Use of satellite and suborbital observations to constrain North American methane emissions in the Carbon Monitoring System Daniel Jacob (PI), Steven Wofsy (Co-I), Kevin Wecht, Alex Turner, Greg Santoni, Melissa Sulprizio Harvard University Vivienne Payne (Co-I), Kevin Bowman (Co-I), Meemong Lee (Co-I), John Worden NASA JPL

Importance of methane for the Carbon Monitoring System Present-day emission-based forcing of methane is 0.95 W m-2 (IPCC AR5) Climate impact of methane is comparable to CO2 over 20-year horizon Methane is a low-hanging fruit for climate policy Natural gas and hydrofracking are changing US sources Methane is a central piece of the President’s Climate Action Plan

Building a methane monitoring system for N America integrated into the CMS EDGAR emission Inventory for methane Can we use satellites together with suborbital observations of methane to monitor methane emissions on the continental scale and test/improve emission inventories in a manner useful to stakeholders?

Methane bottom-up emission inventories for N. America: EDGAR 4.2 (anthropogenic), LPJ (wetlands) N American totals in Tg a-1 Surface/aircraft studies suggest that these emissions are too low by ~factor 2

Methane observing system in North America Satellites Thermal IR AIRS, TES, IASI SCIAMACHY 6-day GOSAT 3-day, sparse TROPOMI GCIRI 1-day geo Shortwave IR 2002 2006 2009 20015 2018 Suborbital 1/2ox2/3o grid of GEOS-Chem chemical transport model (CTM) INTEX-A SEAC4RS CalNex

High-resolution inverse analysis system for quantifying methane emissions in North America Observations EDGAR 4.2 + LPJ a priori 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 at 1/2ox2/3o resolution The same CMS inverse analysis system is used at JPL for CO2 (K. Bowman, PI)

Optimization of state vector for adjoint inversion of SCIAMACHY data Optimal clustering of 1/2ox2/3o gridsquares Native resolution 1000 clusters Correction factor to bottom-up emissions Number of clusters in inversion 1 10 100 1000 10,000 34 28 Optimized US anthropogenic emissions (Tg a-1) posterior cost function SCIAMACHY data cannot constrain emissions at 1/2ox2/3o resolution; use 1000 optimally selected clusters Kevin Wecht, Harvard

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

GOSAT methane column mixing ratios, Oct 2009-2010 Retrieval from U. Leicester

Inversion of GOSAT Oct 2009-2010 methane Correction factors to prior emissions (EDGAR 4.2 + LPJ) Nested inversion with 1/2ox2/3o resolution Alex Turner, Harvard Next step: clustering of emissions in the inversion, use of ACOS data

Testing the information content of satellite data with CalNex inversion of methane emissions Correction factors to EDGAR (analytical inversion) CalNex observations GEOS-Chem w/EDGAR v4.2 May-Jun 2010 S. Wofsy (Harvard) 1800 2000 ppb 0.1 1 3 2x underestimate of livestock emissions Emisssions, Tg a-1 Wecht et al., in prep.

GOSAT observations are too sparse to spatially resolve California emissions Correction factors to methane emissions from inversion GOSAT data (CalNex period)) GOSAT (CalNex period) GOSAT (1 year) Each point = 1-10 observations 0.5 1.5 CalNex aircraft data GOSAT (CalNex) (1 year) Degrees of Freedom for Signal (DOFS) in inversion of methane emissions 15 1.2 2.8 Wecht et al., in prep.

TROPOMI and GCIRI constrain state-level methane emissions better than a dedicated aircraft mission Correction factors to EDGAR v4.2 a priori emissions from a 1-year OSSE TROPOMI (global daily coverage) GCIRI (geostationary 1-h return coverage) 0.2 1 5 A priori CalNex TROPOMI GCIRI TROPOMI+GCIRI DOFS 15 17 23 26 California emissions (Tg a-1) 1.9 3.1 3.0 Wecht et al., in prep.

Working with stakeholders at the US state level State-by-state analysis of SCIAMACHY correction factors to EDGARv4.2 emissions with Iowa Dept. of Natural Resources (Marnie Stein) State emissions computed w/EPA tools too low by x3.5; now investigating EPA livestock emission factors Hog manure? 0 1 2 correction factor with New York Attorney General Office (John Marschilok) State-computed emissions too high by x0.6, reflects overestimate of gas/waste/landfill emissions Large EDGAR source from gas+landfills is just not there Melissa Sulprizio and Kevin Wecht, Harvard