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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 6 | Copyright

Special issue: “Global Challenges for our Common Future: a paleoscience...

Clim. Past, 14, 763-788, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 12 Jun 2018

Research article | 12 Jun 2018

Novel automated inversion algorithm for temperature reconstruction using gas isotopes from ice cores

Michael Döring1,2 and Markus C. Leuenberger1,2 Michael Döring and Markus C. Leuenberger
  • 1Climate and Environmental Physics, University of Bern, Bern, Switzerland
  • 2Oeschger Centre for Climate Change Research (OCCR), Bern, Switzerland

Abstract. Greenland past temperature history can be reconstructed by forcing the output of a firn-densification and heat-diffusion model to fit multiple gas-isotope data (δ15N or δ40Ar or δ15Nexcess) extracted from ancient air in Greenland ice cores using published accumulation-rate (Acc) datasets. We present here a novel methodology to solve this inverse problem, by designing a fully automated algorithm. To demonstrate the performance of this novel approach, we begin by intentionally constructing synthetic temperature histories and associated δ15N datasets, mimicking real Holocene data that we use as true values (targets) to be compared to the output of the algorithm. This allows us to quantify uncertainties originating from the algorithm itself. The presented approach is completely automated and therefore minimizes the subjective impact of manual parameter tuning, leading to reproducible temperature estimates. In contrast to many other ice-core-based temperature reconstruction methods, the presented approach is completely independent from ice-core stable-water isotopes, providing the opportunity to validate water-isotope-based reconstructions or reconstructions where water isotopes are used together with δ15N or δ40Ar. We solve the inverse problem T(δ15N, Acc) by using a combination of a Monte Carlo based iterative approach and the analysis of remaining mismatches between modelled and target data, based on cubic-spline filtering of random numbers and the laboratory-determined temperature sensitivity for nitrogen isotopes. Additionally, the presented reconstruction approach was tested by fitting measured δ40Ar and δ15Nexcess data, which led as well to a robust agreement between modelled and measured data. The obtained final mismatches follow a symmetric standard-distribution function. For the study on synthetic data, 95% of the mismatches compared to the synthetic target data are in an envelope between 3.0 to 6.3permeg for δ15N and 0.23 to 0.51K for temperature (2σ, respectively). In addition to Holocene temperature reconstructions, the fitting approach can also be used for glacial temperature reconstructions. This is shown by fitting of the North Greenland Ice Core Project (NGRIP) δ15N data for two Dansgaard–Oeschger events using the presented approach, leading to results comparable to other studies.

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Short summary
We present a novel approach for ice-core-based temperature reconstructions, which is based on gas-isotope data measured on enclosed air bubbles in ice cores. The processes of air movement and enclosure are highly temperature dependent due to heat diffusion in and densification of the snow and ice. Our method inverts a model, which describes these processes, to desired temperature histories. This paper examines the performance of our novel approach on different synthetic isotope-data scenarios.
We present a novel approach for ice-core-based temperature reconstructions, which is based on...