Water isotopes in ice cores are used as a climate proxy
for local temperature and regional atmospheric circulation as well as
evaporative conditions in moisture source regions. Traditional measurements
of water isotopes have been achieved using magnetic sector isotope ratio
mass spectrometry (IRMS). However, a number of recent studies have shown
that laser absorption spectrometry (LAS) performs as well or better than
IRMS. The new LAS technology has been combined with continuous-flow analysis
(CFA) to improve data density and sample throughput in numerous prior ice
coring projects. Here, we present a comparable semi-automated LAS-CFA system
for measuring high-resolution water isotopes of ice cores. We outline new
methods for partitioning both system precision and mixing length into liquid
and vapor components – useful measures for defining and improving the
overall performance of the system. Critically, these methods take into
account the uncertainty of depth registration that is not present in IRMS
nor fully accounted for in other CFA studies. These analyses are achieved
using samples from a South Pole firn core, a Greenland ice core, and the
West Antarctic Ice Sheet (WAIS) Divide ice core. The measurement system
utilizes a 16-position carousel contained in a freezer to consecutively
deliver
The measurement of water isotopes in ice cores provides records of past
hydrologic cycle variability (Dansgaard, 1964). The parameters
Traditional measurements of water isotopes have been accomplished using
customized preparation systems and isotope ratio mass spectrometry (IRMS;
see a full list of abbreviations in Table 1). Typically, water samples are
analyzed discretely using 3–5 cm intervals of ice. Isotopic ratios of oxygen
are usually obtained using a CO
List of abbreviations used in this paper.
Advances in high-precision laser absorption spectroscopy (LAS) methods are now widely adopted as an alternative to IRMS methods (Kerstel et al., 1999; Lis et al., 2008; Gupta et al., 2009; Brand et al., 2009). There are currently two main LAS methods used: cavity ring-down laser spectroscopy (CRDS; manufactured by Picarro, Inc.) and off-axis integrated cavity output laser spectroscopy (OA-ICOS; manufactured by Los Gatos Research). The CRDS method (utilized in this study) requires the input of water vapor into a detection cavity that confines and reflects laser pulses using a series of mirrors. By comparing the extinction of a laser pulse at different frequencies in an empty cavity and in a cavity filled with water vapor, water isotope concentrations can be determined (Crosson, 2008).
Systems have been developed that continuously deliver water vapor into LAS measurement devices. The technique, known as continuous-flow analysis (CFA), is accomplished by slowly melting a solid ice stick into a continuous liquid stream, which is then vaporized and injected into the LAS instrument. Gkinis et al. (2010, 2011) established the CFA framework for water isotope analysis using LAS. In particular, Gkinis reproduced traditional IRMS water isotope measurements in Greenland ice using a Picarro L1102-i CRDS instrument. Gkinis found that the precision of hydrogen and oxygen isotope measurements was comparable to IRMS. Substantial increases in depth resolution and shorter analysis time were realized. Maselli et al. (2013) expanded on Gkinis' technique by testing multiple new-generation Picarro devices (L2120-i and L2130-i) and found similar results. Later, Emanuelsson et al. (2015) used OA-ICOS to continuously analyze water samples from an ice core, achieving reductions in isotopic step-change response time and memory effects. Other water isotope CFA techniques (not considered in this paper) have utilized a platinum catalyst for continuous mass spectrometry measurements (Huber and Leuenberger, 2005) or a thermal conversion elemental analyzer (TC/EA) coupled to a mass spectrometer (Sharp et al., 2001). CFA has also widely been used for chemical measurements in ice cores (e.g., Röthlisberger, 2000; Osterberg et al., 2006; Bigler et al., 2011; and Rhodes et al., 2013).
In this study, we present a semi-automated water isotope CRDS-CFA system
developed at the Institute of Arctic and Alpine Research (INSTAAR) Stable
Isotope Lab (SIL). We analyze
The ratio of heavy to light water isotopes in a water sample is expressed in delta notation (Epstein, 1953; Mook, 2000) relative to Vienna Standard Mean Ocean Water, where VSMOW has been set to 0 ‰ by the International Atomic Energy Agency (IAEA):
The dxs variable is not a direct isotopic measurement, but rather it is calculated from the direct measurement of oxygen and hydrogen isotope ratios. The precise calculation of dxs (‰) has historically been challenging because IRMS methodology requires that oxygen and hydrogen water isotope ratios be analyzed on separate systems using separate samples, which could increase uncertainty. The CRDS-CFA technique removes the potential for multi-system uncertainty because both isotopic values are measured simultaneously on a single sample with the same system.
The INSTAAR CRDS-CFA system, showing sample flow from the ice melt head through the filter block and debubbler and onto the Valco stream selection valve, nebulizer, furnace, and Picarro L-2130-i CRDS instrument.
The CRDS-CFA system is composed of three parts: (1) the ice core melting
component, (2) the liquid-to-gas conversion component, and (3) the isotopic
analyzer. A full system schematic is shown in Fig. 1. The ice core melting
component can accommodate 16 sticks of ice that measure
For the liquid-to-gas conversion component of the system, liquid water is
drawn away from the melt head interface using peristaltic pumps (Masterflex
L/S 7534-04). Liquid water from the outer square catchment of the melt head
is collected for other purposes. The inner square catchment is pumped at a
rate matching that of the ice stick melt. This liquid water is pushed
through an 8
A typical CRDS-CFA analysis day consisting of the analysis of 12 m of ice core sticks from the WDC. The light grey regions show the analysis of four calibrated laboratory isotopic standards. The dark grey regions show the analysis of mock ice. The prominent white region shows the analysis of ice core sticks separated by short sections of push ice every fourth ice stick.
The filtered water then enters a 2 mL open-top glass vial where bubbles can
escape (debubbler). The flow rate into the debubbler is 3.1 m min
At the Valco six-port stream selection valve, the primary water flow can be switched off, allowing another auxiliary port to be switched on. Each auxiliary port is connected to 30 mL glass vials of laboratory isotopic water standards, which can be analyzed and used for calibration of ice core measurements to international reporting standards. Additional water streams (secondary flow) are pumped from the debubbler to an electrical conductivity (EC) measurement cell (Amber Science 1056) and to a fraction collector (Gilson 215). The EC measurement allows for the comparison of chemical signatures at a known depth between labs. The fraction collector is used to archive water samples for discrete analysis and as a safeguard against system failures. It is programmed to partially fill approximately twenty-five 2 mL glass vials for every meter of ice melted, approximating a discrete ice sampling resolution of about 4.0 cm.
Water isotope standards in units of per mil
(‰). The laboratory standards (BSW, ASW, GSW, and PSW)
were calibrated to primary standards (VSMOW2, SLAP2, and GISP) in March
2010. The primary standards are reported with errors given by the IAEA. The
laboratory standards are reported with a precision value given by the
average standard deviation (1
On a given analysis day, we perform a sequence of measurements related to
calibration and correction (Fig. 2). At the beginning and end of an
analysis day, established laboratory isotopic water standards are analyzed
to calibrate the ice core data. These laboratory isotopic water standards
are annually calibrated to IAEA primary standards (VSMOW2, SLAP2, and GISP).
We use four laboratory standards – Boulder standard water (BSW),
Antarctic standard water (ASW), Greenland standard water (GSW), and Polar
standard water (PSW) – that together provide a range of
To characterize isotopic mixing throughout the system, three small
(20 cm
An ice core melt sequence occurs in between analysis of laboratory isotopic
water standards and mock ice. Key variables are closely monitored during
this time, including water concentration in the Picarro instrument, the melt
rate, and depth registration. During the ice core melt sequence, every set
of four ice cores is separated by 20 cm sections of isotopically homogenous
ice with a
A graphical user interface (GUI) developed in Python is used to automate
procedures and collect auxiliary data related to carousel positioning,
active Valco port, ice core depth registration, quality control (e.g., water
level in the debubbler and melt rate), electrical conductivity, and
commenting. These auxiliary data are collected using serial ports via a Moxa
UPort 1610-8, and all data are recorded at the same data frequency as the
isotopic data generated by the Picarro L2130-i (in this case, at
approximately
During post-measurement data processing, we first identify and separate various analysis sections (e.g., laboratory standards, mock ice, push ice, and ice cores) using integer values in a comment field (e.g., mock ice section are commented “159”, while ice core sections are commented “175”). For every ice core section preceded by push ice, we assign an initial depth to the ice core section at the minimum derivative of the isotopic step change. This effectively marks the mid-point of the isotopic step change and corresponds to the point when ice core isotopic values begin to dominate the preceding push ice signal. We tested and verified this depth assignment scheme using discrete samples of the same ice sections analyzed on the CRDS-CFA system.
Raw values from the CRDS system require calibration to known values of
isotopic laboratory standards (as described in Sect. 2.3). Since isotopic
mixing effects in the CRDS-CFA impede instrument response to the large and
abrupt changes in laboratory standards, we use the average of the last 5 min of a 20 min total analysis to minimize the mixing effect
(discussed further in Sect. 2.4.3). The “measured” (i.e., raw) values are
plotted versus their “known” (i.e., assigned) VSMOW2 calibrated values.
From the plot of measured (
In addition to calibration, we also use the laboratory isotopic standards to
define measures of precision and bias for the CRDS-CFA system downstream of
the Valco stream selection valve. These values can be tracked through time
as a performance indicator of the system. Precision is defined as the degree
of internal agreement among independent measurements made under specific
conditions, while bias is defined as the difference of the test results and
an accepted reference value. In this case, the precision (downstream of the
selector valve) is determined by taking the average standard deviation of
each of the last 5 min of each 20 min laboratory standard run, while
the bias (downstream of the selector valve) is defined as the difference of
the measured GSW value from its known value. Over a period
of 216 measurement days, the average precision and average bias for
Selected Allan deviation values (‰) for a 7 h analysis of isotopically homogenous water on the CRDS-CFA system.
A 7 h analysis of Allan deviation for
The stability of the Picarro CRDS analyzer is determined by tests of Allan variance (Allan, 1966), which provides a measure of the intrinsic noise in a measurement system as a function of the integration time (i.e., the amount of time a parcel of vapor is present in the laser cavity). The Allan variance is defined as
We determine Allan deviation (the square root of Allan variance) for a
continuous flow of isotopically homogenous water into the CRDS-CFA on a
daily basis. The Allan deviation values for an 11 h run are plotted in
Fig. 3, and selected Allan deviation values are shown in Table 3. At small
The log–log CDF transfer functions (left) and skew-normal
impulse response functions (right) of the CRDS-CFA system for
Relative to a set flow rate, an increase in integration time decreases the resolution of measured ice core data. Therefore, a choice must be made between the integration time and the amount of smoothing introduced into an ice core record. The Allan deviation tests described above inform these decisions relative to sample input originating at the Valco stream selection valve. In Sect. 3.1, we show that it is important to also use replicate ice sticks to analyze system performance for sample input originating at the melt head.
Isotopic mixing effects occur in the CRDS-CFA system. Possible contributors
to the mixing effect include liquid mixing in tubing and the debubbler,
liquid drag on tubing walls, vapor mixing downstream of the nebulizer, vapor
interactions with two Picarro instrument filters (Mykrolis Wafergard) prior
to entering the laser cavity, adsorption of water molecules onto the laser
cavity walls, and diffusional effects that can occur at any point in the
CRDS-CFA system. To characterize the mixing, a transfer function and impulse
response function of the system can be defined using mock ice or laboratory
water standards. The transfer function is the system response to an
instantaneous isotopic step change at the melt head or Valco stream
selection valve. The impulse response function is the first derivative of
the transfer function. The standard deviation of the impulse response
corresponds to the mixing length (often referred to as diffusion length in
other publications), which defines the average movement of a water molecule
in the time or depth domain relative to its original position in the ice
sample or within a vial of water. Figure 4 shows transfer functions and
impulse response functions of the CRDS-CFA system for
In previous work by Gkinis et al. (2010, 2011), a transfer function is fit to an isotopic step change using a scaled version of the cumulative distribution function (CDF) of a normal distribution described by
Since mixing effects in the system may stem from potentially different
processes occurring in liquid and vapor phases of the CRDS-CFA system, we
attempted to separate the portion of the system where vapor dominates. We
evaluated mixing effects for the whole system (liquid
Mean mixing length values from 50 determinations of mock
ice step changes (
A comparison of the WAIS Divide ice core
Using Eqs. (5)–(10), mixing length values were determined for
The
The average bias and the standard deviation of the mean
(‰) for 50 determinations of isotopic step-change
corrections using mock ice (i.e.,
Mixing corrections for mock ice (left) and the beginning of an ice core section (right). The ice core mixing correction is based upon a known step-function change defined by the mock ice, from which mixing coefficients are determined. Red represents raw uncorrected isotopic data with no memory correction, black represents VSMOW corrected data with the memory correction applied, blue dots represent 3 cm IRMS discrete samples, and the grey region represents the depth range of corrected data that would otherwise be discarded. Note that the time variable represents the amount of run time on a given day.
During a typical analysis day, every fourth ice core stick is separated by
isotopically distinct push ice. Ideally, the isotopic shift between push ice
and the beginning of an ice core stick at the melt head would register
instantaneously on the CRDS instrument. However, a substantial mixing effect
is evident in the first
To test CRDS-CFA precision for the entire system, nine sticks of solid ice
were cut from a single meter of Greenland test ice (GTI) at about 400 m
depth. The ice sticks were obtained from the National Science Foundation
National Ice Core Laboratory (Lakewood, Colorado) and were a by-product of
the initial tests of the US Deep Ice Sheet Coring Drill performed in
Greenland in 2006 (Johnson et al., 2007). The same drill was deployed in
West Antarctica to obtain the WAIS Divide ice core, which we discuss in
Sect. 3.2. Seven of the nine GTI sticks were analyzed on the CRDS-CFA
system, three of which were broken into parts to test depth registration
methodology. As stated previously, these ice sticks are melted at a rate of
2.5 cm min
Greenland test ice (GTI) for a nominal 1 m section. In
the left column, seven GTI sticks measured on the CRDS-CFA system averaged
to 1 cm increments for
The isotopic data for the seven 1 m long GTI sticks analyzed on the
CRDS-CFA system were measured at sub-millimeter resolution. These data were
then averaged to 1 cm successive values (GTI-1 cm) for each stick, resulting
in seven values at each 1 cm increment, from which a standard deviation was
determined. This resulted in a total of 100 standard deviation values, and
the mean of these values is used to estimate the full-system precision
(Fig. 7). We find full-system precision values (1
The full-system precision can be subdivided into two parts: noise added to
the isotopic signal on the preparation side of the system (i.e., noise
occurring prior to the Valco stream selection valve, including depth
registration) and noise added to the isotopic signal on the vapor side of
the system (i.e., noise occurring downstream of the Valco stream selection
valve, including the nebulizer and CRDS laser cavity). We define noise as a
disturbance that obscures or reduces the clarity of the original water
isotope signal in the ice core. We can isolate the noise added on the vapor
side of the system by analyzing a continuous stream of IHW inputted directly into the nebulizer from the Valco
valve and subsequently analyzed on the CRDS instrument. We take averages of
the IHW over consecutive 24 s intervals, which correspond to the time
needed to melt 1 cm of ice at a melt rate of 2.5 cm min
Scatterplots of GTI comparisons of
CRDS-CFA vs. CRDS-discrete data. Seven GTI sticks were analyzed on the
CRDS-CFA system, down-sampled to 1 cm, and averaged. The discrete data were
sampled at 1 cm and analyzed on a separate Picarro L-2130i using the average
of three injections. The black line is the best-fit linear regression. The
1
As a final analysis using GTI, the reproducibility of GTI-1 cm CRDS-CFA data
and 1 cm discrete CRDS data is compared. We define reproducibility as the
closeness of the agreement between results of measurements of the same
measure and carried out under changed conditions of measurement. In this
case, the same water samples are analyzed using a Picarro L2130-i,
but the exact conditions of measurement are different, in that some samples
are analyzed on the CFA-CRDS system, while others are analyzed by discrete
sample injection into a CRDS instrument. A scatterplot and linear
regression of the resulting data for the two measurement types give a
slope,
Data from the WDC over a depth range of
The reproducibility of traditional IRMS discrete measurements and CRDS-CFA
measurements (both analyzed at the INSTAAR SIL) was tested on the WAIS
WDC over depths of
The isotopic measurement precision (1
A second WDC reproducibility test was performed between high-resolution
CRDS-CFA measurements and low-resolution discrete CRDS measurements (Fig. 9).
The low-resolution data were measured at the University of Washington (UW)
using a Picarro 2120i at
The combination of WDC test results leads to the following conclusions: (1) the
Replicate SPF ice measurements of
Until this point, the analyses in this paper have focused on solid ice; we
now shift focus to firn ice, which involves additional complexities. Due to
the increased porosity and lower density of firn ice, liquid water at the
melt head can wick upwards by capillary action through the pore spaces,
artificially smoothing the isotopic signal and decreasing signal amplitude.
This can, for example, alter frequency analysis calculations or cause the
misinterpretation of the size of summer–winter signals. The melt rate is
also difficult to control, as density variations over a few centimeters can
cause the melt rate to increase or decrease unexpectedly. Furthermore, we
have not devised a reliable way to test
Based upon a steady-state Herron and Langway density model (Herron and
Langway, 1980), the SPF sticks have estimated densities of 340, 386, and
562 kg m
The data show that a loss in
The CRDS-CFA setup presented in this paper (INSTAAR; CRDS L-2130i)
can be compared with similar studies from Victoria University of Wellington
(VUW; OA-ICOS “custom 2014 setup”) and the University of Copenhagen (UC;
CRDS L-2140i; Emanuelsson et al., 2015), as well as the Desert
Research Institute (DRI; CRDS L-2120i and L-2130i; Maselli et al., 2012). Allan deviation values of
The important difference between this study and prior studies is that we perform tests of replicate ice sticks originating at the melt head, rather than testing Allan deviation of sample input originating at the selector valve. This is an important distinction, as there are factors that affect the isotopic data prior to the selector valve, such as depth registration and mixing in the debubbler.
We have presented a high-resolution CFA system based on CRDS technology that is
specifically designed for water isotope analysis of ice cores. The CFA
system converts
We have tested two types of ice cores on the CRDS-CFA system: firn ice and
solid ice beneath the bubble close-off depth in the firn. Using South Pole
firn samples, we find that the CRDS-CFA measurements are not of high-enough
quality for most scientific analyses, due mainly to difficulties in
controlling the melt rate with low-density firn ice. We suggest that
researchers instead utilize high-resolution discrete samples for firn column
measurements. For solid ice, we used seven identical Greenland ice core
sticks to quantify the precision of the CRDS-CFA system. This technique is
previously unpublished and helps quantify as many sources of uncertainty on
the CRDS-CFA system as possible, including depth registration and sample
mixing. Additionally, we measured West Antarctic ice core sticks to test the
reproducibility of two sets of measurements: (1) traditional magnetic sector
IRMS vs. CRDS-CFA system measurements, and
(2) inter-lab comparisons of CFA and discrete measurements on the same type
of CRDS instrument. We find that the CRDS-CFA system provides a
One exception to these solid ice results is found in the
dxs measurements. At the highest frequencies (multi-year to decadal), we
found discrepancies in comparisons of both inter-lab CRDS measurements of
dxs and in CRDS and IRMS measurements of dxs. Contrary to this, however, are
the dxs results measured solely on the CRDS-CFA system presented in this
paper. We found that dxs can be replicated at high frequencies, demonstrated
by tests of identical Greenland ice core sticks. Furthermore, despite
slightly different impulse response functions for
The overall results are in line with prior ice core CFA studies. Gkinis et
al. (2011) cite combined measurement uncertainty values of 0.2, 0.06, and
0.5 ‰ for
The high-resolution WDC Water Isotope Data can be found at
The low-resolution WDC Water Isotope Data can be found at
Additional data inquiries can be made to the corresponding author, Tyler R. Jones (tyler.jones@colorado.edu).
The authors declare that they have no conflict of interest.
This work was supported by US National Science Foundation (NSF) grants 0537930, 0537593, 1043092, and 1043167. The authors would like to thank all field crew, laboratory staff, students, and principal investigators involved with the project. Field and logistical activities were managed by the WAIS Divide Science Coordination Office at the Desert Research Institute, Reno, NE, USA, and the University of New Hampshire, USA (NSF grants 0230396, 0440817, 0944266, and 0944348). The National Science Foundation Division of Polar Programs funded the Ice Drilling Program Office (IDPO), the Ice Drilling Design and Operations (IDDO) group, the National Ice Core Laboratory (NICL), the Antarctic Support Contractor, and the 109th New York Air National Guard. Finally, we would like to thank the Centre for Ice and Climate at the Niels Bohr Institute, University of Copenhagen, for support in the design of the analysis system used in this study. Edited by: R. Koppmann Reviewed by: J. Rudolph and one anonymous referee