Fortunat Joos


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Expert assessment of future vulnerability of the global peatland carbon sink
Julie Loisel, Angela Gallego‐Sala, Matthew J. Amesbury, Gabriel Magnan, Gusti Z. Anshari, David W. Beilman, Juan C. Benavides, Jerome Blewett, Philip Camill, Dan J. Charman, Sakonvan Chawchai, A. Hedgpeth, Thomas Kleinen, Atte Korhola, David J. Large, Claudia A Mansilla, Jurek Müller, Simon van Bellen, Jason B. West, Zicheng Yu, Jill L. Bubier, Michelle Garneau, Tim R. Moore, A. Britta K. Sannel, Susan Page, Minna Vӓliranta, Michel Bechtold, Victor Brovkin, Lydia E.S. Cole, Jeffrey P. Chanton, Torben R. Christensen, Marissa A. Davies, François De Vleeschouwer, Sarah A. Finkelstein, Steve Frolking, Mariusz Gałka, Laure Gandois, Nicholas T. Girkin, Lorna I. Harris, Andreas Heinemeyer, Alison M. Hoyt, Miriam C. Jones, Fortunat Joos, Sari Juutinen, Karl Kaiser, Terri Lacourse, Mariusz Lamentowicz, Tuula Larmola, Jens Leifeld, Annalea Lohila, Alice M. Milner, Kari Minkkinen, Patrick Moss, B. David A. Naafs, J. E. Nichols, J. A. O’Donnell, Richard J. Payne, Michael Philben, Sanna Piilo, Anne Quillet, Amila Sandaruwan Ratnayake, Thomas P. Roland, Sofie Sjögersten, Oliver Sonnentag, Graeme T. Swindles, Ward Swinnen, Julie Talbot, Claire C. Treat, Amy Valach, Jiequn Wu
Nature Climate Change, Volume 11, Issue 1

The carbon balance of peatlands is predicted to shift from a sink to a source this century. However, peatland ecosystems are still omitted from the main Earth system models that are used for future climate change projections, and they are not considered in integrated assessment models that are used in impact and mitigation studies. By using evidence synthesized from the literature and an expert elicitation, we define and quantify the leading drivers of change that have impacted peatland carbon stocks during the Holocene and predict their effect during this century and in the far future. We also identify uncertainties and knowledge gaps in the scientific community and provide insight towards better integration of peatlands into modelling frameworks. Given the importance of the contribution by peatlands to the global carbon cycle, this study shows that peatland science is a critical research area and that we still have a long way to go to fully understand the peatland–carbon–climate nexus. Peatlands are impacted by climate and land-use changes, with feedback to warming by acting as either sources or sinks of carbon. Expert elicitation combined with literature review reveals key drivers of change that alter peatland carbon dynamics, with implications for improving models.


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Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations
Olli Peltola, Timo Vesala, Yao Gao, Olle Räty, Pavel Alekseychik, Mika Aurela, Bogdan H. Chojnicki, Ankur R. Desai, A. J. Dolman, Eugénie Euskirchen, Thomas Friborg, Mathias Göckede, Manuel Helbig, Elyn Humphreys, Robert B. Jackson, Georg Jocher, Fortunat Joos, Janina Klatt, Sara Knox, Natalia Kowalska, Lars Kutzbach, Sebastian Lienert, Annalea Lohila, Ivan Mammarella, Daniel F. Nadeau, Mats Nilsson, Walter C. Oechel, Matthias Peichl, Thomas G. Pypker, William L. Quinton, Janne Rinne, Torsten Sachs, Mateusz Samson, Hans Peter Schmid, Oliver Sonnentag, Christian Wille, Donatella Zona, Tuula Aalto
Earth System Science Data, Volume 11, Issue 3

Abstract. Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at (Peltola et al., 2019).