1. Constituent Assimilation in GMAO Ivanka Štajner Hiroo Hayashi Kris Wargan Steven Pawson

2. Ozone Assimilation Established off-line system, using CTM with parameterized P & L in stratosphere TOMS and/or SBUV total columns, along with SBUV partial columns and/or MLS data Implementation on-line in GCM (transport, feedback to meteorology) New data types: MIPAS, occultation sensors EOS-Aura package Tropospheric ozone with Harvard P & L

3. Ozone: MIPAS O-F statistics  O-F RMS values for MIPAS & SBUV both improve (decrease) when MIPAS data are assimilated  Chi^2 statistics indicate that the reported MIPAS standard deviations may be too small K. Wargan, I Stajner: preliminary

4. Ozone: GEOS-2 vs GEOS-4 winds Assimilation with GEOS-4 winds (red) agrees with sondes (black) better than that with GEOS-2 winds (blue) Tahiti 18S, 211E Reunion 21S, 56E 1000 200 500 100 50 100 0 0 Ozone mixing ratio (ppbv)

5. Plans for Other Constituents Aerosol: assimilation development (da Silva) CO: using Harvard OH parameterization, in GCM - MOPPIT, TES, AIRS CO 2 : comprehensive land-atmosphere-ocean modeling and assimilation system is planned Preliminary AIRS CO and CO 2 data from Chris Barnet

6. Constituent Modeling Full chemistry in GCM: –Stratospheric ozone-climate (underway) –Tropospheric chemistry (in planning) – relationships between climate and (say) air quality, including aerosols Full chemistry will also allow multi-constituent assimilation in 3D framework –Have box-model capability (Lary) –Multi-dimensional, multi-constituent assimilation may be feasible in the 5-10-year timeframe

7. Summary GMAO welcomes collaboration: we offer expertise in assimilation and in global modeling GMAO will not attempt to reinvent wheels (especially square ones) Aim is to build a flexible infrastructure that can be used for useful science CDAWG? (Chemical Data Assimilation Working Group)