CAWCR Hindcast* and ECMWF ERA-5** model predictions of wave spectral properties (wave height and period) and corresponding observed data from ACE. Observations are mapped to model grids. Quality control is applied, i.e. cells with a number of points less than 5 and/or with high data variation (Standard Deviation/Mean greater than 0.2) are eliminated. Files are named as follows: WaMoS_vs_CAWCR_Hs.mat WaMoS_vs_CAWCR_Tm.mat WaMoS_vs_ERA5_Hs.mat WaMoS_vs_ERA5_Tp.mat In each file, columns show Latitude (deg.), Longitude (deg.), Time (number of days from January 0, 0000), Model Parameters (Hs, Tp or Tm) and Observed Parameters (Hs, Tp or Tm), respectively. Hs denotes significant wave height in meters, Tp is peak wave period in seconds and Tm is mean wave period based on the first moment of wave spectrum in seconds. The MATLAB file, WaMoSvsModel_FigurePlot.m, can be used to visualise the results. The files dscatter.m and polyfix.m are functions used in the MATLAB script. A sample figure (SampleFigure.png) is also included for users’ reference. * Durrant, T., Greenslade, D., Hemer, M. and Trenham, C., 2014. A global wave hindcast focussed on the Central and South Pacific (Vol. 40, No. 9, pp. 1917-1941). ** Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate . Copernicus Climate Change Service Climate Data Store (CDS), Dec. 12, 2018.

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