1. Methane emissions in North America and their relevance for climate policy Daniel J. Jacob
with Alexander J. Turner, J.D. (Bram) Maasakkers
Supported by the NASA Carbon Monitoring System
“ The Administration is announcing a new goal to cut methane emissions from the oil and gas sector by 40 – 45 percent from 2012 levels by 2025”
[President’s Updated Climate Action Plan, 2015]

2. Gorillas and chimpanzees of climate change
CO2: the 800-lbs gorilla
Methane and black carbon:
the chimps
Do we care about the chimps?

3. Radiative forcing of climate change
Terrestrial flux
Fout =σ T 4
Solar flux
Fin
Global radiative equilibrium: Fin = Fout
Perturb greenhouse gases or aerosols radiative forcing F = Fin - Fout
Global equilibrium surface temperature response: To ~ F

4. Radiative forcing referenced to emissions, 1750-2011
Radiative forcing from methane emissions is 0.97 W m-2, compared to 1.68 W m-2 for CO2
Radiative forcing from black carbon aerosol (BC) is 0.65 W m-2, highly uncertain
Together methane and BC have radiative forcing comparable to CO2  they have made comparable contribution to past climate change
But atmospheric lifetimes of methane (10 years) and BC (~1 week) are shorter than CO2 (> 100 years)
What does that mean for priorities in controlling future emissions?
[IPCC, 2014]

5. Climate policy metrics consider the integrated future impact of a pulse unit emission of a radiative forcing agent
Inject 1 kg of agent X at time t = 0
Concentration C(t) from pulse
time
time
Impact from pulse = f(C(t))
time
Discount rate
time
Climate metric =
(impact)(discount rate)dt
…usually normalized to CO2

6. Standard IPCC metric: Global Warming Potential (GWP)
Integrated radiative forcing over time horizon [0, H]
Radiative forcing F vs. time
for pulse unit emission of X
at t = 0
CO2 methane BC
Discount rate: step function
time H
IPCC [2014]
GWP for methane
vs. chosen time horizon:
28 for H = 100 years
 1 Tg CH4 = 28 Tg CO2 (eq)
GWP is easy to compute,
but it does not correspond
to any physical impact

7. Towards a temperature-based climate metric
Cancun UN Climate Change Conference (2010):
hold global surface temperature change to less than 2oC above pre-industrial levels
Intent is to avoid catastrophic climate change

8. Global temperature potential (GTP) metric introduced by IPCC AR5 Global mean surface temperature change at t = H
Temperature change vs. time
for pulse unit emission at t = 0
CO2 methane BC
Discount rate:
Dirac function
time H
IPCC [2014]
Temperature response
to actual 2008 emissions
taken as a 1-year pulse
Methane as important as CO2
for 10-year horizon, unimportant
for 100-year horizon

9. Simple calculation of Global Temperature Potential (GTP)
Use impulse response function of surface To to yearly pulse F of 1 W m-2 at time t = 0:
t in years
obtained by fitting results of HadCM3 climate model
GTP is then given by
Boucher and Reddy [2008]

10. Why does methane cause only a short-term temperature response?
Fin
Fout To To
To + To To
t < t = t = 20 years t = 100 years
climate
equilibrium
emission
pulse
climate
response
back to
original
equilibrium
F = 0
F > 0
F < 0
F = 0

11. Implication of GTP-based policy for near-term climate forcers
Aiming to optimize for a maximum temperature change on a 100-year horizon:
Right now we’ll just worry about CO2.
But in 70 years please start acting on methane,
and in 95 years go all after black carbon, baby!
GTP potential
IPCC [2014]

12. Sole focus on temperature change over long-term horizon sacrifices immediate climate emergencies
No summer Arctic sea ice in 20 years? More hurricane Sandys?

13. Methane and BC should be part of climate policy … but for reasons totally different than CO2
It addresses climate change on time scales of decades – which we care about
It offers decadal-scale results for accountability of climate policy
It has important air quality co-benefits
It is an alternative to geoengineering by aerosols
Reducing methane emissions makes money
BC has additional regional, hydrological climate impacts
How about having TWO climate metrics year GTP and 100-year GTP ?

14. Long-term trends of methane are not understood
Source attribution is difficult due to diversity, complexity of sources
the last 1000 years
the last 30 years
E. Dlugokencky, NOAA
Other: 30
Global emission (2012):
540 Tg a-1
Waste: 60
Wetlands: 160
Coal: 50
Fires: 20
Oil/Gas: 70
Livestock: 110
EDGAR4.2 inventory
Rice: 40

15. Satellite observations of methane
Instruments: SCIAMACHY ( ), GOSAT (2009-), TROPOMI (2016 launch)
Methane column
mixing ratio
Turner et al. [2015]

16. Satellite data as constraints on methane emissions
“Bottom-up” emissions (EDGAR):
best understanding of processes
Satellite data for methane columns
537 Tg a-1
Optimal estimate inversion
using GEOS-Chem model adjoint
Ratio of optimal estimate
to bottom-up emissions
Turner et al. [2015]

17. 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
Other 1.1
EPA inventory
for contiguous US:
27 Tg a-1
Waste 5.6
Wetlands 5.9
Fires 0.1
Coal 2.9
Livestock 9.2
Oil/gas 7.7
Rice 0.4

18. “Top-down” constraints on emissions from satellite data
Satellite observations
of methane concentrations
Chemical transport model
Prior “bottom-up”inventory
(EDGAR + wetlands)
Emissions Concentrations
Inverse
Optimal
estimation
Aircraft and
surface
observations
verification
Optimized “top=down”
inventory

19. Global inversion of GOSAT data feeds boundary conditions for North American inversion
GOSAT observations,
Dynamic
boundary
conditions
Analytical inversion
with 369 Gaussians
Adjoint-based inversion
at 4ox5o resolution
correction factors to EDGAR v4.2 + LPJ prior
Turner et al. [2015]

20. Optimized emissions improves simulation of independent data sets in contiguous US
GEOS-Chem simulation with optimized vs. prior emissions
Comparison of California results
to previous inversions of CalNex data
(Los Angeles)
Turner et al. [2015]

21. Correction factors to bottom-up EDGAR inventory
CONUS anthropogenic emission of Tg a-1 vs. EPA value of 27 Tg a-1
Is the underestimate in livestock or oil/gas emissions or both?
Turner et al. [2015]

22. Optimized top-down inventory
CONUS anthropogenic emission of Tg a-1 vs. EPA value of 27 Tg a-1
Is the underestimate in livestock or oil/gas emissions or both?
Turner et al. [2015]

23. Attribution of emission correction to oil/gas or livestock is complicated by uncertainty in location, spatial overlap
Eagle Ford Shale, Texas
Oil/gas fields and cattle
often share quarters
Gas emissions occur at exploration, production, processing, transmission, distribution
EDGAR inventory oil/gas source pattern likely overemphasizes distribution vs. production
Turner et al. [2015]

24. Constructing a gridded version of the EPA national inventory
Best process-based knowledge of sources, granular representation of processes, national inventory reported to the UNFCCC
Large point sources
(oil/gas/coal, waste)
reporting emissions to EPA
GIS data for location of wells, pipelines, coal mines,…
National bottom-up US inventory of methane emissions at 0.1ox0.1o monthly resolution
Livestock and rice data at
sub-county level
Process-level emission factors including seasonal variation
J.D. Maasakkers (in prep.)
with M. Weitz, T. Wirth, C. Hight, M. DeFiguereido [EPA]

25. New EPA-based gridded emission inventory: natural gas production
J.D. Maasakkers (in prep.)

26. Natural gas processing
New EPA-based gridded emission inventory: natural gas production + processing
J.D. Maasakkers (in prep.)

27. Natural gas transmission
New EPA-based gridded emission inventory: natural gas production + processing + transmission
J.D. Maasakkers (in prep.)

28. Total natural gas: production + processing + transmission + distribution
New EPA-based gridded emission inventory: natural gas production + processing + transmission + distribution
J.D. Maasakkers (in prep.)

29. Difference with EDGAR
Using the EPA gridded emission inventory as prior will considerably increase
The quality of information from inverse modeling estimates
J.D. Maasakkers (in prep.)