This interactive app explores how individual parameter perturbations affect model outputs in the CLM and CLM-FATES models in Satellite Phenology (SP) mode. Each parameter is varied one at a time to assess its influence on key biophysical variables.
Each tab in the app focuses on a specific way to explore and interpret the one-at-a-time parameter ensemble results:
Use the navigation bar above to switch between sections. Hover over plots for details, and use the download buttons to save data or figures.
High-level summary of CLM and CLM-FATES models:
Both models were run in satellite phenology (SP) mode, where canopy structure (LAI, SAI, height) is prescribed from satellite observations.
When CLM is coupled to FATES, CLM provides site and soil conditions and atmospheric forcing, while FATES simulates plant physiological, vegetation demography, and biogeochemical processes (Fig. 1).
For more details, see CLM documentation and FATES documentation .
Both CLM and CLM-FATES were run in SP mode using prescribed GSWP3 meteorology (2000–2014).
A 45-year spinup ensured stable soil and energy conditions, and results from an additional 15 years were analyzed.
To reduce computational cost, we used a 400-point ‘sparse grid’ that represents global variability following Kennedy et al. (2025) . (Fig. 2)
We tested how individual parameter changes affect model outputs using one-at-a-time parameter perturbations.
Vegetation-related parameters were perturbed together across all PFTs to limit simulation count.
Adrianna Foster, NCAR — afoster@ucar.edu
If you use this data or figures please cite the paper associated with the project:
Foster, A., Hawkins, L. R., Kennedy, D., Bonan, G., Fisher, R., Needham, J., Knox, R., Koven, C., Wieder, W., Dagon, K., & Lawrence, D. (2026). Contrasting parametric sensitivities in two global vegetation models using parameter perturbation ensembles [Data set]. In Journal of Advances in Modeling Earth Systems. Zenodo https://doi.org/10.5281/zenodo.18203140
You can also find the full dataset associated with this project at:
Foster, A., Hawkins, L. R., Kennedy, D., Bonan, G., Fisher, R., Needham, J., Knox, R., Koven, C., Wieder, W., Dagon, K., & Lawrence, D. (2026). Contrasting parametric sensitivities in two global vegetation models using parameter perturbation ensembles [Data set]. In Journal of Advances in Modeling Earth Systems. Zenodo https://doi.org/10.5281/zenodo.18203140
This material is based upon work supported by the NSF National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. Computing and data storage resources, including the Derecho supercomputer (doi:10.5065/qx9a-pg09) were provided by the Climate Simulation Laboratory at NSF-NCAR's Computational and Information Systems Laboratory (CISL).
Difference maps show the annual value for the ensemble member for the maximum parameter value ensemble member for the minimum parameter value.
Each dot represents a global annual mean or interannual variance for the chosen variable from an ensemble member.
Black dots and error bars show mean, minimum, and maximum values for the entire enemble.
Hover over a point to see what parameter was perturbed and in which direction for that ensemble member.
Variance contribution of a parameter to the global annual mean or interannual variance was calculated as the sum of the squared differences from the default simulation:
$$V_i = (\bar{x}_i - x_{i,min})^2 + (x_{i,max} - \bar{x}_i)^2$$Dots show the global annual mean or interannual variance for that ensemble member.
Files will be organized by dataset, with metadata included if selected.