Estimating Ship emitted NO 2 in the Indian Ocean using satellite data

Transcription

1 Institut für Umweltphysik/Fernerkundung Fachbereich 1 Physik/Elektrotechnik Estimating Ship emitted NO 2 in the Indian Ocean using satellite data K. Franke, A. Richter, J.P. Burrows, H. Bovensmann, Institute of Environmental Physics, University of Bremen FB1, Heinrich.Bovensmann@iup.physik.uni-bremen.de P. Hoor Max Planck Institute for Chemistry, Mainz, Germany P. Jöckel, V. Eyring DLR, Institute of Atmospheric Physics, Oberpfaffenhofen, Germany, Veronika.Eyring@dlr.de ESA Atmospheric Science Conference, Barcelona,

2 Further reading Ship emitted NO 2 in the Indian Ocean: comparison of model results with satellite data K. Franke, A. Richter, H. Bovensmann, V. Eyring, P. Jöckel, P. Hoor, and J. P. Burrows Atmos. Chem. Phys. Discuss., 8, , 2008 ACP: coming very soon! Transport Impacts on atmosphere and climate: Shipping V. Eyring et al, Atmospheric Environment, 2009

3 Some history SCIAMACHY Ensemble Average Eyring et al., ACP, : identification of enhanced NO2 along shipping lanes in GOME (Beierle et al.) and SCIAMACHY Data (Richter et al.) 2007 ENVISAT Symposium: Measured increase of NO 2 along major ship routes may allow verification of emission inventories, but models needs improvements first. 2009: Identification of enhanced HCHO along shipping lanes (Marbach et al.) 2009: Quantitative interpretations of measured ship emissions (Franke et al. ACPD)

4 Magnitude of Ship Emissions Emissions from ships are already now comparable to emissions from the other transport sectors. Global Shipping is assumed to significantly grow during the next decades. No or weak international regulation Eyring et al., JGR, 2005A

5 Area of Investigation: Indian Ocean Ship traffic concentrated along a narrow East-West corridor at 6 N (Region S) Small to negligible other NO2 sources (Regions B1/B2) % of the global ship traffic in S Range of Emissions: 41Gg(N)/yr (Endresen et al using ICOADS) 90Gg(N)/yr (Eyring et al., 2005, using AMVER)

6 NOx ship emissions from previous studies Region S: 83 E 94.2 E 4.4 N 7.2 N AMVER, COADS: different spatial ship activity pattern Emission rates for 2000 are scaled with the increase of total seaborne trade to the year 2005.

7 Apply tropospheric excess method consistently on satellite and model data Method Remove stratospheric NO 2 by subtracting clean reference sector column at same latitude Reference Sector: 180 E to 140 W Assumes longitudinal homogeneity of the stratospheric column at the same local time NOx emission inventory used (EDGAR3.2 + ship emissions from Eyring 2005)

8 Data Used satellite data is spatially averaged to match the coarser resolution of the model

9 Satellite Data: Details Tropospheric Excess Method applied (TEM), Richter et al Reference sector 180 E to 140 W Air Mass factor: 4% albedo (ocean) Trop. NO 2 confined in 700 m well mixed boundary layer Cloud fraction threshold 20% Maritime aerosol Estimated AMF error: 10-30% Monthly mean values were calculated on a grid of x Random uncertainty monthly mean: 3-4 E13 mol/cm2

10 Model Data: ECHAM5/MESSy1 Atmospheric Chemistry General Circulation Model ECHAM5/MESSy1 (E5/M1) (P. Jöckel et al., 2006). Anthropogenic & natural emissions from EDGAR3.2FT year climatological average ( ) to minimise impact of interanual variations Sub-grid plume processes currently not taken into account NOx emissions include Anthropogenic: 31 Tg(N)/year biomass burning: 9.3 Tg(N)/year Lightning: 2 Tg(N)/year Ship NOx emissions Run 1 (TEC): 6.3 Tg(N)/yr (Eyring et al., 2005), spatial distributed from AMVER,, Run 2 (QFY): Global NOx shipping are 4.4 Tg(N)/yr (Endresen et al. 2007), Lightning NOx 5Tg(N)/yr, from EU-project QUANTIFY (P. Hoor et al., 2009)

11 February mean tropospheric NO 2 columns ( ) SCIA TEC E5/M1 TEC Enhancement of approx. 8E14 mol/cm2 in S compared to B1/B2 As model grid box is roughly twice the width of the shipping lane, concentrations in satellite data and model data roughly consistent

12 Seasonal Variation of Tropospheric Excess Columns SCIAMACHY and E5/M1 (TEM) in good agreement, RMS difference of 0.8 E14 molec/cm2. small discrepancies in B1 and B2 coincide with the biomass burning seasons in adjacent landmasses as seen in the TRMM Fire Index, i.e. typically February to May/June in India/Indochinese Peninsula and August to October in Indonesia. GOME-2 > SCIAMACHY > GOME, but roughly consistent within error bars (exception Jan. 2008)

13 Trop. NO2 vs. Seaborne Trade Volume (STV)* GOME to SCIA: increase by 26%, STV: +29% SCIA to GOME %, STV: +10% Interpretation complex due to diurnal variation, instrument/sampling effects etc. * STV = cargo weight x transported distance

14 Impact of shipping emission inventories on model 2003 B1/B2 only slightly affected S: E5/M1 (TEM) being 2E14 molec/cm2 higher than E5/M1 (QFY). NO 2 TEC over region S is mainly controlled by ship induced NOx lightning plays only a minor role.

15 Model vs. Satellite Data (Monthly Mean, Region S) GOME NO2 TECs ( ) in slightly better agreement with E5/M1 (TEM), indicating consistency with an emission slightly lower than 6.3 Tg(N)/yr. SCIAMACHY NO2 TECs ( ) in much better agreement with E5/M1 (TEM) than with E5/M1 (QFY), indicating that the global emissions of 6.3 Tg(N)/yr are much more consistent with the satellite data than a value of 4.4 Tg(N)/yr

16 Summary and Outlook Ship emissions of NOx in the Indian Ocean have been analysed with the help of measurements from GOME ( ), SCIAMACHY ( ), and GOME-2 (2007/2008) in a consistent comparison to two global model simulations using different inventories. Diurnal variation, instrumental biases and consistent data analysis complicate interpretation Difference of 26% in mean NO2 TEC between GOME and SCIAMACHY is consistent with the rise of 29% in mean seaborne trade volume, indicating that satellite data could detect emission increases. Ship emission inventory with around 6 Tg(N)/yr globally (around 90 Gg(N)/yr in the Indian Ocean) are better agreement with measurements in the Indian Ocean for than lower ship emissions estimates of 3 4 Tg(N)/yr globally. GOME-2 on METOP will continue time series

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19 Introduction CO 2 H 2 O NO x, SO x, HC, CO, soot/ash Seagoing ships emit exhaust gases and particles into the marine boundary layer and significantly contribute to the total budget of anthropogenic emissions. Emissions of ozone precursors, like nitrogen oxides (NO x ), carbon monoxide (CO) and unburned hydrocarbons (HC) contribute to the formation of ground-level ozone, which may damage human health and vegetation, and which changes the radiative budget as ozone is radiatively active. In addition, SOx and particles contribute to the cloud formation and/or modify clouds, thereby affecting the Earth radiation budget. MAN B&W, 2002

20 Tropospheric NO 2 Data Analysis Spectral data analysis: DOAS using TOA reflectance data (here: SCIA) nm fitting window Stratospheric Columns stratosphere separated by measurement over pacific ( clean troposphere), the so-called reference sector Assumption: stratospheric NO 2 longitudinal homogeneous Tropospheric Columns: Tropospheric column = total column stratospheric column (Pacific reference sector ) Trop. AMF simulated with RTM simple cloud threshold only

21 Monthly Zonal Mean tropospheric excess NO2 SCIA E5/M AMVER ship activity

22 Is diurnal variation consistent?

23 Overview Introduction Trace Gases from Shipping What can we learn from global models? Summary & Outlook

24 METOP/GOME-2: March 2007