Investigation of the relationship between primary production and sea ice in the arctic seas: assessments based on a small-component model of marine ecosystem
https://doi.org/10.7868/S2073667318020107
Abstract
The work is focused on the further development of a regional coupled eco-thermohydrodynamic model of the Arctic seas with the aim of using it to better understand the interaction of dynamic and ecosystem processes in the ocean under a changing climate in the Arctic. We used the MITgcm as a thermohydrodynamic block and an original 7-component ecosystem model which includes the carbon cycle as an ocean biogeochemistry block. The results of a model climatic run for a 40-year modern period for the Arctic shelf region (Kara, Barents and White Seas) are presented. The estimates of the spatial distribution of the chlorophyll-a concentration in the surface layer have clarified the effect of sea ice on primary production in the Arctic seas, including under conditions of a changing climate that leads to a significant reduction of ice cover in the Arctic Ocean. The clear relationship between the area of the marginal ice zone and primary production has been obtained: the moments of their spring-summer peaks coincide completely and they are highly correlated (0.87), proving the importance of this zone in the functioning of the marine ecosystem. As expected, the interannual variability of the integrated primary production and the total sea ice area (both averaged over the hydrological year — from October to September) have demonstrated an antiphase oscillation which means that the reduced sea ice cover area in the previous winter is one of the main reasons for the increase in primary production in the current year.
Keywords
About the Authors
S. D. MartyanovRussian Federation
Moscow
A. Yu. Dvornikov
Russian Federation
Moscow
V. A. Ryabchenko
Russian Federation
Moscow
D. V. Sein
Russian Federation
Moscow; Bremerhaven, Germany
S. M. Gordeeva
Russian Federation
Moscow; St. Petersburg
References
1. Pabi S., van Dijken G. L., Arrigo K. R. Primary production in the Arctic Ocean, 1998—2006 // J. Geophys. Res. 2008. 113. C08005. doi:10.1029/2007JC004578.
2. Research Fronts 2014: The National Science Library, Chinese Academy of Sciences, Thomson Reuters IP & Science, The Joint Research Center of Emerging Technology Analysis, 2014, 62 p.
3. Research Fronts 2016. Institutes of Science and Development, Chinese Academy of Sciences, The National Science Library, Chinese Academy of Sciences, Clarivate Analytics, 2016, 104 p.
4. Research Fronts 2017. Institutes of Science and Development, Chinese Academy of Sciences, The National Science Library, Chinese Academy of Sciences, Clarivate Analytics, 2017, 94 p.
5. Fasham M. J. R., Ducklow H. W., McKelvie S. M. A nitrogen-based model of plankton dynamics in the oceanic mixed layer // Journal of Marine Research. 1990. 48. 591—639.
6. Popova E. E., Yool A., Aksenov Y., Coward A. C., Anderson T. R. Regional variability of acidification in the Arctic: a sea of contrasts // Biogeosciences. 2014. 11. 293—308. doi:10.5194/bg-11-293-2014
7. Yool A., Popova E. E., Anderson T. R. Medusa-1.0: a new intermediate complexity plankton ecosystem model for the global domain // Geosci. Model Dev. 2011. 4. 381—417. doi:10.5194/gmd-4-381-2011
8. Yool A., Popova E. E., Anderson T. R. MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies // Geosci. Model Dev. 2013. 6. 1767—1811. doi:10.5194/gmd-6-1767-2013
9. Popova E. E., Yool A., Coward A. C., Aksenov Y. K., Alderson S. G., de Cuevas B. A., Anderson T. R. Control of primary production in the Arctic by nutrients and light: Insights from a high-resolution ocean general circulation model // Biogeosci. 2010. 7(11). 3569—3591. doi:10.5194/bg-7-3569-2010.
10. Popova E. E., Yool A., Aksenov Y., Coward A. C. Role of advection in Arctic Ocean lower trophic dynamics: a modeling perspective // J. Geophys. Res. Oceans. 2013. 118. doi:10.1002/jgrc.20126
11. Zhang J., Spitz Y. H., Steele M., Ashjian C., Campbell R., Berline L., Matrai P. Modeling the impact of declining sea ice on the Arctic marine planktonic ecosystem // J. Geophys. Res. 2010. 115. C10015. doi:10.1029/2009JC005387.
12. Kishi M. J. M. et al. NEMURO — A lower trophic level model for the North Pacific marine ecosystem // Ecol. Model. 2007. 202. 12—25.
13. Meibing Jin, Clara Deal, Sang H. Lee, Scott Elliott, Elizabeth Hunke, Mathew Maltrud, Nicole Jeffery. Investigation of Arctic sea ice and ocean primary production for the period 1992—2007 using a 3-D global ice–ocean ecosystem model // Deep-Sea Research II 2012. 81—84, 28—35.
14. Moore J. K., Doney S. C., Kleypas J. C., Glover D. M., Fung I. Y. An intermediate complexity marine ecosystem model for the global domain // Deep-Sea Res. 2002. II 49. 403—462.
15. Moore J. K., Doney S. C., Lindsay K. Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model // Global Biogeochem. Cycles 18. 2004. GB4028. doi:10.1029/2004GB002220.
16. Lebedeva L. P., Shushkina E. A., Vinogradov M. E. A dynamic model of the pelagic ecosystem of the Kara Sea // Oceanology. 1995. 34 (5). 661—666.
17. Аверкиев А. С. Моделирование формирования зоны высокой первичной продуктивности над поднятием дна в Баренцевом море при прохождении циклона // Вестник Северного (Арктического) Федерального Университета, Сер. Естественные науки. 2014. №3. С. 5—14.
18. Filatov N., Pozdnyakov D., Johannessen O. M., Pettersson L. H., Bobylev L. P. White Sea: Its marine Environment and ecosystem dynamics influenced by global change. Chichester: Springer-Praxis Publ., 2007. 476 р.
19. Толстиков А. В., Чернов И. А., Мурзина С. А., Мартынова Д. М., Яковлев Н. Г. Разработка комплекса GREEN JASMINE для изучения и прогнозирования состояния экосистем Белого моря // Труды Карельского научного центра РАН. 2017. № 5. С. 23—32.
20. Vichi M., Pinardi N., Masina S. A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: theory // Journal of Marine Systems. 2007a. 64. С. 89—109.
21. Vichi M., Masina S., Navarra A. A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part II: numerical simulations // Journal of Marine Systems, 2007b. 64. P. 110—134.
22. Sein D. V., Mikolajewicz U., Groger M., Fast I., Cabos W., Pinto J. G., Hagemann S., Semmler T., Izquierdo A., Jacob D. Regionally coupled atmosphere-ocean-sea ice-marine biogeochemistry model ROM: 1. Description and validation // J. Adv. Model. Earth Syst. 2015. 7. P. 268—304.
23. Marshall J., Adcroft A., Hill C., Perelman L., Heisey C. A finite-volume, incompressible navier-stokes model for studies of the ocean on parallel computers // J. Geophys. Res. 1997. 102(C3). 5753—5766. 1997.
24. Danilov S. Ocean modeling on unstructured meshes // Ocean Modelling. 69. 195—210. 2013. DOI: 10.1016/j.ocemod.2013.05.005
25. Hibler III W. D. A dynamic thermodynamic sea ice model // J. Phys. Oceanogr. 9. 815—846. 1979.
26. Hibler III W. D. Modeling a variable thickness sea ice cover // Mon. Wea. Rev. 1. 1943—1973. 1980.
27. Zhang J., Hibler III W. D. On an efficient numerical method for modeling sea ice dynamics // J. Geophys. Res. 102(C4). 8691—8702. 1997.
28. Losch M., Menemenlis D., Campin J.-M., Heimbach P., Hill C. On the formulation of sea-ice models. Part 1: Effects of different solver implementations and parameterizations // Ocean Modelling. 2010. 33(1–2), 129—144. doi:10.1016/j.ocemod.2009.12.008
29. Martyanov S. D., Dvornikov A. Yu., Gorchakov V. A., Losa S. N. Model estimates of the ecosystem contribution in the carbon dioxide exchange between the ocean and the atmosphere in the Barents Sea // Фундаментальная и прикладная гидрофизика. 2017. Т. 10, № 1. С. 11—16.
30. Zhang J. L., Rothrock D. A. Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates // Mon. Weather Rev. 2003. 131. P. 845—861.
31. Schweiger A., Lindsay R., Zhang J., Steele M., Stern H. Uncertainty in modeled arctic sea ice volume // J. Geophys. Res. 2011. 116, C00D06. doi:10.1029/2011JC007084.
32. Matrai P. and Bigelow Laboratory for Ocean Sciences (2011). Productivity, chlorophyll a, Photosynthetically Active Radiation (PAR) and other phytoplankton data from the Arctic Ocean, Bering Sea, Chukchi Sea, Beaufort Sea, East Siberian Sea, Kara Sea, Barents Sea, and Arctic Archipelago measured between 17 April, 1954 and 30 May, 2006 compiled as part of the Arctic System Science Primary Production (ARCSS-PP) observational synthesis project (NODC Accession 0063065). Version 1.1. National Oceanographic Data Center, NOAA. Dataset. (Дата обращения: 01.07.2015).
33. Radach G., Moll A. Review of three-dimensional ecological modelling related to the North Sea shelf system. Part II: model validation and data needs // Oceanography and Marine Biology: An Annual Review, 2006, 44, P. 1—60.
34. Kushnir V., Pavlov V., Morozov A., Pavlova O. “Flashes” of chlorophyll-a concentration derived from in Situ and remote sensing data at the Polar Front in the Barents sea // The Open Oceanography Journal. 2011. 5, P. 14—21.
35. Jin M., Deal C., Lee S. H., Elliott S., Hunke E., Maltrud M., Jeffery N. Investigation of Arctic sea ice and ocean primary production for the period 1992—2007 using a 3-D global ice–ocean ecosystem model // Deep-Sea Res. 2012. II. 81—84, 28—35.
36. Рябченко В. А., Горчаков В. А., Дворников А. Ю., Пугалова С. С. Оценки влияния ледового покрова на первичную продукцию фитопланктона в Баренцевом море (по результатам трехмерного моделирования) // Фундаментальная и прикладная гидрофизика. 2016. Т. 9, № 1. С. 41—51.
Review
For citations:
Martyanov S.D., Dvornikov A.Yu., Ryabchenko V.A., Sein D.V., Gordeeva S.M. Investigation of the relationship between primary production and sea ice in the arctic seas: assessments based on a small-component model of marine ecosystem. Fundamental and Applied Hydrophysics. 2018;11(2):108-117. https://doi.org/10.7868/S2073667318020107