In:
Geoscientific Model Development, Copernicus GmbH, Vol. 12, No. 4 ( 2019-04-04), p. 1351-1364
Abstract:
Abstract. Methane is a powerful greenhouse gas produced in wetland
environments via microbial action in anaerobic conditions. If the location and extent of
wetlands are unknown, such as for the Earth many millions of years in the past, a model
of wetland fraction is required in order to calculate methane emissions and thus help
reduce uncertainty in the understanding of past warm greenhouse climates. Here we present
an algorithm for predicting inundated wetland fraction for use in calculating wetland
methane emission fluxes in deep-time paleoclimate simulations. For each grid cell in a
given paleoclimate simulation, the algorithm determines the wetland fraction predicted by
a nearest-neighbour search of modern-day data in a space described by a set of
environmental, climate and vegetation variables. To explore this approach, we first test
it for a modern-day climate with variables obtained from observations and then for an
Eocene climate with variables derived from a fully coupled global climate model
(HadCM3BL-M2.2; Valdes et al., 2017). Two independent dynamic vegetation models were used
to provide two sets of equivalent vegetation variables which yielded two different
wetland predictions. As a first test, the method, using both vegetation models,
satisfactorily reproduces modern day wetland fraction at a course grid resolution, similar to those used in
paleoclimate simulations. We then applied the method to an early Eocene climate, testing
its outputs against the locations of Eocene coal deposits. We predict global mean monthly
wetland fraction area for the early Eocene of 8×106 to 10×106 km2 with a
corresponding total annual methane flux of 656 to 909 Tg CH4 yr−1,
depending on which of the two different dynamic global vegetation models are used to
model wetland fraction and methane emission rates. Both values are significantly higher
than estimates for the modern day of 4×106 km2 and around
190 Tg CH4 yr−1 (Poulter et al., 2017; Melton et al., 2013).
Type of Medium:
Online Resource
ISSN:
1991-9603
DOI:
10.5194/gmd-12-1351-2019
DOI:
10.5194/gmd-12-1351-2019-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2019
detail.hit.zdb_id:
2456725-5
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