Articles | Volume 14, issue 4
Geosci. Model Dev., 14, 1949–1985, 2021
https://doi.org/10.5194/gmd-14-1949-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 14, 1949–1985, 2021
https://doi.org/10.5194/gmd-14-1949-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper 13 Apr 2021
Model description paper | 13 Apr 2021
SPEAD 1.0 – Simulating Plankton Evolution with Adaptive Dynamics in a two-trait continuous fitness landscape applied to the Sargasso Sea
Guillaume Le Gland et al.
Related authors
Guillaume Le Gland, Laurent Mémery, Olivier Aumont, and Laure Resplandy
Biogeosciences, 14, 3171–3189, https://doi.org/10.5194/bg-14-3171-2017, https://doi.org/10.5194/bg-14-3171-2017, 2017
Short summary
Short summary
In this study, we computed the fluxes of radium-228 from the continental shelf to the open ocean by fitting a numerical model to observations. After determining appropriate model parameters (cost function and number of source regions), we found a lower and more precise global flux than previous estimates: 8.01–8.49×1023 atoms yr−1. This result can be used to assess nutrient and trace element fluxes to the open ocean, but we cannot identify specific pathways like submarine groundwater discharge.
Onur Kerimoglu, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-396, https://doi.org/10.5194/gmd-2020-396, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
In large-scale models, variations in cellular composition of phytoplankton are often insufficiently represented. Detailed modelling approaches exist, but they require additional state variables that increase the computational costs. In this study, we test an instantaneous acclimation model in a spatially explicit setup, and show that its behavior is similar to that of a variant with an additional state variable, but significantly different from that of a fixed composition variant.
Stephanie Dutkiewicz, Pedro Cermeno, Oliver Jahn, Michael J. Follows, Anna E. Hickman, Darcy A. A. Taniguchi, and Ben A. Ward
Biogeosciences, 17, 609–634, https://doi.org/10.5194/bg-17-609-2020, https://doi.org/10.5194/bg-17-609-2020, 2020
Short summary
Short summary
Phytoplankton are an essential component of the marine food web and earth's carbon cycle. We use observations, ecological theory and a unique trait-based ecosystem model to explain controls on patterns of marine phytoplankton biodiversity. We find that different dimensions of diversity (size classes, biogeochemical functional groups, thermal norms) are controlled by a disparate combination of mechanisms. This may explain why previous studies of phytoplankton diversity had conflicting results.
Jose Luis Otero-Ferrer, Pedro Cermeño, Antonio Bode, Bieito Fernández-Castro, Josep M. Gasol, Xosé Anxelu G. Morán, Emilio Marañon, Victor Moreira-Coello, Marta M. Varela, Marina Villamaña, and Beatriz Mouriño-Carballido
Biogeosciences, 15, 6199–6220, https://doi.org/10.5194/bg-15-6199-2018, https://doi.org/10.5194/bg-15-6199-2018, 2018
Short summary
Short summary
The effect of inorganic nutrients on planktonic assemblages has been traditionally assessed by looking at concentrations rather than fluxes of nutrient supply. However, in near-steady-state systems such as subtropical gyres, nitrate concentrations are kept close to the detection limit due to phytoplankton uptake. Our results, based on direct measurements of nitrate diffusive fluxes, support the key role of nitrate supply in controlling the structure of marine picoplankton communities.
Chris J. Daniels, Alex J. Poulton, William M. Balch, Emilio Marañón, Tim Adey, Bruce C. Bowler, Pedro Cermeño, Anastasia Charalampopoulou, David W. Crawford, Dave Drapeau, Yuanyuan Feng, Ana Fernández, Emilio Fernández, Glaucia M. Fragoso, Natalia González, Lisa M. Graziano, Rachel Heslop, Patrick M. Holligan, Jason Hopkins, María Huete-Ortega, David A. Hutchins, Phoebe J. Lam, Michael S. Lipsen, Daffne C. López-Sandoval, Socratis Loucaides, Adrian Marchetti, Kyle M. J. Mayers, Andrew P. Rees, Cristina Sobrino, Eithne Tynan, and Toby Tyrrell
Earth Syst. Sci. Data, 10, 1859–1876, https://doi.org/10.5194/essd-10-1859-2018, https://doi.org/10.5194/essd-10-1859-2018, 2018
Short summary
Short summary
Calcifying marine algae (coccolithophores) are key to oceanic biogeochemical processes, such as calcium carbonate production and export. We compile a global database of calcium carbonate production from field samples (n = 2756), alongside primary production rates and coccolithophore abundance. Basic statistical analysis highlights global distribution, average surface and integrated rates, patterns with depth and the importance of considering cell-normalised rates as a simple physiological index.
Bingzhang Chen and Sherwood Lan Smith
Geosci. Model Dev., 11, 467–495, https://doi.org/10.5194/gmd-11-467-2018, https://doi.org/10.5194/gmd-11-467-2018, 2018
Short summary
Short summary
Marine phytoplankton accounts for half of global primary production. Phytoplankton size is an important trait affecting its fitness and ecosystem functioning. We have developed a plankton model with continuous size distribution for phytoplankton and applied it in the North Pacific. This model is able to capture the general patterns of phytoplankton size distribution in the real ocean and can be used for understanding the mechanisms controlling phytoplankton size structure and diversity.
Guillaume Le Gland, Laurent Mémery, Olivier Aumont, and Laure Resplandy
Biogeosciences, 14, 3171–3189, https://doi.org/10.5194/bg-14-3171-2017, https://doi.org/10.5194/bg-14-3171-2017, 2017
Short summary
Short summary
In this study, we computed the fluxes of radium-228 from the continental shelf to the open ocean by fitting a numerical model to observations. After determining appropriate model parameters (cost function and number of source regions), we found a lower and more precise global flux than previous estimates: 8.01–8.49×1023 atoms yr−1. This result can be used to assess nutrient and trace element fluxes to the open ocean, but we cannot identify specific pathways like submarine groundwater discharge.
Virginia García-Bernal, Óscar Paz, and Pedro Cermeño
Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-4, https://doi.org/10.5194/bg-2017-4, 2017
Manuscript not accepted for further review
Short summary
Short summary
Marine diatoms are responsible for roughly 40 % of modern ocean primary production and contribute disproportionately to the drawdown of atmospheric carbon dioxide through the export of organic carbon into the deep sea and sediments. Over the past 40 Myr their rise to ecological prominence and consequential decline of coccolithophores is linked to the silicon to phosphorus weathering ratio, which controls the oceanic nutrient inventories and hence the competitive ability of diatoms.
Esteban Acevedo-Trejos, Gunnar Brandt, S. Lan Smith, and Agostino Merico
Geosci. Model Dev., 9, 4071–4085, https://doi.org/10.5194/gmd-9-4071-2016, https://doi.org/10.5194/gmd-9-4071-2016, 2016
Short summary
Short summary
Marine phytoplankton plays a prominent role in regulating Earth’s climate. Numerical models are important tools that help us investigate the interactions between these microbes and their environment. We proposed PhytoSFDM as an open-source model to quantify size structure and functional diversity of marine phytoplankton communities. This tool allows us, in a manageable and computationally efficient way, to study patterns in planktonic ecosystems and their feedbacks with a changing environment.
Fatima Abrantes, Pedro Cermeno, Cristina Lopes, Oscar Romero, Lélia Matos, Jolanda Van Iperen, Marta Rufino, and Vitor Magalhães
Biogeosciences, 13, 4099–4109, https://doi.org/10.5194/bg-13-4099-2016, https://doi.org/10.5194/bg-13-4099-2016, 2016
Short summary
Short summary
Diatoms are the dominant primary producers of the most productive and best fishing areas of the modern ocean, the coastal upwelling systems. This turns them into important contributors to the biological pump and climate change. To help untangle their response to warming climate, we compare the worldwide diatom sedimentary abundance (SDA) to environmental variables and find that the capacity of diatoms to take up silicic acid sets an upper limit on global export production in these ocean regions.
Related subject area
Biogeosciences
A model-independent data assimilation (MIDA) module and its applications in ecology
Optical model for the Baltic Sea with an explicit CDOM state variable: a case study with Model ERGOM (version 1.2)
WAP-1D-VAR v1.0: development and evaluation of a one-dimensional variational data assimilation model for the marine ecosystem along the West Antarctic Peninsula
SCOPE 2.0: a model to simulate vegetated land surface fluxes and satellite signals
SolveSAPHE-r2 (v2.0.1): revisiting and extending the Solver Suite for Alkalinity-PH Equations for usage with CO2, HCO3− or CO32− input data
Modeling gas exchange and biomass production in West African Sahelian and Sudanian ecological zones
Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval® thermal analysis (PARTYSOCv2.0 and PARTYSOCv2.0EU)
Addressing biases in Arctic–boreal carbon cycling in the Community Land Model Version 5
Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance
Ecosystem age-class dynamics and distribution in the LPJ-wsl v2.0 global ecosystem model
CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 2: Global benchmarking
A model for marine sedimentary carbonate diagenesis and paleoclimate proxy signal tracking: IMP v0.9
How to reconstruct aerosol-induced diffuse radiation scenario for simulating GPP in land surface models? An evaluation of reconstruction methods with ORCHIDEE_DFv1.0_DFforc
Rapid development of fast and flexible environmental models: the Mobius framework v1.0
Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences
Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum (STEMMUS–SCOPE v1.0.0)
FABM-NflexPD 1.0: Assessing an Instantaneous Acclimation Approach for Modelling Phytoplankton Growth
CoupModel (v6.0): an ecosystem model for coupled phosphorus, nitrogen, and carbon dynamics – evaluated against empirical data from a climatic and fertility gradient in Sweden
Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0
Numerical model to simulate long-term soil organic carbon and ground ice budget with permafrost and ice sheets (SOC-ICE-v1.0)
Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9333): Implementation and evaluation of simulations for Europe
Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O3 experiment
Modeling long-term fire impact on ecosystem characteristics and surface energy using a process-based vegetation–fire model SSiB4/TRIFFID-Fire v1.0
Energy, water and carbon exchanges in managed forest ecosystems: description, sensitivity analysis and evaluation of the INRAE GO+ model, version 3.0
Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation
Oceanic and atmospheric methane cycling in the cGENIE Earth system model – release v0.9.14
Modeling the impacts of diffuse light fraction on photosynthesis in ORCHIDEE (v5453) land surface model
Description and evaluation of the process-based forest model 4C v2.2 at four European forest sites
A multi-isotope model for simulating soil organic carbon cycling in eroding landscapes (WATEM_C v1.0)
One-dimensional models of radiation transfer in heterogeneous canopies: a review, re-evaluation, and improved model
An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen version 2 (FANv2)
Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0)
Short-term forecasting of regional biospheric CO2 fluxes in Europe using a light-use-efficiency model (VPRM, MPI-BGC version 1.2)
FLiES-SIF version 1.0: three-dimensional radiative transfer model for estimating solar induced fluorescence
The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage
Evaluation of CH4MODwetland and Terrestrial Ecosystem Model (TEM) used to estimate global CH4 emissions from natural wetlands
Simulating stable carbon isotopes in the ocean component of the FAMOUS general circulation model with MOSES1 (XOAVI)
Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project
Marine biogeochemical cycling and oceanic CO2 uptake simulated by the NUIST Earth System Model version 3 (NESM v3)
CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 1: Model framework and site-level performance
HR3DHG version 1: modeling the spatiotemporal dynamics of mercury in the Augusta Bay (southern Italy)
Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for land-surface models
BPOP-v1 model: exploring the impact of changes in the biological pump on the shelf sea and ocean nutrient and redox state
P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production
HETEROFOR 1.0: a spatially explicit model for exploring the response of structurally complex forests to uncertain future conditions – Part 2: Phenology and water cycle
Dynamic upscaling of decomposition kinetics for carbon cycling models
CE-DYNAM (v1): a spatially explicit process-based carbon erosion scheme for use in Earth system models
A coupled pelagic–benthic–sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model
FORests and HYdrology under Climate Change in Switzerland v1.0: a spatially distributed model combining hydrology and forest dynamics
ORCHIDEE MICT-LEAK (r5459), a global model for the production, transport, and transformation of dissolved organic carbon from Arctic permafrost regions – Part 2: Model evaluation over the Lena River basin
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
Short summary
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Thomas Neumann, Sampsa Koponen, Jenni Attila, Carsten Brockmann, Kari Kallio, Mikko Kervinen, Constant Mazeran, Dagmar Müller, Petra Philipson, Susanne Thulin, Sakari Väkevä, and Pasi Ylöstalo
Geosci. Model Dev., 14, 5049–5062, https://doi.org/10.5194/gmd-14-5049-2021, https://doi.org/10.5194/gmd-14-5049-2021, 2021
Short summary
Short summary
The Baltic Sea is heavily impacted by surrounding land. Therefore, the concentration of colored dissolved organic matter (CDOM) of terrestrial origin is relatively high and impacts the light penetration depth. Estimating a correct light climate is essential for ecosystem models. In this study, a method is developed to derive riverine CDOM from Earth observation methods. The data are used as boundary conditions for an ecosystem model, and the advantage over former approaches is shown.
Hyewon Heather Kim, Ya-Wei Luo, Hugh W. Ducklow, Oscar M. Schofield, Deborah K. Steinberg, and Scott C. Doney
Geosci. Model Dev., 14, 4939–4975, https://doi.org/10.5194/gmd-14-4939-2021, https://doi.org/10.5194/gmd-14-4939-2021, 2021
Short summary
Short summary
The West Antarctic Peninsula (WAP) is a rapidly warming region, revealed by multi-decadal observations. Despite the region being data rich, there is a lack of focus on ecosystem model development. Here, we introduce a data assimilation ecosystem model for the WAP region. Experiments by assimilating data from an example growth season capture key WAP features. This study enables us to glue the snapshots from available data sets together to explain the observations in the WAP.
Peiqi Yang, Egor Prikaziuk, Wout Verhoef, and Christiaan van der Tol
Geosci. Model Dev., 14, 4697–4712, https://doi.org/10.5194/gmd-14-4697-2021, https://doi.org/10.5194/gmd-14-4697-2021, 2021
Short summary
Short summary
Since the first publication 12 years ago, the SCOPE model has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary productivity (GPP), and directional thermal signals. Here, we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements.
Guy Munhoven
Geosci. Model Dev., 14, 4225–4240, https://doi.org/10.5194/gmd-14-4225-2021, https://doi.org/10.5194/gmd-14-4225-2021, 2021
Short summary
Short summary
SolveSAPHE (Munhoven, 2013) was the first package to calculate pH reliably from any physically sensible pair of total alkalinity (AlkT) and dissolved inorganic carbon (CT) data and to do so in an autonomous and efficient way. Here, we extend it to use CO2, HCO3 or CO3 instead of CT. For each one of these pairs, the new SolveSAPHE calculates all of the possible pH values (0, 1, or 2), again without any prior knowledge of the solutions.
Jaber Rahimi, Expedit Evariste Ago, Augustine Ayantunde, Sina Berger, Jan Bogaert, Klaus Butterbach-Bahl, Bernard Cappelaere, Jean-Martial Cohard, Jérôme Demarty, Abdoul Aziz Diouf, Ulrike Falk, Edwin Haas, Pierre Hiernaux, David Kraus, Olivier Roupsard, Clemens Scheer, Amit Kumar Srivastava, Torbern Tagesson, and Rüdiger Grote
Geosci. Model Dev., 14, 3789–3812, https://doi.org/10.5194/gmd-14-3789-2021, https://doi.org/10.5194/gmd-14-3789-2021, 2021
Short summary
Short summary
West African Sahelian and Sudanian ecosystems are important regions for global carbon exchange, and they provide valuable food and fodder resources. Therefore, we simulated net ecosystem exchange and aboveground biomass of typical ecosystems in this region with an improved process-based biogeochemical model, LandscapeDNDC. Carbon stocks and exchange rates were particularly correlated with the abundance of trees. Grass and crop yields increased under humid climatic conditions.
Lauric Cécillon, François Baudin, Claire Chenu, Bent T. Christensen, Uwe Franko, Sabine Houot, Eva Kanari, Thomas Kätterer, Ines Merbach, Folkert van Oort, Christopher Poeplau, Juan Carlos Quezada, Florence Savignac, Laure N. Soucémarianadin, and Pierre Barré
Geosci. Model Dev., 14, 3879–3898, https://doi.org/10.5194/gmd-14-3879-2021, https://doi.org/10.5194/gmd-14-3879-2021, 2021
Short summary
Short summary
Partitioning soil organic carbon (SOC) into fractions that are stable or active on a century scale is key for more accurate models of the carbon cycle. Here, we describe the second version of a machine-learning model, named PARTYsoc, which reliably predicts the proportion of the centennially stable SOC fraction at its northwestern European validation sites with Cambisols and Luvisols, the two dominant soil groups in this region, fostering modelling works of SOC dynamics.
Leah Birch, Christopher R. Schwalm, Sue Natali, Danica Lombardozzi, Gretchen Keppel-Aleks, Jennifer Watts, Xin Lin, Donatella Zona, Walter Oechel, Torsten Sachs, Thomas Andrew Black, and Brendan M. Rogers
Geosci. Model Dev., 14, 3361–3382, https://doi.org/10.5194/gmd-14-3361-2021, https://doi.org/10.5194/gmd-14-3361-2021, 2021
Short summary
Short summary
The high-latitude landscape or Arctic–boreal zone has been warming rapidly, impacting the carbon balance both regionally and globally. Given the possible global effects of climate change, it is important to have accurate climate model simulations. We assess the simulation of the Arctic–boreal carbon cycle in the Community Land Model (CLM 5.0). We find biases in both the timing and magnitude photosynthesis. We then use observational data to improve the simulation of the carbon cycle.
Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin
Geosci. Model Dev., 14, 2603–2633, https://doi.org/10.5194/gmd-14-2603-2021, https://doi.org/10.5194/gmd-14-2603-2021, 2021
Short summary
Short summary
Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare models against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeastern US.
Leonardo Calle and Benjamin Poulter
Geosci. Model Dev., 14, 2575–2601, https://doi.org/10.5194/gmd-14-2575-2021, https://doi.org/10.5194/gmd-14-2575-2021, 2021
Short summary
Short summary
We developed a model to simulate and track the age of ecosystems on Earth. We found that the effect of ecosystem age on net primary production and ecosystem respiration is as important as climate in large areas of every vegetated continent. The LPJ-wsl v2.0 age-class model simulates dynamic age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.
Christian Seiler, Joe R. Melton, Vivek K. Arora, and Libo Wang
Geosci. Model Dev., 14, 2371–2417, https://doi.org/10.5194/gmd-14-2371-2021, https://doi.org/10.5194/gmd-14-2371-2021, 2021
Short summary
Short summary
This study evaluates how well the CLASSIC land surface model reproduces the energy, water, and carbon cycle when compared against a wide range of global observations. Special attention is paid to how uncertainties in the data used to drive and evaluate the model affect model skill. Our results show the importance of incorporating uncertainties when evaluating land surface models and that failing to do so may potentially misguide future model development.
Yoshiki Kanzaki, Dominik Hülse, Sandra Kirtland Turner, and Andy Ridgwell
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-405, https://doi.org/10.5194/gmd-2020-405, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
Sedimentary carbonate plays a central role to regulation of Earth’s carbon cycle and climate, and also serves as an archive of paleo environments hosting various elements/isotopes. To help obtain “true” environmental changes from carbonate records over diagenetic distortion, a new model IMP has been developed. Signal tracking is enabled by simulating diagenesis of multiple carbonate particles and different styles of particle mixing by benthos are also reflected with a variable transition matrix.
Yuan Zhang, Olivier Boucher, Philippe Ciais, Laurent Li, and Nicolas Bellouin
Geosci. Model Dev., 14, 2029–2039, https://doi.org/10.5194/gmd-14-2029-2021, https://doi.org/10.5194/gmd-14-2029-2021, 2021
Short summary
Short summary
We investigated different methods to reconstruct spatiotemporal distribution of the fraction of diffuse radiation (Fdf) to qualify the aerosol impacts on GPP using the ORCHIDEE_DF land surface model. We find that climatological-averaging methods which dampen the variability of Fdf can cause significant bias in the modeled diffuse radiation impacts on GPP. Better methods to reconstruct Fdf are recommended.
Magnus Dahler Norling, Leah Amber Jackson-Blake, José-Luis Guerrero Calidonio, and James Edward Sample
Geosci. Model Dev., 14, 1885–1897, https://doi.org/10.5194/gmd-14-1885-2021, https://doi.org/10.5194/gmd-14-1885-2021, 2021
Short summary
Short summary
In order to allow researchers to quickly prototype and build models of natural systems, we have created the Mobius framework. Such models can, for instance, be used to ask questions about what the impacts of land-use changes are to water quality in a river or lake, or the response of biogeochemical systems to climate change. The Mobius framework makes it quick to build models that run fast, which enables the user to explore many different scenarios and model formulations.
Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, and Sylvain Pellerin
Geosci. Model Dev., 14, 1639–1656, https://doi.org/10.5194/gmd-14-1639-2021, https://doi.org/10.5194/gmd-14-1639-2021, 2021
Short summary
Short summary
We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential yield. We build a GCCM emulator based on generic formalism and fit its parameters against aboveground biomass and yield at harvest simulated by eight GGCMs. Despite huge differences between GGCMs, we show that the calibration of a few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model could help to improve the global simulation of potential yield.
Yunfei Wang, Yijian Zeng, Lianyu Yu, Peiqi Yang, Christiaan Van der Tol, Qiang Yu, Xiaoliang Lü, Huanjie Cai, and Zhongbo Su
Geosci. Model Dev., 14, 1379–1407, https://doi.org/10.5194/gmd-14-1379-2021, https://doi.org/10.5194/gmd-14-1379-2021, 2021
Short summary
Short summary
This study integrates photosynthesis and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model. The results indicated that the simulation of land surface fluxes was significantly improved by considering the root water uptake, especially when vegetation was experiencing severe water stress. This finding highlights the importance of enhanced soil heat and moisture transfer in simulating ecosystem functioning.
Onur Kerimoglu, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-396, https://doi.org/10.5194/gmd-2020-396, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
In large-scale models, variations in cellular composition of phytoplankton are often insufficiently represented. Detailed modelling approaches exist, but they require additional state variables that increase the computational costs. In this study, we test an instantaneous acclimation model in a spatially explicit setup, and show that its behavior is similar to that of a variant with an additional state variable, but significantly different from that of a fixed composition variant.
Hongxing He, Per-Erik Jansson, and Annemieke I. Gärdenäs
Geosci. Model Dev., 14, 735–761, https://doi.org/10.5194/gmd-14-735-2021, https://doi.org/10.5194/gmd-14-735-2021, 2021
Short summary
Short summary
This study presents the integration of the phosphorus (P) cycle into CoupModel (v6.0, Coup-CNP). The extended Coup-CNP, which explicitly considers the symbiosis between soil microbes and plant roots, enables simulations of coupled C, N, and P dynamics for terrestrial ecosystems. Simulations from the new Coup-CNP model provide strong evidence that P fluxes need to be further considered in studies of how ecosystems and C turnover react to climate change.
Theresa Boas, Heye Bogena, Thomas Grünwald, Bernard Heinesch, Dongryeol Ryu, Marius Schmidt, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, https://doi.org/10.5194/gmd-14-573-2021, 2021
Short summary
Short summary
In this study we were able to significantly improve CLM5 model performance for European cropland sites by adding a winter wheat representation, specific plant parameterizations for important cash crops, and a cover-cropping and crop rotation subroutine to its crop module. Our modifications should be applied in future studies of CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.
Kazuyuki Saito, Hirokazu Machiya, Go Iwahana, Tokuta Yokohata, and Hiroshi Ohno
Geosci. Model Dev., 14, 521–542, https://doi.org/10.5194/gmd-14-521-2021, https://doi.org/10.5194/gmd-14-521-2021, 2021
Short summary
Short summary
Soil organic carbon (SOC) and ground ice (ICE) are essential but under-documented information to assess the circum-Arctic permafrost degradation impacts. A simple numerical model of essential SOC and ICE dynamics, developed and integrated north of 50° N for 125,000 years since the last interglacial, reconstructed the history and 1° distribution of SOC and ICE consistent with current knowledge, together with successful demonstration of climatic and topographical controls on SOC evolution.
Mats Lindeskog, Fredrik Lagergren, Benjamin Smith, and Anja Rammig
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-440, https://doi.org/10.5194/gmd-2020-440, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
Forests play an important role in the global carbon cycle and for carbon storage. In Europe, forests are highly managed. To understand how management influences carbon storage in European forests, we implement detailed forest management into the dynamic vegetation model LPJ-GUESS. We test the model by comparing model output to typical forestry measures such as growing stock and harvest data for different countries in Europe.
Felix Leung, Karina Williams, Stephen Sitch, Amos P. K. Tai, Andy Wiltshire, Jemma Gornall, Elizabeth A. Ainsworth, Timothy Arkebauer, and David Scoby
Geosci. Model Dev., 13, 6201–6213, https://doi.org/10.5194/gmd-13-6201-2020, https://doi.org/10.5194/gmd-13-6201-2020, 2020
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.
Huilin Huang, Yongkang Xue, Fang Li, and Ye Liu
Geosci. Model Dev., 13, 6029–6050, https://doi.org/10.5194/gmd-13-6029-2020, https://doi.org/10.5194/gmd-13-6029-2020, 2020
Short summary
Short summary
We developed a fire-coupled dynamic vegetation model that captures the spatial distribution, temporal variability, and especially the seasonal variability of fire regimes. The fire model is applied to assess the long-term fire impact on ecosystems and surface energy. We find that fire is an important determinant of the structure and function of the tropical savanna. By changing the vegetation composition and ecosystem characteristics, fire significantly alters surface energy balance.
Virginie Moreaux, Simon Martel, Alexandre Bosc, Delphine Picart, David Achat, Christophe Moisy, Raphael Aussenac, Christophe Chipeaux, Jean-Marc Bonnefond, Soisick Figuères, Pierre Trichet, Rémi Vezy, Vincent Badeau, Bernard Longdoz, André Granier, Olivier Roupsard, Manuel Nicolas, Kim Pilegaard, Giorgio Matteucci, Claudy Jolivet, Andrew T. Black, Olivier Picard, and Denis Loustau
Geosci. Model Dev., 13, 5973–6009, https://doi.org/10.5194/gmd-13-5973-2020, https://doi.org/10.5194/gmd-13-5973-2020, 2020
Short summary
Short summary
The model GO+ describes the functioning of managed forests based upon biophysical and biogeochemical processes. It accounts for the impacts of forest operations on energy, water and carbon exchanges within the soil–vegetation–atmosphere continuum. It includes versatile descriptions of management operations. Its sensitivity and uncertainty are detailed and predictions are compared with observations about mass and energy exchanges, hydrological data, and tree growth variables from different sites.
Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski
Geosci. Model Dev., 13, 5959–5971, https://doi.org/10.5194/gmd-13-5959-2020, https://doi.org/10.5194/gmd-13-5959-2020, 2020
Short summary
Short summary
The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.
Christopher T. Reinhard, Stephanie L. Olson, Sandra Kirtland Turner, Cecily Pälike, Yoshiki Kanzaki, and Andy Ridgwell
Geosci. Model Dev., 13, 5687–5706, https://doi.org/10.5194/gmd-13-5687-2020, https://doi.org/10.5194/gmd-13-5687-2020, 2020
Short summary
Short summary
We provide documentation and testing of new developments for the oceanic and atmospheric methane cycles in the cGENIE Earth system model. The model is designed to explore Earth's methane cycle across a wide range of timescales and scenarios, in particular assessing the mean climate state and climate perturbations in Earth's deep past. We further document the impact of atmospheric oxygen levels and ocean chemistry on fluxes of methane to the atmosphere from the ocean biosphere.
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423, https://doi.org/10.5194/gmd-13-5401-2020, https://doi.org/10.5194/gmd-13-5401-2020, 2020
Short summary
Short summary
We improved the ORCHIDEE LSM by distinguishing diffuse and direct light in canopy and evaluated the new model with observations from 159 sites. Compared with the old model, the new model has better sunny GPP and reproduced the diffuse light fertilization effect observed at flux sites. Our simulations also indicate different mechanisms causing the observed GPP enhancement under cloudy conditions at different times. The new model has the potential to study large-scale impacts of aerosol changes.
Petra Lasch-Born, Felicitas Suckow, Christopher P. O. Reyer, Martin Gutsch, Chris Kollas, Franz-Werner Badeck, Harald K. M. Bugmann, Rüdiger Grote, Cornelia Fürstenau, Marcus Lindner, and Jörg Schaber
Geosci. Model Dev., 13, 5311–5343, https://doi.org/10.5194/gmd-13-5311-2020, https://doi.org/10.5194/gmd-13-5311-2020, 2020
Short summary
Short summary
The process-based model 4C has been developed to study climate impacts on forests and is now freely available as an open-source tool. This paper provides a comprehensive description of the 4C version (v2.2) for scientific users of the model and presents an evaluation of 4C. The evaluation focused on forest growth, carbon water, and heat fluxes. We conclude that 4C is widely applicable, reliable, and ready to be released to the scientific community to use and further develop the model.
Zhengang Wang, Jianxiu Qiu, and Kristof Van Oost
Geosci. Model Dev., 13, 4977–4992, https://doi.org/10.5194/gmd-13-4977-2020, https://doi.org/10.5194/gmd-13-4977-2020, 2020
Short summary
Short summary
This study developed a spatially distributed carbon cycling model applicable in an eroding landscape. It includes all three carbon isotopes so that it is able to represent the carbon isotopic compositions. The model is able to represent the observations that eroding area is enriched in 13C and depleted of 14C compared to depositional area. Our simulations show that the spatial variability of carbon isotopic properties in an eroding landscape is mainly caused by the soil redistribution.
Brian N. Bailey, María A. Ponce de León, and E. Scott Krayenhoff
Geosci. Model Dev., 13, 4789–4808, https://doi.org/10.5194/gmd-13-4789-2020, https://doi.org/10.5194/gmd-13-4789-2020, 2020
Short summary
Short summary
Numerous models of plant radiation interception based on a range of assumptions are available in the literature, but the importance of each assumption is not well understood. In this work, we evaluate several assumptions common in simple models of radiation interception in canopies with widely spaced plants by comparing against a detailed 3-D model. This yielded a simple model based on readily measurable parameters that could accurately predict interception for a wide range of architectures.
Julius Vira, Peter Hess, Jeff Melkonian, and William R. Wieder
Geosci. Model Dev., 13, 4459–4490, https://doi.org/10.5194/gmd-13-4459-2020, https://doi.org/10.5194/gmd-13-4459-2020, 2020
Short summary
Short summary
Mostly emitted by the agricultural sector, ammonia has an important role in atmospheric chemistry. We developed a model to simulate how ammonia emissions respond to changes in temperature and soil moisture, and we evaluated agricultural ammonia emissions globally. The simulated emissions agree with earlier estimates over many regions, but the results highlight the variability of ammonia emissions and suggest that emissions in warm climates may be higher than previously thought.
Emily Kyker-Snowman, William R. Wieder, Serita D. Frey, and A. Stuart Grandy
Geosci. Model Dev., 13, 4413–4434, https://doi.org/10.5194/gmd-13-4413-2020, https://doi.org/10.5194/gmd-13-4413-2020, 2020
Short summary
Short summary
Microbes drive carbon (C) and nitrogen (N) transformations in soil, and soil models have started to include explicit microbial physiology and functioning to try to reduce uncertainty in soil–climate feedbacks. Here, we add N cycling to a microbially explicit soil C model and reproduce C and N dynamics in soil during litter decomposition across a range of sites. We discuss model-generated hypotheses about soil C and N cycling and highlight the need for landscape-scale model evaluation data.
Jinxuan Chen, Christoph Gerbig, Julia Marshall, and Kai Uwe Totsche
Geosci. Model Dev., 13, 4091–4106, https://doi.org/10.5194/gmd-13-4091-2020, https://doi.org/10.5194/gmd-13-4091-2020, 2020
Short summary
Short summary
One of the essential challenge for atmospheric CO2 forecasting is predicting CO2 flux variation on synoptic timescale. For CAMS CO2 forecast, a process-based vegetation model is used.
In this research we evaluate another type of model (i.e., the light-use-efficiency model VPRM), which is a data-driven approach and thus ideal for realistic estimation, on its ability of flux prediction. Errors from different sources are assessed, and overall the model is capable of CO2 flux prediction.
Yuma Sakai, Hideki Kobayashi, and Tomomichi Kato
Geosci. Model Dev., 13, 4041–4066, https://doi.org/10.5194/gmd-13-4041-2020, https://doi.org/10.5194/gmd-13-4041-2020, 2020
Short summary
Short summary
Chlorophyll fluorescence is one of the energy release pathways of excess incident light in the photosynthetic process. The canopy-scale Sun-induced chlorophyll fluorescence (SIF), which potentially provides a direct pathway to link leaf-level photosynthesis to global GPP, can be observed from satellites. We develop the three-dimensional Monte Carlo plant canopy radiative transfer model to understand the biological and physical mechanisms behind SIF emission from complex forest canopies.
Femke Lutz, Stephen Del Grosso, Stephen Ogle, Stephen Williams, Sara Minoli, Susanne Rolinski, Jens Heinke, Jetse J. Stoorvogel, and Christoph Müller
Geosci. Model Dev., 13, 3905–3923, https://doi.org/10.5194/gmd-13-3905-2020, https://doi.org/10.5194/gmd-13-3905-2020, 2020
Short summary
Short summary
Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analyses on global estimates of tillage effects on N2O emissions. By comparing model results with observational data of four experimental sites and outputs from field-scale DayCent model simulations, we show that advancing information on agricultural management, as well as the representation of soil moisture dynamics, improves LPJmL5.0-tillage and the estimates of tillage effects on N2O emissions.
Tingting Li, Yanyu Lu, Lingfei Yu, Wenjuan Sun, Qing Zhang, Wen Zhang, Guocheng Wang, Zhangcai Qin, Lijun Yu, Hailing Li, and Ran Zhang
Geosci. Model Dev., 13, 3769–3788, https://doi.org/10.5194/gmd-13-3769-2020, https://doi.org/10.5194/gmd-13-3769-2020, 2020
Short summary
Short summary
Reliable models are required to estimate global wetland CH4 emissions, which are the largest and most uncertain source of atmospheric CH4. This paper evaluated CH4MODwetland and TEM models against CH4 measurements from different continents and wetland types. Based on best-model performance, we estimated 117–125 Tg yr−1 of global CH4 emissions from wetlands for the period 2000–2010. Efforts should be made to reduce estimate uncertainties for different wetland types and regions.
Jennifer E. Dentith, Ruza F. Ivanovic, Lauren J. Gregoire, Julia C. Tindall, and Laura F. Robinson
Geosci. Model Dev., 13, 3529–3552, https://doi.org/10.5194/gmd-13-3529-2020, https://doi.org/10.5194/gmd-13-3529-2020, 2020
Short summary
Short summary
We have added a new tracer (13C) into the ocean of the FAMOUS climate model to study large-scale circulation and the marine carbon cycle. The model captures the large-scale spatial pattern of observations but the simulated values are consistently higher than observed. In the first instance, our new tracer is therefore useful for recalibrating the physical and biogeochemical components of the model.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, https://doi.org/10.5194/gmd-13-3299-2020, 2020
Short summary
Short summary
Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
Yifei Dai, Long Cao, and Bin Wang
Geosci. Model Dev., 13, 3119–3144, https://doi.org/10.5194/gmd-13-3119-2020, https://doi.org/10.5194/gmd-13-3119-2020, 2020
Short summary
Short summary
NESM v3 is one of the CMIP6 registered Earth system models. We evaluate its ocean carbon cycle component and present its present-day and future oceanic CO2 uptake based on the CMIP6 historical and SSP5–8.5 scenarios. We hope that this paper can serve as a documentation of the marine biogeochemical cycle in NESM v3. Also, the model defects found and their underlying causes analyzed in this paper could help users and further model development.
Joe R. Melton, Vivek K. Arora, Eduard Wisernig-Cojoc, Christian Seiler, Matthew Fortier, Ed Chan, and Lina Teckentrup
Geosci. Model Dev., 13, 2825–2850, https://doi.org/10.5194/gmd-13-2825-2020, https://doi.org/10.5194/gmd-13-2825-2020, 2020
Short summary
Short summary
We transitioned the CLASS-CTEM land surface model to an open-source community model format by modernizing the code base to make the model easier to use and understand, providing a complete software environment to run the model within, developing a benchmarking suite for model evaluation, and creating an infrastructure to support community involvement. The new model, the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC), is now available for the community to use and develop.
Giovanni Denaro, Daniela Salvagio Manta, Alessandro Borri, Maria Bonsignore, Davide Valenti, Enza Quinci, Andrea Cucco, Bernardo Spagnolo, Mario Sprovieri, and Andrea De Gaetano
Geosci. Model Dev., 13, 2073–2093, https://doi.org/10.5194/gmd-13-2073-2020, https://doi.org/10.5194/gmd-13-2073-2020, 2020
Short summary
Short summary
The HR3DHG (high-resolution 3D mercury model) investigates the spatiotemporal behavior, in seawater and marine sediments, of three mercury species (elemental, inorganic, and organic mercury) in a highly polluted marine environment (Augusta Bay, southern Italy). The model shows fair agreement with the experimental data collected during six different oceanographic cruises and can possibly be used for a detailed exploration of the effects of climate change on mercury distribution.
Jina Jeong, Jonathan Barichivich, Philippe Peylin, Vanessa Haverd, Matthew J. McGrath, Nicolas Vuichard, Michael N. Evans, Flurin Babst, and Sebastiaan Luyssaert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-29, https://doi.org/10.5194/gmd-2020-29, 2020
Revised manuscript accepted for GMD
Elisa Lovecchio and Timothy M. Lenton
Geosci. Model Dev., 13, 1865–1883, https://doi.org/10.5194/gmd-13-1865-2020, https://doi.org/10.5194/gmd-13-1865-2020, 2020
Short summary
Short summary
We present here the newly developed BPOP box model. BPOP is aimed at studying the impact of large-scale changes in the biological pump, i.e. the cycle of production, export and remineralization of the marine organic matter, on the nutrient and oxygen concentrations in the shelf and open ocean. This model has been developed to investigate the global consequences of the evolution of larger and heavier phytoplankton cells but can be applied to a variety of past and future case studies.
Benjamin D. Stocker, Han Wang, Nicholas G. Smith, Sandy P. Harrison, Trevor F. Keenan, David Sandoval, Tyler Davis, and I. Colin Prentice
Geosci. Model Dev., 13, 1545–1581, https://doi.org/10.5194/gmd-13-1545-2020, https://doi.org/10.5194/gmd-13-1545-2020, 2020
Short summary
Short summary
Estimating terrestrial photosynthesis relies on satellite data of vegetation cover and models simulating the efficiency by which light absorbed by vegetation is used for CO2 assimilation. This paper presents the P-model, a light use efficiency model derived from a carbon–water optimality principle, and evaluates its predictions of ecosystem-level photosynthesis against globally distributed observations. The model is implemented and openly accessible as an R package (rpmodel).
Louis de Wergifosse, Frédéric André, Nicolas Beudez, François de Coligny, Hugues Goosse, François Jonard, Quentin Ponette, Hugues Titeux, Caroline Vincke, and Mathieu Jonard
Geosci. Model Dev., 13, 1459–1498, https://doi.org/10.5194/gmd-13-1459-2020, https://doi.org/10.5194/gmd-13-1459-2020, 2020
Short summary
Short summary
Given their key role in the simulation of climate impacts on tree growth, phenological and water balance processes must be integrated in models simulating forest dynamics under a changing environment. Here, we describe these processes integrated in HETEROFOR, a model accounting simultaneously for the functional, structural and spatial complexity to explore the forest response to forestry practices. The model evaluation using phenological and soil water content observations is quite promising.
Arjun Chakrawal, Anke M. Herrmann, John Koestel, Jerker Jarsjö, Naoise Nunan, Thomas Kätterer, and Stefano Manzoni
Geosci. Model Dev., 13, 1399–1429, https://doi.org/10.5194/gmd-13-1399-2020, https://doi.org/10.5194/gmd-13-1399-2020, 2020
Short summary
Short summary
Soils are heterogeneous, which results in a nonuniform spatial distribution of substrates and the microorganisms feeding on them. Our results show that the variability in the spatial distribution of substrates and microorganisms at the pore scale is crucial because it affects how fast substrates are used by microorganisms and thus the decomposition rate observed at the soil core scale. This work provides a methodology to include microscale heterogeneity in soil carbon cycling models.
Victoria Naipal, Ronny Lauerwald, Philippe Ciais, Bertrand Guenet, and Yilong Wang
Geosci. Model Dev., 13, 1201–1222, https://doi.org/10.5194/gmd-13-1201-2020, https://doi.org/10.5194/gmd-13-1201-2020, 2020
Short summary
Short summary
In this study we present the Carbon Erosion DYNAMics model (CE-DYNAM) that links sediment dynamics resulting from water erosion with the soil carbon cycle along a cascade of hillslopes, floodplains, and rivers. The model can simulate the removal of soil and carbon from eroding areas and their destination at regional scale. We calibrated and validated the model for the Rhine catchment, and we show that soil erosion is a potential large net carbon sink over the period 1850–2005.
Kelly Kearney, Albert Hermann, Wei Cheng, Ivonne Ortiz, and Kerim Aydin
Geosci. Model Dev., 13, 597–650, https://doi.org/10.5194/gmd-13-597-2020, https://doi.org/10.5194/gmd-13-597-2020, 2020
Short summary
Short summary
We describe an ecosystem model for the Bering Sea. Biological components in the Bering Sea can be found in the water column, on and within the bottom sediments, and within the porous lower layer of seasonal sea ice. This model simulates the exchange of material between nutrients and plankton within all three environments. Here, we thoroughly document the model and assess its skill in capturing key biophysical features across the Bering Sea.
Matthias J. R. Speich, Massimiliano Zappa, Marc Scherstjanoi, and Heike Lischke
Geosci. Model Dev., 13, 537–564, https://doi.org/10.5194/gmd-13-537-2020, https://doi.org/10.5194/gmd-13-537-2020, 2020
Short summary
Short summary
Climate change is expected to substantially affect natural processes, and simulation models are a valuable tool to anticipate these changes. In this study, we combine two existing models that each describe one aspect of the environment: forest dynamics and the terrestrial water cycle. The coupled model better described observed patterns in vegetation structure. We also found that including the effect of water availability on tree height and rooting depth improved the model.
Simon P. K. Bowring, Ronny Lauerwald, Bertrand Guenet, Dan Zhu, Matthieu Guimberteau, Pierre Regnier, Ardalan Tootchi, Agnès Ducharne, and Philippe Ciais
Geosci. Model Dev., 13, 507–520, https://doi.org/10.5194/gmd-13-507-2020, https://doi.org/10.5194/gmd-13-507-2020, 2020
Short summary
Short summary
In this second part of the study, we performed simulations of the carbon and water budget of the Lena catchment with the land surface model ORCHIDEE MICT-LEAK, enabled to simulate dissolved organic carbon (DOC) production in soils and its transport and fate in high-latitude inland waters. We compare simulations using this model to existing data sources to show that it is capable of reproducing dissolved carbon fluxes of potentially great importance for the future of the global permafrost.
Cited articles
Ackley, S. F. and Sullivan, C. W.: Physical controls on the development and
characteristics of Antarctic sea ice biological communities: a review and
synthesis, Deep-Sea Res. Pt. I, 41, 1583–1604, https://doi.org/10.1016/0967-0637(94)90062-0, 1994. a
Allen, A. P., Gillooly, J. F., and Brown, J. H.: Linking the global carbon
cycle to individual metabolism, Funct. Ecol., 19, 202–213,
https://doi.org/10.1111/j.1365-2435.2005.00952.x, 2005. a
Álvarez, E., Nogueira, E., and López-Urrutia, Á.: In-vivo
single-cell fluorescence and the size-scaling of phytoplankton chlorophyll
content, Appl. Environ. Microb., 83, e03317-16,
https://doi.org/10.1128/AEM.03317-16, 2017. a
Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P.: An ecosystem model of the global ocean including Fe, Si, P colimitations,
Global Biogeochem. Cy., 17, 1060, https://doi.org/10.1029/2001GB001745, 2003. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a
Aumont, O., van Hulten, M., Roy-Barman, M., Dutay, J.-C., Éthé, C., and Gehlen, M.: Variable reactivity of particulate organic matter in a global ocean biogeochemical model, Biogeosciences, 14, 2321–2341, https://doi.org/10.5194/bg-14-2321-2017, 2017. a
Baretta, J. W., Ebenhöh, W., and Ruardij, P.: The European Regional Seas
Ecosystem Model, a complex marine ecosystem model, Neth. J. Sea Res., 33, 233–246, https://doi.org/10.1016/0077-7579(95)90047-0, 1995. a
Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J., and Follows, M. J.:
Patterns of diversity in marine phytoplankton, Science, 327, 1509–1511,
https://doi.org/10.1126/science.1184961, 2010. a
Beardall, J., Allen, D., Bragg, J., Finkel, Z. V., Flynn, K. J., Quigg, A.,
Rees, T. A. V., Richardson, A., and Raven, J. A.: Allometry and stoichiometry
of unicellular, colonial and multicellular phytoplankton, New Phytol.,
181, 295–309, https://doi.org/10.1111/j.1469-8137.2008.02660.x, 2009. a
Beardmore, R., Gudelj, I., Lipson, D. A., and Hurst, L. D.: Metabolic
trade-offs and the maintenance of the fittest and the flattest, Nature, 472,
342–346, https://doi.org/10.1038/nature09905, 2011. a, b
Beckmann, A., Schaum, C.-E., and Hense, I.: Phytoplankton adaptation in
ecosystem models, J. Theor. Biol., 468, 60–71,
https://doi.org/10.1016/j.jtbi.2019.01.041, 2019. a, b, c, d
Bennett, A. F., Dao, K. M., and Lenski, R. E.: Rapid evolution in response to
high-temperature selection, Nature, 346, 79–81, https://doi.org/10.1038/346079a0,
1990. a
Biller, S. J., Berube, P. M., Lindell, D., and Chisholm, S. W.:
Prochlorococcus: the structure and function of collective
diversity, Nat. Rev. Microbiol., 13, 13–27, https://doi.org/10.1038/nrmicro3378, 2015. a, b
Bruggeman, J. and Bolding, K.: A general framework for aquatic biogeochemical
models, Environ. Modell. Softw., 61, 249–265,
https://doi.org/10.1016/j.envsoft.2014.04.002, 2014. a
Bruggeman, J. and Kooijman, A. L. M.: A biodiversity-inspired approach to
aquatic ecosystem modeling, Limnol. Oceanogr., 52, 1533–1544,
https://doi.org/10.4319/lo.2007.52.4.1533, 2007. a, b, c, d
Brzezinski, M. A.: Vertical distribution of ammonium in stratified oligotrophic waters, Limnol. Oceanogr., 33, 1176–1182, 1988. a
Button, D. K., Robertson, B., Gustafson, E., and Zhao, X.: Experimental and
theoretical bases of specific affinity, a cytoarchitecture-based formulation
of nutrient collection proposed to supercede the Michaelis-Menten paradigm of
microbial kinetics, Appl. Environ. Microb., 70, 5511–5521,
https://doi.org/10.1128/AEM.70.9.5511-5521.2004, 2004. a
Castenholz, R. W.: Thermophilic blue-green algae and the thermal environment,
Bacteriol. Rev., 33, 476–504, https://doi.org/10.1128/MMBR.33.4.476-504.1969, 1969. a
Cermeño, P. and Figueiras, F. G.: Species richness and cell-size
distribution: size structure of phytoplankton
communities, Mar. Ecol. Prog. Ser., 357, 75–85, https://doi.org/10.3354/meps07293, 2008. a, b, c
Cermeño, P., Lee, J.-B., Wyman, K., Schofield, O., and Falkowski, P. G.:
Competitive dynamics in two species of marine phytoplankton under
non-equilibrium conditions, Mar. Ecol. Prog. Ser., 429, 19–28,
https://doi.org/10.3354/meps09088, 2011. a
Champagnat, N., Ferrière, R., and Méléard, S.: Unifying
evolutionary dynamics: From individual stochastic processes to macroscopic
models, Theor. Popul. Biol., 69, 297–321, https://doi.org/10.1016/j.tpb.2005.10.004, 2006. a
Chen, B. and Laws, E. A.: Is there a difference of temperature sensitivity
between marine phytoplankton and heterotrophs, Limnol. Oceanogr.,
62, 806–817, https://doi.org/10.1002/lno.10462, 2017. a
Chen, B., Smith, S. L., and Wirtz, K. W.: Effect of phytoplankton size
diversity on primary productivity in the North Pacific: trait distributions
under environment variability, Ecol. Lett., 22, 56–66,
https://doi.org/10.1111/ele.13167, 2019. a, b, c
Chisholm, S. W., Olson, R. J., Zettler, E. R., Goericke, R., Waterbury, J. B., and Welschmeyer, N. A.: A novel free-living prochlorophyte abundant in the oceanic euphotic zone, Nature, 334, 340–343, https://doi.org/10.1038/334340a0, 1988. a
Coutinho, R. M., Klauschies, T., and Gaedke, U.: Bimodal trait distributions
with large variances question the reliability of trait-based aggregate
models, Theor. Ecol., 9, 389–408, https://doi.org/10.1007/s12080-016-0297-9, 2016. a, b, c, d
DeVries, T. and Primeau, F.: Dynamically and Observationally Constrained
Estimates of Water-Mass Distributions and Ages in the Global Ocean,
J. Phys. Oceanogr., 41, 2381–2401, https://doi.org/10.1175/jpo-d-10-05011.1, 2011. a
DeVries, T., Primeau, F., and Deutsch, C.: The sequestration efficiency of the biological pump, Geophys. Res. Lett., 39, L13601,
https://doi.org/10.1029/2012GL051963, 2012. a
Dieckmann, U. and Law, R.: The dynamical theory of coevolution: a derivation
from stochastic ecological processes, J. Math. Biol., 34, 579–612, https://doi.org/10.1007/bf02409751, 1996. a
Doebeli, M., Ispolatov, Y., and Simon, B.: Towards a mechanistic foundation of evolutionary Theory, Elife, 6, e23804, https://doi.org/10.7554/eLife.23804.001, 2017. a
Downing, A. S., Hajdu, S., Hjerne, O., Otto, S. A., Blenckner, T., Larsson, U., and Winder, M.: Zooming in on size distribution pattern under species
coexistence in Baltic Sea phytoplankton, Ecol. Lett., 17, 1219–1227,
https://doi.org/10.1111/ele.12327, 2014. a, b
Dutkiewicz, S., Follows, M. J., and Bragg, J. G.: Modeling the coupling of
ocean ecology and biogeochemistry, Global Biogeochem. Cy., 23, GB4017,
https://doi.org/10.1029/2008GB003405, 2009. a, b
Dutkiewicz, S., Scott, J. R., and Follows, M. J.: Winners and losers:
Ecological and biogeochemical changes in a warming ocean,
Global Biogeochem. Cy., 27, 463–477, https://doi.org/10.1002/gbc.20042, 2013. a
Edwards, K. F., Thomas, M. K., Klausmeier, C. A., and Litchman, E.: Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton, Limnol. Oceanogr., 57, 554–566,
https://doi.org/10.4319/lo.2012.57.2.0554, 2012. a, b, c
Edwards, K. F., Thomas, M. K., Klausmeier, C. A., and Litchman, E.: Light and
growth in marine phytoplankton: allometric, taxonomic, and environmental
variation, Limnol. Oceanogr., 60, 540–552, https://doi.org/10.1002/lno.10033, 2015. a, b
Einstein, A.: On the Movement of Small Particles Suspended in Stationary
Liquids Required by the Molecular-Kinetic Theory of Heat, Ann. Phys.,
322, 549–560, https://doi.org/10.1002/andp.19053220806, 1905. a
Elbing, K., Larsson, C., Bill, R. M., Albers, E., Snoep, J. L., Boles, E.,
Hohmann, S., and Gustafson, L.: Role of hexose transport in control of
glycolytic flux in Saccharomyces cerevisiae,
Appl. Environ. Microb., 70, 5323–5330, https://doi.org/10.1128/AEM.70.9.5323-5330.2004, 2004. a
Falkowski, P.: The power of plankton, Nature, 483, 17–20,
https://doi.org/10.1038/483S17a, 2012. a
Falkowski, P. G., Barber, R. T., and Smetacek, V.: Biogeochemical Controls and Feedbacks on Ocean Primary Production, Science, 281, 200–206,
https://doi.org/10.1126/science.281.5374.200, 1998. a
Falkowski, P. G., Katz, M. E., Knoll, A. H., Quigg, A., Raven, J. A.,
Schofield, O., and Taylor, F. J. R.: The Evolution of Modern Eukaryotic
Phytoplankton, Science, 305, 354–360, https://doi.org/10.1126/science.1095964, 2004. a
Fasham, M. J. R., Ducklow, H. W., and McKelvie, S. M.: A nitrogen-based model
of plankton dynamics in the oceanic mixed layer, J. Mar. Res.,
48, 591–639, https://doi.org/10.1357/002224090784984678, 1990. a
Field, C. B., Behrenfield, M. J., Randerson, J. T., and Falkowski, P.: Primary production of the biosphere: integrating terrestrial and oceanic components, Science, 281, 237–240, https://doi.org/10.1126/science.281.5374.237, 1998. a
Follows, M. J. and Dutkiewicz, S.: Modeling diverse communities in marine
microbes, Annu. Rev. Mar. Sci., 3, 427–451,
https://doi.org/10.1146/annurev-marine-120709-142848, 2011. a, b
Frederickson, A. G. and Stephanopoulos, G.: Microbial competition, Science,
213, 972–979, https://doi.org/10.1126/science.7268409, 1981. a
Fussmann, G. F., Loreau, M., and Abrams, P. A.: Eco-evolutionary dynamics of
communities and ecosystems, Funct. Ecol., 21, 465–477,
https://doi.org/10.1111/j.1365-2435.2007.01275.x, 2007. a, b, c
Gaedke, U. and Klauschies, T.: Analyzing the shape of observed trait
distributions enables a data-based moment closure of aggregate models,
Limnol. Oceanogr.-Meth., 15, 979–994, https://doi.org/10.1002/lom3.10218, 2017. a
Ghalambor, C. K., McKay, J. K., Carroll, S. P., and Reznick, D. N.: Adaptive
versus non-adaptive phenotypic plasticity and the potential for contemporary
adaptation in new environments, Funct. Ecol., 21, 394–407,
https://doi.org/10.1111/j.1365-2435.2007.01283.x, 2007. a
Gillooly, J., Brown, J., West, G., Savage, V., and Charnov, E.: Effects of size and temperature on metabolic rate, Science, 293, 2248–2251,
https://doi.org/10.1126/science.1061967, 2001. a
Goericke, R. and Welschmeyer, N. A.: Response of Sargasso Sea phytoplankton
biomass, growth rrate and primary production to seasonally varying physical
forcing, J. Plankton Res., 20, 2223–2249, https://doi.org/10.1093/plankt/20.12.2223, 1998. a
Hansen, P. J., Bjørnsen, P. K., and Hansen, B. W.: Zooplankton grazing and
growth: Scaling with the 2–2000-µm body size
range, Limnol. Oceanogr., 42, 687–704, https://doi.org/10.1016/0077-7579(95)90047-0, 1997. a
Hardin, G.: The competitive exclusion principle, Science, 131, 1292–1297,
https://doi.org/10.1126/science.131.3409.1292, 1960. a, b
Healey, F. P. and Hendzel, L. L.: Physiological indicators of nutrient
deficiency in lake phytoplankton,
Can. J. Fish. Aquat. Sci., 37, 442–453, https://doi.org/10.1139/f80-058, 1980. a
Holling, C. S.: The components of predation as revealed by a study of small
mammal predation of the European pine sawfly, Can. Entomol., 91,
293–320, https://doi.org/10.4039/Ent91293-5, 1959. a
Huete-Ortega, M., Cermeño, P., Calvo-Díaz, A., and Marañon, E.:
Isometric size-scaling of metabolic rate and the size abundance distribution
of phytoplankton, P. Roy. Soc. B-Biol. Sci.,
279, 1815–1823, https://doi.org/10.1098/rspb.2011.2257, 2012. a
Hutchinson, G. E.: The paradox of the plankton, Am. Nat., 95,
137–145, https://doi.org/10.1086/282171, 1961. a
Irwin, A. J., Nelles, A. M., and Finkel, Z. V.: Phytoplankton niches estimated from field data, Limnol. Oceanogr., 57, 787–797,
https://doi.org/10.4319/lo.2012.57.3.0787, 2012. a, b, c
Irwin, A. J., Finkel, Z. V., Müller-Karger, F. E., and Ghinaglia, L. T.:
Phytoplankton adapt to changing ocean environments,
P. Natl. Acad. Sci. USA, 112, 5762–5766, https://doi.org/10.1073/pnas.1414752112,
2015. a, b, c
Isserlis, L.: On certain probable errors and correlation coefficients of
multiple frequency distributions with skew regression, Biometrika, 11,
185–190, https://doi.org/10.1093/biomet/11.3.185, 1916. a
Jakobsen, H. H. and Markager, S.: Carbon-to-chlorophyll ratio for
phytoplanktoon in temperate coastal waters: Seasonal pattern and relationship
to nutrients, Limnol. Oceanogr., 61, 1853–1868,
https://doi.org/10.1002/lno.10338, 2016. a
Kinnison, M. T. and Hairston, N. G.: Eco-evolutionary conservation biology:
contemporary evolution and the dynamics of persistence, Funct. Ecol.,
21, 444–454, https://doi.org/10.1111/j.1365-2435.2007.01278.x, 2007. a, b
Kiørboe, T.: Turbulence, phytoplankton cell size, and the structure of
pelagic food webs, Adv. Mar. Biol., 29, 1–72,
https://doi.org/10.1016/S0065-2881(08)60129-7, 1993. a
Kiørboe, T., Saiz, E., and Viitasalo, M.: Prey switching behaviour in the
planktonic copepod Acartia tonsa, Mar. Ecol. Prog. Ser., 143,
65–75, https://doi.org/10.3354/meps143065, 1996. a
Klauschies, T., Coutinho, R. M., and Gaedke, U.: A beta distribution-based
moment closure enhances the reliability of trait-based aggregate models for
natural populations and communities, Ecol. Model., 381, 46–77,
https://doi.org/10.1016/j.ecolmodel.2018.02.001, 2018. a, b, c
Kremer, C. T. and Klausmeier, C. A.: Coexistence in a variable environment:
Eco-evolutionary perspectives, J. Theor. Biol., 339, 14–25,
https://doi.org/10.1016/j.jtbi.2013.05.005, 2013. a
Lännergren, C.: Buoyancy of natural populations of marine
phytoplankton, Mar. Biol., 54, 1–10, https://doi.org/10.1007/BF00387045, 1979. a
Le Gland, G. and Vallina, S. M.: SPEAD v1.1 (Version v1.1), Zenodo, https://doi.org/10.5281/zenodo.4673500, 2021. a
Le Quéré, C., Harrison, S. P., Prentice, I. C., Buitenhuis, E. T., Aumont, O., Bopp, L., Claustre, H., Cotrim Da Cunha, L., Geider, R., Giraud, X., Klaas, C., Kohfeld, K. E., Legendre, L., Manizza, M., Platt, T., Rivkin,
R. B., Sathyendranath, S., Uitz, J., Watson, A. J., and Wolf-Gradow, D.:
Ecosystem dynamics based on plankton functional types and for global ocean
biogeochemistry models, Global Change Biol., 11, 2016–2040,
https://doi.org/10.1111/j.1365-2486.2005.1004.x, 2005. a, b
Lefèvre, M., Vézina, A., Levasseur, M., and Dacey, J. W. H.: A model of
dimethylsulfide dynamics for the subtropical North Atlantic,
Deep-Sea Res. Pt. I, 49, 2221–2239, https://doi.org/10.1016/S0967-0637(02)00121-8, 2002. a, b
Lenski, R. E., Rose, M. R., Simpson, S. C., and Tadler, S. C.: Long-term
experimental evolution in Escherichia coli, I. Adaptation and
divergence during 2000 generations, Am. Nat., 138,
1315–1341, https://doi.org/10.1086/285289, 1991. a
Levins, R.: The strategy of model building in population
biology, Am. Sci., 54, 421–431, 1966. a
Li, W. K. W.: Cytometric diversity in marine ultraphytoplankton, Limnol. Oceanogr., 42, 874–880, https://doi.org/10.4319/lo.1997.42.5.0874, 1997. a
Litchman, E., Klausmeier, C. A., Schofield, O. M., and Falkowski, P. G.: The
role of functional traits and trade-offs in structuring phytoplankton
communities: scaling from cellular to ecosystem level, Ecol. Lett., 10,
1170–1181, https://doi.org/10.1111/j.1461-0248.2007.01117.x, 2007. a, b
López-Urrutia, A., San Martin, E., Harris, R. P., and Irigoien, X.: Scaling
the metabolic balance of the oceans, P. Natl. Acad. Sci. USA, 103, 8739–8744, https://doi.org/10.1073/pnas.0601137103, 2006. a
Maier-Reimer, E.: Geochemical cycles in an ocean general circulation model,
Preindustrial tracer distributions, Global Biogeochem. Cy., 7,
645–677, https://doi.org/10.1029/93GB01355, 1993. a
Marañon, E.: Cell size as a key determinant of phytoplankton metabolism and
community structure, Annu. Rev. Mar. Sci., 7, 241–264,
https://doi.org/10.1146/annurev-marine-010814-015955, 2015. a, b
Marañon, E., Cermeño, P., López-Sandoval, D. C., Rodríguez-Ramos, T.,
Sobrino, C., Huete-Ortega, M., Blanco, J. M., and Rodríguez, J.: Unimodal
size scaling of phytoplankton growth and the size dependence of nutrient
uptake and use, Ecol. Lett., 16, 371–379, https://doi.org/10.1111/ele.12052, 2013. a
Marshall, J., Hill, C., Perelman, L., and Adcroft, A.: Hydrostatic,
quasi-hydrostatic, and nonhydrostatic ocean modeling,
J. Geophys. Res., 102, 5733–5752, https://doi.org/10.1029/96JC02776, 1997. a
Menzel, D. W. and Spaeth, J. P.: Occurrence of ammonia in Sargasso Sea waters
and in rain water at Bermuda, Limnol. Oceanogr., 7, 159–162,
https://doi.org/10.4319/lo.1962.7.2.0159, 1962. a
Meyer, J. R., Gudelj, I., and Beardmore, R.: Biophysical mechanisms that
maintain biodiversity through trade-offs, Nat. Commun., 6, 6278,
https://doi.org/10.1038/ncomms7278, 2015. a
Monod, J.: The growth of bacterial cultures, Annu. Rev. Microbiol., 3, 371–395, https://doi.org/10.1146/annurev.mi.03.100149.002103, 1949. a
Murdoch, W. W.: Switching in general predators: experiments on predator
specificity and stability of prey populations, Ecol. Monogr., 39,
335–354, https://doi.org/10.2307/1942352, 1969. a
Norberg, J.: Biodiversity and ecosystem functioning: A complex adaptive systems approach, Limnol. Oceanogr., 49, 1269–1277,
https://doi.org/10.4319/lo.2004.49.4_part_2.1269, 2004. a
Norberg, J., Urban, M. C., Vellend, M., Klausmeier, C. A., and Loeuille, N.: Eco-evolutionary responses of biodiversity to climate change,
Nat. Clim. Change, 2, 747–751, https://doi.org/10.1038/NCLIMATE1588, 2012. a, b, c, d
Platt, T., Gallegos, C. L., and Harrison, W. G.: Photoinhibition of
photosynthesis in natural assemblages of marine phytoplankton, J. Mar. Res., 38, 687–701, 1980. a
Quintana, X. D., Brucet, S., Boix, D., López-Flores, R., Gascón, S.,
Badosa, A., Sala, J., Moreno-Amich, R., and Egozcue, J. J.: A nonparametric
method for the measurement of size diversity with emphasis on data
standardization, Limnol. Oceanogr.-Meth., 6, 75–86,
https://doi.org/10.4319/lom.2008.6.75, 2008. a, b
Redfield, A. C.: On the proportions of organic derivatives in sea water and their relation to the composition of plankton, in: James Johnstone memorial volume, University Press of Liverpool, 176–192, 1934. a
Riley, G.: Factors controlling phytoplankton populations on Georges Bank, J. Mar. Res., 6, 54–73, 1946. a
Riley, G.: A mathematical model of regional variations in plankton, Limnol. Oceanogr., 10, 202–215, https://doi.org/10.4319/lo.1965.10.suppl2.r202, 1965. a
Rodríguez, J.: Some comments on the size-based structural analysis of the
pelagic ecosystem, Sci. Mar., 58, 1–10, 1994. a
Sauterey, B., Ward, B., Rault, J., Bowler, C., and Claessen, D.: The
implications of eco-evolutionary processes for the emergence of marine
plankton community biogeography, Am. Nat., 190, 116–130,
https://doi.org/10.1086/692067, 2017. a
Schartau, M., Landry, M. R., and Armstrong, R. A.: Density estimation of
plankton size spectra: a reanalysis of IronEx II data, J. Plankton Res., 32, 1167–1184, https://doi.org/10.1093/plankt/fbq072, 2010. a
Schlüter, L., Lohbeck, K. T., Gröger, J. P., Riebesell, U., and Reusch, T. B. H.: Long-term dynamics of adaptive evolution in a global important phytoplankton species to ocean acidification, Science Advances, 2,
e1501660, https://doi.org/10.1126/sciadv.1501660, 2016. a, b, c, d
Steele, J. H.: Plant production in the northern North Sea, H. M. Stationery
Office, Marine Res., Scot. Home Dept., 1958, 1–36, 1958. a
Steinberg, D. K., Carlson, C. A., Bates, N. R., Johnson, R. J., Michaels,
A. F., and Knap, A. H.: Overview of the US JGOFS Bermuda Atlantic Time-series
Study (BATS): a decade-scale look at ocean biology and biogeochemistry,
Deep-Sea Res. Pt. II, 48, 1405–1447, https://doi.org/10.1016/S0967-0645(00)00148-X, 2001. a, b
Strzepek, R. F. and Harrison, P. J.: Photosynthetic architecture differs in
coastal and oceanic diatoms, Nature, 431, 689–692, https://doi.org/10.1038/nature02954, 2004. a
Swift, E.: The marine diatom Ethmodiscus rex: Its morphology and occurrence in the plankton of the Sargasso Sea, J. Phycol., 9, 456–460,
https://doi.org/10.1111/j.1529-8817.1973.tb04121.x, 1973. a
Taguchi, S.: Relationship between photosynthesis and cell size of marine
diatoms, J. Phycol., 12, 185–189, https://doi.org/10.1111/j.1529-8817.1976.tb00499.x, 1976. a
Taylor, A. H., Geider, R., and Gilbert, F. J. H.: Seasonal and latitudinal
dependencies of phytoplankton carbon-to-chlorophyll a ratio: results of a
modeling study, Mar. Ecol. Prog. Ser., 152, 51–66,
https://doi.org/10.3354/meps152051, 1997. a
Terseleer, N., Bruggeman, J., Lancelot, C., and Gypens, N.: Trait-based
representation of diatom functional diversity in a plankton functional type
model of the eutrophied southern North Sea, Limnol. Oceanogr., 59,
1958–1972, https://doi.org/10.4319/lo.2014.59.6.1958, 2014. a, b
Thingstad, T. F.: Elements of a theory for the mechanisms controlling
abundance, diversity, and biogeochemical role of lytic bacterial viruses in
aquatic systems, Limnol. Oceanogr., 45, 1320–1328,
https://doi.org/10.4319/lo.2000.45.6.1320, 2000. a
Thingstad, T. F. and Lignell, R.: Theoretical models for the control of
bacterial growth rate, abundance, diversity and carbon
demand, Aquat. Microb. Ecol., 13, 19–27, https://doi.org/10.3354/ame013019, 1997. a
Thingstad, T. F., Øvreås, L., Egge, J. K., Løvdal, T., and Heldal,
M.: Use of non-limiting substrates to increase size, a genereic strategy to
simultaneously optimize uptake and minimize predation in pelagic osmotrophs?, Ecol. Lett., 8, 675–682, https://doi.org/10.1111/j.1461-0248.2005.00768.x, 2005. a
Thomas, M. K., Kremer, C. T., Klausmeier, C. A., and Litchman, E.: A global
pattern of thermal adaptation in marine phytoplankton, Science, 338,
1085–1088, https://doi.org/10.1126/science.1224836, 2012. a, b
Timofeeff-Ressovsky, N. W.: Mutations and geographical variation, in: The new systematics, Clarendon Press Oxford, 73–136, 1940. a
Travisano, M., Mongold, J. A., Bennett, A. F., and Lenski, R. E.: Experimental tests of the role of adaptation, chance, and history in evolution, Science, 267, 87–90, https://doi.org/10.1126/science.7809610, 1995. a
Vallina, S. M.: Reply to a comment by Larsen, S. H. on “Analysis of a potential “solar radiation dose-dimethylsulfide-cloud condensation nuclei” link from global mapped seasonal correlations”, Global Biogeochem. Cy., 22,
GB3006, https://doi.org/10.1029/2007GB003099, 2008. a
Vallina, S. M., Sim'o, R., Anderson, T. R., Gabric, A., and Cropp, R.: A
dynamic model of oceanic sulfure (DMOS) applied to the Sargasso Sea:
Simulating the dimethylsulfide (DMS) summer paradox, J. Geophys. Res., 113, G01009, https://doi.org/10.1029/2007JG000415, 2008. a, b, c
Vallina, S. M., Follows, M. J., Dutkiewicz, S., Montoya, J. M., Cermeño, P., and Loreau, M.: Global relationship between phytoplankton diversity and
productivity in the ocean, Nat. Commun., 5, 4299,
https://doi.org/10.1038/ncomms5299, 2014a. a, b
Vallina, S. M., Ward, B. A., Dutkiewicz, S., and Follows, M. J.: Maximum
feeding with active prey-switching: A kill-the-winner functional response ad
its effect on global diversity and biogeography, Prog. Oceanogr.,
120, 93–109, https://doi.org/10.1016/j.pocean.2013.08.001, 2014b. a, b, c
Vallina, S. M., Martinez-Garcia, R., Smith, S. L., and Bonachela, J. A.:
Models in microbial ecology, in: Encyclopedia of microbiology, Elsevier, Amsterdam, Netherlands, 211–246, https://doi.org/10.1016/B978-0-12-809633-8.20789-9, 2019.
a, b, c
Villareal, T. A.: Positive buoyancy in the oceanic diatom Rhizosolenia debyana H. Peragallo, Deep-Sea Res. Pt. I, 35, 1037–1045, https://doi.org/10.1016/0198-0149(88)90075-1, 1988. a
Villareal, T. A. and Carpenter, E. J.: Chemical composition and photosynthetic characteristics of Ethmodiscus rex (Bacillariophyceae): Evidence for vertical migration, J. Phycol., 30, 1–8, https://doi.org/10.1111/j.0022-3646.1994.00001.x, 1994. a
Volk, T. and Hoffert, M. I.: Ocean Carbon Pumps: Analysis of Relative Strengths and Efficiencies in Ocean-Driven Atmospheric CO2 Changes, in: The Carbon Cycle and Atmospheric CO2: Natural Variations Archean to Present, edited by: Sundquist, E. and Broecker, W., American Geophysical Union, Washington, D.C., United States, 99–110, https://doi.org/10.1029/GM032p0099, 1985. a
Walsh, J. J., Dieterle, D. A., and Lenes, J.: A numerical analysis of carbon
dynamics of the Southern Ocean phytoplankton community: the role of light and
grazing in effecting both sequestration of atmospheric CO2 and food
availability to larval krill, Deep-Sea Res. Pt. I, 48, 1–48, https://doi.org/10.1016/S0967-0637(00)00032-7, 2001. a
Ward, B. A. and Follows, M. J.: Marine mixotrophy increases trophic transfer
efficiency, mean organism size, and vertical carbon flux, P. Natl. Acad. Sci. USA, 113, 2958–2963, https://doi.org/10.1073/pnas.1517118113,
2016. a
Ward, B. A., Dutkiewicz, S., Jahn, O., and Follows, M. J.: A size-structured
food-web model for the global ocean, Limnol. Oceanogr., 57,
1877–1891, https://doi.org/10.4319/lo.2012.57.6.1877, 2012. a
Ward, B. A., Collins, S., Dutkiewicz, S., Gibbs, S., Bown, P., Ridgwell, A., Sauterey, B., Wilson, J. D., and Oschlies, A.: Considering the role of adaptive evolution in models of the ocean and climate system, J. Adv. Model. Earth Sy., 11, 3343–3361, 2019. a
Whitelam, G. C. and Codd, G. A.: Photoinhibition of photosynthesis in the
cyanobacterium Microcystis areruginosa, Planta, 157, 561–566,
https://doi.org/10.1007/BF00396889, 1983. a
Wickman, J., Diehl, S., and Bränström, Â.: Evolution of resource
specialisation in competitive metacommunities, Ecol. Lett., 22,
1746–1756, https://doi.org/10.1111/ele.13338, 2019. a
Wirtz, K. W.: A generic model for changes in microbial kinetic coefficients,
J. Biotechnol., 97, 147–162, https://doi.org/10.1016/S0168-1656(02)00064-0, 2002. a
Wirtz, K. W.: Mechanistic origins of variability in phytoplankton dynamics:
Part I: niche formation revealed by a size-based model, Mar. Biol., 160,
2319–2335, https://doi.org/10.1007/s00227-012-2163-7, 2013. a, b, c
Yoshida, T., Hairston, N. G., and Ellner, S. P.: Evolutionary trade-off between defence against grazing and competitive ability in a simple unicellular alga, Chlorella vulgaris, P. Roy. Soc. B-Biol. Sci., 271, 1947–1953, https://doi.org/10.1098/rspb.2004.2818, 2004. a
Short summary
We present an ecological model called SPEAD wherein various phytoplankton compete for nutrients. Phytoplankton in SPEAD are characterized by two continuously distributed traits: optimal temperature and nutrient half-saturation. Trait diversity is sustained by allowing the traits to mutate at each generation. We show that SPEAD agrees well with a more classical discrete model for only a fraction of the cost. We also identify realistic values for the mutation rates to be used in future models.
We present an ecological model called SPEAD wherein various phytoplankton compete for nutrients....
