X-radiographs were assessed from the skeleton slices to expose density banding. Chronologies were achieved by counting the well-developed density couplets. BDA 210 revealed continuous growth between 1825 and 1993, whereas BDA 215 and BDA 221 contain hiatuses. The bases of the colonies, which were separated from the upper living parts by algal growth, were 14C dated by AMS in the Leibnitz Laboratory in Kiel. Precision of Dating came to  30 years. The stratigraphy obtained from 14C dating was confirmed by the strong correspondence of the present proxy time series with a continuous 800-year coral skeletal chronology from Bermuda, presented by P�tzold et al. (1998; see Fig. 3c).
X-radiographs were assessed from the skeleton slices to expose density banding. Chronologies were achieved by counting the well-developed density couplets. BDA 210 revealed continuous growth between 1825 and 1993, whereas BDA 215 and BDA 221 contain hiatuses. The bases of the colonies, which were separated from the upper living parts by algal growth, were 14C dated by AMS in the Leibnitz Laboratory in Kiel. Precision of Dating came to  30 years. The stratigraphy obtained from 14C dating was confirmed by the strong correspondence of the present proxy time series with a continuous 800-year coral skeletal chronology from Bermuda, presented by P�tzold et al. (1998; see Fig. 3c).
The coral chronology is constructed by setting the maximum d18O value in  a given year equal to mid-February (consistent with the climatological  minimum in sea surface temperature) and assuming a constant coral growth  rate between February of every year. Therefore we interpolated linearly  between these fixed points for further age assignments. The resulting values  were interpolated linearly to six equally spaced values per year, providing a  bimonthly resolution.
The age model in the data set is based on the density banding of the skeleton and the seasonality in d18O, whithin each year the highest (least depleted) d18O value was tied to mid-September, which is the coldest month on average.
The age model in the data set is based on the density banding of the skeleton and the seasonality in d18O, whithin each year the highest (least depleted) d18O value was tied to mid-September, which is the coldest month on average.
d13C and ENSO events
The chronologies were subsequently developed on the basis of seasonality in skeletal d18O and d13C, supported by examination of annual skeletal density and fluorescent banding.
The chronologies were subsequently developed on the basis of seasonality in skeletal d18O and d13C, supported by examination of annual skeletal density and fluorescent banding.
The top and base of the core section were U/Th-dated (measured at the Institut fur Umweltphysik, Heidelberg, Germany), with a precision of �15 yr. The midpoint of the top dated sample is 14 density band couplets above the profile for geochemical measurements, and the midpoint of the bottom dated sample is seven band couplets below the profile (Fig. 2). In other words, the age model within the chronology is based on the annual banding. According to band couplet counts and the number of annual cycles in the d18O, the U/Th-dated sections differ in age by 104 yr, while the difference is 130 yr according to their radiometric dates (base: 1499, top: 1629 AD). To remove the surplus of 26 yr we adjusted the radiometric ages by adding 13 yr to the bottom and subtracting 13 yr from the top sample age. Therefore, individual years in our record are correct with respect to their positions within the time series, but they do not necessarily match calendar years. Years in the isotopic record are defined by cyclic variations in d18O, which are assumed to be annual, with each cycle spanning ca. 2.5-4 mm core length. Subannual dating is based on the annual d18O cycles, where the most positive d18O value in each cycle is assigned to the coldest average month, which, for Bermuda, is March. This introduces a non-cumulative error of approximately �1 month. The ages of other sampling points were linearly interpolated according to their spatial distances along the sampling profile.
Chronological assignments for the record were generally straightforward, because of the clear seasonal cycle in d18O and the especially clear expression of annual growth bands. The seasonal extremes were an- chored to April and September of each year
anchoring seasonal maxima and minima of the d18O depth series to March and October of each year. Crosschecks on the age assignments were available through examination of the annual growth bands and the strong semi-annual carbon isotope signal
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
The d18O  were tuned to the SST record by assigning the lowest (highest) d18O values of each year to the highest (lowest) SST. Age assignments were then linearly interpolated between these two annual anchor points using the Arand software package (P. Howell, personal commu- nication, 2002). A continuous monthly SST data set is available only as far back as 1970, so for age assignments before 1970 the lowest (highest) d18O and Sr/Ca values were assigned to March (August), March and August being the average times of highest and lowest SST, respectively, between 1970 and 1997
The d18O  were tuned to the SST record by assigning the lowest (highest) d18O values of each year to the highest (lowest) SST. Age assignments were then linearly interpolated between these two annual anchor points using the Arand software package (P. Howell, personal commu- nication, 2002). A continuous monthly SST data set is available only as far back as 1970, so for age assignments before 1970 the lowest (highest) d18O and Sr/Ca values were assigned to March (August), March and August being the average times of highest and lowest SST, respectively, between 1970 and 1997
The d18O  were tuned to the SST record by assigning the lowest (highest) d18O values of each year to the highest (lowest) SST. Age assignments were then linearly interpolated between these two annual anchor points using the Arand software package (P. Howell, personal commu- nication, 2002). A continuous monthly SST data set is available only as far back as 1970, so for age assignments before 1970 the lowest (highest) d18O and Sr/Ca values were assigned to March (August), March and August being the average times of highest and lowest SST, respectively, between 1970 and 1997
Age determination was carried out by matching the oxygen isotope record mainly reflects the seasonal SST cycle of 1–2 C at this location and indicates that the core provides a continuous record from 1957 to 1999.  Positive anomalies agreed with the timing of known El Nino events (when SSTs are cooler in this region)
Time was assigned to the Sr/Ca and coral d18O depth series by matching Sr/Ca minima with SST maxima (and vice versa), using AnalySeries software [Paillard et al., 1996].
tied the annual minima in d18O to February (on average the warmest month) and maximum  d18O to August/September (on average the coolest months). And comparing the d18O  records to Nino3.4 (central equatorial Pacific region bounded by 5�S?5�N and 120�?170�W) SST anomaly (SSTa)
tied the annual minima in d18O to February (on average the warmest month) and maximum  d18O to August/September (on average the coolest months). And comparing the d18O  records to Nino3.4 (central equatorial Pacific region bounded by 5�S?5�N and 120�?170�W) SST anomaly (SSTa)
tied the annual minima in d18O to February (on average the warmest month) and maximum  d18O to August/September (on average the coolest months). And comparing the d18O  records to Nino3.4 (central equatorial Pacific region bounded by 5�S?5�N and 120�?170�W) SST anomaly (SSTa)
tied the annual minima in d18O to February (on average the warmest month) and maximum  d18O to August/September (on average the coolest months). And comparing the d18O  records to Nino3.4 (central equatorial Pacific region bounded by 5�S?5�N and 120�?170�W) SST anomaly (SSTa)
A simple chronology based on these density bands in both corals temporally aligned anomalous 18O-depleted years precisely in the same years (e.g., 1972?1973, 1982? 1983). During these years, there were noted positive precipitation and/or SST anomalies. We then assigned February 15 to the most negative isotopic values. This is, on average, the wettest month. We then interpolated linearly between the anchor points to obtain bimonthly records of coral y18O.
A first chronology was developed based on the density bands evident in the X-rays, which are presumed to represent one year of growth (Figure 4). The age model was then refined using the strong annual cycle in coral d18O (Figures 5 and 6). The heaviest isotopic value in any given year was assigned to 1 September, which is, on average, the coldest month. We then interpolated linearly between these anchor points into 12 equidistant points per year for the monthly, and 6 points per year for the bimonthly resolved series
We use the pronounced seasonal cycle in d18O and Sr/Ca to develop the final chronology of the coral core and assigned the highest d18O and Sr/Ca values to august 15 of each year, which is on average the coldest month. [...] The d18O and Sr/Ca time series were interpolated linearly into 6 and 12 equi- distant points for any given year between these anchor points using the Analyseries software,.083 to 0.167 yrs,chronological,months,on a seasonal scale coral d18O  and Sr/Ca at this location primarily reflects SST variability (Fig. 5; Table 1). "
maximum and minimum ?18Ocoral values in a given year were assigned to February and August; linear interpolation between these fixed tie points
The d18O  were tuned to the SST record by assigning the lowest (highest) d18O values of each year to the highest (lowest) SST. Age assignments were then linearly interpolated between these two annual anchor points using the Arand software package (P. Howell, personal commu- nication, 2002). A continuous monthly SST data set is available only as far back as 1970, so for age assignments before 1970 the lowest (highest) d18O and Sr/Ca values were assigned to March (August), March and August being the average times of highest and lowest SST, respectively, between 1970 and 1997
The age model for the record is based on the seasonal banding pattern, as seen in the X-radiographs, and the annual cycles in the skeletal geochemistry. To be consistent with earlier work [Kuhnert et al., 2002], we used the cyclicity in d18O (which is not further discussed) rather than that in Sr/Ca (the resulting age models are almost identical). The heaviest value in each annual cycle was assigned to March, which is the coldest month on average; i.e., the chronology uses one tie point per year. Ages for the other sampling points were interpolated according to their positions along the sampling profile. An equidistant (monthly) time series was interpolated from the original Sr/Ca data and was used as the base for further calculations.
chronology constructed using seasonal cycles in d18O as well as density couplets in xrays. "we assigned the most enriched value of d18O in each cycle to consecutive Augusts (the average coldest month) down core. We inter- polated linearly between age-assigned d18O data points (sequential Augusts) to assign intra-annual ages, using programs in the ARAND software package (available at http://www.ncdc.noaa.gov/paleo/softlib/arand/arand.html) (P. Howell, personal communication, 1998). "
tied the annual minima in d18O to February (on average the warmest month) and maximum  d18O to August/September (on average the coolest months). And comparing the d18O  records to Nino3.4 (central equatorial Pacific region bounded by 5�S?5�N and 120�?170�W) SST anomaly (SSTa)
The high-resolution samples were line- arly interpolated to 12 ??monthly?? values for comparison to climate reconstructions
Annual extension rates based on sclerochronology were measured on X-radiograph positive prints from the beginning of one high-density band to the beginning of the next
Annual extension rates based on sclerochronology were measured on X-radiograph positive prints from the beginning of one high-density band to the beginning of the next
Age models were constructed using the AnalySeries program [Paillard et al., 1996]. We used tie points to connect the lowest (highest) d18O values to the warmest (coolest) temperatures in the instrumental SST record to establish each year.The use of coral Sr/Ca values instead of coral d18O values to construct the age model results produces a similar chronology. The geochemical data were then interpolated to give monthly d18O values. The geochemically based age models were confirmed via comparison with ages determined by counting of skeletal density-band couplets in the X-ray images of the coral slab.
Chronologies were built by counting density couplets in x-rays.
annual cycles of d18O, verified using annual growth bands
Age models were developed using density banding visible in the X radiographs to identify the calendar year. X radiographs for these corals have been previously published [Goodkin, 2007; Goodkin et al., 2007, 2005]. Some error is expected within this age model because of years of very little or no growth, not visible on the X ray and also not clearly discernible in the seasonal cycles of Sr/Ca. While there is the potential for error generated by noise in the Sr/Ca or the annual band counting to result in the inappropriate addition of a year, the majority of age model error will arise because of missing years, generating a bias in which coral bands are assigned to artificially recent (younger) dates. For example, if the coral did not grow in 1950, 1949 would be inappropriately assigned the date 1950, and similarly for all coral years prior to 1950 (1781?1949). Such age model bias is suspected during the early to mid-1800s when growth rates were extremely slow for an extended period of time and hiatuses in growth may have occurred [Goodkin et al., 2005].
Chronology was developed on the basis of the seasonal cycle in coral ?18O, and by counting the well-developed annual density bands. The coral ?18O-record extends from 1918 to 2004. The measured skeletal d18O minimum (maximum) was assigned to the month September (February) which represents the average seasonal SST maximum (minimum) at the study site. Linear interpolation was used in order to obtain monthly resolution for statistical analysis.
Counting annual density bands provided an age model for these samples. The exact position of the annual banding over core breaks was reconstructed by stacking the coral slabs together into round core sections with spacers representing the material lost in cutting the cores and with a life-size copy of the X-radiograph taped to the microdrilled slab. The two pieces of core were joined by aligning the corallites of each section, and the X-radiographs on both sides of the core break were marked exactly where they met. Once the breaks were aligned, we used the X-radiographs to count density bands multiple times. We estimate an error of �1 year at the bottom of the core.
chronology inferred from seasonal cycles in d18O record
Chronology is built by tying lowest d18O values to Aug, when FanningSST are at their warmest, and tying the highest d18O values to Feb, when Fanning SST are coolest. Ages are interpolated for dates between tie-points
age model was established on the basis of the pronounced seasonal cycle in the Sr/Ca record. The maximum (minimum) Sr/Ca was tied to March (September), which is on average the coolest (warmest) month in the study area. The coral d18O and Sr/Ca time series were linearly interpolated between these anchor points using the Analyseries software [Paillard et al., 1996] to obtain monthly proxy time series.
d18O tied to warmest month (Aug) and coolest month (Feb), linear interpolation used to obtain dates between
Geochemical variations versus depth were converted to variations versus time using AnalySeries software [Paillard et al., 1996]. Geochemical variations in d18O were used to determine a first-order age model, with the maximum intra-annual peaks in d18O being assigned as the coldest month of the year (August), beginning with the year 2007, when the living coral was cored. This first-order age model places d18O variations in the time domain, with uneven time increments (Dt). A second-order age model, with a monthly Dt, was created using the AnalySeries software program, which was verified by comparing years with anomalous d18O values to known ENSO events where possible. This second-order age model is the final age model used in all plots and data analysis.
counting annual density bands in x-rays from known collection date
counting annual density bands in x-rays from known collection date
counting annual density bands in x-rays from known collection date
counting annual density bands in x-rays from known collection date
Chronology is built by tying lowest d18O values to Aug, when FanningSST are at their warmest, and tying the highest d18O values to Feb, when Fanning SST are coolest. Ages are interpolated for dates between tie-points
Relative chronologies were constructed by assigning the maximum ?18O point for a given year to January 15th and linearly interpolating ?18O datapoints in between these tie points, following the procedure outlined in Cobb et al., 2002 for the Palmyra modern and fossil corals U/Th dates used to estimate absolute ages
The geochemical age model was shifted within error of the 230Th dates (Table DR1) to match the timing of the 1578 CE El Ni�o event known from historical records (Ortlieb, 2000). The U-th dates have errors of 2-4 year for the fossil corals and the 20th century corals had U-Th erros of 3-5 years.
The geochemical age model was shifted within error of the 230Th dates (Table DR1) to match the timing of the 1578 CE El Ni�o event known from historical records (Ortlieb, 2000). The U-th dates have errors of 2-4 year for the fossil corals and the 20th century corals had U-Th erros of 3-5 years.
The geochemical age model was shifted within error of the 230Th dates (Table DR1) to match the timing of the 1578 CE El Ni�o event known from historical records (Ortlieb, 2000). The U-th dates have errors of 2-4 year for the fossil corals and the 20th century corals had U-Th erros of 3-5 years.
Sr/Ca data to monthly SST from the 2� � 2� grid square centered on 4�N, 172�E from the extended reconstructed SST version 3b (ERSSTv3b) data product [Smith et al., 2008; ttp://www.esrl.noaa.gov/psd] [?] Annual Sr/Ca maxima were tied to the coolest points in the SST record using Analyseries [Paillard et al., 1996]. The resulting spacing of Sr/Ca data remained relatively constant throughout the record, and the chronology was verified against growth band counts from the X-radiograph. After converting depth to time in Analyseries, Sr/Ca were interpolated to six equally spaced samples per year using ARAND time series analysis software [Howell, 2001], corresponding  to bimonthly resolution for the length of the record.
Sr/Ca data to monthly SST from the 2� � 2� grid square centered on 4�N, 172�E from the extended reconstructed SST version 3b (ERSSTv3b) data product [Smith et al., 2008; ttp://www.esrl.noaa.gov/psd] [?] Annual Sr/Ca maxima were tied to the coolest points in the SST record using Analyseries [Paillard et al., 1996]. The resulting spacing of Sr/Ca data remained relatively constant throughout the record, and the chronology was verified against growth band counts from the X-radiograph. After converting depth to time in Analyseries, Sr/Ca were interpolated to six equally spaced samples per year using ARAND time series analysis software [Howell, 2001], corresponding  to bimonthly resolution for the length of the record.
Sr/Ca data to monthly SST from the 2� � 2� grid square centered on 4�N, 172�E from the extended reconstructed SST version 3b (ERSSTv3b) data product [Smith et al., 2008; ttp://www.esrl.noaa.gov/psd] [?] Annual Sr/Ca maxima were tied to the coolest points in the SST record using Analyseries [Paillard et al., 1996]. The resulting spacing of Sr/Ca data remained relatively constant throughout the record, and the chronology was verified against growth band counts from the X-radiograph. After converting depth to time in Analyseries, Sr/Ca were interpolated to six equally spaced samples per year using ARAND time series analysis software [Howell, 2001], corresponding  to bimonthly resolution for the length of the record.
Sr/Ca tie points to warmest (Dec) and coolest (Aug) months; linear interpolation using Analyseries
Sr/Ca tie points to warmest (Dec) and coolest (Aug) months; linear interpolation using Analyseries
We developed age models by visually comparing coral banding with seasonal d18O and d13C cycles. Dark regions of the coral X-ray positives corresponded with minima in d18O and d13C. We then associated local d18O and d13C minima with March, based on the mean Palau SSS and SST climatology. We converted samples to monthly resolution by performing linear interpolations between chronological anchor points.
We developed age models by visually comparing coral banding with seasonal d18O and d13C cycles. Dark regions of the coral X-ray positives corresponded with minima in d18O and d13C. We then associated local d18O and d13C minima with March, based on the mean Palau SSS and SST climatology. We converted samples to monthly resolution by performing linear interpolations between chronological anchor points.The UC record is based upon a composite of the living and deceased colonies assembled by ?wiggle matching? chronologies for the period of overlap
We used the published bimonthly coral d18O record of Kuhnert et al.12 as our reference coral because the presence of seasonal cycles provided the best age control for all cores.The age model of the new corals was determined by assigning 1 year of growth to a couplet of high- and low-density banding in X-ray-positive prints cross-checked with luminescence banding. The high-density bands were equivalent to summer and low density to winter. The coral d18O record of Kuhnert et al. was averaged annually (January?December), and then each individual annually resolved coral proxy record was normalized to its variance relative to the 1900?1993 reference period. We used the COFECHA tool to aid in cross-dating the annual time series. We aligned the new annually resolved Sr/Ca and d18O records (normalized relative to the 1900?1993 reference period) with the Kuhnert et al. annual time series, and averaged all records to form a composite chronology that extends from 1795 to 2010.
assigning the annual ?18O maximum to the month of the climatological SST minimum at both sites; linear interpolation between annual tie points and resampled to generate monthly series
Ages for samples were assigned on the basis of well-defined density banding, assuming that the base of the dense band coresponds to February and that extensions rates between these points are linear
Ages were determined independently for the two cores on the basis of seasonal d13C variability, which is supported by radiometric and density-banding observations of young Tarawa corals.  We normalized the records, spliced them together using diagnostic density and isotopic patterns, and interpolated them to monthly resolution for statistical analysis
The annual UR-87 chronology is based on counts of dense bands observed in X radiograph positive prints as well as stable isotope profiles
cross-checking the annual d18O variation against the annual dense bands counted from the positive X-rays as well as setting the lowest d18O value in a given year equal to November
In our corals we used a combination of distinctive skeletal fluorescent banding (visible under illumination with longwave UV light; Fig. 2) to provide an initial chronology. The accuracy of this chronology was subsequently verified on a year-byyear basis by consideration of the annual cycle in skeletal d18O
Sampling resolution is four samples per year, one sample in the middle of each density band. The 0.5 mm diameter, discrete point sampling strategy used in this study creates samples that are a homogeneous mixture of-26 (7 mm/year) to -20 (9 mm/year) days of skeletal growth, assuming a linear extension rate. (Quinn et al, 1993). Isotope sampling methods were also identical to Quinn et al. [1993], with new samples being midway between the original samples reported in that study.
Counting annual density bands provided an age model for these samples assuming high density band is July. Data linearly interpolated to monthly for less than 12 samples per year and used rectanglar interpolation for years with more than 12 samples. From NOAA wb site: The skeleton was sampled at about 10-15 samples a year and  these data have been pooled to construct the annual means.
In South Florida, the dense band forms in the late summer, but it is generally thought that there is as much as a � two month variation in timing. By counting the number of bands in a coral, the age can be assessed. Errors in age interpretation arise when dense bands are indistinct or when so-called "stress bands", resulting from events such as temperature extremes, occur. Generally speaking, a year in which a stress band forms may actually contain two dense bands or a thicker dense band which is a combination of the stress and normal dense band. Although it is possible to misinterpret a stress band as an annual band, the annual bands can be recognized by their regular spacing. Therefore, density changes can still provide a clear annual chronology.
used the d18O cycle to count annual layers; assigned the high d18O extremes to Aug 1 of every year and interpolated linearly between these tie points.  The largest d18O anomaly (late 1877) matches the most catastrophic monsoon failure on record
The chronology is then established by associating each maximum and minimum in d18O with each minimum (August) maximum (March) in the SST record (Figure2), assuming a constant linear extension rate between these 2 months
Chronology built by counting density couplets in x-rays.
~annual spikes in d13C assumed to take place in April, ages for depths between spikes are linearly interpolated. Chronology is constrained by density banding in xrays and growth rates.
For the upper part of the record (1992-1952) a calendar year was first assigned to each density-band couplet and hence to every 12 samples assuming linear subannual skeletal extension. Second, systematic variations in oxygen isotopic composition were correlated with monthly SST values by using the AnalySeties program [Paillard et al., 1996] to match the peaks and troughs in the two time series. For the lower part of the record (1657-1951), simple band counting was used to extend the chronology back to the beginning of the record, which we estimate to be 1657. Although we have no absolute dates on this coral, Crowley et al. [1997] recently demonstrated that some of the distinctive cool excursions in the mean annual record coincide within 1 year of known volcanic eruptions as determined from the volcano chronology of Sirekin and Siebert [1994] and various ice core records
Chronologies were built by counting density couplets in x-rays.
we created a preliminary age model using the seasonal (doublet) structure of the respective d13C time series. The age model of Nauru 2 was then optimized through linear mapping of the coral d18O to the local precipitation record with an estimated error of 2-3 months. Over the interval where the two corals have similar sampling resolution (i.e., 1 and 2 mm density) the Nauru 1 d18O record was linearly mapped to the Nauru 2 d18O record
we created a preliminary age model using the seasonal (doublet) structure of the respective d13C time series. The age model of Nauru 2 was then optimized through linear mapping of the coral d18O to the local precipitation record with an estimated error of 2-3 months. Over the interval where the two corals have similar sampling resolution (i.e., 1 and 2 mm density) the Nauru 1 d18O record was linearly mapped to the Nauru 2 d18O record
A first rough age assignment was based on the density banding pattern evident in x-radiographs over most portions of the coral core. Each couplet of one low- and one high-density band represents the growth of one year. The higher-resolving age model is based on the assumption, that coral d18O primarily depends on temperature. In the d18O data deriving from an annual banding couplet the highest value was assigned to the mid of September, the coldest month on average. The chronology is thus tied to one point within each year; the ages of the other sampling points were linearly interpolated according to their spatial distances along the sampling profile. In skeletal portions with unclear density banding pattern the stratigraphy is solely based on cyclic variations in coral d18O. Each cycle was assumed to represent an annual cycle, as long as it occurred over a skeletal portion extending between 10 and 20 mm. Assignments of sub-annual dates were performed as described
A first rough age assignment was based on the density banding pattern evident in x-radiographs over most portions of the coral core. Each couplet of one low- and one high-density band represents the growth of one year. The higher-resolving age model is based on the assumption, that coral d18O primarily depends on temperature. In the d18O data deriving from an annual banding couplet the highest value was assigned to the mid of September, the coldest month on average. The chronology is thus tied to one point within each year; the ages of the other sampling points were linearly interpolated according to their spatial distances along the sampling profile. In skeletal portions with unclear density banding pattern the stratigraphy is solely based on cyclic variations in coral d18O. Each cycle was assumed to represent an annual cycle, as long as it occurred over a skeletal portion extending between 10 and 20 mm. Assignments of sub-annual dates were performed as described
Using density band because they were very clear, then the chronology was tunnd by aligning d18O variability to SST variability
Using density band because they were very clear, then the chronology was tunnd by aligning d18O variability to SST variability
Using density band because they were very clear, then the chronology was tunnd by aligning d18O variability to SST variability
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
based on counting of annual layers (seasonal variations of stable isotopes) and reference horizons
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on reference horizons, counting of annual layers
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
chronology based on 36Cl reference horizon, wiggle matching, 14C dating and ice-flow modelling
chronology based on 36Cl reference horizon, wiggle matching, 14C dating and ice-flow modelling
chronology based on 36Cl reference horizon, wiggle matching, 14C dating and ice-flow modelling
chronology based on 36Cl reference horizon, wiggle matching, 14C dating and ice-flow modelling
chronology based on 36Cl reference horizon, wiggle matching, 14C dating and ice-flow modelling
chronology based on annual layer counting, volcanic and bomb reference horizons
based on volcanic horizons, beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on flow model and volcanic reference horizons
Nye age depth model
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on counting of annual layers (seasonal variations of dust and stable isotopes), beta activity and ice-flow modelling
age model based on counting of annual layers (seasonal variations of dust and stable isotopes), beta activity and ice-flow modelling
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
based on volcanic horizons, beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
chronology based on annual layer counting, bomb reference horizon
based on volcanic horizons, beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
counting of annual layers (seasonal variations of stable isotopes and major ions) and beta activity
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
based on counting of annual layers (seasonal variations of stable isotopes) and reference horizons
based on counting of annual layers (major ions) and reference horizons (Tritium, Saharan dust and volcanic)
based on counting of annual layers (major ions) and reference horizons (Tritium, Saharan dust and volcanic)
based on counting of annual layers (major ions) and reference horizons (Tritium, Saharan dust and volcanic)
based on counting of annual layers (major ions) and reference horizons (Tritium, Saharan dust and volcanic)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
based on counting of annual layers (seasonal variations of stable isotopes) and reference horizons
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
based on volcanic horizons, beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
based on counting of annual layers (stable isotopes, major ions)
based on counting of annual layers (stable isotopes, major ions)
based on counting of annual layers (stable isotopes, major ions)
based on counting of annual layers (stable isotopes, major ions)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
based on counting of annual layers (seasonal variations of stable isotopes)
based on counting of annual layers (seasonal variations of stable isotopes)
based on counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on  volcanic horizons and beta activity peaks as reference horizons, annual layers and flow modelling
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
chronology based on annual layer counting, 36Cl reference horizon, and ice-flow modelling
chronology based on annual layer counting, 36Cl reference horizon, and ice-flow modelling
chronology based on annual layer counting, 36Cl reference horizon, and ice-flow modelling
chronology based on annual layer counting, 36Cl reference horizon, and ice-flow modelling
chronology based on annual layer counting, 36Cl reference horizon, and ice-flow modelling
chronology based on annual layer counting, 36Cl reference horizon, and ice-flow modelling
chronology based on annual layer counting, 36Cl reference horizon, and ice-flow modelling
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on beta activity and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
chronology based on annual layer counting, volcanic reference horizon, 14C dating and matching to GISP2 ice core
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
age model based on volcanic horizons and counting of annual layers (seasonal variations of stable isotopes)
based on counting of annual layers (seasonal variations of stable isotopes) and reference horizons
stack of paired isotope/radiocarbon dates
stack of paired isotope/radiocarbon dates
estimate of lake residence time from mass balance modelling (Jones et al., 2005)
lake equilibrium with the mean annual balance of precipitation and evaporation across the watershed
varve counts
varve counts
average sedimentation rate over 0.5-cm-thick sample
Leaf wax hydrogen isotopes exhibit a surprising amount of variability at the sub-decadal scale (Fig. 6). This high degree of variability indicates that leaf waxes are likely produced, transported and deposited in a relatively short period of time.
Leaf wax hydrogen isotopes exhibit a surprising amount of variability at the sub-decadal scale (Fig. 6). This high degree of variability indicates that leaf waxes are likely produced, transported and deposited in a relatively short period of time.
Leaf wax hydrogen isotopes exhibit a surprising amount of variability at the sub-decadal scale (Fig. 6). This high degree of variability indicates that leaf waxes are likely produced, transported and deposited in a relatively short period of time.
Leaf wax hydrogen isotopes exhibit a surprising amount of variability at the sub-decadal scale (Fig. 6). This high degree of variability indicates that leaf waxes are likely produced, transported and deposited in a relatively short period of time.
Leaf wax hydrogen isotopes exhibit a surprising amount of variability at the sub-decadal scale (Fig. 6). This high degree of variability indicates that leaf waxes are likely produced, transported and deposited in a relatively short period of time.
These lines of evidence strongly suggest that conditions favor- able for B. braunii growth occur during moderate-to-strong El Nin?o events and thus that C34 botryococcene ?D records the hydrologic conditions of the lake during these events. In contrast, the avail- able evidence suggests that the dinoflagellate population persists throughout the varying hydrologic conditions experienced by El Junco Lake and thus that sedimentary dinosterol ?D records the long-term (decadal-to-centennial) mean lake conditions.
Rapid transfer from plant to sediment is likely within the small, steep-sided watershed, suggesting that the leaf wax record would be capable of reconstructing up to decadal resolution variation in leaf wax dD values.
Rapid transfer from plant to sediment is likely within the small, steep-sided watershed, suggesting that the leaf wax record would be capable of reconstructing up to decadal resolution variation in leaf wax dD values.
estimate of lake residence time from mass balance modelling (Jones et al., 2005)
lake residence time claculations
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age model (not shown)
age model (not shown)
age model
age model
age model
age model
age model
age model
age model
age model
age model
age model
age model
age model
age model
age model constructed by linear interpolation
age model constructed by linear interpolation
age model constructed by linear interpolation
age model
Lack of seasonal signal in stable isotopic composition + the drip-water trace element concentrations strongly suggesta that most of the water discharging into this cave has a mean residence time well in excess of one year.
age model
age model: Markov Chain Monte-Carlo sampling and Bayesian statistics implemented in OxCal 4.1
age model: Markov Chain Monte-Carlo sampling and Bayesian statistics implemented in OxCal 4.1
iscam method (Fohlmeister et al., 2012), based on simulations and correlation calculations between pairs of climate  records (this is a stack of 5 records). Point-wise linear interpolation between data points. Typical age errors are smaller than 2%.
The age model was computed in MATLAB® based on a cubic interpolation between the 230Th ages, its distance from the stalagmite top, and the corresponding dating errors. Multiple age models are produced to account for the analytical uncertainties and the errors in the location of each drill hole (assumed to be <0.2 mm). Therefore, multiple iterations of the age model were calculated assuming a normally distributed deviation from the 230Th age and the standard deviation given by the error range (Monte Carlo simulation). Iterations with reversals were dropped and an ensemble of 100 age models was created (i.e., all reversals could be removed within the error margins). In this model the median age in the distribution was used for each point.
The age model was computed in MATLAB® based on a cubic interpolation between the 230Th ages, its distance from the stalagmite top, and the corresponding dating errors. Multiple age models are produced to account for the analytical uncertainties and the errors in the location of each drill hole (assumed to be <0.2 mm). Therefore, multiple iterations of the age model were calculated assuming a normally distributed deviation from the 230Th age and the standard deviation given by the error range (Monte Carlo simulation). Iterations with reversals were dropped and an ensemble of 100 age models was created (i.e., all reversals could be removed within the error margins). In this model the median age in the distribution was used for each point.
U-Th age model, lamina counting and good correlation with local tree ring chronology
The data are supported by U-Th results
age model
age model
age model
age model
age model
age model
U/Th dating
1. Baker, Andy, and Chris Bradley. "Modern stalagmite ? 18 O: Instrumental calibration and forward modelling." Global and Planetary Change 71.3 (2010): 201-206. 2. Oster, Jessica L., Isabel P. Monta�ez, and Neil P. Kelley. "Response of a modern cave system to large seasonal precipitation variability." Geochimica et Cosmochimica Acta 91 (2012): 92-108. 3. Tooth, Anna F., and Ian J. Fairchild. "Soil and karst aquifer hydrological controls on the geochemical evolution of speleothem-forming drip waters, Crag Cave, southwest Ireland." Journal of Hydrology 273.1 (2003): 51-68. Supplementary information.
Karst Hydrologic Model
age model
age model and cave monitoring
210Pb, 14C age model and extrapolation of acumulation rate
age model
age model
age model
age model
age model
For U-Th analysis, 0.3 to 1g of sponge material was drilled along visible growth layers. The sample size was chosen so that the width and depth of the sampling groove was equivalent to a period of about 20 years, given a growth rate on the order of 0.2 mm/a. 11 samples were anayzed for U-Th. The external reproducibility of the U/Th dating results was determined as about �20 years (2sem) by five replicate analyses of sample Ce96-40 and confirmed by analysis of three closely spaced sample pairs (Ce96-45/51, Ce96-170/172, Ce96-123/40). For one sample pair (Ce96-184/186) U/Th ages could not be confirmed within external reproducibility. As the age data of this specimen deviate significantly from a linear growth curve, we fitted a third order polynomial to the U/Th ages.
For U-Th analysis, 0.3 to 1g of sponge material was drilled along visible growth layers. The sample size was chosen so that the width and depth of the sampling groove was equivalent to a period of about 20 years, given a growth rate on the order of 0.2 mm/a. 4 samples were anayzed for U-Th. 10 samples for radiocarbon analysis were removed. They compared D14C data with a D14C curve measured on a Florida surface water coral [Druffel, 1989]. The fit was constrained by assuming that the growth rate was constant in the relevant part of the sponge skeleton. They further assume that the timing of the radiocarbon increase at 125 mbsl was similar to surface waters in that region because the amplitude of change in D14C is similar to the surface water change. For specimen Pb19 a mean growth rate of 0.19 mm/a was calculated from the slope of a linear fit to the dated tie points. A lifespan from about 1400 A.D. to 1996 A.D. was determined by extrapolating the mean growth rate to the base of the skeleton. The U/Th chronology is independently confirmed by the correct localization of the nuclear weapon test radiocarbon increase at a depth of about 5.9 mm below the surface of the skeleton (Figure 6, Table 2). Further independent support for the reliability of our chronologies is provided from the fit of d13C data of the investigated sponge skeletons and atmospheric CO2 records [Bo�hm et al., 2002].
sclerosponges were dated using U/Th radiometric techniques and then analyzed using high resolution micromilling for oxygen isotope analysis via isotope ratio mass spectrometry and laser ablation sampling for Sr/Ca analysis via ICP-MS. Constant-growth age models constructed from U/Th dating indicate growth rates of 145 mmy1 for the Exuma sclerosponge and 172 mmy1 for the TOTO sclerosponge (auxiliary material1). The radiometric age model of the TOTO sclerosponge was tuned to annual Sr/ Ca variations after Swart et al. [2002], however because tuned age models and constant growth models are within 5% [Swart et al., 2002], the U/Th constant growth model was used for the Exuma sclerosponge.
sclerosponges were dated using U/Th radiometric techniques and then analyzed using high resolution micromilling for oxygen isotope analysis via isotope ratio mass spectrometry and laser ablation sampling for Sr/Ca analysis via ICP-MS. Constant-growth age models constructed from U/Th dating indicate growth rates of 145 mmy1 for the Exuma sclerosponge and 172 mmy1 for the TOTO sclerosponge (auxiliary material1). The radiometric age model of the TOTO sclerosponge was tuned to annual Sr/ Ca variations after Swart et al. [2002], however because tuned age models and constant growth models are within 5% [Swart et al., 2002], the U/Th constant growth model was used for the Exuma sclerosponge.