A simple model for estimating methane concentration and lifetime variations View Full Text


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Article Info

DATE

1994-01

AUTHORS

T. J. Osborn, T. M. L. Wigley

ABSTRACT

A simple methane model is presented in which lifetime changes are expressed as a function of CH4 concentration and emissions of NOx CO and NMHCs. The model parameters define the relative sensitivities of lifetime to these determining factors. The parameterized model is fitted to results from five more complex atmospheric chemistry models and to 1990 IPCC concentration projections. The IPCC data and four of the five models are well fitted, implying that the models have similar relative sensitivities. However, overall sensitivities of lifetime to changes in atmospheric composition vary widely from model to model. The parameterized model is used to estimate the history of past methane emissions, lifetime changes and OH variations, with estimates of uncertainties. The pre-industrial lifetime is estimated to be 15–34% lower than today. This implies that 23–55% of past concentration changes are due to lifetime changes. Pre-industrial emissions are found to be much higher (220–330 TgCH4/y) than the best estimate of present natural emissions (155 TgCH4/y). The change in emissions since pre-industrial times is estimated to lie in the range 160–260 TgCH4/y, compared with the current best guess for anthropogenic emissions of 360 TgCH4/y. These results imply either that current estimates of anthropogenic emissions are too high and/or that there have been large changes in natural emissions. 1992 IPCC emissions scenarios are used to give projections of future concentration and lifetime changes, together with their uncertainties. For any given emissions scenario, these uncertainties are large. In terms of future radiative forcing and global-mean temperature changes over 1990–2100 they correspond to uncertainties of at least ±0.2 Wm−2 and ± 0.1° C, respectively. More... »

PAGES

181-193

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00208251

DOI

http://dx.doi.org/10.1007/bf00208251

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049765350


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