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Uncertainty analysis of carbon turnover time and sequestration potential in terrestrial ecosystems of the Conterminous USA Xuhui Zhou 1, Tao Zhou 1, Yiqi.

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Presentation on theme: "Uncertainty analysis of carbon turnover time and sequestration potential in terrestrial ecosystems of the Conterminous USA Xuhui Zhou 1, Tao Zhou 1, Yiqi."— Presentation transcript:

1 Uncertainty analysis of carbon turnover time and sequestration potential in terrestrial ecosystems of the Conterminous USA Xuhui Zhou 1, Tao Zhou 1, Yiqi Luo 1 1 Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019, USA 2 K ey laboratory of Environmental Change and Natural Disaster, Ministry of Education of China, Beijing Normal University, Beijing, P. R. China Email Address: zxuhui14@ou.edu  Carbon (C) turnover time and increases in NPP quantifies the capacity for C storage in plant and soil pools.  Accurate spatially distributed estimates of C turnover time over the conterminous USA is critical to the understanding of terrestrial C sequestration and prediction of climate change.  However, the spatial patterns of C turnover time have not been quantified for the conterminous USA, although NPP changes are relatively well qualified. I t is largely unknown what probabilistic densities are of C turnover time and sequestration potential.  In this study, the Bayesian probability inversion and Markov Chain Monte Carlo (MCMC) technique were applied to a regional TECOR model to generate PDF of C turnover time. And ecosystem C turnover times and sequestration potential and their standard deviation (SD) were estimated using MLEs and SD of the parameters in the conterminous USA. Fig. 5 Spatial patterns of ecosystem carbon turnover time (a) and its standard deviation (b). CONCLUSIONS  Over half of 22 parameters were well constrained by 12 data sets. The poorly constrained parameters were attributable to either the lack of experimental data or the mismatch of timescales.  Using MLEs or means of estimated parameters, ecosystem C turnover time ranged from 16 (cropland) to 86 years (ENF) with an average of 56 years. The C turnover times have highly spatial heterogeneity and its values depend on the vegetation type and climate condition.  Along the latitude, C turnover times displayed a strong positive correlation, suggesting that temperature is the most important factor in influencing carbon turnover time change (R 2 = 0.91).  The estimated C sequestration potential of the whole conterminous USA was 0.22 Pg C yr -1 with large portion in MF, grassland, and cropland. ACKNOWLEDGEMENTS We thank US DOE (DE-FG03-99R62800) and NSF (DEB 0092642, DEB 0444518) for financial support. IntroductionRESULTS METHODS Method: Bayesian probability inversion and Markov Chain Monte Carlo (MCMC) technology Fig. 3 Carbon allocation coefficients for eight biomes Model structure (Regional TECOR) for inversion analysis of carbon turnover time. ε* is maximum light use efficiency, α L, α W, and α R are allocations of NPP to leaves, wood, and roots (three layers ξ R1,ξ R2, and ξ R3 ), θ F and θ C are C partitioning coefficients of fine and coarse litter pools, η is a fraction of C exiting the coarse litter pool by mechanical breakdown, τ F,τ C, τ Ri, and τ Si are C turnover time in fine litter, coarse litter, roots and SOC in three layers. Fig. 1 Inversion results showing the histograms of estimated parameters with about 40,000 samples from M-H simulation for Evergreen Needleleaf Forest (left) and Grassland (right) 12 Data sets: NPP in leaves, stems, and roots, biomass in leaves, stems, fine litter, and roots and SOC in the three soil layers. Fig. 2 Comparisons between modeled and observed data for 12 data sets. Fig. 4 The relationship of latitude and latitude-averaged turnover time with SD Fig. 8 Ecosystem carbon sequestration and its SD (error bar) for 8 biomes (Pg C yr -1 ) Fig. 6 Ecosystem carbon turnover time and its SD (error bar) for 8 biomes Fig. 7 The potential of ecosystem carbon sequestration (a, g C m -2 yr -1 ) and its SD (b) in 50 years as NPP increase by 0.5% per year.


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