This article describes an update of the PALMOD 130k multiproxy marine palaeoclimate data synthesis (Jonkers et al., 2020). Palaeoclimate data hold the unique promise to inform about climate states and climate variability on time scales beyond the instrumental period. As such, they may help to characterise natural climate variability, shed light on the mechanisms of climate change and provide analogues for future climate; all crucial insights in a rapidly changing world. Marine sediments and the compounds and fossils contained in them allow the reconstructions of largely continuous, long records of past ocean physical and chemical conditions. Consequently, the multiproxy, global-scale PALMOD 130k synthesis provides a basis to evaluate the importance of the ocean for regulating Earth's climate and biogeochemical cycles on a global scale.

The synthesis contains palaeoceanographic and sedimentological data from sediment cores that cover the last glacial–interglacial cycle (up to approximately 130 000 years ago). During this time Earth has undergone marked changes in climate that involve the atmosphere, continental ice sheets and the ocean (Jonkers et al., 2023; NGRIP Members, 2004; Waelbroeck et al., 2002). Up to the influence of humans on global climate, which accelerated around 1950 CE (Steffen et al., 2015), these changes were ultimately controlled by variations in the Earth's orbit around the Sun (Hays et al., 1976). This orbital forcing triggered a multitude of (non-linear) feedback mechanisms causing global and regional climate to vary much faster than the forcing. A large amount of palaeoclimate data that span this period has been collected over the past decades, rendering the last glacial–interglacial cycle well-suited to study the natural variability of ocean and climate conditions across timescales and in particular the interaction between the cryosphere and the rest of the climate system. This is especially relevant given the projected, but uncertain, (partial) disappearance of continental ice-sheets with ongoing anthropogenic warming. The high-density data availability also makes the period an excellent test-bed for climate model simulations.

Whilst for a long time, palaeoclimate modelling focussed on the simulation of timeslices, increasing computational power now also enables long transient simulations. For instance, the paleoclimate modelling intercomparison project (PMIP) now also provides protocols for the simulation of the last deglaciation (Ivanovic et al., 2016) and several simulations spanning this period exist (Liu et al., 2009; Mikolajewicz et al., 2025). This development allows investigating climate dynamics in a new way, but also puts new demands on the (palaeo)observational data needed for model benchmarking. Specifically, paleoclimate time series need to be associated with comprehensive and consistent estimates of their (chronological) uncertainty. Moreover, the stochastic aspects of climate also call for new ways of comparing transient simulations with observations. For instance, given the uncertainty about whether suborbital abrupt climate variability is forced or unforced, the observed temporal evolution of climate may represent just one of many possible trajectories. Therefore, data-model comparison strategies that are independent of the exact timing of events need to be considered (Weitzel et al., 2024).

A particular strength of paleoceanographic data is their multiproxy nature. We can hence gain a more comprehensive insight into the climate system by studying it from a multi-proxy, or multi-parameter, perspective. Moreover, a multi-parameter approach is also likely to be more diagnostic for model performance than a single climate parameter approach (Kurahashi-Nakamura et al., 2017). However, at present, multi-parameter approaches are difficult because different parameters from the same archive (sediment core) have often been generated in different studies, and the current poor state of data archiving poses serious challenges for reuse. These challenges arise mainly from the non-standardised and fragmented way palaeoceanographic data (and palaeoclimate data in general) are archived and the scarcity of machine-readable metadata included in datafiles. Automated and reproducible analyses of palaeoceanographic data thus remain time consuming, even though efforts are underway to alleviate these problems and increase the ease of data reuse (Emile-Geay and Eshleman, 2013; Morrill et al., 2021). Still, such approaches require standardisation and synthesis of raw and inferred data and of metadata from various sources.

The PALMOD 130k marine palaeoclimate data synthesis is an attempt to overcome the fragmented archiving of paleoceanographic data sets and contains multiple climate-relevant parameters in a single framework. The synthesis combines data on sediment composition (carbonate, total organic carbon and opal content), planktonic and benthic foraminifera stable oxygen and carbon isotope ratios and estimates of seawater temperature based on various proxies. These parameters were selected following discussions within the PALMOD project. They include physically and biogeochemically relevant parameters that can be directly compared with climate model simulations and for which reasonable spatiotemporal coverage could be achieved. All data have been compiled on a single depth scale per sediment core allowing for robust alignment of the various parameters and for reproducible updating and changing of chronologies. Whenever possible, the synthesis contains raw observational data together with the inferred data to enable revision of the latter. All data are associated with rich metadata to facilitate interpretation and for comprehensive uncertainty estimates. The synthesis is designed to facilitate evaluation of transient climate model simulations, but the data product can also be used to study climate change over the last glacial–interglacial cycle directly, independent from climate model simulations. Whilst the intended use of the synthesis has remained unchanged, version 1 was limited in spatial and temporal coverage, thus warranting an update to increase its usefulness. This article describes this update of the data synthesis and complements an earlier description of the data product (Jonkers et al., 2020).

Briefly, version 2 of the PALMOD 130k marine palaeoclimate data synthesis contains 1736 additional time series from 332 individual sites, markedly increasing both the spatial and temporal coverage of the data. Age-depth models presented in version 1 that were based on radiocarbon ages are revised using calendar ages calculated with the IntCal20 radiocarbon calibration curve (Hogg et al., 2020; Reimer et al., 2020). In addition, we harmonised seawater temperature estimates based on geochemical proxies and provide uncertainty estimates that are consistent and comparable across the different time series. This version also corrects several errors in the metadata associated with time series included in version 1 and contains two additional metadata fields to facilitate filtering of the data. Finally, we made slight changes in the structure of the data to prevent errors during use.

The structure of the synthesis has slightly changed since version 1. The main change is the inclusion of the revised age information directly with the proxy data. These were kept separate in version 1 to highlight the fact that observations on the (core) depth scale are static, whereas the age-depth model can be updated or revised as per user demand. However, this separation into raw and derived data rendered use of the data more complex and we have hence decided to include the revised age data directly with the proxy data. The second change in the structure is the addition of ensembles of seawater temperature time series.

The synthesis is organised by site, which usually is the sediment core from which the data were extracted, but it may also refer to a spliced record comprising multiple sediment cores. Depending on the availability of alkenone or Mg/Ca-derived seawater temperature estimates, each site is associated with information on seven or eight themes. Names in square brackets below are those used in the data files.

Geographic data [site] includes the position of the site in decimal degrees and its water depth in m and, new in this version, the ocean basin in which the site is located. The basin assignment is based on the Global Oceans and Seas dataset, version 1 (Flanders Marine Institute (VLIZ), Belgium, 2021). This theme also contains additional notes that apply to the entire site.

Each site also contains information about the version, import date and date at which the age-depth model was last updated. These fields are largely for internal use.

The data are available in serialised R format (^*RDS) and structured as a list where the elements contain the information about the themes indicated above. RDS files can be easily read using the open source software R. To ensure interoperability the data product is also available as (simple) JSON formatted data following the LiPD structure and vocabulary. Resources for reading and querying the data are provided in the Sect. 7 Usage notes.

General instructions on data usage and potential applications can be found in Jonkers et al. (2020). The PALMOD synthesis contains data about sediment composition (calcium carbonate, organic carbon and biogenic silica content) that is relatively straightforward to interpret. The data product also contains information derived from measurements on biological material (foraminifera stable carbon and oxygen isotope ratios) and estimates of seawater derived indirectly from the chemical or species composition of microfossils or from biomarkers. The biological origin of the data renders its interpretation less straightforward as environmental preferences of the organisms may affect the recorded climate signal. A detailed description of the proxy sensors and the intricacies of the proxies is provided in the data descriptor of version 1 (Jonkers et al., 2020). We encourage users to read the relevant section. A general proxy system modelling framework for marine sediment time series can e.g. be found in Dolman and Laepple (2018). In this section we focus on the main advance compared to version 1 that is relevant to usage: the harmonisation of seawater temperature estimates.

Estimates of near sea surface temperature based on planktonic foraminifera Mg/Ca and U37K′ have been updated and harmonised whenever possible. For U37K′, these updated estimates required relatively little user input, but the use of different priors may affect the results. Since planktonic foraminifera Mg/Ca is not only affected by seawater temperature, the inference of Mg/Ca-based temperatures is more complex. Estimates of salinity and pH are indirect and, by necessity, rather crude as they ignore existing, but unknown, spatiotemporal variation. Importantly, the estimates of salinity and pH depend on the age and updates to the age-depth model therefore require recalculation of the inferred temperatures. The influence of these nuisance factors can in principle also be corrected using measured estimates of ocean pH based on boron isotope ratios (Gray and Evans, 2019) or indirectly by using oxygen isotopes (Morley et al., 2024), but this requires additional data that is not available for all time series. We have hence chosen a correction approach that is consistent across all time series. Whilst for the species Globigerinoides ruber, Trilobatus sacculifer, Globigerina bulloides, Neogloboquadrina pachyderma and N. incompta the sensitivity of Mg/Ca to temperature, salinity and pH is well constrained, this is not the case for other, generally deeper dwelling species. For these other species we have used the generic calibration. Care thus needs to be taken with the temperature estimates based on this calibration and we encourage the use of anomalies, rather than reliance on absolute values. To allow users to compare the harmonised seawater temperature estimates with the values provided by the data generators, the original temperature values are included in the data product. With regard to the estimated uncertainty of the inferred temperatures, it is important to note that the range of possible nSST trajectories may be overestimated as temporal autocorrelation in the temperature and its uncertainty are ignored in the calibration. Whether such autocorrelation would be preserved in sedimentary time series and how large the effect would be, remains to be investigated. For this, and other reasons, users may also want to use different calibration schemes. To this end, the synthesis also contains the raw U37K′ and Mg/Ca values.

Seawater temperatures based on microfossil assemblage composition are included in the synthesis only in the form reported by the authors. Because of the fragmentary nature of the required metadata, the originally reported estimates could not be updated to a common standard and are associated with uncertainty estimates only where the data generators provided them. From the accompanying publications it is not always clear how the uncertainty has been derived and the methods certainly vary among the different studies because of personal preferences and methodological progress on how to deal with issues like spatial autocorrelation. In light of new insights regarding the uncertainty of seawater temperature estimates based on microfossil assemblages (Telford et al., 2013; Trachsel and Telford, 2016), the reported uncertainties are likely too optimistic. Interested users can refer to Jonkers et al. (2023) for an example of comprehensive estimates of calibration uncertainty associated with planktonic foraminifera assemblage derived seawater temperatures.

Whenever publicly available, the original age-depth models are included in the data product, but they almost universally lack uncertainty estimates. Still, these original age estimates can be used for a comparison of the revised and original age models. Future updates to the radiocarbon calibration curve, to the reservoir age estimates, or the stacks may require updating the age-depth models. The raw chronology data provided in the data product enables this updating in an automated fashion. Please note that the Mg/Ca-based temperature estimates depend on the age of the samples for a correction of the effects of salinity and pH. The temperature estimates thus need to be updated if the age-depth model is revised. Uncertainty estimates on the chronology are to some degree method dependent, but the Bayesian approach we utilised appears to provide reasonable results (Trachsel and Telford, 2017). However, our approach ignores additional uncertainty caused by bioturbation and uncertainty related to alignment of the benthic foraminifera δ18O records. Recent work suggests ways to incorporate these sources of uncertainty (Lee et al., 2023). Other approaches may thus provide different results and the user may wish to compare age-depth models using different age-depth modelling strategies like for instance done by Comas-Bru et al. (2020).

The availability of age and nSST ensembles offers the possibility to estimate the joint chronological and calibration uncertainty of the seawater temperature estimates. A possible way to do so is through combining the age and nSST ensembles in a Markov Chain Monte Carlo approach (Cartapanis et al., 2022; Khider et al., 2017), by randomly joining ensembles (Fig. 8a). However, the large number of ensembles potentially renders this approach excessive and computationally expensive as 10⁶ unique combinations of the ensembles are possible. A preliminary analysis suggests that 500–1000 random combinations of age and nSST ensembles are sufficient to capture the combined age and calibration uncertainty (Fig. 8b). We caution however against universal application of this number as the distributions of age and calibration uncertainty likely vary by time series and different research questions may require different approaches. We hence encourage users to carefully consider the chosen number of ensemble combinations. Alternatively, the joint uncertainty may be quantified analytically, but such an approach needs to consider the potential complexity in the probability density distributions of chronological and calibration uncertainty.

Some example scripts for filtering the data product by location, length, resolution and age control are available from the lead author's GitHub repository (https://github.com/lukasjonkers/PALMODutils, last access: 22 January 2026). Example code to estimate the uncertainty of the nSST time series based on the age and temperature ensembles is available at the same location. An extensive documentation of the LiPD format is available at https://lipdverse.org (last access: 23 May 2025) and utilities to interact with LiPD files in R are available at https://github.com/nickmckay/lipdR (last access: 20 January 2026) and a user guide to the lipdR package is available at https://nickmckay.org/lipdR/ (last access 20 January 2026). Python users can use the PyLiPD package (https://pylipd.readthedocs.io/en/latest/, last access: 20 January 2026), which is documented at https://linked.earth/pylipdTutorials/intro.html (last access: 20 January 2026). Resources for Matlab users are available at https://github.com/nickmckay/LiPD-utilities (last access: 23 May 2025).

Whilst we have done our best to address errors and omissions and aimed for standardisation across the entire data product, it comes with the guarantee of remaining errors and inconsistencies. Users are encouraged to report those to the lead author by email so they can be corrected. Updates to the data synthesis will be published on PANGAEA and linked to the current version. Minor updates with corrections only will not be associated with a new data descriptor. The updating and versioning strategy is described in detail in Jonkers et al. (2020).

The PALMOD 130k marine palaeoclimate data synthesis version 2 is freely available at 10.1594/PANGAEA.984602 (Jonkers et al., 2026). The data can also be visualised and downloaded in LiPD format from https://lipdverse.org/PalMod/current_version/ (last access: 30 September 2025). Python and Matlab serialisations of the data as well as csv files of the palaeoclimate and chronological data can also be downloaded from the lipdverse website. We explicitly encourage users to also cite the original data when using this data product to provide credit to the data generators. For instance, when using a selection of the time series from this data product, we recommend including a statement like “We selected time series from the PALMOD 130k marine palaeoclimate synthesis version 2 (Jonkers et al., 2026). The time series that meet our selection criteria, including their original publications, are detailed in the table below”. See Weitzel et al. (2024) for an additional example and Smith et al. (2023) for a general discussion on the issue of crediting data generators.

This synthesis would not have been possible without good data stewardship of the marine palaeoclimate science community. For many decades researchers have been sharing their research data through (dedicated) publicly accessible data repositories. Even so, this synthesis makes a lot of invaluable metadata and chronological information publicly available for the first time. This is because such metadata and chronology data are often not directly associated with the primary proxy data, but reported in accompanying publications instead. When these publications are publicly accessible, this important information to interpret and reuse the data can be retrieved with considerable additional effort. However, more often than not, the publications are not publicly available and the information remains hidden behind the paywalls of publishers, rendering the data difficult to reuse.

Community guidelines on how to report general palaeoclimate data are available (Khider et al., 2019), as are more specific guidelines for microfossil assemblage data (Jonkers et al., 2025). However, legacy datasets invariably do not adhere to such guidelines, thus necessitating (community) effort to increase the reusability of these data. For new datasets, we recommend data sharing not only from the point of view of reproducing single studies, but explicitly consider reuse when sharing data and ensure that that research data files are scientific works that are comprehensible in themselves. In this way, reuse and reinterpretation of scientific data and knowledge is greatly facilitated, allowing our field to address new questions that cannot be answered using a single palaeoclimate time series.

With the National Climate Data Center (NCDC) at NOAA and PANGAEA the palaeoclimate community has access to at least two dedicated data repositories that enforce standardisation and perform data curation. This is a tremendously advantageous position to facilitate the reusability of palaeoclimate data and we explicitly encourage data generators to continue sharing their data through these recognised and specific data repositories. Nevertheless and despite the existence of the LinkedEarth ontology (https://linked.earth/ontology/, last access: 20 January 2026) and the PaST thesaurus (Morrill et al., 2021), the lack of adaptation of a single standardised vocabulary and consistent ontology across the repositories remains a hurdle to reusability. The PaST thesaurus is used at NCDC, but standardisation is only performed under the hood at PANGAEA (Felden et al., 2023), where structured and standardised data can be accessed in xml format. Synthesis efforts benefit from such standardisation, but increased harmonisation across the repositories, or tools to do so, would further facilitate synthesis efforts and the reusability of data.

The value of this data product arguably not only lies in the standardisation of the format and vocabulary, but also in the updating and standardisation of the age-depth models and of the seawater temperature estimates. Such updating and standardisation is only possible when raw chronology and proxy data are available on a depth scale. Radiocarbon data are often publicly available or can, with additional effort, be scraped from publications. However, information on aligned chronologies, including ages and uncertainty on tuning tie points is seldom reported, rendering reevaluation of such chronologies difficult. Regarding proxy data, measured Mg/Ca and U37K′ can, provided the calibration equation is known, be calculated from reported seawater temperatures, but ambiguities are likely to remain and the chance of deriving erroneous values is non-negligible (especially when the calibration equation is not explicitly included in the data file, or provided in the associated publication). For seawater temperatures based on microfossil assemblage the situation is different. Raw assemblage data are not as often shared as geochemical proxy data and due to the multi-dimensional character of the data, raw assemblage data cannot be inferred from the temperature estimates. This not only prevents revising temperature estimates reported without the underlying raw count data, but also prevents reuse of such assemblage data in other contexts, such as for estimates of past biodiversity change (e.g. Strack et al., 2024). This is unfortunate given the tremendous effort that went into generating such species assemblage data. In our opinion, the amount of work and the relevance of such data is so large that it would justify a coordinated international action to rescue the unpublished microfossil counts underlying seawater temperature reconstructions.