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Abstract. The observed warming in the Arctic is more than doublethe global average, and this enhanced Arctic warming is projected to continuethroughout the 21st century. This rapid warming has a wide range ofimpacts on polar and sub-polar marine ecosystems. One of the examples ofsuch an impact on ecosystems is that of coccolithophores, particularlyEmiliania huxleyi, which have expanded their range poleward during recent decades. Thecoccolithophore E. huxleyi plays an essential role in the global carbon cycle.Therefore, the assessment of future changes in coccolithophore blooms isvery important. Currently, there are a large number of climate models that give projectionsfor various oceanographic, meteorological, and biochemical variables in theArctic. However, individual climate models can have large biases whencompared to historical observations. The main goal of this research was toselect an ensemble of climate models that most accurately reproduces thestate of environmental variables that influence the coccolithophore E. huxleyi bloomover the historical period when compared to reanalysis data. We developed anovel approach for model selection to include a diverse set of measures ofmodel skill including the spatial pattern of some variables, which had notpreviously been included in a model selection procedure. We applied this methodto each of the Arctic and sub-Arctic seas in which E. huxleyi blooms have beenobserved. Once we have selected an optimal combination of climate modelsthat most skilfully reproduce the factors which affect E. huxleyi, the projections ofthe future conditions in the Arctic from these models can be used to predicthow E. huxleyi blooms will change in the future. Here, we present the validation of 34 CMIP5 (fifth phase of theCoupled Model Intercomparison Project) atmosphere–ocean generalcirculation models (GCMs) over the historical period 1979–2005. Furthermore,we propose a procedure of ranking and selecting these models based on themodel's skill in reproducing 10 important oceanographic, meteorological, andbiochemical variables in the Arctic and sub-Arctic seas. These factorsinclude the concentration of nutrients ( NO3 , PO4 , and SI),dissolved CO2 partial pressure ( pCO2 ), pH, sea surface temperature (SST), salinityaveraged over the top 30  m (SS 30 m ), 10  m wind speed (WS), ocean surface current speed (OCS), andsurface downwelling shortwave radiation (SDSR). The validation of the GCMs'outputs against reanalysis data includes analysis of the interannualvariability, seasonal cycle, spatial biases, and temporal trends of thesimulated variables. In total, 60 combinations of models were selected for10 variables over six study regions using the selection procedure we presenthere. The results show that there is neither a combination of models norone model that has high skill in reproducing the regional climatic-relevantfeatures of all combinations of the considered variables in target seas.Thereby, an individual subset of models was selected according to our modelselection procedure for each combination of variable and Arctic orsub-Arcticsea. Following our selection procedure, the number of selected models in theindividual subsets varied from 3 to 11. The paper presents a comparison of the selected model subsets and thefull-model ensemble of all available CMIP5 models to reanalysis data. Theselected subsets of models generally show a better performance than thefull-model ensemble. Therefore, we conclude that within the task addressedin this study it is preferable to employ the model subsets determinedthrough application of our procedure than the full-model ensemble.

biogeosciences

Simulation of factors affecting &amp;lt;i&amp;gt;Emiliania huxleyi&amp;lt;/i&amp;gt; blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection