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

2. Radiative forcing of climate change Solar flux F in Terrestrial flux F out ~ T 4 Global radiative equilibrium: F in = F out Perturb greenhouse gases or aerosols radiative forcing RF = F in - F out Global equilibrium surface temperature responds as  T ~ RF

3. Radiative forcing referenced to emissions, 1750-2011 Radiative forcing from methane emissions is 0.97 W m -2, compared to 1.68 W m -2 for CO 2 Radiative forcing from black carbon aerosol (BC) is 0.65 W m -2, highly uncertain Together methane and BC have radiative forcing comparable to CO 2 But atmospheric lifetimes of methane (10 years) and BC (~1 week) are shorter than CO 2 (> 100 years) [IPCC, 2014]

4. Metrics of climate response to a radiative forcing agent Atmospheric lifetime: CO 2 13 yrs 1.5 yrs Global Warming Potential (GWP): integrated forcing over time horizon t = H for 1-kg instantaneous emission at time t = 0 [IPCC, 2014] Global Temperature Potential (GTP): Mean surface temperature change at t = H

5. Controlling methane and BC should be part of climate policy … but for reasons totally different than CO 2 It addresses climate change on time scales of decades – which we care about It offers decadal-scale results for accountability of climate policy It is an alternative to geoengineering by aerosols It has important air quality co-benefits BC has additional regional, hydrological impacts Trend in Arctic sea ice volume Geoengineering: cloud seeeding

6. Black carbon in the atmosphere diesel engines residential fuel open fires freshly emitted BC particle Global BC emission [Wang et al., 2014] Loss of BC is by wet deposition (lifetime ~ 1 week)

7. BC exported to upper troposphere is major component of forcing frontal lifting deep convection scavenging BC source region (combustion) Ocean Export to upper troposphere Global mean BC profile (chemical transport model) BC forcing efficiency Integral contribution To BC forcing Samset and Myhre [2011] 50% from BC > 5 km …because it’s above white clouds instead of dark surface

8. Multimodel intercomparisons and comparisons to observations Koch et al. [2009], Schwarz et al. [2010] BC, ng kg -1 TC4 aircraft campaign (Costa Rica) Observed Models Such large overestimate must be due to model errors in scavenging AeroCom chemical transport models (CTMs) used by IPCC overestimate BC by order of magnitude in upper troposphere Pressure, hPa obs models 60-80N obs models 20S-20N Pressure, hPa HIPPO aircraft campaign over Pacific BC, ng kg -1

9. Previous application to Arctic spring (ARCTAS) CCN Cloud updraft scavenging Large scale precipitation Anvil precipitation IN+CCN entrainment detrainment BC/aerosol scavenging in GEOS-Chem CTM used at Harvard CCN+IN, impaction Meteorological data including convective mass fluxes from NASA GEOS assimilation system Aerosols are scavenged in cloud by similarity with condensed water Additional scavenging below cloud by rain/snow In-cloud scavenging efficiency from freezing/frozen clouds is highly uncertain Additional uncertainty for BC is its efficiency as cloud condensation nucleus (CCN) and ice nucleus (IN) BC lifetime in GEOS-Chem is 4 days (vs. 7±2 days in AeroCom models)

10. GEOS-Chem BC simulation: source regions and outflow NMB= -27% NMB= -12% NMB= 6.6% Observations (circles) and model (background) surface networks AERONET BC optical depth NMB= -32% Aircraft profiles in continental/outflow regions HIPPO (US) Arctic (ARCTAS) Asian outflow (A-FORCE) US (HIPPO) observed model Wang et al., 2014 Normalized mean bias (NMB) in range of -30% to +10% Tests sources, export

11. 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., 2014 Observed Model PDF PDF, (mg m -3 STP) -1

12. 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 Bond et al. [2013] 170.550.601470.88 Our best estimate of 0.19 W m -2 is much lower than IPCC recommendation of 0.65 (0.25-1.1) W m -2 IPCC value is from models that greatly overestimate BC in upper troposphere Wang et al., 2014 DRF = Emissions X Lifetime X Mass absorption coefficient X Forcing efficiency Global atmospheric load Absorbing aerosol optical depth (AAOD) BC is much less important for climate forcing than stated in IPCC

13. Atmospheric methane: long-term trends are not understood Source attribution is difficult due to diversity, complexity of sources Livestock 90 Landfills 70 Gas 60 Coal 40 Rice 40 Other natural 40 Wetlands 180 Fires 50 Global sources, Tg a -1 Individual sources uncertain by at least factor of 2; emission factors are highly variable, poorly constrained the last 1000 years the last 30 years E. Dlugokencky, NOAA

14. Satellite data as constraints on methane emissions “Bottom-up” emissions (EDGAR): best understanding of processes 2009-2011 537 Tg a -1 Satellite data for methane columns Optimal estimate inversion using GEOS-Chem model adjoint Ratio of optimal estimate to bottom-up emissions Turner et al., submitted

15. Basics of inverse modeling Optimize state vector x (emissions) using obs vector y (atm. concentrations) Prior estimate x A + ε A Bottom-up inventory Forward model y M = F(x) + ε M GEOS-Chem chemical transport model Observations y + ε I atmospheric concentrations from satellite, aircraft Posterior estimate Minimize cost function with error weighting, x A regularization Analytical or numerical (variational) method

16. Using satellite data for high-resolution inversion of methane emissions in North America EDGAR emission Inventory for methane

17. Bottom-up methane emissions for N. America (2009-2011) total: 63 Tg a -1 wetlands: 20 oil/gas: 11livestock: 14 waste: 10coal: 4 CONUS anthropogenic emissions: 25 Tg a -1 (EDGAR) 27 Tg a -1 (EPA) 8 oil/gas 9 livestock 6 waste 3 coal Turner et al., submitted

18. Global inversion of GOSAT data feeds boundary conditions for North American inversion GOSAT observations, 2009-2011 Adjoint-based inversion at 4 o x5 o resolution Dynamic boundary conditions Analytical inversion with 369 Gaussians Turner et al., submitted correction factors to EDGAR v4.2 + LPJ prior

19. Correction factors to bottom-up inventory CONUS anthropogenic emission of 40-43 Tg a -1 vs. EPA value of 27 Tg a -1 Livestock source is underestimated by EPA; What about oil/gas? Turner et al., submitted

20. Methane emissions in CONUS: comparison to previous studies, attribution to source types EPA national inventory underestimates anthropogenic emissions by 30% Livestock is a contributor: oil/gas production probably also Ranges from prior error assumptions Turner et al., submitted 2004 satellite 2007 surface, aircraft 2009-2011 satellite

21. Construction of a 0.1 o x0.1 o monthly gridded version of the EPA national bottom-up inventory Use monthly state/county/GGRP/algorithm info from EPA, further distribute with data from other sources (USDA, EIA, DrillingInfo,…) Done as collaboration between Harvard and EPA Climate Change Division Provide improved prior for inversions and feedback to guide improvement in bottom-up inventory EPA livestock enteric emissions. 2012 Livestock (enteric) EPA, 2012 Maasakkers et al., in progress Livestock (enteric) EDGAR, 2010

22. Construction of a 0.1 o x0.1 o monthly gridded version of the EPA national bottom-up inventory Livestock (manure) EPA, 2012 Livestock (manure) EDGAR, 2010 Maasakkers et al., in progress Use monthly state/county/GGRP/algorithm info from EPA, further distribute with data from other sources (USDA, EIA, DrillingInfo,…) Done as collaboration between Harvard and EPA Climate Change Division Provide improved prior for inversions and feedback to guide improvement in bottom-up inventory

23. Future of satellite observations for methane monitoring GOSAT (2009-): high-quality 5x5 km 2 pixels but sparse TROPOMI (2016 launch): global daily coverage with 7x7 km 2 pixels Geostationary (proposed): hourly coverage over N America with 2x2 km 2 pixels Methane is readily observable over land by solar backscatter at 1.6/2.3 µm Scattering by Earth surface Backscattered intensity I B absorption wavelength   Methane column 