SCIAMACHY Carbon Monoxide Lessons learned. Jos de Laat, KNMI/SRON

Transcription

1 SCIAMACHY Carbon Monoxide Lessons learned Jos de Laat, KNMI/SRON

2 A.T.J. de Laat 1, A.M.S. Gloudemans 2, I. Aben 2, M. Krol 2,3, J.F. Meirink 4, G. van der Werf 5, H. Schrijver 2, A. Piters 1, M. van Weele 1. 1 Royal Dutch Meteorological Institute (KNM) de Bilt, The Netherlands 2 Netherlands Institute for Space Research (SRON) Utrecht, The Netherlands 3 Meteorology and Air Quality Group Wageningen University, The Netherlands 4 Institute for Marine and Atmospheric Research (IMAU) Utrecht University, The Netherlands 5 Faculty of Earth and Life Sciences Free University, Amsterdam., The Netherlands

3 Lesson I: SCIAMACHY CO retrievals: a real challenge! Near-infrared CO lines SWIR ~ nm Weak CO lines, strong H2O and CH4 lines ice layer on the SWIR detectors detector radiation damage (dead pixels) Retrieval issues: instrument noise (dark signal), negative CO columns lumns SCIAMACHY CO vertical uniformly sensitivity (~ few %) sensitive down to Earth surface => sensitive to surface sources CO

4 SCIAMACHY CO total columns CO measurement precision signal level: surface albedo and solar zenith angle (only over land) noisy: single column measurement error of % of CO value Non-instrument noise errors are relatively small: < 10% number of collocations: temporal and spatial averaging Annual mean SCIAMACHY CO (10 18 molec/cm 2 ) IMLM v7.4; Sep 2003 Aug 2004

5 Lesson II: Evaluation/validation is a real challenge FTIR?? very limited spatial coverage, often at mid-latitudes and at low surface albedo locations, on top of mountains & collocation issues due to once-every every-6-days SCIA measurements and clouds. MOPITT?? limited sensitivity to lower troposphere (< 3 km), % of the total CO column < 3 km altitude no true total CO columns from MOPITT. Alternative [de Laat et al., JGR, June 2007]: Comparison with model simulation TM4 (J.F. Meirink) Available for every single SCIAMACHY measurement Good: horizontal & vertical transport, seasonal cycle due to OH breakdown of CO Bad: emissions (especially biomass burning), local circulation patterns, p how good is the chemistry?

6 Chemistry transport model TM4. 3x2 degree horizontal resolution, 25 vertical levels (up to 10 hpa), ECMWF meteorological data, CBM-IV tropospheric chemistry. CO emissions are as described by Dentener et al. [2003] Natural emissions from Houweling et al. [1998] Anthropogenic emissions from the EDGAR emission database [van Aardenne et al., 2001]. Extrapolation of anthropogenic 1990 CO emissions to the years under consideration based on historical CO2 emission increases [Dentener et al., 2003] Seasonal cycle for fossil fuel emissions (amplitude 15%; higher emissions in summer for NH > 45 latitude) Biomass burning emissions from the GFED emisison database [van der Werf et al., 2006] Based on actual satellite observations of fire counts and burnt area. Model output on local SCIAMACHY overpass time (10:30 PM)

7 Time series

8 Lesson III: examples of long range transport Model SCIAMACHY (Sept Dec. 2004) Gloudemans et al., GRL 2006 Australia one month later than South America/Africa SCIAMACHY & model agree outside biomass-burning burning season: average difference <5% During biomass-burning burning season SCIAMACHY generally higher than model

9 Transport of biomass-burning CO TM4 (GFEDv2); off-line CO simulation Gloudemans et al., GRL 2006

10 Recent developments: CO over low clouds (using CH4) over oceans - Clouds are bright sufficient signal to noise - Cloud cover > 20% over oceans - Simultanously measured CH4 column: good proxy for cloud top height. - Measured CH4 column is compared to TM4 modeled CH4 column (modeled CH4 columns accuracy = a few %), R>0.8 = low cloud - TM4 CO columns is sampled according to pressure level associated d with CH4 column thickness comparing apples with apples

11 Recent developments: global CO (oceans low clouds) Sep 2003 S A BI T T I P O M r o ( Y H AC M A I C S d e ov m e r )??? Aug 2004!!!

12 Recent developments: Time series over oceans, long range transport

13 Recent developments: Time series up to December 2005 BIAS BIAS MOPITT, MOPITT, SCIAMACHY SCIAMACHY??????

14 Conclusions - SCIAMACHY is capable of determining the total CO column. - Retrieval is complicated due to various factors - Measurement is noisy, BUT, caused by instrument noise reduce by averaging - other errors, biases are relatively small (~10% or Validation/evaluation is hampered by lack of good observations - Comparison with TM4 model shows generally a good agreement - Comparison with MOPITT shows generally a good agreement, apart from f a bias - Measurements over oceans over low clouds - Various pollution episodes can be identified. 17 ) - To resolve: various calibration biases, MOPITT-SCIA bias, potential southern hemisphere SCIA-bias

15 data available (also daily ridded on 1x1) contact : a.gloudemans@sron.nl

16 De Laat et al., GRL, 2006 Gloudemans et al., GRL, 2006 de Laat et al., JGR, 2007 Joint project (national funding) SRON/KNMI/Wageningen University/MOPITT on inverse modelling of SCIA, MOPITT, FTIR and GMD CO observations. [M. Krol, I. Aben]

17 Financial Times 11 May 2007 That s s all

18 Recent developments: Time series up to December 2005

19 SCIAMACHY TM4 GvdW TM4 Clim albedo x 100 collocations 1.2 CO [10 18 molecules cm 2 ] A (103.5,39) B (115.5,-29) C (-115.5,35) D (136.5,-35) E (-70.5,-25) F (43.5,5.) G (55.5,35) H (10.5,31) I (-67.5,-33) J (16.5,-25) K (4.50,47) Months L (76.5,13) Months M (-91.5,35) Months N (-52.5,-23) Months O (127.5,37) Months SCIA CO, monthly means (weighted), Sep Aug 2004, molecules/cm -2, cloud cover < 20 %, 1-σ error indicated by vertical bars. Two TM4 model simulations (courtesy: J.F. Meirink); climatological [Hao and Liu] and satellite based biomass burning emissions [GFED v2.0; van der Werf et al.]

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