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Compatibility of surface and aircraft station networks for inferring carbon fluxes

Compatibility of surface and aircraft station networks for inferring carbon fluxes. TransCom Meeting, 2005 Nir Krakauer California Institute of Technology niryk@caltech.edu Zhonghua Yang, Jim Randerson, Paul Wennberg. Motivation.

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Compatibility of surface and aircraft station networks for inferring carbon fluxes

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  1. Compatibility of surface and aircraft station networks for inferring carbon fluxes TransCom Meeting, 2005 Nir Krakauer California Institute of Technology niryk@caltech.edu Zhonghua Yang, Jim Randerson, Paul Wennberg

  2. Motivation • The effect of vertical transport on CO2 concentrations near the surface is a major uncertainty in estimating net regional carbon fluxes Gurney et al 2004 • More aerial and column CO2 data is becoming available • The effect of using aircraft and column observations in inversions provides a measure of the influence of error in model vertical transport on flux estimates from inversions

  3. Diagnosing model vertical transport with measurements from aircraft: a schematic CO2 upper troposphere latitudinal profile Aircraft data would imply a northern source Modeled profile Model vertical stratification is too strong – slow mixing away from source Modeled profile Spurious northern sink inferred from surface data CO2 latitudinal profile In the boundary layer S N (fossil) CO2 emissions Note: Mixing rates here can be constant. Additional biases can be introduced by the model representation of variability in mixing (e.g. seasonal and diurnal rectifier effects).

  4. Inversion set-up • GLOBALVIEW-2004 stations • Use mean CO2 concentrations for 2000-2003, when more aircraft data collected • Only stations with 60% actual data for period • Transport operators from TransCom model annual-mean output • Data uncertainty assumed proportional to station residual standard deviation • TransCom regions and priors NOAA CMDL

  5. 2000-3 flux for all northern regions

  6. Distribution of residuals

  7. Models that are too vertically stratified tend to imply a larger northern sink

  8. Interim conclusions • Comparing surface with aircraft data sets suggests that much of the TransCom intermodel variability in northern sink estimates is due to variation in model vertical mixing strength • Most TransCom models may have too little vertical mixing, so that surface observations imply an overly large northern sink • Detailed comparison of modeled vs. observed vertical CO2 distributions is probably required to diagnose just where this mixing bias arises (convection? isentropic transport? diurnal cycle?) and how to reduce it • Next: for an imperfect model, how do we best combine surface with other (e.g. aircraft) observations?

  9. Strategies for choosing weights • 1) Generalized cross validation • Through minimizing a GCV objective function, parameters such as the weighting of prior information (λ) and the differential weighting of high- and low-variability stations (τ) can be set to optimize the model’s prediction of left-out observations • See my GRL paper Parameter values determined with GCV TransCom parameter values Krakauer et al 2004

  10. 2) Here we try a maximum-likelihood Bayesian approach (Koch and Kusche 2002) • Divide inversion data into independent groups (e.g. surface vs. aircraft observations vs. prior flux information) • Scale the initial error covariance matrix of each group so that the residual size is equal to the group degrees of freedom (essentially postulating that χ2 ≈ 1 for each group) • Do the inversion again with the new scaling, until convergence

  11. Weights for data & priors

  12. 2000-3 northern flux with optimized weights

  13. 2000-3 results by region (optimized weights) Source Sink

  14. Conclusions • Observations of the CO2 concentration vertical structure valuably complement surface data • Relative weights for surface and aircraft observations can be assigned using statistical methods • Future work: look at the effect of including (ground-based or satellite) column CO2 measurements, other data (isotopic, CO, etc.) in inversions

  15. Acknowledgments • Funding from the Moore Foundation (NK), NASA and NOAA (JR) • The TransCom modelers

  16. Papers cited Gurney, K. R., R. M. Law, A. S. Denning, et al. (2004), Transcom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks, Global Biogeochem. Cycles, 18(1). Koch, K. R. and J. Kusche (2002), Regularization of geopotential determination from satellite data by variance components, J. Geodesy, 76(5), 259-268. Krakauer, N. Y., T. Schneider, J. T. Randerson, et al. (2004), Using generalized cross-validation to select parameters in inversions for regional carbon fluxes, Geophys. Res. Lett., 31(19).

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