1. 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

2. 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

3. 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?

4. 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

5. 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
Suborbital
1/2ox2/3o grid of GEOS-Chem chemical transport model (CTM)
INTEX-A
SEAC4RS
CalNex

6. High-resolution inverse analysis system
for quantifying methane emissions in North America
Observations
EDGAR 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)

7. Optimization of state vector for adjoint inversion of SCIAMACHY data
Optimal clustering of 1/2ox2/3o gridsquares
Native resolution clusters
Correction factor to bottom-up emissions
Number of clusters in inversion
,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

8. 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 v
EPA
This work
Livestock emissions are underestimated by EPA, oil/gas emissions are not
Wecht et al., in prep.

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

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

11. 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
2x underestimate
of livestock emissions
Emisssions, Tg a-1
Wecht et al., in prep.

12. 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.

13. 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)
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.

14. 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?
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