Model simulations of atmospheric methane (1997–2016) and their evaluation using NOAA and AGAGE surface and IAGOS-CARIBIC aircraft observations
<p>Methane (<span class="inline-formula">CH<sub>4</sub></span>) is an important greenhouse gas, and its atmospheric budget is determined by interacting sources and sinks in a dynamic global environment. Methane observations indicate that after almost a decade...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-05-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/20/5787/2020/acp-20-5787-2020.pdf |
| Summary: | <p>Methane (<span class="inline-formula">CH<sub>4</sub></span>) is an important greenhouse gas, and its atmospheric
budget is determined by interacting sources and sinks in a dynamic global
environment. Methane observations indicate that after almost a decade of
stagnation, from 2006, a sudden and continuing global mixing ratio increase
took place. We applied a general circulation model to simulate the global
atmospheric budget, variability, and trends of methane for the period
1997–2016. Using interannually constant <span class="inline-formula">CH<sub>4</sub></span> a priori emissions from
11 biogenic and fossil source categories, the model results are compared
with observations from 17 Advanced Global Atmospheric Gases Experiment (AGAGE)
and National Oceanic and Atmospheric Administration (NOAA) surface stations and
intercontinental Civil Aircraft for
the Regular observation of the atmosphere Based on an Instrumented
Container (CARIBIC) flights, with > 4800 <span class="inline-formula">CH<sub>4</sub></span> samples,
gathered on > 320 flights in the upper troposphere and lowermost
stratosphere.</p>
<p>Based on a simple optimization procedure, methane emission categories have
been scaled to reduce discrepancies with the observational data for the
period 1997–2006. With this approach, the all-station mean dry air mole
fraction of 1780 nmol mol<span class="inline-formula"><sup>−1</sup></span> could be improved from an a priori root mean
square deviation (RMSD) of 1.31 % to just 0.61 %, associated with a
coefficient of determination (<span class="inline-formula"><i>R</i><sup>2</sup></span>) of 0.79. The simulated a priori
interhemispheric difference of 143.12 nmol mol<span class="inline-formula"><sup>−1</sup></span> was improved to
131.28 nmol mol<span class="inline-formula"><sup>−1</sup></span>, which matched the observations quite well (130.82 nmol mol<span class="inline-formula"><sup>−1</sup></span>).</p>
<p>Analogously, aircraft measurements were reproduced well, with a global RMSD
of 1.1 % for the measurements before 2007, with even better results on a
regional level (e.g., over India, with an RMSD of 0.98 % and <span class="inline-formula"><i>R</i><sup>2</sup>=0.65</span>). With regard to emission optimization, this implied a
30.2 Tg <span class="inline-formula">CH<sub>4</sub></span> yr<span class="inline-formula"><sup>−1</sup></span> reduction in predominantly fossil-fuel-related
emissions and a 28.7 Tg <span class="inline-formula">CH<sub>4</sub></span> yr<span class="inline-formula"><sup>−1</sup></span> increase of biogenic sources.</p>
<p>With the same methodology, the <span class="inline-formula">CH<sub>4</sub></span> growth that started in 2007 and
continued almost linearly through 2013 was investigated, exploring the
contributions by four potential causes, namely biogenic emissions from
tropical wetlands, from agriculture including ruminant animals, and from
rice cultivation, and anthropogenic emissions (fossil fuel sources, e.g.,
shale gas fracking) in North America. The optimization procedure adopted in
this work showed that an increase in emissions from shale gas (7.67 Tg yr<span class="inline-formula"><sup>−1</sup></span>),
rice cultivation (7.15 Tg yr<span class="inline-formula"><sup>−1</sup></span>), and tropical wetlands (0.58 Tg yr<span class="inline-formula"><sup>−1</sup></span>) for the
period 2006–2013 leads to an optimal agreement (i.e., lowest RMSD) between
model results and observations.</p> |
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| ISSN: | 1680-7316 1680-7324 |