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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-213
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-213
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 27 Sep 2018

Development and technical paper | 27 Sep 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

A Predictive Algorithm For Wetlands In Deep Time Paleoclimate Models

David J. Wilton1, Marcus Badger2,3,4, Euripides P. Kantzas1, Richard D. Pancost3, Paul J. Valdes4, and David J. Beerling1 David J. Wilton et al.
  • 1Dept. Animal and Plant Sciences, The University of Sheffield, Sheffield, S10 2TN, UK
  • 2School of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, MK7 6AA
  • 3Organic Geochemistry Unit, The Cabot Institute, School of Chemistry, School of Earth Sciences, The University of Bristol, Bristol, BS8 1TH, UK
  • 4Bristol Research Initiative for the Dynamic Global Environment (BRIDGE), The Cabot Institute, School of Geographical Sciences, The University of Bristol, BS8 1TH, UK

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. The algorithm determines, for each grid cell in a given paleoclimate simulation, the wetland fraction predicted by a nearest neighbours 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). 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 data 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 to 10 × 106km2 with corresponding total annual methane flux of 656 to 909 Tg, depending on which of 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 × 106km2 and around 190Tg (Poulter et. al. 2017, Melton et. al., 2013).

David J. Wilton et al.
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Short summary
Methane is an important greenhouse gas naturally produced in wetlands (areas of land inundated with water). Models of the Earth's past climate need estimates of the amounts of methane wetlands produce and in order to calculate those we need to model wetlands. In this work we set out to develop a method for modelling the fraction of an area of the Earth that is wetland, repeating this over all the Earth's land surface and applied this a study of the Earth as it was around 50 million years ago.
Methane is an important greenhouse gas naturally produced in wetlands (areas of land inundated...
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