Torsten Sachs

2023

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Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions
Masahito Ueyama, Sara Knox, Kyle Delwiche, Sheel Bansal, W. J. Riley, Dennis Baldocchi, Takashi Hirano, Gavin McNicol, K. V. Schäfer, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson, Kuang‐Yu Chang, Jiquen Chen, Housen Chu, Ankur R. Desai, Sébastien Gogo, Hiroki Iwata, Minseok Kang, Ivan Mammarella, Matthias Peichl, Oliver Sonnentag, Eeva‐Stiina Tuittila, Youngryel Ryu, E. S. Euskirchen, Mathias Göckede, Adrien Jacotot, Mats B. Nilsson, Torsten Sachs, Masahito Ueyama, Sara Knox, Kyle Delwiche, Sheel Bansal, W. J. Riley, Dennis Baldocchi, Takashi Hirano, Gavin McNicol, K. V. Schäfer, L. Windham‐Myers, Benjamin Poulter, Robert B. Jackson, Kuang‐Yu Chang, Jiquen Chen, Housen Chu, Ankur R. Desai, Sébastien Gogo, Hiroki Iwata, Minseok Kang, Ivan Mammarella, Matthias Peichl, Oliver Sonnentag, Eeva‐Stiina Tuittila, Youngryel Ryu, E. S. Euskirchen, Mathias Göckede, Adrien Jacotot, Mats B. Nilsson, Torsten Sachs
Global Change Biology, Volume 29, Issue 8

Wetlands are the largest natural source of methane (CH4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.

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Carbon uptake in Eurasian boreal forests dominates the high‐latitude net ecosystem carbon budget
Jennifer D. Watts, Mary Farina, John S. Kimball, Luke D. Schiferl, Zhihua Liu, Kyle A. Arndt, Donatella Zona, Ashley P. Ballantyne, E. S. Euskirchen, Frans‐Jan W. Parmentier, Manuel Helbig, Oliver Sonnentag, Torbern Tagesson, Janne Rinne, Hiroki Ikawa, Masahito Ueyama, Hideki Kobayashi, Torsten Sachs, Daniel F. Nadeau, John Kochendorfer, M. Jackowicz-Korczyński, Anna Virkkala, Mika Aurela, R. Commane, Brendan Byrne, Leah Birch, Matthew S. Johnson, Nima Madani, Brendan M. Rogers, Jinyang Du, Arthur Endsley, K. E. Savage, Benjamin Poulter, Zhen Zhang, L. M. Bruhwiler, Charles E. Miller, S. J. Goetz, Walter C. Oechel, Jennifer D. Watts, Mary Farina, John S. Kimball, Luke D. Schiferl, Zhihua Liu, Kyle A. Arndt, Donatella Zona, Ashley P. Ballantyne, E. S. Euskirchen, Frans‐Jan W. Parmentier, Manuel Helbig, Oliver Sonnentag, Torbern Tagesson, Janne Rinne, Hiroki Ikawa, Masahito Ueyama, Hideki Kobayashi, Torsten Sachs, Daniel F. Nadeau, John Kochendorfer, M. Jackowicz-Korczyński, Anna Virkkala, Mika Aurela, R. Commane, Brendan Byrne, Leah Birch, Matthew S. Johnson, Nima Madani, Brendan M. Rogers, Jinyang Du, Arthur Endsley, K. E. Savage, Benjamin Poulter, Zhen Zhang, L. M. Bruhwiler, Charles E. Miller, S. J. Goetz, Walter C. Oechel
Global Change Biology, Volume 29, Issue 7

Arctic-boreal landscapes are experiencing profound warming, along with changes in ecosystem moisture status and disturbance from fire. This region is of global importance in terms of carbon feedbacks to climate, yet the sign (sink or source) and magnitude of the Arctic-boreal carbon budget within recent years remains highly uncertain. Here, we provide new estimates of recent (2003-2015) vegetation gross primary productivity (GPP), ecosystem respiration (Reco ), net ecosystem CO2 exchange (NEE; Reco - GPP), and terrestrial methane (CH4 ) emissions for the Arctic-boreal zone using a satellite data-driven process-model for northern ecosystems (TCFM-Arctic), calibrated and evaluated using measurements from >60 tower eddy covariance (EC) sites. We used TCFM-Arctic to obtain daily 1-km2 flux estimates and annual carbon budgets for the pan-Arctic-boreal region. Across the domain, the model indicated an overall average NEE sink of -850 Tg CO2 -C year-1 . Eurasian boreal zones, especially those in Siberia, contributed to a majority of the net sink. In contrast, the tundra biome was relatively carbon neutral (ranging from small sink to source). Regional CH4 emissions from tundra and boreal wetlands (not accounting for aquatic CH4 ) were estimated at 35 Tg CH4 -C year-1 . Accounting for additional emissions from open water aquatic bodies and from fire, using available estimates from the literature, reduced the total regional NEE sink by 21% and shifted many far northern tundra landscapes, and some boreal forests, to a net carbon source. This assessment, based on in situ observations and models, improves our understanding of the high-latitude carbon status and also indicates a continued need for integrated site-to-regional assessments to monitor the vulnerability of these ecosystems to climate change.

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Pan‐Arctic soil moisture control on tundra carbon sequestration and plant productivity
Donatella Zona, Peter M. Lafleur, Koen Hufkens, Beniamino Gioli, Barbara Bailey, George Burba, E. S. Euskirchen, Jennifer D. Watts, Kyle A. Arndt, Mary Farina, John S. Kimball, Martin Heimann, Mathias Göckede, Martijn Pallandt, Torben R. Christensen, Mikhail Mastepanov, Efrèn López‐Blanco, A. J. Dolman, R. Commane, Charles E. Miller, Josh Hashemi, Lars Kutzbach, David Holl, Julia Boike, Christian Wille, Torsten Sachs, Aram Kalhori, Elyn Humphreys, Oliver Sonnentag, Gesa Meyer, Gabriel Hould Gosselin, Philip Marsh, Walter C. Oechel, Donatella Zona, Peter M. Lafleur, Koen Hufkens, Beniamino Gioli, Barbara Bailey, George Burba, E. S. Euskirchen, Jennifer D. Watts, Kyle A. Arndt, Mary Farina, John S. Kimball, Martin Heimann, Mathias Göckede, Martijn Pallandt, Torben R. Christensen, Mikhail Mastepanov, Efrèn López‐Blanco, A. J. Dolman, R. Commane, Charles E. Miller, Josh Hashemi, Lars Kutzbach, David Holl, Julia Boike, Christian Wille, Torsten Sachs, Aram Kalhori, Elyn Humphreys, Oliver Sonnentag, Gesa Meyer, Gabriel Hould Gosselin, Philip Marsh, Walter C. Oechel
Global Change Biology, Volume 29, Issue 5

Long-term atmospheric CO2 concentration records have suggested a reduction in the positive effect of warming on high-latitude carbon uptake since the 1990s. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. However, the lack of consistent long-term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. In this study, we used a record of nearly 100 site-years of eddy covariance data from 11 continuous permafrost tundra sites distributed across the circumpolar Arctic to test the temperature (expressed as growing degree days, GDD) responses of gross primary production (GPP), net ecosystem exchange (NEE), and ecosystem respiration (ER) at different periods of the summer (early, peak, and late summer) including dominant tundra vegetation classes (graminoids and mosses, and shrubs). We further tested GPP, NEE, and ER relationships with soil moisture and vapor pressure deficit to identify potential moisture limitations on plant productivity and net carbon exchange. Our results show a decrease in GPP with rising GDD during the peak summer (July) for both vegetation classes, and a significant relationship between the peak summer GPP and soil moisture after statistically controlling for GDD in a partial correlation analysis. These results suggest that tundra ecosystems might not benefit from increased temperature as much as suggested by several terrestrial biosphere models, if decreased soil moisture limits the peak summer plant productivity, reducing the ability of these ecosystems to sequester carbon during the summer.

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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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Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems
Donatella Zona, Peter M. Lafleur, Koen Hufkens, Barbara Bailey, Beniamino Gioli, George Burba, Jordan P. Goodrich, A. K. Liljedahl, E. S. Euskirchen, Jennifer D. Watts, Mary Farina, John S. Kimball, Martin Heimann, Mathias Göckede, Martijn Pallandt, Torben R. Christensen, Mikhail Mastepanov, Efrèn López‐Blanco, M. Jackowicz-Korczyński, A. J. Dolman, Luca Belelli Marchesini, R. Commane, Steven C. Wofsy, Charles E. Miller, David A. Lipson, Josh Hashemi, Kyle A. Arndt, Lars Kutzbach, David Holl, Julia Boike, Christian Wille, Torsten Sachs, Aram Kalhori, Xia Song, Xiaofeng Xu, Elyn Humphreys, Charles D. Koven, Oliver Sonnentag, Gesa Meyer, Gabriel Hould Gosselin, Philip Marsh, Walter C. Oechel
Scientific Reports, Volume 12, Issue 1

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

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The ABCflux database: Arctic–boreal CO2 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.

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Vegetation type is an important predictor of the arctic summer land surface energy budget
Jacqueline Oehri, Gabriela Schaepman‐Strub, Jin‐Soo Kim, Raleigh Grysko, Heather Kropp, Inge Grünberg, Vitalii Zemlianskii, Oliver Sonnentag, E. S. Euskirchen, Merin Reji Chacko, Giovanni Muscari, Peter D. Blanken, Joshua Dean, Alcide di Sarra, R. J. Harding, Ireneusz Sobota, Lars Kutzbach, Elena Plekhanova, Aku Riihelä, Julia Boike, Nathaniel B. Miller, Jason Beringer, Efrèn López‐Blanco, Paul C. Stoy, Ryan C. Sullivan, Marek Kejna, Frans‐Jan W. Parmentier, John A. Gamon, Mikhail Mastepanov, Christian Wille, M. Jackowicz-Korczyński, Dirk Nikolaus Karger, W. L. Quinton, Jaakko Putkonen, Dirk van As, Torben R. Christensen, Maria Z. Hakuba, Robert S. Stone, Stefan Metzger, Baptiste Vandecrux, Gerald V. Frost, Martin Wild, Birger Ulf Hansen, Daniela Meloni, Florent Dominé, Mariska te Beest, Torsten Sachs, Aram Kalhori, Adrian V. Rocha, Scott Williamson, Sara Morris, A. L. Atchley, Richard Essery, Benjamin R. K. Runkle, David Holl, Laura Riihimaki, Hiroki Iwata, Edward A. G. Schuur, Christopher J. Cox, Andrey A. Grachev, J. P. McFadden, Robert S. Fausto, Mathias Göckede, Masahito Ueyama, Norbert Pirk, Gijs de Boer, M. Syndonia Bret‐Harte, Matti Leppäranta, Konrad Steffen, Thomas Friborg, Atsumu Ohmura, Colin W. Edgar, Johan Olofsson, Scott Chambers
Nature Communications, Volume 13, Issue 1

Abstract Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm −2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

2021

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FLUXNET-CH4: 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
Statistical upscaling of ecosystem CO 2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties
Anna‐Maria Virkkala, Juha Aalto, Brendan M. Rogers, Torbern Tagesson, Claire C. Treat, Susan M. Natali, Jennifer D. Watts, Stefano Potter, Aleksi Lehtonen, Marguerite Mauritz, Edward A. G. Schuur, John Kochendorfer, Donatella Zona, Walter C. Oechel, Hideki Kobayashi, Elyn Humphreys, Mathias Goeckede, Hiroki Iwata, Peter M. Lafleur, E. S. Euskirchen, Stef Bokhorst, Maija E. Marushchak, Pertti J. Martikainen, Bo Elberling, Carolina Voigt, Christina Biasi, Oliver Sonnentag, Frans‐Jan W. Parmentier, Masahito Ueyama, Gerardo Celis, Vincent L. St. Louis, Craig A. Emmerton, Matthias Peichl, Jinshu Chi, Järvi Järveoja, Mats B. Nilsson, Steven F. Oberbauer, Margaret Torn, Sang‐Jong Park, A. J. Dolman, Ivan Mammarella, Namyi Chae, Rafael Poyatos, Efrèn López‐Blanco, Torben R. Christensen, Min Jung Kwon, Torsten Sachs, David Holl, Miska Luoto, Anna‐Maria Virkkala, Juha Aalto, Brendan M. Rogers, Torbern Tagesson, Claire C. Treat, Susan M. Natali, Jennifer D. Watts, Stefano Potter, Aleksi Lehtonen, Marguerite Mauritz, Edward A. G. Schuur, John Kochendorfer, Donatella Zona, Walter C. Oechel, Hideki Kobayashi, Elyn Humphreys, Mathias Goeckede, Hiroki Iwata, Peter M. Lafleur, E. S. Euskirchen, Stef Bokhorst, Maija E. Marushchak, Pertti J. Martikainen, Bo Elberling, Carolina Voigt, Christina Biasi, Oliver Sonnentag, Frans‐Jan W. Parmentier, Masahito Ueyama, Gerardo Celis, Vincent L. St. Louis, Craig A. Emmerton, Matthias Peichl, Jinshu Chi, Järvi Järveoja, Mats B. Nilsson, Steven F. Oberbauer, Margaret Torn, Sang‐Jong Park, A. J. Dolman, Ivan Mammarella, Namyi Chae, Rafael Poyatos, Efrèn López‐Blanco, Torben R. Christensen, Min Jung Kwon, Torsten Sachs, David Holl, Miska Luoto
Global Change Biology, Volume 27, Issue 17

The regional variability in tundra and boreal carbon dioxide (CO2) fluxes can be high, complicating efforts to quantify sink-source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 from 148 terrestrial high-latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO2 fluxes and test the accuracy and uncertainty of different statistical models. CO2 fluxes were upscaled at relatively high spatial resolution (1 km2) across the high-latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE-focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO2 sink strength was larger in the boreal biome (observed and predicted average annual NEE −46 and −29 g C m−2 yr−1, respectively) compared to tundra (average annual NEE +10 and −2 g C m−2 yr−1). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO2 budget, estimated using the annual NEE ensemble prediction, suggests the high-latitude region was on average an annual CO2 sink during 1990–2015, although uncertainty remains high.

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
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.

2019

DOI bib abs
Large loss of CO2 in winter observed across the northern permafrost region
Susan M. Natali, Jennifer D. Watts, Brendan M. Rogers, Stefano Potter, S. Ludwig, A. K. Selbmann, Patrick F. Sullivan, Benjamin W. Abbott, Kyle A. Arndt, Leah Birch, Mats P. Björkman, A. Anthony Bloom, Gerardo Celis, Torben R. Christensen, Casper T. Christiansen, R. Commane, Elisabeth J. Cooper, P. M. Crill, C. I. Czimczik, S. P. Davydov, Jinyang Du, J. E. Egan, Bo Elberling, E. S. Euskirchen, Thomas Friborg, Hélène Genet, Mathias Göckede, Jordan P. Goodrich, Paul Grogan, Manuel Helbig, Elchin Jafarov, Julie Jastrow, Aram Kalhori, Yongwon Kim, John S. Kimball, Lars Kutzbach, Mark J. Lara, Klaus Steenberg Larsen, Bang-Yong Lee, Zhihua Liu, M. M. Loranty, Magnus Lund, Massimo Lupascu, Nima Madani, Avni Malhotra, Roser Matamala, Jack W. McFarland, A. David McGuire, Anders Michelsen, Christina Minions, Walter C. Oechel, David Olefeldt, Frans‐Jan W. Parmentier, Norbert Pirk, Benjamin Poulter, W. L. Quinton, Fereidoun Rezanezhad, David Risk, Torsten Sachs, Kevin Schaefer, Niels Martin Schmidt, Edward A. G. Schuur, Philipp Semenchuk, Gaius R. Shaver, Oliver Sonnentag, Gregory Starr, Claire C. Treat, Mark P. Waldrop, Yihui Wang, J. M. Welker, Christian Wille, Xiaofeng Xu, Zhen Zhang, Qianlai Zhuang, Donatella Zona
Nature Climate Change, Volume 9, Issue 11

Recent warming in the Arctic, which has been amplified during the winter1-3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is highly uncertain and has not been well represented by ecosystem models or by empirically-based estimates5,6. Here we synthesize regional in situ observations of CO2 flux from arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1662 Tg C yr-1 from the permafrost region during the winter season (October through April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (-1032 Tg C yr-1). Extending model predictions to warmer conditions in 2100 indicates that winter CO2 emissions will increase 17% under a moderate mitigation scenario-Representative Concentration Pathway (RCP) 4.5-and 41% under business-as-usual emissions scenario-RCP 8.5. Our results provide a new baseline for winter CO2 emissions from northern terrestrial regions and indicate that enhanced soil CO2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions.

DOI bib abs
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).