Abstract

Abstract. Air was sampled from the porous firn layer at the NEEM site in Northern Greenland. We use an ensemble of ten reference tracers of known atmospheric history to characterise the transport properties of the site. By analysing uncertainties in both data and the reference gas atmospheric histories, we can objectively assign weights to each of the gases used for the depth-diffusivity reconstruction. We define an objective root mean square criterion that is minimised in the model tuning procedure. Each tracer constrains the firn profile differently through its unique atmospheric history and free air diffusivity, making our multiple-tracer characterisation method a clear improvement over the commonly used single-tracer tuning. Six firn air transport models are tuned to the NEEM site; all models successfully reproduce the data within a 1σ Gaussian distribution. A comparison between two replicate boreholes drilled 64 m apart shows differences in measured mixing ratio profiles that exceed the experimental error. We find evidence that diffusivity does not vanish completely in the lock-in zone, as is commonly assumed. The ice age- gas age difference (Δage) at the firn-ice transition is calculated to be 182+3−9 yr. We further present the first intercomparison study of firn air models, where we introduce diagnostic scenarios designed to probe specific aspects of the model physics. Our results show that there are major differences in the way the models handle advective transport. Furthermore, diffusive fractionation of isotopes in the firn is poorly constrained by the models, which has consequences for attempts to reconstruct the isotopic composition of trace gases back in time using firn air and ice core records.

Highlights

  • The compacted snow found in the accumulation zone of the major ice sheets acts as a unique archive of old air, preserving a continuous record of atmospheric composition from the present up to a century back in time (Battle et al, 1996)

  • Six firn air transport models are tuned to the NEEM site; all models successfully reproduce the data within a 1σ Gaussian distribution

  • A unique uncertainty estimate has been assigned to each of the 260 individual data points used in this study based on the following seven potential sources of uncertainty: (1) analytical precision as specified by the laboratories; (2) uncertainty in atmospheric reconstructions; (3) contamination with modern air in the deepest firn samples; the estimates are based on HFC-134a, SF6 and CFCs, which should be absent in the deepest samples; (4) sampling effects estimated from inter-laboratory and inter-borehole offsets; (5) possibility of in-situ CO2 artifacts (e.g. Tschumi and Stauffer, 2000) and CO2 enrichment due to close-off fractionation (Huber et al, 2006; Severinghaus and Battle, 2006); (6) undersampling of seasonal cycle in the monthly atmospheric reconstruction (CO2 only); and (7) large unexplained EU-US borehole difference in the diffusive zone (SF6 only)

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Summary

Introduction

The compacted snow (firn) found in the accumulation zone of the major ice sheets acts as a unique archive of old air, preserving a continuous record of atmospheric composition from the present up to a century back in time (Battle et al, 1996) Sampling of this archive has allowed for reconstruction of the recent atmospheric history of many trace gas species The procedure consists of forcing a firn air transport model with the atmospheric history of a selected reference gas, often CO2, and subsequently optimising the fit to measured mixing ratios in the firn by adjusting (“tuning”) the effective diffusivity profile. The firn air modeling studies found in literature tune their effective diffusivity profile to a single tracer. The Supplement includes all the firn air data and atmospheric reconstructions used in this study

Methods
Gravitational correction
Differences between the EU and US boreholes
Modeling firn air transport at NEEM
Model intercomparison and discussion
Borehole comparison
Scenario I: diffusive fractionation
Scenario II: attenuation of a sine wave with depth
Scenario III: balance of transport fluxes
Scenario IV: advective transport
Findings
Summary and conclusions
Full Text
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