### 2.1.1.7. CLM5.0[¶](#clm5-0 "Permalink to this headline")

Developments for CLM5.0 build on the progress made in CLM4.5. Most major components of the model have been updated with particularly notable changes made to soil and plant hydrology, snow density, river modeling, carbon and nitrogen cycling and coupling, and crop modeling. Much of the focus of development centered on a push towards more mechanistic treatment of key processes, in addition to more comprehensive and explicit representation of land use and land-cover change. Prior versions of CLM included relatively few options for physics parameterizations or structure. In CLM5, where new parameterizations or model decisions were made, in most cases, the CLM4.5 parameterization was maintained so that users could switch back and forth between different parameterizations via namelist control where appropriate or desirable. Throughout the CLM5 Technical Descpription, in general only the default parameterization for any given process is described. Readers are referred to the CLM4.5 or CLM4 Technical Descriptions for detailed descriptions of non-default parameterizations.

The hydrology updates include the introduction of a dry surface layer-based soil evaporation resistance parameterization [(Swenson and Lawrence, 2014)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#swensonlawrence2014) and a revised canopy interception parameterization. Canopy interception is now divided into liquid and solid phases, with the intercepted snow subject to unloading events due to wind or above-freezing temperatures. The snow-covered fraction of the canopy is used within the canopy radiation and surface albedo calculation. Instead of applying a spatially uniform soil thickness, soil thickness can vary in space [(Brunke et al. 2016](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#brunkeetal2016) and [Swenson and Lawrence, 2015)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#swensonlawrence2015) and is set to values within a range of 0.4m to 8.5m depth, derived from a spatially explicit soil thickness data product [(Pelletier et al., 2016)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#pelletieretal2016). The explicit treatment of soil thickness allows for the deprecation of the unconfined aquifer parameterization used in CLM4.5, which is replaced with a zero flux boundary condition and explicit modeling of both the saturated and unsaturated zones. The default model soil layer resolution is increased, especially within the top 3m, to more explicitly represent active layer thickness within the permafrost zone. Rooting profiles were used inconsistently in CLM4.5 with [Zeng (2001)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#zeng2001) profiles used for water and [Jackson et al. (1996)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#jacksonetal1996) profiles used for carbon inputs. For CLM5, the Jackson et al. (1996) rooting profiles are used for both water and carbon. Roots are deepened for the broadleaf evergreen tropical tree and broadleaf deciduous tropical tree types. Finally, an adaptive time-stepping solution to the Richard’s equation is introduced, which improves the accuracy and stability of the numerical soil water solution. The River Transport Model (RTM) is replaced with the Model for Scale Adaptive River Transport (MOSART, [Li et al., 2013b)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lietal2013b) in which surface runoff is routed across hillslopes and then discharged along with subsurface runoff into a tributary subnetwork before entering the main channel.

Several changes are included that are mainly targeted at improving the simulation of surface mass balance over ice sheets. The fresh snow density parameterization is updated to more realistically capture temperature effects and to additionally account for wind effects on new snow density [(van Kampenhout et al., 2017)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#vankampenhoutetal2017). The maximum number of snow layers and snow amount is increased from 5 layers and 1m snow water equivalent to 12 layers and 10m snow water equivalent to allow for the formation of firn in regions of persistent snow-cover (e.g., glaciers and ice sheets) [(van Kampenhout et al., 2017)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#vankampenhoutetal2017). The CISM2 ice sheet model is included for Greenland by default. The ice sheet does not evolve for typical configurations, but ice sheet evolution can be turned on by choosing an appropriate compset. The introduction in CLM5 of the capability to dynamically adjust landunit weights means that a glacier can initiate, grow, shrink, or disappear during a simulation when ice evolution is active. That is, there are two-way feedbacks between CLM and CISM. Multiple elevation classes (10 elevation classes by default) and associated temperature, rain/snow partitioning, and downwelling longwave downscaling are used for glacier landunits to account for the strong topographic elevation heterogeneity over glaciers and ice sheets.

A plant hydraulic stress routine is introduced which explicitly models water transport through the vegetation according to a simple hydraulic framework (Kennedy et al., to be submitted). The water supply equations are used to solve for vegetation water potential forced by transpiration demand and a set of layer-by-layer soil water potentials. Stomatal conductance, therefore, is a function of prognostic leaf water potential. Water stress is calculated as the ratio of attenuated stomatal conductance to maximum stomatal conductance. An emergent feature of the plant hydraulics is soil hydraulic redistribution. In CLM5, maximum stomatal conductance is obtained from the Medlyn conductance model [(Medlyn et al., 2011)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#medlynetal2011), rather than the Ball-Berry stomatal conductance model that was utilized in CLM4.5 and prior versions of the model. The Medlyn stomatal conductance model is preferred mainly for it’s more realistic behavior at low humidity levels [(Rogers et al., 2017)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#rogersetal2017). The stress deciduous vegetation phenology trigger is augmented with a antecedent precipitation requirement [(Dahlin et al. 2015)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#dahlinetal2015).

Plant nutrient dynamics are substantially updated to resolve several deficiencies with the CLM4 and CLM4.5 nutrient cycling representation. The Fixation and Update of Nitrogen (FUN) model based on the work of [Fisher et al. (2010)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#fisheretal2010), [Brzostek et al. (2014)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#brzosteketal2014), and [Shi et al. (2016)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#shietal2016) is incorporated. The concept of FUN is that in most cases, N uptake requires the expenditure of energy in the form of carbon, and further, that there are numerous potential sources of N in the environment which a plant may exchange for carbon. The ratio of carbon expended to N acquired is therefore the cost, or exchange rate, of N acquisition. FUN calculates the rate of symbiotic N fixation, with this N passed straight to the plant, not the mineral N pool. Separately, CLM5 also calculates rates of symbiotic (or free living) N fixation as a function of evapotranspiration ([Cleveland et al. 1999](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#clevelandetal1999)), which is added to the soil inorganic ammonium (NH4+) pool. The static plant carbon:nitrogen (C:N) ratios utilized in CLM4 and CLM4.5 are replaced with variable plant C:N ratios which allows plants to adjust their C:N ratio, and therefore their leaf nitrogen content, with the cost of N uptake [(Ghimire et al. 2016)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#ghimireetal2016). The implementation of a flexible C:N ratio means that the model no longer relies on instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability to represent nutrient limitation. Furthermore, stomatal conductance is now based on the N-limited photosynthesis rather than on potential photosynthesis. Finally, the Leaf Use of Nitrogen for Assimilation (LUNA, [Xu et al., 2012](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#xuetal2012) and [Ali et al., 2016)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#alietal2016) model is incorporated. The LUNA model calculates photosynthetic capacity based on optimization of the use of leaf nitrogen under different environmental conditions such that light capture, carboxylation, and respiration are co-limiting.

CLM5 applies a fixed allocation scheme for woody vegetation. The decision to use a fixed allocation scheme in CLM5, rather than a dynamic NPP-based allocation scheme, as was used in CLM4 and CLM4.5, was driven by the fact that observations indicate that biomass saturates with increasing productivity, in contrast to the behavior in CLM4 and CLM4.5 where biomass continuously increases with increasing productivity ([Negron-Juarez et al., 2015](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#negronjuarezetal2015)). Soil carbon decomposition processes are unchanged in CLM5, but a new metric for apparent soil carbon turnover times ([Koven et al., 2017](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#kovenetal2017)) suggested parameter changes that produce a weak intrinsic depth limitation on soil carbon turnover rates (rather than the strong depth limitaiton in CLM4.5) and that the thresholds for soil moisture limitation on soil carbon turnover rates in dry soils should be set at a wetter soil moisture level than that used in CLM4.5.

Representation of human management of the land (agriculture, wood harvest) is augmented in several ways. The CLM4.5 crop model is extended to operate globally through the addition of rice, sugarcane, tropical varieties of corn and soybean [(Badger and Dirmeyer, 2015](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#badgeranddirmeyer2015) and [Levis et al., 2016)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#levisetal2016), and perennial bioenergy crops [(Cheng et al., 2019)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#chengetal2019). These crop types are added to the existing temperate corn, temperate soybean, spring wheat, and cotton crop types. Fertilization rates and irrigation equipped area updated annually based on crop type and geographic region through an input dataset. The irrigation trigger is updated. Additional minor changes include crop phenological triggers that vary by latitude for selected crop types, grain C and N is now removed at harvest to a 1-year product pool with the carbon for the next season’s crop seed removed from the grain carbon at harvest. A fraction of leaf/livestem C and N from bioenergy crops is removed at harvest to the biofuel feedstock pools and added to the 1-year product pool. Through the introduction of the capability to dynamically adjust landunit weights during a simulation, the crop model can now be run coincidentally with prescribed land use, which significantly expands the capabilities of the model. Mass-based rather than area-based wood harvest is applied. Several heat stress indices for both urban and rural areas are calculated and output by default [(Buzan et al., 2015)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#buzanetal2015). A more sophisticated and realistic building space heating and air conditioning submodel that prognoses interior building air temperature and includes more realistic space heating and air conditioning wasteheat factors is incorporated.

The fire model is the same as utilized in CLM4.5 except that a modified scheme is used to estimate the dependence of fire occurrence and spread on fuel wetness for non-peat fires outside cropland and tropical closed forests [(Li and Lawrence, 2017)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lilawrence2017) and the dependence of agricultural fires on fuel load is removed.

Included with the release of CLM5.0 is a functionally supported version of the Functionally-Assembled Terrestrial Ecosystem Simulator (FATES, [Fisher et al., 2015)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#fisheretal2015). A major motivation of FATES is to allow the prediction of biome boundaries directly from plant physiological traits via their competitive interactions. FATES is a cohort model of vegetation competition and co-existence, allowing a representation of the biosphere which accounts for the division of the land surface into successional stages, and for competition for light between height structured cohorts of representative trees of various plant functional types. FATES is not active by default in CLM5.0.

Note that the classical dynamic global vegetation model (CLM-DGVM) that has been available within CLM4 and CLM4.5 remains available, though it is largely untested. The technical description of the CLM-DGVM can be found within the CLM4.5 Technical Description ([Oleson et al. 2013)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#olesonetal2013).

During the course of the development of CLM5.0, it became clear that the increasing complexity of the model combined with the increasing number and range of model development projects required updates to the underlying CLM infrastructure. Many such software improvements are included in CLM5 including a partial transition to an object-oriented modular software structure. Many hard coded model parameters have been extracted into either the parameter file or the CLM namelist, which allows users to more readily calibrate the model for use at specific locations or to conduct parameter sensitivity studies. As part of the effort to increase the scientific utility of the code, in most instances older generation parameterizations (i.e., the parameterizations available in CLM4 or CLM4.5) are retained under namelist switches, allowing the user to revert to CLM4.5 from the same code base or to revert individual parameterizations where the old parameterizations are compatible with the new code. Finally, multiple vertical soil layer structures are defined and it is relatively easy to add additional structures.

