Articles | Volume 9, issue 2
https://doi.org/10.5194/gmd-9-587-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-587-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
12 Feb 2016
Model description paper |
| 12 Feb 2016
A global scale mechanistic model of photosynthetic capacity (LUNA V1.0)
A. A. Ali
Earth and Environmental Sciences Division, Los Alamos National Laboratory,
Los Alamos, New Mexico, USA
Department of Civil and Environmental Engineering, University of California
Irvine, Irvine, California, USA
C. Xu
CORRESPONDING AUTHOR
Earth and Environmental Sciences Division, Los Alamos National Laboratory,
Los Alamos, New Mexico, USA
A. Rogers
Environmental and Climate Sciences Department, Brookhaven
National Laboratory, Upton, New York, USA
R. A. Fisher
Climate and Global Dynamics, National Center for Atmospheric Research,
Boulder, Colorado, USA
S. D. Wullschleger
Climate Change Science Institute, Environmental Sciences Division, Oak Ridge
National Laboratory, Oak Ridge, Tennessee, USA
E. C. Massoud
Department of Civil and Environmental Engineering, University of California
Irvine, Irvine, California, USA
J. A. Vrugt
Department of Civil and Environmental Engineering, University of California
Irvine, Irvine, California, USA
Department of Earth System Science, University of California Irvine, Irvine,
California, USA
J. D. Muss
Earth and Environmental Sciences Division, Los Alamos National Laboratory,
Los Alamos, New Mexico, USA
N. G. McDowell
Earth and Environmental Sciences Division, Los Alamos National Laboratory,
Los Alamos, New Mexico, USA
J. B. Fisher
Jet Propulsion Laboratory, California Institute of Technology, Pasadena,
California, USA
P. B. Reich
Department of Forest Resources, University of Minnesota, St. Paul,
Minnesota, USA
Hawkesbury Institute for the Environment, University of Western Sydney,
Penrith, New South Wales, Australia
C. J. Wilson
Earth and Environmental Sciences Division, Los Alamos National Laboratory,
Los Alamos, New Mexico, USA
Related authors
Christian Stiegler, Ana Meijide, Yuanchao Fan, Ashehad Ashween Ali, Tania June, and Alexander Knohl
Biogeosciences, 16, 2873–2890, https://doi.org/10.5194/bg-16-2873-2019, https://doi.org/10.5194/bg-16-2873-2019, 2019
Short summary
Short summary
We show the response of a commercial oil palm plantation in Indonesia to the extreme El Niño–Southern Oscillation (ENSO) event in 2015. Our measurements and model suggest that without human-induced forest fires and related smoke emissions, the observed negative impact on oil palm carbon dioxide greenhouse gas fluxes, carbon accumulation and yield due to ENSO-related drought would have been less pronounced. With respect to climate change we highlight the importance of fire prevention in the area.
Ashehad A. Ali, Yuanchao Fan, Marife D. Corre, Martyna M. Kotowska, Evelyn Hassler, Fernando E. Moyano, Christian Stiegler, Alexander Röll, Ana Meijide, Andre Ringeler, Christoph Leuschner, Tania June, Suria Tarigan, Holger Kreft, Dirk Hölscher, Chonggang Xu, Charles D. Koven, Rosie Fisher, Edzo Veldkamp, and Alexander Knohl
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-236, https://doi.org/10.5194/gmd-2018-236, 2018
Revised manuscript not accepted
Short summary
Short summary
We used carbon-use and water-use related datasets of small-holder rubber plantations from Jambi province, Indonesia to develop and calibrate a rubber plant functional type for the Community Land Model (CLM-rubber). Increased sensitivity of stomata to soil water stress and enhanced respiration costs enabled the model to capture the magnitude of transpiration and leaf area index. Including temporal variations in leaf life span enabled the model to better capture the seasonality of leaf litterfall.
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.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
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.
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.
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.
Katrina E. Bennett, Greta Miller, Robert Busey, Min Chen, Emma R. Lathrop, Julian B. Dann, Mara Nutt, Ryan Crumley, Shannon L. Dillard, Baptiste Dafflon, Jitendra Kumar, W. Robert Bolton, Cathy J. Wilson, Colleen M. Iversen, and Stan D. Wullschleger
The Cryosphere, 16, 3269–3293, https://doi.org/10.5194/tc-16-3269-2022, https://doi.org/10.5194/tc-16-3269-2022, 2022
Short summary
Short summary
In the Arctic and sub-Arctic, climate shifts are changing ecosystems, resulting in alterations in snow, shrubs, and permafrost. Thicker snow under shrubs can lead to warmer permafrost because deeper snow will insulate the ground from the cold winter. In this paper, we use modeling to characterize snow to better understand the drivers of snow distribution. Eventually, this work will be used to improve models used to study future changes in Arctic and sub-Arctic snow patterns.
Qianyu Li, Shawn P. Serbin, Julien Lamour, Kenneth J. Davidson, Kim S. Ely, and Alistair Rogers
Geosci. Model Dev., 15, 4313–4329, https://doi.org/10.5194/gmd-15-4313-2022, https://doi.org/10.5194/gmd-15-4313-2022, 2022
Short summary
Short summary
Stomatal conductance is the rate of water release from leaves’ pores. We implemented an optimal stomatal conductance model in a vegetation model. We then tested and compared it with the existing empirical model in terms of model responses to key environmental variables. We also evaluated the model with measurements at a tropical forest site. Our study suggests that the parameterization of conductance models and current model response to drought are the critical areas for improving models.
Rachael E. McCaully, Carli A. Arendt, Brent D. Newman, Verity G. Salmon, Jeffrey M. Heikoop, Cathy J. Wilson, Sanna Sevanto, Nathan A. Wales, George B. Perkins, Oana C. Marina, and Stan D. Wullschleger
The Cryosphere, 16, 1889–1901, https://doi.org/10.5194/tc-16-1889-2022, https://doi.org/10.5194/tc-16-1889-2022, 2022
Short summary
Short summary
Degrading permafrost and shrub expansion are critically important to tundra biogeochemistry. We observed significant variability in soil pore water NO3-N in an alder-dominated permafrost hillslope in Alaska. Proximity to alder shrubs and the presence or absence of topographic gradients and precipitation events strongly influence NO3-N availability and mobility. The highly dynamic nature of labile N on small spatiotemporal scales has implications for nutrient responses to a warming Arctic.
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.
Elchin E. Jafarov, Daniil Svyatsky, Brent Newman, Dylan Harp, David Moulton, and Cathy Wilson
The Cryosphere, 16, 851–862, https://doi.org/10.5194/tc-16-851-2022, https://doi.org/10.5194/tc-16-851-2022, 2022
Short summary
Short summary
Recent research indicates the importance of lateral transport of dissolved carbon in the polygonal tundra, suggesting that the freeze-up period could further promote lateral carbon transport. We conducted subsurface tracer simulations on high-, flat-, and low-centered polygons to test the importance of the freeze–thaw cycle and freeze-up time for tracer mobility. Our findings illustrate the impact of hydraulic and thermal gradients on tracer mobility, as well as of the freeze-up time.
Karis J. McFarlane, Heather M. Throckmorton, Jeffrey M. Heikoop, Brent D. Newman, Alexandra L. Hedgpeth, Marisa N. Repasch, Thomas P. Guilderson, and Cathy J. Wilson
Biogeosciences, 19, 1211–1223, https://doi.org/10.5194/bg-19-1211-2022, https://doi.org/10.5194/bg-19-1211-2022, 2022
Short summary
Short summary
Planetary warming is increasing seasonal thaw of permafrost, making this extensive old carbon stock vulnerable. In northern Alaska, we found more and older dissolved organic carbon in small drainages later in summer as more permafrost was exposed by deepening thaw. Younger and older carbon did not differ in chemical indicators related to biological lability suggesting this carbon can cycle through aquatic systems and contribute to greenhouse gas emissions as warming increases permafrost thaw.
Dylan R. Harp, Vitaly Zlotnik, Charles J. Abolt, Bob Busey, Sofia T. Avendaño, Brent D. Newman, Adam L. Atchley, Elchin Jafarov, Cathy J. Wilson, and Katrina E. Bennett
The Cryosphere, 15, 4005–4029, https://doi.org/10.5194/tc-15-4005-2021, https://doi.org/10.5194/tc-15-4005-2021, 2021
Short summary
Short summary
Polygon-shaped landforms present in relatively flat Arctic tundra result in complex landscape-scale water drainage. The drainage pathways and the time to transition from inundated conditions to drained have important implications for heat and carbon transport. Using fundamental hydrologic principles, we investigate the drainage pathways and timing of individual polygons, providing insights into the effects of polygon geometry and preferential flow direction on drainage pathways and timing.
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.
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.
A. D. Collins, C. G. Andresen, L. M. Charsley-Groffman, T. Cochran, J. Dann, E. Lathrop, G. J. Riemersma, E. M. Swanson, A. Tapadinhas, and C. J. Wilson
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-2-2020, 1–8, https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-1-2020, https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-1-2020, 2020
Charles D. Koven, Ryan G. Knox, Rosie A. Fisher, Jeffrey Q. Chambers, Bradley O. Christoffersen, Stuart J. Davies, Matteo Detto, Michael C. Dietze, Boris Faybishenko, Jennifer Holm, Maoyi Huang, Marlies Kovenock, Lara M. Kueppers, Gregory Lemieux, Elias Massoud, Nathan G. McDowell, Helene C. Muller-Landau, Jessica F. Needham, Richard J. Norby, Thomas Powell, Alistair Rogers, Shawn P. Serbin, Jacquelyn K. Shuman, Abigail L. S. Swann, Charuleka Varadharajan, Anthony P. Walker, S. Joseph Wright, and Chonggang Xu
Biogeosciences, 17, 3017–3044, https://doi.org/10.5194/bg-17-3017-2020, https://doi.org/10.5194/bg-17-3017-2020, 2020
Short summary
Short summary
Tropical forests play a crucial role in governing climate feedbacks, and are incredibly diverse ecosystems, yet most Earth system models do not take into account the diversity of plant traits in these forests and how this diversity may govern feedbacks. We present an approach to represent diverse competing plant types within Earth system models, test this approach at a tropical forest site, and explore how the representation of disturbance and competition governs traits of the forest community.
Kurt C. Solander, Brent D. Newman, Alessandro Carioca de Araujo, Holly R. Barnard, Z. Carter Berry, Damien Bonal, Mario Bretfeld, Benoit Burban, Luiz Antonio Candido, Rolando Célleri, Jeffery Q. Chambers, Bradley O. Christoffersen, Matteo Detto, Wouter A. Dorigo, Brent E. Ewers, Savio José Filgueiras Ferreira, Alexander Knohl, L. Ruby Leung, Nate G. McDowell, Gretchen R. Miller, Maria Terezinha Ferreira Monteiro, Georgianne W. Moore, Robinson Negron-Juarez, Scott R. Saleska, Christian Stiegler, Javier Tomasella, and Chonggang Xu
Hydrol. Earth Syst. Sci., 24, 2303–2322, https://doi.org/10.5194/hess-24-2303-2020, https://doi.org/10.5194/hess-24-2303-2020, 2020
Short summary
Short summary
We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.
Dylan R. Harp, Vitaly Zlotnik, Charles J. Abolt, Brent D. Newman, Adam L. Atchley, Elchin Jafarov, and Cathy J. Wilson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-100, https://doi.org/10.5194/tc-2020-100, 2020
Manuscript not accepted for further review
Short summary
Short summary
Polygon shaped land forms present in relatively flat Arctic tundra result in complex landscape scale water drainage. The drainage pathways and the time to transition from inundated conditions to drained have important implications for heat and carbon transport. Using fundamental hydrologic principles, we investigate the drainage pathways and timing of individual polygons providing insights into the effects of polygon geometry and preferential flow direction on drainage pathways and timing.
Nathan A. Wales, Jesus D. Gomez-Velez, Brent D. Newman, Cathy J. Wilson, Baptiste Dafflon, Timothy J. Kneafsey, Florian Soom, and Stan D. Wullschleger
Hydrol. Earth Syst. Sci., 24, 1109–1129, https://doi.org/10.5194/hess-24-1109-2020, https://doi.org/10.5194/hess-24-1109-2020, 2020
Short summary
Short summary
Rapid warming in the Arctic is causing increased permafrost temperatures and ground ice degradation. To study the effects of ice degradation on water distribution, tracer was applied to two end members of ice-wedge polygons – a ubiquitous landform in the Arctic. End member type was found to significantly affect water distribution as lower flux was observed with ice-wedge degradation. Results suggest ice degradation can influence partitioning of sequestered carbon as carbon dioxide or methane.
Christian G. Andresen, David M. Lawrence, Cathy J. Wilson, A. David McGuire, Charles Koven, Kevin Schaefer, Elchin Jafarov, Shushi Peng, Xiaodong Chen, Isabelle Gouttevin, Eleanor Burke, Sarah Chadburn, Duoying Ji, Guangsheng Chen, Daniel Hayes, and Wenxin Zhang
The Cryosphere, 14, 445–459, https://doi.org/10.5194/tc-14-445-2020, https://doi.org/10.5194/tc-14-445-2020, 2020
Short summary
Short summary
Widely-used land models project near-surface drying of the terrestrial Arctic despite increases in the net water balance driven by climate change. Drying was generally associated with increases of active-layer depth and permafrost thaw in a warming climate. However, models lack important mechanisms such as thermokarst and soil subsidence that will change the hydrological regime and add to the large uncertainty in the future Arctic hydrological state and the associated permafrost carbon feedback.
Elchin E. Jafarov, Dylan R. Harp, Ethan T. Coon, Baptiste Dafflon, Anh Phuong Tran, Adam L. Atchley, Youzuo Lin, and Cathy J. Wilson
The Cryosphere, 14, 77–91, https://doi.org/10.5194/tc-14-77-2020, https://doi.org/10.5194/tc-14-77-2020, 2020
Short summary
Short summary
Improved subsurface parameterization and benchmarking data are needed to reduce current uncertainty in predicting permafrost response to a warming climate. We developed a subsurface parameter estimation framework that can be used to estimate soil properties where subsurface data are available. We utilize diverse geophysical datasets such as electrical resistance data, soil moisture data, and soil temperature data to recover soil porosity and soil thermal conductivity.
Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, and Jasper A. Vrugt
Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, https://doi.org/10.5194/gmd-12-4133-2019, 2019
Short summary
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.
Christian Stiegler, Ana Meijide, Yuanchao Fan, Ashehad Ashween Ali, Tania June, and Alexander Knohl
Biogeosciences, 16, 2873–2890, https://doi.org/10.5194/bg-16-2873-2019, https://doi.org/10.5194/bg-16-2873-2019, 2019
Short summary
Short summary
We show the response of a commercial oil palm plantation in Indonesia to the extreme El Niño–Southern Oscillation (ENSO) event in 2015. Our measurements and model suggest that without human-induced forest fires and related smoke emissions, the observed negative impact on oil palm carbon dioxide greenhouse gas fluxes, carbon accumulation and yield due to ENSO-related drought would have been less pronounced. With respect to climate change we highlight the importance of fire prevention in the area.
Charles J. Abolt, Michael H. Young, Adam L. Atchley, and Cathy J. Wilson
The Cryosphere, 13, 237–245, https://doi.org/10.5194/tc-13-237-2019, https://doi.org/10.5194/tc-13-237-2019, 2019
Short summary
Short summary
We present a workflow that uses a machine-learning algorithm known as a convolutional neural network (CNN) to rapidly delineate ice wedge polygons in high-resolution topographic datasets. Our workflow permits thorough assessments of polygonal microtopography at the kilometer scale or greater, which can improve understanding of landscape hydrology and carbon budgets. We demonstrate that a single CNN can be trained to delineate polygons with high accuracy in diverse tundra settings.
Jianqiu Zheng, Taniya RoyChowdhury, Ziming Yang, Baohua Gu, Stan D. Wullschleger, and David E. Graham
Biogeosciences, 15, 6621–6635, https://doi.org/10.5194/bg-15-6621-2018, https://doi.org/10.5194/bg-15-6621-2018, 2018
Short summary
Short summary
Arctic soils store vast amounts of frozen carbon that will thaw, fueling microbes that produce carbon dioxide and methane greenhouse gases. We compared methane producing and oxidizing activities in incubated soils and permafrost of Arctic tundra to improve estimates of net emissions. The methane oxidation profile in these soils differs from temperate ecosystems: maximum methane oxidation potential occurs in suboxic soils and permafrost layers, close to the methanogens that produce methane.
Huikyo Lee, Alexander Goodman, Lewis McGibbney, Duane E. Waliser, Jinwon Kim, Paul C. Loikith, Peter B. Gibson, and Elias C. Massoud
Geosci. Model Dev., 11, 4435–4449, https://doi.org/10.5194/gmd-11-4435-2018, https://doi.org/10.5194/gmd-11-4435-2018, 2018
Short summary
Short summary
The Regional Climate Model Evaluation System (RCMES) is designed to facilitate access to observational data and systematic evaluations of regional climate model simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX). To ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's open-source library, Open Climate Workbench (OCW).
Ashehad A. Ali, Yuanchao Fan, Marife D. Corre, Martyna M. Kotowska, Evelyn Hassler, Fernando E. Moyano, Christian Stiegler, Alexander Röll, Ana Meijide, Andre Ringeler, Christoph Leuschner, Tania June, Suria Tarigan, Holger Kreft, Dirk Hölscher, Chonggang Xu, Charles D. Koven, Rosie Fisher, Edzo Veldkamp, and Alexander Knohl
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-236, https://doi.org/10.5194/gmd-2018-236, 2018
Revised manuscript not accepted
Short summary
Short summary
We used carbon-use and water-use related datasets of small-holder rubber plantations from Jambi province, Indonesia to develop and calibrate a rubber plant functional type for the Community Land Model (CLM-rubber). Increased sensitivity of stomata to soil water stress and enhanced respiration costs enabled the model to capture the magnitude of transpiration and leaf area index. Including temporal variations in leaf life span enabled the model to better capture the seasonality of leaf litterfall.
Anthony P. Walker, Ming Ye, Dan Lu, Martin G. De Kauwe, Lianhong Gu, Belinda E. Medlyn, Alistair Rogers, and Shawn P. Serbin
Geosci. Model Dev., 11, 3159–3185, https://doi.org/10.5194/gmd-11-3159-2018, https://doi.org/10.5194/gmd-11-3159-2018, 2018
Short summary
Short summary
Large uncertainty is inherent in model predictions due to imperfect knowledge of how to describe the processes that a model is intended to represent. Yet methods to quantify and evaluate this model hypothesis uncertainty are limited. To address this, the multi-assumption architecture and testbed (MAAT) automates the generation of all possible models by combining multiple representations of multiple processes. MAAT provides a formal framework for quantification of model hypothesis uncertainty.
Hongjuan Zhang, Harrie-Jan Hendricks Franssen, Xujun Han, Jasper A. Vrugt, and Harry Vereecken
Hydrol. Earth Syst. Sci., 21, 4927–4958, https://doi.org/10.5194/hess-21-4927-2017, https://doi.org/10.5194/hess-21-4927-2017, 2017
Short summary
Short summary
Applications of data assimilation (DA) arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. We want to investigate the roles of data assimilation methods and land surface models (LSMs) in joint estimation of states and parameters in the assimilation experiments. We find that all DA methods can improve prediction of states, and that differences between DA methods were limited but that the differences between LSMs were much larger.
Keith F. Lewin, Andrew M. McMahon, Kim S. Ely, Shawn P. Serbin, and Alistair Rogers
Biogeosciences, 14, 4071–4083, https://doi.org/10.5194/bg-14-4071-2017, https://doi.org/10.5194/bg-14-4071-2017, 2017
Short summary
Short summary
Experiments that manipulate the temperature of plants and ecosystems are used to improve understanding of how they will respond to climate change. In logistically challenging locations passive warming using solar energy is the the only viable option for warming experiments. Unfortunately current passive warming approaches can only raise air temperature by about 1.5 °C. We have developed a novel approach that doubles the warming possible using solar energy and requires no power.
Bradley O. Christoffersen, Manuel Gloor, Sophie Fauset, Nikolaos M. Fyllas, David R. Galbraith, Timothy R. Baker, Bart Kruijt, Lucy Rowland, Rosie A. Fisher, Oliver J. Binks, Sanna Sevanto, Chonggang Xu, Steven Jansen, Brendan Choat, Maurizio Mencuccini, Nate G. McDowell, and Patrick Meir
Geosci. Model Dev., 9, 4227–4255, https://doi.org/10.5194/gmd-9-4227-2016, https://doi.org/10.5194/gmd-9-4227-2016, 2016
Short summary
Short summary
We developed a plant hydraulics model for tropical forests based on established plant physiological theory, and parameterized it by conducting a pantropical hydraulic trait survey. We show that a substantial amount of trait diversity can be represented in the model by a reduced set of trait dimensions. The fully parameterized model is able capture tree-level variation in water status and improves simulations of total ecosystem transpiration, showing how to incorporate hydraulic traits in models.
Anna B. Harper, Peter M. Cox, Pierre Friedlingstein, Andy J. Wiltshire, Chris D. Jones, Stephen Sitch, Lina M. Mercado, Margriet Groenendijk, Eddy Robertson, Jens Kattge, Gerhard Bönisch, Owen K. Atkin, Michael Bahn, Johannes Cornelissen, Ülo Niinemets, Vladimir Onipchenko, Josep Peñuelas, Lourens Poorter, Peter B. Reich, Nadjeda A. Soudzilovskaia, and Peter van Bodegom
Geosci. Model Dev., 9, 2415–2440, https://doi.org/10.5194/gmd-9-2415-2016, https://doi.org/10.5194/gmd-9-2415-2016, 2016
Short summary
Short summary
Dynamic global vegetation models (DGVMs) are used to predict the response of vegetation to climate change. We improved the representation of carbon uptake by ecosystems in a DGVM by including a wider range of trade-offs between nutrient allocation to photosynthetic capacity and leaf structure, based on observed plant traits from a worldwide data base. The improved model has higher rates of photosynthesis and net C uptake by plants, and more closely matches observations at site and global scales.
Xiaofeng Xu, Fengming Yuan, Paul J. Hanson, Stan D. Wullschleger, Peter E. Thornton, William J. Riley, Xia Song, David E. Graham, Changchun Song, and Hanqin Tian
Biogeosciences, 13, 3735–3755, https://doi.org/10.5194/bg-13-3735-2016, https://doi.org/10.5194/bg-13-3735-2016, 2016
Short summary
Short summary
Accurately projecting future climate change requires a good methane modeling. However, how good the current models are and what are the key improvements needed remain unclear. This paper reviews the 40 published methane models to characterize the strengths and weakness of current methane models and further lay out the roadmap for future model improvements.
D. G. Miralles, C. Jiménez, M. Jung, D. Michel, A. Ershadi, M. F. McCabe, M. Hirschi, B. Martens, A. J. Dolman, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 823–842, https://doi.org/10.5194/hess-20-823-2016, https://doi.org/10.5194/hess-20-823-2016, 2016
Short summary
Short summary
The WACMOS-ET project aims to advance the development of land evaporation estimates on global and regional scales. Evaluation of current evaporation data sets on the global scale showed that they manifest large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into several components. Different models perform better under different conditions, highlighting the potential for considering biome- or climate-specific model ensembles.
D. Michel, C. Jiménez, D. G. Miralles, M. Jung, M. Hirschi, A. Ershadi, B. Martens, M. F. McCabe, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 803–822, https://doi.org/10.5194/hess-20-803-2016, https://doi.org/10.5194/hess-20-803-2016, 2016
Short summary
Short summary
In this study a common reference input data set from satellite and in situ data is used to run four established evapotranspiration (ET) algorithms using sub-daily and daily input on a tower scale as a testbed for a global ET product. The PT-JPL model and GLEAM provide the best performance for satellite and in situ forcing as well as for the different temporal resolutions. PM-MOD and SEBS perform less well: the PM-MOD model generally underestimates, while SEBS generally overestimates ET.
D. R. Harp, A. L. Atchley, S. L. Painter, E. T. Coon, C. J. Wilson, V. E. Romanovsky, and J. C. Rowland
The Cryosphere, 10, 341–358, https://doi.org/10.5194/tc-10-341-2016, https://doi.org/10.5194/tc-10-341-2016, 2016
Short summary
Short summary
This paper investigates the uncertainty associated with permafrost thaw projections at an intensively monitored site. Permafrost thaw projections are simulated using a thermal hydrology model forced by a worst-case carbon emission scenario. The uncertainties associated with active layer depth, saturation state, thermal regime, and thaw duration are quantified and compared with the effects of climate model uncertainty on permafrost thaw projections.
R. A. Fisher, S. Muszala, M. Verteinstein, P. Lawrence, C. Xu, N. G. McDowell, R. G. Knox, C. Koven, J. Holm, B. M. Rogers, A. Spessa, D. Lawrence, and G. Bonan
Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, https://doi.org/10.5194/gmd-8-3593-2015, 2015
Short summary
Short summary
Predicting the distribution of vegetation under novel climates is important, both to understand how climate change will impact ecosystem services, but also to understand how vegetation changes might affect the carbon, energy and water cycles. Historically, predictions have been heavily dependent upon observations of existing vegetation boundaries. In this paper, we attempt to predict ecosystem boundaries from the ``bottom up'', and illustrate the complexities and promise of this approach.
A. L. Atchley, S. L. Painter, D. R. Harp, E. T. Coon, C. J. Wilson, A. K. Liljedahl, and V. E. Romanovsky
Geosci. Model Dev., 8, 2701–2722, https://doi.org/10.5194/gmd-8-2701-2015, https://doi.org/10.5194/gmd-8-2701-2015, 2015
Short summary
Short summary
Development and calibration of a process-rich model representation of thaw-depth dynamics in Arctic tundra is presented. Improved understanding of polygonal tundra thermal hydrology processes, of thermal conduction, surface and subsurface saturation and snowpack dynamics is gained by using measured field data to calibrate and refine model structure. The refined model is then used identify future data needs and observational studies.
G. B. Bonan, M. Williams, R. A. Fisher, and K. W. Oleson
Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, https://doi.org/10.5194/gmd-7-2193-2014, 2014
F. Deng, D. B. A. Jones, D. K. Henze, N. Bousserez, K. W. Bowman, J. B. Fisher, R. Nassar, C. O'Dell, D. Wunch, P. O. Wennberg, E. A. Kort, S. C. Wofsy, T. Blumenstock, N. M. Deutscher, D. W. T. Griffith, F. Hase, P. Heikkinen, V. Sherlock, K. Strong, R. Sussmann, and T. Warneke
Atmos. Chem. Phys., 14, 3703–3727, https://doi.org/10.5194/acp-14-3703-2014, https://doi.org/10.5194/acp-14-3703-2014, 2014
M. Sadegh and J. A. Vrugt
Hydrol. Earth Syst. Sci., 17, 4831–4850, https://doi.org/10.5194/hess-17-4831-2013, https://doi.org/10.5194/hess-17-4831-2013, 2013
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
L. M. Verheijen, V. Brovkin, R. Aerts, G. Bönisch, J. H. C. Cornelissen, J. Kattge, P. B. Reich, I. J. Wright, and P. M. van Bodegom
Biogeosciences, 10, 5497–5515, https://doi.org/10.5194/bg-10-5497-2013, https://doi.org/10.5194/bg-10-5497-2013, 2013
D. I. Kelley, I. C. Prentice, S. P. Harrison, H. Wang, M. Simard, J. B. Fisher, and K. O. Willis
Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, https://doi.org/10.5194/bg-10-3313-2013, 2013
Related subject area
Biogeosciences
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Short summary
Short summary
Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
Short summary
Short summary
Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Cited articles
Ainsworth, E. A. and Rogers, A.: The response of photosynthesis and
stomatal conductance to rising (CO2): mechanisms and environmental
interactions, Plant Cell Environ., 30, 258–270, 2007.
Ali, A. A., Xu, C., Rogers, A., McDowell, N. G., Medlyn, B. E., Fisher, R.
A., Wullschleger, S. D., Reich, P. B., Vrugt, J. A., Bauerle, W. L.,
Santiago, L. S., and Wilson, C. J.: Global scale environmental control of
plant photosynthetic capacity, Ecol. Appl., 25, 2349–2365, https://doi.org/10.1890/14-2111.1, 2015,
2015.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal
conductance and its contribution to the control of photosynthesis under
different environmental conditions, Dordrecht, The Netherlands,
221–224, 1987.
Bauerle, W. L., Oren, R., Way, D. A., Qian, S. S., Stoy, P. C., Thornton, P.
E., Bowden, J. D., Hoffman, F. M., and Reynolds, R. F.: Photoperiodic
regulation of the seasonal pattern of photosynthetic capacity and the
implications for carbon cycling, Proc. Natl. Acad. Sci. USA, 109, 8612–8617, 2012.
Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis Jr., A. R., and Long,
S. P.: Improved temperature response functions for models of Rubisco-limited
photosynthesis, Plant Cell Environ., 24, 253–259, 2001.
Bernacchi, C. J., Pimentel, C., and Long, S. P.: In vivo temperature response
functions of parameters required to model RuBP-limited photosynthesis,
Plant Cell Environ., 26, 1419–1430, 2003.
Block, K. and Mauritsen, T.: Forcing and feedback in the MPI-ESM-LR coupled
model under abruptly quadrupled CO2, J. Adv. Model.
Earth Syst., 5, 676–691, 2013.
Bonan, G. B., Levis, S., Sitch, S., Vertenstein, M., and Oelson, K. W.: A
dynamic global vegetation model for use with climate models: concepts and
description of simulated vegetation dynamics, Glob. Change Biol., 9,
1543–1566, 2003.
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., 116, 1–22, 2011.
Breshears, D. D., Myers, O. B., Meyer, C. W., Barnes, F. J., Zou, C. B.,
Allen, C. D., McDowell, N. G., and Pockman, W. T.: Tree die-off in response
to global change-type drought: mortality insights from a decade of plant
water potential measurements, Front. Ecol. Environ., 7,
185–189, 2008.
Canadell, J. G., Le Quéré, C., Raupach, M. R., Field, C. B.,
Buitenhuis, E. T., Ciais, P., Conway, T. J., Gillett, N. P., Houghton, R.
A., and Marland, G.: Contributions to accelerating atmospheric CO2 growth
from economic activity, carbon intensity, and efficiency of natural sinks,
Proc. Natl. Acad. Sci. USA, 104, 18866–18870, 2007.
Cernusak, L. A., Winter, K., and Turner, B. L.: Leaf nitrogen to phosphorus
ratios of tropical trees: experimental assessment of physiological and
environmental controls, New Phytol., 185, 770–779, 2010.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and
environmental regualtion of stomatal conductance, photosynthesis, and
transpiration: A model that includes a laminar boundary layer, Agr.
Forest Meteorol., 54, 107–136, 1991.
Comstock, J. and Ehleringer, J. R.: Photoperiod and photosynthetic capacity
in Lotus scoparius, Plant Cell Environ., 9, 609–612, 1986.
Cowan, I. and Farquhar, G.: Stomatal function in relation to leaf metabolism
and environment, 471–505, 1977.
Crafts-Brandner, S. J. and Law, R. D.: Effect of heat stress on the
inhibition and recovery of ribulose-1,5-bisphosphate carboxylase/oxygenase
activation state, Planta, 212, 67–74, 2000.
Crafts-Brandner, S. J. and Salvucci, M. E.: Rubisco activase constrains the
photosynthetic potential of leaves at high temperature and CO2, Proc. Natl. Acad. Sci. USA,
97, 13430–13435, 2000.
Dewar, R. C.: Maximum entropy production and plant optimization theories,
Philos. T. Roy. Soc. B, 365, 1429–1435, 2010.
Dubois, J.-J. B., Fiscus, E. L., Booker, F. L., Flowers, M. D., and Reid, C.
D.: Optimizing the statistical estimation of the parameters of the
Farquhar–von Caemmerer–Berry model of photosynthesis, New Phytol.,
176, 402–414, 2007.
Evans, J. R. and Poorter, H.: Photosynthetic acclimation of plants to growth
irradiance: the relative importance of specific leaf area and nitrogen
partitioning in maximizing carbon gain, Plant Cell Environ., 24,
755–767, 2001.
Farquhar, G. D. and von Caemmerer, S. (Eds.): Modelling of photosynthetic
response to environmental conditions, Heidelberg-Berlin-New York,
Springer-Verlag, 1982.
Farquhar, G. D., Von Caemmerer, S., and Berry, J.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species, Planta,
149, 78–90, 1980.
Franklin, O., Johansson, J., Dewar, R. C., Dieckmann, U., McMurtrie, R. E.,
Brännström, Å., and Dybzinski, R.: Modeling carbon allocation in
trees: a search for principles, Tree Physiol., 32, 648–666, 2012.
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, 2014.
Friend, A.: Use of a model of photosynthesis and leaf microenvironment to
predict optimal stomatal conductance and leaf nitrogen partitioning, Plant
Cell Environ., 14, 895–905, 1991.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C.,
Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., and Vertenstein,
M.: The community climate system model version 4, J. Climate, 24,
4973–4991, 2011.
Goll, D. S., Brovkin, V., Parida, B. R., Reick, C. H., Kattge, J., Reich, P.
B., van Bodegom, P. M., and Niinemets, Ü.: Nutrient limitation reduces land
carbon uptake in simulations with a model of combined carbon, nitrogen and
phosphorus cycling, Biogeosciences, 9, 3547–3569,
https://doi.org/10.5194/bg-9-3547-2012, 2012.
Hanson, P. J., Amthor, J. S., Wullschleger, S. D., Wilson, K. B., Grant, R.
F., Hartley, A., Hui, D., Hunt, J. E. R., Johnson, D. W., Kimball, J. S.,
King, A. W., Luo, Y., McNulty, S. G., Sun, G., Thornton, P. E., Wang, S.,
Williams, M., Baldocchi, D. D., and Cushman, R. M.: Oak forest carbon and
water simulations: model intercomparisons and evaluations against independent
data, Ecol. Monogr., 74, 443–489, 2004.
Harley, P. C. and Baldocchi, D. D.: Scaling carbon dioxide and water vapour
exchange from leaf to canopy in a decisuous forest. I. Leaf model
parametrization, Plant Cell Environ., 18, 1146–1156, 1995.
Harley, P. C., Thomas, R. B., Reynolds, J. F., and Strain, B. R.: Modelling
photosynthesis of cotton grown in elevated CO2, Plant Cell Environ., 15,
271–282, 1992.
Haxeltine, A. and Prentice, I. C.: A general model for the light-use
efficiency of primary production, Funct. Eocol., 10, 551–561, 1996.
Houlton, B. Z., Marklein, A. R., and Bai, E.: Representation of nitrogen in
climate change forecasts, Nature Clim. Change, 5, 398–401, 2015.
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., Lipscomb, W.
H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus,
S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and
Marshall, S.: The Community Earth System Model: A Framework for Collaborative
Research, B. Am. Meteorol. Soc., 94, 1339–1360, 2013.
Jarvis, P. G.: Coupling of carbon and water interactions in forest stands,
Tree Physiol., 2, 347–368, 1986.
Jordan, D. B. and Ogren, W. L.: The CO2/O2 specificity of ribulose
1,5-biphosphate carboxylase/oxygenase. Dependence on ribulose-biphosphate
concentration, pH and temperature, Planta, 161, 308–313, 1984.
Kattge, J. and Knorr, W.: Temperature acclimation in a biochemical model of
photosynthesis: a reanalysis of data from 36 species, Plant Cell Environ.,
30, 1176–1190, 2007.
Kattge, J., Knorr, W., Raddatz, T., and Wirth, C.: Quantifying
photosynthetic capacity and its relationship to leaf nitrogen content for
global-scale terrestrial biosphere models, Glob. Change Biol., 15, 976–991,
2009.
Knorr, W. and Kattge, J.: Inversion of terrestrial ecosystem model parameter
values against eddy covariance measurements by Monte Carlo sampling, Glob.
Change Biol., 11, 1333–1351, 2005.
Laloy, E. and Vrugt, J. A.: High-dimensional posterior exploration of
hydroligic models using multiple-try DREAM(zs) and high-performance
computing, Water Resour. Res., 48, W01526, https://doi.org/01510.01029/02011WR010608, 2012.
Law, R. D. and Crafts-Brandner, S. J.: Inhibition and acclimation of
photosynthesis to heat stress is closely correlated with activation of
ribulose-1,5-bisphosphate carboxylase/ oxygenase, Plant Physiol., 120,
173–181, 1999.
Leuning, R.: Modeling stomatal behavior and photosynthesis of Eucalyptus grandis,
Austr.
J. Plant Physiol., 17, 159–175, 1990.
Leuning, R.: Scaling to a common temperature improves the correlation
between photosynthesis parameters Jmax and Vcmax, J. Exp.
Bot., 307, 345–347, 1997.
Leuning, R.: Temperature dependence of two parameters in a photosynthesis
model, Plant Cell Environ., 25, 1205–1210, 2002.
Limousin, J.-M., Misson, L., Lavoir, A.-V., Martin, N. K., and Rambal, S.:
Do photosynthetic limitations of evergreen Quercus ilex leaves change with
long-term increased drought severity?, Plant Cell Environ., 33, 863–875,
2010.
Lombardozzi, D. L., Bonan, G. B., Smith, N. G., Dukes, J. S., and Fisher, R.
A.: Temperature acclimation of photosynthesis and respiration: A key
uncertainty in the carbon cycle-climate feedback, Geophys. Res. Lett., 42,
8624–8631, 2015.
Long, S. P., Ainsworth, E. A., Rogers, A., and Ort, D. R.: Rising
atmospheric carbon dioxide: plants FACE the future, Ann. Rev. Plant. Biol,
55, 591–628, 2004.
Maire, V., Martre, P., Kattge, J., Gastal, F., Esser, G., Fontaine, S., and
Soussana, F.: The coordination of leaf photosynthesis links C and N fluxes in
C3 plant species, PLos ONE, 7, e38245, https://doi.org/38310.31371/journal.pone.0038345,
2012.
Maire, V., Wright, I. J., Prentice, I. C., Batjes, N. H., Bhaskar, R., van
Bodegom, P. M., Cornwell, W. K., Ellsworth, D., Niinemets, Ü., Ordonez,
A., Reich, P. B., and Santiago, L. S.: Global effects of soil and climate on
leaf photosynthetic traits and rates, Global Ecol. Biogeogr., 24, 706–717,
2015.
Makino, A. and Osmond, B.: Effects of nitrogen nutrition on nitrogen
partitioning between chloroplasts and mitochondria in pea and wheat, Plant
Physiol., 96, 355–362, 1991.
Maroco, J. P., Breia, E., Faria, T., Pereira, J. S., and Chaves, M. M.:
Effects of long-term exposure to elevated CO2 and N fertilization on the
development of photosynthetic capacity and biomass accumulation in
Quercus suber L., Plant Cell Environ., 25, 105–113, 2002.
Martin, B., Martensson, O., and Öquist, G.: Seasonal effects on
photosynthetic electron transport and fluorescence properties in isolated
chloroplasts of Pinus sylvestris, Physiol. Plantarum, 44, 102–109,
1978.
Mayer, D. G. and Butler, D. G.: Statistical validation, Ecol.
Model., 68, 21–32, 1993.
McDowell, N.: Mechanisms linking drought, hydraulics, carbon metabolism, and
vegetation mortality, Plant Physiol., 155, 1051–1059, 2011.
McMurtrie, R. E., Iversen, C. M., Dewar, R. C., Medlyn, B. E., Näsholm,
T., Pepper, D. A., and Norby, R. J.: Plant root distributions and nitrogen
uptake predicted by a hypothesis of optimal root foraging, Ecology and
Evolution, 2, 1235–1250, 2012.
Medlyn, B. E., Badeck, F.-W., De Pury, D. G. G., Barton, C. V. M.,
Broadmeadow, M., Ceulemans, R., De Angelis, P., Forstreuter, M., Jach, M. E.,
Kellomäki, S., Laitat, E., Marek, M., Philippot, S., Rey, A.,
Strassemeyer, J., Laitinen, K., Liozon, R., Portier, B., Proberntz, P., Wang,
K., and Jarvis, P. G.: Effects of elevated [CO2] on photosynthesis in
European forest species: a meta-analysis of model parameters, Plant Cell
Environ., 22, 1475–1495, 1999.
Medlyn, B. E., Dreyer, E., Ellsworth, D., Forstreuter, M., Harley, P. C.,
Kirschbaum, M. U. F., Le Roux, X., Montpied, P., Strassemeyer, J., Walcroft,
A., Wang, K., and Loustau, D.: Temperature response of parameters of a
biochemically based model of photosynthesis. II. A review of experimental
data, Plant Cell Environ., 25, 1167–1179, 2002a.
Medlyn, B. E., Loustau, D., and Delzon, S.: Temperature response of
parameters of a biochemically based model of photosynthesis. I. Seasonal
changes in mature maritime pine (Pinus pinaster Ait.), Plant Cell
Environ., 25, 1155–1165, 2002b.
Medlyn, B. E., Robinson, B. A., Clement, R., and McMurtrie, R. E.: On the
validation of models of forest CO2 exchange using eddy covariance data:
some perils and pitfalls, Tree Physiol., 25, 839–857, 2005.
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. A., Prentice, I. C.,
Barton, C. V. M., Crous, K. Y., De Angelis, P., Freeman, M., and Wingate, L.:
Reconciling the optimal and empirical approaches to modelling stomatal
conductance, Glob. Change Biol., 10, 1365–2486, 2011.
Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.: The
Coupled Model Intercomparison Project (CMIP), B. Am. Meteorol. Soc., 81,
313–318, 2000.
Miao, Z., Xu, M., Lathrop, R. G., and Wang, Y.: Comparison of the A–Cc
curve fitting methods in determining maximum ribulose 1⚫
5-bisphosphate carboxylase/oxygenase carboxylation rate, potential light
saturated electron transport rate and leaf dark respiration, Plant Cell &
Environ., 32, 109–122, 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, 2001.
Moran, E. V., Hartig, F., and Bell, D. M.: Intraspecific trait variation
across scales: implications for understanding global change responses, Glob.
Change Biol., 22, 137–150, https://doi.org/10.1111/gcb.13000, 2016.
Niinemets, Ü. and Tenhunen, J. D.: A model separating leaf structural
and biphysiological effects on carbon gain along light gradients for the
shade-tolerant species Acer saccharum, Plant Cell Environ., 20,
845–866, 1997.
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. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J.-F.,
Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S., Ricciuto, D. M.,
Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical Description of
version 4.5 of the Community Land Model (CLM), NCAR Technical Note
NCAR/TN-503+STR, National Center for Atmospheric Research, Boulder, CO,
2013.
Öquist, G., Brunes, L., Hällgren, J.-E., Gezelius, K., Hallén,
M., and Malmberg, G.: Effects of artificial frost hardening and winter stress
on net photosynthesis, photosynthetic electron transport and RuBP carboxylase
activity in seedlings of Pinus sylvestris, Physiol. Plantarum, 48,
526–531, 1980.
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V., and Wright, I. J.:
Balancing the costs of carbon gain and water transport: testing a new
theoretical framework for plant functional ecology, Ecol. Lett., 17, 82–91,
2014.
Raddatz, T., Reick, C., Knorr, W., Kattge, J., Roeckner, E., Schnur, R.,
Schnitzler, K. G., Wetzel, R. G., and Jungclaus, J.: Will the tropical land
biosphere dominate the climate-carbon cycle feedback during the twenty-first
century?, Clim. Dynam., 29, 565–574, 2007.
Reich, P. B. and Oleksyn, J.: Global patterns of plant leaf N and P in
relation to temperature and latitude, Proc. Natl. Acad. Sci., 101,
11001–11006, 2004.
Reich, P. B., Kloeppel, B. D., Ellsworth, D., and Walters, M. B.: Different
photosynthesis nitorgen relations in decidious hardwood and evergreen
coniferous tree species, Oecologia, 104, 24–30, 1995.
Reich, P. B., Walters, M. B., Tjoelker, M. G., Vanderklein, D., and
Buschena, C.: Photosynthesis and respiration rates depend on leaf and root
morphology and nitrogen concentration in nine boreal tree species differing
in relative growth rate, Funct. Ecol., 12, 395–405, 1998.
Riebeek, H.: The Carbon Cycle, NASA Earth Observatory, available at: http://earthobservatory.nasa.gov/Features/CarbonCycle/ (last access: 6 August 2015),
2011.
Ripullone, F., Grassi, G., Lauteri, M., and Borghetti, M.:
Photosynthesis-nitrogen relationships: interpretation of different patterns
between Pseudotsuga menziesii and Populus x
euroamericana in a mini-stand experiment, Tree Physiol., 23,
137–144, 2003.
Rogers, A.: The use and misuse of Vc, max in earth system models,
Photosynt. Res., 119, 1–15, 2014.
Ryan, M. G.: Foliar maintenance respiration of subalpine and boral trees and
shrubs in relation to nitrogen concentration, Plant Cell Environ., 18,
765–772, 1995.
Schaefer, K., Schwalm, C. R., Williams, C., Arain, M. A., Barr, A., Chen, J.
M., Davis, K. J., Dimitrov, D., Hilton, T. W., Hollinger, D. Y., Humphreys,
E., Poulter, B., Raczka, B. M., Richardson, A. D., Sahoo, A., Thornton, P.,
Vargas, R., Verbeeck, H., Anderson, R., Baker, I., Black, T. A., Bolstad, P.,
Chen, J., Curtis, P. S., Desai, A. R., Dietze, M., Dragoni, D., Gough, C.,
Grant, R. F., Gu, L., Jain, A., Kucharik, C., Law, B., Liu, S., Lokipitiya,
E., Margolis, H. A., Matamala, R., McCaughey, J. H., Monson, R., Munger, J.
W., Oechel, W., Peng, C., Price, D. T., Ricciuto, D., Riley, W. J., Roulet,
N., Tian, H., Tonitto, C., Torn, M., Weng, E., and Zhou, X.: A model-data
comparison of gross primary productivity: Results from the North American
Carbon Program site synthesis, J. Geophys. Res.-Biogeosci., 117, G03010, https://doi.org/03010.01029/02012JG001960,
2012.
Schymanski, S. J., Sivapalan, M., Roderick, M. L., Hutley, L. B., and
Beringer, J.: An optimality-based model of the dynamic feedbacks between
natural vegetation and the water balance, Water Resour. Res., 45, W01412, https://doi.org/01410.01029/02008WR006841,
2009.
Sellers, P. J., Dickinson, R., Randall, D. A., Betts, A. K., Hall, F. G.,
Berry, J. A., Collatz, G. J., Denning, A. S., Mooney, H. A., Nobre, A. D.,
Sato, N., Field, C. B., and HendersonSellers, A.: Modeling the exchanges of
energy, water, and carbon between continents and the atmosphere, Science,
275, 502–509, 1997.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrail carbon cycling in the LPJ dynamic global vegetation model, Glob.
Change Biol., 9, 161–185, 2003.
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space, Global Ecol. Biogeogr.,
10, 621–637, 2001.
Smith, E.: The influence of light and carbon dioxide on photosynthesis,
General Physiology, 20, 807–830, 1937.
Song, Y. H., Ito, S., and Imaizumi, T.: Flowering time regulation:
photoperiod- and temperature-sensing in leaves, Trends Plant Sci., 18,
575–583, 2013.
Spreitzer, R. J. and Salvucci, M. E.: Rubisco: structure, regulatory
interactions, and possibilities for a better enzyme, Ann. Rev. Plant Bio.,
53, 449–475, 2002.
Strand, M. and Öquist, G.: Effects of frost hardening, dehardening and
freezing trees on in vivo fluorescence of seedlings of Scots pine
(Pinus sylvestris L.), Plant Cell Environ., 11, 231–238, 1988.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2013.
Thomas, R. Q. and Williams, M.: A model using marginal efficiency of
investment to analyze carbon and nitrogen interactions in terrestrial
ecosystems (ACONITE Version 1), Geosci. Model Dev., 7, 2015–2037,
https://doi.org/10.5194/gmd-7-2015-2014, 2014.
Valladares, F., Wright, S. J., Lasso, E., Kitajima, K., and Pearcy, R. W.:
Plastic phenotypic response to light of 16 congeneric shrubs from a
Panamanian rainforest, Ecology, 81, 1925–1936, 2000.
Van Oijen, M., Schapendonk, A., and Hoglind, M.: On the relative magnitudes
of photosynthesis, respiration, growth and carbon storage in vegetation, Ann.
Bot.-London, 105, 793–797, 2010.
Verheijen, L. M., Brovkin, V., Aerts, R., Bönisch, G., Cornelissen, J. H. C.,
Kattge, J., Reich, P. B., Wright, I. J., and van Bodegom, P. M.: Impacts of
trait variation through observed trait–climate relationships on performance
of an Earth system model: a conceptual analysis, Biogeosciences, 10,
5497–5515, https://doi.org/10.5194/bg-10-5497-2013, 2013.
Verheijen, L. M., Aerts, R., Brovkin, V., Cavender-Bares, J., Cornelissen,
J. H. C., Kattge, J., and van Bodegom, P. M.: Inclusion of ecologically based
trait variation in plant functional types reduces the projected land carbon
sink in an earth system model, Glob. Change Biol., 21, 3074–3086, 2015.
Vrugt, J. A., ter Braak, C. J. F., Clark, M. P., Hyman, J. M., and Robinson,
B. A.: Treatment of input uncertainty in hydrologic modeling: Doing hydrology
backward with Markov chain Monte Carlo simulation, Water Resour. Res., 44, W00B09, https://doi.org/10.1029/2007WR006720,
2008.
Vrugt, J. A., ter Braak, C. J. F., Diks, C. G. H., Robinson, B. A., Hyman,
J. M., and Higdon, D.: Accelerating Markov Chain Monte Carlo Simulation by
Differential Evolution with Self-Adaptive Randomized Subspace Sampling, Int.
J. Nonlin. Sci. Num., 10, 273–290, 2009.
Walker, A. P., Beckerman, A. P., Gu, L., Kattge, J., Cernusak, L. A.,
Domingues, T. F., Scales, J. C., Wohlfahrt, G., Wullschleger, S. D., and
Woodward, F. I.: The relationship of leaf photosynthetic traits – Vcmax and
Jmax – to leaf nitrogen, leaf phosphorus, and specific leaf area: a
meta-analysis and modeling study, Ecology and Evolution, 4, 3218–3235, 2014.
Wang, Y. P., Law, R. M., and Pak, B.: A global model of carbon, nitrogen and
phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7,
2261–2282, https://doi.org/10.5194/bg-7-2261-2010, 2010.
White, M. A., Thornton, P. E., Running, S. W., and Nemani, R. R.:
Parameterization and sensitivity analysis of the BIOME-BCG terrestrial
ecosystem model: net primary production controls, Earth Interact., 4, 1–85,
2000.
Whitley, R. J., Catriona, M. O., Macinnis-Ng, C., Hutley, L. B., Beringer,
J., Zeppel, M., Williams, M., Taylor, D., and Eamus, D.: Is productivity of
mesic savannas light limited or water limited? Results of a simulation study,
Glob. Change Biol., 17, 3130–3149, 2011.
Wieder, W. R., Cleveland, C. C., Lawrence, D. M., and Bonan, G. B.: Effects
of model structural uncertainty on carbon cycle projections: biological
nitrogen fixation as a case study, Environ. Res. Lett., 10, 044016,
https://doi.org/10.1088/1748-9326/1010/1084/0440,
2015.
Wilson, K. B., Baldocchi, D. D., and Hanson, P. J.: Leaf age affects the
seasonal pattern of photosynthetic capacity and net ecosystem exchange of
carbon in a deciduous forest, Plant Cell Environ., 24, 571–583, 2001.
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z.,
Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer,
M., Flexas, J., Garnier, E., Groom, P. K., Gulias, J., Hikosaka, K., Lamont,
B. B., Lee, T. D., Lee, W., Lusk, C. H., Midgley, J. J., Navas, M.-L.,
Niinemets, Ü., Olesksyn, J., Osada, N., Poorter, H., Poot, P., Prior, L.,
Pyankov, V. I., Roumet, C., Thomas, S. C., Tjoelker, M. G., Veneklaas, E. J.,
and Villar, R.: The worldwide leaf economics spectrum, Nature, 428, 821–827,
2004.
Wullschleger, S. D.: Biochemical limitations to carbon assimilation in
C3 plants: a retrospective analysis of A∕Ci curves from 109
species, J. Exp. Bot. 44, 907–920, 1993.
Xu, C., Fisher, R., Wullschleger, S. D., Wilson, C. J., Cai, M., and
McDowell, N.: Toward a mechanistic modeling of nitrogen limitation on
vegetation dynamics, PLos ONE, 7, e37914, https://doi.org/10.1371/journal.pone.0037914,
2012.
Xu, L. and Baldocchi, D. D.: Seasonal trends in photosynthetic parameters
and stomatal conductance of blue oak (Quercus douglasii) under
prolonged summer drought and high temperature, Tree Physiol., 23, 865–877,
2003.
Yamori, W., Suzuki, K., Noguchi, K. O., Nakai, M., and Terashima, I.:
Effects of Rubisco kinetics and Rubisco activation state on the temperature
dependence of the photosynthetic rate in spinach leaves from contrasting
growth temperatures, Plant Cell Environ., 29, 1659–1670, 2006.
Short summary
We have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA V1.0) to predict the photosynthetic capacities at the global scale based on the optimization of key leaf-level metabolic processes. LUNA model predicts that future climatic changes would mostly affect plant photosynthetic capabilities in high-latitude regions and that Earth system models using fixed photosynthetic capabilities are likely to substantially overestimate future global photosynthesis.
We have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA...
