Articles | Volume 11, issue 4
https://doi.org/10.5194/gmd-11-1517-2018
© Author(s) 2018. 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-11-1517-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
17 Apr 2018
Model description paper |
| 17 Apr 2018
Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE v1.0: model description and information content
Alexander J. Norton
CORRESPONDING AUTHOR
School of Earth Sciences, University of Melbourne, Melbourne,
Australia
Peter J. Rayner
School of Earth Sciences, University of Melbourne, Melbourne,
Australia
Ernest N. Koffi
European Commission Joint Research Centre, Ispra, Italy
Marko Scholze
Department of Physical Geography and Ecosystem Science, Lund
University, Lund, Sweden
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Revised manuscript has not been submitted
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Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
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Preprint archived
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Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
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Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Alexander J. Norton, A. Anthony Bloom, Nicholas C. Parazoo, Paul A. Levine, Shuang Ma, Renato K. Braghiere, and T. Luke Smallman
Biogeosciences, 20, 2455–2484, https://doi.org/10.5194/bg-20-2455-2023, https://doi.org/10.5194/bg-20-2455-2023, 2023
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Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
Saqr Munassar, Guillaume Monteil, Marko Scholze, Ute Karstens, Christian Rödenbeck, Frank-Thomas Koch, Kai U. Totsche, and Christoph Gerbig
Atmos. Chem. Phys., 23, 2813–2828, https://doi.org/10.5194/acp-23-2813-2023, https://doi.org/10.5194/acp-23-2813-2023, 2023
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Using different transport models results in large errors in optimized fluxes in the atmospheric inversions. Boundary conditions and inversion system configurations lead to a smaller but non-negligible impact. The findings highlight the importance to validate transport models for further developments but also to properly account for such errors in inverse modelling. This will help narrow the convergence of gas estimates reported in the scientific literature from different inversion frameworks.
Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
Atmos. Chem. Phys., 22, 8897–8934, https://doi.org/10.5194/acp-22-8897-2022, https://doi.org/10.5194/acp-22-8897-2022, 2022
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We study the interannual variability in Australian carbon fluxes for 2015–2019 derived from OCO-2 satellite data. Our results suggest that Australia's semi-arid ecosystems are highly responsive to variations in climate drivers such as rainfall and temperature. We found that high rainfall and low temperatures recorded in 2016 led to an anomalous carbon sink over savanna and sparsely vegetated regions, while unprecedented dry and hot weather in 2019 led to anomalous carbon release.
Zhenyi Chen, Robyn Schofield, Melita Keywood, Sam Cleland, Alastair G. Williams, Alan Griffiths, Stephen Wilson, Peter Rayner, and Xiaowen Shu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-104, https://doi.org/10.5194/acp-2022-104, 2022
Revised manuscript not accepted
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This study studied the marine boundary layer (MBL) process and aerosol properties in the Southern Ocean using miniMPL, ceilometer and sodar. Compared to the gradient method, the Image Edge Detection Algorithm provides more reliable boundary layer height estimations, especially when a convective MBL with stratification existed. The diurnal characteristic of BLH with the veering of the wind vector was also observed. Under the continental sources, the MBL maintained a well-mixed layer of 0.3 km.
Yan Yang, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, John Worden, Gregory R. Quetin, T. Luke Smallman, Mathew Williams, Liang Xu, and Sassan Saatchi
Geosci. Model Dev., 15, 1789–1802, https://doi.org/10.5194/gmd-15-1789-2022, https://doi.org/10.5194/gmd-15-1789-2022, 2022
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Global carbon and water have large uncertainties that are hard to quantify in current regional and global models. Field observations provide opportunities for better calibration and validation of current modeling of carbon and water. With the unique structure of CARDAMOM, we have utilized the data assimilation capability and designed the benchmarking framework by using field observations in modeling. Results show that data assimilation improves model performance in different aspects.
Stephanie G. Stettz, Nicholas C. Parazoo, A. Anthony Bloom, Peter D. Blanken, David R. Bowling, Sean P. Burns, Cédric Bacour, Fabienne Maignan, Brett Raczka, Alexander J. Norton, Ian Baker, Mathew Williams, Mingjie Shi, Yongguang Zhang, and Bo Qiu
Biogeosciences, 19, 541–558, https://doi.org/10.5194/bg-19-541-2022, https://doi.org/10.5194/bg-19-541-2022, 2022
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Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
Atmos. Chem. Phys., 21, 17453–17494, https://doi.org/10.5194/acp-21-17453-2021, https://doi.org/10.5194/acp-21-17453-2021, 2021
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Semi-arid ecosystems such as those in Australia are evolving and might play an essential role in the future of climate change. We use carbon dioxide concentrations derived from the OCO-2 satellite instrument and a regional transport model to understand if Australia was a carbon sink or source of CO2 in 2015. Our research's main findings suggest that Australia acted as a carbon sink of about −0.41 ± 0.08 petagrams of carbon in 2015, driven primarily by savanna and sparsely vegetated ecosystems.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
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We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Guillaume Monteil and Marko Scholze
Geosci. Model Dev., 14, 3383–3406, https://doi.org/10.5194/gmd-14-3383-2021, https://doi.org/10.5194/gmd-14-3383-2021, 2021
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LUMIA is a Python library for atmospheric inversions, originally developed at Lund University to perform regional atmospheric CO2 inversions. The inversions rely on coupling the regional transport model FLEXPART and the global transport model TM5. The paper presents the modeling setup and some first results, and it introduces the LUMIA Python package as a toolbox for inversions beyond the use case presented in the paper.
Ana Maria Roxana Petrescu, Matthew J. McGrath, Robbie M. Andrew, Philippe Peylin, Glen P. Peters, Philippe Ciais, Gregoire Broquet, Francesco N. Tubiello, Christoph Gerbig, Julia Pongratz, Greet Janssens-Maenhout, Giacomo Grassi, Gert-Jan Nabuurs, Pierre Regnier, Ronny Lauerwald, Matthias Kuhnert, Juraj Balkovič, Mart-Jan Schelhaas, Hugo A. C. Denier van der
Gon, Efisio Solazzo, Chunjing Qiu, Roberto Pilli, Igor B. Konovalov, Richard A. Houghton, Dirk Günther, Lucia Perugini, Monica Crippa, Raphael Ganzenmüller, Ingrid T. Luijkx, Pete Smith, Saqr Munassar, Rona L. Thompson, Giulia Conchedda, Guillaume Monteil, Marko Scholze, Ute Karstens, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2363–2406, https://doi.org/10.5194/essd-13-2363-2021, https://doi.org/10.5194/essd-13-2363-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CO2 fossil emissions and CO2 land fluxes in the EU27+UK. The data integrate recent emission inventories with ecosystem data, land carbon models and regional/global inversions for the European domain, aiming at reconciling CO2 estimates with official country-level UNFCCC national GHG inventories in support to policy and facilitating real-time verification procedures.
Guillaume Monteil, Grégoire Broquet, Marko Scholze, Matthew Lang, Ute Karstens, Christoph Gerbig, Frank-Thomas Koch, Naomi E. Smith, Rona L. Thompson, Ingrid T. Luijkx, Emily White, Antoon Meesters, Philippe Ciais, Anita L. Ganesan, Alistair Manning, Michael Mischurow, Wouter Peters, Philippe Peylin, Jerôme Tarniewicz, Matt Rigby, Christian Rödenbeck, Alex Vermeulen, and Evie M. Walton
Atmos. Chem. Phys., 20, 12063–12091, https://doi.org/10.5194/acp-20-12063-2020, https://doi.org/10.5194/acp-20-12063-2020, 2020
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The paper presents the first results from the EUROCOM project, a regional atmospheric inversion intercomparison exercise involving six European research groups. It aims to produce an estimate of the net carbon flux between the European terrestrial ecosystems and the atmosphere for the period 2006–2015, based on constraints provided by observed CO2 concentrations and using inverse modelling techniques. The use of six different models enables us to investigate the robustness of the results.
Malte Meinshausen, Zebedee R. J. Nicholls, Jared Lewis, Matthew J. Gidden, Elisabeth Vogel, Mandy Freund, Urs Beyerle, Claudia Gessner, Alexander Nauels, Nico Bauer, Josep G. Canadell, John S. Daniel, Andrew John, Paul B. Krummel, Gunnar Luderer, Nicolai Meinshausen, Stephen A. Montzka, Peter J. Rayner, Stefan Reimann, Steven J. Smith, Marten van den Berg, Guus J. M. Velders, Martin K. Vollmer, and Ray H. J. Wang
Geosci. Model Dev., 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, https://doi.org/10.5194/gmd-13-3571-2020, 2020
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This study provides the future greenhouse gas (GHG) concentrations under the new set of so-called SSP scenarios (the successors of the IPCC SRES and previous representative concentration pathway (RCP) scenarios). The projected CO2 concentrations range from 350 ppm for low-emission scenarios by 2150 to more than 2000 ppm under the high-emission scenarios. We also provide concentrations, latitudinal gradients, and seasonality for most of the other 42 considered GHGs.
Yohanna Villalobos, Peter Rayner, Steven Thomas, and Jeremy Silver
Atmos. Chem. Phys., 20, 8473–8500, https://doi.org/10.5194/acp-20-8473-2020, https://doi.org/10.5194/acp-20-8473-2020, 2020
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Estimated carbon fluxes for Australia are subject to considerable uncertainty. We ran simulation experiments over Australia to determine how much these uncertainties can be constrained using satellite data. We found that the satellite data has the potential to reduce these uncertainties up to 80 % across the whole continent. For 1 month, this percentage corresponds to 0.51 Pg C y-1 for Australia. This method could lead to significantly more accurate estimates of Australia's carbon budget.
Nicholas C. Parazoo, Troy Magney, Alex Norton, Brett Raczka, Cédric Bacour, Fabienne Maignan, Ian Baker, Yongguang Zhang, Bo Qiu, Mingjie Shi, Natasha MacBean, Dave R. Bowling, Sean P. Burns, Peter D. Blanken, Jochen Stutz, Katja Grossmann, and Christian Frankenberg
Biogeosciences, 17, 3733–3755, https://doi.org/10.5194/bg-17-3733-2020, https://doi.org/10.5194/bg-17-3733-2020, 2020
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Satellite measurements of solar-induced chlorophyll fluorescence (SIF) provide a global measure of photosynthetic change. This enables scientists to better track carbon cycle responses to environmental change and tune biochemical processes in vegetation models for an improved simulation of future change. We use tower-instrumented SIF measurements and controlled model experiments to assess the state of the art in terrestrial biosphere SIF modeling and find a wide range of sensitivities to light.
Peter Rayner
Atmos. Chem. Phys., 20, 3725–3737, https://doi.org/10.5194/acp-20-3725-2020, https://doi.org/10.5194/acp-20-3725-2020, 2020
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This work extends previous calculations of carbon dioxide sources and sinks to take account of the varying quality of atmospheric models. It uses an extended version of Bayesian statistics which includes the model as one of the unknowns. I performed the work as an example of including the model in the description of the uncertainty.
Peter J. Rayner, Anna M. Michalak, and Frédéric Chevallier
Atmos. Chem. Phys., 19, 13911–13932, https://doi.org/10.5194/acp-19-13911-2019, https://doi.org/10.5194/acp-19-13911-2019, 2019
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This paper describes the methods for combining models and data to understand how nutrients and pollutants move through natural systems. The methods are analogous to the process of weather forecasting in which previous information is combined with new observations and a model to improve our knowledge of the internal state of the physical system. The methods appear highly diverse but the paper shows that they are all examples of a single underlying formalism.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences, 16, 3069–3093, https://doi.org/10.5194/bg-16-3069-2019, https://doi.org/10.5194/bg-16-3069-2019, 2019
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This study presents an estimate of global terrestrial photosynthesis. We make use of satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and estimate photosynthetic carbon uptake. This new framework incorporates nonlinear, process-based understanding of the link between fluorescence and photosynthesis, an advance on past approaches. This will aid in the utility of fluorescence to quantify terrestrial carbon cycle feedbacks.
Alecia Nickless, Peter J. Rayner, Robert J. Scholes, Francois Engelbrecht, and Birgit Erni
Atmos. Chem. Phys., 19, 7789–7816, https://doi.org/10.5194/acp-19-7789-2019, https://doi.org/10.5194/acp-19-7789-2019, 2019
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Different frameworks for an atmospheric inversion study over Cape Town, South Africa, are considered. We focused particularly on how sensitive the estimates of CO2 fluxes were to changes in the way the uncertainty in these estimates was specified and the impact different prior information had on the final flux estimates. We used atmospheric measurements from two new sites located near Cape Town: Robben Island and Hangklip lighthouses, which were specifically deployed for this inversion study.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-270, https://doi.org/10.5194/bg-2018-270, 2018
Revised manuscript has not been submitted
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This study presents a global estimate of land carbon uptake through photosynthesis. We make use satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and then use the optimized model to estimate photosynthetic carbon uptake. This provides a new tool that can combine measurements and observations in a systematic way and maximise the use of chlorophyll fluorescence to improve our understanding of the land carbon cycle.
Alecia Nickless, Peter J. Rayner, Francois Engelbrecht, Ernst-Günther Brunke, Birgit Erni, and Robert J. Scholes
Atmos. Chem. Phys., 18, 4765–4801, https://doi.org/10.5194/acp-18-4765-2018, https://doi.org/10.5194/acp-18-4765-2018, 2018
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Carbon dioxide emissions and uptake were estimated for Cape Town, South Africa. We placed two high-precision analysers in lighthouses located on either end of Cape Town (Robben Island and Hangklip). The Cape Point GAW station provided background measurements. We were able to improve the agreement between modelled and observed concentrations, relative to initial estimates provided. This methodology could potentially be scaled up to provide monitoring and verification of city emissions.
Thomas Kaminski and Peter Julian Rayner
Biogeosciences, 14, 4755–4766, https://doi.org/10.5194/bg-14-4755-2017, https://doi.org/10.5194/bg-14-4755-2017, 2017
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Observations can reduce uncertainties in past, current, and predicted natural and anthropogenic CO2 fluxes. They provide independent information for verification of actions as requested by the Paris Agreement. Quantitative network design (QND) is an objective approach to optimise in situ networks and space missions to achieve an optimal use of the observational capabilities. We describe recent progress and advocate an integrated QND system that simultaneously evaluates multiple data streams.
Marko Scholze, Michael Buchwitz, Wouter Dorigo, Luis Guanter, and Shaun Quegan
Biogeosciences, 14, 3401–3429, https://doi.org/10.5194/bg-14-3401-2017, https://doi.org/10.5194/bg-14-3401-2017, 2017
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This paper briefly reviews data assimilation techniques in carbon cycle data assimilation and the requirements of data assimilation systems on observations. We provide a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation, focussing on relevant space-based observations.
Malte Meinshausen, Elisabeth Vogel, Alexander Nauels, Katja Lorbacher, Nicolai Meinshausen, David M. Etheridge, Paul J. Fraser, Stephen A. Montzka, Peter J. Rayner, Cathy M. Trudinger, Paul B. Krummel, Urs Beyerle, Josep G. Canadell, John S. Daniel, Ian G. Enting, Rachel M. Law, Chris R. Lunder, Simon O'Doherty, Ron G. Prinn, Stefan Reimann, Mauro Rubino, Guus J. M. Velders, Martin K. Vollmer, Ray H. J. Wang, and Ray Weiss
Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, https://doi.org/10.5194/gmd-10-2057-2017, 2017
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Climate change is primarily driven by human-induced increases of greenhouse gas (GHG) concentrations. Based on ongoing community efforts (e.g. AGAGE and NOAA networks, ice cores), this study presents historical concentrations of CO2, CH4, N2O and 40 other GHGs from year 0 to year 2014. The data is recommended as input for climate models for pre-industrial, historical runs under CMIP6. Global means, but also latitudinal by monthly surface concentration fields are provided.
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.
Cathy M. Trudinger, Paul J. Fraser, David M. Etheridge, William T. Sturges, Martin K. Vollmer, Matt Rigby, Patricia Martinerie, Jens Mühle, David R. Worton, Paul B. Krummel, L. Paul Steele, Benjamin R. Miller, Johannes Laube, Francis S. Mani, Peter J. Rayner, Christina M. Harth, Emmanuel Witrant, Thomas Blunier, Jakob Schwander, Simon O'Doherty, and Mark Battle
Atmos. Chem. Phys., 16, 11733–11754, https://doi.org/10.5194/acp-16-11733-2016, https://doi.org/10.5194/acp-16-11733-2016, 2016
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Perfluorocarbons (PFCs) are potent, long-lived and mostly man-made greenhouse gases released to the atmosphere mainly during aluminium production and semiconductor manufacture. Here we present the first continuous histories of three PFCs from 1800 to 2014, derived from measurements of these PFCs in the atmosphere and in air bubbles in polar ice. The records show how human actions have affected these important greenhouse gases over the past century.
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.
Denis M. O'Brien, Igor N. Polonsky, Steven R. Utembe, and Peter J. Rayner
Atmos. Meas. Tech., 9, 4633–4654, https://doi.org/10.5194/amt-9-4633-2016, https://doi.org/10.5194/amt-9-4633-2016, 2016
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The accuracy with which emissions of CO2, CH4 and CO from a complex city can be estimated from geostationary orbit is assessed via numerical experiment. Sources of the gases, meteorology, clouds and aerosols over the city are simulated, as are spectra of reflected sunlight in absorption bands of the gases. Gas concentrations are estimated from the spectra, and source distributions from the concentrations. Comparison of estimated and true sources measures the accuracy of the observing system.
E. N. Koffi, P. Bergamaschi, U. Karstens, M. Krol, A. Segers, M. Schmidt, I. Levin, A. T. Vermeulen, R. E. Fisher, V. Kazan, H. Klein Baltink, D. Lowry, G. Manca, H. A. J. Meijer, J. Moncrieff, S. Pal, M. Ramonet, H. A. Scheeren, and A. G. Williams
Geosci. Model Dev., 9, 3137–3160, https://doi.org/10.5194/gmd-9-3137-2016, https://doi.org/10.5194/gmd-9-3137-2016, 2016
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We evaluate the capability of the TM5 model to reproduce observations of the boundary layer dynamics and the associated variability of trace gases close to the surface, using 222Rn. Focusing on the European scale, we compare the TM5 boundary layer heights with observations from radiosondes, lidar, and ceilometer. Furthermore, we compare TM5 simulations of 222Rn activity concentrations, using a novel, process-based 222Rn flux map over Europe, with 222Rn harmonized measurements from 10 stations.
Cindy Cressot, Isabelle Pison, Peter J. Rayner, Philippe Bousquet, Audrey Fortems-Cheiney, and Frédéric Chevallier
Atmos. Chem. Phys., 16, 9089–9108, https://doi.org/10.5194/acp-16-9089-2016, https://doi.org/10.5194/acp-16-9089-2016, 2016
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Several hypothesis have been made to attribute current trends in atmospheric methane to particular regions. In this context, this work aims at evaluating how well anomalies in methane emissions can be detected at the regional scale with currently available observing systems: two space-borne instruments and a surface network. Our results show that inter-annual analyses of methane emissions inferred by atmospheric inversions should always include an uncertainty assessment.
Peter Rayner, Anna M. Michalak, and Frédéric Chevallier
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-148, https://doi.org/10.5194/gmd-2016-148, 2016
Revised manuscript not accepted
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Numerical models are among our most important tools for understanding and prediction. Models include quantities or equations that we cannot verify directly. We learn about these unknowns by comparing model output with observations and using some algorithm to improve the inputs. We show here that the many methods for doing this are special cases of underlying statistics. This provides a unified way of comparing and contrasting such methods.
Xia Zhang, Kevin R. Gurney, Peter Rayner, David Baker, and Yu-ping Liu
Atmos. Chem. Phys., 16, 1907–1918, https://doi.org/10.5194/acp-16-1907-2016, https://doi.org/10.5194/acp-16-1907-2016, 2016
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This study presents a complete exploration of the space/time effect of time variations (diurnal, weekly, monthly) in fossil fuel emission on CO2 concentration. The paper identified rectifier effect at local to regional scale that is expected from fossil fuel emission and compared to biospheric rectification, and then extends the subject to column measurement. This study demonstrates the importance of considering sub-annual fossil fuel emissions on model simulation and related studies.
T. Ziehn, R. M. Law, P. J. Rayner, and G. Roff
Geosci. Instrum. Method. Data Syst., 5, 1–15, https://doi.org/10.5194/gi-5-1-2016, https://doi.org/10.5194/gi-5-1-2016, 2016
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This study investigates the optimal location of greenhouse gas (GHG) measurement stations in Australia in order to derive GHG flux estimates from concentration measurements. We find that an optimal network designed for CO2 also performs well for other GHGs such as CH4 and N2O due to large similarities in the flux pattern for each of the three GHGs. Economic costs (i.e. maintenance costs) can be halved by selecting stations closer to the base laboratory with only a slight decrease in performance.
E. N. Koffi, P. J. Rayner, A. J. Norton, C. Frankenberg, and M. Scholze
Biogeosciences, 12, 4067–4084, https://doi.org/10.5194/bg-12-4067-2015, https://doi.org/10.5194/bg-12-4067-2015, 2015
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We investigate the utility of satellite measurements of solar-induced chlorophyll fluorescence (SIF) in constraining gross primary productivity (GPP). We simulate SIF with the biosphere model BETHY coupled with the fluorescence model SCOPE. The model simulates well the patterns of SIF. SIF is sensitive to leaf chlorophyll and incoming radiation but not to the key physiological parameter Vcmax controlling GPP. Thus, further model development is necessary before SIF can be used to constrain GPP.
S. Crowell, P. Rayner, S. Zaccheo, and B. Moore
Atmos. Meas. Tech., 8, 2685–2697, https://doi.org/10.5194/amt-8-2685-2015, https://doi.org/10.5194/amt-8-2685-2015, 2015
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We derive a yes/no requirement for the usefulness of an O2 lidar as part of the ASCENDS mission that incorporates errors due to atmospheric state misspecification as well as instrumental noise. We find that the larger the CO2 instrument's sensitivity to surface pressure errors, the lower the precision requirement for the O2 instrument to be useful. In particular, the 2um CO2 instrument would benefit the most from the inclusion of an O2 lidar with high precision retrievals of surface pressure.
A. Nickless, T. Ziehn, P.J. Rayner, R.J. Scholes, and F. Engelbrecht
Atmos. Chem. Phys., 15, 2051–2069, https://doi.org/10.5194/acp-15-2051-2015, https://doi.org/10.5194/acp-15-2051-2015, 2015
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This study aims to provide an optimal network design for the placement of new atmospheric monitoring stations around South Africa, to best estimate the emission and uptake of carbon dioxide fluxes due to both anthropogenic and natural sources. In addition, a sensitivity analysis was performed on the impact that certain parameters would have on the final network solution, considering the inverse modelling framework, the transport model and the use of a different optimisation routine.
P. J. Rayner, A. Stavert, M. Scholze, A. Ahlström, C. E. Allison, and R. M. Law
Biogeosciences, 12, 835–844, https://doi.org/10.5194/bg-12-835-2015, https://doi.org/10.5194/bg-12-835-2015, 2015
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Recent papers suggest a slow-down in the natural uptake of
anthropogenic CO2. We analyse recent trends in atmospheric concentration and
known inputs to test for such a slow-down. We see, rather, an increase
in uptake compared to a simple response to changing CO2 concentration. Using atmospheric models and statistical techniques we isolate this increased uptake to the northern temperate and boreal continents during summer, suggesting a stronger growing season.
S. R. Utembe, N. Jones, P. J. Rayner, I. Genkova, D. W. T. Griffith, D. M. O'Brien, C. Lunney, and A. J. Clark
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-31551-2014, https://doi.org/10.5194/acpd-14-31551-2014, 2014
Revised manuscript not accepted
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A methodology to estimate CO2 emissions from an isolated power plant
is presented and illustrated for a power station in South Australia. It involves measurement of in-situ and column-averaged CO2 near the power plant, forward modelling of the observed signals (using WRF-Chem) and inverse modelling to obtain an estimate of the power plant fluxes. Better simulation is obtained for column data giving better estimates of fluxes. Our estimated emissions are within 6% of the reported values.
X. Zhang, K. R. Gurney, P. Rayner, Y. Liu, and S. Asefi-Najafabady
Geosci. Model Dev., 7, 2867–2874, https://doi.org/10.5194/gmd-7-2867-2014, https://doi.org/10.5194/gmd-7-2867-2014, 2014
P. J. Rayner, S. R. Utembe, and S. Crowell
Atmos. Meas. Tech., 7, 3285–3293, https://doi.org/10.5194/amt-7-3285-2014, https://doi.org/10.5194/amt-7-3285-2014, 2014
T. Ziehn, A. Nickless, P. J. Rayner, R. M. Law, G. Roff, and P. Fraser
Atmos. Chem. Phys., 14, 9363–9378, https://doi.org/10.5194/acp-14-9363-2014, https://doi.org/10.5194/acp-14-9363-2014, 2014
S. Kemp, M. Scholze, T. Ziehn, and T. Kaminski
Geosci. Model Dev., 7, 1609–1619, https://doi.org/10.5194/gmd-7-1609-2014, https://doi.org/10.5194/gmd-7-1609-2014, 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
P. Peylin, R. M. Law, K. R. Gurney, F. Chevallier, A. R. Jacobson, T. Maki, Y. Niwa, P. K. Patra, W. Peters, P. J. Rayner, C. Rödenbeck, I. T. van der Laan-Luijkx, and X. Zhang
Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, https://doi.org/10.5194/bg-10-6699-2013, 2013
S. M. Burrows, P. J. Rayner, T. Butler, and M. G. Lawrence
Atmos. Chem. Phys., 13, 5473–5488, https://doi.org/10.5194/acp-13-5473-2013, https://doi.org/10.5194/acp-13-5473-2013, 2013
C. M. Trudinger, I. G. Enting, P. J. Rayner, D. M. Etheridge, C. Buizert, M. Rubino, P. B. Krummel, and T. Blunier
Atmos. Chem. Phys., 13, 1485–1510, https://doi.org/10.5194/acp-13-1485-2013, https://doi.org/10.5194/acp-13-1485-2013, 2013
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Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
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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
Parameterisation toolbox for physical-biogeochemical model compatible with FABM. Case study: the coupled 1D GOTM-ECOSMO E2E for the Sylt-Romo Bight, North Sea
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
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NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
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Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
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Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
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We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
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Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
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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 of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-223, https://doi.org/10.5194/gmd-2024-223, 2024
Revised manuscript accepted for GMD
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions have changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
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., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
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.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
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Parameterisation is key in modeling to reproduce observations well but is often done manually. This study presents a Particle Swarm Optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
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Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
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Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
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We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
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.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
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.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
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.
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.
Cited articles
Baker, N. R.: Chlorophyll fluorescence: a probe of photosynthesis in vivo,
Annu. Rev. Plant Biol., 59, 89–113,
https://doi.org/10.1146/annurev.arplant.59.032607.092759, 2008. a
Baldocchi, D., Ryu, Y., and Keenan, T.: Terrestrial Carbon Cycle
Variability, F1000 Research, 5, 2371, https://doi.org/10.12688/f1000research.8962.1,
2016. a
Baldocchi, D. D.: “Breathing” of the terrestrial biosphere: lessons learned
from a global network of carbon dioxide flux measurement systems, Aust. J.
Bot., 56, 1–26, https://doi.org/10.1071/BT07151, 2008. a
Beer, C., Reichstein, M., Tomelleri, E., and Ciais, P.: Terrestrial gross
carbon dioxide uptake: global distribution and covariation with climate,
Science, 329, 834–838, https://doi.org/10.1126/science.1184984, 2010. a, b
Bodman, R. W.: Uncertainty in temperature projections reduced using carbon
cycle and climate observations, Nat. Clim. Change, 3, 725–729,
https://doi.org/10.1038/nclimate1903, 2013. a
Boilley, A. and Wald, L.: Comparison between meteorological re-analyses from
ERA-Interim and MERRA and measurements of daily solar irradiation at
surface, Renew. Energ., 75, 135–143, https://doi.org/10.1016/j.renene.2014.09.042,
2015. a
Carter, G. A., Freedman, A., Kebabian, P. L., and Scott, H. E.: Use of a
prototype instrument to detect short-term changes in solar-excited leaf
fluorescence, Int. J. Remote Sens., 25, 1779–1784,
https://doi.org/10.1080/01431160310001619544, 2004. a
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le
Quéré, C., Myneni, R. B., Piao, S., and Thornton, P.: Carbon
and Other Biogeochemical Cycles, in: Climate Change 2013: The Physical
Science Bases, Contribution of Working Group I to the Fifth Assessment Report
of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F.,
Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A.,
Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge,
465–570, 2013. a, b
Damm, A., Guanter, L., Paul-Limoges, E., van der Tol, C., Hueni, A.,
Buchmann, N., Eugster, W., Ammann, C., and Schaepman, M.: Far-red
sun-induced chlorophyll fluorescence shows ecosystem-specific relationships
to gross primary production: An assessment based on observational and
modeling approaches, Remote Sens. Environ., 166, 91–105,
https://doi.org/10.1016/j.rse.2015.06.004, 2015. a
Daumard, F., Champagne, S., Fournier, A., Goulas, Y., Ounis, A., Hanocq,
J.-F., and Moya, I.: A Field Platform for Continuous Measurement of Canopy
Fluorescence, IEEE T. Geosci. Remote Sens., 48, 3358–3368,
https://doi.org/10.1109/TGRS.2010.2046420, 2010. a, b
Evans, J. R.: Photosynthesis and nitrogen relationships in leaves of C3
plants, Oecologia, 78, 9–19, 1989. a
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species, Planta, 90,
78–90, 1980. a
Flexas, J., Escalona, J. M., and Medrano, H.: Water stress induces different
levels of photosynthesis and electron transport rate regulation in
grapevines, Plant Cell Environ., 22, 39–48,
https://doi.org/10.1046/j.1365-3040.1999.00371.x, 1999. a, b
Flexas, J., Briantais, J.-M., Cerovic, Z., Medrano, H., and Moya, I.:
Steady-State and Maximum Chlorophyll Fluorescence Responses to Water Stress
in Grapevine Leaves: A New Remote Sensing System, Remote Sens. Environ., 73,
283–297, https://doi.org/10.1016/S0034-4257(00)00104-8, 2000. a
Flexas, J., Escalona, J. M., Evain, S., Gulías, J., Moya, I., Osmond,
C. B., and Medrano, H.: Steady-state chlorophyll fluorescence (Fs)
measurements as a tool to follow variations of net CO2 assimilation and
stomatal conductance during water-stress in C3 plants, Physiol. Plantarum,
114, 231–240, 2002. a
Frankenberg, C., Butz, A., and Toon, G. C.: Disentangling chlorophyll
fluorescence from atmospheric scattering effects in O2 A-band spectra of
reflected sun-light, Geophys. Res. Lett., 38, L03801,
https://doi.org/10.1029/2010GL045896, 2011a. a, b
Frankenberg, C., Fisher, J. B., Worden, J., Badgley, G., Saatchi, S. S., Lee,
J.-E., Toon, G. C., Butz, A., Jung, M., Kuze, A., and Yokota, T.: New global
observations of the terrestrial carbon cycle from GOSAT: Patterns of plant
fluorescence with gross primary productivity, Geophys. Res. Lett., 38,
L17706, https://doi.org/10.1029/2011GL048738, 2011b. a
Frankenberg, C., O'Dell, C., Berry, J., Guanter, L., Joiner, J.,
Köhler, P., Pollock, R., and Taylor, T. E.: Prospects for chlorophyll
fluorescence remote sensing from the Orbiting Carbon Observatory-2, Remote
Sens. Environ., 147, 1–12, https://doi.org/10.1016/j.rse.2014.02.007, 2014. a
Freedman, A., Cavender-Bares, J., Kebabian, P., Bhaskar, R., Scott, H., and
Bazzaz, F.: Remote sensing of solar-excited plant fluorescence as a measure
of photosynthetic rate, Photosynthetica, 40, 127–132,
https://doi.org/10.1023/A:1020131332107, 2002. a
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V.,
Cadule, P., Doney, S., Eby, M., Fung, I., Bala, G., John, J., Jones, C.,
Joos, F., Kato, T., Kawamiya, M., Knorr, W., Lindsay, K., Matthews, H.,
Raddatz, T., Rayner, P., Reick, C., Roeckner, E., Schnitzler, K., Schnur, R.,
Strassmann, K., Weaver, A., Yoshikawa, C., and Zeng, N.: Climate–Carbon
Cycle Feedback Analysis: Results from the C 4 MIP Model Intercomparison, J.
Climate, 19, 3337–3353, https://doi.org/10.1175/JCLI3800.1, 2006. a
Guanter, L., Frankenberg, C., Dudhia, A., Lewis, P. E., Gómez-Dans, J.,
Kuze, A., Suto, H., and Grainger, R. G.: Retrieval and global assessment of
terrestrial chlorophyll fluorescence from GOSAT space measurements, Remote
Sens. Environ., 121, 236–251, https://doi.org/10.1016/j.rse.2012.02.006, 2012. a, b, c, d
Guanter, L., Rossini, M., Colombo, R., Meroni, M., Frankenberg, C., Lee,
J.-E., and Joiner, J.: Using field spectroscopy to assess the potential of
statistical approaches for the retrieval of sun-induced chlorophyll
fluorescence from ground and space, Remote Sens. Environ., 133, 52–61,
https://doi.org/10.1016/j.rse.2013.01.017, 2013. a
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. a.,
Frankenberg, C., Huete, A. R., Zarco-Tejada, P., Lee, J.-E., Moran, M. S.,
Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D.,
Klumpp, K., Cescatti, A., Baker, J. M., and Griffis, T. J.: Global and
time-resolved monitoring of crop photosynthesis with chlorophyll
fluorescenc, P. Natl. Acad. Sci. USA, 111, E1327–E1333,
https://doi.org/10.1073/pnas.1320008111, 2014. a
Houborg, R., Cescatti, A., Migliavacca, M., and Kustas, W.: Satellite
retrievals of leaf chlorophyll and photosynthetic capacity for improved
modeling of GPP, Agr. Forest Meteorol., 177, 10–23,
https://doi.org/10.1016/j.agrformet.2013.04.006, 2013. a
Hungershoefer, K., Breon, F.-M., Peylin, P., Chevallier, F., Rayner, P.,
Klonecki, A., Houweling, S., and Marshall, J.: Evaluation of various
observing systems for the global monitoring of CO2 surface fluxes, Atmos.
Chem. Phys., 10, 10503–10520, https://doi.org/10.5194/acp-10-10503-2010,
2010. a
Joiner, J., Yoshida, Y., Vasilkov, A. P., Schaefer, K., Jung, M., Guanter,
L., Zhang, Y., Garrity, S., Middleton, E. M., Huemmrich, K. F., Gu, L., and
Marchesini, L. B.: The seasonal cycle of satellite chlorophyll fluorescence
observations and its relationship to vegetation phenology and ecosystem
atmosphere carbon exchange, Remote Sens. Environ., 152, 375–391,
https://doi.org/10.1016/j.rse.2014.06.022, 2014. a
Kaminski, T., Scholze, M., and Houweling, S.: Quantifying the benefit of
A-SCOPE data for reducing uncertainties in terrestrial carbon fluxes in
CCDAS, Tellus B, 62, 784–796, https://doi.org/10.1111/j.1600-0889.2010.00483.x, 2010. a, b
Kaminski, T., Knorr, W., Scholze, M., Gobron, N., Pinty, B., Giering, R., and
Mathieu, P.-P.: Consistent assimilation of MERIS FAPAR and atmospheric CO2
into a terrestrial vegetation model and interactive mission benefit analysis,
Biogeosciences, 9, 3173–3184, https://doi.org/10.5194/bg-9-3173-2012, 2012. a, b, c, d
Kaminski, T., Knorr, W., Schürmann, G., Scholze, M., Rayner, P. J.,
Zaehle, S., Blessing, S., Dorigo, W., Gayler, V., Giering, R., Gobron, N.,
Grant, J. P., Heimann, M., Houweling, S., Kato, T., Kattge, J., Kelley, D.,
Kemp, S., Koffi, E. N., Köstler, C., Mathieu, P., Pinty, B., Reick,
C. H., Rödenbeck, C., Schnur, R., Scipal, K., Sebald, C., Stacke, T.,
Van, A. T., Vossbeck, M., Widmann, H., and Ziehn, T.: The BETHY/JSBACH
Carbon Cycle Data Assimilation System : experiences and challenges, J.
Geophys. Res.-Biogeo., 118, 1–13, https://doi.org/10.1002/jgrg.20118, 2013. a
Kato, S., Loeb, N. G., Rutan, D. A., Rose, F. G., Sun-Mack, S., Miller,
W. F., and Chen, Y.: Uncertainty Estimate of Surface Irradiances Computed
with MODIS-, CALIPSO-, and CloudSat-Derived Cloud and Aerosol Properties,
Surv. Geophys., 33, 395–412, https://doi.org/10.1007/s10712-012-9179-x, 2012. a, b
Kattge, J., Knorr, W., Raddatz, T., and Wirth, C.: Quantifying
photosynthetic capacity and its relationship to leaf nitrogen content for
global scale terrestrial biosphere models, Glob. Change Biol., 15, 976–991,
https://doi.org/10.1111/j.1365-2486.2008.01744.x, 2009. a
Koffi, E. N., Rayner, P. J., Scholze, M., Chevallier, F., and Kaminski, T.:
Quantifying the constraint of biospheric process parameters by CO2
concentration and flux measurement networks through a carbon cycle data
assimilation system, Atmos. Chem. Phys., 13, 10555–10572,
https://doi.org/10.5194/acp-13-10555-2013, 2013. a, b
Koffi, E. N., Rayner, P. J., Norton, A. J., Frankenberg, C., and Scholze, M.:
Investigating the usefulness of satellite-derived fluorescence data in
inferring gross primary productivity within the carbon cycle data
assimilation system, Biogeosciences, 12, 4067–4084,
https://doi.org/10.5194/bg-12-4067-2015, 2015. a, b, c, d, e, f, g, h
Kuppel, S., Chevallier, F., and Peylin, P.: Quantifying the model structural
error in carbon cycle data assimilation systems, Geosci. Model Dev., 6,
45–55, https://doi.org/10.5194/gmd-6-45-2013, 2013. a
Lee, J.-E., Frankenberg, C., van der Tol, C., Berry, J. A., Guanter, L.,
Boyce, C. K., Fisher, J. B., Morrow, E., Worden, J. R., Asefi, S., Badgley,
G., and Saatchi, S.: Forest productivity and water stress in Amazonia:
observations from GOSAT chlorophyll fluorescence, P. Roy. Soc. B-Biol. Sci.,
280, 20130171, https://doi.org/10.1098/rspb.2013.0171, 2013. a
MacBean, N., Peylin, P., Chevallier, F., Scholze, M., and Schürmann, G.:
Consistent assimilation of multiple data streams in a carbon cycle data
assimilation system, Geosci. Model Dev., 9, 3569–3588,
https://doi.org/10.5194/gmd-9-3569-2016, 2016 a
Magney, T. S., Frankenberg, C., Fisher, J. B., Sun, Y., North, G. B., Davis,
T. S., Kornfeld, A., and Siebke, K.: Connecting active to passive
fluorescence with photosynthesis: a method for evaluating remote sensing
measurements of Chl fluorescence, New Phytol., 215, 1594–1608,
https://doi.org/10.1111/nph.14662, 2017. a
Michalak, A. M., Hirsch, A., Bruhwiler, L., Gurney, K. R., Peters, W., and
Tans, P. P.: Maximum likelihood estimation of covariance parameters for
Bayesian atmospheric trace gas surface flux inversions, J. Geophys. Res.,
110, D24107, https://doi.org/10.1029/2005JD005970, 2005. a, b, c
Parazoo, N. C., Bowman, K., Frankenberg, C., Lee, J.-E., Fisher, J. B.,
Worden, J., Jones, D. B. a., Berry, J., Collatz, G. J., Baker, I. T., Jung,
M., Liu, J., Osterman, G., O'Dell, C., Sparks, A., Butz, A., Guerlet, S.,
Yoshida, Y., Chen, H., and Gerbig, C.: Interpreting seasonal changes in the
carbon balance of southern Amazonia using measurements of XCO2 and
chlorophyll fluorescence from GOSAT, Geophys. Res. Lett., 40, 2829–2833,
https://doi.org/10.1002/grl.50452, 2013. a
Parazoo, N. C., Bowman, K., Fisher, J. B., Frankenberg, C., Jones, D. B. A.,
Cescatti, A., Perez-Priego, O., Wohlfahrt, G., and Montagnani, L.:
Terrestrial gross primary production inferred from satellite fluorescence
and vegetation models, Glob. Change Biol., 20, 3103–3121,
https://doi.org/10.1111/gcb.12652, 2014. a, b
Peylin, P., Bacour, C., MacBean, N., Leonard, S., Rayner, P., Kuppel, S.,
Koffi, E., Kane, A., Maignan, F., Chevallier, F., Ciais, P., and Prunet, P.:
A new stepwise carbon cycle data assimilation system using multiple data
streams to constrain the simulated land surface carbon cycle, Geosci. Model
Dev., 9, 3321–3346, https://doi.org/10.5194/gmd-9-3321-2016, 2016. a
Porcar-Castell, A., Tyystjärvi, E., Atherton, J., van der Tol, C.,
Flexas, J., Pfündel, E. E., Moreno, J., Frankenberg, C., and Berry,
J. A.: Linking chlorophyll a fluorescence to photosynthesis for remote
sensing applications: mechanisms and challenges, J. Exp. Bot., 65, 4065–95,
https://doi.org/10.1093/jxb/eru191, 2014. a, b
Rossini, M., Nedbal, L., Guanter, L., Ač, A., Alonso, L., Burkart, A.,
Cogliati, S., Colombo, R., Damm, A., Drusch, M., Hanus, J., Janoutova, R.,
Julitta, T., Kokkalis, P., Moreno, J., Novotny, J., Panigada, C., Pinto, F.,
Schickling, A., Schüttemeyer, D., Zemek, F., and Rascher, U.: Red and
far red Sun-induced chlorophyll fluorescence as a measure of plant
photosynthesis, Geophys. Res. Lett., 42, 1632–1639,
https://doi.org/10.1002/2014GL062943, 2015. a
Schimel, D., Pavlick, R., Fisher, J. B., Asner, G. P., Saatchi, S., Townsend,
P., Miller, C., Frankenberg, C., Hibbard, K., and Cox, P.: Observing
terrestrial ecosystems and the carbon cycle from space, Glob. Change Biol.,
21, 1762–1776, https://doi.org/10.1111/gcb.12822, 2015. a, b
Scholze, M., Kaminski, T., Rayner, P., Knorr, W., and Giering, R.:
Propagating uncertainty through prognostic carbon cycle data assimilation
system simulations, J. Geophys. Res., 112, D17305,
https://doi.org/10.1029/2007JD008642, 2007. a, b
Scholze, M., Kaminski, T., Knorr, W., Blessing, S., Vossbeck, M., Grant,
J. P., and Scipal, K.: Simultaneous assimilation of SMOS soil moisture and
atmospheric CO2 in-situ observations to constrain the global terrestrial
carbon cycle, Remote Sens. Environ., 180, 334–345,
https://doi.org/10.1016/j.rse.2016.02.058, 2016. a, b, c
van der Tol, C., Berry, J. A., Campbell, P. K. E., and Rascher, U.: Models
of fluorescence and photosynthesis for interpreting measurements of
solar-induced chlorophyll fluorescence, J. Geophys. Res.-Biogeo., 119,
1–16, https://doi.org/10.1002/2014JG002713, 2014. a
Verrelst, J., Rivera, J. P., Tol, C. V. D., Magnani, F., Mohammed, G., and
Moreno, J.: Global sensitivity analysis of the SCOPE model: What drives
simulated canopy-leaving sun-induced fluorescence?, Remote Sens. Environ.,
166, 8–21, https://doi.org/10.1016/j.rse.2015.06.002, 2015. a, b
Walther, S., Voigt, M., Thum, T., Gonsamo, A., Zhang, Y., Koehler, P., Jung,
M., Varlagin, A., and Guanter, L.: Satellite chlorophyll fluorescence
measurements reveal large-scale decoupling of photosynthesis and greenness
dynamics in boreal evergreen forests, Glob. Change Biol., 22, 2979–2996,
https://doi.org/10.1111/gcb.13200, 2016. a, b, c, d
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and
Viterbo, P.: The WFDEI meteorological forcing data set: WATCH Forcing Data
methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50,
7505–7514, https://doi.org/10.1002/2014WR015638, 2014. a
Wilson, M. and Henderson-Sellers, A.: Global archive of land cover and soils
data for use in general-circulation climate models, Int. J. Climatol., 5,
119–143, 1985. a
Yang, X., Tang, J., Mustard, J. F., Lee, J.-e., Rossini, M., Joiner, J.,
Munger, J. W., Kornfeld, A., and Richardson, A. D.: Solar-induced
chlorophyll fluorescence that correlates with canopy photosynthesis on
diurnal and seasonal scales in a temperate deciduous forest, Geophys. Res.
Lett., 42, 2977–2987, https://doi.org/10.1002/2015GL063201, 2015. a, b, c, d, e, f
Zhang, Y., Guanter, L., Berry, J. A., Joiner, J., van der Tol, C., Huete, A.,
and Gitelson, A.: Estimation of vegetation photosynthetic capacity from
space-based measurements of chlorophyll fluorescence for terrestrial
biosphere models, Glob. Change Biol., 20, 3727–3742,
https://doi.org/10.1111/gcb.12664, 2014. a
Zhang, Y., Guanter, L., Berry, J. A., van der Tol, C., Yang, X., Tang, J.,
and Zhang, F.: Model-based analysis of the relationship between sun-induced
chlorophyll fluorescence and gross primary production for remote sensing
applications, Remote Sens. Environ., 187, 145–155,
https://doi.org/10.1016/j.rse.2016.10.016, 2016. a
Ziehn, T., Knorr, W., and Scholze, M.: Investigating spatial differentiation
of model parameters in a carbon cycle data assimilation system, Global
Biogeochem. Cy., 25, 1–13, https://doi.org/10.1029/2010GB003886, 2011. a
Short summary
It is difficult to estimate how much CO2 plants absorb via photosynthesis and even more difficult to model this for the whole globe. Here, we present a framework to combine a new satellite measurement "solar-induced chlorophyll fluorescence" with a global photosynthesis model. We then quantify how this new measurement constrains model uncertainties and find highly effective constraint. These results pave a novel pathway for improving estimates and modelling abilities of photosynthesis globally.
It is difficult to estimate how much CO2 plants absorb via photosynthesis and even more...
