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Data-model assimilation for manipulative experiments Dr. Yiqi Luo Botany and microbiology department University of Oklahoma, USA.

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Presentation on theme: "Data-model assimilation for manipulative experiments Dr. Yiqi Luo Botany and microbiology department University of Oklahoma, USA."— Presentation transcript:

1 Data-model assimilation for manipulative experiments Dr. Yiqi Luo Botany and microbiology department University of Oklahoma, USA

2 Manipulative experiments More manipulative experiments under planning by NEON and DOE

3 What is the nature of manipulative experiments?

4 How can we extrapolate results from the Duke FACE experiment to predict long-term, large-scale change

5 GPP Leaves (X 1 ) Wood (X 2 ) Surface metabolic leaf litter (X 4 ) Soil metabolic root litter (X 7 ) Surface microbes (X 9 ) Soil microbes (X 10 ) Wood litter (X 6 ) Surface structural leaf litter (X 5 ) Soil structural root litter (X 8 ) Slow SOM (X 11 ) Passive SOM (X 12 ) CO 2 Fine roots (X 3 ) CO 2

6 Luo and Reynolds 1999

7 Luo and Reynolds 1999, Ecology, 80:

8 Luo and Reynolds 1999 Observed C and N dynamics in FACE experiments are not applicable to natural ecosystems in response to a gradual CO 2 increase.

9 Hui et al Luo 2001 Results of manipulative experiments can not be simply extrapolated to predict ecosystem responses to global change in the real world

10 Luo and Reynolds (1999) “Rigorous analysis of (results from) step experiments requires not only statistical but also other new approaches, such as deconvolution and inverse modeling”

11 Data-model assimilation at Duke FACE Applications 1.Soil C processes: Luo, et al Transfer coefficients: Luo, et al Uncertainty analysis, Xu et al Forecasting of carbon sequestration Tool development 1.Deconvolution (Luo et al. 2001) 2.Adjoint function (White and Luo. 2002) 3.Stochastic inversion, Xu et al Step-wise inversion, Wu et al. (in review) 5.Linear, nonlinear, ensemble Kalman Filter (Gao et al. see poster)

12 Framework for Uncertainty analysis Stochastic inversion Variability in estimated parameter values Forward model Uncertainty in model predictions Measurement errors Observation error (umol m -2 s -1 ) Frequency Error distribution Norman Distribution Double exponential distribution Frequency Hour Day Month Year Relative uncertainty (%)

13 Multiple data sets Tree biomass growth Soil respiration Litter fall Soil carbon Foliage biomass

14 GPP Leaf (X 1 )Wood (X 3 ) Metabolic litter (X 4 ) Microbes (X 6 ) Structure litter (X 5 ) Slow SOM (X 7 ) Passive SOM (X 8 ) CO 2 MCMC– Metropolis-Hastings algorithm Mathematical and statistical procedure 1. Matrix to describe C flow 2. Mapping functions Q j (A)(t) = q j (A)(t) X(A)(t) 3. Cost function 4. Search method Root (X 2 )

15 Luo et al. 2003, GBC Criteria I: Data-model fitting

16 Observed Data Prior knowledgePosterior distribution Criteria II: Probability Distribution Inverse model Well- constrained Edge-hitting No- information

17 Xu et al. 2006, GBC Uncertainty analysis Variability in estimated parameter values Multiple data sets Stochastic inversion Variability in estimated parameter values

18 Xu et al. 2006, GBC

19 ln (Y)=0.966 ln (X) R 2 = Luo et al. 2003, GBC

20 Model predictions Multiple data sets Stochastic inversion Variability in estimated parameter values Forward model Uncertainty in model predictions Xu et al. 2006, GBC

21 Estimated initial values of pools and residence times to partition C sink to two components caused by climate change and forest regrowth

22 Uncertainty Analysis Measurement errors Stochastic inversion Variability in estimated parameter values Forward model Uncertainty in model predictions 1.Magnitudes (50%, 100%, and 200%) of measurement errors (Weng et al. poster) 2.Distributions (Normal vs. double exponential) of measurement errors of eddy-flux data (Liu et al. in review) 3.Different assimilation algorithms (least squares, maximal likelihood, and Kalman filter (Gao et al. poster) 4.Continental analysis on residence times (Tao Zhou et al. in review), Q 10 values (Tao Zhou et al. in review, and their uncertainties (Xuhui Zhou et al. poster) Applications

23 Summary 1.Data from manipulative experiments can not be directly extrapolated to the real world. We have to extract information from data on fundamental processes 2.Model assimilation of multiple data sets is one of the best approaches to synthesis of experimental results with processing thinking and can better balance evidence from different lines.

24 Acknowledgement Idea and math development Luther White James Reynolds S. Lakshmivarahan Dafeng Hui Tao Xu Tao Zhou Ensheng Weng Chao Gao Ensheng Weng Li Zhang Min Liu Financial support DOE TCP NSF Data from the Duke FACE Schlesinger Ellsworth Finzi DeLucia Katul Oren


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