Rapid and reliable assessment of methane impacts on climate
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Published:2018-10-30
Issue:21
Volume:18
Page:15555-15568
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ISSN:1680-7324
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Container-title:Atmospheric Chemistry and Physics
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language:en
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Short-container-title:Atmos. Chem. Phys.
Author:
Ocko Ilissa B., Naik Vaishali, Paynter DavidORCID
Abstract
Abstract. It is clear that the most effective way to limit global
temperature rise and associated impacts is to reduce human emissions of
greenhouse gases, including methane. However, quantification of the climate
benefits of mitigation options are complicated by the contrast in the
timescales at which short-lived climate pollutants, such as methane, persist
in the atmosphere compared to carbon dioxide. Whereas simple metrics fail
to capture the differential impacts across all timescales, sophisticated
climate models that can address these temporal dynamics are often
inaccessible, time-intensive, require special infrastructure, and include
high unforced interannual variability that makes it difficult to analyse
small changes in forcings. On the other hand, reduced-complexity climate
models that use basic knowledge from observations and complex Earth system
models offer an ideal compromise in that they provide quick, reliable
insights into climate responses, with only limited computational
infrastructure needed. They are particularly useful for simulating the
response to forcings of small changes in different climate pollutants, due to
the absence of internal variability. In this paper, we build on previous
evaluations of the freely available and easy-to-run reduced-complexity
climate model MAGICC by comparing temperature responses to historical methane
emissions to those from a more complex coupled global chemistry–climate
model, GFDL-CM3. While we find that the overall forcings and temperature
responses are comparable between the two models, the prominent role of
unforced variability in CM3 demonstrates how sophisticated models are
potentially inappropriate tools for small forcing scenarios. On the other
hand, we find that MAGICC can easily and rapidly provide robust data on
climate responses to changes in methane emissions with clear signals
unfettered by variability. We are therefore able to build confidence in using
reduced-complexity climate models such as MAGICC for purposes of
understanding the climate implications of methane mitigation.
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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