Articles | Volume 12, issue 9
https://doi.org/10.5194/gmd-12-4133-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4133-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Sep 2019
Development and technical paper |
| 23 Sep 2019
| 23 Sep 2019
Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)
Elias C. Massoud
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Department of Civil and Environmental Engineering, University of California Irvine, Irvine, CA, USA
Chonggang Xu
CORRESPONDING AUTHOR
Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Rosie A. Fisher
Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), Toulouse, France
Ryan G. Knox
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Anthony P. Walker
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Shawn P. Serbin
Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
Bradley O. Christoffersen
Department of Biology, University of Texas Rio Grande Valley, Edinburg, TX, USA
Jennifer A. Holm
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Lara M. Kueppers
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Daniel M. Ricciuto
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Liang Wei
Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Daniel J. Johnson
School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
Jeffrey Q. Chambers
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Charlie D. Koven
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Nate G. McDowell
Earth Systems Analysis and Modeling Division, Pacific Northwest National Laboratory, Richland, WA, USA
Jasper A. Vrugt
Department of Civil and Environmental Engineering, University of California Irvine, Irvine, CA, USA
Department of Earth System Science, University of California Irvine, Irvine, CA, USA
Related authors
Elias C. Massoud, Nathan Collier, Yaoping Wang, Jiafu Mao, Adrian Harpold, Steven A. Kannenberg, Gerbrand Koren, Mukesh Kumar, Pushpendra Raghav, Pallav Ray, Mingjie Shi, Jing Tao, Sreedevi P. Vasu, Huiqi Wang, Qing Zhu, and Forrest M. Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2025-3517, https://doi.org/10.5194/egusphere-2025-3517, 2025
Short summary
Short summary
We studied how well Earth System Models simulate soil moisture and its connection to plant growth and water use. Using a model evaluation tool and real-world data, we found that models generally perform well at the surface but struggle deeper in the soil. These issues vary by region, especially in colder regions. Our results can help improve future model development and support better predictions of how ecosystems respond to a changing environment.
Elias C. Massoud, Lauren Andrews, Rolf Reichle, Andrea Molod, Jongmin Park, Sophie Ruehr, and Manuela Girotto
Earth Syst. Dynam., 14, 147–171, https://doi.org/10.5194/esd-14-147-2023, https://doi.org/10.5194/esd-14-147-2023, 2023
Short summary
Short summary
In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Adrienne M. Wootten, Elias C. Massoud, Duane E. Waliser, and Huikyo Lee
Earth Syst. Dynam., 14, 121–145, https://doi.org/10.5194/esd-14-121-2023, https://doi.org/10.5194/esd-14-121-2023, 2023
Short summary
Short summary
Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivity of projections to multi-model ensemble weighting strategies in the south-central United States. Model weighting and ensemble means are sensitive to the domain and variable used. There are numerous findings regarding the improvement in skill with model weighting and the sensitivity associated with various strategies.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
Short summary
Short summary
The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Jean-Francois Lamarque, Pierre Friedlingstein, Brian Osias, Steve Strongin, Venkatramani Balaji, Kevin W. Bowman, Josep G. Canadell, Philippe Ciais, Heidi Cullen, Kenneth J. Davis, Scott C. Doney, Kevin R. Gurney, Alicia R. Karspeck, Charles D. Koven, Galen McKinley, Glen P. Peters, Julia Pongratz, Britt Stephens, and Colm Sweeney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6457, https://doi.org/10.5194/egusphere-2025-6457, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
This Perspective highlights requirements to scale the carbon credit market and enable the growth in climate solutions funded through such market. The requirements are on the understanding of the value of the proposed carbon credit projects, and on the availability of a verification system. This verification becomes particularly relevant as the carbon credit market scales to significant impacts on CO2 (or other greenhouse gases), such that attribution to collective actions can be identified.
Junyan Ding, Nate McDowell, Vanessa Bailey, Nate Conroy, Donnie J. Day, Yilin Fang, Kenneth M. Kemner, Matthew L. Kirwan, Charlie D. Koven, Matthew Kovach, Patrick Megonigal, Kendalynn A. Morris, Teri O'Meara, Stephanie C. Pennington, Roberta B. Peixoto, Peter Thornton, Mike Weintraub, Peter Regier, Leticia Sandoval, Fausto Machado-Silva, Alice Stearns, Nick Ward, and Stephanie J. Wilson
Biogeosciences, 22, 6963–6978, https://doi.org/10.5194/bg-22-6963-2025, https://doi.org/10.5194/bg-22-6963-2025, 2025
Short summary
Short summary
We used a vegetation model to study why coastal forests are dying due to rising water levels and what happens to the ecosystem when marshes take over. We found that tree death is mainly caused by water-damaged roots, leading to major changes in the environment, such as reduced water use and carbon storage. Our study helps explain how coastal ecosystems are shifting and offers new ideas to explore in future field research.
Victor Brovkin, Benjamin M. Sanderson, Noel G. Brizuela, Tomohiro Hajima, Tatiana Ilyina, Chris D. Jones, Charles Koven, David Lawrence, Peter Lawrence, Hongmei Li, Spencer Liddcoat, Anastasia Romanou, Roland Séférian, Lori T. Sentman, Abigail L. S. Swann, Jerry Tjiputra, Tilo Ziehn, and Alexander J. Winkler
Earth Syst. Dynam., 16, 2021–2034, https://doi.org/10.5194/esd-16-2021-2025, https://doi.org/10.5194/esd-16-2021-2025, 2025
Short summary
Short summary
Idealized experiments with Earth system models provide a basis for understanding the response of the carbon cycle to emissions. We show that most models exhibit a quasi-linear relationship between cumulative carbon uptake on land and in the ocean and hypothesize that this relationship does not depend on emission pathways. We reduce the coupled system to only one differential equation, which represents a powerful simplification of the Earth system dynamics as a function of fossil fuel emissions.
Yaoping Wang, Daniel M. Ricciuto, Jiafu Mao, Sören E. Weber, Verity G. Salmon, Xiaoying Shi, Xiaojuan Yang, Natalie A. Griffiths, Paul J. Hanson, Katherine Duchesneau, Camille E. Defrenne, Jeffrey M. Warren, Stephen D. Sebestyen, Keith Oleheiser, Melanie A. Mayes, and Peter E. Thornton
EGUsphere, https://doi.org/10.5194/egusphere-2025-5471, https://doi.org/10.5194/egusphere-2025-5471, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Boreal peatlands store much of the global soil carbon, a service dependent on nutrient limitation on plant productivity. This study improved a major land surface model to better represent how plants gain nitrogen and phosphorus through fine roots and mycorrhizal association. The new model more accurately captured observed carbon fluxes than the default model at an experimental site in Minnesota, and suggests shifts in nutrient uptake strategy helps peatlands stay carbon-rich under warming.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida B. M. Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre A. G. Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido van der Werf, Sebastiaan J. van de Velde, Anthony Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-659, https://doi.org/10.5194/essd-2025-659, 2025
Preprint under review for ESSD
Short summary
Short summary
The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2025). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Elias C. Massoud, Nathan Collier, Yaoping Wang, Jiafu Mao, Adrian Harpold, Steven A. Kannenberg, Gerbrand Koren, Mukesh Kumar, Pushpendra Raghav, Pallav Ray, Mingjie Shi, Jing Tao, Sreedevi P. Vasu, Huiqi Wang, Qing Zhu, and Forrest M. Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2025-3517, https://doi.org/10.5194/egusphere-2025-3517, 2025
Short summary
Short summary
We studied how well Earth System Models simulate soil moisture and its connection to plant growth and water use. Using a model evaluation tool and real-world data, we found that models generally perform well at the surface but struggle deeper in the soil. These issues vary by region, especially in colder regions. Our results can help improve future model development and support better predictions of how ecosystems respond to a changing environment.
Benjamin M. Sanderson, Victor Brovkin, Rosie A. Fisher, David Hohn, Tatiana Ilyina, Chris D. Jones, Torben Koenigk, Charles Koven, Hongmei Li, David M. Lawrence, Peter Lawrence, Spencer Liddicoat, Andrew H. MacDougall, Nadine Mengis, Zebedee Nicholls, Eleanor O'Rourke, Anastasia Romanou, Marit Sandstad, Jörg Schwinger, Roland Séférian, Lori T. Sentman, Isla R. Simpson, Chris Smith, Norman J. Steinert, Abigail L. S. Swann, Jerry Tjiputra, and Tilo Ziehn
Geosci. Model Dev., 18, 5699–5724, https://doi.org/10.5194/gmd-18-5699-2025, https://doi.org/10.5194/gmd-18-5699-2025, 2025
Short summary
Short summary
This study investigates how climate models warm in response to simplified carbon emissions trajectories, refining the understanding of climate reversibility and commitment. Metrics are defined for warming response to cumulative emissions and for the cessation of emissions or ramp-down to net-zero and net-negative levels. Results indicate that previous concentration-driven experiments may have overstated the Zero Emissions Commitment due to emissions rates exceeding historical levels.
Rubaya Pervin, Scott Robeson, Mallory Barnes, Stephen Sitch, Anthony Walker, Ben Poulter, Fabienne Maignan, Qing Sun, Thomas Colligan, Sönke Zaehle, Kashif Mahmud, Peter Anthoni, Almut Arneth, Vivek Arora, Vladislav Bastrikov, Liam Bogucki, Bertrand Decharme, Christine Delire, Stefanie Falk, Akihiko Ito, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Michael O’Sullivan, Wenping Yuan, and Natasha MacBean
EGUsphere, https://doi.org/10.5194/egusphere-2025-2841, https://doi.org/10.5194/egusphere-2025-2841, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Drylands contribute more than a third of the global vegetation productivity. Yet, these regions are not well represented in global vegetation models. Here, we tested how well 15 global models capture annual changes in dryland vegetation productivity. Models that didn’t have vegetation change over time or fire have lower variability in vegetation productivity. Models need better representation of grass cover types and their coverage. Our work highlights where and how these models need to improve.
Mara Y. McPartland, Tomas Lovato, Charles D. Koven, Jamie D. Wilson, Briony Turner, Colleen M. Petrik, José Licón-Saláiz, Fang Li, Fanny Lhardy, Jaclyn Clement Kinney, Michio Kawamiya, Birgit Hassler, Nathan P. Gillett, Cheikh Modou Noreyni Fall, Christopher Danek, Chris M. Brierley, Ana Bastos, and Oliver Andrews
EGUsphere, https://doi.org/10.5194/egusphere-2025-3246, https://doi.org/10.5194/egusphere-2025-3246, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
Mingjie Shi, Nate McDowell, Huilin Huang, Faria Zahura, Lingcheng Li, and Xingyuan Chen
Biogeosciences, 22, 2225–2238, https://doi.org/10.5194/bg-22-2225-2025, https://doi.org/10.5194/bg-22-2225-2025, 2025
Short summary
Short summary
Using Moderate Resolution Imaging Spectroradiometer data products, we quantitatively estimate the resistance and resilience of ecosystem functions to wildfires that occurred in the Columbia River basin in 2015. The carbon state exhibits lower resistance and resilience than the ecosystem fluxes. The random forest feature importance analysis indicates that burn severity plays a minor role in the resilience of grassland and a relatively major role in the resilience of forest and savanna.
Sarah Camelo da Silva, Bárbara Bomfim, Jeffrey Quintin Chambers, Regison Costa de Oliveira, Cacilda Adélia Sampaio de Souza, Marcelo Nunes Vilas-Boas, Adriano José Nogueira Lima, Niro Higuchi, and Bruno Oliva Gimenez
EGUsphere, https://doi.org/10.5194/egusphere-2025-391, https://doi.org/10.5194/egusphere-2025-391, 2025
Preprint archived
Short summary
Short summary
Tropical forest soils are known for low fertility, but support vegetation with high species diversity and biomass. Nutrients and carbon essential for forest functioning are stored in the soil and biomass. The region’s topographic gradient, with variations in soil texture and water table depth, influences species distribution. This study quantified macronutrients and carbon in trunks, leaves, and soil of generalist and specialist species across different soil types.
James Stegen, Amy J. Burgin, Michelle H. Busch, Joshua B. Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate McDowell, Corianne Tatariw, Anna Braswell, Jillian M. Deines, Julia A. Guimond, Peter Regier, Kenton Rod, Edward K. P. Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin L. Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon N. Sweetman, Jianqiu Zheng, Daniel C. Allen, Elizabeth Herndon, Beth A. Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, and Kaizad F. Patel
Biogeosciences, 22, 995–1034, https://doi.org/10.5194/bg-22-995-2025, https://doi.org/10.5194/bg-22-995-2025, 2025
Short summary
Short summary
The loss and gain of surface water (variable inundation) are common processes across Earth. Global change shifts variable inundation dynamics, highlighting a need for unified understanding that transcends individual variably inundated ecosystems (VIEs). We review the literature, highlight challenges, and emphasize opportunities to generate transferable knowledge by viewing VIEs through a common lens. We aim to inspire the emergence of a cross-VIE community based on a proposed continuum approach.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
Short summary
Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
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
Short summary
Short summary
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.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Junyan Ding, Polly Buotte, Roger Bales, Bradley Christoffersen, Rosie A. Fisher, Michael Goulden, Ryan Knox, Lara Kueppers, Jacquelyn Shuman, Chonggang Xu, and Charles D. Koven
Biogeosciences, 20, 4491–4510, https://doi.org/10.5194/bg-20-4491-2023, https://doi.org/10.5194/bg-20-4491-2023, 2023
Short summary
Short summary
We used a vegetation model to investigate how the different combinations of plant rooting depths and the sensitivity of leaves and stems to drying lead to differential responses of a pine forest to drought conditions in California, USA. We found that rooting depths are the strongest control in that ecosystem. Deep roots allow trees to fully utilize the soil water during a normal year but result in prolonged depletion of soil moisture during a severe drought and hence a high tree mortality risk.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Nathan Alec Conroy, Jeffrey M. Heikoop, Emma Lathrop, Dea Musa, Brent D. Newman, Chonggang Xu, Rachael E. McCaully, Carli A. Arendt, Verity G. Salmon, Amy Breen, Vladimir Romanovsky, Katrina E. Bennett, Cathy J. Wilson, and Stan D. Wullschleger
The Cryosphere, 17, 3987–4006, https://doi.org/10.5194/tc-17-3987-2023, https://doi.org/10.5194/tc-17-3987-2023, 2023
Short summary
Short summary
This study combines field observations, non-parametric statistical analyses, and thermodynamic modeling to characterize the environmental causes of the spatial variability in soil pore water solute concentrations across two Arctic catchments with varying extents of permafrost. Vegetation type, soil moisture and redox conditions, weathering and hydrologic transport, and mineral solubility were all found to be the primary drivers of the existing spatial variability of some soil pore water solutes.
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
Short summary
Short summary
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.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen
Biogeosciences, 20, 2671–2692, https://doi.org/10.5194/bg-20-2671-2023, https://doi.org/10.5194/bg-20-2671-2023, 2023
Short summary
Short summary
Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
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
Short summary
Short summary
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.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Elias C. Massoud, Lauren Andrews, Rolf Reichle, Andrea Molod, Jongmin Park, Sophie Ruehr, and Manuela Girotto
Earth Syst. Dynam., 14, 147–171, https://doi.org/10.5194/esd-14-147-2023, https://doi.org/10.5194/esd-14-147-2023, 2023
Short summary
Short summary
In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Adrienne M. Wootten, Elias C. Massoud, Duane E. Waliser, and Huikyo Lee
Earth Syst. Dynam., 14, 121–145, https://doi.org/10.5194/esd-14-121-2023, https://doi.org/10.5194/esd-14-121-2023, 2023
Short summary
Short summary
Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivity of projections to multi-model ensemble weighting strategies in the south-central United States. Model weighting and ensemble means are sensitive to the domain and variable used. There are numerous findings regarding the improvement in skill with model weighting and the sensitivity associated with various strategies.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
Short summary
Short summary
To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
Short summary
Short summary
Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
Short summary
Short summary
We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
Short summary
Short summary
The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
Short summary
Short summary
The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, https://doi.org/10.5194/bg-18-5639-2021, 2021
Short summary
Short summary
The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Benjamin M. Sanderson, Angeline G. Pendergrass, Charles D. Koven, Florent Brient, Ben B. B. Booth, Rosie A. Fisher, and Reto Knutti
Earth Syst. Dynam., 12, 899–918, https://doi.org/10.5194/esd-12-899-2021, https://doi.org/10.5194/esd-12-899-2021, 2021
Short summary
Short summary
Emergent constraints promise a pathway to the reduction in climate projection uncertainties by exploiting ensemble relationships between observable quantities and unknown climate response parameters. This study considers the robustness of these relationships in light of biases and common simplifications that may be present in the original ensemble of climate simulations. We propose a classification scheme for constraints and a number of practical case studies.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
Short summary
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Polly C. Buotte, Charles D. Koven, Chonggang Xu, Jacquelyn K. Shuman, Michael L. Goulden, Samuel Levis, Jessica Katz, Junyan Ding, Wu Ma, Zachary Robbins, and Lara M. Kueppers
Biogeosciences, 18, 4473–4490, https://doi.org/10.5194/bg-18-4473-2021, https://doi.org/10.5194/bg-18-4473-2021, 2021
Short summary
Short summary
We present an approach for ensuring the definitions of plant types in dynamic vegetation models are connected to the underlying ecological processes controlling community composition. Our approach can be applied regionally or globally. Robust resolution of community composition will allow us to use these models to address important questions related to future climate and management effects on plant community composition, structure, carbon storage, and feedbacks within the Earth system.
Eva Sinha, Kate Calvin, Ben Bond-Lamberty, Beth Drewniak, Dan Ricciuto, Khachik Sargsyan, Yanyan Cheng, Carl Bernacchi, and Caitlin Moore
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-244, https://doi.org/10.5194/gmd-2021-244, 2021
Preprint withdrawn
Short summary
Short summary
Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Model (E3SM) Land Model (ELM) to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion.
Daniel M. Ricciuto, Xiaojuan Yang, Dali Wang, and Peter E. Thornton
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-163, https://doi.org/10.5194/bg-2021-163, 2021
Publication in BG not foreseen
Short summary
Short summary
This paper uses a novel approach to quantify the impacts of the choice of decomposition model on carbon and nitrogen cycling. We compare the models to experimental data that examined litter decomposition over five different biomes. Despite widely differing assumptions, the models produce similar patterns of decomposition when nutrients are limiting. This differs from past analyses that did not consider the impacts of changing environmental conditions or nutrients.
Wu Ma, Lu Zhai, Alexandria Pivovaroff, Jacquelyn Shuman, Polly Buotte, Junyan Ding, Bradley Christoffersen, Ryan Knox, Max Moritz, Rosie A. Fisher, Charles D. Koven, Lara Kueppers, and Chonggang Xu
Biogeosciences, 18, 4005–4020, https://doi.org/10.5194/bg-18-4005-2021, https://doi.org/10.5194/bg-18-4005-2021, 2021
Short summary
Short summary
We use a hydrodynamic demographic vegetation model to estimate live fuel moisture dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. Our results suggest that multivariate climate change could cause a significant net reduction in live fuel moisture and thus exacerbate future wildfire danger in chaparral shrub systems.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Wolfgang A. Obermeier, Julia E. M. S. Nabel, Tammas Loughran, Kerstin Hartung, Ana Bastos, Felix Havermann, Peter Anthoni, Almut Arneth, Daniel S. Goll, Sebastian Lienert, Danica Lombardozzi, Sebastiaan Luyssaert, Patrick C. McGuire, Joe R. Melton, Benjamin Poulter, Stephen Sitch, Michael O. Sullivan, Hanqin Tian, Anthony P. Walker, Andrew J. Wiltshire, Soenke Zaehle, and Julia Pongratz
Earth Syst. Dynam., 12, 635–670, https://doi.org/10.5194/esd-12-635-2021, https://doi.org/10.5194/esd-12-635-2021, 2021
Short summary
Short summary
We provide the first spatio-temporally explicit comparison of different model-derived fluxes from land use and land cover changes (fLULCCs) by using the TRENDY v8 dynamic global vegetation models used in the 2019 global carbon budget. We find huge regional fLULCC differences resulting from environmental assumptions, simulated periods, and the timing of land use and land cover changes, and we argue for a method consistent across time and space and for carefully choosing the accounting period.
Xiaoying Shi, Daniel M. Ricciuto, Peter E. Thornton, Xiaofeng Xu, Fengming Yuan, Richard J. Norby, Anthony P. Walker, Jeffrey M. Warren, Jiafu Mao, Paul J. Hanson, Lin Meng, David Weston, and Natalie A. Griffiths
Biogeosciences, 18, 467–486, https://doi.org/10.5194/bg-18-467-2021, https://doi.org/10.5194/bg-18-467-2021, 2021
Short summary
Short summary
The Sphagnum mosses are the important species of a wetland ecosystem. To better represent the peatland ecosystem, we introduced the moss species to the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. We tested the model against field observations and used the model to make projections of the site's carbon cycle under warming and atmospheric CO2 concentration scenarios.
Cited articles
Adams, H. R., Barnard, H. R., and Loomis, A. K.: Topography alters tree
growth–climate relationships in a semi-arid forested catchment,
Ecosphere, 5, 1–16, 2014. a
Ali, A. A., Xu, C., Rogers, A., Fisher, R. A., Wullschleger, S. D., Massoud, E. C., Vrugt, J. A., Muss, J. D., McDowell, N. G., Fisher, J. B., Reich, P. B., and Wilson, C. J.: A global scale mechanistic model of photosynthetic capacity (LUNA V1.0), Geosci. Model Dev., 9, 587–606, https://doi.org/10.5194/gmd-9-587-2016, 2016. a
Archer, G., Saltelli, A., and Sobol,I.: Sensitivity measures, anova-like
techniques and the use of bootstrap, J. Stat.
Comput. Simu., 58, 99–120,1997. a
Bastidas, L. A., Gupta, H. V., Sorooshian, S., Shuttleworth, W. J., and Yang, Z. L.:
Sensitivity analysis of a land surface scheme using multicriteria
methods, J. Geophys. Res.-Atmos.,
104, 19481–19490, 1999. a
Benton, T. G. and Grant, A.:
Elasticity analysis as an important tool in evolutionary and population ecology
Trends Ecol. Evol.,
14, 467–471, 1999. a
Bonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M.,
Lawrence, D. M., and Swenson, S. C.: Improving canopy processes in the
community land model version 4 (CLM4) using global flux fields empirically
inferred from fluxnet data, J. Geophys. Res.-Biogeo., 116, G02014, https://doi.org/10.1029/2010JG001593, 2011. a
Campolongo, F., Saltelli, A., Sørensen, T. M., and Tarantola, S.:
Hitchhiker's guide to sensitivity analysis, in: Sensitivity analysis, IEEE Computer Society Press,
15–47, 2000. a
Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B.,
Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., and Henry, M.:
Improved allometric models to estimate the aboveground biomass
of tropical trees, Glob. Change Biol., 20,
3177–3190, 2014. a
Christoffersen, B. O., Gloor, M., Fauset, S., Fyllas, N. M., Galbraith, D. R., Baker, T. R., Kruijt, B., Rowland, L., Fisher, R. A., Binks, O. J., Sevanto, S., Xu, C., Jansen, S., Choat, B., Mencuccini, M., McDowell, N. G., and Meir, P.: Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro), Geosci. Model Dev., 9, 4227–4255, https://doi.org/10.5194/gmd-9-4227-2016, 2016. a
Claussen, M., Mysak, L., Weaver, A., Crucifix, M., Fichefet, T., Loutre, M. F.,
Weber, S., Alcamo, J., Alexeev, V., Berger, A., and Calov, R.:
Earth system models of intermediate complexity: closing the gap in the spectrum of climate
system models, Clim. Dynam., 18, 579–586, 2002. a
Collalti, A., Thornton, P. E., Cescatti, A., Rita, A., Borghetti, M.,
Nole, A., Trotta, C., Ciais, P., and Matteucci, G.: The sensitivity of the forest carbon budget
shifts across processes along with stand development and climate change,
Ecol. Appl.,
29, e01837, https://doi.org/10.1002/eap.1837, 2019. a, b, c
Collins, D. C. and Avissar, R.: An evaluation with the fourier amplitude
sensitivity test (FAST) of which land-surface parameters are of greatest
importance in atmospheric modeling, J. Climate,
7, 681–703, 1994. a
Crossley, J. F., Polcher, J., Cox, P. M., Gedney, N. and Planton, S.:
Uncertainties linked to land-surface processes in climate change simulations,
Clim. Dynam., 16, 949–961, 2000. a
Cukier, R., Fortuin, C., Shuler, K. E., Petschek, A., and Schaibly, J.:
Study of the sensitivity of coupled reaction systems to uncertainties in rate
coefficients. I theory, J. Chem. Phys.,
59, 3873–3878, 1973. a
Da Rocha, H. R., Nobre, C. A., Bonatti, J. P., Wright, I. R., and Sellers, P. J.:
A vegetation-atmosphere interaction study for amazonia deforestation
using field data and a 'single column' model, Q. J. Roy. Meteor. Soc., 122, 567–594, 1996. a
Díaz, S., Kattge, J., Cornelissen, J. H., Wright, I. J., Lavorel, S., Dray, S.,
Reu, B., Kleyer, M., Wirth, C., Prentice, I. C., and Garnier, E.:
The global spectrum of plant form and function,
Nature,
529, 167–171, 2016. a
Dietze, M. C., Wolosin, M. S., and Clark, J. S.: Capturing diversity and
interspecific variability in allometries: a hierarchical approach,
Forest Ecol. Manage., 256, 1939–1948, 2008. a
Dietze, M. C., Serbin, 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.-Biogeo.,
119, 286–300, 2014. a, b, c, d, e, f, g, h, i, j, k, l, m
Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E.,
Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J., and Krasting, J. P.:
GFDL's ESM2 global coupled climate-carbon earth system models. part
i: Physical formulation and baseline simulation characteristics,
J. Climate, 25, 6646–6665, 2012. a
Dupuy, J. M. and Chazdon, R. L.: Effects of vegetation cover on seedling
and sapling dynamics in secondary tropical wet forests in costa rica,
J. Trop. Ecol., 22, 65–76, 2006. a
Entekhabi, D. and Eagleson, P. S.: Land surface hydrology
parameterization for atmospheric general circulation models including subgrid
scale spatial variability, J. Climate, 2,
816–831, 1989. a
Farquhar, G. D.: Models of integrated photosynthesis of cells and leaves,
Philos. T. Roy. Soc. B,
323, 357–367,
https://doi.org/10.1098/rstb.1989.0016, 1989. a
Farrior, C. E., Bohlman, S. A., Hubbell, S., and Pacala, S. W.:
Dominance of the suppressed: Power-law size structure in tropical forests,
Science, 351, 155–157, 2016. a
Feldpausch, T. R., Banin, L., Phillips, O. L., Baker, T. R., Lewis, S. L., Quesada, C. A., Affum-Baffoe, K., Arets, E. J. M. M., Berry, N. J., Bird, M., Brondizio, E. S., de Camargo, P., Chave, J., Djagbletey, G., Domingues, T. F., Drescher, M., Fearnside, P. M., França, M. B., Fyllas, N. M., Lopez-Gonzalez, G., Hladik, A., Higuchi, N., Hunter, M. O., Iida, Y., Salim, K. A., Kassim, A. R., Keller, M., Kemp, J., King, D. A., Lovett, J. C., Marimon, B. S., Marimon-Junior, B. H., Lenza, E., Marshall, A. R., Metcalfe, D. J., Mitchard, E. T. A., Moran, E. F., Nelson, B. W., Nilus, R., Nogueira, E. M., Palace, M., Patiño, S., Peh, K. S.-H., Raventos, M. T., Reitsma, J. M., Saiz, G., Schrodt, F., Sonké, B., Taedoumg, H. E., Tan, S., White, L., Wöll, H., and Lloyd, J.: Height-diameter allometry of tropical forest trees, Biogeosciences, 8, 1081–1106, https://doi.org/10.5194/bg-8-1081-2011, 2011. a
Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., Spessa, A., Lawrence, D., and Bonan, G.: Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED), Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, 2015. a, b, c, d, e, f, g, h
Fisher, R. A., Koven, C. D., Anderegg, W. R., Christoffersen, B. O., Dietze, M. C.,
Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence, P. J., and Lichstein, J. W.:
Vegetation demographics in earth system models: A review of progress
and priorities, Glob. Change Biol., 24, 35–54, 2018. a, b, c
Foley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S.,
and Haxeltine, A.: An integrated biosphere model of land surface
processes, terrestrial carbon balance, and vegetation dynamics,
Global Biogeochem. Cy., 10, 603–628, 1996. a
Francos, A., Elorza, F. J., Bouraoui, F., Bidoglio, G., and Galbiati, L.:
Sensitivity analysis of distributed environmental simulation models:
understanding the model behaviour in hydrological studies at the catchment
scale, Reliab. Eng. Syst. Safe., 79,
205–218, 2003. a
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V.,
Cadule, P., Doney, S., Eby, M., Fung, I., and Bala, G.: Climate-carbon cycle
feedback analysis: Results from the C4MIP model intercomparison,
J. Climate, 19, 3337–3353, 2006. a
Geromel, J. C.: Optimal linear filtering under parameter uncertainty,
IEEE T. Signal Proces., 47, 168–175, 1999. a
Golaz, J. C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V.,
Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R.,
Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M.,
Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W.,
Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S.,
Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre, J. E. J.,
Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A.,
Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y.,
Yoon, J.H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.:
The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution,
J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a
Groenendijk, M., Dolman, A. J., Van Der Molen, M. K., Leuning, R., Arneth, A.,
Delpierre, N., Gash, J. H. C., Lindroth, A., Richardson, A. D., Verbeeck, H., and Wohlfahrt, G.:
Assessing parameter variability in a photosynthesis model within and
between plant functional types using global Fluxnet eddy covariance data,
Agr. Forest Meteorol., 151, 22–38, 2011. a
Gupta, H. V., Bastidas, L. A., Sorooshian, S., Shuttleworth, W. J., and Yang, Z. L.:
Parameter estimation of a land surface scheme using multicriteria
methods, J. Geophys. Res.-Atmos.,
104, 19491–19503, 1999. a
Haaker, M., and Verheijen, P.:, Local and global sensitivity analysis for
a reactor design with parameter uncertainty, Chem. Eng.
Res. Des., 82, 591–598, 2004. a
Helton, J. C.: Uncertainty and sensitivity analysis techniques for use
in performance assessment for radioactive waste disposal, Reliab.
Eng. Syst. Safe., 42, 327–367, 1993. a
Holm, J. A., Knox, R. G., Zhu, Q., Fisher, R. A., Koven, C. D., Lima, A. J. N., Riley, W. J.,
Longo, M., Negron-Juarez, R. I., de Araujo, A. C., Manzi, A., Kueppers, L. M., Moorcroft, P. R., Higuchi, N., and Chambers, J. Q.:
The Central Amazon forest sink under current and future atmospheric CO2: Predictions
from big-leaf and demographic vegetation models, J. Geophys. Res., in review, 2019. a
Hunter, M. O., Keller, M., Victoria, D., and Morton, D. C.: Tree height and tropical forest biomass estimation, Biogeosciences, 10, 8385–8399, https://doi.org/10.5194/bg-10-8385-2013, 2013. a, b
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J.,
Lamarque, J. F., Large, W. G., Lawrence, D., Lindsay, K., and Lipscomb, W. H.:
The community earth system model: a framework for collaborative research,
B. Am. Meteorol. Soc., 94,
1339–1360, 2013. a
Johnson, D. J., Needham, J., Xu, C., Massoud, E. C., Davies, S. J., Anderson-Teixeira, K. J.,
Bunyavejchewin, S., Chambers, J. Q., Chang-Yang, C. H., Chiang, J. M., and Chuyong, G. B.:
Climate sensitive size-dependent survival in tropical trees, Nature Ecology and Evolution,
2, 1436–1442, 2018. a
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a
Kattge, J., Diaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G.,
Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., and Cornelissen, J. H. C.:
TRY – a global database of plant traits, Glob. Change Biol.,
17, 2905–2935, 2011. a
Kattge, J., Ogle, K., Bönisch, G., Díaz, S., Lavorel, S., Madin, J., Nadrowski, K.,
Nöllert, S., Sartor, K., and Wirth, C.:
A generic structure for plant trait databases, Methods Ecol.
Evol., 2, 202–213, 2011. a
Kioutsioukis, I., Tarantola, S., Saltelli, A., and Gatelli, D.: Uncertainty
and global sensitivity analysis of road transport emission estimates,
Atmos. Environ., 38, 6609–6620, 2004. a
Kitanidis, P. K.: Parameter uncertainty in estimation of spatial
functions: Bayesian analysis, Water Resour. Res.,
22, 499–507, 1986. a
Knyazikhin, Y., Glassy, J., Privette, J. L., Tian, Y., Lotsch, A., Zhang, Y., Wang, Y., Morisette, J. T., Votava, P., Myneni, R. B., Nemani, R. R., and Running, S. W.:
MODIS leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by
vegetation (FPAR) product (MOD15) algorithm theoretical basis document,
Theoretical Basis Document, NASA Goddard Space Flight Center,
Greenbelt, MD, 20771, 1999. a
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J.,
Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.:
A dynamic global vegetation model for studies of the coupled atmosphere-biosphere
system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005. a
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S.,
Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., and Adams, J.:
Land information system: An interoperable framework for high resolution land
surface modeling, Environ. Modell. Softw.,
21, 1402–1415, 2006. a
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S. C.,
Lawrence, P. J., Zeng, X., Yang, Z. L., Levis, S., Sakaguchi, K., and Bonan, G. B.:
Parameterization improvements and functional and structural advances in
version 4 of the Community Land Model, J. Adv.
Model Earth Sy., 3, https://doi.org/10.1029/2011MS00045, 2012. a
Lieberman, D., Lieberman, M., Hartshorn, G., and Peralta, R.: Growth rates
and age-size relationships of tropical wet forest trees in costa rica,
J. Trop. Ecol., 1, 97–109, 1985. a
Lu, Y. and Mohanty, S.: Sensitivity analysis of a complex, proposed
geologic waste disposal system using the fourier amplitude sensitivity test
method, Reliab. Eng. Syst. Safe., 72,
275–291, 2001. a
Massoud, E. C., Purdy, A. J., Christoffersen, B. O., Santiago, L. S., and Xu, C.:
Bayesian inference of hydraulic properties in and around a white fir using a process-based ecohydrologic model,
Environ. Modell. Softw.,
115, 76–85, 2019a. a
Massoud, E., Xu, C., Fisher, R., Knox, R., Walker, A., Serbin, S., Christoffersen, B., Holm, J., Kueppers, L., Ricciuto, D., Wei, L., Johnson, D., Chambers, J., Koven, C., McDowell, N., and Vrugt, J.: Identification of key parameters controlling demographically structured vegetation dynamics in a Land Surface Model, 1.0, NGEE Tropics Data Collection, (dataset), https://doi.org/10.15486/ngt/1497413, 2019b. a
McDowell, N. G., Williams, A. P., Xu, C., Pockman, W. T., Dickman, L. T., Sevanto, S.,
Pangle, R., Limousin, J., Plaut, J., Mackay, D. S., and Ogee, J.: Multi-scale
predictions of massive conifer mortality due to chronic temperature rise,
Nat. Clim. Change, 6, 295–300, 2016. a
McDowell, N., Allen, C. D., Anderson–Teixeira, K., Brando, P., Brienen, R., Chambers, J.,
Christoffersen, B., Davies, S., Doughty, C., Duque, A., Espirito–Santo, F., Fisher, R.,
Fontes, C. G., Galbraith, D., Goodsman, D., Grossiord, C., Hartmann, H., Holm, J.,
Johnson, D. J., Kassim, A. R., Keller, M., Koven, C., Kueppers, L., Kumagai, T., Malhi, Y.,
McMahon, S. M., Mencuccini, M., Meir, P., Moorcroft, P., Muller–Landau, H. C., Phillips, O. L.,
Powell, T., Sierra, C. A., Sperry, J., Warren, J., Xu, C., and Xu, X.:
Drivers and mechanisms of tree mortality in moist tropical forests,
New Phytol., 219, 851–869, 2018. a
McRae, G. J., Tilden, J. W., and Seinfeld, J. H.: Global sensitivity analysis
– a computational implementation of the Fourier Amplitude Sensitivity Test (FAST),
Comput. Chem. Eng., 6, 15–25, 1982. a
Menberg, K., Heo, Y., and Choudhary, R.: Sensitivity
analysis methods for building energy models: Comparing computational costs and extractable information,
Energy Buildings, 133, 433–445, 2016. a
Noilhan, J. and Planton, S.: A simple parameterization of land surface
processes for meteorological models, Mon. Weather Rev.,
117, 536–549, 1989. a
O'Hagan, A., and Leonard, T.: Bayes estimation subject to uncertainty
about parameter constraints, Biometrika, 63, 201–203, 1976. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., Levis, S.,
Li, F., Riley, W. J., Subin, Z., Swenson, S. C., and Thornton, P. E.:
Technical description of version 4.5 of the Community Land Model (CLM),
NCAR Technical Note NCAR/TN-503+STR, 420 pp., https://doi.org/10.5065/D6RR1W7M, 2013. a
Pan, Y., McGuire, A. D., Melillo, J. M., Kicklighter, D. W., Sitch, S., and
Prentice, I. C.: A biogeochemistry-based dynamic vegetation model and its
application along a moisture gradient in the continental united states,
J. Veg. Sci., 13, 369–382, 2002. a
Razavi, S. and Gupta, H. V.: A new framework for comprehensive, robust,
and efficient global sensitivity analysis: 1. theory, Water Resour.
Res., 52, 423–439, 2016. a
Rogers, A.: The use and misuse of Vc,max in Earth System Models,
Photosynth. Res., 119, 15–29, 2014. a
Rogers, A., Medlyn, B. E., Dukes, J. S., Bonan, G., Caemmerer, S., Dietze, M. C.,
Kattge, J., Leakey, A. D., Mercado, L. M., Niinemets, Ü., and Prentice, I. C.:
A roadmap for improving the representation of photosynthesis in earth system
models, New Phytol., 213, 22–42, 2017. a
Rosolem, R., Gupta, H. V., Shuttleworth, W. J., de Gonçalves, L. G. G., and Zeng, X.:
Towards a comprehensive approach to parameter estimation in
land surface parameterization schemes, Hydrol. Process.,
27, 2075–2097, 2013. a
Ruppert, D., Wand, M. P., and Carroll, R. J.:Semiparametric
regression, Cambridge university press, 2003. a
Saltelli, A., Tarantola, S., and Chan, K. S.: A quantitative
model-independent method for global sensitivity analysis of model output,
Technometrics, 41, 39–56, 1999. a
Sato, H., Itoh, A., and Kohyama, T.: SEIB–DGVM: A new dynamic global
vegetation model using a spatially explicit individual-based approach,
Ecol. Model., 200, 279–307, 2007. a
Scheiter, S., Langan, L., and Higgins, S. I.: Next-generation dynamic
global vegetation models: learning from community ecology, New
Phytol., 198, 957–969, 2013. a
Schwalm, C. R., Williams, C. A., Schaefer, K., Anderson, R., Arain, M. A., Baker, I.,
Barr, A., Black, T. A., Chen, G., Chen, J. M., and Ciais, P.:
A model-data intercomparison of CO2 exchange across North America: Results from
the North American carbon program site synthesis, J.
Geophys. Res.-Biogeo., 115, G00H05, https://doi.org/10.1029/2009JG001229, 2010. a
Sen, O. L., Bastidas, L. A., Shuttleworth, W. J., Yang, Z. L., Gupta, H. V., and Sorooshian, S.:
Impact of field-calibrated vegetation parameters on gcm
climate simulations, Q. J. Roy. Meteorol.
Soc., 127, 1199–1223, 2001. a
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O.,
Levis, S., Lucht, W., Sykes, M. T., and Thonicke, K.:
Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ
dynamic global vegetation model, Glob. Change Biol.,
9, 161–185, 2003. a
Sitch, S., Huntingford, C., Gedney, N., Levy, P. E., Lomas, M., Piao, S. L., Betts, R.,
Ciais, P., Cox, P., Friedlingstein, P., and Jones, C. D.:
Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle
feedbacks using five dynamic global vegetation models (DGVMs),
Glob. Change Biol., 14, 2015–2039, 2008. a
Song, X. M., Zhang, J. Y., Zhan, C. S., Xuan, Y. Q., Ye, M., and Xu, C. G.:
Global sensitivity analysis in hydrological modeling: Review of concepts,
methods, theoretical framework, and applications,
J. Hydrol., 523, 739–757, 2015. a
Thonicke, K., Venevsky, S., Sitch, S., and Cramer, W.: The role of fire
disturbance for global vegetation dynamics: coupling fire into a dynamic
global vegetation model, Global Ecol. Biogeogr.,
10, 661–677, 2001. a
Vrugt, J., Wijk, M. V., Hopmans, J. W., and Šimunek, J.: One-, two-, and three-dimensional root water uptake functions for transient modeling,
Water Resour. Res., 37, 2457–2470, 2001. a
Wahba, G.: Spline models for observational data, SIAM, ISBN: 978-0-89871-244-5, 161 pp., https://doi.org/10.1137/1.9781611970128, 1990. a
Wang, G., Gertner, G., Liu, X., and Anderson, A.: Uncertainty assessment of
soil erodibility factor for revised universal soil loss equation,
Catena, 46, 1–14, 2001. a
Waring, R., Landsberg, J., and Williams, M.: Net primary production of
forests: a constant fraction of gross primary production?, Tree
Physiol., 18, 129–134, 1998. a
Waring, R. H. and Running, S. W.:Forest ecosystems: analysis at
multiple scales, Elsevier, 2010. a
Wood, E. F., Lettenmaier, D. P., and Zartarian, V. G.: A land-surface
hydrology parameterization with subgrid variability for general circulation
models, J. Geophys. Res.-Atmos.,
97, 2717–2728, 1992. a
Worbes, M.: Annual growth rings, rainfall-dependent growth and long-term
growth patterns of tropical trees from the caparo forest reserve in
venezuela, J. Ecol., 87, 391–403, 1999. a
Xu, C. and Gertner, G. Z.: Extending a global sensitivity analysis
technique to models with correlated parameters, Comput.
Stat. Data An., 51, 5579–5590, 2007. a
Xu, C. and Gertner, G. Z.: Uncertainty and sensitivity analysis for models
with correlated parameters, Reliab. Eng. Syst. Safe.,
93, 1563–1573, 2008. a
Xu, C. and Gertner, G. Z.: Reliability of global sensitivity indices,
J. Stat. Comput. Sim., 81, 1939–1969, 2011. a
Xu, C., Legros, M., Gould, F., and Lloyd, A. L.: Understanding uncertainties
in model-based predictions of Aedes aegypti population dynamics,
PLoS Neglect. Trop. D., 4, e830, https://doi.org/10.1371/journal.pntd.0000830, 2010.
a
Xu, C., McDowell, N. G., Sevanto, S., and Fisher, R. A.: Our limited
ability to predict vegetation dynamics under water stress, New
Phytol., 200, 298–300, 2013. a
Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter
uncertainties on the modeling of terrestrial biosphere dynamics,
Global Biogeochem. Cy., 19, GB3020, https://doi.org/10.1029/2004GB002395, 2005. a
Zeng, X.: Global vegetation root distribution for land modeling,
J. Hydrometeorol., 2, 525–530, 2001. a
Zhou, X., Lin, H., and Lin, H.: Global Sensitivity Analysis,
in: Encyclopedia of GIS, 408–409, 2008. a
Short summary
We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic vegetation model within a land surface model. By running the model 5000 times with changing input parameter values, we found that (1) the photosynthetic capacity controls carbon fluxes, (2) the allometry is important for tree growth, and (3) the targeted carbon storage is important for tree survival. These results can provide guidance on improved model parameterization for a better fit to observations.
We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic...
