After manually entering or uploading a data sheet of δ18Oc measurements in .csv format, users may select from 1 of 59 different calibrations from the literature (Bemis et al., 1998; Böhm et al., 2000; Bouvier-Soumagnac and Duplessy, 1985; Duplessy et al., 2002; Epstein et al., 1953; Erez and Luz, 1983; Farmer et al., 2007; Geffen, 2012; Godiksen et al., 2010; Grossman and Ku, 1986; Høie et al., 2004; Juillet-Leclerc and Schmidt, 2001; Kim and O'Neil, 1997; Kim et al., 2007; Lynch-Stieglitz et al., 1999; Malevich et al., 2019; Marchitto et al., 2014; McCrea, 1950; Mulitza et al., 2003; O'Neil et al., 1969; Patterson et al., 1993; Reynaud-Vaganay et al., 1999; Rosenheim et al., 2009; Shackleton, 1974; Storm-Suke et al., 2007; Thorrold et al., 1997; Tremaine et al., 2011; White et al., 1999; Willmes et al., 2019). All data are expressed with δ18Oc in units of ‰ VPDB and δ18Ow in units of ‰ VSMOW, with any standard interconversions expected by the chosen calibration performed automatically. Standard interconversion is notably inconsistent in the literature, with many paleoceanographic papers employing the relationship δ18OVPDB=δ18OVSMOW-0.27 ‰ (Hut, 1987), while many geochemical papers employ the incompatible relationship δ18OVPDB=0.97001×δ18OVSMOW-29.99 ‰  (Brand et al., 2014). The former is actually the isotopic offset between the related VPDB-CO2 and VSMOW-CO2 scales, but the difference is unimportant so long as all data are treated in the manner the calibration expects, as our tool ensures. A full list of included calibrations and standard conversions is given in Tables 1–3.

Where applicable, we use the standardized reformulations of Bemis et al. (1998) and Willmes et al. (2019) or exact algebraic rearrangements of the original equations. For the bayfox core-top calibrations of Malevich et al. (2019), the standard bayfox tool re-fits the calibration coefficients with every run. Since this is computationally expensive, we instead use the linear calibration coefficients fit by runs of the R package bayfoxr 0.0.1 directly in linear functions of the form of Eq. (1) (see Table 1). These yield results equivalent to the full fitting process within numerical error (mean residual = ±0.02 ∘C, identical to the mean scatter between replicates of the full bayfox fit).

Users may specify global δ18Ow manually or choose to draw δ18Ow by sample age from 12 different time series of global δ18Ow from the literature (from Cramer et al., 2011; Henkes et al., 2018; Meckler et al., 2022; Miller et al., 2020; Modestou et al., 2020; Rohling et al., 2021; and Veizer and Prokoph, 2015). These records are typically constructed by assuming that the benthic δ18O record reflects a combination of temperature and ice volume and then subtracting out an independent record of temperature (e.g., using Mg/Ca-based bottom-water temperatures; Cramer et al., 2011) or ice volume (e.g., using a multi-proxy sea level reconstruction; Rohling et al., 2021) to determine the residual δ18Ow. Which global δ18Ow record is most realistic remains a contentious topic in the literature, with sea-level and Mg/Ca-based records (e.g., Cramer et al., 2011; Rohling et al., 2021) predicting up to ∼1 ‰ lower δ18Ow for much of the Cenozoic than records based on clumped isotope paleothermometry (Meckler et al., 2022; see also Agterhuis et al., 2022). We provide both classes of record here for comparison by the user.

Records are mapped to the user data's ages by linear interpolation. The Δ47-based δ18Ow records of Meckler et al. (2022) included in our tool were generated by interpolating the authors' original results to 0.1 Myr resolution using the Monte Carlo LOESS method and parameters described in the original publication (Meckler et al., 2022).

All built-in δ18Ow and temperature records are internally converted to four different timescales, so the user can select the timescale consistent with their data: GTS2004 (Gradstein et al., 2005), GTS2012 (Gradstein et al., 2012), GTS2016 (Ogg et al., 2016), and GTS2020 (Gradstein et al., 2020). These timescale conversions are performed by linear interpolation between magnetochron boundaries; dataset files can be found on the project GitHub.

The user may select a method for estimating local δ18Ow. These are as follows: performing no local correction, using modern δ18Ow from each sample's location and a specified depth (after LeGrande and Schmidt, 2006), using reconstructed Late Holocene or Last Glacial Maximum surface δ18Ow from each sample's location (model output from Tierney et al., 2020), using δ18Ow estimated from latitude alone (after Eq. 1 in Zachos et al., 1994, or the method of Hollis et al., 2019), using δ18Ow estimated from latitude and bottom-water temperature (after Gaskell et al., 2022 Eq. S9), or using δ18Ow estimated from isotope-enabled climate models (GCMs; after the method of Gaskell et al., 2022, presently provided using the datasets of Miocene and Eocene paleogeography used in that publication).

For methods which draw from an existing dataset of δ18Ow, the user may specify a number of degrees latitude/longitude or great-circle radius to average over in order to capture a regional mean when the exact paleocoordinates or local hydrography may not be known. To help determine site locations at the time of deposition, an option is also provided to automatically perform paleocoordinate rotations using the GPlates Web Service (Müller et al., 2018). Ages passed to GPlates are rounded to the nearest 100 ka to reduce the number of calls to the web service.

Our tool does not currently implement any automated consideration of seasonal variation in local δ18Ow, as this is generally treated as negligible by standard methodologies or implicitly baked into the calibration by calibrating against mean annual temperatures and δ18Ow values (e.g., Malevich et al., 2019).

Because δ18Oc is known to vary with aqueous carbonate chemistry (the “carbonate ion effect”; Spero et al., 1997; Bijma et al., 1999; Ziveri et al., 2012), users may also specify a carbonate ion correction factor. This is performed by adjusting δ18Oc with the linear relationship 3δ18Oc′=δ18Oc-sCO32--200s, where δ18Oc is the uncorrected oxygen isotope composition of the carbonate, δ18Oc′ is the corrected oxygen isotope composition of the carbonate, s is the selected slope of the effect (in ‰ VPDB per µmol L-1 CO32-), and [CO32-] is the concentration of carbonate ion in solution (in µmol kg-1). This relationship yields no correction when [CO32-] = 200  µmol kg-1, an approximation of the mean modern surface value (after the long-term record of Zeebe and Tyrrell, 2019). The user may specify [CO32-] manually or select a published long-term record of [CO32-] (Tyrrell and Zeebe, 2004; Zeebe and Tyrrell, 2019).

On completion, the tool presents a formatted table of the resulting temperatures, along with any intermediate values (such as estimated δ18Ow) which were required to generate them. Any rows with potential errors (e.g., paleocoordinates which do not yield a valid δ18Ow estimate or temperatures which exceed the data range of the calibration) are highlighted in color and flagged with warning text, which appears in an adjacent column. For reference, a short summary of methods is also generated, including relevant equations and a complete bibliography of citations in both text and BibTeX formats for the methods employed in each run.

It should be noted that, while the tool automates the process of applying a given calibration method, the user is still responsible for pre-screening their data for diagenetic alteration or other external biases. For example, use of δ18O data from foraminifera must consider factors such as diagenetic recrystallization, depth habitat, shell size, and the presence of gametogenic calcite (for a review, see Pearson, 2012).