Adjoint inversion of CO sources using combined MOPITT, SCIAMACHY and AIRS CO columns Monika Kopacz, Daniel Jacob, Jenny Fisher, Meghan Purdy Michael Buchwitz, Iryna Khlystova, John Burrows, (SCIA Bremen) Annemieke Gloudemans, Jos de Laat (SCIA SRON), W. Wallace McMillan (AIRS) COSPAR Montreal, July 18, 2008

Need better understanding of CO sources tracer of combustion pollution precursor to tropospheric O3 (smog) correlations between CO and CO2 can help improve CO2 flux estimates Suntharalingam et al. [2004] IPCC [2007] CO emissions contribute to radiative forcing (indirect greenhouse gas)

Observational constraints on CO sources Top-down constraints Bottom-up emission estimates Chemical Transport Model (CTM) model EMISSIONS CONCENTRATIONS ?= observations Inverse model

Satellite instruments providing CO data SCIAMACHY AIRS MOPITT 100 200 200 200 400 400 400 Pressure (mb) 600 600 600 800 800 800 1000 1000 1000 0 0.1 0.2 0.3 Averaging kernels 0 0.2 0.4 0.6 Averaging kernels 0.2 0.6 1.0 1.4 Averaging kernels validated data product (~5% high bias) sensitive throughout the column extremely dense coverage advantages v7.4 (SRON retrieval), v0.6 (Bremen retrieval) v5 retrieval v5 retrieval

satellite CO column data MOPITT: 1999 – 2007, SCIAMACHY: 2003 – 2005, AIRS: 2002-present May 2004 averages (on 2° x 2.5° resolution) MOPITT AIRS SCIA Bremen SCIA SRON 0 0.88 1.75 2.62 3.50 1018molec/cm2 CO columns expected to be different due to different vertical sensitivity, but are they consistent?

Chemical Transport Model (CTM): the comparison platform satellite 1 satellite 2 satellite 3 SATELLITE DATA in situ observations TRUTH but very sparse in time and space

global Chemical Transport Model, comparison platform Chemical Transport Model (CTM): the comparison platform satellite 1 satellite 2 satellite 3 SATELLITE DATA global Chemical Transport Model, comparison platform in situ observations TRUTH but very sparse in time and space

Chemical Transport Model (CTM): the comparison platform developed at Harvard University Eulerian model, solves continuity equation for individual gridboxes GEOS-Chem CTM contains detailed chemical/aerosol mechanism horizontal resolution is 2° x 2.5° and vertical resolution is ~ 1 km, temporal resolution is 15 min uses NASA/Goddard data assimilated meteorology

Satellite consistency (via GEOS-Chem CTM): 4 3 2 1 r2 = 0.65 r2 = 0.73 GEOS-Chem May 2004 – May 2005 global daytime columns (averaged to 2°x2.5° resolution) MOPITT AIRS 4 3 2 1 Green line: 1:1 Red line: Reduced Major Axis regression r2 = 0.24 r2 = 0.29 Model/satellite slope: MOPITT: 0.73 AIRS: 0.76 SCIA Bremen: 0.61 SCIA SRON: 0.63 GEOS-Chem SCIA Bremen SCIA SRON 0 1 2 3 4 0 1 2 3 4 All units: 1018 molec/cm2

Satellite consistency (via GEOS-Chem): 4 3 2 1 r2 = 0.65 r2 = 0.73 GEOS-Chem May 2004 – May 2005 global daytime columns (averaged to 2°x2.5° resolution) MOPITT AIRS 4 3 2 1 Green line: 1:1 Red line: Reduced Major Axis regression r2 = 0.24 r2 = 0.56 r2 = 0.34 Model/sat slope: MOPITT: 0.73 AIRS: 0.76 SCIA Bremen: 0.61 SCIA SRON: 0.63 GEOS-Chem MONTHLY MEAN MONTHLY MEAN SCIA Bremen SCIA SRON 0 1 2 3 4 0 1 2 3 4 All units: 1018 molec/cm2

Seasonal variability of CO in datasets Averaged CO columns over N. America 3.0 MOPITT SCIA Bremen (5d mean) 2.5 Buchwitz et al. [2007] AIRS 1018 molec/cm2 2.0 MOPITT and AIRS are consistent in seasonal variation, except in spring 1.5 SCIA SRON (5d mean) 1.0 May July Sept Nov Jan Mar May

Estimating CO sources with an inverse model GEOS-Chem CO column: F( • ) satellite CO column: y ≠ 0 0.88 1.75 2.62 3.50 1018molec/cm2 a priori sources: xa + εa a posteriori sources May 2004 satellite data (0 – 60N) : y + εo model concentrations: F(xa) + εm observation error: εe minimize total mismatch (model-obs)2 (opt source – a priori source)2

Schematic of adjoint source inversion 4°x5° resolution start forward model (GEOS-Chem) MOPITT, SCIA, AIRS obs , y initialization MOPITT May 1, 2004 Improved x end L-BFGS optimization algorithm adjoint model a priori state vector xa 4°x5° resolution

CO emission inventories (xa) Fossil fuel May 2004 Anthropogenic: EDGAR (global), BRAVO (Mexico), EMEP (Europe), NEI99 (US), Streets (Asia) Biomass burning: GFED2 (global) Biomass burning Biofuel 1012 molec/cm2/s 0.50 1.00 1.50 2.00

Observational errors RRE = MOPITT SCIAMACHY Bremen AIRS Relative Residual Error (RRE) RRE = MOPITT Observations used: 0-60N 0 8 15 23 30 % SCIAMACHY Bremen AIRS 0 12 25 38 50 % 0 5 10 15 20 %

Inversion results: CO emission constraints min Do combined datasets provide better CO constraints than individual ones? m=28,590 obs m=2,416 obs m=13,138 obs a posteriori MOPITT-model mismatch is lower in a joint inversion SCIAMACHY data does not contribute to model bias improvement

Constraints from individual datasets AIRS MOPITT Major features of correction factors consistent in all inversions: increase in California and N. Mexico increase in India increase in E. China decrease of W. Siberia biomass burning SCIAMACHY (Bremen) 0.0 0.5 1.0 1.5 2.0 overestimate underestimate

Joint inversion constraints a priori model bias (w/ MOPITT) Joint inversion constraints Joint MOPITT-AIRS inversion a posteriori model bias (w/ MOPITT) 0.0 0.5 1.0 1.5 2.0 Conclusions: AIRS provides valuable information over outflow/ocean region few MOPITT obs. over India and SE Asia  could explain stronger emiss. corrections from AIRS SCIAMACHY constraints consistent with AIRS and MOPITT inversion with MOPITT data only a posteriori model bias (w/ MOPITT) inversion with MOPITT + AIRS data -0.50 -0.25 0.0 0.25 0.50

Acknowledgements: MOPITT data provided by the MOPITT retrieval team Funding provided by END

Temporal variations in data and GEOS-Chem CTM ___SCIA SRON ___SCIA Bremen ___AIRS ___MOPITT Buchwitz et al. [2007] GEOS-Chem columns over N. America Lines: GEOS-Chem + averaging kernels Symbols: daily averaged satellite data ___SCIA SRON ___SCIA Bremen ___AIRS ___MOPITT ___ no AK averaging kernels decrease the amplitude of seasonal cycle MOPITT and AIRS appear consistent May 1, 2004 Nov. 1, 2004 May 1, 2005