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 (δ15N or δ40Ar or δ15Nexcess) extracted from ancient air in Greenland ice cores using published accumulation-rate (Acc) datasets. We present...
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| Vydáno v: | Climate of the past Ročník 14; číslo 6; s. 763 - 788 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Katlenburg-Lindau
Copernicus GmbH
12.06.2018
Copernicus Publications |
| Témata: | |
| ISSN: | 1814-9332, 1814-9324, 1814-9332 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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 6.3 permeg for δ15N and 0.23 to 0.51 K
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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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1814-9332 1814-9324 1814-9332 |
| DOI: | 10.5194/cp-14-763-2018 |