A tale of two near-term climate forcers: black carbon and methane Daniel J. Jacob

1. A tale of two near-term climate forcers:black carbon and methaneDaniel J. Jacob with Qiaoqiao Wang, Kevin Wecht, Alex Turner, Melissa Sulprizio

2. BC exported to the free troposphereis a major component of BC direct radiative forcing Integral contribution To BC forcing Global mean BC profile (Oslo CTM) • • • Export to free troposphere deep convection BC forcing efficiency • 50% from BC > 5 km • • scavenging • • frontal lifting • • • • • • • • • • • • • • • • • • • • • • • • • • BC source region (combustion) Ocean Samset and Myhre [2011]

3. Multimodelintercomparison and comparison to observations Multimodel intercomparisons and comparisons to observations Free tropospheric BC in AeroCom models is ~10x too high This has major implications for IPCC radiative forcing estimates TC4 (Costa Rica, summer) Observed Models Pressure, hPa • Large overestimate must reflect • model errors in scavenging BC, ng kg-1 HIPPO over Pacific (Jan) 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]

4. HIPPO deployments across the Pacific “pole-to-pole” aircraft curtains from boundary layer to tropopause NOAA SP2 BC measurements (D. Fahey) NCAR GV aircraft Jan 2009 Oct-Nov 2009 • BC concentrations • span x105 • Mean BC columns • span x103 Mar-Apr 2010 Jun-Jul 2011 An extraordinary range of variability! Aug-Sep 2011 Latitude

5. GEOS-Chemaerosol scavenging scheme Previous application to Arctic spring (ARCTAS) IN+CCN Dealing with freezing/frozen clouds is key uncertainty CCN Anvil precipitation Cloud updraftscavenging Large scale precipitation CCN+IN, impaction • Below-cloud scavenging (accumulation mode aerosol), • different for rain and snow entrainment • 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 detrainment • 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)

6. GEOS-Chem BC simulation: source regions and outflow BC source (2009): 4.9 Tg a-1 fuel + 1.6 Tg a-1 open fires Observations (circles) and model (background) Normalized mean bias (NMB) in range of -30% to +10% NMB= -27% Wang et al., submitted surface networks AERONET BC AAOD NMB= 6.6% NMB= -32% Aircraft profiles in continental/outflow regions HIPPO (US) Asian outflow (A-FORCE) US (HIPPO) observed model Arctic (ARCTAS) NMB= -12%

7. 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., submitted

8. 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 load • 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

9. 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!

10. Building a methane monitoring system for N America EDGAR emission Inventory for methane Can we use satellites together with suborbital observations of methane to monitor methane emissions on the continental scale?

11. 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

12. Methane observing system in North America Satellites AIRS, TES, IASI Thermal IR TROPOMI GCIRI 1-day geo GOSAT 3-day, sparse SCIAMACHY 6-day Shortwave IR 2002 2006 2009 20015 2018 Suborbital 1/2ox2/3o grid of GEOS-Chem chemical transport model (CTM) INTEX-A SEAC4RS CalNex

13. High-resolution inverse analysis system for quantifying methane emissions in North America Observations EDGAR 4.2 + LPJ a priori bottom-up emissions GEOS-Chem CTM and its adjoint 1/2ox2/3o over N. America nested in 4ox5o global domain Bayesian inversion Validation Verification Optimized emissions (“state vector”) at up to 1/2ox2/3o resolution

14. 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 C. Frankenberg (JPL) C. Frankenberg (JPL) D. Blake (UC Irvine) XCH4 INTEX-A validation profiles H2O correction to SCIAMACHY data SCIAMACHY INTEX-A Wecht et al., in prep.

15. Global and nested simulations with a priori emissions Model mean methane for Jul-Aug 2004 (background) and NOAA data (circles) 4ox5o 1/2o2/3o Time-dependent boundary conditions are optimized iteratively as part of the inversion Wecht et al., in prep.

16. Adjoint-based inversion allows optimization of emissions at native resolution of forward model;but this may not be justified by information content of observations

17. Optimization of state vector for adjoint inversion of SCIAMACHY data Optimal clustering of 1/2ox2/3ogridsquares Native resolution 1000 clusters 34 Optimized US emissions (Tg a-1) Correction factor to bottom-up emissions posterior cost function 28 SCIAMACHY data cannot constrain emissions at 1/2ox2/3o resolution; reduce to 1000 clusters Number of clusters in inversion 1 10 100 1000 10,000 Wecht et al., in prep.

18. Independent verification with INTEX-A aircraft data Optimized emissions A priori emissions Tg CH4 a-1 GEOS-Chemsimulation of INTEX-A aircraft observations below 850 hPa: with a priori emissions with optimized emissions Wecht et al., in prep.

19. North American methane emission estimates optimized by SCIAMACHY + INTEX-A data (Jul-Aug 2004) SCIAMACHY column methane mixing ratio Correction factors to a priori emissions 1000 clusters ppb 1700 1800 EDGAR v4.2 26.6 EPA 28.3 This work 32.7 US anthropogenic emissions (Tg a-1) Livestock emissions are underestimated by EDGAR/EPA, oil/gas emissions are not Wecht et al., in prep.

20. 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 Hog manure? 0 1 2 correction factor with New York Attorney General Office (John Marschilok) State-computed emissions too high by x0.6, reflects overestimate of gas/waste/landfill emissions Large EDGAR source from gas+landfills is just not there Melissa Sulprizio and Kevin Wecht, Harvard

21. GOSAT methane column mixing ratios, Oct 2009-2010 Retrieval from U. Leicester

22. Inversion of GOSAT Oct 2009-2010 methane Correction factors to prior emissions (EDGAR 4.2 + LPJ) Nested inversion with 50x50 km2 resolution Alex Turner, Harvard Need to cluster emissions in the inversion, use new NASA retrieval

23. Constraining methane emissions in California Statewide 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) CARB: 0.39 CARB: 1.51 CARB: 0.18 CARB: 0.86 Wecht et al., in prep.

24. CalNex inversion of methane emissions in California Correction factors to EDGAR CalNex aircraft observations GEOS-Chem w/EDGAR v4.2 May-Jun 2010 May-Jun 2010 G. Santoni (Harvard) State totals California emissions (Tg a-1) EDGAR v4.2 1.92 This work 2.86 ± 0.21 CARB 1.51 Santoni et al. STILT inversion 2.37 ± 0.27 Wecht et al., in prep.

25. What is the information content from the inversion? Here x is the state vector of emissions (n = 157) a priori averaging kernel matrix solution = truth + smoothing + noise • 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 Diagonal elements of

26. GOSAT observations of methane are too sparse to constrain California emissions except in LA Basin Correction factors to EDGAR emissions GOSAT data (CalNex period) diagonal elements of A 0.5 1.5 Each point = 1-10 observations • Constraints on emissions in LA Basin are consistent with CalNex Wecht et al., in prep.

27. Potential of future satellites (TROPOMI, geostationary) for constraining spatial distribution of methane emissions Diagonal elements of A TROPOMI will provide information comparable to a continuous CalNex; a geostationary satellite instrument will provide even more Wecht et al., in prep.