Introduction
The mid-Holocene (MH, 6 ± 0.5 ka BP, 4705–5755 14C yr BP, Reimer
et al., 2009) is one of
the three palaeoclimate experiments included in the fifth phase of the
Coupled Modelling Intercomparison Project (CMIP5: Taylor et al., 2012) which
uses palaeoclimate simulations as an opportunity to evaluate how well models
can reproduce climate changes outside the range of the instrumental period
(Braconnot et al., 2012; Schmidt et al., 2013). The choice of the MH
capitalises on the fact that this period has been a major focus for data
synthesis, model simulations and data–model comparisons within the
Palaeoclimate Intercomparison Project (PMIP: http://pmip.lsce.ipsl.fr).
The MH is the nearest period in the past with similar ice-sheet extent as the
present day but characterised by a large change in the seasonal and
latitudinal distribution of insolation leading to an enhanced seasonal cycle
of temperature in the Northern Hemisphere (NH) and a reduced seasonal cycle
in the Southern Hemisphere (SH) (Braconnot et al., 2007).
Terrestrial archives provide robust reconstructions of the spatial and
seasonal patterns of MH land-based temperature and precipitation anomalies
(Bartlein et al., 2011). Evaluations of the CMIP5 simulations using
terrestrial MH reconstructions show that climate models reproduce the
direction and large-scale spatial patterns of the seasonal reconstructions
(Izumi et al., 2013; Li et al., 2013; Schmidt et al., 2013) but often fail to
reproduce the observed magnitude of regional changes (Hargreaves et al.,
2013; Harrison et al., 2013; Perez et al., 2014).
Sea surface temperature (SST) reconstructions have proved to be a valuable
tool for evaluation of Last Glacial Maximum (LGM) simulations (Kageyama et
al., 2006; Otto-Bliesner et al., 2009; Hargreaves et al., 2011; Wang et al.,
2013), but their potential for evaluation of MH simulations still largely
remains to be explored. There have been several attempts to reconstruct MH
sea surface temperature (SST) for specific regions (e.g. the North Atlantic:
Kerwin et al., 1999; Ruddiman and Mix, 1993), but the Global
database for alkenone-derived HOlocene Sea-surface Temperature (GHOST) data set of Mg / Ca and alkenone-based SSTs provides the
only global product (Kim, 2004; Leduc et al., 2010). Data–model
comparisons using the GHOST data set have shown significant mismatches
between the modelled and reconstructed SST anomalies (Schneider et al.,
2010; Hargreaves et al., 2013; Lohmann et al., 2013). It has been suggested
that these mismatches could reflect organisms whose fossils record the
ambient temperature, analytical uncertainties, and/or issues related to the
ecology of the sensors which may have resulted in changes in depth and/or
seasonal habitat compared to the present day (Lohmann et al.,
2013). Given that the reconstructed MH SST anomalies are generally small,
compared for example to the changes registered at the LGM (MARGO Project
Members, 2009), it is important to assess how such factors affect the
precision of the reconstructions in order to determine whether a global
multi-sensor synthesis of MH SSTs could be used for model benchmarking.
Here, we present a new compilation of SST reconstructions for the MH based
on the alkenone unsaturation index, the Mg / Ca palaeothermometer, and
temperatures obtained using statistical reconstruction techniques for
organic-walled dinoflagellate cyst (dinocysts) and planktonic foraminifera.
Since the Mg / Ca palaeothermometer, the alkenone unsaturation index, and
census counts of planktonic foraminifera and dinocysts can be used to derive
SSTs, and hence provide proxies for temperature, they are often referred to as
palaeo-temperature proxies. Because they provide a wider range of
information than simply SST, we prefer to use the term “sensor”. We assess
the uncertainties originating from using different sensors and different
reconstruction methodologies to evaluate the potential of MH SST
reconstructions to benchmark climate-model simulations.
Material and methods
Data collection and quality control
We have compiled site-based SST reconstructions made using the alkenone
unsaturation index, the Mg / Ca palaeothermometer, and statistical
reconstruction techniques for dinocysts and planktonic foraminifera
assemblages, covering all ocean basins (Supplement Table 1). This is the same set of
sensors as used in the MARGO LGM synthesis (Kucera et al.,
2005a), except that we do not include records based on diatom and
radiolarian transfer functions because of lack of available harmonised data
sets. Most of the Mg / Ca and alkenone reconstructions are from the GHOST
database (Kim et al., 2004; Leduc et al., 2010), but additional Mg / Ca and
alkenone records, and the census counts of planktonic foraminifera and
dinocysts, were obtained from public archives (e.g. Pangaea, NOAA-NGDC World
Data Center for Paleoclimatology) or provided by the original author.
The data set is a selection of the available records from each ocean basin.
Only sites that met the following data quality criteria were included in the
compilation:
The individual records have at least 10 data points between 0 and 10 ka BP,
and at least one data point in the 5.5–6.5 ka BP time window.
The sedimentation rate is at least 2 cm per 1000 years to ensure that
individual samples represent no more than the investigated 1000 years time window, assuming no impact of bioturbation.
The chronology was based on at least two radiocarbon dates or other
stratigraphic markers within the interval between 0 and 10 ka BP.
We generated new SST reconstructions based on assemblage counts for
planktonic foraminifera and dinocysts, using the methods adopted by the
MARGO project for the LGM (de Vernal et al., 2005; Kucera et al., 2005b).
This was necessary because transfer-function reconstructions were not
available for some of the records or because existing transfer-function
reconstructions were made using different calibration data sets. However,
the Mg / Ca and alkenone palaeothermometry SST values were taken directly from
the original publications. In the absence of objective guidelines for
reinterpretation of the original measurements, this is the only possible
approach.
Most of the individual site chronologies were based on radiocarbon dating. A
very few sites have age models based on isotopic stratigraphy, specifically
correlation of the benthic oxygen isotope record from the site with the
standard SPECMAP composite record (Martinson et al., 1987), the Shackleton
benthic oxygen isotope record (Shackleton, 2000), or the LR04 composite record
of Lisiecki and Raymo (2005). The chronology of some cores
was established by attributing ages to key stratigraphic events, such as
sapropel events (e.g. Emeis et al., 2003). Since we only used records that
met certain minimum requirements for chronological control, we had no reason
to change the age models from the original publications. Therefore, we use
the original chronology for each site, including a local reservoir correction
if used in the original age model and without recalibrating the radiocarbon
dates. In doing so, we rely on the assumption that differences between the
different calibrations used in constructing the original age models are
negligible over the Holocene age range.
Sea surface temperature reconstruction
Reconstructions based on planktonic foraminifera
The planktonic foraminifera census counts were initially screened for
taxonomic consistency and counting method, and assessed for the effect of
carbonate dissolution. Only records that passed this pre-screening were used
for further statistical analysis. We did not identify any records from the
Indian Ocean that were suitable. The data set therefore includes 57
planktonic foraminifera-based SST records (Supplement Table 1), with 14 from the
North Atlantic, 2 from the equatorial South Atlantic, 15 from the
Mediterranean Sea, and 26 from the Pacific. The average resolution across the
MH interval is 4 samples per 1000 years, with a range of between 1 and 21
samples per core.
The planktonic foraminifera census counts were converted into SST estimates
using the multi-technique approach described by Kucera et al. (2005b). This
approach is based on the simultaneous application of the Modern Analogue
Technique (MAT) and the Artificial Neural Network (ANN) methods. The
calibration data set was derived from the MARGO LGM project
(Kucera et al., 2005b) and uses six regional calibrations
against seasonal means of SST at 10 m water depth from the 1998 version of
the World Ocean Atlas (WOA98: Conkright et al., 1998). The MAT approach
searches the calibration data set for samples with assemblages that most
resemble the fossil assemblage. We used the 10 best analogues, identified using
the squared chord distance measure, in the Atlantic and Pacific, and the 5 best
analogues in the Mediterranean Sea. The ANN method estimates SSTs by mapping
the foraminifera census counts onto a highly recursive system of equations
iteratively optimised on the training data. The ANN approach is
mathematically entirely independent of MAT, e.g. by permitting extrapolation
outside the range of parameter values in the calibration data set.
The final SST reconstructions represent the consensus between the two
methods. At most of the sites, this is the average of the estimates obtained
by the MAT and ANN methods. The calibration error of the foraminifera-based
SST reconstructions is dependent on method and region, and ranges between ±0.8∘
and ±1.9 ∘C for winter, ±1.2∘ and ±1.6 ∘C for summer, and ±0.9∘ and ±1.7 ∘C for mean annual SST (Kucera et
al., 2005a).
Reconstructions based on dinocysts
The data set includes 28 dinocyst-based SST records (Supplement Table 1), with 24
sites from the North Atlantic and 4 from the Mediterranean Sea. The average
resolution across the MH interval is 6 samples per 1000 years, with a range
of between 1 and 20 samples per core.
The dinocyst-based reconstructions were made using the MAT, as described in
detail by de Vernal et al. (2005, 2013). The modern reference database
includes 940 sites from the North Atlantic, North Pacific, Arctic Ocean, and
adjacent epicontinental seas. The reference sites cover a wide range of
environments, from cold to sub-tropical domains, neritic and open ocean
conditions, and brackish to fully marine settings. Reconstruction
uncertainties were calculated by retaining one-fifth of the data for
verification independent of the original calibration. The reconstruction
uncertainties of the dinocyst-based SST reconstructions are ±1.2 ∘C for winter, ±1.6 ∘C for summer, and
±1.1 ∘C for annual mean SSTs.
Reconstructions based on Mg <inline-formula><mml:math display="inline"><mml:mo>/</mml:mo></mml:math></inline-formula> Ca thermometry
There are 38 Mg / Ca-based MH SST records in the data set (Supplement Table 1), with
19 records from the Pacific, 12 from the North Atlantic, 5 from the Indian
Ocean and 2 from the South Atlantic. Most of these records came from the
GHOST database (Leduc et al., 2010), but we excluded 3 GHOST records because
they did not meet our quality criteria and added 9 records. The average
resolution across the MH interval is 6 samples per 1000 years, with a range
of between 1 and 24 samples per record.
The Mg / Ca temperatures are based on measurements on different
planktonic foraminifera species at the different sites. Furthermore, the
samples are prepared using different cleaning methods (Barker et al., 2003;
Boyle and Rosenthal, 1996; Boyle and Keigwin, 1985; Boyle et al., 1995; Lea
et al., 2000; Martin and Lea, 2002; Rosenthal et al., 1999), measured on
different machines (ICP-OES, ICP-MS, Q-ICP-MS, flow-through ICP-MS), and
calibrated using different equations (Anand et al., 2003; Barker and
Elderfield, 2002; Dekens et al., 2002; Elderfield and Ganssen, 2000; Hastings
et al., 2001; Mashiotta et al., 1999; Nürnberg et al., 1996; Rosenthal
and Lohmann, 2002; Thornalley et al., 2009a; von Langen et al., 2005). Since
we use the published reconstructions in our data set, the results could be
affected by these differences. The impact of using different analytical
methods was addressed in the inter-laboratory comparison studies of Rosenthal
et al. (2004) and Greaves et al. (2008). In some cases, the SST reconstructions from different
laboratories differed by as much as 3 ∘C. Inter-laboratory
differences are dominated by different instrument calibrations (Greaves et
al., 2008) and cleaning methods (Rosenthal et al., 2004). However, each
laboratory uses specific SST calibrations, tailored to the taxa and treatment
procedures they use, and thus the published temperature estimates are
probably more comparable than these straight comparisons would suggest
(Rosenthal et al., 2004).
The partial dissolution of foraminiferal calcite alters the Mg / Ca ratio of
the shells, such that there is an increasing cold bias in reconstructed SST
with increasing water depth (e.g. Regenberg et al., 2006).
However, the basic relationship of Mg / Ca with temperature seems robust
(Rosenthal et al., 2000). This means that corrections can be
applied to compensate for the effect of dissolution, for example by using
size-normalised shell weight as an index of dissolution (Rosenthal and
Lohmann, 2002) or by applying a water depth correction such as in the
calibration of Dekens et al. (2002). We further acknowledge a recent study
reporting a more pessimistic scenario in which calcite dissolution may start
occurring as shallow as 1000 m water depth in the Pacific Ocean and the
Indonesian archipelago, i.e. where most of the Mg / Ca data come from
(Regenberg et al., 2014). Since this potential problem was first
addressed a long time ago (Russell et al., 1994), we here rely on the
expertise of the original authors to have identified whether dissolution is
a problem and to have applied a dissolution correction when necessary.
Following Anand et al. (2003), we assume that the uncertainty on the
estimation of the calcification temperature is ±1.2 ∘C. The
temperature anomalies are calculated by subtracting each record's
calcification temperatures from the modern ocean's SST at 10 m water depth
obtained from the WOA98.
Reconstructions based on alkenone unsaturated ratio
There are 89 alkenone-based MH SST records in the data set (Supplement Table 1),
with 39 records from the Pacific, 26 from the North Atlantic, 6 from the
Indian Ocean, 8 from the Mediterranean Sea, and 10 from the South Atlantic.
The average resolution across the MH interval is 5 samples per 1000 years,
with a range of between 1 and 33 samples per record. Most of the alkenone
records have been obtained from the GHOST database (Kim, 2004; Leduc et
al., 2010). We excluded 11 of the GHOST records because they did not meet
our quality criteria and added 9 new records. Rosell-Melé et al. (2001) examined the analytical precision and reproducibility of
alkenone-based temperature estimates generated by different laboratories,
and found that inter-laboratory differences were on average ±1.6 ∘C.
The original alkenone-derived temperature estimates were converted into SSTs
using several different calibrations (Conte et al., 2006; Müller et
al., 1998; Pelejero et al., 1999; Prahl et al., 1988; Prahl and Wakeham,
1987; Rosell-Melé et al., 1995; Sonzogni et al., 1998). A single
calibration could be applied for most paleoceanographic settings (Conte
et al., 2006), so the use of several different calibrations may introduce a
systematic bias (Prahl et al., 2006). However, the
calibrations are relatively similar for the intermediate range of
temperatures observed in the global ocean, and this issue is only likely to
be important under extreme conditions. The global average mean standard
calibration error is ±1.2 ∘C, but larger deviations have
been observed in upwelling zones and in the Arabian Sea (Conte et al.,
2006).
Defining the “sea surface”
The “sea surface” and its related “sea surface temperature” have been set
to 10 m depth following the decision by MARGO (Kucera et al., 2005a). This
decision reflects a compromise allowing a harmonisation of SST estimates
among different sensors. This choice does not mean that the authors assumed
that all sensors record temperature at that depth. Rather, the decision
reflects an assumption that all sensors and proxies record an SST signal
which is highly correlated to SST at 10 m depth and that it is therefore
possible to calibrate the individual proxies against SST at that depth. In
the context of this study where the focus lies on SST anomalies, the
principle assumptions of this depth-homogenisation are thus that the SST
recorded by each proxy and sensor is highly correlated to SST at 10 m depth
and that this relationship remained the same for the time slice between the present day and the
6 ka BP Holocene. Whereas the SST depth recorded by phytoplankton
sensors is limited to the photic zone, the depth range of species of
planktonic foraminifera can be broader. The foraminifera-based Mg / Ca
SST estimates are based chiefly on symbiont-bearing species with shallow
habitats, whose calcification depth has been constrained to lay within the top
100 m of the water column (e.g. Anand et al., 2003; Regenberg et al.,
2009). In contrast, the foraminifera-based transfer-function SST are based on
analysis of the entire assemblage and as shown by Telford et
al. (2013), it is
possible that assemblage composition is sensitive to subsurface temperature,
particularly in low-latitude regions. This depth mismatch may be significant
when reconstructing temperature of the last glacial maximum, but it remains
unclear whether it also has an effect on early Holocene SST estimates. Thus,
in the absence of a universally applicable set of criteria for assigning
depth to SST estimates by different proxies and sensors, we retained the
10 m depth definition used by MARGO, but we acknowledge that
depth-misattribution of the reconstructed SST may be an additional source of
uncertainty and may account for mismatch among SST proxies, particularly
those based on planktonic foraminifera as a sensor.
The global data set
The final data set (Supplement Table 1) consists of 212 individual SST records, of
which 89 are based on alkenones, 38 on Mg / Ca, 57 on planktonic foraminifera,
and 28 on dinocysts. The planktonic foraminifera and dinocysts provide mean
annual, summer, and winter reconstructions, but the Mg / Ca records are only
used for summer and the alkenones for mean annual SSTs as recommended by the
MARGO LGM group (Kucera et al., 2005a).
Impact of using different modern reference climates on gridded
(5×5∘) mid-Holocene (MH) mean annual sea surface temperature
(SST) anomalies: (a) difference between MH anomalies calculated
relative to the 1998 version of the World Ocean Atlas data set (WOA98) and the
2009 version of this data set (WOA09), (b) differences in MH
anomalies calculated using WOA98 and the Hadley Centre Sea Ice and Sea Surface
Temperature (HADiSST) data set, and (c) differences in MH anomalies
calculated using WOA98 and the Global database for alkenone-derived
HOlocene Sea-surface Temperature (GHOST) data set.
The histograms show the frequency distribution of MH anomalies in
0.5∘ temperature classes reconstructed using each of the reference
climates: (d) WOA98, (e) WOA09, (f) HADiSST, and
(g) GHOST.
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Assigning SST records based on alkenones to mean-annual SST and Mg / Ca to
summer SST eventually lead to shortcomings concerning the interpretation of
palaeothermometers. Alkenone-producing coccolithophores have preferential
blooming seasons varying on the basis of regional hydrological and
climatological patterns. Schneider et al. (2010) have used satellite
observations to compute a seasonality index that depicts where and when
primary productivity is increased with respect to the annual SST cycle. They
showed that primary producers, one generic term that includes the
alkenone-synthesising coccolithophores, preferentially thrive during summer
at high latitude because of light limitation, and during winter at low
latitudes when increased surface ocean mixing brings nutrients to the photic
zone (Schneider et al., 2010). Different foraminifera species also occupy
different ecological niches, and their representative season may vary from
place to place, depending on the species analysed downcore (see e.g. Lombard
et al., 2011). Assigning foraminifera-derived SST to summer temperatures and
alkenone-derived SST to mean-annual temperatures hence provides an overly
simplified template for SST databases, and much progress must be done into
this direction to reduce the uncertainties associated with an SST database
derived from multiple sensors.
Between-sample variability in reconstructed sea surface temperatures
(SSTs). (a) Reconstructed annual SSTs anomalies at individual sites
with sample resolution of < 100 years in the 1000-year window from
6.5 to 5.5 ka BP used for mid-Holocene (MH) reconstructions. The grey bar
shows the smaller 500-year window from 6.25 to 5.75 ka BP. (b)
Standard deviation of mean annual SST anomalies within the
6 ± 0.5 ka BP and 6 ± 0.25 ka BP time windows at individual
sites. (c) Comparison of observed standard deviation of SST and
number of samples used to calculate the mean values within the 1000-year and
500-year windows.
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We calculate MH annual, summer, and winter SST anomalies by subtracting
seasonal SST reconstructions from a modern seasonal reference climate.
Winter is defined as January, February, and March in the NH and July, August,
and September in the SH; summer as July, August, and September in the NH and
January, February, and March in the SH. We follow the protocol established
for the MARGO LGM reconstructions (Kucera et al., 2005a) by
using WOA98 as a modern reference (Supplement Table 2), but we also explore the use
of other potential reference climates (Sect. 3.1). The MH temperature at a
site is the average of all measurements within the 5.5–6.5 ka BP window
(Supplement
Table 2), but we also examined the potential use of a smaller time window
(Sect. 3.2). Although many of our analyses are based on reconstructions at
individual core sites, we have also gridded the reconstructions on a regular
5×5∘ latitude/longitude grid by averaging all of the records for a
given season.
The complete data set is available at www.pangaea.de
(10.1594/PANGAEA.830814; 10.1594/PANGAEA.830811). In addition to
the data provided in the Supplement, it contains age model information of the
previously unpublished records.
Results
Impact of the choice of baseline climate
The most robust way of comparing model outputs and palaeoclimate
reconstructions is through the use of anomalies, the difference between a
palaeoclimate reconstruction or experiment and a corresponding modern
baseline observation or control experiment. In contrast to terrestrial
environments, it is often difficult to obtain modern samples in the ocean.
To reconstruct the change in SSTs at the LGM, MARGO used observed
temperature at 10 m water depths from WOA98 as a modern reference temperature
(MARGO Project Members, 2009). Other studies have used different baselines
(Marcott et al., 2013; Ruddiman and Mix, 1993) or have calculated
anomalies relative to a long-term average (e.g. the last 1000 years:
Harrison et al., 2013; Leduc et al., 2010) derived from the core top
sediments. To test the impact of the choice of baseline climate on the
reconstructed SST anomaly patterns, we examined the effect of using the
updated version of the World Ocean Atlas (WOA09;
Locarnini et al., 2010) and the Hadley Centre Sea
Ice and Sea Surface Temperature data set, which covers the period of 1900 to
2000 (HadiSST; Rayner et al., 2003). We also examined
the impact of using a long-term core-top average to calculate the anomalies,
by comparing data from the GHOST database (which includes a “modern”
reference based on the 1000-year core top average) with the anomalies from
WOA98.
The average of the absolute difference in the MH mean annual SST anomalies
based on WOA98 and WOA09 is 0.3 ∘C (Fig. 1a), while the average
absolute difference between WOA98 and the HadiSST data set is 0.4 ∘C
(Fig. 1b). Differences in the reconstructed anomalies using different
baselines exceed 1 ∘C in some areas (Mediterranean Sea,
mid-latitude eastern Pacific). The differences in the MH anomalies estimated
using the core top reconstructions as the modern reference compared to the
WOA98 reference are even larger (Fig. 1c), with an average of the absolute
difference of 2 ∘C, and again this affects the spatial pattern of
the reconstructed SST anomalies. The impact on the spatial patterning is
reflected in the frequency distributions of the anomalies relative to the
different reference climates (Fig. 1d–f), which are different in terms of
dispersion and skewness. The choice of baseline climate has an equally large
impact on seasonal anomalies (Supplement Figs. 1, 2). Thus, the choice of baseline
climate affects both the magnitude and the spatial pattern of reconstructed
MH SST anomalies.
Impact of the choice of time frame
In developing synthetic data sets for data–model comparisons, the MH has
conventionally been defined as 6.5 to 5.5 ka BP (Kohfeld and Harrison,
2000; Leduc et al., 2010; Prentice et al., 2000) with reconstructions being
made based on all samples falling within this window. The use of average
values within a specified time window prevents the selection of single
samples that represent minor climate oscillations to compare with a
simulation representing long-term average conditions, and also maximises the
geographic coverage of sites. However, it assumes that short-term
(inter-annual to inter-decadal) climate variability has a negligible impact
on the long-term average signal. While this appears to be the case for land
reconstructions (see e.g. Bartlein et al., 2011), this may not be true in
the marine realm where the MH changes are smaller.
More than 80 % of the records in the data set have multiple samples
falling in the conventional MH window, where the anomalies would therefore
normally be estimated as the average of values from multiple samples. We
tested the impact of choosing different sampling windows by examining the
variability at individual sites with resolution of < 100 years
(Fig. 2a and Supplement Figs. 3a, 4a) and also by comparing the results
obtained by averaging over the 6.5 to 5.5 ka BP time window and by averaging
over a shorter time period (6.25 to 5.75 ka BP) (Fig. 2b and Supplement Figs. 3b, 4b). These comparisons show that between-sample differences within
the 1000-year window can be large (range between 1–3 ∘C), and the between-sample variability is not reduced when considering the
500-year window (range between 1–3 ∘C). There is no
difference in the variability as a function of sample size between the
broader and narrower time windows (Fig. 2c and Supplement Figs. 3c, 4c).
As a result, the magnitudes and spatial patterns of the anomalies obtained
using averages for 1000-year and for 500-year windows are similar. However, using the
500-year window would reduce the number of points represented on a synthetic
map. While this means that the convention of defining the MH as 6.5 to 5.5 ka BP for data–model comparisons is acceptable, the considerable
between-sample variability is problematic given that the expected changes in
SSTs are small in most regions.
Sensor comparison
The use of multiple sensors increases the number of data points available to
reconstruct global SST patterns, but raises the issue of the comparability
of reconstructions from different sensors. There are only 21 (out of a total
of 212) records in the data set where reconstructions from two sensors are
available. It is difficult to see any consistent relationship between the
reconstructions made with different sensors at the same site. For example,
although reconstructions based on foraminifera consistently yield colder
mean annual temperatures than reconstructions based on alkenones, the
difference can be negligible at some sites and several ∘C at
others (Fig. 3). In the seasonal reconstructions, even the sign of the
offset between sensors is inconsistent (e.g. dinocyst reconstructions show
conditions both colder and warmer than the corresponding foraminifera-based
reconstructions (Fig. 6)).
However, there is an insufficient number of points, overall and for any one
season, to make site comparisons meaningful. We therefore compare the
individual sensor reconstructions by season for specific ocean regions,
using only regions where there are at least three records for a given
sensor. The different sensors give comparable estimates of the median change
in annual SSTs (taking into account the uncertainty range) in most of the
regions, except in the North Atlantic, where alkenone-based reconstructions
indicate much warmer temperature anomalies than either foraminifera or
dinocysts (Fig. 4). This discrepancy is most marked in comparisons where
the median is calculated from all of the individual samples within the
1000-year window between 6.5 and 5.5 ka BP from each record (Fig. 4a), but
the difference between alkenone-based and foraminifera-based reconstructions
is still outside the range of uncertainties when the median is estimated
from the average MH SST anomaly of each of the individual records (Fig. 4b). Although summer reconstructions from different sensors give similar
estimates (Fig. 4a, b), the median change in the South Atlantic estimated
from foraminifera and Mg / Ca are significantly different, with Mg / Ca
reconstructions indicating very large cooling (Fig. 4a, b). Even in cases
where the median estimates are similar across all sensors (within the range
of uncertainty), the between-sample and between-site variability in SST can
be very large. In the Pacific, for example, where the median values obtained
from alkenones and foraminifera for both mean annual and summer anomalies
are similar, the interquartile range based on all the samples is ca.
3 ∘C and the full range is ca. 10 ∘C (ca. 7 ∘C
when only the record averages are used). Similarly large differences between
sensors, and variability, can be seen along latitudinal transects within
specific regions (Supplement Fig. 5).
Regional sea surface temperature pattern
It is common practice to grid individual site-based reconstructions (e.g.
MARGO Project Members, 2009; Bartlein et al., 2011; Annan and Hargreaves,
2013; Harrison et al., 2013) to facilitate comparison with gridded
climate-model outputs. We derived gridded estimates of summer, winter, and
mean annual MH SST anomalies by averaging values from every sample from
every record within a 5×5∘ latitude/longitude grid. We estimated
the standard deviation (SD) for each grid cell based on all values in the
grid cell. The data set yields values for 122 grid cells (Supplement Table 3), with
grid cell values being based in some cases on a single sample from a single
record and in other cases multiple samples from between one and nine
records.
Comparison of reconstructed annual, summer, and winter sea surface
temperature (SST) anomalies at individual sites where reconstructions were
made on at least two different sensors. The sites are arranged by latitude
for convenience.
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Comparison of reconstructed annual, summer, and winter sea surface
temperature (SST) anomalies for different ocean basins using different
sensors. The box-and-whisker plots show anomalies based on (a) using
all samples that fall within the 6.5 to 5.5 ka BP time window for all of
the individual records in a basin and (b) using the average SST
anomaly for the 6.5 to 5.5 ka BP time window from each record. Only sensors
that are represented by a minimum of three data points in any basin are
plotted. The box-and-whisker plots were drawn using the Golden Software Grapher,
which applies Tukey's method showing the 5 to 95 percentiles. The line shows
the median. The whiskers are drawn down to the 5th percentile and up to the
95th. Points below and above the whiskers are drawn as individual dots.
Outliers are calculated 75th (25th) percentile plus (minus)
1.5 × IQR (interquartile range). If this value is greater than (smaller than or equal
to) the largest value in the data set, the upper whisker are drawn to the
largest value. Any points greater (smaller) than 75th (25th) percentile plus
(minus) 1.5 × IQR are plotted as individual points. The chance of
finding an
“outlier” by Tukey's rule in data sampled from a Gaussian distribution
depends on sample size.
![]()
Gridded reconstructions of mid-Holocene (b) mean annual,
(c) summer, and (d) winter sea surface temperature (SST)
anomalies. The gridded values are averages of all records within the 5×5∘ latitude/longitude grid. The map in (a) shows the
distribution of reconstructions based on individual sensors.
![]()
Assessment of the signal-to-noise ratio in reconstructed sea surface
temperatures (SSTs) at (a) individual sites where there are more
than three samples within the 6.5–5.5 ka time window, (b)
individual grid cells, and (c) individual grid cells where there are
more than two records in the grid. Each plot shows the average change in SST
compared to the standard error (∘C). The bars attached to each
reconstruction represent the seasonally appropriate average measurement or
calibration uncertainties on the sensor (foraminifera: ±1.35 ∘C
winter, ±1.4 ∘C summer, ±1.3 ∘C mean annual;
dinocyst: ±1.2 ∘C winter, ±1.6 ∘C summer,
±1.1 ∘C mean annual; alkenones: ±1.2 ∘C mean
annual, Mg / Ca: ±1.2 ∘C summer). Each dotted line is
defined by the anomaly ± the standard error, i.e. points that fall
outside these lines (taking into account the measurement or calibration
uncertainty) would be considered to show a significant anomaly at the
95 % confidence level.
![]()
The gridded maps (Fig. 5) suggest that annual mean SSTs in the mid- to
high latitude NH and mid-latitude SH were warmer than in the present (Fig. 5b).
The upwelling cells off southwest Africa and off Chile display annual mean
conditions warmer than today, with the signal being more pronounced in the
eastern South Atlantic. In contrast, mean annual SSTs in the tropics appear to
be cooler than today. The reconstructed summer SSTs (Fig. 5c) are cooler
than today everywhere except the high-latitude Arctic Ocean. In winter, the
signal in the North Atlantic is spatially variable, but there is a contrast
between warmer-than-present SSTs in the eastern Pacific Ocean and
cooler-than-present SSTs in the western Pacific (Fig. 5d). However,
consistent with the results shown for individual ocean basins (Fig. 4),
the maps suggest that the overall change in SSTs is small (average of
gridded annual mean = 0.54 ∘C, summer = -1.01 ∘C,
winter = -0.13 ∘C), with high inter-site variability.
Assessment of significance of reconstructed changes in
sea surface temperatures
We assess the significance of the reconstructed changes in SST by comparing
the magnitude of the anomalies with the standard error, based on sites with
at least three samples in the 6.5 to 5.5 ka BP window, assuming that a
reconstructed change is significant when it exceeds twice the standard error
(SE) after taking into account the measurement or calibration uncertainties
associated with the sensor on which the measurement were performed (Fig. 6). Most of the reconstructions, both for individual site records
(Fig. 6a) or gridded reconstructions (Fig. 6b) do not show significant changes
in SST. Specifically, we find that only 34 % of the site-based
reconstructions and 33 % of the gridded reconstructions of mean annual SST
are significant; 28 % of the site-based reconstructions and 33 % of the
gridded reconstructions of summer SST are significant; 29 % of the
site-based reconstructions and 16 % of the gridded reconstructions of
winter SST are significant. Furthermore, more than 75 % of the gridded
reconstructions are based on single records. If we consider only those grid
cells where the reconstruction is based on multiple core records (as well as
multiple samples) from each core, then only one grid cell shows significant
seasonal or mean annual anomalies (Fig. 6c).
Although we assume all uncertainties are independent, a certain level of
dependency may exists nonetheless. However, considering the uncertainties as dependent would lead to the t test identifying even fewer records as
being significant.
Reliability assessment
In the absence of independent evidence, there is no objective way to assess
the reliability of the gridded SST patterns. MARGO (2009) established a
semi-empirical method to assess the uncertainty on individual LGM
reconstructions. This method combines the calibration error and measurement
uncertainty for each sensor, with an arbitrary measure of confidence in the
estimate and a semi-quantitative assessment of uncertainty due to dating and
internal variance based on the number of samples per core lying in the
specified time window and the quality of the age model of each record. This
is then combined with the variability of the SST reconstructions within a
grid cell to provide an assessment of the overall reliability of the gridded
reconstructions. Using the same approach, and considering the SST signal to
be reliable when the reconstructed SST anomaly is at least twice as large as
the weighted uncertainty, only 1 % of the mean annual, 4 % of the
winter, and none of the summer SST reconstructions can be considered as
reliable. The low number of grid cells considered as having reliable
reconstructions casts further doubt on many of the features shown in the
mapped reconstruction.
Impact of sensor distribution on mapped sea surface temperature
patterns
There are regional patterns in the distribution of records derived from
particular sensors (Fig. 5a). Given the discrepancies between
reconstructions obtained with different sensors (Sect. 3.3), this raises
the issue of whether patterns in reconstructed SSTs (Sect. 3.4) are an
artifact of the distribution of sensors. For example, the east–west dipole
in the Pacific during summer is based on planktonic foraminifera in the
eastern and Mg / Ca SSTs in the western part of the basin. Similarly, some of
the noisiness apparent in regional reconstructions (e.g. in the mid- to
high-latitude North Atlantic) clearly reflects adjacent sites where the
records were derived from different sensors. Some patterns are entirely
based on a single type of sensor and could be less apparent if other types
of record were available. For example, the pattern of MH summer warming in
the western Arctic is entirely based on dinocyst reconstructions while the
cooling in mean annual temperature in the Indian Ocean is derived from only
alkenone reconstructions.
Discussion
There have been several attempts to produce regional and/or global SST
syntheses for the MH (Kerwin et al., 1999; Leduc et al., 2010; Ruddiman
and Mix, 1993). Most of these have been based on one or (at best) two types
of sensors, and have used different baseline climates for the calculation of
anomalies, and are thus difficult to combine or compare. Here we have
followed the MARGO LGM multi-sensor approach (MARGO Project Members, 2009)
to produce a data set of MH SST anomalies. The reconstructed changes in SSTs
are small, and rarely exceed the uncertainties of the measurements, and
between-sample and between-site variability for a single sensor. Given that
differences between the measurements obtained from different sensors are
also large, and that only 9 % of the available cores have measurements on
more than one sensor, then the patterns that emerge from the gridded maps
are probably methodological artifacts.
The MH is a key period for climate model evaluation (Braconnot et al.,
2012). Evaluations of the CMIP5 palaeosimulations indicate that the coupled
ocean-atmosphere models are able to capture the very-large-scale pattern of
climate change, and have some limited success in capturing different spatial
patterns over the continents during the MH (Izumi et al., 2013; Li et
al., 2013; Schmidt et al., 2013). However, evaluations using various
different SST compilations, largely based on Mg / Ca and alkenone data, have
shown there are significant mismatches between simulated and reconstructed
SST (Schneider et al., 2010; Lohmann et al., 2013; Mairesse
et al., 2013). Our evaluation of the large uncertainties associated with the
MH SST reconstructions suggests that these mismatches may equally well
reflect data uncertainty as model inadequacy.
Standardisation of laboratory techniques and/or calibrations could remove a
large part of the between-site variability in SST reconstructions from an
individual sensor. Rosenthal et al. (2004) have shown that the use of
different cleaning methods introduces a bias of ±1 ∘C,
while the use of different calibrations introduce differences of ±0.5 ∘C for Mg / Ca reconstructions. Similar problems affect the
comparability of alkenones-based SST reconstructions and may be responsible
for even larger differences between individual reconstructions
(Rosell-Mele et al., 2001).
We have shown that the choice of baseline climate introduces uncertainty in
both the magnitude and the spatial patterns of the SST reconstructions.
Standardisation of the choice of baseline climate, as advocated by the MARGO
LGM project (Kucera et al., 2005a), will remove one source of potential
differences between different SST data sets. However, this does not mean
that the resultant data set will be any more comparable to model
simulations. There has been minimal consideration of whether reconstructed
palaeoclimate anomalies are strictly equivalent to simulated anomalies, but
our analyses show that the choice of a “modern” climate is crucial when
the climate-change signal is small. Due to the dependency of MH SST
anomalies to different baseline climates it may prove inadequate to use
pre-industrial climates as reference state in MH model simulation.
The MH orbital configuration resulted in a seasonal cycle of insolation that
is different from today and therefore should have had a larger impact on
seasonal than mean annual SSTs. Thus, reconstructions of seasonal SSTs are
likely to be more useful for model evaluation than reconstructions of mean
annual SSTs. We followed the same approach as the MARGO project (Kucera et
al., 2005a) to assign alkenone-based and Mg / Ca-based SSTs to specific
seasons: Mg / Ca-based SST reconstructions were assumed to provide summer
temperature estimates and alkenones to provide estimates of mean annual
temperature. These seasonal assignments are pragmatic, but Lohmann et
al. (2013) have shown that it is possible to minimise apparent mismatches
between simulated and reconstructed MH SSTs by accounting for possible shifts
in the seasonality of plankton blooms or in the depth at which the plankton
lived. The empirical evidence for seasonal representation is equivocal.
Ecological considerations suggest most phytoplankton species bloom in the
warmer part of the year and this will also be reflected in the abundance of
the organisms that graze on them (e.g. Mohtadi et al., 2009; Wilke et al.,
2009; Žarić et al., 2005). However, the Mg / Ca-based temperature
signal is based on measurements from different planktonic foraminifera
species, which potentially represent SSTs in different depth habitats of the
ocean surface and/or seasons. Indeed, Mg / Ca-based SSTs have been
interpreted as reflecting annual (e.g. Came et al., 2007; Eggins et al.,
2003; Steinke et al., 2011) or seasonal SSTs (Hessler et al., 2011; Mohtadi
et al., 2009; Steinke et al., 2008), depending on location, or as reflecting
the season of upwelling in coastal regions (Farmer et al., 2008). Similarly,
it has been suggested that the alkenone records represent warm season SSTs in
high-latitudes and the cold season in low latitudes (Leduc et al., 2010;
Schneider et al., 2010). However, Rosell-Melé and Prahl
(2013) showed that there
is no consistent and globally applicable seasonal pattern apparent in the
alkenone flux to sediment. The use of statistical reconstruction techniques,
applied here to reconstruct summer and winter SSTs from planktonic
foraminifera census counts and dinocysts, does not solve the problem. The
derived seasonal SST reconstructions are not independent but necessarily
reflect the covariance among seasonal SSTs in the modern ocean (Kucera et
al., 2005a). This is patently unlikely in the case of the MH and model
analyses suggest that there were significant changes in seasonality even
under LGM conditions (Izumi et al., 2013). As indicated in Sect. 2.3
(defining the “sea surface”), the SST pattern reconstructed in this study is
also likely biased by sensitivity of planktonic foraminifera assemblages to
temperatures at different depths in the water column, as well as by changes
in the SST sensitivity or recording depth of the other sensors and proxies
between the present day and the early Holocene. The former is likely to be
more significant because the recording depth of all other sensors and
proxies used in this compilation is bound to have remained within the photic
zone.
Changes in seasonality affect climate reconstructions based on terrestrial
vegetation, and this has lead to reconstruction approaches that focus on
bioclimatic variables more closely related to the physiological controls on
terrestrial plant growth (Cheddadi et al., 1996) and more
recently to the use of vegetation-model inversion as a reconstruction
technique (e.g. Guiot et al., 2000). We suggest that both of these
approaches could profitably be used to reconstruct SSTs, particularly since
there are now both simple models (e.g. Geider et al., 1997) and more complex
global ocean models that simulate the behaviour of plankton explicitly (e.g.
Aumont et al., 2003; Le Quéré et al., 2005) based on the
growing understanding of the ecology of individual plankton groups. Improved
understanding of the ecology of different plankton groups, and how this
could lead to changes in the seasonality, depth habitat, and adaptation to
changing environmental conditions, could also provide insights into the
causes of differences between the reconstructions obtained from different
sensors (Leduc et al., 2010), thus allowing the reconstruction of more
ecologically sensitive variables from existing data sets.
Although we applied several quality criteria for the selection of suitable
records including a minimum requirement on the chronological control,
differences in the SST pattern may be also related to chronological offsets
between some cores. However, it is questionable how different the SST signal
would actually have been even when considering a chronological error of 1000
years, considering the results of the simple exercise where we used two
different definitions of the early Holocene time window. If, as we believe,
the early Holocene SST signal was weak, then chronology alone is unlikely to
explain the observed difference lest we have made such large error as to
compare Holocene and Glacial sediments.
Our analyses were greatly facilitated by the fact that much of the primary
data and the SST reconstructions are archived at, for example, Pangaea (http://www.pangaea.de) or NOAA's National Climatic Data Center
(http://www.ncdc.noaa.gov/data-access/paleoclimatology-data). However,
target data sets for model evaluation need to be comprehensive because
regional and/or zonal signals could be significantly affected by data gaps.
Following (Kucera et al., 2005a), we strongly urge the
community to ensure that marine data and reconstructions are promptly
archived in order that the modelling community can make full use of these
resources.