Implementation of a sea-ice model for application in the Antarctic
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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.
Projected Sea Ice Thickness change based on CMIP5 multi-model ensembles
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Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in sea ice thickness, based on an ensemble of twenty-six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in sea ice thickness is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensemble of sea ice thickness change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in sea ice thickness (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.
Marginal ice zone drift prediction model
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A Langrangian free drift model is developed, including a term for geostrophic currents that reproduces the 13 h period signature in the ice motion observed in the data (CLSC_WIIOS_2017; parent data). The calibrated model is shown to provide accurate predictions of the ice drift for up to 2 days, and the calibrated parameters provide estimates of wind and ocean drag for pancake floes under storm conditions. Model setup is described in "Drift of pancake ice floes in the winter Antarctic marginal ice zone during polar cyclones", Alberello et. al [https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JC015418; pre-print https://arxiv.org/pdf/1906.10839.pdf]. The dataset includes model data. Six model outputs are included. (i) "full_t00" includes the full 10 days simulation, with all the forcing switched on (ii) "noge_t00" includes the full 10 days simulation, but the geostrophic current is suppressed (iii) "full_t25_noup" includes the simulation with start at 2.5 days, all the forcing switched on, no update of the drag coefficients (iv) "full_t25_newn" includes the simulation with start at 2.5 days, all the forcing switched on, the drag coefficients are recalibrated (v) "full_t50_noup" includes the simulation with start at 5 days, all the forcing switched on, no update of the drag coefficients (vi) "full_t50_newn" includes the simulation with start at 5 days, all the forcing switched on, the drag coefficients are recalibrated In each file: - rho_a the air density (1.3 kg/m3) - rho_w the water density (1028 kg/m3) - rho_i the ice density (910kg/m3) - C_w the water drag coefficient (calibrated) - C_a the air drag coefficient (calibrated) - turn the turning angle (25 degrees) - Nansen the Nansen number evaluated using C_a and C_w - aalpha a model parameter (proportional to air and ice parameters) - abeta a model parameter (proportional to water and ice parameters) - ag amplitude of the geostrophic current (U_g=0.125m/s) - tg initial phase of the geostrophic current (in radians) - to start time (in matlab format, use "datestr(to)" ), after which model resolution is 60 seconds - wo components of wind in the East and North direction (m/s) - wi components of wind in the East and North direction (m/s) - uo components of modelled ice drift speed in the East and North direction (m/s) - lo longitude and latitude of modelled ice position (degrees) - xo position of modelled ice in the East and North direction (m), given with respect to the initial position (0,0) - wco components in the East and North direction of geostrophic current (m/s)
Data for: Modelling ocean wave transfer to Ross Ice Shelf flexure
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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.
Comparison of theoretical and laboratory models of ocean wave transmission by a group of ice floes
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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.
Numerical Simulations wave fields Davis Sea, WAVEWATCHIII, January 2020
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This dataset contains numerical simulation results of the wave fields in the Davis Sea from end of December 2019 to start of February 2020. Hindcasts were obtained through the third-generation spectral wave model WAVEWATCH-III (hereafter WW3). A high resolution Davis Sea regional grid (resolution 0.1 degree, 60-80E longitude, 70-60S latitude) was nested into global grid domain (resolution 0.5 degree, 80S-80N latitude). The global model is forced with 0.5 degree sea ice concentration and 10m-wind fields from ECMWF's ERA5 reanalysis. The Davis sea model is forced with 0.1 degree 10m-wind fields from ECMWF's archived forecasts, and high-resolution (3.125km) AMSR2 satellite data for sea ice concentration (Beitsch et al., 2013 updated). Ice-induced wave attenuation is parameterized following Sutherland et al. (2019, doi:10.1016/j.apor.2019.03.023) whilst the break-up of sea ice is parameterized as 'broken' or 'unbroken' based on the break-up parameter of Voermans et al.(2020, doi:10.5194/tc-14-4265-2020). The numerical simulations have been calibrated using the buoy-observations of Voermans (2022, dataset, doi:10.26179/cdmx-n995). Sensitivity of the simulations to sea ice properties was tested and all results are provided in the dataset. The data tree: * global: model outputs for the global domain - ncfield: gridded wave and ice data for this domain in netCDF-4 format - nests: binary data used by WW3 for boundary conditions for the Davis Sea grid - restarts: binary data used by WW3 for restarting this domain * davis_sea: model outputs for the Davis Sea domain - ncfield: gridded wave and ice data for this domain in netCDF-4 format - ncpoint: spectral wave data for a few points in the Davis Sea in netCDF-4 format - nctrack: spectral wave data following the wave buoys of Voermans et al (2022) in the Davis Sea in netCDF-4 format - restarts: binary data used by WW3 for restarting this domain - IHOT: binary text field of broken and unbroken ice for restarting this domain File naming convention (by example): ww3.20200101_20200103_M3D_IHOT_H0P0325_A0P01_YY9P0_SS0P1_HH0P55.nc * 20200101_20200103 identifies the datespan of the simulation in YYYYMMDD format * A0P01 refers to the attenuation coefficient of the model (where P stands for 'point'), in this case, A=0.01 * YY is the Young's Modulus timed 10^9, here, Y-9.0e9 Pa * SS is the ice strength 'sigma' times 10^6, here sigma=0.1e6 * HH is the ice thickness, here h=0.55 m * H0P0325 is proportional to the epsilon calibration coefficient (H=0.5ice_thicknessepsilon). * M3D refers to the 3rd instantiation of the model * IHOT refers to hot start using the ice breakup field from the previous week. ww3.*_M3D_IHOT_H0P065_A0P05_YY6P0_SS0P55.nc is considered the baseline file (note, this simulation only covers the first two weeks of the study period). Reference: Beitsch, A., Kaleschke, L. and Kern, S. (2013). "AMSR2 ASI 3.125 km Sea Ice Concentration Data, V0.1", Institute of Oceanography, University of Hamburg, Germany, digital media
High-resolution (4-km) circum-Antarctic ocean-ice shelf model: Whole Antarctica Ocean Model (WAOM4)
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The Whole-Antarctic Ocean Model (WAOM) is based on the Regional Ocean Modelling System (ROMS) with thermodynamic ice shelf interactions following Galton-Fenzi et al. (2012)¹. WAOM simulations of the present-day (year 2007) were used to investigate the physical drivers of Antarctic ice shelf basal melting via an ocean heat budget in a longitudinal framework. Ice shelves buttress the Antarctic Ice Sheet and therefore are critical in the dynamics of ice sheet instability and its contribution to sea level rise. New insights into the seasonal melting mechanisms and its importance at circum-Antarctic scale were investigated in Dias et al (2025)². 1) Galton-Fenzi B, Hunter JR, Coleman R, Marsland SJ, & Warner RC. (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf, Journal of Geophysical Research: Oceans, 117 (C9). https://doi.org/10.1029/2012JC008214 2) Boeira Dias F, England MH, Morrison AK, & Galton-Fenzi B. (2025).: On the seasonal variability of ocean heat transport and ice shelf melt around Antarctica, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3905