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

2. 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

3. 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

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

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

6. 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.

7. 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.

8. 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%

9. 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

10. 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: local time
OMI: hrs local time

11. 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

12. 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%

13. 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%

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

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

16. Frost et al.: -20% ( )