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IWGGMS Pasadena, June 24, 2008

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.

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IWGGMS Pasadena, June 24, 2008

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  1. CONSISTENCY amongMOPITT, SCIA, AIRSandTES 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) …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 2.3 µm 4.7 µm 4.7 µm 4.7 µm 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 AIRS MOPITT SCIA Bremen TES (2006) 1018molec/cm2 0 0.88 1.75 2.62 3.50 CO columns expected to be different due to different vertical sensitivity

  5. 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, 2004 - May 1, 2005 • MOPITT: 1999 – 2007 (aircraft validation during summer 2004, Emmons et al. [2007]) • SCIAMACHY: 2 retrievals for 2003 – 2005 (2004 unbiased, 2005 increasing loss of pixels) • AIRS: 2002 – present (v5 retrieval) • TES: August 2004 – present

  6. 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 correlations 4 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 AIRS MOPITT 4 3 2 1 r2 = 0.243 r2 = 0.832 r2 = 0.294 GEOS-Chem CTM SCIA SRON SCIA Bremen TES* 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 Unit: 1018 molec/cm2 *TES data start at the end of August 2004

  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 *TES data starts in late August 2004 SCIAMACHY r2 too low to compute bias Latest MOPITTvalidation 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 = 0.062 and 0.164 respectively)

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

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