Publications

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2016
McNorton, J, Chipperfield MP, Gloor M, Wilson C, Feng WH, Hayman GD, Rigby M, Krummel PB, O'Doherty S, Prinn RG, Weiss RF, Young D, Dlugokencky E, Montzka SA.  2016.  Role of OH variability in the stalling of the global atmospheric CH4 growth rate from 1999 to 2006. Atmospheric Chemistry and Physics. 16:7943-7956.   10.5194/acp-16-7943-2016   AbstractWebsite

The growth in atmospheric methane (CH4) concentrations over the past 2 decades has shown large variability on a timescale of several years. Prior to 1999 the globally averaged CH4 concentration was increasing at a rate of 6.0aEuro-ppbaEuro-yr(-1), but during a stagnation period from 1999 to 2006 this growth rate slowed to 0.6aEuro-ppbaEuro-yr(-1). From 2007 to 2009 the growth rate again increased to 4.9aEuro-ppbaEuro-yr(-1). These changes in growth rate are usually ascribed to variations in CH4 emissions. We have used a 3-D global chemical transport model, driven by meteorological reanalyses and variations in global mean hydroxyl (OH) concentrations derived from CH3CCl3 observations from two independent networks, to investigate these CH4 growth variations. The model shows that between 1999 and 2006 changes in the CH4 atmospheric loss contributed significantly to the suppression in global CH4 concentrations relative to the pre-1999 trend. The largest factor in this is relatively small variations in global mean OH on a timescale of a few years, with minor contributions of atmospheric transport of CH4 to its sink region and of atmospheric temperature. Although changes in emissions may be important during the stagnation period, these results imply a smaller variation is required to explain the observed CH4 trends. The contribution of OH variations to the renewed CH4 growth after 2007 cannot be determined with data currently available.

2013
Fraser, A, Palmer PI, Feng L, Boesch H, Cogan A, Parker R, Dlugokencky EJ, Fraser PJ, Krummel PB, Langenfelds RL, O'Doherty S, Prinn RG, Steele LP, van der Schoot M, Weiss RF.  2013.  Estimating regional methane surface fluxes: the relative importance of surface and GOSAT mole fraction measurements. Atmospheric Chemistry and Physics. 13:5697-5713.   10.5194/acp-13-5697-2013   AbstractWebsite

We use an ensemble Kalman filter (EnKF), together with the GEOS-Chem chemistry transport model, to estimate regional monthly methane (CH4) fluxes for the period June 2009-December 2010 using proxy dry-air column-averaged mole fractions of methane (XCH4) from GOSAT (Greenhouse gases Observing SATellite) and/or NOAA ESRL (Earth System Research Laboratory) and CSIRO GASLAB (Global Atmospheric Sampling Laboratory) CH4 surface mole fraction measurements. Global posterior estimates using GOSAT and/or surface measurements are between 510-516 Tg yr(-1), which is less than, though within the uncertainty of, the prior global flux of 529 +/- 25 Tg yr(-1). We find larger differences between regional prior and posterior fluxes, with the largest changes in monthly emissions (75 Tg yr(-1)) occurring in Temperate Eurasia. In non-boreal regions the error reductions for inversions using the GOSAT data are at least three times larger (up to 45 %) than if only surface data are assimilated, a reflection of the greater spatial coverage of GOSAT, with the two exceptions of latitudes >60 degrees associated with a data filter and over Europe where the surface network adequately describes fluxes on our model spatial and temporal grid. We use CarbonTracker and GEOS-Chem XCO2 model output to investigate model error on quantifying proxy GOSAT XCH4 (involving model XCO2) and inferring methane flux estimates from surface mole fraction data and show similar resulting fluxes, with differences reflecting initial differences in the proxy value. Using a series of observing system simulation experiments (OSSEs) we characterize the posterior flux error introduced by non-uniform atmospheric sampling by GOSAT. We show that clear-sky measurements can theoretically reproduce fluxes within 10% of true values, with the exception of tropical regions where, due to a large seasonal cycle in the number of measurements because of clouds and aerosols, fluxes are within 15% of true fluxes. We evaluate our posterior methane fluxes by incorporating them into GEOS-Chem and sampling the model at the location and time of surface CH4 measurements from the AGAGE (Advanced Global Atmospheric Gases Experiment) network and column XCH4 measurements from TCCON (Total Carbon Column Observing Network). The posterior fluxes modestly improve the model agreement with AGAGE and TCCON data relative to prior fluxes, with the correlation coefficients (r(2)) increasing by a mean of 0.04 (range: -0.17 to 0.23) and the biases decreasing by a mean of 0.4 ppb (range: -8.9 to 8.4 ppb).