Using satellite observations to constrain methane emissions with high resolution

1. Using satellite observations to constrain methane emissions with high resolution Daniel J. Jacob, Alexander J. Turner, J.D. (Bram) Maasakkers, Melissa Sulprizio, Jianxiong Sheng

2. Bottom-up inventories relate processes to emissions Emission rate = (Activity rate)  (Emission factor) EDGAR4.2 +LPJ methane emissions, 2012 Other: 30 Waste: 60 Wetlands: 160 Coal: 50 Fires: 20 Oil/Gas: 70 Livestock: 110 Rice: 40

3. Observed atmospheric concentrations can test inventories compare simulated concentrations F(x) observations (vector y) chemical transport model F gridded bottom-up emission inventory (vector x)

4. Bayesian inverse analysis for optimizing emissions Let x be the true emission field (vector): Bottom-up inventory: prior estimate xA + εA Atmospheric observations: y = F(x) + εO Minimize where are error covariance matrices is the solution to Optimized emission field

5. Present-day observing system for atmospheric methane surface+aircraft monitoring Field campaigns HIPPO Satellite (GOSAT) Barnett Shale (EDF)

6. 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? 1/2ox2/3o grid of GEOS-Chem INTEX-A Other 1.1 Waste 5.6 EPA inventory for contiguous US: 27 Tg a-1 Wetlands 5.9 SEAC4RS Fires 0.1 CalNex Coal 2.9 Livestock 9.2 Oil/gas 7.7 Rice 0.4

7. GOSAT satellite observations • Solar backscatter at 1.6 µm • Proxy retrieval [Parker et al, 2011]; • mean methane column mixing ratio • with near-uniform sensitivity • Mean single-retrieval precision of 13 ppb orbit track 90-280 km 5 cross-track 10.5-km pixels 100 km apart Ocean glint 2010-2014

8. 2009-2011 mean GOSAT observations

9. GOSAT validation using CTM as intercomparison platform Chemical transport model (CTM) provides continuous 3-D concentration fields to compare observations with different viewing domains or schedules GEOS-Chem CTM with prior EDGAR4.2+LPJ emissions Satellite (GOSAT) aircraft+surface data Are the comparisons consistent?

10. GEOS-Chem (with prior emissions) compared to in situ data HIPPO aircraft data over Pacific Aug-Sep11 Oct-Nov09 Jun-Jul11 Jan09 GEOS-Chem HIPPO Methane, ppbv Latitude, degrees NOAA US observations GEOS-Chem • GEOS-Chem is unbiased for background methane • US enhancement is ~30% too low, to be corrected in inversion Turner et al. [2015]

11. GEOS-Chem (prior) comparison to GOSAT data Mean background difference vs. latitude 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]

12. Global inversion of GOSAT datafeeds boundary conditions for North American inversion GOSAT observations, 2009-2011 Dynamic boundary conditions Analytical inversion with 369 Gaussians Adjoint-based inversion at 4ox5o resolution correction factors to EDGAR v4.2 + LPJ prior Turner et al. [2015]

13. State vector choice to balance smoothing & aggregation error Native-resolution 1/2ox2/3o emission state vector x (n = 7096) Reduced-resolution state vector x (here n = 8) Aggregation matrix  x =x observation aggregation smoothing total Choose n = 369 for negligible aggregation error; allows analytical inversion with full error characterization Posterior error depends on choice of state vector dimension Mean error s.d., ppb 10 100 1,000 10,000 Number of state vector elements Posterior error covariance matrix: AggregationSmoothing Observation Turner and Jacob [2015]

14. Using radial basis functions (RBFs) with Gaussian mixing modelas state vector Example: dominant Gaussian elements describing emissions in Southern California • State vector of 369 Gaussian 14-D pdfs optimally selected from similarity criteria in native-resolution state vector • Each 1/2ox2/3o grid square is unique linear combination of these pdfs • This enables native resolution (~50x50 km2) for major sources and much coarser resolution where not needed Turner and Jacob [2015]

15. Inversion results and information content Turner et al. [2015]

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

17. Correction factors to bottom-up EDGAR inventory • CONUS anthropogenic emission of 40-43 Tg a-1 vs. EPA value of 27 Tg a-1 • Is the underestimate in livestock or oil/gas emissions or both? Turner et al. [2015]

18. Optimized top-down inventory • CONUS anthropogenic emission of 40-43 Tg a-1 vs. EPA value of 27 Tg a-1 • Is the underestimate in livestock or oil/gas emissions or both? Turner et al. [2015]

19. Attribution of emission correction to oil/gas or livestockis complicated by uncertainty in location, spatial overlap Eagle Ford Shale, Texas • Oil/gas fields and cattle • often share quarters • Gas emissions occur at exploration, production, processing, transmission, distribution EDGAR inventory oil/gas source pattern likely overemphasizes distribution vs. production EDGAR EDGAR Turner et al. [2015]

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

21. Looking ahead to TROPOMI Observing System Simulation Experiment (OSSE) of different observing platforms for constraining methane emissions in California in May-June 2010 GOSAT CalNexaircraft campaign Geostationary: hourly coverage TROPOMI Averaging kernel sensitivities TROPOMI will provide information comparable to a continuous aircraft campaign Wecht et al. [2014]

22. Constructing a gridded version of the EPA national inventory Best process-based knowledge of sources, granular representation of processes, national inventory reported to the UNFCCC Large point sources (oil/gas/coal, waste) reporting emissions to EPA GIS data for location of wells, pipelines, coal mines,… National bottom-up US inventory of methane emissions at 0.1ox0.1o monthly resolution Livestock and rice data at sub-county level Process-level emission factors including seasonal variation J.D. Maasakkers(in prep.) with M. Weitz, T. Wirth, C. Hight, M. DeFiguereido [EPA]

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

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

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

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

27. Difference with EDGAR Using the EPA gridded emission inventory as prior will considerably increase The quality of information from inverse modeling estimates J.D. Maasakkers(in prep.)

28. Is there a large US methane emission trend? • Atmospheric observations suggest a 3 % a-1 increase in US emissions for 2002-2014 • But EPA bottom-up inventory shows no trend over that period Inverse analyses bottom-up Turner et al., in prep.; NOAA data from E. Dlugokencky

29. GOSAT trend in US methane above background, 2010-2014 Subtracting trend in background glint observations; dots are significant (p < 0.01) trends Turner et al., in prep.

30. What could be driving such a large US trend? Only viable candidates seems to be oil/gas exploration and production Turner et al., in prep; data from EIA [2015]