Transcript NASA Air Quality Applied Sciences Team (AQAST)

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]
Gorillas and chimpanzees of climate change
CO2: the 800-lbs gorilla
Methane and black carbon:
the chimps
Do we care about the chimps?
Radiative forcing of climate change
Solar flux
Fin
Terrestrial flux
Fout =σ T 4
• Global radiative equilibrium: Fin = Fout
• Perturb greenhouse gases or aerosols
radiative forcing F = Fin - Fout
• Global equilibrium surface temperature response: To ~ F
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]
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
Impact from pulse = f(C(t))
time
Discount rate

Climate metric =

0
time
(impact)(discount rate)dt
…usually normalized to CO2
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
H
AGWP(X)   ΔFX (t )dt
0
GWP(X) 
AGWP(X)
AGWP(CO2 )
Discount rate: step function
H
time
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
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
Global temperature potential (GTP) metric introduced by IPCC AR5
Global mean surface temperature change at t = H
CO2 methane BC
GTP ( X ) 
Temperature change vs. time
for pulse unit emission at t = 0
ΔTo , X (H )
ΔTo ,CO 2 (H )
Discount rate:
Dirac function
H
time
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
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:
0.63
0.43
 To (t )  (
exp[ t / 8.4] 
exp[ t / 410])
8.4
410
t in years
obtained by fitting results of HadCM3 climate model
GTP is then given by
H
To ( H )   F (t ) T ( H  t )dt
0
Boucher and Reddy [2008]
Why does methane cause only a short-term temperature response?
Fin
Fout
To
To
t<0
t=0
climate
equilibrium
emission
pulse
F = 0
F > 0
To + To
t = 20 years
climate
response
F < 0
To
t = 100 years
back to
original
equilibrium
F = 0
Implication of GTP-based policy for near-term climate forcers
Aiming to optimize for a maximum temperature change on a 100-year horizon:
GTP potential
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!
IPCC [2014]
Sole focus on temperature change over long-term horizon
sacrifices immediate climate emergencies
No summer Arctic sea ice in 20 years?
More hurricane Sandys?
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 - 20-year GTP and 100-year GTP ?
Long-term trends of methane are not understood
Source attribution is difficult due to diversity, complexity of sources
the last 30 years
the last 1000 years
E. Dlugokencky, NOAA
Other: 30
Global emission (2012):
540 Tg a-1
Waste: 60
Wetlands: 160
Coal: 50
Fires: 20
Oil/Gas: 70
EDGAR4.2 inventory
Rice: 40
Livestock: 110
Satellite observations of methane
Instruments: SCIAMACHY (2002-2005), GOSAT (2009-), TROPOMI (2016 launch)
Methane column
mixing ratio
Turner et al. [2015]
Satellite data as constraints on methane emissions
“Bottom-up” emissions (EDGAR):
best understanding of processes
Satellite data for methane columns
2009-2011
537 Tg a-1
Optimal estimate inversion
using GEOS-Chem model adjoint
Ratio of optimal estimate
to 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?
1/2ox2/3o grid of GEOS-Chem
INTEX-A
CalNex
EPA inventory
for contiguous US:
27 Tg a-1
Waste 5.6
SEAC4RS
Other 1.1
Wetlands 5.9
Fires 0.1
Coal 2.9
Livestock 9.2
Oil/gas 7.7
Rice 0.4
“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
verification
Optimized “top=down”
inventory
Aircraft and
surface
observations
Global inversion of GOSAT data
feeds boundary conditions for North American inversion
GOSAT observations, 2009-2011
Dynamic
boundary
conditions
Adjoint-based inversion
at 4ox5o resolution
Analytical inversion
with 369 Gaussians
correction factors to EDGAR v4.2 + LPJ prior
Turner et al. [2015]
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]
Correction factors to bottom-up EDGAR inventory
• CONUS anthropogenic emission of 40-43 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]
Optimized top-down inventory
• CONUS anthropogenic emission of 40-43 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]
Attribution of emission correction to oil/gas or livestock
is complicated by uncertainty in location, spatial overlap
• Oil/gas fields and cattle
often share quarters
• Gas emissions occur at exploration,
production, processing,
transmission, distribution
Eagle Ford Shale, Texas
EDGAR inventory oil/gas source pattern likely overemphasizes distribution vs. production
Turner et al. [2015]
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]
New EPA-based gridded emission inventory:
natural gas production
J.D. Maasakkers (in prep.)
Natural
gas processing
New
EPA-based
gridded emission inventory:
natural gas production + processing
J.D. Maasakkers (in prep.)
Natural
gas transmission
New
EPA-based
gridded emission inventory:
natural gas production + processing + transmission
J.D. Maasakkers (in prep.)
Total EPA-based
natural gas:gridded
production
+ processing
+ transmission + distribution
New
emission
inventory:
natural gas production + processing + transmission + distribution
J.D. Maasakkers (in prep.)
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.)