Michel Bechtold


2020

DOI bib
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.

DOI bib
Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework
Michel Bechtold, Gabriëlle J. M. De Lannoy, Rolf H. Reichle, Dirk Roose, Nicole Balliston, Iuliia Burdun, K. J. Devito, Juliya Kurbatova, Maria Strack, Evgeny A. Zarov
Remote Sensing of Environment, Volume 246

Abstract There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.