Distributed temperature sensing data from a borehole drilled to the base of Sermeq Kujalleq (Store Glacier), Greenland, in July 2019

This is distributed temperature sensing (DTS) data from a 1,043 m borehole drilled to the base of Sermeq Kujalleq (Store Glacier), Greenland, 28 km inland from the glacier terminus. The DTS system was installed on 5 July 2019, with recordings continuing until cable failure on 13 August 2019. The record resolution is ~0.65 m. This work was primarily funded and conducted as part of the European Research Council RESPONDER project (https://www.erc-responder.eu/) under the European Union's Horizon 2020 research and innovation program (Grant 683043). Robert Law was supported by Natural Environment Research Council Doctoral Training Partnership studentships (Grant NE/L002507/1).

The data was gathered from Store Glacier, Greenland, in 2019 using a borehole drilling system as described in Doyle et al. (2018) with an additional heater unit. Drilling occured from 4-5 July 2019. The vertical spatial resolution is ~0.65 m, with a sampling resolution of 0.25 m. The measurement averaging time was originally set at 2 minutes with near continuous operation from 5 July to 21 July whereupon it was set to 10 minutes with a rest time of 40 minutes, with the rest time increased to 4 hours on 23rd July to reduce power consumption for unattended operation. This raw instrument-produced data was then processed using the DTS processing package 'dtsalibration' written in Python (des Tombe et al., 2020) with the sampling time increased dependent on usage/figure output (8 hours for image plot of entire record, 96 hours for final plot before failure).

The data was collected with a Silixa XT-DTS. The cable was a BRUsens DTS steel-armored cable with 2 single-mode (OS2) fibres and 4 multi-mode (OM3) fibres in a dublex arrangement using a basal turnaround assemblage. The single-mode fibres were used in a seperate distributed acoustic sensing study (Booth et al., 2020). The dtscalibration Python package (v0.9.2) was used to process the data (https://python-dts-calibration.readthedocs.io/ https://pypi.org/project/dtscalibration/ https://github.com/dtscalibration/python-dts-calibration).

The data was calibrated using thermistor data and temperate ice at the phase-transition temperature. 95% confidence intervals are reported in Law et al. (2021) and are provided in this data set, derived from a Monte Carlo simulation. The data was fitted to thermistors, subjectivity in this fitting, and thermistor uncertainty from factors such as ice-bath calibration may further increase unertainty - see Law et al. (2021) for a full discussion.