Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-4939-2021
© Author(s) 2021. 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-14-4939-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Woods Hole Oceanographic Institution, Woods Hole, MA 02543,
USA
University of Virginia, Charlottesville, VA 22904, USA
Ya-Wei Luo
Xiamen University, Xiamen, Fujian 361102, China
Hugh W. Ducklow
Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY
10964, USA
Oscar M. Schofield
Rutgers University, New Brunswick, NJ 80901, USA
Deborah K. Steinberg
Virginia Institute of Marine Science, Gloucester Point, VA 23062,
USA
Scott C. Doney
University of Virginia, Charlottesville, VA 22904, USA
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Earth Syst. Sci. Data, 15, 4023–4045, https://doi.org/10.5194/essd-15-4023-2023, https://doi.org/10.5194/essd-15-4023-2023, 2023
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We present results from a machine learning model that accurately predicts dissolved barium concentrations for the global ocean. Our results reveal that the whole-ocean barium inventory is significantly lower than previously thought and that the deep ocean below 1000 m is at equilibrium with respect to barite. The model output can be used for a number of applications, including intercomparison, interpolation, and identification of regions warranting additional investigation.
Hyewon Heather Kim, Jeff S. Bowman, Ya-Wei Luo, Hugh W. Ducklow, Oscar M. Schofield, Deborah K. Steinberg, and Scott C. Doney
Biogeosciences, 19, 117–136, https://doi.org/10.5194/bg-19-117-2022, https://doi.org/10.5194/bg-19-117-2022, 2022
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Heterotrophic marine bacteria are tiny organisms responsible for taking up organic matter in the ocean. Using a modeling approach, this study shows that characteristics (taxonomy and physiology) of bacteria are associated with a subset of ecological processes in the coastal West Antarctic Peninsula region, a system susceptible to global climate change. This study also suggests that bacteria will become more active, in particular large-sized cells, in response to changing climates in the region.
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Earth Syst. Sci. Data, 15, 4023–4045, https://doi.org/10.5194/essd-15-4023-2023, https://doi.org/10.5194/essd-15-4023-2023, 2023
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We present results from a machine learning model that accurately predicts dissolved barium concentrations for the global ocean. Our results reveal that the whole-ocean barium inventory is significantly lower than previously thought and that the deep ocean below 1000 m is at equilibrium with respect to barite. The model output can be used for a number of applications, including intercomparison, interpolation, and identification of regions warranting additional investigation.
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Earth Syst. Sci. Data, 15, 3673–3709, https://doi.org/10.5194/essd-15-3673-2023, https://doi.org/10.5194/essd-15-3673-2023, 2023
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Yifan Guan, Gretchen Keppel-Aleks, Scott C. Doney, Christof Petri, Dave Pollard, Debra Wunch, Frank Hase, Hirofumi Ohyama, Isamu Morino, Justus Notholt, Kei Shiomi, Kim Strong, Rigel Kivi, Matthias Buschmann, Nicholas Deutscher, Paul Wennberg, Ralf Sussmann, Voltaire A. Velazco, and Yao Té
Atmos. Chem. Phys., 23, 5355–5372, https://doi.org/10.5194/acp-23-5355-2023, https://doi.org/10.5194/acp-23-5355-2023, 2023
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We characterize spatial–temporal patterns of interannual variability (IAV) in atmospheric CO2 based on NASA’s Orbiting Carbon Observatory-2 (OCO-2). CO2 variation is strongly impacted by climate events, with higher anomalies during El Nino years. We show high correlation in IAV between space-based and ground-based CO2 from long-term sites. Because OCO-2 has near-global coverage, our paper provides a roadmap to study IAV where in situ observation is sparse, such as open oceans and remote lands.
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Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-10, https://doi.org/10.5194/bg-2023-10, 2023
Preprint under review for BG
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The Southern Ocean is warming faster than the global average. As a globally important carbon sink and nutrient source, climate driven changes in ecosystems can be expected to cause widespread changes to biogeochemical cycles. We analysed earth system models and showed that productivity is expected to increase across the Southern Ocean, driven by different phytoplankton groups at different latitudes. These predictions carry large uncertainties, we propose targeted studies to reduce this error.
Darren C. McKee, Scott C. Doney, Alice Della Penna, Emmanuel S. Boss, Peter Gaube, Michael J. Behrenfeld, and David M. Glover
Biogeosciences, 19, 5927–5952, https://doi.org/10.5194/bg-19-5927-2022, https://doi.org/10.5194/bg-19-5927-2022, 2022
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As phytoplankton (small, drifting photosynthetic organisms) drift with ocean currents, biomass accumulation rates should be evaluated in a Lagrangian (observer moves with a fluid parcel) as opposed to an Eulerian (observer is stationary) framework. Here, we use profiling floats and surface drifters combined with satellite data to analyse time and length scales of chlorophyll concentrations (a proxy for biomass) and of velocity to quantify how phytoplankton variability is related to water motion.
Zhibo Shao and Ya-Wei Luo
Biogeosciences, 19, 2939–2952, https://doi.org/10.5194/bg-19-2939-2022, https://doi.org/10.5194/bg-19-2939-2022, 2022
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Non-cyanobacterial diazotrophs (NCDs) may be an important player in fixing N2 in the ocean. By conducting meta-analyses, we found that a representative marine NCD phylotype, Gamma A, tends to inhabit ocean environments with high productivity, low iron concentration and high light intensity. It also appears to be more abundant inside cyclonic eddies. Our study suggests a niche differentiation of NCDs from cyanobacterial diazotrophs as the latter prefers low-productivity and high-iron oceans.
Hyewon Heather Kim, Jeff S. Bowman, Ya-Wei Luo, Hugh W. Ducklow, Oscar M. Schofield, Deborah K. Steinberg, and Scott C. Doney
Biogeosciences, 19, 117–136, https://doi.org/10.5194/bg-19-117-2022, https://doi.org/10.5194/bg-19-117-2022, 2022
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Heterotrophic marine bacteria are tiny organisms responsible for taking up organic matter in the ocean. Using a modeling approach, this study shows that characteristics (taxonomy and physiology) of bacteria are associated with a subset of ecological processes in the coastal West Antarctic Peninsula region, a system susceptible to global climate change. This study also suggests that bacteria will become more active, in particular large-sized cells, in response to changing climates in the region.
Le Xie, Wei Wei, Lanlan Cai, Xiaowei Chen, Yuhong Huang, Nianzhi Jiao, Rui Zhang, and Ya-Wei Luo
Earth Syst. Sci. Data, 13, 1251–1271, https://doi.org/10.5194/essd-13-1251-2021, https://doi.org/10.5194/essd-13-1251-2021, 2021
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Claudine Hauri, Cristina Schultz, Katherine Hedstrom, Seth Danielson, Brita Irving, Scott C. Doney, Raphael Dussin, Enrique N. Curchitser, David F. Hill, and Charles A. Stock
Biogeosciences, 17, 3837–3857, https://doi.org/10.5194/bg-17-3837-2020, https://doi.org/10.5194/bg-17-3837-2020, 2020
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Anna Mikis, Katharine R. Hendry, Jennifer Pike, Daniela N. Schmidt, Kirsty M. Edgar, Victoria Peck, Frank J. C. Peeters, Melanie J. Leng, Michael P. Meredith, Chloe L. C. Jones, Sharon Stammerjohn, and Hugh Ducklow
Biogeosciences, 16, 3267–3282, https://doi.org/10.5194/bg-16-3267-2019, https://doi.org/10.5194/bg-16-3267-2019, 2019
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Antarctic marine calcifying organisms are threatened by regional climate change and ocean acidification. Future projections of regional carbonate production are challenging due to the lack of historical data combined with complex climate variability. We present a 6-year record of flux, morphology and geochemistry of an Antarctic planktonic foraminifera, which shows that their growth is most sensitive to sea ice dynamics and is linked with the El Niño–Southern Oscillation.
William J. Jenkins, Scott C. Doney, Michaela Fendrock, Rana Fine, Toshitaka Gamo, Philippe Jean-Baptiste, Robert Key, Birgit Klein, John E. Lupton, Robert Newton, Monika Rhein, Wolfgang Roether, Yuji Sano, Reiner Schlitzer, Peter Schlosser, and Jim Swift
Earth Syst. Sci. Data, 11, 441–454, https://doi.org/10.5194/essd-11-441-2019, https://doi.org/10.5194/essd-11-441-2019, 2019
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This paper describes an assembled dataset containing measurements of certain trace substances in the ocean, their distributions, and evolution with time. These substances, called tracers, result from a combination of natural and artificial processes, and their distribution and evolution provide important clues about ocean circulation, mixing, and ventilation. In addition, they give information about the global hydrologic cycle and volcanic and hydrothermal processes.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Judith Hauck, Julia Pongratz, Penelope A. Pickers, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Almut Arneth, Vivek K. Arora, Leticia Barbero, Ana Bastos, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Scott C. Doney, Thanos Gkritzalis, Daniel S. Goll, Ian Harris, Vanessa Haverd, Forrest M. Hoffman, Mario Hoppema, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Truls Johannessen, Chris D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Peter Landschützer, Nathalie Lefèvre, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Craig Neill, Are Olsen, Tsueno Ono, Prabir Patra, Anna Peregon, Wouter Peters, Philippe Peylin, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Matthias Rocher, Christian Rödenbeck, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Tobias Steinhoff, Adrienne Sutton, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, Rebecca Wright, Sönke Zaehle, and Bo Zheng
Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, https://doi.org/10.5194/essd-10-2141-2018, 2018
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The Global Carbon Budget 2018 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
James C. Orr, Raymond G. Najjar, Olivier Aumont, Laurent Bopp, John L. Bullister, Gokhan Danabasoglu, Scott C. Doney, John P. Dunne, Jean-Claude Dutay, Heather Graven, Stephen M. Griffies, Jasmin G. John, Fortunat Joos, Ingeborg Levin, Keith Lindsay, Richard J. Matear, Galen A. McKinley, Anne Mouchet, Andreas Oschlies, Anastasia Romanou, Reiner Schlitzer, Alessandro Tagliabue, Toste Tanhua, and Andrew Yool
Geosci. Model Dev., 10, 2169–2199, https://doi.org/10.5194/gmd-10-2169-2017, https://doi.org/10.5194/gmd-10-2169-2017, 2017
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The Ocean Model Intercomparison Project (OMIP) is a model comparison effort under Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its physical component is described elsewhere in this special issue. Here we describe its ocean biogeochemical component (OMIP-BGC), detailing simulation protocols and analysis diagnostics. Simulations focus on ocean carbon, other biogeochemical tracers, air-sea exchange of CO2 and related gases, and chemical tracers used to evaluate modeled circulation.
Angus Atkinson, Simeon L. Hill, Evgeny A. Pakhomov, Volker Siegel, Ricardo Anadon, Sanae Chiba, Kendra L. Daly, Rod Downie, Sophie Fielding, Peter Fretwell, Laura Gerrish, Graham W. Hosie, Mark J. Jessopp, So Kawaguchi, Bjørn A. Krafft, Valerie Loeb, Jun Nishikawa, Helen J. Peat, Christian S. Reiss, Robin M. Ross, Langdon B. Quetin, Katrin Schmidt, Deborah K. Steinberg, Roshni C. Subramaniam, Geraint A. Tarling, and Peter Ward
Earth Syst. Sci. Data, 9, 193–210, https://doi.org/10.5194/essd-9-193-2017, https://doi.org/10.5194/essd-9-193-2017, 2017
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KRILLBASE is a data rescue and compilation project to improve the availability of information on two key Southern Ocean zooplankton: Antarctic krill and salps. We provide a circumpolar database that combines 15 194 scientific net hauls (1926 to 2016) from 10 countries. These data provide a resource for analysing the distribution and abundance of krill and salps throughout the Southern Ocean to support ecological and biogeochemical research as well as fisheries management and conservation.
Corinne Le Quéré, Robbie M. Andrew, Josep G. Canadell, Stephen Sitch, Jan Ivar Korsbakken, Glen P. Peters, Andrew C. Manning, Thomas A. Boden, Pieter P. Tans, Richard A. Houghton, Ralph F. Keeling, Simone Alin, Oliver D. Andrews, Peter Anthoni, Leticia Barbero, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Kim Currie, Christine Delire, Scott C. Doney, Pierre Friedlingstein, Thanos Gkritzalis, Ian Harris, Judith Hauck, Vanessa Haverd, Mario Hoppema, Kees Klein Goldewijk, Atul K. Jain, Etsushi Kato, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Joe R. Melton, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Kevin O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Christian Rödenbeck, Joe Salisbury, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Adrienne J. Sutton, Taro Takahashi, Hanqin Tian, Bronte Tilbrook, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, https://doi.org/10.5194/essd-8-605-2016, 2016
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The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
Charlotte Laufkötter, Meike Vogt, Nicolas Gruber, Olivier Aumont, Laurent Bopp, Scott C. Doney, John P. Dunne, Judith Hauck, Jasmin G. John, Ivan D. Lima, Roland Seferian, and Christoph Völker
Biogeosciences, 13, 4023–4047, https://doi.org/10.5194/bg-13-4023-2016, https://doi.org/10.5194/bg-13-4023-2016, 2016
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We compare future projections in marine export production, generated by four ecosystem models under IPCC's high-emission scenario RCP8.5. While all models project decreases in export, they differ strongly regarding the drivers. The formation of sinking particles of organic matter is the most uncertain process with models not agreeing on either magnitude or the direction of change. Changes in diatom concentration are a strong driver for export in some models but of low significance in others.
Roland Séférian, Marion Gehlen, Laurent Bopp, Laure Resplandy, James C. Orr, Olivier Marti, John P. Dunne, James R. Christian, Scott C. Doney, Tatiana Ilyina, Keith Lindsay, Paul R. Halloran, Christoph Heinze, Joachim Segschneider, Jerry Tjiputra, Olivier Aumont, and Anastasia Romanou
Geosci. Model Dev., 9, 1827–1851, https://doi.org/10.5194/gmd-9-1827-2016, https://doi.org/10.5194/gmd-9-1827-2016, 2016
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This paper explores how the large diversity in spin-up protocols used for ocean biogeochemistry in CMIP5 models contributed to inter-model differences in modeled fields. We show that a link between spin-up duration and skill-score metrics emerges from both individual IPSL-CM5A-LR's results and an ensemble of CMIP5 models. Our study suggests that differences in spin-up protocols constitute a source of inter-model uncertainty which would require more attention in future intercomparison exercises.
J. E. Rheuban, S. Williamson, J. E. Costa, D. M. Glover, R. W. Jakuba, D. C. McCorkle, C. Neill, T. Williams, and S. C. Doney
Biogeosciences, 13, 253–265, https://doi.org/10.5194/bg-13-253-2016, https://doi.org/10.5194/bg-13-253-2016, 2016
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We analysed 22 years of water quality data collected through a citizen science program focused on Buzzards Bay, MA. We found that summertime water temperatures warmed by nearly 2C and chlorophyll a nearly doubled across Buzzards Bay from 1992-2013. Although water quality worsened over time, nutrient concentrations remained largely the same in many places. Warming or altered rainfall patterns from a changing climate may partially offset benefits achieved by reducing nutrients.
C. Laufkötter, M. Vogt, N. Gruber, M. Aita-Noguchi, O. Aumont, L. Bopp, E. Buitenhuis, S. C. Doney, J. Dunne, T. Hashioka, J. Hauck, T. Hirata, J. John, C. Le Quéré, I. D. Lima, H. Nakano, R. Seferian, I. Totterdell, M. Vichi, and C. Völker
Biogeosciences, 12, 6955–6984, https://doi.org/10.5194/bg-12-6955-2015, https://doi.org/10.5194/bg-12-6955-2015, 2015
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We analyze changes in marine net primary production (NPP) and its drivers for the 21st century in 9 marine ecosystem models under the RCP8.5 scenario. NPP decreases in 5 models and increases in 1 model; 3 models show no significant trend. The main drivers include stronger nutrient limitation, but in many models warming-induced increases in phytoplankton growth outbalance the nutrient effect. Temperature-driven increases in grazing and other loss processes cause a net decrease in biomass and NPP.
C. Hauri, S. C. Doney, T. Takahashi, M. Erickson, G. Jiang, and H. W. Ducklow
Biogeosciences, 12, 6761–6779, https://doi.org/10.5194/bg-12-6761-2015, https://doi.org/10.5194/bg-12-6761-2015, 2015
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Evaluation of a unique 20-year-long time series of inorganic carbon and nutrient observations from the West Antarctic Peninsula region shows that summertime biological productivity and meltwater input drive a large range of surface aragonite saturation states from values < 1 (undersaturated) up to 3.9. Even though we did not detect any statistically significant long-term trends, ongoing ocean acidification and freshwater input may soon induce more unfavorable conditions than seen today.
R. Arruda, P. H. R. Calil, A. A. Bianchi, S. C. Doney, N. Gruber, I. Lima, and G. Turi
Biogeosciences, 12, 5793–5809, https://doi.org/10.5194/bg-12-5793-2015, https://doi.org/10.5194/bg-12-5793-2015, 2015
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We investigate surface ocean pCO2 and air-sea CO2 fluxes climatological variability through biogeochemical modeling in the southwestern Atlantic Ocean. Surface ocean pCO2 spatio-temporal variability was found to be controlled mainly by temperature and Dissolved Inorganic Carbon (DIC). Biological production, physical transport and solubility are the main controlling processes. With different behaviors on subtropical and subantarctic open ocean, and on inner/outer continental shelves.
R. H. R. Stanley, W. J. Jenkins, S. C. Doney, and D. E. Lott III
Biogeosciences, 12, 5199–5210, https://doi.org/10.5194/bg-12-5199-2015, https://doi.org/10.5194/bg-12-5199-2015, 2015
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A long-standing enigma in oceanography is the process in which nutrients are supplied to the sunlit zone of the low nutrient regions of the ocean. In this work, we present one approach for quantifying the physical supply of nitrate to the euphotic zone in the Sargasso Sea through the use of gas tracers. We find that the nitrate supplied is more than enough to support the rates of net community production (balance of photosynthesis respiration) observed.
B. F. Jonsson, S. Doney, J. Dunne, and M. L. Bender
Biogeosciences, 12, 681–695, https://doi.org/10.5194/bg-12-681-2015, https://doi.org/10.5194/bg-12-681-2015, 2015
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We compare how two global circulation models simulate biological production over the year with observations. Note that models simulate the range of biological production and biomass well but fail with regard to timing and regional structures. This is probably because the physics in the models are wrong, especially vertical processes such as mixed layer dynamics.
S. Sitch, P. Friedlingstein, N. Gruber, S. D. Jones, G. Murray-Tortarolo, A. Ahlström, S. C. Doney, H. Graven, C. Heinze, C. Huntingford, S. Levis, P. E. Levy, M. Lomas, B. Poulter, N. Viovy, S. Zaehle, N. Zeng, A. Arneth, G. Bonan, L. Bopp, J. G. Canadell, F. Chevallier, P. Ciais, R. Ellis, M. Gloor, P. Peylin, S. L. Piao, C. Le Quéré, B. Smith, Z. Zhu, and R. Myneni
Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://doi.org/10.5194/bg-12-653-2015, 2015
M. Gehlen, R. Séférian, D. O. B. Jones, T. Roy, R. Roth, J. Barry, L. Bopp, S. C. Doney, J. P. Dunne, C. Heinze, F. Joos, J. C. Orr, L. Resplandy, J. Segschneider, and J. Tjiputra
Biogeosciences, 11, 6955–6967, https://doi.org/10.5194/bg-11-6955-2014, https://doi.org/10.5194/bg-11-6955-2014, 2014
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This study evaluates potential impacts of pH reductions on North Atlantic deep-sea ecosystems in response to latest IPCC scenarios.Multi-model projections of pH changes over the seafloor are analysed with reference to a critical threshold based on palaeo-oceanographic studies, contemporary observations and model results. By 2100 under the most severe IPCC CO2 scenario, pH reductions occur over ~23% of deep-sea canyons and ~8% of seamounts – including seamounts proposed as marine protected areas.
N. Jiao, C. Robinson, F. Azam, H. Thomas, F. Baltar, H. Dang, N. J. Hardman-Mountford, M. Johnson, D. L. Kirchman, B. P. Koch, L. Legendre, C. Li, J. Liu, T. Luo, Y.-W. Luo, A. Mitra, A. Romanou, K. Tang, X. Wang, C. Zhang, and R. Zhang
Biogeosciences, 11, 5285–5306, https://doi.org/10.5194/bg-11-5285-2014, https://doi.org/10.5194/bg-11-5285-2014, 2014
C. Le Quéré, G. P. Peters, R. J. Andres, R. M. Andrew, T. A. Boden, P. Ciais, P. Friedlingstein, R. A. Houghton, G. Marland, R. Moriarty, S. Sitch, P. Tans, A. Arneth, A. Arvanitis, D. C. E. Bakker, L. Bopp, J. G. Canadell, L. P. Chini, S. C. Doney, A. Harper, I. Harris, J. I. House, A. K. Jain, S. D. Jones, E. Kato, R. F. Keeling, K. Klein Goldewijk, A. Körtzinger, C. Koven, N. Lefèvre, F. Maignan, A. Omar, T. Ono, G.-H. Park, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. Schwinger, J. Segschneider, B. D. Stocker, T. Takahashi, B. Tilbrook, S. van Heuven, N. Viovy, R. Wanninkhof, A. Wiltshire, and S. Zaehle
Earth Syst. Sci. Data, 6, 235–263, https://doi.org/10.5194/essd-6-235-2014, https://doi.org/10.5194/essd-6-235-2014, 2014
I. D. Lima, P. J. Lam, and S. C. Doney
Biogeosciences, 11, 1177–1198, https://doi.org/10.5194/bg-11-1177-2014, https://doi.org/10.5194/bg-11-1177-2014, 2014
Y.-W. Luo, I. D. Lima, D. M. Karl, C. A. Deutsch, and S. C. Doney
Biogeosciences, 11, 691–708, https://doi.org/10.5194/bg-11-691-2014, https://doi.org/10.5194/bg-11-691-2014, 2014
M. Ishii, R. A. Feely, K. B. Rodgers, G.-H. Park, R. Wanninkhof, D. Sasano, H. Sugimoto, C. E. Cosca, S. Nakaoka, M. Telszewski, Y. Nojiri, S. E. Mikaloff Fletcher, Y. Niwa, P. K. Patra, V. Valsala, H. Nakano, I. Lima, S. C. Doney, E. T. Buitenhuis, O. Aumont, J. P. Dunne, A. Lenton, and T. Takahashi
Biogeosciences, 11, 709–734, https://doi.org/10.5194/bg-11-709-2014, https://doi.org/10.5194/bg-11-709-2014, 2014
K. Misumi, K. Lindsay, J. K. Moore, S. C. Doney, F. O. Bryan, D. Tsumune, and Y. Yoshida
Biogeosciences, 11, 33–55, https://doi.org/10.5194/bg-11-33-2014, https://doi.org/10.5194/bg-11-33-2014, 2014
V. V. S. S. Sarma, A. Lenton, R. M. Law, N. Metzl, P. K. Patra, S. Doney, I. D. Lima, E. Dlugokencky, M. Ramonet, and V. Valsala
Biogeosciences, 10, 7035–7052, https://doi.org/10.5194/bg-10-7035-2013, https://doi.org/10.5194/bg-10-7035-2013, 2013
M. Vogt, T. Hashioka, M. R. Payne, E. T. Buitenhuis, C. Le Quéré, S. Alvain, M. N. Aita, L. Bopp, S. C. Doney, T. Hirata, I. Lima, S. Sailley, and Y. Yamanaka
Biogeosciences Discuss., https://doi.org/10.5194/bgd-10-17193-2013, https://doi.org/10.5194/bgd-10-17193-2013, 2013
Revised manuscript has not been submitted
T. Hashioka, M. Vogt, Y. Yamanaka, C. Le Quéré, E. T. Buitenhuis, M. N. Aita, S. Alvain, L. Bopp, T. Hirata, I. Lima, S. Sailley, and S. C. Doney
Biogeosciences, 10, 6833–6850, https://doi.org/10.5194/bg-10-6833-2013, https://doi.org/10.5194/bg-10-6833-2013, 2013
L. Bopp, L. Resplandy, J. C. Orr, S. C. Doney, J. P. Dunne, M. Gehlen, P. Halloran, C. Heinze, T. Ilyina, R. Séférian, J. Tjiputra, and M. Vichi
Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, https://doi.org/10.5194/bg-10-6225-2013, 2013
E. T. Buitenhuis, M. Vogt, R. Moriarty, N. Bednaršek, S. C. Doney, K. Leblanc, C. Le Quéré, Y.-W. Luo, C. O'Brien, T. O'Brien, J. Peloquin, R. Schiebel, and C. Swan
Earth Syst. Sci. Data, 5, 227–239, https://doi.org/10.5194/essd-5-227-2013, https://doi.org/10.5194/essd-5-227-2013, 2013
A. Lenton, B. Tilbrook, R. M. Law, D. Bakker, S. C. Doney, N. Gruber, M. Ishii, M. Hoppema, N. S. Lovenduski, R. J. Matear, B. I. McNeil, N. Metzl, S. E. Mikaloff Fletcher, P. M. S. Monteiro, C. Rödenbeck, C. Sweeney, and T. Takahashi
Biogeosciences, 10, 4037–4054, https://doi.org/10.5194/bg-10-4037-2013, https://doi.org/10.5194/bg-10-4037-2013, 2013
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
C. Beaulieu, S. A. Henson, Jorge L. Sarmiento, J. P. Dunne, S. C. Doney, R. R. Rykaczewski, and L. Bopp
Biogeosciences, 10, 2711–2724, https://doi.org/10.5194/bg-10-2711-2013, https://doi.org/10.5194/bg-10-2711-2013, 2013
S. Khatiwala, T. Tanhua, S. Mikaloff Fletcher, M. Gerber, S. C. Doney, H. D. Graven, N. Gruber, G. A. McKinley, A. Murata, A. F. Ríos, and C. L. Sabine
Biogeosciences, 10, 2169–2191, https://doi.org/10.5194/bg-10-2169-2013, https://doi.org/10.5194/bg-10-2169-2013, 2013
R. Wanninkhof, G. -H. Park, T. Takahashi, C. Sweeney, R. Feely, Y. Nojiri, N. Gruber, S. C. Doney, G. A. McKinley, A. Lenton, C. Le Quéré, C. Heinze, J. Schwinger, H. Graven, and S. Khatiwala
Biogeosciences, 10, 1983–2000, https://doi.org/10.5194/bg-10-1983-2013, https://doi.org/10.5194/bg-10-1983-2013, 2013
U. Schuster, G. A. McKinley, N. Bates, F. Chevallier, S. C. Doney, A. R. Fay, M. González-Dávila, N. Gruber, S. Jones, J. Krijnen, P. Landschützer, N. Lefèvre, M. Manizza, J. Mathis, N. Metzl, A. Olsen, A. F. Rios, C. Rödenbeck, J. M. Santana-Casiano, T. Takahashi, R. Wanninkhof, and A. J. Watson
Biogeosciences, 10, 607–627, https://doi.org/10.5194/bg-10-607-2013, https://doi.org/10.5194/bg-10-607-2013, 2013
C. Hauri, N. Gruber, M. Vogt, S. C. Doney, R. A. Feely, Z. Lachkar, A. Leinweber, A. M. P. McDonnell, M. Munnich, and G.-K. Plattner
Biogeosciences, 10, 193–216, https://doi.org/10.5194/bg-10-193-2013, https://doi.org/10.5194/bg-10-193-2013, 2013
Related subject area
Biogeosciences
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 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
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands
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)
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
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
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)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics
Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon
SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level
Improved representation of plant physiology in the JULES-vn5.6 land surface model: photosynthesis, stomatal conductance and thermal acclimation
Representation of the phosphorus cycle in the Joint UK Land Environment Simulator (vn5.5_JULES-CNP)
CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5)
The impact of hurricane disturbances on a tropical forest: implementing a palm plant functional type and hurricane disturbance module in ED2-HuDi V1.0
A validation standard for area of habitat maps for terrestrial birds and mammals
Soil Cycles of Elements simulator for Predicting TERrestrial regulation of greenhouse gases: SCEPTER v0.9
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)
A map of global peatland extent created using machine learning (Peat-ML)
Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)
ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic
Water Ecosystems Tool (WET) 1.0 – a new generation of flexible aquatic ecosystem model
Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET
Predicting global terrestrial biomes with the LeNet convolutional neural network
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments
Assessing methane emissions for northern peatlands in ORCHIDEE-PEAT revision 7020
A dynamic local-scale vegetation model for lycopsids (LYCOm v1.0)
Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model
Global evaluation of the Ecosystem Demography model (ED v3.0)
A new snow module improves predictions of the isotope-enabled MAIDENiso forest growth model
Calibrating the soil organic carbon model Yasso20 with multiple datasets
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.
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.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla T. 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. Discuss., https://doi.org/10.5194/gmd-2023-47, https://doi.org/10.5194/gmd-2023-47, 2023
Revised manuscript accepted for GMD
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (eg, basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits from C sequestration. ERW could drive changes in the soil emissions of non-CO2 GHGs (N2O), and trace gases (NO & 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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2023-278, https://doi.org/10.5194/egusphere-2023-278, 2023
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This paper introduces a plant hydrodynamic model for the DOE-sponsored dynamic vegetation model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest systems in particular, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We have identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
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.
Jianghui Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-281, https://doi.org/10.5194/gmd-2022-281, 2023
Revised manuscript accepted for GMD
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Trace elements and isotopes (TEIs) are important tools to study the changes of 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.
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.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
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Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
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A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
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Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
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The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
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Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
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Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
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Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
Marcus Falls, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí
Geosci. Model Dev., 15, 5713–5737, https://doi.org/10.5194/gmd-15-5713-2022, https://doi.org/10.5194/gmd-15-5713-2022, 2022
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This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.
Julien Ruffault, François Pimont, Hervé Cochard, Jean-Luc Dupuy, and Nicolas Martin-StPaul
Geosci. Model Dev., 15, 5593–5626, https://doi.org/10.5194/gmd-15-5593-2022, https://doi.org/10.5194/gmd-15-5593-2022, 2022
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A widespread increase in tree mortality has been observed around the globe, and this trend is likely to continue because of ongoing climate change. Here we present SurEau-Ecos, a trait-based plant hydraulic model to predict tree desiccation and mortality. SurEau-Ecos can help determine the areas and ecosystems that are most vulnerable to drying conditions.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
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We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Mahdi André Nakhavali, Lina M. Mercado, Iain P. Hartley, Stephen Sitch, Fernanda V. Cunha, Raffaello di Ponzio, Laynara F. Lugli, Carlos A. Quesada, Kelly M. Andersen, Sarah E. Chadburn, Andy J. Wiltshire, Douglas B. Clark, Gyovanni Ribeiro, Lara Siebert, Anna C. M. Moraes, Jéssica Schmeisk Rosa, Rafael Assis, and José L. Camargo
Geosci. Model Dev., 15, 5241–5269, https://doi.org/10.5194/gmd-15-5241-2022, https://doi.org/10.5194/gmd-15-5241-2022, 2022
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In tropical ecosystems, the availability of rock-derived elements such as P can be very low. Thus, without a representation of P cycling, tropical forest responses to rising atmospheric CO2 conditions in areas such as Amazonia remain highly uncertain. We introduced P dynamics and its interactions with the N and P cycles into the JULES model. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low-fertility soils.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
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Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev., 15, 5107–5126, https://doi.org/10.5194/gmd-15-5107-2022, https://doi.org/10.5194/gmd-15-5107-2022, 2022
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We implemented hurricane disturbance in a vegetation dynamics model and calibrated the model with observations of a tropical forest. We used the model to study forest recovery from hurricane disturbance and found that a single hurricane disturbance enhances AGB and BA in the long term compared with a no-hurricane situation. The model developed and results presented in this study can be utilized to understand the impact of hurricane disturbances on forest recovery under the changing climate.
Prabhat Raj Dahal, Maria Lumbierres, Stuart H. M. Butchart, Paul F. Donald, and Carlo Rondinini
Geosci. Model Dev., 15, 5093–5105, https://doi.org/10.5194/gmd-15-5093-2022, https://doi.org/10.5194/gmd-15-5093-2022, 2022
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This paper describes the validation of area of habitat (AOH) maps produced for terrestrial birds and mammals. The main objective was to assess the accuracy of the maps based on independent data. We used open access data from repositories, such as ebird and gbif to check if our maps were a better reflection of species' distribution than random. When points were not available we used logistic models to validate the AOH maps. The majority of AOH maps were found to have a high accuracy.
Yoshiki Kanzaki, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 15, 4959–4990, https://doi.org/10.5194/gmd-15-4959-2022, https://doi.org/10.5194/gmd-15-4959-2022, 2022
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Increasing carbon dioxide in the atmosphere is an urgent issue in the coming century. Enhanced rock weathering in soils can be one of the most efficient C capture strategies. On the basis as a weathering simulator, the newly developed SCEPTER model implements bio-mixing by fauna/humans and enables organic matter and crushed rocks/minerals at the soil surface with an option to track their particle size distributions. Those features can be useful for evaluating the carbon capture efficiency.
Félicien Meunier, Sruthi M. Krishna Moorthy, Marc Peaucelle, Kim Calders, Louise Terryn, Wim Verbruggen, Chang Liu, Ninni Saarinen, Niall Origo, Joanne Nightingale, Mathias Disney, Yadvinder Malhi, and Hans Verbeeck
Geosci. Model Dev., 15, 4783–4803, https://doi.org/10.5194/gmd-15-4783-2022, https://doi.org/10.5194/gmd-15-4783-2022, 2022
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We integrated state-of-the-art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce such an ecosystem in silico. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
Joe R. Melton, Ed Chan, Koreen Millard, Matthew Fortier, R. Scott Winton, Javier M. Martín-López, Hinsby Cadillo-Quiroz, Darren Kidd, and Louis V. Verchot
Geosci. Model Dev., 15, 4709–4738, https://doi.org/10.5194/gmd-15-4709-2022, https://doi.org/10.5194/gmd-15-4709-2022, 2022
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Peat-ML is a high-resolution global peatland extent map generated using machine learning techniques. Peatlands are important in the global carbon and water cycles, but their extent is poorly known. We generated Peat-ML using drivers of peatland formation including climate, soil, geomorphology, and vegetation data, and we train the model with regional peatland maps. Our accuracy estimation approaches suggest Peat-ML is of similar or higher quality than other available peatland mapping products.
Qianyu Li, Shawn P. Serbin, Julien Lamour, Kenneth J. Davidson, Kim S. Ely, and Alistair Rogers
Geosci. Model Dev., 15, 4313–4329, https://doi.org/10.5194/gmd-15-4313-2022, https://doi.org/10.5194/gmd-15-4313-2022, 2022
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Stomatal conductance is the rate of water release from leaves’ pores. We implemented an optimal stomatal conductance model in a vegetation model. We then tested and compared it with the existing empirical model in terms of model responses to key environmental variables. We also evaluated the model with measurements at a tropical forest site. Our study suggests that the parameterization of conductance models and current model response to drought are the critical areas for improving models.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
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We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
Nicolas Azaña Schnedler-Meyer, Tobias Kuhlmann Andersen, Fenjuan Rose Schmidt Hu, Karsten Bolding, Anders Nielsen, and Dennis Trolle
Geosci. Model Dev., 15, 3861–3878, https://doi.org/10.5194/gmd-15-3861-2022, https://doi.org/10.5194/gmd-15-3861-2022, 2022
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We present the Water Ecosystems Tool (WET) – a new modular aquatic ecosystem model configurable to a wide array of physical setups, ecosystems and research questions based on the popular FABM–PCLake model. We aim for the model to become a community staple, thus helping to consolidate the state of the art under a few flexible models, with the aim of improving comparability across studies and preventing the
re-inventions of the wheelthat are common to our scientific modeling community.
Hamze Dokoohaki, Bailey D. Morrison, Ann Raiho, Shawn P. Serbin, Katie Zarada, Luke Dramko, and Michael Dietze
Geosci. Model Dev., 15, 3233–3252, https://doi.org/10.5194/gmd-15-3233-2022, https://doi.org/10.5194/gmd-15-3233-2022, 2022
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We present a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon
reanalysisproduct that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. Here, we build on a decade of work on uncertainty propagation to generate the most complete and robust uncertainty accounting available to date.
Hisashi Sato and Takeshi Ise
Geosci. Model Dev., 15, 3121–3132, https://doi.org/10.5194/gmd-15-3121-2022, https://doi.org/10.5194/gmd-15-3121-2022, 2022
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Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022, https://doi.org/10.5194/gmd-15-2839-2022, 2022
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By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
Elodie Salmon, Fabrice Jégou, Bertrand Guenet, Line Jourdain, Chunjing Qiu, Vladislav Bastrikov, Christophe Guimbaud, Dan Zhu, Philippe Ciais, Philippe Peylin, Sébastien Gogo, Fatima Laggoun-Défarge, Mika Aurela, M. Syndonia Bret-Harte, Jiquan Chen, Bogdan H. Chojnicki, Housen Chu, Colin W. Edgar, Eugenie S. Euskirchen, Lawrence B. Flanagan, Krzysztof Fortuniak, David Holl, Janina Klatt, Olaf Kolle, Natalia Kowalska, Lars Kutzbach, Annalea Lohila, Lutz Merbold, Włodzimierz Pawlak, Torsten Sachs, and Klaudia Ziemblińska
Geosci. Model Dev., 15, 2813–2838, https://doi.org/10.5194/gmd-15-2813-2022, https://doi.org/10.5194/gmd-15-2813-2022, 2022
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A methane model that features methane production and transport by plants, the ebullition process and diffusion in soil, oxidation to CO2, and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model, which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4, was calibrated and evaluated on 14 peatland sites. Results show that the model is sensitive to temperature and substrate availability over the top 75 cm of soil depth.
Suman Halder, Susanne K. M. Arens, Kai Jensen, Tais W. Dahl, and Philipp Porada
Geosci. Model Dev., 15, 2325–2343, https://doi.org/10.5194/gmd-15-2325-2022, https://doi.org/10.5194/gmd-15-2325-2022, 2022
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A dynamic vegetation model, designed to estimate potential impacts of early vascular vegetation, namely, lycopsids, on the biogeochemical cycle at a local scale. Lycopsid Model (LYCOm) estimates the productivity and physiological properties of lycopsids across a broad climatic range along with natural selection, which is then utilized to adjudge their weathering potential. It lays the foundation for estimation of their impacts during their long evolutionary history starting from the Ordovician.
Dóra Hidy, Zoltán Barcza, Roland Hollós, Laura Dobor, Tamás Ács, Dóra Zacháry, Tibor Filep, László Pásztor, Dóra Incze, Márton Dencső, Eszter Tóth, Katarína Merganičová, Peter Thornton, Steven Running, and Nándor Fodor
Geosci. Model Dev., 15, 2157–2181, https://doi.org/10.5194/gmd-15-2157-2022, https://doi.org/10.5194/gmd-15-2157-2022, 2022
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Biogeochemical models used by the scientific community can support society in the quantification of the expected environmental impacts caused by global climate change. The Biome-BGCMuSo v6.2 biogeochemical model has been created by implementing a lot of developments related to soil hydrology as well as the soil carbon and nitrogen cycle and by integrating crop model components. Detailed descriptions of developments with case studies are presented in this paper.
Lei Ma, George Hurtt, Lesley Ott, Ritvik Sahajpal, Justin Fisk, Rachel Lamb, Hao Tang, Steve Flanagan, Louise Chini, Abhishek Chatterjee, and Joseph Sullivan
Geosci. Model Dev., 15, 1971–1994, https://doi.org/10.5194/gmd-15-1971-2022, https://doi.org/10.5194/gmd-15-1971-2022, 2022
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We present a global version of the Ecosystem Demography (ED) model which can track vegetation 3-D structure and scale up ecological processes from individual vegetation to ecosystem scale. Model evaluation against multiple benchmarking datasets demonstrated the model’s capability to simulate global vegetation dynamics across a range of temporal and spatial scales. With this version, ED has the potential to be linked with remote sensing observations to address key scientific questions.
Ignacio Hermoso de Mendoza, Etienne Boucher, Fabio Gennaretti, Aliénor Lavergne, Robert Field, and Laia Andreu-Hayles
Geosci. Model Dev., 15, 1931–1952, https://doi.org/10.5194/gmd-15-1931-2022, https://doi.org/10.5194/gmd-15-1931-2022, 2022
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We modify the numerical model of forest growth MAIDENiso by explicitly simulating snow. This allows us to use the model in boreal environments, where snow is dominant. We tested the performance of the model before and after adding snow, using it at two Canadian sites to simulate tree-ring isotopes and comparing with local observations. We found that modelling snow improves significantly the simulation of the hydrological cycle, the plausibility of the model and the simulated isotopes.
Toni Viskari, Janne Pusa, Istem Fer, Anna Repo, Julius Vira, and Jari Liski
Geosci. Model Dev., 15, 1735–1752, https://doi.org/10.5194/gmd-15-1735-2022, https://doi.org/10.5194/gmd-15-1735-2022, 2022
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We wanted to examine how the chosen measurement data and calibration process affect soil organic carbon model calibration. In our results we found that there is a benefit in using data from multiple litter-bag decomposition experiments simultaneously, even with the required assumptions. Additionally, due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.
Cited articles
Armstrong, R. A.: Optimality-based modeling of nitrogen allocation and photo acclimation in photosynthesis, Deep-Sea Res. II, 53, 513–531, 2006.
Bertilsson, S., Berglund, O., Karl, D. M., and Chisholm, S. W.: Elemental composition of marine Prochlorococcus and Synechococcus: Implications for the ecological stoichiometry of the sea, Limnol. Oceanogr., 48, 1721–1731, https://doi.org/10.4319/lo.2003.48.5.1721, 2003.
Biddanda, B. and Benner, R.: Carbon, nitrogen and carbohydrate fluxes during the production of particulate and dissolved organic matter by marine phytoplankton, Limnol. Oceanogr., 42, 506–518, 1997.
Bird, D. F. and Karl, D. M.: Uncoupling of bacteria and phytoplankton during the austral spring bloom in Gerlache Strait, Antarctic Peninsula, Aquat. Microb. Ecol., 19, 13–27, https://doi.org/10.3354/ame019013, 1999.
Bjørnsen, P. K.: Phytoplankton exudation of organic matter: Why do healthy cells do it?, Limnol. Oceanogr., 33, 151–154, 1988.
Bowman, J. S. and Ducklow, H. W.: Microbial Communities Can Be Described by Metabolic Structure: A General Framework and Application to a Seasonally Variable, Depth-Stratified Microbial Community from the Coastal West Antarctic Peninsula, Plos One, 10, e0135868, https://doi.org/10.1371/journal.pone.0135868, 2015.
Bowman, J. S., Kavanaugh, M. T., Doney, S. C., and Ducklow, H. W.: Recurrent seascape units identify key ecological processes along the western Antarctic Peninsula, Glob. Change Biol., 24, 3065–3078, https://doi.org/10.1111/gcb.14161, 2018.
Campbell, J. W.: The lognormal distribution as a model for bio-optical variability in the sea, J. Geophys. Res.-Oceans, 100, 13237–13254, https://doi.org/10.1029/95JC00458, 1995.
Caron, D. A., Dennett, M. R., Lonsdale, D. J., Moran, D. M., and Shalapyonok, L.: Microzooplankton herbivory in the Ross sea, Antarctica, Deep-Sea Res. Pt. II, 47, 3249–3272, 2000.
Carlson, C. A., Bates, N. R., Ducklow, H. W., and Hansell, D. A.: Estimation of bacterial respiration and growth efficiency in the Ross Sea, Antarctica, Aquat. Microb. Ecol., 19, 229–244, 1999.
Carvalho, F., Kohut, J., Oliver, M. J., Sherrell, R. M.,
and Schofield, O.: Mixing and phytoplankton dynamics in a submarine
canyon in the West Antarctic Peninsula, J. Geophys. Res.,
121, 5069–5083, https://doi.org/10.1002/2016JC011650, 2016.
Clarke, A., Griffiths, H. J., Barnes, D. K. A., Meredith, M. P., and Grant, S. M.: Spatial variation in seabed temperatures in the Southern Ocean: Implications for benthic ecology and biogeography, J. Geophys. Res.-Biogeo., 114, G03003, https://doi.org/10.1029/2008JG000886, 2009.
Cook, A. J., Fox, A. J., Vaughan, D. G., and Ferrigno, J. G.: Retreating Glacier Fronts on the Antarctic Peninsula over the Past Half-Century, Science, 308, 541–544, https://doi.org/10.1126/science.1104235, 2005.
del Giorgio, P. A. and Cole, J. J.: Bacterial Growth Efficiency in Natural Aquatic Systems, Annu. Rev. Ecol. Syst., 29, 503–541, https://doi.org/10.1146/annurev.ecolsys.29.1.503, 1998.
Doney, S. C., Glover, D. M., McCue, S. J., and Fuentes, M.: Mesoscale variability of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite ocean color: Global patterns and spatial scales, J. Geophys. Res.-Oceans, 108, 3024, https://doi.org/10.1029/2001JC000843, 2003.
Doney, S. C., Lima, I., Moore, J. K., Lindsay, K., Behrenfeld, M. J., Westberry, T. K., Mahowald, N., Glover, D. M., and Takahashi, T.: Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data, J. Marine Syst., 76, 95–112, https://doi.org/10.1016/j.jmarsys.2008.05.015, 2009.
Droop, M. R.: The nutrient status of algal cells in continuous culture, J. Mar. Biol. Assoc. UK, 54, 825–855, https://doi.org/10.1017/S002531540005760X, 1974.
Droop, M. R.: 25 years of algal growth kinetics, A
personal view, Bot. Mar., 26, 99–112, https://doi.org/10.1515/botm.1983.26.3.99, 1983.
Ducklow, H. W.: Bacterial production and biomass in the ocean, in: Microbial Ecology of the Oceans, second edition, John Wiley & Sons, Inc., New York, NY, 85–120, 2000.
Ducklow, H. W. and Doney, S. C.: What is the metabolic state of the oligotrophic ocean? A debate, Annu. Rev. Mar. Sci., 5, 525–533, https://doi.org/10.1146/annurev-marine-121211-172331, 2013.
Ducklow, H. W, Baker, K., Martinson, D. G., Quetin, L. B., Ross, R. M., Smith, R. C., Stammerjohn, S. E., Vernet, M., and Fraser, W.: Marine pelagic ecosystems: The West Antarctic Peninsula, Philos. T. R. Soc. B, 362, 67–94, https://doi.org/10.1098/rstb.2006.1955, 2007.
Ducklow, H. W., Doney, S. C., and Steinberg, D. K.: Contributions of Long-Term Research and Time-Series Observations to Marine Ecology and Biogeochemistry, Annu. Rev. Mar. Sci., 1, 279–302, 2008.
Ducklow, H. W., Myers, K. M. S., Erickson, M., Ghiglione, J.-F., and Murray, A. E.: Response of a summertime Antarctic marine-bacterial community to glucose and ammonium enrichment, Aquat. Microb. Ecol., 64, 205–220, https://doi.org/10.3354/ame01519, 2011.
Ducklow, H. W., Schofield, O., Vernet, M., Stammerjohn, S., and Erickson, M.: Multiscale control of bacterial production by phytoplankton dynamics and sea ice along the western Antarctic Peninsula: A regional and decadal investigation, J. Marine Syst., 98–99, 26–39, https://doi.org/10.1016/j.jmarsys.2012.03.003, 2012.
Ducklow, H. W., Stukel, M. R., Eveleth, R., Doney, S. C., Jickells, T., Schofield, O., Baker, A. R., Brindle, J., Chance, R., and Cassar, N.: Spring–summer net community production, new production, particle export and related water column biogeochemical processes in the marginal sea ice zone of the Western Antarctic Peninsula 2012–2014, Philos. T. R. Soc. A, 376, 2017017, https://doi.org/10.1098/rsta.2017.0177, 2018.
Dugdale, R. C. and Goering, J. J.: Uptake of New and Regenerated Forms of Nitrogen in Primary Productivity1, Limnol. Oceanogr., 12, 196–206, https://doi.org/10.4319/lo.1967.12.2.0196, 1967.
Fennel, K., Losch, M., Schröter, J., and Wenzel, M.: Testing a marine ecosystem model: Sensitivity analysis and parameter optimization, J. Marine Syst., 28, 45–63, https://doi.org/10.1016/S0924-7963(00)00083-X, 2001.
Fogg, G. E.: The extracellular products of algae, Oceanogr. Mar. Biol. Annu. Rev., 4, 195–212, 1966.
Friedrichs, M. A. M.: Assimilation of JGOFS EqPac and SeaWiFS data into a marine ecosystem model of the Central Equatorial Pacific Ocean, Deep-Sea Res. Pt. II, 49, 289–319, 2001.
Friedrichs, M. A. M., Hood, R. R., and Wiggert, J. D.: Ecosystem model complexity versus physical forcing: Quantification of their relative impact with assimilated Arabian Sea data, Deep-Sea Res. Pt. II, 53, 576–600, 2006.
Friedrichs, M. A. M., Dusenberry, J. A., Anderson, L. A.,
Armstrong, R. A., Chai, F., Christian, J. R., Doney, S. C., Dunne,
J., Fujii, M., Hood, R., McGillicuddy, D. J., Moore, J. K.,
Schartau, M., Spitz, Y. H., and Wiggert, J. D.: Assessment of skill
and portability in regional marine biogeochemical models: Role of
multiple planktonic groups, J. Geophys. Res.-Oceans, 112, C08001, https://doi.org/10.1029/2006JC003852, 2007.
Fukuda, R., Ogawa, H., Nagata, T., and Koike, I.: Direct Determination of Carbon and Nitrogen Contents of Natural Bacterial Assemblages in Marine Environments, Appl. Environ. Microb., 64, 3352–3358, 1998.
Garzio, L. and Steinberg, D.: Microzooplankton community composition along the Western Antarctic Peninsula, Deep-Sea Res. Pt. I, 77, 36–49, https://doi.org/10.1016/j.dsr.2013.03.001, 2013.
Garzio, L. M., Steinberg, D. K., Erickson, M., and Ducklow, H. W.: Microzooplankton grazing along the Western Antarctic Peninsula, Aquat. Microb. Ecol., 70, 215–232, https://doi.org/10.3354/ame01655, 2013.
Geider, R. J.: Light and Temperature Dependence of the Carbon to Chlorophyll a Ratio in Microalgae and Cyanobacteria: Implications for Physiology and Growth of Phytoplankton, JSTOR, New Phytol., 106, 1–34, 1987.
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: Dynamic model of phytoplankton growth and acclimation: Responses of the balanced growth rate and the chlorophyll a: carbon ratio to light, nutrient-limitation and temperature, JSTOR, Mar. Ecol. Prog. Ser., 148, 187–200, 1997.
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: A dynamic regulatory model of phytoplanktonic acclimation to light, nutrients, and temperature, Limnol. Oceanogr., 43, 679–694, 1998.
Gilbert, J. C. and Lemaréchal, C.: Some numerical experiments with variable-storage quasi-Newton algorithms, Math. Program., 45, 407–435, 1989.
Glover, D. M., Jenkins, W. J., and Doney, S. C.: 10. Model analysis and optimization, in: Modeling Methods for Marine Science, Cambridge University Press, 2011.
Glover, D. M., Doney, S. C., Oestreich, W. K., and Tullo, A. W.: Geostatistical Analysis of Mesoscale Spatial Variability and Error in SeaWiFS and MODIS/Aqua Global Ocean Color Data, J. Geophys. Res.-Oceans, 123, 22–39, https://doi.org/10.1002/2017JC013023, 2018.
Harmon, R. and Challenor, P.: A Markov chain Monte Carlo method for estimation and assimilation into models, Ecol. Model., 101, 41–59, https://doi.org/10.1016/S0304-3800(97)01947-9, 1997.
Henley, S. F., Schofield, O. M., Hendry, K. R., Schloss, I. R., Steinberg, D. K., Moffat, C., Peck, L. S., Costa, D. P., Bakker, D. C. E., Hughes, C., Rozema, P. D., Ducklow, H. W., Abele, D., Stefels, J., Van Leeuwe, M. A., Brussaard, C. P. D., Buma, A. G. J., Kohut, J., Sahade, R., Friedlaender, A. S., Stammerjohn, S. E., Venables, H. J., and Meredith, M. P.: Variability and change in the west Antarctic Peninsula marine system: Research priorities and opportunities, Prog. Oceanogr., 173, 208–237, https://doi.org/10.1016/j.pocean.2019.03.003, 2019.
Inria at Sophia Antipolis: https://team.inria.fr/ecuador/en/tapenade/ last access: 2 August 2021.
Kim, H. and Ducklow, H. W.: A decadal (2002–2014) analysis for dynamics of heterotrophic bacteria in an Antarctic coastal ecosystem: Variability and physical and biogeochemical Forcings, Front. Mar. Sci., 3, 214, https://doi.org/10.3389/fmars.2016.00214, 2016.
Kim, H., Doney, S. C., Iannuzzi, R. A., Meredith, M. P., Martinson, D. G., and Ducklow, H. W.: Climate forcing for dynamics of dissolved inorganic nutrients at Palmer Station, Antarctica: An interdecadal (1993–2013) analysis, J. Geophys. Res.-Biogeo., 121, 2369–2389, 2016.
Kim, H. H., Luo, Y.-W., Ducklow, H. W., Schofield, O. M., Steinberg, D. K., and Doney, S. C.: WAP-1D-VAR v1.0: A One-Dimensional Variational Data Assimilation Model for the West Antarctic Peninsula (Version v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5041139, 2021.
King, J. C.: Recent climate variability in the vicinity of the antarctic peninsula, Int. J. Climatol., 14, 357–369, https://doi.org/10.1002/joc.3370140402, 1994.
Kirchman, D. L. (Ed.): Uptake and regeneration of inorganic nutrients by marine heterotrophic bacteria, in: Microbial ecology of the oceans, Wiley-Liss, New York, NY, 261–288, 2000.
Kirchman, D. L., Morán, X. A. G., and Ducklow, H.: Microbial growth in the polar oceans – Role of temperature and potential impact of climate change, Nat. Rev. Microbiol., 7, 451–459, 2009.
Kirk, J. T. O.: Light and photosynthesis in aquatic systems, Cambridge University Press, New York, NY, 1994.
Klinck, J. M.: Heat and salt changes on the continental shelf west of the Antarctic Peninsula between January 1993 and January 1994, J. Geophys. Res.-Oceans, 103, 7617–7636, https://doi.org/10.1029/98JC00369, 1998.
Lawson, L. M., Spitz, Y. H., Hofmann, E. E., and Long, R. B.: A data assimilation technique applied to a predator-prey model, B. Math. Biol., 57, 593–617, 1995.
Legendre, L. and Rassoulzadegan, F.: Food-web mediated export of biogenic carbon in oceans: hydrodynamic control, Mar. Ecol. Prog. Ser., 145, 179–193, https://doi.org/10.3354/meps145179, 1996.
Long, M. C., Lindsay, K., and Holland, M. M.: Modeling photosynthesis in sea ice‐covered waters, J. Adv. Model. Earth Sy., 7, 1189–1206, 2015.
Luo, Y.-W., Friedrichs, M. A. M., Doney, S. C., Church, M. J., and Ducklow, H. W.: Oceanic heterotrophic bacterial nutrition by semilabile DOM as revealed by data assimilative modeling, Aquat. Microb. Ecol., 60, 273–287, 2010.
Luria, C. M., Ducklow, H. W., and Amaral-Zettler, L. A.: Marine bacterial, archaeal and eukaryotic diversity and community structure on the continental shelf of the western Antarctic Peninsula, Aquat. Microb. Ecol., 73, 107–121, https://doi.org/10.3354/ame01703, 2014.
Luria, C. M., Amaral-Zettler, L. A., Ducklow, H. W., Repeta, D. J., Rhyne, A. L., and Rich, J. J.: Seasonal Shifts in Bacterial Community Responses to Phytoplankton-Derived Dissolved Organic Matter in the Western Antarctic Peninsula, Front. Microbiol., 8, 2117, https://doi.org/10.3389/fmicb.2017.02117, 2017.
Matear, R. J.: Parameter optimization and analysis of ecosystem models using simulated annealing: A case study at Station P, Oceanographic Literature Review, 43, 579, https://doi.org/10.1357/0022240953213098, 1996.
McCarthy, J.: Nitrogen, in: The physiological ecology of phytoplankton, edited by: Morris, I., Blackwell, Oxford, 191–234, 1980.
Meredith, M. P. and King, J. C.: Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century, Geophys. Res. Lett., 32, L19604, https://doi.org/10.1029/2005GL024042, 2005.
Moline, M., Karnovsky, N., Brown, Z., Divoky, G., Frazer, T., Jacoby, C., Torres, J., and Fraser, W.: High Latitude Changes in Ice Dynamics and Their Impact on Polar Marine Ecosystems, Ann. NY Acad. Sci., 1134, 267–319, https://doi.org/10.1196/annals.1439.010, 2008.
Montégut, C. de B., Madec, G., Fischer, A. S., Lazar, A., and Iudicone, D.: Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology, J. Geophys. Res.-Oceans, 109, C12003, https://doi.org/10.1029/2004JC002378, 2004.
Montes-Hugo, M., Doney, S. C., Ducklow, H. W., Fraser, W., Martinson, D., Stammerjohn, S. E., and Schofield, O.: Recent Changes in Phytoplankton Communities Associated with Rapid Regional Climate Change Along the Western Antarctic Peninsula, Science, 323, 1470–1473, https://doi.org/10.1126/science.1164533, 2009.
Murphy, E. J., Cavanagh, R. D., Hofmann, E. E., Hill, S. L., Constable, A. J., Costa, D. P., Pinkerton, M. H., Johnston, N. M., Trathan, P. N., Klinck, J. M., Wolf-Gladrow, D. A., Daly, K. L., Maury, O., and Doney, S. C.: Developing integrated models of Southern Ocean food webs: Including ecological complexity, accounting for uncertainty and the importance of scale, Prog. Oceanogr., 102, 74–92, https://doi.org/10.1016/j.pocean.2012.03.006, 2012.
NOAA-ESRL: https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surface.html, last access: 2 August 2021.
PAL-LTER, http://pal.lternet.edu/data, last access: 2 August 2021.
Pinker, R. T. and Laszlo, I.: Global distribution of photosynthetically active radiation as observed from satellites, J. Climate, 5, 56–65, 1992.
Pomeroy, L. R. and Wiebe, W. J.: Temperature and substrates as interactive limiting factors for marine heterotrophic bacteria, Aquat. Microb. Ecol., 23, 187–204, https://doi.org/10.3354/ame023187, 2001.
Prunet, P., Minster, J.-F., Echevin, V., and Dadou, I.: Assimilation of surface data in a one-dimensional physical-biogeochemical model of the surface ocean: 2. Adjusting a simple trophic model to chlorophyll, temperature, nitrate, and pCO2 data, Global Biogeochem. Cy., 10, 139–158, https://doi.org/10.1029/95GB03435, 1996a.
Prunet, P., Minster, J.-F., Ruiz-Pino, D., and Dadou, I.: Assimilation of surface data in a one-dimensional physical-biogeochemical model of the surface ocean: 1. Method and preliminary results, Global Biogeochem. Cy., 10, 111–138, https://doi.org/10.1029/95GB03436, 1996b.
Saba, G. K., Fraser, W. R., Saba, V. S., Iannuzzi, R. A., Coleman, K. E., Doney, S. C., Ducklow, H. W., Martinson, D. G., Miles, T. N., Patterson-Fraser, D. L., Stammerjohn, S. E., Steinberg, D. K., and Schofield, O. M.: Winter and spring controls on the summer food web of the coastal West Antarctic Peninsula, Nat. Commun., 5, 1–8, https://doi.org/10.1038/ncomms5318, 2014.
Sailley, S. F., Ducklow, H. W., Moeller, H. V., Fraser, W. R., Schofield, O. M., Steinberg, D. K., Garzio, L. M., and Doney, S. C.: Carbon fluxes and pelagic ecosystem dynamics near two western Antarctic Peninsula Adélie penguin colonies: An inverse model approach, Mar. Ecol. Prog. Ser., 492, 253–272, https://doi.org/10.3354/meps10534, 2013.
Schofield, O., Saba, G., Coleman, K., Carvalho, F., Couto, N., Ducklow, H., Finkel, Z., Irwin, A., Kahl, A., Miles, T., Montes-Hugo, M., Stammerjohn, S., and Waite, N.: Decadal variability in coastal phytoplankton community composition in a changing West Antarctic Peninsula, Deep-Sea Res. Pt. I, 124, 42–54, https://doi.org/10.1016/j.dsr.2017.04.014, 2017.
Sherrell, R. M., Annett, A. L., Fitzsimmons, J. N., Roccanova, V. J., and Meredith, M. P.: A “shallow bathtub ring” of local sedimentary iron input maintains the Palmer Deep biological hotspot on the West Antarctic Peninsula shelf, Philos. T. R. Soc. A, 376, 20170171, https://doi.org/10.1098/rsta.2017.0171, 2018.
Smith, D. A., Hofmann, E. E., Klinck, J. M., and Lascara, C. M.: Hydrography and circulation of the West Antarctic Peninsula Continental Shelf, Deep-Sea Res. Pt. I, 46, 925–949, https://doi.org/10.1016/S0967-0637(98)00103-4, 1999.
Smith, R. C., Martinson, D. G., Stammerjohn, S. E., Iannuzzi, R. A., and Ireson, K.: Bellingshausen and western Antarctic Peninsula region: Pigment biomass and sea-ice spatial/temporal distributions and interannual variabilty, Deep-Sea Res. Pt. II, 55, 1949–1963, https://doi.org/10.1016/j.dsr2.2008.04.027, 2008.
Spitz, Y. H., Moisan, J. R., and Abbott, M. R.: Configuring an ecosystem model using data from the Bermuda Atlantic Time Series (BATS), Deep-Sea Res. Pt. II, 48, 1733–1768, 2001.
Stammerjohn, S. E., Martinson, D. G., Smith, R. C., Yuan, X., and Rind, D.: Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and Southern Annular Mode variability, J. Geophys. Res.-Oceans, 113, C03S90, https://doi.org/10.1029/2007JC004269, 2008.
Steinberg, D. K., Ruck, K. E., Gleiber, M. R., Garzio, L. M., Cope, J. S., Bernard, K. S., Stammerjohn, S. E., Schofield, O. M. E., Quetin, L. B., and Ross, R. M.: Long-term (1993–2013) changes in macrozooplankton off the Western Antarctic Peninsula, Deep-Sea Res. Pt. I, 101, 54–70, https://doi.org/10.1016/j.dsr.2015.02.009, 2015.
Stow, C. A., Jolliff, J., McGillicuddy, D. J., Doney, S. C., Allen, J. I., Friedrichs, M. A. M., Rose, K. A., and Wallhead, P.: Skill assessment for coupled biological/physical models of marine systems, J. Marine Syst., 76, 4–15, https://doi.org/10.1016/j.jmarsys.2008.03.011, 2009.
Stukel, M. R., Asher, E., Couto, N., Schofield, O., Strebel, S., Tortell, P., and Ducklow, H. W.: The imbalance of new and export production in the western Antarctic Peninsula, a potentially “leaky” ecosystem, Global Biogeochem. Cy., 29, 1400–1420, https://doi.org/10.1002/2015GB005211, 2015.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, 2001.
Thibodeau, P. S., Steinberg, D. K., Stammerjohn, S. E., and Hauri, C.: Environmental controls on pteropod biogeography along the Western Antarctic Peninsula, Limnol. Oceanogr., 64, S240–S256, https://doi.org/10.1002/lno.11041, 2019.
Tziperman, E. and Thacker, W. C.: An Optimal-Control/Adjoint-Equations Approach to Studying the Oceanic General Circulation, J. Phys. Oceanogr., 19, 1471–1485, 1989.
Vaughan, D., Marshall, G., Connolley, W., Parkinson, C., Mulvaney, R., Hodgson, D., King, J., Pudsey, C., and Turner, J.: Recent Rapid Regional Climate Warming on the Antarctic Peninsula, Climatic Change, 60, 243–274, https://doi.org/10.1023/A:1026021217991, 2003.
Vaughan, D. G.: Recent Trends in Melting Conditions on the Antarctic Peninsula and Their Implications for Ice-sheet Mass Balance and Sea Level, Arct. Antarct. Alp. Res., 38, 147–152, https://doi.org/10.1657/1523-0430(2006)038[0147:RTIMCO]2.0.CO;2, 2006.
Ward, B. A., Friedrichs, M. A. M., Anderson, T. R., and Oschlies, A.: Parameter optimisation techniques and the problem of underdetermination in marine biogeochemical models, J. Marine Syst., 81, 34–43, 2010.
Weston, K., Jickells, T. D., Carson, D. S., Clarke, A., Meredith, M. P., Brandon, M. A., Wallace, M. I., Ussher, S. J., and Hendry, K. R.: Primary production export flux in Marguerite Bay (Antarctic Peninsula): Linking upper water-column production to sediment trap flux, Deep-Sea Res. Pt. I, 75, 52–66, https://doi.org/10.1016/j.dsr.2013.02.001, 2013.
Whitehouse, M. J., Meredith, M. P., Rothery, P., Atkinson, A., Ward, P., and Korb, R. E.: Rapid warming of the ocean around South Georgia, Southern Ocean, during the 20th century: Forcings, characteristics and implications for lower trophic levels, Deep-Sea Res. Pt. I, 55, 1218–1228, https://doi.org/10.1016/j.dsr.2008.06.002, 2008.
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.
The West Antarctic Peninsula (WAP) is a rapidly warming region, revealed by multi-decadal...