Constraining the magnitude and diurnal variation of NOx sources from space Folkert Boersma

Major uncertainty in models: emissions of NOx EMEP Major uncertainty in models: emissions of NOx What is so uncertain about emissions? quantities locations times trends But we can see the NOx sources from space SCIAMACHY Blond et al. (2007) We have seen that the model is doing a reasonable job in describing transport and chemistry. How good are satellite obs? Examples of recent work: use OMI satellite observations to estimate emissions over the U.S. and Mexico use SCIAMACHY and OMI to illustrate importance of timing emissions Emissions

Ozone Monitoring Instrument Data since September 2004 Nadir-viewing instrument measuring direct and atmosphere-backscattered sunlight from 270 – 500 nm NO2 Wide field of view (2600 km)  global coverage in one day Nadir pixel size 24 x 13 km2 Local overpass time 13:30 hrs

Saturday Sunday GOA is a project in which a.o. IUP and KNMI participate

Weekend effect observed from GOME Sunday NO2 levels 25-50% lower than weekday levels

EPA NEI99 emissions in use in GEOS-Chem Industry (17%) Power Plants (25%) Transport (36%) ‘Other’ (21%) Also write down on slide that GC is too low over Mexico, and give rough % fractions that GC is too high or too low. Your audience will be curious to know what the bias is like over MC. Region is not “eastern and southeastern”, it’s “southeastern U.S. and midwest”. Could also mention indication of low bias over northeast. The LA bias is a distraction. I would recommend cropping the figure to remove the west coast – I think that would be OK.

Top-down lower over industrial Midwest r2 = 0.86 (n=118) Top-down lower over industrial Midwest Top-down higher over northeastern United States TOP-DOWN OMI BOTTOM-UP Also write down on slide that GC is too low over Mexico, and give rough % fractions that GC is too high or too low. Your audience will be curious to know what the bias is like over MC. Region is not “eastern and southeastern”, it’s “southeastern U.S. and midwest”. Could also mention indication of low bias over northeast. The LA bias is a distraction. I would recommend cropping the figure to remove the west coast – I think that would be OK.

March 1999 – 2006: +3.2% (2.9%) Regression bottom-up categories to these differences: Transport: +33% 22% Power Plants: -25%  23% Industry: -26%  30% Other: +9%  40%

Diurnal variation of NO2 columns NNO2: NO2 column (t): NO2:NOx ratio E(t): NOx emissions k(t): NOx loss rate NNOx: NOx column  E k

Diurnal variation of NO2 columns Grid SCIAMACHY and OMI NO2 observations on 0.5 x 0.5 grid Take only those grid cells that were cloud-free for both instruments Compute monthly averages SCIAMACHY: 10.00 local time OMI: 13.30 hrs local time

Difference SCIAMACHY – OMI tropospheric NO2 r = 0.76 (n = 1.9×106) SCIAMACHY 10-40% higher than OMI for most anthropogenic source regions SCIAMACHY lower than OMI for biomass burning regions

Simulating 10am to 1:30pm with GEOS-Chem Relative decrease in NO2 column from 10am to 1:30 pm Observed GEOS-Chem US: -18% -31% EU: -5% -30% China: -37% -29%

2003 2005 2001 2004 2002 Biomass burning mainly in afternoon Jun Wang Relative increase in NO2 column from 10am to 1:30 pm Observed GEOS-Chem Africa: +31% +16% Indon.: +35% +11% Brazil: +37% -2%

Conclusions Decreasing power plant NOx emissions (-20%, 1999-2006) Evidence for increasing mobile emissions (+30%, 1999-2006) 2. SCIAMACHY and OMI observe - fast photochemistry - fast emission changes from space

Credits Daniel Jacob Henk Eskes (KNMI) Rob Pinder (EPA) Jun Wang Ronald van der A (KNMI) Bob Yantosca Rokjin J. Park

Frost et al.: -20% (1999-2004)