Water and sea-ice carbon linkages in an Arctic coastal glaciated system

Description

Samples for carbonate system parameters, including dissolved inorganic carbon (DIC) and total alkalinity (AT), were collected in 250 mL borosilicate bottles following established protocols (Dickson et al., 2007). To preserve the samples, 50 µL of saturated mercuric chloride was added, and they were stored in the dark at 4°C until analysis. Nutrient samples were collected in 20 mL HDPE vials, frozen at −20°C, and later analysed. Sea-ice samples were collected on 20th and 23rd of April from two distinct locations using a KOVACS ice corer (Mark II coring system, inner diameter 9 cm). Cores A and B were obtained from sea ice within the northern region influenced by the glaciers Kongsbreen and Conway, and Core C was collected from the inner part and easternmost side of the study area, located near the glacial front of Kongsvegen. Each core had dimensions between 40 and 50 cm in length and 9 cm in diameter and was segmented into four to five sections from top to bottom. Each 10 cm section was thawed at 5 °C in a dark room. All samples underwent filtration for POC, DOC and CDOM after which they were stored frozen until further analysis. CDOM absorbance was measured using a dual-beam spectrophotometer Cary 4000 UV-Vis and a 100 mm cuvete, with Milli-Q water serving as a reference. Spectral measurements were collected from 200 to 800 nm at intervals of 0.5 nm. Absorbance spectra a(λ) was converted to CDOM absorption coefficients, aCDOM(λ) (m⁻¹). The absorption spectra's characteristics between 250 and 700 nm were delineated by determining the exponential spectral slope coefficient (S). An alternative approach proposed by Helms et al. (2008) was also examined, which involved calculating slopes within two different wavelength ranges (S275-295 and S350-400) and their ratio (SR) (S275–295: S350–400). The software Ocean Data View was used to create x, y plots, and section profiles employing a DIVA gridding technique to interpolate the measurements spatially between each depth profile. A Gaussian decomposition method was employed to model the CDOM absorption spectra and determine specific absorptive components. This method assumes that deviations, such as shoulders and peaks, from a typical exponential pattern in the absorption spectra are due to the significant presence of specific compounds or structures (Massicotte & Markager, 2016). The decomposition was performed over the wavelength range of 270–700 nm to capture all spectral characteristics using the Asfit software (Omanović et al., 2019). The statistical analysis of the decomposed Gaussian components was conducted in Python. To examine the relationship between parameters and the properties of CDOM, we employed Pearson's method to calculate the Pearson coefficient. Kruskal-Wallis in the software test was applied to assess differences between the stations.

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• Scottish Funding Council

  United Kingdom

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