title: Uncertain future soil carbon dynamics under global change predicted by models constrained by total carbon measurements.
abstract: Pool-based carbon (C) models are widely applied to predict soil C dynamics under global change and infer underlying mechanisms. However, it is unclear about the credibility of model-predicted C pool size, decay rate (k), and/or microbial C use efficiency (e) as only data on bulked total C is usually available for model constraining. Using observing system simulation experiments (OSSE), we constrained a two-pool model using simulated data sets of total soil C dynamics under topical hypotheses on responses of soil C dynamics to warming and elevated CO2(i.e., global change scenarios). The results indicated that the model predicted great uncertainties in C pool size, k, and e under all global change scenarios, resulting in the difficulty to correctly infer the presupposed "real" values of those parameters that are used to generate the simulated total soil C for constraining the model. Furthermore, the model using the constrained parameters generated divergent future soil C dynamics. Compared with the predictions using the presupposed real parameters (i.e., the real future C dynamics), the percentage uncertainty in 100-yr predictions using the constrained parameters was up to 45% depending on global change scenarios and data availability for model-constraining. Such great uncertainty was mainly due to the high collinearity among the model parameters. Using pool-based models, we argue that soil C pool size, k, and/or e and their responses to global change have to be estimated explicitly and empirically, rather than through model-fitting, in order to accurately predict C dynamics and infer underlying mechanisms. The OSSE approach provides a powerful way to identify data requirement for the new generation of model development and test model performance.
authors.affiliation: CSIRO Agriculture, GPO Box 1700, Canberra, Australian Capital Territory, 1601, Australia.
authors.forename: Zhongkui
authors.initials: Z
authors.lastname: Luo
authors.affiliation: CSIRO Agriculture, GPO Box 1700, Canberra, Australian Capital Territory, 1601, Australia.
authors.forename: Enli
authors.initials: E
authors.lastname: Wang
authors.affiliation: College of Forest Science, Beijing Forestry University, Beijing, 100083, China.
authors.forename: Osbert J
authors.initials: OJ
authors.lastname: Sun
mesh_terms: D002244:Carbon / Q000737:chemistry*; D057486:Carbon Cycle; D057231:Climate Change; D008962:Models, Theoretical; D012987:Soil / Q000737:chemistry*; D035501:Uncertainty
chemical_list: D012987:Soil; D002244:Carbon
keywords: Bayesian calibration; carbon use efficiency; data assimilation; decay rates; elevated CO2; global warming; identifiability and collinearity