Articles | Volume 14, issue 8
Geosci. Model Dev., 14, 4939–4975, 2021
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.
Geosci. Model Dev., 14, 4939–4975, 2021
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.
Development and technical paper
12 Aug 2021
Development and technical paper
| 12 Aug 2021
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
Hyewon Heather Kim et al.
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Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-103, https://doi.org/10.5194/bg-2022-103, 2022
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Since 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 Eulerian (observer is stationary) framework. Here we use profiling floats and surface drifters combined with satellite data to analyze time and length scales of chlorophyll concentrations (a proxy for biomass) and of velocity to quantify how phytoplankton variability is related to water motion.
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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Viruses play key roles in marine ecosystems by killing their hosts, maintaining diversity and recycling nutrients. In the global viral oceanography database (gVOD), 10 931 viral abundance data and 727 viral production data, along with host and other oceanographic parameters, were compiled. It identified viral data were undersampled in the southeast Pacific and Indian oceans. The gVOD can be used in marine viral ecology investigation and modeling of marine ecosystems and biogeochemical cycles.
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The coastal ecosystem of the Gulf of Alaska (GOA) is especially vulnerable to the effects of ocean acidification and climate change. To improve our conceptual understanding of the system, we developed a new regional biogeochemical model setup for the GOA. Model output suggests that bottom water is seasonally high in CO2 between June and January. Such extensive periods of reoccurring high CO2 may be harmful to ocean acidification-sensitive organisms.
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. Todd, 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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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
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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
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)
SurEau-Ecos v2.0: A trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level
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
The PFLOTRAN Reaction Sandbox
A new approach to simulate peat accumulation, degradation and stability in a global land surface scheme (JULES vn5.8_accumulate_soil) for northern and temperate peatlands
Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)
CLM-FruitTree: A new sub-model for deciduous fruit trees in the Community Land Model (CLM5)
Soil Cycles of Elements simulator for Predicting TERrestrial regulation of greenhouse gases: SCEPTER v0.9
Locating trees to mitigate outdoor radiant load of humans in urban areas using a metaheuristic hill-climbing algorithm – introducing TreePlanter v1.0
Sensitivity of asymmetric oxygen minimum zones to mixing intensity and stoichiometry in the tropical Pacific using a basin-scale model (OGCM-DMEC V1.4)
The importance of turbulent ocean–sea ice nutrient exchanges for simulation of ice algal biomass and production with CICE6.1 and Icepack 1.2
Modeling symbiotic biological nitrogen fixation in grain legumes globally with LPJ-GUESS (v4.0, r10285)
Afforestation impact on soil temperature in regional climate model simulations over Europe
BioRT-Flux-PIHM v1.0: a biogeochemical reactive transport model at the watershed scale
The Impact of Hurricane Disturbances on a Tropical Forest: Implementing a Palm Plant Functional Type and Hurricane Disturbance Module in ED2-HuDi V1.0
Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model
Representation of phosphorus cycle in Joint UK Land Environment Simulator (vn5.5_JULES-CNP)
Explicit silicate cycling in the Kiel Marine Biogeochemistry Model version 3 (KMBM3) embedded in the UVic ESCM version 2.9
Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
OMEN-SED(-RCM) (v1.1): a pseudo-reactive continuum representation of organic matter degradation dynamics for OMEN-SED
Testing stomatal models at the stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1)
Comparing an exponential respiration model to alternative models for soil respiration components in a Canadian wildfire chronosequence (FireResp v1.0)
Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS (v4.0, r9710): implementation and evaluation of simulations for Europe
FABM-NflexPD 1.0: assessing an instantaneous acclimation approach for modeling phytoplankton growth
A model for marine sedimentary carbonate diagenesis and paleoclimate proxy signal tracking: IMP v1.0
Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for global land-surface models
Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
A validation standard for Area of Habitat maps for terrestrial birds and mammals
A model-independent data assimilation (MIDA) module and its applications in ecology
Optical model for the Baltic Sea with an explicit CDOM state variable: a case study with Model ERGOM (version 1.2)
Use of Genetic Algorithms for Ocean Model Parameter Optimisation
SCOPE 2.0: a model to simulate vegetated land surface fluxes and satellite signals
SolveSAPHE-r2 (v2.0.1): revisiting and extending the Solver Suite for Alkalinity-PH Equations for usage with CO2, HCO3− or CO32− input data
Modeling gas exchange and biomass production in West African Sahelian and Sudanian ecological zones
Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval® thermal analysis (PARTYSOCv2.0 and PARTYSOCv2.0EU)
Addressing biases in Arctic–boreal carbon cycling in the Community Land Model Version 5
Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance
Ecosystem age-class dynamics and distribution in the LPJ-wsl v2.0 global ecosystem model
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.
Julien Ruffault, François Pimont, Hervé Cochard, Jean-Luc Dupuy, and Nicolas K. Martin-StPaul
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-17, https://doi.org/10.5194/gmd-2022-17, 2022
Revised manuscript accepted for GMD
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Increasing drought conditions are a threat to forest ecosystems around the world. Here we present SurEau-Ecos v2.0, a plant hydraulic model designed to predict predict tree desiccation and mortality. This model will help to determine the most vulnerable areas and ecosystems to drying conditions, and asses the dynamics of forest flammability.
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.
Glenn E. Hammond
Geosci. Model Dev., 15, 1659–1676, https://doi.org/10.5194/gmd-15-1659-2022, https://doi.org/10.5194/gmd-15-1659-2022, 2022
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This paper describes a simplified interface for implementing and testing new chemical reactions within the reactive transport simulator PFLOTRAN. The paper describes the interface, providing example code for the interface. The paper includes several chemical reactions implemented through the interface.
Sarah E. Chadburn, Eleanor J. Burke, Angela V. Gallego-Sala, Noah D. Smith, M. Syndonia Bret-Harte, Dan J. Charman, Julia Drewer, Colin W. Edgar, Eugenie S. Euskirchen, Krzysztof Fortuniak, Yao Gao, Mahdi Nakhavali, Włodzimierz Pawlak, Edward A. G. Schuur, and Sebastian Westermann
Geosci. Model Dev., 15, 1633–1657, https://doi.org/10.5194/gmd-15-1633-2022, https://doi.org/10.5194/gmd-15-1633-2022, 2022
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We present a new method to include peatlands in an Earth system model (ESM). Peatlands store huge amounts of carbon that accumulates very slowly but that can be rapidly destabilised, emitting greenhouse gases. Our model captures the dynamic nature of peat by simulating the change in surface height and physical properties of the soil as carbon is added or decomposed. Thus, we model, for the first time in an ESM, peat dynamics and its threshold behaviours that can lead to destabilisation.
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
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The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-41, https://doi.org/10.5194/gmd-2022-41, 2022
Revised manuscript accepted for GMD
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Fruit orchards will influence soil carbon storage and food production under climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM-FruitTree that describes growth stages, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and the exchange of carbon, energy, and water of a fruit orchard and can be used to study land surface processes in fruit orchards at different scales.
Yoshiki Kanzaki, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-8, https://doi.org/10.5194/gmd-2022-8, 2022
Revised manuscript accepted for GMD
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Increasing carbon dioxide in the atmosphere is an urgent issue in coming century and enhanced rock weathering in soils can be one of most efficient C capture strategies. On the basis as a weathering simulator, the newly developed model SCEPTER implements bio-mixing by fauna and tilling by humans and enables organic matter and crushed rocks/minerals at soil surface with an option to track their particle size distributions. Those features can be useful to evaluate the carbon capture efficiency.
Nils Wallenberg, Fredrik Lindberg, and David Rayner
Geosci. Model Dev., 15, 1107–1128, https://doi.org/10.5194/gmd-15-1107-2022, https://doi.org/10.5194/gmd-15-1107-2022, 2022
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Exposure to solar radiation on clear and warm days can lead to heat stress and thermal discomfort. This can be alleviated by planting trees providing shade in particularly warm areas. Here, we use a model to locate trees and optimize their blocking of solar radiation. Our results show that locations can differ depending, e.g., tree size (juvenile or mature) and number of trees that are positioned simultaneously. The model is available as a tool for accessibility by researchers and others.
Kai Wang, Xiujun Wang, Raghu Murtugudde, Dongxiao Zhang, and Rong-Hua Zhang
Geosci. Model Dev., 15, 1017–1035, https://doi.org/10.5194/gmd-15-1017-2022, https://doi.org/10.5194/gmd-15-1017-2022, 2022
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We use observational data of dissolved oxygen (DO) and organic nitrogen to calibrate a basin-scale model (OGCM-DEMC V1.4) and then evaluate model capacity for simulating mid-depth DO in the tropical Pacific. Sensitivity studies show that enhanced vertical mixing combined with reduced biological consumption performs well in reproducing asymmetric oxygen minimum zones (OMZs). We find that DO is more sensitive to biological processes in the upper OMZs but to physical processes in the lower OMZs.
Pedro Duarte, Philipp Assmy, Karley Campbell, and Arild Sundfjord
Geosci. Model Dev., 15, 841–857, https://doi.org/10.5194/gmd-15-841-2022, https://doi.org/10.5194/gmd-15-841-2022, 2022
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Sea ice modeling is an important part of Earth system models (ESMs). The results of ESMs are used by the Intergovernmental Panel on Climate Change in their reports. In this study we present an improvement to calculate the exchange of nutrients between the ocean and the sea ice. This nutrient exchange is an essential process to keep the ice-associated ecosystem functioning. We found out that previous calculation methods may underestimate the primary production of the ice-associated ecosystem.
Jianyong Ma, Stefan Olin, Peter Anthoni, Sam S. Rabin, Anita D. Bayer, Sylvia S. Nyawira, and Almut Arneth
Geosci. Model Dev., 15, 815–839, https://doi.org/10.5194/gmd-15-815-2022, https://doi.org/10.5194/gmd-15-815-2022, 2022
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The implementation of the biological N fixation process in LPJ-GUESS in this study provides an opportunity to quantify N fixation rates between legumes and to better estimate grain legume production on a global scale. It also helps to predict and detect the potential contribution of N-fixing plants as
green manureto reducing or removing the use of N fertilizer in global agricultural systems, considering different climate conditions, management practices, and land-use change scenarios.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
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Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Wei Zhi, Yuning Shi, Hang Wen, Leila Saberi, Gene-Hua Crystal Ng, Kayalvizhi Sadayappan, Devon Kerins, Bryn Stewart, and Li Li
Geosci. Model Dev., 15, 315–333, https://doi.org/10.5194/gmd-15-315-2022, https://doi.org/10.5194/gmd-15-315-2022, 2022
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Watersheds are the fundamental Earth surface functioning unit that connects the land to aquatic systems. Here we present the recently developed BioRT-Flux-PIHM v1.0, a watershed-scale biogeochemical reactive transport model, to improve our ability to understand and predict solute export and water quality. The model has been verified against the benchmark code CrunchTope and has recently been applied to understand reactive transport processes in multiple watersheds of different conditions.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-410, https://doi.org/10.5194/gmd-2021-410, 2022
Revised manuscript accepted for GMD
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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 a 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.
Huilin Huang, Yongkang Xue, Ye Liu, Fang Li, and Gregory S. Okin
Geosci. Model Dev., 14, 7639–7657, https://doi.org/10.5194/gmd-14-7639-2021, https://doi.org/10.5194/gmd-14-7639-2021, 2021
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This study applies a fire-coupled dynamic vegetation model to quantify fire impact at monthly to annual scales. We find fire reduces grass cover by 4–8 % annually for widespread areas in south African savanna and reduces tree cover by 1 % at the periphery of tropical Congolese rainforest. The grass cover reduction peaks at the beginning of the rainy season, which quickly diminishes before the next fire season. In contrast, the reduction of tree cover is irreversible within one growing season.
Mahdi 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. Discuss., https://doi.org/10.5194/gmd-2021-403, https://doi.org/10.5194/gmd-2021-403, 2021
Revised manuscript accepted for GMD
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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 response in areas such as Amazonia to rising atmospheric CO2 conditions remains 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.
Karin Kvale, David P. Keller, Wolfgang Koeve, Katrin J. Meissner, Christopher J. Somes, Wanxuan Yao, and Andreas Oschlies
Geosci. Model Dev., 14, 7255–7285, https://doi.org/10.5194/gmd-14-7255-2021, https://doi.org/10.5194/gmd-14-7255-2021, 2021
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We present a new model of biological marine silicate cycling for the University of Victoria Earth System Climate Model (UVic ESCM). This new model adds diatoms, which are a key aspect of the biological carbon pump, to an existing ecosystem model. Our modifications change how the model responds to warming, with net primary production declining more strongly than in previous versions. Diatoms in particular are simulated to decline with climate warming due to their high nutrient requirements.
Shannon de Roos, Gabriëlle J. M. De Lannoy, and Dirk Raes
Geosci. Model Dev., 14, 7309–7328, https://doi.org/10.5194/gmd-14-7309-2021, https://doi.org/10.5194/gmd-14-7309-2021, 2021
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A spatially distributed version of the field-scale crop model AquaCrop v6.1 was developed for applications at various spatial scales. Multi-year 1 km simulations over central Europe were evaluated against biomass and surface soil moisture products derived from optical and microwave satellite missions, as well as in situ observations of soil moisture. The regional version of the AquaCrop model provides a suitable setup for subsequent satellite-based data assimilation.
Philip Pika, Dominik Hülse, and Sandra Arndt
Geosci. Model Dev., 14, 7155–7174, https://doi.org/10.5194/gmd-14-7155-2021, https://doi.org/10.5194/gmd-14-7155-2021, 2021
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OMEN-SED is a model for early diagenesis in marine sediments simulating organic matter (OM) degradation and nutrient dynamics. We replaced the original description with a more realistic one accounting for the widely observed decrease in OM reactivity. The new model reproduces pore water profiles and sediment–water interface fluxes across different environments. This functionality extends the model’s applicability to a broad range of environments and timescales while requiring fewer parameters.
Yujie Wang, Philipp Köhler, Liyin He, Russell Doughty, Renato K. Braghiere, Jeffrey D. Wood, and Christian Frankenberg
Geosci. Model Dev., 14, 6741–6763, https://doi.org/10.5194/gmd-14-6741-2021, https://doi.org/10.5194/gmd-14-6741-2021, 2021
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We present the first step in testing a new land model as part of a new Earth system model. Our model links plant hydraulics, stomatal optimization theory, and a comprehensive canopy radiation scheme. We compared model-predicted carbon and water fluxes to flux tower observations and model-predicted sun-induced chlorophyll fluorescence to satellite retrievals. Our model quantitatively predicted the carbon and water fluxes as well as the canopy fluorescence yield.
John Zobitz, Heidi Aaltonen, Xuan Zhou, Frank Berninger, Jukka Pumpanen, and Kajar Köster
Geosci. Model Dev., 14, 6605–6622, https://doi.org/10.5194/gmd-14-6605-2021, https://doi.org/10.5194/gmd-14-6605-2021, 2021
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Forest fires heavily affect carbon stocks and fluxes of carbon in high-latitude forests. Long-term trends in soil respiration following forest fires are associated with recovery of aboveground biomass. We evaluated models for soil autotrophic and heterotrophic respiration with data from a chronosequence of stand-replacing forest fires in northern Canada. The best model that reproduced expected patterns in soil respiration components takes into account soil microbe carbon as a model variable.
Mats Lindeskog, Benjamin Smith, Fredrik Lagergren, Ekaterina Sycheva, Andrej Ficko, Hans Pretzsch, and Anja Rammig
Geosci. Model Dev., 14, 6071–6112, https://doi.org/10.5194/gmd-14-6071-2021, https://doi.org/10.5194/gmd-14-6071-2021, 2021
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Forests play an important role in the global carbon cycle and for carbon storage. In Europe, forests are intensively managed. To understand how management influences carbon storage in European forests, we implement detailed forest management into the dynamic vegetation model LPJ-GUESS. We test the model by comparing model output to typical forestry measures, such as growing stock and harvest data, for different countries in Europe.
Onur Kerimoglu, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 14, 6025–6047, https://doi.org/10.5194/gmd-14-6025-2021, https://doi.org/10.5194/gmd-14-6025-2021, 2021
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In large-scale models, variations in cellular composition of phytoplankton are often insufficiently represented. Detailed modeling approaches exist, but they require additional state variables that increase the computational costs. In this study, we test an instantaneous acclimation model in a spatially explicit setup and show that its behavior is mostly similar to that of a variant with an additional state variable but different from that of a fixed composition variant.
Yoshiki Kanzaki, Dominik Hülse, Sandra Kirtland Turner, and Andy Ridgwell
Geosci. Model Dev., 14, 5999–6023, https://doi.org/10.5194/gmd-14-5999-2021, https://doi.org/10.5194/gmd-14-5999-2021, 2021
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Sedimentary carbonate plays a central role in regulating Earth’s carbon cycle and climate, and also serves as an archive of paleoenvironments, hosting various trace elements/isotopes. To help obtain
trueenvironmental changes from carbonate records over diagenetic distortion, IMP has been newly developed and has the capability to simulate the diagenesis of multiple carbonate particles and implement different styles of particle mixing by benthos using an adapted transition matrix method.
Jina Jeong, Jonathan Barichivich, Philippe Peylin, Vanessa Haverd, Matthew Joseph McGrath, Nicolas Vuichard, Michael Neil Evans, Flurin Babst, and Sebastiaan Luyssaert
Geosci. Model Dev., 14, 5891–5913, https://doi.org/10.5194/gmd-14-5891-2021, https://doi.org/10.5194/gmd-14-5891-2021, 2021
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We have proposed and evaluated the use of four benchmarks that leverage tree-ring width observations to provide more nuanced verification targets for land-surface models (LSMs), which currently lack a long-term benchmark for forest ecosystem functioning. Using relatively unbiased European biomass network datasets, we identify the extent to which presumed biases in the much larger International Tree-Ring Data Bank might degrade the validation of LSMs.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-287, https://doi.org/10.5194/gmd-2021-287, 2021
Revised manuscript accepted for GMD
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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 analyze these across European forests. We find that uncertainties are dominantly induced by parameters related to water and mortality and climate with 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.
Prabhat Raj Dahal, Maria Lumbierres, Stuart H. M. Butchart, Paul F. Donald, and Carlo Rondinini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-245, https://doi.org/10.5194/gmd-2021-245, 2021
Revised manuscript accepted for GMD
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This paper describes the validation of Area of Habitat (AOH) maps produced for terrestrial birds and mammals. The main objective of the validation was to asses the accuracy of these maps based on independent data. We used the open access data from repositories like 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. We found majority of AOH maps have high accuracy.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
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In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Thomas Neumann, Sampsa Koponen, Jenni Attila, Carsten Brockmann, Kari Kallio, Mikko Kervinen, Constant Mazeran, Dagmar Müller, Petra Philipson, Susanne Thulin, Sakari Väkevä, and Pasi Ylöstalo
Geosci. Model Dev., 14, 5049–5062, https://doi.org/10.5194/gmd-14-5049-2021, https://doi.org/10.5194/gmd-14-5049-2021, 2021
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The Baltic Sea is heavily impacted by surrounding land. Therefore, the concentration of colored dissolved organic matter (CDOM) of terrestrial origin is relatively high and impacts the light penetration depth. Estimating a correct light climate is essential for ecosystem models. In this study, a method is developed to derive riverine CDOM from Earth observation methods. The data are used as boundary conditions for an ecosystem model, and the advantage over former approaches is shown.
Marcus Falls, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-222, https://doi.org/10.5194/gmd-2021-222, 2021
Revised manuscript accepted for GMD
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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 optimized model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to reduction of model uncertainties.
Peiqi Yang, Egor Prikaziuk, Wout Verhoef, and Christiaan van der Tol
Geosci. Model Dev., 14, 4697–4712, https://doi.org/10.5194/gmd-14-4697-2021, https://doi.org/10.5194/gmd-14-4697-2021, 2021
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Since the first publication 12 years ago, the SCOPE model has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary productivity (GPP), and directional thermal signals. Here, we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements.
Guy Munhoven
Geosci. Model Dev., 14, 4225–4240, https://doi.org/10.5194/gmd-14-4225-2021, https://doi.org/10.5194/gmd-14-4225-2021, 2021
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SolveSAPHE (Munhoven, 2013) was the first package to calculate pH reliably from any physically sensible pair of total alkalinity (AlkT) and dissolved inorganic carbon (CT) data and to do so in an autonomous and efficient way. Here, we extend it to use CO2, HCO3 or CO3 instead of CT. For each one of these pairs, the new SolveSAPHE calculates all of the possible pH values (0, 1, or 2), again without any prior knowledge of the solutions.
Jaber Rahimi, Expedit Evariste Ago, Augustine Ayantunde, Sina Berger, Jan Bogaert, Klaus Butterbach-Bahl, Bernard Cappelaere, Jean-Martial Cohard, Jérôme Demarty, Abdoul Aziz Diouf, Ulrike Falk, Edwin Haas, Pierre Hiernaux, David Kraus, Olivier Roupsard, Clemens Scheer, Amit Kumar Srivastava, Torbern Tagesson, and Rüdiger Grote
Geosci. Model Dev., 14, 3789–3812, https://doi.org/10.5194/gmd-14-3789-2021, https://doi.org/10.5194/gmd-14-3789-2021, 2021
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West African Sahelian and Sudanian ecosystems are important regions for global carbon exchange, and they provide valuable food and fodder resources. Therefore, we simulated net ecosystem exchange and aboveground biomass of typical ecosystems in this region with an improved process-based biogeochemical model, LandscapeDNDC. Carbon stocks and exchange rates were particularly correlated with the abundance of trees. Grass and crop yields increased under humid climatic conditions.
Lauric Cécillon, François Baudin, Claire Chenu, Bent T. Christensen, Uwe Franko, Sabine Houot, Eva Kanari, Thomas Kätterer, Ines Merbach, Folkert van Oort, Christopher Poeplau, Juan Carlos Quezada, Florence Savignac, Laure N. Soucémarianadin, and Pierre Barré
Geosci. Model Dev., 14, 3879–3898, https://doi.org/10.5194/gmd-14-3879-2021, https://doi.org/10.5194/gmd-14-3879-2021, 2021
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Partitioning soil organic carbon (SOC) into fractions that are stable or active on a century scale is key for more accurate models of the carbon cycle. Here, we describe the second version of a machine-learning model, named PARTYsoc, which reliably predicts the proportion of the centennially stable SOC fraction at its northwestern European validation sites with Cambisols and Luvisols, the two dominant soil groups in this region, fostering modelling works of SOC dynamics.
Leah Birch, Christopher R. Schwalm, Sue Natali, Danica Lombardozzi, Gretchen Keppel-Aleks, Jennifer Watts, Xin Lin, Donatella Zona, Walter Oechel, Torsten Sachs, Thomas Andrew Black, and Brendan M. Rogers
Geosci. Model Dev., 14, 3361–3382, https://doi.org/10.5194/gmd-14-3361-2021, https://doi.org/10.5194/gmd-14-3361-2021, 2021
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The high-latitude landscape or Arctic–boreal zone has been warming rapidly, impacting the carbon balance both regionally and globally. Given the possible global effects of climate change, it is important to have accurate climate model simulations. We assess the simulation of the Arctic–boreal carbon cycle in the Community Land Model (CLM 5.0). We find biases in both the timing and magnitude photosynthesis. We then use observational data to improve the simulation of the carbon cycle.
Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin
Geosci. Model Dev., 14, 2603–2633, https://doi.org/10.5194/gmd-14-2603-2021, https://doi.org/10.5194/gmd-14-2603-2021, 2021
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Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare models against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeastern US.
Leonardo Calle and Benjamin Poulter
Geosci. Model Dev., 14, 2575–2601, https://doi.org/10.5194/gmd-14-2575-2021, https://doi.org/10.5194/gmd-14-2575-2021, 2021
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We developed a model to simulate and track the age of ecosystems on Earth. We found that the effect of ecosystem age on net primary production and ecosystem respiration is as important as climate in large areas of every vegetated continent. The LPJ-wsl v2.0 age-class model simulates dynamic age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.
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...
