Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3733-2024
© Author(s) 2024. 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-17-3733-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 May 2024
Model description paper |
| 08 May 2024
| 08 May 2024
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, China
Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, China
State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
David H. Y. Yung
Earth and Environmental Sciences Programme and Graduate Division of Earth and Atmospheric Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong SAR, China
Timothy Lam
Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, China
Centre for Doctoral Training in Environmental Intelligence, University of Exeter, Exeter, UK
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Surface ozone (O3) is well-known for posing great threats to both human health and agriculture worldwide. However, a multidecadal assessment of the impacts of O3 on public health and agriculture in China is lacking without sufficient O3 observations. We used a hybrid approach combining a chemical transport model and machine learning to provide a robust dataset of O3 concentrations over the past 4 decades in China, thereby filling the gap in the long-term O3 trend and impact assessment in China.
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We developed and used a terrestrial biosphere model to compare and evaluate widely used empirical dry deposition schemes with different stomatal approaches and found that using photosynthesis-based stomatal approaches can reduce biases in modeled dry deposition velocities in current chemical transport models. Our study shows systematic errors in current dry deposition schemes and the importance of representing plant ecophysiological processes in models under a changing climate.
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Jia Mao, Amos P. K. Tai, David H. Y. Yung, Tiangang Yuan, Kong T. Chau, and Zhaozhong Feng
Atmos. Chem. Phys., 24, 345–366, https://doi.org/10.5194/acp-24-345-2024, https://doi.org/10.5194/acp-24-345-2024, 2024
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Surface ozone (O3) is well-known for posing great threats to both human health and agriculture worldwide. However, a multidecadal assessment of the impacts of O3 on public health and agriculture in China is lacking without sufficient O3 observations. We used a hybrid approach combining a chemical transport model and machine learning to provide a robust dataset of O3 concentrations over the past 4 decades in China, thereby filling the gap in the long-term O3 trend and impact assessment in China.
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
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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.
Olivia E. Clifton, Donna Schwede, Christian Hogrefe, Jesse O. Bash, Sam Bland, Philip Cheung, Mhairi Coyle, Lisa Emberson, Johannes Flemming, Erick Fredj, Stefano Galmarini, Laurens Ganzeveld, Orestis Gazetas, Ignacio Goded, Christopher D. Holmes, László Horváth, Vincent Huijnen, Qian Li, Paul A. Makar, Ivan Mammarella, Giovanni Manca, J. William Munger, Juan L. Pérez-Camanyo, Jonathan Pleim, Limei Ran, Roberto San Jose, Sam J. Silva, Ralf Staebler, Shihan Sun, Amos P. K. Tai, Eran Tas, Timo Vesala, Tamás Weidinger, Zhiyong Wu, and Leiming Zhang
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Yuxuan Wang, Nan Lin, Wei Li, Alex Guenther, Joey C. Y. Lam, Amos P. K. Tai, Mark J. Potosnak, and Roger Seco
Atmos. Chem. Phys., 22, 14189–14208, https://doi.org/10.5194/acp-22-14189-2022, https://doi.org/10.5194/acp-22-14189-2022, 2022
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Short summary
With the rising food need, more intense agricultural activities will cause substantial perturbations to the nitrogen cycle, aggravating surface air pollution and imposing stress on terrestrial ecosystems. We studied how these ecosystem changes may modify biosphere–atmosphere exchanges, and further exert secondary effects on air quality, and demonstrated a link between agricultural activities and ozone air quality via the modulation of vegetation and soil biogeochemistry by nitrogen deposition.
Felix Leung, Karina Williams, Stephen Sitch, Amos P. K. Tai, Andy Wiltshire, Jemma Gornall, Elizabeth A. Ainsworth, Timothy Arkebauer, and David Scoby
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Short summary
Short summary
Ground-level ozone (O3) is detrimental to plant productivity and crop yield. Currently, the Joint UK Land Environment Simulator (JULES) includes a representation of crops (JULES-crop). The parameters for O3 damage in soybean in JULES-crop were calibrated against photosynthesis measurements from the Soybean Free Air Concentration Enrichment (SoyFACE). The result shows good performance for yield, and it helps contribute to understanding of the impacts of climate and air pollution on food security.
Lang Wang, Amos P. K. Tai, Chi-Yung Tam, Mehliyar Sadiq, Peng Wang, and Kevin K. W. Cheung
Atmos. Chem. Phys., 20, 11349–11369, https://doi.org/10.5194/acp-20-11349-2020, https://doi.org/10.5194/acp-20-11349-2020, 2020
Short summary
Short summary
We investigate the effects of future land use and land cover change (LULCC) on surface ozone air quality worldwide and find that LULCC can significantly influence ozone in North America and Europe via modifying surface energy balance, boundary-layer meteorology, and regional circulation. The strength of such “biogeophysical effects” of LULCC is strongly dependent on forest type and generally greater than the “biogeochemical effects” via changing deposition and emission fluxes alone.
Anthony Y. H. Wong, Jeffrey A. Geddes, Amos P. K. Tai, and Sam J. Silva
Atmos. Chem. Phys., 19, 14365–14385, https://doi.org/10.5194/acp-19-14365-2019, https://doi.org/10.5194/acp-19-14365-2019, 2019
Short summary
Short summary
Dry deposition is an important, but highly uncertain, sink for surface ozone. Several popular parameterizations exist to model this process, which vary with respect to how they depend on land cover and environmental variables. Here, we predict ozone dry deposition globally over 30 years, comparing four different approaches. We find that the choice of dry deposition parameterization affects the distribution, seasonal means, long-term trends, and interannual variability of surface ozone.
Shan S. Zhou, Amos P. K. Tai, Shihan Sun, Mehliyar Sadiq, Colette L. Heald, and Jeffrey A. Geddes
Atmos. Chem. Phys., 18, 14133–14148, https://doi.org/10.5194/acp-18-14133-2018, https://doi.org/10.5194/acp-18-14133-2018, 2018
Short summary
Short summary
Surface ozone pollution harms vegetation. As plants play key roles shaping air quality, the plant damage may further worsen air pollution. We use various computer models to examine such feedback effects, and find that ozone-induced decline in leaf density can lead to much higher ozone levels in forested regions, mostly due to the reduced ability of leaves to absorb pollutants. This study highlights the importance of considering the two-way interactions between plants and air pollution.
Danny M. Leung, Amos P. K. Tai, Loretta J. Mickley, Jonathan M. Moch, Aaron van Donkelaar, Lu Shen, and Randall V. Martin
Atmos. Chem. Phys., 18, 6733–6748, https://doi.org/10.5194/acp-18-6733-2018, https://doi.org/10.5194/acp-18-6733-2018, 2018
Short summary
Short summary
This paper investigates how large-scale weather systems control fine particulate matter (PM2.5) air quality in China. We show that winter monsoons, onshore winds and frontal rains can drive daily PM2.5 variability in different regions of China. We further project future PM2.5 concentration change by 2050s due to climate change, and verify that climate change has little benefit on future PM2.5 in Beijing, implying cutting down emissions is necessary to mitigate pollutions in megacities of China.
Yuanhong Zhao, Lin Zhang, Amos P. K. Tai, Youfan Chen, and Yuepeng Pan
Atmos. Chem. Phys., 17, 9781–9796, https://doi.org/10.5194/acp-17-9781-2017, https://doi.org/10.5194/acp-17-9781-2017, 2017
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Human activities have substantially enhanced atmospheric deposition of reactive nitrogen, inducing complex environmental consequences. This study presents a first quantitative investigation of how anthropogenic nitrogen deposition could impact surface ozone air quality through surface–atmosphere exchange processes. We find important surface ozone changes driven by nitrogen deposition, which can be comparable with those due to historical climate and land use changes.
Mehliyar Sadiq, Amos P. K. Tai, Danica Lombardozzi, and Maria Val Martin
Atmos. Chem. Phys., 17, 3055–3066, https://doi.org/10.5194/acp-17-3055-2017, https://doi.org/10.5194/acp-17-3055-2017, 2017
Short summary
Short summary
Surface ozone harms vegetation, which can influence not only climate but also ozone air quality itself. We implement a scheme for ozone damage on vegetation into an Earth system model, so that for the first time simulated vegetation and ozone can coevolve in a fully coupled simulation. With ozone–vegetation coupling, simulated ozone is found to be significantly higher by up to 6 ppbv. Reduced dry deposition and enhanced isoprene emission contribute to most of these increases.
Yu Fu, Amos P. K. Tai, and Hong Liao
Atmos. Chem. Phys., 16, 10369–10383, https://doi.org/10.5194/acp-16-10369-2016, https://doi.org/10.5194/acp-16-10369-2016, 2016
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The effects of climate change would partly counteract the emission-driven increase in PM2.5 in winter in most of eastern China, but exacerbate PM2.5 pollution in summer in North China Plain. Land cover and land use change might partially offset the increase in summertime PM2.5 but further enhance wintertime PM2.5 in the model by modifying the dry deposition of various PM2.5 precursors and biogenic volatile organic compound emissions, which also act as important factors in modulating air quality.
L. Shen, L. J. Mickley, and A. P. K. Tai
Atmos. Chem. Phys., 15, 10925–10938, https://doi.org/10.5194/acp-15-10925-2015, https://doi.org/10.5194/acp-15-10925-2015, 2015
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In this study, we have examined the effect of polar jet and Bermuda High on ozone air quality in the eastern United States. In the Midwest and northeast, the poleward shift of jet wind leads to reduced polar jet frequency, resulting in increased ozone there. In the southeast, the influence of Bermuda High on ozone variability depends on the location of its west edge. Westward movement increases the ozone only when the JJA Bermuda High west edge is located west of 85.4°W.
Y. Fu and A. P. K. Tai
Atmos. Chem. Phys., 15, 10093–10106, https://doi.org/10.5194/acp-15-10093-2015, https://doi.org/10.5194/acp-15-10093-2015, 2015
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Historical land cover and land use change alone between 1980 and 2010 could lead to reduced summertime surface ozone by up to 4ppbv in East Asia. Climate change alone could lead to an increase in summertime ozone by 2-10ppbv in most of East Asia. Land cover change could offset part of the climate effect and lead to a previously unknown public health benefit. The sensitivity of surface ozone to land cover change is more dependent on dry deposition than isoprene emission in most of East Asia.
P. Achakulwisut, L. J. Mickley, L. T. Murray, A. P. K. Tai, J. O. Kaplan, and B. Alexander
Atmos. Chem. Phys., 15, 7977–7998, https://doi.org/10.5194/acp-15-7977-2015, https://doi.org/10.5194/acp-15-7977-2015, 2015
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The atmosphere’s oxidative capacity determines the lifetime of many trace gases important to climate, chemistry, and human health. Yet uncertainties remain about its past variations, its controlling factors, and the radiative forcing of short-lived species it influences. To reduce these uncertainties, we must better quantify the natural emissions and chemical reaction mechanisms of organic compounds in the atmosphere, which play a role in governing the oxidative capacity.
Related subject area
Biogeosciences
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses
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
Exploring the Potential of History Matching for Land Surface Model Calibration
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
Using Deep Learning to integrate paleoclimate and global biogeochemistry over Phanerozoic time
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters
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
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
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)
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
Assimilation of Carbonyl Sulfide (COS) fluxes within the adjoint-based data assimilation system–Nanjing University Carbon Assimilation System (NUCAS v1.0)
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
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
Modeling boreal forest’s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
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)
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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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.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-6, https://doi.org/10.5194/gmd-2024-6, 2024
Revised manuscript accepted for GMD
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This study introduces a new scheme to simulate surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal response to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
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
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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
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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.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2996, https://doi.org/10.5194/egusphere-2023-2996, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. Here we test a technique called “History matching” against more common approaches. This method adapts well to these models, helping better understand how they work and therefore how to make them more realistic.
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
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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
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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.
Dongyu Zheng, Andrew Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-230, https://doi.org/10.5194/gmd-2023-230, 2024
Revised manuscript accepted for GMD
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This study uses a deep learning method to enhance the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model for more accurate predictions of climate shifts. The method may be critical in developing new fully-continuous methods that are able to translate over a moving continental surface in deep time with high-resolution at a reasonable computational expense.
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
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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
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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.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-242, https://doi.org/10.5194/gmd-2023-242, 2024
Revised manuscript accepted for GMD
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems as the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
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
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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.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through “data assimilation”, which blends numerical models and observations. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
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
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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
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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
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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.
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
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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
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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.
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
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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
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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.
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
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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
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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
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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.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1955, https://doi.org/10.5194/egusphere-2023-1955, 2023
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In this work, Nanjing University Carbon Assimilation System (NUCAS) was developed. Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS. The assimilation of COS significantly improved the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture, demonstrating that COS can provide strong constraints on parameters relevant to water, energy and carbon processes.
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
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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.
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
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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.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
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Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
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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.
Cited articles
Agathokleous, E., Feng, Z., Oksanen, E., Sicard, P., Wang, Q., Saitanis, C. J., Araminiene, V., Blande, J. D., Hayes, F., Calatayud, V., Domingos, M., Veresoglou, S. D., Peñuelas, J., Wardle, D. A., Marco, A. D., Li, Z., Harmens, H., Yuan, X., Vitale, M., and Paoletti, E.: Ozone affects plant, insect, and soil microbial communities: A threat to terrestrial ecosystems and biodiversity, Sci. Adv., 6, eabc1176, https://doi.org/10.1126/sciadv.abc1176, 2020.
Ahmed, Z., Gui, D., Qi, Z., Liu, Y., Liu, Y., and Azmat, M.: Agricultural system modeling: current achievements, innovations, and future roadmap, Arab. J. Geosci., 15, 363, https://doi.org/10.1007/s12517-022-09654-7, 2022.
Ainsworth, E. A., Yendrek, C. R., Sitch, S., Collins, W. J., and Emberson, L. D.: The Effects of Tropospheric Ozone on Net Primary Productivity and Implications for Climate Change, Annu. Rev. Plant Biol., 63, 637–661, https://doi.org/10.1146/annurev-arplant-042110-103829, 2012.
Ainsworth, E. A., Lemonnier, P., and Wedow, J. M.: The influence of rising tropospheric carbon dioxide and ozone on plant productivity, Plant Biol. J., 22, 5–11, https://doi.org/10.1111/plb.12973, 2020.
Anav, A., Menut, L., Khvorostyanov, D., and Viovy, N.: Impact of tropospheric ozone on the Euro-Mediterranean vegetation, Glob. Change Biol., 17, 2342–2359, https://doi.org/10.1111/j.1365-2486.2010.02387.x, 2011.
Arain, M. A., Xu, B., Brodeur, J. J., Khomik, M., Peichl, M., Beamesderfer, E., Restrepo-Couple, N., and Thorne, R.: Heat and drought impact on carbon exchange in an age-sequence of temperate pine forests, Ecol. Process., 11, 7, https://doi.org/10.1186/s13717-021-00349-7, 2022.
Arneth, A., Harrison, S. P., Zaehle, S., Tsigaridis, K., Menon, S., Bartlein, P. J., Feichter, J., Korhola, A., Kulmala, M., O'Donnell, D., Schurgers, G., Sorvari, S., and Vesala, T.: Terrestrial biogeochemical feedbacks in the climate system, Nat. Geosci., 3, 525–532, https://doi.org/10.1038/ngeo905, 2010.
Arora, V. K., Boer, G. J., Friedlingstein, P., Eby, M., Jones, C. D., Christian, J. R., Bonan, G., Bopp, L., Brovkin, V., Cadule, P., Hajima, T., Ilyina, T., Lindsay, K., Tjiputra, J. F., and Wu, T.: Carbon–Concentration and Carbon–Climate Feedbacks in CMIP5 Earth System Models, J. Climate, 26, 5289–5314, https://doi.org/10.1175/JCLI-D-12-00494.1, 2013.
Avnery, S., Mauzerall, D. L., Liu, J., and Horowitz, L. W.: Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage, Atmos. Environ., 45, 2284–2296, https://doi.org/10.1016/j.atmosenv.2010.11.045, 2011.
Baldocchi, D., Ryu, Y., and Keenan, T.: Terrestrial Carbon Cycle Variability, F1000Research, 5, 2371, https://doi.org/10.12688/f1000research.8962.1, 2016.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions, in: Progress in Photosynthesis Research, edited by: Biggins, J., and Biggins, J., Dordrecht, 221–224, 1987.
Bamberger, I., Ruehr, N. K., Schmitt, M., Gast, A., Wohlfahrt, G., and Arneth, A.: Isoprene emission and photosynthesis during heatwaves and drought in black locust, Biogeosciences, 14, 3649–3667, https://doi.org/10.5194/bg-14-3649-2017, 2017.
Bastos, A., O'Sullivan, M., Ciais, P., Makowski, D., Sitch, S., Friedlingstein, P., Chevallier, F., Rödenbeck, C., Pongratz, J., Luijkx, I. T., Patra, P. K., Peylin, P., Canadell, J. G., Lauerwald, R., Li, W., Smith, N. E., Peters, W., Goll, D. S., Jain, A. K., Kato, E., Lienert, S., Lombardozzi, D. L., Haverd, V., Nabel, J. E. M. S., Poulter, B., Tian, H., Walker, A. P., and Zaehle, S.: Sources of Uncertainty in Regional and Global Terrestrial CO2 Exchange Estimates, Global Biogeochem. Cy., 34, e2019GB006393, https://doi.org/10.1029/2019gb006393, 2020.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rodenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F. I., and Papale, D.: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate, Science, 329, 834–838, https://doi.org/10.1126/science.1184984, 2010.
Beringer, J., Hutley, L. B., Tapper, N. J., and Cernusak, L. A.: Savanna fires and their impact on net ecosystem productivity in North Australia, Glob. Change Biol., 13, 990–1004, https://doi.org/10.1111/j.1365-2486.2007.01334.x, 2007.
Beringer, J., Hutley, L. B., Hacker, J. M., Neininger, B., and Paw U, K. T.: Patterns and processes of carbon, water and energy cycles across northern Australian landscapes: From point to region, Agr. Forest Meteorol., 151, 1409–1416, https://doi.org/10.1016/j.agrformet.2011.05.003, 2011.
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001.
Bhattarai, H., Tai, A. P. K., Martin, M. V., and Yung, D. H. Y.: Impacts of changes in climate, land use, and emissions on global ozone air quality by mid-21st century following selected Shared Socioeconomic Pathways, Sci. Total Environ., 906, 167759, https://doi.org/10.1016/j.scitotenv.2023.167759, 2023.
Bi, W., He, W., Zhou, Y., Ju, W., Liu, Y., Liu, Y., Zhang, X., Wei, X., and Cheng, N.: A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020, Sci. Data, 9, 213, https://doi.org/10.1038/s41597-022-01309-2, 2022.
Blanco, J. A. and Lo, Y.-H.: Latest Trends in Modelling Forest Ecosystems: New Approaches or Just New Methods?, Current Forestry Reports, 9, 219–229, https://doi.org/10.1007/s40725-023-00189-y, 2023.
Boas, T., Bogena, H., Grünwald, T., Heinesch, B., Ryu, D., Schmidt, M., Vereecken, H., Western, A., and Hendricks Franssen, H.-J.: Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0, Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, 2021.
Boike, J., Kattenstroth, B., Abramova, K., Bornemann, N., Chetverova, A., Fedorova, I., Fröb, K., Grigoriev, M., Grüber, M., Kutzbach, L., Langer, M., Minke, M., Muster, S., Piel, K., Pfeiffer, E.-M., Stoof, G., Westermann, S., Wischnewski, K., Wille, C., and Hubberten, H.-W.: Baseline characteristics of climate, permafrost and land cover from a new permafrost observatory in the Lena River Delta, Siberia (1998–2011), Biogeosciences, 10, 2105–2128, https://doi.org/10.5194/bg-10-2105-2013, 2013.
Bonan, G. B.: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests, Science, 320, 1444–1449, https://doi.org/10.1126/science.1155121, 2008.
Bonan, G. B.: Forests, Climate, and Public Policy: A 500-Year Interdisciplinary Odyssey, Annu. Rev. Ecol. Evol. S., 47, 97–121, https://doi.org/10.1146/annurev-ecolsys-121415-032359, 2016.
Bonan, G. B., Levis, S., Kergoat, L., and Oleson, K. W.: Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models, Global Biogeochem. Cy., 16, 5-1–5-23, https://doi.org/10.1029/2000gb001360, 2002.
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.
Bonan, G. B., Oleson, K. W., Fisher, R. A., Lasslop, G., and Reichstein, M.: Reconciling leaf physiological traits and canopy flux data: Use of the TRY and FLUXNET databases in the Community Land Model version 4, J. Geophys. Res.-Biogeo., 117, G02026, https://doi.org/10.1029/2011jg001913, 2012.
Bytnerowicz, A., Omasa, K., and Paoletti, E.: Integrated effects of air pollution and climate change on forests: A northern hemisphere perspective, Environ. Pollut., 147, 438–445, https://doi.org/10.1016/j.envpol.2006.08.028, 2007.
Cai, W. and Prentice, I. C.: Recent trends in gross primary production and their drivers: analysis and modelling at flux-site and global scales, Environ. Res. Lett., 15, 124050, https://doi.org/10.1088/1748-9326/abc64e, 2020.
Calvete-Sogo, H., Gonzalez-Fernandez, I., Sanz, J., Elvira, S., Alonso, R., Garcia-Gomez, H., Ibanez-Ruiz, M. A., and Bermejo-Bermejo, V.: Heterogeneous responses to ozone and nitrogen alter the species composition of Mediterranean annual pastures, Oecologia, 181, 1055–1067, https://doi.org/10.1007/s00442-016-3628-z, 2016.
Chen, C., Riley, W. J., Prentice, I. C., and Keenan, T. F.: CO2 fertilization of terrestrial photosynthesis inferred from site to global scales, P. Natl. Acad. Sci. USA, 119, e2115627119, https://doi.org/10.1073/pnas.2115627119, 2022.
Chen, L., Dirmeyer, P. A., Guo, Z., and Schultz, N. M.: Pairing FLUXNET sites to validate model representations of land-use/land-cover change, Hydrol. Earth Syst. Sci., 22, 111–125, https://doi.org/10.5194/hess-22-111-2018, 2018.
Cheng, W., Dan, L., Deng, X., Feng, J., Wang, Y., Peng, J., Tian, J., Qi, W., Liu, Z., Zheng, X., Zhou, D., Jiang, S., Zhao, H., and Wang, X.: Global monthly gridded atmospheric carbon dioxide concentrations under the historical and future scenarios, Sci. Data, 9, 83, https://doi.org/10.1038/s41597-022-01196-7, 2022.
Chopin, P., Bergkvist, G., and Hossard, L.: Modelling biodiversity change in agricultural landscape scenarios – A review and prospects for future research, Biol. Conserv., 235, 1–17, https://doi.org/10.1016/j.biocon.2019.03.046, 2019.
Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogée, J., Allard, V., Aubinet, M., Buchmann, N., Bernhofer, C., Carrara, A., Chevallier, F., De Noblet, N., Friend, A. D., Friedlingstein, P., Grünwald, T., Heinesch, B., Keronen, P., Knohl, A., Krinner, G., Loustau, D., Manca, G., Matteucci, G., Miglietta, F., Ourcival, J. M., Papale, D., Pilegaard, K., Rambal, S., Seufert, G., Soussana, J. F., Sanz, M. J., Schulze, E. D., Vesala, T., and Valentini, R.: Europe-wide reduction in primary productivity caused by the heat and drought in 2003, Nature, 437, 529–533, https://doi.org/10.1038/nature03972, 2005.
Clifton, O. E., Fiore, A. M., Massman, W. J., Baublitz, C. B., Coyle, M., Emberson, L., Fares, S., Farmer, D. K., Gentine, P., Gerosa, G., Guenther, A. B., Helmig, D., Lombardozzi, D. L., Munger, J. W., Patton, E. G., Pusede, S. E., Schwede, D. B., Silva, S. J., Sörgel, M., Steiner, A. L., and Tai, A. P. K.: Dry Deposition of Ozone Over Land: Processes, Measurement, and Modeling, Rev. Geophys., 58, e2019RG000670, https://doi.org/10.1029/2019RG000670, 2020.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer, Agr. Forest Meteorol., 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991.
Collatz, G. J., Ribas-Carbo, M., and Berry, J.: Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants, Funct. Plant Biol., 19, 519–538, https://doi.org/10.1071/PP9920519, 1992.
Corcoran, E., Afshar, M., Curceac, S., Lashkari, A., Raza, M. M., Ahnert, S., Mead, A., and Morris, R.: Current data and modeling bottlenecks for predicting crop yields in the United Kingdom, Front. Sustain. Food Syst., 7, 1023169, https://doi.org/10.3389/fsufs.2023.1023169, 2023.
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019ms001916, 2020.
Davies-Barnard, T., Zaehle, S., and Friedlingstein, P.: Assessment of the impacts of biological nitrogen fixation structural uncertainty in CMIP6 earth system models, Biogeosciences, 19, 3491–3503, https://doi.org/10.5194/bg-19-3491-2022, 2022.
Dermody, O., Long, S. P., McConnaughay, K., and DeLucia, E. H.: How do elevated CO2 and O3 affect the interception and utilization of radiation by a soybean canopy?, Glob. Change Biol., 14, 556–564, https://doi.org/10.1111/j.1365-2486.2007.01502.x, 2008.
Deryng, D., Elliott, J., Folberth, C., Müller, C., Pugh, T. A. M., Boote, K. J., Conway, D., Ruane, A. C., Gerten, D., Jones, J. W., Khabarov, N., Olin, S., Schaphoff, S., Schmid, E., Yang, H., and Rosenzweig, C.: Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity, Nat. Clim. Change, 6, 786–790, https://doi.org/10.1038/nclimate2995, 2016.
Dickinson, R. E.: Land Surface Processes and Climate – Surface Albedos and Energy Balance, Adv. Geophys., 25, 305–353, https://doi.org/10.1016/S0065-2687(08)60176-4, 1983.
Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html (last access: 21 March 2024), 2022.
Dušek, J., Èížková, H., Stellner, S., Czerný, R., and Kvìt, J.: Fluctuating water table affects gross ecosystem production and gross radiation use efficiency in a sedge-grass marsh, Hydrobiologia, 692, 57–66, https://doi.org/10.1007/s10750-012-0998-z, 2012.
Ebi, K. L., Anderson, C. L., Hess, J. J., Kim, S.-H., Loladze, I., Neumann, R. B., Singh, D., Ziska, L., and Wood, R.: Nutritional quality of crops in a high CO2 world: an agenda for research and technology development, Environ. Res. Lett., 16, 064045, https://doi.org/10.1088/1748-9326/abfcfa, 2021.
Emberson, L.: Effects of ozone on agriculture, forests and grasslands, Philos T. Roy. A-Math., 378, 20190327, https://doi.org/10.1098/rsta.2019.0327, 2020.
Emberson, L. D., Kitwiroon, N., Beevers, S., Büker, P., and Cinderby, S.: Scorched Earth: how will changes in the strength of the vegetation sink to ozone deposition affect human health and ecosystems?, Atmos. Chem. Phys., 13, 6741–6755, https://doi.org/10.5194/acp-13-6741-2013, 2013.
Emberson, L. D., Pleijel, H., Ainsworth, E. A., van den Berg, M., Ren, W., Osborne, S., Mills, G., Pandey, D., Dentener, F., Büker, P., Ewert, F., Koeble, R., and Van Dingenen, R.: Ozone effects on crops and consideration in crop models, Eur. J. Agron., 100, 19–34, https://doi.org/10.1016/j.eja.2018.06.002, 2018.
Fares, S., Vargas, R., Detto, M., Goldstein, A. H., Karlik, J., Paoletti, E., and Vitale, M.: Tropospheric ozone reduces carbon assimilation in trees: estimates from analysis of continuous flux measurements, Glob. Change Biol., 19, 2427–2443, https://doi.org/10.1111/gcb.12222, 2013.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980.
Felzer, B. S., Cronin, T., Reilly, J. M., Melillo, J. M., and Wang, X.: Impacts of ozone on trees and crops, CR Geosci., 339, 784–798, https://doi.org/10.1016/j.crte.2007.08.008, 2007.
Feng, H., Guo, J., Peng, C., Kneeshaw, D., Roberge, G., Pan, C., Ma, X., Zhou, D., and Wang, W.: Nitrogen addition promotes terrestrial plants to allocate more biomass to aboveground organs: A global meta-analysis, Glob. Chang Biol., 29, 3970–3989, https://doi.org/10.1111/gcb.16731, 2023.
Feng, Z., Kobayashi, K., and Ainsworth, E. A.: Impact of elevated ozone concentration on growth, physiology, and yield of wheat (Triticum aestivum L.): a meta-analysis, Glob. Change Biol., 14, 2696–2708, https://doi.org/10.1111/j.1365-2486.2008.01673.x, 2008.
Franklin, O., Harrison, S. P., Dewar, R., Farrior, C. E., Brannstrom, A., Dieckmann, U., Pietsch, S., Falster, D., Cramer, W., Loreau, M., Wang, H., Makela, 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., Penuelas, J., Hofhansl, F., Terrer, C., Soudzilovskaia, N. A., Midgley, G., and Prentice, I. C.: Organizing principles for vegetation dynamics, Nat. Plants, 6, 444–453, https://doi.org/10.1038/s41477-020-0655-x, 2020.
Franks, P. J., Adams, M. A., Amthor, J. S., Barbour, M. M., Berry, J. A., Ellsworth, D. S., Farquhar, G. D., Ghannoum, O., Lloyd, J., McDowell, N., Norby, R. J., Tissue, D. T., and von Caemmerer, S.: Sensitivity of plants to changing atmospheric CO2 concentration: from the geological past to the next century, New Phytol., 197, 1077–1094, https://doi.org/10.1111/nph.12104, 2013.
Franks, P. J., Berry, J. A., Lombardozzi, D. L., and Bonan, G. B.: Stomatal Function across Temporal and Spatial Scales: Deep-Time Trends, Land-Atmosphere Coupling and Global Models, Plant Physiol., 174, 583–602, https://doi.org/10.1104/pp.17.00287, 2017.
Franz, M. and Zaehle, S.: Competing effects of nitrogen deposition and ozone exposure on northern hemispheric terrestrial carbon uptake and storage, 1850–2099, Biogeosciences, 18, 3219–3241, https://doi.org/10.5194/bg-18-3219-2021, 2021.
Franz, M., Simpson, D., Arneth, A., and Zaehle, S.: Development and evaluation of an ozone deposition scheme for coupling to a terrestrial biosphere model, Biogeosciences, 14, 45–71, https://doi.org/10.5194/bg-14-45-2017, 2017.
Fuhrer, J., Val Martin, M., Mills, G., Heald, C. L., Harmens, H., Hayes, F., Sharps, K., Bender, J., and Ashmore, M. R.: Current and future ozone risks to global terrestrial biodiversity and ecosystem processes, Ecol. Evol., 6, 8785–8799, https://doi.org/10.1002/ece3.2568, 2016.
Gang, C., Wang, Z., You, Y., Liu, Y., Xu, R., Bian, Z., Pan, N., Gao, X., Chen, M., and Zhang, M.: Divergent responses of terrestrial carbon use efficiency to climate variation from 2000 to 2018, Global Planet. Change, 208, 103709, https://doi.org/10.1016/j.gloplacha.2021.103709, 2022.
Ganzeveld, L. and Lelieveld, J.: Dry deposition parameterization in a chemistry general circulation model and its influence on the distribution of reactive trace gases, J. Geophys. Res., 100, 20999, https://doi.org/10.1029/95JD02266, 1995.
Ganzeveld, L., Bouwman, L., Stehfest, E., van Vuuren, D. P., Eickhout, B., and Lelieveld, J.: Impact of future land use and land cover changes on atmospheric chemistry-climate interactions, J. Geophys. Res., 115, D23301, https://doi.org/10.1029/2010JD014041, 2010.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gerber, S., Hedin, L. O., Oppenheimer, M., Pacala, S. W., and Shevliakova, E.: Nitrogen cycling and feedbacks in a global dynamic land model, Global Biogeochem. Cy., 24, GB1001, https://doi.org/10.1029/2008gb003336, 2010.
Gleason, S. M., Barnard, D. M., Green, T. R., Mackay, S., Wang, D. R., Ainsworth, E. A., Altenhofen, J., Brodribb, T. J., Cochard, H., Comas, L. H., Cooper, M., Creek, D., DeJonge, K. C., Delzon, S., Fritschi, F. B., Hammer, G., Hunter, C., Lombardozzi, D., Messina, C. D., Ocheltree, T., Stevens, B. M., Stewart, J. J., Vadez, V., Wenz, J., Wright, I. J., Yemoto, K., and Zhang, H.: Physiological trait networks enhance understanding of crop growth and water use in contrasting environments, Plant Cell Environ., 45, 2554–2572, https://doi.org/10.1111/pce.14382, 2022.
Gong, C., Liao, H., Yue, X., Ma, Y., and Lei, Y.: Impacts of Ozone-Vegetation Interactions on Ozone Pollution Episodes in North China and the Yangtze River Delta, Geophys. Res. Lett., 48, e2021GL093814, https://doi.org/10.1029/2021GL093814, 2021.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Halladay, K. and Good, P.: Non-linear interactions between CO2 radiative and physiological effects on Amazonian evapotranspiration in an Earth system model, Clim. Dynam., 49, 2471–2490, https://doi.org/10.1007/s00382-016-3449-0, 2017.
Hardacre, C., Wild, O., and Emberson, L.: An evaluation of ozone dry deposition in global scale chemistry climate models, Atmos. Chem. Phys., 15, 6419–6436, https://doi.org/10.5194/acp-15-6419-2015, 2015.
Harrison, S. P., Cramer, W., Franklin, O., Prentice, I. C., Wang, H., Brannstrom, A., de Boer, H., Dieckmann, U., Joshi, J., Keenan, T. F., Lavergne, A., Manzoni, S., Mengoli, G., Morfopoulos, C., Penuelas, 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.
Haverd, V., Raupach, M. R., Briggs, P. R., Canadell, J. G., Isaac, P., Pickett-Heaps, C., Roxburgh, S. H., van Gorsel, E., Viscarra Rossel, R. A., and Wang, Z.: Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles, Biogeosciences, 10, 2011–2040, https://doi.org/10.5194/bg-10-2011-2013, 2013.
He, P., Ma, X., and Sun, Z.: Interannual variability in summer climate change controls GPP long-term changes, Environ. Res., 212, 113409, https://doi.org/10.1016/j.envres.2022.113409, 2022.
Hidy, D., Barcza, Z., Hollós, R., Dobor, L., Ács, T., Zacháry, D., Filep, T., Pásztor, L., Incze, D., Dencső, M., Tóth, E., Merganičová, K., Thornton, P., Running, S., and Fodor, N.: Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model, Geosci. Model Dev., 15, 2157–2181, https://doi.org/10.5194/gmd-15-2157-2022, 2022.
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.
Hou, H., Zhou, B. B., Pei, F., Hu, G., Su, Z., Zeng, Y., Zhang, H., Gao, Y., Luo, M., and Li, X.: Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity, Earth's Future, 10, e2021EF002628, https://doi.org/10.1029/2021ef002628, 2022.
Hu, L., Keller, C. A., Long, M. S., Sherwen, T., Auer, B., Da Silva, A., Nielsen, J. E., Pawson, S., Thompson, M. A., Trayanov, A. L., Travis, K. R., Grange, S. K., Evans, M. J., and Jacob, D. J.: Global simulation of tropospheric chemistry at 12.5 km resolution: performance and evaluation of the GEOS-Chem chemical module (v10-1) within the NASA GEOS Earth system model (GEOS-5 ESM), Geosci. Model Dev., 11, 4603–4620, https://doi.org/10.5194/gmd-11-4603-2018, 2018.
Hu, Y., Schäfer, K. V. R., Zhu, L., Zhao, P., Zhao, X., Ni, G., Zhang, Y., Ye, H., Zhao, W., Shen, W., and Fu, S.: Impacts of Canopy and Understory Nitrogen Additions on Stomatal Conductance and Carbon Assimilation of Dominant Tree Species in a Temperate Broadleaved Deciduous Forest, Ecosystems, 24, 1468–1484, https://doi.org/10.1007/s10021-020-00595-4, 2021.
Huntingford, C., Oliver, R. J., Mercado, L. M., and Sitch, S.: Technical note: A simple theoretical model framework to describe plant stomatal “sluggishness” in response to elevated ozone concentrations, Biogeosciences, 15, 5415–5422, https://doi.org/10.5194/bg-15-5415-2018, 2018.
Hutley, L. B., Beringer, J., Isaac, P. R., Hacker, J. M., and Cernusak, L. A.: A sub-continental scale living laboratory: Spatial patterns of savanna vegetation over a rainfall gradient in northern Australia, Agr. Forest Meteorol., 151, 1417–1428, https://doi.org/10.1016/j.agrformet.2011.03.002, 2011.
Im, U., Brandt, J., Geels, C., Hansen, K. M., Christensen, J. H., Andersen, M. S., Solazzo, E., Kioutsioukis, I., Alyuz, U., Balzarini, A., Baro, R., Bellasio, R., Bianconi, R., Bieser, J., Colette, A., Curci, G., Farrow, A., Flemming, J., Fraser, A., Jimenez-Guerrero, P., Kitwiroon, N., Liang, C.-K., Nopmongcol, U., Pirovano, G., Pozzoli, L., Prank, M., Rose, R., Sokhi, R., Tuccella, P., Unal, A., Vivanco, M. G., West, J., Yarwood, G., Hogrefe, C., and Galmarini, S.: Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3, Atmos. Chem. Phys., 18, 5967–5989, https://doi.org/10.5194/acp-18-5967-2018, 2018.
Imer, D., Merbold, L., Eugster, W., and Buchmann, N.: Temporal and spatial variations of soil CO2, CH4 and N2O fluxes at three differently managed grasslands, Biogeosciences, 10, 5931–5945, https://doi.org/10.5194/bg-10-5931-2013, 2013.
Jain, A., Yang, X., Kheshgi, H., McGuire, A. D., Post, W., and Kicklighter, D.: Nitrogen attenuation of terrestrial carbon cycle response to global environmental factors, Global Biogeochem. Cy., 23, GB4028, https://doi.org/10.1029/2009gb003519, 2009.
Jin, Z., Yan, D., Zhang, Z., Li, M., Wang, T., Huang, X., Xie, M., Li, S., and Zhuang, B.: Effects of Elevated Ozone Exposure on Regional Meteorology and Air Quality in China Through Ozone-Vegetation Coupling, J. Geophys. Res.-Atmos., 128, e2022JD038119, https://doi.org/10.1029/2022jd038119, 2023.
Karlsson, P. E., Uddling, J., Braun, S., Broadmeadow, M., Elvira, S., Gimeno, B. S., Le Thiec, D., Oksanen, E., Vandermeiren, K., Wilkinson, M., and Emberson, L.: New critical levels for ozone effects on young trees based on AOT40 and simulated cumulative leaf uptake of ozone, Atmos. Environ., 38, 2283–2294, https://doi.org/10.1016/j.atmosenv.2004.01.027, 2004.
Karlsson, P. E., Klingberg, J., Engardt, M., Andersson, C., Langner, J., Karlsson, G. P., and Pleijel, H.: Past, present and future concentrations of ground-level ozone and potential impacts on ecosystems and human health in northern Europe, Sci. Total Environ., 576, 22–35, https://doi.org/10.1016/j.scitotenv.2016.10.061, 2017.
Keller, C. A., Long, M. S., Yantosca, R. M., Da Silva, A. M., Pawson, S., and Jacob, D. J.: HEMCO v1.0: a versatile, ESMF-compliant component for calculating emissions in atmospheric models, Geosci. Model Dev., 7, 1409–1417, https://doi.org/10.5194/gmd-7-1409-2014, 2014.
Kimmins, J. P., Blanco, J. A., Seely, B., Welham, C., and Scoullar, K.: Complexity in modelling forest ecosystems: How much is enough?, Forest Ecol. Manage., 256, 1646–1658, https://doi.org/10.1016/j.foreco.2008.03.011, 2008.
Kou-Giesbrecht, S. and Arora, V. K.: Compensatory Effects Between CO2, Nitrogen Deposition, and Nitrogen Fertilization in Terrestrial Biosphere Models Without Nitrogen Compromise Projections of the Future Terrestrial Carbon Sink, Geophys. Res. Lett., 50, e2022GL102618, https://doi.org/10.1029/2022gl102618, 2023.
Lai, J., Lortie, C. J., Muenchen, R. A., Yang, J., and Ma, K.: Evaluating the popularity of R in ecology, Ecosphere, 10, e02567, https://doi.org/10.1002/ecs2.2567, 2019.
Lam, T. and Tai, A. P. K.: Simulating Gross Primary Productivity of vegetation under soil water stress using in-situ and reanalysis soil moisture data inputs, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21397, https://doi.org/10.5194/egusphere-egu2020-21397, 2020.
Lam, J. C. Y., Tai, A. P. K., Ducker, J. A., and Holmes, C. D.: Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality, Geosci. Model Dev., 16, 2323–2342, https://doi.org/10.5194/gmd-16-2323-2023, 2023.
Lasslop, G., Reichstein, M., Papale, D., Richardson, A. D., Arneth, A., Barr, A., Stoy, P., and Wohlfahrt, G.: Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation, Glob. Change Biol., 16, 187–208, https://doi.org/10.1111/j.1365-2486.2009.02041.x, 2010.
Lawrence, D., Rosie, F., Charles, K., Keith, O., Sean, S., Mariana, V., Ben, A., Gordon, B., Bardan, G., Leo van, K., Daniel, K., Erik, K., Ryan, K., Peter, L., Fang, L., Hongyi, L., Danica, L., Yaqiong, L., Justin, P., William, R., William, S., Mingjie, S., Will, W., Chonggang, X., Ashehad, A., Andrew, B., Gautam, B., Patrick, B., Michael, B., Jonathan, B., Martyn, C., Tony, C., Kyla, D., Beth, D., Louisa, E., Josh, F., Mark, F., Pierre, G., Jan, L., Sam, L., Leung, L. R., William, L., Jon, P., Daniel, M. R., Ben, S., Jacquelyn, S., Andrew, S., Zachary, S., Jinyun, T., Ahmed, T., Quinn, T., Simone, T., Francis, V., and Xubin, Z.: Technical Description of version 5.0 of the Community Land Model (CLM), http://www.cesm.ucar.edu/models/cesm2/land/CLM50_Tech_Note.pdf (last access: 21 March 2024), 2020.
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0), J. Geophys. Res., 112, G01023, https://doi.org/10.1029/2006JG000168, 2007.
Lee, E., Zeng, F.-W., Koster, R. D., Weir, B., Ott, L. E., and Poulter, B.: The impact of spatiotemporal variability in atmospheric CO2 concentration on global terrestrial carbon fluxes, Biogeosciences, 15, 5635–5652, https://doi.org/10.5194/bg-15-5635-2018, 2018.
Legates, D. R. and McCabe, G. J.: Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233–241, https://doi.org/10.1029/1998WR900018, 1999.
Lei, Y., Yue, X., Liao, H., Gong, C., and Zhang, L.: Implementation of Yale Interactive terrestrial Biosphere model v1.0 into GEOS-Chem v12.0.0: a tool for biosphere–chemistry interactions, Geosci. Model Dev., 13, 1137–1153, https://doi.org/10.5194/gmd-13-1137-2020, 2020.
Leung, F., Williams, K., Sitch, S., Tai, A. P. K., Wiltshire, A., Gornall, J., Ainsworth, E. A., Arkebauer, T., and Scoby, D.: Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O3 experiment, Geosci. Model Dev., 13, 6201–6213, https://doi.org/10.5194/gmd-13-6201-2020, 2020.
Leung, F., Sitch, S., Tai, A. P. K., Wiltshire, A. J., Gornall, J. L., Folberth, G. A., and Unger, N.: CO2 fertilization of crops offsets yield losses due to future surface ozone damage and climate change, Environ. Res. Lett., 17, 074007, https://doi.org/10.1088/1748-9326/ac7246, 2022.
Li, J., Mahalov, A., and Hyde, P.: Simulating the impacts of chronic ozone exposure on plant conductance and photosynthesis, and on the regional hydroclimate using WRF/Chem, Environ. Res. Lett., 11, 114017, https://doi.org/10.1088/1748-9326/11/11/114017, 2016.
Li, X. and Xiao, J.: Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2, Remote Sens., 11, 2563, https://doi.org/10.3390/rs11212563, 2019.
Li, X., Xiao, J., He, B., Altaf Arain, M., Beringer, J., Desai, A. R., Emmel, C., Hollinger, D. Y., Krasnova, A., Mammarella, I., Noe, S. M., Ortiz, P. S., Rey-Sanchez, A. C., Rocha, A. V., and Varlagin, A.: Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO-2 and flux tower observations, Glob. Change Biol., 24, 3990–4008, https://doi.org/10.1111/gcb.14297, 2018.
Lin, H., Jacob, D. J., Lundgren, E. W., Sulprizio, M. P., Keller, C. A., Fritz, T. M., Eastham, S. D., Emmons, L. K., Campbell, P. C., Baker, B., Saylor, R. D., and Montuoro, R.: Harmonized Emissions Component (HEMCO) 3.0 as a versatile emissions component for atmospheric models: application in the GEOS-Chem, NASA GEOS, WRF-GC, CESM2, NOAA GEFS-Aerosol, and NOAA UFS models, Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021, 2021.
Lin, S., Hu, Z., Wang, Y., Chen, X., He, B., Song, Z., Sun, S., Wu, C., Zheng, Y., Xia, X., Liu, L., Tang, J., Sun, Q., Joos, F., and Yuan, W.: Underestimated Interannual Variability of Terrestrial Vegetation Production by Terrestrial Ecosystem Models, Global Biogeochem. Cy., 37, e2023GB007696, https://doi.org/10.1029/2023gb007696, 2023.
Lindauer, M., Schmid, H. P., Grote, R., Mauder, M., Steinbrecher, R., and Wolpert, B.: Net ecosystem exchange over a non-cleared wind-throw-disturbed upland spruce forest – Measurements and simulations, Agr. Forest Meteorol., 197, 219–234, https://doi.org/10.1016/j.agrformet.2014.07.005, 2014.
Liu, S., Yan, Z., Wang, Z., Serbin, S., Visser, M., Zeng, Y., Ryu, Y., Su, Y., Guo, Z., Song, G., Wu, Q., Zhang, H., Cheng, K. H., Dong, J., Hau, B. C. H., Zhao, P., Yang, X., Liu, L., Rogers, A., and Wu, J.: Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy, Remote Sens. Environ., 293, 113612, https://doi.org/10.1016/j.rse.2023.113612, 2023.
Liu, X., Tai, A. P. K., and Fung, K. M.: Responses of surface ozone to future agricultural ammonia emissions and subsequent nitrogen deposition through terrestrial ecosystem changes, Atmos. Chem. Phys., 21, 17743–17758, https://doi.org/10.5194/acp-21-17743-2021, 2021.
Liu, Y., Xiao, J., Ju, W., Zhu, G., Wu, X., Fan, W., Li, D., and Zhou, Y.: Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes, Remote Sens. Environ., 206, 174–188, https://doi.org/10.1016/j.rse.2017.12.024, 2018.
Lombardozzi, D., Levis, S., Bonan, G., and Sparks, J. P.: Predicting photosynthesis and transpiration responses to ozone: decoupling modeled photosynthesis and stomatal conductance, Biogeosciences, 9, 3113–3130, https://doi.org/10.5194/bg-9-3113-2012, 2012.
Lombardozzi, D., Levis, S., Bonan, G., Hess, P. G., and Sparks, J. P.: The Influence of Chronic Ozone Exposure on Global Carbon and Water Cycles, J. Climate, 28, 292–305, https://doi.org/10.1175/JCLI-D-14-00223.1, 2015.
Loveland, T. R. and Belward, A. S.: The IGBP-DIS global 1 km land cover data set, DISCover: First results, Int. J. Remote Sens., 18, 3289–3295, https://doi.org/10.1080/014311697217099, 1997.
Lu, X., Gilliam, F. S., Yue, X., Wang, B., and Kuang, Y.: Shifts in Above- Versus Below-Ground Carbon Gains to Terrestrial Ecosystems Carbon Sinks Under Excess Nitrogen Inputs, Global Biogeochem. Cy., 37, e2022GB007638, https://doi.org/10.1029/2022gb007638, 2023.
Marcolla, B., Cescatti, A., Manca, G., Zorer, R., Cavagna, M., Fiora, A., Gianelle, D., Rodeghiero, M., Sottocornola, M., and Zampedri, R.: Climatic controls and ecosystem responses drive the inter-annual variability of the net ecosystem exchange of an alpine meadow, Agr. Forest Meteorol., 151, 1233–1243, https://doi.org/10.1016/j.agrformet.2011.04.015, 2011.
Mason, R. E., Craine, J. M., Lany, N. K., Jonard, M., Ollinger, S. V., Groffman, P. M., Fulweiler, R. W., Angerer, J., Read, Q. D., Reich, P. B., Templer, P. H., and Elmore, A. J.: Evidence, causes, and consequences of declining nitrogen availability in terrestrial ecosystems, Science, 376, eabh3767, https://doi.org/10.1126/science.abh3767, 2022.
McLaughlin, S. B., Wullschleger, S. D., Sun, G., and Nosal, M.: Interactive effects of ozone and climate on water use, soil moisture content and streamflow in a southern Appalachian forest in the USA, New Phytol., 174, 125–136, https://doi.org/10.1111/j.1469-8137.2007.01970.x, 2007.
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, I. C., Barton, C. V. M., Crous, K. Y., De Angelis, P., Freeman, M., and Wingate, L.: Reconciling the optimal and empirical approaches to modelling stomatal conductance, Glob. Change Biol., 17, 2134–2144, https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011.
Merbold, L., Eugster, W., Stieger, J., Zahniser, M., Nelson, D., and Buchmann, N.: Greenhouse gas budget (CO2, CH4 and N2O) of intensively managed grassland following restoration, Glob. Change Biol., 20, 1913–1928, https://doi.org/10.1111/gcb.12518, 2014.
Miner, G. L., Bauerle, W. L., and Baldocchi, D. D.: Estimating the sensitivity of stomatal conductance to photosynthesis: a review: The sensitivity of conductance to photosynthesis, Plant Cell Environ., 40, 1214–1238, https://doi.org/10.1111/pce.12871, 2017.
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cy., 22, GB1022, https://doi.org/10.1029/2007gb002947, 2008.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Tr. Akad. Nauk SSSR Geofiz Inst., 24, 163–187, 1954.
Monks, P. S., Archibald, A. T., Colette, A., Cooper, O., Coyle, M., Derwent, R., Fowler, D., Granier, C., Law, K. S., Mills, G. E., Stevenson, D. S., Tarasova, O., Thouret, V., von Schneidemesser, E., Sommariva, R., Wild, O., and Williams, M. L.: Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer, Atmos. Chem. Phys., 15, 8889–8973, https://doi.org/10.5194/acp-15-8889-2015, 2015.
Moura, B. B., Alves, E. S., Marabesi, M. A., de Souza, S. R., Schaub, M., and Vollenweider, P.: Ozone affects leaf physiology and causes injury to foliage of native tree species from the tropical Atlantic Forest of southern Brazil, Sci. Total Environ., 610–611, 912–925, https://doi.org/10.1016/j.scitotenv.2017.08.130, 2018.
Muller, B. and Martre, P.: Plant and crop simulation models: powerful tools to link physiology, genetics, and phenomics, J. Exp. Bot., 70, 2339–2344, https://doi.org/10.1093/jxb/erz175, 2019.
Myers, S. S., Zanobetti, A., Kloog, I., Huybers, P., Leakey, A. D. B., Bloom, A. J., Carlisle, E., Dietterich, L. H., Fitzgerald, G., Hasegawa, T., Holbrook, N. M., Nelson, R. L., Ottman, M. J., Raboy, V., Sakai, H., Sartor, K. A., Schwartz, J., Seneweera, S., Tausz, M., and Usui, Y.: Increasing CO2 threatens human nutrition, Nature, 510, 139–142, https://doi.org/10.1038/nature13179, 2014.
Noormets, A., Kull, O., Sôber, A., Kubiske, M. E., and Karnosky, D. F.: Elevated CO2 response of photosynthesis depends on ozone concentration in aspen, Environ. Pollut., 158, 992–999, https://doi.org/10.1016/j.envpol.2009.10.009, 2010.
Norman, J. M.: Simulation of microclimates, Biometeorology and Integrated Pest Management, 65–99, https://doi.org/10.1016/B978-0-12-332850-2.50009-8, 1982.
Nowak, D., Hirabayashi, S., Bodine, A., and Greenfield, E.: Tree and forest effects on air quality and human health in the United States, Environ. Pollut., 193, 119–129, https://doi.org/10.1016/j.envpol.2014.05.028, 2014.
Oikawa, S. and Ainsworth, E. A.: Changes in leaf area, nitrogen content and canopy photosynthesis in soybean exposed to an ozone concentration gradient, Environ. Pollut., 215, 347–355, https://doi.org/10.1016/j.envpol.2016.05.005, 2016.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Levis, S., Li, F., Riley, W. J., Swenson, S. C., Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, F., Lawrence, P. J., Leung, L. R., Muszala, S., Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical Description of version 4.5 of the Community Land Model (CLM), https://doi.org/10.5065/D6RR1W7M, 2013.
Oliver, R. J., Mercado, L. M., Sitch, S., Simpson, D., Medlyn, B. E., Lin, Y.-S., and Folberth, G. A.: Large but decreasing effect of ozone on the European carbon sink, Biogeosciences, 15, 4245–4269, https://doi.org/10.5194/bg-15-4245-2018, 2018.
O'Sullivan, M., Spracklen, D. V., Batterman, S. A., Arnold, S. R., Gloor, M., and Buermann, W.: Have Synergies Between Nitrogen Deposition and Atmospheric CO2 Driven the Recent Enhancement of the Terrestrial Carbon Sink?, Global Biogeochem. Cy., 33, 163–180, https://doi.org/10.1029/2018GB005922, 2019.
Pacifico, F., Harrison, S. P., Jones, C. D., Arneth, A., Sitch, S., Weedon, G. P., Barkley, M. P., Palmer, P. I., Serça, D., Potosnak, M., Fu, T.-M., Goldstein, A., Bai, J., and Schurgers, G.: Evaluation of a photosynthesis-based biogenic isoprene emission scheme in JULES and simulation of isoprene emissions under present-day climate conditions, Atmos. Chem. Phys., 11, 4371–4389, https://doi.org/10.5194/acp-11-4371-2011, 2011.
Pacifico, F., Folberth, G. A., Jones, C. D., Harrison, S. P., and Collins, W. J.: Sensitivity of biogenic isoprene emissions to past, present, and future environmental conditions and implications for atmospheric chemistry, J. Geophys. Res.-Atmos., 117, D22302, https://doi.org/10.1029/2012JD018276, 2012.
Parrington, M., Jones, D. B. A., Bowman, K. W., Horowitz, L. W., Thompson, A. M., Tarasick, D. W., and Witte, J. C.: Estimating the summertime tropospheric ozone distribution over North America through assimilation of observations from the Tropospheric Emission Spectrometer, J. Geophys. Res., 113, D18307, https://doi.org/10.1029/2007JD009341, 2008.Please provide article number or page range.
Peichl, M., Brodeur, J. J., Khomik, M., and Arain, M. A.: Biometric and eddy-covariance based estimates of carbon fluxes in an age-sequence of temperate pine forests, Agr. Forest Meteorol., 150, 952–965, https://doi.org/10.1016/j.agrformet.2010.03.002, 2010.
Pleijel, H., Danielsson, H., Ojanperä, K., Temmerman, L. D., Högy, P., Badiani, M., and Karlsson, P. E.: Relationships between ozone exposure and yield loss in European wheat and potato – a comparison of concentration- and flux-based exposure indices, Atmos. Environ., 38, 2259–2269, https://doi.org/10.1016/j.atmosenv.2003.09.076, 2004.
Pongratz, J., Reick, C. H., Raddatz, T., and Claussen, M.: Effects of anthropogenic land cover change on the carbon cycle of the last millennium, Global Biogeochem. Cy., 23, GB4001, https://doi.org/10.1029/2009GB003488, 2009.
Pongratz, J., Reick, C. H., Raddatz, T., and Claussen, M.: Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change, Geophys. Res. Lett., 37, L08702, https://doi.org/10.1029/2010GL043010, 2010.
Porporato, A., Laio, F., Ridol, L., and Rodriguez-Iturbe, I.: Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress III. Vegetation water stress, Adv. Water Resour., 24, 725–744, 2001.
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochem. Cy., 24, GB1011, https://doi.org/10.1029/2008gb003435, 2010.
Prescher, A.-K., Grünwald, T., and Bernhofer, C.: Land use regulates carbon budgets in eastern Germany: From NEE to NBP, Agr. Forest Meteorol., 150, 1016–1025, https://doi.org/10.1016/j.agrformet.2010.03.008, 2010.
Proietti, C., Anav, A., De Marco, A., Sicard, P., and Vitale, M.: A multi-sites analysis on the ozone effects on Gross Primary Production of European forests, Sci. Total Environ., 556, 1–11, https://doi.org/10.1016/j.scitotenv.2016.02.187, 2016.
Pu, X., Wang, T. J., Huang, X., Melas, D., Zanis, P., Papanastasiou, D. K., and Poupkou, A.: Enhanced surface ozone during the heat wave of 2013 in Yangtze River Delta region, China, Sci. Total Environ., 603–604, 807–816, https://doi.org/10.1016/j.scitotenv.2017.03.056, 2017.
Rahman, M. H. U., Ahrends, H. E., Raza, A., and Gaiser, T.: Current approaches for modeling ecosystem services and biodiversity in agroforestry systems: Challenges and ways forward, Frontiers in Forests and Global Change, 5, 1032442, https://doi.org/10.3389/ffgc.2022.1032442, 2023.
R Core Team: R: A language and environment for statistical computing, Vienna, Austria, 2022.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Glob. Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005.
Rhea, L., King, J., Kubiske, M., Saliendra, N., and Teclaw, R.: Effects of elevated atmospheric CO2 and tropospheric O3 on tree branch growth and implications for hydrologic budgeting, Environ. Pollut., 158, 1079–1087, https://doi.org/10.1016/j.envpol.2009.08.038, 2010.
Roberts, H. R., Dodd, I. C., Hayes, F., and Ashworth, K.: Chronic tropospheric ozone exposure reduces seed yield and quality in spring and winter oilseed rape, Agr. Forest Meteorol., 316, 108859, https://doi.org/10.1016/j.agrformet.2022.108859, 2022.
Sadiq, M., Tai, A. P. K., Lombardozzi, D., and Val Martin, M.: Effects of ozone–vegetation coupling on surface ozone air quality via biogeochemical and meteorological feedbacks, Atmos. Chem. Phys., 17, 3055–3066, https://doi.org/10.5194/acp-17-3055-2017, 2017.
Sanderson, M. G., Collins, W. J., Hemming, D. L., and Betts, R. A.: Stomatal conductance changes due to increasing carbon dioxide levels: Projected impact on surface ozone levels, Tellus B, 59, 404-411, https://doi.org/10.1111/j.1600-0889.2007.00277.x, 2007.
Sandor, R., Ehrhardt, F., Brilli, L., Carozzi, M., Recous, S., Smith, P., Snow, V., Soussana, J. F., Dorich, C. D., Fuchs, K., Fitton, N., Gongadze, K., Klumpp, K., Liebig, M., Martin, R., Merbold, L., Newton, P. C. D., Rees, R. M., Rolinski, S., and Bellocchi, G.: The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands, Sci. Total Environ., 642, 292–306, https://doi.org/10.1016/j.scitotenv.2018.06.020, 2018.
Schimel, D., Stephens, B. B., and Fisher, J. B.: Effect of increasing CO2 on the terrestrial carbon cycle, P. Natl. Acad. Sci. USA, 112, 436–441, https://doi.org/10.1073/pnas.1407302112, 2015.
Scott, R. L., Biederman, J. A., Hamerlynck, E. P., and Barron-Gafford, G. A.: The carbon balance pivot point of southwestern U.S. semiarid ecosystems: Insights from the 21st century drought, J. Geophys. Res.-Biogeo., 120, 2612–2624, https://doi.org/10.1002/2015JG003181, 2015.
Seiler, C., Melton, J. R., Arora, V. K., Sitch, S., Friedlingstein, P., Anthoni, P., Goll, D., Jain, A. K., Joetzjer, E., Lienert, S., Lombardozzi, D., Luyssaert, S., Nabel, J. E. M. S., Tian, H., Vuichard, N., Walker, A. P., Yuan, W., and Zaehle, S.: Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?, J. Adv. Model. Earth Sy., 14, e2021MS002946, https://doi.org/10.1029/2021ms002946, 2022.
Sellers, P. J.: Canopy reflectance, photosynthesis and transpiration, Int. J. Remote Sens., 6, 1335–1372, https://doi.org/10.1080/01431168508948283, 1985.
Serrano-Ortiz, P., Domingo, F., Cazorla, A., Were, A., Cuezva, S., Villagarcía, L., Alados-Arboledas, L., and Kowalski, A. S.: Interannual CO2 exchange of a sparse Mediterranean shrubland on a carbonaceous substrate, J. Geophys. Res., 114, G04015, https://doi.org/10.1029/2009JG000983, 2009.
Shekhar, A., Buchmann, N., and Gharun, M.: How well do recently reconstructed solar-induced fluorescence datasets model gross primary productivity?, Remote Sens. Environ., 283, 113282, https://doi.org/10.1016/j.rse.2022.113282, 2022.
Sicard, P., Anav, A., De Marco, A., and Paoletti, E.: Projected global ground-level ozone impacts on vegetation under different emission and climate scenarios, Atmos. Chem. Phys., 17, 12177–12196, https://doi.org/10.5194/acp-17-12177-2017, 2017.
Sitch, S., Cox, P. M., Collins, W. J., and Huntingford, C.: Indirect radiative forcing of climate change through ozone effects on the land-carbon sink, Nature, 448, 791–794, https://doi.org/10.1038/nature06059, 2007.
Slevin, D., Tett, S. F. B., Exbrayat, J.-F., Bloom, A. A., and Williams, M.: Global evaluation of gross primary productivity in the JULES land surface model v3.4.1, Geosci. Model Dev., 10, 2651–2670, https://doi.org/10.5194/gmd-10-2651-2017, 2017.
Sun, G., McLaughlin, S. B., Porter, J. H., Uddling, J., Mulholland, P. J., Adams, M. B., and Pederson, N.: Interactive influences of ozone and climate on streamflow of forested watersheds, Glob. Change Biol., 18, 3395–3409, https://doi.org/10.1111/j.1365-2486.2012.02787.x, 2012.
Sun, S., Tai, A. P. K., Yung, D. H. Y., Wong, A. Y. H., Ducker, J. A., and Holmes, C. D.: Influence of plant ecophysiology on ozone dry deposition: comparing between multiplicative and photosynthesis-based dry deposition schemes and their responses to rising CO2 level, Biogeosciences, 19, 1753–1776, https://doi.org/10.5194/bg-19-1753-2022, 2022.
Tai, A. P. K., Sadiq, M., Pang, J. Y. S., Yung, D. H. Y., and Feng, Z.: Impacts of Surface Ozone Pollution on Global Crop Yields: Comparing Different Ozone Exposure Metrics and Incorporating Co-effects of CO2, Front. Sustain. Food Syst., 5, 534616, https://doi.org/10.3389/fsufs.2021.534616, 2021.
Tai, A. P. K. and Yung, D. H. Y.: TEMIR v1.0 Public Release (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.8215332, 2023a.
Tai, A. P. K. and Yung, D. H. Y.: Input Data for TEMIR v1.0 (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8215152, 2023b.
Terrer, C., Phillips, R. P., Hungate, B. A., Rosende, J., Pett-Ridge, J., Craig, M. E., van Groenigen, K. J., Keenan, T. F., Sulman, B. N., Stocker, B. D., Reich, P. B., Pellegrini, A. F. A., Pendall, E., Zhang, H., Evans, R. D., Carrillo, Y., Fisher, J. B., Van Sundert, K., Vicca, S., and Jackson, R. B.: A trade-off between plant and soil carbon storage under elevated CO(2), Nature, 591, 599–603, https://doi.org/10.1038/s41586-021-03306-8, 2021.
Tharammal, T., Bala, G., Narayanappa, D., and Nemani, R.: Potential roles of CO2 fertilization, nitrogen deposition, climate change, and land use and land cover change on the global terrestrial carbon uptake in the twenty-first century, Clim. Dynam., 52, 4393–4406, https://doi.org/10.1007/s00382-018-4388-8, 2018.
Tian, J., Zhang, Y., and Zhang, X.: Impacts of heterogeneous CO2 on water and carbon fluxes across the global land surface, International J. Digit. Earth, 14, 1175–1193, https://doi.org/10.1080/17538947.2021.1937352, 2021.
Ueyama, M., Ichii, K., Kobayashi, H., Kumagai, T. O., Beringer, J., Merbold, L., Euskirchen, E. S., Hirano, T., Marchesini, L. B., Baldocchi, D., Saitoh, T. M., Mizoguchi, Y., Ono, K., Kim, J., Varlagin, A., Kang, M., Shimizu, T., Kosugi, Y., Bret-Harte, M. S., Machimura, T., Matsuura, Y., Ohta, T., Takagi, K., Takanashi, S., and Yasuda, Y.: Inferring CO2 fertilization effect based on global monitoring land-atmosphere exchange with a theoretical model, Environ. Res. Lett., 15, 084009, https://doi.org/10.1088/1748-9326/ab79e5, 2020.
Val Martin, M., Heald, C. L., and Arnold, S. R.: Coupling dry deposition to vegetation phenology in the Community Earth System Model: Implications for the simulation of surface O3, Geophys. Res. Lett., 41, 2988–2996, https://doi.org/10.1002/2014GL059651, 2014.
Van Dingenen, R., Dentener, F. J., Raes, F., Krol, M. C., Emberson, L., and Cofala, J.: The global impact of ozone on agricultural crop yields under current and future air quality legislation, Atmos. Environ., 43, 604–618, https://doi.org/10.1016/j.atmosenv.2008.10.033, 2009.
Verbeke, T., Lathière, J., Szopa, S., and de Noblet-Ducoudré, N.: Impact of future land-cover changes on HNO3 and O3 surface dry deposition, Atmos. Chem. Phys., 15, 13555–13568, https://doi.org/10.5194/acp-15-13555-2015, 2015.
Verhoef, A. and Egea, G.: Modeling plant transpiration under limited soil water: Comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models, Agr. Forest Meteorol., 191, 22–32, https://doi.org/10.1016/j.agrformet.2014.02.009, 2014.
von Caemmerer, S. and Farquhar, G. D.: Some relationships between the biochemistry of photosynthesis and the gas exchange of leaves, Planta, 153, 376–387, https://doi.org/10.1007/BF00384257, 1981.
Wang, S., Zhang, Y., Ju, W., Chen, J. M., Ciais, P., Cescatti, A., Sardans, J., Janssens, I. A., Wu, M., Berry, J. A., Campbell, E., Fernández-Martínez, M., Alkama, R., Sitch, S., Friedlingstein, P., Smith, W. K., Yuan, W., He, W., Lombardozzi, D., Kautz, M., Zhu, D., Lienert, S., Kato, E., Poulter, B., Sanders, T. G. M., Krüger, I., Wang, R., Zeng, N., Tian, H., Vuichard, N., Jain, A. K., Wiltshire, A., Haverd, V., Goll, D. S., and Peñuelas, J.: Recent global decline of CO2 fertilization effects on vegetation photosynthesis, Science, 370, 1295–1300, https://doi.org/10.1126/science.abb7772, 2020.
Wang, X., Chen, J. M., Ju, W., and Zhang, Y.: Seasonal Variations in Leaf Maximum Photosynthetic Capacity and Its Dependence on Climate Factors Across Global FLUXNET Sites, J. Geophys. Res.-Biogeo., 127, e2021JG006709, https://doi.org/10.1029/2021jg006709, 2022.
Wang, Y., Jacob, D. J., and Logan, J. A.: Global simulation of tropospheric O3-NOx-hydrocarbon chemistry: 3. Origin of tropospheric ozone and effects of nonmethane hydrocarbons, J. Geophys. Res.-Atmos., 103, 10757–10767, https://doi.org/10.1029/98JD00156, 1998.
Wesely, M. L.: Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models, Atmos. Environ., 23, 1293–1304, 1989.
Wild, B., Teubner, I., Moesinger, L., Zotta, R.-M., Forkel, M., van der Schalie, R., Sitch, S., and Dorigo, W.: VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing, Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, 2022.
Wild, O.: Modelling the global tropospheric ozone budget: exploring the variability in current models, Atmos. Chem. Phys., 7, 2643–2660, https://doi.org/10.5194/acp-7-2643-2007, 2007.
Wiltshire, A. J., Burke, E. J., Chadburn, S. E., Jones, C. D., Cox, P. M., Davies-Barnard, T., Friedlingstein, P., Harper, A. B., Liddicoat, S., Sitch, S., and Zaehle, S.: JULES-CN: a coupled terrestrial carbon–nitrogen scheme (JULES vn5.1), Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, 2021.
Wolf, S., Keenan, T. F., Fisher, J. B., Baldocchi, D. D., Desai, A. R., Richardson, A. D., Scott, R. L., Law, B. E., Litvak, M. E., Brunsell, N. A., Peters, W., and van der Laan-Luijkx, I. T.: Warm spring reduced carbon cycle impact of the 2012 US summer drought, P. Natl. Acad. Sci. USA, 113, 5880–5885, https://doi.org/10.1073/pnas.1519620113, 2016.
Wong, A. Y. H., Geddes, J. A., Tai, A. P. K., and Silva, S. J.: Importance of dry deposition parameterization choice in global simulations of surface ozone, Atmos. Chem. Phys., 19, 14365–14385, https://doi.org/10.5194/acp-19-14365-2019, 2019.
Wu, Q., Chen, S., Zhang, Y., Song, C., Ju, W., Wang, L., and Jiang, J.: Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016, Remote Sens., 15, 1172, https://doi.org/10.3390/rs15051172, 2023a.
Wu, Q., Wang, X., Chen, S., Wang, L., and Jiang, J.: Land Surface Greening and CO2 Fertilization More than Offset the Gross Carbon Sequestration Decline Caused by Land Cover Change and the Enhanced Vapour Pressure Deficit in Europe, Remote Sens., 15, 1372, https://doi.org/10.3390/rs15051372, 2023b.
Xia, L., Lam, S. K., Kiese, R., Chen, D., Luo, Y., van Groenigen, K. J., Ainsworth, E. A., Chen, J., Liu, S., Ma, L., Zhu, Y., and Butterbach-Bahl, K.: Elevated CO2 negates O3 impacts on terrestrial carbon and nitrogen cycles, One Earth, 4, 1752–1763, https://doi.org/10.1016/j.oneear.2021.11.009, 2021.
Xu, B., Arain, M. A., Black, T. A., Law, B. E., Pastorello, G. Z., and Chu, H.: Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems, Glob. Change Biol., 26, 901–918, https://doi.org/10.1111/gcb.14843, 2020.
Yan, M., Yue, X., Zhou, B., Sun, X., and Xin, N.: Projected changes of ecosystem productivity and their responses to extreme heat events in northern asia, Front. Earth Sci., 10, 970296, https://doi.org/10.3389/feart.2022.970296, 2022.
Yang, R., Wang, J., Zeng, N., Sitch, S., Tang, W., McGrath, M. J., Cai, Q., Liu, D., Lombardozzi, D., Tian, H., Jain, A. K., and Han, P.: Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products, Earth Syst. Dynam., 13, 833–849, https://doi.org/10.5194/esd-13-833-2022, 2022.
Yang, X., Wittig, V., Jain, A. K., and Post, W.: Integration of nitrogen cycle dynamics into the Integrated Science Assessment Model for the study of terrestrial ecosystem responses to global change, Global Biogeochem. Cy., 23, GB4029, https://doi.org/10.1029/2009gb003474, 2009.
Yang, X., Chen, X., Ren, J., Yuan, W., Liu, L., Liu, J., Chen, D., Xiao, Y., Song, Q., Du, Y., Wu, S., Fan, L., Dai, X., Wang, Y., and Su, Y.: A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests, Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, 2023.
Yi, K., Dragoni, D., Phillips, R. P., Roman, D. T., and Novick, K. A.: Dynamics of stem water uptake among isohydric and anisohydric species experiencing a severe drought, Tree Physiol, 37, 1379–1392, https://doi.org/10.1093/treephys/tpw126, 2017.
Yuan, H., Dai, Y., Xiao, Z., Ji, D., and Shangguan, W.: Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling, Remote Sens. Environ., 115, 1171–1187, https://doi.org/10.1016/j.rse.2011.01.001, 2011.
Yue, X. and Unger, N.: Ozone vegetation damage effects on gross primary productivity in the United States, Atmos. Chem. Phys., 14, 9137–9153, https://doi.org/10.5194/acp-14-9137-2014, 2014.
Yue, X. and Unger, N.: The Yale Interactive terrestrial Biosphere model version 1.0: description, evaluation and implementation into NASA GISS ModelE2, Geosci. Model Dev., 8, 2399–2417, https://doi.org/10.5194/gmd-8-2399-2015, 2015.
Yue, X., Unger, N., Harper, K., Xia, X., Liao, H., Zhu, T., Xiao, J., Feng, Z., and Li, J.: Ozone and haze pollution weakens net primary productivity in China, Atmos. Chem. Phys., 17, 6073–6089, https://doi.org/10.5194/acp-17-6073-2017, 2017.
Zeng, X., Zhao, M., and Dickinson, R. E.: Intercomparison of Bulk Aerodynamic Algorithms for the Computation of Sea Surface Fluxes Using TOGA COARE and TAO Data, J. Climate, 11, 2628–2644, https://doi.org/10.1175/1520-0442(1998)011<2628:IOBAAF>2.0.CO;2, 1998.
Zeng, X., Shaikh, M., Dai, Y., Dickinson, R. E., and Myneni, R.: Coupling of the Common Land Model to the NCAR Community Climate Model, J. Climate, 15, 1832–1854, https://doi.org/10.1175/1520-0442(2002)015<1, 2002.
Zhang, L., Moran, M. D., Makar, P. A., Brook, J. R., and Gong, S.: Modelling gaseous dry deposition in AURAMS: a unified regional air-quality modelling system, Atmos. Environ., 36, 537–560, https://doi.org/10.1016/S1352-2310(01)00447-2, 2002.
Zhang, L., Brook, J. R., and Vet, R.: A revised parameterization for gaseous dry deposition in air-quality models, Atmos. Chem. Phys., 3, 2067–2082, https://doi.org/10.5194/acp-3-2067-2003, 2003.
Zhang, L., Jacob, D. J., Liu, X., Logan, J. A., Chance, K., Eldering, A., and Bojkov, B. R.: Intercomparison methods for satellite measurements of atmospheric composition: application to tropospheric ozone from TES and OMI, Atmos. Chem. Phys., 10, 4725–4739, https://doi.org/10.5194/acp-10-4725-2010, 2010.
Zhang, L., Hoshika, Y., Carrari, E., Burkey, K. O., and Paoletti, E.: Protecting the photosynthetic performance of snap bean under free air ozone exposure, J. Environ. Sci., 66, 31–40, https://doi.org/10.1016/j.jes.2017.05.009, 2018.
Zhang, X., Ward, B. B., and Sigman, D. M.: Global Nitrogen Cycle: Critical Enzymes, Organisms, and Processes for Nitrogen Budgets and Dynamics, Chem. Rev., 120, 5308–5351, https://doi.org/10.1021/acs.chemrev.9b00613, 2020.
Zhang, Y. and Wang, Y.: Climate-driven ground-level ozone extreme in the fall over the Southeast United States, P. Natl. Acad. Sci. USA, 113, 10025–10030, https://doi.org/10.1073/pnas.1602563113, 2016.
Zhang, Y. and Ye, A.: Improving global gross primary productivity estimation by fusing multi-source data products, Heliyon, 8, e09153, https://doi.org/10.1016/j.heliyon.2022.e09153, 2022.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.: A global moderate resolution dataset of gross primary production of vegetation for 2000–2016, Sci. Data, 4, 170165, https://doi.org/10.1038/sdata.2017.165, 2017.
Zhao, J., Liu, D., Zhu, Y., Peng, H., and Xie, H.: A review of forest carbon cycle models on spatiotemporal scales, J. Clean. Prod., 339, https://doi.org/10.1016/j.jclepro.2022.130692, 2022.
Zhao, Y., Zhang, L., Tai, A. P. K., Chen, Y., and Pan, Y.: Responses of surface ozone air quality to anthropogenic nitrogen deposition in the Northern Hemisphere, Atmos. Chem. Phys., 17, 9781–9796, https://doi.org/10.5194/acp-17-9781-2017, 2017.
Zhou, S. S., Tai, A. P. K., Sun, S., Sadiq, M., Heald, C. L., and Geddes, J. A.: Coupling between surface ozone and leaf area index in a chemical transport model: strength of feedback and implications for ozone air quality and vegetation health, Atmos. Chem. Phys., 18, 14133–14148, https://doi.org/10.5194/acp-18-14133-2018, 2018.
Zhu, J., Tai, A. P. K., and Hung Lam Yim, S.: Effects of ozone–vegetation interactions on meteorology and air quality in China using a two-way coupled land–atmosphere model, Atmos. Chem. Phys., 22, 765–782, https://doi.org/10.5194/acp-22-765-2022, 2022.
Zielis, S., Etzold, S., Zweifel, R., Eugster, W., Haeni, M., and Buchmann, N.: NEP of a Swiss subalpine forest is significantly driven not only by current but also by previous year's weather, Biogeosciences, 11, 1627–1635, https://doi.org/10.5194/bg-11-1627-2014, 2014.
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and...
