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 CO 2 concentrations near the surface is a major uncertainty in estimating net regional carbon fluxes More aerial and column CO 2 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 Gurney et al 2004

3. Diagnosing model vertical transport with measurements from aircraft: a schematic SN CO 2 latitudinal profile In the boundary layer Modeled profile Spurious northern sink inferred from surface data Model vertical stratification is too strong – slow mixing away from source CO 2 upper troposphere latitudinal profile Modeled profile Aircraft data would imply a northern source (fossil) CO 2 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 CO 2 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. Long-term aircraft profiles CodeLocationLat/LonAltitude range (m) Start atEnd atFlights / year PFAPoker Flats, AK65.07N, 147.29W 1500-75001999.06.27-10.5 ORLOrleans, France47.80N, 2.50E 500-35001997.01.02-? HFMHarvard Forest, MA 42.54N, 72.17W 500-65001999.11.11-13.7 CARCarr, CO40.90N, 104.80W 3000-80001992.11.19-30.0 HAAMolokai, HI21.23N, 158.95W 500-75001999.05.31-15.5 RTARarotonga, Cook Islands 21.25S, 159.83W 500-45002000.04.16-16.0 AIACape Grim, Australia 39S, 145E500-65001991.06.242000.09.1810.5

6. 2000-3 flux for all northern regions (land + ocean), Pg C/ y Northern flux Intermodel SD Priors-2.06±3.46 Land-0.74±3.17 Ocean-1.32±1.40 Surface data-1.780.44 Aircraft data-1.530.25 Combined data-1.690.37

7. Distribution of residuals

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

9. 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 CO 2 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?

10. 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 Krakauer et al 2004 Parameter values determined with GCV TransCom parameter values

11. 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 χ 2 ≈ 1 for each group) –Do the inversion again with the new scaling, until convergence

12. Weights for data & priors GroupSizeInitial mean variance Maximum-likelihood mean variance (intermodel range) Surface data 1081.01 ppm0.66 (0.61-0.74) Aircraft data 250.50 ppm0.20 (0.08-0.50) Prior region fluxes 220.80 Pg C/ y0.58 (0.23-1.18)

13. 2000-3 northern flux with optimized weights (land + ocean), Pg C/ y Northern flux Intermodel SD Priors-2.06±3.46 Land-0.74±3.17 Ocean-1.32±1.40 Surface data-1.780.44 Aircraft data-1.530.25 Combined data (original weights) -1.690.37 Combined data (optimized weights) -1.600.33

14. 2000-3 results by region (optimized weights) Source Sink

15. Conclusions Observations of the CO 2 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 CO 2 measurements, other data (isotopic, CO, etc.) in inversions

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

17. 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).