The AA4528 corridor dataset contains the Matlab scripts for the corridor algorithm, ice shelf locations and file extensions. The corridor algorithm is designed to calculate the parts of the ocean which can directly propagate swell into an exposed ice shelf. The algorithm achieves this as an expansion of the coastal exposure algorithm (Reid and Massom, 2021), with the details of the inner working of the algorithm work presented in the paper attached with this dataset. Corridors can be used to calculate the frequency of swell reaching an ice shelf per year and can be combined with hindcasts to extract relevant wave data to an ice shelf for modelling or data analysis purposes. The corridor algorithm requires sea ice concentration data, which was provided by the NSIDC Sea ice concentrations from the Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1 (https://nsidc.org/data/nsidc-0051). Ice shelf coordinates were extracted from the gfsc_25s.msk that come with the sea ice data, with the aid of Antarctic Mapping Toolbox (Greene et al., 2017), and were attached separately to make editing more consistent. As this is designed to use daily sea ice data from the 1st of January 1979 onwards, I’ve also attached the sea ice files for the off-days when the sea-ice data was taken every 2nd day. Th file extensions script was also included to be able to switch through off-day files and changes that occur with the NSIDC file format. The ocean hindcast that the corridor algorithm was built around is the CAWCR Wave Hindcast – Aggregated Collection (https://data.csiro.au/collections/collection/CI39819v005). The corridor algorithm uses daily data to make it consistent with the sea ice data and calculated the maximum significant wave height for each cell present in the hindcast. Data that was extracted from it was the maximum daily significant wave height recorded in the corridor and the direction of that cell. Data was taken from 01/09/1979 to 31/08/2019 giving 40 years of data which accounts for seasonality of corridors. The excel spreadsheet attached contains relevant corridor data for each ice shelf with an area greater than 500 km^2. Area was determined by either the supplementary files from Rignot et. al., 2013, or ice shelf areas from the Antarctic mapping toolbox (Greene et al., 2017). Angle1 and Angle2 were the ones used in the direction filter, and there should be a comment in the filter with how it handles if Angle 1 is greater than Angle 2 or vice versa. Ac is the corridor area, PA is potential corridor area (i.e. the absolute max it could be with the settings we used, Ac_max is the maximum corridor area, D_cor is the days that corridors were present, Hs is significant wave height and LW (large waves) is counting days per year when significant wave heights greater than or equal to 6 m (Morim et al., 2021). Refs: Greene, C. A., Gwyther, D. E. and Blankenship, D. D. (2017) ‘Antarctic Mapping Tools for MATLAB’, Computers and Geosciences, 104, pp. 151–157. doi: 10.1016/j.cageo.2016.08.003. Morim, J. et al. (2021) ‘Global-scale changes to extreme ocean wave events due to anthropogenic warming’, Environmental Research Letters, 16(7), p. 074056. doi: 10.1088/1748-9326/ac1013. Reid, P. and Massom, R. (2021) ‘Change and Variability in Antarctic Coastal Exposure , 1979-2020’. In pre-print (https://assets.researchsquare.com/files/rs-636839/v1/02002d0b-2c6c-402b-8e14-7f77075d8f90.pdf?c=1631885736) Rignot, E. et al. (2013) ‘Ice-shelf melting around antarctica’, Science, 341(6143), pp. 266–270. doi: 10.1126/science.1235798.

The data are from our Nature Article from June 2018: "Antarctic ice shelf disintegration triggered by sea ice loss and ocean swell". The abstract is: "Understanding the causes of recent catastrophic ice shelf disintegrations is a crucial step towards improving coupled models of the Antarctic Ice Sheet and predicting its future state and contribution to sea-level rise. An overlooked climate-related causal factor is regional sea ice loss. Here we show that for the disintegration events observed (the collapse of the Larsen A and B and Wilkins ice shelves), the increased seasonal absence of a protective sea ice buffer enabled increased flexure of vulnerable outer ice shelf margins by ocean swells that probably weakened them to the point of calving. This outer-margin calving triggered wider-scale disintegration of ice shelves compromised by multiple factors in preceding years, with key prerequisites being extensive flooding and outer-margin fracturing. Wave-induced flexure is particularly effective in outermost ice shelf regions thinned by bottom crevassing. Our analysis of satellite and ocean-wave data and modelling of combined ice shelf, sea ice and wave properties highlights the need for ice sheet models to account for sea ice and ocean waves." Details of the analyses and data used, and the data generated by this study, are given in the paper: https://www.nature.com/articles/s41586-018-0212-1. Code availability: Analytical scripts used in this study are freely available from the authors via the corresponding author upon reasonable request. Data availability: The datasets and products generated during the current study are available from the corresponding author on reasonable request. The datasets forming the basis of the study are available as follows: (1) Sea ice: Daily estimates of satellite-derived sea ice concentration (gridded at a spatial resolution of 25 x 25 km) derived by the NASA Bootstrap algorithm for the period 1979-2010 were obtained from the US National Snow and Ice Data Center (NSIDC) dataset at: http://nsidc.org/data/NSIDC-0079. Accessed August 2015. (2) Waves: Ocean wave-field data were obtained from the CAWCR (Collaboration for Australian Weather and Climate Research) Wave Hindcast 1979–2010 dataset run on a 0.4 x 0.4° global grid: https://doi.org/10.4225/08/523168703DCC5. Accessed September 2017. (3) Satellite visible and thermal infrared imagery of ice shelves and disintegration events: The NOAA AVHRR image of the Larsen1995 disintegration used in Figure 2 was obtained from the British Antarctic Survey: http://www.nerc-bas.ac.uk/icd/bas_publ.html. Accessed June 2015. MODIS visible and 839 thermal infrared imagery from the US NSIDC archive at: http://nsidc.org/data/iceshelves_images/. Accessed June 2012. The study involved 2 model components, and model output is described below. The 2 models are: (i) a model of ocean swell attenuation by sea ice; and (ii) an ice shelf-ocean wave interaction model. Descriptions of both are given in the Nature paper (Methods section). DESCRIPTIONS OF THE 13 INDIVIDUAL DATA FILES PROVIDED (NB DESCRIPTIONS OF DATASETS GENERATED RELATIVE TO THE FIGURES) ARE GIVEN IN THE FILES: (1) Source data for Figures 4 (parts a-d), 5 and 6a are given in Excel spreadsheet files "Source-Data_2017-07-09041A_Figure.....xlsx". (2) Source data for Extended Data Figures 1 (parts a-b), 3 (parts b,d and parts a,c), 4 (parts b,d and a,c) and 6 are given in Excel spreadsheet files "Source-Data_2017-07-09041A_EDFig.....xlsx".

This dataset contains the Voyage Data from voyage 202122050 undertaken by the RSV Nuyina between February 12th and March 27th 2022. The principal objectives of the voyage were to retrieve equipment and exchange personnel from Davis Station, and resupply Macquarie Island Station. The EK80 acoustic instruments, underway oceanographic instruments in the OceanPack system, the ice and wave radar, and meteorological instruments were all run during this voyage. Whole of voyage data from the RSV Nuyina underway instruments. Includes uncontaminated seawater, meteorological, and wave radar data interpolated to 1 minute measurements. Wherever possible, each parameter and its associated unit of measurement complies with the NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table (Version 29) - “voyage_202122050\underway_merger\netcdf\202122050_1min_all.nc