Methane and Nitrous Oxide in North America: Using an LPDM to Constrain Emissions

1. Methane and Nitrous Oxidein North America: Using an LPDM to Constrain Emissions Eric Kort kort@fas.harvard.edu Non-CO2 Workshop October 23, 2008

2. Approach • Atmospheric Measurements • Use receptor oriented framework (STILT) to link measurements with emissions • Forward Model Concentrations, Optimizations, Inversions STILT developed by John Lin and Christoph Gerbig

3. Trajectories • Receptor Points- Locations in space-time where measurements are made • Release an ensemble of ‘particles’, which travel backwards in time, stochastically sampling the turbulence • Driving wind fields are of crucial importance: i.e. mass conservation

4. Trajectories • Meteorological Driver: WRF v2.2 • Use time-averaged mass fluxes- ensures good mass conservation • Uses analysis nudging to increase realism • Turbulence included • Release 500 particles backwards 10 days in time from each receptor • Case study used 100 particles 6 days back Using STILT, comparison w/ FLEXPART underway…, preliminary results encouraging, wind fields dominate answer, choice of LPDM does not strongly bias footprints

5. Go to Thomas Slides

6. TrajectoriestoFootprint

7. Footprint • Critical Item which links measurements to emissions (Unit: ppb/flux) • With this calculated can: • Interface w/ prior emissions field- bottom-up model concentrations • Follow this with simple scalar optimization • Or Perform a Bayesian optimization • Or go straight to a Geostatistical Inversion

8. Footprint * Prior Emission Field * Result = Enhancement of gas at measurement point due to source

9. Prior Emissions Fields • Nitrous Oxide • Anthropogenic- EDGAR32FT2000 • Anthropogenic & Biogenic- GEIA • Methane • Anthropogenic- EDGAR32FT2000 • Biogenic- Jed Kaplan wetland inventory FIRES

10. Boundary Condition • To do even a Geostatistical Inversion, need ‘background’ values from where particles are 10 days back in time • Crucial to have good values here, as any error here directly propagates into any emissions analysis • Biases in particular are of large concern

11. Boundary Condition • Data-derived: Globalview type product (MBL, time/lat) • Add vertical shape?? • Model-output: Forward model runs • Atmospheric Inversion output- carbontracker methane

12. Boundary Condition Insights2 Crucial Points • Latitude Dependence • Vertical gradient over ocean (for ch4) is negligible in comparison • Seasonal Variation • This must be correct, in order to prevent seasonal biases • Must check with measurement points in free troposphere with minimal surface influence– aircraft measurements are crucial

13. Bottom-Up Model Values • Enhancement + Boundary Value = Modeled Mixing Ratio @ measurement point • Facilitates direct comparison, and optimization of emissions

14. Case Study- COBRA-NA 2003 • ~300 flasks measured @ NOAA/Boulder, UND Citation II, 23 May to 28 June 2003

15. Measurements- Footprint

16. Results- Methane Slope: 0.924 ± 0.13 Scaling Factor: 1.08 ± 0.15 Note: Prior Emissions Field EDGAR32FT 2000 & JK wetland

17. Results- Nitrous Oxide Note: Prior Emissions Field EDGAR32FT 2000, similar results using GEIA Slope: 0.381 ± 0.072 Scaling Factor: 2.62 ± 0.50

18. But . . . • Limitations in coverage • Only a snapshot in time (May- June of 2003) • Seasonality in agricultural Nitrous Oxide emissions is likely at play. • Want to do with measurements over multiple years, get full seasonality picture.

19. Concept Here • Goal: Incorporate all measurements of CH4 and N2O over North America for 2004-2008 • Start: NOAA network, aircraft and tower flask samples, for 1 calendar year, CH4: under way • Gives an initial framework from which to expand from • Natural path is to start with same simple approach used previously

20. Combined Footprint, Aircraft Flasks, September 2006

21. Midday Footprint, LEF, Spring 04

22. Midday Footprint, AMT, Spring 04

23. Midday Footprint, WKT, Spring 04

24. Intensive Aircraft Campaigns & Continuous measurements • Incorparation of Intensive Aircraft Campaigns and continuous measurments at towers can strongly supplement the flask measurement framework Pre-HIPPO flight, from Rodrigo Jimenez

25. LEF mjj, Model PredictionsModel runs at 19 GMTData Boundary,Note: -large day to day variation-dominance of anthropogenic emissions

26. LEF 2004 Model runs at 19 GMT Note Different slopes w/ different boundaries, indicating different seasonality in boundaries

27. Texas- model systematically too low

28. Maine: Model systematically too high

29. Acknowledgements • Harvard • Bruce Daube, Elaine Gottlieb, Steve Wofsy • AER • Janusz Eluszkiewicz & Thomas Nehrkorn • MPI- Jena • Christoph Gerbig & Stefan Korner • Netherlands & Switzerland • Sander Houweling & Jed Kaplan • NOAA & NCAR • Arlyn Andrews, Adam Hirsch, John B. Miller, Brit Stephens, Colm Sweeney, Lori Bruhwiler, Ed Dlugokencky, Pieter Tans • U Michagan • Anna Michalak • University of Waterloo • John Lin