Articles | Volume 15, issue 19
https://doi.org/10.5194/bg-15-5801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-15-5801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Oct 2018
Research article |
| 04 Oct 2018
| 04 Oct 2018
Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation
Istem Fer
CORRESPONDING AUTHOR
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Ryan Kelly
RK Analytics, Durham, NC 27712, USA
Paul R. Moorcroft
Department Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
Andrew D. Richardson
School of Informatics, Computing and Cyber Systems, Northern Arizona University Flagstaff, AZ 86011, USA
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA
Elizabeth M. Cowdery
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Michael C. Dietze
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
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M. Verma, M. A. Friedl, A. D. Richardson, G. Kiely, A. Cescatti, B. E. Law, G. Wohlfahrt, B. Gielen, O. Roupsard, E. J. Moors, P. Toscano, F. P. Vaccari, D. Gianelle, G. Bohrer, A. Varlagin, N. Buchmann, E. van Gorsel, L. Montagnani, and P. Propastin
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R. G. Knox, M. Longo, A. L. S. Swann, K. Zhang, N. M. Levine, P. R. Moorcroft, and R. L. Bras
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-15295-2013, https://doi.org/10.5194/hessd-10-15295-2013, 2013
Preprint withdrawn
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
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Terrestrial biosphere models can either prescribe the geographical distribution of biomes or simulate them dynamically, capturing climate-change-driven biome shifts. We isolate and examine the differences between these different land cover implementations. We find that the simulated terrestrial carbon sink at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover due to important range shifts in the Arctic and Amazon.
Elizabeth S. Duan, Luciana Chavez Rodriguez, Nicole Hemming-Schroeder, Baptiste Wijas, Habacuc Flores-Moreno, Alexander W. Cheesman, Lucas A. Cernusak, Michael J. Liddell, Paul Eggleton, Amy E. Zanne, and Steven D. Allison
Biogeosciences, 21, 3321–3338, https://doi.org/10.5194/bg-21-3321-2024, https://doi.org/10.5194/bg-21-3321-2024, 2024
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Understanding the link between climate and carbon fluxes is crucial for predicting how climate change will impact carbon sinks. We estimated carbon dioxide (CO2) fluxes from deadwood in tropical Australia using wood moisture content and temperature. Our model predicted that the majority of deadwood carbon is released as CO2, except when termite activity is detected. Future models should also incorporate wood traits, like species and chemical composition, to better predict fluxes.
Daniel Nadal-Sala, Rüdiger Grote, David Kraus, Uri Hochberg, Tamir Klein, Yael Wagner, Fedor Tatarinov, Dan Yakir, and Nadine K. Ruehr
Biogeosciences, 21, 2973–2994, https://doi.org/10.5194/bg-21-2973-2024, https://doi.org/10.5194/bg-21-2973-2024, 2024
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A hydraulic model approach is presented that can be added to any physiologically based ecosystem model. Simulated plant water potential triggers stomatal closure, photosynthesis decline, root–soil resistance increases, and sapwood and foliage senescence. The model has been evaluated at an extremely dry site stocked with Aleppo pine and was able to represent gas exchange, soil water content, and plant water potential. The model also responded realistically regarding leaf senescence.
Patrick Neri, Lianhong Gu, and Yang Song
Biogeosciences, 21, 2731–2758, https://doi.org/10.5194/bg-21-2731-2024, https://doi.org/10.5194/bg-21-2731-2024, 2024
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A first-of-its-kind global-scale model of temperature resilience and tolerance of photosystem II maximum quantum yield informs how plants maintain their efficiency of converting light energy to chemical energy for photosynthesis under temperature changes. Our finding explores this variation across plant functional types and habitat climatology, highlighting diverse temperature response strategies and a method to improve global-scale photosynthesis modeling under climate change.
Liyuan He, Jorge L. Mazza Rodrigues, Melanie A. Mayes, Chun-Ta Lai, David A. Lipson, and Xiaofeng Xu
Biogeosciences, 21, 2313–2333, https://doi.org/10.5194/bg-21-2313-2024, https://doi.org/10.5194/bg-21-2313-2024, 2024
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Soil microbes are the driving engine for biogeochemical cycles of carbon and nutrients. This study applies a microbial-explicit model to quantify bacteria and fungal biomass carbon in soils from 1901 to 2016. Results showed substantial increases in bacterial and fungal biomass carbon over the past century, jointly influenced by vegetation growth and soil temperature and moisture. This pioneering century-long estimation offers crucial insights into soil microbial roles in global carbon cycling.
Tao Chen, Félicien Meunier, Marc Peaucelle, Guoping Tang, Ye Yuan, and Hans Verbeeck
Biogeosciences, 21, 2253–2272, https://doi.org/10.5194/bg-21-2253-2024, https://doi.org/10.5194/bg-21-2253-2024, 2024
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Chinese subtropical forest ecosystems are an extremely important component of global forest ecosystems and hence crucial for the global carbon cycle and regional climate change. However, there is still great uncertainty in the relationship between subtropical forest carbon sequestration and its drivers. We provide first quantitative estimates of the individual and interactive effects of different drivers on the gross primary productivity changes of various subtropical forest types in China.
Stefano Manzoni and Francesca Cotrufo
EGUsphere, https://doi.org/10.5194/egusphere-2024-1092, https://doi.org/10.5194/egusphere-2024-1092, 2024
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Organic carbon and nitrogen are stabilized in soils via microbial assimilation and stabilization of necromass (in vivo pathway) or via adsorption of the products of extra-cellular decomposition (ex vivo pathway). Here we use a diagnostic model to quantify which stabilization pathway is prevalent, using data on residue-derived carbon and nitrogen incorporation in mineral associated organic matter. We find that the in vivo pathway is dominant in fine-textured soils with low organic matter content.
Ke Liu, Yujie Wang, Troy S. Magney, and Christian Frankenberg
Biogeosciences, 21, 1501–1516, https://doi.org/10.5194/bg-21-1501-2024, https://doi.org/10.5194/bg-21-1501-2024, 2024
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Stomata are pores on leaves that regulate gas exchange between plants and the atmosphere. Existing land models unrealistically assume stomata can jump between steady states when the environment changes. We implemented dynamic modeling to predict gradual stomatal responses at different scales. Results suggested that considering this effect on plant behavior patterns in diurnal cycles was important. Our framework also simplified simulations and can contribute to further efficiency improvements.
Melanie A. Thurner, Silvia Caldararu, Jan Engel, Anja Rammig, and Sönke Zaehle
Biogeosciences, 21, 1391–1410, https://doi.org/10.5194/bg-21-1391-2024, https://doi.org/10.5194/bg-21-1391-2024, 2024
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Due to their crucial role in terrestrial ecosystems, we implemented mycorrhizal fungi into the QUINCY terrestrial biosphere model. Fungi interact with mineral and organic soil to support plant N uptake and, thus, plant growth. Our results suggest that the effect of mycorrhizal interactions on simulated ecosystem dynamics is minor under constant environmental conditions but necessary to reproduce and understand observed patterns under changing conditions, such as rising atmospheric CO2.
Jinyun Tang and William J. Riley
Biogeosciences, 21, 1061–1070, https://doi.org/10.5194/bg-21-1061-2024, https://doi.org/10.5194/bg-21-1061-2024, 2024
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A chemical kinetics theory is proposed to explain the non-monotonic relationship between temperature and biochemical rates. It incorporates the observed thermally reversible enzyme denaturation that is ensured by the ceaseless thermal motion of molecules and ions in an enzyme solution and three well-established theories: (1) law of mass action, (2) diffusion-limited chemical reaction theory, and (3) transition state theory.
Nina Raoult, Louis-Axel Edouard-Rambaut, Nicolas Vuichard, Vladislav Bastrikov, Anne Sofie Lansø, Bertrand Guenet, and Philippe Peylin
Biogeosciences, 21, 1017–1036, https://doi.org/10.5194/bg-21-1017-2024, https://doi.org/10.5194/bg-21-1017-2024, 2024
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Observations are used to reduce uncertainty in land surface models (LSMs) by optimising poorly constraining parameters. However, optimising against current conditions does not necessarily ensure that the parameters treated as invariant will be robust in a changing climate. Manipulation experiments offer us a unique chance to optimise our models under different (here atmospheric CO2) conditions. By using these data in optimisations, we gain confidence in the future projections of LSMs.
Kelsey T. Foster, Wu Sun, Yoichi P. Shiga, Jiafu Mao, and Anna M. Michalak
Biogeosciences, 21, 869–891, https://doi.org/10.5194/bg-21-869-2024, https://doi.org/10.5194/bg-21-869-2024, 2024
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Assessing agreement between bottom-up and top-down methods across spatial scales can provide insights into the relationship between ensemble spread (difference across models) and model accuracy (difference between model estimates and reality). We find that ensemble spread is unlikely to be a good indicator of actual uncertainty in the North American carbon balance. However, models that are consistent with atmospheric constraints show stronger agreement between top-down and bottom-up estimates.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
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We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Xin Chen, Tiexi Chen, Xiaodong Li, Yuanfang Chai, Shengjie Zhou, Renjie Guo, and Jie Dai
EGUsphere, https://doi.org/10.5194/egusphere-2024-114, https://doi.org/10.5194/egusphere-2024-114, 2024
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We provides an ensemble model-based GPP dataset (ERF_GPP) that explains 83.7 % of the monthly variation in GPP across 171 sites, higher than other single remote sensing model. In addition, ERF_GPP improves the phenomenon of “high value underestimation and low value overestimation” in GPP estimation to some extent. Overall, ERF_GPP provides a more reliable estimate of global GPP and will facilitate further development of carbon cycle research.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billdesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Gharun Mana, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
EGUsphere, https://doi.org/10.5194/egusphere-2024-165, https://doi.org/10.5194/egusphere-2024-165, 2024
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The movement of water, carbon, and energy from the earth surface to the atmosphere, or flux, is an important process to understand that impacts all of our lives. Here we outline a method to estimate global water and CO2 fluxes based on direct measurements from site around the world called FLUXCOM-X. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
Bertrand Guenet, Jérémie Orliac, Lauric Cécillon, Olivier Torres, Laura Sereni, Philip A. Martin, Pierre Barré, and Laurent Bopp
Biogeosciences, 21, 657–669, https://doi.org/10.5194/bg-21-657-2024, https://doi.org/10.5194/bg-21-657-2024, 2024
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Heterotrophic respiration fluxes are a major flux between surfaces and the atmosphere, but Earth system models do not yet represent them correctly. Here we benchmarked Earth system models against observation-based products, and we identified the important mechanisms that need to be improved in the next-generation Earth system models.
Shuyue Li, Bonnie Waring, Jennifer Powers, and David Medvigy
Biogeosciences, 21, 455–471, https://doi.org/10.5194/bg-21-455-2024, https://doi.org/10.5194/bg-21-455-2024, 2024
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We used an ecosystem model to simulate primary production of a tropical forest subjected to 3 years of nutrient fertilization. Simulations parameterized such that relative allocation to fine roots increased with increasing soil phosphorus had leaf, wood, and fine root production consistent with observations. However, these simulations seemed to over-allocate to fine roots on multidecadal timescales, affecting aboveground biomass. Additional observations across timescales would benefit models.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
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In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Joe R. McNorton and Francesca Di Giuseppe
Biogeosciences, 21, 279–300, https://doi.org/10.5194/bg-21-279-2024, https://doi.org/10.5194/bg-21-279-2024, 2024
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Wildfires have wide-ranging consequences for local communities, air quality and ecosystems. Vegetation amount and moisture state are key components to forecast wildfires. We developed a combined model and satellite framework to characterise vegetation, including the type of fuel, whether it is alive or dead, and its moisture content. The daily data is at high resolution globally (~9 km). Our characteristics correlate with active fire data and can inform fire danger and spread modelling efforts.
Reza Kusuma Nurrohman, Tomomichi Kato, Hideki Ninomiya, Lea Végh, Nicolas Delbart, Tatsuya Miyauchi, Hisashi Sato, Tomohiro Shiraishi, and Ryuichi Hirata
EGUsphere, https://doi.org/10.5194/egusphere-2024-105, https://doi.org/10.5194/egusphere-2024-105, 2024
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SPITFIRE fire module was integrated into SEIB Dynamic Global Vegetation Model to improve the model's accuracy in depicting forest fire frequency, intensity, and extent in Siberia. Projected fires showed a continuous increase in higher emissions of greenhouse gases and aerosols from 2023 to 2100 under all RCP scenarios. This study contributes to a better understanding of fire dynamics, land ecosystem-climate interactions, and global material cycles under the threat of escalating fires in Siberia.
Brooke A. Eastman, William R. Wieder, Melannie D. Hartman, Edward R. Brzostek, and William T. Peterjohn
Biogeosciences, 21, 201–221, https://doi.org/10.5194/bg-21-201-2024, https://doi.org/10.5194/bg-21-201-2024, 2024
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We compared soil model performance to data from a long-term nitrogen addition experiment in a forested ecosystem. We found that in order for soil carbon models to accurately predict future forest carbon sequestration, two key processes must respond dynamically to nitrogen availability: (1) plant allocation of carbon to wood versus roots and (2) rates of soil organic matter decomposition. Long-term experiments can help improve our predictions of the land carbon sink and its climate impact.
Jan De Pue, Sebastian Wieneke, Ana Bastos, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Maral Maleki, Fabienne Maignan, Françoise Gellens-Meulenberghs, Ivan Janssens, and Manuela Balzarolo
Biogeosciences, 20, 4795–4818, https://doi.org/10.5194/bg-20-4795-2023, https://doi.org/10.5194/bg-20-4795-2023, 2023
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The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. To estimate this flux, models can rely on remote sensing data (RS-driven), meteorological data (meteo-driven) or a combination of both (hybrid). An intercomparison of 11 models demonstrated that RS-driven models lack the sensitivity to short-term anomalies. Conversely, the simulation of soil moisture dynamics and stress response remains a challenge in meteo-driven models.
Chad A. Burton, Luigi J. Renzullo, Sami W. Rifai, and Albert I. J. M. Van Dijk
Biogeosciences, 20, 4109–4134, https://doi.org/10.5194/bg-20-4109-2023, https://doi.org/10.5194/bg-20-4109-2023, 2023
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Australia's land-based ecosystems play a critical role in controlling the variability in the global land carbon sink. However, uncertainties in the methods used for quantifying carbon fluxes limit our understanding. We develop high-resolution estimates of Australia's land carbon fluxes using machine learning methods and find that Australia is, on average, a stronger carbon sink than previously thought and that the seasonal dynamics of the fluxes differ from those described by other methods.
Yuan Yan, Anne Klosterhalfen, Fernando Moyano, Matthias Cuntz, Andrew C. Manning, and Alexander Knohl
Biogeosciences, 20, 4087–4107, https://doi.org/10.5194/bg-20-4087-2023, https://doi.org/10.5194/bg-20-4087-2023, 2023
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A better understanding of O2 fluxes, their exchange ratios with CO2 and their interrelations with environmental conditions would provide further insights into biogeochemical ecosystem processes. We, therefore, used the multilayer canopy model CANVEG to simulate and analyze the flux exchange for our forest study site for 2012–2016. Based on these simulations, we further successfully tested the application of various micrometeorological methods and the prospects of real O2 flux measurements.
Jie Zhang, Elisabeth Larsen Kolstad, Wenxin Zhang, Iris Vogeler, and Søren O. Petersen
Biogeosciences, 20, 3895–3917, https://doi.org/10.5194/bg-20-3895-2023, https://doi.org/10.5194/bg-20-3895-2023, 2023
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Manure application to agricultural land often results in large and variable N2O emissions. We propose a model with a parsimonious structure to investigate N transformations around such N2O hotspots. The model allows for new detailed insights into the interactions between transport and microbial activities regarding N2O emissions in heterogeneous soil environments. It highlights the importance of solute diffusion to N2O emissions from such hotspots which are often ignored by process-based models.
Jukka Alm, Antti Wall, Jukka-Pekka Myllykangas, Paavo Ojanen, Juha Heikkinen, Helena M. Henttonen, Raija Laiho, Kari Minkkinen, Tarja Tuomainen, and Juha Mikola
Biogeosciences, 20, 3827–3855, https://doi.org/10.5194/bg-20-3827-2023, https://doi.org/10.5194/bg-20-3827-2023, 2023
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In Finland peatlands cover one-third of land area. For half of those, with 4.3 Mha being drained for forestry, Finland reports sinks and sources of greenhouse gases in forest lands on organic soils following its UNFCCC commitment. We describe a new method for compiling soil CO2 balance that follows changes in tree volume, tree harvests and temperature. An increasing trend of emissions from 1.4 to 7.9 Mt CO2 was calculated for drained peatland forest soils in Finland for 1990–2021.
Siqi Li, Bo Zhu, Xunhua Zheng, Pengcheng Hu, Shenghui Han, Jihui Fan, Tao Wang, Rui Wang, Kai Wang, Zhisheng Yao, Chunyan Liu, Wei Zhang, and Yong Li
Biogeosciences, 20, 3555–3572, https://doi.org/10.5194/bg-20-3555-2023, https://doi.org/10.5194/bg-20-3555-2023, 2023
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Physical soil erosion and particulate carbon, nitrogen and phosphorus loss modules were incorporated into the process-oriented hydro-biogeochemical model CNMM-DNDC to realize the accurate simulation of water-induced erosion and subsequent particulate nutrient losses at high spatiotemporal resolution.
Ivan Cornut, Nicolas Delpierre, Jean-Paul Laclau, Joannès Guillemot, Yann Nouvellon, Otavio Campoe, Jose Luiz Stape, Vitoria Fernanda Santos, and Guerric le Maire
Biogeosciences, 20, 3093–3117, https://doi.org/10.5194/bg-20-3093-2023, https://doi.org/10.5194/bg-20-3093-2023, 2023
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Potassium is an essential element for living organisms. Trees are dependent upon this element for certain functions that allow them to build their trunks using carbon dioxide. Using data from experiments in eucalypt plantations in Brazil and a simplified computer model of the plantations, we were able to investigate the effect that a lack of potassium can have on the production of wood. Understanding nutrient cycles is useful to understand the response of forests to environmental change.
Ivan Cornut, Guerric le Maire, Jean-Paul Laclau, Joannès Guillemot, Yann Nouvellon, and Nicolas Delpierre
Biogeosciences, 20, 3119–3135, https://doi.org/10.5194/bg-20-3119-2023, https://doi.org/10.5194/bg-20-3119-2023, 2023
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After simulating the effects of low levels of potassium on the canopy of trees and the uptake of carbon dioxide from the atmosphere by leaves in Part 1, here we tried to simulate the way the trees use the carbon they have acquired and the interaction with the potassium cycle in the tree. We show that the effect of low potassium on the efficiency of the trees in acquiring carbon is enough to explain why they produce less wood when they are in soils with low levels of potassium.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
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We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Kevin R. Wilcox, Scott L. Collins, Alan K. Knapp, William Pockman, Zheng Shi, Melinda D. Smith, and Yiqi Luo
Biogeosciences, 20, 2707–2725, https://doi.org/10.5194/bg-20-2707-2023, https://doi.org/10.5194/bg-20-2707-2023, 2023
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The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems store carbon in the future. Here, we employ novel data–model integration techniques to identify the carbon capacity of six grassland sites spanning the US Great Plains. Hot and dry sites had low C capacity due to less plant growth and high turnover of soil C, so they may be a C source in the future. Alternately, cooler and wetter ecosystems had high C capacity, so these systems may be a future C sink.
Ara Cho, Linda M. J. Kooijmans, Kukka-Maaria Kohonen, Richard Wehr, and Maarten C. Krol
Biogeosciences, 20, 2573–2594, https://doi.org/10.5194/bg-20-2573-2023, https://doi.org/10.5194/bg-20-2573-2023, 2023
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Carbonyl sulfide (COS) is a useful constraint for estimating photosynthesis. To simulate COS leaf flux better in the SiB4 model, we propose a novel temperature function for enzyme carbonic anhydrase (CA) activity and optimize conductances using observations. The optimal activity of CA occurs below 40 °C, and Ball–Woodrow–Berry parameters are slightly changed. These reduce/increase uptakes in the tropics/higher latitudes and contribute to resolving discrepancies in the COS global budget.
Yunyao Ma, Bettina Weber, Alexandra Kratz, José Raggio, Claudia Colesie, Maik Veste, Maaike Y. Bader, and Philipp Porada
Biogeosciences, 20, 2553–2572, https://doi.org/10.5194/bg-20-2553-2023, https://doi.org/10.5194/bg-20-2553-2023, 2023
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We found that the modelled annual carbon balance of biocrusts is strongly affected by both the environment (mostly air temperature and CO2 concentration) and physiology, such as temperature response of respiration. However, the relative impacts of these drivers vary across regions with different climates. Uncertainty in driving factors may lead to unrealistic carbon balance estimates, particularly in temperate climates, and may be explained by seasonal variation of physiology due to acclimation.
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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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.
Libo Wang, Vivek K. Arora, Paul Bartlett, Ed Chan, and Salvatore R. Curasi
Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, https://doi.org/10.5194/bg-20-2265-2023, 2023
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Plant functional types (PFTs) are groups of plant species used to represent vegetation distribution in land surface models. There are large uncertainties associated with existing methods for mapping land cover datasets to PFTs. This study demonstrates how fine-resolution tree cover fraction and land cover datasets can be used to inform the PFT mapping process and reduce the uncertainties. The proposed largely objective method makes it easier to implement new land cover products in models.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
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Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
Veronika Kronnäs, Klas Lucander, Giuliana Zanchi, Nadja Stadlinger, Salim Belyazid, and Cecilia Akselsson
Biogeosciences, 20, 1879–1899, https://doi.org/10.5194/bg-20-1879-2023, https://doi.org/10.5194/bg-20-1879-2023, 2023
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In a future climate, extreme droughts might become more common. Climate change and droughts can have negative effects on soil weathering and plant health.
In this study, climate change effects on weathering were studied on sites in Sweden using the model ForSAFE, a climate change scenario and an extreme drought scenario. The modelling shows that weathering is higher during summer and increases with global warming but that weathering during drought summers can become as low as winter weathering.
Agustín Sarquis and Carlos A. Sierra
Biogeosciences, 20, 1759–1771, https://doi.org/10.5194/bg-20-1759-2023, https://doi.org/10.5194/bg-20-1759-2023, 2023
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Although plant litter is chemically and physically heterogenous and undergoes multiple transformations, models that represent litter dynamics often ignore this complexity. We used a multi-model inference framework to include information content in litter decomposition datasets and studied the time it takes for litter to decompose as measured by the transit time. In arid lands, the median transit time of litter is about 3 years and has a negative correlation with mean annual temperature.
Qi Guan, Jing Tang, Lian Feng, Stefan Olin, and Guy Schurgers
Biogeosciences, 20, 1635–1648, https://doi.org/10.5194/bg-20-1635-2023, https://doi.org/10.5194/bg-20-1635-2023, 2023
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Understanding terrestrial sources of nitrogen is vital to examine lake eutrophication changes. Combining process-based ecosystem modeling and satellite observations, we found that land-leached nitrogen in the Yangtze Plain significantly increased from 1979 to 2018, and terrestrial nutrient sources were positively correlated with eutrophication trends observed in most lakes, demonstrating the necessity of sustainable nitrogen management to control eutrophication.
Vivek K. Arora, Christian Seiler, Libo Wang, and Sian Kou-Giesbrecht
Biogeosciences, 20, 1313–1355, https://doi.org/10.5194/bg-20-1313-2023, https://doi.org/10.5194/bg-20-1313-2023, 2023
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The behaviour of natural systems is now very often represented through mathematical models. These models represent our understanding of how nature works. Of course, nature does not care about our understanding. Since our understanding is not perfect, evaluating models is challenging, and there are uncertainties. This paper illustrates this uncertainty for land models and argues that evaluating models in light of the uncertainty in various components provides useful information.
Benjamin S. Felzer
Biogeosciences, 20, 573–587, https://doi.org/10.5194/bg-20-573-2023, https://doi.org/10.5194/bg-20-573-2023, 2023
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The future of the terrestrial carbon sink depends upon the legacy of past land use, which determines the stand age of the forest and nutrient levels in the soil, both of which affect vegetation growth. This study uses a modeling approach to determine the effects of land-use legacy in the conterminous US from 1750 to 2099. Not accounting for land legacy results in a low carbon sink and high biomass, while water variables are not as highly affected.
Bailu Zhao and Qianlai Zhuang
Biogeosciences, 20, 251–270, https://doi.org/10.5194/bg-20-251-2023, https://doi.org/10.5194/bg-20-251-2023, 2023
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In this study, we use a process-based model to simulate the northern peatland's C dynamics in response to future climate change during 1990–2300. Northern peatlands are projected to be a C source under all climate scenarios except for the mildest one before 2100 and C sources under all scenarios afterwards.
We find northern peatlands are a C sink until pan-Arctic annual temperature reaches −2.09 to −2.89 °C. This study emphasizes the vulnerability of northern peatlands to climate change.
Lin Yu, Silvia Caldararu, Bernhard Ahrens, Thomas Wutzler, Marion Schrumpf, Julian Helfenstein, Chiara Pistocchi, and Sönke Zaehle
Biogeosciences, 20, 57–73, https://doi.org/10.5194/bg-20-57-2023, https://doi.org/10.5194/bg-20-57-2023, 2023
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In this study, we addressed a key weakness in current ecosystem models regarding the phosphorus exchange in the soil and developed a new scheme to describe this process. We showed that the new scheme improved the model performance for plant productivity, soil organic carbon, and soil phosphorus content at five beech forest sites in Germany. We claim that this new model could be used as a better tool to study ecosystems under future climate change, particularly phosphorus-limited systems.
Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, https://doi.org/10.5194/bg-19-5521-2022, 2022
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Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
Jie Zhang, Wenxin Zhang, Per-Erik Jansson, and Søren O. Petersen
Biogeosciences, 19, 4811–4832, https://doi.org/10.5194/bg-19-4811-2022, https://doi.org/10.5194/bg-19-4811-2022, 2022
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In this study, we relied on a properly controlled laboratory experiment to test the model’s capability of simulating the dominant microbial processes and the emissions of one greenhouse gas (nitrous oxide, N2O) from agricultural soils. This study reveals important processes and parameters that regulate N2O emissions in the investigated model framework and also suggests future steps of model development, which have implications on the broader communities of ecosystem modelers.
Jan De Pue, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Manuela Balzarolo, Fabienne Maignan, and Françoise Gellens-Meulenberghs
Biogeosciences, 19, 4361–4386, https://doi.org/10.5194/bg-19-4361-2022, https://doi.org/10.5194/bg-19-4361-2022, 2022
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The functioning of ecosystems involves numerous biophysical processes which interact with each other. Land surface models (LSMs) are used to describe these processes and form an essential component of climate models. In this paper, we evaluate the performance of three LSMs and their interactions with soil moisture and vegetation. Though we found room for improvement in the simulation of soil moisture and drought stress, the main cause of errors was related to the simulated growth of vegetation.
Jarmo Mäkelä, Laura Arppe, Hannu Fritze, Jussi Heinonsalo, Kristiina Karhu, Jari Liski, Markku Oinonen, Petra Straková, and Toni Viskari
Biogeosciences, 19, 4305–4313, https://doi.org/10.5194/bg-19-4305-2022, https://doi.org/10.5194/bg-19-4305-2022, 2022
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Soils account for the largest share of carbon found in terrestrial ecosystems, and accurate depiction of soil carbon decomposition is essential in understanding how permanent these carbon storages are. We present a straightforward way to include carbon isotope concentrations into soil decomposition and carbon storages for the Yasso model, which enables the model to use 13C as a natural tracer to track changes in the underlying soil organic matter decomposition.
Cited articles
Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal. Process., 50, 174–188, https://doi.org/10.1109/78.978374, 2002.
Aslanyan, G., Easther, R., and Price, L. C.: Learn-as-you-go acceleration of cosmological parameter estimates, J. Cosmol. Astropart. P., 2015, 005, 2015.
Bradford, J. B., Weishampel, P., Smith, M.-L., Kolka, R., Birdsey, R. A., Ollinger, S. V., and Ryan, M. G.: Carbon pools and fluxes in small temperate forest landscapes: Variability and implications for sampling design, Forest Ecol. Manag., 259, 1245–1254, https://doi.org/10.1016/j.foreco.2009.04.009, 2010.
Braswell, B. H., Sacks, W. J., Linder, E., and Schimel, D. S.: Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations, Global Change Biol., 11, 335–355, https://doi.org/10.1111/j.1365-2486.2005.00897.x, 2005.
Brynjarsdóttir, J. and O'Hagan, A.: Learning about physical parameters: the importance of model discrepancy, Inverse Problems, 30, 114007, 2014.
Clark, J. S.: Why environmental scientists are becoming Bayesians, Ecol. Lett., 8, 2–14, https://doi.org/10.1111/j.1461-0248.2004.00702.x, 2005.
Dancik, G. M.: mlegp: Maximum Likelihood Estimates of Gaussian Processes, R package version 3.1.4, available at: https://CRAN.R-project.org/package=mlegp (last access: 30 September 2018), 2013.
Datta, A., Banerjee, S., Finley, A. O., and Gelfand, A. E.: On nearest-neighbor Gaussian process models for massive spatial data, WIRES Comput. Statistics, 8, 162–171, https://doi.org/10.1002/wics.1383, 2016.
Dietze, M. C.: Ecological Forecasting, https://doi.org/10.1002/eap.1589, 2017a.
Dietze, M. C.: Prediction in ecology: a first-principles framework, Ecol. Appl., 27, 2048–2060, https://doi.org/10.1002/eap.1589, 2017b.
Dietze, M. C. and Moorcroft, P. R.: Tree mortality in the eastern and central United States: patterns and drivers, Global Change Biol., 17, 3312–3326, https://doi.org/10.1111/j.1365-2486.2011.02477.x, 2011.
Dietze, M. C., Shawn, S. P., Davidson, C., Desai, A. R., Feng, X., Kelly, R., Kooper, R., LeBauer, D., Mantooth, J., McHenry, K., and Wang, D.: A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes, J. Geophys. Res.-Biogeosci., 119, 286–300, https://doi.org/10.1002/2013JG002392, 2014.
Erickson, C. B., Ankenman, B. E., and Sanchez, S. M.: Comparison of Gaussian process modeling software, Eur. J. Oper. Res., 266, 179–192, https://doi.org/10.1016/j.ejor.2017.10.002, 2018.
Fisher, J., Huntzinger, D., Schwalm, C., and Sitch, S.: Modeling the terrestrial biosphere, Annu. Rev. Env. Resour., 39, 91–123, https://doi.org/10.1146/annurev-environ-012913-093456, 2014.
Fisher, R. A.: On the mathematical foundations of theoretical statistics, Phil. T. R. Soc. A, 222, https://doi.org/10.1098/rsta.1922.0009, 1922.
Forrester, A. I. and Keane, A. J.: Recent advances in surrogate-based optimization, Prog. Aerosp. Sci., 45, 50–79, https://doi.org/10.1016/j.paerosci.2008.11.001, 2009.
Fox, A., Williams, M., Richardson, A. D., Cameron, D., Gove, J. H., Quaife, T., Ricciuto, D., Reichstein, M., Tomelleri, E., Trudinger, C. M., and Wijk, M. T. V.: The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data, Agr. Forest Meteorol., 149, 1597–1615, https://doi.org/10.1016/j.agrformet.2009.05.002, 2009.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A., Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks, J. Climate, 27, 511–526, https://doi.org/10.1175/JCLI-D-12-00579.1, 2014.
Gong, W. and Duan, Q.: An adaptive surrogate modeling-based sampling strategy for parameter optimization and distribution estimation (ASMO-PODE), Environ. Modell. Softw., 95, 61–75, https://doi.org/10.1016/j.envsoft.2017.05.005, 2017.
Gupta, H. V., Clark, M. P., Jasper, J. A. V., Abramowitz, G., and Ye, M.: Towards a comprehensive assessment of model structural adequacy, Water Resour. Res., 48, W08301, https://doi.org/10.1029/2011WR011044, 2012.
Gutmann, M. U. and Corander, J.: Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models, J. Mach. Learn. Res., 17, 1–47, 2016.
Haario, H., Saksman, E., and Tamminen, J.: An adaptive Metropolis algorithm, Bernoulli, 7, 223–242, 2001.
Hartig, F., Dyke, J., Hickler, T., Higgins, S. I., O'Hara, R. B., Scheiter, S., and Huth, A.: Connecting dynamic vegetation models to data – an inverse perspective, J. Biogeogr., 39, 2240–2252, https://doi.org/10.1111/j.1365-2699.2012.02745.x, 2012.
Hartig, F., Minuno, F., and Paul, S.: BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics, R package version 0.1.3, available at: https://CRAN.R-project.org/package=BayesianTools (last access: 30 September 2018, 2017.
Huang, M., Ray, J., Hou, Z., Ren, H., Liu, Y., and Swiler, L.: On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites, J. Geophys. Res.-Atmos., 121, 7548–7563, https://doi.org/10.1002/2015JD024339, 2016.
Jandarov, R., Haran, M., Bjørnstad, O., and Grenfell, B.: Emulating a gravity model to infer the spatiotemporal dynamics of an infectious disease, J. R. Stat. Soc. C-Appl., 63, 423–444, https://doi.org/10.1111/rssc.12042, 2014.
Jenkins, J. C., Chojnacky, D. C., Heath, L. S., and Birdsey, R. A.: Comprehensive database of diameter-based biomass regressions for North American tree species, United States Department of Agriculture, available at: https://www.fs.fed.us/ne/durham/4104/papers/ne_gtr319_jenkins_and_others.pdf (last access: 30 September 2018), 2004.
Joyce, P. and Marjoram, P.: Approximately sufficient statistics and Bayesian computation, Stat. Appl. Genet. Mo. B., 7, https://doi.org/10.2202/1544-6115.1389, 2008.
Kandasamy, K., Schneider, J., and Póczos, B.: Bayesian Active Learning for Posterior Estimation, AAAI Publications, Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015.
Keenan, T. F., Carbone, M. S., Reichstein, M., and Richardson, A. D.: The model-data fusion pitfall: assuming certainty in an uncertain world, Oecologia, 167, 587, https://doi.org/10.1007/s00442-011-2106-x, 2011.
Keenan, T. F., Davidson, E. A., Munger, J. W., and Richardson, A. D.: Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle, Ecol. Appl., 23, 273–286, https://doi.org/10.1890/12-0747.1, 2013.
Kennedy, M., Anderson, C., O'Hagan, A., Lomas, M., Woodward, I., Gosling, J. P., and Heinemeyer, A.: Quantifying uncertainty in the biospheric carbon flux for England and Wales, J. R. Stat. Soc. A Stat., 171, 109–135, https://doi.org/10.1111/j.1467-985X.2007.00489.x, 2008.
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J. R. Stat. Soc. B Stat., 63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001.
Laloy, E. and Vrugt, J. A.: High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing, Water Resour. Res., 48, W01526, https://doi.org/10.1029/2011WR010608, 2012.
Lasslop, G., Reichstein, M., Kattge, J., and Papale, D.: Influences of observation errors in eddy flux data on inverse model parameter estimation, Biogeosciences, 5, 1311–1324, https://doi.org/10.5194/bg-5-1311-2008, 2008.
LeBauer, D., Kooper, R., Mulrooney, P., Rohde, S., Wang, D., Long, S. P., and Dietze, M. C.: BETYdb: a yield, trait, and ecosystem service database applied to second-generation bioenergy feedstock production, GCB Bioenergy, 10, 61–71, https://doi.org/10.1111/gcbb.12420, 2017.
LeBauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., and Dietze, M. C.: Facilitating feedbacks between field measurements and ecosystem models, Ecol. Monogr., 83, 133–154, https://doi.org/10.1890/12-0137.1, 2013.
Lee, M. S., Hollinger, D. Y., Keenan, T. F., Ouimette, A. P., Ollinger, S. V., and Richardson, A. D.: Model-based analysis of the impact of diffuse radiation on CO2 exchange in a temperate deciduous forest, Agr. Forest Meteorol., 249, 377–389, https://doi.org/10.1016/j.agrformet.2017.11.016, 2018.
Li, J., Duan, Q., Wang, Y.-P., Gong, W., Gan, Y., and Wang, C.: Parameter optimization for carbon and water fluxes in two global land surface models based on surrogate modelling, Int. J. Climatol., 38, e1016–e1031, https://doi.org/10.1002/joc.5428, 2018.
Loeppky, J. L., Sacks, J., and Welch, W. J.: Choosing the Sample Size of a Computer Experiment: A Practical Guide, Technometrics, 51, 366–376, https://doi.org/10.1198/TECH.2009.08040, 2009.
Lu, D., Ricciuto, D., Walker, A., Safta, C., and Munger, W.: Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods, Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017, 2017.
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.
McMahon, S. M., Dietze, M. C., Hersh, M. H., Moran, E. V., and Clark, J. S.: A Predictive Framework to Understand Forest Responses to Global Change, Ann. NY Acad. Sci., 1162, 221–236, https://doi.org/10.1111/j.1749-6632.2009.04495.x, 2009.
Medlyn, B. E., Zaehle, S., De Kauwe, M. G., Walker, A. P., Dietze, M. C., Hanson, P. J., Hickler, T., Jain, A. K., Luo, Y., Parton, W., Prentice, I. C., Thornton, P. E., Wang, S., Wang, Y.-P., Weng, E., Iversen, C. M., McCarthy, H. R., Warren, J. M., Oren, R., and Norby, R. J.: Using ecosystem experiments to improve vegetation models, Nat. Clim. Change, 5, 528–534, https://doi.org/10.1038/nclimate2621, 2015.
Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y., and Moorcroft, P. R.: Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2, J. Geophys. Res.-Biogeosci., 114, G01002, https://doi.org/10.1029/2008JG000812, 2009.
Moorcroft, P. R., Hurtt, G. C., and Pacala, S. W.: A method for scaling vegetation dynamics: The Ecosystem Demography model (ED), Ecol. Monogr., 71, 557–586, https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2, 2001.
Oakley, J. E. and Youngman, B. D.: Calibration of Stochastic Computer Simulators Using Likelihood Emulation, Technometrics, 59, 80–92, https://doi.org/10.1080/00401706.2015.1125391, 2017.
Phillips, C. L., Bond-Lamberty, B., Desai, A. R., Lavoie, M., Risk, D., Tang, J., Todd-Brown, K., and Vargas, R.: The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling, Plant Soil, 413, 1–25, https://doi.org/10.1007/s11104-016-3084-x, 2017.
Post, H., Vrugt, J. A., Fox, A., Vereecken, H., and Hendricks, F. H.: Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites, J. Geophys. Res.-Biogeosci., 122, 661–689, https://doi.org/10.1002/2015JG003297, 2017.
Rasmussen, C. E. and Williams, C. K. I.: Gaussian processes for machine learning, available at: http://www.gaussianprocess.org/gpml/chapters/RW.pdf (last access: 30 September 2018), 2006.
Raupach, M. R., Rayner, P. J., Barrett, D. J., DeFries, R. S., Heimann, M., Ojima, D. S., Quegan, S., and Schmullius, C. C.: Model–data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications, Global Change Biol., 11, 378–397, https://doi.org/10.1111/j.1365-2486.2005.00917.x, 2005.
Ray, J., Hou, Z., Huang, M., Sargsyan, K., and Swiler, L.: Bayesian calibration of the Community Land Model using surrogates, SIAM/ASA J. Uncertain. Quantif., 3, 199–233, https://doi.org/10.1137/140957998, 2015.
Ricciuto, D. M., Davis, K. J., and Keller, K.: A Bayesian calibration of a simple carbon cycle model: The role of observations in estimating and reducing uncertainty, Global Biogeochem. Cy., 22, https://doi.org/10.1029/2006GB002908, 2008.
Richardson, A. D., Hollinger, D. Y., Burba, G. G., Davis, K. J., Flanagan, L. B., Katul, G. G., Munger, J. W., Ricciuto, D. M., Stoy, P. C., Suyker, A. E., Verma, S. B., and Wofsy, S. C.: A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes, Agr. Forest Meteorol., 136, 1–18, https://doi.org/10.1016/j.agrformet.2006.01.007, 2006.
Richardson, A. D., Williams, M., Hollinger, D. Y., Moore, D. J. P., Dail, D. B., Davidson, E. A., Scott, N. A., Evans, R. S., Hughes, H., Lee, J. T., Rodrigues, C., and Savage, K.: Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints, Oecologia, 164, 25–40, https://doi.org/10.1007/s00442-010-1628-y, 2010.
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P.: Design and Analysis of Computer Experiments, Stat. Sci., 4, 409–423, https://doi.org/10.1214/ss/1177012413, 1989.
Sacks, W. J., Schimel, D. S., Monson, R. K., and Braswell, B. H.: Model-data synthesis of diurnal and seasonal CO2 fluxes at Niwot Ridge, Colorado, Global Change Biol., 12, 240–259, https://doi.org/10.1111/j.1365-2486.2005.01059.x, 2006.
Thomas, R. Q., Brooks, E. B., Jersild, A. L., Ward, E. J., Wynne, R. H., Albaugh, T. J., Dinon-Aldridge, H., Burkhart, H. E., Domec, J.-C., Fox, T. R., Gonzalez-Benecke, C. A., Martin, T. A., Noormets, A., Sampson, D. A., and Teskey, R. O.: Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments, Biogeosciences, 14, 3525–3547, https://doi.org/10.5194/bg-14-3525-2017, 2017.
Trudinger, C. M., Raupach, M. R., Rayner, P. J., Kattge, J., Liu, Q., Pak, B., Reichstein, M., Renzullo, L., Richardson, A. D., Roxburgh, S. H., Styles, J., Wang, Y. Y., Briggs, P., Barrett, D., and Nikolova, S.: OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models, J. Geophys. Res.-Biogeosci., 112, G02027, https://doi.org/10.1029/2006JG000367, 2007.
van Oijen, M.: Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models, Current Forestry Reports, 3, 269–280, https://doi.org/10.1007/s40725-017-0069-9, 2017.
van Oijen, M., Cameron, D. R., Butterbach-Bahl, K., Farahbakhshazad, N., Jansson, P.-E., Kiese, R., Rahn, K.-H., Werner, C., and Yeluripati, J. B.: A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest, Agr. Forest Meteorol., 151, 1609–1621, https://doi.org/10.1016/j.agrformet.2011.06.017, 2011.
Walker, A. P., Ye, M., Lu, D., De Kauwe, M. G., Gu, L., Medlyn, B. E., Rogers, A., and Serbin, S. P.: The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources, Geosci. Model Dev., 11, 3159–3185, https://doi.org/10.5194/gmd-11-3159-2018, 2018.
Wang, C., Duan, Q., Gong, W., Ye, A., Di, Z., and Miao, C.: An evaluation of adaptive surrogate modeling based optimization with two benchmark problems, Environ. Modell. Softw., 60, 167–179, https://doi.org/10.1016/j.envsoft.2014.05.026, 2014.
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y.-P.: Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341–1359, https://doi.org/10.5194/bg-6-1341-2009, 2009.
Short summary
The computer models we use to understand and forecast the ecosystem changes have multiple components that determine their outcomes. Due to our limited observation capacities, these components bear uncertainties that in return affect our predictions. While there are techniques for reducing these uncertainties, they are not applicable to every model due to computational and statistical barriers. This research presents a method that lowers those barriers and allows us to improve model predictions.
The computer models we use to understand and forecast the ecosystem changes have multiple...
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