NACP Meeting, New Orleans February 2, 2011

1. Impact of the expanding measurement network on top-down budgeting of CO2 surface fluxes in North America Kim Mueller, Sharon Gourdji, VineetYadav, Michael Trudeau, AbhishekChatterjee, Deborah Huntzinger, Arlyn Andrews, Andrew Schuh, Yoichi Shiga, Kenneth Davis, Britton Stephens, Beverly E. Law, Colm Sweeney, Marc Fischer, DaniloDragoni, Doug Worthy, Matt Parker, Mathias Goeckede, Scott Richardson, Natasha Miles, Anna M. Michalak • NACP Meeting, New Orleans • February 2, 2011

2. CO2 observations – flask measurements CO2 observations –continuous measurements log10(ppm/μmolCO2/m2s) log10(ppm/μmolCO2/m2s) 2008 2oo4 2oo5 2oo6 2oo7 2oo8

3. ? Regional Atmospheric Inverse Modeling Boundary Conditions Subtract ? CO2 observations ? Transport How well can you match your data (R)? R ? Approach to quantify R Regularization Method Synthesis Bayesian Inverse Modeling (Bayes) Geostatistical Inverse Modeling (GIM) Sources and Sinks, Uncertainties Inversion ? Approach to quantify Q Coefficients (β) ^ @ what temporal scale How is the underlying flux field spatially and temporally correlated (Q)? How much you trust prior guess (Q)? Q

4. 1pm - 1050 obs shortaft -3000 obs CT-BC 35 towers 10 towers all – 8400 obs 4Ddiurnal 3hrly GV-BC • (III) Real data (Bayes) • experiment • (I) Synthetic data • experiment • (II) Real data (GIM) • experiment SPACE Estimation Scale Boundary Conditions TIME BETTER COVERAGE IN SPACE & TIME Sources and Sinks Inversion Shortaft ~ afternoon data at short towers, all data at tall towers Explicit prior

5. Synthetic Data • Experiment Results Using GIM • No explicit prior so experiment test how much information is within atmospheric content in measurements w/out transport error

6. JUNE (all fluxes post aggregated to monthly scale) • TIME • “Truth” Adding more measurement in time and space improves both the spatial pattern and grid scale flux estimates. Biggest “bang for the buck” when adding in more data throughout the day with expanded network. Could draw opposite conclusion if estimating fluxes at a coarser scale. • SPACE 3hrly Estimation Scale 4Ddiurnal Estimation Scale • Synthetic Data (GIM) Courtesy of J. Randerson

7. WRF-STILT Transport Model (Nehkorn et al., 2010) • Real Data (GIM) • Experiment Results • No explicit prior -fluxes are based almost solely on atmospheric content in measurements.

8. Boundary Conditions Boundary conditions account for the influence of fluxes that occurred outside of the North American domain Difference of GV-BCs and CT-BCs is approximately 0.5-1ppm with GV-BCs always being lower and therefore are associate with less sinks (more sinks occurred outside of domain of interest)

9. JUNE,JULY,AUGUST (all fluxes post aggregated to monthly scale) • TIME • BC • SPACE 3hrly Estimation Scale The choice of boundary conditions doesn’t have much impact on monthly grid-scale fluxes except in boreal north More constraint provided by increasing the number of measurements per day • Real Data (GIM)

10. Annual grid-scale GV-BC (10TN) GV-BC (35TN) CT-BC (35TN) Inventory More constraint provided by the expanded network Inversion using CT-BC results in very strong uptake that is not present in inventory estimates Boundary conditional have a large impact on annual totals from MCI Courtesy of S. Ogle, MCI Campaign • Real Data (GIM)

11. WRF-STILT Transport Model (Nehkorn et al., 2010) • Real Data (Bayes) • Experiment Results • Used explicit prior (CASA) to see how much atmospheric measurements correct our first guess of grid-scale fluxes

12. Seasonal grid-scale CASA - prior UMich-Bayes(10twrs) UMich-Bayes(35twrs) Start to pull away from explicit prior in South with the use of more towers More corrections in the SouthWest and stronger sources in agricultural belt Not many deviations from prior in growing season with 10TN compared to 35TN. Corrections across the contiguous US. As with Dec-Feb, start to pull away from explicit prior in South with expanded network • Real Data (Bayes)

13. WRF-STILT Transport Model (Nehkorn et al., 2010) • Annual Budgets (synthetic, GIM and Bayes)

14. 9TN(04) Annual biospheric budgets for NA 35TN(08) 10TN(08) Bayes GIM Synthetic data experiments indicates that with the expanded measurement network, we should be able to recover annual budget using GIM. No boundary conditions needed but did simulate real measurement gaps. The Bayesian results have less of a spread of the estimates due to choice of boundary conditions but still wider than the differences between estimates from the smaller and expanded network. The ability of the expanded measurement network to budget continental sources and sinks is hampered by the influence of boundary conditions. The spread is likely the same if not wider when using more data is space. Spread of the budgets due to boundary conditions is wide (>1GtC/year). This spread may be exacerbated by the setup of GIM to recover 3hrly fluxes for the year. The impact of the boundary conditions was also apparent in the 2004 results. CarbonTrackercourtesy of A. Jacobsen and NOAA Orchidee courtesy of D.N. Huntzinger and Interim-Synthesis Team 2004 results courtesy of S. Gourdji

15. Conclusions Can the expanded observational network help us to identify sources and sinks at regional scales? Results look promising but more work to be done … To help maximize the extent to which the inversion can extract information content of measurements need to: • Estimate fluxes to account for underlying variability in transport or flux field (e.g. 3hrly) • Use more observations from more times of the day • Need a method to verify simulated atmospheric transport at these additional times • Better means of validating our boundary conditions (A.E. Andrews has new version available) • Improve atmospheric transport models • Better ways to assess uncertainty • Assess at what spatial and temporal scales we can trust estimates

16. Acknowledgements Other Contributors: NOAA-ESRL: Adam Hirsch, Andy Jacobsen AER: Thomas Nehrkorn, John Henderson, JanuszEluszkiewicz NACP-Interim Sythensis Team Members NASA NAS: technical support staff (Johnny Chang and others) Funding: NASA (NNX06AE84G Constraining North American Fluxes of Carbon Dioxide and Inferring their Spatiotemporal Covariances through Assimilation of Remote Sensing and Atmospheric Data in Geostatistical Framework) Questions? Check out: The Top-Down Constraint on North American CO2 Fluxes: and Inter-comparison of Region Inversion Results for 2004, Gourdji et al., 2004 Friday at 8:50am Check out: Come to the data-assimilation side meeting (Yadav & Michalak) Form 5:15-6:15 in the Lafitte Room

17. Boundary conditions account for the influence of fluxes that occurred outside of the North American domain Even though we saw big differences in MCI region with choice of boundary conditions, differences are the greatest in the West Coast and under-constrained regions of the continent. Annual difference between fluxes using GVBCs and CTBCs Difference of GV-BCs and CT-BCs is approximately 0.5-1ppm with GV-BCs always being lower and therefore are associate with less sinks (more sinks occurred outside of domain of interest)

18. Post aggregated fluxes June More spatial locations reduces the spread of the monthly budget and improves ability to recover the “truth” More spatial data reduces spread but still a lot of variability in estimates associated with different setup choices MidContinental Intensive 9 measurement locations

19. Average grid-scale diagnostics MCI 9 measurement locations

20. Post aggregated scales • Post aggregated scales Boundary conditions only shift seasonal cycle up or down. Not shown. More difference in growing season with choice of observations to use throughout the day with more measurement locations. Temporal aggregation error has an influence at aggregated areas size of MCI but less so at continental scale.

21. What is the information content of the expanding measurement network in terms of budgeting sources and sinks? How does the inversion setup influence our ability to extract the information from the measurements?

22. SPACE & TIME Other Estimates Courtesy of S. Gourdji Courtesy of J. Randerson