Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8153-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8153-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
14 Nov 2022
Model description paper |
| 14 Nov 2022
| 14 Nov 2022
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Center for Climate Systems Research, Columbia University, New York, NY 10025, USA
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Igor Aleinov
Center for Climate Systems Research, Columbia University, New York, NY 10025, USA
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Ram Singh
Center for Climate Systems Research, Columbia University, New York, NY 10025, USA
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Michael J. Puma
Center for Climate Systems Research, Columbia University, New York, NY 10025, USA
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Sonali S. McDermid
Department of Environmental Studies, New York University, New York, NY 10003, USA
Nancy Y. Kiang
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Maxwell Kelley
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Kevin Wilcox
Department of Ecosystem Science and Management, University of Wyoming, Laramie, WY 82071, USA
Ray Dybzinski
School of Environmental Sustainability, Loyola University Chicago, Chicago, IL 60660, USA
Caroline E. Farrior
Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA
Stephen W. Pacala
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
Benjamin I. Cook
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Related authors
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
Short summary
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Ensheng Weng, Ray Dybzinski, Caroline E. Farrior, and Stephen W. Pacala
Biogeosciences, 16, 4577–4599, https://doi.org/10.5194/bg-16-4577-2019, https://doi.org/10.5194/bg-16-4577-2019, 2019
Short summary
Short summary
Our study illustrates that the competition processes for light and soil resources in a game-theoretic vegetation demographic model can substantially change the prediction of the contribution of ecosystems to the global carbon cycle. The model that tracks the competitive allocation strategies can generate significantly different ecosystem-level predictions than those with fixed allocation strategies.
Zhenggang Du, Ensheng Weng, Lifen Jiang, Yiqi Luo, Jianyang Xia, and Xuhui Zhou
Geosci. Model Dev., 11, 4399–4416, https://doi.org/10.5194/gmd-11-4399-2018, https://doi.org/10.5194/gmd-11-4399-2018, 2018
Short summary
Short summary
In this study, based on a traceability analysis technique, we evaluated alternative representations of C–N interactions and their impacts on the C cycle using the TECO model framework. Our results showed that different representations of C–N coupling processes lead to divergent effects on plant production, C residence time, and thus the ecosystem C storage capacity. Identifying those effects can help us to improve the N limitation assumptions employed in terrestrial ecosystem models.
E. S. Weng, S. Malyshev, J. W. Lichstein, C. E. Farrior, R. Dybzinski, T. Zhang, E. Shevliakova, and S. W. Pacala
Biogeosciences, 12, 2655–2694, https://doi.org/10.5194/bg-12-2655-2015, https://doi.org/10.5194/bg-12-2655-2015, 2015
Short summary
Short summary
We present a model, LM3-PPA, which simulates vegetation dynamics and biogeochemical processes by explicitly scaling from individual plants to ecosystems using the perfect plasticity approximation. It includes height-structured competition for light- and root-allocation-dependent competition for belowground resources. Because of the tractability of the PPA, the coupled LM3-PPA model is able to retain computational tractability, as well as close linkages to mathematically tractable special cases.
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Nikolina Mileva, Julia Pongratz, Vivek K. Arora, Akihiko Ito, Sebastiaan Luyssaert, Sonali S. McDermid, Paul A. Miller, Daniele Peano, Roland Séférian, Yanwu Zhang, and Wolfgang Buermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-979, https://doi.org/10.5194/egusphere-2025-979, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Despite forests being so important for mitigating climate change, there are still uncertainties about how much the changes in forest cover contribute to the cooling/warming of the climate. Climate models and real-world observations often disagree about the magnitude and even the direction of these changes. We constrain climate models scenarios of widespread deforestation with satellite and in-situ data and show that models still have difficulties representing the movement of heat and water.
Yona Silvy, Thomas L. Frölicher, Jens Terhaar, Fortunat Joos, Friedrich A. Burger, Fabrice Lacroix, Myles Allen, Raffaele Bernardello, Laurent Bopp, Victor Brovkin, Jonathan R. Buzan, Patricia Cadule, Martin Dix, John Dunne, Pierre Friedlingstein, Goran Georgievski, Tomohiro Hajima, Stuart Jenkins, Michio Kawamiya, Nancy Y. Kiang, Vladimir Lapin, Donghyun Lee, Paul Lerner, Nadine Mengis, Estela A. Monteiro, David Paynter, Glen P. Peters, Anastasia Romanou, Jörg Schwinger, Sarah Sparrow, Eric Stofferahn, Jerry Tjiputra, Etienne Tourigny, and Tilo Ziehn
Earth Syst. Dynam., 15, 1591–1628, https://doi.org/10.5194/esd-15-1591-2024, https://doi.org/10.5194/esd-15-1591-2024, 2024
Short summary
Short summary
The adaptive emission reduction approach is applied with Earth system models to generate temperature stabilization simulations. These simulations provide compatible emission pathways and budgets for a given warming level, uncovering uncertainty ranges previously missing in the Coupled Model Intercomparison Project scenarios. These target-based emission-driven simulations offer a more coherent assessment across models for studying both the carbon cycle and its impacts under climate stabilization.
Ram Singh, Alexander Koch, Allegra N. LeGrande, Kostas Tsigaridis, Riovie D. Ramos, Francis Ludlow, Igor Aleinov, Reto Ruedy, and Jed O. Kaplan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-219, https://doi.org/10.5194/gmd-2024-219, 2024
Preprint under review for GMD
Short summary
Short summary
This study presents and demonstrates an experimental framework for asynchronous land-atmosphere coupling using the NASA GISS ModelE and LPJ-LMfire models for the 2.5ka period. This framework addresses the limitation of NASA ModelE, which does not have a fully dynamic vegetation model component. It also shows the role of model performance metrics, such as model bias and variability, and the simulated climate is evaluated against the multi-proxy paleoclimate reconstructions for the 2.5ka climate.
Ram Singh, Kostas Tsigaridis, Diana Bull, Laura P. Swiler, Benjamin M. Wagman, and Kate Marvel
EGUsphere, https://doi.org/10.5194/egusphere-2024-2280, https://doi.org/10.5194/egusphere-2024-2280, 2024
Short summary
Short summary
Analysis of post-eruption climate conditions using the impact metrics is crucial for understanding the hydroclimatic responses. We used NASA’s Earth system model to perform the experiments and utilize the moisture-based impact metrics and hydrological variables to investigate the effect of volcanically induced conditions that govern plant productivity. This study demonstrates the Mt. Pinatubo’s impact on drivers of plant productivity and regional and seasonal dependence of different drivers.
Kevin R. Wilcox, Scott L. Collins, Alan K. Knapp, William Pockman, Zheng Shi, Melinda D. Smith, and Yiqi Luo
Biogeosciences, 20, 2707–2725, https://doi.org/10.5194/bg-20-2707-2023, https://doi.org/10.5194/bg-20-2707-2023, 2023
Short summary
Short summary
The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems store carbon in the future. Here, we employ novel data–model integration techniques to identify the carbon capacity of six grassland sites spanning the US Great Plains. Hot and dry sites had low C capacity due to less plant growth and high turnover of soil C, so they may be a C source in the future. Alternately, cooler and wetter ecosystems had high C capacity, so these systems may be a future C sink.
Ram Singh, Kostas Tsigaridis, Allegra N. LeGrande, Francis Ludlow, and Joseph G. Manning
Clim. Past, 19, 249–275, https://doi.org/10.5194/cp-19-249-2023, https://doi.org/10.5194/cp-19-249-2023, 2023
Short summary
Short summary
This work is a modeling effort to investigate the hydroclimatic impacts of a volcanic
quartetduring 168–158 BCE over the Nile River basin in the context of Ancient Egypt's Ptolemaic era (305–30 BCE). The model simulated a robust surface cooling (~ 1.0–1.5 °C), suppressing the African monsoon (deficit of > 1 mm d−1 over East Africa) and agriculturally vital Nile summer flooding. Our result supports the hypothesized relation between volcanic eruptions, hydroclimatic shocks, and societal impacts.
Elizabeth Klovenski, Yuxuan Wang, Susanne E. Bauer, Kostas Tsigaridis, Greg Faluvegi, Igor Aleinov, Nancy Y. Kiang, Alex Guenther, Xiaoyan Jiang, Wei Li, and Nan Lin
Atmos. Chem. Phys., 22, 13303–13323, https://doi.org/10.5194/acp-22-13303-2022, https://doi.org/10.5194/acp-22-13303-2022, 2022
Short summary
Short summary
Severe drought stresses vegetation and causes reduced emission of isoprene. We study the impact of including a new isoprene drought stress (yd) parameterization in NASA GISS ModelE called DroughtStress_ModelE, which is specifically tuned for ModelE. Inclusion of yd leads to better simulated isoprene emissions at the MOFLUX site during the severe drought of 2012, reduced overestimation of OMI satellite ΩHCHO (formaldehyde column), and improved simulated O3 (ozone) during drought.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
Short summary
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Sian Kou-Giesbrecht, Sergey Malyshev, Isabel Martínez Cano, Stephen W. Pacala, Elena Shevliakova, Thomas A. Bytnerowicz, and Duncan N. L. Menge
Biogeosciences, 18, 4143–4183, https://doi.org/10.5194/bg-18-4143-2021, https://doi.org/10.5194/bg-18-4143-2021, 2021
Short summary
Short summary
Representing biological nitrogen fixation (BNF) is an important challenge for land models. We present a novel representation of BNF and updated nitrogen cycling in a land model. It includes a representation of asymbiotic BNF by soil microbes and the competitive dynamics between nitrogen-fixing and non-fixing plants. It improves estimations of major carbon and nitrogen pools and fluxes and their temporal dynamics in comparison to previous representations of BNF in land models.
Ensheng Weng, Ray Dybzinski, Caroline E. Farrior, and Stephen W. Pacala
Biogeosciences, 16, 4577–4599, https://doi.org/10.5194/bg-16-4577-2019, https://doi.org/10.5194/bg-16-4577-2019, 2019
Short summary
Short summary
Our study illustrates that the competition processes for light and soil resources in a game-theoretic vegetation demographic model can substantially change the prediction of the contribution of ecosystems to the global carbon cycle. The model that tracks the competitive allocation strategies can generate significantly different ecosystem-level predictions than those with fixed allocation strategies.
Grégory Cesana, Anthony D. Del Genio, Andrew S. Ackerman, Maxwell Kelley, Gregory Elsaesser, Ann M. Fridlind, Ye Cheng, and Mao-Sung Yao
Atmos. Chem. Phys., 19, 2813–2832, https://doi.org/10.5194/acp-19-2813-2019, https://doi.org/10.5194/acp-19-2813-2019, 2019
Short summary
Short summary
The response of low clouds to climate change (i.e., cloud feedbacks) is still pointed out as being the largest source of uncertainty in climate models. Here we use CALIPSO observations to discriminate climate models that reproduce observed interannual change of cloud fraction with SST forcings, referred to as a present-day cloud feedback. Modeling moist processes in the planetary boundary layer is crucial to produce large stratocumulus decks and realistic present-day cloud feedbacks.
Isabel Martínez Cano, Helene C. Muller-Landau, S. Joseph Wright, Stephanie A. Bohlman, and Stephen W. Pacala
Biogeosciences, 16, 847–862, https://doi.org/10.5194/bg-16-847-2019, https://doi.org/10.5194/bg-16-847-2019, 2019
Zhenggang Du, Ensheng Weng, Lifen Jiang, Yiqi Luo, Jianyang Xia, and Xuhui Zhou
Geosci. Model Dev., 11, 4399–4416, https://doi.org/10.5194/gmd-11-4399-2018, https://doi.org/10.5194/gmd-11-4399-2018, 2018
Short summary
Short summary
In this study, based on a traceability analysis technique, we evaluated alternative representations of C–N interactions and their impacts on the C cycle using the TECO model framework. Our results showed that different representations of C–N coupling processes lead to divergent effects on plant production, C residence time, and thus the ecosystem C storage capacity. Identifying those effects can help us to improve the N limitation assumptions employed in terrestrial ecosystem models.
Katia Lamer, Ann M. Fridlind, Andrew S. Ackerman, Pavlos Kollias, Eugene E. Clothiaux, and Maxwell Kelley
Geosci. Model Dev., 11, 4195–4214, https://doi.org/10.5194/gmd-11-4195-2018, https://doi.org/10.5194/gmd-11-4195-2018, 2018
Short summary
Short summary
Weather and climate predictions of cloud, rain, and snow occurrence remain uncertain, in part because guidance from observation is incomplete. We present a tool that transforms predictions into observations from ground-based remote sensors. Liquid water and ice occurrence errors associated with the transformation are below 8 %, with ~ 3 % uncertainty. This (GO)2-SIM forward-simulator tool enables better evaluation of cloud, rain, and snow occurrence predictions using available observations.
Sam S. Rabin, Daniel S. Ward, Sergey L. Malyshev, Brian I. Magi, Elena Shevliakova, and Stephen W. Pacala
Geosci. Model Dev., 11, 815–842, https://doi.org/10.5194/gmd-11-815-2018, https://doi.org/10.5194/gmd-11-815-2018, 2018
Short summary
Short summary
This paper describes a new fire model that for the first time simulates how fire is used on cropland and pasture in the modern day, as imposed using a recently developed dataset. A non-agricultural fire module is fit algorithmically against non-agricultural burned area. Fitting improves performance and the general global pattern of fire is represented, but some gaps remain. The novel separation of agricultural burning from other fire may necessitate new design thinking in the future.
PAGES Hydro2k Consortium
Clim. Past, 13, 1851–1900, https://doi.org/10.5194/cp-13-1851-2017, https://doi.org/10.5194/cp-13-1851-2017, 2017
Short summary
Short summary
Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited due to a paucity of modern instrumental observations. We review how proxy records of past climate and climate model simulations can be used in tandem to understand hydroclimate variability over the last 2000 years and how these tools can also inform risk assessments of future hydroclimatic extremes.
Nir Y. Krakauer, Michael J. Puma, Benjamin I. Cook, Pierre Gentine, and Larissa Nazarenko
Earth Syst. Dynam., 7, 863–876, https://doi.org/10.5194/esd-7-863-2016, https://doi.org/10.5194/esd-7-863-2016, 2016
Short summary
Short summary
We simulated effects of irrigation on climate with the NASA GISS global climate model. Present-day irrigation levels affected air pressures and temperatures even in non-irrigated land and ocean areas. The simulated effect was bigger and more widespread when ocean temperatures in the climate model could change, rather than being fixed. We suggest that expanding irrigation may affect global climate more than previously believed.
Jonathan M. Gregory, Nathaelle Bouttes, Stephen M. Griffies, Helmuth Haak, William J. Hurlin, Johann Jungclaus, Maxwell Kelley, Warren G. Lee, John Marshall, Anastasia Romanou, Oleg A. Saenko, Detlef Stammer, and Michael Winton
Geosci. Model Dev., 9, 3993–4017, https://doi.org/10.5194/gmd-9-3993-2016, https://doi.org/10.5194/gmd-9-3993-2016, 2016
Short summary
Short summary
As a consequence of greenhouse gas emissions, changes in ocean temperature, salinity, circulation and sea level are expected in coming decades. Among the models used for climate projections for the 21st century, there is a large spread in projections of these effects. The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to investigate and explain this spread by prescribing a common set of changes in the input of heat, water and wind stress to the ocean in the participating models.
Y. Kim, P. R. Moorcroft, I. Aleinov, M. J. Puma, and N. Y. Kiang
Geosci. Model Dev., 8, 3837–3865, https://doi.org/10.5194/gmd-8-3837-2015, https://doi.org/10.5194/gmd-8-3837-2015, 2015
Short summary
Short summary
The Ent Terrestrial Biosphere Model is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models. This study describes the leaf phenology submodel implemented in the Ent TBM. We evaluate the performance in reproducing observed leaf seasonal growth as well as water and carbon fluxes for four plant functional types at five Fluxnet sites.
E. S. Weng, S. Malyshev, J. W. Lichstein, C. E. Farrior, R. Dybzinski, T. Zhang, E. Shevliakova, and S. W. Pacala
Biogeosciences, 12, 2655–2694, https://doi.org/10.5194/bg-12-2655-2015, https://doi.org/10.5194/bg-12-2655-2015, 2015
Short summary
Short summary
We present a model, LM3-PPA, which simulates vegetation dynamics and biogeochemical processes by explicitly scaling from individual plants to ecosystems using the perfect plasticity approximation. It includes height-structured competition for light- and root-allocation-dependent competition for belowground resources. Because of the tractability of the PPA, the coupled LM3-PPA model is able to retain computational tractability, as well as close linkages to mathematically tractable special cases.
G. A. Schmidt, J. D. Annan, P. J. Bartlein, B. I. Cook, E. Guilyardi, J. C. Hargreaves, S. P. Harrison, M. Kageyama, A. N. LeGrande, B. Konecky, S. Lovejoy, M. E. Mann, V. Masson-Delmotte, C. Risi, D. Thompson, A. Timmermann, L.-B. Tremblay, and P. Yiou
Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, https://doi.org/10.5194/cp-10-221-2014, 2014
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
N. Unger, K. Harper, Y. Zheng, N. Y. Kiang, I. Aleinov, A. Arneth, G. Schurgers, C. Amelynck, A. Goldstein, A. Guenther, B. Heinesch, C. N. Hewitt, T. Karl, Q. Laffineur, B. Langford, K. A. McKinney, P. Misztal, M. Potosnak, J. Rinne, S. Pressley, N. Schoon, and D. Serça
Atmos. Chem. Phys., 13, 10243–10269, https://doi.org/10.5194/acp-13-10243-2013, https://doi.org/10.5194/acp-13-10243-2013, 2013
N. Y. Krakauer, M. J. Puma, and B. I. Cook
Hydrol. Earth Syst. Sci., 17, 1963–1974, https://doi.org/10.5194/hess-17-1963-2013, https://doi.org/10.5194/hess-17-1963-2013, 2013
Related subject area
Biogeosciences
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
Parameterisation toolbox for physical-biogeochemical model compatible with FABM. Case study: the coupled 1D GOTM-ECOSMO E2E for the Sylt-Romo Bight, North Sea
China Wildfire Emission (ChinaWED v1) for the period 2012–2022
H2MV (v1.0): Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation
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
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
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)
Alquimia v1.0: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements
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
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Process-based Modeling of Solar-induced Chlorophyll Fluorescence with VISIT-SIF version 1.0
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
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)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
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.
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.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-223, https://doi.org/10.5194/gmd-2024-223, 2024
Revised manuscript accepted for GMD
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 have changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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.
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.
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.
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.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
Short summary
Short summary
Parameterisation is key in modeling to reproduce observations well but is often done manually. This study presents a Particle Swarm Optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
Short summary
Short summary
We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2592, https://doi.org/10.5194/egusphere-2024-2592, 2024
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they’ve changed from 1901 to 2018. We found that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation has increased, especially in the tropics, while N limitation has decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
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.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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.
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.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1914, https://doi.org/10.5194/egusphere-2024-1914, 2024
Short summary
Short summary
Under climate change, the conditions for wildfires to form are becoming more frequent 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 crucial platform for future developments.
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.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
Short summary
Short summary
When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those 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 see what changes on our lands.
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.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
EGUsphere, https://doi.org/10.5194/egusphere-2024-1542, https://doi.org/10.5194/egusphere-2024-1542, 2024
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical process-based model developed to represent the global SIF observed by GOSAT. Our model simulation reproduced the global distribution and seasonal variations of GOSAT SIF. The model can be utilized to improve photosynthetic process through the combination of biogeochemical modeling and GOSAT SIF.
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.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Cited articles
Aakala, T., Fraver, S., Palik, B. J., and D'Amato, A. W.:
Spatially random mortality in old-growth red pine forests of northern Minnesota, Can. J. Forest Res., 42, 899–907, https://doi.org/10.1139/x2012-044, 2012.
Abramoff, R. Z. and Finzi, A. C.:
Are above- and below-ground phenology in sync?, New Phytol., 205, 1054–1061, https://doi.org/10.1111/nph.13111, 2015.
Aerts, R.:
The advantages of being evergreen, Trends Ecol. Evol., 10, 402–407, https://doi.org/10.1016/S0169-5347(00)89156-9, 1995.
Ainsworth, E. A. and Long, S. P.:
What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2: Tansley review, New Phytol., 165, 351–372, https://doi.org/10.1111/j.1469-8137.2004.01224.x, 2004.
Aleixo, I., Norris, D., Hemerik, L., Barbosa, A., Prata, E., Costa, F., and Poorter, L.:
Amazonian rainforest tree mortality driven by climate and functional traits, Nat. Clim. Change, 9, 384–388, https://doi.org/10.1038/s41558-019-0458-0, 2019.
Alemohammad, S. H., Fang, B., Konings, A. G., Aires, F., Green, J. K., Kolassa, J., Miralles, D., Prigent, C., and Gentine, P.:
Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence, Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, 2017.
Alexander, K. and Easterbrook, S. M.:
The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations, Geosci. Model Dev., 8, 1221–1232, https://doi.org/10.5194/gmd-8-1221-2015, 2015.
Allen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D. D., Hogg, E. H. (Ted), Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J.-H., Allard, G., Running, S. W., Semerci, A., and Cobb, N.:
A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests, Forest Ecol. Manag., 259, 660–684, https://doi.org/10.1016/j.foreco.2009.09.001, 2010.
Anderegg, W. R. L., Kane, J. M., and Anderegg, L. D. L.:
Consequences of widespread tree mortality triggered by drought and temperature stress, Nat. Clim. Change, 3, 30–36, https://doi.org/10.1038/nclimate1635, 2012.
Anten, N. P.:
Evolutionarily stable leaf area production in plant populations, J. Theor. Biol., 217, 15–32, 2002.
Argles, A. P. K., Moore, J. R., Huntingford, C., Wiltshire, A. J., Harper, A. B., Jones, C. D., and Cox, P. M.:
Robust Ecosystem Demography (RED version 1.0): a parsimonious approach to modelling vegetation dynamics in Earth system models, Geosci. Model Dev., 13, 4067–4089, https://doi.org/10.5194/gmd-13-4067-2020, 2020.
Arora, V. K. and Boer, G. J.:
A parameterization of leaf phenology for the terrestrial ecosystem component of climate models, Glob. Change Biol., 11, 39–59, https://doi.org/10.1111/j.1365-2486.2004.00890.x, 2005.
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.:
Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020.
Avissar, R. and Werth, D.:
Global Hydroclimatological Teleconnections Resulting from Tropical Deforestation, J. Hydrometeorol., 6, 134–145, https://doi.org/10.1175/JHM406.1, 2005.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw U, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.:
FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Beer: Bestimmung der Absorption des rothen Lichts in farbigen Flüssigkeiten, Ann. Phys., 162, 78–88, https://doi.org/10.1002/andp.18521620505, 1852.
Berzaghi, F., Wright, I. J., Kramer, K., Oddou-Muratorio, S., Bohn, F. J., Reyer, C. P. O., Sabaté, S., Sanders, T. G. M., and Hartig, F.:
Towards a New Generation of Trait-Flexible Vegetation Models, Trends Ecol. Evol., 35, 191–205, https://doi.org/10.1016/j.tree.2019.11.006, 2019.
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, G02014, https://doi.org/10.1029/2010JG001593, 2011.
Brando, P. M., Paolucci, L., Ummenhofer, C. C., Ordway, E. M., Hartmann, H., Cattau, M. E., Rattis, L., Medjibe, V., Coe, M. T., and Balch, J.:
Droughts, Wildfires, and Forest Carbon Cycling: A Pantropical Synthesis, Annu. Rev. Earth Pl. Sc., 47, 555–581, https://doi.org/10.1146/annurev-earth-082517-010235, 2019.
Briones, M. J. I., McNamara, N. P., Poskitt, J., Crow, S. E., and Ostle, N. J.:
Interactive biotic and abiotic regulators of soil carbon cycling: evidence from controlled climate experiments on peatland and boreal soils, Glob. Change Biol., 20, 2971–2982, https://doi.org/10.1111/gcb.12585, 2014.
Brodribb, T. J., Powers, J., Cochard, H., and Choat, B.:
Hanging by a thread? Forests and drought, Science, 368, 261–266, https://doi.org/10.1126/science.aat7631, 2020.
Caldararu, S., Purves, D. W., and Palmer, P. I.:
Phenology as a strategy for carbon optimality: a global model, Biogeosciences, 11, 763–778, https://doi.org/10.5194/bg-11-763-2014, 2014.
Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., and Zanne, A. E.:
Towards a worldwide wood economics spectrum, Ecol. Lett., 12, 351–366, https://doi.org/10.1111/j.1461-0248.2009.01285.x, 2009.
Chen, M., Melaas, E. K., Gray, J. M., Friedl, M. A., and Richardson, A. D.:
A new seasonal-deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios, Glob. Change Biol., 22, 3675–3688, https://doi.org/10.1111/gcb.13326, 2016.
Chen, Y., Xia, J., Sun, Z., Li, J., Luo, Y., Gang, C., and Wang, Z.:
The role of residence time in diagnostic models of global carbon storage capacity: model decomposition based on a traceable scheme, Sci. Rep.-UK, 5, 16155, https://doi.org/10.1038/srep16155, 2015.
Choat, B., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar, R., Bucci, S. J., Feild, T. S., Gleason, S. M., Hacke, U. G., Jacobsen, A. L., Lens, F., Maherali, H., Martínez-Vilalta, J., Mayr, S., Mencuccini, M., Mitchell, P. J., Nardini, A., Pittermann, J., Pratt, R. B., Sperry, J. S., Westoby, M., Wright, I. J., and Zanne, A. E.:
Global convergence in the vulnerability of forests to drought, Nature, 491, 752–755, https://doi.org/10.1038/nature11688, 2012.
Chuine, I.:
Why does phenology drive species distribution?, Philos. T. Roy. Soc. B, 365, 3149–3160, https://doi.org/10.1098/rstb.2010.0142, 2010.
Clark, J. S., Iverson, L., Woodall, C. W., Allen, C. D., Bell, D. M., Bragg, D. C., D'Amato, A. W., Davis, F. W., Hersh, M. H., Ibanez, I., Jackson, S. T., Matthews, S., Pederson, N., Peters, M., Schwartz, M. W., Waring, K. M., and Zimmermann, N. E.:
The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States, Glob. Change Biol., 22, 2329–2352, https://doi.org/10.1111/gcb.13160, 2016.
Coomes, D. A., Allen, R. B., Bentley, W. A., Burrows, L. E., Canham, C. D., Fagan, L., Forsyth, D. M., Gaxiola-Alcantar, A., Parfitt, R. L., Ruscoe, W. A., Wardle, D. A., Wilson, D. J., and Wright, E. F.:
The hare, the tortoise and the crocodile: the ecology of angiosperm dominance, conifer persistence and fern filtering, J. Ecol., 93, 918–935, https://doi.org/10.1111/j.1365-2745.2005.01012.x, 2005.
Crowther, T. W., Todd-Brown, K. E. O., Rowe, C. W., Wieder, W. R., Carey, J. C., Machmuller, M. B., Snoek, B. L., Fang, S., Zhou, G., Allison, S. D., Blair, J. M., Bridgham, S. D., Burton, A. J., Carrillo, Y., Reich, P. B., Clark, J. S., Classen, A. T., Dijkstra, F. A., Elberling, B., Emmett, B. A., Estiarte, M., Frey, S. D., Guo, J., Harte, J., Jiang, L., Johnson, B. R., Kröel-Dulay, G., Larsen, K. S., Laudon, H., Lavallee, J. M., Luo, Y., Lupascu, M., Ma, L. N., Marhan, S., Michelsen, A., Mohan, J., Niu, S., Pendall, E., Peñuelas, J., Pfeifer-Meister, L., Poll, C., Reinsch, S., Reynolds, L. L., Schmidt, I. K., Sistla, S., Sokol, N. W., Templer, P. H., Treseder, K. K., Welker, J. M., and Bradford, M. A.:
Quantifying global soil carbon losses in response to warming, Nature, 540, 104–108, https://doi.org/10.1038/nature20150, 2016.
Cui, E., Huang, K., Arain, M. A., Fisher, J. B., Huntzinger, D. N., Ito, A., Luo, Y., Jain, A. K., Mao, J., Michalak, A. M., Niu, S., Parazoo, N. C., Peng, C., Peng, S., Poulter, B., Ricciuto, D. M., Schaefer, K. M., Schwalm, C. R., Shi, X., Tian, H., Wang, W., Wang, J., Wei, Y., Yan, E., Yan, L., Zeng, N., Zhu, Q., and Xia, J.:
Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region, Global. Biogeochem. Cy., 33, 668–689, https://doi.org/10.1029/2018GB005909, 2019.
Dahlin, K. M., Fisher, R. A., and Lawrence, P. J.:
Environmental drivers of drought deciduous phenology in the Community Land Model, Biogeosciences, 12, 5061–5074, https://doi.org/10.5194/bg-12-5061-2015, 2015.
Davidson, E. A. and Janssens, I. A.:
Temperature sensitivity of soil carbon decomposition and feedbacks to climate change, Nature, 440, 165–173, https://doi.org/10.1038/nature04514, 2006.
De Kauwe, M. G., Zhou, S.-X., Medlyn, B. E., Pitman, A. J., Wang, Y.-P., Duursma, R. A., and Prentice, I. C.:
Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe, Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, 2015.
Dieckmann, U., Brannstrom, A., HilleRisLambes, R., and Ito, H. C.:
The Adaptive Dynamics of Community Structure, in: Mathematics for Ecology and Environmental Sciences, edited by: Takeuchi, Y., Iwasa, Y., and Sato, K., Springer, 145–177, ISBN 978-3-540-34427-8, https://doi.org/10.1007/978-3-540-34428-5_8, 2007.
Dietze, M. C.:
Gaps in knowledge and data driving uncertainty in models of photosynthesis, Photosynth. Res., 119, 3–14, https://doi.org/10.1007/s11120-013-9836-z, 2014.
Duncanson, L., Neuenschwander, A., Hancock, S., Thomas, N., Fatoyinbo, T., Simard, M., Silva, C. A., Armston, J., Luthcke, S. B., Hofton, M., Kellner, J. R., and Dubayah, R.:
Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California, Remote Sens. Environ., 242, 111779, https://doi.org/10.1016/j.rse.2020.111779, 2020.
Dybzinski, R., Farrior, C., Wolf, A., Reich, P. B., and Pacala, S. W.:
Evolutionarily Stable Strategy Carbon Allocation to Foliage, Wood, and Fine Roots in Trees Competing for Light and Nitrogen: An Analytically Tractable, Individual-Based Model and Quantitative Comparisons to Data, Am. Nat., 177, 153–166, https://doi.org/10.1086/657992, 2011.
Dybzinski, R., Farrior, C. E., and Pacala, S. W.:
Increased forest carbon storage with increased atmospheric CO2 despite nitrogen limitation: a game-theoretic allocation model for trees in competition for nitrogen and light, Glob. Change Biol., 21, 1182–1196, https://doi.org/10.1111/gcb.12783, 2015.
Emanuel, W. R. and Killough, G. G.:
Modeling terrestrial ecosystems in the global carbon cycle with Shifts in carbon storage capacity by land-use change, Ecology, 65, 970–983, https://doi.org/10.2307/1938069, 1984.
Eriksson, E.:
Compartment Models and Reservoir Theory, Annu. Rev. Ecol. Syst., 2, 67–84, https://doi.org/10.1146/annurev.es.02.110171.000435, 1971.
Euskirchen, E. S., Edgar, C. W., Turetsky, M. R., Waldrop, M. P., and Harden, J. W.:
Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost, J. Geophys. Res.-Biogeo., 119, 1576–1595, https://doi.org/10.1002/2014JG002683, 2014.
Falster, D. and Westoby, M.:
Plant height and evolutionary games, Trends Ecol. Evol., 18, 337–343, https://doi.org/10.1016/S0169-5347(03)00061-2, 2003.
Falster, D. S., FitzJohn, R. G., Brannstrom, A., Dieckmann, U., and Westoby, M.:
plant: A package for modelling forest trait ecology and evolution, Methods Ecol. Evol., 7, 136–146, https://doi.org/10.1111/2041-210X.12525, 2016.
Falster, D. S., Braennstroem, A., Westoby, M., and Dieckmann, U.:
Multitrait successional forest dynamics enable diverse competitive coexistence, P. Natl. Acad. Sci. USA, 114, E2719–E2728, https://doi.org/10.1073/pnas.1610206114, 2017.
Famiglietti, C. A., Smallman, T. L., Levine, P. A., Flack-Prain, S., Quetin, G. R., Meyer, V., Parazoo, N. C., Stettz, S. G., Yang, Y., Bonal, D., Bloom, A. A., Williams, M., and Konings, A. G.:
Optimal model complexity for terrestrial carbon cycle prediction, Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, 2021.
Farrior, C. E.:
Theory predicts plants grow roots to compete with only their closest neighbours, P. R. Soc. B, 286, 20191129, https://doi.org/10.1098/rspb.2019.1129, 2019.
Farrior, C. E., Dybzinski, R., Levin, S. A., and Pacala, S. W.:
Competition for Water and Light in Closed-Canopy Forests: A Tractable Model of Carbon Allocation with Implications for Carbon Sinks, Am. Nat., 181, 314–330, https://doi.org/10.1086/669153, 2013.
Fisher, R. A. and Koven, C. D.:
Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems, J. Adv. Model. Earth Sy., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020.
Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., Spessa, A., Lawrence, D., and Bonan, G.:
Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes, CLM4.5(ED), Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, 2015.
Forster, P.:
Half a century of robust climate models, Nature, 545, 296–297, https://doi.org/10.1038/545296a, 2017.
Franklin, O., Johansson, J., Dewar, R. C., Dieckmann, U., McMurtrie, R. E., Brannstrom, A., and Dybzinski, R.:
Modeling carbon allocation in trees: a search for principles, Tree Physiol., 32, 648–666, https://doi.org/10.1093/treephys/tpr138, 2012.
Franklin, O., Harrison, S. P., Dewar, R., Farrior, C. E., Brännström, Å., Dieckmann, U., Pietsch, S., Falster, D., Cramer, W., Loreau, M., Wang, H., Mäkelä, A., Rebel, K. T., Meron, E., Schymanski, S. J., Rovenskaya, E., Stocker, B. D., Zaehle, S., Manzoni, S., van Oijen, M., Wright, I. J., Ciais, P., van Bodegom, P. M., Peñuelas, J., Hofhansl, F., Terrer, C., Soudzilovskaia, N. A., Midgley, G., and Prentice, I. C.:
Organizing principles for vegetation dynamics, Nat. Plants, 1–10, https://doi.org/10.1038/s41477-020-0655-x, 2020.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.:
MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A., Liddicoat, S. K., and Knutti, R.:
Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks, J. Climate, 27, 511–526, https://doi.org/10.1175/JCLI-D-12-00579.1, 2014.
Friend, A. D. and Kiang, N. Y.: Land Surface Model Development for the GISS GCM: Effects of Improved Canopy Physiology on Simulated Climate, J. Climate, 18, 2883–2902, https://doi.org/10.1175/JCLI3425.1, 2005.
Friend, A. D., Stevens, A. K., Knox, R. G., and Cannell, M. G. R.:
A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0), Ecol. Model., 95, 249–287, https://doi.org/10.1016/S0304-3800(96)00034-8, 1997.
Friend, A. D., Arneth, A., Kiang, N. Y., Lomas, M., Ogee, J., Roedenbeckk, C., Running, S. W., Santaren, J.-D., Sitch, S., Viovy, N., Woodward, F. I., and Zaehle, S.:
FLUXNET and modelling the global carbon cycle, Glob. Change Biol., 13, 610–633, https://doi.org/10.1111/j.1365-2486.2006.01223.x, 2007.
Garcia, E. S., Swann, A. L. S., Villegas, J. C., Breshears, D. D., Law, D. J., Saleska, S. R., and Stark, S. C.:
Synergistic Ecoclimate Teleconnections from Forest Loss in Different Regions Structure Global Ecological Responses, PLOS ONE, 11, e0165042, https://doi.org/10.1371/journal.pone.0165042, 2016.
Givnish, T.:
Adaptive significance of evergreen vs. deciduous leaves: solving the triple paradox, Silva Fenn., 36, 703–743, https://doi.org/10.14214/sf.535, 2002.
Gleason, K. E., Bradford, J. B., Bottero, A., D'Amato, A. W., Fraver, S., Palik, B. J., Battaglia, M. A., Iverson, L., Kenefic, L., and Kern, C. C.:
Competition amplifies drought stress in forests across broad climatic and compositional gradients, Ecosphere, 8, e01849, https://doi.org/10.1002/ecs2.1849, 2017.
Green, J. K., Konings, A. G., Alemohammad, S. H., Berry, J., Entekhabi, D., Kolassa, J., Lee, J.-E., and Gentine, P.:
Regionally strong feedbacks between the atmosphere and terrestrial biosphere, Nat. Geosci., 10, 410–414, https://doi.org/10.1038/ngeo2957, 2017.
Hansen, J., Sato, M., Ruedy, R., Kharecha, P., Lacis, A., Miller, R., Nazarenko, L., Lo, K., Schmidt, G. A., Russell, G., Aleinov, I., Bauer, S., Baum, E., Cairns, B., Canuto, V., Chandler, M., Cheng, Y., Cohen, A., Del Genio, A., Faluvegi, G., Fleming, E., Friend, A., Hall, T., Jackman, C., Jonas, J., Kelley, M., Kiang, N. Y., Koch, D., Labow, G., Lerner, J., Menon, S., Novakov, T., Oinas, V., Perlwitz, Ja., Perlwitz, Ju., Rind, D., Romanou, A., Schmunk, R., Shindell, D., Stone, P., Sun, S., Streets, D., Tausnev, N., Thresher, D., Unger, N., Yao, M., and Zhang, S.:
Climate simulations for 1880–2003 with GISS modelE, Clim. Dynam., 29, 661–696, https://doi.org/10.1007/s00382-007-0255-8, 2007.
Harper, A. B., Williams, K. E., McGuire, P. C., Duran Rojas, M. C., Hemming, D., Verhoef, A., Huntingford, C., Rowland, L., Marthews, T., Breder Eller, C., Mathison, C., Nobrega, R. L. B., Gedney, N., Vidale, P. L., Otu-Larbi, F., Pandey, D., Garrigues, S., Wright, A., Slevin, D., De Kauwe, M. G., Blyth, E., Ardö, J., Black, A., Bonal, D., Buchmann, N., Burban, B., Fuchs, K., de Grandcourt, A., Mammarella, I., Merbold, L., Montagnani, L., Nouvellon, Y., Restrepo-Coupe, N., and Wohlfahrt, G.:
Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements, Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, 2021.
Harrison, S. P., Cramer, W., Franklin, O., Prentice, I. C., Wang, H., Brännström, Å., de Boer, H., Dieckmann, U., Joshi, J., Keenan, T. F., Lavergne, A., Manzoni, S., Mengoli, G., Morfopoulos, C., Peñuelas, J., Pietsch, S., Rebel, K. T., Ryu, Y., Smith, N. G., Stocker, B. D., and Wright, I. J.:
Eco-evolutionary optimality as a means to improve vegetation and land-surface models, New Phytol., 231, 2125–2141, https://doi.org/10.1111/nph.17558, 2021.
Hengeveld, G. M., Gunia, K., Didion, M., Zudin, S., Clerkx, A. P. P. M., and Schelhaas, M. J.:
Global 1-degree Maps of Forest Area, Carbon Stocks, and Biomass, 1950–2010, ORNL DAAC, Oak Ridge, Tennessee, USA [data set], https://doi.org/10.3334/ORNLDAAC/1296, 2015.
Hikosaka, K.:
Leaf Canopy as a Dynamic System: Ecophysiology and Optimality in Leaf Turnover, Ann. Bot.-London, 95, 521–533, https://doi.org/10.1093/aob/mci050, 2005.
Hikosaka, K. and Anten, N. P. R.:
An evolutionary game of leaf dynamics and its consequences for canopy structure, Funct. Ecol., 26, 1024–1032, https://doi.org/10.1111/j.1365-2435.2012.02042.x, 2012.
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D.:
The Art and Science of Climate Model Tuning, B. Am. Meteorol. Soc., 98, 589–602, https://doi.org/10.1175/BAMS-D-15-00135.1, 2017.
Huang, M., Piao, S., Sun, Y., Ciais, P., Cheng, L., Mao, J., Poulter, B., Shi, X., Zeng, Z., and Wang, Y.:
Change in terrestrial ecosystem water-use efficiency over the last three decades, Glob. Change Biol., 21, 2366–2378, https://doi.org/10.1111/gcb.12873, 2015.
Huntzinger, D. N., Schwalm, C., Michalak, A. M., Schaefer, K., King, A. W., Wei, Y., Jacobson, A., Liu, S., Cook, R. B., Post, W. M., Berthier, G., Hayes, D., Huang, M., Ito, A., Lei, H., Lu, C., Mao, J., Peng, C. H., Peng, S., Poulter, B., Riccuito, D., Shi, X., Tian, H., Wang, W., Zeng, N., Zhao, F., and Zhu, Q.:
The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design, Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, 2013.
Ito, G., Romanou, A., Kiang, N. Y., Faluvegi, G., Aleinov, I., Ruedy, R., Russell, G., Lerner, P., Kelley, M., and Lo, K.:
Global Carbon Cycle and Climate Feedbacks in the NASA GISS ModelE2.1, J. Adv. Model. Earth Sy., 12, e2019MS002030, https://doi.org/10.1029/2019MS002030, 2020.
Jiang, L., Shi, Z., Xia, J., Liang, J., Lu, X., Wang, Y., and Luo, Y.:
Transient Traceability Analysis of Land Carbon Storage Dynamics: Procedures and Its Application to Two Forest Ecosystems, J. Adv. Model. Earth Sy., 9, 2822–2835, https://doi.org/10.1002/2017MS001004, 2017.
Keenan, T. F., Hollinger, D. Y., Bohrer, G., Dragoni, D., Munger, J. W., Schmid, H. P., and Richardson, A. D.:
Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise, Nature, 499, 324–327, https://doi.org/10.1038/nature12291, 2013.
Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C., García-Pando, C. P., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.:
GISS-E2.1: Configurations and Climatology, J. Adv. Model. Earth Sy., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020.
Kim, Y., Moorcroft, P. R., Aleinov, I., Puma, M. J., and Kiang, N. Y.:
Variability of phenology and fluxes of water and carbon with observed and simulated soil moisture in the Ent Terrestrial Biosphere Model (Ent TBM version 1.0.1.0.0), Geosci. Model Dev., 8, 3837–3865, https://doi.org/10.5194/gmd-8-3837-2015, 2015.
Kitajima, K., Mulkey, S. S., Samaniego, M., and Joseph Wright, S.:
Decline of photosynthetic capacity with leaf age and position in two tropical pioneer tree species, Am. J. Bot., 89, 1925–1932, https://doi.org/10.3732/ajb.89.12.1925, 2002.
Kyker-Snowman, E., Lombardozzi, D. L., Bonan, G. B., Cheng, S. J., Dukes, J. S., Frey, S. D., Jacobs, E. M., McNellis, R., Rady, J. M., Smith, N. G., Thomas, R. Q., Wieder, W. R., and Grandy, A. S.:
Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model, Glob. Change Biol., 28, 665–684, https://doi.org/10.1111/gcb.15894, 2022.
Levine, J. I., Levine, J. M., Gibbs, T., and Pacala, S. W.:
Competition for water and species coexistence in phenologically structured annual plant communities, Ecol. Lett., 25, 1110–1125, https://doi.org/10.1111/ele.13990, 2022.
Litton, C. M., Raich, J. W., and Ryan, M. G.:
Carbon allocation in forest ecosystems, Glob. Change Biol., 13, 2089–2109, https://doi.org/10.1111/j.1365-2486.2007.01420.x, 2007.
Liu, H., Gleason, S. M., Hao, G., Hua, L., He, P., Goldstein, G., and Ye, Q.:
Hydraulic traits are coordinated with maximum plant height at the global scale, Sci. Adv., 5, eaav1332, https://doi.org/10.1126/sciadv.aav1332, 2019.
Lloret, F., Escudero, A., Iriondo, J. M., Martínez-Vilalta, J., and Valladares, F.:
Extreme climatic events and vegetation: the role of stabilizing processes, Glob. Change Biol., 18, 797–805, https://doi.org/10.1111/j.1365-2486.2011.02624.x, 2012.
Lu, R., Qiao, Y., Wang, J., Zhu, C., Cui, E., Xu, X., He, Y., Zhao, Z., Du, Y., Yan, L., Shen, G., Yang, Q., Wang, X., and Xia, J.:
The U-shaped pattern of size-dependent mortality and its correlated factors in a subtropical monsoon evergreen forest, J. Ecol., 109, 2421–2433, https://doi.org/10.1111/1365-2745.13652, 2021.
Luo, Y.:
Terrestrial carbon-cycle feedback to climate warming, Annu. Rev. Ecol. Evol. S., 38, 683–712, https://doi.org/10.1146/annurev.ecolsys.38.091206.095808, 2007.
Luo, Y. and Schuur, E. A. G.:
Model parameterization to represent processes at unresolved scales and changing properties of evolving systems, Glob. Change Biol., 26, 1109–1117, https://doi.org/10.1111/gcb.14939, 2020.
Luo, Y., Weng, E., Wu, X., Gao, C., Zhou, X., and Zhang, L.:
Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models, Ecol. Appl., 19, 571–574, https://doi.org/10.1890/08-0561.1, 2009.
Luo, Y., Ogle, K., Tucker, C., Fei, S., Gao, C., LaDeau, S., Clark, J. S., and Schimel, D. S.:
Ecological forecasting and data assimilation in a data-rich era, Ecol. Appl., 21, 1429–1442, https://doi.org/10.1890/09-1275.1, 2011.
Luo, Y. Q., Randerson, J. T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., Ciais, P., Dalmonech, D., Fisher, J. B., Fisher, R., Friedlingstein, P., Hibbard, K., Hoffman, F., Huntzinger, D., Jones, C. D., Koven, C., Lawrence, D., Li, D. J., Mahecha, M., Niu, S. L., Norby, R., Piao, S. L., Qi, X., Peylin, P., Prentice, I. C., Riley, W., Reichstein, M., Schwalm, C., Wang, Y. P., Xia, J. Y., Zaehle, S., and Zhou, X. H.:
A framework for benchmarking land models, Biogeosciences, 9, 3857–3874, https://doi.org/10.5194/bg-9-3857-2012, 2012.
MacBean, N., Peylin, P., Chevallier, F., Scholze, M., and Schürmann, G.:
Consistent assimilation of multiple data streams in a carbon cycle data assimilation system, Geosci. Model Dev., 9, 3569–3588, https://doi.org/10.5194/gmd-9-3569-2016, 2016.
Manzoni, S., Trofymow, J. A., Jackson, R. B., and Porporato, A.:
Stoichiometric controls on carbon, nitrogen, and phosphorus dynamics in decomposing litter, Ecol. Monogr., 80, 89–106, 2010.
Manzoni, S., Vico, G., Thompson, S., Beyer, F., and Weih, M.:
Contrasting leaf phenological strategies optimize carbon gain under droughts of different duration, Adv. Water Resour., 84, 37–51, https://doi.org/10.1016/j.advwatres.2015.08.001, 2015.
McDowell, N. G.:
Mechanisms Linking Drought, Hydraulics, Carbon Metabolism, and Vegetation Mortality, Plant Physiol., 155, 1051–1059, https://doi.org/10.1104/pp.110.170704, 2011.
McDowell, N. G., Allen, C. D., Anderson-Teixeira, K., Aukema, B. H., Bond-Lamberty, B., Chini, L., Clark, J. S., Dietze, M., Grossiord, C., Hanbury-Brown, A., Hurtt, G. C., Jackson, R. B., Johnson, D. J., Kueppers, L., Lichstein, J. W., Ogle, K., Poulter, B., Pugh, T. A. M., Seidl, R., Turner, M. G., Uriarte, M., Walker, A. P., and Xu, C.:
Pervasive shifts in forest dynamics in a changing world, Science, 368, eaaz9463, https://doi.org/10.1126/science.aaz9463, 2020.
McNickle, G. G., Gonzalez-Meler, M. A., Lynch, D. J., Baltzer, J. L., and Brown, J. S.:
The world's biomes and primary production as a triple tragedy of the commons foraging game played among plants, P. R. Soc. B, 283, 20161993, https://doi.org/10.1098/rspb.2016.1993, 2016.
Meir, P., Cox, P., and Grace, J.:
The influence of terrestrial ecosystems on climate, Trends Ecol. Evol., 21, 254–260, https://doi.org/10.1016/j.tree.2006.03.005, 2006.
Montané, F., Fox, A. M., Arellano, A. F., MacBean, N., Alexander, M. R., Dye, A., Bishop, D. A., Trouet, V., Babst, F., Hessl, A. E., Pederson, N., Blanken, P. D., Bohrer, G., Gough, C. M., Litvak, M. E., Novick, K. A., Phillips, R. P., Wood, J. D., and Moore, D. J. P.:
Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests, Geosci. Model Dev., 10, 3499–3517, https://doi.org/10.5194/gmd-10-3499-2017, 2017.
Niinemets, Ü.:
Photosynthesis and resource distribution through plant canopies, Plant Cell Environ., 30, 1052–1071, https://doi.org/10.1111/j.1365-3040.2007.01683.x, 2007.
Niinemets, Ü. and Anten, N. P. R.:
Packing the Photosynthetic Machinery: From Leaf to Canopy, in: Photosynthesis in silico: Understanding Complexity from Molecules to Ecosystems, edited by: Laisk, A., Nedbal, L., and Govindjee, Springer Netherlands, Dordrecht, 363–399, https://doi.org/10.1007/978-1-4020-9237-4_16, 2009.
Niinemets, Ü., Keenan, T. F., and Hallik, L.:
A worldwide analysis of within-canopy variations in leaf structural, chemical and physiological traits across plant functional types, New Phytol., 205, 973–993, https://doi.org/10.1111/nph.13096, 2015.
Niklas, K.:
Plant Height and the Properties of Some Herbaceous Stems, Ann. Bot.-London, 75, 133–142, https://doi.org/10.1006/anbo.1995.1004, 1995.
Nobre, C. A., Sellers, P. J., and Shukla, J.:
Amazonian Deforestation and Regional Climate Change, J. Climate, 4, 957–988, https://doi.org/10.1175/1520-0442(1991)004<0957:ADARCC>2.0.CO;2, 1991.
Oliveira, R. S., Eller, C. B., Barros, F. de V., Hirota, M., Brum, M., and Bittencourt, P.:
Linking plant hydraulics and the fast–slow continuum to understand resilience to drought in tropical ecosystems, New Phytol., 230, 904–923, https://doi.org/10.1111/nph.17266, 2021.
Osnas, J. L. D., Lichstein, J. W., Reich, P. B., and Pacala, S. W.:
Global Leaf Trait Relationships: Mass, Area, and the Leaf Economics Spectrum, Science, 340, 741–744, https://doi.org/10.1126/science.1231574, 2013.
Pan, Y., Birdsey, R. A., Phillips, O. L., and Jackson, R. B.:
The Structure, Distribution, and Biomass of the World's Forests, Annu. Rev. Ecol. Evol. S., 44, 593–622, https://doi.org/10.1146/annurev-ecolsys-110512-135914, 2013.
Park, H. and Jeong, S.:
Leaf area index in Earth system models: how the key variable of vegetation seasonality works in climate projections, Environ. Res. Lett., 16, 034027, https://doi.org/10.1088/1748-9326/abe2cf, 2021.
Parton, W., Schimel, D., Cole, C., and Ojima, D.:
Analysis of factors controlling soil organic matter levels in Great Plains grasslands, Soil Sci. Soc. Am. J., 51, 1173–1179, https://doi.org/10.2136/sssaj1987.03615995005100050015x, 1987.
Parton, W. J., Stewart, J., and Cole, C.:
Dynamics of C, N, P and S in grassland soils: a model, Biogeochemistry, 5, 109–131, https://doi.org/10.1007/BF02180320, 1988.
Pavlick, R., Drewry, D. T., Bohn, K., Reu, B., and Kleidon, A.:
The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs, Biogeosciences, 10, 4137–4177, https://doi.org/10.5194/bg-10-4137-2013, 2013.
Pielke Sr, R. A., Avissar, R., Raupach, M., Dolman, A. J., Zeng, X., and Denning, A. S.:
Interactions between the atmosphere and terrestrial ecosystems: influence on weather and climate, Glob. Change Biol., 4, 461–475, https://doi.org/10.1046/j.1365-2486.1998.t01-1-00176.x, 1998.
Potter, C., Klooster, S., Myneni, R., Genovese, V., Tan, P., and Kumar, V.:
Continental-scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982-1998, Global Planet. Change, 39, 201–213, https://doi.org/10.1016/j.gloplacha.2003.07.001, 2003.
Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A., and Klooster, S. A.:
Terrestrial ecosystem production: A process model based on global satellite and surface data, Global. Biogeochem. Cy., 7, 811–841, https://doi.org/10.1029/93GB02725, 1993.
Powell, T. L., Galbraith, D. R., Christoffersen, B. O., Harper, A., Imbuzeiro, H. M. A., Rowland, L., Almeida, S., Brando, P. M., da Costa, A. C. L., Costa, M. H., Levine, N. M., Malhi, Y., Saleska, S. R., Sotta, E., Williams, M., Meir, P., and Moorcroft, P. R.:
Confronting model predictions of carbon fluxes with measurements of Amazon forests subjected to experimental drought, New Phytol., 200, 350–365, https://doi.org/10.1111/nph.12390, 2013.
Prentice, I. C., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A., and Solomon, A. M.:
A global biome model based on plant physiology and dominance, soil properties and climate, J. Biogeogr., 19, 117–134, https://doi.org/10.2307/2845499, 1992.
Prentice, I. C., Bondeau, A., Cramer, W., Harrison, S. P., Hickler, T., Lucht, W., Sitch, S., Smith, B., and Sykes, M. T.:
Dynamic Global Vegetation Modeling: Quantifying Terrestrial Ecosystem Responses to Large-Scale Environmental Change, in: Terrestrial Ecosystems in a Changing World, edited by: Canadell, J. G., Pataki, D. E., and Pitelka, L. F., Springer Berlin Heidelberg, Berlin, Heidelberg, 175–192, https://doi.org/10.1007/978-3-540-32730-1_15, 2007.
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, https://doi.org/10.1111/ele.12211, 2014.
Purves, D. and Pacala, S.:
Predictive models of forest dynamics, Science, 320, 1452–1453, https://doi.org/10.1126/science.1155359, 2008.
Purves, D. W., Lichstein, J. W., Strigul, N., and Pacala, S. W.:
Predicting and understanding forest dynamics using a simple tractable model, P. Natl. Acad. Sci. USA, 105, 17018–17022, https://doi.org/10.1073/pnas.0807754105, 2008.
Randerson, J., Thompson, M., Conway, T., Fung, I., and Field, C.:
The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide, Global. Biogeochem. Cy., 11, 535–560, https://doi.org/10.1029/97GB02268, 1997.
Reich, P. B.:
The world-wide `fast–slow' plant economics spectrum: a traits manifesto, J. Ecol., 102, 275–301, https://doi.org/10.1111/1365-2745.12211, 2014.
Reyer, C. P. O., Leuzinger, S., Rammig, A., Wolf, A., Bartholomeus, R. P., Bonfante, A., de Lorenzi, F., Dury, M., Gloning, P., Abou Jaoudé, R., Klein, T., Kuster, T. M., Martins, M., Niedrist, G., Riccardi, M., Wohlfahrt, G., de Angelis, P., de Dato, G., François, L., Menzel, A., and Pereira, M.:
A plant's perspective of extremes: terrestrial plant responses to changing climatic variability, Glob. Change Biol., 19, 75–89, https://doi.org/10.1111/gcb.12023, 2013.
Richardson, A. D., Anderson, R. S., Arain, M. A., Barr, A. G., Bohrer, G., Chen, G., Chen, J. M., Ciais, P., Davis, K. J., Desai, A. R., Dietze, M. C., Dragoni, D., Garrity, S. R., Gough, C. M., Grant, R., Hollinger, D. Y., Margolis, H. A., McCaughey, H., Migliavacca, M., Monson, R. K., Munger, J. W., Poulter, B., Raczka, B. M., Ricciuto, D. M., Sahoo, A. K., Schaefer, K., Tian, H., Vargas, R., Verbeeck, H., Xiao, J., and Xue, Y.:
Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis, Glob. Change Biol., 18, 566–584, https://doi.org/10.1111/j.1365-2486.2011.02562.x, 2012.
Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham, V., and Coxi, D. R.:
Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation, P. Roy. Soc. Lond. A Mat., 455, 3789–3805, https://doi.org/10.1098/rspa.1999.0477, 1999.
Rosenzweig, C. and Abramopoulos, F.:
Land-Surface Model Development for the GISS GCM, J. Climate, 10, 2040–2054, https://doi.org/10.1175/1520-0442(1997)010<2040:LSMDFT>2.0.CO;2, 1997.
Scheiter, S., Langan, L., and Higgins, S. I.:
Next-generation dynamic global vegetation models: learning from community ecology, New Phytol., 198, 957–969, https://doi.org/10.1111/nph.12210, 2013.
Schmidt, G. A.: GISS GCM ModelE – NASA Goddard Institute for Space Studies, NASA [code], https://www.giss.nasa.gov/tools/modelE/, last access: 27 October 2022.
Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I., Bauer, M., Bauer, S. E., Bhat, M. K., Bleck, R., Canuto, V., Chen, Y.-H., Cheng, Y., Clune, T. L., Del Genio, A., de Fainchtein, R., Faluvegi, G., Hansen, J. E., Healy, R. J., Kiang, N. Y., Koch, D., Lacis, A. A., LeGrande, A. N., Lerner, J., Lo, K. K., Matthews, E. E., Menon, S., Miller, R. L., Oinas, V., Oloso, A. O., Perlwitz, J. P., Puma, M. J., Putman, W. M., Rind, D., Romanou, A., Sato, M., Shindell, D. T., Sun, S., Syed, R. A., Tausnev, N., Tsigaridis, K., Unger, N., Voulgarakis, A., Yao, M.-S., and Zhang, J.:
Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive, J. Adv. Model. Earth Sy., 6, 141–184, https://doi.org/10.1002/2013MS000265, 2014.
Sellers, P. J.:
Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere, Science, 275, 502–509, https://doi.org/10.1126/science.275.5299.502, 1997.
Sierra, C. A., Ceballos-Núñez, V., Metzler, H., and Müller, M.:
Representing and Understanding the Carbon Cycle Using the Theory of Compartmental Dynamical Systems, J. Adv. Model. Earth Sy., 10, 1729–1734, https://doi.org/10.1029/2018MS001360, 2018.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.:
Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Biogeo., 116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
Singh, A. K., Dhanapal, S., and Yadav, B. S.:
The dynamic responses of plant physiology and metabolism during environmental stress progression, Mol. Biol. Rep., 47, 1459–1470, https://doi.org/10.1007/s11033-019-05198-4, 2020.
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 terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003.
Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G., Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford, C., Levis, S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng, N., Arneth, A., Bonan, G., Bopp, L., Canadell, J. G., Chevallier, F., Ciais, P., Ellis, R., Gloor, M., Peylin, P., Piao, S. L., Le Quéré, C., Smith, B., Zhu, Z., and Myneni, R.:
Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, 2015.
Strigul, N., Pristinski, D., Purves, D., Dushoff, J., and Pacala, S.:
Scaling from trees to forests: tractable macroscopic equations for forest dynamics, Ecol. Monogr., 78, 523–545, https://doi.org/10.1890/08-0082.1, 2008.
Swenson, N. G. and Enquist, B. J.:
Ecological and evolutionary determinants of a key plant functional trait: wood density and its community-wide variation across latitude and elevation, Am. J. Bot., 94, 451–459, https://doi.org/10.3732/ajb.94.3.451, 2007.
Swinehart, D. F.:
The Beer–Lambert Law, J. Chem. Educ., 39, 333, https://doi.org/10.1021/ed039p333, 1962.
Tian, Y., Woodcock, C. E., Wang, Y., Privette, J. L., Shabanov, N. V., Zhou, L., Zhang, Y., Buermann, W., Dong, J., Veikkanen, B., Häme, T., Andersson, K., Ozdogan, M., Knyazikhin, Y., and Myneni, R. B.:
Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment, Remote Sens. Environ., 83, 414–430, https://doi.org/10.1016/S0034-4257(02)00047-0, 2002.
Tian, Y., Wang, Y., Zhang, Y., Knyazikhin, Y., Bogaert, J., and Myneni, R. B.:
Radiative transfer based scaling of LAI retrievals from reflectance data of different resolutions, Remote Sens. Environ., 84, 143–159, https://doi.org/10.1016/S0034-4257(02)00102-5, 2003.
Tifafi, M., Guenet, B., and Hatté, C.:
Large Differences in Global and Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and NCSCD: Intercomparison and Evaluation Based on Field Data From USA, England, Wales, and France, Global. Biogeochem. Cy., 32, 42–56, https://doi.org/10.1002/2017GB005678, 2018.
Tilman, D.:
Plant strategies and the dynamics and structure of plant communities, Princeton University Press, Princeton, NJ, 360 pp., ISBN 9780691084893, https://doi.org/10.1515/9780691209593, 1988.
van der Molen, M. K., Dolman, A. J., Ciais, P., Eglin, T., Gobron, N., Law, B. E., Meir, P., Peters, W., Phillips, O. L., Reichstein, M., Chen, T., Dekker, S. C., Doubková, M., Friedl, M. A., Jung, M., van den Hurk, B. J. J. M., de Jeu, R. A. M., Kruijt, B., Ohta, T., Rebel, K. T., Plummer, S., Seneviratne, S. I., Sitch, S., Teuling, A. J., van der Werf, G. R., and Wang, G.:
Drought and ecosystem carbon cycling, Agr. Forest Meteorol., 151, 765–773, https://doi.org/10.1016/j.agrformet.2011.01.018, 2011.
Verryckt, L. T., Vicca, S., Van Langenhove, L., Stahl, C., Asensio, D., Urbina, I., Ogaya, R., Llusià, J., Grau, O., Peguero, G., Gargallo-Garriga, A., Courtois, E. A., Margalef, O., Portillo-Estrada, M., Ciais, P., Obersteiner, M., Fuchslueger, L., Lugli, L. F., Fernandez-Garberí, P.-R., Vallicrosa, H., Verlinden, M., Ranits, C., Vermeir, P., Coste, S., Verbruggen, E., Bréchet, L., Sardans, J., Chave, J., Peñuelas, J., and Janssens, I. A.:
Vertical profiles of leaf photosynthesis and leaf traits and soil nutrients in two tropical rainforests in French Guiana before and after a 3-year nitrogen and phosphorus addition experiment, Earth Syst. Sci. Data, 14, 5–18, https://doi.org/10.5194/essd-14-5-2022, 2022.
Volaire, F.:
A unified framework of plant adaptive strategies to drought: Crossing scales and disciplines, Glob. Change Biol., 24, 2929–2938, https://doi.org/10.1111/gcb.14062, 2018.
von Foerster, H.: Some Remarks on Changing Populations, in: The Kinetics of Cellular Proliferation, edited by: Stohlman Jr., F., Grune and Stratton, New York, 382–407, 1959.
Wang, H., Prentice, I. C., Keenan, T. F., Davis, T. W., Wright, I. J., Cornwell, W. K., Evans, B. J., and Peng, C.:
Towards a universal model for carbon dioxide uptake by plants, Nat. Plants, 3, 734–741, https://doi.org/10.1038/s41477-017-0006-8, 2017.
Wang, Y.-P. and Goll, D. S.:
Modelling of land nutrient cycles: recent progress and future development, Faculty Reviews, 10, 53, https://doi.org/10.12703/r/10-53, 2021.
Wang, Y.-P., Trudinger, C. M., and Enting, I. G.:
A review of applications of model–data fusion to studies of terrestrial carbon fluxes at different scales, Agr. Forest Meteorol., 149, 1829–1842, https://doi.org/10.1016/j.agrformet.2009.07.009, 2009.
Wei, N., Xia, J., Zhou, J., Jiang, L., Cui, E., Ping, J., and Luo, Y.:
Evolution of Uncertainty in Terrestrial Carbon Storage in Earth System Models from CMIP5 to CMIP6, J. Climate, 1, 1–33, https://doi.org/10.1175/JCLI-D-21-0763.1, 2022.
Weiskopf, S. R., Myers, B. J. E., Arce-Plata, M. I., Blanchard, J. L., Ferrier, S., Fulton, E. A., Harfoot, M., Isbell, F., Johnson, J. A., Mori, A. S., Weng, E., HarmáČková, Z. V., Londoño-Murcia, M. C., Miller, B. W., Pereira, L. M., and Rosa, I. M. D.:
A Conceptual Framework to Integrate Biodiversity, Ecosystem Function, and Ecosystem Service Models, BioScience, biac074, in press, https://doi.org/10.1093/biosci/biac074, 2022.
Weng, E.: A standalone demographic vegetation model (BiomeE 1.0) (GMD), Zenodo [code], https://doi.org/10.5281/zenodo.7261019, 2022.
Weng, E. and Luo, Y.:
Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics, Ecol. Appl., 21, 1490–1505, 2011.
Weng, E., Luo, Y., Gao, C., and Oren, R.:
Uncertainty analysis of forest carbon sink forecast with varying measurement errors: a data assimilation approach, J. Plant Ecol., 4, 178–191, https://doi.org/10.1093/jpe/rtr018, 2011.
Weng, E., Farrior, C. E., Dybzinski, R., and Pacala, S. W.:
Predicting vegetation type through physiological and environmental interactions with leaf traits: evergreen and deciduous forests in an earth system modeling framework, Glob. Change Biol., 23, 2482–2498, https://doi.org/10.1111/gcb.13542, 2017.
Weng, E., Dybzinski, R., Farrior, C. E., and Pacala, S. W.:
Competition alters predicted forest carbon cycle responses to nitrogen availability and elevated CO2: simulations using an explicitly competitive, game-theoretic vegetation demographic model, Biogeosciences, 16, 4577–4599, https://doi.org/10.5194/bg-16-4577-2019, 2019.
Weng, E. S., Malyshev, S., Lichstein, J. W., Farrior, C. E., Dybzinski, R., Zhang, T., Shevliakova, E., and Pacala, S. W.:
Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of height-structured competition, Biogeosciences, 12, 2655–2694, https://doi.org/10.5194/bg-12-2655-2015, 2015.
Weng, E., Aleinov, I., Singh, R., Puma, M. J., McDermid, S. S., Kiang, N. Y., Kelley, M., Wilcox, K., Dybzinski, R., Farrior, C. E., Pacala, S. W., and Cook, B. I.: Model codes and simulation data for “Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)” (1.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.7125963, 2022.
Wieder, W. R.:
Regridded Harmonized World Soil Database v1.2, ORNL DAAC, Oak Ridge, Tennessee, USA [data set], https://doi.org/10.3334/ORNLDAAC/1247, 2014.
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., and Bonan, G. B.:
Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, Biogeosciences, 11, 3899–3917, https://doi.org/10.5194/bg-11-3899-2014, 2014.
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y.-P.:
Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341–1359, https://doi.org/10.5194/bg-6-1341-2009, 2009.
Woodward, F. I., Lomas, M. R., and Betts, R. A.:
Vegetation-climate feedbacks in a greenhouse world, Philos. T. Roy. Soc. B, 353, 29–39, https://doi.org/10.1098/rstb.1998.0188, 1998.
Xia, J., Luo, Y., Wang, Y.-P., and Hararuk, O.:
Traceable components of terrestrial carbon storage capacity in biogeochemical models, Glob. Change Biol., 19, 2104–2116, https://doi.org/10.1111/gcb.12172, 2013.
Xia, J., Yuan, W., Wang, Y.-P., and Zhang, Q.:
Adaptive Carbon Allocation by Plants Enhances the Terrestrial Carbon Sink, Sci. Rep.-UK, 7, 3341, https://doi.org/10.1038/s41598-017-03574-3, 2017.
Xia, J., Yuan, W., Lienert, S., Joos, F., Ciais, P., Viovy, N., Wang, Y., Wang, X., Zhang, H., Chen, Y., and Tian, X.:
Global Patterns in Net Primary Production Allocation Regulated by Environmental Conditions and Forest Stand Age: A Model-Data Comparison, J. Geophys. Res.-Biogeo., 124, 2039–2059, https://doi.org/10.1029/2018JG004777, 2019.
Xu, T., White, L., Hui, D., and Luo, Y.:
Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction, Global. Biogeochem. Cy., 20, GB2007, https://doi.org/10.1029/2005GB002468, 2006.
Yuan, W., Luo, Y., Liang, S., Yu, G., Niu, S., Stoy, P., Chen, J., Desai, A. R., Lindroth, A., Gough, C. M., Ceulemans, R., Arain, A., Bernhofer, C., Cook, B., Cook, D. R., Dragoni, D., Gielen, B., Janssens, I. A., Longdoz, B., Liu, H., Lund, M., Matteucci, G., Moors, E., Scott, R. L., Seufert, G., and Varner, R.:
Thermal adaptation of net ecosystem exchange, Biogeosciences, 8, 1453–1463, https://doi.org/10.5194/bg-8-1453-2011, 2011.
Zeng, Z., Piao, S., Li, L. Z. X., Zhou, L., Ciais, P., Wang, T., Li, Y., Lian, X., Wood, E. F., Friedlingstein, P., Mao, J., Estes, L. D., Myneni, R. B., Peng, S., Shi, X., Seneviratne, S. I., and Wang, Y.:
Climate mitigation from vegetation biophysical feedbacks during the past three decades, Nat. Clim. Change, 7, 432–436, https://doi.org/10.1038/nclimate3299, 2017.
Zhou, G., Houlton, B. Z., Wang, W., Huang, W., Xiao, Y., Zhang, Q., Liu, S., Cao, M., Wang, X., Wang, S., Zhang, Y., Yan, J., Liu, J., Tang, X., and Zhang, D.:
Substantial reorganization of China's tropical and subtropical forests: based on the permanent plots, Glob. Change Biol., 20, 240–250, https://doi.org/10.1111/gcb.12385, 2014.
Zhou, J., Xia, J., Wei, N., Liu, Y., Bian, C., Bai, Y., and Luo, Y.:
A traceability analysis system for model evaluation on land carbon dynamics: design and applications, Ecol. Process., 10, 12, https://doi.org/10.1186/s13717-021-00281-w, 2021.
Zuleta, D., Arellano, G., Muller-Landau, H. C., McMahon, S. M., Aguilar, S., Bunyavejchewin, S., Cárdenas, D., Chang-Yang, C.-H., Duque, A., Mitre, D., Nasardin, M., Pérez, R., Sun, I.-F., Yao, T. L., and Davies, S. J.:
Individual tree damage dominates mortality risk factors across six tropical forests, New Phytol., 233, 705–721, https://doi.org/10.1111/nph.17832, 2022.
Short summary
We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
We develop a demographic vegetation model to improve the representation of terrestrial...
