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Understanding Methane Concentrations Nicola Warwick 1, Euan Nisbet 2, John Pyle 1 1- Centre for Atmospheric Science University of Cambridge 2 – Royal.

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Presentation on theme: "Understanding Methane Concentrations Nicola Warwick 1, Euan Nisbet 2, John Pyle 1 1- Centre for Atmospheric Science University of Cambridge 2 – Royal."— Presentation transcript:

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2 Understanding Methane Concentrations Nicola Warwick 1, Euan Nisbet 2, John Pyle 1 1- Centre for Atmospheric Science University of Cambridge 2 – Royal Holloway, University of London Quest CH 4 Workshop, June 2004

3 Outline CH 4 observations Understanding the global distribution and seasonal cycles Understanding variability and trends Outlook

4 Factors controlling CH 4 Emissions (natural and anthropogenic) Sinks (OH, soil) Meteorology (winds, temperatures, rainfall) July model surface CH 4 (ppbv) July Surface OH in model (10 6 molecules/cm 3 )

5 Methane Measurements In-situ and flask data: Global network of 40-50 stations mainly including: US-NOAA-CMDL Australian CSIRO New Zealand NIWA Meth-MonitEUr – European Network Satellite observations Ice-cores

6 Observed CH 4 1992-2002 Observed CH 4 mixing ratios (Marine Boundary Layer) from 1992 to 2001, NOAA-CMDL Southern Hemisphere Northern Hemisphere

7 CH 4 Measuring Stations

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

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

10 Testing our understanding of CH 4 Global modelling (forward and inverse) Regional modelling Back trajectory analyses / diurnal experiments Isotopes

11 December surface CH 4 by MATCH model (provided by Kim Holmen, NILU)

12 13 C-CH 4 Isotopic Fractionation of CH 4 Sources Light Heavy Methane sources have a wide range of distinct 13 CH 4 signatures further constraint on emission scenarios. Adapted from Chanton et al. [2000] -47% KIE

13 Global CH 4 and 13 CH 4 Distributions Wetlands Fossil Fuel Rice Surface 13 CH 4 () - January monthly mean Surface CH 4 (ppbv) - January monthly mean

14 How well do measurements describe the CH 4 burden? Blue line: modelled zonal mean CH 4 and 13 C-CH 4 Red Circles: modelled CH 4 and 13 C- C-CH 4 at measuring station locations Black squares: observed CH 4 and 13 C-CH 4 [Dlugokencky et al., 1998, Miller et al., 2002] Houwelling et al. (1999): relative contribution of NH sources decrease from 77% to 67%.

15 Breakdown of seasonal cycles by source Southern Hemisphere (Modelled CH 4 at Ascension Island, Atlantic, 7°S) Northern Hemisphere (Modelled CH 4 at Alert, Canada, 82°N)

16 Alternative Emission Scenarios Source / TgScenario 1 (base) Scenario 2 (wtnd) Scenario 3 (ff) Scenario 4 (wtld&ff) Wetlands1156111585 Fossil Fuel89 3565 Biomass Burning 54108 Other254 Global Total (Tg/yr) 512

17 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

18 Understanding Interannual Variability and Trends

19 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 #2001-076. NOAA/NGDC Paleoclimatology Program, Boulder CO, USA. Dlugokencky et al., 1998, 2003

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

21 CH 4 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

22 Modelled and Observed CH 4 Variations Simulated CH 4 interannual variability arising from changes in the circulation Observed CH 4 growth rate (NOAA CMDL Carbon Cycle Greenhouse Gases) Changes in modelled interpolar gradient resulting from meteorology: ~up to 0.5%

23 Influence of Meteorology on CH 4 Tropics: –OH (will vary with humidity) –Tropical forest fires / wetlands –El Niño Influence of el Nino on fire emissions – van der Werf et al., 2004. Measured CH 4 at Ascension Island (D. Lowry, E. Nisbet)

24 Influence of Meteorology on CH 4 Tropics: –OH (will vary with humidity) –Tropical forest fires / wetlands –El Nino –Winds (alter inter-hemispheric mixing) Northern Latitudes: –Northern wetlands CH 4 measurements at Yamal Peninsula compared to Teriberka background level

25 Problems and Outlook Model simulations still have trouble reproducing CH 4 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)

26 The End

27 The global CH 4 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 2008-12, Kyoto Protocol envisages reduction in GHG emissions of ~5% w.r.t. 1990 values Modelled annual mean surface CH 4 / ppbv

28 Observed and modelled CH 4 seasonal cycles

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

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

31 CH 4 Variability due to Sinks Prinn et al. 2001: 1.4 ± 2.1% yr -1 upward trend 1979-1989 2.3% yr -1 downward trend 1990-2000 Krol et al. 1998, 2001: 1978-1993, 0.46 ± 0.6% yr -1 Krol and Lelieveld 2003: possible problems with CH 3 CCl 3 data. Dentener et al. 2003: 1979-1993, 0.24 ± 0.06% yr -1 large interannual variability of OH (1.5%)


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