ESSD Earth System Science Data ESSDEarth Syst. Sci. Data 1866-3516 Copernicus Publications Göttingen, Germany 10.5194/essd-18-6485-2026C-PEAT's Global Peatland Carbon Database (v.2025)C-PEAT's Global Peatland Carbon Database (v.2025) LoiselJulie jloisel@unr.edu https://orcid.org/0000-0002-8861-4618 Gallego-SalaAngela RansbyDaniela https://orcid.org/0000-0002-3643-333X RabelEmily BehrensCornelia https://orcid.org/0000-0002-0306-0047 SchumacherStefanie https://orcid.org/0000-0002-8310-9743 Todd-BrownKathe https://orcid.org/0000-0002-3109-8130 MarupakaVaasuki https://orcid.org/0000-0002-3854-1764 C-PEAT Working Group Members Dept. of Geography, Texas A&M University, College Station, TX, USA Dept. of Geography, University of Nevada, Reno, NV, USA Dept. of Geography, University of Exeter, Exeter, UK PANGAEA, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany PANGAEA, MARUM – Center for Marine Environmental Sciences, University of Bremen, Germany Dept. of Environmental Engineering Sciences, University of Florida, Gainesville, FL, USA A full list of authors appears at the end of the paper. Julie Loisel (jloisel@unr.edu)7September2026 18 9 64856502 31March2026 11May2026 6August2026 12August2026 Copyright: © 2026 Julie Loisel et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/18/6485/2026/essd-18-6485-2026.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/18/6485/2026/essd-18-6485-2026.pdf Abstract

Field-based measurements are foundational to the study of short- and long-term peatland carbon dynamics. For decades, the scientific community has amassed hundreds of valuable empirical datasets in the form of peat core records from around the world. Those records typically include peat depth, basal age, peat organic matter content, peat dry bulk density, peat organic density, and/or carbon and nitrogen content. Once combined with chronological constraints and models, peat core time series can be used to estimate changes in peat-carbon accumulation rates through time. Consolidating these peat records can help improve global peat-carbon stock estimates and quantifications of past, present, and future greenhouse gas exchanges between peatlands and the atmosphere. Large-scale synthesis can also shed light on the sensitivity of peat-carbon accumulation processes to climate change and provide context for current and future global environmental change. We can also use spatial and temporal peat data to inform, validate, and benchmark existing models that include peatland representations. This paper presents the first formal version of PAGES' C-PEAT Global Peatland Carbon Database (GD), which is available for download in the PANGAEA and International Soil Carbon Network (ISCN) data repositories. The C-PEAT GD contains 267 independently catalogued peat cores and a large number of observations from those cores, including: peat depths, organic matter content values, dry bulk density values, organic density values, as well as carbon and nitrogen content values. Raw and calibrated chronological data are included for each individual dataset when available. The metadata fields are easily searchable and interoperable, as per PANGAEA's standards. The main objective of this article is to describe the structure and content of the database, itself aimed at increasing the use, assimilation, and interoperability of peat-core data across disciplines. The C-PEAT GD can be accessed at the PANGAEA data repository (10.1594/PANGAEA.986891; Loisel et al., 2025).

 National Science Foundation 2142177 1802838 Introduction

Peatlands have stored a vast quantity of carbon (C) in their thick organic soils over the past ∼ 10 000 years (Gorham, 1991; Yu et al., 2010). The effect of this long-term carbon sink on the global climate system has been that of a net cooling (Smith et al., 2004; Frolking et al., 2006; Yu, 2012), despite large methane (CH4) emissions to the atmosphere (Spahni et al., 2013; Packalen et al., 2014; Treat et al., 2021). While peatlands only occupy 3 % of the global land area, their current carbon store has been estimated at ∼ 500–1000 gigatonnes (Gt) (Yu, 2012; Nichols and Peteet, 2019), with an apparent long-term averaged carbon accumulation rate (aCAR) of 23 g C m−2 yr−1 (Loisel et al., 2014). We note that peat soil carbon density and aCAR are known to vary over space and time, as they depend on a number of internal factors that include local vegetation communities, hydrological setting, and topography, as well as a suite of external factors such as air temperature, moisture regime, and climate seasonality (Jones and Yu, 2010; Charman et al., 2013; Cobb et al., 2017; Hoyt et al., 2019). Other key agents capable of altering a peatland's carbon store include wildfire, permafrost dynamics, sea-level change, atmospheric pollution and dust deposition, and anthropogenic land-use change (Dommain et al., 2011; Gibson et al., 2018; Hooijer et al., 2012; Hoyt et al., 2020; Turetsky et al., 2020).

Over the past ∼ 35 years, the literature has become rich with empirical- and model-based studies aimed at quantifying peatland carbon storage from around the world as well as their past, present, and future CARs (Fig. 1a). While the majority of these studies have focused on northern high-latitude peatlands (> 45° N; Gorham, 1991; Turunen et al., 2002; Yu, 2012), there has been an increase in the number of studies based in the temperate, tropical, and southern high-latitude regions (Kurnianto et al., 2015; Kock et al., 2020; Ruwaimana et al., 2020), as well as global assessments (Gallego-Sala et al., 2018; Qiu et al., 2020; Loisel et al., 2021; Müller and Joos, 2021). In particular, studies of tropical peatlands have been on the rise since 2010 (Fig. 1b).

Number of peer-reviewed publications that relate to peat carbon, per year (1991–2024), according to Web of Science. (a) Global peatlands (search terms: peat* AND carbon AND (stock* OR stor*) AND rate*). (b) Tropical peatlands (search terms: peat* AND carbon AND (stock* OR stor*) AND rate* AND tropic*).

Timeline of some key synthesis papers on peatland carbon. Blue: northern hemisphere; Orange: global.

The 2010s have also brought along many large-scale peat-data compilations (Fig. 2), perhaps in part thanks to the broad goals of increasing scientific availability/reproducibility (Ellison, 2010; Bond-Lamberty et al., 2016). The need for transparency in methods, code, and data has also been fueling an increase in collaborative, open science (Adams, 2012; Wolkovich et al., 2012), which helps fulfill a growing need for integrated knowledge and actionable science, in addition to bringing an additional element of trust to the body of science (Grand et al., 2012). In particular, synthesis work has been contributing to initiating new collaborations and building working groups across the scientific community (Baron et al., 2017). In the case of ecological data, syntheses and meta-analyses increase visibility of, and accessibility to, individual case studies (Markowitz et al., 2015). These efforts may help the safeguarding, rescuing, and disseminating of ecological data that would otherwise be lost or remain inaccessible, particularly if unpublished (Dietze et al., 2014; Vines et al., 2014; Harden et al., 2017). Indeed, access to raw ecological data is generally thought to range between 1 % and 10 % (Reichman et al., 2011; Wolkovich et al., 2012), making data curation, data accessibility, and data compatibility essential parts of a scientist's work today. This is particularly the case for field ecological observations and measurements that cannot be reproduced in a context of climate change (Wolkovich et al., 2012).

It has been argued that synthesis work provides critical knowledge to solving environmental problems and informing policy (Carpenter et al., 2009; Hampton and Parker, 2011; Dicks et al., 2014). By integrating multiple lines of evidence, the power of synthesis work lies in its ability to develop new knowledge that broadens perspectives. The synthesis of existing research provides a means to assess individual case studies and explore generalities among datasets; it limits emphasis on specificities and instead focuses on distilling information to improve scientific understanding. In the case of peat core data, the combination of hundreds of peat records can help improve global peat-carbon stock estimates and quantifications of past, present, and future greenhouse gas exchanges between peatlands and the atmosphere (e.g., MacDonald et al., 2006; Yu, 2012; Yu et al., 2013). Large-scale synthesis can also shed light on the sensitivity of peat-carbon accumulation processes to climate change (e.g., Charman et al., 2013; Loisel et al., 2014; Treat et al., 2019) and provide context for current and future global environmental change (e.g., Gallego-Sala et al., 2018; Hugelius et al., 2020; Qiu et al., 2020). We can also use spatial and temporal peat data to inform, validate, and benchmark existing Earth System Models that include peatland representations (Kleinen et al., 2012; Spahni et al., 2013; Qiu et al., 2018; Alexandrov et al., 2020; Chaudhary et al., 2020; Müller and Joos, 2021; Chadburn et al., 2022).

Here we present the first formal version of PAGES' C-PEAT Global Peatland Carbon Database (GD), which is now available for download on the PANGAEA data repository. The C-PEAT GD contains 267 previously published independent peat cores and a large number of observations from those cores: 267 peat core depths, 43 467 peat dry bulk density values, 31 473 peat organic matter content values, 4016 peat organic matter density values, 42 566 carbon content values, and 10 771 nitrogen content values. The main objective of this article is to describe the structure and content of the database, itself aimed at increasing the use, assimilation, and interoperability of peat-core data across disciplines. The database is made of four synthesis papers that were published as part of international community efforts deployed by the C-PEAT working group over the past decade. Those four synthesis papers are introduced below. Figure 2 also acknowledges a number of additional synthesis papers on long-term peatland dynamics and carbon stock. C-PEAT is a group of over 350 scientists dedicated to synthesizing data and knowledge on the evolution of peatlands through Earth's recent history via collaborations between international researchers. C-PEAT operates under the umbrella of the parent organizations Past Global Changes (PAGES) and Future Earth, and has received further support from the International Union of Quaternary Research (INQUA) and UNESCO's International Geoscience Programme (IGCP) to organize community workshops and training sessions.

Yu et al. (2010) compiled carbon accumulation records from 76 sites, which were used to reconstruct changes in the peat-carbon pool since the Last Glacial Maximum for the northern, tropical, and southern peatland regions. Charman et al. (2013) then focused on 24 well-dated peat profiles from the northern high-latitude regions to examine changes in C accumulation over the past millennium. Loisel et al. (2014) augmented the Yu et al. (2010)'s northern dataset by using peat core data from over 200 sites to report Holocene peat properties (i.e., bulk density, organic matter, carbon, and nitrogen (N) content) and aCARs for 127 of those sites. Gallego-Sala et al. (2018) further extended the last millennium work from Charman et al. (2013) by combining over 250 millennium-aged peat-core records from around the globe with a modeling approach to quantify the future global peatland carbon sink function. We note that the C-PEAT group has worked on other peatland databases worth mentioning here. For instance, Treat et al. (2015) looked at peatland plant macrofossil records from 280 sites across the northern permafrost zone and reported differences in peat properties as well as aCARs among the different vegetation types and environmental classes. Yu et al. (2013) and Loisel et al. (2017) combined 2808 basal peat ages from previously published papers to reconstruct spatial and temporal patterns of Holocene peatland area change. Lastly, Treat et al. (2019) used over 1000 stratigraphic records of buried peatlands from around the world to reconstruct peat-forming wetland dynamics over the past 130 000 years. The datasets from those last few papers are also available on PANGAEA, but the peat cores have not yet been cataloged individually. Recently, the compilations by Widyastuti et al. (2025) and Skye et al. (2026) have also contributed global peat depth datasets.

 Methods Definitions and potential uses for the C-PEAT GD

The C-PEAT GD includes high-resolution peat-core geochemical and chronological data that can be used to calculate a number of soil carbon indices (Fig. 3). At the peat-core level, the information can be used to quantify peatland carbon storage (units vary, but typically shown as g C cm−2, kg C m−2, or Mg C ha−1) (e.g., Armentano and Menges, 1986; Gorham, 1991; Petrokofsky et al., 2012). Apparent carbon accumulation rates (aCAR; g C m−2 yr−1) can be obtained by dividing a core's carbon density by the basal age of that core (Tolonen and Turunen, 1996); multiple ages and carbon measurements along a peat profile can also be used to estimate temporal changes in aCAR (e.g., Yu et al., 2010), though aCAR should not be misinterpreted as net carbon balance (Young et al., 2019, 2021). In previous synthesis work, aCAR was calculated for each peat layer (Fig. 3); the layer data were then combined into bins (e.g., 50, 100, 500, 1000 years) to provide time-weighted aCAR (Yu et al., 2010; Jones and Yu, 2010; Loisel et al., 2014; Fig. 3). The latter makes it easier to compare apparent accumulation rates across sites and build regional assessments. It is important to note that aCAR is an imperfect measure of past accumulation rates because peatlands may be subject to secondary peat loss – from fire, erosion, a lasting drought, or management – that takes place long after initial peat deposition and early-stage diagenesis (Yu, 2012; Frolking et al., 2014; Young et al., 2019, 2021). Thus, a fundamental problem with aCAR is that it cannot rule out whether secondary peat loss has taken place. Aggregating a large number of core site data for which multiple age determinations and carbon measurements are available could alleviate the issue, if one assumes that, secondary decomposition is site-specific “noise” that would be subdued in a synthesis product, though large-scale changes could affect several sites at once. Recent aCAR calculations (past ∼ 150 years; Fig. 3) are technically possible using our database. But here again, the use of these datasets comes with important limitations. For instance, we emphasize that near-surface records tend to represent incompletely decomposed, young peat, such that recent aCAR should not be directly compared against longer-term aCAR (Piilo et al., 2019; Young et al., 2019). In fact, the past ∼ 150 years of peat accumulation are often omitted from synthesis work (e.g., Charman et al., 2013; Gallego-Sala et al., 2018). Lastly, it should be mentioned that the C-PEAT GD database also includes nitrogen content; nitrogen stock, soil nitrogen density, and aNAR can thus be calculated.

Datasets available from the C-PEAT Global Peatland Carbon Database. The * denotes soil carbon indices that can be derived from the database. For additional details, refer to Tables 2–3–4. Photo credit: Julie Loisel.

 Peat carbon database architecture

Here we present the first version (coined v.2025) of the C-PEAT GD. The following paragraphs provide the general database structure. We start with a brief introduction of the collaborative process that gave birth to the database. We then describe data aggregation and formatting, list the types of data/metadata that are available, and we finish by presenting data selection criteria as well as data coverage. The C-PEAT GD can be accessed via PANGAEA at: https://www.pangaea.de/?q=project:label:PAGES_C-PEAT (last access: 17 August 2026). The latter landing page contains links to download individual records; the entire database can also be mass-downloaded using R or Python (Ransby et al., 2026). Metadata and data can be obtained from the PANGAEA repository with scripts using the “pangaear” R package (Chamberlain et al., 2021) and the “pangaeapy” Python library (Huber et al., 2025). More information on submission of new or revised datasets can be found in Sect. 4.1.

This database is the product of approximately 10 years of collaborative work by the C-PEAT working group, which is endorsed by PAGES. This synthesis effort focuses on peatland-C records; it is a contribution to C-PEAT's second developmental phase and fulfills the objective of making the community's peat-C data widely available (Loisel and Gallego-Sala, 2018). The C-PEAT GD is the culmination of multiple synthesis efforts that have taken place over the past decade (see Sect. 1 and Fig. 2). For each one of these previous studies, calls for participation were widely distributed. Data inclusion criteria differed between papers to match their different scopes; in each case, decisions were made by a group of lead authors in consultation with the data contributors (Table 1). The workload for assembling the data and metadata that make up C-PEAT's GD was coordinated at Texas A&M University. All data from the four compilation papers are included in the C-PEAT GD.

Criteria for inclusion used by the four peat-data synthesis articles that form the C-PEAT Global Peatland Carbon Database (v.2025).

References Chronological constraints Geochemical constraints Geographic constraints Yu et al. (2010) multiple calibrated ages some bulk density measurements none Charman et al. (2013) at least 3 evenly spaced dates spanning the last 1000 years contiguous bulk density measurements at < 5 cm resolution latitude: > 45° N Loisel et al. (2014) at least 1 date per thousand years (on average for the entire record) some bulk density or organic matter density measurements latitude: > 45° N Gallego-Sala et al. (2018) at least 2 radiocarbon dates from the past 1000 years contiguous bulk density measurements at <2 cm resolution none Data aggregation, formatting, and availability in PANGAEA

As a data publisher for Earth & Environmental Sciences, PANGAEA (Felden et al., 2023) has an almost 40-year history as an open-access library for archiving, publishing, and disseminating georeferenced data from the Earth, environmental, and biodiversity sciences. Originally evolving from a database for sediment cores, it is operated as a joint facility of the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, and the Center for Marine Environmental Sciences at the University of Bremen. PANGAEA has been certified with the Core Trust Seal since 2019. Each published dataset can be cited with a specific data citation and an associated unique Digital Object Identifier (DOI) to cross-link to related resources such as manuscripts and data. A broad spectrum of contextual information (“metadata”) describing the where, how, when, and why of a measurement is given. Additionally, the methods and devices used for sampling and analysis as well as references of related literature and external resources are provided. All data and metadata are compiled in close collaboration between the scientists and trained field experts acting as data editors. Both data and metadata are checked for completeness and plausibility, ensuring high quality standards according to the FAIR data principles. Semantic interoperability during data curation is ensured through strict application and dynamic evolution of terminologies according to international protocols and standards, such as Environment ontology (EnvO), the Integrated Taxonomic Information System (ITIS), and Quantities, Units, Dimensions and DataTypes Ontology (QUDT). This harmonization and standardization promote not only readability and further processability by machines, but also a high degree of reusability of the data stock. In addition to the classic access to data via the website, an integrative use of data in the form of a DataWarehouse and a set of tools for programmatic data processing are available for this purpose. The two applications, “pangaeapy” (Huber et al., 2025) and “pangaear” (Chamberlain et al., 2021), respectively, are written for the scripting languages Python and R, and make use of the well-developed interoperability framework of PANGAEA.

Each peat core record that is a part of our GD was either directly contributed by the original data owner or obtained from the published literature by the different synthesis author teams (Fig. 4). In most cases, records that were identified in the literature were then obtained from the corresponding author via email. In a few cases, datasets were digitized from publication tables, appendices, and supplementary materials. The synthesis team leads provided templates to the data contributors to facilitate aggregation. In the case of data acquired from publications, the synthesis teams entered the data into the templates themselves. At this stage, data quality was manually assessed by said teams (Table 1). The data were then compiled and used for analysis. During the second stage (i.e., in preparation for the C-PEAT GD), the data and metadata were reviewed and formatted to harmonize and standardize them with the PANGAEA database. To date, PANGAEA hosts 758 C-PEAT datasets obtained from 267 distinct cores (267 geochemistry datasets, 264 age determination datasets, and 227 calibrated ages datasets). These datasets were authored by 81 individuals (or groups of individuals). The datasets refer to 84 journal articles as well as 4 theses, dissertations, and conference papers. Each dataset is associated with an “Event”, which is a metadata descriptor used by PANGAEA for characterizing the location where a physical sample or measurement took place. For the C-PEAT database, “Event” is a synonym for a “peat core”. While the database contains individual peatlands that harbor several cores, each “core” is represented individually. There is no direct way to differentiate by “peatland site”, besides by looking at the “event” names: cores collected from a single site typically have the same first letters in their name (e.g., event: Mariana_coreMF03-1 vs. event: Mariana_coreMF03-2). Mandatory metadata for each Event comprises a unique “Label” as well as “Latitude” and “Longitude” information (in decimal degrees). Further optional metadata include an additional/alternative core Label, Sampling method, Elevation (meters above sea level) and a Comment (free text) (Fig. 4). Duplicate cores across the 4 synthesis papers were reconciled to avoid duplications in the database. Cores from the most recent synthesis were selected over data from the same core site that were previously published (e.g., we would include a core presented by Charman et al. (2013) and leave its older duplicate from Yu et al., 2010). This selection allowed to integrate the most updated values into the C-PEAT database, should the core data were updated over time.

Workflow to integrate C-PEAT data into PANGAEA. Each peat core (called an Event in PANGAEA) is linked to Location and Methods metadata. Each peat layer includes geochemical data, raw age determinations, and calibrated ages (referred to as Data in PANGAEA). Three datasets per core are created, each with a distinct DOI. Those data are linked to Methods and References from the original publication (if published).

Geochemical parameters included in the C-PEAT Global Peatland Carbon Database, with associated descriptions and metadata.

Parameter [unit] Description Parameter short name DEPTH, sediment/rock [m] sample midpoint depth, measured from the surface Depth sed AGE [ka BP] calibrated age (in thousands of years Before Present, based on the original age-depth curve) Age Sample thickness [cm] distance (cm) between top and bottom of a sample Samp thick Sample volume [cm3] amount of material Samp vol Water content, wet mass [%] weight-% Water wm Peat type [ ] category Peat type Sedimentation rate per year [cm a−1] amount of peat accumulated per year SR Peat carbon accumulation rate per year [g m−2 a−1] weight (g) of carbon accumulated per unit of surface area (m2) per year PCAR Organic matter [%]* weight-% OM Carbon [%] calculated from other data such as LOI C Carbon, inorganic, total [%] weight-% TIC Carbon, organic, total [%] weight-% TOC Carbon, total [%] weight-% TC Comment [ ] free text Comment Cumulative mass [g cm−2] total accumulated peat dry mass up to the sample's depth, where CM = 0 is the deepest sample of the core and CM = max at the surface of the core Cum mass Density, dry bulk [g cm−3] ratio of dry mass to peat bulk volume DBD Density, organic carbon [g cm3] ratio of organic carbon to peat bulk volume Corg dens Density, organic matter [g cm−3] ratio of organic matter to peat bulk volume OM dens Nitrogen, total [%] weight-% TN

* In some datasets, the parameter is named Loss on ignition [%] – both were merged for the synthesis.

The geochemistry datasets contain the key peat geochemical properties along with the ancillary data that may complement, or help interpret, the data (Table 2). These ancillary data include entries such as the main peat type. Age determination datasets provide raw and/or calibrated age control data from different chronological sources, such as radiocarbon (14C), lead-210 (210Pb), or tephra chronostratigraphy, together with information on the type of dated material (bulk, wood, pollen etc.) (Table 3). Since different authors opt for different nomenclatures and notation systems when presenting their raw dates and calibrated ages, PANGAEA uses a relatively long list of parameters aimed at maintaining the original meaning of the original data entries. For example, the parameters “AGE [ka BP]” and “Age model [ka]” are used to store the calibrated age for each sample along a peat core, and the data are presented in thousands of calibrated years Before Present (BP). In publications where age is instead presented in calendar years, the PANGAEA parameters “AGE [a]”, “AGE [a, AD/CE]”, or “AGE [a, years ago]” are used. The same goes with archiving age uncertainties: some authors calculate their own uncertainty range (PANGAEA parameter: “Age, dated, uncertainty [±]”) while others opt to present one standard deviation (PANGAEA parameter: “Age, dated standard deviation [±]”). Age uncertainties can also be presented in ka or in years, and different parameters are used accordingly. Table 3 presents the complete list of parameters (and associated short descriptions) that are used to archived the C-PEAT GD data entries pertaining to dates and ages. Age-depth relationships of individual peat cores are presented in the calibrated ages datasets (Table 4). Note that these ages were obtained from the original publications and are thus based on different age-depth modeling techniques and calibration curves.

Age determination parameters included in the C-PEAT Global Peatland Carbon Database, with associated descriptions and metadata. Descriptions that pertain to “original age-depth curve or publications” mean that the data that we report in our database were directly extracted from the original datasets and may have been calibrated a long time ago. The user is advised to use the uncalibrated datasets and run their own calibrations.

Parameter [unit] Description Parameter short name DEPTH, sediment/rock [m] sample midpoint depth, measured from the surface Depth sed Depth, top/min [m] sample top depth, measured from the surface Depth top Depth, bottom/max [m] sample bottom depth, measured from the surface Depth bot Sample code/label [ ] sample name Sample label Sample thickness [cm] distance (cm) between top and bottom of a sample Samp thick Laboratory code/label [ ] sample code (provided by a dating laboratory) Lab label Method comment [ ] comment on dating method Method comm Fraction modern carbon [ ] radiocarbon concentration as fraction modern carbon (FMC) F14C Fraction modern carbon, error [±] laboratory error associated with fraction modern carbon (FMC) F14C e Δ14C [‰] radiocarbon concentration Δ14C Δ14C, standard deviation [±] laboratory error associated with Δ14C Δ14C std dev Activity of radiocarbon in percent of modern carbon [pMC] percent of modern carbon in a sample Activity Activity of radiocarbon in percent of modern carbon, standard deviation [±] laboratory error associated with percent modern carbon in a sample Activity std dev Age, dated [ka] 14C uncalibrated age (in thousands of years Before Present, based on the original age-depth curve) Age dated Age, dated, uncertainty [±] age uncertainty (in ka) as reported in the original publication Age dated unc Age, dated standard deviation [±] age uncertainty if reported as 1 standard deviation in the original publication Age dated std dev Age, dated material [ ] type of material dated (e.g., leaf, bulk sediment, charcoal, etc.) Dated material Age, comment [ ] free text Comm Age model [ka] calibrated age (in thousands of years Before Present, based on the original age-depth curve) Age model Age, error [±] age error as reported in the original publication Age e Age [a] calibrated age (in calendar years, based on the original age-depth curve) Age Age, uncertainty [±] age uncertainty (in calendar years) as reported in the original publication Age unc Age, standard deviation [±] age uncertainty (in calendar years) if reported as 1 standard deviation in the original publication Age std dev Age [a AD/CE] calibrated age (in calendar years/Common Era, based on the original age-depth curve) Age Age, maximum/old [a AD/CE] oldest calibrated age (in calendar years/Common Era, based on the original age-depth curve) Age max Age, minimum/young [a AD/CE] youngest calibrated age (in calendar years/Common Era, based on the original age-depth curve) Age min

Continued.

Parameter [unit] Description Parameter short name Age, relative number of years [a] Relative age, number of years relative to the surface sample (based on the original age-depth curve) Age Calendar age [ka BP] 14C age or 210Pb calibrated age (in thousands of calendar years, based on the original age-depth curve) Cal age Calendar age, standard deviation [±] age uncertainty (in thousands of calendar years) as reported in the original publication Cal age std dev Comment [ ] free text Comment

Calibrated age parameters included in the C-PEAT Global Peatland Carbon Database, with associated descriptions and metadata.

Parameter [unit] Parameter Description short name DEPTH, sediment/rock [m] Depth sed Sample midpoint depth Calendar age [ka BP] Cal age Contains a method comment (e.g., OxCal, Bacon) Calendar age, maximum/old [ka BP] Cal age max Contains a method comment (e.g., OxCal, Bacon) Calendar age, minimum/young [ka BP] Cal age min Contains a method comment (e.g., OxCal, Bacon)

Geochemical metadata were obtained from the contributing authors (or extracted from the peer-reviewed articles) and they relate to study core site and author information, as well as analytical and sampling methods (Table 2). Chronological metadata relate to the different types of dating methods, laboratories where the analysis were performed, and they also include additional sampling information (when available from the original articles) (Table 3).

 Data coverage, resolution, duration, and chronological constraints

Data coverage of the database is dependent upon data availability, which is mostly out of the control of the data analysts. In the case of the C-PEAT GD, all available records that fulfilled our criteria for inclusion (Table 1) were accepted. As for the resolution and duration of the peat records included in the C-PEAT GD, they vary greatly. For example, the minimal geochemical data resolution was set at every 2 cm by Gallego-Sala et al. (2018) whereas it was set at 5 cm by Charman et al. (2013). This is because those previous synthesis activities had different research objectives (Table 1).

 Analysis performed on the C-PEAT GD

To illustrate overall data coverage, we used a climate envelope and climate region analysis (Köppen) approach. For each core, mean annual temperature (MAT) and total annual precipitation (MAP) were extracted from a gridded climate product (Fick and Hijmans, 2017). We also investigated the peat geochemical properties of our peat samples. For each variable, central tendencies (mean, median and mode) and dispersions (minimum, maximum, variance, and standard deviation) were calculated.

 Results and Discussion Data coverage over space and time

Spatial coverage of the dataset is global, with the majority of cores (220/267) located in the northern extratropical region (Fig. 5). With the exception of southern South America (Patagonia; 11 cores), data availability is very sparse across the southern extratropical region. As for the tropical belt (around 20° N to 20° S), the C-PEAT dataset includes 34 peat core records (Fig. 5). Future efforts should prioritize the synthesis of tropical core records.

Spatial distribution of peat cores included in the C-PEAT Global Peatland Carbon Database. Orange circles represent individual peat cores. The global peatland area (in green) is from UNEP (2022). Map source: © Esri, TomTom, FAO, USGS | Powered by Esri.

Temporal resolution of peat cores included in the C-PEAT Global Peatland Carbon Database. (a) Cumulative number of peat core records over time. (b) Number of chronological constraints per core. (c) Mean apparent rate of peat accumulation for each core (in cm yr−1). On panels (b) and (c), the 25th–75th percentile ranges (IQR – interquartile range) are displayed as the boxes, with the medians (50th percentile) marked by horizontal black lines inside. The whiskers represent 1.5 × IQR and the circles represent outliers.

Temporal coverage of the peat cores varies over time (Fig. 6a). To our knowledge, all peat records found in this database are continuous and their surface is assumed to be of modern age. We note that the oldest age available along a peat core sets the record's duration; in most cases, peat cores extend back to peatland initiation, though many cores do not. In total, 24 peat core records are younger than 1000 years (of which 11 core chronologies were solely constrained by 210Pb ages), 110 records are at least 5000 years old, and 16 cores initiated prior to 10 000 years ago. The oldest records are found in southern Patagonia and Alaska, presumably because of the earlier deglacial history of those regions. The oldest tropical core available in this database is approximately 11 600 years old, though we note that most of the tropical cores included in the dataset are less than 5000 years old (median = 2940 years).

The temporal resolution of the peat core records varies considerably. For instance, the number of chronological constraints along a single peat core varies from 1 to 41 (median = 6; Fig. 6b). Dividing the maximum age of a record by the number of dates available for said record yields an average of 0.76 dates/1000 years, with 64 records characterized by an averaged temporal resolution that is greater than 1 date/1000 years. In general, there are more chronological constraints for the late Holocene than the mid- and early-Holocene time periods, in part because the data from Gallego-Sala et al. (2018) focused on the past millennium. Peat accumulation rates also vary widely between cores. In general, the peatland archives therein offer a multi-decadal resolution, with an average peat accumulation rate of 0.057 cm yr−1, ranging from 0.005 to 0.5 cm yr−1 (Fig. 6c).

 Data coverage

The climate envelope and climate region analyses confirm that peatland cores within this database are found across a broad range of hydroclimatic conditions and climate classes, from the tropical rainforest and the tropical savannah to the polar tundra, and many other classes in between. It is clear that cold climate cores dominate the dataset (222/267 sites) and that more cores from the warmer bioclimatic regions would help balance representation in the database (Fig. 7).

Distribution of core sites included in the C-PEAT Global Peatland Carbon Database in the context of the Köppen-Geiger climate classification. Peatland cores (orange circles on the map) are the same as on Fig. 6. The climate map is from Beck et al. (2023) and the global peatland area (in black) is from UNEP (2022). The biplot presents those same peatland cores (color-coded using the Köppen climate classes) in a climate space. The legend reads as follows (from Beck et al. 2023): 1 Af (Tropical, rainforest), 2 Am (Tropical, monsoon), 3 Aw (Tropical, savannah), 4 BWh (Arid, desert, hot), 5 BWk (Arid, desert, cold), 6 BSh (Arid, steppe, hot), 7 BSk (Arid, steppe, cold), 8 Csa (Temperate, dry summer, hot summer), 9 Csb (Temperate, dry summer, warm summer), 10 Csc (Temperate, dry summer, cold summer), 11 Cwa (Temperate, dry winter, hot summer), 12 Cwb (Temperate, dry winter, warm summer), 13 Cwc (Temperate, dry winter, cold summer), 14 Cfa (Temperate, no dry season, hot summer), 15 Cfb (Temperate, no dry season, warm summer), 16 Cfc (Temperate, no dry season, cold summer), 17 Dsa (Cold, dry summer, hot summer), 18 Dsb (Cold, dry summer, warm summer), 19 Dsc (Cold, dry summer, cold summer), 20 Dsd (Cold, dry summer, very cold winter), 21 Dwa (Cold, dry winter, hot summer), 22 Dwb (Cold, dry winter, warm summer), 23 Dwc (Cold, dry winter, cold summer), 24 Dwd Cold, dry winter, very cold winter), 25 Dfa (Cold, no dry season, hot summer), 26 Dfb (Cold, no dry season, warm summer), 27 Dfc (Cold, no dry season, cold summer), 28 Dfd (Cold, no dry season, very cold winter), 29 ET (Polar, tundra), 30 EF (Polar, frost). Map source: © Esri, TomTom, FAO, USGS | Powered by Esri.

 Key peat properties and characteristics

When combined, the peat records yield a total of 267 peat core depths, 43 467 dry bulk density values, 31 473 organic matter content values, 4016 peat organic matter density values, 42 566 carbon content values (%C, %TC, %TOC combined), and 10 771 nitrogen content values. Descriptive statistics of the dataset are presented in the paragraphs below as well as in Fig. 8 and Table 5.

Descriptive statistics for the C-PEAT Global Peatland Carbon Database. (a) Dry bulk density; (b) Organic matter content; (c) Carbon content, where the 3 datasets are displayed (see Sect. 3.3 for details), and (d) Nitrogen content. For each variable, the left inner panel displays the interquartile range (IQR) (represented as the box, with the median shown as a horizontal black line) while the right inner panel presents the entire data range (including the outliers, denoted as circles). Whiskers present 1.5 × IQR.

Central tendencies and dispersions for the C-PEAT Global Peatland Carbon Database (267 cores).

Variable name and sample count Central tendencies Dispersions n mean median minimum maximum SD total peat depth (m) 267 2.24 1.61 0.30 10.95 1.90 DBD (g cm−3) 43 467 0.140 0.098 0.001 3.380 0.178 OM (%) 31 473 88.5 96.5 0.0 100.0 20.1 OMD (g OM cm−3) 4016 0.117 0.109 0.008 0.399 0.046 C (%) 26 842 46.7 50.8 0.0 71.7 10.5 TC (%) 14 430 45.4 48.4 0.0 100.0 16.4 TOC (%) 1532 37.7 46.0 0.0 77.0 19.6 OCD (g C cm−3) 3952 0.058 0.055 0.005 0.195 0.024 TN (%) 10 771 1.2 1.0 0.0 56.6 0.7

Peat core depth is provided for all 267 cores and ranges from 0.30 to 10.95 m, with a mean value of 2.24 ± 1.9 m (1 standard deviation (SD); Table 5). We note that “peat core depth” refers to the deepest sample provided along a core; while most peat cores in the dataset extend back to peatland initiation, many cores may not. When in doubt, it is recommended to refer to the original publication to confirm whether the record starts at peat inception.

Peat dry bulk density values (n = 43 467) are available for 263 cores and range from 0.01 to 3.380 g cm−3, with a mean value of 0.140 ± 0.178 g cm−3 (1 SD; Fig. 8a; Table 5). Organic matter content data (n = 31 473) are available for 174 cores and range from 0 % to 100 % (though there are some data points > 100 %), with a mean value of 88.5 ± 20.1 % (1 SD; Fig. 8b; Table 5). Organic matter density values (OMD; n = 4016) are available for 20 cores and range from 0.008 to 0.399 g OM cm−3, with a mean value of 0.117 ± 0.046 g OM cm−3 (1 SD; Table 5).

In the database, carbon content is presented as three distinct variables: C%, TC%, and TOC%. While C% refers to estimated carbon content based on organic matter measurements (authors often multiply their OM% by 50 % to estimate C% and data users are prompted to refer to the original publications to know more about the conversion factors that were used), reports of TC (total carbon) and TOC (total organic carbon) data refer to values that were measured directly, typically through elemental analysis. Here we report C% data from 151 cores (n = 26 842), TC% from 114 cores (n = 14 430), and TOC% data from 11 cores (n = 1532), for a total of 42 804 measurements. We note that 16 cores report both C% and TC% values, that 7 cores report C% and TOC% values, that 1 cores reports TC% and TOC% values, and that 1 core reports C%, TC%, and TOC% values. Carbon content (C%) estimates range from 0.0 % to 71.7 %, with a mean value of 46.7 ± 10.5 % (1 SD; Fig. 8c; Table 5). Total carbon content (TC%) values range from 0.0 % to 100 %, with a mean value of 45.4 ± 16.4 % (1 SD; Fig. 8c; Table 5). Total organic carbon content (TOC%) values range from 0.0 % to 77.0 %, with a mean value of 37.7 ± 19.6 % (1 SD; Fig. 8c; Table 5). Lastly, organic carbon density values (OCD; n = 3952) are available for 28 cores and range from 0.005 to 0.195 g C cm−3, with a mean value of 0.058 ± 0.024 g C cm−3 (1 SD; Table 5). We acknowledge that a few individual data points are above the expected data ranges (e.g., %C > 60 %); we did not exclude said data points from the database. Data users can easily filter outliers as they see fit.

Total nitrogen content (TN) data are reported for 87 cores (n = 10 770) and range from 0 % to 6.3 % (there is an outlier at 56.6 % that was omitted from Fig. 8d), with a mean value of 1.2 ± 0.7 % (1 SD; Fig. 8d; Table 5).

While the averaged data (Table 5) may be used as reference values, we advise data users to further dig into the dataset, as the database structure allows for much finer analysis. For instance, it is possible (and simple) to query the dataset for a particular region of the world and/or time period, with the goal to obtain more specific peat characteristics.

 Code and data availability

The database is publicly available via the PANGAEA data repository (10.1594/PANGAEA.986891; Loisel et al., 2025). The datasets can be explored and queried at https://www.pangaea.de/?q=project:label:PAGES_C-PEAT. Code for accessing the data using Python and R is available on Zenodo (10.5281/zenodo.20734314; Ransby et al., 2026).

 Usage notes Database availability, updates, and submission of new datasets

The C-PEAT GD v.2025 presented therein can be accessed via PANGAEA, under the project label C-PEAT. There also exist R and Python scripts that allow for mass-downloads of the entire dataset (Ransby et al., 2026). The database is expected to keep evolving over time, as new datasets are integrated, existing datasets are extended (or corrected), and new sharing standards are developed. Formal database updates will be tentatively initiated every two to three years; said updates will be described online, directly within the data repository. Future updates will be reflected in the database identifier, which will include the update year (e.g., v.2027, v.2030, etc.). Between updates, database users are asked to flag any existing issues by contacting the PANGAEA editors through the contact form https://www.pangaea.de/contact/ (last access: 17 August 2026); a new version of the erroneous data can then be published. The inclusion of new datasets or the expansion of existing ones are performed manually. Data contributors interested in sharing one or a handful of datasets are asked to follow the instructions and use the templates provided by PANGAEA (https://wiki.pangaea.de/wiki/Best_practice_manuals_and_templates, last access: 17 August 2026). Contributors who wish to share a larger quantity of datasets (> 10) should contact a PANGAEA editor through the contact form linked above.

Whether they used the database in its entirety or only a subset, users are asked to cite the C-PEAT GD v.2025. When possible, and particularly when a subsequent product makes use of a few specific records, we request that the original publication(s) from which the primary data were extracted be cited as well.

 Outstanding usage notes

Lastly, it is important to recognize the limitations of this database. Most importantly, the dataset is incomplete, in that there remain numerous peat core sites that have not yet been integrated into this dataset. As a reminder, the C-PEAT GD v.2025 only includes cores that were already integrated to previously published data synthesis products. Second, and as mentioned in the text, not all peat core records are complete, meaning that some cores may not have reached the bottom of the peatland. Similarly, we remind the users that peat cores are not necessarily representative of an entire peat basin; rather, their record should be interpreted as a snapshot of an ecosystem's history. Additionally, individual research teams might have had different goals than the data users when they selected coring locations and collected their samples, such that some cores therein could represent the deepest portion of a peatland basin, but others might have been purposefully collected near basin margins. It is always advised to read the primary data source to gain further knowledge about the study sites and core locations.

 Team list

C-PEAT Working Group Members and data contributors: Atte Korhola (University of Helsinki, Helsinki, Finland), Alexandre Lamarre (Université du Québec – Montréal, Montréal, Canada), Barbara Fiałkiewicz-Kozieł (Adam Mickiewicz University, Poznan, Poland), Bas van Geel (University of Amsterdam, Amsterdam, Netherlands), Christopher Bochicchio (Lehigh University, Bethlehem, USA), Charles Tarnocai (Agriculture and Agri-Food Canada, Ottawa, Canada), David Anderson (Eton College, Windsor, UK), Dan Hammarlund (Lund University, Lund, Sweden), David Large (University of Nottingham, Nottingham, UK), Dmitri Mauquoy (University of Aberdeen, Aberdeen, UK), Dorothy Peteet (Columbia University, Palisades, USA), Dale Vitt (Southern Illinois University, Carbondale, USA), Dan Charman (University of Exeter, Exeter, UK), David Beilman (University of Hawai'i – Manoa, Honolulu, USA), Edgar Karofeld (University of Tartu, Tartu, Estonia), Eric Klein (University of Alaska Anchorage, Anchorage, USA), Eeva-Stiina Tuittila (University of Eastern Finland, Joensuu, Finland), Frank Chambers (University of Gloucestershire, Cheltenham, UK), François De Vleeschouwer (Université de Toulouse, Castanet, France), Fraser Mitchell (Trinity College Dublin, Dublin, Ireland), Gael Le Roux (Université de Toulouse, Castanet Tolosan, France), Gabriel Magnan (Université du Québec – Montréal, Montréal, Canada), Gunnar Mallon (University of Groningen, Groningen, the Netherlands), Glen MacDonald (University of California – Los Angeles, Los Angeles, USA), Graeme Swindles (Queen's University Belfast, Belfast, UK), Ilka Bauer (Memorial University of Newfoundland, Corner Brook, Canada), Colin Prentice (University of Bristol, Bristol, UK), Jukka Alm (University of Eastern Finland, Joensuu, Finland), Joan Bunbury (University of Wisconsin – La Crosse, La Crosse, USA), James Holmquist (Smithsonian Environmental Research Center, Washington D.C., USA), Julia McCarroll (University of Gloucestershire, Cheltenham, UK), Jonathan Nichols (Columbia University, Palisades, USA), Andres Christen (Centro de Investigacion en Matematicas, Guanajuato, Mexico), Keith Barber (University of Southampton, Southampton, UK), Liisa Ukonmaanaho (The Finnish Forest Research Institute, Vantaa, Finland), Lisa Belyea (Queen Mary University of London, London, UK), Matthew Amesbury (University of Exeter, Exeter, UK), Maarten Blaauw (Queen's University Belfast, Belfast, UK), Marius Gałka (University of Lodz, Lodz, Poland), Michelle Garneau (Université du Québec – Montréal, Montréal, Canada), Marius Lamentowicz (Adam Mickiewicz University, Poznań, Poland), Martin Lavoie (Université Laval, Québec, Canada), Markku Mäkilä (Geological Survey of Finland, Espoo, Finland), Maara Packalen (Ministry of Natural Resources and Forestry, Sault Ste. Marie, Canada), Mats Rundgren (Lund University, Lund, Sweden), Merritt Turetsky (University of Colorado – Boulder, Boulder, USA), Minna Väliranta (University of Helsinki, Helsinki, Finland), Marjolein van der Linden (BIAX Consult, Zaandam, Netherlands), Miriam Jones (U.S. Geological Survey, Reston, USA), Netajirao Phadtare (Wadia Institute of Himalayan Geology, Dehra Dun, India), Nicole Rausch (University of Heidelberg, Heidelberg, Germany), Philip Camill (Bowdoin College, Brunswick, USA), Peter Kuhry (University of Alberta, Edmonton, Canada), Paul Mathijssen (University of Helsinki, Helsinki, Finland), Pirita Oksanen (Centre for Economic Development, Transport and the Environment, Vaasa, Finland), Paul Hughes (University of Southampton, Southampton, UK), Pierre Richard (Université de Montréal, Montréal, Canada), Robert Moschen (Institute of Bio- and Geosciences, Julich, Germany), Robert Booth (Lehigh University, Bethlehem, USA), Sofia Andersson (Stockholm University, Stockholm, Sweden), Simon Brewer (University of Utah, Salt Lake City, USA), Sarah Finkelstein (University of Toronto, Toronto, Canada), Sandy Harrison (University of Bristol, Bristol, UK), Stephen Robinson (Champlain College – Dublin Campus, Dublin, Ireland), Simon van Bellen (Université de Montréal, Montréal, Canada), Stephen Jackson (University of Wyoming, Laramie, USA), Tiina Nieminen (The Finnish Forest Research Institute, Vantaa, Finland), Tiina Ronkainen (University of Helsinki, Helsinki, Finland), Tim Thom (Yorkshire Wildlife Trust, York, UK), Tim Moore (McGill University, Montreal, Canada), Ulla Kokfelt (University of Copenhagen, Copenhagen, Denmark), Üulle Sillasoo (Tallinn University, Uus-Sadama, Estonia), William Hinchcliffe (University of Exeter, Exeter, UK), Yvonne Corish (Trinity College Dublin, Dublin, Ireland), Yongsong Huang (Brown University, Providence, USA), Zicheng Yu (Northeast Normal University, Changchun, China), Antonio Martínez Cortizas (Universidade de Santiago de Compostela, Spain), Britta Sannel (Stockholm University, Stockholm, Sweden), Colin Courtney-Mustaphi (University of York, York, UK), Chris Jones (University of Southampton, Southampton, UK), Charly Massa (University of Hawaii – Manoa, Honolulu, USA), Donna Carless (University of Exeter, Exeter, UK), Elizabeth Cressey (University of Exeter, Exeter, UK), Esther Githumbi (University of York, York, UK), Guoping Wang (Chinese Academy of Science, Changchun, China), Helen Mackay (Newcastle University, Newcastle, UK), John Hribjlan (Michigan Technical University, Houghton, USA), Joanna Uglow (University of Exeter, Exeter, UK), Joana Zaragoza-Castells (University of Exeter, Exeter, UK), Katarzyna Marcisz (Adam Mickiewicz University, Poznań, Poland), Lisa Orme (University of Exeter, Exeter, UK), Michael Clifford (The Nature Conservancy, Las Vegas, USA), Nicole Sanderson (Université du Québec – Montréal, Montréal, Canada), Noemi Silva-Sánchez (Universidade de Santiago de Compostela, Spain), Natascha Steinberg (University of Exeter, Exeter, UK), Pierre Friedlingstein (University of Exeter, Exeter, UK), Patrick Moss (The University of Queensland, Brisbane, Australia), Rodney Chimner (Michigan Technical University, Houghton, USA), Rixt de Jong (Lund University, Lund, Sweden), Robert Marchant (University of York, York, UK), Svante Björck (Lund University, Lund, Sweden), Susan Page (University of Leicester, Leicester, UK), Tatyana Blyakharchuk (Tomsk State University, Tomsk, Russia), Terri Lacourse (University of Victoria, Victoria, Canada), Timothy Mighall (University of Aberdeen, Aberdeen, UK), Thomas Roland (University of Exeter, Exeter, UK), Edward Turner (University of Leeds, Leeds, UK), Weijian Zhou (Chinese Academy of Sciences, Xi'an, China), Yan Zhao (Chinese Academy of Sciences, Beijing, China).

Author contributions

JL & AGS built and managed the C-PEAT GD, and coordinated the broader C-PEAT network; JL, AGS, SU & ER assembled or entered datasets and/or metadata into the database; DR, CB & SS performed quality control, term standardization, database cleaning, and/or QC certification; JL, KTB & VM analyzed data and generated figures for this paper; JL wrote and revised the manuscript; AGS, DR, SU, ER, CB, SS, KTB & VM reviewed and improved preliminary versions of this manuscript.

Competing interests

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

Disclaimer

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. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Acknowledgements

PAGES received support from the Swiss Academy of Sciences, the US National Science Foundation, and the Chinese Academy of Sciences at the time of database preparation and redaction of this manuscript.

Financial support

Support for this work includes NSF-2142177 and NSF-1802838 to JL. The C-PEAT working group acknowledges financial support from its parent organizations Past Global Changes (PAGES) and Future Earth, and support from the International Union of Quaternary Research (INQUA) and UNESCO's International Geoscience Programme (IGCP) for workshops and training events.

Review statement

This paper was edited by Zihao Bian and reviewed by two anonymous referees.

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