Quantification and attribution of errors in the simulated annual gross primary production and latent heat fluxes by two global land surface models. (16th August 2016)

• Record Type: Journal Article
• Authors: Li, Jianduo; Wang, Ying‐Ping; Duan, Qingyun; Lu, Xingjie; Pak, Bernard; Wiltshire, Andy; Robertson, Eddy; Ziehn, Tilo
• Is Part Of: Journal of advances in modeling earth systems. Volume 8:Number 3(2016)
• Page Start: 1270
• Page End: 1288
• Publication Date: 2016-08-16
• Subjects: global land surface model -- model error quantification -- sensitivity analysis -- ensemble simulation -- model error attribution; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1002/2015MS000583
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Differences in the predicted carbon and water fluxes by different global land models have been quite large and have not decreased over the last two decades. Quantification and attribution of the uncertainties of global land surface models are important for improving the performance of global land surface models, and are the foci of this study. Here we quantified the model errors by comparing the simulated monthly global gross primary productivity (GPP) and latent heat flux (LE) by two global land surface models with the model‐data products of global GPP and LE from 1982 to 2005. By analyzing model parameter sensitivities within their ranges, we identified about 2–11 most sensitive model parameters that have strong influences on the simulated GPP or LE by two global land models, and found that the sensitivities of the same parameters are different among the plant functional types (PFT). Using parameter ensemble simulations, we found that 15%–60% of the model errors were reduced by tuning only a few (<4) most sensitive parameters for most PFTs, and that the reduction in model errors varied spatially within a PFT or among different PFTs. Our study shows that future model improvement should optimize key model parameters, particularly those parameters relating to leaf area index, maximum carboxylation rate, and stomatal conductance. Key Points: There are 2–11 most sensitive model parameters in two land surface models Fifteen percent to 60% of the model errors can be reduced by tuning the few (<4) most sensitive parameters Future model improvement should optimize model parameters, particularly those parameters relating to leaf area index, maximum carboxylation rate, and stomatal conductance.