Tropospheric NO2 Ronald van der A, Michel Van Roozendael, Isabelle De Smedt, Ruud Dirksen, Folkert Boersma KNMI and BIRA-IASB Beijing, October 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

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

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

OMI version 1.1: October 2004 till today Local time: Format: HDF5-EOS Monthly mean files –TOMS ASCII-format –ESRI grid format Pixel size: > 24x12 km

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

Trend in tropospheric NO 2 ( ) Trends with 95% confidence criterium

Trends in China Trend van der A et al., J. Geophys. Res., 111, 2006

Sources of tropospheric NO 2 NO x sources: –Anthropogenic (traffic, industry, power plants) –Soil emissions (grasslands, induced by rain) –Biomass burning (tropics, dry season) –Lightning (tropics) Phase shift to identify sources: –Anthropogenicwinter maximum –Soil emissionssummer maximum, rainfall –Biomass burningdry season –Lightning-

Observed NO 2 timeseries (monthly means ) Anthropogenic (Tehran) Biomass burning Ghana (10°N,0°) Soil West-China (40°N,100°E)

Month of maximum NO 2 GOME/SCIAMACHY TM model

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%

Summary Tropospheric NO2  GOME/SCIAMACHY NO2 data available for today, allowing trend analysis.  OMI data 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)  Overpass files for OMI pixels within 100 km from station.

GOME tropospheric O3 columns R. van der A, J. de Laat, J. van Peet, O. Tuinder (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)

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 –Period 1996 to 2001 is available –Height depend error propagation in the assimilation included

Tropospheric Ozone data GOME tropospheric columns:  Data set complete  Low resolution => low quality  Small improvements possible  GOME-2 tropospheric columns:  Data set not available yet  High quality  Long processing time  GOME-2 tropospheric profiles:  Data set is available  Data can is difficult to interpret: averaging kernel and a- priori profile needed

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Scheme of source identification AnthropogenicWinter maximum (outside tropics) Low variability BiomassWinter/Spring maximumHigh variability SoilSummer maximumHigh variability LightningNO 2 (above clouds) > NO 2 (clear-sky)

Source identification van der A et al., JGR, 2008