Articles | Volume 16, issue 4
Data description paper
26 Apr 2024
Data description paper |  | 26 Apr 2024

SISALv3: a global speleothem stable isotope and trace element database

Nikita Kaushal, Franziska A. Lechleitner, Micah Wilhelm, Khalil Azennoud, Janica C. Bühler, Kerstin Braun, Yassine Ait Brahim, Andy Baker, Yuval Burstyn, Laia Comas-Bru, Jens Fohlmeister, Yonaton Goldsmith, Sandy P. Harrison, István G. Hatvani, Kira Rehfeld, Magdalena Ritzau, Vanessa Skiba, Heather M. Stoll, József G. Szűcs, Péter Tanos, Pauline C. Treble, Vitor Azevedo, Jonathan L. Baker, Andrea Borsato, Sakonvan Chawchai, Andrea Columbu, Laura Endres, Jun Hu, Zoltán Kern, Alena Kimbrough, Koray Koç, Monika Markowska, Belen Martrat, Syed Masood Ahmad, Carole Nehme, Valdir Felipe Novello, Carlos Pérez-Mejías, Jiaoyang Ruan, Natasha Sekhon, Nitesh Sinha, Carol V. Tadros, Benjamin H. Tiger, Sophie Warken, Annabel Wolf, Haiwei Zhang, and SISAL Working Group members

Palaeoclimate information on multiple climate variables at different spatiotemporal scales is becoming increasingly important to understand environmental and societal responses to climate change. A lack of high-quality reconstructions of past hydroclimate has recently been identified as a critical research gap. Speleothems, with their precise chronologies, widespread distribution, and ability to record changes in local to regional hydroclimate variability, are an ideal source of such information. Here, we present a new version of the Speleothem Isotopes Synthesis and AnaLysis database (SISALv3), which has been expanded to include trace element ratios and Sr isotopes as additional, hydroclimate-sensitive geochemical proxies. The oxygen and carbon isotope data included in previous versions of the database have been substantially expanded. SISALv3 contains speleothem data from 365 sites from across the globe, including 95 Mg/Ca, 85 Sr/Ca, 52 Ba/Ca, 25 U/Ca, 29 P/Ca, and 14 Sr-isotope records. The database also has increased spatiotemporal coverage for stable oxygen (892) and carbon (620) isotope records compared with SISALv2 (which consists of 673 and 430 stable oxygen and carbon records, respectively). Additional meta information has been added to improve the machine-readability and filtering of data. Standardized chronologies are included for all new entities along with the originally published chronologies. Thus, the SISALv3 database constitutes a unique resource of speleothem palaeoclimate information that allows regional to global palaeoclimate analyses based on multiple geochemical proxies, permitting more robust interpretations of past hydroclimate and comparisons with isotope-enabled climate models and other Earth system and hydrological models. The database can be accessed at (Kaushal et al., 2024).

1 Introduction

Speleothems, secondary cave carbonate precipitates, are a rich palaeoenvironmental archive of geochemical data (Wong and Breecker, 2015). Due to their widespread distribution (Comas-Bru et al., 2020) and their precise chronologies (Henderson, 2006), they can provide palaeoclimate data at a seasonal (Baldini et al., 2021) to multi-annual resolution spanning millennial and longer timescales (Cheng et al., 2016; Stoll et al., 2022).

The Speleothem Isotopes Synthesis and AnaLysis working group (SISAL WG) is an international effort to synthesize speleothem data under the umbrella of the Past Global Changes (PAGES) project (Comas-Bru et al., 2017; Comas-Bru and Harrison, 2019). The SISAL WG aims to answer critical open questions in palaeoclimate science with a focus on regional to global trends and event synchronization. To address these questions, the SISAL WG has been developing standardized and quality-checked databases. The first three versions of the database (SISALv1, SISALv1b, and SISALv2) provided the palaeoclimate community with a growing resource of speleothem geochemical data (Atsawawaranunt et al., 2018; Comas-Bru et al., 2020, 2019), specifically oxygen (δ18O) and carbon (δ13C) isotope records, and age-model ensembles, along with an online tool – the SISAL webApp – to increase accessibility to the SISAL database (Hatvani et al., 2024). The SISAL database versions have been exploited (i) to better understand the drivers of speleothem environmental proxies and improve their interpretations (Baker et al., 2019, 2021; Fohlmeister et al., 2020; Treble et al., 2022; Skiba and Fohlmeister, 2023); (ii) to provide a resource for the interpretation of speleothem records at a regional level, identifying key gaps and future work (Kaushal et al., 2018; Lechleitner et al., 2018; Braun et al., 2019b; Burstyn et al., 2019; Deininger et al., 2019; Kern et al., 2019; Oster et al., 2019; Zhang et al., 2019; Lorrey et al., 2020); and (iii) to understand the mechanisms of past climate change, including through comparison with isotope-enabled climate models (Comas-Bru et al., 2019; Parker et al., 2021b; Bühler et al., 2022; Parker and Harrison, 2022; Parker et al., 2021a) and other modelling approaches (Skiba et al., 2023).

The new SISALv3 database provides an increased dataset of oxygen and carbon isotope data, interpreted as records of hydroclimate and vegetation dynamics/bioproductivity (Wong and Breecker, 2015), and has been significantly expanded to include data on Sr, Mg, Ba, and U, which are typically tracers for hydrological processes in the karst and cave (Fairchild et al., 2000; Johnson et al., 2006; Fairchild and Treble, 2009; Wassenburg et al., 2016), and data on P, which is recognized as a tracer for surface bioproductivity (Treble et al., 2003; Borsato et al., 2007; McDonough et al., 2022) (Table 1). Also included are data on Sr isotopes, as these are an important proxy for hydroclimatic processes and may provide information on local hydrology and soil source, production, and/or erosion (e.g. (Li et al., 2005; Ünal-İmer et al., 2016; Wortham et al., 2017; Weber et al., 2018; Ward et al., 2019; Utida et al., 2020). Ratios of Sr/Ca, Mg/Ca, Ba/Ca, and U/Ca, coupled with δ13C information, are sensitive to water–rock interactions and residence time (Fairchild et al., 2000; Johnson et al., 2006). An important mechanism that drives variability in these multiple proxies in quantifiable ways is the process of prior calcite/carbonate precipitation (PCP), through which carbonate precipitated along flow paths in the karst and on the cave roof, leading to an altered element concentration in cave drip waters from which the speleothem ultimately precipitates (Fairchild et al., 2000; Day and Henderson, 2013). An increase in PCP usually occurs in times of drought that facilitate increased water–rock residence times and degassing in the karst (Fairchild et al., 2000). The strength of these proxies is that they provide robust climatic and environmental information via a multi-proxy approach that will need to be tailored for different karst and climatic settings (Table 1). The SISAL WG is currently working on projects with the new additional proxies to explore and gain more detailed insights. We provide examples of proxy interpretations with linked references, but we must emphasize that this list is not exhaustive, the interpretations are timescale dependent, and (in most cases) multi-proxy approaches are necessary (Table 1). Thus, the SISALv3 database augmented with trace element proxies provides a multi-proxy dataset that can be used for long-term drought reconstructions in the past as well as to better understand the forcings, mechanisms, and periodicities of such events. In addition to the new geochemical data, extensive metadata including information on parameters such as vegetation and karst type as well as entity (i.e. speleothem dataset) images are provided to aid robust interpretations.

The SISALv3 database will allow the systematic and global analysis of stable isotope and trace element variability and will elucidate how trace element data can be used to strengthen climatic interpretations from speleothem oxygen (δ18O) and carbon (δ13C) records. The database can be accessed at (Kaushal et al., 2024).

Table 1Summary of speleothem geochemical proxies included in SISALv3, examples of their possible interpretations, and relevant references.

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Figure 1Structure of the SISALv3 database. Fields and tables marked with (*) refer to new information added in SISALv3; see Table 2 for details. The colours refer to the format of that field: Enum, Int, Varchar, Double, or Decimal. More information on the list of predefined menus can be found in the Supplement (Table S1). For trace element records, a series of identical tables was generated (labelled X_Ca, where X stands for the specific element: Mg, Sr, Ba, U, or P).


2 Data and methods

2.1 New data formatting and processing

All trace elements are reported normalized as ratios with respect to Ca (X/Ca, where X stands for the individual elements) in units of millimoles per mole. In the following paper, “trace element” refers to the normalized ratio to Ca. A standardized conversion sheet is used to facilitate conversions from grams to moles (available in the repository). Sr-isotope data are reported as 87Sr/86Sr values. For internal consistency and to facilitate future intercomparison and synthesis studies, the measurement method and reference materials used as well as the measurement precision are also reported for both trace elements and Sr isotopes.

Table 2Changes made to the Site, Entity, and stable isotope tables compared with SISALv2.

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Mechanisms relevant to hydroclimate interpretations from speleothems are based on a multi-proxy approach of stable isotopes and one or more trace element ratios. Therefore, the SISALv3 database structure allows for trace element measurements to be added at the depths of the stable isotope measurements on a given entity. However, between 35 % and 86 % of the records (depending on the element) were measured using in situ techniques, such as laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and these datasets are typically generated at a higher resolution (10–100 µm) than the stable isotope records (Jochum et al., 2012). These data have been downsampled to the resolution of the stable isotope data for the same speleothem. Downsampling was performed by computing averages (and standard deviations) of the trace element measurements for the corresponding stable isotope sampling depths. This implicitly assumes that the same or a (depth-equivalent) parallel sampling track was used for trace elements and stable isotopes and that the isotope sampling was continuous. Downsampling allows the trace element data to be represented by the same age–depth model as the stable isotope record. For records submitted by the authors in which the originally published dataset was at a higher resolution than reported in the SISAL database, standardized *.txt data files are also available in the repository (see Sect. 5.1 on code and data accessibility). No new chronological information or separate age models are reported for these datasets.

2.2 Additional metadata

New metadata fields are included in the Entity table (see the database structure in Fig. 1) to allow users to select sites with similar environmental conditions and to account for factors that might influence the interpretation of individual records. These include information on vegetation, land use, land cover, and host rock type above the cave. This information is often missing from publications and was not available from data contributors; therefore, information from data products has been added as additional fields to the database for completeness. Information on vegetation type and land use was provided by the original investigators. Additionally, information on land use and land cover was taken from the Copernicus Global Land Service Land Cover database (LCC v3.0.1; Epoch 2019; Buchhorn et al., 2021, 2020), extracted with a radius of 250 m around the cave site. Information on the carbonate/evaporite host rock at the cave sites was taken from the World Karst Aquifer Map (WoKAM) database (Goldscheider et al., 2020), extracted with a radius of 1000 m.

The database also indicates if the trace element content of the host rock and drip water feeding the speleothem is available (but does not include the actual values). Drip height (i.e. the distance the drip falls from the ceiling of the cave to the speleothem) and the difference between drip water and carbonate δ18O values are given, based on information provided by the original investigators.

The SISAL WG repository now hosts images of the entities (speleothem sections) and maps of cave sites. These allow users to evaluate petrographic features that may influence the trace element and stable isotopic records and to check whether cave morphology could potentially influence the climate in the cave (Covington and Perne, 2015). The Entity table in the database contains fields indicating whether maps and images are available.

2.3 Changes to database structure

The structure of the SISALv3 database (Fig. 1) has been changed to accommodate additional data and metadata as well as to optimize the organization of information, as described below.

2.3.1 New geochemical data and metadata fields

The elemental ratio for each trace element and the Sr-isotope data are given in individual tables that contain sample identifiers (sample_id), the measurement value, and the measurement precision. The sample_id provides the link to the Sample table and, thus, links these data to the stable isotope data (Fig. 1, Table 2).

Metadata for the measurements are stored in the Entity table. For each elemental ratio (Sr/Ca, Mg/Ca, U/Ca, Ba/Ca, and P/Ca), the Entity table indicates whether the data are available (“yes/no/other/unknown”), the measurement method, the laboratory reference materials used, and (where applicable) the downsampling methods used. The table also indicates if high-resolution trace element data are available. For Sr isotopes, the Entity table specifies whether this dataset is available, what measurement method was employed, and how the measurement was standardized (Fig. 1, Table 2).

SISALv3 now provides a unique, persistent identifier for each speleothem (persist_id) in the Entity table (Fig. 1, Table 2). This was needed because there was an increasing issue with non-unique entity names; it was also required to deal with the fact that different datasets from the same stalagmite had different entity_id information (e.g. for datasets covering different time periods in the same speleothem). Thus, the field entity_id provides a unique identifier for a specific dataset, but not necessarily for a specific speleothem, while the persist_id uniquely identifies the speleothem. The persist_id information was created by combining the site_id and entity_name (without special characters). There are 838 unique persist_id entries and 902 unique entity_id entries in the database.

2.3.2 Changes in existing database fields and options

The fields “geology” and “rock age” were moved from the Site table to the Entity table (Fig. 1, Table 2). This was done to allow for variability in these parameters within the same cave system, which is particularly relevant for the interpretation of δ13C and trace element data. The field “trace elements” (yes/no) in the Entity table was removed, as it was now redundant. The field “iso_std”, describing the reference material used for the measurement of δ18O and δ13C values, was moved from the stable isotope tables to the Entity metadata table. A number of options for entries in the metadata fields were changed (Table 3). The majority of these changes were additions to the previously available options in light of the entries made in the “Notes” section to allow for more “metadata-filterable” database mining. A few options were removed from the metadata fields because they had never been used in previous database versions.

Table 3Changes made to the predefined options for metadata fields compared with SISALv2.

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Figure 2Quality checking workflow adopted for the inclusion of datasets in SISAL. The colours indicate different quality check levels: blue – data contribution sources (original authors or datasets deposited in repositories and publication supplementary information); yellow – SISAL regional coordinator group with regional expertise; orange – SISAL database managers.


3 Quality control

The SISAL WG has used several levels of quality control (QC), and this practice was continued for SISALv3 (Fig. 2). The initial data compilation is performed by SISAL regional coordinators and/or in liaison with the data contributors into standardized Excel workbooks. The first QC level consists of expert assessment by the SISAL regional coordinators, who double-check the completeness of entered data and the correctness of measurement units where applicable. Standardized unit conversion sheets for common conversions (e.g. degrees–minutes–seconds to decimal degrees for site information, atomic ratios to activity ratios for dating information, or milligrams per gram to millimoles per mole for trace-element-to-Ca ratios for trace element information) have been provided to regional coordinators (see repository). The completed workbook(s) are subjected to a series of automated QC (e.g. checking if the age model matches the discreet dating information or if hiatuses are placed at the correct depth) by the database managers. When the datasets pass automated QC and no further corrections are necessary, the dataset workbook and the automatically generated QC figures are sent to the data contributors for final evaluation and approval. The same workflow has been followed for the *.txt trace element data files. The new metadata fields of vegetation_type and land_use have been added to SISALv3; for entities that were included in SISALv2, the information on these metadata fields has been added from publications. Data already included in SISALv2 have been checked, and mistakes or unknowns identified during previous data analysis or during the process of trace element data addition have been corrected. A comprehensive summary of the changes made to existing entities between SISALv2 and SISALv3 is shown in Table 4.

Table 4Summary of the modifications applied to records in version 2 (Comas-Bru et al., 2020) of the SISAL database. Note that the changes in the Dating table and the Sample table were counted by dating_id and sample_id, respectively, which led to a large number of changes.

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Figure 3Trace element ratios and Sr-isotope records included in SISALv3 by region. Abbreviations: S. America – South America; N./C America – North and Central America.


Figure 4Temporal coverage of the trace element and Sr-isotope records in SISALv3 by region. Entities with multiple trace elements were counted multiple times. Bin sizes are as follows: (a) 0–2000 years BP – 20 years; (b) 2000–21 000 years BP – 250 years; (c) 21 000–750 000 years BP – 10 000 years.


Table 5Summary of the number of trace element records in SISAL and the downsampling methods applied.

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Table 6Summary of the new δ18O and δ13C records added to SISALv3 compared with SISALv2.

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4 Overview of database contents

4.1 Trace element and Sr-isotope records

SISALv3 contains 95 Mg/Ca, 85 Sr/Ca, 52 Ba/Ca, 25 U/Ca, 29 P/Ca, and 14 Sr-isotope records (Table 5). This corresponds to ∼60 % of the known published data, based on an assessment by the SISAL WG. There is a clear regional bias in the database, with European entities dominating every elemental ratio (Fig. 3). The Sr-isotope records are more evenly distributed, with records from every region except Asia and Oceania. Temporal coverage for the combined trace element and Sr-isotope dataset is high during the last 2000 years (∼60 entities per 20-year interval) and the Holocene (∼60 entities per 250-year interval); it then drops to 20–40 entities per 10 000-year interval for the last glacial cycle (12–120 kyr BP, where “kyr” stands for 1000 years and BP for “before present”, defined as 1950; Fig. 4). Beyond ∼120 kyr BP, the number of entities gradually decreases until the U–Th dating limit is reached (∼640 ka).

Where the original measured laser ablation data have been provided by data contributors, these have been made available as *.txt data files in the repository (Table 5). A total of 46 trace element records (15 Mg/Ca, 17 Sr/Ca, 4 Ba/Ca, 5 U/Ca, 5 P/Ca, and 2 Sr-isotope records) are only provided in the original format (*.txt files), either because they could not be converted to millimoles per mole or because the trace element data were not measured at stable-isotope-equivalent depths and were at an insufficiently high resolution for accurate resampling. Additional elements that are not included in the database but have been submitted by data contributors are also provided as *.txt files (e.g. Mn, Fe, Zn, Th, Pb, K, and Na).

4.2 New stable isotope records

SISALv3 provides a significantly expanded oxygen isotope dataset compared with SISALv2 (Tables 6, 7; Fig. 5), with 892 δ18O records from 365 sites, compared with 673 records in SISALv2. The most significant increases in δ18O records are in Africa (28 additional records), Europe (73 additional records), and Asia (50 additional records; Table 6). SISALv3 contains 334 entities covering the last 2000 years, of which 78 are new (Fig. 6). As record density begins to decrease with age (Fig. 6), the spatial distribution is reduced as well. For the Last Glacial Maximum (20–22 kyr BP), SISALv3 contains 92 entities (11 new), while for the Last Interglacial (124–126 kyr BP), 66 entities are available (15 new). Four δ18O records previously included in SISALv2 have been modified to correct previous mistakes (Table 4); these are entity_id 110 (CUR4; Novello et al., 2016), 169 (Dim-E3; Ünal-İmer et al., 2015), 447 (JAR4; Novello et al., 2017), and 573 (Gej-1; Flohr et al., 2017).

There has also been a significant increase in the number of δ13C records added, with 620 records in SISALv3 compared with 430 in SISALv2 (Table 6, Fig. 7). At the regional scale, the most significant increases in δ13C records are for Africa (23 additional records), Asia (33 additional records), and Europe (66 additional records; Table 6). The δ13C record coverage decreases following the same patterns as the trace element and δ18O records (Fig. 8). Two δ13C records previously included in SISALv2 have been modified to correct previous mistakes (Table 4); these are entity_id 169 (Dim-E3; Ünal-İmer et al., 2015) and 573 (Gej-1; Flohr et al., 2017).

Table 7New entities added to SISALv3.

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Figure 5Global map of δ18O records included in SISAL v2 and v3. The shaded background shows the global karst distribution extracted from the World Karst Aquifer Map (WoKAM; Goldscheider et al., 2020).

Figure 6Temporal coverage of the δ18O records in SISALv3 by region. Bin sizes are as follows: (a) 0–2000 years BP – 20 years; (b) 2000–21 000 years BP – 250 years; (c) 21 000–750 000 years BP – 10 000 years.


Figure 7Map of available δ13C records in SISALv3 compared with all records in the database. The shaded background shows the global karst distribution extracted from the World Karst Aquifer Map (WoKAM; Goldscheider et al., 2020).

Figure 8Temporal coverage of the δ13C records in SISALv3 by region. Bin sizes are as follows: (a) 0–2000  years BP – 20 years; (b) 2000–21 000  years BP – 250 years; (c) 21 000–750 000 years BP – 10 000 years.


4.3 Vegetation and land cover metadata

Interpretation of the site-to-site variability in speleothem data sensitive to vegetation changes is facilitated by providing information on vegetation_type and land_use. The dropdown list for these fields includes options typically used in speleothem publications. Additional information provided by the authors (e.g. species names) has been added to the Notes table. About 40 % of the database entries lack the author-reported information on land cover (Fig. 9c). Satellite-derived land cover classifications provide information for many more sites (unknown: 1.7 %; Fig. 9d). Forested sites (evergreen, deciduous, and mixed) comprise ∼56.5 % (Fig. 9d), shrub- and grassland make up 25.1 %, and this dataset also denotes sites that are affected by anthropogenic land use (managed vegetation, agriculture, and urban), which make up 13 %.

Figure 9(a) Vegetation description from the original publications or provided by authors and (b) land cover categories extracted from the Copernicus LCC database (Buchhorn et al., 2021, 2020) with a radius of 250 m around the cave sites. (c) Pie chart showing the relative proportions of vegetation types as reported by authors. (d) Pie chart showing the relative proportions of land cover types as extracted from the Copernicus LCC database. Background shading in the map shows the global karst distribution extracted from the World Karst Aquifer Map (WoKAM; Goldscheider et al., 2020). To allow comparison between the two datasets, the Copernicus LCC vegetation data were grouped into broader categories, e.g. “deciduous” includes all closed and open broadleaf and needleleaf forest marked as deciduous. The entries in the database are more detailed.

5 Recommendations for use

The SISALv3 database is a standardized, quality-checked dataset that allows regional to global assessments of spatial and temporal trends in multiple environmental proxies from speleothem records. The addition of trace element data at stable-isotope-equivalent depths to the database and machine-readable metadata fields allow the examination of hydroclimatic controls on the speleothem trace element distribution. Metadata fields, including distance from coast (latitude, longitude, and elevation), lithology (geology and wokam), and land cover (cover_type, cover_thickness, vegetation_type, land_use, and copernicus_lcc), allow the identification of the primary controls on trace elements. We recommend using multiple cover fields together, based on the analysis type and scope (e.g. time interval considered), as they provide complementary information. Anthropogenic and natural changes in the cover parameters over time need to be considered, and this applies particularly for the cover fields vegetation_type, land_use, and copernicus_lcc, which (in most cases) may only be applicable for very recent speleothem growth.

Where trace elements are measured on aliquots of the same powder as stable isotopes, the sample-to-sample variability in depth–time space is minimal. Where samples for stable isotopes and trace elements have been drilled at different times or in situ methods have been used for trace element measurements, there may be depth–time variability that may impact the results. Extensive metadata on sampling and measurement methods as well as the original high-resolution in situ measurements against depth are provided in the database and linked repository and should be used to check for such impacts. Measurements may also be sensitive to stalagmite petrography; image scans have been provided in the linked repository so that the user can evaluate whether this is important for the interpretation of the record.

5.1 Code and data availability

The database is available in CSV and SQL format in a repository at (Kaushal et al., 2024). This dataset is licensed by the rights holder(s) under a Creative Commons Attribution 4.0 International licence: (last access: 8 March 2024). Apart from the workbook used to submit data to the SISAL database and the codes for automatic quality checking, the repository contains additional standardization sheets (coordinate conversion, grams to moles conversion for trace elements, and atomic activity calculator for U-series data). Moreover, the repository contains all submitted cave maps and entity images in separate zip folders as well as copyright information for the individual images and an entity scan “wish list” that details best practices for entity scan images. Standardized trace element data files are included separately with their metadata (see Sect. 2) and the codes needed to connect and use the database (described in the README file).

The codes for standardization and downsampling of trace element and Sr-isotope records are available at Zenodo (, Skiba, 2023; licensed by the right holder(s) under Creative Commons Attribution 4.0 International).

The database contains both the original age model for individual entities and a standardized age-modelling ensemble. The original age model often takes account of site- and sample-specific conditions; the standardized age-model ensemble allows for robust assessment of age uncertainties and sensitivity testing (Comas-Bru et al., 2020). All codes for constructing the age-model ensembles using linear interpolation, linear regression, Bchron, Bacon, copRa, and StalAge can be found at (last access: 23 July 2020; Roesch and Rehfeld, 2020; codes licensed by the right holder(s) under a GPL-3). All age-model ensembles are available at (Rehfeld and Bühler, 2024). These codes are licensed by the right holder(s) under a Creative Commons Attribution 4.0 International licence.

The SISALv3 database, like its predecessors, lists the original references, and users are encouraged to consult original authors for interpretative details. The “SISAL webApp” (; Hatvani et al., 2024) has been updated to provide an easy-to-use front-end interface for exploring the latest SISALv3 database. It now allows one to run queries on various data and metadata fields, such as stable isotope records and trace element proxies.

5.2 How to cite the database

The SISALv3 database is a community-driven effort to synthesize and standardize speleothem data and make them available to the wider palaeoclimate community. In agreement with the FAIR principles for scientific data management and stewardship, the database itself should be cited (available at; Kaushal et al., 2024), along with this publication (and previous publication versions). If individual records are extracted from the database, the original publications should also be listed. More details on the terms of use are provided in the repository (; Kaushal et al., 2024).


The supplement related to this article is available online at:

Team list

The following SISAL WG members contributed either data or age-modelling advice to SISALv3: Asfawossen Asrat (Department of Mining and Geological Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana), Charlotte Honiat (Institute of Geology, University of Innsbruck, Innrain 52, Innsbruck, Austria), Dana Felicitas Christine Riechelmann (Institute for Geosciences, Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 21, 55128 Mainz, Germany), Denis Scholz (Institute for Geosciences, Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 21, 55128 Mainz, Germany), Dianbing Liu (School of Geography, Nanjing Normal University, Nanjing, China), Dominik Fleitmann (Department of Environmental Sciences, University of Basel, Bernoullistrasse 32, 4056 Basel, Switzerland), Dominik Hennhoefer (Department of Earth Sciences, Khalifa University, SAN Campus, Abu Dhabi, 127788, United Arab Emirates), Ezgi Ünal İmer (Geological Engineering Department, Middle East Technical University, 06800 Çankaya, Ankara, Türkiye), Gina E. Moseley (Institute of Geology, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria), Giselle Utida (Institute of Geosciences, University of São Paulo, São Paulo, 05508-080, Brazil), Hai Cheng (Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an, China), Helen Green (The University of Melbourne, Parkville, VIC 3010, Australia), Hsun-Ming Hu (High-Precision Mass Spectrometry and Environment Change Laboratory – HISPEC, Department of Geosciences, National Taiwan University, Taipei 10617, Taiwan), James Apaéstegui (Instituto Geofísico del Perú, Lima, 15012, Peru), Jan Esper (Department of Geography, Johannes Gutenberg University, Johann-Joachim-Becher-Weg 21, 55099 Mainz, Germany), Jasper A. Wassenburg (Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea; Pusan National University, Busan, 46241, Republic of Korea), Jeronimo Aviles Olguin (Museo del Desierto. Blvd. Carlos Abedrop Dávila 3745, Nuevo Centro Metropolitano de Saltillo, 25022 Saltillo, Coah. Mexico), Jessica Leigh Oster (Department of Earth and Environmental Sciences, Vanderbilt University, Nashville, TN 37240, USA), Jesús M. Pajón Morejón (National Museum of Natural History of Cuba, Department of Paleogeography and Paleobiology, Obispo 61, Plaza de Armas, Habana Vieja, CP 10 100, La Habana, Cuba), Judit Torner (CRG Marine Geosciences, Facultat de Ciències de la Terra, Universitat de Barcelona, 08028 Barcelona, Spain), Kathleen A Wendt (College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA), Liangcheng Tan (State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China), Lijuan Sha (Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an, China), Liza Kathleen McDonough (ANSTO, New Illawarra Road, Lucas Heights, NSW 2234, Australia), Maša Surić (Department of Geography, University of Zadar, Ul. dr. F. Tuđmana 24 i, 23000 Zadar, Croatia), Matthew J. Jacobson (Division of Agrarian History, Department of Urban and Rural Development, Swedish University of Agricultural Sciences, 756 51 Uppsala, Sweden), Mercè Cisneros (GRC Geociències Marines, Departament de Dinàmica de la Terra i de l'Oceà, Facultat de Ciències de la Terra, Universitat de Barcelona, c/ Martí i Franqués s/n, 08028 Barcelona, Spain; Centre en Canvi Climàtic, Department de Geografia, Facultat de Turisme i Geografia, Universitat Rovira i Virgili, c/ Joanot Martorell 15, 43480 Vila-seca, Tarragona, Spain), Michael L. Griffiths (Department of Environmental Science, William Paterson University, Wayne, NJ 07739, USA), Michael Weber (Institute for Geosciences, Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 21, 55128 Mainz, Germany), Nick Scroxton (Irish Climate and Analysis Research UnitS – ICARUS, Department of Geography, Maynooth University, Maynooth, Kildare, Ireland), Paul S. Wilcox (Institute of Geology, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria), R. Lawrence Edwards (Department of Earth and Environmental Sciences, University of Minnesota, Minneapolis, MN 55455, USA), Romina Belli (Proteomics and Mass Spectrometry Core Facility, Department of Cellular, Computational and Integrative Biology – DeCIBIO, University of Trento, Via Sommarive 9, 38123 Trento, Italy), Sebastian F. M. Breitenbach (Department of Geography and Environmental Sciences, Northumbria, Newcastle upon Tyne, NE1 8ST, UK), Shraddha T Band (National Taiwan University, Institute of Oceanography, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan), Simon Dominik Steidle (Institute of Geology, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria), Stacy Anne Carolin (Department of Earth Sciences, University of Cambridge, Downing Street, Cambridge, CB23 8AD, UK), Vanessa E. Johnston (Karst Research Institute ZRC SAZU, Titov trg 2, 6230 Postojna, Slovenia), and Wuhui Duan (Key Laboratory of Cenozoic Geology and Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Life and Paleoenvironment, Beijing, China).

Author contributions

NK coordinated this project. NK, FAL, and MW designed the new version of the database. KR and JLB ran the SISAL standardized age–depth models for new entities. Downsampling of trace element records to stable isotope resolution was performed by VS and MR. Standardization of trace element data files was done by YB and NK. Reworking and additions to the metadata fields were done by KB and KA. JGS and NK collected citations, copyright information, and licence terms for the cave maps and speleothem images. Regional data collection and screening was coordinated by VA, JLB, SC, AC, LE, JH, IGH, ZK, AK, KK, MM, BM, SMA, CN, VFN, CPM, JR, NaS, NiS, CVT, BHT, SW, AW, and HZ. Quality control of the submitted datasets was performed by MW, FAL, and NK, with additional code provided by JF. Figures 1 and 2 were created by FAL, and Figs. 3–9 were created by JCB. All authors listed as “Data contributors” provided data for this version of the database or helped to complete existing data entries. FL wrote the paper with input from NK, JCB, KR, AB, PT, and SPH. All authors contributed to the final version of the paper.

Competing interests

The contact author has declared that none of the authors has any competing interests.


Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.


We thank all SISAL members who contributed their data to this project and were available to provide additional information where necessary. We also acknowledge Ana Moreno, Christoph Spötl, and Laura A. Dupont for specific data contributions to the database. We thank the editorial staff at Earth System Science Data, the reviewers (Ewan Gowan, Christopher Hancock, and Sang Chen), and the anonymous referee for their supportive and critical feedback on this manuscript.

Financial support

This study was conducted by SISAL (Speleothem Isotopes Synthesis and Analysis), a working group of the Past Global Changes (PAGES) project. In this framework, we received financial support from the Swiss Academy of Sciences and the Chinese Academy of Sciences. The design and construction of the SISALv3 database were financially supported by a PAGES Data Stewardship Scholarship to Franziska A. Lechleitner and Nikita Kaushal (grant no. DSS-108). We also received funding from PAGES, the Minerva Stiftung (grant no. 3063000253), and the Institute of Earth Sciences at the Hebrew University Jerusalem (Israel) to support the organization of a workshop to kick-start the initiative.

Review statement

This paper was edited by Xingchen Wang and reviewed by Chris Hancock, Evan Gowan, Sang Chen, and one anonymous referee.


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Short summary
Speleothems are a popular, multi-proxy climate archive that provide regional to global insights into past hydroclimate trends with precise chronologies. We present an update to the SISAL (Speleothem Isotopes
Synthesis and AnaLysis) database, SISALv3, which, for the first time, contains speleothem trace element records, in addition to an update to the stable isotope records available in previous versions of the database, cumulatively providing data from 365 globally distributed sites.
Final-revised paper