@Article{SmallmanMiSoGeOmWi:2021:PaUnDo,
               author = "Smallman, Thoma Luke and Milodowski, David Thomas and Sousa Neto, 
                         Eraclito Rodrigues de and Gerbrand, Koren and Ometto, Jean Pierre 
                         Henry Balbaud and Williams, Mathew",
          affiliation = "{} and {} and {} and {} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Parameter uncertainty dominates C-cycle forecast errors over most 
                         of Brazil for the 21st century",
              journal = "Earth System Dynamics",
                 year = "2021",
               volume = "12",
               number = "4",
                pages = "1191--1237",
                month = "Nov.",
             abstract = "Identification of terrestrial carbon (C) sources and sinks is 
                         critical for understanding the Earth system as well as mitigating 
                         and adapting to climate change resulting from greenhouse gas 
                         emissions. Predicting whether a given location will act as a C 
                         source or sink using terrestrial ecosystem models (TEMs) is 
                         challenging due to net flux being the difference between far 
                         larger, spatially and temporally variable fluxes with large 
                         uncertainties. Uncertainty in projections of future dynamics, 
                         critical for policy evaluation, has been determined using 
                         multi-TEM intercomparisons, for various emissions scenarios. This 
                         approach quantifies structural and forcing errors. However, the 
                         role of parameter error within models has not been determined. 
                         TEMs typically have defined parameters for specific plant 
                         functional types generated from the literature. To ascertain the 
                         importance of parameter error in forecasts, we present a Bayesian 
                         analysis that uses data on historical and current C cycling for 
                         Brazil to parameterise five TEMs of varied complexity with a 
                         retrieval of model error covariance at 1 degrees spatial 
                         resolution. After evaluation against data from 2001-2017, the 
                         parameterised models are simulated to 2100 under four climate 
                         change scenarios spanning the likely range of climate projections. 
                         Using multiple models, each with per pixel parameter ensembles, we 
                         partition forecast uncertainties. Parameter uncertainty dominates 
                         across most of Brazil when simulating future stock changes in 
                         biomass C and dead organic matter (DOM). Uncertainty of simulated 
                         biomass change is most strongly correlated with net primary 
                         productivity allocation to wood (NPPwood) and mean residence time 
                         of wood (MRTwood). Uncertainty of simulated DOM change is most 
                         strongly correlated with MRTsoil and NPPwood. Due to the coupling 
                         between these variables and C stock dynamics being bi-directional, 
                         we argue that using repeat estimates of woody biomass will provide 
                         a valuable constraint needed to refine predictions of the future 
                         carbon cycle. Finally, evaluation of our multi-model analysis 
                         shows that wood litter contributes substantially to fire 
                         emissions, necessitating a greater understanding of wood litter C 
                         cycling than is typically considered in large-scale TEMs.",
                  doi = "10.5194/esd-12-1191-2021",
                  url = "http://dx.doi.org/10.5194/esd-12-1191-2021",
                 issn = "2190-4979",
             language = "en",
           targetfile = "smallman-2021.pdf",
        urlaccessdate = "11 jun. 2024"
}