20 Ice Trackers were deployed at the MOSAiC drifting site. The deployment of the trackers was made from the helicopter onboard RV Polarstern during Leg 5 of the expedition. The data contain the GPS positioning of the trackers (and the motion of the ice on which the trackers were deployed). The data record starts from early September 2020 and lasted until July 2021 for the longest-surviving trackers. The trackers started their drift near the North Pole and move to the south through the Fram Strait. The deployment of the trackers was done in collaboration with the MOSAiC ice team. This work was funded by NERC MOSAiC program NE/S002545/1.

This dataset contains the floe size distribution (FSD) data derived from multi-satellite imagery data acquired across the Arctic Ocean. Satellite imagery data includes high-resolution visible images from the USGS Global Fiducials Library (MEDEA), TerraSAR-X/TanDEM-X and Worldview-3 (WV3). The derived data contain floe size (calliper diameter), shape factor, minor/major axis, perimeter and area of the floes. This data set has been used to investigate the characteristics of the FSD during major seasonal evaluation stages of Arctic sea ice floes. The retrieval of the FSD data was done by the University of Huddersfield team. This work was funded by NERC MOSAiC program NE/S002545/1.

This dataset contains the post-processed GNSS/INS buoy data for a kinematic correction of a moving base station. The GNSS/INS buoys were deployed on sea ice during the 2019-20 MOSAiC expedition. These buoys recorded raw GNSS/INS data at a sampling rate of 10 Hz. For the kinematic correction, two buoys (with overlapping measurements of each other) were selected, and one of the buoys was used as a moving "base" and the other as the "rover". The post-processed dataset contains kinematically corrected latitude, longitude and velocity of the rover, as well as the baseline distance between the rover and base. The main objective of the kinematic correction is to create high-precision and high-frequency data to measure ice dynamics at a few centimetre accuracies. The buoys were assembled by the University of Huddersfield team and the deployment was done by the MOSAiC ice team throughout the expedition. This work was funded by NERC MOSAiC program NE/S002545/1.

This dataset contains the raw data from GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) buoys deployed during the 2019-2020 MOSAiC expedition. These buoys recorded the data from GNSS and Sensors. The raw GNSS data contain time, latitude, longitude, velocity, and fix type. The raw Sensors data contain time, acceleration, gyroscope, magnetometer, and temperature. These data were sampled at 10 Hz. The original data was in ANPP format (see advancednavigation.com), which have been converted to structured ASCII formats (such as RINEX, CSV) using Spatial Manager software. The buoys were assembled by the University of Huddersfield team and the deployment was done by the MOSAiC ice team throughout the expedition. This work was funded by NERC MOSAiC program NE/S002545/1.

This dataset presents biweekly gridded sea ice thickness and uncertainty for the Arctic derived from the European Space Agency's satellite CryoSat-2. An associated 'developer's product' also includes intermediate parameters used or output in the sea ice thickness processing chain. Data are provided as biweekly grids with a resolution of 80 km, mapped onto a Northern Polar Stereographic Grid, covering the Arctic region north of 50 degrees latitude, for all months of the year between October 2010 and July 2020. CryoSat-2 Level 1b Baseline-D observed radar waveforms have been retracked using two different approaches, one for the 'cold season' months of October-April and the second for 'melting season' months of May-September. The cold season retracking algorithm uses a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a physical treatment of the effect of ice surface roughness on retracked ice and ocean elevations. The method for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, and deriving sea ice radar freeboard are detailed in Landy et al. (2020). The melting season retracking algorithm uses the SAMOSA+ analytical echo model with optimization to observed CryoSat-2 waveforms through the SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation) service available through ESA Grid Processing on Demand (GPOD). The method for classifying radar returns and deriving sea ice radar freeboard in the melting season are detailed in Dawson et al. (2022). The melting season sea ice radar freeboards require a correction for an electromagnetic range bias, as described in Landy et al. (2022). After applying the correction, year-round freeboards are converted to sea ice thickness using auxiliary satellite observations of the sea ice concentration and type, as well as snow depth and density estimates from a Lagrangian snow evolution scheme: SnowModel-LG (Stroeve et al., 2020; Liston et al., 2020). The sea ice thickness uncertainties have been estimated based on methods described in Landy et al. (2022). NetCDF files contain detailed descriptions of each parameter. Funding was provided by the NERC PRE-MELT grant NE/T000546/1 and the ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS.