Impact model: CLM4.5

The Community Land Model is the land model for the Community Earth System Model (CESM). It examines the physical, chemical, and biological processes by which terrestrial ecosystems affect and are affected by climate across a variety of spatial and temporal scales. The central theme is that terrestrial ecosystems, through their cycling of energy, water, chemical elements, and trace gases, are important determinants of climate. Model components consist of: biogeophysics, hydrologic cycle, biogeochemistry and dynamic vegetation. The land surface is represented by 5 primary sub-grid land cover types (glacier, lake, wetland, urban, vegetated) in each grid cell. The vegetated portion of a grid cell is further divided into patches of plant functional types, each with its own leaf and stem area index and canopy height. Each subgrid land cover type and PFT patch is a separate column for energy and water calculations. The current version of the Community Land Model is CLM4.5. Simulations for ISIMIP2b were conducted with CLM4.5, and include an interactive Carbon and Nitrogen cycle (CN) and a an interactive crop model (CROP). ISIMIP2a simulations were conducted either with CLM4.0 (global water) or CLM4.5post (agriculture, at 2° resolution).

Contact Person

Information for the model CLM4.5 is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.

Basic information
Model Version: 4.5
Model output license: CC BY 4.0
Reference Paper: Main Reference: Thiery, W., et al. et al. Present-day irrigation mitigates heat extremes. J. Geophys. Res. Atm.,122,1403-1422,2017
Reference Paper: Other References:
Person Responsible For Model Simulations In This Simulation Round: Wim Thiery
Output Data
Experiments: I, II, III and VIII
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2017-12-13
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: subdaily
Temporal Resolution Of Input Data: Co2: annual
Temporal Resolution Of Input Data: Land Use/Land Cover: annual
Temporal Resolution Of Input Data: Soil: constant
Additional Temporal Resolution Information: ISIMIP daily atmospheric input data is temporally disaggregated to 6-hourly meteorological forcing fields for CLM (algorithm courtesy of Guoyong Leng, PNNL)
Input data sets used
Simulated Atmospheric Climate Data Sets Used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Land Use Data Sets Used: Historical, gridded land use (HYDE 3.2)
Climate Variables: ta, rlds, huss, sfcWind, rsds, pr
Additional Input Data Sets: GDP (for fire model)
Exceptions to Protocol
Exceptions: 1. Atmospheric CO2 concentrations are held constant at 284.7 ppm for picontrol runs (whereas the ISIMIP protocol prescribes 286.38 ppm). Note that this values is only used for the land carbon cycle, i.e. this difference does not affect the atmospheric forcing provided by ISIMIP. CO2 concentrations for the historical simulations follow observations and are consistent with the ISIMIP protocol. 2. Historical simulations are conducted with fixed, present-day land-use (2005soc), this due to the inability of CLM4.5 to account for transient irrigation extend. 3. 365_day calendar instead of proleptic_gregorian 4. For some pixels and time steps negative values were obtained for the variables 'tws', 'qtot', 'qr', 'maxdis', 'mindis', and 'dis'. In those cases, values were forced to be zero. We note however that the occurrence of negative values was very rare, and that values were small whenever they occurred.
Was A Spin-Up Performed?: Yes
Spin-Up Design: picontrol simulations were branched from an existing picontrol spinup run which was run at 1.9° x 2.5° resolution and interpolated to 0.5° x 0.5° resolution using the CESM interpinic tool. Historical simulations were branched from the respective 0.5° x 0.5° ISIMIP picontrol run, scenario runs were branched from the respective 0.5° x 0.5° ISIMIP historical runs
Natural Vegetation
Natural Vegetation Partition: tile approach including 24 PFTs for the vegatated land unit.
Natural Vegetation Dynamics: no dynamic vegetation
Management & Adaptation Measures
Management: Irrigation (see Thiery et al., 2017 for a short description of the irrigation module)
Extreme Events & Disturbances
Key Challenges: Representation of human water management (only irrigation is included in CLM4.5)
Additional Comments: In total >450 variables are stored, additional variables are available upon request (
Key model processes
Dynamic Vegetation: no
Nitrogen Limitation: yes. In addition to the relatively rapid cycling of nitrogen within the plant – litter – soil organic matter system, CLM also represents several slow processes which couple the internal nitrogen cycle to external sources and sinks. Inputs of new mineral nitrogen are from atmospheric deposition and biological nitrogen fixation. Losses of mineral nitrogen are due to nitrification, denitrification, leaching, and losses in fire. While the short-term dynamics of nitrogen limitation depend on the behavior of the internal nitrogen cycle, establishment of total ecosystem nitrogen stocks depends on the balance between sources and sinks in the external nitrogen cycle.
Co2 Effects: yes
Light Interception: yes
Light Utilization: yes. Photosynthesis and transpiration depend non-linearly on solar radiation, via the light response of stomata. The canopy is treated as two leaves (sunlit and shaded) and the solar radiation in the visible waveband (< 0.7 μm) absorbed by the vegetation is apportioned to the sunlit and shaded leaves
Phenology: yes, dynamic. The CLM phenology model consists of several algorithms controlling the transfer of stored carbon and nitrogen out of storage pools for the display of new growth and into litter pools for losses of displayed growth. PFTs are classified into three distinct phenological types that are represented by separate algorithms: an evergreen type, for which some fraction of annual leaf growth persists in the displayed pool for longer than one year; a seasonal-deciduous type with a single growing season per year, controlled mainly by temperature and daylength; and a stress-deciduous type with the potential for multiple growing seasons per year, controlled by temperature and soil moisture conditions.
Water Stress: yes. soil water influences stomatal resistance directly by multiplying the minimum conductance by a soil water stress function βt (which ranges from 0 to 1) and also indirectly through A n , as in (Sellers et al. 1996).
Heat Stress: no
Evapo-Transpiration Approach: Monin-Obukhov similarity theory
Differences In Rooting Depth: different per pft, but constant in time
Root Distribution Over Depth: plant types differ in leaf and stem optical properties that determine reflection, transmittance, and absorption of solar radiation, root distribution parameters that control the uptake of water from the soil, aerodynamic parameters that determine resistance to heat, moisture, and momentum transfer, and photosynthetic parameters that determine stomatal resistance, photosynthesis, and transpiration.
Closed Energy Balance: Yes, checked every time step at the grid cell level
Coupling/Feedback Between Soil Moisture And Surface Temperature: yes
Latent Heat: Monin-Obukhov similarity theory
Sensible Heat: Monin-Obukhov similarity theory
Causes of mortality in vegetation models
Fire: "The fire parameterization in CLM contains four components: non-peat fires outside cropland and tropical closed forests, agricultural fires, deforestation fires in the tropical closed forests, and peat fires (Li et al. 2012a, b, 2013a). In this fire parameterization, burned area is affected by climate and weather conditions, vegetation composition and structure, and human activities. After burned area is calculated, we estimate the fire impact, including biomass and peat burning, fire-induced vegetation mortality, and the adjustment of the carbon and nitrogen (C/N) pools. Justification for all equations and parameter values is given by Li et al. (2012a, b; 2013a) in detail." [Oleson et al., 2013] For a more detailed description of the fire model, see Oleson, K. W., et al. (2013), Technical description of version 4.5 of the Community Land Model (CLM), Tech. Rep., Natl. Center for Atmos. Res. [Available at]
Remarks: Plant mortality as described here applies to perennial vegetation types, and is intended to represent the death of individuals from a stand of plants due to the aggregate of processes such as wind throw, insect attack, disease, extreme temperatures or drought, and age-related decline in vigor. These processes are referred to in aggregate as “gap-phase” mortality. Mortality due to fire and anthropogenic land cover change are treated separately.
Model output specifications
Output Format: output per grid cell or per tile, depending on the variable
Output Per Pft?: output per PFT is per unit area of that PFT
Considerations: All processes are described in the following technical report: Oleson, K. W., et al. (2013), Technical description of version 4.5 of the Community Land Model (CLM), Tech. Rep., Natl. Center for Atmos. Res. [Available at]
Land-use change implementation
Is crop harvest included? If so, how?: Harvest is assumed to occur as soon as the crop reaches maturity. When GDD T 2m reaches 100% of GDDmat or the number of days past planting reaches a crop-specific maximum, then the crop is harvested. Harvest occurs in one time step using CN’s leaf offset algorithm.
Is cropland soil management included? If so, how?: irrigation is included
Is grass harvest included? If so, how?: no
Is shifting cultivation included?: no
Is wood harvest included? If so, how?: no
Which transition rules are applied to decide where agriculture is conducted?: prescribed from LUH2 data