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WG-C2: Remote and land-based atmospheric methane monitoring Ko van Huissteden

WG-C2: Remote and land-based atmospheric methane monitoring Ko van Huissteden. vrije universiteit amsterdam faculty of earth and life sciences. Remote and land-based atmospheric methane monitoring. Monitoring: the Cinderella work of Science (Nisbet)

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WG-C2: Remote and land-based atmospheric methane monitoring Ko van Huissteden

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  1. WG-C2: Remote and land-based atmospheric methane monitoring Ko van Huissteden vrije universiteit amsterdam faculty of earth and life sciences

  2. Remote and land-based atmospheric methane monitoring Monitoring: the Cinderella work of Science (Nisbet) very often difficult to get continued funding! • Atmospheric measurements incl isotopes, flask and bag samples (gradual difference with surface flux measurements!) • Remote sensing data • Modelling Goals: Identifying major global sources Understanding source emission processes

  3. Surface observations Flask and bag samples, aircraft clear signature of high latitude emission GLOBALVIEW-CH4: Cooperative Atmospheric Data Integration Project Methane. CD-ROM, NOAA ESRL, Boulder, Colorado [Also available on Internet via anonymous FTP to ftp.cmdl.noaa.gov, Path: ccg/ch4/GLOBALVIEW], 2009.

  4. Tracing sources with isotopes Ambient air methane δ13C and trajectory analysis indicate West-Siberian source - Samples collected on ship west of Spitsbergen 23/8/08-23/9/08 Source: NILU (Norwegian Institute for Air Research) and Euan Nisbet

  5. A clear bias...... the major climate-sensitive natural source areas are missing Surface observation network

  6. Remote sensing: the success of ENVISAT SCIAMACHY column averaged CH4, animation by Michael Buchwitz Arctic regions complicated because - high solar zenith angles - snow and ice cover

  7. First data from Japanese GOSAT In theory: Quality improvement with respect to SCIAMACHY smaller ground pixel, less noise, les interference with other gases, less clouds data quality in practice still has to be evaluated poor arctic coverage remains GOSAT September 2009

  8. Inverse modelling: estimating sources from atmospheric observations (Thanks to Philippe Bousquet) good quality observations with good spatial coverage are crucial

  9. Surface flux modelling Petrescu et al. accepted Modeling regional to hemispheric CH4 emissions of boreal, subarctic and arctic wetlands Global Biogeochemical Cycles Wetland methane emission model coupled to global hydrological model and different wetland extent maps: Widely varying results Possible improvement with SMOS satellite soil moisture data?

  10. Surface flux modelling Sensitivity testing of Walter-Heimann model J. van Huissteden, A. M. R. Petrescu, D. M. D. Hendriks, and K. T. Rebel 2009 Sensitivity analysis of a wetland methane emission model based on temperate and Arctic wetland sites, Biogeosciences Discussions High sensitivity to: - Vegetation parameters related to CH4 transport and oxidation in plants - Low sensitivity to soil characteristics Improvement of wetland vegetation characterization is important - Remote Sensing Carex Sphagnum within plant oxidation Sphagnum sites: symbiosis of Sphagnum with methanogens Transport rate Carex: high transport, Sphagnum: Low

  11. Surface emission and atmospheric monitoring Surface emission studies and atmospheric monitoring: a fruitful cooperation Mastepanov et al., 2008: autumn freeze-up CH4 burst from wetlands at Zackenberg, Greenland observations from atmospheric sampling network model without autumn emission model with autumn emission

  12. Problems to tackle Surface measurements: poor coverage in the arctic, in particular Siberia cannot be resolved entirely remote sensing only continuity: short project term funding vs long term monitoring needs Remote sensing: with GOSAT still poor coverage in the arctic maybe promising for wetlands: SMOS Modelling: Bottum-up models need improvement in particular wetland hydrology

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