Tropospheric NO2 and ozone Ronald van der A, Michel Van Roozendael, Isabelle De Smedt, Jos de Laat, Ruud Dirksen, Folkert Boersma KNMI and BIRA-IASB Thessaloniki, May 2008

Tropospheric NO 2

Tropospheric NO 2 from satellite measurements Observed total slant column Subtract stratospheric part (model) Convert slant column into vertical column (air mass factor, RT) Surface Stratospheric NO 2 Cloud Tropospheric NO 2

Retrieval approach CTM (TM4) Assimilation fed by satellite measurements (unpolluted pixels only) and ECMWF data gives: Estimation of stratospheric column A priori NO 2 profile  AMF trop Surface albedo + cloud info + a priori profile  AMF trop Measured column + stratospheric column + AMF trop  Tropospheric NO 2 column

1: TM4 forecast How does it work?

GOME Archive: Apr Jul Format: HDF4 Monthly mean files –TOMS ASCII-format –ESRI grid format Pixel size: 320x80 km

SCIAMACHY version 1.1: July 2002 till today version 1.1 uses FRESCO+ Format: HDF4 Monthly mean files: –TOMS ASCII-format –ESRI grid format –Regional images Pixel size is 60x30 km

OMI L1b reprocessing of entire mission completed version 1.1: October 2004 till March 2008 NRT processing has to be updated Format: HDF5 Monthly mean files –TOMS ASCII-format –ESRI grid format Pixel size: > 24x12 km

GOME-2 Data available: April 2007 till today NRT processing is operational Format: HDF5 Monthly mean files –TOMS ASCII-format –ESRI grid format Pixel size: 30x60 km

Based on monthly averaged NO 2 columns from GOME ( ) and SCIAMACHY ( ) measurements. Large increase of NO 2 over industrial and economical areas of eastern China. Trend detection of tropospheric NO 2 over China van der A et al., J. Geophys. Res., 111, 2006 Mean NO Trend (%/yr) Error on trend Beijing Shanghai Chongqing ShenYang Seoul

NO 2 correlated with vehicles population during 1996~2006 in Beijing (top) and Shanghai (bottom)

OMI Concurrent measurements of tropospheric NO 2 from OMI and SCIAMACHY Retrieved with common, consistent algorithm Collocated, cloud-free measurements at common grid (0.5°x0.5°) Differences large over source regions, and larger than combined errors Differences in spectra (slant columns) and in AMF (profile shape) 10:00 hrs SCIAMACHY 13:40 hrs OMI August 2006

What does GEOS-Chem simulate? Relative decrease in NO2 column from 10am to 1:30 pm ObservedGEOS-Chem US: -16% -28% EU: -6%-13% China: -26%-22%

Biomass burning mainly in afternoon Jun Wang Relative increase in NO 2 column from 10am to 1:30 pm ObservedGEOS-Chem Africa:+48% +16% Indon.:+60%+10% Brazil:+54%+13%

Summary Tropospheric NO2  GOME/SCIAMACHY NO2 data available for today, allowing trend analysis.  OMI data (based on collection 3) and GOME-2 is also available now.  Monitoring of China with SCIAMACHY/GOME-2 (overpass at 10.00/9.30 AM ) and OMI (overpass at PM)  Data for China will soon be presented on the AMFIC web site.

GOME tropospheric O3 columns J. de Laat, R. van der A, M. van Weele, R. van Oss, O. Tuinder, B. Mijling and A. Segers. Royal Dutch Meteorological Institute (KNMI)

Measuring tropospheric O3 column from space - air pollution/air quality & greenhouse gas - Stratosphere > 90 % of total O3 column - Troposphere < 10 % of total O3 column - Tropospheric O3 is highly variable in space and time: - Global: - in situ production (tropics and extra-tropics) - complex chemistry - Extratropics: - Stratosphere-troposphere exchange - Tropopause height variations - Clouds, aerosols (interpretation)

MethodSatelliteGeographical location Time resolution Fishman et al TORTOMS/SBUVTropicsMonthly means Kim et al. 1996WAVE 1TOMSTropicsMonthly means Hudson and Thompson 1998 MRMTOMSTropicsMonthly means (15 –days) Munro et al. 1998GOMEGlobal?? Ziemke et al CCDTOMSTropicsMonthly means Ziemke et al TOMS/MLS/HALOETropicsMonhtly means Fishman and Balok 1999 TORTOMS/SBUVTropics Mid-latitudes (- 50°N) Monthly means Kim et al. 2001SCAN-ANGLETOMSTropicsMonthly means Newchurch et al LTOTOMSMountainsMonthly means Fishman et al TORTOMS/SBUVTropics Mid-latitudes Monthly means Newchurch et al CCPTOMSTropicsMonthly means Liu et al. 2005DirectGOMEGlobalDaily Previous work

Limited height information [Degrees of Freedom for Signal] About 5 independent pieces of vertical information  Smoothing of actual profile !!  about 1 piece of tropospheric information [cf. Liu et al., JGR, 2005] separate troposphere – stratosphere !!! Directly measuring tropospheric O3 from space: GOME OPERA algorithm (Ozone ProfilE Retrieval Algorithm) Tuinder, Van der A, van Oss, Mijling [KNMI] Non-linear Optimal Estimation [Rodgers], iterative, use of a-priori Courtesy J. Landgraf, SRON

New approach: data assimilation Determine SOC by assimilating OPERA O3 profiles using CTM (TM5) Then: TTOC = TOC - SOC Advantages: - No gaps in SOC  internal consistency - Better solution for “smoothing error” - Internally consistent tropopause - different O3 (profile) measurements can be used and even combined  Best available estimate of SOC

This figure was originally published in Liu et al. [JGR, 2006]. The upper panel shows the tropospheric O3 columns from GOME O3 profiles for a region over Indonesia in 1997 (7S, E). Added are also results from a chemistry-transport model calculation (GEOS-CHEM) with realistic emissions and nearby O3 sonde measurements. The lower panel is for a region over the central Pacific. Added are the black lines: the GOME TOC values from the KNMI OPERA algorithm.

Algorithm development and current status Assimilation system/algorithm development –Several problems in the system and algorithm – hampering long assimilation runs – were detected –Slow progress of solving problems due to long duration of assimilation runs to test stability –In the mean time … OPERA algorithm was also considerably improved (Tuinder and Mijling) New linearized strat. O3 chemistry scheme published Current status –Stable assimilation algorithm –6 test runs with different settings performed for year 2000: –1 run (iterative assimilation, Cariole v2.1) for 1996 to 2001, currently running year 2001 [ are finished] Height depend error propagation in the assimilation included