Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-4663-2020
© Author(s) 2020. 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-13-4663-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
02 Oct 2020
Model description paper |
| 02 Oct 2020
| 02 Oct 2020
Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 1: Implementation and model behaviour
Markus Pahlow
CORRESPONDING AUTHOR
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Chia-Te Chien
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Lionel A. Arteaga
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
present address: NASA Global Modeling and Assimilation Office, Universities Space Research Association, Columbia, MD, USA
Andreas Oschlies
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
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In this paper, we evaluate the newest version of the University of Victoria Earth System Climate Model (UVic ESCM 2.10). Combining recent model developments as a joint effort, this version is to be used in the next phase of model intercomparison and climate change studies. The UVic ESCM 2.10 is capable of reproducing changes in historical temperature and carbon fluxes well. Additionally, the model is able to reproduce the three-dimensional distribution of many ocean tracers.
Cited articles
Ågren, G. I.: The C : N : P stoichiometry of autotrophs – theory and
observations, Ecol. Lett., 7, 185–191,
https://doi.org/10.1111/j.1461-0248.2004.00567.x, 2004. a
Anderson, L. A. and Sarmiento, J. L.: Redfield ratios of remineralization
determined by nutrient data analysis, Global Biogeochem. Cycles, 8, 65–80,
https://doi.org/10.1029/93GB03318, 1994. a
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020. a
Arteaga, L., Pahlow, M., and Oschlies, A.: Global patterns of phytoplankton
nutrient and light colimitation inferred from an optimality-based model,
Global Biogeochem. Cycles, 28, 648–661, https://doi.org/10.1002/2013GB004668, 2014. a
Arteaga, L., Pahlow, M., and Oschlies, A.: Modelled Chl:C ratio and derived
estimates of phytoplankton carbon biomass and its contribution to total
particulate organic carbon in the global surface ocean, Global Biogeochem.
Cycles, 30, 1791–1810, https://doi.org/10.1002/2016GB005458, 2016. a, b
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from
satellite-based chlorophyll concentration, Limnol. Oceanogr., 42, 1–20,
https://doi.org/10.4319/lo.1997.42.1.0001, 1997. a
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013. a
Carr, M.-E., Friedrichs, M. A., Schmeltz, M., Noguchi Aita, M., Antoine, D., Arrigo, K. R., Asanuma, I., Aumont, O., Barber, R., Behrenfeld, M., Bidigare, R., Buitenhuis, E. T., Campbell, J., Ciotti, A., Dierssen, H., Dowell, M., Dunne, J., Esaias, W., Gentili, B., Gregg, W., Groom, S., Hoepffner, N., Ishizaka, J., Kameda, T., Le Quéré, C., Lohrenz, S., Marra, J., Mélin, F., Moore, K., Morel, A., Reddy, T. E., Ryan, J., Scardi, M., Smyth, T., Turpie, K., Tilstone, G., Waters, K., and Yamanaka, Y.: A
comparison of global estimates of marine primary production from ocean color,
Deep-Sea Res. Pt. II, 53, 741–770, https://doi.org/10.1016/j.dsr2.2006.01.028, 2006. a
Chen, B. and Smith, S. L.: Optimality-based approach for computationally
efficient modeling of phytoplankton growth, chlorophyll-to-carbon, and
nitrogen-to-carbon ratios, Ecol. Model., 385, 197–212,
https://doi.org/10.1016/j.ecolmodel.2018.08.001, 2018. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv.
Model. Earth Syst., 12, e2019MS001916, https://doi.org/10.1029/2019ms001916, 2020. a
Deutsch, C., Sarmiento, J. L., Sigman, D. M., Gruber, N., and Dunne, J. P.:
Spatial coupling of nitrogen inputs and losses in the ocean, Nature, 445,
163–167, https://doi.org/10.1038/nature05392, 2007. a
DeVries, T. and Weber, T.: The export and fate of organic matter in the ocean:
New constraints from combining satellite and oceanographic tracer
observations, Global Biogeochem. Cycles, 31, 535–555,
https://doi.org/10.1002/2016GB005551, 2017. a
DeVries, T., Deutsch, C., Primeau, F., Chang, B., and Devol, A.: Global rates
of water-column denitrification derived from nitrogen gas measurements,
Nature Geosci., 5, 547–550, https://doi.org/10.1038/ngeo1515, 2012. a
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. a
Dunne, J. P., Armstrong, R. A., Gnanadesikan, A., and Sarmiento, J. L.:
Empirical and mechanistic models for the particle export ratio, Global
Biogeochem. Cycles, 19, GB4026, https://doi.org/10.1029/2004GB002390, 2005. a
Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E., Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J., Krasting, J. P., Malyshev, S. L., Milly, P. C. D., Phillipps, P. J., Sentman, L. T., Samuels, B. L., Spelman, M. J., Winton, M., Wittenberg, A. T., and Zadeh, N.: GFDL's ESM2 Global Coupled Climate–Carbon Earth System Models.
Part I: Physical Formulation and Baseline Simulation Characteristics, J.
Climate, 25, 6646–6665, https://doi.org/10.1175/jcli-d-11-00560.1, 2012. a
Eby, M., Zickfeld, K., Montenegro, A., Archer, D., Meissner, K. J., and Weaver,
A. J.: Lifetime of Anthropogenic Climate Change: Millennial Time Scales of
Potential CO2 and Surface Temperature Perturbations, J. Climate, 22,
2501–2511, https://doi.org/10.1175/2008JCLI2554.1, 2009. a, b
Eby, M., Weaver, A. J., Alexander, K., Zickfeld, K., Abe-Ouchi, A., Cimatoribus, A. A., Crespin, E., Drijfhout, S. S., Edwards, N. R., Eliseev, A. V., Feulner, G., Fichefet, T., Forest, C. E., Goosse, H., Holden, P. B., Joos, F., Kawamiya, M., Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov, I. I., Monier, E., Olsen, S. M., Pedersen, J. O. P., Perrette, M., Philippon-Berthier, G., Ridgwell, A., Schlosser, A., Schneider von Deimling, T., Shaffer, G., Smith, R. S., Spahni, R., Sokolov, A. P., Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., Zeng, N., and Zhao, F.: Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity, Clim. Past, 9, 1111–1140, https://doi.org/10.5194/cp-9-1111-2013, 2013. a
Fernández-Castro, B., Pahlow, M., Mouriño-Carballido, B.,
Marañón, E., and Oschlies, A.: Optimality-based Trichodesmium
Diazotrophy in the North Atlantic Subtropical Gyre, J. Plankton Res., 38,
946–963, https://doi.org/10.1093/plankt/fbw047, 2016. a, b, c, d
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Mishonov, A. V.,
Baranova, O. K., Zweng, M. M., Reagan, J. R., and Johnson, D. R.: Dissolved
Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation, in: World Ocean
Atlas 2013, edited by: Levitus, S., vol. 3, NOAA Atlas NESDIS 75, available at:
http://www.nodc.noaa.gov/OC5/indprod.html (last access: 1 August 2018), 2013a. a, b
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Mishonov, A. V.,
Baranova, O. K., Zweng, M. M., Reagan, J. R., and Johnson, D. R.: Dissolved
Inorganic Nutrients (phosphate, nitrate, silicate), in: World Ocean Atlas
2013, edited by: Levitus, S., vol. 4, NOAA Atlas NESDIS 76, available at:
http://www.nodc.noaa.gov/OC5/indprod.html (last access: 1 August 2018), 2013b. a, b
Getzlaff, J. and Dietze, H.: Effects of increased isopycnal diffusivity
mimicking the unresolved equatorial intermediate current system in an earth
system climate model, Geophys. Res. Lett., 40, 2166–2170,
https://doi.org/10.1002/grl.50419, 2013. a, b
Gismervik, I.: Numerical and functional responses of choreo- and oligotrich
planktonic ciliates, Aquat. Microb. Ecol., 40, 163–173,
https://doi.org/10.3354/ame040163, 2005. a
Gruber, N.: The dynamics of the marine nitrogen cycle and its influence on
atmospheric CO2 variations, in: The Ocean Carbon Cycle and Climate,
edited by: Follows, M. and Oguz, T., pp. 97–148, Kluwer, Dordrecht, 2004. a
Gruber, N. and Sarmiento, J. L.: Global Patterns of marine nitrogen fixation
and denitrification, Global Biogeochem. Cycles, 11, 235–266,
https://doi.org/10.1029/97GB00077, 1997. a
Holling, C. S. and Buckingham, S.: A behavioral model of predator-prey
functional responses, Behav. Sci., 21, 183–195, https://doi.org/10.1002/bs.3830210305,
1976. a
Houlton, B. Z., Wang, Y.-P., Vitousek, P. M., and Field, C. B.: A unifying
framework for dinitrogen fixation in the terrestrial biosphere, Nature, 454,
327–330, https://doi.org/10.1038/nature07028, 2008. a, b, c, d
Hu, C., Lee, Z., and Franz, B.: Chlorophyll a algorithms for oligotrophic
oceans: A novel approach based on three-band reflectance difference, J.
Geophys. Res.-Oceans, 117, C01011, https://doi.org/10.1029/2011jc007395, 2012. a, b
Hülse, D., Arndt, S., Wilson, J. D., Munhoven, G., and Ridgwell, A.:
Understanding the causes and consequences of past marine carbon cycling
variability through models, Earth Sci. Rev., 171, 349–382,
https://doi.org/10.1016/j.earscirev.2017.06.004, 2017. a
Keller, D. P., Lenton, A., Scott, V., Vaughan, N. E., Bauer, N., Ji, D., Jones, C. D., Kravitz, B., Muri, H., and Zickfeld, K.: The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP): rationale and experimental protocol for CMIP6, Geosci. Model Dev., 11, 1133–1160, https://doi.org/10.5194/gmd-11-1133-2018, 2018. a
Key, R., Olsen, A., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X.,
Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S.,
Steinfeldt, R., Jeansson, E., Ishi, M., Perez, F. F., and Suzuki, T.: Global
Ocean Data Analysis Project, Version 2 (GLODAPv2), ORNL/CDIAC-162, NDP-P093,
Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US
Department of Energy, https://doi.org/10.3334/CDIAC/OTG.NDP093_GLODAPv2, 2015. a, b
Kiørboe, T., Møhlenberg, F., and Hamburger, K.: Bioenergetics of the
planktonic copepod Acartia tonsa: relation between feeding, egg
production and respiration, and composition of specific dynamic action, Mar.
Ecol. Prog. Ser., 26, 85–97, https://doi.org/10.3354/meps026085, 1985. a
Klausmeier, C. A., Litchman, E., Daufresne, T., and Levin, S. A.: Phytoplankton
stoichiometry, Ecol. Res., 23, 479–485, https://doi.org/10.1007/s11284-008-0470-8,
2008. a
Kriest, I.: Calibration of a simple and a complex model of global marine biogeochemistry, Biogeosciences, 14, 4965–4984, https://doi.org/10.5194/bg-14-4965-2017, 2017. a, b
Kriest, I. and Oschlies, A.: MOPS-1.0: towards a model for the regulation of the global oceanic nitrogen budget by marine biogeochemical processes, Geosci. Model Dev., 8, 2929–2957, https://doi.org/10.5194/gmd-8-2929-2015, 2015. a
Krishna, S., Pahlow, M., and Schartau, M.: Comparison of two carbon-nitrogen
regulatory models calibrated with mesocosm data, Ecol. Model., 411, 108711,
https://doi.org/10.1016/j.ecolmodel.2019.05.016, 2019. a
Kvale, K. F., Khatiwala, S., Dietze, H., Kriest, I., and Oschlies, A.: Evaluation of the transport matrix method for simulation of ocean biogeochemical tracers, Geosci. Model Dev., 10, 2425–2445, https://doi.org/10.5194/gmd-10-2425-2017, 2017. a, b
Kwiatkowski, L., Aumont, O., Bopp, L., and Ciais, P.: The Impact of Variable
Phytoplankton Stoichiometry on Projections of Primary Production, Food
Quality, and Carbon Uptake in the Global Ocean, Global Biogeochem. Cycles,
32, 516–528, https://doi.org/10.1002/2017gb005799, 2018. a
Landolfi, A., Dietze, H., Koeve, W., and Oschlies, A.: Overlooked runaway feedback in the marine nitrogen cycle: the vicious cycle, Biogeosciences, 10, 1351–1363, https://doi.org/10.5194/bg-10-1351-2013, 2013. a
Landolfi, A., Kaehler, P., Koeve, W., and Oschlies, A.: Global Marine N2
Fixation Estimates: From Observations to Models, Front. Microbiol., 9, 2112,
https://doi.org/10.3389/fmicb.2018.02112, 2018. a
Laufkötter, C., Vogt, M., Gruber, N., Aita-Noguchi, M., Aumont, O., Bopp, L., Buitenhuis, E., Doney, S. C., Dunne, J., Hashioka, T., Hauck, J., Hirata, T., John, J., Le Quéré, C., Lima, I. D., Nakano, H., Seferian, R., Totterdell, I., Vichi, M., and Völker, C.: Drivers and uncertainties of future global marine primary production in marine ecosystem models, Biogeosciences, 12, 6955–6984, https://doi.org/10.5194/bg-12-6955-2015, 2015. a
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the 1∘ × 1∘ GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, https://doi.org/10.5194/essd-8-325-2016, 2016. a, b
Laws, E. A., Falkowski, P. G., Walker O. Smith, J., Ducklow, H., and McCarthy,
J. J.: Temperature effects on export production in the open ocean, Global
Biogeochem. Cycles, 14, 1231–1246, https://doi.org/10.1029/1999GB001229, 2000. a
Lessard, E. J. and Murrell, M. C.: Microzooplankton herbivory and phytoplankton
growth in the northwestern Sargasso Sea, Aquat. Microb. Ecol., 16,
173–188, https://doi.org/10.3354/ame016173, 1998. a
Letscher, R. T. and Moore, J. K.: Preferential remineralization of dissolved
organic phosphorus and non-Redfield DOM dynamics in the global ocean:
Impacts on marine productivity, nitrogen fixation, and carbon export, Global
Biogeochem. Cycles, 29, 2014GB004904, https://doi.org/10.1002/2014GB004904, 2015. a
Löptien, U. and Dietze, H.: Effects of parameter indeterminacy in pelagic
biogeochemical modules of Earth System Models on projections into a warming
future: the scale of the problem, Global Biogeochem. Cycles, 31, 1155–1172,
https://doi.org/10.1002/2017GB005690, 2017. a
Löscher, C. R., Mohr, W., Bange, H. W., and Canfield, D. E.: No nitrogen fixation in the Bay of Bengal?, Biogeosciences, 17, 851–864, https://doi.org/10.5194/bg-17-851-2020, 2020. a
Luo, Y.-W., Doney, S. C., Anderson, L. A., Benavides, M., Berman-Frank, I., Bode, A., Bonnet, S., Boström, K. H., Böttjer, D., Capone, D. G., Carpenter, E. J., Chen, Y. L., Church, M. J., Dore, J. E., Falcón, L. I., Fernández, A., Foster, R. A., Furuya, K., Gómez, F., Gundersen, K., Hynes, A. M., Karl, D. M., Kitajima, S., Langlois, R. J., LaRoche, J., Letelier, R. M., Marañón, E., McGillicuddy Jr., D. J., Moisander, P. H., Moore, C. M., Mouriño-Carballido, B., Mulholland, M. R., Needoba, J. A., Orcutt, K. M., Poulton, A. J., Rahav, E., Raimbault, P., Rees, A. P., Riemann, L., Shiozaki, T., Subramaniam, A., Tyrrell, T., Turk-Kubo, K. A., Varela, M., Villareal, T. A., Webb, E. A., White, A. E., Wu, J., and Zehr, J. P.: Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates, Earth Syst. Sci. Data, 4, 47–73, https://doi.org/10.5194/essd-4-47-2012, 2012. a, b, c, d, e, f
Martiny, A. C., Vrugt, J. A., and Lomas, M. W.: Concentrations and ratios of
particulate organic carbon, nitrogen, and phosphorus in the global ocean,
Sci. Data, 1, 140048, https://doi.org/10.1038/sdata.2014.48, 2014. a, b, c
Matrai, P. A. and Keller, M. D.: Total organic sulfur and
dimethylsulfoniopropionate in marine phytoplankton: intracellular variations,
Mar. Biol., 119, 61–68, https://doi.org/10.1007/BF00350107, 1994. a
Millero, F. J., Fiol, S., Campbell, D. M., and Parilla, G.: Carbon dioxide,
hydrographic, and chemical data obtained during the R/V Hespérides
cruise in the Atlantic Ocean (WOCE section A5, July 14–August
15, 1992), Carbon Dioxide Information Analysis Center, Oak Ridge National
Laboratory, U.S. Department of Energy, https://doi.org/10.3334/CDIAC/otg.ndp074, 2000. a, b
Mills, M. M., Brown, Z. W., Lowry, K. E., van Dijken, G. L., Becker, S., Pal,
S., Benitez-Nelson, C. R., Downer, M. M., Strong, A. L., Swift, J. H.,
Pickart, R. S., and Arrigo, K. R.: Impacts of low phytoplankton
Monteiro, F. M. and Follows, M. J.: On nitrogen fixation and preferential
remineralization of phosphorus, Geophys. Res. Lett., 39, L06607,
https://doi.org/10.1029/2012GL050897, 2012. a
Monteiro, F. M., Dutkiewicz, S., and Follows, M. J.: Biogeographical controls
on the marine nitrogen fixers, Global Biogeochem. Cycles, 25, GB2003,
https://doi.org/10.1029/2010GB003902, 2011. a
Mulholland, M. R., Bernhardt, P. W., Widner, B. N., Selden, C. R., Chappell,
P. D., Clayton, S., Mannino, A., and Hyde, K.: High Rates of N2 Fixation
in Temperate, Western North Atlantic Coastal Waters Expand the Realm of
Marine Diazotrophy, Global Biogeochem. Cycles, 33, 826–840,
https://doi.org/10.1029/2018gb006130, 2019. a, b
Niemeyer, D., Kemena, T. P., Meissner, K. J., and Oschlies, A.: A model study of warming-induced phosphorus–oxygen feedbacks in open-ocean oxygen minimum zones on millennial timescales, Earth Syst. Dynam., 8, 357–367, https://doi.org/10.5194/esd-8-357-2017, 2017. a, b
Norberg, J.: Biodiversity and ecosystem functioning: A complex adaptive systems
approach, Limnol. Oceanogr., 49, 1269–1277,
https://doi.org/10.4319/lo.2004.49.4_part_2.1269, 2004. a
O'Malley, R.: Ocean Productivity,
available at: http://www.science.oregonstate.edu/ocean.productivity/index.php (last access: 20 June 2019),
2017. a
Oschlies, A., Koeve, W., and Garçon, V.: An Eddy-Permitting Coupled
Physical-Biological Model of the North Atlantic 2. Ecosystem Dynamics and
Comparison With Satellite and JGOFS Local Studies Data, Global Biogeochem.
Cycles, 14, 499–523, 2000. a
Oschlies, A., Duteil, O., Getzlaff, J., Koeve, W., Landolfi, A., and Schmidtko,
S.: Patterns of deoxygenation: sensitivity to natural and anthropogenic
drivers, Phil. Trans. R. Soc. Lond. A, 375, 20160325,
https://doi.org/10.1098/rsta.2016.0325, 2017. a, b
Pahlow, M.: Linking chlorophyll-nutrient dynamics to the Redfield N:C ratio
with a model of optimal phytoplankton growth, Mar. Ecol. Prog. Ser., 287,
33–43, https://doi.org/10.3354/meps287033, 2005. a, b, c
Pahlow, M.: UVic-updates-opem: Optimality-based Plankton Ecosystem Model (OPEM v1.0) for the UVic-ESCM, OceanRep, https://doi.org/10.3289/SW_1_2020, 2020. a
Park, J.-Y., Stock, C. A., Dunne, J. P., Yang, X., and Rosati, A.: Seasonal to
multiannual marine ecosystem prediction with a global Earth system model,
Science, 365, 284–288, https://doi.org/10.1126/science.aav6634, 2019. a, b
Prowe, A. E. F., Visser, A. W., Andersen, K. H., Chiba, S., and Kiørboe, T.:
Biogeography of zooplankton feeding strategy, Limnol. Oceanogr., 64,
661–678, https://doi.org/10.1002/lno.11067, 2018. a
Smith, S. L., Yamanaka, Y., Pahlow, M., and Oschlies, A.: Optimal uptake
kinetics: physiological acclimation explains the pattern of nitrate uptake by
phytoplankton in the ocean, Mar. Ecol. Prog. Ser., 384, 1–12,
https://doi.org/10.3354/meps08022, 2009. a, b, c
Smith, S. L., Pahlow, M., Merico, A., and Wirtz, K. W.: Optimality-based
modeling of planktonic organisms, Limnol. Oceanogr., 56, 2080–2094,
https://doi.org/10.4319/lo.2011.56.6.2080, 2011. a
Smith, S. L., Pahlow, M., Merico, A., Acevedo-Trejos, E., Sasai, Y., Yoshikawa,
C., Sasaoka, K., Fujiki, T., Matsumoto, K., and Honda, M. C.: Flexible
phytoplankton functional type (FlexPFT) model: size-scaling of traits and
optimal growth, J. Plankton Res., 38, 977–992, https://doi.org/10.1093/plankt/fbv038,
2016. a
Strom, S. L.: Growth and grazing rates of the herbivorous dinoflagellate
Gymnodinium sp. from the open subarctic Pacific Ocean, Mar. Ecol.
Prog. Ser., 78, 103–113, https://doi.org/10.3354/meps078103, 1991. a
Strom, S. L., Miller, C. B., and Frost, B. W.: What sets the lower limit to
phytoplankton stocks in high-nitrate, low-chlorophyll regions of the open
ocean?, Mar. Ecol. Prog. Ser., 193, 19–31, https://doi.org/10.3354/meps193019, 2000. a
Su, B., Pahlow, M., and Prowe, F.: The role of microzooplankton trophic
interactions in modelling a suite of mesocosm ecosystems, Ecol. Model., 368,
169–179, https://doi.org/10.1016/j.ecolmodel.2017.11.013, 2018. a, b
Talmy, D., Blackford, J., Hardman-Mountford, N. J., Polimene, L., Follows, M. J., and Geider, R. J.: Flexible C:N ratio enhances metabolism of large phytoplankton when resource supply is intermittent, Biogeosciences, 11, 4881–4895, https://doi.org/10.5194/bg-11-4881-2014, 2014. a
Talmy, D., Martiny, A. C., Hill, C., Hickman, A. E., and Follows, M. J.:
Microzooplankton regulation of surface ocean POC:PON ratios, Global
Biogeochem. Cycles, 30, 311–332, https://doi.org/10.1002/2015GB005273, 2016. a
Taucher, J. and Oschlies, A.: Can we predict the direction of marine primary
production change under global warming?, Geophys. Res. Lett., 38, L02603,
https://doi.org/10.1029/2010GL045934, 2011. a, b
Vichi, M., Pinardi, N., and Masina, S.: A generalized model of pelagic
biogeochemistry.for the global ocean ecosystem. Part I: Theory, J. Mar.
Syst., 64, 89–109, https://doi.org/10.1016/j.jmarsys.2006.03.006, 2007. a
Wang, W.-L., Moore, J. K., Martiny, A. C., and Primeau, F. W.: Convergent
estimates of marine nitrogen fixation, Nature, 566, 205–211,
https://doi.org/10.1038/s41586-019-0911-2, 2019. a, b
Weaver, A., Eby, M., Wiebe, E., Bitz, C., Duffy, P., Ewen, T., Fanning, A.,
Holland, M., MacFadyen, A., Matthews, H., Meissner, K., Saenko, O.,
Schmittner, A., Wang, H., and Yoshimori, M.: The UVic Earth System Climate
Model: Model description, climatology, and applications to past, present and
future climates, Atmos.-Ocean, 39, 361–428,
https://doi.org/10.1080/07055900.2001.9649686, 2001. a, b, c
Short summary
The stoichiometry of marine biotic processes is important for the regulation of atmospheric CO2 and hence the global climate. We replace a simplistic, fixed-stoichiometry plankton module in an Earth system model with an optimal-regulation model with variable stoichiometry. Our model compares better to the observed carbon transfer from the surface to depth and surface nutrient distributions. This work could aid our ability to describe and project the role of marine ecosystems in the Earth system.
The stoichiometry of marine biotic processes is important for the regulation of atmospheric CO2...
