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
Yona Silvy, Thomas L. Frölicher, Jens Terhaar, Fortunat Joos, Friedrich A. Burger, Fabrice Lacroix, Myles Allen, Raffaele Bernadello, 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-488, https://doi.org/10.5194/egusphere-2024-488, 2024
Short summary
Short summary
We apply the Adaptive Emission Reduction Approach with Earth System Models to provide simulations in which all ESMs converge at 1.5 °C and 2 °C warming levels. These simulations provide compatible emission pathways for a given warming level, uncovering uncertainty ranges previously missing in the CMIP scenarios. This new type of target-based emission-driven simulations offers a more coherent assessment across ESMs for studying both the carbon cycle and impacts under climate stabilization.
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
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)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
Dynamic ecosystem assembly and escaping the “fire-trap” in the tropics: Insights from FATES_15.0.0
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
A global behavioural model of human fire use and management: WHAM! v1.0
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): calibration-free calculations of water and energy fluxes
biospheremetrics v1.0.1: An R package to calculate two complementary terrestrial biosphere integrity indicators: human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
In-silico calculation of soil pH by SCEPTER v1.0
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Optimal enzyme allocation leads to the constrained enzyme hypothesis: The Soil Enzyme Steady Allocation Model (SESAM v3.1)
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: Simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics
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.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles D. Koven, Ryan G. Knox, Lara M. Kueppers, and Chonggang Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-191, https://doi.org/10.5194/gmd-2023-191, 2023
Preprint under review for GMD
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.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James Millington
EGUsphere, https://doi.org/10.5194/egusphere-2023-2162, https://doi.org/10.5194/egusphere-2023-2162, 2023
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.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
EGUsphere, https://doi.org/10.5194/egusphere-2023-1626, https://doi.org/10.5194/egusphere-2023-1626, 2023
Short summary
Short summary
Numerous estimations of water and energy balances heavily depend on empirical equations that necessitate site-specific calibration. This equifinality poses the risk of obtaining 'right answers for wrong reasons.' In this paper, we introduce novel formulations based on first-principles to calculate calibration-free quantities, such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
EGUsphere, https://doi.org/10.5194/egusphere-2023-2503, https://doi.org/10.5194/egusphere-2023-2503, 2023
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 >25 % of the potential pre-industrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of land, water, and fertilizer use, as well as climate change.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
Short summary
Short summary
Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Short summary
In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
Short summary
Short summary
Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Elin Ristorp Aas, Heleen A. de Wit, and Terje Koren Berntsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2069, https://doi.org/10.5194/egusphere-2023-2069, 2023
Short summary
Short summary
By including microbial processes in soil models, we learn about how the soil system interacts with its environment, and responds to climate change. We present a soil process model, MIMICS+, able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also found that when adding nitrogen, the relationship between soil microbes changed notably. Coupling the model to a vegetation model, will allow further investigation of these mechanisms.
Yannek Käber, Florian Hartig, and Harald Bugmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2114, https://doi.org/10.5194/egusphere-2023-2114, 2023
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 with forest inventory data, and climatic effects are challenging to capture.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
EGUsphere, https://doi.org/10.5194/egusphere-2023-2003, https://doi.org/10.5194/egusphere-2023-2003, 2023
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.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-137, https://doi.org/10.5194/gmd-2023-137, 2023
Revised manuscript accepted for GMD
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 pH and bulk soil pH is thus crucial to accurate evaluation of alkalinity required to counteract soil acidification and resulting capture of anthropogenic carbon dioxide through the Enhanced Rock Weathering technique. This has been enabled by the updated reactive-transport SCEPTER code and newly developed framework to simulate soil pH.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
EGUsphere, https://doi.org/10.5194/egusphere-2023-1492, https://doi.org/10.5194/egusphere-2023-1492, 2023
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 behavior at low microbial biomass led us formulate the constrained enzyme hypothesis. We now can better describe how microbial mediated linkages of elemental fluxes adapt across decades.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
Short summary
Short summary
Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-1287, https://doi.org/10.5194/egusphere-2023-1287, 2023
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predict their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that important questions related to plant-atmosphere interactions can be addressed, such as the effects of rising CO2 and ozone pollution on carbon uptake of the biosphere. The model has been well validated with both ground and satellite observations.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
Short summary
Short summary
Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
Short summary
Short summary
Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
Short summary
Short summary
Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
Short summary
Short summary
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
Short summary
Short summary
There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
Short summary
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
Short summary
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
Short summary
Short summary
Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
Short summary
Short summary
To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
Short summary
Short summary
Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
Short summary
Short summary
A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
Short summary
Short summary
Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
Short summary
Short summary
The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
Short summary
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
Short summary
Short summary
Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
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...
