Coupling the Earth system model INMCM with the biogeochemical flux model
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Ilya A. Chernov
Abstract
In the present paper we consider the first results of modelling the World Ocean biogeochemistry system within the framework of the Earth system model: a global atmosphere-ocean-ice-land-biogeochemistry model. It is based on the INMCM climate model (version INMCM39) coupled with the pelagic ecosystem model BFM. The horizontal resolution was relatively low: 2∘ × 2.5∘ for the ‘longitude’ and ‘latitude’ in transformed coordinates with the North Pole moved to land, 33 non-equidistant σ-horizons, 1 hour time step. We have taken into account 54 main rivers worldwide with run–off supplied by the atmosphere submodel. The setup includes nine plankton groups, 60 tracers in total. Some components sink with variable speed. We discuss challenges of coupling the BFM with the σ-coordinate ocean model. The presented results prove that the model output is realistic in comparison with the observed data, the numerical efficiency is high enough, and the coupled model may serve as a basis for further simulations of the long-term climate change.
Funding: This work was supported by the Russian Science Foundation (project 14-27-00126).
Acknowledgment
This work was carried out in the INM RAS and IAMR KRC RAS under the project sponsored by the Russian Science Foundation grant 14-27-00126. Authors thank Prof. E. Volodin, Prof. A. Gritsun, Dr. E. Mortikov and Dr. A. Danilov (all from INM RAS) for their valuable support on running the INMCM and using the INM RAS cluster. We also would like to express our gratitude to our climate community leader Academician Prof. V. P. Dymnikov, who took much interest in the subject of research and supported us while we were despaired of success on the thorny path of biochemical modelling.
References
[1] Biogeochemical Flux Model. URL: http://bfm-community.euSuche in Google Scholar
[2] I. Chernov, P. Lazzari, A. Tolstikov, M. Kravchishina, and N. Iakovlev, Hydrodynamical and biogeochemical spatiotemporal variability in the White Sea: A modeling study. J. Marine Systems 187 (2018), 23–35.10.1016/j.jmarsys.2018.06.006Suche in Google Scholar
[3] Climate Data Guide. SeaWiFs: ocean biooptical and carbon properties. URL: https://climatedataguide.ucar.edu/climatedata/seawifs-ocean-bio-optical-and-carbon-propertiesSuche in Google Scholar
[4] G. Cossarini, S. Querin, C. Solidoro, G. Sannino, P. Lazzari, V. Di Biagio, and G. Bolzon, Development of BFMCOUPLER (v1. 0), the coupling scheme that links the MITgcm and BFM models for ocean biogeochemistry simulations. Geosci. Model Devel. 10 (2017), No. 4, 1423.10.5194/gmd-10-1423-2017Suche in Google Scholar
[5] N. A. Dianskii, V. Ya. Galin, A. V. Gusev, S. P. Smyshlyaev, E. M. Volodin, and N. G. Iakovlev, The model of the Earth system developed at the INM RAS. Russ. J. Numer. Anal. Math. Modelling 25 (2010), No. 5, 419–429.10.1515/rjnamm.2010.027Suche in Google Scholar
[6] V. P. Dymnikov, V. N. Lykosov, and E. M. Volodin, Mathematical simulation of Earth system dynamics. Izv. Atmosph. Oceanic Physics 51 (2015), No. 3, 227–240.10.1134/S0001433815030044Suche in Google Scholar
[7] A. V. Gusev and N. A. Diansky, Numerical simulation of the world ocean circulation and its climatic variability for 1948– 2007 using the INMOM. Izv. Atmosph. Oceanic Physics 50 (2014), No. 1, 1–12.10.1134/S0001433813060078Suche in Google Scholar
[8] N. G. Iakovlev, E. M. Volodin, and A. S. Gritsun, Simulation of the spatiotemporal variability of the World Ocean sea surface height by the INM climate models. Izv. Atm. Ocean. Phys. 52 (2016), 376–385.10.1134/S0001433816040125Suche in Google Scholar
[9] P. Lazzari, C. Solidoro, S. Salon, and G. Bolzon, Spatial variability of phosphate and nitrate in the Mediterranean Sea: A modeling approach. Deep Sea Research Part I: Oceanographic Research Papers 108 (2016), 39–52.10.1016/j.dsr.2015.12.006Suche in Google Scholar
[10] E. Maier-Reimer, I. Kriest, J. Segschneider, and P. Wetzel, The HAMburg ocean carbon cycle model HAMOCC5.1 — Technical description release 1.1. Max Planck Institute for Meteorology, Ser. Reports on Earth System Science 14 (2005), p. 50.Suche in Google Scholar
[11] J. C. McWilliams, Submesoscale currents in the ocean. Proc. Roy. Soc. London A. 472 (2016), 20160117.10.1098/rspa.2016.0117Suche in Google Scholar
[12] J. K. Moore, S. C. Doney, J. C. Kleypas, D. M. Glover, and I. Y. Fung, An intermediate complexity marine ecosystem model for the global domain. Deep Sea Res. Part II 49 (2002), 403–462.10.1016/S0967-0645(01)00108-4Suche in Google Scholar
[13] J. K. Moore, S. C. Doney, and K. Lindsay, Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model. Global Biogeochem. Cycles 18 (2004), GB4028.10.1029/2004GB002220Suche in Google Scholar
[14] N. V. Mordasova, Indirect estimation of water productivity on chlorophyll content. In: Proc. of VNIRO 152 (2014), 41–56 (in Russian).Suche in Google Scholar
[15] L. Patara, M. Visbeck, S. Masina, G. Krahmann, and M. Vichi, Marine biogeochemical responses to the North Atlantic Oscillation in a coupled climate model. J. Geophys. Research: Oceans 116 (2011), C07O23.10.1029/2010JC006785Suche in Google Scholar
[16] L. Patara, M. Vichi, S. Masina, P. G. Fogli, and E. Manzini, Global response to solar radiation absorbed by phytoplankton in a coupled climate model. CDYN (2012), 1–18.10.1007/s00382-012-1300-9Suche in Google Scholar
[17] L. Patara, M. Vichi, and S. Masina, Impacts of natural and anthropogenic climate variations on North Pacific plankton in an Earth System Model. Ecological Modelling 244 (2012), 132–147.10.1016/j.ecolmodel.2012.06.012Suche in Google Scholar
[18] Ch. Piroddi, H. Teixeira, Ch. P. Lynam, Ch. Smith, M. C. Alvarez, Kr. Mazik, E. Andonegi, T. Churilova, L. Tedesco, M. Chifflet, G. Chust, I. Galparsoro, A. C. Garcia, M. Kämäri, O. Kryvenko, G. Lassalle, S. Neville, N. Niquil, N. Papadopoulou, A. G. Rossberg, V. Suslin, and M. C. Uyarra, Using ecological models to assess ecosystem status in support of the European marine strategy framework directive. Ecological Indicators 58 (2015), 175–191.10.1016/j.ecolind.2015.05.037Suche in Google Scholar
[19] L. Polimene, N. Pinardi, M. Zavatarelli, J. I. Allen, M. Giani, and M. Vichi, A numerical simulation study of dissolved organic carbon accumulation in the northern Adriatic Sea. J. Geophys. Res. 112 (2007), C03S20.10.1029/2006JC003529Suche in Google Scholar
[20] L. Tedesco, E. Miettunen, B. W. An, J. Haapala, and H. Kaartokallio, Long-term mesoscale variability of modelled sea–ice primary production in the northern Baltic Sea. Elem. Sci. Anth. 5 (2017).10.1525/elementa.223Suche in Google Scholar
[21] M. Vichi, J. I. Allen, S. Masina, and N. J. Hardman-Mountford, The emergence of ocean biogeochemical provinces: A quantitative assessment and a diagnostic for model evaluation. Global Biogeochem. Cycles 25 (2011), No. 2, 1–17.10.1029/2010GB003867Suche in Google Scholar
[22] M. Vichi, T. Lovato, P. Lazzari, G. Cossarini, E. Gutierrez Mlot, G. Mattia, S. Masina, W. J. McKiver, N. Pinardi, C. Solidoro, L. Tedesco, and M. Zavatarelli, The Biogeochemical Flux Model (BFM): Equation Description and User Manual. BFM version 5.1 2015.Suche in Google Scholar
[23] M. Vichi, N. Pinardi, and S. Masina, A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: theory. JMS 64 (20017), 89–109.10.1016/j.jmarsys.2006.03.006Suche in Google Scholar
[24] M. Vichi, S. Masina, and A. Navarra, A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part II: numerical simulations. JMS 64 (2017), 110–134.10.1016/j.jmarsys.2006.03.014Suche in Google Scholar
[25] M. Vichi, T. Lovato, E. Gutierrez Mlot, and W. J. McKiver, Coupling BFM with Ocean models: the NEMO model (Nucleus for the European Modelling of the Ocean) BFM Consortium, August 2015.Suche in Google Scholar
[26] E. M. Volodin and N. A. Dianskii, and A. V. Gusev, Simulating present-day climate with the INMCM 4.0 coupled model of the atmospheric and oceanic general circulations. Izv. Atmosph. Oceanic Physics 46 (2010), No. 4, 414–431.Suche in Google Scholar
[27] E. M. Volodin, E. V. Mortikov, S. V. Kostrykin, V. Ya. Galin, V. N. Lykossov, A. S. Gritsoun, N. A. Diansky, A. V. Gusev, and N. G. Iakovlev, Simulation of the present-day climate with the climate model INMCM5. Climate Dynamics 49 (2017), No. 11– 12, 3715–3734.10.1007/s00382-017-3539-7Suche in Google Scholar
[28] World Ocean Atlas. URL: https://www.nodc.noaa.gov/OC5/indprod.Suche in Google Scholar
[29] J. W. Zillman, A study of some aspects of the radiation and heat budgets of the southern hemisphere oceans. Meteorol. Stud. 26 (1972), 562–565.Suche in Google Scholar
© 2018 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Coupling the Earth system model INMCM with the biogeochemical flux model
- Design and development of the SLAV-INMIO-CICE coupled model for seasonal prediction and climate research
- Low frequency variability and sensitivity of the Atlantic meridional overturning circulation in selected IPCC climate models
- INM RAS coupled atmosphere–ionosphere general circulation model INMAIM (0–130 km)
- The nature of 60-year oscillations of the Arctic climate according to the data of the INM RAS climate model
- Simulation of the modern climate using the INM-CM48 climate model
Artikel in diesem Heft
- Frontmatter
- Coupling the Earth system model INMCM with the biogeochemical flux model
- Design and development of the SLAV-INMIO-CICE coupled model for seasonal prediction and climate research
- Low frequency variability and sensitivity of the Atlantic meridional overturning circulation in selected IPCC climate models
- INM RAS coupled atmosphere–ionosphere general circulation model INMAIM (0–130 km)
- The nature of 60-year oscillations of the Arctic climate according to the data of the INM RAS climate model
- Simulation of the modern climate using the INM-CM48 climate model
