Top-down bottom-up comparisons of the Mid-Continental Intensive (MCI) Region

1. Top-down bottom-up comparisons of the Mid-Continental Intensive (MCI) Region • Colorado State University, 2. The Pennsylvania State University, 3. Pacific Northwest National Laboratory, 4. NOAA Earth System Research Laboratory, 5. U.S. Forest Service, 6. Arizona State University, 7. U.S. Forest Service Andrew Schuh1, Thomas Lauvaux2, , Ken Davis2, Marek Uliasz1, Dan Cooley1, Tristram West3, Liza Diaz2, Scott Richardson2, Natasha Miles2, F. Jay Breidt1, Arlyn Andrews4, Kevin Gurney6, Erandi Lokupitiya1, Linda Heath7, James Smith7, Scott Denning1 , and Stephen M. Ogle1 We gratefully acknowledge funding support from the National Aeronautics and Space Administration, Earth Sciences Division, to Colorado State University (agreement #NNX08AK08G).

2. Atmospheric Inversions CO2 CO2 CO2 CO2 CO2 CO2 C C Inventories Main Goal of MCI Synthesis • Compare and reconcile to the extent possible CO2 fluxes from inventories and atmospheric inversions

3. “Top-down” vs “Bottom-up” Atmospheric Inversions Inventories • 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 • 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. Main Goal of MCI Synthesis • P. Tans white paper (2002) proposed an area of the country that might be used as testbed and minimized the potential problems of each method. • Homogeneous managed landscape, soy, corn, some grasslands, a little forest • Relatively flat landscape, minimizes possible transport problems due to complex topography

5. Inventory: CROP NEE West et al. 2010, Ecol Apps

6. Inventory: CROP NEE CORN/SOY WHEAT HAY SOY/COTTON West et al. 2010, Ecol Apps

7. Inventory + FIA (Heath & Smith, US Forest Service) West et al. 2011 (in prep)

8. Inventory + FIA (Heath & Smith, US Forest Service) + Human/Cattle Respiration (West, PNNL) West et al. 2011 (in prep)

9. Inventory + FIA (Heath & Smith, US Forest Service) + Human/Cattle Respiration (West, PNNL) + fossil fuel (K. Gurney, ASU) West et al. 2011 (in prep)

10. Inventory + FIA (Heath & Smith, US Forest Service) + Human/Cattle Respiration (West, PNNL) + fossil fuel (K. Gurney, ASU) + additional contributions (PNNL, CSU, USGS) West et al. 2011 (in prep)

11. Total 2007 NEE (Inventory net fossil) +350gCm-2yr-1 +250gCm-2yr-1 SD MEAN • 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 -350gCm-2yr-1 0gCm-2yr-1

12. Summing over MCI Region

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

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

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

16. CarbonTracker vs MCI Inventory 100gCm-2yr-1 MAX CROP SIGNAL MAX CROP SIGNAL -350gCm-2yr-1 • 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.

17. Regional Inversions? • While some global inversions do reasonably well (CarbonTracker), can we improve the estimates with regional higher resolution inversions? • We ran two add’l inversions: • with WRF, regionally at 10KM, w/ prior from offline SiBCROP fluxes • with RAMS, continentally at 40km, w/ prior from “coupled” SiBCROP fluxes • both use Marek Uliasz’s LPDM particle model POSTER A51C-0128. M. Uliasz Regional Modeling Support for Planning Airborne Campaigns to Observe CO2 and Other Trace Gases.

18. 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. Posterior NEE (TgC/deg2) (June 1 – Dec 31, 2007) SiB-CROP Prior NEE (TgC/deg2) (June 1 – Dec 31, 2007) Lauvaux et al. 2011 (in prep)

19. 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. Posterior NEE (June 1 – Dec 31, 2007) SiB-CROP Prior NEE (June 1 – Dec 31, 2007) Yields were worse than expected Yields were better than expected Lauvaux et al. 2011 (in prep)

20. Inversion Priors/Posteriors (Jun – Dec, 2007)(GgC /0.5 deg 2 )

21. Inversion Priors/Posteriors (Jun – Dec, 2007)(GgC /0.5 deg 2 ) Shift in max C drawdown but much stronger sink than inventory Shift in max C drawdown but sink “appearing” closer to inventory

22. Inversion Priors/Posteriors (Jun – Dec, 2007)(GgC /0.5 deg 2 ) Magnitude of sink looks reasonable and decently placed but no ability to move source/sink on finer scales

23. Time series of Inversion Results • CarbonTracker posterior adheres fairly strongly to CASA prior over MCI • CSU inversion might be biased high (sink) based on uniform inversion adjustment in sink direction. • All inversions agree on fairly strong drawdown peak not seen in priors

24. Conclusions • CarbonTracker estimates stronger regional sink than inventory over MCI but not unreasonable and probably close to accurate at regional scales. • Mesoscale regional inversions seem to be able to allocate source/sinks better spatially • Spatial structure of sources/sink seem robust to different driving transport although overall strength of source/sink over region likely varies as a function of uncertainty in vertical transport