Sedimentary charcoal records are widely used to reconstruct regional changes
in fire regimes through time in the geological past. Existing global
compilations are not geographically comprehensive and do not provide
consistent metadata for all sites. Furthermore, the age models provided for
these records are not harmonised and many are based on older calibrations of
the radiocarbon ages. These issues limit the use of existing compilations
for research into past fire regimes. Here, we present an expanded database
of charcoal records, accompanied by new age models based on recalibration of
radiocarbon ages using IntCal20 and Bayesian age-modelling software. We
document the structure and contents of the database, the construction of the
age models, and the quality control measures applied. We also record the
expansion of geographical coverage relative to previous charcoal
compilations and the expansion of metadata that can be used to inform
analyses. This first version of the Reading Palaeofire Database contains
1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at
10.17864/1947.000345.
Introduction
Wildfires have major impacts on terrestrial ecosystems (Bond et al., 2005;
Bowman et al., 2016; He et al., 2019; Lasslop et al., 2020), the global
carbon cycle (Li et al., 2014; Arora and Melton, 2018; Pellegrini et al.,
2018; Lasslop et al., 2019), atmospheric chemistry (van der Werf et al.,
2010; Voulgarakis and Field, 2015; Sokolik et al., 2019), and climate
(Randerson et al., 2006; Li et al., 2017; Harrison et al., 2018; Liu et al.,
2019). Although the climatic, vegetation, and anthropogenic controls on
wildfires are relatively well understood (e.g. Harrison et al., 2010;
Bistinas et al., 2014; Knorr et al., 2016; Forkel et al., 2017; Li et al.,
2019), recent years have seen wildfires occurring in regions where they were
historically rare (e.g. northern Alaska, Greenland, northern Scandinavia – Evangeliou et al., 2019; Hayasaka, 2021) and an increase in fire frequency
and severity in more fire-prone regions (e.g. California, the
circum-Mediterranean, eastern Australia; e.g. Abatzoglou and Williams, 2016;
Dutta et al., 2016; Williams et al., 2019; Nolan et al., 2020). It is useful
to look at the pre-industrial era (conventionally defined as pre-1850 CE) to
understand whether these events are atypical. The pre-industrial past also
provides an opportunity to characterise fire regimes before anthropogenic
influences, in terms of both ignitions and fire suppression, became
important.
Ice-core records provide a global picture of changes in wildfire in the
geologic past (Rubino et al., 2016). However, wildfires exhibit considerable
local to regional variability because of the spatial heterogeneity of the
various factors controlling their occurrence and intensity (Bistinas et al.,
2014; Andela et al., 2019; Forkel et al., 2019). Thus, it is useful to use
information that can provide a picture of regional changes through time.
Charcoal, preserved in lake, peat, or marine sediments, can provide a picture
of such changes (Clark and Patterson, 1997; Conedera et al., 2009). The
wildfire regime can be characterised from sedimentary charcoal records
through total charcoal abundance per unit of sediment, which can be
considered a measure of the total biomass burned (e.g. Marlon et al.,
2006), or by the presence of peaks in charcoal accumulation, which, in records
with a sufficiently high temporal resolution, can indicate individual episodes
of fire (e.g. Power et al., 2006).
The Global Paleofire Working Group (GPWG) was established in 2006 to
coordinate the compilation and analysis of charcoal data globally, through
the construction of the Global Charcoal Database (GCD – Power et al., 2008).
The GPWG was initiated by the International Geosphere-Biosphere Programme
(IGBP) Fast-Track Initiative on Fire and subsequently recognised as a
working group of the Past Global Changes (PAGES) project in 2008. There have
now been several iterations of the GCD (Power et al., 2008,
2010; Daniau et al., 2012; Blarquez et al., 2014; Marlon et al., 2016),
which since 2020 has been managed by the International Paleofire Network as
the Global Paleofire Database (GPD; https://paleofire.org, last access: 21 February 2022). The GCD has
been used to examine changes in fire regimes over the past 2 millennia
(Marlon et al., 2008), during the current interglacial (Marlon et al.,
2013), on glacial–interglacial timescales (Power et al., 2008; Daniau et
al., 2012; Williams et al., 2015), and in response to rapid climate changes
(Marlon et al., 2009; Daniau et al., 2010), as well as to examine regional
fire histories (e.g. Mooney et al., 2011; Vannière et al., 2011; Marlon
et al., 2012; Power et al., 2013a, b; Feurdean et al., 2020). However, there are
a number of limitations to the use of the GCD for analyses of palaeofire
regimes. Firstly, the database does not include many recently published
records and needs to be updated. Secondly, there are inconsistencies among
the various versions of the database including duplicated and/or missing
sites, differences in the metadata included for each site or record, and
missing metadata and dating information for some sites or records. Perhaps
most crucially, the age models included in the database were made at
different times, using different radiocarbon calibration curves, and using
different age-modelling methods. The disparities between the archived age
models preclude a detailed comparison of changes in wildfire regimes across
regions.
Here, we present an expanded database of charcoal records (the Reading
Palaeofire Database, RPD), accompanied by new age models based on
recalibration of radiocarbon ages using IntCal20 (Reimer et al., 2020) and
using a consistent Bayesian approach (Bacon – Blaauw et al., 2021) to
age-model construction. However, we have retained the original age models
for all the sites for comparison and to allow the user to choose a preferred
age model. The RPD is designed to facilitate regional analyses of fire
history; it is not designed as a permanent repository. We document the
structure and contents of the database, the construction of the new age
models, the expanded metadata available, and the quality control measures
applied to check the data entry. We also document the expansion of the
geographic and temporal coverage and the availability of metadata,
relative to previous GCD compilations.
Data and methodsCompilation of data
The database contains sedimentary charcoal records, metadata to facilitate
the interpretation of these records, and information on the dates used to
construct the original age model for each record. Some records were obtained
from the GCD. There are multiple versions of the GCD which differ in terms
of the sites and the types of metadata included. We compared the GCDv3
(Marlon et al., 2016), GCDv4 (Blarquez, 2018), and GCD web page versions
(http://paleofire.org, last access: 21 February 2022) and extracted a single unique version of each site
and entity across the three versions. Where sites or entities were
duplicated in different versions of the GCD, we used the latest version.
Missing metadata and dating information for these records were obtained from
the literature or from the original data providers. Some sites in the GCD
were represented by both concentration data and the same data expressed as
influx (i.e. concentration per year) from the same samples; because influx
calculations are time dependent, we have only retained concentration data
for such sites to allow for future improvements to age models. Influx can be
easily computed using data available in the RPD. We also removed duplicates
where the GCD contained both raw data and concentration data from the same
entity. We extracted published charcoal records from public repositories, specifically PANGAEA
(https://www.pangaea.de/, last access: 21 February 2022), NOAA National Centers for Environmental
Information (https://www.ncdc.noaa.gov/data-access/paleoclimatology-data, last access: 21 February 2022),
the Neotoma Paleoecology Database (https://www.neotomadb.org/, last access: 21 February 2022), the European
Pollen Database (http://www.europeanpollendatabase.net/index.php, last access: 21 February 2022), and the
Arctic Data Center (https://arcticdata.io/catalog/, last access: 21 February 2022); if these records were
also in the GCD, we replaced the GCD version. Additional charcoal data,
dating information, and metadata were provided directly by the authors. All
the records in the current version of the database are listed in the
Supplement (Table S1).
Structure of the database
The data are stored in a relational database (MySQL), which consists of 10
linked tables, specifically “site”, “entity”, “sample”, “date info”, “unit”,
“entity link publication”, “publication”, “chronology”, “age model”, and
“model name”. Figure 1 shows the relationships between these tables. A
description of the structure and content of each of the tables is given
below, and more detailed information about individual fields is given in the
Supplement (Table S2).
Diagram showing the structure of the database, individual tables and their contents, and the nature of the relationships between the component tables. One-to-many linkages indicate that it is possible to have several entries in one table linked to a single entry in another table. The database uses both primary and foreign keys. The primary key ensures that data included in a specific field are unique. The foreign key refers to the field in a table which is the primary key of another table and ensures that there is a link between these tables.
Site metadata (table name – site)
A site is defined as the hydrological basin from which charcoal records have
been obtained (Table 1). There may be several charcoal records from the same
site, for example where charcoal records have been obtained on central and
marginal cores from the same lake or where there is a lake core and
additional cores from peatlands and/or terrestrial deposits (e.g. small
hollows, soils) within the same hydrological basin. A site may therefore be
linked to several charcoal records, where each record is treated as a
separate entity. The site table contains basic metadata about the basin,
including site ID, site name, latitude, longitude, elevation, site type, and
maximum water depth. The site names are expressed without diacritics to
facilitate database querying and subsequent analyses in programming
languages that do not handle these characters. Latitude and longitude are
given in decimal degrees, truncated to six decimal places since this gives
an accuracy of < 1 m at the Equator. Broad categories of site type
are differentiated (e.g. terrestrial, lacustrine, marine), with subdivisions
according to geomorphic origin (e.g. lakes are recorded according to whether
they are, for example, fluvial, glacial, or volcanic in origin). In addition to
coastal salt marshes and estuaries, we include a generic coastal category
for all types of sites that lie within the coastal zone and whose hydrology
may therefore have been affected by changes in sea level. Wherever possible,
the size of the basin and the catchment are recorded (in km2), but if
accurate quantified information is not available, the basin and catchment
size are recorded by size classes. The site table also contains information
on whether the lake or peatland is hydrologically closed or has inflows and
outflows, which can affect the source, quantity, and preservation of charcoal
in the sediments. A complete listing of the sites and entities in the RPD is
given in Table S1. A list of the valid choices for fields that are selected
from a pre-defined list (e.g. site type) is given in Table S2.
Definition of the site table.
Field nameDefinitionData typeConstraints/notesID_SITEUnique identifier for each siteUnsigned integerPositive integersite_nameSite name as given by original authors or as defined by us where there was no unique name given to the siteTextRequiredlatitudeLatitude of the sampling site, given in decimal degrees, where N is positive and S is negativeDoubleNumeric value between -90 and 90longitudeLongitude of the sampling site in decimal degrees, where E is positive and W is negativeDoubleNumeric value between -180 and 180elevationElevation of the sampling site in metres above (+) or below (-) sea levelDoubleNonesite_typeInformation about the type of site (e.g. lake, peatland, terrestrial)TextSelected from pre-defined listwater_depthWater depth of the sampling site in metresDoubleNoneflow_typeIndication of whether there is inflow and/or outflow from the sampled siteTextSelected from pre-defined listbasin_size_km2Size of sampled site (e.g. lake or bog) in km2DoubleNonecatch_size_km2Size of hydrological catchment in km2DoubleNonebasin_size_classCategorical estimate of basin sizeTextSelected from pre-defined listcatch_size_classCategorical estimate of hydrological catchment sizeTextSelected from pre-defined listEntity metadata (table name – entity)
This table provides metadata for each individual entity (Table 2). In
addition to distinguishing multiple cores from the same basin as separate
entities, we also distinguish different size classes of charcoal from the
same core when these data are available. Different charcoal size classes
from the same core are also treated as separate entities in the database.
However, we have removed duplicates where the same record was expressed in
different ways (e.g. as both raw counts and concentration or as
concentration and influx) to avoid confusion and mistakes when subsequently
processing these data. The RPD contains raw data wherever possible and
concentration data when the raw data are not available, and it only includes
influx data if neither raw nor concentration data are available. When specific cores were given
distinctive names in the original publication or by the original author, we
include this information in the entity name for ease of cross-referencing.
The entity metadata include information that can be used to interpret the
charcoal records, including depositional context, core location, measurement
method, and measurement unit. There is no standard measurement unit for
charcoal, and in fact, there are > 100 different units employed
in the database. For convenience, there is a link table to the measurement
units (table name – unit). In addition, the entity table provides the source
from which the charcoal data were obtained, including whether these data are
from a version of the GCD or a data repository or were provided by the
original author, and an indication of when the record was last updated. A
list of the valid choices for fields that are selected from a pre-defined
list (e.g. depositional context) is given in Table S2. A list of the
charcoal measurement units currently in use in the RPD is given in Table S3.
Definition of the entity table.
Field nameDefinitionData typeConstraints/notesID_ENTITYUnique identifier for each entityUnsigned integerPositive integerID_SITERefers to unique identifier for each site (as given in site table)Unsigned integerAuto-numeric, foreign key of the site table, a positive integerentity_nameName of entity, where an entity may be a separate core from the site or a separate type of measurement on the same coreTextRequiredlatitudeLatitude of the entity, given in decimal degrees, where N is positive and S is negativeDoubleA numeric value between -90 and 90longitudeLongitude of the entity, given in decimal degrees, where E is positive and W is negativeDoubleA numeric value between -180 and 180elevationElevation of the sampling site, in metres above (+) or below (-) sea levelDoubleNonedepositional_contextType of sediment sampled for charcoalTextSelected from pre-defined listmeasurement_methodMethod used to measure the amount of charcoalTextSelected from pre-defined listTYPEThe unit type of the measured charcoal values (e.g. concentration, influx)TextSelected from pre-defined listsourceSource of charcoal dataTextSelected from pre-defined listcore_locationLocation of the entity within the site (e.g. central core or marginal core)TextSelected from pre-defined listlast_updatedDate when the entity or its linked data were last updatedDateIn format YYYY/mm/ddID_UNITUnique identifier for measurement unit (as in unit table)Unsigned integerAuto-numeric, foreign key of the unit table, a positive integerSample metadata and data (table name – sample)
The sample table provides information on the average depth in the core or
profile and the thickness of the sample on which charcoal was measured (Table 3). The
thickness measurements relate to the total thickness of the charcoal sample
and provide an indication of whether the sampling was contiguous downcore.
The sample table also provides information on the sample size and units and the
quantity of charcoal present. The charcoal measurement units have been
standardised by converting units expressed as multiples (e.g. fragments × 100) back to the whole numbers and by converting units expressed in milligrams or
kilograms to grams. As a result, the values in the RPD may apparently differ from
published values.
Definition of the sample table.
Field nameDefinitionData typeConstraints/notesID_SAMPLEUnique identifier for each charcoal sampleUnsigned integerAuto-numeric, primary key, a positive integerID_ENTITYUnique identifier for the entity (as in entity table)Unsigned integerAuto-numeric, foreign key of the entity table, a positive integeravg_depthAverage sampling depth, in metresDoubleNonesample_thicknessSample thickness, in metresDoubleNonecharcoal_measurementQuantity of charcoal measured in the sampleDoubleNoneanalytical_sample_sizeTotal amount of sediment sampledText255-character maximum lengthanalytical_sample_size_unitUnits used for the samplingText255-character maximum lengthDating information (table name – date_info)
This table provides information about the dates available for each entity
that can be used to construct an age model (Table 4). We include information about the
age of the core top for records that were known to be actively accumulating
sediment at the time of collection. In addition to radiometric dates, we
include information about the presence of tephras (either dated at the site
or independently dated elsewhere) and stratigraphic events that can be used
to establish correlative ages (e.g. changes in the pollen assemblage that
are dated in other cores from the region or evidence of known fires in the
catchment). Wherever possible the name of a tephra is given to facilitate
the use of subsequent and more accurate estimates of its age. Similarly, the
basis for correlative dates is given, again to facilitate the use of updated
estimates of the age of the event. Radiocarbon ages are given in radiocarbon
years, but all other ages are given in calendar years before present (BP) using 1950 CE as
the reference zero date. Error estimates are given for radiometric ages and
wherever possible for calendar ages. We provide an indication of whether a
specific date was used in the original age model for the entity and an
explanation for why specific dates were rejected, since this can be a guide
as to whether the dates should be incorporated in the construction of new
age models. A list of the valid choices for fields that are selected from a
pre-defined list (e.g. material dated) is given in Table S2.
Definition of the date info table.
Field nameDefinitionData typeConstraints/notesID_DATE_INFOUnique identifier for the date recordUnsigned integerAuto-numeric, primary key, a positive integerID_ENTITYUnique identifier for the entity (as in entity table)Unsigned integerAuto-numeric, foreign key of the entity table, a positive integermaterial_datedMaterial from which the date was obtained if applicableTextSelected from pre-defined listdate_typeTechnique used to obtain the date measurementTextSelected from pre-defined listavg_depthAverage depth in the sedimentary sequence where the date was measured, in metresDoubleNonethicknessThickness of the sample used for dating, in metresDoubleNonelab_numberUnique identifying code assigned by the dating laboratoryText65 535-character maximum lengthage_C14Uncalibrated radiocarbon ageDoubleNoneage_calibThe calendar age of a dateDoubleNoneerrorAnalytical or measurement error of the dateDoubleNonecorrelation_infoIndication of basis for correlative dating (e.g. pollen, tephra, or stratigraphic correlations)TextSelected from pre-defined listage_usedIndicates whether date was used by the author(s) in the construction of the original age modelTextSelected from pre-defined listreason_age_not_usedIndication of why a date was not used in the original age model, blank if dates were used in original modelTextSelected from pre-defined listnotesAdditional comments regarding a date recordText65 535-character maximum lengthPublication information (table name – publication)
This table provides full bibliographic citations for the original references
documenting the charcoal records and/or their age models. There may be
multiple publications for a single charcoal record, and all of these
references are listed. Conversely, there may be a single publication for
multiple charcoal records. There is also a table (table name – entity_link_publication) that links the
publications to the specific entity.
Original age-model information (table name – chronology)
This table provides information about the original age model for each
record and the ages assigned to individual samples. There can be many
records that use the same type of age model (e.g. linear interpolation,
spline, regression), and for convenience, there is a table that links the
records to the age-model name (table name – model_name).
New age-model information (table name – age_model)
This table contains information about the age models that have been
constructed for this version of the database using the IntCal20
calibration curve (Reimer et al., 2020) and the Bacon (Blaauw et al., 2021)
age-modelling R package (see Sect. 2.3) (Table 5). We preserve information on the
mean and median ages, as well as the quantile ranges for each sample.
Definition of the age-model table.
Field nameDefinitionData typeConstraints/notesID_MODELUnique identifier for the technique used to generate the age model (original age models from existing authors in the chronology table and new age models in the age_model table)Unsigned integerAuto-numeric, composite primary key with ID_SAMPLE, foreign key of the model_name table, positive integerID_SAMPLEUnique identifier for the sample (as in sample table)Unsigned integerAuto-numeric, composite primary key with ID_MODEL, foreign key of the sample table, positive integermeanMean age of the sampleIntegerNonemedianMedian age of the sampleIntegerNoneUNCERT_5Lower bound of the 95 % confidence interval for the median ageIntegerNoneUNCERT_95Upper bound of the 95 % confidence interval for the median ageIntegerNoneUNCERT_25Lower bound of the 75 % confidence interval for the median ageIntegerNoneUNCERT_75Upper bound of the 75 % confidence interval for the median ageIntegerNoneConstruction of new age models
The original age models for the charcoal records were made at different
times, using different radiocarbon calibration curves, and using different
age-modelling methods. We standardised the age modelling, using rbacon
(Blaauw and Christen, 2011; Blaauw et al., 2021) to construct new Bayesian
age–depth models in the ageR package (Villegas-Diaz et al., 2021). The ageR
package provides functions that facilitate the supervised creation of
multiple age models for many cores and different data sources, including
databases and comma- and tab-separated files. The IntCal20 Northern Hemisphere
calibration curve (Reimer et al., 2020) and the SHCal20 Southern Hemisphere
calibration curve (Hogg et al., 2020) were used for entities between the
latitudes of 90 and 15∘ N and 15 to 90∘ S
respectively. Entities in equatorial latitudes (15∘ N to
15∘ S) used a 50:50 mixed calibration curve to account for
north–south air mass mixing following Hogg et al. (2020), and radiocarbon
ages from marine entities were calibrated using the Marine20 calibration
curve (Heaton et al., 2020).
To estimate the optimum age-modelling scenarios based upon the date and
sample information for each entity, multiple rbacon age models were run
using different prior accumulation rate (acc.mean) and thickness values. Prior accumulation
rate values were selected using an initial linear regression of the ages in
each entity, which was then increased (decreased) sequentially from the
default value to up to two times more (less) than the initial value. As an example,
if the initial accumulation rate value selected from the linear regression
was 20 yr cm-1, age models would also be run using values of 10, 15, 20, 30,
and 40 yr cm-1. In cases where the regional accumulation rate was known, the
upper and lower values of the accumulation rate scenarios were manually
constrained. The range of prior thicknesses used in the models was calculated by
increasing and decreasing the rbacon default thickness value (5 cm) to up to a
value one-eighth of the overall length of the core. For a 400 cm core for
example, the thickness scenarios would be 5, 10, 15, 20, 25, 30, 35, 40, 45,
and 50 cm. Thus, the number of scenarios created by possible accumulation
rates and thicknesses varies between different entities. Depths of known
hiatuses reported in the original publications were included in the date_info table (Sect. 2.1.5) and have also been included in the age models run
in ageR. In instances where the sedimentation rates were different above and
below a hiatus, separate age models were run before and after the
non-deposition period to account for these variations (Blaauw and Christen,
2011).
A three-step procedure was used to select the best model for each entity.
First, an optimum model was selected by ageR, using the lowest quantified
area between the prior and posterior accumulation rate distribution curves (Supplement
Fig. S1). This selection was checked manually using comparisons between the
distance of the estimated ages and the controls to check the accuracy of the
model interpolation. Finally, the age model was visually inspected to ensure
that final interpolation accurately represented the date information and did
not show abrupt shifts in accumulation rates or changes at the dated depths.
If the ageR model selection was deemed to be erroneous or inaccurate, the
next suitable model with the lowest area between the prior and posterior curves, which
accurately represented the distribution of dates in the sequence, was
selected (Supplement Fig. S2).
Map showing the location of sites included in the RPD. As shown here, some sites have multiple records, either representing separate cores from the same hydrological basin or representing measurements of different charcoal size fractions on the same core. These records are treated as separate entities in the database itself.
Plots showing the temporal coverage of individual entities in the database. Panel (a) shows records covering the past 2000 years (2 kyr BP); panel (b) shows records covering the past 12 000 years (12 kyr BP); panel (c) shows records for the past 22 000 years (22 kyr BP), thus encompassing the Last Glacial Maximum (LGM); panel (d) shows records that cover the interval of the last glacial prior to the LGM (22–115 kyr BP).
Quality control
Individual records in the RPD were compiled either by the original authors
or from published and open-access material by specialists in the collection
and interpretation of charcoal records. Records that were obtained from
published and open-access material were cross-checked against publications
or with the original authors of those publications whenever possible. Null
values for metadata fields were identified during the initial checking
procedure, and checks were made with the data contributors to determine
whether these genuinely corresponded to missing information. In the
database, null values are reserved for fields where the required information
is not applicable, for example water depth for terrestrial sites or
laboratory sample numbers for correlative dates. We distinguish fields where
information could be available but was never recorded or has subsequently
been lost (represented by -999999) and fields where we were unable to
obtain this information but it could be included in subsequent updates of
the database (represented by -777777). We also distinguish fields where
specific metadata are not applicable (represented by -888888), for example
basin size for a marine core or water depth for a terrestrial small hollow.
Availability of metadata that can be used to select suitable sites for specific analyses or for quality control. Plot (a) shows the distribution of sites by type. Some site types have finer distinctions recorded in the database: lacustrine environments, for example, are subdivided according to origin. Plot (b) shows the number of sites with quantitative estimates versus categorical assessments of basin size, and plot (c) shows the number of sites in specific basin size ranges. Plot (d) shows the distribution of different hydrological types for lake records.
Prior to entry in the database, the records were automatically checked using
specially designed database scripts (in R) to ensure that the entries to
individual fields were in the format expected (e.g. text, decimal numeric,
positive integers) or were selected from the pre-defined lists provided for
specific fields. Checks were also performed to find duplicated rows (e.g.
duplicated sampling depths within the same entity).
Overview of database contents
This first version of the RPD contains 1676 individual charcoal records from
1480 sites worldwide. This represents a 128 % increase compared to the
number of records in version 3 of the Global Charcoal Database (GCDv3; Marlon et al., 2016; 736 records), a 79 % increase compared to version 4 (Blarquez, 2018; 935 records), and a 36 % increase compared to the online
version of the GCD (1232 records). The RPD includes 840 records that are not
available in any version of the GCD and provides updated or corrected
information for a further 485 records that were included in the GCD. Raw
data are available for 14 % of the entities and concentration for 67 %
of the entities; influx based on the original age models is given for 16 %
of the entities. The original age models for 67 (4 %) of the records
included in the RPD were derived solely by layer counting, U–Th or Pb dates,
or isotopic correlation and therefore are already expressed in calendar
ages. However, we have provided new age models for 22 of these records
(33 %), where the dates or correlation points were specified, using the
supervised age-modelling procedure for consistency. New age models have been
created for 807 (50 %) of the remaining charcoal records where the
original chronology was based on radiometric dating. The geographic coverage
of the RPD (Fig. 2) is biased towards the northern extratropics. However,
there is a growing representation of records from China, the neotropics
(Central and South America), southern and eastern Africa, and eastern
Australia. The largest gaps geographically are in currently dry regions,
which often lack sites with anoxic sedimentation suitable for the
preservation of charcoal and are generally under-represented in palaeofire
reconstructions (Leys et al., 2018). The temporal coverage of the records is
excellent for the interval starting 22 000 years ago, with 774 records with a
minimum resolution of 10 years for the past 2000 years, 1335 records with a
minimum resolution of 500 years for the past 12 000 years, and 1382 records
with a minimum resolution of 1000 years for the past 22 000 years (Fig. 3). There are
fewer records for earlier intervals. Nevertheless, there are 70 records that
provide evidence for the interval of the last glacial period before the Last
Glacial Maximum (22–115 ka) including the response of fire to rapid climate
warming (Dansgaard–Oeschger events).
Information about site type (Fig. 4a) is included in the database because
this could influence whether the charcoal is of local origin or represents a
more regional palaeofire signal. For example, records from small forest
hollows provide a very local signal of fire activity and records from peat
bogs most likely sample fires on the peatland itself, whereas records from
lakes could provide both local and regional fire signals. More than half
(55 %) of the records in the RPD are derived from lakes (811 entities).
Records from peatlands are also well represented (471 entities, 32 %).
Basin size, particularly in the case of lakes, influences the source area
for charcoal particles transported by wind. However, the existence of
inflows and outflows to the system can also affect the charcoal record.
Quantitative information is now available for more than half of the lake
sites (Fig. 4b), and most (691 sites, 81 %) of the records (Fig. 4c)
are from relatively small lakes (< 1 km2). A quarter of the
charcoal records from lakes (Fig. 4d) are from closed basins (334 sites).
Data availability
Version 1 of the Reading Palaeofire Database (RPDv1b – Harrison et al., 2021)
is available in SQL format at 10.17864/1947.000345. The individual tables are also
available as .csv files. The R package used to create the new age models is
available at https://github.com/special-uor/ageR (last access: 21 February 2022) and 10.5281/zenodo.4636716 (Villegas-Diaz et al., 2021).
Conclusions
The Reading Palaeofire Database (RPD) is an effort to improve the coverage
of charcoal records that can be used to investigate palaeofire regimes. New
age models have been developed for 48 % of the records to take account of
recent improvements in radiocarbon calibration and age-modelling methods. In
addition to expanded coverage and improved age models, considerable effort
has been made to include metadata and quality control information to allow
the selection of records appropriate to address specific questions and to
document potential sources of uncertainty in the interpretation of the
records. The first version of the RPD contains 1676 individual charcoal
records (entities) from 1480 sites worldwide. Geographic coverage is best
for the northern extratropics, but the coverage is good overall except for in semi-arid
and arid regions. Temporal coverage is good for the past 2000 years, the
Holocene, and back to the LGM, but there are a reasonable number of longer
records. The database is publicly available, both as an SQL database and as
.csv files.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-14-1109-2022-supplement.
Author contributions
SPH and RVD designed the database; RVD, DK, PL, and
SPH were responsible for construction of the database; ALD advised on
incorporation of data from the GCD and the standardisation of charcoal
units; ECS, DG, DK, PL, YS, and LS provided updated age models; the other
authors provided original data or metadata and quality control on individual
records; SPH wrote the first draft of the paper, and all authors contributed
to the final draft.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Roberto Villegas-Diaz, Sandy P. Harrison, Esmeralda Cruz-Silva, and Yicheng Shen acknowledge support from the
ERC-funded project GC2.0 (Global Change 2.0: Unlocking the past for a
clearer future, grant number 694481). Sandy P. Harrison, Paul Lincoln, Daniel Gallagher, David Kesner, and Luke Sweeney acknowledge
support from the Leverhulme Centre for Wildfires, Environment and Society
through the Leverhulme Trust, grant number RC-2018-023. Angelica Feurdean acknowledges
support from the German Research Foundation (grant no. FE-1096/6-1). Katarzyna Marcisz
acknowledges support from the Swiss Government Excellence Postdoctoral
Scholarship (grant no. FIRECO 2016.0310); the National Science Centre in
Poland (grant no. 2015/17/B/ST10/01656); grant PSPB-013/2010 from
Switzerland through the Swiss contribution to the enlarged European Union; and
the Scientific Exchange Programme from the Swiss Contribution to the New
Member States of the European Union and Switzerland (Sciex-NMSch) – SCIEX Scholarship
Fund, project RE-FIRE 12.286. Olga Lisitsyna acknowledges support from the Mobilitas
Plus post-doctoral research grant of the Estonian Research Council
(MOBJD313). We would like to thank our many colleagues from the PAGES Global
Paleofire Working Group for their contributions to the construction of the
Global Charcoal Database, which provided the starting point for the current
compilation, and our colleagues from the Leverhulme Centre for Wildfires,
Environment and Society for discussions on the use of palaeodata to
reconstruct past fire regimes. We thank Manfred Rösch for providing
information on dating for several sites. We also thank Dan Gavin and Jack
Williams for helpful reviews of the original manuscript.
Financial support
This research has been supported by the Leverhulme Trust (grant no. RC-2018-023), the European Research Council (grant no. 694481), the German Research Foundation (grant no. FE-1096/6-1), the Swiss Government Excellence Postdoctoral Scholarships (grant no. FIRECO 2016.0310), the National Science Centre of Poland (grant no. 2015/17/B/ST10/01656), the SCIEX Scholarship Fund (grant no. PSPB-013/2010), and the Estonian Research Council (grant no. MOBJD313).
Review statement
This paper was edited by David Carlson and reviewed by Daniel Gavin and one anonymous referee.
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