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2. Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing

Author

Xie, Mingjuan, Ma, Xiaofei, Wang, Yuangang, Li, Chaofan, Shi, Haiyang, Yuan, Xiuliang, Hellwich, Olaf, Chen, Chunbo, Zhang, Wenqiang, Zhang, Chen, Ling, Qing, Gao, Ruixiang, Zhang, Yu, Ochege, Friday Uchenna, Frankl, Amaury, De Maeyer, Philippe, Buchmann, Nina, Feigenwinter, Iris, Olesen, Jørgen E., Juszczak, Radoslaw, Jacotot, Adrien, Korrensalo, Aino, Pitacco, Andrea, Varlagin, Andrej, Shekhar, Ankit, Lohila, Annalea, Carrara, Arnaud, Brut, Aurore, Kruijt, Bart, Loubet, Benjamin, Heinesch, Bernard, Chojnicki, Bogdan, Helfter, Carole, Vincke, Caroline, Shao, Changliang, Bernhofer, Christian, Brümmer, Christian, Wille, Christian, Tuittila, Eeva-Stiina, Nemitz, Eiko, Meggio, Franco, Dong, Gang, Lanigan, Gary, Niedrist, Georg, Wohlfahrt, Georg, Zhou, Guoyi, Goded, Ignacio, Gruenwald, Thomas, Olejnik, Janusz, Jansen, Joachim, Neirynck, Johan, Tuovinen, Juha-Pekka, Zhang, Junhui, Klumpp, Katja, Pilegaard, Kim, Šigut, Ladislav, Klemedtsson, Leif, Tezza, Luca, Hörtnagl, Lukas, Urbaniak, Marek, Roland, Marilyn, Schmidt, Marius, Sutton, Mark A., Hehn, Markus, Saunders, Matthew, Mauder, Matthias, Aurela, Mika, Korkiakoski, Mika, Du, Mingyuan, Vendrame, Nadia, Kowalska, Natalia, Leahy, Paul G., Alekseychik, Pavel, Shi, Peili, Weslien, Per, Chen, Shiping, Fares, Silvano, Friborg, Thomas, Tallec, Tiphaine, Kato, Tomomichi, Sachs, Torsten, Maximov, Trofim, di Cella, Umberto Morra, Moderow, Uta, Li, Yingnian, He, Yongtao, Kosugi, Yoshiko, and Luo, Geping

Published
2023
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6. Interannual and seasonal variability of the air–sea CO2 exchange at Utö in the coastal region of the Baltic Sea.

Author

Honkanen, Martti, Aurela, Mika, Hatakka, Juha, Haraguchi, Lumi, Kielosto, Sami, Mäkelä, Timo, Seppälä, Jukka, Siiriä, Simo-Matti, Stenbäck, Ken, Tuovinen, Juha-Pekka, Ylöstalo, Pasi, and Laakso, Lauri

Subjects
ATMOSPHERIC carbon dioxide, WIND speed measurement, WATER chemistry, CARBON dioxide, MIXING height (Atmospheric chemistry)
Abstract

Oceans alleviate the accumulation of atmospheric CO 2 by absorbing approximately a quarter of all anthropogenic emissions. In the deep oceans, carbon uptake is dominated by aquatic phase chemistry, whereas in biologically active coastal seas the marine ecosystem and biogeochemistry play an important role in the carbon uptake. Coastal seas are hotspots of organic and inorganic matter transport between the land and the oceans, and thus they are important for the marine carbon cycling. In this study, we investigate the net air–sea CO 2 exchange at the Utö Atmospheric and Marine Research Station, located at the southern edge of the Archipelago Sea within the Baltic Sea, using the data collected during 2017–2021. The air–sea fluxes of CO 2 were measured using the eddy covariance technique, supported by the flux parameterization based on the p CO 2 and wind speed measurements. During the spring–summer months (April–August), the sea was gaining carbon dioxide from the atmosphere, with the highest monthly sink fluxes typically occurring in May, being -0.26 µ mol m -2 s -1 on average. The sea was releasing the CO 2 to the atmosphere in September–March, and the highest source fluxes were typically observed in September, being 0.42 µ mol m -2 s -1 on average. On an annual basis, the study region was found to be a net source of atmospheric CO 2 , and on average, the annual net exchange was 27.1 gC m -1 yr -1 , which is comparable to the exchange observed in the Gulf of Bothnia, the Baltic Sea. The annual net air–sea CO 2 exchanges varied between 18.2 (2018) and 39.1 gC m -1 yr -1 (2017). During the coldest year, 2017, the spring–summer sink fluxes remained low compared to the other years, as a result of relatively high seawater p CO 2 in summer, which never fell below 220 µ atm during that year. The spring–summer phytoplankton blooms of 2017 were weak, possibly due to the cloudy summer and deeply mixed surface layer, which restrained the photosynthetic fixation of dissolved inorganic carbon in the surface waters. The algal blooms in spring–summer 2018 and the consequent p CO 2 drawdown were strong, fueled by high pre-spring nutrient concentrations. The systematic positive annual CO 2 balances suggest that our coastal study site is affected by carbon flows originating from elsewhere, possibly as organic carbon, which is remineralized and released to the atmosphere as CO 2. This coastal source of CO 2 fueled by the organic matter originating probably from land ecosystems stresses the importance of understanding the carbon cycling in the land–sea continuum. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Increasing contribution of peatlands to boreal evapotranspiration in a warming climate

Author

Helbig, Manuel, Waddington, James Michael, Alekseychik, Pavel, Amiro, Brian D., Aurela, Mika, Barr, Alan G., Black, T. Andrew, Blanken, Peter D., Carey, Sean K., Chen, Jiquan, Chi, Jinshu, Desai, Ankur R., Dunn, Allison, Euskirchen, Eugenie S., Flanagan, Lawrence B., Forbrich, Inke, Friborg, Thomas, Grelle, Achim, Harder, Silvie, Heliasz, Michal, Humphreys, Elyn R., Ikawa, Hiroki, Isabelle, Pierre-Erik, Iwata, Hiroki, Jassal, Rachhpal, Korkiakoski, Mika, Kurbatova, Juliya, Kutzbach, Lars, Lindroth, Anders, Löfvenius, Mikaell Ottosson, Lohila, Annalea, Mammarella, Ivan, Marsh, Philip, Maximov, Trofim, Melton, Joe R., Moore, Paul A., Nadeau, Daniel F., Nicholls, Erin M., Nilsson, Mats B., Ohta, Takeshi, Peichl, Matthias, Petrone, Richard M., Petrov, Roman, Prokushkin, Anatoly, Quinton, William L., Reed, David E., Roulet, Nigel T., Runkle, Benjamin R. K., Sonnentag, Oliver, Strachan, Ian B., Taillardat, Pierre, Tuittila, Eeva-Stiina, Tuovinen, Juha-Pekka, Turner, Jessica, Ueyama, Masahito, Varlagin, Andrej, Wilmking, Martin, Wofsy, Steven C., and Zyrianov, Vyacheslav

Published
2020
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10. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M. Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D’Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., De Cinti, Bruno, de Grandcourt, Agnes, De Ligne, Anne, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, de Dios, Victor Resco, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario

Published
2021
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12. Early snowmelt significantly enhances boreal springtime carbon uptake

Author

Pulliainen, Jouni, Aurela, Mika, Laurila, Tuomas, Aalto, Tuula, Takala, Matias, Salminen, Miia, Kulmala, Markku, Barr, Alan, Heimann, Martin, Lindroth, Anders, Laaksonen, Ari, Derksen, Chris, Mäkelä, Annikki, Markkanen, Tiina, Lemmetyinen, Juha, Susiluoto, Jouni, Dengel, Sigrid, Mammarella, Ivan, Tuovinen, Juha-Pekka, and Vesala, Timo

Published
2017

13. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M. Altaf, Ardö, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brümmer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D’Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., Cinti, Bruno De, Grandcourt, Agnes de, Ligne, Anne De, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, Tommasi, Paul di, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, Dios, Victor Resco de, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario

Published
2020
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15. Interannual and seasonal variability of the air-sea CO2 exchange at Utö in the coastal region of the Baltic Sea.

Author

Honkanen, Martti, Aurela, Mika, Hatakka, Juha, Haraguchi, Lumi, Kielosto, Sami, Mäkelä, Timo, Seppälä, Jukka, Siiriä, Simo-Matti, Stenbäck, Ken, Tuovinen, Juha-Pekka, Ylöstalo, Pasi, and Laakso, Lauri

Subjects
ATMOSPHERIC carbon dioxide, WIND speed measurement, WATER chemistry, ALGAL blooms, CARBON cycle, ATMOSPHERE, BIOGEOCHEMISTRY
Abstract

Oceans alleviate the accumulation of atmospheric CO2 by absorbing approximately a quarter of all anthropogenic emissions. In the deep oceans, carbon uptake is dominated by aquatic phase chemistry, whereas in biologically active coastal seas the marine ecosystem and biogeochemistry play an important role in the carbon uptake. Coastal seas are hotspots of organic and inorganic matter transport between the land and the oceans, and thus important for the marine carbon cycling. In this study, we investigate the net air-sea CO2 exchange at the Utö Atmospheric and Marine Research Station, located at the southern edge of the Archipelago Sea within the Baltic Sea, using the data collected during 2017–2021. The air-sea fluxes of CO2 were measured using the eddy covariance technique, supported by the flux parametrization based on the p CO2 and wind speed measurements. During the spring-summer months (April–August), the sea was gaining carbon dioxide from the atmosphere, with the highest monthly sink fluxes typically occurring in May, being -0.26 μmol m-2 s-1 on average. The sea was releasing the CO2 to the atmosphere in September–March, and the highest source fluxes were typically observed in September, being 0.42 μmol m-2 s-1 on average. On the annual basis, the study region was found to be a net source of atmospheric CO2, and on average, the annual net exchange was 27.1 gC m-2 y-1, which is comparable to the exchange observed in the Gulf of Bothnia, the Baltic Sea. The annual net air-sea CO2 exchanges varied between 18.2 gC m-2 y-1 (2018) and 39.1 gC m-2 y-1 (2017). During the coldest year, 2017, the spring-summer sink fluxes remained low compared to the other years, as a result of relatively high seawater p CO2 in summer, which never fell below 220 μatm during that year. The spring-summer phytoplankton blooms of 2017 were weak, possibly due to the cloudy summer and deeply mixed surface layer, which restrained the photosynthetic fixation of dissolved inorganic carbon in the surface waters. The algal blooms in spring-summer 2018 and the consequent p CO2 drawdown were strong, fueled by high pre-spring nutrient concentrations. The systematic positive annual CO2 balances suggest that our coastal study site is affected by carbon flows originating from elsewhere, possibly as organic carbon which is remineralized and released to the atmosphere as CO2. This coastal source of CO2 fueled by the organic matter originating probably from land ecosystems stresses the importance of understanding the carbon cycling in the land-sea continuum. [ABSTRACT FROM AUTHOR]

Published
2024
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17. The uncertain climate footprint of wetlands under human pressure

Author

Petrescu, Ana Maria Roxana, Lohila, Annalea, Tuovinen, Juha-Pekka, Baldocchi, Dennis D., Desai, Ankur R., Roulet, Nigel T., Vesala, Timo, Dolman, Albertus Johannes, Oechel, Walter C., Marcolla, Barbara, Friborg, Thomas, Rinne, Janne, Matthes, Jaclyn Hatala, Merbold, Lutz, Meijide, Ana, Kiely, Gerard, Sottocornola, Matteo, Sachs, Torsten, Zona, Donatella, Varlagin, Andrej, Lai, Derrick Y. F., Veenendaal, Elmar, Parmentier, Frans-Jan W., Skiba, Ute, Lund, Magnus, Hensen, Arjan, van Huissteden, Jacobus, Flanagan, Lawrence B., Shurpali, Narasinha J., Grünwald, Thomas, Humphreys, Elyn R., Jackowicz-Korczyński, Marcin, Aurela, Mika A., Laurila, Tuomas, Grüning, Carsten, Corradi, Chiara A. R., Schrier-Uijl, Arina P., Christensen, Torben R., Tamstorf, Mikkel P., Mastepanov, Mikhail, Martikainen, Pertti J., Verma, Shashi B., Bernhofer, Christian, and Cescatti, Alessandro

Published
2015

20. Author Correction:The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (Scientific Data, (2020), 7, 1, (225), 10.1038/s41597-020-0534-3)

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You-Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Reichstein, Markus, Ribeca, Alessio, van Ingen, Catharine, Vuichard, Nicolas, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Arain, M. Altaf, Ardo, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Marchesini, Luca Belelli, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean-Marc, Bowling, David R., Bracho, Rosvel, Brodeur, Jason, Brummer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Isaac, Christensen, Torben R., Cleverly, James, Collalti, Alessio, Consalvo, Claudia, Cook, Bruce D., Cook, David, Coursolle, Carole, Cremonese, Edoardo, Curtis, Peter S., D'Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth J., De Cinti, Bruno, de Grandcourt, Agnes, De Ligne, Anne, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrene, Eric, Dunn, Allison, Dusek, Jiri, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim Abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grunwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hortnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janous, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, Lopez-Ballesteros, Ana, Lopez-Blanco, Efren, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Luers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jorgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Ullar, Raz-Yaseef, Naama, Rebmann, Corinna, Reed, David, de Dios, Victor Resco, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sanchez-Canete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlak, Pavel, Serrano-Ortiz, Penelope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Sigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario

Abstract

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.

Published
2021
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21. Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange.

Author

Kämäräinen, Matti, Tuovinen, Juha-Pekka, Kulmala, Markku, Mammarella, Ivan, Aalto, Juha, Vekuri, Henriikka, Lohila, Annalea, and Lintunen, Anna

Subjects
MACHINE learning, CARBON dioxide, TAIGAS, GRID cells, BIOSPHERE, RANDOM forest algorithms, CARBON cycle
Abstract

Accurate estimates of net ecosystem CO 2 exchange (NEE) would improve the understanding of natural carbon sources and sinks and their role in the regulation of global atmospheric carbon. In this work, we use and compare the random forest (RF) and the gradient boosting (GB) machine learning (ML) methods for predicting year-round 6 h NEE over 1996–2018 in a pine-dominated boreal forest in southern Finland and analyze the predictability of NEE. Additionally, aggregation to weekly NEE values was applied to get information about longer term behavior of the method. The meteorological ERA5 reanalysis variables were used as predictors. Spatial and temporal neighborhood (predictor lagging) was used to provide the models more data to learn from, which was found to improve considerably the accuracy of both ML approaches compared to using only the nearest grid cell and time step. Both ML methods can explain temporal variability of NEE in the observational site of this study with meteorological predictors, but the GB method was more accurate. Only minor signs of overfitting could be detected for the GB algorithm when redundant variables were included. The accuracy of the approaches, measured mainly using cross-validated R2 score between the model result and the observed NEE, was high, reaching a best estimate value of 0.92 for GB and 0.88 for RF. In addition to the standard RF approach, we recommend using GB for modeling the CO 2 fluxes of the ecosystems due to its potential for better performance. [ABSTRACT FROM AUTHOR]

Published
2023
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22. Meteorological responses of carbon dioxide and methane fluxes in the terrestrial and aquatic ecosystems of a subarctic landscape.

Author

Heiskanen, Lauri, Tuovinen, Juha-Pekka, Vekuri, Henriikka, Räsänen, Aleksi, Virtanen, Tarmo, Juutinen, Sari, Lohila, Annalea, Mikola, Juha, and Aurela, Mika

Subjects
DROUGHTS, ATMOSPHERIC carbon dioxide, BOGS, CARBON dioxide, HEAT waves (Meteorology), SOIL moisture, LAKE sediments
Abstract

The subarctic landscape consists of a mosaic of forest, peatland, and aquatic ecosystems and their ecotones. The carbon (C) exchange between ecosystems and the atmosphere through carbon dioxide (CO2) and methane (CH4) fluxes varies spatially and temporally among these ecosystems. Our study area in Kaamanen in northern Finland covered 7 km2 of boreal subarctic landscape with upland forest, open peatland, pine bogs, and lakes. We measured the CO2 and CH4 fluxes with eddy covariance and chambers between June 2017 and June 2019 and studied the C flux responses to varying meteorological conditions. The landscape area was an annual CO2 sink of - 45 ± 22 and - 33 ± 23 gCm-2 and a CH4 source of 3.0 ± 0.2 and 2.7 ± 0.2 gCm-2 during the first and second study years, respectively. The pine forest had the largest contribution to the landscape-level CO2 sink, - 126 ± 21 and - 101 ± 19 gCm-2 , and the fen to the CH4 emissions, 7.8 ± 0.2 and 6.3 ± 0.3 gCm-2 , during the first and second study years, respectively. The lakes within the area acted as CO2 and CH4 sources to the atmosphere throughout the measurement period, and a lake located downstream from the fen with organic sediment showed 4-fold fluxes compared to a mineral sediment lake. The annual C balances were affected most by the rainy peak growing season in 2017, the warm summer in 2018, and a heatwave and drought event in July 2018. The rainy period increased ecosystem respiration (ER) in the pine forest due to continuously high soil moisture content, and ER was on a level similar to the following, notably warmer, summer. A corresponding ER response to abundant precipitation was not observed for the fen ecosystem, which is adapted to high water table levels, and thus a higher ER sum was observed during the warm summer 2018. During the heatwave and drought period, similar responses were observed for all terrestrial ecosystems, with decreased gross primary productivity and net CO2 uptake, caused by the unfavourable growing conditions and plant stress due to the soil moisture and vapour pressure deficits. Additionally, the CH4 emissions from the fen decreased during and after the drought. However, the timing and duration of drought effects varied between the fen and forest ecosystems, as C fluxes were affected sooner and had a shorter post-drought recovery time in the fen than forest. The differing CO2 flux response to weather variations showed that terrestrial ecosystems can have a contrasting impact on the landscape-level C balance in a changing climate, even if they function similarly most of the time. [ABSTRACT FROM AUTHOR]

Published
2023
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25. The effect of rainfall amount and timing on annual transpiration in a grazed savanna grassland.

Author

Räsänen, Matti, Aurela, Mika, Vakkari, Ville, Beukes, Johan P., Tuovinen, Juha-Pekka, Van Zyl, Pieter G., Josipovic, Miroslav, Siebert, Stefan J., Laurila, Tuomas, Kulmala, Markku, Laakso, Lauri, Rinne, Janne, Oren, Ram, and Katul, Gabriel

Subjects
GRASSLANDS, RAINFALL, DROUGHTS, SAVANNAS, WATER efficiency, HEAT flux measurement, WATER supply
Abstract

The role of precipitation (P) variability with respect to evapotranspiration (ET) and its two components, transpiration (T) and evaporation (E), from savannas continues to draw significant research interest given its relevance to a number of ecohydrological applications. Our study reports on 6 years of measured ET and estimated T and E from a grazed savanna grassland at Welgegund, South Africa. Annual P varied significantly with respect to amount (508 to 672 mm yr -1), with dry years characterized by infrequent early-season rainfall. T was determined using annual water-use efficiency and gross primary production estimates derived from eddy-covariance measurements of latent heat flux and net ecosystem CO2 exchange rates. The computed annual T for the 4 wet years with frequent early wet-season rainfall was nearly constant, 326±19 mm yr -1 (T/ET=0.51), but was lower and more variable between the 2 dry years (255 and 154 mm yr -1 , respectively). Annual T and T/ET were linearly related to the early wet-season storm frequency. The constancy of annual T during wet years is explained by the moderate water stress of C4 grasses as well as trees' ability to use water from deeper layers. During extreme drought, grasses respond to water availability with a dieback–regrowth pattern, reducing leaf area and transpiration and, thus, increasing the proportion of transpiration contributed by trees. The works suggest that the early-season P distribution explains the interannual variability in T , which should be considered when managing grazing and fodder production in these grasslands. [ABSTRACT FROM AUTHOR]

Published
2022
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26. Carbon and climate implications of rewetting a raised bog in Ireland.

Author

Wilson, David, Mackin, Francis, Tuovinen, Juha‐Pekka, Moser, Gerald, Farrell, Catherine, and Renou‐Wilson, Florence

Subjects
GREENHOUSE gas mitigation, CLIMATE change mitigation, RADIATIVE forcing, BOGS
Abstract

Peatland rewetting has been proposed as a vital climate change mitigation tool to reduce greenhouse gas emissions and to generate suitable conditions for the return of carbon (C) sequestration. In this study, we present annual C balances for a 5‐year period at a rewetted peatland in Ireland (rewetted at the start of the study) and compare the results with an adjacent drained area (represents business‐as‐usual). Hydrological modelling of the 230‐hectare site was carried out to determine the likely ecotopes (vegetation communities) that will develop post‐rewetting and was used to inform a radiative forcing modelling exercise to determine the climate impacts of rewetting this peatland under five high‐priority scenarios (SSP1‐1.9, SS1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). The drained area (marginal ecotope) was a net C source throughout the study and emitted 157 ± 25.5 g C m−2 year−1. In contrast, the rewetted area (sub‐central ecotope) was a net C sink of 78.0 ± 37.6 g C m−2 year−1, despite relatively large annual methane emissions post‐rewetting (average 19.3 ± 5.2 g C m−2 year−1). Hydrological modelling predicted the development of three key ecotopes at the site, with the sub‐central ecotope predicted to cover 24% of the site, the sub‐marginal predicted to cover 59% and the marginal predicted to cover 16%. Using these areal estimates, our radiative forcing modelling projects that under the SSP1‐1.9 scenario, the site will have a warming effect on the climate until 2085 but will then have a strong cooling impact. In contrast, our modelling exercise shows that the site will never have a cooling impact under the SSP5‐8.5 scenario. Our results confirm the importance of rapid rewetting of drained peatland sites to (a) achieve strong C emissions reductions, (b) establish optimal conditions for C sequestration and (c) set the site on a climate cooling trajectory. [ABSTRACT FROM AUTHOR]

Published
2022
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27. Tracking vegetation phenology of pristine northern boreal peatlands by combining digital photography with CO2 flux and remote sensing data.

Author

Linkosalmi, Maiju, Tuovinen, Juha-Pekka, Nevalainen, Olli, Peltoniemi, Mikko, Taniş, Cemal M., Arslan, Ali N., Rainne, Juuso, Lohila, Annalea, Laurila, Tuomas, and Aurela, Mika

Subjects
DIGITAL photography, REMOTE sensing, PHENOLOGY, PEATLANDS, VEGETATION dynamics, PLANT phenology
Abstract

Vegetation phenology, which refers to the seasonal changes in plant physiology, biomass and plant cover, is affected by many abiotic factors, such as precipitation, temperature and water availability. Phenology is also associated with the carbon dioxide (CO 2) exchange between ecosystems and the atmosphere. We employed digital cameras to monitor the vegetation phenology of three northern boreal peatlands during five growing seasons. We derived a greenness index (green chromatic coordinate, GCC) from the images and combined the results with measurements of CO 2 flux, air temperature and high-resolution satellite data (Sentinel-2). From the digital camera images it was possible to extract greenness dynamics on the vegetation community and even species level. The highest GCC and daily maximum gross photosynthetic production (GPP max) were observed at the site with the highest nutrient availability and richest vegetation. The short-term temperature response of GCC depended on temperature and varied among the sites and months. Although the seasonal development and year-to-year variation in GCC and GPP max showed consistent patterns, the short-term variation in GPP max was explained by GCC only during limited periods. GCC clearly indicated the main phases of the growing season, and peatland vegetation showed capability to fully compensate for the impaired growth resulting from a late growing season start. The GCC data derived from Sentinel-2 and digital cameras showed similar seasonal courses, but a reliable timing of different phenological phases depended upon the temporal coverage of satellite data. [ABSTRACT FROM AUTHOR]

Published
2022
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30. Peatland Heterogeneity Impacts on Regional Carbon Flux and Its Radiative Effect Within a Boreal Landscape.

Author

Kou, Dan, Virtanen, Tarmo, Treat, Claire C., Tuovinen, Juha‐Pekka, Räsänen, Aleksi, Juutinen, Sari, Mikola, Juha, Aurela, Mika, Heiskanen, Lauri, Heikkilä, Maija, Weckström, Jan, Juselius, Teemu, Piilo, Sanna R., Deng, Jia, Zhang, Yu, Chaudhary, Nitin, Huang, Conghong, Väliranta, Minna, Biasi, Christina, and Liu, Xiangyu

Abstract

Peatlands, with high spatial variability in ecotypes and microforms, constitute a significant part of the boreal landscape and play an important role in the global carbon (C) cycle. However, the effects of this peatland heterogeneity within the boreal landscape are rarely quantified. Here, we use field‐based measurements, high‐resolution land cover classification, and biogeochemical and atmospheric models to estimate the atmosphere‐ecosystem C fluxes and the corresponding radiative effect (RE) for a boreal landscape (Kaamanen) in northern Finland. Our result shows that the Kaamanen catchment currently functioned as a sink of carbon dioxide (CO2) and a source of methane (CH4). Peatlands (26% of the area) contributed 22% of the total CO2 uptake and 89% of CH4 emissions; forests (61%) accounted for 78% of CO2 uptake and offset 6% of CH4 emissions; water bodies (13%) offset 7% of CO2 uptake and contributed 11% of CH4 emissions. The heterogeneity of peatlands accounted for 11%, 88%, and 75% of the area‐weighted variability (deviation from the area‐weighted mean among different land cover types (LCTs) within the catchment) in CO2 flux, CH4 flux, and the combined RE of CO2 and CH4 exchanges over the 25‐year time horizon, respectively. Aggregating peatland LCTs or misclassifying them as nonpeatland LCTs can significantly (p < 0.05) bias the regional CH4 exchange and RE estimates, while differentiating between drier noninundated and wetter inundated peatlands can effectively reduce the bias. Current land cover products lack such details in peatland heterogeneity, which would be needed to better constrain boreal C budgets and global C‐climate feedbacks. Plain Language Summary: Peatlands form part of the boreal landscapes exhibiting diverse types and microforms that have different characteristics of topography, hydrology, vegetation, and soil. Our understanding is still limited concerning how boreal peatlands, especially their inherent heterogeneities, affect the regional biosphere‐atmosphere exchange of carbon and related climate effects, and what level of detail is needed to characterize them in land cover maps. By combining remote sensing information, field measurements, and biogeochemical modeling, we showed that, among different land cover types, peatlands played a dominant role in the variability of methane (CH4) flux (88%) and the combined radiative climate effect due to carbon dioxide and CH4 exchanges (75% over the 25‐year time horizon). Possible aggregation and misclassification of peatland types could induce significant biases in the regional CH4 balances and radiative effect estimates, but the distinction of noninundated drier and inundated wetter peatland types could reduce these biases effectively. Key Points: The atmosphere‐ecosystem carbon exchange of a heterogeneous boreal landscape was determinedPeatlands (26% area) contributed 22% total carbon dioxide (CO2) uptake and 89% methane (CH4) emission; forests offset 6% CH4 emission and water bodies 7% CO2 uptakeDifferentiating between noninundated drier and inundated wetter peatlands improved radiative effect estimates [ABSTRACT FROM AUTHOR]

Published
2022
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31. Identifying main uncertainties in estimating past and present radiative forcing of peatlands.

Author

Mathijssen, Paul J. H., Tuovinen, Juha‐Pekka, Lohila, Annalea, Väliranta, Minna, and Tuittila, Eeva‐Stiina

Subjects
*RADIATIVE forcing, *PEATLANDS, *METHANE, *CARBON dioxide, *PLASMA beam injection heating, *GREENHOUSE gases
Abstract

Reconstructions of past climate impact, that is, radiative forcing (RF), of peatland carbon (C) dynamics show that immediately after peatland initiation the climate warming effect of CH4 emissions exceeds the cooling effect of CO2 uptake, but thereafter the net effect of most peatlands will move toward cooling, when RF switches from positive to negative. Reconstructing peatland C dynamics necessarily involves uncertainties related to basic assumptions on past CO2 flux, CH4 emission and peatland expansion. We investigated the effect of these uncertainties on the RF of three peatlands, using either apparent C accumulation rates, net C balance (NCB) or NCB plus C loss during fires as basis for CO2 uptake estimate; applying a plausible range for CH4 emission; and assuming linearly interpolated expansion between basal dates or comparatively early or late expansion. When we factored that some C would only be stored temporarily (NCB and NCB+fire), the estimated past cooling effect of CO2 uptake increased, but the present‐day RF was affected little. Altering the assumptions behind the reconstructed CO2 flux or expansion patterns caused the RF to peak earlier and advanced the switch from positive to negative RF by several thousand years. Compared with NCB, including fires had only small additional effect on RF lasting less than 1000 year. The largest uncertainty in reconstructing peatland RF was associated with CH4 emissions. As shown by the consistently positive RF modelled for one site, and in some cases for the other two, peatlands with high CH4 emissions and low C accumulation rates may have remained climate warming agents since their initiation. Although uncertainties in present‐day RF were mainly due to the assumed CH4 emission rates, the uncertainty in lateral expansion still had a significant effect on the present‐day RF, highlighting the importance to consider uncertainties in the past peatland C balance in RF reconstructions. [ABSTRACT FROM AUTHOR]

Published
2022
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32. Variation in CO2 and CH4 fluxes among land cover types in heterogeneous Arctic tundra in northeastern Siberia.

Author

Juutinen, Sari, Aurela, Mika, Tuovinen, Juha-Pekka, Ivakhov, Viktor, Linkosalmi, Maiju, Räsänen, Aleksi, Virtanen, Tarmo, Mikola, Juha, Nyman, Johanna, Vähä, Emmi, Loskutova, Marina, Makshtas, Alexander, and Laurila, Tuomas

Subjects
TUNDRAS, LAND cover, CARBON sequestration, LEAF area index, METHANE, CARBON dioxide
Abstract

Arctic tundra is facing unprecedented warming, resulting in shifts in the vegetation, thaw regimes, and potentially in the ecosystem–atmosphere exchange of carbon (C). However, the estimates of regional carbon dioxide (CO 2) and methane (CH 4) budgets are highly uncertain. We measured CO 2 and CH 4 fluxes, vegetation composition and leaf area index (LAI), thaw depth, and soil wetness in Tiksi (71 ∘ N, 128 ∘ E), a heterogeneous site located within the prostrate dwarf-shrub tundra zone in northeastern Siberia. Using the closed chamber method, we determined the net ecosystem exchange (NEE) of CO 2 , ecosystem respiration in the dark (ER), ecosystem gross photosynthesis (Pg), and CH 4 flux during the growing season. We applied a previously developed high-spatial-resolution land cover map over an area of 35.8 km 2 for spatial extrapolation. Among the land cover types varying from barren to dwarf-shrub tundra and tundra wetlands, the NEE and Pg at the photosynthetically active photon flux density of 800 µ mol m -2 h -1 (NEE 800 and Pg 800) were greatest in the graminoid-dominated habitats, i.e., streamside meadow and fens, with NEE 800 and Pg 800 of up to - 21 (uptake) and 28 mmol m -2 h -1 , respectively. Vascular LAI was a robust predictor of both NEE 800 and Pg 800 and, on a landscape scale, the fens were disproportionately important for the summertime CO 2 sequestration. Dry tundra, including the dwarf-shrub and lichen tundra, had smaller CO 2 exchange rates. The fens were the largest source of CH 4 , while the dry mineral soil tundra consumed atmospheric CH 4 , which on a landscape scale amounted to - 9 % of the total CH 4 balance during the growing season. The largest seasonal mean CH 4 consumption rate of 0.02 mmol m -2 h -1 occurred in sand- and stone-covered barren areas. The high consumption rate agrees with the estimate based on the eddy covariance measurements at the same site. We acknowledge the uncertainty involved in spatial extrapolations due to a small number of replicates per land cover type. This study highlights the need to distinguish different land cover types including the dry tundra habitats to account for their different CO 2 and CH 4 flux patterns, especially the consumption of atmospheric CH 4 , when estimating tundra C exchange on a larger spatial scale. [ABSTRACT FROM AUTHOR]

Published
2022
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33. The FLUXNET 20165 dataset and the ONEFlux processing pipeline for eddy covariance data

Author

Pastorello, Gilberto, Trotta, Carlo, Canfora, Eleonora, Chu, Housen, Christianson, Danielle, Cheah, You - Wei, Poindexter, Cristina, Chen, Jiquan, Elbashandy, Abdelrahman, Humphrey, Marty, Isaac, Peter, Polidori, Diego, Riveca, Alessio, van Ingen, Catharine, Zhang, Leiming, Amiro, Brian, Ammann, Christof, Altaf Arain, M., Ardo, Jonas, Arkebauer, Timothy, Arndt, Stefan K., Arriga, Nicola, Aubinet, Marc, Aurela, Mika, Baldocchi, Dennis, Barr, Alan, Beamesderfer, Eric, Belelli Marchesini, Luca, Bergeron, Onil, Beringer, Jason, Bernhofer, Christian, Berveiller, Daniel, Billesbach, Dave, Black, Thomas Andrew, Blanken, Peter D., Bohrer, Gil, Boike, Julia, Bolstad, Paul V., Bonal, Damien, Bonnefond, Jean - Marc, Bowling, David R., Bracho, Rosuel, Brodeur, Jason, Brummer, Christian, Buchmann, Nina, Burban, Benoit, Burns, Sean P., Buysse, Pauline, Cale, Peter, Cavagna, Mauro, Cellier, Pierre, Chen, Shiping, Chini, Issac, Christensen, Storben, Cleverly, James, Collatti, Alessio, Consalvo, Claudia, Cook, Bruce, Cook, David, Coversolle, Carole, Cremonese, Edoardo, Curtis, Peter, D'Andrea, Ettore, da Rocha, Humberto, Dai, Xiaoqin, Davis, Kenneth, De Cinti, Bruno, de Grandcourt, agnes, De Ligne, Anne, De Oliveira, Raimundo C., Delpierre, Nicolas, Desai, Ankur R., Di Bella, Carlos Marcelo, di Tommasi, Paul, Dolman, Han, Domingo, Francisco, Dong, Gang, Dore, Sabina, Duce, Pierpaolo, Dufrêne, Eric, Dunn, Allison, Dušek, Jiří, Eamus, Derek, Eichelmann, Uwe, ElKhidir, Hatim abdalla M., Eugster, Werner, Ewenz, Cacilia M., Ewers, Brent, Famulari, Daniela, Fares, Silvano, Feigenwinter, Iris, Feitz, Andrew, Fensholt, Rasmus, Filippa, Gianluca, Fischer, Marc, Frank, John, Galvagno, Marta, Gharun, Mana, Gianelle, Damiano, Gielen, Bert, Gioli, Beniamino, Gitelson, Anatoly, Goded, Ignacio, Goeckede, Mathias, Goldstein, Allen H., Gough, Christopher M., Goulden, Michael L., Graf, Alexander, Griebel, Anne, Gruening, Carsten, Grünwald, Thomas, Hammerle, Albin, Han, Shijie, Han, Xingguo, Hansen, Birger Ulf, Hanson, Chad, Hatakka, Juha, He, Yongtao, Hehn, Markus, Heinesch, Bernard, Hinko-Najera, Nina, Hörtnagl, Lukas, Hutley, Lindsay, Ibrom, Andreas, Ikawa, Hiroki, Jackowicz-Korczynski, Marcin, Janouš, Dalibor, Jans, Wilma, Jassal, Rachhpal, Jiang, Shicheng, Kato, Tomomichi, Khomik, Myroslava, Klatt, Janina, Knohl, Alexander, Knox, Sara, Kobayashi, Hideki, Koerber, Georgia, Kolle, Olaf, Kosugi, Yoshiko, Kotani, Ayumi, Kowalski, Andrew, Kruijt, Bart, Kurbatova, Julia, Kutsch, Werner L., Kwon, Hyojung, Launiainen, Samuli, Laurila, Tuomas, Law, Bev, Leuning, Ray, Li, Yingnian, Liddell, Michael, Limousin, Jean-Marc, Lion, Marryanna, Liska, Adam J., Lohila, Annalea, López-Ballesteros, Ana, López-Blanco, Efrén, Loubet, Benjamin, Loustau, Denis, Lucas-Moffat, Antje, Lüers, Johannes, Ma, Siyan, Macfarlane, Craig, Magliulo, Vincenzo, Maier, Regine, Mammarella, Ivan, Manca, Giovanni, Marcolla, Barbara, Margolis, Hank A., Marras, Serena, Massman, William, Mastepanov, Mikhail, Matamala, Roser, Matthes, Jaclyn Hatala, Mazzenga, Francesco, McCaughey, Harry, McHugh, Ian, McMillan, Andrew M. S., Merbold, Lutz, Meyer, Wayne, Meyers, Tilden, Miller, Scott D., Minerbi, Stefano, Moderow, Uta, Monson, Russell K., Montagnani, Leonardo, Moore, Caitlin E., Moors, Eddy, Moreaux, Virginie, Moureaux, Christine, Munger, J. William, Nakai, Taro, Neirynck, Johan, Nesic, Zoran, Nicolini, Giacomo, Noormets, Asko, Northwood, Matthew, Nosetto, Marcelo, Nouvellon, Yann, Novick, Kimberly, Oechel, Walter, Olesen, Jørgen Eivind, Ourcival, Jean-Marc, Papuga, Shirley A., Parmentier, Frans-Jan, Paul-Limoges, Eugenie, Pavelka, Marian, Peichl, Matthias, Pendall, Elise, Phillips, Richard P., Pilegaard, Kim, Pirk, Norbert, Posse Beaulieu, Gabriela, Powell, Thomas, Prasse, Heiko, Prober, Suzanne M., Rambal, Serge, Rannik, Üllar, Raz-Yaseef, Naama, Reed, David, Resco de Dios, Victor, Restrepo-Coupe, Natalia, Reverter, Borja R., Roland, Marilyn, Sabbatini, Simone, Sachs, Torsten, Saleska, Scott R., Sánchez-Cañete, Enrique P., Sanchez-Mejia, Zulia M., Schmid, Hans Peter, Schmidt, Marius, Schneider, Karl, Schrader, Frederik, Schroder, Ivan, Scott, Russell L., Sedlák, Pavel, Serrano-Ortíz, Penélope, Shao, Changliang, Shi, Peili, Shironya, Ivan, Siebicke, Lukas, Šigut, Ladislav, Silberstein, Richard, Sirca, Costantino, Spano, Donatella, Steinbrecher, Rainer, Stevens, Robert M., Sturtevant, Cove, Suyker, Andy, Tagesson, Torbern, Takanashi, Satoru, Tang, Yanhong, Tapper, Nigel, Thom, Jonathan, Tiedemann, Frank, Tomassucci, Michele, Tuovinen, Juha-Pekka, Urbanski, Shawn, Valentini, Riccardo, van der Molen, Michiel, van Gorsel, Eva, van Huissteden, Ko, Varlagin, Andrej, Verfaillie, Joseph, Vesala, Timo, Vincke, Caroline, Vitale, Domenico, Vygodskaya, Natalia, Walker, Jeffrey P., Walter-Shea, Elizabeth, Wang, Huimin, Weber, Robin, Westermann, Sebastian, Wille, Christian, Wofsy, Steven, Wohlfahrt, Georg, Wolf, Sebastian, Woodgate, William, Li, Yuelin, Zampedri, Roberto, Zhang, Junhui, Zhou, Guoyi, Zona, Donatella, Agarwal, Deb, Biraud, Sebastien, Torn, Margaret, and Papale, Dario

Subjects
Remote Sensing, Red de Datos, Tratados, Treaties, Teledetección, Covarianza de Remolinos, Garantía de Calidad, Quality Assurance, Eddy Covariance, Data Network
Abstract

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, 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. Fil: Pastorello, Gilberto. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Trotta, Carlo. University of Tuscia. DiBAf; Italia Fil: Canfora, Eleonora. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Chu, Housen. Lawrence Berkeley national Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Christianson, Danielle. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Cheah, You - Wei. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Poindexter, Cristina. California State University. Department of Civil Engineering; Estados Unidos Fil: Chen, Jiquan. Michigan State University. Department of Geography, Environment, and Spatial Sciences; Estados Unidos Fil: Elbashandy, Abdelrah man. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Humphrey, Marty. University of Virginia. Department of Computer Science; Estados Unidos Fil: Isaac, Peter. TeRn Ecosystrem Processes; Australia Fil: Polidori, Diego. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Riveca, Alessio. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: van Ingen, Catharine. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Zhang, Leiming. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China Fil: Amiro, Brian. University of Manitoba. Department of Soil Science; Canadá Fil: Ammann, Christof. Agroscope Research Institute. Department of Agroecology and Environment; Suiza Fil: Altaf Arain, M. McMaster University. School of Geography and Earth Sciences; Canadá. Fil: Ardo, Jonas. Lund University. Department of Physical Geography and Ecosystem Science; Suecia Fil: Arkebauer, Timothy. University of Nebraska-Lincoln. Department of Agronomy and Horticulture; Estados Unidos Fil: Arndt, Stefan K. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia Fil: Arriga, Nicola. University of Antwerp. Department of Biology, Research Group PLECO; Bélgica. European Commission. Joint Research Centre; Italia Fil: Aubinet, Marc. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Aurela, Mika. Finnish Meteorological Institute; Finlandia Fil: Baldocchi, Dennis. University of California Berkeley. ESPM; Estados Unidos Fil: Barr, Alan. University of Saskatchewan. Global Institute for Water Security; Canadá. Environment and Climate Change Canada. Climate Research Division; Canadá. Fil: Beamesderfer, Eric. McMaster University. School of Geography and Earth Sciences; Canadá. Fil: Belelli Marchesini, Luca. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia. RUDN University. Agrarian-Technological Institute. Department of Landscape Design and Sustainable Ecosystems; Rusia Fil: Bergeron, Onil. Ministère du Développement durable de l’Environnement et de la Lutte contre les Changements Climatiques. Direction du Marché du Carbone; Canadá. Fil: Beringer, Jason. University of Western Australia. School of Agriculture and Environment; Australia. Fil: Bernhofer, Christia. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Berveiller, Daniel. Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolution; Francia Fil: Billesbach, Dave. University of Nebraska-Lincoln. Biological Systems Engineering; Estados Unidos Fil: Black, Tomas Andrew. University of British Columbia. Faculty of Land and Food Systems; Canadá. Fil: Blanken, Peter D. University of Colorado. Department of Geography; Estados Unidos Fil: Bohrer, Gil. Ohio State University. Department of Civil, Environmental & Geodetic Engineering; Estados Unidos Fil: Boike, Julia Alfred. Wegener Institute. Helmholtz Centre for Polar and Marine Research; Alemania. Humboldt-Universität zu Berlin. Geography Department; Alemania Fil: Bolstad, Paul V. University of Minnesota. Forest Resources; Estados Unidos Fil: Bonal, Damien. Université de Lorraine, AgroParisTech, INRAE, UMR Silva; Francia Fil: Bonnefond, Jean - Marc. ISPA, Bordeaux Sciences Agro, INRAE; Francia Fil: Bowling, David R. University of Utah. School of Biological Sciences; Estados Unidos Fil: Bracho, Rosuel. University of Florida.School of Forest Resources and Conservation; Estados Unidos Fil: Brodeur, Jason. McMaster University. McMaster University Library; Estados Unidos Fil: Brummer, Christian. Federal Research Institute of Rural Areas, Forestry and Fisheries. Thünen Institute of Climate-Smart Agriculture; Alemania Fil: Buchmann, Nina. ETH. Department of Environmental Systems Science; Suiza Fil: Burban, Benoit. INRAE UMR ECOFOG; Guyana Francesa Fil: Burns, Sean P. University of Colorado. Department of Geography; Estados Unidos. National Center for Atmospheric Research. Mesoscale and Microscale Meteorology Laboratory; Estados Unidos Fil: Buysse, Pauline. Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Cale, Peter. Australian Landscape Trust; Australia Fil: Cavagna, Mauro. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Cellier, Pierre. Université Paris-Saclay. INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Che, Shiping. Chinese Academy of Sciences. Institute of Botany. State Key Laboratory of Vegetation and Environmental Change; China Fil: Chini, Issac. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Christensen, Storben. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Cleverly, James. University of Technology. School of Life Sciences; Australia. University of Technology. Terrestrial Ecosystem Research Network; Australia. Fil: Collalti, Alessio. University of Tuscia. DiBAf; Italia. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Consalvo, Claudia. University of Tuscia. DiBAf; Italia. National Research Council of Italy. Research Institute on Terrestrial Ecosystems; Italia Fil: Cook, Bruce. NASA Goddard Space Flight Center. Biospheric Sciences Laboratory; Estados Unidos Fil: Cook, David. Argonne National Laboratory. Environmental Science Division; Estados Unidos Fil: Coursolle, Carole. Natural Resources Canada. Canadian Forest Service; Canadá. Université Laval. Faculté de Foresterie, de Géographie et de Géomatique. Centre d’étude de la Forêt; Canadá Fil: Cremonose, Edoardo. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Curtis, Peter. Ohio State University. Department of Evolution, Ecology, and Organismal Biology; Estados Unidos Fil: D'Andrea, Ettore. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: da Rocha, Humberto. Universidade de São Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil Fil: Dai, Xiaoqin. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. Fil: Davis, Kenneth. The Pennsylvania State University. Department of Meteorology and Atmospheric Science; Estados Unidos Fil: De Cinti, Bruno. National Research Council of Italy. Institute of Research on Terrestrial Ecosystems; Italia Fil: de Grandcourt, Agnes. UMR Eco&Sols, CIRAD; Francia Fil: De Ligne, Anne. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: De Oliveira, Raimundo C. Pedology, Embrapa Amazonia Oriental; Brasil. Fil: Delpierre, Nicolas. Université Paris-Saclay. CNRS, AgroParisTech, Ecologie Systématique et Evolution; Francia Fil: Desai, Ankur R. University of Wisconsin-Madison. Atmospheric and Oceanic Sciences; Estados Unidos Fil: Di Bella, Carlos Marcelo. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina Fil: di Tommasi, Paul. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Dolman, Han. Vrije Universiteit Amsterdam. Department of Earth Sciences; Holanda Fil: Domingo, Francisco. CSIC. Experimental Station of Arid Zones. Desertification and Geoecology Department; España Fil: Dong, Gang. Shanxi University. School of Life Science; China. Fil: Dore, Sabina. HydroFocus; Estados Unidos Fil: Duce, Pierpaolo. National Research Council of Italy. Institute of Bioeconomy; Italia Fil: Dufrêne, Eric. Université Paris-Saclay, CNRS., AgroParisTech. Ecologie Systématique et Evolution; Francia Fil: Dunn, Allison. Worcester State University. Department of Earth, Environment, and Physics; Estados Unidos Fil: Dušek, Jiri. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Eamus Derek. University of Technology. School of Life Sciences; Australia. Fil: Eichelmann, Uwe.Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: ElKhidir, Hatim abdalla M. elObeid. Agricultural Research Corporation. Research Station; Sudán Fil: Eugster, Wener. ETH. Department of Environmental Systems Science; Suiza Fil: Ewenz, Cacilia M. Airborne Research Australia. TERN Ecosystem Processes Central Node; Australia Fil: Ewers, Brent. University of Wyoming. Department of Botany. Program in Ecology; Estados Unidos Fil: Famulari, Daniela. National Research Council of Italy. Institute for Agricultural and Forestry Systems in the Mediterranean; Italia Fil: Fares, Silvano. National Research Council of Italy. Institute of BioEconomy; Italia Fil: Feigenwinter, Iris. ETH. Department of Environmental Systems Science; Suiza Fil: Feitz, Andrew. Geoscience Australia; Australia Fil: Fensholt, Rasmus. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Filippa, Gianluca. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Fischer, Marc. Lawrence Berkeley National Laboratory. Energy Analysis & Environmental Impacts Division; Estados Unidos Fil: Frank, John. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Galvagno, Marta. Environmental Protection Agency of Aosta Valley. Climate Change Unit; Italia Fil: Gharun, Mana. ETH. Department of Environmental Systems Science; Suiza Fil: Gianelle, Damiano. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Gielen, Bert. University of Antwerp. Department of Biology. Research Group PLECO; Bélgica Fil: Gioli, Beniamino. National Research Council of Italy. Institute of BioEconomy; Italia Fil: Gitelson, Anatoly. University of Nebraska-Lincoln. School of natural Resources; Estados Unidos Fil: Goded, Ignacio. Joint Research Centre, European Commission; Italia Fil: Goeckede, Mathias. Max Planck Institute for Biogeochemistry; Alemania Fil: Goldstein. Allen H. University of California Berkeley. ESPM; Estados Unidos Fil: Gough, Christopher M. Virginia Commonwealth University. Department of Biology; Estados Unidos Fil: Goulden, Michael L. University of California Irvine. Department of Earth System Science; Estados Unidos Fil: Graf, Alexander. Agrosphere. (IBG3), Forschungszentrum Jülich; Alemania Fil: Griebel, Anne. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia Fil: Gruening, Carsten. Joint Research Centre, European Commission; Italia Fil: Grünwald, Thomas. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Hammerle, Albin. University of Innsbruck. Department of Ecology; Austria. Fil: Han, Shijie. Henan University. School of Life Sciences. International Joint Research Laboratory for Global Change Ecology; China. Chinese Academy of Sciences. Institute of Applied Ecology; China. Fil: Han, Xingguo. Chinese Academy of Sciences.Institute of Botany. State Key Laboratory of Vegetation and Environmental Change; China Fil: Hansen, Birger Ulf. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Hanson, Chad. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: , Juha Hatakka, Juha. Finnish Meteorological Institute; Finlandia Fil: He, Yongtao. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. University of Chinese Academy of Sciences. College of Resources and Environment; China Fil: Hehn, Markus. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Heinesch, Bernard. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Hinko-Najera, Nina. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia. Fil: Hörtnagl, Lukas. ETH. Department of Environmental Systems Science; Suiza. Fil: Hutley, Lindsay. Charles Darwin University. Research Institute for the Environment and Livelihoods; Australia Fil: Ibrom, Andreas. Technical University of Denmark. Department of Environmental Engineering; Dinamarca Fil: Ikawa, Hiroki. National Agriculture and Food Research Organization. Institute for Agro-Environmental Sciences; Japón Fil: Jackowicz-Korczynski, Marcin. Lund University. Department of Physical Geography and Ecosystem Science; Suecia. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Janouš, Dalibor. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Jans, Wilma. Wageningen University and Research. Wageningen Environmental Research; Holanda Fil: Jassal, Rachhpal. University of British Columbia. Faculty of Land and Food Systems; Canadá. Fil: Jiang, Shicheng. Ministry of Education, Northeast Normal University. Key Laboratory of Vegetation Ecology; China Fil: Kato, Tomomichi. Hokkaido University. Research Faculty of Agriculture; Japón. Hokkaido University. GICore; Japón Fil: Khomik, Myroslava. Geography and Environmental Management; Canadá. Fil: Klatt, Janina. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Knohl, Alexander. University of Goettingen. Bioclimatology; Alelmania. University of Goettingen. Centre of Biodiversity and Sustainable Land Use; Alemania Fil: Knox, Sara. The University of British Columbia. Department of Geography; Canadá Fil: Kobayashi, Hideki. Japan Agency for Marine-Earth Science and Technology. Research Institute for Global change, Institute of Arctic Climate and Environment Research; Japón Fil: Koerber, Georgia. University of Adelaide. Biological Sciences; Australia. Fil: Kolle, Olaf. Max Planck Institute for Biogeochemistry; Alemania Fil: Kosugi, Yoshiko. Kyoto University. Graduate School of Agriculture; Japón Fil: Kotani, Ayumi. Nagoya University. Graduate School of Bioagricultural Sciences; Japón Fil: Kowalski, Andrew. University of Granada. Department of Applied Physics; España Fil: Kruijt, Bart. Wageningen University, Wageningen. Water systems and Global Change group; Holanda Fil: Kurbatova; Julia A. Russian Academy of Sciences. Severtsov institute of Ecology and Evolution; Rusia Fil: Kutsch, Werner L. Integrated Carbon Observation System (ICOS ERIC). Head Office; Finlandia Fil: Kwon, Hyojung. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: Launiainen, Samuli. Natural Resources Institute Finland; Finlandia Fil: Laurila, Tuomas. Finnish Meteorological Institute; Finlandia Fil: Law, Bev. Oregon State University. Department of Forest Ecosystems and Society; Estados Unidos Fil: Leuning. Ray. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Li, Yingnian. Chinese Academy of Sciences. Northwest institute of Plateau Biology. Key Laboratory of Adaptation and Evolution of Plateau Biota; China Fil: Liddell, Michael. James Cook University. Centre for Tropical Environmental Sustainability Studies; Australia. Fil: Limousin, Jean-Marc. CEFE, CNRS, Univ Montpellier: Francia Fil: Lion, Marryanna. Forest Research Institute Malaysia. Forestry and Environment Division; Malasia Fil: Lohila, Annalea. University of Helsinki. Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: López-Ballesteros, Ana. Trinity College Dublin. School of Natural Sciences. Department of Botany; Irlanda Fil: López-Blanco, Efren. Aarhus University. Arctic Research Center. Department of Bioscience; Dinamarca Fil: Loubet, Benjamin. Université Paris-Saclay. INRAE, AgroParisTech, UMR ECOSYS; Francia Fil: Lucas-Moffat, Antje. Centre for Agrometeorological Research. German Meteorological Service; Alemania Fil: Lüers, Johannes. University of Bayreuth. Micrometeorology; Alemania. Bayreuth Center of Ecology and Environmental Research; Alemania Fil: Ma, Siyan. University of California Berkeley. ESPM; Estados Unidos Fil: Macfarlane, Craig. CSIRO Land and Water; Australia Fi: Magliulo, Vincenzo. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Mammarella, Ivan. University of Helsinki . Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: Manca, Giovanni. Joint Research Centre, European Commission; Italia Fil: Marras, Serena. University of Sassari. Department of Agriculture; Italia Fil: Massman, William. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Mastepanov, Mikhail. University of Oulu. Oulanka Research Station; Finlandia Fil: Matamala, Roser. Argonne National Laboratory. Environmental Science Division; Estados Unidos Fil: Matthes, Jaclyn Hatala. Wellesley College. Department of Biological Sciences; Estados Unidos Fil: , Mazzenga, Francesco. National Research Council of Italy. Research Institute on Terrestrial Ecosystems; Italia Fil: McCaughey, Harry. Queen’s University. Department of Geography and Planning; Canadá. Fil: McHugh, Ian. The University of Melbourne. School of Ecosystem and Forest Sciences; Australia. Fil: McMillan, Andrew M.S. Environmental Analytics nZ, Ltd.; Nueva Zelanda Fil: Merbold, Lutz. International Livestock Research Institute. Mazingira Centre; Kenia. Fil: Meyer, Wayne. University of Adelaide. Biological Sciences; Australia. Fil: Meyers, Tilden. NOAA/OAR/Air Resources Laboratory; Estados Unidos Fil: Miller, Scott D. State University of New York at Albany. Atmospheric Sciences Research Center; Estados Unidos Fil: Minerbi, Stefano. Forest Department of South Tyrol; Italia Fil: Monson, Russell K. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Montagnani, Leonardo. Forest Department of South Tyrol; Italia. Free University of Bolzano. Faculty of Science and Technology; Italia Fil: Moore, Caitlin E. University of Illinois at Urbana-Champaign. Department of Plant Biology; Estados Unidos Fil: Moors, Eddy. IHE Delft: Holanda. VU Amsterdam. Faculty of Science; Holanda Fil: Moreaux, Virginie. University Grenoble Alpes; Francia Fil: Moureaux, Christine. University of Liege. TeRRA Teaching and Research Center; Bélgica Fil: Munger, J. William. Harvard University. School of engineering and Applied Sciences; Estados Unidos. Harvard University. Department of Earth and Planetary Sciences; Estados Unidos Fil: Nakai, Taro. National Taiwan University. School of forestry and Resource conservation; Taiwan. University of Alaska Fairbanks. International Arctic Research Center; Estados Unidos Fil: Neirynck, Johan. Research Institute for Nature and Forest. Environment and Climate; Bélgica Fil: Nesic, Zoran. University of British Columbia. Faculty of Land and Food Systems; Canadá Fil: Nicolini, Giacomo. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Noormets, Asko. Texas A&M University. College Station. Department of Ecosystem Science and Management; Estados Unidos Fil: Northwood, Matthew. Charles Darwin University. Research Institute for the Environment and Livelihoods; Australia Fil: Nosetto, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática Aplicada San Luis. Grupo de Estudios Ambientales. Universidad Nacional de San Luis; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina Fil: Nouvellon, Yann. Université Montpellier-CIRAD-INRA-IRD-Montpellier SupAgro. Eco&Sols; Francia Fil: Novick, Kimberly O’Neill. Indiana University Bloomington. School of Public and Environmental Affairs; Estados Unidos Fil: Oechel, Walter. San Diego State University. Department of Biology. Global Change Research Group; Estados Unidos. University of Exeter. College of Life and Environmental Sciences. Department of Geography; Reino Unido Fil: Olesen, Jørgen Eivind. Aarhus University. Department of Agroecology; Dinamarca. Aarhus University. iCLIMATE; Dinamarca Fil: Ourcival, Jean-Marc. CEFE, CNRS, Université Montpellier; Francia Fil: Papuga, Shirley A. Wayne State University. Department of Geology; Estados Unidos Fil: Parmentier, Frans-Jan. University of Oslo. Department of Geosciences; Noruega Fil: Paul-Limoges, Eugenie. University of Zurich. Department of Geography; Suiza Fil: Pavelka, Marian. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Peichl, Matthias. Swedish University of Agricultural Sciences. Department of Forest Ecology and Management; Suecia Fil: Pendall, Elise. Western Sydney University. Hawkesbury Institute for the Environment; Australia Fil: Phillips, Richard P. Indiana University Bloomington. Department of Biology; Estados Unidos Fil: Pilegaard, Kim. Technical University of Denmark. Department of Environmental Engineering; Dinamarca Fil: Pirk, Norbert. CSiRO Land and Water; Australia. Fil: Posse Beaulieu, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Powell, Thomas. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Prasse, Heiko. Technische Universität Dresden. Institute of Hydrology and Meteorology; Alemania Fil: Reed, David. Michigan State University. Center for Global Change & Earth Observations: Estados Unidos. Michigan State University. Center for Global Change & Earth Observations; Estados Unidos Fil: Resco de Dios, Víctor. Western Sydney University. Hawkesbury Institute for the Environment; Australia. Southwest University of Science and Technology. School of Life Science and Engineering; China Fil: Restrepo-Coupe, Natalia. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Reverter, Borja R. Universidade Federal da Paraiba. Departamento de Química e Física; Brasil Fil: Roland, Marilyn. University of Antwerp. Department of Biology. Research Group PLECO; Bélgica Fil: Sabbatini, Simone. University of Tuscia. DiBAf; Italia. Fil: Sachs, Torsten. GfZ German Research Centre for Geosciences. Remote Sensing and Geoinformatics; Alemania Fil: Saleska, Scott R. University of Arizona. Department of Ecology and Evolutionary Biology; Estados Unidos Fil: Sánchez-Cañete, Enrique P. University of Granada. Department of Applied Physics; España. Andalusian Institute for Earth System Research (CEAMA-IISTA); España Fil: Sanchez-Mejia, Zulia M. Instituto Tecnológico de Sonora. Ciencias del Agua y Medioambiente; México Fil: Schmid, Hans Peter. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Schmidt, Marius. Agrosphere (IBG3), Forschungszentrum Jülich; Alemania Fil: Schneider, Karl. University of Cologne. Geographical Institute; Alemania Fil: Schrader, Frederik Thünen. Federal Research Institute of Rural Areas, Forestry and Fisheries. Institute of Climate-Smart Agriculture; Alemania Fil: Schroder, Ivan. Geoscience Australia. Department of industry, Innovation and Science; Australia. Fil: Scott, Russell L. USDA-ARS. Southwest Watershed Research Center; Estados Unidos Fil: Sedlák, Pavel. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa. Institute of Atmospheric Physics of the Czech Academy of Sciences; República Checa Fil: Serrano-Ortíz, Penélope. Andalusian Institute for Earth System Research (CEAMA-IISTA); España. University of Granada. Department of Ecology; España Fil: Shao, Changliang. Chinese Academy of Agricultural Sciences. National Hulunber Grassland Ecosystem Observation and Research Station & Institute of Agricultural Resources and Regional Planning; China. Fil: Shi, Peili. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China. Fil: Shironya, Ivan A.n. Russian Academy of Sciences. Severtsov Institute of Ecology and Evolution; Rusia Fil: Siebicke, Lukas. University of Goettingen. Bioclimatology; Alemania Fil: Šigut, Ladislav. Global Change Research Institute of the Czech Academy of Sciences. Department of Matter and Energy Fluxes; República Checa Fil: Silberstein, Richard. University of Western Australia. School of Agriculture and Environment; Australia. Edith Cowan University. School of Science; Australia. Fil: Sirca, Costantino. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia. University of Sassari. Department of Agriculture; Italia Fil: Spano, Donatella. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia. University of Sassari. Department of Agriculture; Italia Fil: Steinbrecher, Rainer. Karlsruhe Institute of Technology. Institute of Meteorology and Climate Research; Alemania Fil: Stevens, Robert M. Sentek Pty Ltd.; Australia Fil: Sturtevant, Cove. National ecological Observatory Network Program; Estados Unidos Fil: Suyker, Andy. University of Nebraska-Lincoln. School of Natural Resources; Estados Unidos Fil: Tagesson, Torbem. Lund University. Department of Physical Geography and Ecosystem Science; Suecia. University of Copenhagen. Department of Geosciences and Natural Resource Management; Dinamarca Fil: Takanashi, Satoru. Forestry and Forest Products Research Institute. Kansai Research Center; Japón Fil: Tang, Yanhong. Peking University. College of Urban and Environmental Sciences; China. Fil: Tapper, Nigel. Monash University. School of Earth, Atmosphere and Environment; Australia Fil: Thom, Jonathan. University of Wisconsin-Madison. Space Science and Engineering Center; Estados Unidos Fil: Tiedemann, Frank. University of Goettingen. Bioclimatology; Alemania Fil: Tomassucci, Michele. University of Tuscia. DiBAf; Italia. Terrasystem srl; Italia Fil: Tuovinen, Juha-Pekka. Finnish Meteorological Institute; Finlandia Fil: Urbanski, Shawn. USDA Forest Service. Rocky Mountain Research Station; Estados Unidos Fil: Valentini, Riccardo. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: van der Molen, Michiel. Wageningen University. Meteorology and Air Quality Group; Holanda Fil: van Gorsel, Eva. Australian National University Canberra. Fenner School of Environment and Society; Australia. Fil: van Huissteden, Ko. Vrije Universiteit Amsterdam. Department of Earth Sciences; Holanda Fil: Varlagin, Andrej. Agroscope Research Institute. Department of Agroecology and Environment; Suiza Fil: Verfaillie, Joseph. University of California Berkeley. ESPM; Estados Unidos Fil: Vesala, Timo. University of Helsinki. Institute for Atmosphere and Earth System Research/Physics; Finlandia Fil: Vincke, Caroline. Chinese Academy of Sciences. South China Botanical Garden; China. Fil: Vitale, Domenico. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia Fil: Vygodskaya, Natalia. University of Bayreuth. Micrometeorology; Alemania Fil: Walker, Jeffrey P. Monash University. Department of Civil Engineering; Australia Fil: Walter-Shea, Elizabeth. University of Nebraska-Lincoln. School of natural Resources; Estados Unidos Fil: Wang, Huimin. Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research. Key Laboratory of Ecosystem Network Observation and Modeling; China Fil: Weber, Robin. University of California Berkeley. ESPM; Estados Unidos Fil: Westermann, Sebastian. Instituto Nacional de Tecnologia Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Fil: Wille, Christian. GfZ German Research centre for Geosciences. Remote Sensing and Geoinformatics; Alemania Fil: Wofsy, Steven. Harvard University. School of engineering and Applied Sciences; Estados Unidos. Harvard University. Department of Earth and Planetary Sciences; Estados Unidos Fil: Wohlfahrt, Georg. University of Innsbruck. Department of Ecology; Austria. Fil: Woodgate, William. CSIRO Land and Water; Australia. Fil: Li, Yuelin. Chinese Academy of Sciences. South China Botanical Garden; China. Fil: Zampedri, Roberto. Fondazione Edmund Mach. Research and Innovation Centre. Department of Sustainable Agro-ecosystems and Bioresources; Italia Fil: Zhang, Junhui. Chinese Academy of Sciences. Institute of Applied Ecology; China. Fil: Zhou, Guoyi. Nanjing University of Information Science & Technology. College of Applied Meteorology; China. Fil: Zona, Donatella. San Diego State University. Department of Biology. Global Change Research Group; Estados Unidos. University of Sheffield. Department of Animal and Plant Sciences; Reino Unido Fil: Agarwal, Deb. Lawrence Berkeley National Laboratory. Computational Research Division; Estados Unidos Fil: Biraud, Sebastien. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Torn, Margaret. Lawrence Berkeley National Laboratory. Climate & Ecosystem Sciences Division; Estados Unidos Fil: Papale, Dario. University of Tuscia. DiBAf; Italia. Euro-Mediterranean Centre on Climate Change Foundation (CMCC); Italia

Published
2020

34. Evaluation of gradient boosting and random forest methods to model subdaily variability of the atmosphere–forest CO2 exchange.

Author

Kämäräinen, Matti, Lintunen, Anna, Kulmala, Markku, Tuovinen, Juha-Pekka, Mammarella, Ivan, Aalto, Juha, Vekuri, Henriikka, and Lohila, Annalea

Subjects
ATMOSPHERIC carbon dioxide, RANDOM forest algorithms, CARBON cycle, CARBON dioxide, ROOT-mean-squares, PEARSON correlation (Statistics), TAIGAS
Abstract

Accurate estimates of the net ecosystem CO2 exchange (NEE) would improve the understanding of the natural carbon sources and sinks and their role in the regulation of the global atmospheric carbon. In this work, we use and compare the random forest (RF) and the gradient boosting (GB) machine learning (ML) methods for predicting the year-round 6 hourly NEE over 1996–2018 in a pine-dominated boreal forest in southern Finland and analyze the predictability of the NEE. Additionally, aggregation to weekly NEE values was applied to get information about longer term behavior of the method. The meteorological ERA5 reanalysis variables were used as predictors. Spatial and temporal neighborhood (predictor lagging) was used to provide the models more data to learn from, which was found to improve the accuracy compared to using only the nearest grid cell and time step. Both ML methods can explain the temporal variability of the NEE in the observational site of this study with the meteorological predictors, but the GB method was more accurate. It was more effective in separating the important predictors from non-important ones, showing no signs of overfitting despite many redundant variables. The accuracy of the GB (RF), here measured mainly using cross-validated Pearson correlation coefficient between the model result and the observed NEE, was high (good), reaching a best estimate value of 0.96 (0.94) and the root mean square value of 1.18 µmol m-2 s-1 (1.35 µmol m² s-1). We recommend using GB instead of RF for modeling the CO2 fluxes of the ecosystems due to its better performance. [ABSTRACT FROM AUTHOR]

Published
2022
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35. Meteorological responses of carbon dioxide and methane fluxes in the terrestrial and aquatic ecosystems of a subarctic landscape.

Author

Heiskanen, Lauri, Tuovinen, Juha-Pekka, Räsänen, Aleksi, Virtanen, Tarmo, Juutinen, Sari, Vekuri, Henriikka, Lohila, Annalea, Mikola, Juha, and Aurela, Mika

Subjects
BOGS, TUNDRAS, ATMOSPHERIC carbon dioxide, CARBON dioxide, DROUGHTS, HEAT waves (Meteorology), SOIL moisture, LAKE sediments
Abstract

The subarctic landscape consists of a mosaic of forest, peatland and aquatic ecosystems and their ecotones. The carbon (C) exchange between ecosystems and the atmosphere through carbon dioxide (CO2) and methane (CH4) fluxes varies spatially and temporally among these ecosystems. Our study area in Kaamanen in northern Finland covered 7 km² of boreal subarctic landscape with upland forest, open peatland, pine bogs and lakes. We measured the CO2 and CH4 fluxes with eddy covariance and chambers between June 2017 and June 2019 and studied the C flux responses to varying meteorological conditions. The landscape area was an annual CO2 sink of -25.9 ± 65.7 and -41.3 ± 64.9 g C m-2, and a CH4 source of 2.4 ± 0.7 and 2.3 ± 0.7 g C m-2 during the first and second study year, respectively. The pine forest had the largest contribution to the landscape-level CO2 sink, -78.3 ± 50.8 and -118.9 ± 26.8 g C m-2, and the fen to the CH4 emissions, 7.0 ± 0.2 and 6.3 ± 0.3 g C m-2, during the first and second study year, respectively. The lakes within the area acted as CO2 and CH4 sources to the atmosphere throughout the measurement period, with an organic sediment lake located downstream from the fen showing sixfold fluxes compared to a mineral sediment lake. The annual C balances were affected most by the rainy peak growing season of 2017 and the heatwave and drought event in July 2018. The rainy period increased the ecosystem respiration of the pine forest due to continuously high soil moisture content. A similar flux response to abundant precipitation was not observed for the fen ecosystem, which is adapted to high water table levels. During the heatwave and drought period, similar responses were observed for all terrestrial ecosystems, with decreased gross primary productivity and net CO2 uptake, caused by the unfavourable growing conditions and plant stress due to the soil moisture and vapour pressure deficits. Additionally, the CH4 emissions from the fen decreased during and after the drought. However, the timing and duration of drought effects varied between fen and forest ecosystems, as C fluxes were affected sooner and had a shorter post-drought recovery time in the fen than forests. The differing CO2 flux response to weather variations showed that terrestrial ecosystems can have a contrasting impact on the landscape-level C balance in a changing climate, even if they function similarly most of the time. [ABSTRACT FROM AUTHOR]

Published
2022
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37. Tracking vegetation phenology of pristine northern boreal peatlands by combining digital photography with CO2 flux and remote sensing data.

Author

Linkosalmi, Maiju, Tuovinen, Juha-Pekka, Nevalainen, Olli, Peltoniemi, Mikko, Tanis, Cemal Melih, Arslan, Ali Nadir, Rainne, Juuso, Lohila, Annalea, Laurila, Tuomas, and Aurela, Mika

Subjects
DIGITAL photography, REMOTE sensing, PHENOLOGY, PEATLANDS, VEGETATION dynamics, PLANT phenology
Abstract

Vegetation phenology, which refers to the seasonal changes in plant physiology, biomass and leaf area, is affected by many abiotic factors, such as precipitation, temperature and water availability. Phenology is also associated with the carbon dioxide (CO2) exchange between ecosystems and the atmosphere. We employed digital cameras to monitor the vegetation phenology of three northern boreal peatlands during five growing seasons. We derived a greenness index (Green Chromatic Coordinate, GCC) from the images and combined the results with measurements of CO2 flux, temperature and water table level, and with high-resolution satellite data (Sentinel-2). From the digital camera images it was possible to extract greenness dynamics on the vegetation community and even species level. The highest GCC and daily maximum gross photosynthetic production (GPPmax) were observed at the site with the highest nutrient availability and richest vegetation. The short-term temperature response of GCC depended on temperature and varied among the sites and months. Although the seasonal development and year-to-year variation of GCC and GPPmax showed consistent patterns, the short-term variation in GPPmax was explained by GCC only during limited periods. GCC clearly indicated the main phases of the growing season and peatland vegetation showed capability to fully compensate for the impaired growth resulting from a late growing season start. The GCC data derived from Sentinel-2 and digital cameras showed similar seasonal courses, but a reliable timing of different phenological phases depended upon the temporal coverage of satellite data. [ABSTRACT FROM AUTHOR]

Published
2022
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38. Variation in CO2 and CH4 Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia.

Author

Juutinen, Sari, Aurela, Mika, Tuovinen, Juha-Pekka, Ivakhov, Viktor, Linkosalmi, Maiju, Räsänen, Aleksi, Virtanen, Tarmo, Mikola, Juha, Nyman, Johanna, Vähä, Emmi, Loskutova, Marina, Makshtas, Alexander, and Laurila, Tuomas

Subjects
TUNDRAS, LAND cover, LEAF area index, COMPOSITION of leaves, GROWING season, FOREIGN exchange rates
Abstract

Arctic tundra is facing unprecedented warming, resulting in shifts in the vegetation, thaw regimes, and potentially in the ecosystem-atmosphere exchange of carbon (C). The estimates of regional carbon dioxide (CO2) and methane (CH4) budgets, however, are highly uncertain. We measured CO2 and CH4 fluxes, vegetation composition and leaf area index (LAI), thaw depth, and soil wetness in Tiksi (71° N, 128° E), a heterogeneous site located within the prostrate dwarf-shrub tundra zone in northeastern Siberia. Using the closed chamber method, we determined net ecosystem exchange (NEE) of CO2, dark ecosystem respiration (ER), ecosystem gross photosynthesis (Pg), and CH4 fluxes during the growing season. We applied a previously developed high-spatial-resolution land-cover map over an m area of 35.8 km². Among the land-cover types varying from barrens to dwarf-shrub tundra and tundra wetlands, the light-saturated NEE and Pg scaled with the LAI of vascular plants. Thus, the graminoid-dominated tundra wetlands, with high LAI and the deepest thaw depth, had the highest light-saturated NEE and Pg (up to -21 (uptake) and 28 mmol m-2 h-1, respectively) and were disproportionately important for the summertime CO2 sequestration on a landscape scale. Dry tundra, including the dwarf-shrub-dominated vegetation and only sparsely vegetated lichen tundra, had only small CO2 exchange rates. While tundra wetlands were sources of CH4, lichen tundra, including bare ground habitats, consumed atmospheric CH4 at a substantial rate. On a landscape scale, the consumption by lichen tundra and barrens could offset ca. 10% of the CH4 emissions. We acknowledge the uncertainty involved in spatial extrapolations due to a small number of replicates per land-cover type. This study, however, highlights the need for distinguishing different land-cover types including the dry tundra habitats to account for their consumption of the atmospheric CH4 when estimating tundra C-exchange on a larger spatial scale. [ABSTRACT FROM AUTHOR]

Published
2022
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39. Towards agricultural soil carbon monitoring, reporting, and verification through the Field Observatory Network (FiON).

Author

Nevalainen, Olli, Niemitalo, Olli, Fer, Istem, Juntunen, Antti, Mattila, Tuomas, Koskela, Olli, Kukkamäki, Joni, Höckerstedt, Layla, Mäkelä, Laura, Jarva, Pieta, Heimsch, Laura, Vekuri, Henriikka, Kulmala, Liisa, Stam, Åsa, Kuusela, Otto, Gerin, Stephanie, Viskari, Toni, Vira, Julius, Hyväluoma, Jari, and Tuovinen, Juha-Pekka

Subjects
CARBON in soils, CARBON sequestration, OBSERVATORIES, AGRICULTURAL forecasts, EMISSIONS (Air pollution)
Abstract

Better monitoring, reporting, and verification (MRV) of the amount, additionality, and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies, and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, modeling, and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called the Field Observatory (v1.0, https://www.fieldobservatory.org/ , last access: 3 February 2022). The Field Observatory is designed as an online service for near-real-time model–data synthesis, forecasting, and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site, which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices as well as provide an MRV tool for decision support. [ABSTRACT FROM AUTHOR]

Published
2022
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40. Measuring turbulent CO2 fluxes with a closed-path gas analyzer in a marine environment

Author

Honkanen, Martti, Tuovinen, Juha-Pekka, Laurila, Tuomas, Mäkelä, Timo, Hatakka, Juha, Kielosto, Sami, and Laakso, Lauri

Abstract

In this study, we introduce new observations of sea–air fluxes of carbon dioxide using the eddy covariance method. The measurements took place at the Utö Atmospheric and Marine Research Station on the island of Utö in the Baltic Sea in July–October 2017. The flux measurement system is based on a closed-path infrared gas analyzer (LI-7000, LI-COR) requiring only occasional maintenance, making the station capable of continuous monitoring. However, such infrared gas analyzers are prone to significant water vapor interference in a marine environment, where CO2 fluxes are small. Two LI-7000 analyzers were run in parallel to test the effect of a sample air drier which dampens water vapor fluctuations and a virtual impactor, included to remove liquid sea spray, both of which were attached to the sample air tubing of one of the analyzers. The systems showed closely similar (R2=0.99) sea–air CO2 fluxes when the latent heat flux was low, which proved that neither the drier nor the virtual impactor perturbed the CO2 flux measurement. However, the undried measurement had a positive bias that increased with increasing latent heat flux, suggesting water vapor interference. For both systems, cospectral densities between vertical wind speed and CO2 molar fraction were distributed within the expected frequency range, with a moderate attenuation of high-frequency fluctuations. While the setup equipped with a drier and a virtual impactor generated a slightly higher flux loss, we opt for this alternative for its reduced water vapor cross-sensitivity and better protection against sea spray. The integral turbulence characteristics were found to agree with the universal stability dependence observed over land. Nonstationary conditions caused unphysical results, which resulted in a high percentage (65 %) of discarded measurements. After removing the nonstationary cases, the direction of the sea–air CO2 fluxes was in good accordance with independently measured CO2 partial pressure difference between the sea and the atmosphere. Atmospheric CO2 concentration changes larger than 2 ppm during a 30 min averaging period were found to be associated with the nonstationarity of CO2 fluxes.

Published
2019

42. The effect of rainfall amount and timing on annual transpiration in a grazed savanna grassland.

Author

Rasanen, Matti, Aurela, Mika, Vakkari, Ville, Beukes, Johan P., Tuovinen, Juha-Pekka, Zyl, Pieter G. Van, Josipovic, Miroslav, Siebert, Stefan J., Laurila, Tuomas, Kulmala, Markku, Laakso, Lauri, Rinne, Janne, Oren, Ram, and Katul, Gabriel

Abstract

The role of precipitation (P) variability on evapotranspiration (ET) and its two components, transpiration (T) and evaporation (E) from savannas, continues to draw significant research interest given its relevance to a number of eco-hydrological applications. Our study reports on six years of measured ET and estimated T and E from a grazed savanna grassland in Welgegund, South Africa. Annual P varied significantly in amount (508 to 672 mm yr-1), with dry years characterized by infrequent early-season rainfall. T was determined using annual water-use efficiency and gross primary production estimates derived from eddy covariance measurements of latent heat flux and net ecosystem CO2 exchange rates. The computed annual T was nearly constant, 331 ± 11 mm yr-1 (T/ET=0.52), for the four wet years with frequent early wet-season rainfall, whereas annual T was 268 and 175 mm yr-1 during the dry years. Annual T/ET was linearly related to the early wet-season storm frequency. The constancy of annual T during wet years is explained by the moderate water stress of C4 grass and constant annual tree transpiration covering 15% of the landscape. However, grass transpiration declines during dry spells. Moreover, grasses respond to water availability with a dieback-regrowth pattern, reducing leaf area and transpiration during drought. These changes lead to an anomalous monthly T/ET relation to leaf-area index (LAI). The results highlight the role of the C4 grass layer in the hydrological balance and suggest that the grass response to dry spells and drought is reasonably described by precipitation timing. [ABSTRACT FROM AUTHOR]

Published
2021
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43. Towards agricultural soil carbon monitoring, reporting and verification through Field Observatory Network (FiON).

Author

Nevalainen, Olli, Niemitalo, Olli, Fer, Istem, Juntunen, Antti, Mattila, Tuomas, Koskela, Olli, Kukkamäki, Joni, Höckerstedt, Layla, Mäkelä, Laura, Jarva, Pieta, Heimsch, Laura, Vekuri, Henriikka, Kulmala, Liisa, Stam, Åsa, Kuusela, Otto, Gerin, Stephanie, Viskari, Toni, Vira, Julius, Hyväluoma, Jari, and Tuovinen, Juha-Pekka

Subjects
CARBON in soils, CARBON sequestration, OBSERVATORIES, AGRICULTURAL forecasts, REMOTE-sensing images
Abstract

Better monitoring, reporting and verification (MRV) of the amount, additionality and persistence of the sequestered soil carbon is needed to understand the best carbon farming practices for different soils and climate conditions, as well as their actual climate benefits or cost-efficiency in mitigating greenhouse gas emissions. This paper presents our Field Observatory Network (FiON) of researchers, farmers, companies and other stakeholders developing carbon farming practices. FiON has established a unified methodology towards monitoring and forecasting agricultural carbon sequestration by combining offline and near real-time field measurements, weather data, satellite imagery, modeling and computing networks. FiON's first phase consists of two intensive research sites and 20 voluntary pilot farms testing carbon farming practices in Finland. To disseminate the data, FiON built a web-based dashboard called Field Observatory (v1.0, fieldobservatory.org). Field Observatory is designed as an online service for near real-time model-data synthesis, forecasting and decision support for the farmers who are able to monitor the effects of carbon farming practices. The most advanced features of the Field Observatory are visible on the Qvidja site which acts as a prototype for the most recent implementations. Overall, FiON aims to create new knowledge on agricultural soil carbon sequestration and effects of carbon farming practices, and provide an MRV tool for decision-support. [ABSTRACT FROM AUTHOR]

Published
2021
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44. ICOS eddy covariance flux-station site setup : a review

Author

Rebmann, Corinna, Aubinet, Marc, Schmid, Hape, Arriga, Nicola, Aurela, Mika, Burba, George, Clement, Robert, De Ligne, Anne, Fratini, Gerardo, Gielen, Bert, Grace, John, Graf, Alexander, Gross, Patrick, Haapanala, Sami, Herbst, Mathias, Hortnagl, Lukas, Ibrom, Andreas, Joly, Lilian, Kljun, Natascha, Kolle, Olaf, Kowalski, Andrew, Lindroth, Anders, Loustau, Denis, Mammarella, Ivan, Mauder, Matthias, Merbold, Lutz, Metzger, Stefan, Molder, Meelis, Montagnani, Leonardo, Papale, Dario, Pavelka, Marian, Peichl, Matthias, Roland, Marilyn, Serrano-Ortiz, Penelope, Siebicke, Lukas, Steinbrecher, Rainer, Tuovinen, Juha-Pekka, Vesala, Timo, Wohlfahrt, Georg, Franz, Daniela, INAR Physics, Micrometeorology and biogeochemical cycles, Institute for Atmospheric and Earth System Research (INAR), Viikki Plant Science Centre (ViPS), and Ecosystem processes (INAR Forest Sciences)

Subjects
eddy covariance technique, INDUCED FLOW DISTORTION, CO2 STORAGE, NET ECOSYSTEM EXCHANGE, QUALITY ASSESSMENT, SONIC ANEMOMETERS, GAS ANALYZER, SCALAR FLUXES, 114 Physical sciences, CARBON-DIOXIDE, ICOS, tower set up, greenhouse gas, WATER-VAPOR, FOOTPRINT ESTIMATION, protocol
Abstract

The Integrated Carbon Observation System Re-search Infrastructure aims to provide long-term, continuous observations of sources and sinks of greenhouse gases such as carbon dioxide, methane, nitrous oxide, and water vapour. At ICOS ecosystem stations, the principal technique for measurements of ecosystem-atmosphere exchange of GHGs is the eddy-covariance technique. The establishment and setup of an eddy-covariance tower have to be carefully reasoned to ensure high quality flux measurements being representative of the investigated ecosystem and comparable to measurements at other stations. To fulfill the requirements needed for flux determination with the eddy-covariance technique, variations in GHG concentrations have to be measured at high frequency, simultaneously with the wind velocity, in order to fully capture turbulent fluctuations. This requires the use of high-frequency gas analysers and ultrasonic anemometers. In addition, to analyse flux data with respect to environmental conditions but also to enable corrections in the post-processing procedures, it is necessary to measure additional abiotic variables in close vicinity to the flux measurements. Here we describe the standards the ICOS ecosystem station network has adopted for GHG flux measurements with respect to the setup of instrumentation on towers to maximize measurement precision and accuracy while allowing for flexibility in order to observe specific ecosystem features.

Published
2018

45. Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe's terrestrial ecosystems : a review

Author

Franz, Daniela, Acosta, Manuel, Altimir, Nuria, Arriga, Nicola, Arrouays, Dominique, Aubinet, Marc, Aurela, Mika, Ayres, Edward, Lopez-Ballesteros, Ana, Barbaste, Mireille, Berveiller, Daniel, Biraud, Sebastien, Boukir, Hakima, Brown, Timothy, Bruemmer, Christian, Buchmann, Nina, Burba, George, Carrara, Arnaud, Cescatti, Allessandro, Ceschia, Eric, Clement, Robert, Cremonese, Edoardo, Crill, Patrick, Darenova, Eva, Dengel, Sigrid, D'Odorico, Petra, Filippa, Gianluca, Fleck, Stefan, Fratini, Gerardo, Fuss, Roland, Gielen, Bert, Gogo, Sebastien, Grace, John, Graf, Alexander, Grelle, Achim, Gross, Patrick, Gruenwald, Thomas, Haapanala, Sami, Hehn, Markus, Heinesch, Bernard, Heiskanen, Jouni, Herbst, Mathias, Herschlein, Christine, Hortnagl, Lukas, Hufkens, Koen, Ibrom, Andreas, Jolivet, Claudy, Joly, Lilian, Jones, Michael, Kiese, Ralf, Klemedtsson, Leif, Kljun, Natascha, Klumpp, Katja, Kolari, Pasi, Kolle, Olaf, Kowalski, Andrew, Kutsch, Werner, Laurila, Tuomas, de Ligne, Anne, Linder, Sune, Lindroth, Anders, Lohila, Annalea, Longdoz, Bernhard, Mammarella, Ivan, Manise, Tanguy, Maranon Jimenez, Sara, Matteucci, Giorgio, Mauder, Matthias, Meier, Philip, Merbold, Lutz, Mereu, Simone, Metzger, Stefan, Migliavacca, Mirco, Molder, Meelis, Montagnani, Leonardo, Moureaux, Christine, Nelson, David, Nemitz, Eiko, Nicolini, Giacomo, Nilsson, Mats B., Op de Beeck, Maarten, Osborne, Bruce, Lofvenius, Mikaell Ottosson, Pavelka, Marian, Peichl, Matthias, Peltola, Olli, Pihlatie, Mari, Pitacco, Andrea, Pokorny, Radek, Pumpanen, Jukka, Ratie, Celine, Rebmann, Corinna, Roland, Marilyn, Sabbatini, Simone, Saby, Nicolas P. A., Saunders, Matthew, Schmid, Hans Peter, Schrumpf, Marion, Sedlak, Pavel, Serrano Ortiz, Penelope, Siebicke, Lukas, Sigut, Ladislav, Silvennoinen, Hanna, Simioni, Guillaume, Skiba, Ute, Sonnentag, Oliver, Soudani, Kamel, Soule, Patrice, Steinbrecher, Rainer, Tallec, Tiphaine, Thimonier, Anne, Tuittila, Eeva-Stiina, Tuovinen, Juha-Pekka, Vestin, Patrik, Vincent, Gaelle, Vincke, Caroline, Vitale, Domenico, Waldner, Peter, Weslien, Per, Wingate, Lisa, Wohlfahrt, Georg, Zahniser, Mark, Vesala, Timo, Institute for Atmospheric and Earth System Research (INAR), INAR Physics, Micrometeorology and biogeochemical cycles, Ecosystem processes (INAR Forest Sciences), Department of Agricultural Sciences, Environmental Soil Science, Methane and nitrous oxide exchange of forests, Viikki Plant Science Centre (ViPS), University Management, and Faculty of Agriculture and Forestry

Subjects
GHG exchange, MEASUREMENT NETWORK, CO2 EXCHANGE, 4111 Agronomy, FOREST ECOSYSTEMS, DIOXIDE EXCHANGE, ICOS, standardised monitoring, 1.5 DEGREES-C, carbon cycle, CLIMATE EXTREMES, observational network, CH4 EMISSIONS, INTERANNUAL VARIABILITY, NET CARBON, 1172 Environmental sciences, PRIMARY PRODUCTIVITY
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; CO2, CH4, N2O, H2O) 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.

Published
2018

46. Digital photography for assessing the link between vegetation phenology and CO2 exchange in two contrasting northern ecosystems

Author

Linkosalmi, Maiju, Aurela, Mika, Tuovinen, Juha-Pekka, Peltoniemi, Mikko, Tanis, Cemal M., Arslan, Ali N., Kolari, Pasi, Bottcher, Kristin, Aalto, Tuula, Rainne, Juuso, Hatakka, Juha, Laurila, Tuomas, Department of Physics, Ecosystem processes (INAR Forest Sciences), and Micrometeorology and biogeochemical cycles

Subjects
BUD BURST, 1171 Geosciences, CLIMATE-CHANGE, CARBON-DIOXIDE EXCHANGE, EDDY COVARIANCE, lcsh:QC801-809, SEASONAL DYNAMICS, SPRING PHENOLOGY, FOREST, TRENDS, REPEAT PHOTOGRAPHY, lcsh:Geophysics. Cosmic physics, TEMPERATURE, 1172 Environmental sciences
Abstract

Digital repeat photography has become a widely used tool for assessing the annual course of vegetation phenology of different ecosystems. By using the green chromatic coordinate (GCC) as a greenness measure, we examined the feasibility of digital repeat photography for assessing the vegetation phenology in two contrasting high-latitude ecosystems. Ecosystem–atmosphere CO2 fluxes and various meteorological variables were continuously measured at both sites. While the seasonal changes in GCC were more obvious for the ecosystem that is dominated by annual plants (open wetland), clear seasonal patterns were also observed for the evergreen ecosystem (coniferous forest). Daily and seasonal time periods with sufficient solar radiation were determined based on images of a grey reference plate. The variability in cloudiness had only a minor effect on GCC, and GCC did not depend on the sun angle and direction either. The daily GCC of wetland correlated well with the daily photosynthetic capacity estimated from the CO2 flux measurements. At the forest site, the correlation was high in 2015 but there were discernible deviations during the course of the summer of 2014. The year-to-year differences were most likely generated by meteorological conditions, with higher temperatures coinciding with higher GCCs. In addition to depicting the seasonal course of ecosystem functioning, GCC was shown to respond to environmental changes on a timescale of days. Overall, monitoring of phenological variations with digital images provides a powerful tool for linking gross primary production and phenology.

Published
2018

48. Carbon dioxide fluxes and carbon balance of an agricultural grassland in southern Finland.

Author

Heimsch, Laura, Lohila, Annalea, Tuovinen, Juha-Pekka, Vekuri, Henriikka, Heinonsalo, Jussi, Nevalainen, Olli, Korkiakoski, Mika, Liski, Jari, Laurila, Tuomas, and Kulmala, Liisa

Subjects
GRASSLAND soils, CARBON dioxide, LEAF area index, WATER efficiency, EDDY flux, ORGANIC fertilizers
Abstract

A significant proportion of the global carbon emissions to the atmosphere originate from agriculture. Therefore, continuous long-term monitoring of CO 2 fluxes is essential to understand the carbon dynamics and balances of different agricultural sites. Here we present results from a new eddy covariance flux measurement site located in southern Finland. We measured CO 2 and H 2 O fluxes at this agricultural grassland site for 2 years, from May 2018 to May 2020. In particular the first summer experienced prolonged dry periods, which affected the CO 2 fluxes, and substantially larger fluxes were observed in the second summer. During the dry summer, leaf area index (LAI) was notably lower than in the second summer. Water use efficiency increased with LAI in a similar manner in both years, but photosynthetic capacity per leaf area was lower during the dry summer. The annual carbon balance was calculated based on the CO 2 fluxes and management measures, which included input of carbon as organic fertilizers and output as yield. The carbon balance of the field was -57 ± 10 and -86 ± 12 g C m -2 yr -1 in the first and second study years, respectively. [ABSTRACT FROM AUTHOR]

Published
2021
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49. Carbon dioxide and methane exchange of a patterned subarctic fen during two contrasting growing seasons.

Author

Heiskanen, Lauri, Tuovinen, Juha-Pekka, Räsänen, Aleksi, Virtanen, Tarmo, Juutinen, Sari, Lohila, Annalea, Penttilä, Timo, Linkosalmi, Maiju, Mikola, Juha, Laurila, Tuomas, and Aurela, Mika

Subjects
GROWING season, CARBON dioxide, WATER levels, WATER table, METHANE, TUNDRAS, ATMOSPHERIC carbon dioxide
Abstract

The patterned microtopography of subarctic mires generates a variety of environmental conditions, and carbon dioxide (CO 2) and methane (CH 4) dynamics vary spatially among different plant community types (PCTs). We studied the CO 2 and CH 4 exchange between a subarctic fen and the atmosphere at Kaamanen in northern Finland based on flux chamber and eddy covariance measurements in 2017–2018. We observed strong spatial variation in carbon dynamics between the four main PCTs studied, which were largely controlled by water table level and differences in vegetation composition. The ecosystem respiration (ER) and gross primary productivity (GPP) increased gradually from the wettest PCT to the drier ones, and both ER and GPP were larger for all PCTs during the warmer and drier growing season 2018. We estimated that in 2017 the growing season CO 2 balances of the PCTs ranged from - 20 g C m -2 (Trichophorum tussock PCT) to 64 g C m -2 (string margin PCT), while in 2018 all PCTs were small CO 2 sources (10–22 g C m -2). We observed small growing season CH 4 emissions (< 1 g C m -2) from the driest PCT, while the other three PCTs had significantly larger emissions (mean 7.9, range 5.6–10.1 g C m -2) during the two growing seasons. Compared to the annual CO 2 balance (- 8.5 ± 4.0 g C m -2) of the fen in 2017, in 2018 the annual balance (- 5.6 ± 3.7 g C m -2) was affected by an earlier onset of photosynthesis in spring, which increased the CO 2 sink, and a drought event during summer, which decreased the sink. The CH 4 emissions were also affected by the drought. The annual CH 4 balance of the fen was 7.3 ± 0.2 g C m -2 in 2017 and 6.2 ± 0.1 g C m -2 in 2018. Thus, the carbon balance of the fen was close to zero in both years. The PCTs that were adapted to drier conditions provided ecosystem-level resilience to carbon loss due to water level drawdown. [ABSTRACT FROM AUTHOR]

Published
2021
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50. Lateral expansion and carbon exchange of a boreal peatland in Finland resulting in 7000 years of positive radiative forcing

Author

Mathijssen, Paul J. H., Kahkola, Noora, Tuovinen, Juha-Pekka, Lohila, Annalea, Minkkinen, Kari, Laurila, Tuomas, Väliranta, Minna, Environmental Sciences, Department of Forest Sciences, Kari Minkkinen / Principal Investigator, Environmental Change Research Unit (ECRU), and Forest Ecology and Management

Subjects
1171 Geosciences, DRAINED PINE MIRES, CLIMATE-CHANGE, ACCUMULATION RATES, HOLOCENE CARBON, NORTHERN PEATLAND, WATER-LEVEL DRAWDOWN, SOUTHERN FINLAND, VEGETATION SUCCESSION, FINNISH LAPLAND, 1172 Environmental sciences, GREENHOUSE-GAS FLUXES
Abstract

Data on past peatland growth patterns, vegetation development, and carbon (C) dynamics during the various Holocene climate phases may help us to understand possible future climate-peatland feedback mechanisms. In this study, we analyzed and radiocarbon dated several peat cores from Kalevansuo, a drained bog in southern Finland. We investigated peatland succession and C dynamics throughout the Holocene. These data were used to reconstruct the long-term atmospheric radiative forcing, i.e., climate impact of the peatland since initiation. Kalevansuo peat records revealed a general development from fen to bog, typical for the southern boreal zone, but the timing of ombrotrophication varied in different parts of the peatland. Peat accumulation patterns and lateral expansion through paludification were influenced by fires and climate conditions. Long-term C accumulation rates were overall lower than the average values found from literature. We suggest the low accumulation rates are due to repeated burning of the peat surface. Drainage for forestry resulted in a nearly complete replacement of typical bog mosses by forest species within 40 years after drainage. The radiative forcing reconstruction suggested positive values ( warming) for the first similar to 7000 years following initiation. The change from positive to negative forcing was triggered by an expansion of bog vegetation cover and later by drainage. The strong relationship between peatland area and peat type with radiative forcing suggests a possible feedback for future changing climate, as high-latitude peatlands may experience prominent regime shifts, such as fen to bog transitions.

Published
2017

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