1. 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

2. Satellite instruments providing CO data
MOPITT
AIRS
SCIAMACHY
TES
100
Averaging kernels
1000
800
600
400
200
100
Pressure (mb)
300
500
700
1000
Averaging kernels
Averaging kernels
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

3. Available satellite CO (column) data
May 2004 averages (on 2° x 2.5° resolution)
MOPITT
AIRS
SCIA Bremen
TES (2006)
1018molec/cm2
CO columns expected to be different due to different vertical sensitivity, but are they consistent?

4. 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

5. 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

6. Model: satellite correlations4 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
*TES data start at the end of September 2004
Unit: 1018 molec/cm2

7. Amount of a priori information in model-satellite correlations4 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 4 3 2 1
r2 = slope 0.88
r2 = 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

8. Amount of a priori information in model-satellite correlations4 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 4 3 2 1
r2 = slope 0.89
r2 = 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 8

9. 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

10. 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

11. 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

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