1. Andrew Schuh 1, Thomas Lauvaux 2,, Ken Davis 2, Marek Uliasz 1, Dan Cooley 1, Tristram West 3, Liza Diaz 2, Scott Richardson 2, Natasha Miles 2, F. Jay Breidt 1, Arlyn Andrews 4, Kim Mueller 5, Sharon Gourdji 5, Kevin Gurney 6, Erandi Lokupitiya 1, Linda Heath 7, James Smith 7, Mathias Goeckede, Scott Denning 1, and Stephen M. Ogle 1 Contrasting atmospheric CO2 inversion results at a regional scale over the highly-inventoried area of the Mid Continental Intensive. 1.Colorado State University, 2. The Pennsylvania State University, 3. Pacific Northwest National Laboratory, 4. NOAA Earth System Research Laboratory, 5. University of Michigan, 6. Arizona State University, 7. U.S. Forest Service We gratefully acknowledge funding support from the National Aeronautics and Space Administration, Earth Sciences Division, to Colorado State University (agreement #NNX08AK08G).

2. Main Goal of MCI Synthesis Compare and reconcile to the extent possible CO 2 fluxes from inventories and atmospheric inversions C CO 2 C Atmospheric Inversions Inventories

3. “Top-down” vs “Bottom-up” Accurately captures all C contributions, whether known or unknown Integrates and mixes signals, thus generally better used at larger spatial scales then inventory Depends on accurate modeling of transport which can be difficult InventoriesAtmospheric Inversions Process based and thus fluxes are “attributable”, good for policy decisions Generally tied to valuable commodities and thus tracked well, e.g. crop production, forest inventory, etc. Generally sampled at point locations and upscaled and thus possibly not as accurate at larger scales

4. Total 2007 NEE (Inventory minus fossil) Note largest sink driven by crop signal over corn belt Largest uncertainty is over non-crop lands, presumably forest driven, on scale of 50% of max sink strength Note human respiration component over Chicago MEA N SD -450gCm -2 yr -1 +350gCm -2 yr -1 +250gCm -2 yr -1 0gCm -2 yr -1 Posters Ogle H-184 Ogle F-129 West G-167

5. CarbonTracker as Baseline

6. Summing over MCI Region

7. CarbonTracker vs MCI Inventory -350gCm -2 yr -1 100gCm -2 yr -1

8. CarbonTracker vs MCI Inventory In general, looks pretty reasonable -350gCm -2 yr -1 100gCm -2 yr -1

9. CarbonTracker vs MCI Inventory MAX CROP SIGNAL In general, looks pretty reasonable However, max crop signal might be reversed? -350gCm -2 yr -1 100gCm -2 yr -1

10. CarbonTracker vs MCI Inventory MAX CROP SIGNAL In general, looks pretty reasonable However, max crop signal might be reversed? CarbonTracker has little flexibility to adjust sub-ecoregion scale fluxes, even if fine spatial scale data is available. -350gCm -2 yr -1 100gCm -2 yr -1

11. Regional Inversions? While some global inversions do reasonably well (CarbonTracker), can we improve the estimates with regional higher resolution inversions? Three add’l inversions: – Penn State: with WRF, regionally at 10KM, w/ prior from offline SiBCROP fluxes (w/ Uliasz LPDM particle model) – CSU: with RAMS, continentally at 40km, w/ prior from “coupled” SiBCROP fluxes (w/ Uliasz LPDM particle model) – UMich: with WRF, at 40km, w/ geostatistical inversion (and STILT particle model)

12. Anchoring Data A ring of towers instrumented by Penn State U. (Davis/Miles/Richardson) NOAA/ESRL tall towers Calibrated Ameriflux sites (Penn State)

13. SiB-CROP Prior NEE (TgC/deg 2 ) (June 1 – Dec 31, 2007) Posterior NEE (TgC/deg 2 ) (June 1 – Dec 31, 2007) Lauvaux et al. 2011 (in prep) Notice the max C drawdown in prior is somewhat similarly placed (NW Iowa/SW MN) to CarbonTracker (CASA). The posterior appears to ‘spread’ out the crop signal as well as relocate the max C drawdown location to central/northern Illinois.

14. SiB-CROP Prior NEE (TgC/deg 2 ) (June 1 – Dec 31, 2007) Posterior NEE (TgC/deg 2 ) (June 1 – Dec 31, 2007) Lauvaux et al. 2011 (in prep) Notice the max C drawdown in prior is somewhat similarly placed (NW Iowa/SW MN) to CarbonTracker (CASA). The posterior appears to ‘spread’ out the crop signal as well as relocate the max C drawdown location to central/northern Illinois. Yields were higher than normal Yields were lower than expected

15. Inverse flux sensitivity to a priori fluxes

16. Network Design: How many towers? Lauvaux Poster H-182

17. Time Series of Inversion Results (2007) Weeks of 2007 Both CT and PSU inversions provide deep summer sink that isn’t observed quite so strongly in general global suite of models

18. The CSU inversion appears to capture the location of the maximum sink but can’t obtain the overall source/sink total for area GgC/0.5°

19. How do we interpret overall? The two mesoscale inversions share some spatial similarity in 2007 as far as location of maximum local sink However, we see significantly different magnitudes of total sources/sinks between CSU and the other two, with CSU having an estimated sink much weaker than the MCI inventory How do we investigate an inversion that appears to have a problem?

20. Transport Uncertainty Summer time sensitivity (to surface) is stronger in CSU than PSU More similar in winter time Could this be why flux corrections are too weak in CSU’s inversion in summer? Poster Andrews E-119

21. Transport Uncertainty Stronger sensitivity in LPDM-RAMS than STILT- WRF for top of LEF tower (afternoon obs) Poster Andrews E-119 … however LPDM- SiBRAMS seems to match observations pretty well

22. Transport Uncertainty Stronger sensitivity in LPDM-RAMS than STILT- WRF for top of LEF tower (afternoon obs) Poster Andrews E-119 … however LPDM- SiBRAMS seems to match observations pretty well Relating to Dan Cooley’s talk yesterday: Not only could this help “debug” transport problems, but it could also be used to provide a more formal estimate of transport uncertainty into inversion results, for example swapping in/out transport.

23. Summary on inversions over MCI Mesoscale inversions show promise of increasing resolution of inversion results (fine scale PSU) but in certain cases (continental CSU) need work Fine tower placement appears to be needed to resolve certain high resolution features such as case with WBI / KW towers Continued work is needed to compare the transport fields which currently show some significant differences (WRF-STILT, WRF-LPDM and RAMS-LPDM). These transport comparisons can be used to provide higher quality uncertainty estimates for the inversions We would like to continue investigation of differences between inventory and inversion results which have appeared for 2008.