1. Near-term climate forcers and climate policy: black carbon and methane Daniel J. Jacob

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

3. Radiative forcing of climate change
Terrestrial flux
Fout ~ T 4
Solar flux
Fin
Global radiative equilibrium: Fin = Fout
Perturb greenhouse gases or aerosols radiative forcing F = Fin - Fout
Global equilibrium surface temperature responds as 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
But atmospheric lifetimes of methane (10 years) and BC (~1 week) are shorter than CO2 (> 100 years)
[IPCC, 2014]

5. Metrics of climate response to a radiative forcing agent
for 1-kg instantaneous emission at time t = 0
Global Warming Potential (GWP): integrated forcing over time horizon t = H
Atmospheric lifetime:
CO yrs yrs
Global Temperature Potential (GTP): Mean surface temperature change at t = H
Surface T response
from 2008 emissions
taken as pulse
[IPCC, 2014]

6. Why the ephemeral response from a pulse of methane?
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

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

8. Implication of GTP-based policy for near-term climate forcers
Start controlling methane 40 years before target, BC 10 years before target
IPCC [2014]

9. Other climate policy metrics (M) have been proposed
C is the atmospheric variable perturbed by emission E
I is the impact function of interest (T, sea level, precip, GNP, health…)
W(t) is the temporal weighting factor
 W(t) = 1 for t < tH , = 0 for t > tH (as for GWP)
 W(t) = (t – tH) Dirac function (as for GTP)
 W(t) = exp[-t/tH] exponential discount rate
As societal relevance of the metrics increase,
so does uncertainty
Flugestvedt et al. [2003]

10. Controlling 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 is less sensitive to arguments over what discount rates should be used
It is an alternative to geoengineering by aerosols
It has important air quality co-benefits
BC has additional regional, hydrological impacts
Measures to reduce emissions can have lasting effects over long time horizons
Trend in Arctic sea ice volume Geoengineering: cloud seeeding

11. Black carbon in the atmosphere
diesel engines residential fuel open fires
freshly emitted
BC particle
Global BC emission [Wang et al., 2014]
Loss of BC is by wet deposition (lifetime ~ 1 week)

12. BC exported to upper troposphere is major component of forcing
…because it’s above white clouds instead of dark surface
Integral contribution
To BC forcing
Global mean
BC profile
(chemical
transport model) • • •
Export to upper
troposphere
deep
convection • •
50% from
BC > 5 km •
scavenging
BC forcing
efficiency • • • •
frontal
lifting • • • • • • • • • • • • • • • • • • • • • • • •
BC source
region
(combustion)
Ocean
Samset and Myhre [2011]

13. AeroCom chemical transport models (CTMs) used by IPCC
Multimodel intercomparisons and comparisons to observations
AeroCom chemical transport models (CTMs) used by IPCC
overestimate BC by order of magnitude in upper troposphere
TC4 aircraft campaign (Costa Rica)
Observed
Models
Pressure, hPa
Such large overestimate must be due to model errors in scavenging
BC, ng kg-1
HIPPO aircraft campaign over Pacific
Pressure, hPa
obs
models
20S-20N
obs
models
60-80N
BC, ng kg-1
BC, ng kg-1
Koch et al. [2009], Schwarz et al. [2010]

14. Previous application to Arctic spring (ARCTAS)
BC/aerosol scavenging in GEOS-Chem CTM used at Harvard
Cloud updraft scavenging
Anvil precipitation
Large scale precipitation
CCN
IN+CCN
CCN+IN,
impaction
Meteorological data including convective mass fluxes from NASA GEOS assimilation system
Aerosols are scavenged in cloud by similarity with condensed water
Additional scavenging below cloud by rain/snow
In-cloud scavenging efficiency from freezing/frozen clouds is highly uncertain
Additional uncertainty for BC is its efficiency as
cloud condensation nucleus (CCN) and ice nucleus (IN)
entrainment
detrainment
vertical position of the aerosol particle, the scavenging process can be in-cloud or below-cloud.
In-cloud: aerosols enter cloud droplets or ice crystals when they act as cloud condensation or ice nuclei and also by the process of impaction with the cloud droplets and crystals
Below-cloud: is the capture of aerosol particles by precipitating rain droplets or snow particles, and is usually described by a first order decay equation.
BC lifetime in GEOS-Chem is 4 days (vs. 7±2 days in AeroCom models)

15. GEOS-Chem BC simulation: source regions and outflow
Tests sources, export
Observations (circles) and model (background)
Normalized mean bias (NMB) in range of -30% to +10%
NMB= -27%
surface
networks
Wang et al., 2014
NMB= 6.6%
AERONET BC optical depth
NMB= -32%
Steven Barrett , Federal aviation administration;
The dataset was generated using the Aviation Emissions Inventory Code (AEIC).
Anthropogenic emission dominates globally, biomass burning may dominate regionally and seasonally
Aircraft profiles in continental/outflow regions
HIPPO
(US) US
(HIPPO)
Asian outflow
(A-FORCE)
observed
model
Arctic
(ARCTAS)
NMB= -12%

16. Comparison to HIPPO BC observations across the Pacific
Observed Model
PDF
PDF, (mg m-3 STP)-1
Model doesn’t capture low tail, is too high at N mid-latitudes
Mean column bias is +48%
Still much better than the AeroCom models
Wang et al., 2014

17. BC top-of-atmosphere direct radiative forcing (DRF)
Absorbing aerosol optical depth (AAOD)
Mass absorption
coefficient
Forcing
efficiency
DRF = Emissions X Lifetime X X
Global atmospheric load
Emission
Tg C a-1
Global load
(mg m-2)
[% above 5 km]
BC AAOD
x100
Forcing efficiency
(W m-2/AAOD)
Direct radiative forcing (W m-2)
fuel+fires
This work
6.5
0.15 [8.7%]
0.17 88
0.19 ( )
AeroCom [2006]
7.8 ±0.4
0.28 ± 0.08
[21±11%]
0.22±0.10
168 ± 53
0.34 ± 0.07
Bond et al. [2013] 17
0.55
0.60
147
0.88
Our best estimate of 0.19 W m-2 is much lower than IPCC recommendation of 0.65 ( ) W m-2
IPCC value is from models that greatly overestimate BC in upper troposphere
BC is much less important for climate forcing than stated in IPCC
Wang et al., 2014

18. Atmospheric methane: long-term trends are not understood
the last 1000 years
the last 30 years
E. Dlugokencky, NOAA
Source attribution is difficult due to diversity, complexity of sources
Livestock 90
Landfills 70
Gas 60
Coal 40
Rice
Other natural
Wetlands
180
Fires 50
Global sources,
Tg a-1
Individual sources uncertain
by at least factor of 2; emission factors are highly variable, poorly constrained

19. 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.,
submitted

20. Basics of inverse modeling
Optimize state vector x (emissions) using obs vector y (atm. concentrations)
Observations
y + εI
atmospheric concentrations
from satellite, aircraft
Minimize cost function
with error weighting,
xA regularization
Prior estimate
xA + εA
Bottom-up
inventory
Posterior estimate
Analytical
or numerical
(variational) method
Forward model
yM = F(x) + εM
GEOS-Chem
chemical transport model

21. Using satellite data for high-resolution inversion of methane emissions in North America
EDGAR emission
Inventory for methane

22. Bottom-up methane emissions for N. America (2009-2011)
CONUS anthropogenic
emissions:
25 Tg a-1 (EDGAR)
27 Tg a-1 (EPA)
8 oil/gas
9 livestock
6 waste
3 coal
total: 63 Tg a-1
wetlands: 20
livestock: 14
oil/gas: 11
waste: 10
coal: 4
Turner et al., submitted

23. 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., submitted

24. Correction factors to bottom-up inventory
CONUS anthropogenic emission of Tg a-1 vs. EPA value of 27 Tg a-1
Livestock source is underestimated by EPA; What about oil/gas?
Turner et al., submitted

25. Methane emissions in CONUS: comparison to previous studies, attribution to source types
Ranges from
prior error
assumptions
2004
satellite
2007
surface,
aircraft
satellite
EPA national inventory underestimates anthropogenic emissions by 30%
Livestock is a contributor: oil/gas production probably also
Turner et al., submitted

26. Future of satellite observations for methane monitoring
Methane is readily observable over land by solar backscatter at 1.6/2.3 µm
Methane column 
Backscattered
intensity IB
absorption
Scattering by
Earth surface l1 l2
wavelength
GOSAT (2009-): high-quality 5x5 km2 pixels but sparse
TROPOMI (2016 launch): global daily coverage with 7x7 km2 pixels
Geostationary (proposed): hourly coverage over N America with 2x2 km2 pixels