Research Projects and Groups

Ent Dynamic Terrestrial Biosphere Model

The Ent Terrestrial Biosphere Model (Ent TBM) is a demographic dynamic global vegetation model (DGVM) compatible with the Earth System Modeling Framework (ESMF). It is designed to couple biophysics (fluxes of water, energy, carbon, other trace gases) with land surface models and atmospheric general circulations models (GCMs); biogeochemistry (seasonal growth and decay of vegetation); and biogeography or ecological dynamics (decadal- to century-scale vegetation cover change due to competition, mortality, establishment, and disturbance). Process-based dynamics in Ent are computationally efficient and suitable for two-way coupling and parallel computing in GCMs. The Ent TBM is coupled to the NASA Goddard Institute for Space Studies (GISS) GCM ModelE, and may be run in stand-alone modes.

Flowchart of Ent process modules

Figure 1. Flowchart of the Ent TBM's process modules and their interface with a GCM atmosphere and land surface hydrology model.

Ent is a demographic DGVM following the approach introduced by the Ecosystem Demography (ED) model of Moorcroft et al. (2001) to represent heterogeneous landscape structure through height-stratified canopies in subgrid landscape patches. Vegetation communities consist of mixes of "cohorts" that are ensembles of identical individuals plants, with cohorts distinguished by height and plant functional type. This height binning allows for simulation of light competition. Ent's distinguishing feature is that it incorporates canopy geometry consistent with geometric optical radiative transfer (GORT) theory (Li & Strahler, 1992; Ni et al., 1999). This theoretical framework describes the heterogeneous distribution of light scattering elements in space, wherein the spatial distribution may be approximated by bounding geometries around the scattering elements, such as foliage within plant crowns that are on trunks. It provides a theory for quantifying the gap probability of plant canopies and for calculating various quantities measurable through lidar remote sensing, such as foliage clumping, the bidirectional reflectance distribution function (BRDF), canopy albedo, and vertical foliage profiles. Plant allometry in Ent incorporates both biomass allometry of different plant carbon pools and geometry of ellipsoidal crowns and tapering woody stems. This geometric allometry with cohort structure then provides boundary conditions to Ent's GORT canopy radiative transfer model, the Analytical Clumped Two-Stream (ACTS) model, which analytically calculates the clumping of foliage in mixed, vertically layered canopies (Ni-Meister et al., 2010; Yang et al., 2010).

The biophysics of Ent utilizes the well-known photosynthesis functions of Farqhuar, von Caemmerer, and Berry (1980) and Farqhuar and von Caemmerer (1982), and stomatal conductance of Ball and Berry (1985, 1987). Phenology is based on temperature, drought, and radiation cues, and growth is via allocation of carbon from labile carbohydrate reserve storage to different plant components (Kim et al., 2015). Soil biogeochemistry is based on the Carnegie-Ames-Stanford (CASA) model of Potter et al. (1993), and we introduce variable Q10 values for soil respiration response to moisture and temperature based on data from Del Grosso et al. (2005). The Ent TBM code is set up to capture mixed canopy processes within dynamically changing linked-list data structures.

The Ent TBM can run at different spatial scales (column or site, regional masks, and global), and with different levels of dynamics turned on or off (biophysics, biogeochemistry, biogeography). The functionality of Ent available in ModelE currently is only the biophysics portion for calculating fluxes of carbon dioxide and water vapor at the physical time step of the GCM. Prognostic seasonal growth is undergoing global testing and is available only to developers. Ecological dynamics is not yet available. Ent supplied the land carbon dynamics for the NASA GISS contribution to the Intergovernment Panel on Climate Change (IPCC) Sixth Coupled Model Intercomparison Project (CMIP6) Coupled Climate-Carbon Cycle MIP (C4MIP) (Ito et al. 2020).

Model Overview

Fig. 1 shows a diagram of the Ent model (green box), and how it is coupled with a climate driver (which can be an off-line data set of meteorological drivers or an atmospheric GCM) and a land hydrology model. Ent's biophysics modules operate at the physical time step of the GCM or land surface hydrology models. The photosynthetic uptake of carbon is accumulated over a day so that growth allocation and phenological behavior are updated once per day.

Conceptual diagram of the coupling of Ent to a GCM

Figure 2. Conceptual illustration of Ent subgrid heterogeneity..

Fig. 2 illustrates how subgrid heterogeneity is represented in Ent. Ent represents a grid cell or catchment hydrologic subregion as an “ent cell”, and subgrid heterogeneity as dynamic patches of vegetation communities, comprised of cohorts of plants that are ensembles of identical individuals. Patches dynamically open or merge according to disturbances and development of plant communities. Canopy conductances from each patch are summed to the ent cell level to couple with the atmosphere. All patches in one ent cell experience the same atmospheric conditions. Currently, conductances of water vapor from each patch are summed over the ent cell, such that all patches then share the same water balance. Subgrid heterogeneity of water balances is a development area.

Individuals

Ent does not simulate individual plants, but instead simulates cohorts that are ensembles of identical individuals that are distinguished by plant functional type and size (geometry, biomass pools). The biomass pools are constrained by fixed allometry, with seasonally varying foliage and fine roots limited within this allometry.

Biomass pools for woody plants consist of carbon pools for foliage, live and dead stem, sapwood, fine and coarse roots, and a labile carbon reserve pool, as illustrated in Fig. 3. The labile carbon pool stores carbon assimilated from photosynthetic uptake or retranslocated from senescence, and carbon is allocated from it for respiration and growth. Herbaceous plants have only foliage, fine root, and labile pools. Nitrogen content is currently specified by fixed carbon:nitrogen ratios by PFT and carbon pool.

Plant geometry is specified by height, crown diameter and ellipticity, stem diameter (diameter at breast height, dbh), and leaf area index. These variables allow determination of foliage density within crowns as part of the calculation of canopy radiative transfer. Leaf characteristics are described in the section on Biophysics.

Cohorts

Cohorts of ensembles of identical individuals in a patch community are organized in linked list data structures, from tallest to shortest. This stratification organizes the cohorts in their competition for light. Horizontal spatial distribution of individuals is statistical and not explicit, but the canopy radiative transfer model imposes a constraint that crowns do not intersect.

Routines to summarize cohort properties to the patch level are called half-hourly for fluxes, daily for biomass pools, and intermittently for significant community structural changes.

Patch Communities

Patches of subgrid areas within an ent cell contain communities of plant cohorts. Patches are not spatially explicit within an ent cell, but are organized in linked lists, stratified by age, that is, the time since the disturbance that opened a patch area as bare soil. The number of patches in a grid cell may be restricted as an option if necessary; experience with the ED model indicates that a grid cell generally requires ~10 patches to capture a realistic level of surface heterogeneity and dynamics. For land surface models with representations of different hydrological zones of catchments, since this level of land surface heterogeneity is described by the hydrological model, Ent can be set with static patches that correspond to each catchment zone.

Utilities for managing patches include those for reading in prescribed land cover from a data file; insertion, for new patches; partitioning, for when disturbance (fire) leads to new patches due to clearing parts of existing patches; and merging, and when ecosystem dynamics lead to similar communities on different patches, such that managing them separately is redundant. In addition, a utility to write patch data to a text file is provided.

Routines to summarize patch properties to the Ent cell level (grid cell) are called half-hourly for fluxes, daily for soil carbon and nitrogen pools, and intermittently according to occurrence of disturbance and patch merging.

Ent Cells

Ent cells correspond to the basic unit of the land surface hydrology model, which may be a GCM grid cell or a catchment zone. At the Ent cell level, atmospheric drivers and surface temperature are supplied to Ent, and Ent returns vegetation conductance of water vapor and fluxes of CO2, as diagrammed in Fi. 1. Soil depth structure for soil moisture also follows that of the land surface scheme. Note that the Ent model does not calculate energy balances, but relies on the land surface and atmosphere model to calculate canopy temperature and soil temperature.

Ent cells are stored in an array, set up at initialization of the model according to the land surface hydrology model's structure. The Ent cell array and soil moisture vertical structure interface requires customized setup through a driver file according to different land surface schemes.

The cell-level meteorological drivers are all stored in the Ent cell data structure for access by Ent modules that perform operations at the patch and cohort levels. Pointers between a parent Ent cell, its children patches, and their children cohorts, allow straightforward access to and passing of driver data.

Ent vertical and horizontal soil structure

Soil type in Ent depends on the soil types specified by the land surface hydrology model to which Ent is coupled, which must provide sand, silt, and clay fractions, and soil depth. Soil type is expected to of one kind in an ent cell. Ent soil biogeochemistry requires distinction of soil temperature, moisture, and texture in a 0-30 cm layer, and optionally in a 30-100 cm deep layer. Ent canopy conductance requires interfacing vertical root distributions with the layering scheme of the land surface model to calculate soil moisture stress; the layering of vertical root distributions is currently based on continuous functions by Rosenszweig and Abramopoulos (1997).

Plant functional types

The Ent TBM code is designed to allow flexible swapping out of different PFT sets, so that users can add new PFTs, or introduce their own PFT categorizations. For example, the 8 vegetation types of Matthews (1983) originally in the GISS GCM can also be simulated through a compile option. The default parameter set for Ent supports 16 plant functional types (PFTs), as listed below. The Ent TBM can support a 17 PFTs, distinguishing C3 and C4 crops; however, the current manner in which the GISS ModelE updates historical crop cover restricts Ent to simulate a single crop type in coupled runs, so all crops are merged into a single C3 type.

Table 1. Plant functional types

  1. Evergreen broadleaf early successional
  2. Evergreen broadleaf late successional
  3. Evergreen needleleaf early successional
  4. Evergreen needleleaf late successional
  5. Cold deciduous broadleaf early successional
  6. Cold deciduous broadleaf late successional
  7. Drought deciduous broadleaf
  8. Decidous needleleaf
  9. Cold adapted shrub
  10. Arid adapted shrub
  11. C3 grass - perennial
  12. C4 grass - perennial
  13. C3 grass arctic
  14. C3 grass annual
  15. Crops - C4 herbaceous
  16. Crops - broadleaf woody
  17. Crops - C3 herbaceous

Following the rationale first advocated by Defries et al. (1995) and adopted by all vegetation models since to varying degrees, Ent's PFTs distinguish physiogonomic characteristics: photosynthetic pathway (C3, C4), leaf type (broadleaf, needleleaf), growth form (woody, herbaceous), phenotype (evergreen, cold deciduous, drought deciduous; for herbs, annual vs. perennial), and cultivated (crops). To better capture community dynamics in mixed canopies, Ent is set up to distinguish early and late successional species if provided parameters for different leaf life span, as in Moorcroft, et al. (2001), which is based on leaf physiological relations found in Reich et al. (1997). Currently, the default parameter set does not distinguish successional types, because available satellite data products cannot generally distinguish them.

Canopy radiative transfer

Ent can be run with two different canopy radiative transfer schemes: the two-stream, layered Beer's Law scheme of Friend and Kiang (2005); and the Analytical Clumped Two-Stream scheme (ACTS) of Ni-Meister et al. (2010) and Yang et al., (2010). The Ent/ACTS model convolves multi-cohort canopies to calculate the canopy vertical foliage profile (VFP), and analytically solve for a whole canopy foliage clumping factor. The enables calculation of the effective vertical foliage profile whose gap probability preserves the light transmittance to the ground. ACTS then provides the vertical profiles of incident light and fractions of sunlit and shaded foliage in each layer. These whole-canopy light profiles then are seen by the individual cohorts in Ent for simulation of their photosynthetic activity. In addition, the ACTS model calculates the zenith angle dependent canopy albedo for coupling with atmospheric models.

The ACTS model theory considers potential height variation within cohorts, assuming crown dimensions are otherwise the same, as illustrated in Fig. 4, where two parameters, h1 and h2, denote the top and bottom heights of crown centers in the cohort. The effective vertical foliage profile calculated by ACTS is as would be derived from satellite lidar waveforms. In Ent, cohorts are considered to have identical individuals with no height variation, while the possibility of introducing a height variance in the future is possible.

Biophysics

Biophysics modules provide the conductance of water vapor and fluxes of CO2 from the land surface to the atmosphere, via the processes of photosynthetic uptake of CO2 coupled with transpiration of water vapor. These processes are calculated at the physical time step of the land surface model. Physical time step fluxes of CO2 also include plant and soil respiration.

Photosynthesis and conductance in Ent are calculated at the leaf level using the well-known Michaelis-Menten photosynthesis relationships of Farquhar, von Caemmerer and Berry (1980) and Farquhar and von Caemmerer (1982) and stomatal conductance of Ball and Berry (1985, 1987). Photosynthetic capacity varies according to immediate temperature, and phenology. The solution to the coupled photosynthesis-conductance equations utilizes leaf boundary conductances similarly to the approach by Collatz et al. (1991) but with the boundary layer conductance derived from canopy surface layer conductance; equations for a cubic solution are a variation on the approaches by Baldocchi (1994), Su et al. (1996), and Zhan et al. (2003). Photosynthetic uptake of CO2 is accumulated into a carbon reserve pool, from which other processes may allocate uses.

Scaling of the leaf to canopy level is through stratification of canopy light levels and leaf area profiles. Two options for canopy radiative transfer are available, described in that section above. Canopy fluxes of CO2, conductance of water vapor, canopy heat capacity, and canopy roughness length are simulated by Ent and can be coupled to atmospheric models.

Autotrophic respiration consists of 1) maintenance respiration as a function of plant biomass pool size, its carbon:nitrogen ratio, with a Q10 temperature response and acclimation to 10-day average temperature; 2) growth respiration as a function of photosynthetic activity; and 3) growth respiration resulting from tissue growth. The latter is calculated once a day when tissue turnover and growth are calculated, and the resulting respiration fluxes are distributed uniformly at the physical time step over the next day.

Seasonality

Phenology, carbon allocation, and soil biogeochemistry are documented in Kim et al. (2015). Assimilated carbon from photosynthetic uptake of CO2 is stored in the plant's labile carbon pool, which is withdrawn through autotrophic respiraton at the physical time step, growth at the daily time step, and partially restored through retranslocation from senescence at the daily time step. Seasonal variation in carbon stocks due to plant growth can be either prescribed from an input file or prognostic. With prescribed seasonal leaf area, the only prognostic carbon pools are plant labile carbon and soil carbon. To conserve carbon and maintain an equilibrium of the labile carbon pool, excesss labile carbon relative to plant size is dumped as litter to the soil. Soil biogeochemistry in either case is driven by litterfall from the daily change in plant carbon pools. To avoid a daily pulse of plant respiration when plant carbon stocks are updated, the tissue growth respiration is released uniformly at the physical time step over the next day.

With prognostic leaf growth and senescence, seasonal leaf dynamics are driven by CO2 uptake and by climate cues. Since this mode does not include community dynamics or cover change (competition, mortality, establishment, disturbance), to prevent woody plants from unlimited increase in size, any allocation of carbon that would have been for woody structural growth or reproduction is dumped into litterfall so that the carbon cycle is closed.

Soil biogeochemistry

The soil biogeochemistry submodel of Ent is documented in Kim et al. (2015). It is based largely on the CASA’ biosphere submodel used in the NCAR LSM and CSM 1.4 (Bonan, 1996; Randerson et al., 1997; Fung et al., 2005; Doney et al., 2006), which itself is a modified version of the original NASA-CASA biosphere model (Potter et al., 1993). Modifications have been made for soil respiration response to moisture and temperature based on data from Del Grosso et al. (2005). The soil model mechanistically determines terrestrial soil carbon pools and CO2 fluxes from microbial respiration. Ent combines soil respiration, photosynthesis, and autotrophic respiration to predict net ecosystem exchange (NEE) of carbon with the atmosphere.

Meteorological drivers to Ent

Table 2. Input variables from the atmosphere and land surface hydrology models to the Ent TBM ("patch"=subgrid cover fraction).
Inputs Variable Units Spatial Resolution
From atmosphere Air pressure millibar Ent cell
Air temperature Celsius
Vapor pressure mixing ratio at foliage surface kg/kg
Atmospheric CO2 concentration mol/m3
Cosine of solar zenith angle cosine
Incident direct photosynthetically active radiation W/m2
Incident diffuse photosynthetically active radiation W/m2
Wind speed m/s
Ground to surface layer heat transfer coefficient dimensionless
From land surface model Soil texture (sand, silt, clay) fractions (init.) Ent cell
Soil albedo fraction by band (init.) patch
Soil temperature Celsius Ent cell
Soil moisture saturated fraction Ent cell
Soil ice fraction of soil water fraction by layer Ent cell
Canopy temperature Celsius Ent cell
Snow albedo fraction by band Ent cell
Canopy wetness fraction of leaf area patch
Table 3. Outputs from the Ent TBM to the atmospheric and land surface hydrology models.
Outputs Variable Units Spatial Resolution
To atmosphere Canopy albedo fraction (bands) patches avg. to Ent cell
Net CO2 flux kg-C/m2-ground/s patches sum to Ent cell
Canopy height m (daily) patch
(Roughness length TBA) m (daily) patch
(Aerosols from fire TBA) TBA patches sum to Ent cell
(Volatile organic carbons TBA) TBA patches sum to Ent cell
To land surface model Canopy conductance of water vapor
m/s
patch
Plant water stress
fraction (0-1) by layer
patch
Vegetation structure - leaf area index (LAI)
m2/m2 (daily)
patch
Vegetation structure - root depth distribution
fraction biomass per layer
cohorts avg. to patch
Transmittance of shortwave to the ground fraction patch

References

Baldocchi, D., 1994: An analytical solution for coupled leaf photosynthesis and stomatal conductance models. Tree Physiol, 14(7-9), 1069-1079.

Ball, J. T. and J.A. Berry, 1987: A model predicting stomatal conductance and its contribution to photosynthesis under different environmental conditions. In Progress in Photosynthesis Research. I. Biggins. Nijhoff, Dordrecht, Netherlands. IV: 110-112.

Ball, T. and J. Berry, 1985: A Simple Empirical Model of Stomatal Control. Plant Physiol., 77(n. Supplement 4), 91.

Bonan, G. B., 1996: A land surface model (LSM Version 1.0) for ecological, hydrological, and atmospheric studies: technical description and user's guide. Boulder, Colorado, National Center of Atmospheric Research: 122.

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. Agric. Forest Meteorol., 54, 107-136.

Defries, R. S., C. B. Field, I. Fung, C. O. Justice, S. O. Los, P. A. Matson, E. Matthews, H. A. Mooney, C. Potter, K. C. Prentice, P. J. Sellers, J. Townshend, C. J. Tucker, S. L. Ustin and P. Vitousek, 1995: Mapping the land surface for global atmosphere-biosphere models - toward continuous distributions of vegetation's functional properties. J. Geophys. Rese., 100, 20867-20882.

Del Grosso, S. J., W. J. Parton, A. R. Mosier, E. A. Holland, E. Pendall, D. S. Schimel and D. S. Ojima, 2005: Modeling soil CO2 emissions from ecosystems. Biogeochemistry, 73, 71-91

Doney, S.C., K. Lindsay, I. Fung, and J. John, 2006: Natural variability in a stable, 1000-yr global coupled climate-carbon cycle simulation. J. Climate, 19, 3033-3052.

Farquhar, G. D. and S. von Caemmerer, 1982: 16 Modelling photosynthetic response to environmental conditions. In Encyclopedia of Plant Physiology (NS). Eds. O.L. Lange, P.S. Nobel, C.B. Osmond, and H. Ziegler. Berlin, Springer. 12B: 549-587.

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

Friend, A. D. and N. Y. Kiang, 2005: Land surface model development for the GISS GCM: Effects of improved canopy physiology on simulated climate. J. Climate, 18(15), 2883-2902.

Fung I, S. Doney, K. Lindsay, and J. John, 2005: Evolution of carbon sinks in a changing climate. Proc. Natl. Acad. Sci., 102, 11201–11206

Kim, Y., P.R. Moorcroft, I. Aleinov, M.J. Puma, and N.Y. Kiang, 2015: Variability of phenology and fluxes of water and carbon with observed and simulated soil moisture in the Ent Terrestrial Biosphere Model (Ent TBM version 1.0.1.0.0). Geosci. Model Dev., 8, 3837-3865, doi:10.5194/gmd-8-3837-2015.

Matthews, E., 1983: Global vegetation and land use: New high-resolution data bases for climate studies. J. Clim. Appl. Meteorol., 22, 474-487.

Medvigy, D., S. C. Wofsy, J. W. Munger, D. Y. Hollinger, and P. R. Moorcroft, 2009: Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. J. Geophys. Res., 114, G01002.

Moorcroft, P., G. C. Hurtt and S. W. Pacala, 2001: A method for scaling vegetation dynamics: The Ecosystem Demography Model (ED). Ecol. Monographs, 71(4), 557-586.

Ni-Meister, W., Yang, W.Z., Kiang, N.Y., 2010: A clumped-foliage canopy radiative transfer model for a global dynamic terrestrial ecosystem model. I: Theory. Agric. Forest Meteorol., 150, 881-894.

Potter, C. S., J. T. Randerson, C. B. Field, P. A. Matson, P. M. Vitousek, H. A. Mooney and S. A. Klooster, 1993: Terrestrial ecosystem production: A process model based on global satellite and surface data. Glob. Biogeochem. Cycles, 7(4), 811-841.

Randerson, J. T., T. M.V., T. J. Conway, I. Y. Fung and C. B. Field, 1997: The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide. Glob. Biogeochemi. Cycles, 11(4), 535-560.

Rosenzweig, C. and F. Abramopoulos, 1997: Land-surface model development for the GISS GCM. J. Climate, 10, 2040-2054.

Su, H.-B., K. T. Paw and R. H. Shaw 1996: Development of a coupled leaf and canopy model for the simulation of plant-atmosphere interaction. J. Appl. Meteorol., 35(5), 733-748.

Yang, W.Z., Ni-Meister, W., Kiang, N.Y., Moorcroft, P.R., Strahler, A.H., Oliphant, A., 2010: A clumped-foliage canopy radiative transfer model for a Global Dynamic Terrestrial Ecosystem Model II: Comparison to measurements. Agric. Forest Meteorol., 150, 895-907.

Zhan, X. W., Y. K. Xue and G. J. Collatz 2003: An analytical approach for estimating CO2 and heat fluxes over the Amazonian region. Ecol. Model., 162(1-2), 97-117.

Contacts

Please address inquiries about the Ent project to Dr. Nancy Kiang.