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

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.

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