Future methane, hydroxyl, and their uncertainties: key climate and emission...

Holmes, C. D., M. Prather, O. A. Søvde, and G. Myhre (2013), Future methane, hydroxyl, and their uncertainties: key climate and emission parameters for future predictions, Atmos. Chem. Phys., 13, 285-302, doi:10.5194/acp-13-285-2013.

Accurate prediction of future methane abundances following a climate scenario requires understanding the lifetime changes driven by anthropogenic emissions, meteorological factors, and chemistry-climate feedbacks. Uncertainty in any of these influences or the underlying processes implies uncertainty in future abundance and radiative forcing. We simulate methane lifetime in three chemical transport models (CTMs) – UCI CTM, GEOS-Chem, and Oslo CTM3 – over the period 1997–2009 and compare the models’ year-to-year variability against constraints from global methyl chloroform observations. Using sensitivity tests, we find that temperature, water vapor, stratospheric ozone column, biomass burning and lightning NOx are the dominant sources of interannual changes in methane lifetime in all three models. We also evaluate each model’s response to forcings that have impacts on decadal time scales, such as methane feedback, and anthropogenic emissions. In general, these different CTMs show similar sensitivities to the driving variables. We construct a parametric model that reproduces most of the interannual variability of each CTM and use it to predict methane lifetime from 1980 through 2100 following a specified emissions and climate scenario (RCP 8.5). The parametric model propagates uncertainties through all steps and provides a foundation for predicting methane abundances in any climate scenario. Our sensitivity tests also enable a new estimate of the methane global warming potential (GWP), accounting for stratospheric ozone effects, including those mediated by water vapor. We estimate the 100-yr GWP to be 32, which is 25 % larger than past assessments.

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Atmospheric Composition Modeling and Analysis Program (ACMAP)
Modeling Analysis and Prediction Program (MAP)