Greenland GNSS Network consist of more than 70 high grade GNSS (Global Navigation Satellite System) stations placed along the perimeter of Greenland. With this work, we present the processed position solution for the 20+ year record in a daily resolution. Along with the processed time series, we also publish the extensive metadata record for the network + all the raw data. A comparison with other subsets of the data showed an increased stability in the full processed dataset we here publish.

Mountain glaciers have a layered structure which contains information about past snow accumulation and ice flow. Using ground-penetrating radar instruments, the internal structure can be observed. The detection of layers in the deeper parts of a glacier is often difficult. Here, we present a new approach for imaging the englacial structure of an Alpine glacier (Colle Gnifetti, Switzerland and Italy) using a phase-sensitive radar that can detect reflection depth changes at sub-wavelength scales.

To better understand the microbial ecosystems that underlay Earth’s glaciers, studies often rely on indirect sampling of the subglacial environment via proglacial meltwater runoff. Our research in Greenland reveals that fluctuations in glacier melt can affect microbial composition in runoff, highlighting important biases often overlooked in studies of glacial runoff that might skew interpretations as to the subglacial origin of microbial communities exported within meltwaters.

Ice crystal alignment in the sheared margins of fast-flowing polar ice is important as it may control the ice sheet flow rate, from land to the ocean. Sampling shear margins is difficult because of logistical and safety considerations. We show that crystal alignments in a glacier shear margin in Antarctica can be measured using sound waves. Results from a seismic experiment on the 50 m scale and from ultrasonic experiments on the decimetre scale match ice crystal measurements from an ice core.

Ice sheet and ice shelf models rely on data from experiments to accurately represent the way ice moves. Performing experiments at the temperatures and stresses that are generally present in nature takes a long time, and so there are few of these datasets. Here, we test the method of speeding up an experiment by running it initially at a higher temperature, before dropping to a lower target temperature to generate the relevant data. We show that this method can reduce experiment time by 55 %.