A tale of two near-term climate forcers: black carbon and methane Daniel J. Jacob
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Transcript of A tale of two near-term climate forcers: black carbon and methane Daniel J. Jacob
A tale of two near-term climate forcers:black carbon and methane
Daniel J. Jacob
with Qiaoqiao Wang, Kevin Wecht, Alex Turner, Melissa Sulprizio
BC exported to the free troposphereis a major component of BC direct radiative forcing
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frontallifting
deep convection
scavenging
BC source region (combustion)
Ocean
Export to free troposphere
Global mean BC profile(Oslo CTM)
BC forcingefficiency
Integral contributionTo BC forcing
Samset and Myhre [2011]
50% fromBC > 5 km
Multimodel intercomparison and comparison to
observations
Multimodel intercomparisons and comparisons to observations
Koch et al. [2009], Schwarz et al. [2010]
BC, ng kg-1
TC4 (Costa Rica, summer)
ObservedModels
Large overestimate must reflect model errors in scavenging
Free tropospheric BC in AeroCom models is ~10x too highP
ress
ure
, h
Pa
obsmodels60-80N
obsmodels20S-20N
Pre
ssu
re,
hP
a
HIPPO over Pacific (Jan)
BC, ng kg-1 BC, ng kg-1
This has major implications for IPCC radiative forcing estimates
HIPPO deployments across the Pacific
“pole-to-pole” aircraft curtains from boundary layer to tropopause
NOAA SP2 BC measurements (D. Fahey)
NCAR GV aircraft
• BC concentrations span x105
• Mean BC columns span x103
An extraordinary rangeof variability!
Latitude
Oct-Nov2009
Mar-Apr2010
Jun-Jul2011
Aug-Sep2011
Jan2009
Previous application to Arctic spring (ARCTAS)
CCN
Cloud updraft scavenging
Large scale precipitationAnvil precipitation
IN+CCN
entrainment
detrainment
GEOS-Chem aerosol scavenging scheme
CCN+IN,impaction • Below-cloud scavenging (accumulation mode aerosol),
different for rain and snow• BC has 1-day time scale for conversion from
hydrophobic (IN but not CCN) to hydrophilic (CCN but not IN)
• Homogeneous freezing below 237K scavenges all aerosol
• Scheme evaluated with aerosol observations worldwide• 210Pb tropospheric lifetime of 8.6 days (consistent with best estimate of 9 days)• BC tropospheric lifetime of 4.2 days (vs. 6.8 ± 1.8 days in AeroCom models)
Dealing with freezing/frozen clouds is key uncertainty
GEOS-Chem BC simulation: source regions and outflow
NMB= -27%
NMB= -12%
NMB= 6.6%
Observations (circles) and model (background)
surfacenetworks
AERONETBC AAOD
NMB= -32%
Aircraft profiles in continental/outflow regionsHIPPO(US)
Arctic(ARCTAS)
Asian outflow(A-FORCE)
US(HIPPO)
observedmodel
Wan
g e
t al
., s
ub
mit
tedNormalized mean bias (NMB) in range of -30% to +10%
BC source (2009): 4.9 Tg a-1 fuel + 1.6 Tg a-1 open fires
Comparison to HIPPO BC observations across the Pacific
• 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., submitted
Observed Model PDF
PDF,
(mg
m-3
STP
)-1
BC top-of-atmosphere direct radiative forcing (DRF)
EmissionTg C a-1
Global load(mg m-2)[% above 5 km]
BC AAODx100
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 (0.17-0.31)
AeroCom [2006]
7.8 ±0.4 0.28 ± 0.08[21±11%]
0.22±0.10 168 ± 53 0.34 ± 0.07
Chung et al. [2012]
0.77 84 0.65
Bond et al. [2013]
17 0.55 0.60 147 0.88
• Our best estimate of 0.19 W m-2 is at the low end of literature and of IPCC AR5 recommendation of 0.40 (0.05-0.8) W m-2 for fuel-only
• Models that cannot reproduce observations in the free troposphere should not be trusted for DRF estimates Wang et al., submitted
DRF = Emissions X Lifetime XMass absorption
coefficientX
Forcingefficiency
Global load
Absorbing aerosol optical depth (AAOD)
Importance of methane for climate policy
• Present-day emission-based forcing of methane is 0.95 W m-2 (IPCC AR5), compared to 1.8 W m-2 for CO2
• Climate impact of methane is comparable to CO2 over 20-year horizon
• Methane is cheap to control - if we know which sources to control!
Building a methane monitoring system for N America
EDGAR emissionInventory for methane
Can we use satellites together with suborbital observations of methane to monitor methane emissions on the continental scale?
Methane bottom-up emission inventories for N. America: EDGAR 4.2 (anthropogenic), LPJ (wetlands)
N American totals in Tg a-1 (2004)
Surface/aircraft studies suggest that these emissions are too low by ~x2
AIRS, TES, IASI
Methane observing system in North America
Satellites
2002 2006 2009 20015 2018
Thermal IR
SCIAMACHY 6-day
GOSAT3-day, sparse
TROPOMI GCIRI 1-day geoShortwave IR
Suborbital
CalNex
INTEX-A
SEAC4RS
1/2ox2/3o grid of GEOS-Chem chemical transport model (CTM)
High-resolution inverse analysis system for quantifying methane emissions in North America
GEOS-Chem CTM and its adjoint1/2ox2/3o over N. America
nested in 4ox5o global domain
Observations
Bayesianinversion
Optimized emissions (“state vector”)at up to 1/2ox2/3o resolution
Validation Verification
EDGAR 4.2 + LPJa priori bottom-up emissions
Optimization of methane emissions using SCIAMACHY data for Jul-Aug 2004
Concurrent INTEX-A aircraft data allow SCIAMACHY validation, evaluation of inversion
SCIAMACHY column methane mixing ratio XCH4 INTEX-A methane below 850 hPa
INTEX-A validation profiles H2O correction to SCIAMACHY data
Wecht et al., in prep.
C. Frankenberg(JPL)
SCIA
MAC
HY
INTEX-A
XCH4
D. Blake(UC Irvine)
C. Frankenberg(JPL)
Global and nested simulations with a priori emissions
Model mean methane for Jul-Aug 2004 (background) and NOAA data (circles)
Wecht et al., in prep.
4ox5o 1/2o2/3o
Time-dependent boundary conditionsare optimized iteratively as part of the inversion
Adjoint-based inversion allows optimization of emissions at native resolution of forward model;
but this may not be justified by information content of observations
Optimization of state vector for adjoint inversion of SCIAMACHY data
Optimal clustering of 1/2ox2/3o gridsquares
Correction factor to bottom-up emissions
Number of clusters in inversion1 10 100 1000 10,000
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Optimized US emissions (Tg a-1)
posterior cost function
Native resolution 1000 clusters
SCIAMACHY data cannot constrain emissions at 1/2ox2/3o resolution; reduce to 1000 clusters
Wecht et al., in prep.
Independent verification with INTEX-A aircraft data
A prioriemissions
Optimizedemissions
GEOS-Chem simulation of INTEX-A aircraft observations below 850 hPa:
with a priori emissions with optimized emissions
Wecht et al., in prep.
Tg CH4 a-1
North American methane emission estimatesoptimized by SCIAMACHY + INTEX-A data (Jul-Aug 2004)
1700 1800ppb
SCIAMACHY column methane mixing ratio Correction factors to a priori emissions
Livestock Oil & Gas Landfills Coal Mining Other0
5
10
15US anthropogenic emissions (Tg a-1)
EDGAR v4.2 26.6
EPA 28.3
This work 32.7
Wecht et al., in prep.
1000 clusters
Livestock emissions are underestimated by EDGAR/EPA, oil/gas emissions are not
Working with stakeholders at the US state level
State-by-state analysis of SCIAMACHY correction factors to EDGARv4.2 emissions
with Iowa Dept. of Natural Resources (Marnie Stein)
State emissions computed w/EPA tools too low by x3.5;now investigating EPA livestock emission factors
with New York Attorney General Office (John Marschilok)State-computed emissions too high by x0.6,reflects overestimate of gas/waste/landfill emissions
Melissa Sulprizio and Kevin Wecht, Harvard
Hog manure?
Large EDGAR source from gas+landfillsis just not there
0 1 2correction factor
GOSAT methane column mixing ratios, Oct 2009-2010
Retrieval from U. Leicester
Inversion of GOSAT Oct 2009-2010 methane
Nested inversionwith 50x50 km2 resolution
Correction factors to prior emissions (EDGAR 4.2 + LPJ)
Alex Turner, Harvard
Need to cluster emissions in the inversion, use new NASA retrieval
Constraining methane emissions in CaliforniaStatewide greenhouse gas emissions must decrease to 1990 levels by 2020
EDGAR v4.2 emissions and patterns for 2010 (Tg a-1)compared to state estimates from California Air Resources Board (CARB)
Wecht et al., in prep.
CARB: 1.51
CARB: 0.86CARB: 0.18
CARB: 0.39
CalNex inversion of methane emissions in CaliforniaCalNex aircraft observations GEOS-Chem w/EDGAR v4.2 Correction factors to EDGAR
May-Jun2010
Wecht et al., in prep.
California emissions (Tg a-1)
G. Santoni (Harvard)
May-Jun2010
EDGAR v4.2 1.92
This work 2.86 ± 0.21
CARB 1.51
Santoni et al.
STILT inversion 2.37 ± 0.27
State totals
Livestock Gas/oil Landfills Other0
0.20.40.60.8
11.21.4
What is the information content from the inversion?
ˆ Ax = Ax + (I - A)x +Gεsolution = truth + smoothing + noise
averaging kernel matrix a priori
Here x is the state vector of emissions (n = 157)
Diagonal elements of ˆ / A x x
• Diagonal elements of A range from 0 (no constraint from observations) to 1 (no constraint from a priori)
• Degrees Of Freedom for Signal (DOFS) = tr(A) = total # pieces of information constrained by inversion
GOSAT observations of methane are too sparseto constrain California emissions except in LA Basin
GOSAT data (CalNex period)Correction factors
to EDGAR emissions
Each point =1-10 observations
0.5 1.5
Wecht et al., in prep.
• Constraints on emissions in LA Basin are consistent with CalNex
diagonal elements of A
Potential of future satellites (TROPOMI, geostationary) for constraining spatial distribution of methane emissions
TROPOMI will provide information comparable to a continuous CalNex; a geostationary satellite instrument will provide even more
Wecht et al., in prep.
Diagonal elements of A