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Your search keyword '"Wilton, David A."' showing total 5 results
Start Over Author "Wilton, David A." Publisher copernicus gesellschaft mbh
5 results on '"Wilton, David A."'

1. GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet

Author

Fettweis, Xavier, Hofer, Stefan, Krebs-Kanzow, Uta, Amory, Charles, Aoki, Teruo, Berends, Constantijn J., Born, Andreas, Box, Jason E., Delhasse, Alison, Fujita, Koji, Gierz, Paul, Goelzer, Heiko, Hanna, Edward, Hashimoto, Akihiro, Huybrechts, Philippe, Kapsch, Marie-Luise, King, Michalea D., Kittel, Christoph, Lang, Charlotte, Langen, Peter L., Lenaerts, Jan T. M., Liston, Glen E., Lohmann, Gerrit, Mernild, Sebastian H., Mikolajewicz, Uwe, Modali, Kameswarrao, Mottram, Ruth H., Niwano, Masashi, Noël, Brice, Ryan, Jonathan C., Smith, Amy, Streffing, Jan, Tedesco, Marco, van de Berg, Willem Jan, van den Broeke, Michiel, van de Wal, Roderik S. W., van Kampenhout, Leo, Wilton, David, Wouters, Bert, Ziemen, Florian, Zolles, Tobias, Fettweis, Xavier, Hofer, Stefan, Krebs-Kanzow, Uta, Amory, Charles, Aoki, Teruo, Berends, Constantijn J., Born, Andreas, Box, Jason E., Delhasse, Alison, Fujita, Koji, Gierz, Paul, Goelzer, Heiko, Hanna, Edward, Hashimoto, Akihiro, Huybrechts, Philippe, Kapsch, Marie-Luise, King, Michalea D., Kittel, Christoph, Lang, Charlotte, Langen, Peter L., Lenaerts, Jan T. M., Liston, Glen E., Lohmann, Gerrit, Mernild, Sebastian H., Mikolajewicz, Uwe, Modali, Kameswarrao, Mottram, Ruth H., Niwano, Masashi, Noël, Brice, Ryan, Jonathan C., Smith, Amy, Streffing, Jan, Tedesco, Marco, van de Berg, Willem Jan, van den Broeke, Michiel, van de Wal, Roderik S. W., van Kampenhout, Leo, Wilton, David, Wouters, Bert, Ziemen, Florian, and Zolles, Tobias

Published
2020

2. A predictive algorithm for wetlands in deep time paleoclimate models.

Author

Wilton, David J., Badger, Marcus P. S., Kantzas, Euripides P., Pancost, Richard D., Valdes, Paul J., and Beerling, David J.

Subjects
*WETLANDS, *WETLAND soils, *ATMOSPHERIC methane, *GRID cells, *VEGETATION & climate, *CLIMATE in greenhouses, *ATMOSPHERIC models, *GREENHOUSE gases
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 km 2 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 km 2 and around 190 Tg CH4 yr -1 (Poulter et al., 2017; Melton et al., 2013). [ABSTRACT FROM AUTHOR]

Published
2019
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3. Variability and quasi-decadal changes in the methane budget over the period 2000-2012

Author

Saunois, Marielle, Bousquet, Philippe, Poulter, Ben, Peregon, Anna, Ciais, Philippe, Canadell, Josep G., Dlugokencky, Edward J., Etiope, Giuseppe, Bastviken, David, Houweling, Sander, Janssens-Maenhout, Greet, Tubiello, Francesco N., Castaldi, Simona, Jackson, Robert B., Alexe, Mihai, Arora, Vivek K., Beerling, David J., Bergamaschi, Peter, Blake, Donald R., Brailsford, Gordon, Bruhwiler, Lori, Crevoisier, Cyril, Crill, Patrick, Covey, Kristofer, Frankenberg, Christian, Gedney, Nicola, Hoeglund-Isaksson, Lena, Ishizawa, Misa, Ito, Akihiko, Joos, Fortunat, Kim, Heon-Sook, Kleinen, Thomas, Krummel, Paul, Lamarque, Jean-Francois, Langenfelds, Ray, Locatelli, Robin, Machida, Toshinobu, Maksyutov, Shamil, Melton, Joe R., Morino, Isamu, Naik, Vaishali, ODoherty, Simon, Parmentier, Frans-JanW., Patra, Prabir K., Peng, Changhui, Peng, Shushi, Peters, Glen P., Pison, Isabelle, Prinn, Ronald, Ramonet, Michel, Riley, William J., Saito, Makoto, Santini, Monia, Schroeder, Ronny, Simpson, Isobel J., Spahni, Renato, Takizawa, Atsushi, Thornton, Brett F., Tian, Hanqin, Tohjima, Yasunori, Viovy, Nicolas, Voulgarakis, Apostolos, Weiss, Ray, Wilton, David J., Wiltshire, Andy, Worthy, Doug, Wunch, Debra, Xu, Xiyan, Yoshida, Yukio, Zhang, Bowen, Zhang, Zhen, Zhu, Qiuan, Saunois, Marielle, Bousquet, Philippe, Poulter, Ben, Peregon, Anna, Ciais, Philippe, Canadell, Josep G., Dlugokencky, Edward J., Etiope, Giuseppe, Bastviken, David, Houweling, Sander, Janssens-Maenhout, Greet, Tubiello, Francesco N., Castaldi, Simona, Jackson, Robert B., Alexe, Mihai, Arora, Vivek K., Beerling, David J., Bergamaschi, Peter, Blake, Donald R., Brailsford, Gordon, Bruhwiler, Lori, Crevoisier, Cyril, Crill, Patrick, Covey, Kristofer, Frankenberg, Christian, Gedney, Nicola, Hoeglund-Isaksson, Lena, Ishizawa, Misa, Ito, Akihiko, Joos, Fortunat, Kim, Heon-Sook, Kleinen, Thomas, Krummel, Paul, Lamarque, Jean-Francois, Langenfelds, Ray, Locatelli, Robin, Machida, Toshinobu, Maksyutov, Shamil, Melton, Joe R., Morino, Isamu, Naik, Vaishali, ODoherty, Simon, Parmentier, Frans-JanW., Patra, Prabir K., Peng, Changhui, Peng, Shushi, Peters, Glen P., Pison, Isabelle, Prinn, Ronald, Ramonet, Michel, Riley, William J., Saito, Makoto, Santini, Monia, Schroeder, Ronny, Simpson, Isobel J., Spahni, Renato, Takizawa, Atsushi, Thornton, Brett F., Tian, Hanqin, Tohjima, Yasunori, Viovy, Nicolas, Voulgarakis, Apostolos, Weiss, Ray, Wilton, David J., Wiltshire, Andy, Worthy, Doug, Wunch, Debra, Xu, Xiyan, Yoshida, Yukio, Zhang, Bowen, Zhang, Zhen, and Zhu, Qiuan

Abstract

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000-2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000-2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000-2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008-2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16-32] Tg CH4 yr(-1) higher methane emissions over the period 2008-2012 compared to 2002-2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002-2006 and 2008-2012 differs from one atmospheric inversion study to another. However, all top-down studies su, Funding Agencies|EU FP7 GEOCARBON project; National Environmental Science Program - Earth Systems and Climate Change Hub; NASA [NNX07AK10G]; Swiss National Science Foundation; National Science and Engineering Research Council of Canada (NSERC) discovery grant; Chinas QianRen Program; Research Council of Norway project [209701]; Swedish Research Council VR; ERC [725546]; Environment Research and Technology Development Fund of the Ministry of the Environment, Japan [A2-1502]; Office of Science, Office of Biological and Environmental Research of the US Department of Energy as part of the RGCM BGC-Climate Feedbacks SFA [DE-AC02-05CH11231]; European Commission Seventh Framework Programme (FP7) project MACCII [283576]; European Commission Horizon Programme project MACC-III [633080]; ESA Climate Change Initiative Greenhouse Gases Phase 2 project; NASA Carbon Monitoring Program [NNX12AP84G, NNX14AO73G]; Joint DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]; ERC Advanced grant (CDREG) [322998]; NERC [NE/J00748X/1]; CSIRO Australia; Australian Bureau of Meteorology; Australian Institute of Marine Science; Australian Antarctic Division; NOAA USA; Meteorological Service of Canada; National Aeronautic and Space Administration (NASA) [NAG5-12669, NNX07AE89G, NNX11AF17G, NNX07AE87G, NNX07AF09G, NNX11AF15G, NNX11AF16G]; Department of Energy and Climate Change (DECC, UK) [GA01081]; Commonwealth Scientific and Industrial Research Organisation (CSIRO Australia); Bureau of Meteorology (Australia)

Published
2017
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4. The global methane budget 2000-2012

Author

Saunois, Marielle, Bousquet, Philippe, Poulter, Ben, Peregon, Anna, Ciais, Philippe, Canadell, Josep G., Dlugokencky, Edward J., Etiope, Giuseppe, Bastviken, David, Houweling, Sander, Janssens-Maenhout, Greet, Tubiello, Francesco N., Castaldi, Simona, Jackson, Robert B., Alexe, Mihai, Arora, Vivek K., Beerling, David J., Bergamaschi, Peter, Blake, Donald R., Brailsford, Gordon, Brovkin, Victor, Bruhwiler, Lori, Crevoisier, Cyril, Crill, Patrick, Covey, Kristofer, Curry, Charles, Frankenberg, Christian, Gedney, Nicola, Hoeglund-Isaksson, Lena, Ishizawa, Misa, Ito, Akihiko, Joos, Fortunat, Kim, Heon-Sook, Kleinen, Thomas, Krummel, Paul, Lamarque, Jean-Francois, Langenfelds, Ray, Locatelli, Robin, Machida, Toshinobu, Maksyutov, Shamil, McDonald, Kyle C., Marshall, Julia, Melton, Joe R., Morino, Isamu, Naik, Vaishali, ODoherty, Simon, Parmentier, Frans-Jan W., Patra, Prabir K., Peng, Changhui, Peng, Shushi, Peters, Glen P., Pison, Isabelle, Prigent, Catherine, Prinn, Ronald, Ramonet, Michel, Riley, William J., Saito, Makoto, Santini, Monia, Schroeder, Ronny, Simpson, Isobel J., Spahni, Renato, Steele, Paul, Takizawa, Atsushi, Thornton, Brett F., Tian, Hanqin, Tohjima, Yasunori, Viovy, Nicolas, Voulgarakis, Apostolos, van Weele, Michiel, van der Werf, Guido R., Weiss, Ray, Wiedinmyer, Christine, Wilton, David J., Wiltshire, Andy, Worthy, Doug, Wunch, Debra, Xu, Xiyan, Yoshida, Yukio, Zhang, Bowen, Zhang, Zhen, Zhu, Qiuan, Saunois, Marielle, Bousquet, Philippe, Poulter, Ben, Peregon, Anna, Ciais, Philippe, Canadell, Josep G., Dlugokencky, Edward J., Etiope, Giuseppe, Bastviken, David, Houweling, Sander, Janssens-Maenhout, Greet, Tubiello, Francesco N., Castaldi, Simona, Jackson, Robert B., Alexe, Mihai, Arora, Vivek K., Beerling, David J., Bergamaschi, Peter, Blake, Donald R., Brailsford, Gordon, Brovkin, Victor, Bruhwiler, Lori, Crevoisier, Cyril, Crill, Patrick, Covey, Kristofer, Curry, Charles, Frankenberg, Christian, Gedney, Nicola, Hoeglund-Isaksson, Lena, Ishizawa, Misa, Ito, Akihiko, Joos, Fortunat, Kim, Heon-Sook, Kleinen, Thomas, Krummel, Paul, Lamarque, Jean-Francois, Langenfelds, Ray, Locatelli, Robin, Machida, Toshinobu, Maksyutov, Shamil, McDonald, Kyle C., Marshall, Julia, Melton, Joe R., Morino, Isamu, Naik, Vaishali, ODoherty, Simon, Parmentier, Frans-Jan W., Patra, Prabir K., Peng, Changhui, Peng, Shushi, Peters, Glen P., Pison, Isabelle, Prigent, Catherine, Prinn, Ronald, Ramonet, Michel, Riley, William J., Saito, Makoto, Santini, Monia, Schroeder, Ronny, Simpson, Isobel J., Spahni, Renato, Steele, Paul, Takizawa, Atsushi, Thornton, Brett F., Tian, Hanqin, Tohjima, Yasunori, Viovy, Nicolas, Voulgarakis, Apostolos, van Weele, Michiel, van der Werf, Guido R., Weiss, Ray, Wiedinmyer, Christine, Wilton, David J., Wiltshire, Andy, Worthy, Doug, Wunch, Debra, Xu, Xiyan, Yoshida, Yukio, Zhang, Bowen, Zhang, Zhen, and Zhu, Qiuan

Abstract

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (similar to biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH(4) yr(-1), range 540-568. About 60% of global emissions are anthropogenic (range 50-65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scen, Funding Agencies|Swiss National Science Foundation; NASA [NNX14AF93G, NNX14AO73G]; National Environmental Science Program - Earth Systems and Climate Change Hub; European Commission [283576, 633080]; ESA Climate Change Initiative Greenhouse Gases Phase 2 project; US Department of Energy, BER [DE-AC02-05CH11231]; FAO member countries; Environment Research and Technology Development Fund of the Ministry of the Environment, Japan [2-1502]; ERC [322998]; NERC [NE/J00748X/1]; Swedish Research Council VR; Research Council of Norway [244074]; NSF [1243232, 1243220]; National Science and Engineering Research Council of Canada (NSERC); Chinas QianRen Program; CSIRO Australia; Australian Bureau of Meteorology; Australian Institute of Marine Science; Australian Antarctic Division; NOAA USA; Meteorological Service of Canada; National Aeronautic and Space Administration (NASA) [NAG5-12669, NNX07AE89G, NNX11AF17G, NNX07AE87G, NNX07AF09G, NNX11AF15G, NNX11AF16G]; Department of Energy and Climate Change (DECC, UK) [GA01081]; Commonwealth Scientific and Industrial Research Organization (CSIRO Australia); Bureau of Meteorology (Australia); Joint DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]

Published
2016
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5. A Predictive Algorithm For Wetlands In Deep Time Paleoclimate Models.

Author

Wilton, David J., Badger, Marcus, Kantzas, Euripides P., Pancost, Richard D., Valdes, Paul J., and Beerling, David J.

Subjects
*WETLANDS, *PALEOCLIMATOLOGY
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 x 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 x 106km² and around 190Tg (Poulter et. al. 2017, Melton et. al., 2013). [ABSTRACT FROM AUTHOR]

Published
2018
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