Hans Peter Schmid


2022

DOI bib
Causality guided machine learning model on wetland CH4 emissions across global wetlands
Kunxiaojia Yuan, Qing Zhu, Fa Li, William J. Riley, M. S. Torn, Housen Chu, Gavin McNicol, Min Chen, Sara Knox, Kyle Delwiche, Huayi Wu, Dennis Baldocchi, Hengbo Ma, Ankur R. Desai, Jiquan Chen, Torsten Sachs, Masahito Ueyama, Oliver Sonnentag, Manuel Helbig, Eeva‐Stiina Tuittila, Gerald Jurasinski, Franziska Koebsch, David I. Campbell, Hans Peter Schmid, Annalea Lohila, Mathias Goeckede, Mats Nilsson, Thomas Friborg, Joachim Jansen, Donatella Zona, Eugénie Euskirchen, Eric J. Ward, Gil Bohrer, Zhenong Jin, Licheng Liu, Hiroyasu Iwata, Jordan P. Goodrich, Robert B. Jackson
Agricultural and Forest Meteorology, Volume 324

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.

2021

DOI bib
Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
Kuang‐Yu Chang, William J. Riley, Sara Knox, Robert B. Jackson, Gavin McNicol, Benjamin Poulter, Mika Aurela, Dennis Baldocchi, Sheel Bansal, Gil Bohrer, David I. Campbell, Alessandro Cescatti, Housen Chu, Kyle Delwiche, Ankur R. Desai, Eugénie Euskirchen, Thomas Friborg, Mathias Goeckede, Manuel Helbig, Kyle S. Hemes, Takashi Hirano, Hiroyasu Iwata, Minseok Kang, Trevor F. Keenan, Ken W. Krauss, Annalea Lohila, Ivan Mammarella, Bhaskar Mitra, Akira Miyata, Mats Nilsson, Asko Noormets, Walter C. Oechel, Dario Papale, Matthias Peichl, M. L. Reba, Janne Rinne, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Karina V. R. Schäfer, Hans Peter Schmid, Narasinha Shurpali, Oliver Sonnentag, Angela C. I. Tang, M. S. Torn, Carlo Trotta, Eeva‐Stiina Tuittila, Masahito Ueyama, Rodrigo Vargas, Timo Vesala, Lisamarie Windham‐Myers, Zhen Zhang, Donatella Zona
Nature Communications, Volume 12, Issue 1

Abstract Wetland methane (CH 4 ) emissions ( $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> and temperature using observations from the FLUXNET-CH 4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>C</mml:mi> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH 4 production are thus needed to improve global CH 4 budget assessments.

DOI bib
Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello, Carlo Trotta, E. Canfora, Housen Chu, Danielle Christianson, You-Wei Cheah, C. Poindexter, Jiquan Chen, Abdelrahman Elbashandy, Marty Humphrey, Peter Isaac, Diego Polidori, Markus Reichstein, Alessio Ribeca, Catharine van Ingen, Nicolas Vuichard, Leiming Zhang, B.D. Amiro, Christof Ammann, M. Altaf Arain, Jonas Ardö, Timothy J. Arkebauer, Stefan K. Arndt, Nicola Arriga, Marc Aubinet, Mika Aurela, Dennis Baldocchi, Alan Barr, Eric Beamesderfer, Luca Belelli Marchesini, Onil Bergeron, Jason Beringer, Christian Bernhofer, Daniel Berveiller, D. P. Billesbach, T. Andrew Black, Peter D. Blanken, Gil Bohrer, Julia Boike, Paul V. Bolstad, Damien Bonal, Jean-Marc Bonnefond, David R. Bowling, Rosvel Bracho, Jason Brodeur, Christian Brümmer, Nina Buchmann, Benoît Burban, Sean P. Burns, Pauline Buysse, Peter Cale, M. Cavagna, Pierre Cellier, Shiping Chen, Isaac Chini, Torben R. Christensen, James Cleverly, Alessio Collalti, Claudia Consalvo, Bruce D. Cook, David Cook, Carole Coursolle, Edoardo Cremonese, Peter S. Curtis, Ettore D’Andrea, Humberto da Rocha, Xiaoqin Dai, Kenneth J. Davis, Bruno De Cinti, A. de Grandcourt, Anne De Ligne, Raimundo Cosme de Oliveira, Nicolas Delpierre, Ankur R. Desai, Carlos Marcelo Di Bella, Paul Di Tommasi, A. J. Dolman, Francisco Domingo, Gang Dong, Sabina Dore, Pierpaolo Duce, Éric Dufrêne, Allison L. Dunn, J.T. Dusek, Derek Eamus, Uwe Eichelmann, Hatim Abdalla M. ElKhidir, Werner Eugster, Cäcilia Ewenz, B. E. Ewers, D. Famulari, Silvano Fares, Iris Feigenwinter, Andrew Feitz, Rasmus Fensholt, Gianluca Filippa, M. L. Fischer, J. M. Frank, Marta Galvagno, Mana Gharun, Damiano Gianelle, Bert Gielen, Beniamino Gioli, Anatoly A. Gitelson, Ignacio Goded, Mathias Goeckede, Allen H. Goldstein, Christopher M. Gough, Michael L. Goulden, Alexander Graf, Anne Griebel, Carsten Gruening, Thomas Grünwald, Albin Hammerle, Shijie Han, Xingguo Han, Birger Ulf Hansen, Chad Hanson, Juha Hatakka, Yongtao He, Markus Hehn, Bernard Heinesch, Nina Hinko‐Najera, Lukas Hörtnagl, Lindsay B. Hutley, Andreas Ibrom, Hiroki Ikawa, Marcin Jackowicz-Korczyński, Dalibor Janouš, W.W.P. Jans, Rachhpal S. Jassal, Shicheng Jiang, Tomomichi Kato, Myroslava Khomik, Janina Klatt, Alexander Knohl, Sara Knox, Hideki Kobayashi, Georgia R. Koerber, Olaf Kolle, Yukio Kosugi, Ayumi Kotani, Andrew S. Kowalski, B. Kruijt, Juliya Kurbatova, Werner L. Kutsch, Hyojung Kwon, Samuli Launiainen, Tuomas Laurila, B. E. Law, R. Leuning, Yingnian Li, Michael J. Liddell, Jean‐Marc Limousin, Marryanna Lion, Adam Liska, Annalea Lohila, Ana López‐Ballesteros, Efrén López‐Blanco, Benjamin Loubet, Denis Loustau, Antje Lucas-Moffat, Johannes Lüers, Siyan Ma, Craig Macfarlane, Vincenzo Magliulo, Regine Maier, Ivan Mammarella, Giovanni Manca, Barbara Marcolla, Hank A. Margolis, Serena Marras, W. J. Massman, Mikhail Mastepanov, Roser Matamala, Jaclyn Hatala Matthes, Francesco Mazzenga, Harry McCaughey, Ian McHugh, Andrew M. S. McMillan, Lutz Merbold, Wayne S. Meyer, Tilden P. Meyers, S. D. Miller, Stefano Minerbi, Uta Moderow, Russell K. Monson, Leonardo Montagnani, Caitlin E. Moore, E.J. Moors, Virginie Moreaux, Christine Moureaux, J. William Munger, T. Nakai, Johan Neirynck, Zoran Nesic, Giacomo Nicolini, Asko Noormets, Matthew Northwood, Marcelo D. Nosetto, Yann Nouvellon, Kimberly A. Novick, W. C. Oechel, Jørgen E. Olesen, Jean‐Marc Ourcival, S. A. Papuga, Frans‐Jan W. Parmentier, Eugénie Paul‐Limoges, Marián Pavelka, Matthias Peichl, Elise Pendall, Richard P. Phillips, Kim Pilegaard, Norbert Pirk, Gabriela Posse, Thomas L. Powell, Heiko Prasse, Suzanne M. Prober, Serge Rambal, Üllar Rannik, Naama Raz‐Yaseef, Corinna Rebmann, David E. Reed, Víctor Resco de Dios, Natalia Restrepo‐Coupe, Borja R. Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, S. R. Saleska, Enrique P. Sánchez-Cañete, Z. M. Sánchez-Mejía, Hans Peter Schmid, Marius Schmidt, Karl Schneider, Frederik Schrader, Ivan Schroder, Russell L. Scott, Pavel Sedlák, Penélope Serrano-Ortíz, Changliang Shao, Peili Shi, Ivan Shironya, Lukas Siebicke, Ladislav Šigut, Richard Silberstein, Costantino Sirca, Donatella Spano, R. Steinbrecher, Robert M. Stevens, Cove Sturtevant, Andy Suyker, Torbern Tagesson, Satoru Takanashi, Yanhong Tang, Nigel Tapper, Jonathan E. Thom, Michele Tomassucci, Juha‐Pekka Tuovinen, S. P. Urbanski, Р. Валентини, M. K. van der Molen, Eva van Gorsel, J. van Huissteden, Andrej Varlagin, Joe Verfaillie, Timo Vesala, Caroline Vincke, Domenico Vitale, N. N. Vygodskaya, Jeffrey P. Walker, Elizabeth A. Walter‐Shea, Huimin Wang, R. J. Weber, Sebastian Westermann, Christian Wille, Steven C. Wofsy, Georg Wohlfahrt, Sebastian Wolf, William Woodgate, Yuelin Li, Roberto Zampedri, Junhui Zhang, Guoyi Zhou, Donatella Zona, D. Agarwal, S. Biraud, M. S. Torn, Dario Papale
Scientific Data, Volume 8, Issue 1

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

2020

DOI bib
The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello, Carlo Trotta, E. Canfora, Housen Chu, Danielle Christianson, You-Wei Cheah, C. Poindexter, Jiquan Chen, Abdelrahman Elbashandy, Marty Humphrey, Peter Isaac, Diego Polidori, Markus Reichstein, Alessio Ribeca, Catharine van Ingen, Nicolas Vuichard, Leiming Zhang, B.D. Amiro, Christof Ammann, M. Altaf Arain, Jonas Ardö, Timothy J. Arkebauer, Stefan K. Arndt, Nicola Arriga, Marc Aubinet, Mika Aurela, Dennis Baldocchi, Alan Barr, Eric Beamesderfer, Luca Belelli Marchesini, Onil Bergeron, Jason Beringer, Christian Bernhofer, Daniel Berveiller, D. P. Billesbach, T. Andrew Black, Peter D. Blanken, Gil Bohrer, Julia Boike, Paul V. Bolstad, Damien Bonal, Jean-Marc Bonnefond, David R. Bowling, Rosvel Bracho, Jason Brodeur, Christian Brümmer, Nina Buchmann, Benoît Burban, Sean P. Burns, Pauline Buysse, Peter Cale, M. Cavagna, Pierre Cellier, Shiping Chen, Isaac Chini, Torben R. Christensen, James Cleverly, Alessio Collalti, Claudia Consalvo, Bruce D. Cook, David Cook, Carole Coursolle, Edoardo Cremonese, Peter S. Curtis, Ettore D’Andrea, Humberto da Rocha, Xiaoqin Dai, Kenneth J. Davis, Bruno De Cinti, A. de Grandcourt, Anne De Ligne, Raimundo Cosme de Oliveira, Nicolas Delpierre, Ankur R. Desai, Carlos Marcelo Di Bella, Paul Di Tommasi, A. J. Dolman, Francisco Domingo, Gang Dong, Sabina Dore, Pierpaolo Duce, Éric Dufrêne, Allison L. Dunn, J.T. Dusek, Derek Eamus, Uwe Eichelmann, Hatim Abdalla M. ElKhidir, Werner Eugster, Cäcilia Ewenz, B. E. Ewers, D. Famulari, Silvano Fares, Iris Feigenwinter, Andrew Feitz, Rasmus Fensholt, Gianluca Filippa, M. L. Fischer, J. M. Frank, Marta Galvagno, Mana Gharun, Damiano Gianelle, Bert Gielen, Beniamino Gioli, Anatoly A. Gitelson, Ignacio Goded, Mathias Goeckede, Allen H. Goldstein, Christopher M. Gough, Michael L. Goulden, Alexander Graf, Anne Griebel, Carsten Gruening, Thomas Grünwald, Albin Hammerle, Shijie Han, Xingguo Han, Birger Ulf Hansen, Chad Hanson, Juha Hatakka, Yongtao He, Markus Hehn, Bernard Heinesch, Nina Hinko‐Najera, Lukas Hörtnagl, Lindsay B. Hutley, Andreas Ibrom, Hiroki Ikawa, Marcin Jackowicz-Korczyński, Dalibor Janouš, W.W.P. Jans, Rachhpal S. Jassal, Shicheng Jiang, Tomomichi Kato, Myroslava Khomik, Janina Klatt, Alexander Knohl, Sara Knox, Hideki Kobayashi, Georgia R. Koerber, Olaf Kolle, Yukio Kosugi, Ayumi Kotani, Andrew S. Kowalski, B. Kruijt, Juliya Kurbatova, Werner L. Kutsch, Hyojung Kwon, Samuli Launiainen, Tuomas Laurila, B. E. Law, R. Leuning, Yingnian Li, Michael J. Liddell, Jean‐Marc Limousin, Marryanna Lion, Adam Liska, Annalea Lohila, Ana López‐Ballesteros, Efrén López‐Blanco, Benjamin Loubet, Denis Loustau, Antje Maria Moffat, Johannes Lüers, Siyan Ma, Craig Macfarlane, Vincenzo Magliulo, Regine Maier, Ivan Mammarella, Giovanni Manca, Barbara Marcolla, Hank A. Margolis, Serena Marras, W. J. Massman, Mikhail Mastepanov, Roser Matamala, Jaclyn Hatala Matthes, Francesco Mazzenga, Harry McCaughey, Ian McHugh, Andrew M. S. McMillan, Lutz Merbold, Wayne S. Meyer, Tilden P. Meyers, S. D. Miller, Stefano Minerbi, Uta Moderow, Russell K. Monson, Leonardo Montagnani, Caitlin E. Moore, E.J. Moors, Virginie Moreaux, Christine Moureaux, J. William Munger, T. Nakai, Johan Neirynck, Zoran Nesic, Giacomo Nicolini, Asko Noormets, Matthew Northwood, Marcelo D. Nosetto, Yann Nouvellon, Kimberly A. Novick, W. C. Oechel, Jørgen E. Olesen, Jean‐Marc Ourcival, S. A. Papuga, Frans‐Jan W. Parmentier, Eugénie Paul‐Limoges, Marián Pavelka, Matthias Peichl, Elise Pendall, Richard P. Phillips, Kim Pilegaard, Norbert Pirk, Gabriela Posse, Thomas L. Powell, Heiko Prasse, Suzanne M. Prober, Serge Rambal, Üllar Rannik, Naama Raz‐Yaseef, Corinna Rebmann, David E. Reed, Víctor Resco de Dios, Natalia Restrepo‐Coupe, Borja R. Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, S. R. Saleska, Enrique P. Sánchez-Cañete, Z. M. Sánchez-Mejía, Hans Peter Schmid, Marius Schmidt, Karl Schneider, Frederik Schrader, Ivan Schroder, Russell L. Scott, Pavel Sedlák, Penélope Serrano-Ortíz, Changliang Shao, Peili Shi, Ivan Shironya, Lukas Siebicke, Ladislav Šigut, Richard Silberstein, Costantino Sirca, Donatella Spano, R. Steinbrecher, Robert M. Stevens, Cove Sturtevant, Andy Suyker, Torbern Tagesson, Satoru Takanashi, Yanhong Tang, Nigel Tapper, Jonathan E. Thom, Michele Tomassucci, Juha‐Pekka Tuovinen, S. P. Urbanski, Р. Валентини, M. K. van der Molen, Eva van Gorsel, J. van Huissteden, Andrej Varlagin, Joe Verfaillie, Timo Vesala, Caroline Vincke, Domenico Vitale, N. N. Vygodskaya, Jeffrey P. Walker, Elizabeth A. Walter‐Shea, Huimin Wang, R. J. Weber, Sebastian Westermann, Christian Wille, Steven C. Wofsy, Georg Wohlfahrt, Sebastian Wolf, William Woodgate, Yuelin Li, Roberto Zampedri, Junhui Zhang, Guoyi Zhou, Donatella Zona, D. Agarwal, S. Biraud, M. S. Torn, Dario Papale
Scientific Data, Volume 7, Issue 1

Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

2019

DOI bib
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 https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).

2018

DOI bib
Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe’s terrestrial ecosystems: a review
Daniela Franz, Manuel Acosta, Núria Altimir, Nicola Arriga, Dominique Arrouays, Marc Aubinet, Mika Aurela, Edward Ayres, Ana López‐Ballesteros, Mireille Barbaste, Daniel Berveiller, S. Biraud, Hakima Boukir, Thomas S. Brown, Christian Brümmer, Nina Buchmann, George Burba, Arnaud Carrara, A. Cescatti, Éric Ceschia, Robert Clement, Edoardo Cremonese, Patrick Crill, Eva Dařenová, Sigrid Dengel, Petra D’Odorico, Gianluca Filippa, Stefan Fleck, Gerardo Fratini, Roland Fuß, Bert Gielen, Sébastien Gogo, J. Grace, Alexander Graf, Achim Grelle, Patrick Gross, Thomas Grünwald, Sami Haapanala, Markus Hehn, Bernard Heinesch, Jouni Heiskanen, Mathias Herbst, Christine Herschlein, Lukas Hörtnagl, Koen Hufkens, Andreas Ibrom, Claudy Jolivet, Lilian Joly, Michael B. Jones, Ralf Kiese, Leif Klemedtsson, Natascha Kljun, Katja Klumpp, Pasi Kolari, Olaf Kolle, Andrew S. Kowalski, Werner L. Kutsch, Tuomas Laurila, Anne De Ligne, Sune Linder, Anders Lindroth, Annalea Lohila, Bernhard Longdoz, Ivan Mammarella, Tanguy Manise, Sara Marañón-Jiménez, Giorgio Matteucci, Matthias Mauder, Philip Meier, Lutz Merbold, Simone Mereu, Stefan Metzger, Mirco Migliavacca, Meelis Mölder, Leonardo Montagnani, Christine Moureaux, David D. Nelson, Eiko Nemitz, Giacomo Nicolini, Mats Nilsson, Maarten Op de Beeck, Bruce Osborne, Mikaell Ottosson Löfvenius, Marián Pavelka, Matthias Peichl, Olli Peltola, Mari Pihlatie, Andrea Pitacco, Radek Pokorný, Jukka Pumpanen, Céline Ratié, Corinna Rebmann, Marilyn Roland, Simone Sabbatini, Nicolas Saby, Matthew Saunders, Hans Peter Schmid, Marion Schrumpf, Pavel Sedlák, Penélope Serrano-Ortiz, Lukas Siebicke, Ladislav Šigut, Hanna Silvennoinen, Guillaume Simioni, U. Skiba, Oliver Sonnentag, Kamel Soudani, Patrice Soulé, R. Steinbrecher, Tiphaine Tallec, Anne Thimonier, Eeva‐Stiina Tuittila, Juha‐Pekka Tuovinen, Patrik Vestin, Gaëlle Vincent, Caroline Vincke, Domenico Vitale, Peter Waldner, Per Weslien, Lisa Wingate, Georg Wohlfahrt, M. S. Zahniser, Timo Vesala
International Agrophysics, Volume 32, Issue 4

Abstract Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO 2 , CH 4 , N 2 O, H 2 O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.
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