Abstract

Abstract. Since September 2011, a wavelength-scanned cavity ring-down spectroscopy analyser has been remotely operated in Ivittuut, southern Greenland, providing the first record of surface water vapour isotopic composition based on continuous measurements in South Greenland and the first record including the winter season in Greenland. The comparison of vapour data with measurements of precipitation isotopic composition suggest an equilibrium between surface vapour and precipitation. δ18O and deuterium excess are generally anti-correlated and show important seasonal variations, with respective amplitudes of ~10 and ~20‰, as well as large synoptic variations. The data depict small summer diurnal variations. At the seasonal scale, δ18O has a minimum in November–December and a maximum in June–July, while deuterium excess has a minimum in May–June and a maximum in November. The approach of low-pressure systems towards South Greenland leads to δ18O increase (typically +5‰) and deuterium excess decrease (typically −15‰). Seasonal and synoptic variations coincide with shifts in the moisture sources, estimated using a quantitative moisture source diagnostic based on a Lagrangian back-trajectory model. The atmospheric general circulation model LMDZiso correctly captures the seasonal and synoptic variability of δ18O, but does not capture the observed magnitude of deuterium excess variability. Covariations of water vapour isotopic composition with local and moisture source meteorological parameters have been evaluated. δ18O is strongly correlated with the logarithm of local surface humidity, consistent with Rayleigh distillation processes, and with local surface air temperature, associated with a slope of ~0.4‰ °C−1. Deuterium excess correlates with local surface relative humidity as well as surface relative humidity from the dominant moisture source area located in the North Atlantic, south of Greenland and Iceland.

Highlights

  • Stable water isotopes in atmospheric waters are widely used as tracers of the hydrological cycle (Jouzel, 2003)

  • The isotopic composition of water is expressed in ‰ units, using δ notation, defined as a deviation of the sample isotopic ratio R compared to a standard isotopic ratio RVSMOW: δ = 1000 × (R/RVSMOW − 1)

  • The surface snow and the snowfall isotopic composition being consistent with water vapour isotopic composition assuming equilibrium, this could indicate that a large water exchange could take place between the snowpack and the lower atmosphere

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Summary

Introduction

Stable water isotopes (designating the water stable isotopologues H2O, H128O and HDO) in atmospheric waters are widely used as tracers of the hydrological cycle (Jouzel, 2003). The combination of different ice core records has recently made it possible to investigate the regional patterns of past changes in precipitation stable water isotopes and climate (Steen-Larsen et al, 2011; Guillevic et al, 2012) All these studies indicate that the quantitative understanding of Greenland ice core records requires improved comprehension of the different processes controlling Greenland surface snow isotopic composition. The surface snow and the snowfall isotopic composition being consistent with water vapour isotopic composition assuming equilibrium, this could indicate that a large water exchange could take place between the snowpack and the lower atmosphere These NEEM data show synoptic events with high d values, a potential signature of Arctic air masses (Steen-Larsen et al, 2013).

Methods and data
Sampling site
Meteorological data
Water vapour isotope monitoring
Nov 2012 12 Apr 2013
Data gaps and data quality control
Independent humidity measurements
Lagrangian moisture source diagnostic
Water isotope enabled AGCM
Diurnal variability
Synoptic timescale variability and moisture source diagnostic
Summer 2012 heat wave
Seasonal variability and moisture source diagnostic
Precipitation isotopic composition and equilibrium with vapour
Statistical relationships between water vapour isotopes and local climate
Discussion: comparison with LMDZiso simulations
Statistical relationships with local climatic parameters in LMDZiso
Spatial representativeness of Ivittuut observations in LMDZiso
Zone 1 dv obs RH obs dv LMDZiso RH LMDZiso dv LMDZiso RH LMDZiso dv obs
Findings
Conclusions

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