Lago_Pato_Read_Me.txt

DATA AND CODE FOR LAKE SEDIMENT RECORDS EXTRACTED FROM LAGO PATO, TORRES DEL PAINE, SOUTHERN CHILE

This directory contains data (Lago_Pato_Data.zip folder) and code (Lago_Pato_Code folder) for site data and multiproxy analysis of lake sediment cores extracted from Lago Pato, a small lake basin at S51°18.020’, W72°40.716’ and ~ 33 m a.s.l. in Torres del Paine National Park, Southern Chile collected between November 2007 to May 2017. 

The data are used to constrain glacier dynamics and lake level change in the TdP and Última Esperanza region over the last ~30,000 cal a BP (30 ka). The code folder contains Matlab and R code used to analyse the data and construct figures for the following paper: 

Roberts SJ, McCulloch RDM, Emmings J, Davies SJ, Van Nieuwenhuyze W, Sterken M, et al. (2022) Late glacial and Holocene palaeolake history of the Última Esperanza region of Southern Patagonia. Frontiers in Earth Science.

LOCATION
Lago Pato, Torres del Paine National Park, Patagonia, Chile

FUNDING SOURCE

This is NERC-funded data and the UK Open Government Licence for its use applies.

This project was funded by the Natural Environment Research Council (NERC) through the British Antarctic Survey (BAS) and an UGent BOF bilateral collaboration project. RMcC was supported by Programa Regional R17A10002 and R20F0002 (PATSER) ANID. We gratefully acknowledge the University of Magallanes (UMAG) and the University of Santiago (Carolina Diaz) for assistance with fieldwork; the NERC/SUERC AMS Radiocarbon Facility for providing initial range-finder radiocarbon dates; the NERC Isotope Geosciences Laboratory (NIGL, now National Environmental Isotope Facility, NEIF, at the British Geological Survey) and Melanie Lang for stable carbon isotope analysis; Aberystwyth University (David Kelly), Durham University (Neil Tunstall and Christopher Longley) and Edinburgh University (Chris Hayward) for use of their core scanning and microprobe facilities and technical support.


KEYWORDS
Last Glacial Maximum (LGM), palaeoclimate, palaeolimnology, glaciation, lake level changes, Patagonia, Southern Hemisphere Westerly Winds.

DATA COLLECTORS & ANALYSTS

Data collectors (ORCID code where known)
Stephen J. Roberts1 (0000-0001-5542-3703) – sediment core extraction, chronology, geochemistry, sedimentology
Robert D. McCulloch2 (0000-0001-5542-3703) – pollen, chronology
Sarah J. Davies4 – geochemistry
Joseph F. Emmings3 – geochemistry, sedimentology
Mieke Sterken5 – sediment core extraction, diatoms
Evelien Van de Vyver5 – diatoms
Wim Van Nieuwenhuyze5 – diatoms
Katrien Heirman6 – sediment core extraction
Jeroen Van Wichelen7 – sediment core extraction
Carolina Diaz8 – sediment core extraction

Data analysts
Stephen J. Roberts1 – all data
Robert D. McCulloch2 – pollen
Joseph F. Emmings3 – geochemistry, sedimentology
Sarah J. Davies4 – geochemistry
Wim Van Nieuwenhuyze5 – diatoms
Mieke Sterken5 – diatoms
Elie Verleyen5 – sediment cores, diatoms

Affiliations
1British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge, CB3 0ET, UK.
2Centro de Investigación en Ecosistemas de la Patagonia (CIEP), Coyhaique, Chile.
3British Geological Survey, Keyworth, Nottingham, NG12 5GG, UK.
4Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, SY23 3DB, UK.
5Protistology and Aquatic Ecology, Ghent University, Krijgslaan 281 S8, 9000 Gent, Belgium.
6TNO - Geological Survey of the Netherlands, Princetonlaan 6, NL-3584 CB Utrecht, Netherlands.
7Research Institute for Nature and Forest, Herman Teirlinckgebouw, Havenlaan 88 bus 73, 1000 Brussels, Belgium.
8Universidad de Santiago de Chile, Avenida Libertador Bernardo O'Higgins nº 3363. Estación Central, Santiago, Chile, and Alcalde Eduardo Castillo Velasco 3197, Ñuñoa, Santiago, Chile.
9Flanders Environment Agency Dokter De Moorstraat 24-26 9300 Aalst Belgium

METHODS

Sediment cores were collected using a UWITEC-gravity corer, Livingston piston corer from the deepest point (~3.5 m of water depth) in Lago Pato (LP08: S51° 18’01.2’’, W72° 40’43.0’’, 32 m a.s.l., 600 cm total recovered sediment depth).

Biology
Subsamples for diatom and stomatocyst analysis were taken at 4 cm and 8 cm intervals from the LP08 core. At least 400 valves were counted per slide and species were identified to at least genus level using taxonomic studies from the region. Microscopic pollen, charcoal and cryptotephra shards were counted at 8 cm intervals in the LP08 core) with Lycopodium spores of known concentration added for quantification. The total pollen sum from each subsample is at least 300 land pollen grains (Total Land Pollen, TLP) above 470 cm in the LP08. Charcoal was classified in five different size classes, <25 µm, 25–50 µm, 50–75 µm, 75–100 µm, and >100 µm, to distinguish between proximal and distal fires.

Chronology
A chronology was established using Accelerator Mass Spectrometry (AMS) radiocarbon dating. Calibration of radiocarbon ages was undertaken in OXCAL v.4.4 using the SHCal20.14C Southern Hemisphere atmosphere calibration curve (SH20). Radiocarbon ages are reported as conventional radiocarbon years BP (14C years BP) ±1σ and calibrated ages as 2σ (95.4%) ranges, median and mean calendar years BP (cal a BP and cal ka BP, relative to 1950 CE), rounded to the nearest ten years. Age-depth models were developed using Bayesian age-depth modelling software (rBACON v.2.5). Modelled age data mean ages produced by the SH20M1H (Southern Hemisphere, SHcal20, radiocarbon calibration curve) in rBACON, where M1 indicates Model 1 and H indicates the inclusion of a hiatus in the model.

Geochemistry
Sediment cores were collected using a UWITEC-gravity corer, Livingston piston corer and a Russian corer from the deepest point (~3.5 m of water depth) in Lago Pato:
-	LP08 record from  S51° 18’01.2’’, W72° 40’43.0’’, 32 m a.s.l. is 600 cm long
-	LP16 record from S51°18’11.3’’, W72° 40’53.7’’, 33–34 m a.s.l. is 295 cm long

Contiguous downcore wet-sediment Energy Dispersive Spectrometry (EDS) X-ray fluorescence core scanning (XRF-CS) data was collected using an ITRAX XRF core scanner at Aberystwyth University fitted with a Molybdenum (Mo) anode X-ray tube (settings: 30 kV, 50 mA, count time 10 seconds, at 2 mm contiguous intervals and for LP08 Unit 6 (equivalent to mean ± 2-sigma: 4.5±7.0 years), at 200 μm intervals for LP08 Unit 1 (1.3±4.2 years),  with LP08 basal Unit 1 scanned at 100 μm, and at 500 μm for LP16 Units 2-6 (9.6±17.4 years) and at 200 μm for LP16 Unit 1 200 μm (1.1±1.6 years). Data from finely laminated glaciolacustrine sediments in Units 1-2 were measured at or smoothed to 200 μm (from 100 μm interval data) before analysis.

Sedimentology
Physical properties were measured with a Geotek® multi-sensor core logger (MSCL) (gamma-ray wet density (ɣ-density), resistivity and magnetic susceptibility (MSκ; SI x 10-5) data (Bartington Instruments; LP08: MS2C loop sensor, 2 mm intervals, 10 seconds; LP16: MS2E point sensor, 0.5 mm intervals; 10 seconds) and density-corrected MSχ (κ/ρ; kg m-3)). Digital X-radiographs were obtained from split cores using a rotating anode mobile digital Celtic SMR CR computerised X radiography unit at Cambridge University Vet School (48kV; 4 mAs; no grid) and as ITRAX generated digital X-radiographs (45 kV, 50 mA.ms, 200 ms, 60 µm interval) at Aberystwyth University. Subsample data includes Loss-on-ignition (LOI) (12 hrs drying at 110°C, 4 hrs at 550°C (LOI550), and 2 hrs at 950°C for carbonate-proxy (LOI950x1.36), TOC (Total Organic Carbon (%C or %Corg) and total nitrogen (%N) and carbon isotopic ratios (δ13C). 

Time series analysis 
Log-n element/Ti ratio XRF-CS Z-scores were used for time series analysis (Fast Fourier Transform, FFT, periodograms, Lomb-Scargle Power Spectrum, Wavelet Power Spectrum, Peak Identification) in MATLAB. Equally spaced (10-year and 100-year) time-intervals were generated using a Piecewise Cubic Hermite Interpolated Polynomial (PCHIP) function, which avoids spline artefacts and preserve the shape of the original XRF-CS data series (Grinsted et al., 2004; Trauth, 2015). Time series data were detrended (polynomial linear best fit) to remove the long-term linear trend. Second order polynomial Locally Weighted Scatterplot Smoothing (LOESS) 100-year smoothing (0.1 sampling interval with outliers removed) was also used to compare datasets to published data.

Data were analysed in MATLAB v. R2021a, R v. 4.1.0/Rstudio v. 1.4.171,  using the R packages Vegan, Rioja, Tidyverse, ggplot2, Ggally v. 2.1.2, and in Sigmaplot v. 14.0 and C2 v.1.7.7. Code is available from: https://github.com/stever60/Lago_Pato 

Instrumentation
Sedimentology: Geotek® multi-sensor core logger (MSCL)
Geochemistry: ITRAX XRF core scanner fitted with a Molybdenum (Mo) anode X-ray tube
X-rays: Celtic SMR CR computerised X radiography and ITRAX-XRF CS generated digital X-radiographs
δ13C: Costech EA interfaced with the VG Triple Trap and Optima IRMS at NIGL (NERC Isotope Geosciences Laboratory).

DATA QUALITY

Chronology
Radiocarbon ages were rounded to the nearest 10 calendar years (cal a BP) in file LP08- C14_data.csv and the results section and to the nearest 100 years (0.1 cal ka BP) in the discussion to reflect dating and age-depth modelling uncertainties.

Geochemistry
ITRAX-XRF Raw count per second (cps) data were analysed using the Q-spec software v8.6.0 (Cox Analytical), with MSE values minimised to optimise the fit of ‘as measured’ spectra to a modelled spectrum. data  are presented as percentages of the Total Scatter Normalised ratio sum (%∑TSN or, more simply, %TSN, which are equivalent to percentages of the cps sum, or %cps) to account for downcore variations in count rate, density, water and organic content. Data less than mean minus two-sigma kcps (mainly due to gaps in the core) and greater than MSE plus two-sigma (representing a poor fit between measured to modelled spectra) were filtered before analysis. ‘Noisy’ elements were eliminated by comparing cps and using %TSN thresholds of >0.1% mean and >0.5% maximum, and by examining autocorrelation profiles for each element (Bishop, 2021). For LP08, this left 12 ‘measurable’ elements for the LP08 record (Si, S ,K, Ca, Ti, Mn, Fe, Zn, Br, Rb, Sr, Zr, and inc., coh. scatter). Elements are presented as natural log (log n or Ln) ratios. Ti-normalised log n ratios are used to estimate changes in relation to the background bedrock input. Duplicates runs were undertaken on ~10% of the total core depth (LP08-1F at 2 mm and 200 microns). For LP16, this left 17 ‘measurable’ elements for the LP16 record (Si , S, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Zn, As, Br, Rb, Sr, Zr , Ba, and inc., coh. scatter). Elements are presented as natural log (log n or Ln) ratios. Ti-normalised log n ratios are used to estimate changes in relation to the background bedrock input. Duplicates runs were undertaken on ~10% of the total core depth (LP16-3B at 200 and 500 microns). 

Sedimentology
Carbon isotope δ13C data, values were calculated to the VPDB scale using a within-run laboratory standard calibrated against NBS-19 and NBS-22. Replicate analyses of sample material gave a precision of ± 0.1 (per mil) for δ13Corg and 10% for C/N. Flux data (g cm a-2) were calculated from the product of dry mass accumulation rates (g cm a-1), dry bulk density (g cm-3), sedimentation rates (cm a-1) and proxy concentration measurements.

DATA RESOLUTION
Geochemical XRF-Core scanning data were measured at 2 mm contiguous intervals and for LP08 Unit 6 (equivalent to mean ± 2-sigma: 4.5±7.0 years), at 200 μm intervals for LP08 Unit 1 (1.3±4.2 years), with LP08 basal Unit 1 scanned at 100 μm. Data from finely laminated glaciolacustrine sediments in Units 1-2 were measured at or smoothed to 200 μm (from 100 μm interval data) before analysis.Geochemical XRF-Core scanning data were measured at 500 μm for LP16 Units 2-6 (9.6±17.4 years) and at 200 μm for LP16 Unit 1 200 μm (1.1±1.6 years). XRF-CS data from finely laminated glaciolacustrine sediments in Units 1-2 were smoothed to 200 μm, other data to 2mm, and equal spaced time-intervals (10-years and 100-years) for use in time series analysis were generated. Time series datasets were centred, standardised (Z-scores), detrended and interpolated using a 10-year or 100-year Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function, which avoids spline artefacts and preserve the shape of the original XRF-CS data series.

SPATIAL & TEMPORAL COVERAGE
Cores were extracted and data analysed between 10/2007 – 3/2008 & between 2013–2018
The LP08 and LP16 records cover the time period between 0–30,000 years

Sediment cores are from two sites:
-	LP08 record from  S51° 18’01.2’’, W72° 40’43.0’’, 32 m a.s.l. is 600 cm long
-	LP16 record from S51°18’11.3’’, W72° 40’53.7’’, 33–34 m a.s.l. is 295 cm long

REFERENCES
Roberts SJ, McCulloch RDM, Emmings J, Davies SJ, Van Nieuwenhuyze W, Sterken M, et al. (2022) Late glacial and Holocene palaeolake history of the Última Esperanza region of Southern Patagonia. Frontiers in Earth Science.

Grinsted, A., Moore, J.C., and Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics 11, 561-566.

Trauth, M.H. (2015). "Time-Series Analysis," in MATLAB® Recipes for Earth Sciences, ed. M. H. Trauth.  (Berlin, Heidelberg: Springer Berlin Heidelberg), 151-213.

DATA STRUCTURE AND FORMATS

1) Lago_Pato_LP08_core_data folder

This entry contains the following data folders and subfolders (in alphabetical order):
Biology
Diatoms
Count, percentage and flux diatom data for LP08 for all species and species that make up more than 10% of the total counts
Pollen
Percentage and concentration pollen data for LP08

Chronology
As measured radiocarbon age data and BACON age-depth model input and output data for LP08
LP08_C14_data.csv – Measured and calibrated Radiocarbon data from LP08. Aw = acid wash; a/a/a = acid-alkali-acid pre-treatment; M=Macrofossil age; P=paired bulk-macrofossil ages; R = age reversal and length of reversal in years in brackets. Reasons for rejection: X-a = Organic sediments emplaced on extraction; X-b = Drag down of younger roots during coring.
Age_depth_models folder:
LP08_SH20M1H_BACON_input.csv – data used in Southern Hemisphere (SH20) atmosphere calibration curve  for LP08 Model 1 with hiatus included (M1H)
LP08_SH20M1H_BACON_output.csv – calibrated age data for LP08

Geochemistry
ITRAX_XRF_CS folder for LP08 containing:
Cluster Analysis folder
LP08_2mm_TSN_sqrt_CONISS_Element1.csv and LP08_2mm_TSN_sqrt_CONISS_Element2.csv - Square-root transformed %TSN data used in constrained cluster analysis (CONISS) to define the lithological zones undertaken on two different element combinations.
Data folder 
LP08_13_3 subfolder, analysed in March 2013, containing the following core section subfolders. 
LP081A_XRF
LP081B_XRF 
LP081C_XRF
LP081D_XRF
LP081E_XRF
LP081F_XRF
LP081G_XRF
LP081H_XRF
LP081IC_XRF
Each core section subfolder contains:
document.txt – run and set up data for the core section scan
optical.tif – raw optical image of the core section scan
optical1.tif – autotune/autocontrast/autocolour processed optical image of the core section scan in Photoshop v23.2.0
radiograph.raw – raw X ray image of the core section scan
radiograph.tif – raw X ray image of the core section scan
radiograph1.tif – autotune/autocontrast/autocolour processed X ray image of the core section scan section scan in Photoshop v23.2.0
result.txt – summary set up and cps datafile for the core section
sumspectra.txt – average spectra output datafile the core section
XRF data folder – individual run data and spectra channel energy data for the core section in sequence from the top of the section, e.g., L000001.txt, L00001.txt etc. 

Summary data
LP08_filtered_2mm_MC.csv and LP08_filtered_200um_MC.csv –filtered cps mastercore (MC) X ray fluorescence (XRF) core scanning (XRF-CS) data 
LP08_LnFe_Mn_Ratio.txt and LP08_LnFe-Mn_Ratio_100yr.txt – natural log Fe/Mn ratio and Z-scores as measured and 100 year smoothed X ray fluorescence (XRF) core scanning (XRF-CS) data
LP08_result_200um_ALL.csv – all as measured cps (count per second) X ray fluorescence (XRF) core scanning (XRF-CS) data 
LP08_TSN_percent_200um_PCA.csv - %TSN (percentage of the total scatter normalised sum) data and PCA input and output X ray fluorescence (XRF) core scanning (XRF-CS) data
LP08_Unit1basal_100um.csv – cps X ray fluorescence (XRF) core scanning (XRF-CS) data from the basal section at 100 micron intervals

Sedimentology
GEOTEK_MSCL folder containing
LP08_MSCL_2mm.csv - Geotek multi-sensor core logger data for LP08 and LP16
LP08_Sedimentology_4cm – subsample data summary (LOI, TOC, d13C, C/N) 


2) Lago_Pato_LP16_core_data

This entry contains the following data folders and subfolders (in alphabetical order):

Chronology
As measured radiocarbon age data and BACON age-depth model input and output data for LP16
LP16_C14_data.csv – Measured and calibrated Radiocarbon data from LP08. Aw = acid wash; a/a/a = acid-alkali-acid pre-treatment; M=Macrofossil age; P=paired bulk-macrofossil ages; R = age reversal and length of reversal in years in brackets. Reasons for rejection: X-a = Organic sediments emplaced on extraction; X-b = Drag down of younger roots during coring.
Age_depth_models folder:
LP16_SH20M1H_BACON_input.csv – data used in Southern Hemisphere (SH20) atmosphere calibration curve  for LP08 Model 1 with hiatus included (M1H)
LP16_SH20M1H_BACON_output.csv – calibrated age data for LP16

Geochemistry
ITRAX_XRF_CS folder for LP16 containing:
Cluster Analysis folder
LP16_2mm_TSN_sqrt_CONISS_Element1.csv, LP16_2mm_TSN_sqrt_CONISS_Element2.csv, and LP16_1mm_TSN_sqrt_CONISS_Element3.csv - Square-root transformed %TSN data used in constrained cluster analysis (CONISS) to define the lithological zones undertaken using three different element combinations.
Data folder 
LP16_16_5 subfolder, analysed in May 2016, containing the following core section subfolders. 
LP161A_XRF 
LP161C_XRF
LP162A_XRF
LP162B_XRF
LP163A_XRF
LP163B_XRF
LP164A_XRF
LP164B_XRF
Each core section subfolder contains:
document.txt – run and set up data for the core section scan
optical.tif – raw optical image of the core section scan
optical1.tif – autotune/autocontrast/autocolour processed optical image of the core section scan in Photoshop v23.2.0
radiograph.raw – raw X ray image of the core section scan
radiograph.tif – raw X ray image of the core section scan
radiograph1.tif – autotune/autocontrast/autocolour processed X ray image of the core section scan section scan in Photoshop v23.2.0
result.txt – summary set up and cps datafile for the core section
sumspectra.txt – average spectra output datafile the core section
XRF data folder – individual run data and spectra channel energy data for the core section in sequence from the top of the section, e.g., L000001.txt, L00001.txt etc. 
Summary_data folder containing
LP16_filtered_2mm_MC.csv and LP16_filtered_200um_MC.csv – filtered cps mastercore (MC) X ray fluorescence (XRF) core scanning (XRF-CS) data 
LP16_LnFe_Mn_Ratio.txt and LP16_LnFe-Mn_Ratio_100yr.txt – natural log Fe/Mn ratio and Z-scores as measured and 100 year smoothed X ray fluorescence (XRF) core scanning (XRF-CS) data
LP16_result_200um_ALL.csv and LP16_result_500um_ALL.csv – all as measured cps (count per second) X ray fluorescence (XRF) core scanning (XRF-CS) data 
LP16_TSN_percent_200um_PCA.csv – %TSN (percentage of the total scatter normalised sum) data and PCA input and output X ray fluorescence (XRF) core scanning (XRF-CS) data
Summary_data folder containing
Tephra_EPMA subfolder containing
	EPMA_ACID_PCA.csv – Electron microprobe tephra data, acid >63% SiO2 only
EPMA_SAM_db_references.txt – references for the EPMA_SAM_db.csv file
EPMA_SAM_db.csv – database of electron microprobe tephra data from Southern South America
LP16_EMPA_tephra.csv – Electron microprobe data tephra from selected cryptotephra layers within the LP16 core

Sedimentology
GEOTEK_MSCL folder containing
LP08_MSCL_2mm.csv - Geotek multi-sensor core logger data for LP08 and LP16

3) Lago_Pato_R_Data

csv input files used for use with code in the Lago_Pato_Code/R_code

4) Lago_Pato_Site_Data

This dataset is arranged as follows
/Lake_water_chemistry_Patagonia_2007.csv – lake water chemistry dataset
/LP_Bathymetry_data.csv – Lago Pato bathymetry dataset
/LP_GPS_data.csv – Lago Pato site and lake GPS data
/Modelled_precipitation – folder containing modelled annual, summer (December, January, February; DJF) and winter (June, July, August) precipitation data for the Lago Pato latitude and longitude

5) Lago_Pato_Time_Series_Data

The LP08 and LP16 time series datasets are arranged as below

The input output csv files are arranged into subfolders as follows. These subfolders represent different time periods in the LP08 and LP16 records. Changing the time period investigated alters the Z-scores produced. 

LP08_5ka - covering the last 5000 years
LP08_8ka - covering the last 8000 years
LP08_10ka - covering the last 10,000 years
LP08_Unit1 - covering 21,180-29,780 cal a BP
LP08_Unit1_basal - covering 26,490-29,780 cal a BP

LP16_11ka - covering the last 11,000 years
LP16_14ka - covering the last 14,000 years
LP16_Unit1 - covering 20,400-27,550 cal a BP

LP08_LP16_10ka - comparison of dataset pairs for LP08 and LP16 records covering the last 10,000 years
LP08_LP16_21_27ka - comparison of dataset pairs for LP08 and LP16 records covering the 21-27 ka cal BP
LP08_LP16_Fig9_LnFe_Mn – Fe/Mn natural log ratio time series data used in the Frontiers paper for LP008 and LP16 records

The following log ratio dataset pairs were run:
1_Fe_Mn_&_Mn_Ti
2_Fe_Mn_&_Br_Ti
3_Mn_Ti_&_Br_Ti
4_Fe_Mn_&_Inc_Coh
5_Mn_Ti_&_Ca_Ti
6_Br_Ti_&_Inc_Coh

Input datafiles:

The filename within each folder matches the folder name and indicates the time period covered by the time series. For example:

LP08_Unit1_basal_inputs.csv

Output datafiles (e.g., LP08_Unit1_basal_ouput.csv)

The filename within each folder matches the folder name and indicates the time period covered by the time series. 

The key for the columns in each dataset pair (e.g., 1_Fe_Mn_&_Mn_Ti)  are as follows (in order from left to right. 

% As measured data	
s1x, s1xn	series 1x time scale (cal a BP)
s2x, s2xn	series 2x time scale (cal a BP)
series1_input	series 1y input data (log ratios)
series2_input	series 2y input data (log ratios)
s1n	 	series 1y normalised (Z-scores)
s2n	 	series 2y normalised (Z-scores)
series1ydl	series 1y detrended - linear best fit
series1ydlnorm	series 1y standardised (Z-scores) detrended (linear best fit)
series2ydl	series 2y detrended - linear best fit
series2ydlnorm	series 2y standardised (Z-scores) detrended (linear best fit)
	
% Interpolated data	
sx1inter	series  	1x time scale interpolated (10 or 100 year intervals)
sy1inter	series  	1y interpolated (10 or 100 year intervals)
sy1norminter	series 1y standardised (Z-scores) & interpolated (10 or 100 year intervals)
sx2inter	series  	2x time scale interpolated (10 or 100 year intervals)
sy2inter	series  	2y interpolated (10 or 100 year intervals)
sy2norminter	series 2y standardised (Z-scores) & interpolated (10 or 100 year intervals)
	
% Detrended & interpolated data (s = reshaped array into wide format)
x2 or x2s	series 1x time scale interpolated (10 or 100 year intervals)
x4 or x4s	series 2x time scale interpolated (10 or 100 year intervals)
y2 or y2s 	series 1y detrended and factor interpolated data - subtracted from mean
y2l or y2sl 	series 1y detrended and factor interpolated data - subtracted from linear
y4 or y4s 	detrended and factor interpolated data series 2 - subtracted from mean
y4l or y4sl 	detrended and factor interpolated data series 2 - subtracted from linear
	
% Standardized, Detrended & interpolated data	
y2ln or y2sln 	series 1y standardised and detrended data - subtracted from linear best fit
y4ln or y4sln 	series 2y standardised (Z-scores) and detrended and factor interpolated data - subtracted from linear
	
% Standardized, Detrended & interpolated periodicity data	
s1period	 	periodicity of series 1y standardized (Z-scores) and detrended (subtracted from
		linear best fit) interpolated data (i.e., y2ln or y2sln)
s2period	 	periodicity of series 2y standardized (Z-scores) and detrended (subtracted from
		linear best fit) interpolated data (i.e., y4ln or y4sln)
msc	 	Magnitude squared coherence (MSC) of s1period and s2period for the time series
frcspect	 	Frequency cross spectrum (FCS) of s1period and s2period for the time series

ACCESS CONSTRAINTS
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