Coupled land-atmosphere modeling of methane emissions with WRF
Abstract
This project aims to couple a soil model for methane transport to an atmospheric model to predict methane emissions and dispersion. Methane is a potent greenhouse gas, 20 times as efficient at trapping heat in the atmosphere as the most prevalent greenhouse gas, carbon dioxide. It has been estimated that 60% of methane emissions in the earth's atmosphere come from anthropogenic sources, 17% of which comes from landfills, making landfills the third largest contributor of human-generated methane. Due to high costs and non-ideal weather conditions, field measurements of methane concentration at landfills are difficult and infrequent, so estimates of annual emissions from landfills are not very accurate. We plan to create a coupled land-atmosphere model that takes production and oxidation of methane into account when calculating methane emissions. This model will give a better understanding of how much methane is emitted annually from a given landfill and assist with monitoring efforts. It will also demonstrate the magnitude of diurnal and seasonal variations in methane emissions, which may identify errors in yearly methane emissions estimates made by extrapolating from a small number of field measurements. As a first step, an existing land-surface model, Noah, is modified to compute the transport of oxygen and methane along a 1-D soil column. Surface emissions are calculated using a gradient flux method with a boundary layer conductance that depends on the wind speed. These modifications to the land-surface model will be added to the Weather Research and Forecasting model to predict atmospheric dispersion of methane emitted by landfills. Comparisons to observations are made at two different landfill sites to validate the coupled model.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFM.H43D1467T
- Keywords:
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- 3322 ATMOSPHERIC PROCESSES Land/atmosphere interactions;
- 1843 HYDROLOGY Land/atmosphere interactions;
- 3333 ATMOSPHERIC PROCESSES Model calibration