1. Using satellite observations of atmospheric methane
to quantify the methane budget and trends from the global scale down to point sources
Daniel J. Jacob, Harvard University
Bram Maasakkers
Yuzhong Zhang
Daniel Varon
Dan Cusworth

2. increase greenhouse gas
Methane: 2nd anthropogenic greenhouse gas after CO2
The last 1000 years (ice cores)
CO2
Methane
Climate equilibrium: Fin = Fout
Radiative forcing: ΔF = Fin - Fout
Fin
Fout
Fin
Fout
increase greenhouse gas
by ΔX ΔX
Solar
Terrestrial
μm μm
μm μm

3. Radiative forcing of climate referenced to emissions, 1750-2011
Methane: 2nd anthropogenic greenhouse gas after CO2
[IPCC, 2014]
Methane is 60% as important as CO2 in explaining warming since pre-industrial time
Climate policy treats methane emissions as 25 CO2 equivalents but in fact the two gases operate on very different time horizons

4. Complexity of methane sources
Methane is a major greenhouse gas… but where does it come from?
Livestock
Oil/gas
Wetlands
Satellite observations hold the key!
Wetlands: 180
Fires: 15
Livestock: 120
Rice: 26
Oil/Gas: 70
Coal: 38
Waste: 68
Other: 42
CH4
Lifetime 9.1±0.9 years
Emission
550  60 Tg a-1
CO2
Tropospheric OH
Landfills, wastewater
Fires
Global emissions (Tg a-1): EDGAR4.3.2, WetCHARTS

5. Methane fits and starts over past 40 years
CO2
Methane fits and starts over past 40 years
Leveling off in the 1990s is not understood
Renewed growth after 2007 (accelerating after 2014) is not understood either

6. Using atmospheric methane observations to test emission inventories
compare
predicted concentrations
observed atmospheric concentrations
optimize emissions
(posterior estimate)
3-D chemical transport model
Prior estimate from bottom-up inventory: activity rates x emission factors

7. Observing methane from space in the shortwave IR (SWIR)
GOSAT
TROPOMI
GHGSat
SWIR atmospheric optical depths
Wavelength [µm]
2.3 µm
1.65 µm
solar
backscatter
CH4 column
Measures methane column
with uniform vertical sensitivity
GOSAT ( )
GHGSat ( )
TROPOMI ( )
Jacob et al., ACP 2016

8. Mean GOSAT methane observations, 2010-2015
10 km pixels
250 km apart
U. Leicester v7 retrieval [Parker et al., AMT 2015]
1.2 million observations
Invert GOSAT methane data with GEOS-Chem chemical transport model to optimize:
mean methane emissions on 4ox5o grid
emission trends on same grid
Global mean annual OH concentration and trend
Analytical inversion with Gaussian errors → posterior errors as part of solution
Maasakkers et al., ACP 2019

9. Global optimization of mean 2010-2015 emissions
EDGARv4.3
USEPA
WetCHARTS
Livestock and oil/gas are the dominant anthropogenic sources
Maasakkers et al., ACP 2019

10. Attribution of 2010-2015 methane trend
global emission trend
+0.8 ± 0.1% a-1
global OH trend
-0.2 ± 0.8% a-1
Tropical wetlands account for half of methane trend
OH may contribute to trend but very uncertain
SWIR methane can constrain global OH but not its trend; addition of TIR methane could effectively constrain trend [Zhang et al., ACP 2018]
Maasakkers et al., ACP 2019

11. More recent inversion with GOSAT data to end of 2017
Improved prior estimates, seasonal optimization of wetlands
linear trend for non-wetland emissions
Wetlands emission trend
prior
livestock
posterior
Wetlands and livestock each contribute half of growth
Wetlands drive the post-2014 growth acceleration
Zhang et al., in prep.

12. New TROPOMI data are providing much higher coverage
global daily coverage, 7x7 km2 pixel resolution
May 2018 – January 2019 methane column data

13. TROPOMI methane over UK
SRON retrieval
[Hu et al., GRL 2018]
Yuzhong Zhang, Harvard

14. Methane inventories require monitoring at facility scale
A difficult problem because sources are numerous, relatively small, and highly variable
Barnett Shale
East Texas
“fat tail”: a few sites
can dominate emissions
A large methane point source is tons h-1…compare to 1,000-10,000 tons h-1 for CO2
Cusworth et al., ACP 2018

15. AVIRIS-NG airborne remote sensing of methane plumes
Raster sampling with 3x3 m2 pixels, 5 nm spectral resolution
Plumes are typically km in size
western US
Four Corners region
Frankenberg et al., PNAS 2016

16. GHGSat space-based observation of methane point sources
Effective pixel resolution of 50x50 m2 over selected 12x12 km2 scenes
15 kg Fabry-Perot spectrometer
0.1 nm spectral resolution
Design precision 1-5%
First demonstration instrument
launched in June 2016: precision ~13%
methane
plume
Point source
12 km
12 km
Unique platform to monitor methane emissions
from individual point sources
Focus on plumes relaxes precision/accuracy requirements
Varon et al., AMT 2018

17. How to relate instantaneous plume observations to emissions?
WRF large-eddy simulations (LES) at 50x50 m2 resolution
Varon et al., AMT 2018

18. Inferring point source rates Q from instantaneous observation of column plume enhancements ΔΩ
Varon et al., AMT 2018
1. Gaussian plume inversion U
Fails for plumes < 10 km
due to non-Gaussian behavior
2. Source pixel mass balance U
Fails for pixels < 1 km because
eddy flow dominates ventilation W
3. Cross-sectional flux
Ueff
Derive Ueff = f(U10) from LES simulations
4. Integrated mass enhancement (IME)
plume mass (IME),
size L
Ueff

19. GHGSat discovery of massive source from oil/gas production in Turkmenistan
Feb Jan 2019 observations
13 Jan 2019
buried
pipeline
buried
pipeline
Korpezhe gas compressor station
Compressor station: tons h-1 (persistent)
Pipeline: 30 tons h-1 (once)
Varon et al., submitted to GRL

20. TROPOMI observation of Turkmenistan plumes
GHGSat
scene
Inferred source rates over GHGSat scene
TROPOMI confirms large persistent source, mean source rate 2x GHGSat
Varon et al.,
submitted to GRL

21. Source estimated at 8 tons h-1

22. 2014
estimated emission 8 tons h-1
2014: dam under construction
2014

23. 2016 estimated emission 8 tons h-1 2016 2016: dam completed,
former vegetation flooded
2016

24. Hydroelectric dams may be a large unrecognized methane source
low CH4
Dam O2
CH4
CO2
oxicline
high CH4
no O2
CH4
turbine flow
dead organic material
Lom Pangar dam
view from top
view from bottom

25. Can we retrieve methane from next generation of imaging spectrometers?
These instruments observe Earth’s surface with fine pixels but coarse spectral resolution
Landsat:
100 nm spectral resolution

26. Next-generation imaging spectrometers have ~10 nm resolution in SWIR
PRISMA (Italy): launched in Jan 2019
EnMAP (Germany); to be launched in 2020
EnMAP, PRISMA
TROPOMI
Spectral window, nm
900–2450
Spectral resolution, nm
8-12
0.25
Pixel resolution, m
30x30
7000x7000
Revisit time, days 6 1
SWIR top-of-atmosphere transmission spectra
Cusworth et al., AMTD 2019

27. Synthetic EnMAP scene of Berlin

28. Add LES methane plumes to EnMAP scenes and try to retrieve them:
Challenge is to separate fine spectral features (gases) from coarse features (surface)
EnMAP
solar
backscatter
Grass scene
CH4 column
surface reflectance
Forward model (Beer’s Law):
Urban scene
backscattered
radiance
solar
radiation
AMF
absorption by
atmospheric gases
surface reflectance
(Legendre polynomials)
State vector for retrieval:
x = (XCH4, XH2O, XN2O , a1,…aK)T
Cusworth et al., AMTD 2019

29. Retrieval results over grass and urban scenes
with methane plume Retrieval: Q = 500 kg h Q = 900 kg h-1
Grass scene
Urban scene
Source with Q = 500 kg h-1 can be retrieved for grass scene;
even better for bright scene
Surface reflectivity artifacts prevent successful retrieval for urban scene;
limited by spectral resolution
Cusworth et al., AMTD 2019

30. RGB image AVIRIS-NG retrieval EnMAP retrieval
EnMAP resampling of AVIRIS-NG spectra (3x3 m2 pixels, 5 nm spectral resolution) measured from aircraft over oil/gas facilities in California
RGB image AVIRIS-NG retrieval EnMAP retrieval
EnMAP can retrieve ~100 kg h-1 sources over bright surfaces with factor of 2 precision
Cusworth et al., AMTD 2019