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Improving Understanding of Global and Regional Carbon Dioxide Flux Variability through Assimilation of in Situ and Remote Sensing Data in a Geostatistical.

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Presentation on theme: "Improving Understanding of Global and Regional Carbon Dioxide Flux Variability through Assimilation of in Situ and Remote Sensing Data in a Geostatistical."— Presentation transcript:

1 Improving Understanding of Global and Regional Carbon Dioxide Flux Variability through Assimilation of in Situ and Remote Sensing Data in a Geostatistical Framework Kim Mueller 1 Sharon Gourdji 1 Anna M. Michalak 1,2 1 Department of Civil and Environmental Engineering 2 Department of Atmospheric, Oceanic and Space Sciences The University of Michigan

2 Synthesis Bayesian Inversion Meteorological Fields Transport Model Sensitivity of observations to fluxes (H) Residual covariance structure (Q, R) Prior flux estimates (s p ) CO 2 Observations (y) Inversion Flux estimates and covariance ŝ, V ŝ Biospheric Model Auxiliary Variables Slide from Anna Michalak

3 Key Questions  Is there another inversion approach available to estimate: Spatial and temporal autocorrelation structure of fluxes and/or flux residuals? Sources and sinks of CO 2 without relying on prior estimates? Significance of available auxiliary data? Relationship between auxiliary data and flux distribution? Realistic grid-scale flux variability

4 Geostatistical Approach to Inverse Modeling  Geostatistical inverse modeling objective function: H = transport information, s = unknown fluxes, y = CO 2 measurements X and  define the model of the trend R = model data mismatch covariance Q = spatio-temporal covariance matrix for the flux deviations from the trend Deterministic componentStochastic component

5 Global Gridscale CO 2 Flux Estimation  Estimate monthly CO 2 fluxes (ŝ) and their uncertainty on 3.75° x 5° global grid from 1997 to 2001 in a geostatistical inverse modeling framework using: CO 2 flask data from NOAA-ESRL network (y) TM3 (atmospheric transport model) (H) Assume spatial correlation but no temporal correlation a priori (Q )  Three models of trend flux (X β) considered: Simple monthly land and ocean constants Terrestrial latitudinal flux gradient and ocean constants Terrestrial gradient, ocean constants and auxiliary variables

6 Inversion Results –

7 Transcom Regions TransCom, Gurney et al. 2003

8 Regional comparison of seasonal cycle GtC/yr

9 Regional comparison of inter annual variability GtC/yr

10 Key Questions  Is there another inversion approach available to estimate: Spatial and temporal autocorrelation structure of fluxes and/or flux residuals? Sources and sinks of CO 2 without relying on prior estimates? Significance of available auxiliary data? Relationship between auxiliary data and flux distribution? Realistic grid-scale flux variability …. Sharon

11 Key Questions  Is there another inversion approach available to estimate: Spatial and temporal autocorrelation structure of fluxes and/or flux residuals? Sources and sinks of CO 2 without relying on prior estimates? Significance of available auxiliary data? Relationship between auxiliary data and flux distribution? Realistic grid-scale flux variability

12 Sample Auxiliary Data Gourdji et al. (in prep.)

13 Variance-Ratio Test uses atmospheric data to assess significant improvement in fit of more complex trend Physical understanding combined with results of VRT to choose final set of auxiliary variables: % AgLAISST % ForestfPARdSSt/dt % ShrubNDVIPalmer Drought Index % GrassPrecipitationGDP Density Land Air Temp.Population Density Variance-Ratio Test uses atmospheric data to assess significant improvement in fit of more complex trend Physical understanding combined with results of VRT to choose final set of auxiliary variables: % AgLAISST % ForestfPARdSSt/dt % ShrubNDVIPalmer Drought Index % GrassPrecipitationGDP Density Land Air Temp.Population Density Variance-Ratio Test and Auxiliary Variables  Three models of trend flux (X β) considered: Monthly land and ocean constants (simple) Terrestrial latitudinal flux gradient and ocean constants (modified) Latitudinal gradient, ocean constants and auxiliary variables (variable) ˆ

14 Deterministic component Stochastic component Building up the best estimate in January 2000 Gourdji et al. (in prep.) ˆ

15 Uncertainty Reduction from Simple to Variable Trend Gourdji et al. (in prep.) %

16 Regional comparison of seasonal cycle Gourdji et al. (in prep.)

17 Comparison of annual average non-fossil fuel flux Gourdji et al. (in prep.)

18 Conclusions  Atmospheric data information and geostatistical approach can: Quantify model-data mismatch and flux covariance structure Identify significant auxiliary environmental variables and estimate their relationship with flux Constrain continental-scale fluxes independently of biospheric model and oceanic exchange estimates  Uncertainties at grid scale are high, but uncertainties of continental and global estimates are comparable to synthesis Bayesian studies  Upscaling fluxes a posteriori minimizes the risk of aggregation errors associated with inversions that estimate fluxes directly at large scale  Auxiliary data inform grid-scale flux variability; seasonal cycle at larger scales is consistent across models  Use of auxiliary variables within a geostatistical framework can be used to derive process-based understanding directly from an inverse model

19 North American CO 2 Flux Estimation  Estimate North American CO 2 fluxes at 1°x1° resolution & daily/weekly/monthly timescales using: CO 2 concentrations from 3 tall towers in Wisconsin (Park Falls), Maine (Argyle) and Texas (Moody) STILT – Lagrangian atmospheric transport model Auxiliary remote- sensing and in situ environmental data Pseudodata and recovered fluxes (Source: Adam Hirsch, NOAA-ESRL)

20 Acknowledgements  Collaborators: Advisor: Anna Michalak Research group: Alanood Alkhaled, Abhishek Chatterjee, Sharon Gourdji, Charles Humphriss, Meng Ying Li, Miranda Malkin, Kim Mueller, Shahar Shlomi, and Yuntao Zhou  Data providers: NOAA-ESRL cooperative air sampling network Christian Rödenbeck, MPIB Kevin Schaefer, NSIDC  Funding sources:

21 QUESTIONS? &

22 Drift Coefficients (β) Aux. Variable  CV X (GtC/yr)  GDP  LAI  fPAR  % Shrub  L. Temp GDP LAI fPAR % Shrub LandTemp Land Constants Ocean Constants Complete trend Gourdji et al. (in prep.) ˆ Inversion estimates drift coefficients (β) for variable trend: ˆ ˆ ˆ

23 Variogram Model  Used to describe spatial correlation


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