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

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

3. 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) Observed Models Large overestimate must reflect model errors in scavenging Free tropospheric BC in AeroCom models is ~10x too high Pressure, hPa obs models 60-80N obs models 20S-20N Pressure, hPa HIPPO over Pacific (Jan) BC, ng kg -1 This has major implications for IPCC radiative forcing estimates

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 BC concentrations span x10 5 Mean BC columns span x10 3 An extraordinary range of variability! Latitude Oct-Nov 2009 Mar-Apr 2010 Jun-Jul 2011 Aug-Sep 2011 Jan 2009

5. Previous application to Arctic spring (ARCTAS) CCN Cloud updraft scavenging Large scale precipitation Anvil 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 210 Pb 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

6. GEOS-Chem BC simulation: source regions and outflow NMB= -27% NMB= -12% NMB= 6.6% Observations (circles) and model (background) surface networks AERONET BC AAOD NMB= -32% Aircraft profiles in continental/outflow regions HIPPO (US) Arctic (ARCTAS) Asian outflow (A-FORCE) US (HIPPO) observed model Wang et al., submitted Normalized 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

7. 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

8. BC top-of-atmosphere direct radiative forcing (DRF) 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 work6.50.15 [8.7%]0.17880.19 (0.17-0.31) AeroCom [2006] 7.8 ±0.40.28 ± 0.08 [21±11%] 0.22±0.10168 ± 530.34 ± 0.07 Chung et al. [2012] 0.77840.65 Bond et al. [2013] 170.550.601470.88 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 X Mass absorption coefficient X Forcing efficiency Global load Absorbing aerosol optical depth (AAOD)

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 CO 2 Climate impact of methane is comparable to CO 2 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. AIRS, TES, IASI Methane observing system in North America Satellites 2002 2006 2009 20015 2018 Thermal IR SCIAMACHY 6-day GOSAT 3-day, sparse TROPOMI GCIRI 1-day geo Shortwave IR Suborbital CalNex INTEX-A SEAC 4 RS 1/2 o x2/3 o grid of GEOS-Chem chemical transport model (CTM)

13. High-resolution inverse analysis system for quantifying methane emissions in North America GEOS-Chem CTM and its adjoint 1/2 o x2/3 o over N. America nested in 4 o x5 o global domain Observations Bayesian inversion Optimized emissions (“state vector”) at up to 1/2 o x2/3 o resolution Validation Verification EDGAR 4.2 + LPJ a priori bottom-up emissions

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 X CH4 INTEX-A methane below 850 hPa INTEX-A validation profiles H 2 O correction to SCIAMACHY data Wecht et al., in prep. C. Frankenberg (JPL) SCIAMACHY INTEX-A X CH4 D. Blake (UC Irvine) C. Frankenberg (JPL)

15. 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. 4 o x5 o 1/2 o 2/3 o Time-dependent boundary conditions are optimized iteratively as part of the inversion

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 1/2 o x2/3 o gridsquares Correction factor to bottom-up emissions Number of clusters in inversion 1 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 1/2 o x2/3 o resolution; reduce to 1000 clusters Wecht et al., in prep.

18. Independent verification with INTEX-A aircraft data A priori emissions Optimized emissions GEOS-Chem simulation of INTEX-A aircraft observations below 850 hPa: with a priori emissions with optimized emissions Wecht et al., in prep. Tg CH 4 a -1

19. North American methane emission estimates optimized by SCIAMACHY + INTEX-A data (Jul-Aug 2004) 17001800 ppb SCIAMACHY column methane mixing ratio Correction factors to a priori emissions US 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

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 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+landfills is just not there 0 1 2 correction factor

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

22. Inversion of GOSAT Oct 2009-2010 methane Nested inversion with 50x50 km 2 resolution Correction factors to prior emissions (EDGAR 4.2 + LPJ) 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) Wecht et al., in prep. CARB: 1.51 CARB: 0.86CARB: 0.18 CARB: 0.39

24. CalNex inversion of methane emissions in California CalNex aircraft observationsGEOS-Chem w/EDGAR v4.2 Correction factors to EDGAR May-Jun 2010 Wecht et al., in prep. California emissions (Tg a -1 ) G. Santoni (Harvard) May-Jun 2010 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

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

26. GOSAT observations of methane are too sparse to constrain California emissions except in LA Basin GOSAT data (CalNex period) Correction factors to EDGAR emissions Each point = 1-10 observations 0.51.5 Wecht et al., in prep. Constraints on emissions in LA Basin are consistent with CalNex diagonal elements of A

27. 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