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

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 0.4-0.7 μm 5-20 μm 0.4-0.7 μm 5-20 μm

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

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

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

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

Observing methane from space in the shortwave IR (SWIR) GOSAT TROPOMI GHGSat SWIR atmospheric optical depths 1.6 1.8 2.0 2.2 2.4 Wavelength [µm] 2.3 µm 1.65 µm solar backscatter CH4 column Measures methane column with uniform vertical sensitivity GOSAT (2010 - ) GHGSat (2016 - ) TROPOMI (2017 - ) Jacob et al., ACP 2016

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 2010-2015 methane emissions on 4ox5o grid 2010-2015 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

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

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

More recent inversion with GOSAT data to end of 2017 Improved prior estimates, seasonal optimization of wetlands 2010-2017 linear trend for non-wetland emissions Wetlands emission trend prior livestock posterior Wetlands and livestock each contribute half of 2010-2017 growth Wetlands drive the post-2014 growth acceleration Zhang et al., in prep.

New TROPOMI data are providing much higher coverage global daily coverage, 7x7 km2 pixel resolution May 2018 – January 2019 methane column data http://www.tropomi.eu/data-products/methane

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

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 0.1-1 tons h-1…compare to 1,000-10,000 tons h-1 for CO2 Cusworth et al., ACP 2018

AVIRIS-NG airborne remote sensing of methane plumes Raster sampling with 3x3 m2 pixels, 5 nm spectral resolution Plumes are typically 0.1-1 km in size western US Four Corners region Frankenberg et al., PNAS 2016

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

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

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

GHGSat discovery of massive source from oil/gas production in Turkmenistan Feb 2018- Jan 2019 observations 13 Jan 2019 buried pipeline buried pipeline Korpezhe gas compressor station Compressor station: 10-42 tons h-1 (persistent) Pipeline: 30 tons h-1 (once) Varon et al., submitted to GRL

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

Source estimated at 8 tons h-1

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

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

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

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

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 2310-2390 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

Synthetic EnMAP scene of Berlin

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

Retrieval results over grass and urban scenes with methane plume Retrieval: Q = 500 kg h-1 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

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