1. CONSISTENCY among MOPITT, SCIA, AIRS and TES measurements of CO using the GEOS-Chem model as a comparison platform
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)
MOPITT and TES data provided by the respective retrieval teams for data
IWGGMS
Pasadena, June 24, 2008
Funding provided by

2. Carbon Monoxide (CO) observing CO from space is much easier than CO2 !
…as relevant to IWGGMS
important and uncertain indirect greenhouse gas
correlations between CO and CO2 can help improve CO2 flux estimates (H. Wang’s talk during OCO STM)
IPCC [2007]
Accurate CO estimates AND experience with measuring CO from space are very valuable
observing CO from space is much easier than CO2 !
Suntharalingam et al. [2004]

3. Satellite instruments providing (daytime) CO column measurements
relatively unexplored, provides collocated information on tropospheric O3
extremely dense coverage (daily global!), v5 retrieval not used so far
sensitive throughout the column, large errors, relatively unexplored
validated data product, global coverage every 3 days, used in inversions and comparisons previously

4. Available satellite CO (column) data
May 2004
MOPITT
AIRS
SCIA Bremen
TES (2006)
1018molec/cm2
CO columns expected to be different due to different vertical sensitivity

5. Scientific questions: (1) Are the satellite datasets consistent
Scientific questions: (1) Are the satellite datasets consistent? (Can we treat them as one big dataset for flux inversion etc?)
(2) What are the biases and how can we account for them? ?= ?= ?=
Data selection: May 1, May 1, 2005
MOPITT: 1999 – 2007 (aircraft validation during summer 2004, Emmons et al. [2007])
SCIAMACHY: 2 retrievals for 2003 – (2004 unbiased, 2005 increasing loss of pixels)
AIRS: 2002 – present (v5 retrieval)
TES: August 2004 – present

6. (global) Chemical Transport Model, CTM GEOS-Chem, comparison platform
GEOS-Chem CTM: the comparison platform
SATELLITE DATA
biased?
biased?
biased?
biased?
(global) Chemical Transport Model, CTM GEOS-Chem, comparison platform
compute model-satellite correlations for each dataset
biased?
validate
validate
in situ observations ?
TRUTH
but very sparse in time and space

7. Model: satellite correlations4 3 2 1
r2 = 0.645
r2 = 0.731
May 2004 – May 2005 global daytime columns (averaged to 2x2.5 resolution)
GEOS-Chem CTM
Red line: Reduced Major Axis regression
MOPITT
AIRS 4 3 2 1
r2 = 0.832
r2 = 0.243
r2 = 0.294
GEOS-Chem CTM
TES*
SCIA Bremen
SCIA SRON
*TES data start at the end of August 2004
Unit: 1018 molec/cm2

8. 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

9. Satellite bias based on sat-model correlations
GEOS-Chem/satellite slope (Correlation coefficient r2)
global NH
0-60N
Satellite
year
year/land
spring
no spring
MOPIT
.76(.65)
.78(.69)
.64(.46)
.75(.53)
.59(.46)
.70(.50)
.65(.51)
.73(.54)
AIRS
.71(.73)
.74(.70)
.70(.52)
.78(.56)
.68(.49)
.76(.57)
.69(.56)
.76(.61)
TES*
.89(.83)
.93(.50)
.83(.42)
.88(.46)
*TES data starts in late August SCIAMACHY r2 too low to compute bias
Latest MOPITT validation during summer 2004: 5% high, Emmons et al. [2007]
Conclusions:
MOPITT – GC bias consistent throughout time and space, and values of MOPITT bias alone can be extrapolated from Emmons at al. [2008]
AIRS bias slightly lower than MOPITT
SCIA Bremen and SRON not correlate with the model due to scatter (r2 = and respectively)

10. END
Next: Adjoint inversion using MOPITT, SCIAMACHY and AIRS data to estimate CO sources (COSPAR July 2008)