Create and run land models in integrated (multi- component) or standalone (single component) modes.

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This is the repository of the CliMA land model code. Here are some notable features:

• ClimaLand has a modular design, models can be run as standalone (e.g., soil moisture/energy only) or integrated (e.g., soil moisture/energy AND canopy AND snow, etc.)
• ClimaLand can simulate single columns, regional boxes, and global runs
• ClimaLand is CPU and GPU compatible
• ClimaLand welcomes contributions! Please feel free to reach out to us with questions about how to get started, create a branch, and extend our code.

To use ClimaLand.jl, first you need to install Julia. Then, you can install ClimaLand.jl by doing:

julia> using Pkg
julia> Pkg.add(ClimaLand)

You are now ready to use ClimaLand.jl. To run a simple first simulation, please see our documentation page Running your first simulation.

In our code base, a "model" define a set of prognostic variables which must be timestepped. The equations which govern the time evolution likely contain parameters and are informed by parameterization and physical domain choices. Any ClimaLand model contains all of the information needed to evaluate these equations. Below are the current models we support:

Component Models:

• RichardsModel: Soil model option; runnable only in standalone mode

• EnergyHydrology: Soil model option; runnable in standalone mode, or as part of an integrated model

• CanopyModel: runnable in standalone mode, or as part of an integrated model

• SnowModel: runnable in standalone mode, or as part of an integrated model

Combined Models:

• SoilCanopyModel: an integrated model made of individual component models EnergyHydrology + CanopyModel
• LandModel: an integrated model made of individual component models EnergyHydrology + CanopyModel + SnowModel + SoilCO2Model

Recommended Julia Version: Stable release v1.11.x. CI tests Julia v1.10 and 1.11.

ClimaLand.jl is a different model from the original CliMA Land, which aims to utilize remote sensing data through more complex canopy RT and plant physiology modules. For more details, please refer to https://github.com/CliMA/Land.

• Wang, Yujie, et al. "Testing stomatal models at the stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0. 1)." Geoscientific Model Development 14.11 (2021): 6741-6763.
• R. K. Braghiere, Y. Wang, R. Doughty, D. Souza, T. Magney, J. Widlowski, M. Longo, A. Bloom, J. Worden, P. Gentine, and C. Frankenberg. 2021. Accounting for canopy structure improves hyperspectral radiative transfer and sun-induced chlorophyll fluorescence representations in a new generation Earth System model. Remote Sensing of Environment. 261: 112497.
• Wang, Yujie, and Christian Frankenberg. "On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes." Biogeosciences 19.1 (2022): 29-45.
• Wang, Yujie, et al. "GriddingMachine, a database and software for Earth system modeling at global and regional scales." Scientific data 9.1 (2022): 258.
• Holtzman, Nataniel, et al. "Constraining plant hydraulics with microwave radiometry in a land surface model: Impacts of temporal resolution." Water Resources Research 59.11 (2023): e2023WR035481.
• Braghiere, R. K., Wang, Y., Gagné-Landmann, A., Brodrick, P. G., Bloom, A. A., Norton, A. J., Ma, S., Levine, P., Longo, M., Deck, K., Gentine, P., Worden, J. R., Frankenberg, C., & Schneider, T. (2023). The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections. AGU Advances, 4(4), e2023AV000910. link
• Wang, Y., Braghiere, R. K., Yin, Y., Yao, Y., Hao, D., & Frankenberg, C. (2024). Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets. Global Change Biology, 30(5), e17346. link