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Understanding Methane Concentrations

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Presentation on theme: "Understanding Methane Concentrations"— Presentation transcript:

1 Understanding Methane Concentrations
Nicola Warwick1, Euan Nisbet2, John Pyle1 1- Centre for Atmospheric Science University of Cambridge 2 – Royal Holloway, University of London Quest CH4 Workshop, June 2004

2 Outline CH4 observations
Understanding the global distribution and seasonal cycles Understanding variability and trends Outlook

3 Factors controlling CH4
Emissions (natural and anthropogenic) Sinks (OH, soil) Meteorology (winds, temperatures, rainfall) July model surface CH4 (ppbv) July Surface OH in model (106molecules/cm3)

4 Methane Measurements In-situ and flask data:
Global network of stations mainly including: US-NOAA-CMDL Australian CSIRO New Zealand NIWA Meth-MonitEUr – European Network Satellite observations Ice-cores

5 Observed CH Northern Hemisphere Southern Hemisphere Observed CH4 mixing ratios (Marine Boundary Layer) from 1992 to 2001, NOAA-CMDL

6 CH4 Measuring Stations

7 Ny-Alesund Mace Head Meth-MonitEUr: Methane monitoring in the European region.

8 Royal Holloway College, W London (provided by E. Nisbet, D. Lowry)
Ny-Alesund Mace Head Royal Holloway College, W London (provided by E. Nisbet, D. Lowry) Meth-MonitEUr: Methane monitoring in the European region.

9 Testing our understanding of CH4
Global modelling (forward and inverse) Regional modelling Back trajectory analyses / diurnal experiments Isotopes

10 December surface CH4 by MATCH model (provided by Kim Holmen, NILU)

11 13C-CH4 Isotopic Fractionation of CH4 Sources
Methane sources have a wide range of distinct 13CH4 signatures  further constraint on emission scenarios. -47% KIE Adapted from Chanton et al. [2000] Light Heavy

12 Global CH4 and 13CH4 Distributions
Fossil Fuel Surface CH4 (ppbv) - January monthly mean Rice Surface 13CH4 (‰) - January monthly mean Wetlands

13 How well do measurements describe the CH4 burden?
Houwelling et al. (1999): relative contribution of NH sources decrease from 77% to 67%. Blue line: modelled zonal mean CH4 and 13C-CH4 Red Circles: modelled CH4 and 13C-C-CH4 at measuring station locations Black squares: observed CH4 and 13C-CH4 [Dlugokencky et al., 1998, Miller et al., 2002]

14 Breakdown of seasonal cycles by source
Northern Hemisphere (Modelled CH4 at Alert, Canada, 82°N) Southern Hemisphere (Modelled CH4 at Ascension Island, Atlantic, 7°S)

15 Alternative Emission Scenarios
Source / Tg Scenario 1 (base) Scenario 2 (wtnd) Scenario (ff) Scenario 4 (wtld&ff) Wetlands 115 61 85 Fossil Fuel 89 35 65 Biomass Burning 54 108 Other 254 Global Total (Tg/yr) 512

16 Alternative Emission Scenarios: Results
Niwot Ridge, Colorado (106W, 40N) Cape Grim, Tasmania (145E, 41S) South Pole (25W, 90S) Measurements courtesy of NOAA/CMDL/CCGG

17 Understanding Interannual Variability and Trends

18 Year-to-Year Millennial and Longer…
Feedback Timescales Year-to-Year Millennial and Longer… Petit, J.R., et al., 2001, Vostok Ice Core Data for 420,000 Years, IGBP PAGES/World Data Center for Paleoclimatology Data Contribution Series # NOAA/NGDC Paleoclimatology Program, Boulder CO, USA. Dlugokencky et al., 1998, 2003

19 NOAA Observed Interpolar Gradient
Dlugokencky et al., GRL, 2003

20 CH4 Variability due to Meteorology
Black line = observed growth rate Dashed line = modelled growth rate Correlation Coefficients: Key Biscayne, r=0.61 Ascension, r=0.59

21 Modelled and Observed CH4 Variations
Simulated CH4 interannual variability arising from changes in the circulation Observed CH4 growth rate (NOAA CMDL Carbon Cycle Greenhouse Gases) Changes in modelled interpolar gradient resulting from meteorology: ~up to 0.5%

22 Influence of Meteorology on CH4
Tropics: OH (will vary with humidity) Tropical forest fires / wetlands El Niño Measured CH4 at Ascension Island (D. Lowry, E. Nisbet) Influence of el Nino on fire emissions – van der Werf et al., 2004.

23 Influence of Meteorology on CH4
Tropics: OH (will vary with humidity) Tropical forest fires / wetlands El Nino Winds (alter inter-hemispheric mixing) Northern Latitudes: Northern wetlands CH4 measurements at Yamal Peninsula compared to Teriberka background level

24 Problems and Outlook Model simulations still have trouble reproducing CH4 observations Need more continental data (e.g. South America, Africa Asia) to test models Satellites: look for abrupt changes (e.g tropical fires) infer surface fluxes (need high precision) Isotopes: High-precision ground-based isotope measurements can distinguish sources (help quantify wetland source)

25 The End

26 The global CH4 burden may be higher than previously thought.....
Use modelled radon concentrations to distinguish between ‘clean’ and ‘dirty’ air True global mean = 1.3% greater than ‘clean’ global mean By , Kyoto Protocol envisages reduction in GHG emissions of ~5% w.r.t values Modelled annual mean surface CH4 / ppbv

27 Observed and modelled CH4 seasonal cycles

28 The Atmospheric CH4 Record: 1000 to 2000
Change in CH4 abundance for the last 1000 years. IPCC TAR Fig 4-1.

29 Carbon Gas Measurements: 1996-2003
Carbon gas measurements at Royal Holloway College, W London (provided by E. Nisbet, D. Lowry)

30 CH4 Variability due to Sinks
Prinn et al. 2001: ± 2.1% yr-1 upward trend 2.3% yr-1 downward trend Krol et al. 1998, 2001: , 0.46 ± 0.6% yr-1 Krol and Lelieveld 2003: possible problems with CH3CCl3 data. Dentener et al. 2003: , 0.24 ± 0.06% yr-1 large interannual variability of OH (1.5%)

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