Aura Science Team Meeting CONSISTENCY among MOPITT, SCIAMACHY, AIRS and TES measurements of CO using the GEOS-Chem model as a comparison platform Motivation: towards estimating CO sources Monika Kopacz, Jenny Fisher, Daniel Jacob, Jennifer Logan, Lin Zhang, Meghan Purdy Michael Buchwitz, Iryna Khlystova, John Burrows, (SCIA Bremen), Annemieke Gloudemans, Jos de Laat (SCIA SRON), W. Wallace McMillan (AIRS) Aura Science Team Meeting Columbia, MD, October 30, 2008

Satellite instruments providing CO data MOPITT AIRS SCIAMACHY TES 100 0 0.2 0.4 0.6 Averaging kernels 1000 800 600 400 200 100 Pressure (mb) 300 500 700 1000 0 0.1 0.2 0.3 Averaging kernels 0.2 0.6 1.0 1.4 Averaging kernels 0 0.1 0.3 Averaging kernels validated data product (~5% high bias) sensitive throughout the column provides O3 data extremely dense (daily) coverage v7.4 (SRON retrieval), v0.6 (Bremen retrieval) v3 retrieval v5 retrieval v2 retrieval

Available satellite CO (column) data May 2004 averages (on 2° x 2.5° resolution) MOPITT AIRS SCIA Bremen TES (2006) 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 global Chemical Transport Model (CTM) in situ observations TRUTH but very sparse in time and space

GEOS-Chem Chemical Transport Model (CTM): the comparison platform Chemistry: detailed chemical mechanism Meteorology: NASA/Goddard data assimilated meteorology Resolution: horizontal 2° x 2.5°, vertical ~ 1 km, temporal 15 min Compare with in situ data Compare with satellite data 200 150 100 50 MOPITT CO columns model data MOZAIC 200 150 100 50 GMD GEOS-Chem+ MOPITT AK 300 200 100 Vienna

Model: satellite correlations 4 3 2 1 r2 = 0.65 r2 = 0.73 May 2004 – May 2005 global daytime columns (averaged on 2x2.5 resolution) GEOS-Chem CTM Red line: Reduced Major Axis regression MOPITT AIRS 4 3 2 1 r2 = 0.83 r2 = 0.24 r2 = 0.29 GEOS-Chem CTM GEOS-Chem CTM TES* SCIA Bremen SCIA SRON 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 *TES data start at the end of September 2004 Unit: 1018 molec/cm2

Amount of a priori information in model-satellite correlations 4 3 2 1 r2 = 0.65 slope 0.76 r2 = 0.73 slope 0.71 GEOS-Chem CTM Measure of information content: degrees of freedom (DOFs) MOPITT AIRS 0.5 1.0 1.5 0.5 1.0 1.5 4 3 2 1 r2 = 0.83 slope 0.88 r2 = 0.65 slope 0.74 GEOS-Chem CTM Note: DOFs not available for SCIA; reprocessing with MOPITT a priori does not change SCIA correlations TES w/ MOPITT a priori TES 0.5 1.0 1.5 0.5 1.0 1.5 0 1 2 3 4 0 1 2 3 4

Amount of a priori information in model-satellite correlations 4 3 2 1 r2 = 0.65 slope 0.76 r2 = 0.73 slope 0.71 GEOS-Chem CTM Measure of information content: degrees of freedom (DOFs) MOPITT AIRS 0.5 1.0 1.5 0.5 1.0 1.5 4 3 2 1 r2 = 0.85 slope 0.89 r2 = 0.63 slope 0.70 GEOS-Chem CTM Note: DOFs not available for SCIA; reprocessing with MOPITT a priori does not change SCIA correlations TES w/ MOPITT a priori TES 0.5 1.0 1.5 0.5 1.0 1.5 0 1 2 3 4 0 1 2 3 4 8

Seasonal variability of CO in datasets and model satellite CO columns averaged over NH Seasonal variability of CO in datasets and model 3.0 MOPITT SCIA Bremen 2.5 AIRS 2.0 1018 molec/cm2 spring deviation partly due to differences in vertical sensitivity TES 1.5 SCIA SRON 1.0 May’04 July Sept Nov Jan Mar May 2.2 GEOS-Chem CO columns averaged over NH w/ SCIA SRON AK, SCIA Bremen AK, AIRS AK, MOPITT AK, TES AK, GEOS-Chem w/ no AK 2.0 1018 molec/cm2 1.8 1.6 May’04 July Sept Nov Jan Mar May

Time and space consistency of datasets (data-model) % difference = model % data-model difference Consistency: Spring CO >> non spring CO in all datasets MOPITT CO ~ AIRS CO Inconsistencies: TES differs from MOPITT or AIRS SCIA Bremen and SCIA SRON differ all data NH spring

Implications for further use of data vs. MOPITT and AIRS are very consistent, but differ slightly over ocean Small inconsistencies in the interhemispheric gradient SCIA Bremen dataset has valuable info in the boundary layer, despite noise; quite consistent with MOPITT and AIRS SCIA SRON is very different from SCIA Bremen and does not appear useful for source inversion TES (or its Averaging Kernels?) are systematically different from MOPITT and AIRS Data requirements: Each (datai-model) difference provides consistent CO source constraints, not necessarily data1 = data2

Acknowledgements: NASA funding (graduate fellowship), MOPITT and TES teams for providing data END