et al. 2013). The introduction of N. seminae in the Nordic
Seas can be associated with an increased influence of
Pacific waters via the Arctic, probably due to diminished
Arctic sea ice and/or changed ocean circulation in the Arctic Ocean. This might suggest the initiation of a unique climatic transition of the scale seen during the
mid-Pleistocene transition though now the appearance
may indicate a transition to warmer conditions
(Miettinen et al. 2013).
Diatom-based sea-ice reconstructions
Sea ice has a prominent effect on climate and thus paleosea-ice reconstructions are important for climate modeling
and for setting the recent sea-ice variability into a geological context. For the sea-ice reconstructions, three diatombased methods are available.
The qualitative method is based on the statistically
(Imbrie-Kipp method and a Q-mode factor analysis)
defined occurrence of diatom assemblages according to
their relation to modern surface hydrography (Andersen
et al. 2004). The dominant marine diatom species in the
specific sea-ice assemblage are Fragilariopsis cylindrus,
F. oceanica, Bacterosira bathyomphala, Thalassiosira
hyalina, T. nordenskioeldii, and resting spores of
T. gravida (Koç-Karpuz and Schrader 1990). For example, the sea-ice cover of the Nordic Seas for the last
14,000 years was reconstructed by this method (Koç
et al. 1993). In addition to the previous marine assemblage, some brackish-water diatom species indicating sea
ice occur in the Arctic, such as Melosira arctica (Boetius
et al. 2013 and references therein).
The transfer function for May sea-ice cover is based on
diatoms preserved in surface sediment samples from the
North Atlantic and the associated modern sea-ice concentrations, using the maximum likelihood method (Justwan
and Koç 2008). The results attained by this method are
comparable to the ones obtained by other paleo-proxies
(e.g., dinoflagellates) indicating the reliability of this
new technique for the quantitative reconstruction of May
sea ice. The method has been used, e.g., for sea-ice reconstruction from the Norwegian Sea showing a marked
decrease in May sea-ice concentration at the transition
between the Younger Dryas and the Holocene (Justwan
and Koç 2008).
Recently, the highly branched isoprenoid lipid, IP 25 , is
an approach based on long carbon chains produced by diatoms living in association with seasonal sea ice (Belt
et al. 2007). IP 25 has emerged as a potential sea-icespecific proxy for the past sea-ice cover as it seems to reliably reconstruct trends in observed sea-ice concentrations
(Weckström et al. 2013).
Diatom-based sea surface temperature (SST)
reconstructions
Diatom SST transfer functions
Diatom-based quantitative SST reconstructions from the
North Atlantic have been generated using data sets
consisting of modern diatom assemblages of the surface
sediment samples and modern SST data from the North
Atlantic (e.g., Andersen et al. 2004; Jiang et al. 2005;
Miller and Chapman 2013) and the transfer function
methods, such as weighted averaging partial least square
(WA-PLS; ter Braak and Juggins 1993), Imbrie-Kipp
transfer function method (IKM; Imbrie and Kipp 1971),
maximum likelihood (ML), the modern analog technique
(MAT; Hutson 1980), and the artificial neural network
approach (ANN; Malmgren and Nordlund 1997).
Recently, Miettinen et al. (unpublished) expanded the
most commonly used diatom SST calibration data set of
139 surface sediment samples with 52 diatom species
(Andersen et al. 2004) to 184 surface samples and refined
the SST equation. This new WA-PLS diatom transfer
function has a root mean square error (RMSE) of 0.8
C,
a coefficient of determination between observed and
inferred values (r
2
) of 0.96, and a maximum bias of
0.6
C. These statistical criteria indicate that diatoms show
good analogue relations to modern oceanic conditions and
are linearly related to SST.
The WA-PLS method is a commonly used transfer
function method for SST reconstructions because it shows
the best statistical fit between observed SST and
estimated SST through the temperature range of modern
calibration data. The reconstructions indicate summer
temperatures; as for diatom transfer functions, August
SST gives the best match (Berner et al. 2008). The
WA-PLS method can be regarded as the unimodal-based
equivalent of multiple linear regression, i.e., a diatom species has an optimal abundance along the environmental
gradient being investigated. The method uses several
components in the final transfer function. These components are selected to maximize the covariance between
the environmental variables to be reconstructed to
improve predictive power of the method (ter Braak and
Juggins 1993).
Late Holocene SSTs in the North Atlantic and the
Nordic seas
Recently, several quantitative high-resolution SST reconstructions have been generated from the North Atlantic
in order to investigate the variability of the SST pattern,
the surface currents, and the forcing factors behind the variability. For example, the most recent high-resolution diatom August SST records show persistent opposite climate
trends toward warming in the subpolar North Atlantic
(Miettinen et al. 2011, 2012) and cooling in the Norwegian Sea (Berner et al. 2011) during the late Holocene
(Figure 1). An apparent tendency to coherent antiphased
SST variations between the regions is also revealed for
the multicentennial time scales implying an SST seesaw
between the northern subpolar North Atlantic and the
Norwegian Sea (Miettinen et al. 2012). This seesaw might
have had a strong effect on two major climate anomalies in
northwest Europe during the past millennium: the Medieval Warm Period (MWP) and the Little Ice Age (LIA).
186
DIATOMS
Seas can be associated with an increased influence of
Pacific waters via the Arctic, probably due to diminished
Arctic sea ice and/or changed ocean circulation in the Arctic Ocean. This might suggest the initiation of a unique climatic transition of the scale seen during the
mid-Pleistocene transition though now the appearance
may indicate a transition to warmer conditions
(Miettinen et al. 2013).
Diatom-based sea-ice reconstructions
Sea ice has a prominent effect on climate and thus paleosea-ice reconstructions are important for climate modeling
and for setting the recent sea-ice variability into a geological context. For the sea-ice reconstructions, three diatombased methods are available.
The qualitative method is based on the statistically
(Imbrie-Kipp method and a Q-mode factor analysis)
defined occurrence of diatom assemblages according to
their relation to modern surface hydrography (Andersen
et al. 2004). The dominant marine diatom species in the
specific sea-ice assemblage are Fragilariopsis cylindrus,
F. oceanica, Bacterosira bathyomphala, Thalassiosira
hyalina, T. nordenskioeldii, and resting spores of
T. gravida (Koç-Karpuz and Schrader 1990). For example, the sea-ice cover of the Nordic Seas for the last
14,000 years was reconstructed by this method (Koç
et al. 1993). In addition to the previous marine assemblage, some brackish-water diatom species indicating sea
ice occur in the Arctic, such as Melosira arctica (Boetius
et al. 2013 and references therein).
The transfer function for May sea-ice cover is based on
diatoms preserved in surface sediment samples from the
North Atlantic and the associated modern sea-ice concentrations, using the maximum likelihood method (Justwan
and Koç 2008). The results attained by this method are
comparable to the ones obtained by other paleo-proxies
(e.g., dinoflagellates) indicating the reliability of this
new technique for the quantitative reconstruction of May
sea ice. The method has been used, e.g., for sea-ice reconstruction from the Norwegian Sea showing a marked
decrease in May sea-ice concentration at the transition
between the Younger Dryas and the Holocene (Justwan
and Koç 2008).
Recently, the highly branched isoprenoid lipid, IP 25 , is
an approach based on long carbon chains produced by diatoms living in association with seasonal sea ice (Belt
et al. 2007). IP 25 has emerged as a potential sea-icespecific proxy for the past sea-ice cover as it seems to reliably reconstruct trends in observed sea-ice concentrations
(Weckström et al. 2013).
Diatom-based sea surface temperature (SST)
reconstructions
Diatom SST transfer functions
Diatom-based quantitative SST reconstructions from the
North Atlantic have been generated using data sets
consisting of modern diatom assemblages of the surface
sediment samples and modern SST data from the North
Atlantic (e.g., Andersen et al. 2004; Jiang et al. 2005;
Miller and Chapman 2013) and the transfer function
methods, such as weighted averaging partial least square
(WA-PLS; ter Braak and Juggins 1993), Imbrie-Kipp
transfer function method (IKM; Imbrie and Kipp 1971),
maximum likelihood (ML), the modern analog technique
(MAT; Hutson 1980), and the artificial neural network
approach (ANN; Malmgren and Nordlund 1997).
Recently, Miettinen et al. (unpublished) expanded the
most commonly used diatom SST calibration data set of
139 surface sediment samples with 52 diatom species
(Andersen et al. 2004) to 184 surface samples and refined
the SST equation. This new WA-PLS diatom transfer
function has a root mean square error (RMSE) of 0.8
C,
a coefficient of determination between observed and
inferred values (r
2
) of 0.96, and a maximum bias of
0.6
C. These statistical criteria indicate that diatoms show
good analogue relations to modern oceanic conditions and
are linearly related to SST.
The WA-PLS method is a commonly used transfer
function method for SST reconstructions because it shows
the best statistical fit between observed SST and
estimated SST through the temperature range of modern
calibration data. The reconstructions indicate summer
temperatures; as for diatom transfer functions, August
SST gives the best match (Berner et al. 2008). The
WA-PLS method can be regarded as the unimodal-based
equivalent of multiple linear regression, i.e., a diatom species has an optimal abundance along the environmental
gradient being investigated. The method uses several
components in the final transfer function. These components are selected to maximize the covariance between
the environmental variables to be reconstructed to
improve predictive power of the method (ter Braak and
Juggins 1993).
Late Holocene SSTs in the North Atlantic and the
Nordic seas
Recently, several quantitative high-resolution SST reconstructions have been generated from the North Atlantic
in order to investigate the variability of the SST pattern,
the surface currents, and the forcing factors behind the variability. For example, the most recent high-resolution diatom August SST records show persistent opposite climate
trends toward warming in the subpolar North Atlantic
(Miettinen et al. 2011, 2012) and cooling in the Norwegian Sea (Berner et al. 2011) during the late Holocene
(Figure 1). An apparent tendency to coherent antiphased
SST variations between the regions is also revealed for
the multicentennial time scales implying an SST seesaw
between the northern subpolar North Atlantic and the
Norwegian Sea (Miettinen et al. 2012). This seesaw might
have had a strong effect on two major climate anomalies in
northwest Europe during the past millennium: the Medieval Warm Period (MWP) and the Little Ice Age (LIA).
186
DIATOMS