Annalea Lohila

2023

DOI bib abs
Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison
Gavin McNicol, Etienne Fluet‐Chouinard, Zutao Ouyang, Sara Knox, Zhen Zhang, Tuula Aalto, Sheel Bansal, Kuang‐Yu Chang, Min Chen, Kyle Delwiche, Sarah Féron, Mathias Goeckede, Jinxun Liu, Avni Malhotra, Joe R. Melton, W. J. Riley, Rodrigo Vargas, Kunxiaojia Yuan, Qing Ying, Qing Zhu, Pavel Alekseychik, Mika Aurela, David P. Billesbach, David I. Campbell, Jiquan Chen, Housen Chu, Ankur R. Desai, E. S. Euskirchen, Jordan P. Goodrich, Timothy J. Griffis, Manuel Helbig, Takashi Hirano, Hiroki Iwata, Gerald Jurasinski, John S. King, Franziska Koebsch, Randall K. Kolka, Ken W. Krauss, Annalea Lohila, Ivan Mammarella, Mats E Nilson, Asko Noormets, Walter C. Oechel, Matthias Peichl, Torsten Sachs, Ayaka Sakabe, Christopher Schulze, Oliver Sonnentag, Ryan C. Sullivan, Eeva‐Stiina Tuittila, Masahito Ueyama, Timo Vesala, Eric J. Ward, Christian Wille, Guan Xhuan Wong, Donatella Zona, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson
AGU Advances, Volume 4, Issue 5

Abstract Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data‐driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH 4 emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4 y −1 for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH 4 y −1 ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH 4 y −1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra‐tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC ( https://doi.org/10.3334/ORNLDAAC/2253 ).

2022

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Warming response of peatland CO2 sink is sensitive to seasonality in warming trends
Manuel Helbig, Tatjana Živković, Pavel Alekseychik, Mika Aurela, Tarek S. El‐Madany, E. S. Euskirchen, Lawrence B. Flanagan, Timothy J. Griffis, Paul J. Hanson, J. Hattakka, Carole Helfter, Takashi Hirano, Elyn Humphreys, Gerard Kiely, Randall K. Kolka, Tuomas Laurila, Paul Leahy, Annalea Lohila, Ivan Mammarella, Mats B. Nilsson, A. V. Panov, Frans‐Jan W. Parmentier, Matthias Peichl, Janne Rinne, D. Tyler Roman, Oliver Sonnentag, Eeva‐Stiina Tuittila, Masahito Ueyama, Timo Vesala, Patrik Vestin, Simon Weldon, Per Weslien, Sönke Zaehle
Nature Climate Change, Volume 12, Issue 8

Peatlands have acted as net CO2 sinks over millennia, exerting a global climate cooling effect. Rapid warming at northern latitudes, where peatlands are abundant, can disturb their CO2 sink function. Here we show that sensitivity of peatland net CO2 exchange to warming changes in sign and magnitude across seasons, resulting in complex net CO2 sink responses. We use multiannual net CO2 exchange observations from 20 northern peatlands to show that warmer early summers are linked to increased net CO2 uptake, while warmer late summers lead to decreased net CO2 uptake. Thus, net CO2 sinks of peatlands in regions experiencing early summer warming, such as central Siberia, are more likely to persist under warmer climate conditions than are those in other regions. Our results will be useful to improve the design of future warming experiments and to better interpret large-scale trends in peatland net CO2 uptake over the coming few decades.

DOI bib abs
The ABCflux database: Arctic–boreal CO<sub>2</sub> flux observations and ancillary information aggregated to monthly time steps across terrestrial ecosystems
Anna‐Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, K. E. Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, D. L. Peter, Christina Minions, Julia Nojeim, R. Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroki Iwata, Hideki Kobayashi, Pasi Kolari, Efrèn López‐Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans‐Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret‐Harte, Sigrid Dengel, A. J. Dolman, Colin W. Edgar, Bo Elberling, E. S. Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yojiro Matsuura, Gesa Meyer, Mats B. Nilsson, Steven F. Oberbauer, Sang‐Jong Park, Roman Petrov, Anatoly Prokushkin, Christopher Schulze, Vincent L. St. Louis, Eeva‐Stiina Tuittila, Juha‐Pekka Tuovinen, W. L. Quinton, Andrej Varlagin, Donatella Zona, Viacheslav I. Zyryanov
Earth System Science Data, Volume 14, Issue 1

Abstract. Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).

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Causality guided machine learning model on wetland CH4 emissions across global wetlands
Kunxiaojia Yuan, Qing Zhu, Fa Li, W. J. Riley, Margaret Torn, Housen Chu, Gavin McNicol, Min Chen, Sara Knox, Kyle Delwiche, Huayi Wu, Dennis Baldocchi, Hongxu 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 B. Nilsson, Thomas Friborg, Joachim Jansen, Donatella Zona, E. S. Euskirchen, Eric J. Ward, Gil Bohrer, Zhenong Jin, Licheng Liu, Hiroki 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 abs
FLUXNET-CH<sub>4</sub>: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
Kyle Delwiche, Sara Knox, Avni Malhotra, Etienne Fluet‐Chouinard, Gavin McNicol, Sarah Féron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, A. J. Dolman, Elke Eichelmann, E. S. Euskirchen, D. Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Y. Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John S. King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y.F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim C. Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, W. J. Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey‐Sánchez, Edward A. G. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart‐Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne Szutu, Jonathan E. Thom, Margaret Torn, Eeva‐Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vázquez‐Lule, Joseph Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson, Kyle Delwiche, Sara Knox, Avni Malhotra, Etienne Fluet‐Chouinard, Gavin McNicol, Sarah Féron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, A. J. Dolman, Elke Eichelmann, E. S. Euskirchen, D. Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Y. Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John S. King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y.F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim C. Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, W. J. Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey‐Sánchez, Edward A. G. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart‐Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne Szutu, Jonathan E. Thom, Margaret Torn, Eeva‐Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vázquez‐Lule, Joseph Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson
Earth System Science Data, Volume 13, Issue 7

Abstract. Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

DOI bib abs
Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
Kuang‐Yu Chang, W. 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, E. S. Euskirchen, Thomas Friborg, Mathias Goeckede, Manuel Helbig, Kyle S. Hemes, Takashi Hirano, Hiroki Iwata, Minseok Kang, Trevor F. Keenan, Ken W. Krauss, Annalea Lohila, Ivan Mammarella, Bhaskar Mitra, Akira Miyata, Mats B. Nilsson, Asko Noormets, Walter C. Oechel, Dario Papale, Matthias Peichl, Michele 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, Margaret Torn, Carlo Trotta, Eeva‐Stiina Tuittila, Masahito Ueyama, Rodrigo Vargas, Timo Vesala, L. Windham‐Myers, Zhen Zhang, Donatella Zona, Kuang‐Yu Chang, W. 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, E. S. Euskirchen, Thomas Friborg, Mathias Goeckede, Manuel Helbig, Kyle S. Hemes, Takashi Hirano, Hiroki Iwata, Minseok Kang, Trevor F. Keenan, Ken W. Krauss, Annalea Lohila, Ivan Mammarella, Bhaskar Mitra, Akira Miyata, Mats B. Nilsson, Asko Noormets, Walter C. Oechel, Dario Papale, Matthias Peichl, Michele 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, Margaret Torn, Carlo Trotta, Eeva‐Stiina Tuittila, Masahito Ueyama, Rodrigo Vargas, Timo Vesala, L. 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 abs
Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Jeremy Irvin, Sharon Zhou, Gavin McNicol, Fred Lu, Vincent Liu, Etienne Fluet‐Chouinard, Zutao Ouyang, Sara Knox, Antje Lucas-Moffat, Carlo Trotta, Dario Papale, Domenico Vitale, Ivan Mammarella, Pavel Alekseychik, Mika Aurela, Anand Avati, Dennis Baldocchi, Sheel Bansal, Gil Bohrer, David I. Campbell, Jiquan Chen, Housen Chu, Higo J. Dalmagro, Kyle Delwiche, Ankur R. Desai, E. S. Euskirchen, Sarah Féron, Mathias Goeckede, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, Hiroki Iwata, Gerald Jurasinski, Aram Kalhori, Andrew Kondrich, Derrick Y.F. Lai, Annalea Lohila, Avni Malhotra, Lutz Merbold, Bhaskar Mitra, Andrew Y. Ng, Mats B. Nilsson, Asko Noormets, Matthias Peichl, Camilo Rey‐Sánchez, Andrew D. Richardson, Benjamin R. K. Runkle, Karina VR Schäfer, Oliver Sonnentag, Ellen Stuart‐Haëntjens, Cove Sturtevant, Masahito Ueyama, Alex Valach, Rodrigo Vargas, George L. Vourlitis, Eric J. Ward, Guan Xhuan Wong, Donatella Zona, Ma. Carmelita Alberto, David P. Billesbach, Gerardo Celis, A. J. Dolman, Thomas Friborg, Kathrin Fuchs, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Lukas Hörtnagl, Adrien Jacotot, Franziska Koebsch, Kuno Kasak, Regine Maier, Timothy H. Morin, Eiko Nemitz, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Torsten Sachs, Ayaka Sakabe, Edward A. G. Schuur, Robert Shortt, Ryan C. Sullivan, Daphne Szutu, Eeva‐Stiina Tuittila, Andrej Varlagin, Joeseph G Verfaillie, Christian Wille, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson, Jeremy Irvin, Sharon Zhou, Gavin McNicol, Fred Lu, Vincent Liu, Etienne Fluet‐Chouinard, Zutao Ouyang, Sara Knox, Antje Lucas-Moffat, Carlo Trotta, Dario Papale, Domenico Vitale, Ivan Mammarella, Pavel Alekseychik, Mika Aurela, Anand Avati, Dennis Baldocchi, Sheel Bansal, Gil Bohrer, David I. Campbell, Jiquan Chen, Housen Chu, Higo J. Dalmagro, Kyle Delwiche, Ankur R. Desai, E. S. Euskirchen, Sarah Féron, Mathias Goeckede, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, Hiroki Iwata, Gerald Jurasinski, Aram Kalhori, Andrew Kondrich, Derrick Y.F. Lai, Annalea Lohila, Avni Malhotra, Lutz Merbold, Bhaskar Mitra, Andrew Y. Ng, Mats B. Nilsson, Asko Noormets, Matthias Peichl, Camilo Rey‐Sánchez, Andrew D. Richardson, Benjamin R. K. Runkle, Karina VR Schäfer, Oliver Sonnentag, Ellen Stuart‐Haëntjens, Cove Sturtevant, Masahito Ueyama, Alex Valach, Rodrigo Vargas, George L. Vourlitis, Eric J. Ward, Guan Xhuan Wong, Donatella Zona, Ma. Carmelita Alberto, David P. Billesbach, Gerardo Celis, A. J. Dolman, Thomas Friborg, Kathrin Fuchs, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Lukas Hörtnagl, Adrien Jacotot, Franziska Koebsch, Kuno Kasak, Regine Maier, Timothy H. Morin, Eiko Nemitz, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Torsten Sachs, Ayaka Sakabe, Edward A. G. Schuur, Robert Shortt, Ryan C. Sullivan, Daphne Szutu, Eeva‐Stiina Tuittila, Andrej Varlagin, Joeseph G Verfaillie, Christian Wille, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson
Agricultural and Forest Meteorology, Volume 308-309

• We evaluate methane flux gap-filling methods across 17 boreal-to-tropical wetlands • New methods for generating realistic artificial gaps and uncertainties are proposed • Decision tree algorithms perform slightly better than neural networks on average • Soil temperature and generic seasonality are the most important predictors • Open-source code is released for gap-filling steps and uncertainty evaluation Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

DOI bib abs
Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
Sara Knox, Sheel Bansal, Gavin McNicol, Karina V. R. Schäfer, Cove Sturtevant, Masahito Ueyama, Alex Valach, Dennis Baldocchi, Kyle Delwiche, Ankur R. Desai, E. S. Euskirchen, Jinxun Liu, Annalea Lohila, Avni Malhotra, Lulie Melling, W. J. Riley, Benjamin R. K. Runkle, Jessica Turner, Rodrigo Vargas, Qing Zhu, Tuula Alto, Etienne Fluet‐Chouinard, Mathias Goeckede, Joe R. Melton, Oliver Sonnentag, Timo Vesala, Eric J. Ward, Zhen Zhang, Sarah Féron, Zutao Ouyang, Pavel Alekseychik, Mika Aurela, Gil Bohrer, David I. Campbell, Jiquan Chen, Housen Chu, Higo J. Dalmagro, Jordan P. Goodrich, Pia Gottschalk, Takashi Hirano, Hiroki Iwata, Gerald Jurasinski, Minseok Kang, Franziska Koebsch, Ivan Mammarella, Mats B. Nilsson, Keisuke Ono, Matthias Peichl, Olli Peltola, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Jed P. Sparks, Eeva‐Stiina Tuittila, George L. Vourlitis, Guan Xhuan Wong, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson, Sara Knox, Sheel Bansal, Gavin McNicol, Karina V. R. Schäfer, Cove Sturtevant, Masahito Ueyama, Alex Valach, Dennis Baldocchi, Kyle Delwiche, Ankur R. Desai, E. S. Euskirchen, Jinxun Liu, Annalea Lohila, Avni Malhotra, Lulie Melling, W. J. Riley, Benjamin R. K. Runkle, Jessica Turner, Rodrigo Vargas, Qing Zhu, Tuula Alto, Etienne Fluet‐Chouinard, Mathias Goeckede, Joe R. Melton, Oliver Sonnentag, Timo Vesala, Eric J. Ward, Zhen Zhang, Sarah Féron, Zutao Ouyang, Pavel Alekseychik, Mika Aurela, Gil Bohrer, David I. Campbell, Jiquan Chen, Housen Chu, Higo J. Dalmagro, Jordan P. Goodrich, Pia Gottschalk, Takashi Hirano, Hiroki Iwata, Gerald Jurasinski, Minseok Kang, Franziska Koebsch, Ivan Mammarella, Mats B. Nilsson, Keisuke Ono, Matthias Peichl, Olli Peltola, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Jed P. Sparks, Eeva‐Stiina Tuittila, George L. Vourlitis, Guan Xhuan Wong, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson
Global Change Biology, Volume 27, Issue 15

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

DOI bib abs
Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello, Carlo Trotta, Eleonora 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, D. 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, K. 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, Jiří Dušek, 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, A. 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, M. 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, Yoshiko Kosugi, Ayumi Kotani, Andrew S. Kowalski, Bart Kruijt, Julia 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, Efrèn 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, Walter C. Oechel, Jørgen E. Olesen, Jean‐Marc Ourcival, S. A. Papuga, Frans‐Jan W. Parmentier, Eugénie Paul‐Limoges, Marian 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‐Coupé, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, S. R. Saleska, Enrique P. Sánchez‐Cañete, Zulia Mayari 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, Riccardo Valentini, M. K. van der Molen, Eva van Gorsel, J. van Huissteden, Andrej Varlagin, Joseph 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, Yuanman Hu, Guoyi Zhou, Donatella Zona, D. Agarwal, Sébastien Biraud, Margaret Torn, Dario Papale, Gilberto Pastorello, Carlo Trotta, Eleonora 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, D. 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, K. 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, Jiří Dušek, 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, A. 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, M. 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, Yoshiko Kosugi, Ayumi Kotani, Andrew S. Kowalski, Bart Kruijt, Julia 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, Efrèn 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, Walter C. Oechel, Jørgen E. Olesen, Jean‐Marc Ourcival, S. A. Papuga, Frans‐Jan W. Parmentier, Eugénie Paul‐Limoges, Marian 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‐Coupé, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, S. R. Saleska, Enrique P. Sánchez‐Cañete, Zulia Mayari 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, Riccardo Valentini, M. K. van der Molen, Eva van Gorsel, J. van Huissteden, Andrej Varlagin, Joseph 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, Yuanman Hu, Guoyi Zhou, Donatella Zona, D. Agarwal, Sébastien Biraud, Margaret 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 abs
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, Alexandra L. 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 Väliranta, 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, Jonathan 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, Alex Valach, Jianghua 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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Increasing contribution of peatlands to boreal evapotranspiration in a warming climate
Manuel Helbig, J. M. Waddington, Pavel Alekseychik, B. D. Amiro, Mika Aurela, Alan Barr, T. Andrew Black, Peter D. Blanken, Sean K. Carey, Jiquan Chen, Jinshu Chi, Ankur R. Desai, Allison L. Dunn, E. S. Euskirchen, Lawrence B. Flanagan, Inke Forbrich, Thomas Friborg, Achim Grelle, Silvie Harder, Michal Heliasz, Elyn Humphreys, Hiroki Ikawa, Pierre‐Erik Isabelle, Hiroki Iwata, Rachhpal S. Jassal, Mika Korkiakoski, J. Kurbatova, Lars Kutzbach, Anders Lindroth, Mikaell Ottosson Löfvenius, Annalea Lohila, Ivan Mammarella, Philip Marsh, Trofim C. Maximov, Joe R. Melton, Paul Moore, Daniel F. Nadeau, Erin M. Nicholls, Mats B. Nilsson, Takeshi Ohta, Matthias Peichl, Richard M. Petrone, Roman Petrov, Anatoly Prokushkin, W. L. Quinton, David E. Reed, Nigel T. Roulet, Benjamin R. K. Runkle, Oliver Sonnentag, Ian B. Strachan, Pierre Taillardat, Eeva‐Stiina Tuittila, Juha‐Pekka Tuovinen, Jessica Turner, Masahito Ueyama, Andrej Varlagin, Martin Wilmking, Steven C. Wofsy, Vyacheslav Zyrianov
Nature Climate Change, Volume 10, Issue 6

The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2 kPa, peatland ET exceeds forest ET by up to 30%. Future (2091–2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections.

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The biophysical climate mitigation potential of boreal peatlands during the growing season
Manuel Helbig, J. M. Waddington, Pavel Alekseychik, B. D. Amiro, Mika Aurela, Alan Barr, T. Andrew Black, Sean K. Carey, Jiquan Chen, Jinshu Chi, Ankur R. Desai, Allison L. Dunn, E. S. Euskirchen, Lawrence B. Flanagan, Thomas Friborg, Michelle Garneau, Achim Grelle, Silvie Harder, Michal Heliasz, Elyn Humphreys, Hiroki Ikawa, Pierre‐Erik Isabelle, Hiroki Iwata, Rachhpal S. Jassal, Mika Korkiakoski, J. Kurbatova, Lars Kutzbach, Е. Д. Лапшина, Anders Lindroth, Mikaell Ottosson Löfvenius, Annalea Lohila, Ivan Mammarella, Philip Marsh, Paul Moore, Trofim C. Maximov, Daniel F. Nadeau, Erin M. Nicholls, Mats B. Nilsson, Takeshi Ohta, Matthias Peichl, Richard M. Petrone, Anatoly Prokushkin, W. L. Quinton, Nigel T. Roulet, Benjamin R. K. Runkle, Oliver Sonnentag, Ian B. Strachan, Pierre Taillardat, Eeva‐Stiina Tuittila, Juha‐Pekka Tuovinen, Jessica Turner, Masahito Ueyama, Andrej Varlagin, Timo Vesala, Martin Wilmking, Vyacheslav Zyrianov, Christopher Schulze
Environmental Research Letters, Volume 15, Issue 10

Peatlands and forests cover large areas of the boreal biome and are critical for global climate regulation. They also regulate regional climate through heat and water vapour exchange with the atmosphere. Understanding how land-atmosphere interactions in peatlands differ from forests may therefore be crucial for modelling boreal climate system dynamics and for assessing climate benefits of peatland conservation and restoration. To assess the biophysical impacts of peatlands and forests on peak growing season air temperature and humidity, we analysed surface energy fluxes and albedo from 35 peatlands and 37 evergreen needleleaf forests - the dominant boreal forest type - and simulated air temperature and vapour pressure deficit (VPD) over hypothetical homogeneous peatland and forest landscapes. We ran an evapotranspiration model using land surface parameters derived from energy flux observations and coupled an analytical solution for the surface energy balance to an atmospheric boundary layer (ABL) model. We found that peatlands, compared to forests, are characterized by higher growing season albedo, lower aerodynamic conductance, and higher surface conductance for an equivalent VPD. This combination of peatland surface properties results in a ∼20% decrease in afternoon ABL height, a cooling (from 1.7 to 2.5 °C) in afternoon air temperatures, and a decrease in afternoon VPD (from 0.4 to 0.7 kPa) for peatland landscapes compared to forest landscapes. These biophysical climate impacts of peatlands are most pronounced at lower latitudes (∼45°N) and decrease toward the northern limit of the boreal biome (∼70°N). Thus, boreal peatlands have the potential to mitigate the effect of regional climate warming during the growing season. The biophysical climate mitigation potential of peatlands needs to be accounted for when projecting the future climate of the boreal biome, when assessing the climate benefits of conserving pristine boreal peatlands, and when restoring peatlands that have experienced peatland drainage and mining. © 2020 The Author(s). Published by IOP Publishing Ltd. (Less)

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The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Gilberto Pastorello, Carlo Trotta, Eleonora 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, D. 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, K. 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, Jiří Dušek, 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, A. 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, M. 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, Yoshiko Kosugi, Ayumi Kotani, Andrew S. Kowalski, Bart Kruijt, Julia 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, Efrèn 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, Walter C. Oechel, Jørgen E. Olesen, Jean‐Marc Ourcival, S. A. Papuga, Frans‐Jan W. Parmentier, Eugénie Paul‐Limoges, Marian 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‐Coupé, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, S. R. Saleska, Enrique P. Sánchez‐Cañete, Zulia Mayari 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, Riccardo Valentini, M. K. van der Molen, Eva van Gorsel, J. van Huissteden, Andrej Varlagin, Joseph 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, Yuanman Hu, Guoyi Zhou, Donatella Zona, D. Agarwal, Sébastien Biraud, Margaret 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.

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Stomatal response to decreased relative humidity constrains the acceleration of terrestrial evapotranspiration
Mingzhong Xiao, Zhongbo Yu, Dongdong Kong, Xihui Gu, Ivan Mammarella, Leonardo Montagnani, M. Altaf Arain, Lutz Merbold, Vincenzo Magliulo, Annalea Lohila, Nina Buchmann, Sebastian Wolf, Mana Gharun, Lukas Hörtnagl, Jason Beringer, Beniamino Gioli
Environmental Research Letters, Volume 15, Issue 9

Abstract Terrestrial evapotranspiration (ET) is thermodynamically expected to increase with increasing atmospheric temperature; however, the actual constraints on the intensification of ET remain uncertain due to a lack of direct observations. Based on the FLUXNET2015 Dataset, we found that relative humidity (RH) is a more important driver of ET than temperature. While actual ET decrease at reduced RH, potential ET increases, consistently with the complementary relationship (CR) framework stating that the fraction of energy not used for actual ET is dissipated as increased sensible heat flux that in turn increases potential ET. In this study, we proposed an improved CR formulation requiring no parameter calibration and assessed its reliability in estimating ET both at site-level with the FLUXNET2015 Dataset and at basin-level. Using the ERA-Interim meteorological dataset for 1979–2017 to calculate ET, we found that the global terrestrial ET showed an increasing trend until 1998, while the trend started to decline afterwards. Such decline was largely associated with a reduced RH, inducing water stress conditions that triggered stomatal closure to conserve water. For the first time, this study quantified the global-scale implications of changes in RH on terrestrial ET, indicating that the temperature-driven acceleration of the terrestrial water cycle will be likely constrained by terrestrial vegetation feedbacks.

2019

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Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests
Simon Besnard, Nuno Carvalhais, M. Altaf Arain, T. Andrew Black, Benjamin Brede, Nina Buchmann, Jiquan Chen, J.G.P.W. Clevers, L.P. Dutrieux, Fabian Gans, Martin Herold, Martin Jung, Yoshiko Kosugi, Alexander Knohl, B. E. Law, Eugénie Paul‐Limoges, Annalea Lohila, Lutz Merbold, Olivier Roupsard, Riccardo Valentini, Sebastian Wolf, Xudong Zhang, Markus Reichstein
PLOS ONE, Volume 14, Issue 2

Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

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Refining the role of phenology in regulating gross ecosystem productivity across European peatlands
Franziska Koebsch, Oliver Sonnentag, Järvi Järveoja, Mikko Peltoniemi, Pavel Alekseychik, Mika Aurela, Ali Nadir Arslan, Kerry J. Dinsmore, Damiano Gianelle, Carole Helfter, M. Jackowicz-Korczyński, Aino Korrensalo, Fraser Leith, Maiju Linkosalmi, Annalea Lohila, Magnus Lund, Martin Maddison, Ivan Mammarella, Ülo Mander, Kari Minkkinen, Amy Pickard, Johannes Wilhelmus Maria Pullens, Eeva‐Stiina Tuittila, Mats B. Nilsson, Matthias Peichl
Global Change Biology, Volume 26, Issue 2

The role of plant phenology as a regulator for gross ecosystem productivity (GEP) in peatlands is empirically not well constrained. This is because proxies to track vegetation development with daily coverage at the ecosystem scale have only recently become available and the lack of such data has hampered the disentangling of biotic and abiotic effects. This study aimed at unraveling the mechanisms that regulate the seasonal variation in GEP across a network of eight European peatlands. Therefore, we described phenology with canopy greenness derived from digital repeat photography and disentangled the effects of radiation, temperature and phenology on GEP with commonality analysis and structural equation modeling. The resulting relational network could not only delineate direct effects but also accounted for possible effect combinations such as interdependencies (mediation) and interactions (moderation). We found that peatland GEP was controlled by the same mechanisms across all sites: phenology constituted a key predictor for the seasonal variation in GEP and further acted as a distinct mediator for temperature and radiation effects on GEP. In particular, the effect of air temperature on GEP was fully mediated through phenology, implying that direct temperature effects representing the thermoregulation of photosynthesis were negligible. The tight coupling between temperature, phenology and GEP applied especially to high latitude and high altitude peatlands and during phenological transition phases. Our study highlights the importance of phenological effects when evaluating the future response of peatland GEP to climate change. Climate change will affect peatland GEP especially through changing temperature patterns during plant phenologically sensitive phases in high latitude and high altitude regions.

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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, E. S. 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 B. Nilsson, Walter C. Oechel, Matthias Peichl, Thomas G. Pypker, W. 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

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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, Sébastien Biraud, Hakima Boukir, Tim Brown, Christian Brümmer, Nina Buchmann, George Burba, Arnaud Carrara, A. Cescatti, Éric Ceschia, Robert Clement, Edoardo Cremonese, P. M. 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, Gioṙgio 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 B. Nilsson, Maarten Op de Beeck, Bruce Osborne, Mikaell Ottosson Löfvenius, Marian 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-Ortíz, 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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Ancillary vegetation measurements at ICOS ecosystem stations
Bert Gielen, Manuel Acosta, Núria Altimir, Nina Buchmann, Alessandro Cescatti, Éric Ceschia, Stefan Fleck, Lukas Hörtnagl, Katja Klumpp, Pasi Kolari, Annalea Lohila, Denis Loustau, Sara Marañón‐Jiménez, Tanguy Manise, Gioṙgio Matteucci, Lutz Merbold, Christine Metzger, Christine Moureaux, Leonardo Montagnani, Mats B. Nilsson, Bruce Osborne, Dario Papale, Marian Pavelka, Matthew Saunders, Guillaume Simioni, Kamel Soudani, Oliver Sonnentag, Tiphaine Tallec, Eeva‐Stiina Tuittila, Matthias Peichl, Radek Pokorný, Caroline Vincke, Georg Wohlfahrt
International Agrophysics, Volume 32, Issue 4

Abstract The Integrated Carbon Observation System is a Pan-European distributed research infrastructure that has as its main goal to monitor the greenhouse gas balance of Europe. The ecosystem component of Integrated Carbon Observation System consists of a multitude of stations where the net greenhouse gas exchange is monitored continuously by eddy covariance measurements while, in addition many other measurements are carried out that are a key to an understanding of the greenhouse gas balance. Amongst them are the continuous meteorological measurements and a set of non-continuous measurements related to vegetation. The latter include Green Area Index, aboveground biomass and litter biomass. The standardized methodology that is used at the Integrated Carbon Observation System ecosystem stations to monitor these vegetation related variables differs between the ecosystem types that are represented within the network, whereby in this paper we focus on forests, grasslands, croplands and mires. For each of the variables and ecosystems a spatial and temporal sampling design was developed so that the variables can be monitored in a consistent way within the ICOS network. The standardisation of the methodology to collect Green Area Index, above ground biomass and litter biomass and the methods to evaluate the quality of the collected data ensures that all stations within the ICOS ecosystem network produce data sets with small and similar errors, which allows for inter-comparison comparisons across the Integrated Carbon Observation System ecosystem network.