1. I MPACT OF THE EXPANDING MEASUREMENT NETWORK ON TOP - DOWN BUDGETING OF CO 2 SURFACE FLUXES IN N ORTH A MERICA Kim Mueller, Sharon Gourdji, Vineet Yadav, Michael Trudeau, Abhishek Chatterjee, Deborah Huntzinger, Arlyn Andrews, Andrew Schuh, Yoichi Shiga, Kenneth Davis, Britton Stephens, Beverly E. Law, Colm Sweeney, Marc Fischer, Danilo Dragoni, Doug Worthy, Matt Parker, Mathias Goeckede, Scott Richardson, Natasha Miles, Anna M. Michalak NACP Meeting, New Orleans February 2, 2011

2. CO 2 observations – flask measurements CO 2 observations –continuous measurements 2008 log 10 (ppm/μmolCO 2 /m 2 s) 2oo42oo52oo62oo72oo8

3. Inversion Sources and Sinks, Uncertainties Sources and Sinks, Uncertainties Transport CO 2 observations How well can you match your data (  R )? How much you trust prior guess (  Q )? Approach to quantify  Q Approach to quantify  R Regional Atmospheric Inverse Modeling Synthesis Bayesian Inverse Modeling (Bayes) How is the underlying flux field spatially and temporally correlated (  Q )? Geostatistical Inverse Modeling (GIM) Coefficients ( β) ^ RR QQ Boundary Conditions Subtract ? ? @ what temporal scale ? ? ? Regularization Method

4. BETTER COVERAGE IN SPACE & TIME SPACE TIME Inversion Sources and Sinks Boundary Conditions Estimation Scale (I) Synthetic data experiment (II) Real data (GIM) experiment Shortaft ~ afternoon data at short towers, all data at tall towers (III) Real data (Bayes) experiment

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. “Truth” 3hrly Estimation Scale4Ddiurnal Estimation Scale Adding more measurement in time and space improves both the spatial pattern and grid scale flux estimates. TIME SPACE 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. JUNE (all fluxes post aggregated to monthly scale) Courtesy of J. Randerson Synthetic Data (GIM)

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

8. 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 ) Boundary Conditions

9. 3hrly Estimation Scale TIME SPACE BC 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 JUNE,JULY,AUGUST (all fluxes post aggregated to monthly scale) Real Data (GIM)

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

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

12. CASA - priorUMich-Bayes (10twrs)UMich-Bayes (35twrs) Seasonal grid-scale 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. Annual Budgets (synthetic, GIM and Bayes) WRF-STILT Transport Model (Nehkorn et al., 2010)

14. GIM Annual biospheric budgets for NA 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. 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 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 impact of the boundary conditions was also apparent in the 2004 results. 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. Orchidee courtesy of D.N. Huntzinger and Interim-Synthesis Team CarbonTracker courtesy of A. Jacobsen and NOAA Bayes 35TN(08) 10TN(08) 9TN(04) 2004 results courtesy of S. Gourdji

15. Conclusions 1.Estimate fluxes to account for underlying variability in transport or flux field (e.g. 3hrly) 2.Use more observations from more times of the day 1.Need a method to verify simulated atmospheric transport at these additional times 3.Better means of validating our boundary conditions (A.E. Andrews has new version available) Can the expanded observational network help us to identify sources and sinks at regional scales? Results look promising but more work to be done … 4.Improve atmospheric transport models 5.Better ways to assess uncertainty 1.Assess at what spatial and temporal scales we can trust estimates To help maximize the extent to which the inversion can extract information content of measurements need to:

16. ACKNOWLEDGEMENTS Other Contributors: NOAA-ESRL: Adam Hirsch, Andy Jacobsen AER: Thomas Nehrkorn, John Henderson, Janusz Eluszkiewicz 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. Annual difference between fluxes using GVBCs and CTBCs 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 ) 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.

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. MCI 9 measurement locations Average grid-scale diagnostics

20. 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. Post aggregated scales

21. INDIRECT = Atmospheric CO 2 observations Atmospheric Inversion Models Estimation CO 2 flux Use atmospheric transport model to translate atmospheric CO 2 mixing ratio into surface exchange Observations  Cannot match data perfectly  Error associated w/ transport, measurements, etc. LIMITED IN TIME & SPACE  Use a “prior” or regularization method

22. 1.What is the information content of the expanding measurement network in terms of budgeting sources and sinks? 2.How does the inversion setup influence our ability to extract the information from the measurements?

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