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1. Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Author

Chu, H, Chu, H, Luo, X, Ouyang, Z, Chan, WS, Dengel, S, Biraud, SC, Torn, MS, Metzger, S, Kumar, J, Arain, MA, Arkebauer, TJ, Baldocchi, D, Bernacchi, C, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Bracho, R, Brown, S, Brunsell, NA, Chen, J, Chen, X, Clark, K, Desai, AR, Duman, T, Durden, D, Fares, S, Forbrich, I, Gamon, JA, Gough, CM, Griffis, T, Helbig, M, Hollinger, D, Humphreys, E, Ikawa, H, Iwata, H, Ju, Y, Knowles, JF, Knox, SH, Kobayashi, H, Kolb, T, Law, B, Lee, X, Litvak, M, Liu, H, Munger, JW, Noormets, A, Novick, K, Oberbauer, SF, Oechel, W, Oikawa, P, Papuga, SA, Pendall, E, Prajapati, P, Prueger, J, Quinton, WL, Richardson, AD, Russell, ES, Scott, RL, Starr, G, Staebler, R, Stoy, PC, Stuart-Haëntjens, E, Sonnentag, O, Sullivan, RC, Suyker, A, Ueyama, M, Vargas, R, Wood, JD, Zona, D, Chu, H, Chu, H, Luo, X, Ouyang, Z, Chan, WS, Dengel, S, Biraud, SC, Torn, MS, Metzger, S, Kumar, J, Arain, MA, Arkebauer, TJ, Baldocchi, D, Bernacchi, C, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Bracho, R, Brown, S, Brunsell, NA, Chen, J, Chen, X, Clark, K, Desai, AR, Duman, T, Durden, D, Fares, S, Forbrich, I, Gamon, JA, Gough, CM, Griffis, T, Helbig, M, Hollinger, D, Humphreys, E, Ikawa, H, Iwata, H, Ju, Y, Knowles, JF, Knox, SH, Kobayashi, H, Kolb, T, Law, B, Lee, X, Litvak, M, Liu, H, Munger, JW, Noormets, A, Novick, K, Oberbauer, SF, Oechel, W, Oikawa, P, Papuga, SA, Pendall, E, Prajapati, P, Prueger, J, Quinton, WL, Richardson, AD, Russell, ES, Scott, RL, Starr, G, Staebler, R, Stoy, PC, Stuart-Haëntjens, E, Sonnentag, O, Sullivan, RC, Suyker, A, Ueyama, M, Vargas, R, Wood, JD, and Zona, D

Abstract

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applicat

Published
2021

2. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (vol 7, 225, 2020)

Author

Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brummer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grunwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hortnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Luers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brummer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grunwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hortnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Luers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D

Abstract

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

Published
2021

3. Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Author

Chu, H, Chu, H, Luo, X, Ouyang, Z, Chan, WS, Dengel, S, Biraud, SC, Torn, MS, Metzger, S, Kumar, J, Arain, MA, Arkebauer, TJ, Baldocchi, D, Bernacchi, C, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Bracho, R, Brown, S, Brunsell, NA, Chen, J, Chen, X, Clark, K, Desai, AR, Duman, T, Durden, D, Fares, S, Forbrich, I, Gamon, JA, Gough, CM, Griffis, T, Helbig, M, Hollinger, D, Humphreys, E, Ikawa, H, Iwata, H, Ju, Y, Knowles, JF, Knox, SH, Kobayashi, H, Kolb, T, Law, B, Lee, X, Litvak, M, Liu, H, Munger, JW, Noormets, A, Novick, K, Oberbauer, SF, Oechel, W, Oikawa, P, Papuga, SA, Pendall, E, Prajapati, P, Prueger, J, Quinton, WL, Richardson, AD, Russell, ES, Scott, RL, Starr, G, Staebler, R, Stoy, PC, Stuart-Haëntjens, E, Sonnentag, O, Sullivan, RC, Suyker, A, Ueyama, M, Vargas, R, Wood, JD, Zona, D, Chu, H, Chu, H, Luo, X, Ouyang, Z, Chan, WS, Dengel, S, Biraud, SC, Torn, MS, Metzger, S, Kumar, J, Arain, MA, Arkebauer, TJ, Baldocchi, D, Bernacchi, C, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Bracho, R, Brown, S, Brunsell, NA, Chen, J, Chen, X, Clark, K, Desai, AR, Duman, T, Durden, D, Fares, S, Forbrich, I, Gamon, JA, Gough, CM, Griffis, T, Helbig, M, Hollinger, D, Humphreys, E, Ikawa, H, Iwata, H, Ju, Y, Knowles, JF, Knox, SH, Kobayashi, H, Kolb, T, Law, B, Lee, X, Litvak, M, Liu, H, Munger, JW, Noormets, A, Novick, K, Oberbauer, SF, Oechel, W, Oikawa, P, Papuga, SA, Pendall, E, Prajapati, P, Prueger, J, Quinton, WL, Richardson, AD, Russell, ES, Scott, RL, Starr, G, Staebler, R, Stoy, PC, Stuart-Haëntjens, E, Sonnentag, O, Sullivan, RC, Suyker, A, Ueyama, M, Vargas, R, Wood, JD, and Zona, D

Abstract

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applicat

Published
2021

4. The three major axes of terrestrial ecosystem function.

Author

Migliavacca, M, Musavi, T, Mahecha, MD, Nelson, JA, Knauer, J, Baldocchi, DD, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, TA, Blanken, PD, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, AR, El-Madany, TS, Farella, MM, Fernández-Martínez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, CM, Göckede, M, Ibrom, A, Ikawa, H, Janssens, IA, Jung, M, Kattge, J, Keenan, TF, Knohl, A, Kobayashi, H, Kraemer, G, Law, BE, Liddell, MJ, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, DE, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, RP, Reich, PB, Rossini, M, Rotenberg, E, Scott, RL, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, IJ, Yakir, D, Zaehle, S, Reichstein, M, Migliavacca, M, Musavi, T, Mahecha, MD, Nelson, JA, Knauer, J, Baldocchi, DD, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, TA, Blanken, PD, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, AR, El-Madany, TS, Farella, MM, Fernández-Martínez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, CM, Göckede, M, Ibrom, A, Ikawa, H, Janssens, IA, Jung, M, Kattge, J, Keenan, TF, Knohl, A, Kobayashi, H, Kraemer, G, Law, BE, Liddell, MJ, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, DE, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, RP, Reich, PB, Rossini, M, Rotenberg, E, Scott, RL, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, IJ, Yakir, D, Zaehle, S, and Reichstein, M

Abstract

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.

Published
2021

5. The three major axes of terrestrial ecosystem function.

Author

Migliavacca, M, Musavi, T, Mahecha, MD, Nelson, JA, Knauer, J, Baldocchi, DD, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, TA, Blanken, PD, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, AR, El-Madany, TS, Farella, MM, Fernández-Martínez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, CM, Göckede, M, Ibrom, A, Ikawa, H, Janssens, IA, Jung, M, Kattge, J, Keenan, TF, Knohl, A, Kobayashi, H, Kraemer, G, Law, BE, Liddell, MJ, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, DE, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, RP, Reich, PB, Rossini, M, Rotenberg, E, Scott, RL, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, IJ, Yakir, D, Zaehle, S, Reichstein, M, Migliavacca, M, Musavi, T, Mahecha, MD, Nelson, JA, Knauer, J, Baldocchi, DD, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, TA, Blanken, PD, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, AR, El-Madany, TS, Farella, MM, Fernández-Martínez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, CM, Göckede, M, Ibrom, A, Ikawa, H, Janssens, IA, Jung, M, Kattge, J, Keenan, TF, Knohl, A, Kobayashi, H, Kraemer, G, Law, BE, Liddell, MJ, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, DE, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, RP, Reich, PB, Rossini, M, Rotenberg, E, Scott, RL, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, IJ, Yakir, D, Zaehle, S, and Reichstein, M

Abstract

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.

Published
2021

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

Author

Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D

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

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

Author

Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D

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

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

Author

Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Ribeca, A, van Ingen, C, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Bruemmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Gruenwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hoertnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lueers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tiedemann, F, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Ribeca, A, van Ingen, C, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Bruemmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Gruenwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hoertnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lueers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tiedemann, F, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D

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.

Published
2020

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

Author

Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, Papale D, Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, and Papale D

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.

Published
2020

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

Author

Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, Papale D, Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, and Papale D

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.

Published
2020

11. Comparison of Big-Leaf, Two-Big-Leaf, and Two-Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon-Water Modeling

Author

Luo, X, Luo, X, Chen, JM, Liu, J, Black, TA, Croft, H, Staebler, R, He, L, Arain, MA, Chen, B, Mo, G, Gonsamo, A, McCaughey, H, Luo, X, Luo, X, Chen, JM, Liu, J, Black, TA, Croft, H, Staebler, R, He, L, Arain, MA, Chen, B, Mo, G, Gonsamo, A, and McCaughey, H

Abstract

Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2 = 0.71, root-mean-square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.

Published
2018

12. Temporal Dynamics of Aerodynamic Canopy Height Derived From Eddy Covariance Momentum Flux Data Across North American Flux Networks

Author

Chu, H, Chu, H, Baldocchi, DD, Poindexter, C, Abraha, M, Desai, AR, Bohrer, G, Arain, MA, Griffis, T, Blanken, PD, O'Halloran, TL, Thomas, RQ, Zhang, Q, Burns, SP, Frank, JM, Christian, D, Brown, S, Black, TA, Gough, CM, Law, BE, Lee, X, Chen, J, Reed, DE, Massman, WJ, Clark, K, Hatfield, J, Prueger, J, Bracho, R, Baker, JM, Martin, TA, Chu, H, Chu, H, Baldocchi, DD, Poindexter, C, Abraha, M, Desai, AR, Bohrer, G, Arain, MA, Griffis, T, Blanken, PD, O'Halloran, TL, Thomas, RQ, Zhang, Q, Burns, SP, Frank, JM, Christian, D, Brown, S, Black, TA, Gough, CM, Law, BE, Lee, X, Chen, J, Reed, DE, Massman, WJ, Clark, K, Hatfield, J, Prueger, J, Bracho, R, Baker, JM, and Martin, TA

Abstract

Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface-atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum-flux data. At 69 forest sites, annual ha robustly predicted site-to-site and year-to-year differences in canopy heights (R2 = 0.88, 111 site-years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2 > 0.70 in 74 site-years). Our results demonstrate the potential of flux-derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large-scale and time-varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.

Published
2018

13. Comparison of Big-Leaf, Two-Big-Leaf, and Two-Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon-Water Modeling

Author

Luo, X, Luo, X, Chen, JM, Liu, J, Black, TA, Croft, H, Staebler, R, He, L, Arain, MA, Chen, B, Mo, G, Gonsamo, A, McCaughey, H, Luo, X, Luo, X, Chen, JM, Liu, J, Black, TA, Croft, H, Staebler, R, He, L, Arain, MA, Chen, B, Mo, G, Gonsamo, A, and McCaughey, H

Abstract

Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2 = 0.71, root-mean-square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.

Published
2018

14. Temporal Dynamics of Aerodynamic Canopy Height Derived From Eddy Covariance Momentum Flux Data Across North American Flux Networks

Author

Chu, H, Chu, H, Baldocchi, DD, Poindexter, C, Abraha, M, Desai, AR, Bohrer, G, Arain, MA, Griffis, T, Blanken, PD, O'Halloran, TL, Thomas, RQ, Zhang, Q, Burns, SP, Frank, JM, Christian, D, Brown, S, Black, TA, Gough, CM, Law, BE, Lee, X, Chen, J, Reed, DE, Massman, WJ, Clark, K, Hatfield, J, Prueger, J, Bracho, R, Baker, JM, Martin, TA, Chu, H, Chu, H, Baldocchi, DD, Poindexter, C, Abraha, M, Desai, AR, Bohrer, G, Arain, MA, Griffis, T, Blanken, PD, O'Halloran, TL, Thomas, RQ, Zhang, Q, Burns, SP, Frank, JM, Christian, D, Brown, S, Black, TA, Gough, CM, Law, BE, Lee, X, Chen, J, Reed, DE, Massman, WJ, Clark, K, Hatfield, J, Prueger, J, Bracho, R, Baker, JM, and Martin, TA

Abstract

Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface-atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum-flux data. At 69 forest sites, annual ha robustly predicted site-to-site and year-to-year differences in canopy heights (R2 = 0.88, 111 site-years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2 > 0.70 in 74 site-years). Our results demonstrate the potential of flux-derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large-scale and time-varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.

Published
2018

15. Semiempirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites

Author

Migliavacca, M, Reichstein, M, Richardson, A, Colombo, R, Sutton, M, Lasslop, G, Tomelleri, E, Wohlfahrt, G, Carvalhais, N, Cescatti, A, Mahecha, M, Montagnani, L, Papale, D, Zaehle, S, Arain, A, Arneth, A, Black, T, Carrara, A, Dore, S, Gianelle, D, Helfter, C, Hollinger, D, Kutsch, W, Lafleur, P, Nouvellon, Y, Rebmann, C, Humberto, R, Rodeghiero, M, Roupsard, O, Sebastià, M, Seufert, G, Soussana, J, Michiel, K, Richardson, AD, COLOMBO, ROBERTO, Sutton, MA, Mahecha, MD, Black, TA, Kutsch, WL, Lafleur, PM, Humberto R, Michiel, K., Migliavacca, M, Reichstein, M, Richardson, A, Colombo, R, Sutton, M, Lasslop, G, Tomelleri, E, Wohlfahrt, G, Carvalhais, N, Cescatti, A, Mahecha, M, Montagnani, L, Papale, D, Zaehle, S, Arain, A, Arneth, A, Black, T, Carrara, A, Dore, S, Gianelle, D, Helfter, C, Hollinger, D, Kutsch, W, Lafleur, P, Nouvellon, Y, Rebmann, C, Humberto, R, Rodeghiero, M, Roupsard, O, Sebastià, M, Seufert, G, Soussana, J, Michiel, K, Richardson, AD, COLOMBO, ROBERTO, Sutton, MA, Mahecha, MD, Black, TA, Kutsch, WL, Lafleur, PM, Humberto R, and Michiel, K.

Abstract

In this study we examined ecosystem respiration (RECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of RECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of RECOfailed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of RECO. The maximum seasonal leaf area index (LAIMAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature Tref=15 °C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r2=0.52, P<0.001, n=104) even within each PFT. Besides LAIMAX, we found that reference respiration may be explained partially by total soil carbon content (SoilC). For undisturbed temperate and boreal forests a negative control of total nitrogen deposition (Ndepo) on reference respiration was also identified. We developed a new semiempirical model incorporating abiotic factors (climate), recent productivity (daily GPP), general site productivity and canopy structure (LAIMAX) which performed well in predicting the spatio-temporal variability of RECO, explaining >70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands. © 2010 Blackwell Publishing Ltd.

Published
2011

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