Trends and seasonal variability in tropospheric NO 2 Ronald van der A, David Peters, Henk Eskes, Folkert Boersma ESA-NRSCC DRAGON Cooperation Programme.

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Presentation transcript:

Trends and seasonal variability in tropospheric NO 2 Ronald van der A, David Peters, Henk Eskes, Folkert Boersma ESA-NRSCC DRAGON Cooperation Programme

Combined retrieval-modelling- assimilation approach 1. DOAS  slant column (GwinDOAS, developed at BIRA-IASB) 2. Assimilation  strat. slant column (TM4-DAM, developed at KNMI) 3. Modelling  tropospheric amf (DAK, developed at KNMI)

Annual mean 2004

NO 2 over China Annual mean of the tropospheric NO2 column GOME-1997 SCIAMACHY-2004

NO 2 data set Time series gridded to 1 x 1 degree GOME: April 1996 – March 2003 SCIAMACHY: April 2003 – September 2005 Convolution of SCIAMACHY data to spatial resolution of GOME. Cloud filtering (only pixels with radiance from clouds of less than 50 %)

Analysis of time series Y t = Y 0 + Bt + A∙sin(ωt + δ) + N t + U t N t = φN t-1 + ε t Y monthly mean of tropospheric NO 2 t number of months after t=0 Y 0 start value at t=0 B monthly trend A amplitude of seasonal variability ω period (fixed at π/6 month -1 ) δ phase shift U t bias between GOME and SCIAMACHY N t remainder φautocorrelation in N εnoise

Statistical significance From E.C. Weatherhead et al., JGR 103, 1998: Error on trend: Φautocorrelation in N nnumber of data points σ N variance in N Trend B is real with 95 % confidence level if B/σ B > 2

Trends of tropospheric NO 2

Some hotspots in Asia [10 15 molec/cm 2 /year] NO 2 in 1996TrendError on trendGrowth (Ref. year 1996) Hong Kong % Beijing % Shanghai % Seoul %

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

Month of maximum concentration GOME/SCIAMACHY TM model Grey areas: no significant amplitude

Other parameters Variability, defined as seasonal signal over the yearly mean: –To distinguish anthropogenic sources (constant emissions) and biomass burning (strong seasonality) Comparing cloudy pixels with clear-sky pixels –To distinguish lightning, whose signal is higher in cloudy pixels

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

Lightning Lightning occurs mainly in clouds above 500 hPa. NO 2 measured in pixels with clouds above 500 hPa (derived with FRESCO) minus background value. Background value derived from cloudy pixels between 1000 hPa and 500 hPa.

NO 2 due to lightning Diurnal variation OTD/LIS source:K.Driscoll

Summary 10 year data set of tropospheric NO 2 from GOME and SCIAMACHY ( ) retrieved. Global trend study: strong pos. trends in Asia (China, Iran). Identifying sources using fitted seasonality. Observing NO 2 from lightning in satellite observations with clouds above 500 hPa. Outlook OMI data can largely improve this study because –much larger amount of observation per month –overpass time more interesting for lightning observations