@Article{Kennedy_JAMES_20250809,
 author		= {D. Kennedy and K. Dagon and D. M. Lawrence and R. A. Fisher and B. M. Sanderson and N. Collier and F. M. Hoffman and C. D. Koven and E. Kluzek and S. Levis and X. Lu and K. W. Oleson and C. M. Zarakas and Y. Cheng and A. C. Foster and M. D. Fowler and L. R. Hawkins and T. Kavoo and S. Kumar and A. J. Newman and P. J. Lawrence and F. Li and D. L. Lombardozzi and Y. Luo and J. K. Shuman and A. L. S. Swann and S. C. Swenson and G. Tang and W. R. Wieder and A. W. Wood},
 title		= {One-at-a-Time Parameter Perturbation Ensemble of the {C}ommunity {L}and {M}odel, Version 5.1},
 journal	= JAMES,
 volume		= 17,
 number		= 8,
 pages		= {e2024MS004715},
 doi		= {10.1029/2024MS004715},
 day		= 9,
 month		= aug,
 year		= 2025,
 abstract	= {Comprehensive land models are subject to significant parametric uncertainty, which can be hard to quantify due to the large number of parameters and high model computational costs. We constructed a large parameter perturbation ensemble (PPE) for the Community Land Model version 5.1 with biogeochemistry configuration (CLM5.1-BGC). We performed more than 2,000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive data set, which can be used to identify the most influential parameters on a wide range of output variables globally, by biome, or by plant functional type. We found that parameter effects can exceed scenario effects and that a small number of parameters explains a large fraction of variance across our ensemble. The most important parameters can differ regionally and also based on the forcing scenario. The software infrastructure developed for this experiment has greatly reduced the human and computer time needed for CLM PPEs, which can facilitate routine investigation of parameter sensitivity and uncertainty, as well as automated calibration.}
}