scattering by earth surface instruments: backscattered intensity i b absorption methane column ...

Scattering by Earth surface

Instruments:

Backscatteredintensity IB

abso

rpti

on

l1 l2

Methane column

2 1ln[ ( ) / ( )]

AMF

B BI I

Application of inverse methodsto constrain methane emissions from satellite data

Methane observable by solar backscatter at 1.6 and 2.3 µmnear-unit sensitivityat all altitudes

Remove air mass factor (AMF) dependence using CO2 retrieval for nearby wavelengths:

44 2

2

CHCH CO

CO

X X dry column mixing ratio

2002 2005 2009 20016 ?

SCIAMACHY

60 km, 6-day GOSAT5 km, 3-day, sparse

TROPOMI Geostationary 7 km, 1-day 2 km, 1-hour

Global distribution of methane observed from space

Sources: wetlands, livestock, landfills, natural gas… Sink: atmospheric oxidation (10-year lifetime)

Global source is 550 60 Tg a-1, constrained by knowledge of global sink

Long-term trends of methane are not understood

Source attribution is difficult due to diversity, complexity of sources

Livestock90

Landfills70

Gas60

Coal40

Rice40

Other natural40

Wetlands180

Fires50

Global sources,Tg a-1

Individual sources uncertain by at least factor of 2; emission factors are highly variable, poorly constrained

the last 1000 years

the last 30 years

E. Dlugokencky, NOAA

Satellite data as constraints on methane emissions

“Bottom-up” emissions (EDGAR):best understanding of processes

2009-2011537 Tg a-1

Satellite data for methane columns

Optimal estimate inversionusing GEOS-Chem model adjoint

Ratio of optimal estimateto bottom-up emissions

Turner et al. [2015]

Building a continental-scale methane monitoring system

Can we use satellites together with suborbital observations of methane to monitor methane emissions on the continental scale?

CalNex

INTEX-A

SEAC4RS

1/2ox2/3o grid of GEOS-Chem

Bottom-up methane emissions for N. America (2009-2011)

total: 63 Tg a-1 wetlands: 20

oil/gas: 11livestock: 14

waste: 10 coal: 4

CONUS anthropogenic emissions: 25 Tg a-1 (EDGAR) 27 Tg a-1 (EPA) 8 oil/gas 9 livestock 6 waste 3 coal Aircraft/surface data indicate that these bottom-up estimates are too low

Turner et al. [2015]

High-resolution inversion of methane emissions

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

nested in 4ox5o global domain

Observations

Bayesianinversion

Optimized emissions (“state vector”)at up to 1/2ox2/3o resolution

Validation Verification

EDGAR 4.2 + LPJprior bottom-up emissions

Three applications: 1. Summer 2004 using SCIAMACHY 2. CalNex May-June 2010 aircraft campaign over California 3. 2009-2011 using GOSAT

First step: validate the satellite methane data

SCIAMACHY validation using vertical profiles from INTEX-A aircraft campaignSCIAMACHY column methane mixing ratio XCH4 INTEX-A methane below 850 hPa

C. Frankenberg(JPL)

D. Blake(UC Irvine)

C. Frankenberg(JPL)

H2O retrieval bias: remove it!Differencebetween satelliteand aircraft

after bias correction

Wecht et al. [2014a]

Second step: check model background

Model mean methane for Jul-Aug 2004 (background) and NOAA data (circles)

Wecht et al. [2014a]

4ox5o 1/2o2/3o

Include time-dependent boundary conditionsin state vector

Third step: choose state vector

1-1 ) () ( )( ) OAx AS K S F x -(x - x 0yx TJ

If state vector is too large, cost function is dominated by prior: smoothing error

Correct this by aggregating state vector elements, but this incurs aggregation error

There is an optimal state vector dimension for fitting observations:1ˆ ˆ( ) ( )T Oy F(x) S y F(x)

# state vector elements

aggregation smoothing

As dim(x) increases, the importance of the prior terms increases

Prior Observations

native grid aggregated grid

Selection of state vector for inversion of SCIAMACHY dataOptimal clustering

of 1/2ox2/3o gridsquares

Correction factor to bottom-up emissions

Number of clusters in inversion1 10 100 1000 10,000

34

28

Optim

ized US

emissions (T

g a-1)

Native resolution (7,906 gridsquares) 1000 clusters

Wecht et al. [2014a]

1ˆ ˆ( ) ( )T Oy F(x) S y F(x)

aggregation smoothing

Inverse model fit to observations

Verification of inversion results with INTEX-A aircraft data

Prioremissions

Optimizedemissions

GEOS-Chem simulation of INTEX-A aircraft observations below 850 hPa:

with prior emissions with optimized emissions

Wecht et al. [2014a]

Tg CH4 a-1

Attribution of geographical source contributions to source type is complicated by spatial overlap

For a given cluster, assume that prior emission attribution by source type (i) is correct:

,A i A ii

E f Eand apply inversion scaling factor for that cluster to all source types weighted by fi

with 1ii

f

Livestock and natural gas emissions are often collocated

Eagle Ford Shale, Texas

North American methane emission estimatesoptimized by SCIAMACHY (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. [2014a]

1000 clusters

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

Constraining methane emissions in CaliforniaStatewide greenhouse gas emissions must decrease to 1990 levels by 2020

Large difference between bottom-up emission inventories:EDGAR v4.2 (2010) vs. California Air Resources Board (CARB)

Wecht et al. [2014b]

CARB: 1.51

CARB: 0.86CARB: 0.18

CARB: 0.39

Tg a-1

Inversion of methane emissions using aircraft campaign data

CalNex aircraft observations GEOS-Chem w/EDGAR v4.2Correction factors to EDGAR(analytical inversion, n= 157)

May-Jun2010

Wecht et al. [2014b]

California emissions (Tg a-1)

G. Santoni (Harvard)

May-Jun2010

EDGAR v4.2 1.92

CARB 1.51

This work 2.86 ± 0.21

State totals

Livestock Gas/oil Landfills Other0

0.20.40.60.8

11.2

Diagnosing the information content from the inversion

ˆ ( )Ax = x + (I - A) x x + Gεsolution = truth + smoothing + noise

averaging kernel matrix prior

x is the state vector of emissions (n = 157)

Diagonal elements of ˆ / A x x

• Diagonal elements of A range from 0 (no local constraint from observations) to 1 (no constraint from prior)

• Degrees Of Freedom for Signal (DOFS) = tr(A) = total # pieces of information constrained by inversion

Comparing information content from aircraft and satellites

TROPOMI will provide information comparable to a continuous aircraft campaign; a geostationary satellite instrument will provide even more Wecht et al. [2014b]

Diagonal elements of A

OSSE of satellite observations during CalNex period (May-June 2010)

CalNex GOSAT:precise but sparse

TROPOMI (2016):daily coverage

Geostationary:hourly coverage

Temporal averaging can overcome GOSAT data sparsity

2.5 years of GOSAT data

Turner et al. [2015]

GOSAT validation using CTM as intercomparison platform

Model provides continuous 3-D fields to compare different observational data sets

Satellite (GOSAT)

GEOS-Chem with prior emissions

aircraft+surface data

Are the comparisons consistent?

GEOS-Chem (with prior emissions) compared to in situ data

Latitude, degrees

GEOS-ChemHIPPO

Jan09 Oct-Nov09 Jun-Jul11 Aug-Sep11

Met

hane

, ppb

v

GE

OS

-Ch

em

NOAA US observations

• GEOS-Chem is unbiased for background methane• US enhancement is ~30% too low, to be corrected

in inversion

Turner et al. [2015]

HIPPO aircraft data over Pacific

GEOS-Chem (prior) comparison to GOSAT data

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]

State vector choice to balance smoothing & aggregation error

Native-resolution 1/2ox2/3o emission state vector x (n = 7096)

Aggregation matrix

x =x

Reduced-resolutionstate vector x (here n = 8)

Posterior error covariance matrix: ˆ T T

ω ω ω A ω ω ωTT

ω O ωAG (K - K Γ )S (K - K Γ ) G (IS = + +- A) G SS I- ) G( A Aggregation Smoothing Observation

Choose n = 369 for negligible aggregation error; allows analytical inversion with full error characterization

1 10 100 1,000 10,000Number of state vector elements

M

ean

erro

r s.

d., p

pb

Posterior errordepends on choice

of state vectordimension

observation aggregationsmoothingtotal

Turner and Jacob [2015]

Using radial basis functions (RBFs) with Gaussian mixing model

as state vector

• 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

Dominant Gaussians for emissionsin Southern California

Turner and Jacob [2015]

Global inversion of GOSAT datafeeds boundary conditions for North American inversion

GOSAT observations, 2009-2011

Adjoint-based inversionat 4ox5o resolution

Dynamicboundaryconditions

Analytical inversionwith 369 Gaussians

Turner et al. [2015]

correction factors to EDGAR v4.2 + LPJ prior

Averaging kernel sensitivities and inversion results

Turner et al. [2015]

Evaluation of posterior emissionswith independent data sets in contiguous US

Comparison of California resultsto previous inversions of CalNex data

(Los Angeles)

Turner et al. [2015]

GEOS-Chem simulationwith posterior vs. prior emissions

Methane emissions in US:comparison to previous studies, attribution to source

types

• EPA national inventory underestimates anthropogenic emissions by 30%• Livestock is a contributor: oil/gas production probably also

Ranges from prior errorassumptions

Turner et al. [2015]

2004satellite

2007surface,aircraft

2009-2011satellite

• What is needed to improve source attribution in future?• Better observing system (more GOSAT years, TROPOMI,…)• Better bottom-up inventory (gridded EPA inventory, wetlands)

Source attribution is only as good as bottom-up prior pattern

Little confidence and detail in EDGAR gridded inventory; construct our ownin collaboration with US EPA data including detailed info on processes

Large point sources(oil/gas/coal, waste)reporting emissions to EPA

GIS data for location of wells, pipelines, coal mines,…

Livestock and rice data at (sub)-county level

Process-level emission factors including seasonal variation

National bottom-up US inventory of methane emissions at 0.1ox0.1o grid resolution

J.D. Maasakkers (in prep.)

New EPA-based gridded emission inventory: natural gas production

J.D. Maasakkers (in prep.)

Natural gas processing

J.D. Maasakkers (in prep.)

New EPA-based gridded emission inventory: natural gas production + processing

Natural gas transmission

J.D. Maasakkers (in prep.)

New EPA-based gridded emission inventory: natural gas production + processing + transmission

Total natural gas: production + processing + transmission + distribution

J.D. Maasakkers (in prep.)

New EPA-based gridded emission inventory: natural gas production + processing + transmission + distribution

EDGAR v4.2FT 2010 total natural gas emissions

J.D. Maasakkers (in prep.)

Difference with EDGAR

J.D. Maasakkers (in prep.)