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 Americaintegrated into the CMS

EDGAR emissionInventory 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

AIRS, TES, IASI

Methane observing system in North America

Satellites

2002 2006 2009 20015 2018

Thermal IR

SCIAMACHY 6-day

GOSAT3-day, sparse

TROPOMI GCIRI 1-day geoShortwave IR

Suborbital

CalNex

INTEX-A

SEAC4RS

1/2ox2/3o grid of GEOS-Chem chemical transport model (CTM)

High-resolution inverse analysis system for quantifying methane emissions in North America

GEOS-Chem CTM and its adjoint1/2ox2/3o over N. America

nested in 4ox5o global domain

Observations

Bayesianinversion

Optimized emissionsat 1/2ox2/3o resolution

Validation Verification

EDGAR 4.2 + LPJa priori bottom-up emissions

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

Correction factor to bottom-up emissions

Number of clusters in inversion1 10 100 1000 10,000

34

28

Optimized US anthropogenic emissions (Tg a-1)

posterior cost function

Native resolution 1000 clusters

SCIAMACHY data cannot constrainemissions at 1/2ox2/3o resolution;use 1000 optimally selected clusters

Kevin Wecht, Harvard

North American methane emission estimatesoptimized by SCIAMACHY + INTEX-A data (Jul-Aug 2004)

1700 1800ppb

SCIAMACHY column methane mixing ratio Correction factors to a priori emissions

Livestock Oil & Gas Landfills Coal Mining Other0

5

10

15US anthropogenic emissions (Tg a-1)

EDGAR v4.2 26.6

EPA 28.3

This work 32.7

Wecht et al., in prep.

1000 clusters

Livestock emissions are underestimated by EPA, oil/gas emissions are not

GOSAT methane column mixing ratios, Oct 2009-2010

Retrieval from U. Leicester

Inversion of GOSAT Oct 2009-2010 methane

Nested inversionwith 1/2ox2/3o resolution

Correction factors to prior emissions (EDGAR 4.2 + LPJ)

Alex Turner, Harvard

Next step: clustering of emissions in the inversion, use of ACOS data

Testing the information content of satellite datawith CalNex inversion of methane emissions

CalNex observations GEOS-Chem w/EDGAR v4.2

0.1 1 3

Correction factors to EDGAR(analytical inversion)

1800 2000ppb

May-Jun2010

State of California Los Angeles Basin0

0.51

1.52

2.53

3.5CA Air Resources BoardEDGAR v4.2Santoni et al. Lagrangian (STILT) inversionGEOS-Chem inversion

Emis

ssio

ns, T

g a-1

Wecht et al., in prep.

2x underestimateof livestock emissions

S. Wofsy (Harvard)

GOSAT observations are too sparseto spatially resolve California emissions

GOSAT data (CalNex period))Correction factors to methane emissions from inversion

GOSAT (CalNex period) GOSAT (1 year)

CalNex aircraft data

GOSAT(CalNex)

GOSAT(1 year)

Degrees of Freedom for Signal (DOFS) in inversion of methane emissions

15 1.2 2.8

Each point =1-10 observations

0.5 1.5

Wecht et al., in prep.

TROPOMI and GCIRI constrain state-level methane emissions better than a dedicated aircraft mission

TROPOMI (global daily coverage) GCIRI (geostationary 1-h return coverage)Correction factors to EDGAR v4.2 a priori emissions from a 1-year OSSE

A priori CalNex TROPOMI GCIRI TROPOMI+GCIRI

DOFS 15 17 23 26

California emissions (Tg a-1) 1.9 3.1 3.0 3.0 3.0

0.2 1 5

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

with New York Attorney General Office (John Marschilok)State-computed emissions too high by x0.6,reflects overestimate of gas/waste/landfill emissions

Melissa Sulprizio and Kevin Wecht, Harvard

Hog manure?

Large EDGAR source from gas+landfillsis just not there

0 1 2correction factor