High-resolution continuous-flow analysis setup for water isotopic measurement from ice cores using laser spectroscopy

B D Emanuelsson,E D Keller,V Gkinis,N A N Bertler,W T Baisden

doi:10.5194/amt-8-2869-2015

Abstract

Abstract. Here we present an experimental setup for water stable isotope (δ18O and δD) continuous-flow measurements and provide metrics defining the performance of the setup during a major ice core measurement campaign (Roosevelt Island Climate Evolution; RICE). We also use the metrics to compare alternate systems. Our setup is the first continuous-flow laser spectroscopy system that is using off-axis integrated cavity output spectroscopy (OA-ICOS; analyzer manufactured by Los Gatos Research, LGR) in combination with an evaporation unit to continuously analyze water samples from an ice core. A Water Vapor Isotope Standard Source (WVISS) calibration unit, manufactured by LGR, was modified to (1) enable measurements on several water standards, (2) increase the temporal resolution by reducing the response time and (3) reduce the influence from memory effects. While this setup was designed for the continuous-flow analysis (CFA) of ice cores, it can also continuously analyze other liquid or vapor sources. The custom setups provide a shorter response time (~ 54 and 18 s for 2013 and 2014 setup, respectively) compared to the original WVISS unit (~ 62 s), which is an improvement in measurement resolution. Another improvement compared to the original WVISS is that the custom setups have a reduced memory effect. Stability tests comparing the custom and WVISS setups were performed and Allan deviations (σAllan) were calculated to determine precision at different averaging times. For the custom 2013 setup the precision after integration times of 103 s is 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 custom setups and WVISS setup are influenced by instrumental drift with δ18O being more drift sensitive than δD. The σAllan values for δ18O are 0.30 and 0.18 ‰ for the custom 2013 and WVISS setup, respectively, after averaging times of 104 s (2.78 h). Using response time tests and stability tests, we show that the custom setups are more responsive (shorter response time), whereas the University of Copenhagen (UC) setup is more stable. More broadly, comparisons of different setups address the challenge of integrating vaporizer/spectrometer isotope measurement systems into a CFA campaign with many other analytical instruments.

Highlights

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)
In addition to this we present preliminary data and results from the updated custom 2014 setup and new previously unpublished results from a cavity ring-down spectroscopy (CRDS) L2140-i Picarro ice core setup (UC setup) with a custom-made vaporizer
We reduced the internal volume of the Water Vapor Isotope Standard Source (WVISS) evaporation chamber of 1.1 L to 40 mL in the 2013 custom vaporizer

Summary

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). 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.) influence the δ signal (Jouzel et al, 1997; Masson-Delmotte et al, 2008; Küttel et al, 2012; Sinclair et al, 2013).

Methods

Results

Conclusion