Constraining methane emissions in North America by high-resolution inversion of satellite data:

International Conference "Towards a Global Carbon Observing System: Progresses and Challenges", Geneva, 1-2 October 2013

Constraining methane emissions in North America by high-resolution inversion of satellite data:

from SCIAMACHY to GOSAT and beyond

Daniel Jacob, Kevin Wecht, AlexTurner, Melissa Sulprizio

Towards a Global Carbon Observing System: Progresses and Challenges

Geneva, 1-2 October 2013


Methane emission inventories for N. America: EDGAR 4.2 (anthropogenic), Kaplan (wetlands)

N American totals in Tg a-1

Surface/aircraft observations in US suggest 2-3x underestimate of emissions


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)


Use GEOS-Chem CTM with observing systemfor optimized estimate of methane emissions

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 + Kaplana priori bottom-up emissions


Testing GEOS-Chem methane backgroundwith HIPPO aircraft data across Pacific

GEOS-ChemHIPPO

Latitude, degrees

Jan09 Oct-Nov09 Jun-Jul11 Aug-Sep11

Alex Turner and Kevin Wecht, Harvard

Boundary conditions for N. American window are optimized as part of the inversion

Met

hane

, ppb

v


Optimization of methane emissions using SCIAMACHY data for Jul-Aug 2004

Concurrent INTEX-A aircraft mission allows validation of SCIAMACHY, evaluation of inversionSCIAMACHY column methane mixing ratio XCH4 INTEX-A methane below 850 hPa

INTEX-A validation profiles H2O correction to SCIAMACHY data

Kevin Wecht, Harvard

D. Blake(UC Irvine)

C. Frankenberg(JPL)

SCIA

MAC

HY

INTEX-A

XCH4


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



N. American methane emission estimatesoptimized by SCIAMACHY data (Jul-Aug 2004)

1700 1800ppb

SCIAMACHY column methane mixing ratio Correction factors to 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



GOSAT methane column mixing ratios, Oct 2009-2010

Retrieval from U. Leicester


Preliminary global adjoint inversion of GOSAT Oct 2009-2010 methane data

Nested adjoint inversionwith 1/2ox2/3o resolution

Correction factors to prior emissions (EDGAR 4.2 + Kaplan)

Alex Turner, Harvard


Testing information content of satellite datawith CalNex inversion of methane emissions

CalNex observations with GEOS-Chem prior

0.1 1 3

Correction factors to EDGAR v4.2

1800 2000ppb

May-Jun2010

State of California Los Angeles Basin0

0.51

1.52

2.53

3.5

CA Air Resources BoardEDGAR v4.2Santoni et al. Lagrangian inversionGEOS-Chem inversion

Emis

ssio

ns, T

g a-1


2x underestimateof livestock emissions


GOSAT observations are too sparsefor spatial resolution of 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

14.7 0.55 1.41

Each point =1-10 observations

0.5 1.5



Potential of TROPOMI and GCIRIfor constraining methane emissions

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 14.7 9.5 14.1 16.7

California emissions (Tg a-1) 1.9 3.2 2.9 3.0 3.1

0.2 1 5



Working with stakeholdersat the US state level

State-by-state analysis of SCIAMACHY correction factors to bottom-up emissions

with Iowa Dept. of Natural Resources

State emissions computed w/EPA tools too low by x3.5;now investigating EPA livestock emission factors

with New York Attorney General Office

State-computed emissions too high by x0.6,reflects overestimate of gas/waste/landfill emissions

Melissa Sulprizio and Kevin Wecht, Harvard


Policy-relevant recommendationsfor monitoring N American methane emissions

• Need better understanding of methane emissions from livestock; these seem to be seriously underestimated in US EPA emission inventory

• No apparent underestimate of methane emissions from oil/gas production, but careful monitoring is needed in view of industry expansion

• TROPOMI (2015 launch) holds considerable promise for monitoring methane emissions; need to prepare inverse methods for exploiting the data

• GCIRI (proposed geostationary launch in 2018/2019) would greatly augment capability for methane monitoring

• Ground measurement sites play a critical role in evaluating satellite data; targeted aircraft campaigns can provide verification and better understanding of source regions

Documents

Constraining methane emissions in North America by high-resolution inversion of satellite data: