Articles | Volume 17, issue 4
https://doi.org/10.5194/bg-17-1199-2020
https://doi.org/10.5194/bg-17-1199-2020
Research article
 | 
03 Mar 2020
Research article |  | 03 Mar 2020

Simulation of factors affecting Emiliania huxleyi blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection

Natalia Gnatiuk, Iuliia Radchenko, Richard Davy, Evgeny Morozov, and Leonid Bobylev

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Cited articles

Agosta, C., Fettweis, X., and Datta, R.: Evaluation of the CMIP5 models in the aim of regional modelling of the Antarctic surface mass balance, The Cryosphere, 9, 2311–2321, https://doi.org/10.5194/tc-9-2311-2015, 2015. 
Almazroui, M., Nazrul Islam, M., Saeed, S., Alkhalaf, A. K., and Dambul, R.: Assessment of Uncertainties in Projected Temperature and Precipitation over the Arabian Peninsula Using Three Categories of Cmip5 Multimodel Ensembles, Earth Syst. Environ., 1, 23, https://doi.org/10.1007/s41748-017-0027-5, 2017. 
Balch, W. M., Drapeau, D. T., and Bowler, B. C.: Step-changes in the physical, chemical and biological characteristics of the Gulf of Maine, as documented by the GNATS time series, Mar. Ecol. Prog. Ser., 450, 11–35, https://doi.org/10.3354/meps09555, 2012. 
Balch, W. M., Bates, N. R., Lam, P. J., Twining, B. S., Rosengard, S. Z., Bowler, B. C., Drapeau, D. T., Garley, R., Lubelczyk, L. C., Mitchell, C., and Rauschenberg, S.: Factors regulating the Great Calcite Belt in the Southern Ocean and its biogeochemical significance, Global Biogeochem. Cycles, 30, 1124–1144, https://doi.org/10.1002/2016GB005414, 2016. 
Brown, C. W. and Yoder, J. A.: Coccolithophorid blooms in the global ocean, J. Geophys. Res., 99, 7467, https://doi.org/10.1029/93JC02156, 1994. 
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We analysed the ability of 34 climate models to reproduce main factors affecting the coccolithophore Emiliania huxleyi blooms in six Arctic and sub-Arctic seas. Furthermore, we proposed a procedure of ranking and selecting these models based on the model’s skill in reproducing 10 important oceanographic, meteorological, and biochemical variables in comparison with observation data and demonstrated that the proposed methodology shows a better result than commonly used all-model averaging.
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