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).
Highlights
Methane (CH4) is a powerful greenhouse gas
Since the modern-day test set is the reference climate data interpolated from 0.5◦ to the courser HadCM3BL-M2.2 model grid of 2.5◦ by 3.75◦, we expect the NN algorithm to yield predicted FW reasonably consistent with a Observed Observed excluding SDGVM bare land SDGVM LPJ
SDGVM consistently underestimates the amount of tropical wetland, whilst LPJ agrees reasonably well with observations: mean monthly values are 2.11 × 106, 1.47 × 106 and 1.90 × 106 km2 for the observed, SDGVM and LPJ data, respectively
Summary
Methane (CH4) is a powerful greenhouse gas. As well as absorbing infrared radiation from the Earth’s surface, it contributes to additional indirect warming through its photochemistry and oxidation to CO2 in the atmosphere (IPCC, 2013). Along with other trace gases, methane is an important component of the Earth’s climate system; but for studies of the past, such as warm greenhouse paleoclimates, we lack suitable geochemical or biological proxies for methane concentration. Earth system models used to reconstruct ancient climate or develop future climate scenarios must either assume atmospheric methane concentrations as a boundary condition and/or incorporate dynamic methane fluxes from natural sources and sinks (Beerling et al, 2011). The main natural source of methane is wetland environments via microbial action in anaerobic conditions (Whiticar, 1999), but methane fluxes from wetlands are . In order to model fluxes of methane to the atmosphere both the extent and locations of wetlands need to be known. Recent past and nearfuture scenarios, maps of observed wetland extent (Prigent et al, 2007; Papa et al, 2010; Schroeder et al, 2015; Poulter et al, 2017) can be used or wetland extent can be calculated at a sub-grid level from fine-resolution topographical data (as in the TOPMODEL approach of Beven and Kirkby, 1979; Lu and Zhuang, 2012; Stocker et al, 2014), as wetlands only form where the ground is relatively flat
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