Introduction
Ice cores are valuable archives from which we can gain knowledge of past
atmospheric processes and climate by investigating records that are
preserved in the ice or in entrapped gas bubbles, e.g., from water molecules,
chemical impurities, particulates and methane gas (e.g., Petit et al., 1999;
EPICA Community Members, 2004; WAIS Divide Project Members, 2013). Water
stable isotopes (δ18O and δD, hereafter referred to as
δ) preserved in ice are among the most powerful proxy records of
past climate and atmospheric processes, which greatly extend limited
instrumental and observational records from Antarctica that are available
only over the last century (e.g., Steig et al., 2013; Thomas et al., 2013).
δ records in ice cores have most frequently been used as a site
temperature proxy (Epstein and Mayeda, 1953; Dansgaard, 1964), but sea ice
extent, atmospheric circulation, transportation pathways, changes in source
region as well as post-depositional effects (wind scour, diffusion, etc.)
also influence the δ signal (Jouzel et al., 1997; Masson-Delmotte et
al., 2008; Küttel et al., 2012; Sinclair et al., 2013). Deuterium excess
(d-excess) is a second-order proxy (d-excess =δD-8×δ18O; Craig, 1963; Dansgaard, 1964) commonly interpreted as
describing relative humidity and temperature at the moisture source region,
as a result of the kinetic isotope effect during evaporation (Merlivat and
Jouzel, 1979).
Laser spectrometry has made continuous-flow analysis (CFA) of δ in
ice core melt streams possible (Gkinis et al., 2010; Maselli et al., 2013),
replacing measurement of discrete samples (e.g., Rhodes et al., 2012;
Sinclair et al., 2013). Following the first measurements of δ from
laser absorption spectrometry (Kerstel et al., 1999), improvements in
commercially available analyzers (Baer et al., 2002; Crosson, 2008;
Berman et al., 2013; Steig et al., 2014) have made them increasingly
suitable to analyze water vapor continuously. High temporal resolution
measurement applications have included atmospheric sciences (e.g., Johnson et
al., 2011; Aemisegger et al., 2012; Steen-Larsen et al., 2013), ice core
records (Gkinis et al., 2010; Steig et al., 2013; Maselli et al., 2013),
ecology (e.g., Lee et al., 2007) and hydrology (e.g., Goebel and Lascano,
2012).
Successful measurement of a liquid water stream using laser spectrometry
requires that the liquid sample stream is converted to vapor and compared to
water standards of known isotopic composition to achieve calibration.
Reliable calibration vaporizing units have been developed for research
(Gkinis et al., 2010; Schmidt et al., 2010; Sturm and Knohl, 2010; Steig et
al., 2014), and calibration units are available commercially: the Water
Vapor Isotope Standard Source (WVISS) manufactured by Los Gatos Research (LGR) and
Standards Delivery Module (SDM) by Picarro. The WVISS system vaporizes one
standard using a self-aspirating nebulizer into a heated (75 ∘C)
evaporation jar, whereas the SDM uses a syringe-pump-based system that can
switch between two standards. For δ CFA of ice cores, a range of
criteria including the stability and response time of the
vaporizer/spectrometer system must be understood and matched to practical
requirements, including the operation of other CFA analyzers and the
collection of discrete samples.
Here, we present new experimental setups and identify the following aims
for the characterization of new and previously reported systems: (1) enable
accurate calibration to several water standards, (2) increase the temporal
resolution by reducing the response time and (3) reduce memory effects.
With regard to the first aim, calibration to two standards allows us to
properly normalize our results on the VSMOW/SLAP scale (Gonfiantini, 1978;
Coplen, 1996). In addition, we are able to characterize the instrumental
response time using the step changes between standards. We define the
response time as the time from the first 5 to the last 5 % of the
response to a step change in δ. A short response time is desirable
to minimize the time required for completing calibration cycles and to
maximize the resolution with which the δ signal that is preserved in
the ice is captured. Alongside the response time, we define a memory effect
as causing an asymmetric tail in the last 5 % of the step in δ
values, with an extended tail compared to our empirical fit. Avoiding memory
effects is desirable given the large range of δ in ice cores and in
the standards required for normalization.
A key challenge we address is the integration of vaporizer/spectrometer
isotope measurement systems into a CFA campaign with many other instruments.
In the case of a multi-instrument CFA campaign, the competing demands of
different instrumentation and associated logistics of extended continuous
operation may require the CFA isotope measurement system to be constructed
so that the entire multi-instrument CFA system can be optimized. For
example, CFA methane measurements require long uninterrupted periods of
melting due to large memory effects, whereas δ measurements for the
custom and WVISS setups will benefit from more frequent calibration.
Therefore, the number of consecutive core sections that are stacked on top
of one another and melted continuously without interruption for calibration
can necessitate a compromise between the ideal time intervals of continuous
melting for each analysis type.
Here, we describe the design and performance characterization of two custom
vaporizer/spectrometer δ CFA employed for analysis of an ice core –
the Roosevelt Island Climate Evolution (RICE) Antarctic ice core, retrieved
from 79∘21′46′′ S, 161∘42′3′′ W (560 m a.s.l.) on an
ice rise (Conway et al., 1999) situated at the northeastern edge of the
Ross Ice Shelf. The δ-CFA systems were required to yield maximum
response resolutions for relating ice core δ to sea ice extent,
atmospheric circulation modes and local temperature using reanalysis data
(1979 to 2012) and in response to past climate events while maintaining
stable and accurate δ CFA throughout the analyzed depths of the 763 m ice core. Our δ-CFA setups are compared in response times and
stability to a similar δ-CFA ice core setup, the University of
Copenhagen (UC) setup and the commercially available WVISS (Table 1). This
is the first study within the field of ice core science that provides a
detailed characterization and comparison between δ-CFA setups that
are using the new generation of off-axis integrated cavity output
spectroscopy (OA-ICOS) and cavity
ring-down spectroscopy (CRDS) spectroscopy techniques.
The performance of the δ-CFA systems is described and evaluated in
the context of the RICE project, with a focus on the characterization to aid
the selection and potential customization of δ-CFA systems for
future CFA campaigns.
Technical detail and properties for the different
setups.
Setup name
Analyzer
Manufacturer
Technology
Vaporizer
Reference
Custom 2013
IWA-EP35
LGR
OA-ICOS
modified WVISS
This study
Custom 2014
TIWA-EP45
LGR
OA-ICOS
modified WVISS
This study
WVISS
TIWA-EP45
LGR
OA-ICOS
WVISS
UC
L2130-i
Picarro
CRDS
Capillary and a furnace,
Gkinis et al. (2010, 2011);
see ref.
Steig et al. (2013)
Experimental
Results are reported in δ notation, representing the abundance of
rare isotopes as a deviation from a reference ratio: δ= R/RSMOW-1, where R and RSMOW are the ratio between rare and
abundant isotopes 18O/16O, or D/H in the sample and in VSMOW
(Vienna Standard Mean Ocean Water), respectively. Results are reported in
per mil, ‰.
Isotope water analyzer (IWA-35EP)/triple isotope water analyzer (TIWA-45EP) laser spectroscopy system
In this study, we use an absorption spectroscopy instrument based on OA-ICOS
technology in combination with an evaporation unit to continuously analyze
sample from an ice core or water standards during calibration. The
absorption spectroscopy instrument is an IWA
manufactured by LGR.
The main system we describe (custom 2013) was developed and employed for the
RICE CFA melting campaign (0 to 500 m) in 2013. It consists of a
commercially available IWA from LGR and a customized furnace and
evaporation chamber, which was built and fitted inside a modified WVISS
calibration unit. In addition to its function as a calibration unit, the
custom 2013 setup (Table 1) was used as a sample introduction system, in
which the
ice core melt stream is continuously vaporized and introduced to the IWA. A
similar system (custom 2014) incorporating improvements was assembled for
the 2014 RICE CFA melting campaign (500 to 761 m) and is described briefly.
The IWA-35EP analyzer uses a near-infrared, tunable diode laser that scans
over three nearby absorption peaks, H216O, H218O and HDO,
located near the 1.4 µm wavelength. The instrument uses an OA-ICOS
technique (Baer et al., 2002), in which the laser is directed off axis into an
optical cavity. The semi-transparent cavity absorption cell has highly
reflective mirrors, yielding an effective path length of several kilometers.
The transmitted intensities are recorded by a photo detector. Laser
spectroscopy analyzers are able to provide simultaneous measurements of
δ18O,δD and water vapor mixing ratios.
Aemisegger et al. (2012) characterized the response time characteristics of
commercially available analyzers, finding that the L1115-i Picarro CRDS analyzer has a longer response time compared
to the LGR IWA by ∼ 31 and 22 s for δD
and δ18O, respectively. Additionally, the signal for the
L1115-i is biased by isotope-specific time lags (henceforth called lag
bias), with δD having a ∼ 10 s longer response time than
δ18O. A δ signal without lag bias is important when
calculating high-frequency d-excess values. The faster response time and
lack of lag bias of our custom setups allows us to explore the maximum
extent to which the rapidly changing δ signal preserved in an ice
core can be resolved. The pumping rate of the cavity is higher for the IWA
(500 to 800 mL min-1) compared to the L1115-i (25 mL min-1) setup,
which increases the turnover rate of the cavity, a contributing
factor to the short response time of the IWA (Aemisegger et al., 2012).
Sturm and Knohl (2010) reported on temperature sensitivity of the IWA. Recent
IWA models have an enhanced performance (EP) feature, which improved the
thermal control of the cavity by keeping the temperature of the cavity
(∼ 46.3 ∘C) stable and elevated above the ambient
temperature. Recent models also have the capability to measure δ17O, such as the TIWA-45EP from LGR
(Berman et al., 2013) and L2140-i from Picarro (Steig et al., 2014). Our
IWA-35EP analyzer was updated in December 2013 to a TIWA-45EP analyzer,
which added the capability of measuring δ17O using a second
tunable diode laser. The update of the analyzer enabled us to include
δ17O in our evaluation of the WVISS and the 2014 custom setup.
Evaporation/vapor introduction systems
We evaluate the performance of two water evaporation units, the WVISS
calibration unit and the custom 2013 setup. In addition to this we present
preliminary data and results from the updated custom 2014 setup and new
previously unpublished results from a CRDS L2140-i Picarro ice core setup
(UC setup) with a custom-made vaporizer (vaporizer and setup described in
Gkinis et al., 2010; Steig et al., 2014). The UC setup was optimized with
stability in mind.
WVISS system
The original WVISS unit (WVISS v.2 evaporation unit; manufactured December 2013)
was set up to run a single water standard. A stream of water standard is
continuously evaporated by the WVISS during calibration events. The WVISS
unit consists of a heated (75 ∘C) 1.1 L jar into which a nebulizer
injects a constant stream of minuscule water droplets that rapidly
evaporate. For incoming dry air, the WVISS incorporates a built-in
compressor and drier, but we chose to plumb to an external compressed dry
air source to maximize stability and minimize noise and vibration in the
laboratory. The dry air is split up into two flows inside the WVISS: one
constant flow that goes through the nebulizer and the second, constituting
the majority of the dry air flow, that is regulated by a mass flow controller
(MFC) and is introduced to the evaporation jar. Tests of the WVISS unit were
performed using external compressed dry air (< 20 ppm). When
provided by the manufacturer, the nebulizer (Savillex, PFA C-flow 50
nebulizer) is set up to self-aspirate a flow of 50 µL min-1 from a
0.5 L glass water bottle. We use a multi-port valve (C25-3186EMH, VICI),
which enables us to use more than one water standard. Additional flow
resistance introduced by the multi-port valve necessitated the use of a
peristaltic pump (P2; MP2, Elemental Scientific) to provide a more
stable water flow to the evaporation chamber. The WVISS unit is described
more in depth in Rambo et al. (2011) and Kurita et al. (2012).
The performance of an unaltered WVISS evaporation unit was also evaluated,
using the TIWA-45EP LGR analyzer.
Experimental setup for water vapor isotope measurements
Building on the design of the WVISS as a calibration unit supporting
measurement of vapor samples, a key principle of our customized design was
that both the ice core melt stream and isotopic standards passed through the
same vaporization process. All changes to the WVISS involved readily
available components that could easily be integrated within the WVISS shell
and controlled with the IWA-WVISS system.
In the custom setups, we modified the following aspects of the setup: (1)
volume of the evaporation chamber, (2) materials, (3) evaporation
temperature, (4) introduction of the sample into the carrier gas and (5)
reduction of travel distances of the samples. We will explain the rationale
and outcome of these changes in the following sections.
Volume of the evaporation chamber
We reduced the internal volume of the WVISS evaporation chamber of 1.1 L to
40 mL in the 2013 custom vaporizer. The substantially smaller volume
increases the turnover rate and reduces the response time. This is
particularly important when analyzing multiple standards and/or a rapidly
changing continuous flow signal (e.g., from an ice core). The maximum
resolution will be determined by the time it takes to replace the vapor
volume in the evaporation chamber.
The evaporation chamber volume is less important for one-standard
calibration setups (without the requirement of rapid volume exchange), as
used for example to obtain real time measurements of atmospheric vapor for
which the WVISS was designed (Rambo et al., 2011; Aemisegger et al., 2012).
Material
The WVISS setup has a Savilex sealed 1.1 L jar evaporation chamber. For the
custom 2013 setup we use borosilicate glass for the evaporation chamber. A
cavity was milled within an aluminum block to hold the glass evaporation
chamber in place and to conduct heat from the furnace efficiently into the
evaporation chamber. Glass was chosen as the material for the evaporation
chamber because it can be molded when heated to form the precise desired
shape of the chamber (Fig. 1).
Diagram of glass evaporation chamber. A moist air stream is
generated from the nebulizer, merged with the MFC regulated dry air,
mixed at 170 ∘C and subsequently flows out of the glass
evaporation chamber to the IWA.
Evaporation temperature
To achieve complete evaporation at higher vapor throughput rates, we
increased the temperature of our custom furnace to ∼ 170 ∘C compared to the WVISS, which operates at 75 ∘C.
The temperature of the custom furnace and WVISS evaporation jar is regulated
using a PID regulated Omega temperature controller (CN7500).
Introduction of the sample into the carrier gas
Filtered compressed dry air (< 20 ppm) was used for evaporation of
water sample flow and to transport the vapor to the IWA. Only a small
portion of the total dry air flow goes through the nebulizer. The nebulizer
injects a mixture of water and dry air into a glass evaporation chamber
inside a customized furnace, where the sample water is instantaneously
evaporated.
In the 2013 evaporation chamber, the moist air stream generated by the
nebulizer (Fig. 1) and a line with dry air flow passes through the furnace
parallel to each other in separate lines. These two gas streams are later
merged, with the vapor line from the nebulizer being centered inside the dry
air stream at the merger. Separating the two lines has the advantage that it
provides a longer residence time in the furnace for the sample that is
injected by the nebulizer. This ensures complete evaporation; if all of the
dry air were to be introduced directly adjacent to the nebulizer, the
throughput rate of moist air could exceed the rate at which the dry air can be
heated, causing inefficient and incomplete evaporation at a high vapor
throughput rate. The dry air line represents the majority of the total air
flow (0 to 10 L min-1) and the dry air flow is controlled by an Omega
MFC.
After the vapor has left the furnace the internal pluming of the WVISS was
used to transport the sample vapor to the analyzer. The water vapor
concentration introduced to the analyzer was ∼ 20 000 ppm.
Excess vapor was vented to the atmosphere through the WVISS exhaust.
Further modifications were made in a custom 2014 setup, which uses two
ceramic heating elements (122 mm, 250 W, and 230 V) to heat a stainless
steel evaporation block to 165 ∘C. The block is painted with high-temperature black paint to uniformly absorb radiant heat generated by the
elements within a reflective cavity. The inner surface of the block is
electro-polished. The same nebulizer is used as in the 2013 setup, and the
mixing chamber is of similar dimensions. Dry air is pre-warmed in baffles
and introduced adjacent to the nebulizer. Compared to the 2013 setup, a
higher sample flow of ∼ 150 µL min-1 matched with
dry air flow to achieve ∼ 20 000 ppm water vapor
concentrations is used. Preliminary results from the 2014 setup are
reported here and differ only in the vaporizer construction and in
delivery of mixed vapor to the IWA directly through an open split. The 2014
open split is a simple step down in PFA tubing sizes rather than within the
WVISS plumbing and exhaust system: 1/4” O.D. tubing is connected to the
IWA and inserted 5 cm and centered within the larger size tubing 1/2” O.D.
carrying flow from the vaporizer.
Reduction of travel distance of the sample
The 1/4” PFA tubing between the WVISS and the IWA, which provides the
analyzer with sample, was kept short (59 cm) and a heat tape was wrapped
around the tubing to prevent condensation and reduce adsorption to the
tubing walls.
The sample flow from the ice core melt head separates the sample stream from
the inner and outer parts of the core, allowing for clean chemical sampling
from the inner section. For CFA δ measurements it is not necessary
to have an ultra-clean sampling regime, but we recommend taking the sample
stream from the inner line to prevent blocking of the nebulizer. The δ-measurement flow requires only 50 to 150 µL min-1 and thus
represents a minimal draw on the available sample volume. The melt rate was
monitored to allow the association of each continuous ice core measurement
with a depth.
The old air entrapped in the ice is first separated from the water sample by
a debubbler (DB1) and led to spectroscopic analyzers to measure CH4 and
δ18O in the air. After this step air bubbles are introduced to
the keep the water sample flow segmented. To keep the water vapor mixing
ratio generated from the evaporation unit constant, the introduced bubbles
are removed in DB2 before the evaporation unit and multi-port valve (V2). A
more detailed description of the melt head and the sealed debubbler (DB1,
Fig. 2) used during the 2013 and 2014 RICE melting campaign is provided in
Bigler et al. (2011). The multi-port valve (C25-3186EMH, VICI) enables us to
switch between samples from the ice core and multiple water standards (V2,
Fig. 2 and Table 2). Switching between sample and calibration cycles was
initiated through a control and data acquisition interface that was
built in LabVIEW software (National Instruments). Once the calibration cycle
was initiated, switches between water standards were automated.
RS-232-to-USB cables were used to control and log positions of the valves
(stored in calibration log files). P2 provides a constant water flow rate of 50 to 150 µL min-1 to the nebulizer (Savillex, PFA C-flow 50 nebulizer, part
no. 800-1-005-01-00). PFA tubing was used for the water and vapor flows.
δ18O and δD discrete-IWA measurements of
water standards in relation to the VSMOW/SLAP scale and their role during
two-point calibration.
Standard
N
δ18O (‰)
± σ
δD (‰)
± σ
Calibration
WS 1
4
-10.84
0.099
-74.15
0.938
QA/QC
RICE
30
-22.54
0.049
-175.02
0.193
Slope and normalization
ITASE
30
-37.39
0.046
-299.66
0.183
Slope
Flow chart of CFA IWA-custom vaporizer setup, where MH
stands for melt head, V for valve, P for peristaltic pump and DB for
debubbler. Blue lines represent the liquid part of the setup and the black
lines represent the dry and moist air portion.
The custom 2013 setup has proven to be reliable. The system performed
without malfunction during the entire 2013 RICE melting campaign of 35
processing days.
Results and discussion
Signal stability (instrument drift)
Stability tests were performed to determine precision for different lengths
of averaging times (τm) and to quantify at which timescales
instrumental drift affects the δ signal (Allan, 1966; Werle, 2011).
Allan deviation (σAllan, square root of Allan variance, Eq. (1);
Werle, 2011) was calculated using several stability tests consisting of
measurements on the Lower Hutt Milli-Q
water (LHW) standard for extended time periods. Tests were
performed on the 2013 and 2014 custom and the WVISS setups at water vapor
concentrations of ∼ 20 000 ppm (Table 3).
σAllan2(τm)=12m∑j=1mδ‾j+1-δ‾j2,
where τm is the averaging time and δ¯j+1 and δ¯j are the mean
values of neighboring time intervals j and j+1.
Representative results from the 29 June 2013 are provided in Fig. 3. For
δ18O two relatively stable periods can be seen separated by a
transition period of instrumental drift between 11 and 13 h. We find that
the instrumental drift is nonlinear and therefore challenging to correct
for.
Date, duration, mean water vapor concentration and standard
deviation for stability tests.
Date
Setup
Duration (h)
Mean (ppm)
Std (ppm)
24 May 2013
Custom 2013
18.45
21 466
639
01 June 2013
Custom 2013
23.81
19 941
479
09 June 2013
Custom 2013
22.04
19 530
400
23 June 2013
Custom 2013
23.98
19 870
456
29 June 2013
Custom 2013
23.26
19 642
401
24 January 2014
WVISS
31.22
18 092
145
28 January 2014
WVISS
24.47
19 770
135
30 January 2014
WVISS
31.67
19 641
144
19 July 2014
Custom 2014
30.49
20 216
304
20 July 2014
Custom 2014
18.92
21 486
132
30 May 2014
Gkinis 2014
30.00
–
–
Results from a stability test using the 2013 custom
vaporizer setup, measured over a 24 h period. Moving averages of 60 s of the
2 Hz data are displayed. Deviation from mean value is shown for each point in
(a) for δD and (b) for δ18O. (c) Water vapor concentration (ppm) and (d) analyzer cavity
temperature (∘C).
Custom vaporizer
The Allan deviation analysis for the custom 2013 setup shows that precision
increases with longer averaging times until the optimal averaging time is
reached after ∼ 200 and ∼ 600 s with a
precision of ∼ 0.04 and ∼ 0.07 ‰ for δ18O and δD, respectively
(Figs. 4 and 5). For longer averaging times, instrumental drift becomes
apparent. For this reason, averaging times beyond these values will have
poorer precision.
Allan deviation as a function of averaging time for δ18O from stability tests of the custom vaporizer setups (2013 setup
brown lines and 2014 setup blue lines), the WVISS setup (green line) and the
University of Copenhagen setup (UC black line). Average precision (1σ; standard deviation) for individual discrete samples measured on the
IWA-35EP analyzer is shown as a black dashed horizontal line.
After an averaging time of 60 s (approximately the response time of the
whole ice core CFA setup) a precision of 0.05 and
0.15 ‰ is achieved for δ18O and δD, respectively. This level of precision is comparable to IRMS analyzes for
δ18O (IRMS δ18O: 0.024 to 0.1 ‰) and outperforms IRMS analyzes for δD (IRMS δ D: 0.5
to 1.0 ‰; Sturm and Knohl, 2010, and references
therein).
Due to competing requirements of various CFA analytical lines, calibrations
were not run frequently enough during the 2013 RICE ice core processing
campaign to avoid influence from instrumental drift on the CFA δ data. Calibrations were on average conducted every 2.4 h. Thus, the
maximum temporal distance of any δ-CFA measurement to a calibration
event is ∼ 1.2 h. The peak uncertainty is therefore given by
the Allan deviation after 1.2 h, which is ∼ 0.17 and ∼ 0.13 ‰ for
δ18O and δD, respectively (Figs. 4. and 5). Analytical
uncertainty in d-excess was estimated to range between 0.31 and
1.37 ‰ using Eq. (2). We note that Eq. (2) assumes δ18O and δD are uncorrelated, which was verified to be true for
the vast majority of our measurements on standards averaged at 15 s
intervals. Peak uncertainty (1.37 ‰) occurs at the
center point between calibrations (Figs. 4 and 5; 1.2 h = 4320 s) and the
minimum uncertainty (0.31 ‰) occurs near calibration
points, within the optimal integration time for δ18O (200 s).
σd=(σδD)2+(8⋅σδ18O)21/2
For d-excess ice core measurements, a precision of ≤ 0.1 and ≤ 1.0 ‰ for δ18O and δD, respectively, is required (σd= 1.28 ‰; Masson-Delmotte et al., 2008). The δ18O measurements are more drift sensitive compared to δD data
and are therefore the limiting factor for d-excess data. To achieve
high-precision measurements for δ18O (< 0.1 ‰), a standard would have to be run for drift
correction every ∼ 1 h, which is incompatible with CFA methane
gas analyses.
To reduce the response time and the influence from instrumental drift, we
are currently working towards a new setup, focusing on reducing the response
time and the influence from instrumental drift even further. A reduced
response time would allow us to run a drift standard during melting, only
missing a small quantity of ice core analysis. Such an approach would
accommodate competing requirements, such as frequent calibrations for
isotope measurements and long periods of uninterrupted melting for methane
measurements. Furthermore, if the instrumental drift is reduced then less
frequent calibrations will be required.
WVISS system
For the WVISS system the optimum averaging times are 700, 1000 and 1500 s with precisions of 0.04,
0.06 and 0.04 ‰ for δ18O, δD and
δ17O, respectively.
The optimal averaging time is reached faster for the custom setups compared
to the WVISS system, indicating that the custom setup has higher precision
compared to the WVISS during shorter integration times (e.g., 0 to 300 s for
δ18O, Fig. 4). However, the 2013 custom setup is more
susceptible to long-term drift beyond the optimal averaging time (Figs. 4 and 5).
The variability observed at long averaging times (beyond the optimum
averaging time) shows the randomness of the drift, as drift occurs after
different lengths of times for different tests (Figs. 4, 5 and S1 in the Supplement). Previous studies have shown results from singular tests (Gkinis et al.,
2010; Sturm and Knohl, 2010; Aemisegger et al., 2012), averages from several
tests (Steig et al., 2014) or from several tests but only for short
integration times (≤1000 s; Maselli et al., 2013). Showing results from
a suite of tests provides an improved estimate of the uncertainty of the
results and the nonlinear nature of the drift. However, if the system is
less drift sensitive, a single long-term test can be sufficient to
characterize the stability of the system.
Aemisegger et al. (2012) conducted stability test comparisons for averaging
times from 0 to 104 s (2.78 h). However, to investigate drift for
longer averaging times, we consider averaging times up to 105 s (27.8 h) and compare these results to the UC setup (Picarro instrument; L2140-i).
Our results show that the custom and WVISS setups are affected by
instrumental drift. At averaging times of 104 s the WVISS setup has
σAllan values of 0.15,
0.06 and 0.10 ‰ for δ18O, δD and δ17O, respectively. For the custom
2013 setup the σAllan values at 104 s are and
0.3 and 0.25 ‰ for δ18O
and δD, respectively.
For the custom 2013 setup the precision after integration times of 103 s are 0.060 and 0.070 ‰ for
δ18O and δD, respectively. For the WVISS setup the
precision is 0.035, 0.070 and
0.042 ‰ after 103 s for δ18O, δD and δ17O, respectively. Preliminary data from the updated
custom 2014 setup (blue lines, Figs. 4, 5, and S1) show that this
system is less affected by instrumental drift compared to the 2013 setup;
after integration times of 103 s the 2014 system achieves σAllan values of 0.030 ‰ for δ18O,
0.043 ‰ for δ17O and
0.060 ‰ for δD.
Allan deviation as a function of averaging time for δD from stability tests of the custom vaporizer setups (2013 setup brown
lines and 2014 setup blue lines), the WVISS setup (green line) and the
University of Copenhagen setup (UC black line). Average precision (1σ; standard deviation) for individual discrete samples measured on the
IWA-35EP analyzer is shown as a black dashed horizontal line.
Results from the University of Copenhagen setup show that the Picarro CRDS
analyzer (L2140-i) and vaporizer achieve σAllan values of
0.011 ‰ for δ18O, 0.010 ‰
for δ17O and 0.048 ‰ for δD,
after averaging times of 103 s. The University of Copenhagen setup
achieves higher precision and is less affected by drift compared to the
custom and WVISS setups. No instrumental drift can be detected for δ18O and δ17O for the L2140-i setup (Figs. 4 and S1).
When analyzing for secondary parameters, such as d-excess and
17O-excess, a system that is optimized for stability, like the UC
setup, has an advantage over the custom and WVISS setups (Fig. S2),
which to a larger extent are influenced by instrumental drift (with δ18O being more drift sensitive than δD). The susceptibility of
OA-ICOS analyzers to instrumental drift for δ18O has also been
shown by Aemisegger et al. (2012). Therefore, more frequent measurements of
drift correction standards will have to be performed for the custom setups
in order to achieve the high precision measurements achieved by the L2140-i
setup.
Response time
Isotopic step changes between water standards are used to calculate response
times for the customized and the WVISS setup (Tables 4 and 5). The water
vapor concentration was kept constant over the isotopic step change
(∼ 20 000 ppm). Cumulative distribution functions of the log
normal distribution were fitted to the isotopic steps following Gkinis et
al. (2010).
δfit(t)=K121+erflnt-tvalveS2+K2,
where t is the time and K1, K2, tvalve and S are constants
estimated using least square optimization (LSO). The isotopic
transition period, and thus the response time, was defined between 5 and
95 % of the total isotopic step change (Eqs. 4 and 5). Beyond these limits,
it becomes difficult to distinguish a step change from random signal noise.
Δδstep=δstd1-δstd2,δ%=δfit-δstd2Δδstep,
where Δδstep is the size of the δ step between
standard 1 (δstd1) and standard 2 (δstd2) and
δ% is the percent change using the δ signal from the
fitted function in Eq. (3) δfit.
Response times for δ18O and δD from
isotopic step tests between water standards.
Response time (s)
Isotopic step
Setup
Step
Δ18O
ΔD
# steps
δ18O
±σ
Fit mean
δD
±σ
Fit mean
(‰)
(‰)
mean
rmse
mean
rmse
ITASE
RICE
Custom 2013
Pos.
14.5
124.2
5
53.4
1.9
0.51
54.1
1.7
0.51
RICE
ITASE
Custom 2013
Neg.
14.5
124.2
10
54.6
1.8
0.54
55.6
0.9
1.56
RICE
ITASE
WVISS
Neg.
14.5
124.2
7
61.3
2.6
0.55
61.8
2.4
1.82
ITASE
RICE
WVISS
Pos.
14.5
124.2
3
61.7
3.3
0.57
63.1
3.6
0.81
RICE
ITASE
Custom 2014
Neg.
14.5
124.2
10
18.4
0.8
0.48
18.5
1.0
1.56
ITASE
WS 1
Custom 2014
Pos.
26.5
225.5
7
18.4
0.9
0.44
18.8
0.5
0.67
Standard -40
CPH-DI
Gkinis 2014
Pos.
31.6
252.6
1
90.3
–
0.33
93.6
–
1.03
Response times for δ17O for water standard
isotopic step tests.
Response time (s)
Isotopic step
Setup
Step
Δ17O
# steps
δ17O
±σ
Fit mean
(‰)
mean
rmse
RICE
ITASE
WVISS
Neg.
7.7
7
61.2
3.9
0.79
ITASE
RICE
WVISS
Pos.
7.7
3
60.2
1.5
0.81
RICE
ITASE
Custom 2014
Neg.
7.7
10
19.2
1.7
0.74
ITASE
WS 1
Custom 2014
Pos.
13.7
7
19.2
2.2
0.67
Standard -40
CPH-DI
Gkinis 2014
Pos.
17.0
1
90.3
–
0.34
The response times for the customized setups are ∼ 54 and
∼ 18 s for the 2013 and 2014 setup, respectively. This is an
improvement compared to the WVISS setup, which has a response time of
∼ 62 s (Fig. 6, Tables 4 and 5).
(a–f) Shows δ-CFA data (blue dots and green dots for
the 2013 and 2014 custom setup, respectively), a LSO fitted curve (black
line), 5 and 95 % of change in the response time (RT) transition
period (red line) for the custom setups (a and b) and WVISS setup (c and d) and the
University of Copenhagen setup (UC; e and f).
(g–h) The
impulse response function for the fit for the 2013 custom (black line), the
2014 custom (green line), the Gkinis 2014 (blue line) and the WVISS (red
line) setup. The left column of plots (a, c, e and g) are for δ18O
and the right column of plots (b, d, f and h) are for δD.
The UC L2140-i (Picarro) analyzer and vaporizer unit setup achieves response
times of 90 s for δ18O and δ17O and 94 s for
δD (Fig. 6e and f, Tables 4 and 5). The custom setups (the 2014
version in particular) are more responsive compared to the WVISS and UC
setup.
We hypothesize that a more responsive system can often become less stable
when optimized for responsiveness by reducing evaporation chamber volume or
increasing the amount of dry air flow. This could partially be due to the
fact that it can be harder to control and keep environmental parameters
constant in a more responsive system. For example, if the dry air flow is
increased to reduce the response time, the system can become more sensitive
to ambient temperature changes (as it will be harder to preheat a larger
volume of air), which can induce drift. However, another way to obtain a
more responsive system would be to minimize the amount of dead volume and
mixing volumes in the water sample lines, which would not necessarily result
in a less stable system. The dead volume in the sample lines for the custom
setups was not minimized due to lab space limitations and due to the fact
that the δ-CFA system shared sample lines with other CFA analytical
equipment.
Furthermore, the δD isotopic step transition for the WVISS setup
(Fig. 6d) is influenced by memory effects. A long tail is evident in the
data before the final isotopic value is reached. The fit with the cumulative
distribution function (Eq. 3) is poor while an exponential function improves
the fit with the data. In contrast, the 2013 and 2014 custom setup reaches
the final stable isotopic value faster (Fig. 6b), which suggests it is less
influenced by memory effects.
Figure 6 provides an example of a step change between two water standards
RICE and ITASE for the custom setups (Fig. 6a and b) and WVISS (Fig. 6c and
d) setup. A step change from the UC setup of similar isotopic size is also provided for comparison (Fig. 6e and f). The impulse response
functions, the derivative of the isotopic fit (∂δfit/∂t) for the setups,
are shown in Fig. 6g and h.
The response time values presented in Table 4 are valid as a comparison
between the WVISS and custom setups. However, the response times for the
custom setups are not representative of the whole CFA system, as the V2
valve is located downstream of the melter and debubbler (Fig. 2). Hence the
attenuation of the δ signal prior to the valve is not taken into
account. Gkinis et al. (2011) presented a method to calculate the attenuation
for the CFA system using a power spectrum of the CFA data and a spectrum
from discrete offline measurements (ice pieces cut directly from the core)
over the same depth interval as the CFA measurements.
Aemisegger et al. (2012) reported δD, δ18O and water
vapor mixing ratio response times of 4.5, 3 and 2.9 s, respectively,
for an IWA-WVISS setup. However, their isotopic step change is between one
standard from the WVISS unit and the ambient air, which provides the
response times of the IWA. This way the evaporation chamber volume does not
have to be replaced for the isotopic step to be complete. When an
evaporation unit is used for switching between multiple standards and/or a
changing CFA signal (e.g., from CFA sample stream from an ice core), it
becomes critical to have a small evaporation chamber volume, as the vapor
volume in the evaporation chamber needs to be replaced before the current
signal can be analyzed.
The lag bias introduced by the IWA–vaporizer setups are negligible (for both
the custom setups and WVISS setups). On average δD lags
δ18O by 1 s (Table 4). A δ signal without lag bias
enables the calculation of high-frequency d-excess values. For the WVISS and
the custom 2014 setup we can also confirm there is no observable bias
between δ18O and δ17O, which is relevant for
17O-excess measurements (Tables 4 and 5). Furthermore, for the custom
setups we find no relationship between the size of the isotopic step and the
response time or the response time and the direction of the step (positive
or negative).
We reduced the tubing length in our system for the vapor introduction to the
analyzer compared to a typical atmospheric science setup, where it is
necessary to have longer air intake lines. We hypothesize that our setup
experiences less adsorption to tubing walls. This is important, as
adsorption can cause a bias between δ18O and δD. The
tubing between evaporation chamber and analyzer is only 59 cm, which
required orientating the analyzer with the back towards the WVISS. Moreover,
we applied heat tape to the tubing to reduce adsorption to tubing walls.
To conclude discussion on this topic, we note that the response times
reported above do not represent the full response time of the entire CFA
system, as measured from the melt head. Limited experimentation produced an
estimate of ∼ 43 s for the response time of the entire 2014
CFA system. This is a more rapid response than all systems reported in Table 3
except the custom 2014 setup. Thus, to take full advantage of the fastest
response times, the isotope analysis system could be placed closer to the
melt head by changing the overall CFA design or shortening the sample line
between the melt head and DB1.
Calibration
We use four internal standards (Table 2 and Fig. 7): LHW, Working Standard 1 (WS1), RICE (derived from RICE snow) and
ITASE (derived from US-ITASE, West Antarctic snow). The values of the
internal standards in relation to the VSMOW/SLAP scale were determined using
discrete laser absorption spectroscopy measurements on the IWA-35EP analyzer
(Table 2).
Example of raw data from a melting session and calibration
events from 25 June 2013. Ice core sections of 1 m were stacked on top each
other during melting sessions. The ice core data are bracketed by
calibration events. Water standards from the first calibration event are
color marked. Yellow marking is for LHW standard, red for WS1, green for
RICE and blue for the ITASE water standard.
The CFA data import and processing is handled with MATLAB routines (MATLAB
version 8.0.0.783 (R2012b), MathWorks, Inc., Natick, Massachusetts,
United States). A semi-automatic MATLAB script was set up for extracting
standard measurements during calibrations and associating calibration events
with the corresponding ice core melt section.
Water vapor generated from Milli-Q water (18 MΩ water) is supplied
to the analyzer between calibration and ice core melting periods, allowing
us to easily distinguish between these events. We identify the start of the
calibration measurement after the transition from Milli-Q to standard by
finding the first time where the derivative of the δ CFA measurement
drops below a threshold. The time lag between the V2 valve and the analyzer
is found using the calculated start time of the standard measurement and the
time of the valve switch recorded in the calibration log file. Running
Milli-Q water when the sample or standards are not analyzed also has the
advantage that there is less risk of deposit buildup that can block the
nebulizer.
Figure 7 shows an example of a typical section from δ-CFA processing
of the RICE ice core. Three or four 1 m ice core sections were typically
melted between calibrations, by stacking consecutive cores on top of each
other during melting. Normally the stack of cores takes up to 2.4 h between
calibrations, and one calibration cycle takes ∼ 30 min.
The multiple water standard calibration cycle consists of three internal
standards: WS1, RICE and ITASE. The values of the internal standards in
relation to the VSMOW/SLAP scale are provided in Table 2. The isotope
standards bracket the isotopic ice core record (Fig. 7).
Normalized measurement of QA/QC standard (WS1) over 35 days.
Raw data are shown as gray stars and corrected data as black dots
(a) for δ18O and (b) for δD. The corrected data have a
standard deviation of 0.11 and
0.75 ‰ for δ18O and δD,
respectively. The mean corrected anomaly (black line) from the true WS1
standard value (thick red line) is -0.07 and
-0.51 ‰ for δ18O and δD,
respectively.
Throughout our measurements the water vapor mixing ratio was kept at
∼ 20 000 ppm in order to ensure data stability. To remove data
that are affected by sudden changes in water mixing ratios (often caused by
small air bubbles or drips) we used the following criteria: CFA data are
removed when the difference between the 30 s moving average and 200 s moving
average of water mixing ratio (ppm) exceeds 1 standard deviation (σ)
of the 60 s moving average. In addition, a cut-off limit of 15 000 ppm was
implemented, which removes data that are stable but measured at mixing
ratios that deviate from our set measurement level (∼ 20 000 ppm). If an offset occurred from the target water mixing ratio, the
analyzer's water vapor dependence is corrected accurately if the magnitude
of the offset in between calibrations is constant.
Each standard is analyzed for 500 s; the first 100 and last 100 s of
each standard measurement are discarded to conservatively avoid influence
from memory effects. Measurements shorter than 250 s are omitted. Figure 7
shows an example of the standards analyzed during a calibration cycle.
Average values over 300 s were calculated for each standard.
The 60 s moving average of the corrected 2 Hz δ CFA data (black line), results from discrete measurements (green dots for
measurements from the IWA-35EP analyzer and yellow dots for measurements on
the DLT-100 analyzer) and δ-CFA data integrated over the discrete
vial depth intervals (red dots); (a) for δ18O, (b) for δD and (c) for d-excess.
We follow the recommendation by the International Atomic and Energy Agency
(IAEA) of measuring multiple water standards for calibration (Kurita et al.,
2012). We fit a multi-port valve to switch between different water
standards to the nebulizer to perform calibrations. The RICE and ITASE
standards are used for the two-point linear correction of the CFA data.
Correction slopes were calculated using the RICE and ITASE standards
directly before and directly after each melting period. The data are
normalized to the RICE standard to reduce the influence from instrument
drift and WS1 is used as a QA/QC standard. The calibration and normalization
were linearly time weighted between the calibration events.
The averages of correction slopes from calibrations throughout the RICE
processing campaign are δ18O = 0.941 ± 0.0057 (mean ±1σ; N= 324) and δD = 0.997 ± 0.0043. The
average of the correction slopes are shown here for characterization
purposes. However, for isotope raw-data correction we use adjacent
calibration slopes to calibrate the data. This approach was applied as
correction slopes have been shown to be instrument specific and vary
slightly over time due to instrumental drift (Kurita et al., 2012). To
correct for drift, our system is designed for calibrations performed at a
time interval averaging 2.4 h. Recent studies have obtained similar
correction slopes (δ18O = 0.941 ± 0.008 and δD = 0.994 ± 0.003 (Aemisegger et al., 2012) and
δ18O = 0.946 ± 0.005 and δD = 1.00 ± 0.003; Kurita et al.,
2012) using an IWA-WVISS setup. It is important to use a two-point
calibration correction, as it is not feasible to calculate correction slopes
using a single-standard correction approach, and any resulting deviation
from the predominant slope would bias the calibration.
The custom setups can be applied to the field of atmospheric science,
enabling rapid, automated and robust calibration cycles using multiple water
standards (two-point calibration), compared to the one-standard setup that
the unaltered WVISS is fitted with by the manufacturer. Reducing the length
of calibration while including multiple standards should maximize data
quality while minimizing the loss of atmospheric measurements during
calibration cycles. Additionally, the setup has proven to be a robust system
that can run continuously for months and operate unattended for days.
Long-term precision and accuracy
The RICE and ITASE standards are used for the two-point linear correction of
the δ-CFA data, and the RICE standard is also used for normalization
to minimize influence from drift. Measurement of a QA/QC standard (WS1) was
conducted as a check throughout the RICE processing campaign (35 days). The
corrected CFA measurements of the QA/QC standard provide a measure of the
long-term precision and accuracy of the corrected δ-CFA data
measured on the custom 2013 setup (Fig. 8). The data in Fig. 8 have been
normalized using the VSMOW/SLAP value of the QA/QC standard (WS1; Table 2).
The overall precision of the 177 standard measurements over the 35 days was
0.11 and 0.75 ‰ for δ18O and δD, respectively. The mean anomaly values of the
corrected QA/QC standard values are -0.07 and
-0.51 ‰ for δ18O and δD,
respectively, and they provide an estimate of the overall accuracy of the
measurements (black dashed line in Fig. 8). On 16 June 2013 (day 167),
problems were diagnosed with the vacuum pump for the IWA (N940, KNF), which
appears to have affected the accuracy of δD (Fig. 8b).
High-resolution ice core record
To evaluate the quality of the calibration procedure, the corrected δ-CFA data were compared to discrete data (Fig. 9). Discrete measurements
measured on an IWA-35EP (green dots) and DLT-100 analyzer (yellow dots) can
be compared with δ-CFA data integrated over the discrete vial depth
intervals (red dots). Figure 9 verifies the validity of the calibration
procedure.
Histogram showing the difference between discrete and the
CFA data: (a) for δ18O, (b) for δD and (c) d-excess
for depths from 133 to 144 m of the RICE ice core.
The discrete and CFA data for the 133 to 144 m section of the RICE ice core
were investigated further by creating histograms of the difference between
the discrete data measured on the IWA-35EP analyzer and the CFA data. A
difference was calculated for each discrete sample (N= 215). The averages
of the CFA δ over the discrete vial depth intervals were calculated
to make a direct comparison with the lower resolution discrete measurements.
The difference between the averaged CFA and discrete data was calculated to
be 0.09 ± 0.16 and 0.70 ± 1.07 ‰ (mean ± 1σ) for δ18O
and δD, respectively (Fig. 10). d-excess was calculated for the
discrete and averaged CFA data, the difference being -0.05 ± 0.25 ‰ (Fig. 10c).
Conclusions
This study outlines the process used to develop experimental CFA equipment
for δ measurements with high temporal resolution (sub-annual) in the
RICE ice cores and describes the performance and operation of the equipment
as well as potential improvements. This continuous-flow laser system is the
first to use OA-ICOS in combination with a vaporizer unit to continuously
analyze sample from an ice core.
Stability tests comparing the custom and the WVISS setups were performed and
Allan deviations (σAllan) were calculated to determine
precision at different averaging times. For the 2013 modified setup, the
σAllan after integration times of 103 s are
0.060 and 0.070 ‰ for δ18O and δD, respectively. The corresponding σAllan values for the custom 2014 setup are 0.030, 0.060 and 0.043 ‰ for δ18O, δD and δ17O, respectively. For the WVISS
setup the precision is 0.035,
0.070 and 0.042 ‰ after 103 s
for δ18O, δD and δ17O, respectively. Both
the modified and WVISS setup are influenced by instrumental drift, and
δ18O is more drift sensitive than δD.
The peak precision uncertainty for the custom 2013 CFA δ data is
given by the Allan deviation after 1.2 h (center point between
calibrations), which is ∼ 0.17 and
∼ 0.13 ‰ for δ18O and
δ D, respectively. Allan deviation for d-excess was estimated
to range between 0.31 and 1.37 ‰ using Eq. (2).
1.37 ‰ is the peak uncertainty (1.2 h) and the minimum
uncertainty (0.31 ‰) occurs near calibration points,
within the optimal integration time for δ18O (200 s).
Results from the UC setup show that the Picarro CRDS
analyzer (L2140-i) and vaporizer achieves σAllan values of
0.011 ‰ for δ18O, 0.010 ‰
for δ17O and 0.048 ‰ for δD,
after averaging times of 103 s. The UC setup outperforms the custom
setups on the basis of precision.
The mean response times for the customized setup are 54 and 18 s for 2013
and 2014 setup, respectively. This is an improvement compared to the WVISS
setup, which has a response time of 62 s. The UC L2140-i (Picarro) analyzer
and vaporizer unit setup achieves response times of 90 s for δ18O and δ17O and 94 s for δD. The custom setups
(the 2014 version in particular) are more responsive compared to the WVISS
and UC setup and can therefore provide measurements with higher temporal
resolution.
The two-point calibration process was evaluated by comparing the CFA data to
discrete measurements. The overall difference between CFA and IWA-35EP
discrete measurements was 0.09 ± 0.16 and 0.70 ± 1.07 ‰ (mean ±1σ) for δ18O and δD, respectively.
The custom setups used during the 2013 and 2014 RICE ice core processing campaign achieved high precision
measurements, in particular for δD, with high temporal (sub-annual)
resolution for the upper part of the core.