Palaeoclimate data hold the unique promise of providing a long-term
perspective on climate change and as such can serve as an important
benchmark for climate models. However, palaeoclimate data have generally
been archived with insufficient standardisation and metadata to allow for
transparent and consistent uncertainty assessment in an automated way.
Thanks to improved computation capacity, transient palaeoclimate simulations
are now possible, calling for data products containing multi-parameter time
series rather than information on a single parameter for a single time
slice. Efforts are underway to simulate a complete glacial–interglacial
cycle using general circulation models (https://www.palmod.de/, last access: 6 May 2020), and to confront these
simulations with palaeoclimate data, we have compiled a multi-parameter
marine palaeoclimate data synthesis that contains time series spanning 0 to
130 000 years ago. We present the first version of the data product that
focuses exclusively on time series for which a robust chronology based on
benthic foraminifera δ18O and radiocarbon dating is available.
The product contains 896 time series of eight palaeoclimate parameters from
143 individual sites, each associated with rich metadata, age–depth model
ensembles, and information to refine and update the chronologies. This
version contains 205 time series of benthic foraminifera δ18O;
169 of benthic foraminifera δ13C; 131 of seawater temperature;
174 and 119 of planktonic foraminifera δ18O and δ13C; and 44, 38 and 16 of carbonate, organic carbon and biogenic silica
content, respectively. The data product is available in three formats (R,
LiPD and netCDF) facilitating use across different software and operating
systems and can be downloaded at 10.1594/PANGAEA.908831 (Jonkers et al., 2019). This
data descriptor presents our data synthesis strategy and describes the
contents and format of the data product in detail. It ends with a set of
recommendations for data archiving.
Introduction
Global climate has varied dramatically over the last glacial–interglacial
cycle. Since the previous interglacial (approximately 130 000 years ago) the
Earth had slowly been cooling until the Last Glacial Maximum (LGM; approximately
21 000 years ago). This cooling was associated with the growth of massive
ice sheets in North America and Eurasia, leading to a sea level drop of
about 120 m (Waelbroeck et al., 2002) and pronounced climate
variability on millennial timescales (Voelker and workshop participants,
2002). From the LGM, the Earth warmed rapidly until the onset of the
current relatively stable warm period, the Holocene (Shakun et al., 2012).
The ultimate cause of the large-scale variations in the Earth's climate is
changes in the orbit of the Earth around the Sun (Hays et al., 1976).
However, complex feedback and non-linear mechanisms, involving ocean
(atmosphere, cryosphere) circulation and biogeochemical cycles, are required
to explain how slow changes in the orbital configuration led to the observed
evolution of global climate and how these processes led to the
manifestation of abrupt climate change.
For these reasons the last glacial–interglacial cycle has been a key target
for palaeoclimate modelling. Initially this only involved equilibrium
simulations for key time slices, such as the LGM, or transient simulations
for short periods, such as the last millennium. The motivation to simulate
past climate states is given by the possibility of palaeoclimate data serving as a benchmark for the models. Indeed, this possibility contributed to
the development of large palaeodata syntheses (CLIMAP project members, 1981;
MARGO project, 2009). The time-slice modelling approach is still being
pursued; for example in phase 4 of the Paleoclimate Modelling Intercomparison Project
(PMIP), four of the five target intervals fall into the time frame of the
last glacial cycle (Kageyama et al., 2018). However, with increasing
computing power, the focus is now shifting towards transient climate
simulations (Liu et al., 2009; Latif et al., 2016), and the simulation of the
last deglaciation is now also considered in the PMIP protocol (Ivanovic et
al., 2016).
This development calls for a different type of palaeodata synthesis, with
its focus on time series rather than on time slices. Time series of climate data
are needed to evaluate aspects of transient simulations that are not
available in equilibrium simulations, such as rates of change, phase
relationships and spectral properties of climate variability. It is also
clear that an evaluation in multi-parameter space using different aspects of
the climate system and multiple proxies will be more powerful and diagnostic
(Kurahashi-Nakamura et al., 2017), calling for multi-parameter synthesis
products.
Observations of the evolution of past climate are based on proxies
(measurable approximations of climate-related variables) and hence are, by
definition, indirect. Comparison of proxy-based reconstructions with climate
model simulations is therefore far from straightforward, as discrepancies
may arise from both model and proxy uncertainty. Proxy uncertainty derives
from reconstruction uncertainty (related to calibration, recording bias,
archive specifics and instrumental approach) and chronological uncertainty.
The latter is particularly relevant to the comparison of transient climate
change, and chronological uncertainty thus requires a comprehensive treatment
in data syntheses of palaeoclimate time series.
Accounting for proxy uncertainties in a comprehensive and transparent manner
requires not only expert knowledge but also the availability of extensive
metadata in addition to the proxy data. However, due to a lack of
standardisation and inconsistent archiving of metadata, synthesising
palaeoclimate data in a way that allows for robust uncertainty assessment
remains challenging and time consuming. Efforts are underway to alleviate
these challenges. The largest palaeoclimate data repositories (World Data
Service for Paleoclimatology, operated by the national centres for
environmental information (NCEIs) at NOAA, and PANGAEA) are both striving for
more standardisation and to store data in (more) machine-readable formats. In
addition, standardisation is progressing through the use of existing data
formats from other communities (netCDF; Langner and Mulitza, 2019) as well
as the implementation of new data formats specifically targeted to
palaeodata (Linked Paleo Data (LiPD); McKay and Emile-Geay, 2016). At the
same time there is ongoing discussion on data and metadata requirements and
standards (Khider et al., 2019). Traceability of datasets is also improved
through data citations, not only ensuring that data producers receive proper
credit for their work but also allowing for better linking of different
datasets. Nevertheless, these initiatives have only recently been emerging and the
majority of the palaeoclimate data remains inconsistently formatted,
non-standardised and scattered over various data repositories. The need for
synthesis products and documentation of potential synthesis approaches is
therefore as large as ever.
Here we present the first version of a new multi-proxy marine palaeoclimate
data synthesis that covers the past 130 000 years developed within the
German climate modelling initiative PalMod (Latif et al., 2016). We focus on
the ocean as it is a large reservoir of heat and CO2 and allows for global
coverage with consistent chronological control. This synthesis goes beyond
the time frame of many existing multi-proxy and multi-parameter data syntheses
(PAGES2k Consortium et al., 2017; Routson et al., 2019), expands existing
data products that provide long palaeoclimate time series to multiple
parameters (Shakun et al., 2012; Marcott et al., 2013; Peterson and
Lisiecki, 2018; Snyder, 2016), and is based on a strategy of semi-automated
data harvesting (Cartapanis et al., 2016). This version of the synthesis
contains data on nine climate-sensitive parameters: benthic and planktonic
foraminifera stable oxygen and carbon isotopes, seawater temperature,
radiocarbon and bulk sediment carbonate, organic carbon, and biogenic silica
content.
In this paper we describe our synthesis approach, the contents and
structure of version 1.0.0 of the data,
plans for future updates, and recommendations for archiving new data and
retrieving dark data in a way that allows for optimal future reuse. The data
product is intended to be used to investigate spatio-temporal changes in a
multi-parameter domain. Thanks to rich metadata that allow for the rigorous quantification of reconstruction uncertainties, we also envision that this data
product will provide the building blocks for intelligent palaeoclimate data
model comparison (Weitzel et al., 2019), for instance through proxy system
modelling (Dolman and Laepple, 2018) or data assimilation (Breitkreuz et
al., 2019).
The structure of this data descriptor is as follows. Section 2 describes the
synthesis strategy, including the data discovery approach, standardisation
and age modelling. In Sect. 3 we provide general information on
palaeoclimate proxies from marine-sediment archives that is used to guide
the metadata selection. Section 4 details the structure of the database, and
the contents of version 1.0.0 are outlined in Sect. 5. The formats of the
data product, future plans and versioning are described in Sects. 6 and 7. Section 8 describes where the data can be accessed. In the last section,
Sect. 9, we reflect on the data synthesis effort and provide
recommendations for data archiving and data rescue.
Data synthesis strategy
Our data product focuses on time series from marine-sediment archives. A
single marine-sediment archive (sediment core) can be used for measurements
of different parameters, each providing information on different aspects of
the environmental conditions at the time of deposition. However, for the
purpose of analysis, the various proxy time series must refer to a single
age–depth model for the sediment core they are derived from. For this
reason, the basis of our synthesis is formed by a collection of sediment
cores, each associated with its own age–depth model.
Marine sediments are dated using absolute age controls, where specific
layers are dated using, for instance, radiocarbon, tephra or palaeomagnetic
properties, and/or relative age controls, where time series are aligned
based on the hypothesised synchronicity of the changes recorded by some
properties of the sediment. A well-established hypothesis-based age modelling approach with a solid theoretical basis is the alignment of
benthic foraminifera stable-oxygen-isotope ratio (δ18O) time
series (Lisiecki and Raymo, 2005). We thus base our chronological framework
on a combination of radiocarbon dates and benthic foraminifera δ18O and have selected time series where both parameters are available
as the foundation of this data product. This approach of blending absolute
and relative age controls is required to provide age–depth models for
sediment cores that extend beyond the radiocarbon dating range
(∼40 000 years). If available, further proxy time series were
then added, thus ensuring a common chronology among all proxy time series
measured on the same sediment core.
We selected palaeoclimate parameters to synthesise the following discussion with
climate modellers within the PalMod project. The high-priority selection
includes both physically and biogeochemically relevant parameters, of which
some are based on measurements that can be compared with climate model
output using (forward proxy) models (e.g. benthic δ18O) and
others represent inferred parameters that can be compared with model output
more directly but for which proxy models are still in their infancy (e.g.
temperature based on foraminifera Mg/Ca). Also considered in parameter selection was the expected spatial and temporal coverage of data availability
as well as the existence of previous data products. The high-priority
parameters for which data are presented here are listed in Table 1. If
available, raw data were synthesised and in cases where raw data were not
available and it was possible to derive the raw data from the inferred
palaeoclimate data, raw data were back-calculated. Raw data time series
obtained in this way are flagged with a note describing the calculation.
Palaeoclimate parameters in the PalMod 130k marine data synthesis.
ParameterBenthic foraminifera δ18O and δ13CPlanktonic foraminifera δ18O and δ13CSeawater temperature*RadiocarbonCarbonate contentTotal organic carbon contentBiogenic silica content
* Inferred from various proxies (foraminifera Mg/Ca, alkenones, microfossil
assemblages).
We note that our approach of first building the stratigraphic framework
based on radiocarbon dates and benthic foraminifera δ18O means
that the synthesis is not necessarily comprehensive as it does not include
time series where one of the parameters of interest has been measured but
where the components of the stratigraphic framework are not available.
However, at this stage, we opted to include only sediment cores where an age
modelling strategy that is consistent and comparable across the entire data
product could be achieved.
Data discovery
In principle, data synthesis can proceed by expansion or reduction (Fig. 1).
The first, more traditional, approach relies on expert knowledge of what
data are available and/or on asystematic literature search. In this approach
the synthesis grows by including more data until sufficient data that meet
inclusion criteria are compiled. In this way, a lot of time is spent on
discovering and retrieving datasets, and it is possible that valuable, but
less exposed, data are missed. On the other hand, this approach has a chance
of uncovering dark data that are not publicly available (Fig. 1).
Data synthesis approaches. In the expansion approach the database
size increases slowly as records are added. The database size follows an
opposite pathway using the reduction approach and reaches a stable size more quickly,
with less effort. Since the expansion approach is not restricted to data
that are available in the public domain, this approach may lead to a database
that includes data that are not publicly available (dark data). The reduction
approach on the other hand is arguably more objective, can be automated and
is therefore more efficient. This approach also encourages good data
stewardship.
The second approach starts from a large and crude synthesis of data from
public sources and proceeds by weeding out data that do not meet criteria
for inclusion in the data product. This approach faces different challenges:
making sure that the initial bulk database is comprehensive (efficient data
mining) and assuring that the data filtering is efficient (fast and
accurate). In contrast to the expansion approach, this reduction approach
cannot discover dark data. However, it is more objective (less reliant on
expert knowledge), can be automated more easily and focuses on data that are
already in the public domain so that no time is lost to finding data that
ultimately prove unavailable. Because this second approach focuses on data that are publicly available, it also rewards and
encourages good data stewardship.
In theory, both approaches can lead to a similarly sized and exhaustive
synthesis, but they differ in the allocation of effort (Fig. 1). In practice
both approaches are often combined, especially towards the end of a
synthesis project, when the data product is benchmarked against existing
syntheses.
SynthesisInitial synthesis
We followed the reduction approach and used a semi-automated pipeline to
compile data from public sources. Keywords (Supplement) were
used to make lists of URLs of potentially relevant data on https://pangaea.de (last access: September 2016), and
the linked files were then downloaded in bulk (n=108 239). A slightly
different approach was followed for the NCEI archive. Here, all files that
were machine-readable at the time of download (September 2016, n=1925)
were obtained from the FTP server (ftp://ftp.ncdc.noaa.gov/pub/data/paleo/paleocean//sediment_files/complete, last access: September 2016). Custom scripts in R were used to put all data in a common
format and merge time series that could be unambiguously assigned to the
same core (based on name and x, y, z position). This resulted in a mixture
of records that were merged to the same core and those that could not be,
either because there was only one data file for the core or because of
ambiguous labelling. We refer to the locations of these records as
“sites”. In order to facilitate the analysis (filtering) of the sites, a
uniform attribution of the various parameter names had to be developed.
Because no standardised names exist for palaeoclimate parameters, the
uniform attribution required the development of attribution libraries for
each desired palaeoclimate parameter. The initial synthesis contained time
series from 38 511 sites.
Data reduction and standardisation of ontologies
The initial synthesis was reduced by removing non-marine sites (using
elevation) and further constrained by only considering sites where at
least one data point of any of the parameters measured in that core fell
within the target time frame (disambiguating age units in the synonym
library of the category “age”) and the site had benthic oxygen isotope
data. This resulted in 781 sites. At this stage, no criteria for length or
resolution were applied but we prioritised processing time series that we
estimated to contain at least 50 data points within the 130 000-year
timeframe. Further data processing started with dereplication of the
selected sites. This was necessary because no standards exist for the naming
of cores and the repositories store data with different renditions of the
same core name, sometimes even associated with erroneous geographic
coordinates. This process was carried out manually and proceeded by constructing a
list of disambiguated sites through sequential one-by-one comparison. Where
a strict synonym was found (different labels for the same core but the same
data), only unique data were retained. At this stage, disambiguation of site
names was only performed for sites that had at least benthic δ18O,
so time series of other parameters, which were associated with inconsistent core
labels, could have been missed in the synthesis. However, those sites are
contained in the initial bulk synthesis and are hence not lost but will be
salvaged in updates of the data product (see Sect. 7).
Further steps required a manual standardisation of the names of the
parameters and their attributes (such as the species name that was analysed for
oxygen isotopes). This was accomplished by deciding on a final, uniform list
of parameters and associated metadata and their possible values. Original
parameter names were preserved to allow for cross-checking. By metadata, we
refer to aspects of the individual parameters that were deemed essential to
facilitate a meaningful analysis in a palaeoclimatic context, considering
potential sources of uncertainty, such as species of foraminifera analysed
or the calibration equation used for palaeotemperature estimates. The list of
metadata is provided in Table 2. The standardisation was accompanied by
further dereplication of individual time series that were already associated
with the same site name but archived more than once.
Metadata terms.
NameDescriptionParameterOriginal*Original parameter name as in data repositoryParameter*Standardised parameter name (see Table 4)ParameterType*Parameter type, measured or inferredParameterUnit*ParameterAnalyticalErrorError based on repeat measurements of standardsParameterReproducibilityError based on repeat measurements of samplesInstrumentLaboratorySampleThickness_cmMaterial*Measurement material or parameter on which inferred parameter is basedSpeciesNshellsSizeFraction_micromNotesRecordingSeasonRecordingDepthEquilibriumOffsetCalibrationEquationCalibrationUncertaintyCalibrationDOITransferFunctionTrainingSetTransferFunctionUncertaintyTransferFunctionDOIPublicationDOI*AuthorsPublicationTitleJournalYearVolumeIssuePagesReportNumberDataDOIDataLink*RetrievalNumberFor internal use only
* Essential terms.
Metadata and chronology
Subsequently, as far as possible, metadata values were added manually, often by scraping the information from the original publication.
Next, all time series from a single site (core) were put on a common depth
scale to allow for age modelling. Data that could not be put on a depth
scale were excluded from the synthesis. This was the case where parameter
values were archived only against age and where no other data file was
available that allowed unambiguous reassignment to depth. Ambiguity also
resulted from the use of multiple (composite) depth scales for the same
archive. Finally, chronological data (all absolute markers, including
radiocarbon dates and associated metadata) were manually added, where
necessary also by consulting the original publications. Throughout the
process, publication information (digital object identifier (DOI) or, if not
available, full bibliographic details) and the data source (URL and/or DOI) were
preserved in order to trace the source of the data. This applies both to the
source of the individual data files from repositories and to the sources of
the metadata and chronological data.
Age modelling
Whereas the initial steps of data discovery and synthesis could rely on
published chronology, analysis of the complete dataset requires the
development of a common chronological framework. This framework must be
constructed in a way that not only allows for a consistent method of assignment
of ages to depths within each core but also allows for the consistent and
quantitative assessment of age uncertainty. To this end, we follow an
approach that combines absolute ages (radiocarbon ages, tephra layers and
palaeomagnetic events) with δ18O stratigraphy. As a result, our
age models may differ from those reported in the original publication(s).
This does not mean that the updated age models are better (constrained),
but they are constructed in a way that allows for applicability and consistency
across the synthesis. The consistent approach allows for an assessment of age
uncertainty jointly for all records by a Bayesian approach, generating
ensembles of sedimentation histories consistent with the available age
control points for each core, allowing for uncertainty estimates at each depth
by considering the distribution of ages given by the ensemble.
With respect to the reporting of the chronology, we follow a transparent
approach, preserving the initial age model and providing the new age models
as well as all information needed to revise or update the new age models. In
the final step, the age information from absolute ages and δ18O
tie points was combined and the age model and its uncertainty was assessed in
a Bayesian framework using “Bacon” (Blaauw and Christen, 2011). The entire age
modelling routine was carried out in PaleoDataView (PDV; Langner and
Mulitza, 2019).
To ensure a common chronological framework for all time series in the
synthesis, radiocarbon ages were recalibrated using the IntCal13 curve
(Reimer et al., 2013). Since reservoir ages vary in space as well as in
time, we used reservoir age estimates based on a comprehensive ocean general
circulation model (Butzin et al., 2017) to account for this variability in a
physically plausible way. To derive the reservoir age and uncertainty for a
measured radiocarbon age, PDV (i) extracts all modelled radiocarbon ages from
the nearest grid cell in the modelled dataset, (ii) finds all modelled
radiocarbon ages that are possible within the error of the measured
radiocarbon age, and (iii) takes the mean and the standard deviation of all
corresponding reservoir ages to correct for the measured radiocarbon age.
By definition, this approach cannot account for processes affecting the
reservoir ages on subgrid spatial scales. Given the relatively coarse
resolution of the model, this means that processes such as upwelling are not
fully accounted for. In addition, no modelled reservoir age data are
available for the Mediterranean and Red seas; we use the reservoir ages
reported by the authors of the original publication and an assumed
uncertainty of 100 years for these basins (five sites). Absolute ages based on
North Atlantic tephra layers and palaeomagnetic events were updated and
harmonised using Svensson et al. (2008).
In addition, and beyond the 14C dating realm (∼40 000 years), the age models rely on manual tuning of the benthic foraminifera
δ18O time series from each core to regional benthic
foraminifera δ18O stacks (Lisiecki and Stern, 2016). Stable-isotope stratigraphy in theory provides a range of events to correlate;
however, in order to not inflate confidence in the tuned age models and to
ensure comparability between different cores, in our approach, the tuning
was carried out as far as possible by only matching the position of marine isotope
stage boundaries. We updated the age–depth models only for the 0–130 000 years time frame of this synthesis, but data and original age models
extending beyond 130 000 years are preserved in the data product. To obtain
uncertainty for the age control points obtained by δ18O tuning,
we used the chronological uncertainty in the δ18O stacks, as
reported by Lisiecki and Stern (2016). Additional uncertainty associated
with the identification of the control points in the individual records or
with the assumption on synchronicity was ignored, as these are difficult, if
not impossible, to quantify.
Notes on palaeoclimate proxies in marine-sediment archives and metadata
This synthesis contains climate-sensitive proxy data based on measurements
using various biological sensors. It is not the intention here to provide a
full overview of marine palaeoclimate proxies and their uncertainties (for
this, see for example Hillaire-Marcel and De Vernal, 2007; Moffa-Sánchez et al.,
2019), but the fact that the proxies are based on biological sensors means
that they are affected by different ecological bias in addition to
observational noise. Basic knowledge of the recording system is therefore
essential for the interpretation of the data and may aid in explaining
differences between proxies for the same climate parameter. These
considerations were also essential to choosing the range of metadata to be
recorded alongside each palaeoclimatic parameter to allow for a proxy-specific
assessment of uncertainty.
Foraminifera are among the most widely used proxy sensors in
palaeoceanography. They are unicellular marine zooplankton. The species used
here all build a calcite skeleton that is preserved in the sediment.
Foraminifera can be divided into two main groups: benthic foraminifera
living at the seafloor level or at shallow depth in the sediment and planktonic
foraminifera living in the upper hundreds of metres of the ocean. In the
data product, proxies measured on these two groups are clearly distinguished
by a benthic or planktonic prefix. The chemical composition of foraminifera
reflects environmental conditions of the seawater that the organisms calcified
in. For the purpose of this data product, the parameters of interest are
stable-oxygen-isotope, stable-carbon-isotope and Mg/Ca ratios. Stable-oxygen-isotope
ratios in foraminifera calcite reflect a combination of temperature and
δ18O of seawater (Urey, 1948), which is in turn related to ice
volume and salinity. Species-specific calibrations exist to quantitatively
link δ18Oforaminifera and δ18Oseawater
to temperature (e.g. Marchitto et al., 2014; Bemis et al., 1998). Stable-carbon-isotope ratios (δ13C) reflect the δ13C of
the dissolved inorganic carbon in seawater. In particular the benthic
foraminifera species Cibicidoides wuellerstorfi generally incorporates δ13CDIC
without a biological offset and can serve as a tracer of bottom-water
δ13CDIC which is commonly used as non-passive circulation
tracer (Curry and Oppo, 2005). The δ13C of other benthic
foraminifera species is generally not indicative of bottom-water δ13CDIC, and the δ13C of planktonic
foraminifera is also influenced by temperature and carbonate ion
concentration, rendering interpretation complicated (Spero et al., 1997).
The Mg/Ca ratio in foraminifera calcite can be used to infer calcification
temperature and, in combination with δ18Oforaminifera, the
δ18Oseawater (Elderfield and Ganssen, 2000). Similar to
stable oxygen isotopes, species-specific calibrations exist to
quantitatively reconstruct past temperature from Mg/Ca ratios (Anand et al.,
2003; Lear et al., 2002), and whenever indicated in the original
publication, the calibration is included in the metadata. Carbonate system
parameters and salinity have a secondary influence on Mg/Ca ratios in
foraminifera calcite (Gray et al., 2018b). Whereas benthic foraminifera live
in a generally stable environment, the near-sea-surface habitat of
planktonic foraminifera shows large seasonal and vertical gradients.
Species-specific seasonal and/or depth habitat preferences may therefore
leave a considerable imprint on the proxy signal contained in their shells
(Jonkers and Kučera, 2017; Mix, 1987). For all proxies based on
foraminifera, it is relevant to record the species as well as the number of
individuals that were pooled for geochemical analysis. The latter is because
the short lifespan and variable habitat of foraminifera species cause
large variability among individuals. Planktonic foraminifera shell size may
for several reasons also affect their chemistry (Jonkers et al., 2013;
Friedrich et al., 2012) as well as their assemblage composition (Al-Sabouni
et al., 2007). Therefore, the size fraction of the analysed shells was
included in the metadata whenever this information was available.
Besides planktonic foraminifera Mg/Ca ratios, the UK′37
unsaturation index can provide information about near-sea-surface
temperature. The UK′37 index is based on the relative degree of
unsaturation of C37 alkenones, which is linearly related to temperature
(Prahl et al., 1988). Alkenones are produced by coccolithophores, marine
phytoplankton living in the photic zone. The production of alkenones is in
many regions not constant during the year, thus potentially causing a
seasonal recording bias in the UK′37 temperature proxy
(Rosell-Melé and Prahl, 2013). Several calibrations exist that relate
the index to sea surface temperature, and if the calibration was mentioned in
the original publication, it was preserved in the metadata.
A large proportion of the temperature estimates in this data product are
based on microfossil (planktonic foraminifera, diatoms, Radiolaria,
dinoflagellate cysts) assemblages. These reconstructions are based on a
statistical relationship between species assemblages and temperature (Imbrie
and Kipp, 1971). In theory, microfossil assemblages can be used to
reconstruct temperatures of different seasons or different environmental
parameters from the same assemblage. However, it is not always clear that
such reconstructions are truly independent (Telford and Birks, 2011).
Several different methods exist to relate fossil assemblages to temperature,
and researchers often apply more than a single method in their
reconstructions to increase confidence (Kucera et al., 2005). When
available, these different reconstructions are included in the data product.
The bulk sediment data (CaCO3, TOC and BSi) form a category of their
own. They are not proxies in the strict sense but properties of the
sediment that reflect a combination of export productivity, sedimentation
and preservation. However, they can provide crucial information about the
ocean–climate system, in particular about biogeochemical cycles (Cartapanis et
al., 2016). With the advent of explicit sediment modules in climate models
(Heinze et al., 1999; Kurahashi-Nakamura et al., 2020), sediment composition
can also be directly compared with model output and potentially provide
additional constraints on the simulations.
Structure of the database
Following the data synthesis strategy outlined above, we generated a first
data product for time series of eight parameters in sediment cores with
radiocarbon and benthic δ18O stratigraphy. Following the logic
of our approach, the synthesis is organised by the physical object from
which the records were extracted (cores), here called site, to account for the
inclusion of records from spliced cores.
Each site in the data product has information on seven different themes
(Fig. 2):
Geographic data contain the site name, latitude and longitude (in decimal degrees
N and E), elevation or water depth (in metres), and possible notes that are
relevant to the site or core as a whole. All fields except notes are essential
and always included.
Metadata include the original parameter name as given in the online data
file, a standardised parameter name, parameter type (measured or inferred),
unit, an estimate of analytical error as determined from repeat measurements
of a standard and an estimate of reproducibility as determined by repeat measurements on
samples. For measured parameters, information on the instrument and
laboratory is given. All metadata terms are listed in Table 2, and an
overview of the standardised parameter names is provided in Table 3.
Chronology data contain raw data on absolute age control points used for
age modelling. This includes not only depth, radiocarbon ages including their
uncertainty, dated material and laboratory codes but also calendar ages of
tephra layers and palaeomagnetic events. Age control points not used in the
age model by the authors of the original publication(s) are indicated, and if
available, the source (DOI or URL) of the data is shown in addition to the
original publication DOI. A complete list of chronology data terms is given
in Table 4.
The actual time series data are provided on a common depth scale. Original
age models are preserved alongside the data time series as they may differ
for different time series from the same site. The data also contain
information on the sample number or label (mainly for DSDP, ODP and IODP cores) for
spliced records and sample-specific notes.
The revised age model contains ages for depths bracketed by age control
points (absolute and relative). Mean and median ages are given as well as an
uncertainty range (2.5th and 97.5th percentiles) based on the full suite of age
model ensembles. No attempts were made to extrapolate the age models beyond
the tie points in the 130 000-year time frame, so original age models may
extend to either side.
The Bacon data contain all information to reproduce the revised age model.
Besides the 14C and absolute age control points, this includes the tie
points for the alignment, the alignment target and all parameters used to
construct the age–depth model.
Age ensembles are provided for further assessment of chronological
uncertainty. In order to keep file sizes manageable, 1000 randomly selected
age model ensembles are preserved.
Structure of the PalMod 130k marine palaeoclimate data synthesis.
Each file in the database contains information on seven different themes on
a single site (sediment core). Links between different themes are indicated.
Standardised parameter names.
NameDescriptionbenthic.d18OBenthic foraminifera δ18Obenthic.d13CBenthic foraminifera δ13Cplanktonic.d18OPlanktonic foraminifera δ18Oplanktonic.d13CPlanktonic foraminifera δ13Csurface.tempInferred (near-)sea-surface temperature (based on microfossils, planktonic foraminifera Mg/Ca, UK′37)deep.tempInferred bottom-water temperature (based on benthic foraminifera Mg/Ca)CaCO3Calcium carbonate contentTOCTotal organic carbon contentBSiBiogenic silica contentDBDDry bulk densityIRDIce-rafted detritusplanktonic.MgCaPlanktonic foraminifera Mg/Ca ratiobenthic.MgCaBenthic foraminifera Mg/Ca ratioUK37UK37 ratio (rare cases where this is not UK′37 mentioned in notes)C37.concentrationAlkenone concentrationPalMod 130k marine palaeoclimate data synthesis v1.0.0 contents
The data product contains 896 time series of the palaeoclimate parameters
listed in Table 1 from 143 sites. An overview of all datasets used in this synthesis,
including URLs to the data, can be found at
10.5281/zenodo.3739019 (Jonkers et al., 2020). Data sources for each site are listed in the Appendix. By design all sites have both
benthic stable-oxygen-isotope and radiocarbon data.. The majority of the
sites are close to the continents and in the Northern Hemisphere, with a
concentration in the North Atlantic Ocean (Fig. 3). This reflects both
research attention as well as the challenges of obtaining sediment cores
with high accumulation rates and well-preserved foraminifera. The effect of
research focus is also visible in the temporal coverage of the time series,
where every parameter is characterised by a clear maximum around the last
glacial termination ca. 15 ka (Fig. 4). The median resolution of the time
series varies by 2 orders of magnitude but is generally better than one
sample per 1000 years and fairly similar among the different parameters
(Fig. 5). The updated age models are based on chronological control points
(tie points) from radiocarbon dating and absolutely dated layers (using tephra
and/or palaeomagnetic event stratigraphy) as well as alignment to the
regional benthic δ18O stacks. The majority of the time series
has a chronological control point at least every 5000 years (Fig. 6).
Taken together, the coverage in space, time and across parameters indicates
that the PalMod marine palaeoclimate data product allows for analysis of
palaeoclimate on a supra-regional scale over the entire 130 000-year time
frame.
Spatial distribution of sites in version 1.0.0 of the PalMod 130k
marine palaeoclimate data synthesis. The distribution of the sites reflects
research effort and the possibility of obtaining sediment cores containing
well-preserved foraminifera and is hence skewed towards the Northern
Hemisphere (Atlantic) and the continental margins. Since no explicit search
was carried out for parameters other than benthic foraminifera δ18O,
the distribution of the other parameters is restricted to sites that have
benthic foraminifera δ18O. For benthic foraminifera δ13C, only sites with data based on the genus Cibicidoides are shown.
Chronology terms.
NameDescriptionChronType*Type of absolute chronology tie point (14C, tephra, palaeomag)ChronDepthTop_cmChronDepthBottom_cmChronDepthMid_cm*ChronSampleThickness_cmChronAge_kaBP*Age of non-14C tie point (tephra, palaeomag)ChronAgeError_ka*Age error of non-14C tie point (tephra, palaeomag)ChronDatedMaterialChronDatedSpeciesChronNshellsDatedChron14CLabcodeChronAge14C_kaBP*ChronAge14CError_ka*ChronAge14CErrorUp_kaChronAge14CErrorDown_kaChronReservoirAge_ka*ChronReservoirAgeError_ka*ChronCalibCurveChronCalibAge14C_kaBPCalibrated 14C ageChronCalibAge14C1sigLo_kaChronCalibAge14C1sigUp_kaChronAgemodelMethodChronAgeRejectedChronNotesChronSourceChronDOI*
* Essential terms.
This data product builds upon previous syntheses. Virtually all of the sites
are also part of the benthic foraminifera δ18O and δ13C compilations of Lisiecki and Stern (2016) and Peterson and
Lisiecki (2018). Our synthesis, however, also includes data on other
palaeoclimate parameters and contains more metadata and information on the
age–depth models. Some of the planktonic foraminifera δ18O and
Mg/Ca time series in the PalMod 130k data product are also included in the
Iso2k synthesis effort (Konecky et al., 2018), and a number of sea surface
temperature time series are also part of the Temperature12k
synthesis (Kaufman et al., 2020).
Data formats
We provide the data products in three different formats in order to
facilitate access and analysis using different software and across operating
systems. Given the structure of the formats, each representation is slightly
different in its level of metadata detail and the way metadata and data are
stored. The differences are described below.
Since the data product was built using R, the data product is presented in
R-readable RDS files that contain for each site a list with data for each
theme (Sect. 4). This is the format that is most complete, yet in the
interest of memory space it preserves a random selection of 1000 age models
from the larger ensemble produced using Bacon. In this format, all data and
metadata for each site are contained within a single file. Example scripts
(https://github.com/lukasjonkers/PALMODutils, last access: 6 May 2020) allow the user to extract a
quick overview of the contents of the data product
with information on the temporal range, resolution and age
control of the time series. Additional code is available to query the data
product by parameter, parameter detail, sensor species, temporal range,
resolution and age control.
The data product is also provided in the LiPD format, which is built around
JSON-LD and CSV formats and is widely readable across different platforms.
As with RDS, all data for each site are presented in a single file. Utilities
to interact with LiPD files in R, Matlab and Python are available at
https://github.com/nickmckay/LiPD-utilities (last access: 6 May 2020).
Finally, the data are also provided in netCDF format in a way that allows
reading with PDV. This means that a single site has separate files for each
individual palaeoclimate parameter as well as for the age model. In this
format, some metadata are stored as concatenated strings rather than easily
searchable attributes. The netCDF format allows however for the storage of the full
suite of age model ensembles without excessive file sizes. The PDV software
to read and process the data can be downloaded at https://www.marum.de/en/Stefan-Mulitza/PaleoDataView.html (last access: 6 May 2020).
Temporal distribution of time series in version 1.0.0 of the
PalMod 130k marine palaeoclimate data synthesis. The number of time series
(y axis) is counted per 1000-year bin. The temporal availability of all
parameters shows a clear maximum around 15 ka, reflecting the research
focus on the last glacial termination. For benthic foraminifera δ13C, only time series with data based on the genus Cibicidoides are shown.
Median resolution of the time series in version 1.0.0 of the
PalMod 130k marine palaeoclimate data synthesis. Box-and-whisker plots show the
spread of resolution per parameter. For benthic foraminifera δ13C the resolution data are restricted to time series containing data
measured on shells of the genus Cibicidoides. For all parameters the median resolution
is more than one data point per 1000 years.
Future plans and versioning
To increase the spatio-temporal coverage over the entire 130 000-year time
frame of the database, updates of this data product will first aim for
quantitative growth of the database by adding more time series with
chronological control based on benthic foraminifera δ18O and
absolute age control points other than 14C. If available, these updates
will also include the parameters listed in Table 1. They will be named using
the counter following the first decimal separator. The structure of the data
product is designed to be flexible, allowing for the addition of different
metadata fields and parameters. Further updates that include new parameters
and/or require a new age modelling approach (i.e. no benthic δ18O alignment) will be named using the counter before the first
decimal separator. Any updates to add or correct (meta)data to an existing
version that do not increase the number of sites will be indicated using
a counter following the second decimal separator. Future versions will be made available on PANGAEA, and links to the updates will be provided at 10.1594/PANGAEA.908831 (Jonkers et al., 2019).
Data availability
The
PalMod 130k marine palaeoclimate data product can be downloaded in R, LiPD
and netCDF format at 10.1594/PANGAEA.908831 (Jonkers et al., 2019). The data
can also be visualised and downloaded in LiPD and CSV formats at
http://lipdverse.org/PalMod/current_version/. We encourage users of the data product to also cite the primary source of the data when using (individual time series of) this product.
Average temporal spacing between chronology tie points
(avgPoints). Tie points are also split into 14C, absolute
(tephra or palaeomagnetics) and tuned. The majority of the time series in the
synthesis has a spacing of chronology tie points that is below 5000 years.
Lessons learned: recommendations for data archiving
Data reuse and sharing are both made easier when data are archived in a
standardised manner. Even though a large number of palaeoceanographic data
are publicly available, metadata to facilitate interpretation of the raw or
inferred palaeodata are often not, or only partially, made available and
need to be obtained from the original publication, which may be behind a paywall and not freely accessible. Synthesis efforts
therefore still require a lot of time and effort to find, compile and
standardise data and metadata. Only recently has the palaeoclimate community
started to discuss data-archiving standards (Khider et al., 2019). However,
implementation of the proposed Paleoclimate Community reporTing Standard
(PaCTS 1.0) will only affect new uploads to public repositories, and data
already available (legacy data) are likely only going to be made compliant
with the PaCTS through dedicated synthesis efforts. Below we list some of the
main issues that we encountered during data synthesis. Our aims with
mentioning these are to raise awareness of how the lack of standardisation
affects data synthesis and thereby to encourage best practice in data
reporting. We encourage researchers and also reviewers to treat data
handling not as an afterthought but as an integral part of their study.
After all, compared to generating the data, data handling is not a
time-consuming task. Time spent on proper documenting and archiving is not
wasted as it facilitates reuse of data and enables scientific progress in
our field.
Disambiguate core names. An apparently trivial, but surprisingly common,
first-order issue is that core names are inconsistently archived. Different
names for the same core arise not only from differences in hyphenation; truncation of
(long) names; and minor variations in the same name that can, with expert
knowledge, be linked but also from the use of altogether different names for the
same core, e.g. reflecting differences in the labelling during an expedition
and in the repository. This naming confusion renders it difficult to combine
datasets from the same core, especially in an automated way, and to assess
the uniqueness of time series from the same core for dereplication. We
recommend using the full name as indicated in the cruise report where the
core was first described.
The problem of the absence of standardisation in parameter names. The
cumulative frequency of synonyms for seawater temperature in our initial
database (Sect. 2.2.1), showing that there are over 500 different names
for the same parameter and that many of these are unique.
Standardise vocabularies. Even though a vast number of palaeoclimate data are
available in public repositories, the lack of standardisation of parameter
vocabularies hinders efficient data processing. This problem is clearly
illustrated by the fact that, for this synthesis, long synonym lists needed to
be generated in order to group parameters. Each parameter in this synthesis
had tens to hundreds of different names, of which many were unique (Fig. 7). This
issue can be partly addressed by a consistent separation of parameter and
attribute names (e.g. parameter δ18O, species Globigerina bulloides, instead of a
single parameter “d18OGbul”), but even that calls for a standardisation of
parameters and attributes.
Report sampling depth. All data reported here are based on measurements of
discrete samples from a specific depth interval in a given core. Therefore,
the synthesis requires information on the position of each sample. Since
ages of the samples are always estimates and may differ among studies of the
same archive, unambiguous information on sample depth is essential to
reproduce and update the time series. Despite this, many studies fail to
report sample depth and instead report only age. This problem is worse for
spliced records that rely on a composite depth scale. Splicing approaches
are often opaque, and original sample depths, or sample codes that identify
unique samples, are not always available. We recommend therefore that sample
depth should be essential for palaeodata time series from (marine) sediments
and that sample labels are archived for spliced records. This includes
(I)OPD or DSDP sample labels (in full) or IGSN (if available).
Publish raw data. To ensure the reproducibility of inferred parameters (in this
synthesis only temperature) and to ensure the harmonisation or updating of the
calibration, the raw measured data are needed. Provided that the calibration
is known, measured data can in some cases be calculated from the inferred
data, but this is impossible for data based on microfossil transfer
functions. Related to this is unclear information about the calibration that
was used, particularly if the publication describing the calibration
includes multiple different equations.
Include metadata. To assess ecological imprints on proxy signals (recording
bias), temperature estimates as well as oxygen and carbon isotope data based
on planktonic foraminifera also require that key metadata, such as species
name, are archived in a standardised way. This is not universally carried out, and
for example the species information is often only available in the original
publication. Additional information to assess the uncertainty in proxy
measurements, such as foraminifera shell size, the sample size (e.g. number
of shells, concentration of alkenones) or reproducibility of repeat
measurements, was often not available from the paper or from the archived
data, and we encourage the archiving of such data in a standardised way. A
similar issue applies to chronological data. Thanks to a longer history of
reporting standards, radiocarbon (Stuiver and Polach, 1977) (meta)data are
often rather complete. However, this information is often not included
alongside the digitally available data, and for this synthesis a large proportion
of the radiocarbon (meta)data had to be scraped from the literature. In our age
modelling approach, we took reservoir age uncertainty into account, using
data derived from the modelled reservoir ages (see Sect. 2.3). Alternative
approaches are hindered by the fact that reservoir age uncertainty is almost
never reported.
Avoid redundancy. A considerable amount of time was spent on the dereplication
of time series of the same parameter from the same core that were archived
multiple times. Repeat archiving happens when data are reused or, less
commonly, updated. The dereplication task is not made easier by (incomplete
or inconsistent) metadata reporting and can be avoided through better
linking of existing datasets when, instead of re-uploading the data, the
DOI or URL of the original data is provided.
Help rescue dark data. This synthesis is based on data that are publicly
available, yet many palaeoceanographic time series are not archived in
public repositories. Even though the proportion of this so-called dark data
is by definition not known exactly, it likely affects every branch of palaeoceanography, and as a
result palaeoceanographical data syntheses cannot be exhaustive. This
problem is clearly exacerbated for syntheses relying on automated data
mining. There are several shades of dark data, each requiring their own
approach to retrieve them and make them available. Some data are only partially
available, for instance datasets that lack sample depths. Such data only
require additional data to make them reusable. These additional data can
sometimes be calculated or obtained from cross-referencing different data
files but in many cases will need to be retrieved from the data producers.
Other datasets, in particular those from before the digital age, are
presented in tables in the original publications. Progress has been made
with digitising those datasets (especially in PANGAEA), but this work is not
finished, and more effort is needed to make this data available to the
community. There are also data that are used in publications but are not made
available in any way (print or digital). This third shade of data can so far
only be obtained from the original data producers or authors of the original
publication or, if this proves impossible, needs to be digitised from
graphs. Digitisation inevitably leads to a loss of accuracy of the data, and a
dataset retrieved in this way should be flagged. A final category of dark
data consists of data that are not part of a publication. Such datasets can
be made publicly available and be associated with a DOI to ensure
traceability. To reward data sharing, the use of data citations needs to be
encouraged and data citations should be included in the evaluation of a
researcher's impact.
Data sources for version 1.0.0 of the PalMod 130k marine palaeoclimate data synthesis. An extended version of this table can be accessed at https://doi.org/10.5281/zenodo.3739019 (Jonkers et al., 2020).
SiteSource167_1017EKennett et al. (2000), Tada et al. (2000)182_1132BHolbourn et al. (2002), Brooks et al. (2002)323_U1340ASchlung et al. (2013)90_594Nelson et al. (1993), Wells and Okada (1997)AHF_16832Stott et al. (2000), Mortyn et al. (1996)ASV13_1200Duplessy et al. (2005), Ivanova (2002)B997_328Castañeda et al. (2004)B997_330Castañeda et al. (2004)BP00_07_05Simstich et al. (2008), Simstich et al. (2004)CH69_K09Govin et al. (2012), Cortijo et al. (1999), Labeyrie et al. (1999)CHN82_24_4PCBoyle and Keigwin (1985), Sarnthein et al. (1988), Ku et al. (1972)EW9209_1JPCCurry and Oppo (1997)EW9209_2JPCCurry et al. (1999)EW9209_3JPCCurry et al. (1999), Curry (1996)EW9504_02PCStott et al. (2000)EW9504_03PCStott et al. (2000)EW9504_04PCStott et al. (2000)EW9504_05PCStott et al. (2000)EW9504_08PCStott et al. (2000)EW9504_09PCStott et al. (2000)FR01_97_12Bostock et al. (2004), Bostock et al. (2009)GeoB12615_4Romahn et al. (2014)GeoB13731_1Fink et al. (2013)GeoB1711_4Little et al. (1997), Kirst et al. (1999), Vidal et al. (1999)GeoB1720_2Dickson et al. (2009)GeoB3104_1Arz et al. (1999), Arz et al. (1998)GeoB3202_1Arz et al. (1999)GeoB3808_6Jonkers et al. (2015)GeoB4223_2Freudenthal et al. (2002), Henderiks et al. (2002)GeoB5844_2Arz et al. (2003), Arz et al. (2007)GeoB9508_5Mulitza et al. (2008), Niedermeyer et al. (2009), Zarriess et al. (2011), Bouimetarhan et al. (2013)GeoB9526_5Zarriess and Mackensen (2011), Zarriess et al. (2011), Zarriess and Mackensen (2010)GeoTu_SL148Ehrmann et al. (2007)GIK13289_2Sarnthein et al. (1994)GIK15612_2Sarnthein et al. (1994), Kiefer (1998)GIK15637_1Sarnthein et al. (1994), Kiefer (1998), Zahn-Knoll (1986)GIK17045_2Sarnthein et al. (1994), Vogelsang et al. (2001)GIK17045_3Sarnthein et al. (1994)GIK17049_6Jung (1996), Vogelsang et al. (2001)GIK17051_3Jung (1996), Vogelsang et al. (2001)GIK17940_2Wang et al. (1999), Pelejero et al. (1999), Wang et al. (1999), Jian et al. (1999), Hu et al. (2012)GIK17961_2Wang et al. (1999), Pelejero et al. (1999)GIK18471_1Lo Giudice Cappelli et al. (2016)GIK23258_2Sarnthein et al. (2008), Martrat et al. (2003)GIK23258_3Sarnthein et al. (2008)GIK23415_9Jung (1996), Weinelt et al. (2003)GIK23519_5Millo et al. (2008)GPC_5Keigwin and Boyle (1999), Keigwin and Jones (1989), Keigwin and Jones (1994)H214Samson et al. (2005)HLY02_02_17Brunelle et al. (2007), Cook et al. (2005)HU91_045_093Hoogakker et al. (2014), Hoogakker et al. (2011)
Continued.
SiteSourceKF13Richter (1998)KH94_3_LM_8Oba and Murayama (2004)KNR110_82Sarnthein et al. (1988), Curry and Crowley (1987), Broecker et al. (1988)KNR159_36Oppo and Horowitz (2000), Curry and Oppo (2005), Carlson et al. (2008), Carlson et al. (2008)KNR166_2_1Lynch-Stieglitz et al. (2009)KNR166_2_105Lynch-Stieglitz et al. (2009)KNR166_2_106Lynch-Stieglitz et al. (2009)KNR166_2_113Lynch-Stieglitz et al. (2009)KNR166_2_119Lynch-Stieglitz et al. (2009)KNR166_2_127Lynch-Stieglitz et al. (2009)KNR166_2_135Lynch-Stieglitz et al. (2009)KNR166_2_2Lynch-Stieglitz et al. (2009)KNR166_2_51Lynch-Stieglitz et al. (2009)KT90_9_21Oba and Murayama (2004)KT90_9_5Oba and Murayama (2004)LV28_42_4Nürnberg and Tiedemann (2004), Kaiser (2001)LV29_114_3Riethdorf et al. (2013), Max et al. (2012), Max et al. (2014)M35003_4Rühlemann et al. (1999), Hüls (2000)M77_2_056_5Nürnberg et al. (2015), Mollier-Vogel et al. (2013)M77_2_059_1Nürnberg et al. (2015), Mollier-Vogel et al. (2013)MD01_2378Holbourn et al. (2005), Xu et al. (2006), Zuraida et al. (2009), Xu et al. (2008), Kawamura et al. (2006), Dürkop et al. (2008), Sarnthein et al. (2011)MD01_2416Sarnthein et al. (2004), Gebhardt et al. (2008), Gray et al. (2018a), Sarnthein et al. (2015), Sarnthein et al. (2013)MD02_2489Gebhardt et al. (2008)MD02_2575Ziegler et al. (2008), Nürnberg et al. (2008)MD02_2589Diz et al. (2007), Molyneux et al. (2007)MD06_2986Ronge et al. (2015)MD06_2990Ronge et al. (2015)MD06_3067Bolliet et al. (2011)MD06_3075Fraser et al. (2014)MD07_3076Gottschalk et al. (2015), Gottschalk et al. (2016), Waelbroeck et al. (2011), Roberts et al. (2016), Skinner et al. (2010)MD73_025Cline et al. (1984), Labeyrie and Duplessy (1985), Labracherie et al. (1989)MD84_527Sarnthein et al. (1988), Pichon et al. (1992), Duplessy et al. (1988), Labracherie et al. (1989)MD88_770Labeyrie et al. (1996)MD95_2010Dokken and Jansen (1999), Risebrobakken et al. (2005), Govin et al. (2012)MD95_2024Hoogakker et al. (2011), Hoogakker et al. (2014), Weber et al. (2001), Korte and Hesselbo (2011)MD95_2039Thomson et al. (1999), Salgueiro et al. (2014), Eynaud et al. (2009), Schönfeld et al. (2003)MD95_2040Voelker and de Abreu (2011), de Abreu et al. (2003), Pailler and Bard (2002), Voelker et al. (2009), Moreno et al. (2002), Schönfeld et al. (2003)MD95_2043Cacho et al. (2006), Cacho et al. (1999), Martrat et al. (2014)MD96_2098Pichevin et al. (2005), Daniau et al. (2013)MD97_2120Sachs and Anderson (2005), Pahnke and Sachs (2006), Pahnke (2003), Pahnke (2005)MD98_2181Stott et al. (2007), Saikku et al. (2009), Stott (2002), Stott (2007)MD99_2236Jennings et al. (2015)MD99_2339Voelker et al. (2006), Voelker et al. (2009), Voelker and de Abreu (2011)MD99_2343Sierro et al. (2005), Frigola et al. (2008)MSM05_5_712_1Werner et al. (2011), Spielhagen et al. (2011)MSM05_5_712_2Werner et al. (2013), Müller et al. (2012), Müller and Stein (2014)MSM05_5_723_2Werner et al. (2016), Müller et al. (2012)MV0502_4JCWaddell et al. (2009)NA87_22Duplessy et al. (1992), Vogelsang et al. (2001), Gherardi et al. (2009)NEAP_04KRickaby and Elderfield (2005), Hall et al. (2004)ODP1145Oppo and Sun (2005)
Continued.
SiteSourceODP846Mix et al. (1995), Lawrence (2006), Martinez et al. (2003)ODP980Oppo et al. (2003), McManus et al. (1999), Oppo et al. (2006), Ortiz et al. (1999)ODP984CPraetorius et al. (2008), Came et al. (2007), Ortiz et al. (1999)P7Pedersen et al. (1988), Pedersen et al. (1991)POS200_10_6_2Baas et al. (1997)PS1730_2Nam (1997)PS1878_3Telesinski et al. (2014), Telesinski et al. (2013)PS2138_1Wollenburg et al. (2001), Knies and Vogt (2003), Knies and Stein (1998), Nowaczyk et al. (2003), Knies et al. (2000)PS75_059_2Ullermann et al. (2016), Lamy et al. (2014), Ronge et al. (2016)R657Weaver et al. (1998), Sikes et al. (2002)RAPiD_15_4PThornalley et al. (2010), Thornalley et al. (2010), Thornalley et al. (2011)RAPiD_17_5PThornalley et al. (2010), Thornalley et al. (2011)RC11_83Charles et al. (1996), Charles and Fairbanks (1992), Piotrowski et al. (2004)RC16_119Oppo and Horowitz (2000)RC16_84Oppo and Horowitz (2000)RS147_GC07Sikes et al. (2009)SK157_14Ahmad et al. (2008)SO136_003GCRonge et al. (2015), Barrows et al. (2007)SO164_17_2Bahr et al. (2011)SO201_2_12KLRiethdorf et al. (2013), Max et al. (2012), Max et al. (2014)SO201_2_85Riethdorf et al. (2013), Riethdorf et al. (2013), Max et al. (2014), Max et al. (2012), Max et al. (2014), Riethdorf et al. (2016)SO213_2_59_2Tapia et al. (2015)SO213_2_82_1Ronge et al. (2015), Ronge et al. (2016)SO213_2_84_1Ronge et al. (2015), Ronge et al. (2016)SO42_74KLSirocko (2000), Sirocko et al. (1993), Sirocko et al. (1991), Kim et al. (2004), Schulz (1995)SO82_5_2Jung (1996), van Kreveld et al. (2000)SU81_18Bard et al. (1989), Bard (2000), Waelbroeck et al. (2001)SU90_11Labeyrie et al. (1995), Jullien et al. (2006)SU90_24Elliot et al. (2002), Elliot et al. (1998), Elliot et al. (2001)TR163_22Lea et al. (2006)V19_27Lyle et al. (2002), Koutavas and Lynch-Stieglitz (2003)V19_28Lyle et al. (2002), Koutavas and Lynch-Stieglitz (2003)V19_30Shackleton and Pisias (2013), Lyle et al. (2002), Bond (1997, 1976)V23_81Jansen and Veum (1990), Broecker et al. (1988), CLIMAP project members (1981)V24_253Oppo and Horowitz (2000)V25_59Sarnthein et al. (1988), Waelbroeck et al. (1998), CLIMAP project members (1981)V28_14CLIMAP project members (1981), Kellogg et al. (1978)V29_202Oppo and Lehman (1995)W8709A_13Lyle et al. (1992), Lyle et al. (2000), Kienast et al. (2002), Gardner et al. (1997), Lund and Mix (1998)WIND_28KMcCave et al. (2005), Johnstone et al. (2014), Kiefer et al. (2006)Y69_106Lyle et al. (2002)
The supplement related to this article is available online at: https://doi.org/10.5194/essd-12-1053-2020-supplement.
Author contributions
LJ, MK and SM conceptualised the study; LJ, OC and MK developed the
workflow; LJ designed the database structure with help from OC and MK; LJ
and AS compiled (meta)data; LJ carried out quality control; LJ updated
age–depth models; LJ, SM and ML ensured integration with the PDV format; LJ and NM
ensured the integration with the LiPD format; MK and SM acquired funding; LJ and MK
wrote the manuscript, and all authors reviewed and commented on the
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Paleoclimate data synthesis and analysis of associated uncertainty (BG/CP/ESSD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
This research was supported by PalMod, the German palaeoclimate modelling
initiative (http://www.palmod.de, last access: 6 May 2020). PalMod is part of the Research for
Sustainable Development initiative (FONA; http://www.fona.de, last access: 6 May 2020) funded by the
German Federal Ministry of Education and Research (BMBF). We thank
colleagues in working group 3 from PalMod for discussion on data and
metadata requirements and Rangelys Sorrentino for help with data processing.
We also thank Blanca Ausin and the anonymous reviewer for their constructive
feedback on an earlier version of this manuscript.
Financial support
This research has been supported by the Bundesministerium für Bildung und Forschung (PalMod grant). Olivier Cartapanis was funded by
the Swiss National Science Foundation (grant no. PP00P2-144811).
Review statement
This paper was edited by David Carlson and reviewed by Blanca Ausin and one anonymous referee.
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