Case study: using GOSAT to estimate US emissions Daniel J. Jacob with Alex Turner, Jianxiong Sheng Turner, A.J., D.J. Jacob, K.J. Wecht, J.D. Maasakkers, E. Lundgren, A.E. Andrews, S.C. Biraud, H. Boesch, K.W. Bowman, N.M. Deutscher, M.K. Dubey, D.W.T. Griffith, F. Hase, A. Kuze, J. Notholt, H. Ohyama, R. Parker, V.H. Payne, R. Sussmann, C. Sweeney, V.A. Velazco, T. Warneke, P.O. Wennberg, and D. Wunch, Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data, Atmos. Chem. Phys., 15, 7049-7069, 2015. 

Building a continental-scale monitoring system for methane emissions in North America Can we use satellites together with suborbital observations to monitor methane emissions and their trends? GOSAT (+soon TROPOMI, GHGSat) CalNex INTEX-A SEAC4RS 1/2ox2/3o grid of GEOS-Chem Forward model: GEOS-Chem CTM Prior: EDGAR (soon EPA) A project supported by the NASA Carbon Monitoring System (CMS)

The GOSAT observational record 3 cross-track 10-km pixels 250 km apart orbit track 250 km CO2 proxy retrieval [Parker et al, 2011] Mean single-retrieval precision 13 ppb Use here data for 6/2009-12/2011

GEOS-Chem Chemical Transport Model Input data NASA GEOS meteorological fields other Model solves 3-D chemical continuity equations on global or nested Eulerian grid Modules emissions transport chemistry aerosols deposition sub-surface Applications chemical processes, transport, budgets inverse analyses radiative forcing air quality biogeochemistry … Model adjoint Model capabilities: Tropospheric and stratospheric chemistry, aerosol microphysics, CO2, methane, mercury, various tracers 1980-present GEOS meteorological data, past and future climates (GCMs) Horizontal resolution: 0.25ox0.3125o (native), 1/2ox2/3o , 2ox2.5o, 4ox5o, other grids Used by a community of over 100 research groups worldwide

GEOS-Chem methane simulation Global simulation at 4ox5o resolution North America simulation at 1/2ox2/3o resolution 1/2ox2/3o GEOS-Chem grid dynamic boundary conditions 72 vertical levels extending to mesosphere Prior emission inventory from EDGAR + LPJ wetlands OH concentrations archived from full-chemistry simulation

with prior EDGAR4.2+LPJ emissions First step: conduct GEOS-Chem simulation with prior emissions, compare to GOSAT and suborbital data GEOS-Chem CTM with prior EDGAR4.2+LPJ emissions Satellite (GOSAT) aircraft+surface data Are the comparisons consistent? Three goals: Indirect validation of satellite data – demonstrate consistency with suborbital data Correct any biases that would be irreducible in inversions Identify any anomalous patterns that the inversion would not be able to correct.

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

GEOS-Chem (with prior emissions) compared 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]

Constructing the observational error covariance matrix 𝐲−𝐅 𝐱 𝐀 ) stdev(ԑO) Estimate observational error variance as residual standard deviation: difference mean error to be corrected In inversion GOSAT instrument precision is 13 ppb; use it if higher than residual standard deviation Error correlation length scale ~100 km, can ignore for GOSAT → diagonal matrix

Inversion strategy 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 Inversion assumes 50% error standard deviation on prior emissions Turner et al. [2015]

State vector chosen 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: Aggregation Smoothing Observation Turner and Jacob [2015]

Using radial basis functions (RBFs) with Gaussian mixing model as 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]

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

Evaluation of posterior emissions with 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]

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 due to livestock or oil/gas emissions or both? Turner et al. [2015]

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

Use prior emission pattern for source type attribution EDGAR + LPJ wetlands Problem is that EDGAR patterns have large errors total: 63 Tg a-1 wetlands: 20 livestock: 14 oil/gas: 11 waste: 10 coal: 4 Turner et al. [2015]

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]

Current evaluation of Turner et al Current evaluation of Turner et al. (2015) results with SEAC4RS aircraft data over Southeast US (Aug-Sep 2013) Data below 2 km from Don Blake, UC Irvine

Using SEAC4RS data to correct Turner et al. (2015) emissions aircraft data Optimal emission estimate from Turner et al. [2015] Correction factors Inversion used as prior estimate GEOS-Chem CTM 0.25ox0.3125o resolution Sheng et al., in prep.

SEAC4RS inversion provides better fit to SEAC4RS data… Scaling factor Prior Posterior Sheng et al., in prep

…and also to NOAA surface data in Sep-Oct 2013… SCT WKT SGP Prior Posterior Sheng et al., in prep

…and to GOSAT data (but noisy because of data sparsity) September-October 2013 Prior Posterior Could emissions have gone down in 2013? Need to examine consistency between GOSAT data for 2013 and 2009-2011 Sheng et al., in prep

Potential of TROPOMI and GEO-CAPE to improve observing system OSSE applied to SEAC4RS viewing domain and 2-month period Diagonal elements of averaging kernel matrix A: DOFS=68.7 DOFS=9.7 SEAC4RS GEO-CAPE TROPOMI DOFs = tr(A) : number of pieces of information to constrain emissions DOFS=34.1 Sheng et al., in prep