Contrasting parametric sensitivities in two global vegetation models using parameter perturbation ensembles
Foster, A. C., Hawkins, L. R., Kennedy, D., Bonan, G. B., Fisher, R. A., et al. (2026). Contrasting parametric sensitivities in two global vegetation models using parameter perturbation ensembles. Journal of Advances in Modeling Earth Systems, doi:https://doi.org/10.1029/2025ms005590
| Title | Contrasting parametric sensitivities in two global vegetation models using parameter perturbation ensembles |
|---|---|
| Genre | Article |
| Author(s) | Adrianna C. Foster, L. R. Hawkins, Daniel Kennedy, Gordon B. Bonan, R. A. Fisher, J. F. Needham, R. G. Knox, C. D. Koven, William Wieder, Katherine Dagon, David M. Lawrence |
| Abstract | Uncertainty in land model projections remains high and the roles of parametric and structural uncertainty are difficult to disentangle. To compare parametric sensitivity across model structures we present two parameter perturbation ensembles using the Community Land Model (CLM) operating in satellite phenology mode. The ensembles contrast two vegetation modules: (a) the default CLM vegetation module and (b) the Functionally Assembled Terrestrial Ecosystem Simulator (CLM-FATES). We perturbed over 300 parameters and quantified their effects on biophysical fluxes globally and across biomes. Most parameters have minimal impact on biophysical fluxes, with only a few substantially influencing results. While both models exhibit similar parameter sensitivity for some fluxes, CLM-FATES shows larger spread in gross primary productivity (GPP), driven by strong sensitivity to carboxylation rate. CLM-FATES also shows a weaker GPP response to soil hydrology parameters and exhibits higher water use efficiency (WUE). Cross-model comparisons reveal similar sensitivities for some parameters (e.g., leaf dimension) but divergent responses to others (e.g., stomatal intercept), highlighting underlying structural differences. Differences in WUE and sensitivity to hydrology and stomatal conductance parameters underscore how model structure fundamentally alters parametric sensitivity. The data sets generated from these ensembles can be used to identify influential parameters and guide future calibration efforts. |
| Publication Title | Journal of Advances in Modeling Earth Systems |
| Publication Date | Mar 1, 2026 |
| Publisher's Version of Record | https://doi.org/10.1029/2025ms005590 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7mw2nnt |
| OpenSky Listing | View on OpenSky |
| CGD Affiliations | TSS, CAS, CGDAO, ESP |