass quantities under advection. Energy is conserved in the thermodynamic scheme to a maximum error of 1e-5 Wm-2 per grid cell by iterating the scheme until the error falls below this value.
Description:The key parameters values specified include ice strength, snow conductivity and minimum ice thickness.
Realm’s Processes
Description: The ice dynamics are solved by integrating a two-dimensional simplification of the momentum equation to obtain ice velocities. The five forces which affect the sea ice in this equation are: atmosphere-ice stress, ocean-ice stress, Coriolis force, internal ice stress and sea surface tilt. The internal ice stress is calculated using the elastic-viscous-plastic formulation of Hunke (1997).
Citations:
Description: The sea ice thermodynamics scheme is based on the energy-conserving method of Bitz and Lipscomb (1999), with four sea ice layers and optionally one snow layer. Conductivity and specific heat capacity are allowed to vary depending on temperature and salinity (the latter being a constant, horizontally-uniform prescribed profile). Unlike in Bitz and Lipscomb (1999), the surface variables (e.g. surface temperature, surface fluxes) are calculated separately in the surface exchange scheme. Ice temperature and effective conductivity form the lower boundary condition for the surface exchange, while top conductive flux, sublimation flux and top melting flux are calculated by the surface exchange and act as forcing for the sea ice thermodynamics. More details can be found in West et al (2016). In situations of very thin ice, or of very cold ice occurring in small concentrations, conductive flux is transferred directly to the ice base to preserve the thermodynamic solver, as described in Ridley et al. (2018).
Citations:
Subtopics:
Description:Energy is accounted in CICE using sea ice enthalpy, the energy required to bring a unit volume of sea ice (or snow) to the melting point. Enthalpy is treated as a tracer variable and is remapped during sea ice advection with all other such variables; during thermodynamic growth and melt enthalpy is remapped between layers as the boundaries shift, and between categories as required. During sensible ice heating or cooling energy is conserved by an iterative solver to within 10^-5 Wm-2.
Citations:
Description:Ice and snow are assumed to be of constant density, hence ice and snow mass are directly related to volume. Volume is conserved during advection using incremental remapping. It is also conserved during ridging. Processes which can change ice and snow mass are: top ice melting, basal ice melting, lateral ice melting, top snow melting, congelation ice growth, new ice growth (also known as frazil ice growth), snowfall, and net sublimation. In addition, snow mass can be converted to ice mass by snow-ice formation.
Citations:
Description:Ice salinity is used in the calculation of ice conductivity and heat capacity, as specified by Bitz and Lipscomb (1999). The ice salinity used in the thermodynamic calculations is constant and horizontally uniform, but varies in the vertical. A different constant salinity, 8, is used to calculate salt flux to and from the ocean during ice growth and melt.
Citations:
Properties:
Description: Mass transport of saltSalinity type: Constant
Constant salinity value: 8
Additional details: nil:inapplicable
Description: Salt thermodynamicsSalinity type: Prescribed salinity profile
Constant salinity value: nil:inapplicable
Additional details: salinity = saltmax/2 * (1 – cos(pi * layer number – 0.5 * (ns/(ms + depth)))) where saltmax, ns, ms = 9.6, 0.407, 0.573 respectively, where layer varies from 1 to 4.
Description:The sea ice thickness distribution is represented explicitly using five thickness categories.
Description:Only the impact of melt ponds on surface albedo is included; the impacts on freshwater flux and on thermodynamics are neglected. The melt pond area fraction and depth for ice in each thickness category are calculated with the CICE topographic melt pond formulation (Flocco et al., 2010, 2012; Hunke et al., 2015). Where the pond depth on ice of a particular thickness category is shallower than 4 mm, the ponds are assumed to have no impact on albedo, and the albedo of such ponded ice is simply equal to that of bare ice. Where the pond depth is greater than 20 cm, the underlying bare ice is assumed to have no impact, and the ponded ice albedo is assumed to be equal to that of the melt pond. For ponds deeper than 4 mm but shallower than 20 cm, the underlying bare ice is assumed to have an impact on the total pond albedo, and the bare ice and melt pond albedos are combined linearly as described by Ridley et al. (2018).
Citations:
Description:Snow becomes thermodynamically active when it reaches a thickness of 10 cm; below this thickness, it is ignored by the thermodynamics scheme. For the purposes of thermodynamics, snow is assumed to cover each thickness category to a uniform thickness. Density and conductivity of snow are assumed to be constant, and heat capacity is that of fresh ice. Only a single layer of snow is permitted by the thermodynamics scheme.
Description:The floe size is specified as a single parameter, 300 m.
Description: Radiative processes at the surface are handled in the surface exchange scheme using a multi-band albedo scheme based on that used in the CCSM3 model. This has separate albedos for visible (< 700 nm) and near-infrared (> 700 nm) wavelengths for both bare ice and snow, and is described in the CICE Users Manual (Hunke et al., 2015). Penetration of radiation into the ice is not included. A correction is therefore applied to the surface albedo to account for scattering within the ice pack (Semtner, 1976). The impact of surface melt ponds on albedo is included as an addition to the CCSM3 albedo scheme, as described in the Melt Ponds subtopic under Thermodynamics below. Because the impact of melt ponds on albedo has been included explicitly, the reduction in bare ice albedo with increasing temperature, which was intended to account for melt pond formation, is not included. However, the reduction in snow albedo with increasing surface skin temperature, intended to take account of the lower albedo of melting snow, has been retained. The total gridbox albedo of ice in each thickness category is calculated for each of the two wavebands by combining the melt pond, bare ice and snow albedos, weighted by the melt pond and snow fractions. Further details of the scheme and its implementation are given by Ridley et al. (2018).
Citations:
Canonical name: land
Short name:
Description: Land Realm
Overview: Land Realm
Grid
Description: The land-surface grid maps to the atmospheric model grid.
Subtopics:
Description:Horizontal grid matches the atmospheric model grid.
Description:The total depth of the soil is 3 m with the vertical grid structured as four layers for water and energy at 0.1, 0.25, 0.65 and 2.0 m.
Realm’s Key Properties
Description: Covers the key properties of the model including its details, processes modelled, flux exchanges, atmospheric coupling treatment, land cover types, tiling, conservation properties, software properties and timestepping framework.
Citations:
Subtopics:
Description:Energy conservation has been assessed as part of the coupled model configuration development process.
Description:Land-surface is called during each atmospheric timestep (900s in GC3.1).
Description:Details of the software properties for the model, including repository, version and coding language.
Description:n/a
Realm’s Processes
Description: There is no lakes module in GC3.1.
Subtopics:
Description:No lakes model is included
Description:No lakes model is included
Description: There is no nitrogen cycling in GC3.1.
Description: The surface and sub-surface runoff fluxes are supplied to an implementation of the TRIP river routing model.
Citations:
Subtopics:
Description:The river routing oceanic discharge is claculated by direct flow from major basins.
Description: Energy balance is resolved for each land-surface tile.
Description: There is no carbon module in GC3.1.
Subtopics:
Description:There is no representation of permafrost in GC3.1 beyond the fact that some soil layers may stay frozen year round.
Description:Soil carbon is not a prognostic in the model. There is a fixed soil carbon map and one carbon pool.
Description:Ther is no carbon cycle module in GC3.1.
Properties:
Description: Allocation treatment in carbon cycleAllocation fractions: nil:inapplicable
Method: nil:inapplicable
Allocation bins: nil:inapplicable
Description: Autotrophic respiration treatment in carbon cycleMaintainance respiration: nil:inapplicable
Growth respiration: nil:inapplicable
Description: Vegetation mortality treatment in carbon cycleMethod: nil:inapplicable
Description: Phenology treatment in carbon cycleMethod: nil:inapplicable
Description: Photosynthesis treatment in carbon cycleMethod: nil:inapplicable
Description:There is no litter module GC3.1.
Description: The snow module included is a layered snow module. A maximum number of layers is preset (three in this configuration) but layers are only created when the snow depth requires it. Variable density allows compaction of the snow pack. For needleleaf trees snow is partitioned between canopy interception and throughfall, with canopy snow being stored in a single reservoir.
Citations:
Subtopics:
Description:n/a
Description: The vegetation coverage, LAI and height are prescribed by maps read from files and dynamic vegetation is turned off.
Citations:
Description: The soil hydraulics in the model is an implementation of the van Genuchten equations.
Citations:
Subtopics:
Description:Incoming precipitation is divided between canopy intercepted and throughfall. Throughfall at a rate lower than a (hydraulic conductivity based) threshold infiltrates the soil. Surplus is partitioned into surface runoff. Movement of moisture between soil layers is based on the hydraulic relationships of van Genuchten.
Properties:
Description: Drainage treatment in the soilDescription: Incoming precipitation is divided between canopy intercepted and throughfall. Throughfall at a rate lower than a (hydraulic conductivity based) threshold infiltrates the soil. Surplus is partitioned into surface runoff. Movement of moisture between soil layers is based on the hydraulic relationships of van Genuchten.
Types: Horton mechanism | topmodel-based | Dunne mechanism |
Description: Frozen Soil TreatmentNumber of ground ice layers: 4
Ice storage method: Soil water may freeze with the amount dependent on the minimisation of Gibbs free energy.
Permafrost: nil:inapplicable
Description:Heat treatment within surface / soil is a simplified version of the scheme described in Johansen (1975) and is documented in Dharssi et al. (2009). Heat fluxes take into account frozen and unfrozen components of soil moisture.
Citations:
Description:The soil map is derived from the Harmonized World Soil Database (HWSD) and the hydraulic parameters are derived from the texture properties and equations from Brookes Corey. The hydraulic and thermal parameters are derived from the grid box mean ratios of sand, silt and clay.
Description:Snow free albedo is calculated as the fraction area covered weight sum of individual tile albedos, with lake and urban albedo taking a globally prescribed value, soil albedo prescribed from a soil colour map (Best et al., 2011). Vegetated tile albedo are calculated based on radiative transfer through vegetation as described by Sellers (1985) and implemented by Essery et al. (2001). This scheme uses separate direct-beam and diffuse albedos in the visible and near-infrared for each vegetation type and requires four parameter values for leaf reflection and leaf scattering coefficients for both near-infrared and photosynthetically active radiation. Beams are intercepted/scattered through the canopy, where leaves are approximated as isotropic scattering elements. A fraction of both beams penetrate the canopy and is reflected by the bare soils prescribed albedo.
Citations:
Canonical name: ocean
Short name:
Description: Ocean Realm
Overview: Ocean Realm
Grid
Description: The horizontal grid is based on the ORCA tripolar Arakawa C-grid (Madec, 2008). The vertical grid varies in thickness over time (the z* coordinate of Adcroft and Campin, 2004). Cells spanning partial model levels are allowed next to the bathymetry (Barnier et al., 2006).
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Subtopics:
Description:The horizontal grid is based on the ORCA tripolar Arakawa C-grid (Madec, 2008). The vertical grid varies in thickness over time (the z* coordinate of Adcroft and Campin, 2004). Cells spanning partial model levels are allowed next to the bathymetry (Barnier et al., 2006).
Properties:
Description: Type of horizontal discretisation scheme in oceanStaggering: Arakawa C-grid
Type: Two north poles (ORCA-style)
Scheme: Finite difference / Arakawa C-grid
Description: Properties of vertical discretisation in oceanCoordinates: Z*-coordinate
Partial steps: true
Realm’s Key Properties
Description: Key properties provides an overview of the model and summary properties for bathymetry, non-oceanic water, seawater and the software. Sub-topics address conservation, resolution and tuning.
Citations:
Properties:
Description: Properties of bathymetry in oceanReference dates: Present day
Type: true
Ocean smoothing: The model bathymetry is based on the ETOPO1 dataset (NOAA, 2009) and GEBCO (GEBCO, 2008) with bathymetry on the Antarctic shelf based on IBSCO (Arndt et al., 2013). The Antarctic coastline is smoothed to remove single grid point inlets and avoid the spurious accumulation of sea ice related to the different grid of the coupled sea ice model.
Source: ETOPO1, GEBCO, IBSCO
Description: Non oceanic waters treatement in oceanIsolated seas: Caspian Sea, Aral Sea, Lake Victoria, American Great Lakes as ocean points
River mouth: nil:inapplicable
Description: Physical properties of seawater in oceanEos type: Polynomial EOS-80
Eos functional temp: Potential temperature
Eos functional salt: Practical salinity Sp
Eos functional depth: Depth (meters)
Ocean freezing point: UNESCO 1983
Ocean specific heat: 3991.8679571196
Ocean reference density: 1026
Description: Software properties of ocean codeRepository: http://forge.ipsl.jussieu.fr/nemo/browser/branches/UKMO/dev_r5518_GO6_package
Code version: 3.6 stable
Code languages: Fortran
Subtopics:
Description:The horizontal grid is eORCA025, which is a tripolar Arakawa C-grid (Madec, 2008), with the southern boundary extended from 77S to 85S to permit the modelling of circulation under ice shelves in Antarctica (Mathiot et al., 2017). This has nominal 0.25 degree resolution (1442 x 1050 grid points) at the equator decreasing poleward and uses an isotropic Mercator grid between 22N and 67S, with meridional refinement in the tropics. Three quasi-isotropic bipolar grids are joined to the Mercator grid; one north of 22N with poles in Siberia and Canada, and two south of 67S in the Weddell Sea and Bellingshausen, Amundsen and Ross Sea sectors. The model has 75 vertical levels which are a double tanh function of depth such that the thickness increases from 1 m near the surface to 200 m at 6000 m. This provides high resolution near the surface for short- to mid-range forecasting purposes, while retaining reasonable resolution at mid-depths for long-term climate studies.
Citations:
Description:Tuning details covered in coupled model information.
Description:The tracer equations are written in flux form and are conservative. The use of a nonlinear free surface (z* vertical coordinate) ensures the conservation of heat and salt. A vector-invariant form of the momentum equations is used with an energy and enstrophy conserving scheme for advection.
Realm’s Processes
Description: Timestepping of tracers and dynamics uses a leap-frog scheme and Asselin filter with an integration time step of 45 minutes. Vertical diffusion of tracers uses an implicit backward time stepping scheme.
Properties:
Description: Baroclinic dynamics in oceanType: Preconditioned conjugate gradient
Scheme: Leap-frog + Asselin filter
Time step: 900
Description: Barotropic time stepping in oceanSplitting: None
Time step: 900
Description: Properties of tracers time stepping in oceanScheme: Leap-frog + Asselin filter
Time step: 900
Description: Vertical physics time stepping in oceanMethod: implicit / backward stepping
Description: Advection of momentum uses a vector-invariant (rotational and irrotational) formulation. The irrotational component is formulated according to Hollingsworth et al. (1983) in order to avoid vertical numerical instabilities. The vorticity term is calculated using the energy and enstrophy conserving scheme of Arakawa and Lamb (1981). Advection of tracers uses the Total Variance Dissipation (TVD) scheme of Zalesak (1979).
Citations:
Subtopics:
Description:Advection of momentum uses a vector-invariant (rotational and irrotational) formulation. The irrotational component is formulated according to Hollingsworth et al. (1983) in order to avoid vertical numerical instabilities. The vorticity term is calculated using the energy and enstrophy conserving scheme of Arakawa and Lamb (1981). Advection of tracers uses the Total Variance Dissipation (TVD) scheme of Zalesak (1979).
Citations:
Description:Advection of momentum uses a vector-invariant (rotational and irrotational) formulation. The irrotational component is formulated according to Hollingsworth et al. (1983) in order to avoid vertical numerical instabilities. The vorticity term is calculated using the energy and enstrophy conserving scheme of Arakawa and Lamb (1981). Advection of tracers uses the Total Variance Dissipation (TVD) scheme of Zalesak (1979).
Citations:
Description:Advection of momentum uses a vector-invariant (rotational and irrotational) formulation. The irrotational component is formulated according to Hollingsworth et al. (1983) in order to avoid vertical numerical instabilities. The vorticity term is calculated using the energy and enstrophy conserving scheme of Arakawa and Lamb (1981). Advection of tracers uses the Total Variance Dissipation (TVD) scheme of Zalesak (1979).
Citations:
Description: Lateral diffusion of momentum is on geopotential surfaces and uses a laplacian operator with a coefficient of 2×10^4 m2s-1, reducing linearly with the grid spacing in order to avoid numerical diffusion instabilities and reducing with a hyperbolic profile over depth. Lateral diffusion of tracers is along isoneutral surfaces and uses a laplacian operator with a coefficient of 1000 m2s-1, reducing linearly with the grid spacing. Adiabatic mixing by transient mesoscale eddies is parameterised according to Held and Larichev (1996).
Citations:
Subtopics:
Description:Lateral diffusion of momentum is on geopotential surfaces and uses a laplacian operator with a coefficient of 2×10^4 m2s-1, reducing linearly with the grid spacing in order to avoid numerical diffusion instabilities and reducing with a hyperbolic profile over depth. Lateral diffusion of tracers is along isoneutral surfaces and uses a laplacian operator with a coefficient of 1000 m2s-1, reducing linearly with the grid spacing. Adiabatic mixing by transient mesoscale eddies is parameterised according to Held and Larichev (1996).
Properties:
Description: Properties of eddy viscosity coeff in lateral physics momemtum scheme in the oceanType: Space varying
Constant coefficient: nil:inapplicable
Variable coefficient: -1.5e11 m4/s at equator reducing with cube of grid spacing
Coeff background: 0
Coeff backscatter: false
Description: Properties of lateral physics operator for momentum in oceanDirection: Geopotential
Order: Bi-harmonic
Discretisation: Second order
Description:Lateral diffusion of momentum is on geopotential surfaces and uses a laplacian operator with a coefficient of 2×10^4 m2s-1, reducing linearly with the grid spacing in order to avoid numerical diffusion instabilities and reducing with a hyperbolic profile over depth. Lateral diffusion of tracers is along isoneutral surfaces and uses a laplacian operator with a coefficient of 1000 m2s-1, reducing linearly with the grid spacing. Adiabatic mixing by transient mesoscale eddies is parameterised according to Held and Larichev (1996).
Citations:
Properties:
Description: Properties of eddy diffusity coeff in lateral physics tracers scheme in the oceanType: Space varying
Constant coefficient: nil:inapplicable
Variable coefficient: 150 m2/s at equator reducing linearly with the grid spacing
Coeff background: 0
Coeff backscatter: false
Description: Properties of eddy induced velocity (EIV) in lateral physics tracers scheme in the oceanType: nil:inapplicable
Constant val: nil:inapplicable
Flux type: nil:inapplicable
Added diffusivity: none
Description: Properties of lateral physics operator for tracers in oceanDirection: Isoneutral
Order: Harmonic
Discretisation: Second order
Description: The vertical mixing of tracers and momentum is parameterised using a modified version of the Gaspar et al. (1990) Turbulent Kinetic Energy (TKE) scheme. Unresolved mixing due to internal wave breaking is represented by a background vertical diffusivity of 1.2×10^-5 m2/s which decreases linearly from 15 degrees latitude to 1.2×10^-6 m2/s at 5 degrees latitude (Gregg et al., 2003), and a globally constant background viscosity of 1.2×10^-4 m2/s. The wave breaking parameterisation of Craig and Banner (1994) is used as the TKE surface boundary condition. Vertical mixing in the boundary layer includes an ad-hoc parameterisation of near-inertial wave breaking (Madec, 2008; Rodgers et al., 2014) with an e-decay vertical length scale increasing sinusoidally from 0.5 m at the equator to 10 m and 30 m at ~13 N and ~40 S respectively (Storkey et al., 2018). Langmuir turbulence is parameterised following Axell (2002). Vertical mixing in the ocean interior includes parameterisations of tidal mixing with a special formulation for the Indonesian Throughflow (Simmons et al., 2004; Koch-Larrouy et al., 2008) and double diffusive mixing (Merryfield et al., 1999). Convection is parameterised as an enhanced vertical diffusivity of 10 m2/s where the density profile is statically unstable.
Citations:
Subtopics:
Description:The vertical mixing of tracers and momentum is parameterised using a modified version of the Gaspar et al. (1990) Turbulent Kinetic Energy (TKE) scheme. Unresolved mixing due to internal wave breaking is represented by a background vertical diffusivity of 1.2×10^-5 m2/s which decreases linearly from 15 degrees latitude to 1.2×10^-6 m2/s at 5 degrees latitude (Gregg et al., 2003), and a globally constant background viscosity of 1.2×10^-4 m2/s. The wave breaking parameterisation of Craig and Banner (1994) is used as the TKE surface boundary condition. Vertical mixing in the boundary layer includes an ad-hoc parameterisation of near-inertial wave breaking (Madec, 2008; Rodgers et al., 2014) with an e-decay vertical length scale increasing sinusoidally from 0.5 m at the equator to 10 m and 30 m at ~13 N and ~40 S respectively (Storkey et al., 2018). Langmuir turbulence is parameterised following Axell (2002). Vertical mixing in the ocean interior includes parameterisations of tidal mixing with a special formulation for the Indonesian Throughflow (Simmons et al., 2004; Koch-Larrouy et al., 2008) and double diffusive mixing (Merryfield et al., 1999). Convection is parameterised as an enhanced vertical diffusivity of 10 m2/s where the density profile is statically unstable.
Properties:
Description: Properties of vertical physics in oceanLangmuir cells mixing: true
Description: Properties of boundary layer (BL) mixing on momentum in the oceanType: Turbulent closure – TKE
Closure order: 1.5
Constant: nil:inapplicable
Background: constant, 1.2e-4 m2/s
Description: Properties of boundary layer (BL) mixing on tracers in the oceanType: Turbulent closure – TKE
Closure order: 1.5
Constant: nil:inapplicable
Background: constant but reduced in tropics, 1.2e-5 m2/s
Description:The vertical mixing of tracers and momentum is parameterised using a modified version of the Gaspar et al. (1990) Turbulent Kinetic Energy (TKE) scheme. Unresolved mixing due to internal wave breaking is represented by a background vertical diffusivity of 1.2×10^-5 m2/s which decreases linearly from 15 degrees latitude to 1.2×10^-6 m2/s at 5 degrees latitude (Gregg et al., 2003), and a globally constant background viscosity of 1.2×10^-4 m2/s. The wave breaking parameterisation of Craig and Banner (1994) is used as the TKE surface boundary condition. Vertical mixing in the boundary layer includes an ad-hoc parameterisation of near-inertial wave breaking (Madec, 2008; Rodgers et al., 2014) with an e-decay vertical length scale increasing sinusoidally from 0.5 m at the equator to 10 m and 30 m at ~13 N and ~40 S respectively (Storkey et al., 2018). Langmuir turbulence is parameterised following Axell (2002). Vertical mixing in the ocean interior includes parameterisations of tidal mixing with a special formulation for the Indonesian Throughflow (Simmons et al., 2004; Koch-Larrouy et al., 2008) and double diffusive mixing (Merryfield et al., 1999). Convection is parameterised as an enhanced vertical diffusivity of 10 m2/s where the density profile is statically unstable.
Properties:
Description: Properties of interior mixing in the oceanConvection type: Enhanced vertical diffusion
Tide induced mixing: baroclinic based on climatologies
Double diffusion: true
Shear mixing: false
Description: Properties of interior mixing on momentum in the oceanType: Turbulent closure / TKE
Constant: nil:inapplicable
Profile: false
Background: constant, 1.2e-4 m2/s
Description: Properties of interior mixing on tracers in the oceanType: Turbulent closure / TKE
Constant: nil:inapplicable
Profile: false
Background: constant but reduced at tropics, 1.2e-5 m2/s
Description: The model uses a non-linear free surface in which the cell thicknesses throughout the water column are allowed to vary with time (the z* coordinate of Adcroft and Campin, 2004). This permits an exact representation of the surface freshwater flux. The equation for the surface pressure gradient is solved using a filtered solution in which the fast gravity waves are damped by an additional force in the equation (Roullet and Madec, 2000). An advective and diffusive bottom boundary layer scheme is used (Beckmann and Doscher, 1997) with a lateral mixing coefficient of 1000 m2/s.
Citations:
Properties:
Description: Properties of bottom boundary layer in oceanOverview: The model uses a non-linear free surface in which the cell thicknesses throughout the water column are allowed to vary with time (the z* coordinate of Adcroft and Campin, 2004). This permits an exact representation of the surface freshwater flux. The equation for the surface pressure gradient is solved using a filtered solution in which the fast gravity waves are damped by an additional force in the equation (Roullet and Madec, 2000). An advective and diffusive bottom boundary layer scheme is used (Beckmann and Doscher, 1997) with a lateral mixing coefficient of 1000 m2/s.
Type of bbl: Advective and Diffusive
Lateral mixing coef: 1000
Sill overflow: nil:inapplicable
Description: Properties of free surface in oceanScheme: Non-linear filtered
Embeded seaice: false
Description: Freshwater runoff from land is added to the surface layer of the ocean, assuming the runoff is fresh and at the same temperature as the local SST. An enhanced vertical diffusion of 2×10^-3 m2/s is added over the top 10 m of the water column at runoff points to avoid instabilities associated with very shallow fresh layers at the surface. Meltwater fluxes from land ice are parameterised using a Lagrangian iceberg model (Bigg et al., 1997; Martin and Adcroft, 2010) and a prescribed freshwater flux at the edge of ice shelves to represent basal melting (Mathiot et al., 2017). Penetration of the shortwave heat flux into the ocean is parameterised using a 3-band RGB scheme (Lengaigne et al., 2007) assuming a constant chlorophyll concentration of 0.05 g.Chl/L. Dense overflows are parameterised using an advective and diffusive bottom boundary layer scheme (Beckmann and Doscher, 1997). A quadratic bottom friction is used with enhancements in the Indonesian Throughflow, Denmark Strait and Bab el Mandab regions.
Citations:
Subtopics:
Description:Freshwater runoff from land is added to the surface layer of the ocean, assuming the runoff is fresh and at the same temperature as the local SST. An enhanced vertical diffusion of 2×10^-3 m2/s is added over the top 10 m of the water column at runoff points to avoid instabilities associated with very shallow fresh layers at the surface. Meltwater fluxes from land ice are parameterised using a Lagrangian iceberg model (Bigg et al., 1997; Martin and Adcroft, 2010) and a prescribed freshwater flux at the edge of ice shelves to represent basal melting (Mathiot et al., 2017). Penetration of the shortwave heat flux into the ocean is parameterised using a 3-band RGB scheme (Lengaigne et al., 2007) assuming a constant chlorophyll concentration of 0.05 g.Chl/L. Dense overflows are parameterised using an advective and diffusive bottom boundary layer scheme (Beckmann and Doscher, 1997). A quadratic bottom friction is used with enhancements in the Indonesian Throughflow, Denmark Strait and Bab el Mandab regions.
Properties:
Description: Properties of momentum bottom friction in oceanType: Non-linear
Description: Properties of momentum lateral friction in oceanType: Free-slip
Description:Freshwater runoff from land is added to the surface layer of the ocean, assuming the runoff is fresh and at the same temperature as the local SST. An enhanced vertical diffusion of 2×10^-3 m2/s is added over the top 10 m of the water column at runoff points to avoid instabilities associated with very shallow fresh layers at the surface. Meltwater fluxes from land ice are parameterised using a Lagrangian iceberg model (Bigg et al., 1997; Martin and Adcroft, 2010) and a prescribed freshwater flux at the edge of ice shelves to represent basal melting (Mathiot et al., 2017). Penetration of the shortwave heat flux into the ocean is parameterised using a 3-band RGB scheme (Lengaigne et al., 2007) assuming a constant chlorophyll concentration of 0.05 g.Chl/L. Dense overflows are parameterised using an advective and diffusive bottom boundary layer scheme (Beckmann and Doscher, 1997). A quadratic bottom friction is used with enhancements in the Indonesian Throughflow, Denmark Strait and Bab el Mandab regions.
Citations:
Properties:
Description: Properties of surface fresh water forcing in oceanFrom atmopshere: Freshwater flux
From sea ice: Freshwater flux
Forced mode restoring: Restoration to monthly climatology using a -33.33 mm/day/psu retroaction coefficient.
Description: Properties of sunlight penetration scheme in oceanExtinction depths: 2.619 m (red), 12.713 m (green), 39.984 m (blue)
Scheme: 3 extinction depth
Ocean colour: false
Extinction depth description: The three-band RGB model of Lengaigne et al. (2007) is used, in which visible light is split into red (600-700nm), green (500-600nm) and blue (400-500nm) wavebands. The given extinction depths are valid for a constant 0.05 g.Chl/L chlorophyll concentration.
Canonical name: aerosol
Short name:
Description: n/a
Overview: n/a
Grid
Description: The grid used for the aerosols model is the same as for the atmosphere model.
Subtopics:
Description:432×325 grid cells in horizontal with resolution of 0.83 x 0.56 degrees respectively, translating to approx 60 km in mid-latitudes. 85 hybrid levels in the vertical extending upto 85.0 km
Realm’s Key Properties
Description: Multi-component and multi-modal aerosol model that transports aerosol particle number and component mass concentrations of sulfate, sea salt, black carbon and organic carbon in five internally mixed log-normal modes. Mineral dust is simulated separately using the 6-bin mass-based CLASSIC scheme.
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Properties:
Description: Software properties of aerosol codeRepository: https://code.metoffice.co.uk/trac/um
Code version: UM11.0
Code languages: Fortran 90/2003
Subtopics:
Description:Specific timestepping (operator splitting) performed with a timestep of 900 seconds done in the atmosphere model for aerosol advection and a timestep of 3600 seconds for aerosol physics.
Description:Information on 3D and 2D forcing variables and the frequency with which the forcings are applied.
Description:432×325 grid cells in horizontal with resolution of 0.83 x 0.56 degrees respectively, translating to approx 60 km in mid-latitudes. 85 Hybrid Levels in the vertical extending upto 85.0 km.
Description:Mineral dust emissions have three tuning parameters described in Woodward (2001), these tune the global total emission, soil moisture dependence and threshold frictional velocity dependence. Tuned to give as good agreement as possible with observed dust surface concentration and optical depths. Biomass burning emissions of BC and OC are scaled to give improved AOD agreement in biomass burning regions following Johnson et al., 2016. SOA yield from Monoterpene has a scaling factor of 2.0. In-cloud oxidation of SO2 is reduced by 25% following Mulcahy et al., 2018 in prep.
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Realm’s Processes
Description: Transport of aerosols and aerosol-chemistry tracers is handled by the atmospheric model.
Description: For HadGEM3/GC3.1 prescribed emissions of SO2, BC, OC, land and oceanic DMS, Isoprene and Monoterpene. Online emissions for sea-salt and dust.
Description: For HadGEM3 the concentrations in the model are unknown with no species listed.
Description: Aerosol direct and indirect effects included. Optical properties calculated from Mie theory assuming homogeneous spheres where all components present in a size mode are internally mixed. Mineral dust aerosol also modelled as spheres and externally mixed with the modal aerosol.
Subtopics:
Description:Information on absorption mass coefficients for black carbon, dust and organics.
Description:Information on internal and external mixing with respect to chemical composition.
Description:The impact of H2O on aerosols
Description:Aerosol-radiation scheme RADAER is described in Bellouin et al., 2013. Optical properties for each aerosol mode vary interactively depending on modal radius and chemical composition (including any water).
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Description:Aerosol activation of cloud droplets is simulated using the UKCA-ACTIVATE scheme (West et al., 2014), which implements the Abdul Razzak Ghan (2000) parameterisation. There is no minimum limit on CCN, but the activation scheme assumes a minimum CDNC of 5 cm-3 to avoid numerical failures.
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Description: UKCA-GLOMAP-mode (Mann et al., 2010, 2012) is a multi-component and multi-modal aerosol model representing microphysical processes in the form of size-resolved primary emissions, new particle formation, condensation, coagulation, cloud processing, dry deposition, sedimentation, nucleation scavenging and impaction scavenging. The model transports aerosol particle number and component mass concentrations of sulfate, sea salt, black carbon and organic carbon in five internally mixed log-normal modes. Mineral dust is simulated separately using the 6-bin emission scheme of Woodward (2001). This represents the direct interaction of dust with radiation, but not interactions with cloud microphysics. Dust does not mix internally with the aerosols represented by the modal scheme.
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Coupled components
MetUM-HadGEM3-GA7.1 N216
Canonical ID: MetUM-HadGEM3-GA7.1 N216
Long name: MetUM-HadGEM3-GA7.1 N216
Version: 7.1
Description: The atmosphere component of HadGEM3-GC3.1 is labelled GA7.1 and is fully described in Walters et al. (2017) and Mulcahy et al. (2018). This atmosphere component is developed for weather forecasting applications as well as seasonal-to-centennial climate simulations. The model includes a number of major developments since HadGEM2-AO, principally: a new dynamical core; increased vertical resolution with a higher model top to fully resolve the stratosphere; prognostic treatment of cloud, condensate and rain amounts; and a 2-moment 5-mode aerosol scheme. Other developments include an improved treatment of gaseous absorption in the radiation scheme, improvements to the treatment of warm rain and ice clouds and an improvement to the numerics in the model’s convection scheme.
Model type: Atm Only
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CICE-HadGEM3-GSI8 N216
Canonical ID: CICE-HadGEM3-GSI8 N216
Long name: CICE-HadGEM3-GSI8 N216
Version: 5.1.2
Description: The sea ice configuration in HadGEM3 GC3.1, GSI8.1, is described by Ridley et al. (2018). It is largely based on the the Los Alamos sea ice model CICE, version 5.1.2. It is modified as described in West et al. (2016) with surface variables calculated in the surface exchange scheme JULES, on the atmosphere grid. It is configured to use a 5-category thickness distribution, multilayer thermodynamics and explicit meltponds for albedo calculation.
Model type: Process
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JULES-HadGEM3-GL7.1 N216
Canonical ID: JULES-HadGEM3-GL7.1 N216
Long name: JULES-HadGEM3-GL7.1 N216
Version: UM10.7 VN4.8
Description: Land-surface component of HadGEM3 GC3.1. The joint UK Land Environment Simulator (JULES) model is based on the Met Office Surface Exchange Scheme (MOSES). Sub-grid scale surface heterogeneity is represented by a tiling system. A multi-layer snow scheme has been implemented since HadGEM2.
Model type: Process
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NEMO-HadGEM3-GO6.0 eORCA025
Canonical ID: NEMO-HadGEM3-GO6.0 eORCA025
Long name: NEMO-HadGEM3-GO6.0 eORCA025
Version: 3.6 stable
Description: The ocean component of the low-resolution version of HadGEM3 GC3.1 is the 0.25 degree GO6 configuration (Kuhlbrodt et al., 2018; Storkey et al., 2018) of version 3.6_stable of the NEMO primitive equation model (Madec, 2008). Since 2010 the UK Met Office, the National Oceanography Centre and the British Antarctic Survey have collaborated on the development of standard global ocean model configurations based on the NEMO code. These are intended to be used for a variety of applications across a range of timescales from ocean forecasting a few days ahead to century-scale climate modelling. The use of a single ocean model configuration for multiple applications is in the spirit of the seamless forecasting approach. The GO6 ocean model is the ocean component of the GC3.1 version of the Met Office Hadley Centre coupled climate model (Williams et al., 2017) and the ocean component of the UKESM1 UK Earth System model (Kuhlbrodt et al., 2018), both of which will be used in CMIP6 simulations and associated OMIP simulations. GO6 is expected to be incorporated into future versions of the FOAM ocean forecasting system, the GloSea seasonal forecasting system and the DePreSys decadal forecasting system. The previous configuration GO5 was only released at a single resolution of a nominal 1/4 degree horizontal grid spacing. GO6 is a traceable hierarchy of three horizontal resolutions: 1 degree, 1/4 degree and 1/12 degree, all with the same vertical grid – traceable means that the only differences between the three configurations are those that can be justified as necessitated by the change in resolution, an example being tuning of the horizontal viscosity.
Model type: Ocean Only
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UKCA-GLOMAP-mode N216
Canonical ID: UKCA-GLOMAP-mode N216
Long name: UKCA-GLOMAP-mode N216
Version: part of UM11.0
Description: UKCA-GLOMAP-mode (Mann et al., 2010, 2012) is a multi-component and multi-modal aerosol model representing microphysical processes in the form of size-resolved primary emissions, new particle formation, condensation, coagulation, cloud processing, dry deposition, sedimentation, nucleation scavenging and impaction scavenging. The model transports aerosol particle number and component mass concentrations of sulfate, sea salt, black carbon and organic carbon in five internally mixed log-normal modes. Mineral dust is simulated separately using the 6-bin emission scheme of Woodward et al. (2001). This represents the direct interaction of dust with radiation, but not interactions with cloud microphysics. Dust does not mix internally with the aerosols represented by the modal scheme.
Model type: Process
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