the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Nov 2023
120 years of sea-ice cover on the Northeast Greenland continental shelf: a biomarker and observational record comparison
Joanna Davies
Kirsten Fahl
Matthias Moros
Alice Carter-Champion
Henrieka Detlef
Ruediger Stein
Christof Pearce
Marit-Solveig Seidenkrantz
Abstract. This study reconstructs recent changes (ca. 120 years) in sea-ice cover, using biomarkers (IP25 and phytoplankton sterols) from three sediment cores located in a transect across Belgica Trough, on the Northeast Greenland continental shelf. These results are evaluated using instrumental and historical data from the same region and time period. Over the entire 120-year study period, IP25 concentrations are highest at the inner shelf (site 90R) and decrease towards the mid-shelf (site 109R), with lowest values found at the outer shelf (site 134R). The PIP25 index yields the highest sea-ice cover at sites 109R and 90R and lowest at 134R, in agreement with observational records. A decline in sea-ice cover, identified visually and using change point analysis, occurs from 1971 in the observational sea-ice data at sites 90R and 109R. A change in sea-ice cover occurs in 1984 at site 134R. Sea-ice cover in these years aligns with an increase in sterol biomarkers and IP25 at all three sites and decline in the PIP25 index at sites 90R and 134R. The outcomes of this study support the reliability of biomarkers for sea-ice reconstructions in this region.
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Joanna Davies et al.
Status: open (until 03 Jan 2024)
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RC1: 'Comment on egusphere-2023-2363', Anonymous Referee #1, 28 Nov 2023
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The authors present a comprehensive biomarker data set from three NE Greenland shelf records together with instrumental/observational data for sea ice fluctuations over the last century. A well-balanced and interesting manuscript with plenty of new datasets certainly of interest for the readership of EGUsphere. I would like to highlight a few critical points that might be addressed before the manuscript can be accepted for publication:
- First, the authors have access to bulk organic information including TOC, TN, and d13Corg While the bulk organics give you a comprehensive overview on the organic matter sources, the biomarkers cover only a tiny fraction of it. You could use the data better to inform the readership of dominant organic matter source in the records. You may even consider a rough semi-quantification of marine and terrestrial organic matter and use is more actively for your interpretation. The d13Corg data vary between -23 and -27 permille implying quite a bit of variation in terms of terrestrial organic matter supply to your shelf system.
- The authors (desperately) try to argue that the near-surficial deposits are less influenced by bio-degradation compared to the climate signal preserved within the biomarker records. Rontani et al. (2018) is often referred while only the ration of epi-brassicasterol and 24-methylenecholesterol is shown. Why don’t you analyse the autoxidation products of IP25 in some of your samples? You have the co-authors to do this experiment. It would strengthen your dataset immensely and avoid mis-interpretation of your data.
- Clearly, from the discussion, core 109R is affected by biodegradable products. (from the Bra/24-Me) ratio. You may run some of your fractions again for potential prevalence of autoxidation products of IP25 as well. Also, the gradual decline in brassicasterol concentration in all records could be interpreted as a result of diagenesis. Perhaps the application of PIP25 is here rather speculative and taken the uncertainties of biodegradation into account, I would suggest to leave it out. You have a visually good correlation with declining sea cover from your observational data set. According to Rontani et al. (2018) this is your strongest argument against significant bio-degradational control.
Minor comments
- You mention X-ray fluorescence scanning and grain size analysis in the methods, but you hardly use these data for your interpretation. Consider showing the data actively or omit. X-ray fluorescence data can also provide you with information on diagenesis (redox boundaries).
- You may provide more details to your bulk analysis including d13Corg measurements, uncertainties, errors, standards etc.
Citation: https://doi.org/10.5194/egusphere-2023-2363-RC1
Joanna Davies et al.
Joanna Davies et al.
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