Novel automated inversion algorithm for temperature reconstruction using gas isotopes from ice cores
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 ( δ 15 N or δ 40 Ar or δ 15 N excess ) extracted from ancient air in Greenland ice cores using published accumulation-rate (Acc) datasets. W...
| Published in: | Climate of the Past |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2018
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/cp-14-763-2018 https://doaj.org/article/3f8e6e4eaff84bb6948cf5318bd0a2a1 |
| Summary: | 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 ( δ 15 N or δ 40 Ar or δ 15 N excess ) 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 δ 15 N datasets, mimicking real Holocene data that we use as <q>true values</q> (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 <q>subjective</q> 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 δ 15 N or δ 40 Ar. We solve the inverse problem T ( δ 15 N, 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 δ 40 Ar and δ 15 N excess 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 6.3 permeg for δ 15 N and 0.23 to 0.51 K for ... |
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