Simulation of factors affecting Emiliania huxleyi blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection
Authors: Gnatiuk Natalia , Radchenko Iuliia , Davy Richard , Morozov Evgeny , Bobylev Leonid 
Published: March 2020

Abstract. The observed warming in the Arctic is more than double
the global average, and this enhanced Arctic warming is projected to continue
throughout the 21st century. This rapid warming has a wide range of
impacts on polar and sub-polar marine ecosystems. One of the examples of
such an impact on ecosystems is that of coccolithophores, particularly
Emiliania huxleyi, which have expanded their range poleward during recent decades. The
coccolithophore E. huxleyi plays an essential role in the global carbon cycle.
Therefore, the assessment of future changes in coccolithophore blooms is
very important. Currently, there are a large number of climate models that give projections
for various oceanographic, meteorological, and biochemical variables in the
Arctic. However, individual climate models can have large biases when
compared to historical observations. The main goal of this research was to
select an ensemble of climate models that most accurately reproduces the
state of environmental variables that influence the coccolithophore E. huxleyi bloom
over the historical period when compared to reanalysis data. We developed a
novel approach for model selection to include a diverse set of measures of
model skill including the spatial pattern of some variables, which had not
previously been included in a model selection procedure. We applied this method
to each of the Arctic and sub-Arctic seas in which E. huxleyi blooms have been
observed. Once we have selected an optimal combination of climate models
that most skilfully reproduce the factors which affect E. huxleyi, the projections of
the future conditions in the Arctic from these models can be used to predict
how E. huxleyi blooms will change in the future. Here, we present the validation of 34 CMIP5 (fifth phase of the
Coupled Model Intercomparison Project) atmosphere–ocean general
circulation models (GCMs) over the historical period 1979–2005. Furthermore,
we propose a procedure of ranking and selecting these models based on the
model's skill in reproducing 10 important oceanographic, meteorological, and
biochemical variables in the Arctic and sub-Arctic seas. These factors
include the concentration of nutrients (NO3, PO4, and SI),
dissolved CO2 partial pressure (pCO2), pH, sea surface temperature (SST), salinity
averaged over the top 30 m (SS30 m), 10 m wind speed (WS), ocean surface current speed (OCS), and
surface downwelling shortwave radiation (SDSR). The validation of the GCMs'
outputs against reanalysis data includes analysis of the interannual
variability, seasonal cycle, spatial biases, and temporal trends of the
simulated variables. In total, 60 combinations of models were selected for
10 variables over six study regions using the selection procedure we present
here. The results show that there is neither a combination of models nor
one model that has high skill in reproducing the regional climatic-relevant
features of all combinations of the considered variables in target seas.
Thereby, an individual subset of models was selected according to our model
selection procedure for each combination of variable and Arctic or
sub-Arctic
sea. Following our selection procedure, the number of selected models in the
individual subsets varied from 3 to 11. The paper presents a comparison of the selected model subsets and the
full-model ensemble of all available CMIP5 models to reanalysis data. The
selected subsets of models generally show a better performance than the
full-model ensemble. Therefore, we conclude that within the task addressed
in this study it is preferable to employ the model subsets determined
through application of our procedure than the full-model ensemble.