Articles | Volume 8, issue 7
https://doi.org/10.5194/gmd-8-2035-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-2035-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
13 Jul 2015
Development and technical paper |
| 13 Jul 2015
A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
MPI-Meteorology, Hamburg, Germany
J. Ryder
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
M. J. McGrath
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
Helmholtz-Zentrum Geesthacht, Climate Service Center 2.0, Hamburg, Germany
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
A. Valade
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
V. Bellasen
INRA, 21079 Dijon, France
G. Berhongaray
University of Antwerp, 2610 Wilrijk, Belgium
G. Bönisch
MPI-Biogeochemistry, Jena, Germany
M. Campioli
University of Antwerp, 2610 Wilrijk, Belgium
J. Ghattas
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
T. De Groote
University of Antwerp, 2610 Wilrijk, Belgium
VITO, 2400 Mol, Belgium
V. Haverd
CSIRO-Ocean and Atmosphere Flagship, 2600 Canberra, Australia
J. Kattge
MPI-Biogeochemistry, Jena, Germany
N. MacBean
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
F. Maignan
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
P. Merilä
METLA, Oulu, Finland
J. Penuelas
CSIC, Global Ecology Unit CREAF-CSIC-UAB, Cerdanyola del Valles, Spain
CREAF, Cerdanyola del Vallès, Spain
P. Peylin
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
B. Pinty
European Commission, Joint Research Centre, Ispra, Italy
H. Pretzsch
TUM, Munich, Germany
E. D. Schulze
MPI-Biogeochemistry, Jena, Germany
D. Solyga
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
CGG, 91341 Massy, France
N. Vuichard
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
Y. Yan
University of Antwerp, 2610 Wilrijk, Belgium
S. Luyssaert
LSCE, IPSL, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
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Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
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Thomas Janssen, Ype van der Velde, Florian Hofhansl, Sebastiaan Luyssaert, Kim Naudts, Bart Driessen, Katrin Fleischer, and Han Dolman
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Jonathan Barichivich, Philippe Peylin, Thomas Launois, Valerie Daux, Camille Risi, Jina Jeong, and Sebastiaan Luyssaert
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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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Carlos A. Sierra, Susan E. Crow, Martin Heimann, Holger Metzler, and Ernst-Detlef Schulze
Biogeosciences, 18, 1029–1048, https://doi.org/10.5194/bg-18-1029-2021, https://doi.org/10.5194/bg-18-1029-2021, 2021
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The climate benefit of carbon sequestration (CBS) is a metric developed to quantify avoided warming by two separate processes: the amount of carbon drawdown from the atmosphere and the time this carbon is stored in a reservoir. This metric can be useful for quantifying the role of forests and soils for climate change mitigation and to better quantify the benefits of carbon removals by sinks.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
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.
Thomas A. M. Pugh, Tim Rademacher, Sarah L. Shafer, Jörg Steinkamp, Jonathan Barichivich, Brian Beckage, Vanessa Haverd, Anna Harper, Jens Heinke, Kazuya Nishina, Anja Rammig, Hisashi Sato, Almut Arneth, Stijn Hantson, Thomas Hickler, Markus Kautz, Benjamin Quesada, Benjamin Smith, and Kirsten Thonicke
Biogeosciences, 17, 3961–3989, https://doi.org/10.5194/bg-17-3961-2020, https://doi.org/10.5194/bg-17-3961-2020, 2020
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The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle. Estimates from six contemporary models found this time to range from 12.2 to 23.5 years for the global mean for 1985–2014. Future projections do not give consistent results, but 13 model-based hypotheses are identified, along with recommendations for pragmatic steps to test them using existing and novel observations, which would help to reduce large current uncertainty.
Christopher P. O. Reyer, Ramiro Silveyra Gonzalez, Klara Dolos, Florian Hartig, Ylva Hauf, Matthias Noack, Petra Lasch-Born, Thomas Rötzer, Hans Pretzsch, Henning Meesenburg, Stefan Fleck, Markus Wagner, Andreas Bolte, Tanja G. M. Sanders, Pasi Kolari, Annikki Mäkelä, Timo Vesala, Ivan Mammarella, Jukka Pumpanen, Alessio Collalti, Carlo Trotta, Giorgio Matteucci, Ettore D'Andrea, Lenka Foltýnová, Jan Krejza, Andreas Ibrom, Kim Pilegaard, Denis Loustau, Jean-Marc Bonnefond, Paul Berbigier, Delphine Picart, Sébastien Lafont, Michael Dietze, David Cameron, Massimo Vieno, Hanqin Tian, Alicia Palacios-Orueta, Victor Cicuendez, Laura Recuero, Klaus Wiese, Matthias Büchner, Stefan Lange, Jan Volkholz, Hyungjun Kim, Joanna A. Horemans, Friedrich Bohn, Jörg Steinkamp, Alexander Chikalanov, Graham P. Weedon, Justin Sheffield, Flurin Babst, Iliusi Vega del Valle, Felicitas Suckow, Simon Martel, Mats Mahnken, Martin Gutsch, and Katja Frieler
Earth Syst. Sci. Data, 12, 1295–1320, https://doi.org/10.5194/essd-12-1295-2020, https://doi.org/10.5194/essd-12-1295-2020, 2020
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Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale to support systematic model intercomparisons and model development in Europe.
Thomas Janssen, Katrin Fleischer, Sebastiaan Luyssaert, Kim Naudts, and Han Dolman
Biogeosciences, 17, 2621–2645, https://doi.org/10.5194/bg-17-2621-2020, https://doi.org/10.5194/bg-17-2621-2020, 2020
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The frequency and severity of droughts are expected to increase in the tropics, impacting the functioning of tropical forests. Here, we synthesized observed responses to drought in Neotropical forests. We find that, during drought, trees generally close their leaf stomata, resulting in reductions in photosynthesis, growth and transpiration. However, on the ecosystem scale, these responses are not visible. This indicates that resistance to drought increases from the leaf to ecosystem scale.
Bettina Steuri, Tanja Blome, Katharina Bülow, Juliane El Zohbi, Peter Hoffmann, Juliane Petersen, Susanne Pfeifer, Diana Rechid, and Daniela Jacob
Adv. Sci. Res., 17, 9–17, https://doi.org/10.5194/asr-17-9-2020, https://doi.org/10.5194/asr-17-9-2020, 2020
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The goal of an interdisciplinary team of scientists at the Climate Service Center Germany (GERICS) was to make the findings of the special report IPCC SR1.5 more accessible to the citizens of Hamburg. Therefore, a flyer was created that is understandable to non-climate scientists, visually attractive and generates interest.
In this article, the authors provide insights into their teamwork, the underlying guiding principles as well as lessons learned that are of great value for future projects.
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.
Giovanni Forzieri, Matteo Pecchi, Marco Girardello, Achille Mauri, Marcus Klaus, Christo Nikolov, Marius Rüetschi, Barry Gardiner, Julián Tomaštík, David Small, Constantin Nistor, Donatas Jonikavicius, Jonathan Spinoni, Luc Feyen, Francesca Giannetti, Rinaldo Comino, Alessandro Wolynski, Francesco Pirotti, Fabio Maistrelli, Ionut Savulescu, Stéphanie Wurpillot-Lucas, Stefan Karlsson, Karolina Zieba-Kulawik, Paulina Strejczek-Jazwinska, Martin Mokroš, Stefan Franz, Lukas Krejci, Ionel Haidu, Mats Nilsson, Piotr Wezyk, Filippo Catani, Yi-Ying Chen, Sebastiaan Luyssaert, Gherardo Chirici, Alessandro Cescatti, and Pieter S. A. Beck
Earth Syst. Sci. Data, 12, 257–276, https://doi.org/10.5194/essd-12-257-2020, https://doi.org/10.5194/essd-12-257-2020, 2020
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Strong winds may uproot and break trees and represent a risk for forests. Despite the importance of this natural disturbance and possible intensification in view of climate change, spatial information about wind-related impacts is currently missing on a pan-European scale. We present a new database of wind disturbances in European forests comprised of more than 80 000 records over the period 2000–2018. Our database is a unique spatial source for the study of forest disturbances at large scales.
Julia E. M. S. Nabel, Kim Naudts, and Julia Pongratz
Geosci. Model Dev., 13, 185–200, https://doi.org/10.5194/gmd-13-185-2020, https://doi.org/10.5194/gmd-13-185-2020, 2020
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Models need to account for forest age structures when investigating land use influences on land–atmosphere feedbacks. We present a consolidated scheme to introduce forest age classes, combining age-dependent simulations of important processes with the possibility to trace forest age, and describe its implementation in JSBACH4, the land surface model of the ICON Earth system model. We evaluate simulations with and without age classes demonstrating the benefit of forest age classes in JSBACH4.
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.
Nicolas Vuichard, Palmira Messina, Sebastiaan Luyssaert, Bertrand Guenet, Sönke Zaehle, Josefine Ghattas, Vladislav Bastrikov, and Philippe Peylin
Geosci. Model Dev., 12, 4751–4779, https://doi.org/10.5194/gmd-12-4751-2019, https://doi.org/10.5194/gmd-12-4751-2019, 2019
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In this research, we present a new version of the global terrestrial ecosystem model ORCHIDEE in which carbon and nitrogen cycles are coupled. We evaluate its skills at simulating primary production at 78 sites and at a global scale. Based on a set of additional simulations in which carbon and nitrogen cycles are coupled and uncoupled, we show that the functional responses of the model with carbon–nitrogen interactions better agree with our current understanding of photosynthesis.
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.
Johannes Winckler, Christian H. Reick, Sebastiaan Luyssaert, Alessandro Cescatti, Paul C. Stoy, Quentin Lejeune, Thomas Raddatz, Andreas Chlond, Marvin Heidkamp, and Julia Pongratz
Earth Syst. Dynam., 10, 473–484, https://doi.org/10.5194/esd-10-473-2019, https://doi.org/10.5194/esd-10-473-2019, 2019
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For local living conditions, it matters whether deforestation influences the surface temperature, temperature at 2 m, or the temperature higher up in the atmosphere. Here, simulations with a climate model show that at a location of deforestation, surface temperature generally changes more strongly than atmospheric temperature. Comparison across climate models shows that both for summer and winter the surface temperature response exceeds the air temperature response locally by a factor of 2.
Elodie Alice Courtois, Clément Stahl, Benoit Burban, Joke Van den Berge, Daniel Berveiller, Laëtitia Bréchet, Jennifer Larned Soong, Nicola Arriga, Josep Peñuelas, and Ivan August Janssens
Biogeosciences, 16, 785–796, https://doi.org/10.5194/bg-16-785-2019, https://doi.org/10.5194/bg-16-785-2019, 2019
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Measuring greenhouse gases (GHGs) from a natural ecosystem remains a contemporary challenge. We tested the use of appropriate technology for the estimation of soil fluxes of the three main GHGs in a tropical rainforest for 4 months. We showed that our design allowed the continuous high-frequency measurement of the three gases in a tropical biome and provide recommendations for its implementation. This study is a major step in the estimation of the global GHG budget of tropical forests.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
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This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
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.
Yilong Wang, Philippe Ciais, Daniel Goll, Yuanyuan Huang, Yiqi Luo, Ying-Ping Wang, A. Anthony Bloom, Grégoire Broquet, Jens Hartmann, Shushi Peng, Josep Penuelas, Shilong Piao, Jordi Sardans, Benjamin D. Stocker, Rong Wang, Sönke Zaehle, and Sophie Zechmeister-Boltenstern
Geosci. Model Dev., 11, 3903–3928, https://doi.org/10.5194/gmd-11-3903-2018, https://doi.org/10.5194/gmd-11-3903-2018, 2018
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We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Vanessa Haverd, Benjamin Smith, Lars Nieradzik, Peter R. Briggs, William Woodgate, Cathy M. Trudinger, Josep G. Canadell, and Matthias Cuntz
Geosci. Model Dev., 11, 2995–3026, https://doi.org/10.5194/gmd-11-2995-2018, https://doi.org/10.5194/gmd-11-2995-2018, 2018
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CABLE is a terrestrial biosphere model that can be applied stand-alone and provides for land surface–atmosphere exchange within a climate model. We extend CABLE for regional and global carbon–climate simulations, accounting for land use and land cover change mediated by tree demography. A novel algorithm to simulate the coordination of rate-limiting photosynthetic processes is also implemented. Simulations satisfy multiple observational constraints on the global land carbon cycle.
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.
Yasmina Loozen, Karin T. Rebel, Derek Karssenberg, Martin J. Wassen, Jordi Sardans, Josep Peñuelas, and Steven M. De Jong
Biogeosciences, 15, 2723–2742, https://doi.org/10.5194/bg-15-2723-2018, https://doi.org/10.5194/bg-15-2723-2018, 2018
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Nitrogen (N) is an essential nutrient for plant growth. It would be interesting to detect it using satellite data. The goal was to investigate if it is possible to remotely sense the canopy nitrogen concentration and content of Mediterranean trees using a product calculated from satellite reflectance data, the MERIS Terrestrial Chlorophyll Index (MTCI). The tree plots were located in Catalonia, NE Spain. The relationship between MTCI and canopy N was present but dependent on the type of trees.
Marta Camino-Serrano, Bertrand Guenet, Sebastiaan Luyssaert, Philippe Ciais, Vladislav Bastrikov, Bruno De Vos, Bert Gielen, Gerd Gleixner, Albert Jornet-Puig, Klaus Kaiser, Dolly Kothawala, Ronny Lauerwald, Josep Peñuelas, Marion Schrumpf, Sara Vicca, Nicolas Vuichard, David Walmsley, and Ivan A. Janssens
Geosci. Model Dev., 11, 937–957, https://doi.org/10.5194/gmd-11-937-2018, https://doi.org/10.5194/gmd-11-937-2018, 2018
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Global models generally oversimplify the representation of soil organic carbon (SOC), and thus its response to global warming remains uncertain. We present the new soil module ORCHIDEE-SOM, within the global model ORCHIDEE, that refines the representation of SOC dynamics and includes the dissolved organic carbon (DOC) processes. The model is able to reproduce SOC stocks and DOC concentrations in four different ecosystems, opening an opportunity for improved predictions of SOC in global models.
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.
Yi-Ying Chen, Barry Gardiner, Ferenc Pasztor, Kristina Blennow, James Ryder, Aude Valade, Kim Naudts, Juliane Otto, Matthew J. McGrath, Carole Planque, and Sebastiaan Luyssaert
Geosci. Model Dev., 11, 771–791, https://doi.org/10.5194/gmd-11-771-2018, https://doi.org/10.5194/gmd-11-771-2018, 2018
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The inclusion of process-based wind-throw damage simulation in Earth system models has been hampered by the big-leaf approach, which cannot provide the canopy structure information that is required. We adapted the physics from ForestGALES to calculate CWS on large scales. The new model included several numerically efficient solutions, such as handling the landscape heterogeneity, downscaling spatially and temporally aggregated wind fields, and downscaling storm damage within the 2500 km2 pixels.
Chao Yue, Philippe Ciais, Sebastiaan Luyssaert, Wei Li, Matthew J. McGrath, Jinfeng Chang, and Shushi Peng
Geosci. Model Dev., 11, 409–428, https://doi.org/10.5194/gmd-11-409-2018, https://doi.org/10.5194/gmd-11-409-2018, 2018
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Human alteration of land cover has caused CO2 that is stored in forest biomass and soil to be released into the atmosphere and thus contribute to global warming. Global vegetation models that are used to quantify such carbon emissions from land use change traditionally rarely include shifting cultivation and secondary forest age dynamics. In this study, we expanded one vegetation model to include these features. We found that carbon emissions from land use change are estimated to be smaller.
Fei Lun, Junguo Liu, Philippe Ciais, Thomas Nesme, Jinfeng Chang, Rong Wang, Daniel Goll, Jordi Sardans, Josep Peñuelas, and Michael Obersteiner
Earth Syst. Sci. Data, 10, 1–18, https://doi.org/10.5194/essd-10-1-2018, https://doi.org/10.5194/essd-10-1-2018, 2018
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We quantified in detail the P budgets in agricultural systems and PUE on global, regional, and national scales from 2002 to 2010. Globally, half of the total P inputs into agricultural systems accumulated in agricultural soils, with the rest lost to bodies of water. There are great differences in P budgets and PUE in agricultural systems on global, regional, and national scales. International trade played a significant role in P redistribution and P in fertilizer and food among countries.
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gabriel Abramowitz, Martin G. De Kauwe, Bradley Evans, Vanessa Haverd, Longhui Li, Caitlin Moore, Youngryel Ryu, Simon Scheiter, Stanislaus J. Schymanski, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 14, 4711–4732, https://doi.org/10.5194/bg-14-4711-2017, https://doi.org/10.5194/bg-14-4711-2017, 2017
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This paper attempts to review some of the current challenges faced by the modelling community in simulating the behaviour of savanna ecosystems. We provide a particular focus on three dynamic processes (phenology, root-water access, and fire) that are characteristic of savannas, which we believe are not adequately represented in current-generation terrestrial biosphere models. We highlight reasons for these misrepresentations, possible solutions and a future direction for research in this area.
Daniel S. Goll, Nicolas Vuichard, Fabienne Maignan, Albert Jornet-Puig, Jordi Sardans, Aurelie Violette, Shushi Peng, Yan Sun, Marko Kvakic, Matthieu Guimberteau, Bertrand Guenet, Soenke Zaehle, Josep Penuelas, Ivan Janssens, and Philippe Ciais
Geosci. Model Dev., 10, 3745–3770, https://doi.org/10.5194/gmd-10-3745-2017, https://doi.org/10.5194/gmd-10-3745-2017, 2017
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We describe a representation of the terrestrial phosphorus cycle for the ORCHIDEE land surface model. The model is able to reproduce the observed shift from nitrogen to phosphorus limited net primary productivity along a soil formation chronosequence in Hawaii, as well as the contrasting responses of net primary productivity to nutrient addition. However, the simulated nutrient use efficiencies are lower, as observed primarily due to biases in the nutrient content and turnover of woody biomass.
Thomas Kaminski, Bernard Pinty, Michael Voßbeck, Maciej Lopatka, Nadine Gobron, and Monica Robustelli
Biogeosciences, 14, 2527–2541, https://doi.org/10.5194/bg-14-2527-2017, https://doi.org/10.5194/bg-14-2527-2017, 2017
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We present the Joint Research Centre Two-stream Inversion Package (JRC-TIP) for retrieval of variables characterising the state of the vegetation–soil system. The system provides a set of land surface variables that satisfy all requirements for assimilation into the land component of climate and numerical weather prediction models.
Cathy M. Trudinger, Vanessa Haverd, Peter R. Briggs, and Josep G. Canadell
Biogeosciences, 13, 6363–6383, https://doi.org/10.5194/bg-13-6363-2016, https://doi.org/10.5194/bg-13-6363-2016, 2016
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Semi-arid ecosystems in Australia are responsible for a significant part of the variability in global atmospheric carbon dioxide. Here we use Australian observations to estimate parameters in a land surface model of carbon and water cycles. We quantify the variability in Australian carbon fluxes between 1982 and 2013, including the large uptake in 2011 associated with exceptionally wet conditions following a prolonged drought. We estimate the effect of parameter uncertainty on these estimates.
Palmira Messina, Juliette Lathière, Katerina Sindelarova, Nicolas Vuichard, Claire Granier, Josefine Ghattas, Anne Cozic, and Didier A. Hauglustaine
Atmos. Chem. Phys., 16, 14169–14202, https://doi.org/10.5194/acp-16-14169-2016, https://doi.org/10.5194/acp-16-14169-2016, 2016
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We provide BVOC emissions for the present scenario, employing the updated ORCHIDEE emission module and the MEGAN model. The modelling community still faces the problem of emission model evaluation because of the absence of adequate observations. The accurate analysis performed, employing the two models, allowed the various processes modelled to be investigated, in order to fully understand the origin of the mismatch between the model estimates and to quantify the emission uncertainties.
Matthew J. McGrath, James Ryder, Bernard Pinty, Juliane Otto, Kim Naudts, Aude Valade, Yiying Chen, James Weedon, and Sebastiaan Luyssaert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-280, https://doi.org/10.5194/gmd-2016-280, 2016
Revised manuscript not accepted
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.
Eva van Gorsel, Sebastian Wolf, James Cleverly, Peter Isaac, Vanessa Haverd, Cäcilia Ewenz, Stefan Arndt, Jason Beringer, Víctor Resco de Dios, Bradley J. Evans, Anne Griebel, Lindsay B. Hutley, Trevor Keenan, Natascha Kljun, Craig Macfarlane, Wayne S. Meyer, Ian McHugh, Elise Pendall, Suzanne M. Prober, and Richard Silberstein
Biogeosciences, 13, 5947–5964, https://doi.org/10.5194/bg-13-5947-2016, https://doi.org/10.5194/bg-13-5947-2016, 2016
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Temperature extremes are expected to become more prevalent in the future and understanding ecosystem response is crucial. We synthesised measurements and model results to investigate the effect of a summer heat wave on carbon and water exchange across three biogeographic regions in southern Australia. Forests proved relatively resilient to short-term heat extremes but the response of woodlands indicates that the carbon sinks of large areas of Australia may not be sustainable in a future climate.
Jason Beringer, Lindsay B. Hutley, Ian McHugh, Stefan K. Arndt, David Campbell, Helen A. Cleugh, James Cleverly, Víctor Resco de Dios, Derek Eamus, Bradley Evans, Cacilia Ewenz, Peter Grace, Anne Griebel, Vanessa Haverd, Nina Hinko-Najera, Alfredo Huete, Peter Isaac, Kasturi Kanniah, Ray Leuning, Michael J. Liddell, Craig Macfarlane, Wayne Meyer, Caitlin Moore, Elise Pendall, Alison Phillips, Rebecca L. Phillips, Suzanne M. Prober, Natalia Restrepo-Coupe, Susanna Rutledge, Ivan Schroder, Richard Silberstein, Patricia Southall, Mei Sun Yee, Nigel J. Tapper, Eva van Gorsel, Camilla Vote, Jeff Walker, and Tim Wardlaw
Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, https://doi.org/10.5194/bg-13-5895-2016, 2016
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OzFlux is the regional Australian and New Zealand flux tower network that aims to provide a continental-scale national facility to monitor and assess trends, and improve predictions, of Australia’s terrestrial biosphere and climate. We describe the evolution, design, and status as well as an overview of data processing. We suggest that a synergistic approach is required to address all of the spatial, ecological, human, and cultural challenges of managing Australian ecosystems.
Marta Camino-Serrano, Elisabeth Graf Pannatier, Sara Vicca, Sebastiaan Luyssaert, Mathieu Jonard, Philippe Ciais, Bertrand Guenet, Bert Gielen, Josep Peñuelas, Jordi Sardans, Peter Waldner, Sophia Etzold, Guia Cecchini, Nicholas Clarke, Zoran Galić, Laure Gandois, Karin Hansen, Jim Johnson, Uwe Klinck, Zora Lachmanová, Antti-Jussi Lindroos, Henning Meesenburg, Tiina M. Nieminen, Tanja G. M. Sanders, Kasia Sawicka, Walter Seidling, Anne Thimonier, Elena Vanguelova, Arne Verstraeten, Lars Vesterdal, and Ivan A. Janssens
Biogeosciences, 13, 5567–5585, https://doi.org/10.5194/bg-13-5567-2016, https://doi.org/10.5194/bg-13-5567-2016, 2016
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We investigated the long-term trends of dissolved organic carbon (DOC) in soil solution and the drivers of changes in over 100 forest monitoring plots across Europe. An overall increasing trend was detected in the organic layers, but no overall trend was found in the mineral horizons. There are strong interactions between controls acting at local and regional scales. Our findings are relevant for researchers focusing on the link between terrestrial and aquatic ecosystems and for C-cycle models.
Natasha MacBean, Philippe Peylin, Frédéric Chevallier, Marko Scholze, and Gregor Schürmann
Geosci. Model Dev., 9, 3569–3588, https://doi.org/10.5194/gmd-9-3569-2016, https://doi.org/10.5194/gmd-9-3569-2016, 2016
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Model projections of the response of the terrestrial biosphere to anthropogenic emissions are uncertain, in part due to unknown fixed parameters in a model. Data assimilation can address this by using observations to optimise these parameter values. Using multiple types of data is beneficial for constraining different model processes, but it can also pose challenges in a DA context. This paper demonstrates and discusses the issues involved using toy models and examples from existing literature.
Philippe Peylin, Cédric Bacour, Natasha MacBean, Sébastien Leonard, Peter Rayner, Sylvain Kuppel, Ernest Koffi, Abdou Kane, Fabienne Maignan, Frédéric Chevallier, Philippe Ciais, and Pascal Prunet
Geosci. Model Dev., 9, 3321–3346, https://doi.org/10.5194/gmd-9-3321-2016, https://doi.org/10.5194/gmd-9-3321-2016, 2016
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The study describes a carbon cycle data assimilation system that uses satellite observations of vegetation activity, net ecosystem exchange of carbon and water at many sites and atmospheric CO2 concentrations, in order to optimize the parameters of the ORCHIDEE land surface model. The optimized model is able to fit all three data streams leading to a land carbon uptake similar to independent estimates, which opens new perspectives for better prediction of the land carbon balance.
Vanessa Haverd, Matthias Cuntz, Lars P. Nieradzik, and Ian N. Harman
Geosci. Model Dev., 9, 3111–3122, https://doi.org/10.5194/gmd-9-3111-2016, https://doi.org/10.5194/gmd-9-3111-2016, 2016
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CABLE is a global land surface model, which has been used extensively in offline and coupled simulations. We improve CABLE’s simulation of evaporation using a new scheme for drought response and a physically accurate representation of coupled energy and water fluxes in the soil. Marked improvements in predictions of evaporation are demonstrated globally. Results highlight the important roles of deep soil moisture in mediating drought response and litter in dampening soil evaporation.
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.
Yiying Chen, James Ryder, Vladislav Bastrikov, Matthew J. McGrath, Kim Naudts, Juliane Otto, Catherine Ottlé, Philippe Peylin, Jan Polcher, Aude Valade, Andrew Black, Jan A. Elbers, Eddy Moors, Thomas Foken, Eva van Gorsel, Vanessa Haverd, Bernard Heinesch, Frank Tiedemann, Alexander Knohl, Samuli Launiainen, Denis Loustau, Jérôme Ogée, Timo Vessala, and Sebastiaan Luyssaert
Geosci. Model Dev., 9, 2951–2972, https://doi.org/10.5194/gmd-9-2951-2016, https://doi.org/10.5194/gmd-9-2951-2016, 2016
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In this study, we compiled a set of within-canopy and above-canopy measurements of energy and water fluxes, and used these data to parametrize and validate the new multi-layer energy budget scheme for a range of forest types. An adequate parametrization approach has been presented for the global-scale land surface model (ORCHIDEE-CAN). Furthermore, model performance of the new multi-layer parametrization was compared against the existing single-layer scheme.
Albert Rivas-Ubach, Yina Liu, Jordi Sardans, Malak M. Tfaily, Young-Mo Kim, Eric Bourrianne, Ljiljana Paša-Tolić, Josep Peñuelas, and Alex Guenther
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-209, https://doi.org/10.5194/amt-2016-209, 2016
Revised manuscript not accepted
Anna B. Harper, Peter M. Cox, Pierre Friedlingstein, Andy J. Wiltshire, Chris D. Jones, Stephen Sitch, Lina M. Mercado, Margriet Groenendijk, Eddy Robertson, Jens Kattge, Gerhard Bönisch, Owen K. Atkin, Michael Bahn, Johannes Cornelissen, Ülo Niinemets, Vladimir Onipchenko, Josep Peñuelas, Lourens Poorter, Peter B. Reich, Nadjeda A. Soudzilovskaia, and Peter van Bodegom
Geosci. Model Dev., 9, 2415–2440, https://doi.org/10.5194/gmd-9-2415-2016, https://doi.org/10.5194/gmd-9-2415-2016, 2016
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Dynamic global vegetation models (DGVMs) are used to predict the response of vegetation to climate change. We improved the representation of carbon uptake by ecosystems in a DGVM by including a wider range of trade-offs between nutrient allocation to photosynthetic capacity and leaf structure, based on observed plant traits from a worldwide data base. The improved model has higher rates of photosynthesis and net C uptake by plants, and more closely matches observations at site and global scales.
Jinfeng Chang, Philippe Ciais, Mario Herrero, Petr Havlik, Matteo Campioli, Xianzhou Zhang, Yongfei Bai, Nicolas Viovy, Joanna Joiner, Xuhui Wang, Shushi Peng, Chao Yue, Shilong Piao, Tao Wang, Didier A. Hauglustaine, Jean-Francois Soussana, Anna Peregon, Natalya Kosykh, and Nina Mironycheva-Tokareva
Biogeosciences, 13, 3757–3776, https://doi.org/10.5194/bg-13-3757-2016, https://doi.org/10.5194/bg-13-3757-2016, 2016
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We derived the global maps of grassland management intensity of 1901–2012, including the minimum area of managed grassland with fraction of mown/grazed part. These maps, to our knowledge for the first time, provide global, time-dependent information for drawing up global estimates of management impact on biomass production and yields and for global vegetation models to enable simulations of carbon stocks and GHG budgets beyond simple tuning of grassland productivities to account for management.
Lin Wu, Grégoire Broquet, Philippe Ciais, Valentin Bellassen, Felix Vogel, Frédéric Chevallier, Irène Xueref-Remy, and Yilong Wang
Atmos. Chem. Phys., 16, 7743–7771, https://doi.org/10.5194/acp-16-7743-2016, https://doi.org/10.5194/acp-16-7743-2016, 2016
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This paper advances atmospheric inversion of city CO2 emissions as follows: (1) illustrate how inversion methodology can be tailored to deal with very large urban networks of sensors measuring CO2 concentrations; (2) demonstrate that atmospheric inversion could be a relevant tool of Monitoring, Reporting and Verification (MRV) of city CO2 emissions; (3) clarify the theoretical potential of inversion for reducing uncertainties in the estimates of citywide total and sectoral CO2 emissions.
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gab Abramowitz, Martin G. De Kauwe, Remko Duursma, Bradley Evans, Vanessa Haverd, Longhui Li, Youngryel Ryu, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 13, 3245–3265, https://doi.org/10.5194/bg-13-3245-2016, https://doi.org/10.5194/bg-13-3245-2016, 2016
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In this study we assess how well terrestrial biosphere models perform at predicting water and carbon cycling for savanna ecosystems. We apply our models to five savanna sites in Northern Australia and highlight key causes for model failure. Our assessment of model performance uses a novel benchmarking system that scores a model’s predictive ability based on how well it is utilizing its driving information. On average, we found the models as a group display only moderate levels of performance.
X. Wu, N. Vuichard, P. Ciais, N. Viovy, N. de Noblet-Ducoudré, X. Wang, V. Magliulo, M. Wattenbach, L. Vitale, P. Di Tommasi, E. J. Moors, W. Jans, J. Elbers, E. Ceschia, T. Tallec, C. Bernhofer, T. Grünwald, C. Moureaux, T. Manise, A. Ligne, P. Cellier, B. Loubet, E. Larmanou, and D. Ripoche
Geosci. Model Dev., 9, 857–873, https://doi.org/10.5194/gmd-9-857-2016, https://doi.org/10.5194/gmd-9-857-2016, 2016
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The response of crops to changing climate and atmospheric CO2 could have large effects on food production, terrestrial carbon, water, energy fluxes and the climate feedbacks. We developed a new process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module into the land surface model ORCHIDEE. Our model has good ability to capture the spatial gradients of crop phenology, carbon and energy-related variables across Europe.
Bertrand Guenet, Fernando Esteban Moyano, Philippe Peylin, Philippe Ciais, and Ivan A Janssens
Geosci. Model Dev., 9, 841–855, https://doi.org/10.5194/gmd-9-841-2016, https://doi.org/10.5194/gmd-9-841-2016, 2016
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We present a simple conceptual model of soil carbon decomposition (PRIM) able to reproduce priming experiments. Parameters were optimized using a Bayesian framework and evaluated against another set of soil incubation. PRIM better fit data than the original, CENTURY-type soil decomposition model. We then compared both models incorporated into the global land biosphere model ORCHIDEE. Both versions reproduced observed decay litter rates, but only ORCHIDEE-PRIM could simulate the observed priming.
V. Haverd, B. Smith, M. Raupach, P. Briggs, L. Nieradzik, J. Beringer, L. Hutley, C. M. Trudinger, and J. Cleverly
Biogeosciences, 13, 761–779, https://doi.org/10.5194/bg-13-761-2016, https://doi.org/10.5194/bg-13-761-2016, 2016
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We present a new approach for modelling coupled phenology and carbon allocation in savannas, and test it using data from the OzFlux network. Model behaviour emerges from complex feedbacks between the plant physiology and vegetation dynamics, in response to resource availability, and not from imposed hypotheses about the controls on tree-grass co-existence. Results indicate that resource limitation is a stronger determinant of tree cover than disturbance in Australian savannas.
J. Ryder, J. Polcher, P. Peylin, C. Ottlé, Y. Chen, E. van Gorsel, V. Haverd, M. J. McGrath, K. Naudts, J. Otto, A. Valade, and S. Luyssaert
Geosci. Model Dev., 9, 223–245, https://doi.org/10.5194/gmd-9-223-2016, https://doi.org/10.5194/gmd-9-223-2016, 2016
P. Kountouris, C. Gerbig, K.-U. Totsche, A. J. Dolman, A. G. C. A. Meesters, G. Broquet, F. Maignan, B. Gioli, L. Montagnani, and C. Helfter
Biogeosciences, 12, 7403–7421, https://doi.org/10.5194/bg-12-7403-2015, https://doi.org/10.5194/bg-12-7403-2015, 2015
E. D. Schulze, E. Lapshina, I. Filippov, I. Kuhlmann, and D. Mollicone
Biogeosciences, 12, 7057–7070, https://doi.org/10.5194/bg-12-7057-2015, https://doi.org/10.5194/bg-12-7057-2015, 2015
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At the eastern border of the West Siberian Plain, south of permafrost, peat started to accumulate 15000 years ago. Peat accumulated as fen peat at a constant rate of 0.2 mm yr-1 and 0.01 kgC m-2 yr-1. Over the last 2000 years, the bogs have changed into Sphagnum mires, accumulating about 0.1 kgC m-2 yr-1 until the present. The high accumulation rate of peat in unfrozen mires indicates that thawing of permafrost peat may change northern peatlands into long-lasting carbon sinks.
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.
M. J. McGrath, S. Luyssaert, P. Meyfroidt, J. O. Kaplan, M. Bürgi, Y. Chen, K. Erb, U. Gimmi, D. McInerney, K. Naudts, J. Otto, F. Pasztor, J. Ryder, M.-J. Schelhaas, and A. Valade
Biogeosciences, 12, 4291–4316, https://doi.org/10.5194/bg-12-4291-2015, https://doi.org/10.5194/bg-12-4291-2015, 2015
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Studying century-scale ecological processes and their legacy effects requires taking forest management into account. In this study we produce spatially and temporally explicit maps of European forest management from 1600 to 2010. The most important changes between 1600 and 2010 are an increase of 593 000km2 in conifers at the expense of deciduous forest, a 612 000km2 decrease in unmanaged forest, a 152 000km2 decrease in coppice management and a 818 000km2 increase in high stand management.
R. Wang, Y. Balkanski, O. Boucher, L. Bopp, A. Chappell, P. Ciais, D. Hauglustaine, J. Peñuelas, and S. Tao
Atmos. Chem. Phys., 15, 6247–6270, https://doi.org/10.5194/acp-15-6247-2015, https://doi.org/10.5194/acp-15-6247-2015, 2015
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This study makes a first attempt to estimate the temporal trend of Fe emissions from anthropogenic and natural combustion sources from 1960 to 2007 and the emissions of Fe from mineral dust based on a recent mineralogical database. The new emission inventory is introduced into a global aerosol model. The simulated total Fe and soluble Fe concentrations in surface air as well as the deposition of total Fe are evaluated by observations over major continental and oceanic regions globally.
T. De Groote, D. Zona, L. S. Broeckx, M. S. Verlinden, S. Luyssaert, V. Bellassen, N. Vuichard, R. Ceulemans, A. Gobin, and I. A. Janssens
Geosci. Model Dev., 8, 1461–1471, https://doi.org/10.5194/gmd-8-1461-2015, https://doi.org/10.5194/gmd-8-1461-2015, 2015
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This paper describes the modification of the widely used land surface model ORCHIDEE for stand-scale simulations of short rotation coppice (SRC) plantations. The modifications presented in this paper were evaluated using data from two Belgian poplar-based SRC sites, for which multiple measurements and meteorological data were available. The simulations show that the model predicts aboveground biomass production, ecosystem photosynthesis and ecosystem respiration well.
T. Launois, S. Belviso, L. Bopp, C. G. Fichot, and P. Peylin
Atmos. Chem. Phys., 15, 2295–2312, https://doi.org/10.5194/acp-15-2295-2015, https://doi.org/10.5194/acp-15-2295-2015, 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
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.
D. Sperlich, C. T. Chang, J. Peñuelas, C. Gracia, and S. Sabaté
Biogeosciences, 11, 5657–5674, https://doi.org/10.5194/bg-11-5657-2014, https://doi.org/10.5194/bg-11-5657-2014, 2014
I. B. Konovalov, E. V. Berezin, P. Ciais, G. Broquet, M. Beekmann, J. Hadji-Lazaro, C. Clerbaux, M. O. Andreae, J. W. Kaiser, and E.-D. Schulze
Atmos. Chem. Phys., 14, 10383–10410, https://doi.org/10.5194/acp-14-10383-2014, https://doi.org/10.5194/acp-14-10383-2014, 2014
V. Haverd, B. Smith, L. P. Nieradzik, and P. R. Briggs
Biogeosciences, 11, 4039–4055, https://doi.org/10.5194/bg-11-4039-2014, https://doi.org/10.5194/bg-11-4039-2014, 2014
J. P. Greenberg, J. Peñuelas, A. Guenther, R. Seco, A. Turnipseed, X. Jiang, I. Filella, M. Estiarte, J. Sardans, R. Ogaya, J. Llusia, and F. Rapparini
Atmos. Meas. Tech., 7, 2263–2271, https://doi.org/10.5194/amt-7-2263-2014, https://doi.org/10.5194/amt-7-2263-2014, 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
A. Valade, P. Ciais, N. Vuichard, N. Viovy, A. Caubel, N. Huth, F. Marin, and J.-F. Martiné
Geosci. Model Dev., 7, 1225–1245, https://doi.org/10.5194/gmd-7-1225-2014, https://doi.org/10.5194/gmd-7-1225-2014, 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
C. Szczypta, J.-C. Calvet, F. Maignan, W. Dorigo, F. Baret, and P. Ciais
Geosci. Model Dev., 7, 931–946, https://doi.org/10.5194/gmd-7-931-2014, https://doi.org/10.5194/gmd-7-931-2014, 2014
J. Otto, D. Berveiller, F.-M. Bréon, N. Delpierre, G. Geppert, A. Granier, W. Jans, A. Knohl, A. Kuusk, B. Longdoz, E. Moors, M. Mund, B. Pinty, M.-J. Schelhaas, and S. Luyssaert
Biogeosciences, 11, 2411–2427, https://doi.org/10.5194/bg-11-2411-2014, https://doi.org/10.5194/bg-11-2411-2014, 2014
S. Turquety, L. Menut, B. Bessagnet, A. Anav, N. Viovy, F. Maignan, and M. Wooster
Geosci. Model Dev., 7, 587–612, https://doi.org/10.5194/gmd-7-587-2014, https://doi.org/10.5194/gmd-7-587-2014, 2014
A. Loew, P. M. van Bodegom, J.-L. Widlowski, J. Otto, T. Quaife, B. Pinty, and T. Raddatz
Biogeosciences, 11, 1873–1897, https://doi.org/10.5194/bg-11-1873-2014, https://doi.org/10.5194/bg-11-1873-2014, 2014
J. F. Chang, N. Viovy, N. Vuichard, P. Ciais, T. Wang, A. Cozic, R. Lardy, A.-I. Graux, K. Klumpp, R. Martin, and J.-F. Soussana
Geosci. Model Dev., 6, 2165–2181, https://doi.org/10.5194/gmd-6-2165-2013, https://doi.org/10.5194/gmd-6-2165-2013, 2013
B. Guenet, F. E. Moyano, N. Vuichard, G. J. D. Kirk, P. H. Bellamy, S. Zaehle, and P. Ciais
Geosci. Model Dev., 6, 2153–2163, https://doi.org/10.5194/gmd-6-2153-2013, https://doi.org/10.5194/gmd-6-2153-2013, 2013
P. Biber, S. Seifert, M. K. Zaplata, W. Schaaf, H. Pretzsch, and A. Fischer
Biogeosciences, 10, 8283–8303, https://doi.org/10.5194/bg-10-8283-2013, https://doi.org/10.5194/bg-10-8283-2013, 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
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
E. V. Berezin, I. B. Konovalov, P. Ciais, A. Richter, S. Tao, G. Janssens-Maenhout, M. Beekmann, and E.-D. Schulze
Atmos. Chem. Phys., 13, 9415–9438, https://doi.org/10.5194/acp-13-9415-2013, https://doi.org/10.5194/acp-13-9415-2013, 2013
L. M. Verheijen, V. Brovkin, R. Aerts, G. Bönisch, J. H. C. Cornelissen, J. Kattge, P. B. Reich, I. J. Wright, and P. M. van Bodegom
Biogeosciences, 10, 5497–5515, https://doi.org/10.5194/bg-10-5497-2013, https://doi.org/10.5194/bg-10-5497-2013, 2013
R. Seco, J. Peñuelas, I. Filella, J. Llusia, S. Schallhart, A. Metzger, M. Müller, and A. Hansel
Atmos. Chem. Phys., 13, 4291–4306, https://doi.org/10.5194/acp-13-4291-2013, https://doi.org/10.5194/acp-13-4291-2013, 2013
M. Casado, P. Ortega, V. Masson-Delmotte, C. Risi, D. Swingedouw, V. Daux, D. Genty, F. Maignan, O. Solomina, B. Vinther, N. Viovy, and P. Yiou
Clim. Past, 9, 871–886, https://doi.org/10.5194/cp-9-871-2013, https://doi.org/10.5194/cp-9-871-2013, 2013
V. Haverd, M. R. Raupach, P. R. Briggs, J. G. Canadell, P. Isaac, C. Pickett-Heaps, S. H. Roxburgh, E. van Gorsel, R. A. Viscarra Rossel, and Z. Wang
Biogeosciences, 10, 2011–2040, https://doi.org/10.5194/bg-10-2011-2013, https://doi.org/10.5194/bg-10-2011-2013, 2013
M. Schrumpf, K. Kaiser, G. Guggenberger, T. Persson, I. Kögel-Knabner, and E.-D. Schulze
Biogeosciences, 10, 1675–1691, https://doi.org/10.5194/bg-10-1675-2013, https://doi.org/10.5194/bg-10-1675-2013, 2013
V. Haverd, M. R. Raupach, P. R. Briggs, J. G. Canadell., S. J. Davis, R. M. Law, C. P. Meyer, G. P. Peters, C. Pickett-Heaps, and B. Sherman
Biogeosciences, 10, 851–869, https://doi.org/10.5194/bg-10-851-2013, https://doi.org/10.5194/bg-10-851-2013, 2013
M. C. Braakhekke, T. Wutzler, C. Beer, J. Kattge, M. Schrumpf, B. Ahrens, I. Schöning, M. R. Hoosbeek, B. Kruijt, P. Kabat, and M. Reichstein
Biogeosciences, 10, 399–420, https://doi.org/10.5194/bg-10-399-2013, https://doi.org/10.5194/bg-10-399-2013, 2013
Related subject area
Biogeosciences
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCO v4-Hg: the role of surfactants and waves
Lambda-PFLOTRAN 1.0: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie Fisher, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2024-978, https://doi.org/10.5194/egusphere-2024-978, 2024
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land Surface Models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes and variability of carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research on these processes.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-81, https://doi.org/10.5194/gmd-2024-81, 2024
Revised manuscript accepted for GMD
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The estimation of Hg0 fluxes is of great uncertainty due to neglecting wave breaking and sea surfactant. Integrating these factors into MITgcm significantly rise Hg0 transfer velocity. The updated model shows increased fluxes in high wind and wave regions and vice versa, enhancing the spatial heterogeneity. It shows a stronger correlation between Hg0 transfer velocity and wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-34, https://doi.org/10.5194/gmd-2024-34, 2024
Revised manuscript accepted for GMD
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The newly developed Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate respiration and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow via a Jupyter Notebook interface, that digests raw organic matter chemistry data via FTICR-MS, develops the representative reaction network, and completes a biogeochemical simulation with the open source, parallel reactive flow and transport code PFLOTRAN.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
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Short summary
Despite the potential of forest management to mitigate climate change, none of today's predictions of future climate accounts for the impact of forest management. To address this gap in modelling capability, we developed and parametrised a land-surface model to simulate biogeochemical and biophysical effects of forest management. Comparison of model output against data showed an increased model performance in reproducing large-scale spatial patterns and inter-annual variability over Europe.
Despite the potential of forest management to mitigate climate change, none of today's...
