A mathematical model (Bennetts and Meylan, 2021, doi.org/10.1137/20M13851) has been used to make predictions of ocean wave transfer to Ross Ice Shelf flexure. The transfer is considered along transects of the Ross Ice Shelf and adjoining open ocean, where the ice shelf thickness and seabed profiles along the transects are sampled from the Bedmap2 dataset (Fretwell et al, 2013, doi.org/10.5194/tc-7-375-2013). Our dataset consists of MAT-files, where each file is for a particular transect and holds two structures: 'data_I' as input data and 'data_o' for the model output data. The input data are the profiles from Bedmap2: 'thick' is the shelf thickness, 'draft' is the shelf draught; and 'bed' is the seabed elevation. They are all in vector form with 2001 sample points along the shelf, which was found to give model outputs accurate to 95%. The input data also contains: a 1x2 vector 'L_vec', for which the first entry is the shelf length, and the second entry is the length of the adjoining open ocean, where both values are in metres; and a 1x2 vector 'Int_vec', for which the first entry is the total number of sample points (ocean + shelf) and the second entry is the number of points in the shelf only. The output date are the three matrices where the rows correspond to different wave period and columns are distances along the transect: 'eta_w' is the water displacement (dimensionless); 'eta_s' is the shelf displacement (dimensionless); and 'str' is the flexural shelf strain (1/metres). All three outputs are normalised by the incident amplitude, noting that the model is linear. The output data also contains: a 1x300 vector containing the wave periods 'T', which are log-spaced between 10s and 1000s. The data are divided into two folders: validation/ and transects/. The first group (validation/) are used to validate the model predictions against the observations of Chen et al (Geophysical Research Letters, 2019, doi.org/10.1029/2019GL084123) close to 2 km away from the shelf front, where the results of Chen et al (2019) have been digitised and are contained in 'Chen_paper.mat'. The second group (transects/) can be used to study transfer over a 500km wide region of the Ross Ice Shelf. There are 101 transects with 5 km spacing. We also analysed the shelf displacement and strain over different wave periods at 10 km away from shelf front for all transects to investigate the relations between strain and wave period, these data have stored in 'Transfer_function_x_10km.mat'. Three MATLAB scripts (Fig1.m, Fig2.m, Fig3.m) are included to recreate results from Bennetts et al (submitted). Fig1.m produces plots from observation (Chen et al) and our models. Fig2.m performs strain transfer function analysis for different profiles and Fig3.m generate the strain map and selected region of Ross Ice Shelf for given incident ocean wave. For Fig1.m, it requires “Bedmap2 Toolbox for Matlab” to access the bedmap2 for producing Ross Ice Shelf on the Antarctica map. A link to download this software will be stated in the MATLAB scripts. An updated dataset was provided on 2022-10-25.

Although the floating sea ice surrounding the Antarctic damps ocean waves, they may still be detected hundreds of kilometres from the ice edge. Over this distance the waves leave an imprint of broken ice, which is susceptible to winds, currents, and lateral melting. The important omission of wave-ice interactions in ice/ocean models is now being addressed, which has prompted campaigns for experimental data. These exciting developments must be matched by innovative modelling techniques to create a true representation of the phenomenon that will enhance forecasting capabilities. This metadata record details laboratory wave basin experiments that were conducted to determine: (i) the wave induced motion of an isolated wooden floe; (ii) the proportion of wave energy transmitted by an array of 40 floes; and (iii) the proportion of wave energy transmitted by an array of 80 floes. Monochromatic incident waves were used, with different wave periods and wave amplitudes. The dataset provides: (i) response amplitude operators for the rigid-body motions of the isolated floe; and (ii) transmission coefficients for the multiple-floe arrays, extracted from raw experimental data using spectral methods. The dataset also contains codes required to produce theoretical predictions for comparison with the experimental data. The models are based on linear potential flow theory. These data models were developed to be applicable to Southern Ocean conditions.

Simulating WAves Nearshore (SWAN) regional wave model 7-day output with a 5-day hourly forecast for the Hawaiian islands of Kauai and Niihau at approximately 500-m resolution. This high-resolution model is utilized to capture shallow water effects and nearshore coastal dynamics such as refracting, shoaling, and smaller scale shadowing. It is run directly after the Hawaii regional WaveWatch III (WW3) wave model (ww3_hawaii) has completed. Please note that some of the nested model setup is still in the testing and validation phase. While considerable effort has been made to implement all model components in a thorough, correct, and accurate manner, numerous sources of error are possible. As such, please use these data with the caution appropriate for any ocean related activity.

Metadata record for data from ASAC Project 2504 See the link below for public details on this project. In this project a sea-ice model for application in Southern Ocean climate and forecasting studies will be developed to amend identified deficiencies in numerical models (i.e. unaccounted short-term dynamics; or non-suitable ice rheology). In-situ deformation and ice-stress data will be used to derive parameterisations suitable for the Southern Ocean pack. Antarctic sea ice is an important component of the Southern Hemisphere climate. It provides a habitat for algae, plankton and for larger species such as mammals or penguins. It is a transport medium for freshwater and biological matter. On the other hand it acts like a barrier between ocean and atmosphere in regard to the exchange of thermal energy, water vapour and gases. Sea ice affects the polar climate in many ways: E.g., by effectively insulating the ocean from the colder atmosphere the sea ice enables an advection of relatively warm water onto the shallow Antarctic continental shelf. This warmer water is then available to interact with other components of the climate system, such as by basal melting of the continental ice shelves [Jenkins and Holland, 2002]. Also, due to its high albedo, the sea ice has a large-scale effect on the net incoming solar radiation [Ebert et al., 1995] and reduces the absorption of solar energy into the upper ocean. The thermodynamic growth of seaice and the consequent desalination of the ice gives rise to a transport of salt from the ice into the ocean, which increases the water density over the shelf, thereby driving the deep vertical overturning cell in the global ocean circulation. High ice-growth rates (e.g., in regions of polynyas) are generally concentrated in small areas in shallow waters. These regions are often insufficiently resolved or even unresolved in coupled climate models, which are generally configured to run at a spatial resolution of 2 degree longitude by 1 degree latitude or coarser [Zhang and Hunke, 2001]. The specific objectives of this project are to: identify the variabilities in the sea-ice characteristics and the underlying physical processes; identify the time scales, at which the sea ice interacts with the ocean and atmosphere; assess the contribution of sub-daily ice motion and deformation due to tidal forcing and inertial response to changes within the Antarctic ocean-ice-atmosphere system; derive the impact of sub-daily ice dynamics on the sea-ice area, extent and mass on interannual and decadal time scales; determine the scale effect of dynamic processes on the accuracy of modelled sea-ice parameters using a global high-resolution model; identify model uncertainties through comprehensive validation studies. However, logistical problems prevented the project from collecting any data in the field. To overcome the paucity of planned buoy data we used the following data sets to address some of the aspects of the original proposal: 1) Sea-ice buoy data: ISPOL 2004: See AAS #2500 for metadata. 2) Numerical investigations: We have investigated the failure of sea ice using an isotropic model [Hibler, 1979], where ice strength is modelled as a random variable in the model space. In situ weakening was prescribed by a fracture-based Coulombic rheology [Hibler and Schulson, 2000]. We realised this by parameterising weakening with an ice-strength parameter of 1000 and initialising the ice strength across the model grid by random. The simulations were run over a 2000 km by 2000 km region and forced, from rest, with an idealised wind field. We analysed the sensitivity of failure to ice strength and wind stress as well as the intersection angle of the wind stress, and conducted idealised 2D failure experiments.

Simulating WAves Nearshore (SWAN) regional wave model 7-day output with a 5-day hourly forecast for the island of Guam at approximately 500-m resolution. This high-resolution model is utilized to capture shallow water effects and nearshore coastal dynamics such as refracting, shoaling, and smaller scale shadowing. It is run directly after the Mariana Islands regional WaveWatch III (WW3) wave model (ww3_mariana) has completed. Please note that some of the nested model setup is still in the testing and validation phase. While considerable effort has been made to implement all model components in a thorough, correct, and accurate manner, numerous sources of error are possible. As such, please use these data with the caution appropriate for any ocean related activity.

Simulating WAves Nearshore (SWAN) regional wave model 7-day output with a 5-day hourly forecast for the Big Island of Hawaii at approximately 500-m resolution. This high-resolution model is utilized to capture shallow water effects and nearshore coastal dynamics such as refracting, shoaling, and smaller scale shadowing. It is run directly after the Hawaii regional WaveWatch III (WW3) wave model (ww3_hawaii) has completed. Please note that some of the nested model setup is still in the testing and validation phase. While considerable effort has been made to implement all model components in a thorough, correct, and accurate manner, numerous sources of error are possible. As such, please use these data with the caution appropriate for any ocean related activity.

Simulating WAves Nearshore (SWAN) regional wave model 7-day output with a 5-day hourly forecast for the Hawaiian islands of Maui County (Maui, Molokai, Lanai, and Kahoolawe) at approximately 500-m resolution. This high-resolution model is utilized to capture shallow water effects and nearshore coastal dynamics such as refracting, shoaling, and smaller scale shadowing. It is run directly after the Hawaii regional WaveWatch III (WW3) wave model (ww3_hawaii) has completed. Please note that some of the nested model setup is still in the testing and validation phase. While considerable effort has been made to implement all model components in a thorough, correct, and accurate manner, numerous sources of error are possible. As such, please use these data with the caution appropriate for any ocean related activity.

Oceanweather performs basic and applied research in marine meteorology and ocean response numerical modeling, supported by both US and foreign government agencies. Our staff continues to gain international reputations through active participation in international scientific conferences and research programs, and in open publication. Oceanweather functions as a specialized consulting firm serving the coastal and ocean engineering communities with its unique capacity to integrate several areas of expertise into specification of definitive design data on the physical environment. Oceanweather's approach is to consistently develop and apply its high-level technology to satisfy practical requirements in the areas of marine meteorology, ocean wave and current specification, ocean engineering, and statistics of environmental data. In the past quarter century Oceanweather has performed dedicated hindcast studies and Joint Industry Projects (JIPs) in virtually every ocean basin in the world. Since 1983, Oceanweather has operated a real time forecasting division following a unique approach which optimally combines the traditional approach to weather forecasting, which retains the contributions of individual forecasters, and Oceanweather's high-level technology developed and applied so successfully in its hindcasting and consulting divisions. The system includes a global wind and wave forecast system and various high resolution regional applications.

A numerical model of ocean wave interactions with Antarctic sea ice cover, including: (i) attenuation of wave energy due to the ice cover (based on the empirical model of Meylan, Bennetts, Kohout, 2014, Geophys Res Lett, doi:10.1002/2014GL060809); and (ii) breakup of the ice cover into smaller floes due to strains imposed by wave motion (based on the theory of Williams et al, 2013, Ocean Model., doi:10.1016/j.ocemod.2013.05.010). The model is coded in FORTRAN90 for use as a module in a standalone version of the CICEv4.1 sea ice model (http://oceans11.lanl.gov/trac/CICE). It requires incident wave forcing to be specified at some constant latitude outside the ice cover, which can be user chosen or imported from data files (e.g. data given by Wavewatch III hindcasts, see http://doi.org/10.4225/08/523168703DCC5). Modifications to the existing CICE routines are given to allow integration of the broken floe sizes into its lateral melting scheme, and for incorporation of a floe bonding scheme. Bennetts, O'Farrell and Uotila (submitted) use the model to study the impact of wave-induced ice breakup on model predictions of the concentration and volume of Antarctic sea ice.

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.