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Satellite Data for Methane Emissions Constraints

Utilizing satellite data and the inverse method to analyze methane emissions globally, focusing on sources, sinks, and uncertainties to refine emissions estimates. Comparison of bottom-up inventories with satellite observations.

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Satellite Data for Methane Emissions Constraints

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  1. Application of inverse methodsto constrain methane emissions from satellite data • Methane observable by solar backscatter at 1.6and 2.3 µm near-unit sensitivity at all altitudes Methane column TROPOMI Geostationary 7 km, 1-day 2 km, 1-hour GOSAT 5 km, 3-day, sparse SCIAMACHY 60 km, 6-day 2002 2005 2009 20016 ?  absorption Backscattered intensity IB Scattering by Earth surface l1 l2 Remove air mass factor (AMF) dependenceusing CO2 retrieval for nearby wavelengths: dry column mixing ratio Instruments:

  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 the last 30 years the last 1000 years E. Dlugokencky, NOAA Fires 50 Global sources, Tg a-1 Livestock 90 Wetlands 180 Source attribution is difficult due to diversity, complexity of sources Landfills 70 Individual sources uncertain by at least factor of 2; emission factors are highly variable, poorly constrained Gas 60 Coal 40 Other natural 40 Rice 40

  4. Satellite data as constraints on methane emissions “Bottom-up” emissions (EDGAR): best understanding of processes Satellite data for methane columns 2009-2011 537 Tg a-1 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? 1/2ox2/3o grid of GEOS-Chem INTEX-A SEAC4RS CalNex

  6. Bottom-up methane emissions for N. America (2009-2011) 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 total: 63 Tg a-1 wetlands: 20 oil/gas: 11 livestock: 14 waste: 10 coal: 4 Turner et al. [2015]

  7. High-resolution inversion of methane emissions Observations EDGAR 4.2 + LPJ prior bottom-up emissions GEOS-Chem CTM and its adjoint 1/2ox2/3o over N. America nested in 4ox5o global domain Bayesian inversion Validation Verification Optimized emissions (“state vector”) at up to 1/2ox2/3o resolution 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 XCH4 INTEX-A methane below 850 hPa C. Frankenberg (JPL) D. Blake (UC Irvine) C. Frankenberg (JPL) Difference between satellite and aircraft after bias correction H2O retrieval bias: remove it! Wecht et al. [2014a]

  9. Second step: check model background Model mean methane for Jul-Aug 2004 (background) and NOAA data (circles) 4ox5o 1/2o2/3o Include time-dependent boundary conditions in state vector Wecht et al. [2014a]

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

  11. Selection of state vector for inversion of SCIAMACHY data Optimal clustering of 1/2ox2/3ogridsquares Native resolution (7,906 gridsquares) 1000 clusters 34 Correction factor to bottom-up emissions Optimized US emissions (Tg a-1) Inverse model fit to observations 28 smoothing aggregation Number of clusters in inversion 1 10 100 1000 10,000 Wecht et al. [2014a]

  12. Verification of inversion results with INTEX-A aircraft data Optimized emissions Prior emissions Tg CH4 a-1 GEOS-Chemsimulation of INTEX-A aircraft observations below 850 hPa: with prior emissions with optimized emissions Wecht et al. [2014a]

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

  14. North American methane emission estimates optimized by SCIAMACHY (Jul-Aug 2004) SCIAMACHY column methane mixing ratio Correction factors to a priori emissions 1000 clusters ppb 1700 1800 EDGAR v4.2 26.6 EPA 28.3 This work 32.7 US anthropogenic emissions (Tg a-1) Livestock emissions are underestimated by EDGAR/EPA, oil/gas emissions are not Wecht et al. [2014a]

  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) CARB: 0.39 CARB: 1.51 Tg a-1 CARB: 0.18 CARB: 0.86 Wecht et al. [2014b]

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

  17. Diagnosing the information content from the inversion x is the state vector of emissions (n = 157) prior averaging kernel matrix solution = truth + smoothing + noise • 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 Diagonal elements of

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

  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 GEOS-Chem with prior emissions Satellite (GOSAT) aircraft+surface data Are the comparisons consistent?

  21. 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]

  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 1/2ox2/3o 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]

  24. Using radial basis functions (RBFs) with Gaussian mixing modelas state vector Dominant Gaussians for 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]

  25. 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]

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

  27. 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]

  28. Methane emissions in US:comparison to previous studies, attribution to source types Ranges from prior error assumptions 2004 satellite 2007 surface, aircraft 2009-2011 satellite • 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 (more GOSAT years, TROPOMI,…) • Better bottom-up inventory (gridded EPA inventory, wetlands) Turner et al. [2015]

  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,… National bottom-up US inventory of methane emissions at 0.1ox0.1o grid resolution Livestock and rice data at (sub)-county level Process-level emission factors including seasonal variation J.D. Maasakkers(in prep.)

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

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

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

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

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

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

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