a tale of two near-term climate forcers: black carbon and methane daniel j. jacob
DESCRIPTION
A tale of two near-term climate forcers: black carbon and methane Daniel J. Jacob. w ith Qiaoqiao Wang, Kevin Wecht , Alex Turner, Melissa Sulprizio. BC exported to the free troposphere is a major component of BC direct radiative forcing. Integral contribution To BC forcing. Global mean - PowerPoint PPT PresentationTRANSCRIPT
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A tale of two near-term climate forcers:black carbon and methane
Daniel J. Jacob
with Qiaoqiao Wang, Kevin Wecht, Alex Turner, Melissa Sulprizio
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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
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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
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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
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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
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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
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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
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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)
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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!
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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?
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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
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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)
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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
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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)
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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
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Adjoint-based inversion allows optimization of emissions at native resolution of forward model;
but this may not be justified by information content of observations
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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
34
28
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.
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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
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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
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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
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GOSAT methane column mixing ratios, Oct 2009-2010
Retrieval from U. Leicester
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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
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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
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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
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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
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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
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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