Climate model: second generation Canadian earth system model

Canadian Centre for Climate Modelling and Analysis

The second generation Canadian Earth System Model (CanESM2) consists of the physical coupled atmosphere-ocean model CanCM4 coupled to a terrestrial carbon model (CTEM) and an ocean carbon model (CMOC).

1. Treatment of carbon dioxide (CO2)

The land and ocean components of the carbon cycle in CanESM2 are operable for two experimental designs:

  1. an emissions-driven mode, where the atmospheric carbon dioxide (CO2) concentration is a freely evolving 3D tracer in the model;
  2. a concentrations-driven mode, where the atmospheric CO2 concentration is prescribed externally.  

In the emissions-driven mode, the anthropogenic CO2 emissions are specified and since the interactive land and ocean carbon cycle components simulate the atmosphere-land and atmosphere-ocean CO2 fluxes, respectively, the model is able to simulate the evolution of atmospheric CO2 burden.  In this case, the model simulates the transport of CO2 in the atmosphere producing 3D structure, an annual cycle, and inter-annual variability.  In the concentrations-driven mode, the atmosphere-land and atmosphere-ocean CO2 fluxes remain interactively determined and therefore model results can be used to diagnose the anthropogenic CO2 emissions that are compatible with a given atmospheric CO2 pathway at the global scale. A single scalar value of atmospheric CO2 concentration, which may be time evolving, is imposed at all geographical and vertical locations in the model in the concentration-driven mode. 

2. Carbon cycle components of CanESM2

The ocean and land carbon cycle components of CanESM2 are essentially the same as those in CanESM1 and are represented by the Canadian Model of Ocean Carbon (CMOC) (Christian et al., 2010 and references therein) and the Canadian Terrestrial Ecosystem Model (CTEM) (Arora et al., 2009; Arora and Boer, 2010), respectively.

2.1 Canadian Model of Ocean Carbon (CMOC)

The Canadian Model of Ocean Carbon (CMOC) incorporates an inorganic chemistry module (solubility pump) and an ecosystem model (organic and carbonate pumps) for simulating the ocean-atmosphere exchange of CO2 (Zahariev et al., 2008). The inorganic chemistry module is based on protocols from the Ocean Carbon Model Intercomparison Project (OCMIP) - Phase 2, with dissolved inorganic carbon (DIC) and total alkalinity as prognostic variables. The piston or gas transfer velocity is proportional to the square of the 10-meter wind speed and the intercept is adjusted to conserve the global mean piston velocity (Wanninkhof, 1992). There is no gas exchange through sea ice.

Figure 1: The structure of the ecosystem component of the Canadian Model of Ocean Carbon (CMOC) based on an NPZD model.

The ecosystem component of CMOC (Figure 1) is based on the NPZD model of Denman and Peña (1999). A single nutrient variable (N) implicitly represents nitrate, ammonium and urea including inputs from surface dinitrogen fixation. Phytoplankton population (P) growth is limited by light, temperature, nitrogen, and iron. Zooplankton (Z) graze on the phytoplankton population, and their mortality is modelled on the basis of linear and quadratic terms implying predation by unresolved higher tropic levels. The detritus variable (D) implicitly combines dissolved, suspended and sinking organic matter, with a constant sinking rate. The currency of the model is nitrogen, and the biological effect on dissolved inorganic carbon (DIC) is calculated via a constant Redfield C:N ratio. Chlorophyll (Chl) is a separate prognostic variable based on a varying Chl:N ratio. Phytoplankton growth and remineralization rates are temperature-dependent. Iron limitation of photosynthesis is implemented through a surface ocean distribution of an iron limitation factor that is derived from normalized monthly observational estimates of the climatological annual minimum nitrate concentration (Zahariev et al., 2008). This factor is based on the assumption that the lowest nitrate concentration observed during the seasonal cycle is proportional to the degree of iron limitation. A more detailed description of these and other aspects of CMOC is available in Zahariev et al. (2008).

2.2 Canadian Terrestrial Ecosystem Model (CTEM)

Land-atmosphere exchange of CO2 in CanESM2 is modelled using the Canadian Terrestrial Ecosystem Model (CTEM) (Arora, 2003; Arora and Boer, 2003; 2005) that simulates three live vegetation pools (leaves, stem, and root) and two dead carbon pools (litter and soil organic carbon) for nine plant functional types (PFTs) as illustrated in Figure 2. CTEM is coupled to the Canadian Land Surface Scheme (CLASS 2.7) (Verseghy, 1991; Verseghy et al., 1993) to produce fluxes of energy, water, and CO2 at the land surface. The photosynthesis sub-module of CTEM is based on the biochemical model of Farquhar et al. (1980) and Collatz et al. (1991, 1992). The current version uses a single-leaf photosynthesis approach with coupling between photosynthesis and canopy conductance based on vapor pressure deficit (Leuning, 1995). The photosynthesis and autotrophic and heterotrophic respiration sub-modules of CTEM, as described in Arora (2003), are used to calculate net primary and net ecosystem productivity. Positive net primary productivity (NPP) is allocated to leaves, stem, and root based on light, root water, and leaf phenological status. The phenology sub-module of CTEM uses a carbon-gain approach in which leaf onset is initiated when it is beneficial for the plant, in carbon terms, to produce new leaves. Leaf offset is initiated by unfavourable environmental conditions including shorter day length, cooler temperatures, and low soil moisture (Arora and Boer, 2005). Photosynthesis operates at the atmosphere model time step of 20 minutes; all other sub-modules of CTEM operate at a daily time step.

Figure 2: Structure of the Canadian Terrestrial Ecosystem Model (CTEM) and the nine plant functional types for which all ecosystem processes are modelled

Allocation to, and the litter and respiratory losses from, the three vegetation components (leaves, stem, and root) result in time-varying biomasses that are reflected in the structural vegetation attributes used in the energy and water balance calculations of the land surface scheme (Arora and Boer, 2005). CTEM does not include N or P cycles, and the effects of nutrient limitation on photosynthesis are not modelled explicitly.

2.2.1 Land use change emissions

Land use change (LUC) emissions can be specified as an external source or can be modelled explicitly in CTEM on the basis of specified changes in land cover. Explicit modelling of LUC emissions ensures that the modelled net land-atmosphere CO2 exchange is consistent with the specified changes in land cover.

An increase in crop area implies the replacement of natural vegetation by crops (we use the term deforestation). The deforested biomass is divided into three fractions representing the amounts:

  1. combusted or used for fuel wood with no time delay;
  2. left as slash or used for pulp and paper products;
  3. used for durable wood products. The fractions allocated to these three uses depend on the above-ground vegetation biomass density and whether the PFTs are woody or herbaceous (see Table 1 in Arora et al. (2009)). The fraction allocated to slash or pulp and paper products are transferred to the litter pool and the fraction allocated to wood products is allocated to the model's soil carbon pool.

Over the cropland fraction of a grid cell a simple crop model is used. Crops increase their biomass depending on environmental conditions, and harvesting is initiated when the air temperature remains below 8°C for 5 consecutive days, or when the crop LAI reaches a threshold (3.5 m2/m2 for C3 crops and 4.5 m2/m2 for C4 crops) signifying that the crops have matured (Arora and Boer, 2005). Crops are harvested over a period of 15 days and the harvested biomass contributes to the litter pool. Harvesting ensures that vegetation biomass does not keep increasing on crop lands as CO2 increases, and thus prevents croplands from sequestering aboveground carbon like forests. When croplands are abandoned, the fractional coverage of other PFTs is increased. The result is that the vegetation density is reduced, and carbon is sequestered until a new equilibrium is reached, providing a carbon sink associated with regrowth as abandoned croplands revert back to natural vegetation.


Arora, V. K. et al. (2009) The effect of terrestrial photosynthesis down-regulation on the 20th century carbon budget simulated with the CCCma Earth System Model, J. Clim., 22, 6066-6088.

Arora, V.K. (2003) Simulating energy and carbon fluxes over winter wheat using coupled land surface and terrestrial ecosystem models, Agricultural and Forest Meteorology, 118, 21-47.

Arora, V.K. and G.J. Boer (2003) A representation of variable root distribution in dynamic vegetation models, Earth Interactions, Vol. 7, Paper 6, 19 pp.

Arora, V.K. and G.J. Boer (2005) A parameterization of leaf phenology for the terrestrial ecosystem component of climate models, Global Change Biology, 11, 39-59.

Arora, V.K. and G.J. Boer (2010) Uncertainties in the 20th century carbon budget associated with land use change, Global Change Biology, 16(12), 3327.3348.

Christian, J. R. et al. (2010): The global carbon cycle in the CCCma earth system model CanESM1: preindustrial control simulation, J. Geophy. Res. (Biogeosciences), 115, G03014, doi:10.1029/2008JG000920.

Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry (1991) Physiological and environmental regulation of stomatal conductance, photosynthesis, and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest Meteorology, 54, 107-136.

Collatz, G. J., M. Ribas-Carbo, and J. A. Berry (1992) Coupled photosynthesis-stomatal conductance model for leaves of C4 plants. Aust. J. Plant Physiol., 19, 519-538.

Denman, K., and A. Peña (1999) A coupled 1-D biological/physical model of the northeast subarctic Pacific Ocean with iron limitation, Deep-Sea Res. II, 46, 2877.2908.

Farquhar, G.D., S. von Caemmere, and J.A. Berry (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta, 149, 78-90.

Houghton, R. A., and J. L. Hackler (2002) Carbon flux to the atmosphere from land-use changes. In: Trends. CDIAC, ORNL, Oak Ridge.

Khairoutdinov, M. and Y. Kogan (2000) A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus, Mon. Wea. Rev., 128, 229-243.

Large W.G., G. Danabasoglu, J.C. McWilliams, P.R. Gent and F.O. Bryan (2001) Equatorial circulation of a global ocean climate model with aniostropic horizontal viscosity. J. Phys. Oceanogr., 31, 518-536.

Large, W.G., J.C. McWilliams, and S.C. Doney (1994) Oceanic vertical mixing: A review and a model with a vertical K-profile boundary layer parameterization. Rev Geophys., 363-403.

Leuning, R. (1995) A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant, Cell and Environment, 18, 339-355.

Li, J. and H. W. Barker (2005) A radiation algorithm with correlated k-distribution. Part I: local thermal equilibrium. Journal of Atmospheric Science, Journal of Atmospheric Science, 62, 286-309.

Ma, X., von Salzen, K., and Cole, J. (2010) Constraints on interactions between aerosols and clouds on a global scale from a combination of MODIS-CERES satellite data and climate simulations, Atmos. Chem. Phys., 10, 9851-9861, doi:10.5194/acp-10-9851-2010.

Scinocca, J. S., and N. A. McFarlane (2000) The parameterization of drag induced by stratified flow over anisotropic orography, Q. J. R. Meteorol. Soc., 126, 2353-2393.

Scinocca, J. S., N.A. McFarlane, M. Lazare, J. Li, D. Plummer (2008) The CCCma third-generation AGCM and its extension into the middle atmosphere. Atmos. Chem. Phys., 8, 7055.7074.

Verseghy, D. (1991) CLASS - A Canadian land surface scheme for GCMs, I, Soil Model, Int. J. Climatol., 11, 111-133.

Verseghy, D., N.A. McFarlane, and M. Lazare (1993) CLASS - A Canadian land surface scheme for GCMs, II, Vegetation model and coupled runs, Int. J. Climatol., 13, 347-370.

von Salzen, K., N. A. McFarlane, and M. Lazare (2005) The role of shallow convection in the water and energy cycles of the atmosphere, Clim. Dyn., 25, 671-688.

Wanninkhof, R. (1992) Relationship between wind speed and gas exchange over the ocean, J. Geophys. Res., 97, 7373.7382.

Zahariev, K., J.R. Christian, and K.L. Denman (2008) Preindustrial, historical, and fertilization simulations using a global ocean carbon model with new parameterizations of iron limitation, calcification, and N2 fixation. Prog. Oceanogr., 77, 56-82.

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