Articles | Volume 13, issue 10
https://doi.org/10.5194/essd-13-4881-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-4881-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 Oct 2021
Data description paper |
| 26 Oct 2021
| 26 Oct 2021
Mapping global forest age from forest inventories, biomass and climate data
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Wageningen, the Netherlands
Sujan Koirala
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Maurizio Santoro
Gamma Remote Sensing, Bern, Switzerland
Ulrich Weber
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Jacob Nelson
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Jonas Gütter
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Data Management and Analysis, Institute of Data Science, German Aerospace Center (DLR), Jena, Germany
Bruno Herault
Institut National Polytechnique Félix Houphouët-Boigny (INP-HB), Côte d'Ivoire
Research Unit Forests and Societies, CIRAD, University of Montpellier, Montpellier, France
Justin Kassi
Laboratoire de Botanique, UFR Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire
Anny N'Guessan
Laboratoire de Botanique, UFR Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire
Christopher Neigh
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Benjamin Poulter
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Tao Zhang
Department of Biology, University of Florida, Gainesville, Florida, USA
Department of Forest Resources, University of Minnesota, Minneapolis, Minnesota, USA
Nuno Carvalhais
CORRESPONDING AUTHOR
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Departamento de Ciências e Engenharia do Ambiente (DCEA), Faculdade de Ciências e Tecnologia (FCT), Universidade Nova de Lisboa, Lisbon, Portugal
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Basil Kraft, Jacob A. Nelson, Sophia Walther, Fabian Gans, Ulrich Weber, Gregory Duveiller, Markus Reichstein, Weijie Zhang, Marc Rußwurm, Devis Tuia, Marco Körner, Zayd Mahmoud Hamdi, and Martin Jung
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Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
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Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
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We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
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Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Andrew F. Feldman, Zhen Zhang, Yasuko Yoshida, Abhishek Chatterjee, and Benjamin Poulter
Atmos. Chem. Phys., 23, 1545–1563, https://doi.org/10.5194/acp-23-1545-2023, https://doi.org/10.5194/acp-23-1545-2023, 2023
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We investigate the conditions under which satellite-retrieved column carbon dioxide concentrations directly hold information about surface carbon dioxide fluxes, without the use of inversion models. We show that OCO-2 column carbon dioxide retrievals, available at 1–3 month latency, can be used to directly detect and roughly estimate extreme biospheric CO2 fluxes. As such, these OCO-2 retrievals have value for rapidly monitoring extreme conditions in the terrestrial biosphere.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth
Earth Syst. Dynam., 13, 1451–1471, https://doi.org/10.5194/esd-13-1451-2022, https://doi.org/10.5194/esd-13-1451-2022, 2022
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Subseasonal forecasts facilitate early warning of extreme events; however their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
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Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, and Riley M. Duren
Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, https://doi.org/10.5194/acp-22-9617-2022, 2022
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We review the capability of satellite observations of atmospheric methane to quantify methane emissions on all scales. We cover retrieval methods, precision requirements, inverse methods for inferring emissions, source detection thresholds, and observations of system completeness. We show that current instruments already enable quantification of regional and national emissions including contributions from large point sources. Coverage and resolution will increase significantly in coming years.
Sophia Walther, Simon Besnard, Jacob Allen Nelson, Tarek Sebastian El-Madany, Mirco Migliavacca, Ulrich Weber, Nuno Carvalhais, Sofia Lorena Ermida, Christian Brümmer, Frederik Schrader, Anatoly Stanislavovich Prokushkin, Alexey Vasilevich Panov, and Martin Jung
Biogeosciences, 19, 2805–2840, https://doi.org/10.5194/bg-19-2805-2022, https://doi.org/10.5194/bg-19-2805-2022, 2022
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Satellite observations help interpret station measurements of local carbon, water, and energy exchange between the land surface and the atmosphere and are indispensable for simulations of the same in land surface models and their evaluation. We propose generalisable and efficient approaches to systematically ensure high quality and to estimate values in data gaps. We apply them to satellite data of surface reflectance and temperature with different resolutions at the stations.
Colm Sweeney, Abhishek Chatterjee, Sonja Wolter, Kathryn McKain, Robert Bogue, Stephen Conley, Tim Newberger, Lei Hu, Lesley Ott, Benjamin Poulter, Luke Schiferl, Brad Weir, Zhen Zhang, and Charles E. Miller
Atmos. Chem. Phys., 22, 6347–6364, https://doi.org/10.5194/acp-22-6347-2022, https://doi.org/10.5194/acp-22-6347-2022, 2022
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The Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of aircraft profiles for independent evaluation of model-derived emissions and uptake of atmospheric CO2, CH4, and CO from land and ocean. Comparison with the Goddard Earth Observing System (GEOS) modeling system suggests that fluxes of CO2 are very consistent with observations, while those of CH4 have some regional and seasonal biases, and that CO comparison is complicated by transport errors.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
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The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Zhu Deng, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, Piyu Ke, Yanan Cui, Katsumasa Tanaka, Xin Lin, Rona L. Thompson, Hanqin Tian, Yuanzhi Yao, Yuanyuan Huang, Ronny Lauerwald, Atul K. Jain, Xiaoming Xu, Ana Bastos, Stephen Sitch, Paul I. Palmer, Thomas Lauvaux, Alexandre d'Aspremont, Clément Giron, Antoine Benoit, Benjamin Poulter, Jinfeng Chang, Ana Maria Roxana Petrescu, Steven J. Davis, Zhu Liu, Giacomo Grassi, Clément Albergel, Francesco N. Tubiello, Lucia Perugini, Wouter Peters, and Frédéric Chevallier
Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, https://doi.org/10.5194/essd-14-1639-2022, 2022
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In support of the global stocktake of the Paris Agreement on climate change, we proposed a method for reconciling the results of global atmospheric inversions with data from UNFCCC national greenhouse gas inventories (NGHGIs). Here, based on a new global harmonized database that we compiled from the UNFCCC NGHGIs and a comprehensive framework presented in this study to process the results of inversions, we compared their results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein
Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, https://doi.org/10.5194/hess-26-1579-2022, 2022
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We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
Tina Trautmann, Sujan Koirala, Nuno Carvalhais, Andreas Güntner, and Martin Jung
Hydrol. Earth Syst. Sci., 26, 1089–1109, https://doi.org/10.5194/hess-26-1089-2022, https://doi.org/10.5194/hess-26-1089-2022, 2022
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We assess the effect of how vegetation is defined in a global hydrological model on the composition of total water storage (TWS). We compare two experiments, one with globally uniform and one with vegetation parameters that vary in space and time. While both experiments are constrained against observational data, we found a drastic change in the partitioning of TWS, highlighting the important role of the interaction between groundwater–soil moisture–vegetation in understanding TWS variations.
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
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The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
P. E. K. Campbell, K. F. Huemmrich, E. M. Middleton, J. Alfieri, C. van der Tol, and C. S. R. Neigh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 1–8, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-1-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-1-2022, 2022
J. Pacheco-Labrador, U. Weber, X. Ma, M. D. Mahecha, N. Carvalhais, C. Wirth, A. Huth, F. J. Bohn, G. Kraemer, U. Heiden, FunDivEUROPE members, and M. Migliavacca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 49–55, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, 2022
Yeonuk Kim, Monica Garcia, Laura Morillas, Ulrich Weber, T. Andrew Black, and Mark S. Johnson
Hydrol. Earth Syst. Sci., 25, 5175–5191, https://doi.org/10.5194/hess-25-5175-2021, https://doi.org/10.5194/hess-25-5175-2021, 2021
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Here, we present a novel physically based evaporation model to demonstrate that vertical relative humidity (RH) gradients from the land surface to the atmosphere tend to evolve towards zero due to land–atmosphere equilibration processes. Collapsing RH gradients on daily to yearly timescales indicate an emergent land–atmosphere equilibrium, making it possible to determine evapotranspiration using only meteorological information, independent of land surface conditions and vegetation controls.
Yuanyuan Huang, Phillipe Ciais, Maurizio Santoro, David Makowski, Jerome Chave, Dmitry Schepaschenko, Rose Z. Abramoff, Daniel S. Goll, Hui Yang, Ye Chen, Wei Wei, and Shilong Piao
Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, https://doi.org/10.5194/essd-13-4263-2021, 2021
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Roots play a key role in our Earth system. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1 km) root biomass dataset. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10.
Louise Chini, George Hurtt, Ritvik Sahajpal, Steve Frolking, Kees Klein Goldewijk, Stephen Sitch, Raphael Ganzenmüller, Lei Ma, Lesley Ott, Julia Pongratz, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 4175–4189, https://doi.org/10.5194/essd-13-4175-2021, https://doi.org/10.5194/essd-13-4175-2021, 2021
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Carbon emissions from land-use change are a large and uncertain component of the global carbon cycle. The Land-Use Harmonization 2 (LUH2) dataset was developed as an input to carbon and climate simulations and has been updated annually for the Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2 updates and describe the 2019 version which used new cropland and grazing land data inputs for the globally important region of Brazil.
Maurizio Santoro, Oliver Cartus, Nuno Carvalhais, Danaë M. A. Rozendaal, Valerio Avitabile, Arnan Araza, Sytze de Bruin, Martin Herold, Shaun Quegan, Pedro Rodríguez-Veiga, Heiko Balzter, João Carreiras, Dmitry Schepaschenko, Mikhail Korets, Masanobu Shimada, Takuya Itoh, Álvaro Moreno Martínez, Jura Cavlovic, Roberto Cazzolla Gatti, Polyanna da Conceição Bispo, Nasheta Dewnath, Nicolas Labrière, Jingjing Liang, Jeremy Lindsell, Edward T. A. Mitchard, Alexandra Morel, Ana Maria Pacheco Pascagaza, Casey M. Ryan, Ferry Slik, Gaia Vaglio Laurin, Hans Verbeeck, Arief Wijaya, and Simon Willcock
Earth Syst. Sci. Data, 13, 3927–3950, https://doi.org/10.5194/essd-13-3927-2021, https://doi.org/10.5194/essd-13-3927-2021, 2021
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Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we generated a global dataset of forest above-ground biomass, i.e. carbon stocks, from satellite data of 2010. This dataset provides a comprehensive and detailed portrait of the distribution of carbon in forests, although for dense forests in the tropics values are somewhat underestimated. This dataset will have a considerable impact on climate, carbon, and socio-economic modelling schemes.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
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Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Brad Weir, Lesley E. Ott, George J. Collatz, Stephan R. Kawa, Benjamin Poulter, Abhishek Chatterjee, Tomohiro Oda, and Steven Pawson
Atmos. Chem. Phys., 21, 9609–9628, https://doi.org/10.5194/acp-21-9609-2021, https://doi.org/10.5194/acp-21-9609-2021, 2021
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We present a collection of carbon surface fluxes, the Low-order Flux Inversion (LoFI), derived from satellite observations of the Earth's surface and calibrated to match long-term inventories and atmospheric and oceanic records. Simulations using LoFI reproduce background atmospheric carbon dioxide measurements with comparable skill to the leading surface flux products. Available both retrospectively and as a forecast, LoFI enables the study of the carbon cycle as it occurs.
Wolfgang A. Obermeier, Julia E. M. S. Nabel, Tammas Loughran, Kerstin Hartung, Ana Bastos, Felix Havermann, Peter Anthoni, Almut Arneth, Daniel S. Goll, Sebastian Lienert, Danica Lombardozzi, Sebastiaan Luyssaert, Patrick C. McGuire, Joe R. Melton, Benjamin Poulter, Stephen Sitch, Michael O. Sullivan, Hanqin Tian, Anthony P. Walker, Andrew J. Wiltshire, Soenke Zaehle, and Julia Pongratz
Earth Syst. Dynam., 12, 635–670, https://doi.org/10.5194/esd-12-635-2021, https://doi.org/10.5194/esd-12-635-2021, 2021
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We provide the first spatio-temporally explicit comparison of different model-derived fluxes from land use and land cover changes (fLULCCs) by using the TRENDY v8 dynamic global vegetation models used in the 2019 global carbon budget. We find huge regional fLULCC differences resulting from environmental assumptions, simulated periods, and the timing of land use and land cover changes, and we argue for a method consistent across time and space and for carefully choosing the accounting period.
Zhen Zhang, Etienne Fluet-Chouinard, Katherine Jensen, Kyle McDonald, Gustaf Hugelius, Thomas Gumbricht, Mark Carroll, Catherine Prigent, Annett Bartsch, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 2001–2023, https://doi.org/10.5194/essd-13-2001-2021, https://doi.org/10.5194/essd-13-2001-2021, 2021
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The spatiotemporal distribution of wetlands is one of the important and yet uncertain factors determining the time and locations of methane fluxes. The Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset describes the global data product used to quantify the areal dynamics of natural wetlands and how global wetlands are changing in response to climate.
Leonardo Calle and Benjamin Poulter
Geosci. Model Dev., 14, 2575–2601, https://doi.org/10.5194/gmd-14-2575-2021, https://doi.org/10.5194/gmd-14-2575-2021, 2021
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We developed a model to simulate and track the age of ecosystems on Earth. We found that the effect of ecosystem age on net primary production and ecosystem respiration is as important as climate in large areas of every vegetated continent. The LPJ-wsl v2.0 age-class model simulates dynamic age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.
Zichong Chen, Junjie Liu, Daven K. Henze, Deborah N. Huntzinger, Kelley C. Wells, Stephen Sitch, Pierre Friedlingstein, Emilie Joetzjer, Vladislav Bastrikov, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica L. Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Benjamin Poulter, Hanqin Tian, Andrew J. Wiltshire, Sönke Zaehle, and Scot M. Miller
Atmos. Chem. Phys., 21, 6663–6680, https://doi.org/10.5194/acp-21-6663-2021, https://doi.org/10.5194/acp-21-6663-2021, 2021
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NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite observes atmospheric CO2 globally. We use a multiple regression and inverse model to quantify the relationships between OCO-2 and environmental drivers within individual years for 2015–2018 and within seven global biomes. Our results point to limitations of current space-based observations for inferring environmental relationships but also indicate the potential to inform key relationships that are very uncertain in process-based models.
Sudhanshu Pandey, Sander Houweling, Alba Lorente, Tobias Borsdorff, Maria Tsivlidou, A. Anthony Bloom, Benjamin Poulter, Zhen Zhang, and Ilse Aben
Biogeosciences, 18, 557–572, https://doi.org/10.5194/bg-18-557-2021, https://doi.org/10.5194/bg-18-557-2021, 2021
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We use atmospheric methane observations from the novel TROPOspheric Monitoring Instrument (TROPOMI; Sentinel-5p) to estimate methane emissions from South Sudan's wetlands. Our emission estimates are an order of magnitude larger than the estimate of process-based wetland models. We find that this underestimation by the models is likely due to their misrepresentation of the wetlands' inundation extent and temperature dependences.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, https://doi.org/10.5194/gmd-13-5425-2020, 2020
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To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Rachel L. Tunnicliffe, Anita L. Ganesan, Robert J. Parker, Hartmut Boesch, Nicola Gedney, Benjamin Poulter, Zhen Zhang, Jošt V. Lavrič, David Walter, Matthew Rigby, Stephan Henne, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 20, 13041–13067, https://doi.org/10.5194/acp-20-13041-2020, https://doi.org/10.5194/acp-20-13041-2020, 2020
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This study quantifies Brazil’s emissions of a potent atmospheric greenhouse gas, methane. This is in the field of atmospheric modelling and uses remotely sensed data and surface measurements of methane concentrations as well as an atmospheric transport model to interpret the data. Because of Brazil’s large emissions from wetlands, agriculture and biomass burning, these emissions affect global methane concentrations and thus are of global significance.
Naixin Fan, Sujan Koirala, Markus Reichstein, Martin Thurner, Valerio Avitabile, Maurizio Santoro, Bernhard Ahrens, Ulrich Weber, and Nuno Carvalhais
Earth Syst. Sci. Data, 12, 2517–2536, https://doi.org/10.5194/essd-12-2517-2020, https://doi.org/10.5194/essd-12-2517-2020, 2020
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The turnover time of terrestrial carbon (τ) controls the global carbon cycle–climate feedback. In this study, we provide a new, updated ensemble of diagnostic terrestrial carbon turnover times and associated uncertainties on a global scale. Despite the large variation in both magnitude and spatial patterns of τ, we identified robust features in the spatial patterns of τ which could contribute to uncertainty reductions in future projections of the carbon cycle–climate feedback.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Marielle Saunois, Ann R. Stavert, Ben Poulter, Philippe Bousquet, Josep G. Canadell, Robert B. Jackson, Peter A. Raymond, Edward J. Dlugokencky, Sander Houweling, Prabir K. Patra, Philippe Ciais, Vivek K. Arora, David Bastviken, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Kimberly M. Carlson, Mark Carrol, Simona Castaldi, Naveen Chandra, Cyril Crevoisier, Patrick M. Crill, Kristofer Covey, Charles L. Curry, Giuseppe Etiope, Christian Frankenberg, Nicola Gedney, Michaela I. Hegglin, Lena Höglund-Isaksson, Gustaf Hugelius, Misa Ishizawa, Akihiko Ito, Greet Janssens-Maenhout, Katherine M. Jensen, Fortunat Joos, Thomas Kleinen, Paul B. Krummel, Ray L. Langenfelds, Goulven G. Laruelle, Licheng Liu, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Joe McNorton, Paul A. Miller, Joe R. Melton, Isamu Morino, Jurek Müller, Fabiola Murguia-Flores, Vaishali Naik, Yosuke Niwa, Sergio Noce, Simon O'Doherty, Robert J. Parker, Changhui Peng, Shushi Peng, Glen P. Peters, Catherine Prigent, Ronald Prinn, Michel Ramonet, Pierre Regnier, William J. Riley, Judith A. Rosentreter, Arjo Segers, Isobel J. Simpson, Hao Shi, Steven J. Smith, L. Paul Steele, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Francesco N. Tubiello, Aki Tsuruta, Nicolas Viovy, Apostolos Voulgarakis, Thomas S. Weber, Michiel van Weele, Guido R. van der Werf, Ray F. Weiss, Doug Worthy, Debra Wunch, Yi Yin, Yukio Yoshida, Wenxin Zhang, Zhen Zhang, Yuanhong Zhao, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
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Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Shufen Pan, Naiqing Pan, Hanqin Tian, Pierre Friedlingstein, Stephen Sitch, Hao Shi, Vivek K. Arora, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Julia E. M. S. Nabel, Catherine Ottlé, Benjamin Poulter, Sönke Zaehle, and Steven W. Running
Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, https://doi.org/10.5194/hess-24-1485-2020, 2020
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Evapotranspiration (ET) links global water, carbon and energy cycles. We used 4 remote sensing models, 2 machine-learning algorithms and 14 land surface models to analyze the changes in global terrestrial ET. These three categories of approaches agreed well in terms of ET intensity. For 1982–2011, all models showed that Earth greening enhanced terrestrial ET. The small interannual variability of global terrestrial ET suggests it has a potential planetary boundary of around 600 mm yr-1.
Martin Jung, Christopher Schwalm, Mirco Migliavacca, Sophia Walther, Gustau Camps-Valls, Sujan Koirala, Peter Anthoni, Simon Besnard, Paul Bodesheim, Nuno Carvalhais, Frédéric Chevallier, Fabian Gans, Daniel S. Goll, Vanessa Haverd, Philipp Köhler, Kazuhito Ichii, Atul K. Jain, Junzhi Liu, Danica Lombardozzi, Julia E. M. S. Nabel, Jacob A. Nelson, Michael O'Sullivan, Martijn Pallandt, Dario Papale, Wouter Peters, Julia Pongratz, Christian Rödenbeck, Stephen Sitch, Gianluca Tramontana, Anthony Walker, Ulrich Weber, and Markus Reichstein
Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, https://doi.org/10.5194/bg-17-1343-2020, 2020
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We test the approach of producing global gridded carbon fluxes based on combining machine learning with local measurements, remote sensing and climate data. We show that we can reproduce seasonal variations in carbon assimilated by plants via photosynthesis and in ecosystem net carbon balance. The ecosystem’s mean carbon balance and carbon flux trends require cautious interpretation. The analysis paves the way for future improvements of the data-driven assessment of carbon fluxes.
Binghao Jia, Xin Luo, Ximing Cai, Atul Jain, Deborah N. Huntzinger, Zhenghui Xie, Ning Zeng, Jiafu Mao, Xiaoying Shi, Akihiko Ito, Yaxing Wei, Hanqin Tian, Benjamin Poulter, Dan Hayes, and Kevin Schaefer
Earth Syst. Dynam., 11, 235–249, https://doi.org/10.5194/esd-11-235-2020, https://doi.org/10.5194/esd-11-235-2020, 2020
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We quantitatively examined the relative contributions of climate change, land
use and land cover change, and elevated CO2 to interannual variations and seasonal cycle amplitude of gross primary productivity (GPP) in China based on multi-model ensemble simulations. The contributions of major subregions to the temporal change in China's total GPP are also presented. This work may help us better understand GPP spatiotemporal patterns and their responses to regional changes and human activities.
Nora Linscheid, Lina M. Estupinan-Suarez, Alexander Brenning, Nuno Carvalhais, Felix Cremer, Fabian Gans, Anja Rammig, Markus Reichstein, Carlos A. Sierra, and Miguel D. Mahecha
Biogeosciences, 17, 945–962, https://doi.org/10.5194/bg-17-945-2020, https://doi.org/10.5194/bg-17-945-2020, 2020
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Vegetation typically responds to variation in temperature and rainfall within days. Yet seasonal changes in meteorological conditions, as well as decadal climate variability, additionally shape the state of ecosystems. It remains unclear how vegetation responds to climate variability on these different timescales. We find that the vegetation response to climate variability depends on the timescale considered. This scale dependency should be considered for modeling land–atmosphere interactions.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Judith Hauck, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Dorothee C. E. Bakker, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Peter Anthoni, Leticia Barbero, Ana Bastos, Vladislav Bastrikov, Meike Becker, Laurent Bopp, Erik Buitenhuis, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Kim I. Currie, Richard A. Feely, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Daniel S. Goll, Nicolas Gruber, Sören Gutekunst, Ian Harris, Vanessa Haverd, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Jed O. Kaplan, Etsushi Kato, Kees Klein Goldewijk, Jan Ivar Korsbakken, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Gregg Marland, Patrick C. McGuire, Joe R. Melton, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Craig Neill, Abdirahman M. Omar, Tsuneo Ono, Anna Peregon, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Roland Séférian, Jörg Schwinger, Naomi Smith, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Guido R. van der Werf, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, https://doi.org/10.5194/essd-11-1783-2019, 2019
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The Global Carbon Budget 2019 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Sajeev Philip, Matthew S. Johnson, Christopher Potter, Vanessa Genovesse, David F. Baker, Katherine D. Haynes, Daven K. Henze, Junjie Liu, and Benjamin Poulter
Atmos. Chem. Phys., 19, 13267–13287, https://doi.org/10.5194/acp-19-13267-2019, https://doi.org/10.5194/acp-19-13267-2019, 2019
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This research was conducted to quantify the impact of different prior global biosphere models on the estimate of terrestrial CO2 fluxes when assimilating Orbiting Carbon Observatory-2 (OCO-2) satellite observations. To determine the prior model impact, we apply observing system simulation experiments (OSSEs). Even with the substantial spatiotemporal coverage of OCO-2 data, residual differences in posterior CO2 flux estimates remain due to the choice of prior flux mean and uncertainties.
Ana Bastos, Philippe Ciais, Frédéric Chevallier, Christian Rödenbeck, Ashley P. Ballantyne, Fabienne Maignan, Yi Yin, Marcos Fernández-Martínez, Pierre Friedlingstein, Josep Peñuelas, Shilong L. Piao, Stephen Sitch, William K. Smith, Xuhui Wang, Zaichun Zhu, Vanessa Haverd, Etsushi Kato, Atul K. Jain, Sebastian Lienert, Danica Lombardozzi, Julia E. M. S. Nabel, Philippe Peylin, Benjamin Poulter, and Dan Zhu
Atmos. Chem. Phys., 19, 12361–12375, https://doi.org/10.5194/acp-19-12361-2019, https://doi.org/10.5194/acp-19-12361-2019, 2019
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Here we show that land-surface models improved their ability to simulate the increase in the amplitude of seasonal CO2-cycle exchange (SCANBP) by ecosystems compared to estimates by two atmospheric inversions. We find a dominant role of vegetation growth over boreal Eurasia to the observed increase in SCANBP, strongly driven by CO2 fertilization, and an overall negative effect of temperature on SCANBP. Biases can be explained by the sensitivity of simulated microbial respiration to temperature.
Sven Boese, Martin Jung, Nuno Carvalhais, Adriaan J. Teuling, and Markus Reichstein
Biogeosciences, 16, 2557–2572, https://doi.org/10.5194/bg-16-2557-2019, https://doi.org/10.5194/bg-16-2557-2019, 2019
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This study examines how limited water availability during droughts affects water-use efficiency. This metric describes how much carbon an ecosystem can assimilate for each unit of water lost by transpiration. We test how well different water-use efficiency models can capture the dynamics of transpiration decrease due to increased soil-water limitation. Accounting for the interacting effects of radiation and water limitation is necessary to accurately predict transpiration during these periods.
Leonardo Calle, Benjamin Poulter, and Prabir K. Patra
Atmos. Meas. Tech., 12, 2611–2629, https://doi.org/10.5194/amt-12-2611-2019, https://doi.org/10.5194/amt-12-2611-2019, 2019
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Satellite observations of atmospheric carbon dioxide offer extraordinary insights into terrestrial ecosystem activity on Earth. The algorithm we present provides researchers with a great deal more information from these satellite data than has been available in the past. We hope the application of this algorithm and analyses tools provides insight into atmospheric dynamics of carbon dioxide and helps inform the development of global ecosystem models in the future.
Xiaolu Tang, Nuno Carvalhais, Catarina Moura, Bernhard Ahrens, Sujan Koirala, Shaohui Fan, Fengying Guan, Wenjie Zhang, Sicong Gao, Vincenzo Magliulo, Pauline Buysse, Shibin Liu, Guo Chen, Wunian Yang, Zhen Yu, Jingjing Liang, Leilei Shi, Shenyan Pu, and Markus Reichstein
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-37, https://doi.org/10.5194/bg-2019-37, 2019
Preprint withdrawn
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Vegetation CUE is a key measure of carbon transfer from the atmosphere to terrestrial biomass. This study modelled global CUE with published observations using random forest. CUE varied with ecosystem types and spatially decreased with latitude, challenging the previous conclusion that CUE was independent of environmental controls. Our results emphasize a better understanding of environmental controls on CUE to reduce uncertainties in prognostic land-process model simulations.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Judith Hauck, Julia Pongratz, Penelope A. Pickers, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Almut Arneth, Vivek K. Arora, Leticia Barbero, Ana Bastos, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Scott C. Doney, Thanos Gkritzalis, Daniel S. Goll, Ian Harris, Vanessa Haverd, Forrest M. Hoffman, Mario Hoppema, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Truls Johannessen, Chris D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Peter Landschützer, Nathalie Lefèvre, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Craig Neill, Are Olsen, Tsueno Ono, Prabir Patra, Anna Peregon, Wouter Peters, Philippe Peylin, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Matthias Rocher, Christian Rödenbeck, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Tobias Steinhoff, Adrienne Sutton, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, Rebecca Wright, Sönke Zaehle, and Bo Zheng
Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, https://doi.org/10.5194/essd-10-2141-2018, 2018
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The Global Carbon Budget 2018 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
HyeJin Kim, Isabel M. D. Rosa, Rob Alkemade, Paul Leadley, George Hurtt, Alexander Popp, Detlef P. van Vuuren, Peter Anthoni, Almut Arneth, Daniele Baisero, Emma Caton, Rebecca Chaplin-Kramer, Louise Chini, Adriana De Palma, Fulvio Di Fulvio, Moreno Di Marco, Felipe Espinoza, Simon Ferrier, Shinichiro Fujimori, Ricardo E. Gonzalez, Maya Gueguen, Carlos Guerra, Mike Harfoot, Thomas D. Harwood, Tomoko Hasegawa, Vanessa Haverd, Petr Havlík, Stefanie Hellweg, Samantha L. L. Hill, Akiko Hirata, Andrew J. Hoskins, Jan H. Janse, Walter Jetz, Justin A. Johnson, Andreas Krause, David Leclère, Ines S. Martins, Tetsuya Matsui, Cory Merow, Michael Obersteiner, Haruka Ohashi, Benjamin Poulter, Andy Purvis, Benjamin Quesada, Carlo Rondinini, Aafke M. Schipper, Richard Sharp, Kiyoshi Takahashi, Wilfried Thuiller, Nicolas Titeux, Piero Visconti, Christopher Ware, Florian Wolf, and Henrique M. Pereira
Geosci. Model Dev., 11, 4537–4562, https://doi.org/10.5194/gmd-11-4537-2018, https://doi.org/10.5194/gmd-11-4537-2018, 2018
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This paper lays out the protocol for the Biodiversity and Ecosystem Services Scenario-based Intercomparison of Models (BES-SIM) that projects the global impacts of land use and climate change on biodiversity and ecosystem services over the coming decades, compared to the 20th century. BES-SIM uses harmonized scenarios and input data and a set of common output metrics at multiple scales, and identifies model uncertainties and research gaps.
Eunjee Lee, Fan-Wei Zeng, Randal D. Koster, Brad Weir, Lesley E. Ott, and Benjamin Poulter
Biogeosciences, 15, 5635–5652, https://doi.org/10.5194/bg-15-5635-2018, https://doi.org/10.5194/bg-15-5635-2018, 2018
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Land carbon fluxes are controlled in part by the responses of terrestrial ecosystems to atmospheric conditions near the Earth's surface. This study offers a comprehensive evaluation of the consequences of multiple facets of spatiotemporal variability in atmospheric CO2 for carbon cycle dynamics. Globally, consideration of the diurnal CO2 variability reduces the gross primary production and net land carbon uptake. The relative contributions of other variability vary regionally and seasonally.
Tina Trautmann, Sujan Koirala, Nuno Carvalhais, Annette Eicker, Manfred Fink, Christoph Niemann, and Martin Jung
Hydrol. Earth Syst. Sci., 22, 4061–4082, https://doi.org/10.5194/hess-22-4061-2018, https://doi.org/10.5194/hess-22-4061-2018, 2018
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In this study, we adjust a simple hydrological model to several observational datasets, including satellite observations of the land's total water storage. We apply the model to northern latitudes and find that the dominating factor of changes in the total water storage depends on both the spatial and temporal scale of analysis. While snow dominates seasonal variations, liquid water determines year-to-year variations, yet with increasing contribution of snow when averaging over larger regions.
Emilie Joetzjer, Fabienne Maignan, Jérôme Chave, Daniel Goll, Ben Poulter, Jonathan Barichivich, Isabelle Maréchaux, Sebastiaan Luyssaert, Matthieu Guimberteau, Kim Naudts, Damien Bonal, and Philippe Ciais
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-308, https://doi.org/10.5194/bg-2018-308, 2018
Revised manuscript not accepted
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This study explores the relative contributions of tree demographic, canopy structure and hydraulic processes on the Amazonian carbon and water cycles using large-scale process-based model. Our results imply that explicit coupling of the water and carbon cycles improves the representation of biogeochemical cycles and their spatial variability. Representing the variation in the ecological functioning of Amazonia should be the next step to improve the performance and predictive ability of models.
Donghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, and Shilong Piao
Biogeosciences, 15, 3421–3437, https://doi.org/10.5194/bg-15-3421-2018, https://doi.org/10.5194/bg-15-3421-2018, 2018
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Our results indicate that most ecosystem models do not capture the observed asymmetric responses under normal precipitation conditions, suggesting an overestimate of the drought effects and/or underestimate of the watering impacts on primary productivity, which may be the result of inadequate representation of key eco-hydrological processes. Collaboration between modelers and site investigators needs to be strengthened to improve the specific processes in ecosystem models in following studies.
Jacob A. Nelson, Nuno Carvalhais, Mirco Migliavacca, Markus Reichstein, and Martin Jung
Biogeosciences, 15, 2433–2447, https://doi.org/10.5194/bg-15-2433-2018, https://doi.org/10.5194/bg-15-2433-2018, 2018
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Plants have typical daily carbon uptake and water loss cycles. However, these cycles may change under periods of duress, such as water limitation. Here we identify two types of patterns in response to water limitations: a tendency to lose more water in the morning than afternoon and a decoupling of the carbon and water cycles. The findings show differences in responses by trees and grasses and suggest that morning shifts may be more efficient at gaining carbon per unit water used.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Julia Pongratz, Andrew C. Manning, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Robert B. Jackson, Thomas A. Boden, Pieter P. Tans, Oliver D. Andrews, Vivek K. Arora, Dorothee C. E. Bakker, Leticia Barbero, Meike Becker, Richard A. Betts, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Catherine E. Cosca, Jessica Cross, Kim Currie, Thomas Gasser, Ian Harris, Judith Hauck, Vanessa Haverd, Richard A. Houghton, Christopher W. Hunt, George Hurtt, Tatiana Ilyina, Atul K. Jain, Etsushi Kato, Markus Kautz, Ralph F. Keeling, Kees Klein Goldewijk, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Ivan Lima, Danica Lombardozzi, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Yukihiro Nojiri, X. Antonio Padin, Anna Peregon, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Janet Reimer, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Steven van Heuven, Nicolas Viovy, Nicolas Vuichard, Anthony P. Walker, Andrew J. Watson, Andrew J. Wiltshire, Sönke Zaehle, and Dan Zhu
Earth Syst. Sci. Data, 10, 405–448, https://doi.org/10.5194/essd-10-405-2018, https://doi.org/10.5194/essd-10-405-2018, 2018
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The Global Carbon Budget 2017 describes data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. It is the 12th annual update and the 6th published in this journal.
Wei Li, Philippe Ciais, Shushi Peng, Chao Yue, Yilong Wang, Martin Thurner, Sassan S. Saatchi, Almut Arneth, Valerio Avitabile, Nuno Carvalhais, Anna B. Harper, Etsushi Kato, Charles Koven, Yi Y. Liu, Julia E.M.S. Nabel, Yude Pan, Julia Pongratz, Benjamin Poulter, Thomas A. M. Pugh, Maurizio Santoro, Stephen Sitch, Benjamin D. Stocker, Nicolas Viovy, Andy Wiltshire, Rasoul Yousefpour, and Sönke Zaehle
Biogeosciences, 14, 5053–5067, https://doi.org/10.5194/bg-14-5053-2017, https://doi.org/10.5194/bg-14-5053-2017, 2017
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We used several observation-based biomass datasets to constrain the historical land-use change carbon emissions simulated by models. Compared to the range of the original modeled emissions (from 94 to 273 Pg C), the observationally constrained global cumulative emission estimate is 155 ± 50 Pg C (1σ Gaussian error) from 1901 to 2012. Our approach can also be applied to evaluate the LULCC impact of land-based climate mitigation policies.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545, https://doi.org/10.5194/gmd-10-3519-2017, https://doi.org/10.5194/gmd-10-3519-2017, 2017
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Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
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Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Jakob Zscheischler, Miguel D. Mahecha, Valerio Avitabile, Leonardo Calle, Nuno Carvalhais, Philippe Ciais, Fabian Gans, Nicolas Gruber, Jens Hartmann, Martin Herold, Kazuhito Ichii, Martin Jung, Peter Landschützer, Goulven G. Laruelle, Ronny Lauerwald, Dario Papale, Philippe Peylin, Benjamin Poulter, Deepak Ray, Pierre Regnier, Christian Rödenbeck, Rosa M. Roman-Cuesta, Christopher Schwalm, Gianluca Tramontana, Alexandra Tyukavina, Riccardo Valentini, Guido van der Werf, Tristram O. West, Julie E. Wolf, and Markus Reichstein
Biogeosciences, 14, 3685–3703, https://doi.org/10.5194/bg-14-3685-2017, https://doi.org/10.5194/bg-14-3685-2017, 2017
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Here we synthesize a wide range of global spatiotemporal observational data on carbon exchanges between the Earth surface and the atmosphere. A key challenge was to consistently combining observational products of terrestrial and aquatic surfaces. Our primary goal is to identify today’s key uncertainties and observational shortcomings that would need to be addressed in future measurement campaigns or expansions of in situ observatories.
Sven Boese, Martin Jung, Nuno Carvalhais, and Markus Reichstein
Biogeosciences, 14, 3015–3026, https://doi.org/10.5194/bg-14-3015-2017, https://doi.org/10.5194/bg-14-3015-2017, 2017
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For plants, the ratio of carbon uptake to water loss by transpiration is usually thought to depend on characteristic properties (their adaption to water scarcity) and the dryness of the atmosphere at any given moment. We show that, on the ecosystem scale, radiation has an independent effect on this ratio that had not been previously considered. When including this variable in models, predictions of transpiration improve considerably.
Youven Goulamoussène, Caroline Bedeau, Laurent Descroix, Laurent Linguet, and Bruno Hérault
Biogeosciences, 14, 353–364, https://doi.org/10.5194/bg-14-353-2017, https://doi.org/10.5194/bg-14-353-2017, 2017
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Natural gap disturbances are the dominant form of forest regeneration and dynamics in tropical forests. We highlight strong links between gap size distribution and environment, primarily hydrological conditions and topography, with large gaps being more frequent on floodplains and in wind-exposed areas.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
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An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Corinne Le Quéré, Robbie M. Andrew, Josep G. Canadell, Stephen Sitch, Jan Ivar Korsbakken, Glen P. Peters, Andrew C. Manning, Thomas A. Boden, Pieter P. Tans, Richard A. Houghton, Ralph F. Keeling, Simone Alin, Oliver D. Andrews, Peter Anthoni, Leticia Barbero, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Kim Currie, Christine Delire, Scott C. Doney, Pierre Friedlingstein, Thanos Gkritzalis, Ian Harris, Judith Hauck, Vanessa Haverd, Mario Hoppema, Kees Klein Goldewijk, Atul K. Jain, Etsushi Kato, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Joe R. Melton, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Kevin O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Christian Rödenbeck, Joe Salisbury, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Adrienne J. Sutton, Taro Takahashi, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, https://doi.org/10.5194/essd-8-605-2016, 2016
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The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
Fang Zhao, Ning Zeng, Ghassem Asrar, Pierre Friedlingstein, Akihiko Ito, Atul Jain, Eugenia Kalnay, Etsushi Kato, Charles D. Koven, Ben Poulter, Rashid Rafique, Stephen Sitch, Shijie Shu, Beni Stocker, Nicolas Viovy, Andy Wiltshire, and Sonke Zaehle
Biogeosciences, 13, 5121–5137, https://doi.org/10.5194/bg-13-5121-2016, https://doi.org/10.5194/bg-13-5121-2016, 2016
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The increasing seasonality of atmospheric CO2 is strongly linked with enhanced land vegetation activities in the last 5 decades, for which the importance of increasing CO2, climate and land use/cover change was evaluated in single model studies (Zeng et al., 2014; Forkel et al., 2016). Here we examine the relative importance of these factors in multiple models. Our results highlight models can show similar results in some benchmarks with different underlying regional dynamics.
Xiyan Xu, William J. Riley, Charles D. Koven, Dave P. Billesbach, Rachel Y.-W. Chang, Róisín Commane, Eugénie S. Euskirchen, Sean Hartery, Yoshinobu Harazono, Hiroki Iwata, Kyle C. McDonald, Charles E. Miller, Walter C. Oechel, Benjamin Poulter, Naama Raz-Yaseef, Colm Sweeney, Margaret Torn, Steven C. Wofsy, Zhen Zhang, and Donatella Zona
Biogeosciences, 13, 5043–5056, https://doi.org/10.5194/bg-13-5043-2016, https://doi.org/10.5194/bg-13-5043-2016, 2016
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Wetlands are the largest global natural methane source. Peat-rich bogs and fens lying between 50°N and 70°N contribute 10–30% to this source. The predictive capability of the seasonal methane cycle can directly affect the estimation of global methane budget. We present multiscale methane seasonal emission by observations and modeling and find that the uncertainties in predicting the seasonal methane emissions are from the wetland extent, cold-season CH4 production and CH4 transport processes.
Gregor J. Schürmann, Thomas Kaminski, Christoph Köstler, Nuno Carvalhais, Michael Voßbeck, Jens Kattge, Ralf Giering, Christian Rödenbeck, Martin Heimann, and Sönke Zaehle
Geosci. Model Dev., 9, 2999–3026, https://doi.org/10.5194/gmd-9-2999-2016, https://doi.org/10.5194/gmd-9-2999-2016, 2016
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We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS). The system improves the modelled carbon cycle of the terrestrial biosphere by systematically confronting (or assimilating) the model with observations of atmospheric CO2 and fractions of absorbed photosynthetically active radiation. Jointly assimilating both data streams outperforms the single-data stream experiments, thus showing the value of a multi-data stream assimilation.
Rosa Maria Roman-Cuesta, Mariana C. Rufino, Martin Herold, Klaus Butterbach-Bahl, Todd S. Rosenstock, Mario Herrero, Stephen Ogle, Changsheng Li, Benjamin Poulter, Louis Verchot, Christopher Martius, John Stuiver, and Sytze de Bruin
Biogeosciences, 13, 4253–4269, https://doi.org/10.5194/bg-13-4253-2016, https://doi.org/10.5194/bg-13-4253-2016, 2016
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This research provides spatial data on gross emissions from the land use sector for the tropical region for the period 2000–2005. This sector contributes up to 24 % of the global emissions, but there is little understanding of where the hotspots of emissions are, how uncertain they are, and what the human activities behind these emissions are. Data provided here should assist countries to identify priority areas for mitigation action and contrast the effectiveness of their current measures.
Fabien H. Wagner, Bruno Hérault, Damien Bonal, Clément Stahl, Liana O. Anderson, Timothy R. Baker, Gabriel Sebastian Becker, Hans Beeckman, Danilo Boanerges Souza, Paulo Cesar Botosso, David M. J. S. Bowman, Achim Bräuning, Benjamin Brede, Foster Irving Brown, Jesus Julio Camarero, Plínio Barbosa Camargo, Fernanda C. G. Cardoso, Fabrício Alvim Carvalho, Wendeson Castro, Rubens Koloski Chagas, Jérome Chave, Emmanuel N. Chidumayo, Deborah A. Clark, Flavia Regina Capellotto Costa, Camille Couralet, Paulo Henrique da Silva Mauricio, Helmut Dalitz, Vinicius Resende de Castro, Jaçanan Eloisa de Freitas Milani, Edilson Consuelo de Oliveira, Luciano de Souza Arruda, Jean-Louis Devineau, David M. Drew, Oliver Dünisch, Giselda Durigan, Elisha Elifuraha, Marcio Fedele, Ligia Ferreira Fedele, Afonso Figueiredo Filho, César Augusto Guimarães Finger, Augusto César Franco, João Lima Freitas Júnior, Franklin Galvão, Aster Gebrekirstos, Robert Gliniars, Paulo Maurício Lima de Alencastro Graça, Anthony D. Griffiths, James Grogan, Kaiyu Guan, Jürgen Homeier, Maria Raquel Kanieski, Lip Khoon Kho, Jennifer Koenig, Sintia Valerio Kohler, Julia Krepkowski, José Pires Lemos-Filho, Diana Lieberman, Milton Eugene Lieberman, Claudio Sergio Lisi, Tomaz Longhi Santos, José Luis López Ayala, Eduardo Eijji Maeda, Yadvinder Malhi, Vivian R. B. Maria, Marcia C. M. Marques, Renato Marques, Hector Maza Chamba, Lawrence Mbwambo, Karina Liana Lisboa Melgaço, Hooz Angela Mendivelso, Brett P. Murphy, Joseph J. O'Brien, Steven F. Oberbauer, Naoki Okada, Raphaël Pélissier, Lynda D. Prior, Fidel Alejandro Roig, Michael Ross, Davi Rodrigo Rossatto, Vivien Rossi, Lucy Rowland, Ervan Rutishauser, Hellen Santana, Mark Schulze, Diogo Selhorst, Williamar Rodrigues Silva, Marcos Silveira, Susanne Spannl, Michael D. Swaine, José Julio Toledo, Marcos Miranda Toledo, Marisol Toledo, Takeshi Toma, Mario Tomazello Filho, Juan Ignacio Valdez Hernández, Jan Verbesselt, Simone Aparecida Vieira, Grégoire Vincent, Carolina Volkmer de Castilho, Franziska Volland, Martin Worbes, Magda Lea Bolzan Zanon, and Luiz E. O. C. Aragão
Biogeosciences, 13, 2537–2562, https://doi.org/10.5194/bg-13-2537-2016, https://doi.org/10.5194/bg-13-2537-2016, 2016
Zhen Zhang, Niklaus E. Zimmermann, Jed O. Kaplan, and Benjamin Poulter
Biogeosciences, 13, 1387–1408, https://doi.org/10.5194/bg-13-1387-2016, https://doi.org/10.5194/bg-13-1387-2016, 2016
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This study investigates improvements and uncertainties associated with estimating global inundated area and wetland CH4 emissions using TOPMODEL. Different topographic information and catchment aggregation schemes are evaluated against seasonal and permanently inundated wetland observations. Reducing uncertainty in prognostic wetland dynamics modeling must take into account forcing data as well as topographic scaling schemes.
G. Murray-Tortarolo, P. Friedlingstein, S. Sitch, V. J. Jaramillo, F. Murguía-Flores, A. Anav, Y. Liu, A. Arneth, A. Arvanitis, A. Harper, A. Jain, E. Kato, C. Koven, B. Poulter, B. D. Stocker, A. Wiltshire, S. Zaehle, and N. Zeng
Biogeosciences, 13, 223–238, https://doi.org/10.5194/bg-13-223-2016, https://doi.org/10.5194/bg-13-223-2016, 2016
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We modelled the carbon (C) cycle in Mexico for three different time periods: past (20th century), present (2000-2005) and future (2006-2100). We used different available products to estimate C stocks and fluxes in the country. Contrary to other current estimates, our results showed that Mexico was a C sink and this is likely to continue in the next century (unless the most extreme climate-change scenarios are reached).
C. Le Quéré, R. Moriarty, R. M. Andrew, J. G. Canadell, S. Sitch, J. I. Korsbakken, P. Friedlingstein, G. P. Peters, R. J. Andres, T. A. Boden, R. A. Houghton, J. I. House, R. F. Keeling, P. Tans, A. Arneth, D. C. E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, L. P. Chini, P. Ciais, M. Fader, R. A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A. K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S. K. Lauvset, N. Lefèvre, A. Lenton, I. D. Lima, N. Metzl, F. Millero, D. R. Munro, A. Murata, J. E. M. S. Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F. F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, I. T. van der Laan-Luijkx, G. R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, https://doi.org/10.5194/essd-7-349-2015, 2015
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Accurate assessment of anthropogenic carbon dioxide emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to understand the global carbon cycle, support the development of climate policies, and project future climate change. We describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on a range of data and models and their interpretation by a broad scientific community.
O. Perez-Priego, J. Guan, M. Rossini, F. Fava, T. Wutzler, G. Moreno, N. Carvalhais, A. Carrara, O. Kolle, T. Julitta, M. Schrumpf, M. Reichstein, and M. Migliavacca
Biogeosciences, 12, 6351–6367, https://doi.org/10.5194/bg-12-6351-2015, https://doi.org/10.5194/bg-12-6351-2015, 2015
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Sun-induced chlorophyll fluorescence and photochemical reflectance index revealed controls of climate and nutrient availability on photosynthesis (gross primary production, GPP). Meteo-driven models (MMs) were unable to describe nutrient-induced effects on GPP. Important implications can be derived from these results, and uncertainties in the prediction of global GPP still remain when MMs do not account for plant nutrient availability.
M. Aubry-Kientz, V. Rossi, F. Wagner, and B. Hérault
Biogeosciences, 12, 5583–5596, https://doi.org/10.5194/bg-12-5583-2015, https://doi.org/10.5194/bg-12-5583-2015, 2015
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We used a forest dynamic model, calibrated with data from the 20-year study site of Paracou, French Guiana, to test a set of climatic variables on tree growth and mortality probabilities. Severe droughts decreased annual growth and mortality rates, high precipitation increased mortality rates and high temperature decreased tree growth. Best resistance to drought was found for trees with high wood density and for trees with small current diameters.
T. Launois, P. Peylin, S. Belviso, and B. Poulter
Atmos. Chem. Phys., 15, 9285–9312, https://doi.org/10.5194/acp-15-9285-2015, https://doi.org/10.5194/acp-15-9285-2015, 2015
B. Poulter, N. MacBean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny, S. Hagemann, M. Herold, G. Kirches, C. Lamarche, D. Lederer, C. Ottlé, M. Peters, and P. Peylin
Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, https://doi.org/10.5194/gmd-8-2315-2015, 2015
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Land cover is an essential variable in earth system models and determines conditions driving biogeochemical, energy and water exchange between ecosystems and the atmosphere. A methodology is presented for mapping plant functional types used in global vegetation models from a updated land cover classification system and open-source conversion tool, resulting from a consultative process among map producers and modelers engaged in the European Space Agency’s Land Cover Climate Change Initiative.
L. Molina, G. Broquet, P. Imbach, F. Chevallier, B. Poulter, D. Bonal, B. Burban, M. Ramonet, L. V. Gatti, S. C. Wofsy, J. W. Munger, E. Dlugokencky, and P. Ciais
Atmos. Chem. Phys., 15, 8423–8438, https://doi.org/10.5194/acp-15-8423-2015, https://doi.org/10.5194/acp-15-8423-2015, 2015
D. Zhu, S. S. Peng, P. Ciais, N. Viovy, A. Druel, M. Kageyama, G. Krinner, P. Peylin, C. Ottlé, S. L. Piao, B. Poulter, D. Schepaschenko, and A. Shvidenko
Geosci. Model Dev., 8, 2263–2283, https://doi.org/10.5194/gmd-8-2263-2015, https://doi.org/10.5194/gmd-8-2263-2015, 2015
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This study presents a new parameterization of the vegetation dynamics module in the process-based ecosystem model ORCHIDEE for mid- to high-latitude regions, showing significant improvements in the modeled distribution of tree functional types north of 40°N. A new set of metrics is proposed to quantify the performance of ORCHIDEE, which integrates uncertainties in the observational data sets.
S. Hashimoto, N. Carvalhais, A. Ito, M. Migliavacca, K. Nishina, and M. Reichstein
Biogeosciences, 12, 4121–4132, https://doi.org/10.5194/bg-12-4121-2015, https://doi.org/10.5194/bg-12-4121-2015, 2015
C. Le Quéré, R. Moriarty, R. M. Andrew, G. P. Peters, P. Ciais, P. Friedlingstein, S. D. Jones, S. Sitch, P. Tans, A. Arneth, T. A. Boden, L. Bopp, Y. Bozec, J. G. Canadell, L. P. Chini, F. Chevallier, C. E. Cosca, I. Harris, M. Hoppema, R. A. Houghton, J. I. House, A. K. Jain, T. Johannessen, E. Kato, R. F. Keeling, V. Kitidis, K. Klein Goldewijk, C. Koven, C. S. Landa, P. Landschützer, A. Lenton, I. D. Lima, G. Marland, J. T. Mathis, N. Metzl, Y. Nojiri, A. Olsen, T. Ono, S. Peng, W. Peters, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. E. Salisbury, U. Schuster, J. Schwinger, R. Séférian, J. Segschneider, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, G. R. van der Werf, N. Viovy, Y.-P. Wang, R. Wanninkhof, A. Wiltshire, and N. Zeng
Earth Syst. Sci. Data, 7, 47–85, https://doi.org/10.5194/essd-7-47-2015, https://doi.org/10.5194/essd-7-47-2015, 2015
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Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
This study (updated yearly) makes an accurate assessment of anthropogenic CO2 emissions and their redistribution between the atmosphere, ocean, and terrestrial biosphere in order to better understand the global carbon cycle, support the development of climate policies, and project future climate change.
A. C. Spessa, R. D. Field, F. Pappenberger, A. Langner, S. Englhart, U. Weber, T. Stockdale, F. Siegert, J. W. Kaiser, and J. Moore
Nat. Hazards Earth Syst. Sci., 15, 429–442, https://doi.org/10.5194/nhess-15-429-2015, https://doi.org/10.5194/nhess-15-429-2015, 2015
S. Sitch, P. Friedlingstein, N. Gruber, S. D. Jones, G. Murray-Tortarolo, A. Ahlström, S. C. Doney, H. Graven, C. Heinze, C. Huntingford, S. Levis, P. E. Levy, M. Lomas, B. Poulter, N. Viovy, S. Zaehle, N. Zeng, A. Arneth, G. Bonan, L. Bopp, J. G. Canadell, F. Chevallier, P. Ciais, R. Ellis, M. Gloor, P. Peylin, S. L. Piao, C. Le Quéré, B. Smith, Z. Zhu, and R. Myneni
Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://doi.org/10.5194/bg-12-653-2015, 2015
M. Forkel, N. Carvalhais, S. Schaphoff, W. v. Bloh, M. Migliavacca, M. Thurner, and K. Thonicke
Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, https://doi.org/10.5194/bg-11-7025-2014, 2014
C. Yue, P. Ciais, P. Cadule, K. Thonicke, S. Archibald, B. Poulter, W. M. Hao, S. Hantson, F. Mouillot, P. Friedlingstein, F. Maignan, and N. Viovy
Geosci. Model Dev., 7, 2747–2767, https://doi.org/10.5194/gmd-7-2747-2014, https://doi.org/10.5194/gmd-7-2747-2014, 2014
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ORCHIDEE-SPITFIRE model could moderately capture the decadal trend and variation of burned area during the 20th century, and the spatial and temporal patterns of contemporary vegetation fires. The model has a better performance in simulating fires for regions dominated by climate-driven fires, such as boreal forests. However, it has limited capability to reproduce the infrequent but important large fires in different ecosystems, where urgent model improvement is needed in the future.
J. B. Fisher, M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller
Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, https://doi.org/10.5194/bg-11-4271-2014, 2014
P. Ciais, A. J. Dolman, A. Bombelli, R. Duren, A. Peregon, P. J. Rayner, C. Miller, N. Gobron, G. Kinderman, G. Marland, N. Gruber, F. Chevallier, R. J. Andres, G. Balsamo, L. Bopp, F.-M. Bréon, G. Broquet, R. Dargaville, T. J. Battin, A. Borges, H. Bovensmann, M. Buchwitz, J. Butler, J. G. Canadell, R. B. Cook, R. DeFries, R. Engelen, K. R. Gurney, C. Heinze, M. Heimann, A. Held, M. Henry, B. Law, S. Luyssaert, J. Miller, T. Moriyama, C. Moulin, R. B. Myneni, C. Nussli, M. Obersteiner, D. Ojima, Y. Pan, J.-D. Paris, S. L. Piao, B. Poulter, S. Plummer, S. Quegan, P. Raymond, M. Reichstein, L. Rivier, C. Sabine, D. Schimel, O. Tarasova, R. Valentini, R. Wang, G. van der Werf, D. Wickland, M. Williams, and C. Zehner
Biogeosciences, 11, 3547–3602, https://doi.org/10.5194/bg-11-3547-2014, https://doi.org/10.5194/bg-11-3547-2014, 2014
C. Le Quéré, G. P. Peters, R. J. Andres, R. M. Andrew, T. A. Boden, P. Ciais, P. Friedlingstein, R. A. Houghton, G. Marland, R. Moriarty, S. Sitch, P. Tans, A. Arneth, A. Arvanitis, D. C. E. Bakker, L. Bopp, J. G. Canadell, L. P. Chini, S. C. Doney, A. Harper, I. Harris, J. I. House, A. K. Jain, S. D. Jones, E. Kato, R. F. Keeling, K. Klein Goldewijk, A. Körtzinger, C. Koven, N. Lefèvre, F. Maignan, A. Omar, T. Ono, G.-H. Park, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. Schwinger, J. Segschneider, B. D. Stocker, T. Takahashi, B. Tilbrook, S. van Heuven, N. Viovy, R. Wanninkhof, A. Wiltshire, and S. Zaehle
Earth Syst. Sci. Data, 6, 235–263, https://doi.org/10.5194/essd-6-235-2014, https://doi.org/10.5194/essd-6-235-2014, 2014
S. Vicca, M. Bahn, M. Estiarte, E. E. van Loon, R. Vargas, G. Alberti, P. Ambus, M. A. Arain, C. Beier, L. P. Bentley, W. Borken, N. Buchmann, S. L. Collins, G. de Dato, J. S. Dukes, C. Escolar, P. Fay, G. Guidolotti, P. J. Hanson, A. Kahmen, G. Kröel-Dulay, T. Ladreiter-Knauss, K. S. Larsen, E. Lellei-Kovacs, E. Lebrija-Trejos, F. T. Maestre, S. Marhan, M. Marshall, P. Meir, Y. Miao, J. Muhr, P. A. Niklaus, R. Ogaya, J. Peñuelas, C. Poll, L. E. Rustad, K. Savage, A. Schindlbacher, I. K. Schmidt, A. R. Smith, E. D. Sotta, V. Suseela, A. Tietema, N. van Gestel, O. van Straaten, S. Wan, U. Weber, and I. A. Janssens
Biogeosciences, 11, 2991–3013, https://doi.org/10.5194/bg-11-2991-2014, https://doi.org/10.5194/bg-11-2991-2014, 2014
M. Balzarolo, S. Boussetta, G. Balsamo, A. Beljaars, F. Maignan, J.-C. Calvet, S. Lafont, A. Barbu, B. Poulter, F. Chevallier, C. Szczypta, and D. Papale
Biogeosciences, 11, 2661–2678, https://doi.org/10.5194/bg-11-2661-2014, https://doi.org/10.5194/bg-11-2661-2014, 2014
W. Knorr, T. Kaminski, A. Arneth, and U. Weber
Biogeosciences, 11, 1085–1102, https://doi.org/10.5194/bg-11-1085-2014, https://doi.org/10.5194/bg-11-1085-2014, 2014
C. R. Schwalm, D. N. Huntinzger, R. B. Cook, Y. Wei, I. T. Baker, R. P. Neilson, B. Poulter, P. Caldwell, G. Sun, H. Q. Tian, and N. Zeng
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-1801-2014, https://doi.org/10.5194/bgd-11-1801-2014, 2014
Revised manuscript not accepted
D. N. Huntzinger, C. Schwalm, A. M. Michalak, K. Schaefer, A. W. King, Y. Wei, A. Jacobson, S. Liu, R. B. Cook, W. M. Post, G. Berthier, D. Hayes, M. Huang, A. Ito, H. Lei, C. Lu, J. Mao, C. H. Peng, S. Peng, B. Poulter, D. Riccuito, X. Shi, H. Tian, W. Wang, N. Zeng, F. Zhao, and Q. Zhu
Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, https://doi.org/10.5194/gmd-6-2121-2013, 2013
C. Yue, P. Ciais, S. Luyssaert, P. Cadule, J. Harden, J. Randerson, V. Bellassen, T. Wang, S. L. Piao, B. Poulter, and N. Viovy
Biogeosciences, 10, 8233–8252, https://doi.org/10.5194/bg-10-8233-2013, https://doi.org/10.5194/bg-10-8233-2013, 2013
C. Ottlé, J. Lescure, F. Maignan, B. Poulter, T. Wang, and N. Delbart
Earth Syst. Sci. Data, 5, 331–348, https://doi.org/10.5194/essd-5-331-2013, https://doi.org/10.5194/essd-5-331-2013, 2013
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
B. Badawy, C. Rödenbeck, M. Reichstein, N. Carvalhais, and M. Heimann
Biogeosciences, 10, 6485–6508, https://doi.org/10.5194/bg-10-6485-2013, https://doi.org/10.5194/bg-10-6485-2013, 2013
R. Wania, J. R. Melton, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, G. Chen, A. V. Eliseev, P. O. Hopcroft, W. J. Riley, Z. M. Subin, H. Tian, P. M. van Bodegom, T. Kleinen, Z. C. Yu, J. S. Singarayer, S. Zürcher, D. P. Lettenmaier, D. J. Beerling, S. N. Denisov, C. Prigent, F. Papa, and J. O. Kaplan
Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, https://doi.org/10.5194/gmd-6-617-2013, 2013
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
J. R. Melton, R. Wania, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, D. J. Beerling, G. Chen, A. V. Eliseev, S. N. Denisov, P. O. Hopcroft, D. P. Lettenmaier, W. J. Riley, J. S. Singarayer, Z. M. Subin, H. Tian, S. Zürcher, V. Brovkin, P. M. van Bodegom, T. Kleinen, Z. C. Yu, and J. O. Kaplan
Biogeosciences, 10, 753–788, https://doi.org/10.5194/bg-10-753-2013, https://doi.org/10.5194/bg-10-753-2013, 2013
Related subject area
Biogeosciences and biodiversity
Multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands
Remote sensing of young leaf photosynthetic capacity in tropical and subtropical evergreen broadleaved forests
China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022
CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization
Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes
Global patterns and drivers of soil dissolved organic carbon concentrations
The SahulCHAR collection: a palaeofire database for Australia, New Guinea, and New Zealand
ARGO: ARctic greenhouse Gas Observation metadata version 1
WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022
The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive
An expert survey on chamber measurement techniques and data handling procedures for methane fluxes
LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 kyr
A new habitat map of the Lena Delta in Arctic Siberia based on field and remote sensing datasets
Mapping global leaf inclination angle (LIA) based on field measurement data
A post-processed carbon flux dataset for 34 eddy covariance flux sites across the Heihe River basin, China
Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles
Century-long reconstruction of gridded phosphorus surplus across Europe (1850–2019)
High-resolution carbon cycling data from 2019 to 2021 measured at six Austrian long-term ecosystem research sites
The JapanFlux2024 dataset for eddy covariance observations covering Japan and East Asia from 1990 to 2023
An organic matter database (OMD): consolidating global residue data from agriculture, fisheries, forestry and related industries
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
A spectral–structural characterization of European temperate, hemiboreal, and boreal forests
VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring
Crop-specific management history of phosphorus fertilizer input (CMH-P) in the croplands of the United States: reconciliation of top-down and bottom-up data sources
Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation index (NDVI) with MODIS NDVI
A synthesized field survey database of vegetation and active-layer properties for the Alaskan tundra (1972–2020)
A vegetation phenology dataset by integrating multiple sources using the Reliability Ensemble Averaging method
TCSIF: a temporally consistent global Global Ozone Monitoring Experiment-2A (GOME-2A) solar-induced chlorophyll fluorescence dataset with the correction of sensor degradation
Global nitrous oxide budget (1980–2020)
National forest carbon harvesting and allocation dataset for the period 2003 to 2018
Spatial mapping of key plant functional traits in terrestrial ecosystems across China
HiQ-LAI: a high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2022
EUPollMap: the European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach
Reference maps of soil phosphorus for the pan-Amazon region
Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020
Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022
Investigating limnological processes and modern sedimentation at Lake Żabińskie, northeast Poland: a decade-long multi-variable dataset, 2012–2021
Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada
Quantifying exchangeable base cations in permafrost: a reserve of nutrients about to thaw
Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms
The Portuguese Large Wildfire Spread database (PT-FireSprd)
Thirty-meter map of young forest age in China
GRiMeDB: the Global River Methane Database of concentrations and fluxes
A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests
Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100
A remote-sensing-based dataset to characterize the ecosystem functioning and functional diversity in the Biosphere Reserve of the Sierra Nevada (southeastern Spain)
A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT
A global database on holdover time of lightning-ignited wildfires
Yifang Shi, Jinhu Wang, and W. Daniel Kissling
Earth Syst. Sci. Data, 17, 3641–3677, https://doi.org/10.5194/essd-17-3641-2025, https://doi.org/10.5194/essd-17-3641-2025, 2025
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We present a new set of multi-temporal lidar metrics of ecosystem structure derived from four national ALS (airborne laser scanning) surveys of the Netherlands (AHN1–AHN4), capturing vegetation height, cover, and structural variability over the last 2 decades (1998–2022). Around 70 TB point clouds have been processed to ready-to-use raster layers at 10 m resolution (~ 59 GB), enabling a wide use and uptake of ecosystem structure information in biodiversity and habitat monitoring and ecosystem and carbon dynamic modelling.
Xueqin Yang, Qingling Sun, Liusheng Han, Jie Tian, Wenping Yuan, Liyang Liu, Wei Zheng, Mei Wang, Yunpeng Wang, and Xiuzhi Chen
Earth Syst. Sci. Data, 17, 3293–3314, https://doi.org/10.5194/essd-17-3293-2025, https://doi.org/10.5194/essd-17-3293-2025, 2025
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Understanding how leaves absorb carbon from the atmosphere is essential for predicting changes in global forests. Young leaves play a key role in this process, but their efficiency has been difficult to measure at large scales. Using satellite data, we developed a new method to track the seasonal patterns of young leaves’ photosynthetic capacity from 2001 to 2018. Our dataset helps scientists better understand forest growth and how ecosystems respond to climate change.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
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Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Yanghui Kang, Maoya Bassiouni, Max Gaber, Xinchen Lu, and Trevor F. Keenan
Earth Syst. Sci. Data, 17, 3009–3046, https://doi.org/10.5194/essd-17-3009-2025, https://doi.org/10.5194/essd-17-3009-2025, 2025
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CEDAR-GPP provides spatiotemporally upscaled estimates of gross primary productivity (GPP) globally, uniquely incorporating the direct effect of elevated atmospheric CO2 on photosynthesis. This dataset was produced by upscaling eddy covariance data with machine learning and a broad range of satellite and climate variables. Available at monthly and 0.05° resolution from 1982 to 2020, CEDAR-GPP offers critical insights into ecosystem–climate interactions and the global carbon cycle.
Anna C. Talucci, Michael M. Loranty, Jean E. Holloway, Brendan M. Rogers, Heather D. Alexander, Natalie Baillargeon, Jennifer L. Baltzer, Logan T. Berner, Amy Breen, Leya Brodt, Brian Buma, Jacqueline Dean, Clement J. F. Delcourt, Lucas R. Diaz, Catherine M. Dieleman, Thomas A. Douglas, Gerald V. Frost, Benjamin V. Gaglioti, Rebecca E. Hewitt, Teresa Hollingsworth, M. Torre Jorgenson, Mark J. Lara, Rachel A. Loehman, Michelle C. Mack, Kristen L. Manies, Christina Minions, Susan M. Natali, Jonathan A. O'Donnell, David Olefeldt, Alison K. Paulson, Adrian V. Rocha, Lisa B. Saperstein, Tatiana A. Shestakova, Seeta Sistla, Oleg Sizov, Andrey Soromotin, Merritt R. Turetsky, Sander Veraverbeke, and Michelle A. Walvoord
Earth Syst. Sci. Data, 17, 2887–2909, https://doi.org/10.5194/essd-17-2887-2025, https://doi.org/10.5194/essd-17-2887-2025, 2025
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Wildfires have the potential to accelerate permafrost thaw and the associated feedbacks to climate change. We assembled a dataset of permafrost thaw depth measurements from burned and unburned sites contributed by researchers from across the northern high-latitude region. We estimated maximum thaw depth for each measurement, which addresses a key challenge: the ability to assess impacts of wildfire on maximum thaw depth when measurement timing varies.
Tianjing Ren and Andong Cai
Earth Syst. Sci. Data, 17, 2873–2885, https://doi.org/10.5194/essd-17-2873-2025, https://doi.org/10.5194/essd-17-2873-2025, 2025
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This study compiles a global database of soil dissolved organic carbon (DOC) concentrations, a key factor in soil health and climate change. Using machine learning, it identifies the most influential factors affecting soil DOC levels and maps global DOC patterns. The findings help guide soil management and climate strategies, with the dataset available for further research.
Emma Rehn, Haidee Cadd, Scott Mooney, Tim J. Cohen, Henry Munack, Alexandru T. Codilean, Matthew Adeleye, Kristen K. Beck, Mark Constantine IV, Chris Gouramanis, Johanna M. Hanson, Penelope J. Jones, A. Peter Kershaw, Lydia Mackenzie, Maame Maisie, Michela Mariani, Kia Matley, David McWethy, Keely Mills, Patrick Moss, Nicholas R. Patton, Cassandra Rowe, Janelle Stevenson, John Tibby, and Janet Wilmshurst
Earth Syst. Sci. Data, 17, 2681–2692, https://doi.org/10.5194/essd-17-2681-2025, https://doi.org/10.5194/essd-17-2681-2025, 2025
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This paper presents SahulCHAR, a new collection of palaeofire (ancient fire) records from Australia, New Guinea, and New Zealand. SahulCHAR version 1 contains 687 records of sedimentary charcoal or black carbon, including digitized data, records from existing databases, and original author-submitted data. SahulCHAR is a much-needed update to past charcoal compilations that will also provide greater representation of records from this region in future global syntheses to understand past fire.
Judith Vogt, Martijn M. T. A. Pallandt, Luana S. Basso, Abdullah Bolek, Kseniia Ivanova, Mark Schlutow, Gerardo Celis, McKenzie Kuhn, Marguerite Mauritz, Edward A. G. Schuur, Kyle Arndt, Anna-Maria Virkkala, Isabel Wargowsky, and Mathias Göckede
Earth Syst. Sci. Data, 17, 2553–2573, https://doi.org/10.5194/essd-17-2553-2025, https://doi.org/10.5194/essd-17-2553-2025, 2025
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We present a meta-dataset of greenhouse gas observations in the Arctic and boreal regions, including information on sites where greenhouse gases have been measured using different measurement techniques. We provide a novel repository of metadata to facilitate synthesis efforts for regions undergoing rapid environmental change. The meta-dataset shows where measurements are missing and will be updated as new measurements are published.
Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, https://doi.org/10.5194/essd-17-2507-2025, 2025
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We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
Alba Viana-Soto and Cornelius Senf
Earth Syst. Sci. Data, 17, 2373–2404, https://doi.org/10.5194/essd-17-2373-2025, https://doi.org/10.5194/essd-17-2373-2025, 2025
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Europe's forests are undergoing complex changes in response to increasing disturbances driven by climate and land use changes. Here, we present the European Forest Disturbance Atlas, a satellite-based approach for mapping annual forest disturbances across continental Europe from 1985 onwards. Maps provide insights into the year of disturbance occurrence, the actual frequency of disturbances, severity and the underlying causal agent, thus contributing to a future monitoring system envisioned for Europe.
Katharina Jentzsch, Lona van Delden, Matthias Fuchs, and Claire C. Treat
Earth Syst. Sci. Data, 17, 2331–2372, https://doi.org/10.5194/essd-17-2331-2025, https://doi.org/10.5194/essd-17-2331-2025, 2025
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Methane is a greenhouse gas that contributes to global warming, but we do not fully understand how much is released from natural sources like wetlands. To measure methane over large areas, many measurements are needed, often from small chambers that are placed on the ground. However, different researchers use different measurement setups, making it hard to combine data. We surveyed 36 researchers about their methods, summarized the responses, and identified ways to make the data more comparable.
Laura Schild, Peter Ewald, Chenzhi Li, Raphaël Hébert, Thomas Laepple, and Ulrike Herzschuh
Earth Syst. Sci. Data, 17, 2007–2033, https://doi.org/10.5194/essd-17-2007-2025, https://doi.org/10.5194/essd-17-2007-2025, 2025
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This study reconstructed vegetation and tree cover in the Northern Hemisphere from a harmonized dataset of pollen counts from sediment and peat cores for the past 14 000 years. A model was applied to correct for differences in pollen production between different plants, and modern remote-sensing forest cover was used to validate the reconstructed tree cover. Accurate data on past vegetation are invaluable for the investigation of vegetation–climate dynamics and the validation of vegetation models.
Simeon Lisovski, Alexandra Runge, Iuliia Shevtsova, Nele Landgraf, Anne Morgenstern, Ronald Reagan Okoth, Matthias Fuchs, Nikolay Lashchinskiy, Carl Stadie, Alison Beamish, Ulrike Herzschuh, Guido Grosse, and Birgit Heim
Earth Syst. Sci. Data, 17, 1707–1730, https://doi.org/10.5194/essd-17-1707-2025, https://doi.org/10.5194/essd-17-1707-2025, 2025
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The Lena Delta is the largest river delta in the Arctic and represents a biodiversity hotspot. Here, we describe multiple field datasets and a detailed habitat classification map for the Lena Delta. We present context and methods of these openly available datasets and show how they can improve our understanding of the rapidly changing Arctic tundra system.
Sijia Li and Hongliang Fang
Earth Syst. Sci. Data, 17, 1347–1366, https://doi.org/10.5194/essd-17-1347-2025, https://doi.org/10.5194/essd-17-1347-2025, 2025
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Leaf inclination angle (LIA) is a vital trait in radiative transfer, rainfall interception, evapotranspiration, photosynthesis, and hydrological processes. However, global LIA knowledge is still lacking. This study generated the first global 500 m LIA products by gap-filling LIA measurement data. The global LIA is 41.47° ± 9.55° and increases with latitude. LIA products could enhance our understanding of global LIA and assist remote sensing retrieval and land surface modeling studies.
Xufeng Wang, Tao Che, Jingfeng Xiao, Tonghong Wang, Junlei Tan, Yang Zhang, Zhiguo Ren, Liying Geng, Haibo Wang, Ziwei Xu, Shaomin Liu, and Xin Li
Earth Syst. Sci. Data, 17, 1329–1346, https://doi.org/10.5194/essd-17-1329-2025, https://doi.org/10.5194/essd-17-1329-2025, 2025
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In this study, carbon flux and auxiliary meteorological data are post-processed to create an analysis-ready dataset for 34 sites across six ecosystems in the Heihe River basin. Overall, 18 sites have multi-year observations, while 16 were observed only during the 2012 growing season, totaling 1513 site months. This dataset can be used to explore carbon exchange, assess ecosystem responses to climate change, support upscaling studies, and evaluate carbon cycle models.
Hirofumi Hashimoto, Weile Wang, Taejin Park, Sepideh Khajehei, Kazuhito Ichii, Andrew Michaelis, Alberto Guzman, Ramakrishna Nemani, Margaret Torn, Koong Yi, and Ian Brosnan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-33, https://doi.org/10.5194/essd-2025-33, 2025
Revised manuscript accepted for ESSD
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We create the GeoNEX Coincident Ground Observations dataset (GeCGO) by extracting point data at observational network sites across Americas from the gridded GeoNEX products. The GeoNEX dataset is the high temporal frequent dataset of the latest geostationary satellites observations. We also release the software, GeoNEXTools, that helps handling the GeCGO data. GeCGO data and GeoNEXTools could help scientists use geostationary satellite data at their interested ground observational sites.
Masooma Batool, Fanny J. Sarrazin, and Rohini Kumar
Earth Syst. Sci. Data, 17, 881–916, https://doi.org/10.5194/essd-17-881-2025, https://doi.org/10.5194/essd-17-881-2025, 2025
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Our paper presents a reconstruction and analysis of the gridded P surplus in European landscapes from 1850 to 2019 at a 5 arcmin resolution. By utilizing 48 different estimates, we account for uncertainties in major components of the P surplus. Our findings highlight substantial historical changes, with the total P surplus in the EU 27 tripling over 170 years. Our dataset enables flexible aggregation at various spatial scales, providing critical insights for land and water management strategies.
Thomas Dirnböck, Michael Bahn, Eugenio Diaz-Pines, Ika Djukic, Michael Englisch, Karl Gartner, Günther Gollobich, Johannes Ingrisch, Barbara Kitzler, Karl Knaebel, Johannes Kobler, Andreas Maier, Armin Malli, Ivo Offenthaler, Johannes Peterseil, Gisela Pröll, Sarah Venier, Christoph Wohner, Sophie Zechmeister-Boltenstern, Anita Zolles, and Stephan Glatzel
Earth Syst. Sci. Data, 17, 685–702, https://doi.org/10.5194/essd-17-685-2025, https://doi.org/10.5194/essd-17-685-2025, 2025
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Long-term observation sites have been established in six Austrian locations, covering major ecosystem types such as forests, grasslands, and wetlands. The purpose of these observations is to measure baselines for assessing the impacts of extreme climate events on the carbon cycle. The collected datasets include meteorological variables, soil temperature and moisture, carbon dioxide fluxes, and tree stem growth in forests at a resolution of 15–60 min between 2019 and 2021.
Masahito Ueyama, Yuta Takao, Hiromi Yazawa, Makiko Tanaka, Hironori Yabuki, Tomo’omi Kumagai, Hiroki Iwata, Md. Abdul Awal, Mingyuan Du, Yoshinobu Harazono, Yoshiaki Hata, Takashi Hirano, Tsutom Hiura, Reiko Ide, Sachinobu Ishida, Mamoru Ishikawa, Kenzo Kitamura, Yuji Kominami, Shujiro Komiya, Ayumi Kotani, Yuta Inoue, Takashi Machimura, Kazuho Matsumoto, Yojiro Matsuura, Yasuko Mizoguchi, Shohei Murayama, Hirohiko Nagano, Taro Nakai, Tatsuro Nakaji, Ko Nakaya, Shinjiro Ohkubo, Takeshi Ohta, Keisuke Ono, Taku M. Saitoh, Ayaka Sakabe, Takanori Shimizu, Seiji Shimoda, Michiaki Sugita, Kentaro Takagi, Yoshiyuki Takahashi, Naoya Takamura, Satoru Takanashi, Takahiro Takimoto, Yukio Yasuda, Qinxue Wang, Jun Asanuma, Hideo Hasegawa, Tetsuya Hiyama, Yoshihiro Iijima, Shigeyuki Ishidoya, Masayuki Itoh, Tomomichi Kato, Hiroaki Kondo, Yoshiko Kosugi, Tomonori Kume, Takahisa Maeda, Trofim Maximov, Ryo Moriwaki, Hiroyuki Muraoka, Roman Petrov, Jun Suzuki, Shingo Taniguchi, and Kazuhito Ichii
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-615, https://doi.org/10.5194/essd-2024-615, 2025
Revised manuscript accepted for ESSD
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The JapanFlux2024 dataset, created through collaboration across Japan and East Asia, includes eddy covariance data from 79 sites spanning 652 site-years (1990–2023). This comprehensive dataset offers valuable insights into energy, water, and CO2 fluxes, supporting research on land-atmosphere interactions and process models. Compatible with FLUXNET, it fosters global collaboration and advances research in environmental science and regional climate dynamics.
Gudeta Weldesemayat Sileshi, Edmundo Barrios, Johannes Lehmann, and Francesco Nicola Tubiello
Earth Syst. Sci. Data, 17, 369–391, https://doi.org/10.5194/essd-17-369-2025, https://doi.org/10.5194/essd-17-369-2025, 2025
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Agricultural, fishery, forestry and agro-processing activities produce large quantities of residues, by-products and waste materials every year. Here, we present a global organic matter database (OMD), the first of its kind, consolidating estimates of residues and by-products potentially available for use in a circular bio-economy. It also provides definitions, typologies and methods to aid consistent classification, estimation and reporting of the various residues and by-products.
Kelly S. Aho, Kaelin M. Cawley, Robert T. Hensley, Robert O. Hall Jr., Walter K. Dodds, and Keli J. Goodman
Earth Syst. Sci. Data, 16, 5563–5578, https://doi.org/10.5194/essd-16-5563-2024, https://doi.org/10.5194/essd-16-5563-2024, 2024
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Gas exchange is fundamental to many biogeochemical processes in streams and depends on the degree of gas saturation and the gas transfer velocity (k). Currently, k is harder to measure than concentration. Here, we present a processing pipeline to estimate k from tracer-gas experiments conducted in 22 streams by the National Ecological Observatory Network. The processed dataset (n = 339) represents the largest compilation of standardized k estimates available.
Miina Rautiainen, Aarne Hovi, Daniel Schraik, Jan Hanuš, Petr Lukeš, Zuzana Lhotáková, and Lucie Homolová
Earth Syst. Sci. Data, 16, 5069–5087, https://doi.org/10.5194/essd-16-5069-2024, https://doi.org/10.5194/essd-16-5069-2024, 2024
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Radiative transfer models play a key role in monitoring vegetation using remote sensing data such as satellite or airborne images. The development of these models has been hindered by a lack of comprehensive ground reference data on structural and spectral characteristics of forests. Here, we reported datasets on the structural and spectral properties of temperate, hemiboreal, and boreal European forest stands. We anticipate that these data will have wide use in remote sensing applications.
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
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VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
Peiyu Cao, Bo Yi, Franco Bilotto, Carlos Gonzalez Fischer, Mario Herrero, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 4557–4572, https://doi.org/10.5194/essd-16-4557-2024, https://doi.org/10.5194/essd-16-4557-2024, 2024
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This article presents a spatially explicit time series dataset reconstructing crop-specific phosphorus fertilizer application rates, timing, and methods at a 4 km × 4 km resolution in the United States from 1850 to 2022. We comprehensively characterized the spatio-temporal dynamics of P fertilizer management over the last 170 years by considering cross-crop variations. This dataset will greatly contribute to the field of agricultural sustainability assessment and Earth system modeling.
Chad A. Burton, Sami W. Rifai, Luigi J. Renzullo, and Albert I. J. M. Van Dijk
Earth Syst. Sci. Data, 16, 4389–4416, https://doi.org/10.5194/essd-16-4389-2024, https://doi.org/10.5194/essd-16-4389-2024, 2024
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Understanding vegetation response to environmental change requires accurate, long-term data on vegetation condition (VC). We evaluated existing satellite VC datasets over Australia and found them lacking, so we developed a new VC dataset for Australia, AusENDVI. It can be used for studying Australia's changing vegetation dynamics and downstream impacts on the carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change.
Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan T. Berner, Amy L. Breen, Abhishek Chatterjee, Scott J. Davidson, Gerald V. Frost, Teresa N. Hollingsworth, Go Iwahana, Randi R. Jandt, Anja N. Kade, Tatiana V. Loboda, Matt J. Macander, Michelle Mack, Charles E. Miller, Eric A. Miller, Susan M. Natali, Martha K. Raynolds, Adrian V. Rocha, Shiro Tsuyuzaki, Craig E. Tweedie, Donald A. Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
Earth Syst. Sci. Data, 16, 3687–3703, https://doi.org/10.5194/essd-16-3687-2024, https://doi.org/10.5194/essd-16-3687-2024, 2024
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The Arctic tundra is experiencing widespread physical and biological changes, largely in response to warming, yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and studies compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which brings together field datasets and includes vegetation, active-layer, and fire properties.
Yishuo Cui, Shouzhi Chen, Yufeng Gong, Mingwei Li, Zitong Jia, Yuyu Zhou, and Yongshuo H. Fu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-225, https://doi.org/10.5194/essd-2024-225, 2024
Revised manuscript accepted for ESSD
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Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycle. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982 to 2022 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data, 16, 2789–2809, https://doi.org/10.5194/essd-16-2789-2024, https://doi.org/10.5194/essd-16-2789-2024, 2024
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To obtain a temporally consistent satellite solar-induced chlorophyll fluorescence
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
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Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
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This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu
Earth Syst. Sci. Data, 16, 1771–1810, https://doi.org/10.5194/essd-16-1771-2024, https://doi.org/10.5194/essd-16-1771-2024, 2024
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This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
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Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
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Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
João Paulo Darela-Filho, Anja Rammig, Katrin Fleischer, Tatiana Reichert, Laynara Figueiredo Lugli, Carlos Alberto Quesada, Luis Carlos Colocho Hurtarte, Mateus Dantas de Paula, and David M. Lapola
Earth Syst. Sci. Data, 16, 715–729, https://doi.org/10.5194/essd-16-715-2024, https://doi.org/10.5194/essd-16-715-2024, 2024
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Phosphorus (P) is crucial for plant growth, and scientists have created models to study how it interacts with carbon cycle in ecosystems. To apply these models, it is important to know the distribution of phosphorus in soil. In this study we estimated the distribution of phosphorus in the Amazon region. The results showed a clear gradient of soil development and P content. These maps can help improve ecosystem models and generate new hypotheses about phosphorus availability in the Amazon.
Mengyao Zhu, Junhu Dai, Huanjiong Wang, Juha M. Alatalo, Wei Liu, Yulong Hao, and Quansheng Ge
Earth Syst. Sci. Data, 16, 277–293, https://doi.org/10.5194/essd-16-277-2024, https://doi.org/10.5194/essd-16-277-2024, 2024
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This study utilized 24,552 in situ phenology observation records from the Chinese Phenology Observation Network to model and map 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020. These phenology maps are the first gridded, independent and reliable phenology data sources for China, offering a high spatial resolution of 0.1° and an average deviation of about 10 days. It contributes to more comprehensive research on plant phenology and climate change.
Jiabin Pu, Kai Yan, Samapriya Roy, Zaichun Zhu, Miina Rautiainen, Yuri Knyazikhin, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 15–34, https://doi.org/10.5194/essd-16-15-2024, https://doi.org/10.5194/essd-16-15-2024, 2024
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Long-term global LAI/FPAR products provide the fundamental dataset for accessing vegetation dynamics and studying climate change. This study develops a sensor-independent LAI/FPAR climate data record based on the integration of Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products and applies advanced gap-filling techniques. The SI LAI/FPAR CDR provides a valuable resource for researchers studying vegetation dynamics and their relationship to climate change in the 21st century.
Wojciech Tylmann, Alicja Bonk, Dariusz Borowiak, Paulina Głowacka, Kamil Nowiński, Joanna Piłczyńska, Agnieszka Szczerba, and Maurycy Żarczyński
Earth Syst. Sci. Data, 15, 5093–5103, https://doi.org/10.5194/essd-15-5093-2023, https://doi.org/10.5194/essd-15-5093-2023, 2023
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We present a dataset from the decade-long monitoring of Lake Żabińskie, a hardwater and eutrophic lake in northeast Poland. The lake contains varved sediments, which form a unique archive of past environmental variability. The monitoring program was designed to capture a pattern of relationships between meteorological conditions, limnological processes, and modern sedimentation and to verify if meteorological and limnological phenomena can be precisely tracked with varves.
Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, https://doi.org/10.5194/essd-15-4877-2023, 2023
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The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
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Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti
Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
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A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
Elisabeth Mauclet, Maëlle Villani, Arthur Monhonval, Catherine Hirst, Edward A. G. Schuur, and Sophie Opfergelt
Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
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Permafrost ecosystems are limited in nutrients for vegetation development and constrain the biological activity to the active layer. Upon Arctic warming, permafrost degradation exposes organic and mineral soil material that may directly influence the capacity of the soil to retain key nutrients for vegetation growth and development. Here, we demonstrate that the average total exchangeable nutrient density (Ca, K, Mg, and Na) is more than 2 times higher in the permafrost than in the active layer.
Anna G. Boegehold, Ashley M. Burtner, Andrew C. Camilleri, Glenn Carter, Paul DenUyl, David Fanslow, Deanna Fyffe Semenyuk, Casey M. Godwin, Duane Gossiaux, Thomas H. Johengen, Holly Kelchner, Christine Kitchens, Lacey A. Mason, Kelly McCabe, Danna Palladino, Dack Stuart, Henry Vanderploeg, and Reagan Errera
Earth Syst. Sci. Data, 15, 3853–3868, https://doi.org/10.5194/essd-15-3853-2023, https://doi.org/10.5194/essd-15-3853-2023, 2023
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Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
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We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
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Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
Earth Syst. Sci. Data, 15, 2879–2926, https://doi.org/10.5194/essd-15-2879-2023, https://doi.org/10.5194/essd-15-2879-2023, 2023
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The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
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We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
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We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
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This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
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We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
Short summary
Short summary
This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
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Short summary
Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. Yet, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. In this paper, we introduced a new global distribution of forest age inferred from forest inventory, remote sensing and climate data in support of a better understanding of the global dynamics in the forest water and carbon cycles.
Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. Yet, a...
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