1. Scattering by Earth surface Instruments: Backscattered intensity I B absorption   Methane column  Application of inverse methods to constrain methane emissions from satellite data Methane observable by solar backscatter at 1.6 and 2.3 µm near-unit sensitivity at all altitudes Remove air mass factor (AMF) dependence using CO 2 retrieval for nearby wavelengths: dry column mixing ratio 2002 2005 2009 20016 ? SCIAMACHY 60 km, 6-day GOSAT 5 km, 3-day, sparse TROPOMI Geostationary 7 km, 1-day 2 km, 1-hour

2. Global distribution of methane observed from space Sources: wetlands, livestock, landfills, natural gas… Sink: atmospheric oxidation (10-year lifetime) Global source is 550  60 Tg a -1, constrained by knowledge of global sink

3. Long-term trends of methane are not understood Source attribution is difficult due to diversity, complexity of sources Livestock 90 Landfills 70 Gas 60 Coal 40 Rice 40 Other natural 40 Wetlands 180 Fires 50 Global sources, Tg a -1 Individual sources uncertain by at least factor of 2; emission factors are highly variable, poorly constrained the last 1000 years the last 30 years E. Dlugokencky, NOAA

4. Satellite data as constraints on methane emissions “Bottom-up” emissions (EDGAR): best understanding of processes 2009-2011 537 Tg a -1 Satellite data for methane columns Optimal estimate inversion using GEOS-Chem model adjoint Ratio of optimal estimate to bottom-up emissions Turner et al. [2015]

5. Building a continental-scale methane monitoring system Can we use satellites together with suborbital observations of methane to monitor methane emissions on the continental scale? CalNex INTEX-A SEAC 4 RS 1/2 o x2/3 o grid of GEOS-Chem

6. Bottom-up methane emissions for N. America (2009-2011) total: 63 Tg a -1 wetlands: 20 oil/gas: 11livestock: 14 waste: 10coal: 4 CONUS anthropogenic emissions: 25 Tg a -1 (EDGAR) 27 Tg a -1 (EPA) 8 oil/gas 9 livestock 6 waste 3 coal Aircraft/surface data indicate that these bottom-up estimates are too low Turner et al. [2015]

7. High-resolution inversion of methane emissions GEOS-Chem CTM and its adjoint 1/2 o x2/3 o over N. America nested in 4 o x5 o global domain Observations Bayesian inversion Optimized emissions (“state vector”) at up to 1/2 o x2/3 o resolution Validation Verification EDGAR 4.2 + LPJ prior bottom-up emissions Three applications: 1. Summer 2004 using SCIAMACHY 2. CalNex May-June 2010 aircraft campaign over California 3. 2009-2011 using GOSAT

8. First step: validate the satellite methane data SCIAMACHY validation using vertical profiles from INTEX-A aircraft campaign SCIAMACHY column methane mixing ratio X CH4 INTEX-A methane below 850 hPa C. Frankenberg (JPL) D. Blake (UC Irvine) C. Frankenberg (JPL) H 2 O retrieval bias: remove it! Difference between satellite and aircraft after bias correction Wecht et al. [2014a]

9. Second step: check model background Model mean methane for Jul-Aug 2004 (background) and NOAA data (circles) Wecht et al. [2014a] 4 o x5 o 1/2 o 2/3 o Include time-dependent boundary conditions in state vector

10. Third step: choose state vector If state vector is too large, cost function is dominated by prior: smoothing error Correct this by aggregating state vector elements, but this incurs aggregation error There is an optimal state vector dimension for fitting observations: # state vector elements aggregation smoothing As dim(x) increases, the importance of the prior terms increases Prior Observations native grid aggregated grid

11. Selection of state vector for inversion of SCIAMACHY data Optimal clustering of 1/2 o x2/3 o gridsquares Correction factor to bottom-up emissions Number of clusters in inversion 1 10 100 1000 10,000 34 28 Optimized US emissions (Tg a -1 ) Native resolution (7,906 gridsquares) 1000 clusters Wecht et al. [2014a] aggregation smoothing Inverse model fit to observations

12. Verification of inversion results with INTEX-A aircraft data Prior emissions Optimized emissions GEOS-Chem simulation of INTEX-A aircraft observations below 850 hPa: with prior emissions with optimized emissions Wecht et al. [2014a] Tg CH 4 a -1

13. Attribution of geographical source contributions to source type is complicated by spatial overlap For a given cluster, assume that prior emission attribution by source type (i) is correct: and apply inversion scaling factor for that cluster to all source types weighted by f i with Livestock and natural gas emissions are often collocated Eagle Ford Shale, Texas

14. North American methane emission estimates optimized by SCIAMACHY (Jul-Aug 2004) 17001800 ppb SCIAMACHY column methane mixing ratio Correction factors to a priori emissions US anthropogenic emissions (Tg a -1 ) EDGAR v4.2 26.6 EPA 28.3 This work 32.7 Wecht et al. [2014a] 1000 clusters Livestock emissions are underestimated by EDGAR/EPA, oil/gas emissions are not

15. Constraining methane emissions in California Statewide greenhouse gas emissions must decrease to 1990 levels by 2020 Large difference between bottom-up emission inventories: EDGAR v4.2 (2010) vs. California Air Resources Board (CARB) Wecht et al. [2014b] CARB: 1.51 CARB: 0.86CARB: 0.18 CARB: 0.39 Tg a -1

16. Inversion of methane emissions using aircraft campaign data CalNex aircraft observationsGEOS-Chem w/EDGAR v4.2 Correction factors to EDGAR (analytical inversion, n= 157) May-Jun 2010 Wecht et al. [2014b] California emissions (Tg a -1 ) G. Santoni (Harvard) May-Jun 2010 EDGAR v4.2 1.92 CARB 1.51 This work 2.86 ± 0.21 State totals

17. Diagnosing the information content from the inversion solution = truth + smoothing + noise averaging kernel matrix prior x is the state vector of emissions (n = 157) Diagonal elements of Diagonal elements of A range from 0 (no local constraint from observations) to 1 (no constraint from prior) Degrees Of Freedom for Signal (DOFS) = tr(A) = total # pieces of information constrained by inversion

18. Comparing information content from aircraft and satellites TROPOMI will provide information comparable to a continuous aircraft campaign; a geostationary satellite instrument will provide even more Wecht et al. [2014b] Diagonal elements of A OSSE of satellite observations during CalNex period (May-June 2010) CalNex GOSAT: precise but sparse TROPOMI (2016): daily coverage Geostationary: hourly coverage

19. Temporal averaging can overcome GOSAT data sparsity 2.5 years of GOSAT data Turner et al. [2015]

20. GOSAT validation using CTM as intercomparison platform Model provides continuous 3-D fields to compare different observational data sets Satellite (GOSAT) GEOS-Chem with prior emissions aircraft+surface data Are the comparisons consistent?

21. GEOS-Chem (with prior emissions) compared to in situ data Latitude, degrees GEOS-Chem HIPPO Jan09 Oct-Nov09Jun-Jul11 Aug-Sep11 Methane, ppbv GEOS-Chem NOAA US observations GEOS-Chem is unbiased for background methane US enhancement is ~30% too low, to be corrected in inversion Turner et al. [2015] HIPPO aircraft data over Pacific

22. GEOS-Chem (prior) comparison to GOSAT data High-latitude bias could be due to satellite retrieval or GEOS-Chem stratosphere: in any case, we need to remove it before doing inversion Turner et al. [2015]

23. State vector choice to balance smoothing & aggregation error Native-resolution 1/2 o x2/3 o emission state vector x (n = 7096) Aggregation matrix   x  =   x Reduced-resolution state vector x  (here n = 8) Posterior error covariance matrix: Aggregation Smoothing Observation Choose n = 369 for negligible aggregation error; allows analytical inversion with full error characterization 1 10 100 1,000 10,000 Number of state vector elements Mean error s.d., ppb Posterior error depends on choice of state vector dimension observation aggregation smoothing total Turner and Jacob [2015]

24. Using radial basis functions (RBFs) with Gaussian mixing model as state vector State vector of 369 Gaussian 14-D pdfs optimally selected from similarity criteria in native-resolution state vector Each 1/2 o x2/3 o grid square is unique linear combination of these pdfs This enables native resolution (~50x50 km 2 ) for major sources and much coarser resolution where not needed Dominant Gaussians for emissions in Southern California Turner and Jacob [2015]

25. Global inversion of GOSAT data feeds boundary conditions for North American inversion GOSAT observations, 2009-2011 Adjoint-based inversion at 4 o x5 o resolution Dynamic boundary conditions Analytical inversion with 369 Gaussians Turner et al. [2015] correction factors to EDGAR v4.2 + LPJ prior

26. Averaging kernel sensitivities and inversion results Turner et al. [2015]

27. Evaluation of posterior emissions with independent data sets in contiguous US Comparison of California results to previous inversions of CalNex data (Los Angeles) Turner et al. [2015] GEOS-Chem simulation with posterior vs. prior emissions

28. Methane emissions in US: comparison to previous studies, attribution to source types EPA national inventory underestimates anthropogenic emissions by 30% Livestock is a contributor: oil/gas production probably also Ranges from prior error assumptions Turner et al. [2015] 2004 satellite 2007 surface, aircraft 2009-2011 satellite What is needed to improve source attribution in future? Better observing system (more GOSAT years, TROPOMI,…) Better bottom-up inventory (gridded EPA inventory, wetlands)

29. Source attribution is only as good as bottom-up prior pattern Little confidence and detail in EDGAR gridded inventory; construct our own in collaboration with US EPA data including detailed info on processes Large point sources (oil/gas/coal, waste) reporting emissions to EPA GIS data for location of wells, pipelines, coal mines,… Livestock and rice data at (sub)-county level Process-level emission factors including seasonal variation National bottom-up US inventory of methane emissions at 0.1 o x0.1 o grid resolution J.D. Maasakkers (in prep.)

30. New EPA-based gridded emission inventory: natural gas production J.D. Maasakkers (in prep.)

31. Natural gas processing J.D. Maasakkers (in prep.) New EPA-based gridded emission inventory: natural gas production + processing

32. Natural gas transmission J.D. Maasakkers (in prep.) New EPA-based gridded emission inventory: natural gas production + processing + transmission

33. Total natural gas: production + processing + transmission + distribution J.D. Maasakkers (in prep.) New EPA-based gridded emission inventory: natural gas production + processing + transmission + distribution

34. EDGAR v4.2FT 2010 total natural gas emissions J.D. Maasakkers (in prep.)

35. Difference with EDGAR J.D. Maasakkers (in prep.)