ELM Background¶
ELM is based on the Community Land Model Version 4.5 (CLM4.5, Oleson et al. 2013). ELM calculates canopy radiation flux using the two-stream approximation methods; snow albedo using the Snow, Ice, and Aerosol Radiative Model (SNICAR) model (Flanner et al., 2007); and snow cover fraction based on snow water equivalent (Swenson and Lawrence, 2012). ELM also represents the snow hydrological processes including snowfall accumulation, melting, refreezing, compaction, aging, water transfer across layers, etc.
New features in ELM to better represent land surface processes include an updated representation of soil hydrology (Bisht et al., 2018), improved treatment of ecosystem carbon dynamics (Tang and Riley, 2018), a novel topography-based sub-grid spatial structure impacting atmospheric preciptiation and temperature (Tesfa and Leung, 2017, Tesfa et al. 2020) and radiation (Hao et al. 2022), an irrigation scheme constrained by water management (Zhou et al., 2020), and improved snow albedo (Hao et al. 2023).
ELM Background: Snow Processes¶
ELM represents snow as a vertically resolved, multi-layer snowpack that simulates accumulation, compaction, melt–refreeze, and energy exchange with the atmosphere, vegetation canopy, and soil.
Precipitation phase is determined from near-surface air temperature (TBOT) using a mixed rain–snow partitioning scheme:
Snow if
Tair ≤ T_snowRain if
Tair ≥ T_rainMixed phase otherwise
Typical thresholds:
T_snow ≈ 273.15 K(0 °C)T_rain ≈ 275.15 K(~2 °C)
Between these thresholds, precipitation is linearly partitioned between rain and snow.
For more details, see the CLM 4.5 Tech Note (Oleson et al 2013).
ELM Background: Vegetation and Shrubs¶
In ELM, shrubs and other vegetation are represented using a plant functional type (PFT) framework, in which each grid cell contains fractional coverage of multiple PFTs (e.g., Arctic shrubs), each with its own prognostic carbon, water, and energy budgets.
Vegetation structure is characterized by prognostic variables such as leaf area index (LAI) and stem area index (SAI), which control radiative transfer, canopy interception of precipitation (rain and snow), aerodynamic roughness, and transpiration.
Recent improvements following Sulman et al. (2021) expanded the Arctic vegetation representation from two PFTs to nine Arctic-specific PFTs, including distinct classes of deciduous and evergreen shrubs, nitrogen-fixing alder, graminoids, forbs, and nonvascular mosses and lichens.
These PFTs are parameterized using observed traits such as belowground biomass allocation, specific leaf area, carbon-to-nitrogen ratios, and rooting depth, enabling improved representation of tundra trait diversity and spatial variability in biomass and productivity.
This expanded PFT set improves model fidelity in Arctic environments by better capturing functional differences among vegetation growth forms and their biophysical and biogeochemical roles.
Photosynthesis and stomatal conductance are computed using coupled formulations that link carbon uptake to water loss, while vegetation carbon pools evolve through allocation, turnover, and mortality processes.
ELM employs Farquhar photosynthesis (Farquhar et al., 1980; Leuning et al., 1995) coupled to either the Ball–Berry (Ball et al., 1987) or Medlyn (Medlyn et al., 2011) stomatal conductance formulations, depending on model configuration.
Vegetation interacts strongly with snow and soil through canopy masking and interception, particularly for shrubs, which trap wind-blown snow and reduce exposure of high-albedo snow surfaces, indirectly warming underlying soils.
Snow interception by vegetation is typically parameterized as a function of canopy structure, for example:
where \(P_{\text{snow}}\) is snowfall and \(k\) is an extinction coefficient.
Vegetation also influences the surface energy balance through changes in albedo, roughness length, and turbulent heat fluxes, making shrub cover a key mediator of land–atmosphere coupling and permafrost dynamics in ELM.
ELM Background: Permafrost¶
In ELM, permafrost is represented as an emergent property of the prognostic subsurface thermal and hydrologic system rather than as a prescribed or binary state.
ELM solves a vertically discretized soil column with prognostic soil temperature, liquid water, and ice content, explicitly accounting for freezing and thawing through an enthalpy-based (apparent heat capacity) formulation.
Soil temperature is computed by solving the one-dimensional heat diffusion equation through a vertically discretized soil column, with explicit representation of freezing and thawing processes. The governing equation conserves energy within each soil layer and can be written as:
where \(T\) is soil temperature, \(t\) is time, \(z\) is depth, \(C\) is the volumetric heat capacity, \(k\) is the thermal conductivity, and \(Q\) represents internal heat sources or sinks, which are typically zero for soils.
This approach allows phase change to occur continuously as soil temperatures cross the freezing point, conserving energy and capturing realistic seasonal freeze–thaw behavior.
ELM represents phase change using an enthalpy-based or apparent heat capacity formulation, in which latent heat effects are incorporated directly into the heat capacity term.
This can be expressed as:
where \(C_\text{soil}\) is the sensible heat capacity of soil solids, liquid water, and ice, \(L_f\) is the latent heat of fusion, and \(\theta_i\) is the volumetric ice content.
Soil thermal conductivity and heat capacity vary dynamically as functions of soil texture, moisture, and ice content, enabling simulation of active-layer development, thermal buffering, and long-term permafrost stability or degradation. Frozen soils generally exhibit higher conductivity than unfrozen soils.
The subsurface thermal regime is coupled to the land surface through boundary conditions imposed by the surface energy balance at the top of the soil column and a zero-flux or prescribed geothermal heat flux at the lower boundary.
The heat equation is discretized using a finite-difference approach and solved implicitly in time to ensure numerical stability under strong temperature gradients and during freeze–thaw transitions.
Snow and vegetation influence permafrost primarily by modifying heat exchange between the atmosphere and soil, with snow acting as an insulating layer and vegetation altering radiative and turbulent fluxes at the surface.
These surface controls modulate winter soil cooling and summer warming, shaping ground temperature profiles and controlling the depth and timing of seasonal thaw.
Together, the prognostic soil thermal structure and its coupling to surface energy fluxes allow ELM to simulate permafrost dynamics, active-layer thickness, and their sensitivity to climate forcing in a physically consistent manner.
References¶
Ball, J.T., Woodrow, I.E. and Berry, J.A., 1987, October. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In Progress in photosynthesis research: volume 4 proceedings of the VIIth international congress on photosynthesis providence, Rhode Island, USA, august 10–15, 1986 (pp. 221-224). Dordrecht: Springer Netherlands.
Bisht, G., Riley, W.J., Hammond, G.E. and Lorenzetti, D.M., 2018. Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) version 1.0. Geoscientific Model Development, 11(10), pp.4085-4102.
Farquhar, G.D., von Caemmerer, S., and Berry, J.A. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149:78-90.
Flanner, M.G., Arnheim, J., Cook, J.M., Dang, C., He, C., Huang, X., Singh, D., Skiles, S.M., Whicker, C.A. and Zender, C.S., 2021. SNICAR-AD v3: A community tool for modeling spectral snow albedo. Geoscientific Model Development Discussions, 2021, pp.1-49.
Hao, D., Bisht, G., Huang, M., Ma, P.L., Tesfa, T., Lee, W.L., Gu, Y. and Leung, L.R., 2022. Impacts of sub‐grid topographic representations on surface energy balance and boundary conditions in the E3SM land model: A case study in Sierra Nevada. Journal of Advances in Modeling Earth Systems, 14(4), p.e2021MS002862.
Hao, D., Bisht, G., Rittger, K., Bair, E., He, C., Huang, H., Dang, C., Stillinger, T., Gu, Y., Wang, H., Qian, Y., and Leung, L. R., 2023. Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau, Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023.
Leuning R. A critical appraisal of a combined stomatal‐photosynthesis model for C3 plants. Plant, Cell & Environment. 1995 Apr; 18(4):339-55.
Medlyn, B.E., Duursma, R.A., Eamus, D., Ellsworth, D.S., Prentice, I.C., Barton, C.V., Crous, K.Y., De Angelis, P., Freeman, M. and Wingate, L., 2011. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global change biology, 17(6), pp.2134-2144.
Oleson, K.W., Lawrence, D.M., Bonan, G.B., Fisher, R.A., Lawrence, P.J. and Muszala, S.P., 2013. Technical description of version 4.5 of the Community Land Model (CLM). Technical description of version 4.5 of the Community Land Model (CLM)(2013) NCAR/TN-503+ STR, 503.
Sulman, B.N., Salmon, V.G., Iversen, C.M., Breen, A.L., Yuan, F. and Thornton, P.E., 2021. Integrating arctic plant functional types in a land surface model using above‐and belowground field observations. Journal of Advances in Modeling Earth Systems, 13(4), p.e2020MS002396.
Swenson, S.C. and Lawrence, D.M., 2012. A new fractional snow‐covered area parameterization for the Community Land Model and its effect on the surface energy balance. Journal of geophysical research: Atmospheres, 117(D21).
Tang, J. and Riley, W.J., 2018. Predicted land carbon dynamics are strongly dependent on the numerical coupling of nitrogen mobilizing and immobilizing processes: A demonstration with the E3SM land model. Earth Interactions, 22(11), pp.1-18.
Tesfa, T. K. and Leung, L.-Y. R.: Exploring new topography-based subgrid spatial structures for improving land surface modeling, Geosci. Model Dev., 10, 873–888, https://doi.org/10.5194/gmd-10-873-2017, 2017.
Tesfa, T.K., Leung, L.R. and Ghan, S.J., 2020. Exploring topography‐based methods for downscaling subgrid precipitation for use in Earth System Models. Journal of Geophysical Research: Atmospheres, 125(5), p.e2019JD031456.
Zhou, T., Leung, L.R., Leng, G., Voisin, N., Li, H.Y., Craig, A.P., Tesfa, T. and Mao, Y., 2020. Global irrigation characteristics and effects simulated by fully coupled land surface, river, and water management models in E3SM. Journal of Advances in Modeling Earth Systems, 12(10), p.e2020MS002069.
