Described fully in (https://doi.org/10.21203/rs.3.rs-636839/v1 holder). Data The main CEL method, and a subsidiary Coastal Exposure Index or CEI (both described below), are based on daily sea-ice concentration products for the period 1979 through 2020. These products are derived from the multi-satellite passive-microwave brightness temperature time series using the NASA Team algorithm, mapped at 25 km x 25 km resolution and obtained from the NASA National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC). Both algorithms are designed to be adaptable for different resolution data. Complete coverage of the entire Antarctic coastal and sea-ice zones is obtained on a daily basis, except for 1979-July 1987 (once every two days). Missing single days during this period are interpolated from the adjoining day's sea-ice concentration values. Averages and climatologies are based on the period 1979-2020, unless otherwise stated. The continental land mask used (gsfc_25s.msk) is also obtained from NSIDC, and includes ice shelves (the seaward extremities of which are taken here to be coastline). Coastline grid points are defined from the continental land mask as any ocean grid point that has land/ice sheet adjacent to it. Analysis methods For this study, we developed two new but different algorithms for quantifying and monitoring Antarctic coastal exposure: the Coastal Exposure Index (CEI) and Coastal Exposure Length (CEL) method. The CEI technique is based on the detection of sea ice presence/absence radially out (northwards) from the coastline along each meridian (at one degree longitudinal spacing), following masking of the ice sheet. The CEI is simply defined as the number of longitudes with no sea ice (threshold set to less than 15% following convention) to the north of the continent, and hence runs from zero to 360. This methodology is trivial and code for this is not included. CEL is defined as the length (in kms) of the Antarctic coastal perimeter with no adjacent sea ice anywhere offshore (i.e. total exposure of the coast to the open Southern Ocean with no intervening sea ice), but excluding coastal polynyas. By this method, we use the land mask to determine if each coastal grid point has an immediately-adjacent ocean grid point that is ice-free (i.e. has a sea-ice concentration of less than 15%). If this criterion is met, then a nearest (adjoining) neighbour-testing technique is used to determine whether that ocean grid point is exposed in some way to the wider open ocean or is bound by neighbouring sea ice offshore. If any of the neighbouring grid points are classified as “exposed”, or if the total area of neighbouring ice-free grid points exceeds an arbitrary cut-off of 500,000 km2, then that coastal grid point is classified as “exposed”. Otherwise, the grid point and all sea-ice-free neighbouring grid points are deemed to be bounded by sea ice and are classified as a coastal polynya. The length of individual exposed coastal grid points is estimated by taking the square root of the respective pixel area. The length of coastal exposure, either regionally or net circum-Antarctic, is then simply the sum of the length of exposed coastal grid points. The IDL code used for calculating CEL is included here.

This dataset contains the digitisation of one U.S. Navy/NOAA Joint Ice Facility sea ice extent and concentration map monthly to give the latitude and longitude of the northern extent of the Antarctic sea ice. Maps were produced weekly, but have been digitised monthly, since distribution began in January 1973 (except August 1985), until December 1996. Maps were digitised at each 10 degrees of longitude, and the longitude, distance from the south pole to the northern edge of the sea ice at that longitude, and latitude of that edge is given, as well as the mean distance and latitude for that map. Summary tabulations (sea ice northern extent latitudes at each 10 degree of longitude each year, grouped by month) and mean monthly sea ice extent statistics are also available.

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.

This indicator is no longer maintained, and is considered OBSOLETE. INDICATOR DEFINITION The northern limit of the pack ice as defined by the 15% concentration of sea ice determined by the SSM/I instrument or its replacement. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION RATIONALE FOR INDICATOR SELECTION Climate is affected by complex interactions between the sea ice and the atmosphere and ocean. The sea ice extent and concentration is determined by the oceanic and atmospheric forcing. There is evidence of variations in the sea ice extent and concentration on a synoptic time scale as storms pass through the region, and variations in sea ice extent on a multi-year time frame with forcing caused by the Antarctic circumpolar wave. Over the past 20 years, there is limited evidence of an increase in spatial ice extent and in the length of time that ice is present. Continued monitoring of sea ice extent and concentration may provide insights into the dynamics of the Southern Ocean and help to predict future climate. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM NASA uses a combination of satellite passive microwave sensors to measure the brightness values over sea ice covered regions. They then use an algorithm (referred to as the 'team' algorithm) to calculate the ice concentration and to determine the ice edge. The data are available globally on a daily or monthly basis. RESEARCH ISSUES Currently, NASA intends to maintain a series of satellite microwave sensors to continue to monitor sea ice extent and concentration. Ongoing research to interpret the data are currently being carried out at the AAD and the Antarctic and Southern Ocean CRC. Links with other indicators The sea ice extent and concentration has a large impact on the surface salinity and temperatures. Thus strong links with sea surface salinity and sea surface temperatures.

This dataset represents extents of Antarctic sea ice derived from passive microwave data. It includes: maximum and minimum sea ice extent based on 1989 - 99 data; maximum sea ice extent by month for the period October - March based on 1973 - 98 data; mean sea ice extent by month based on 1973 - 1998 data; and maximum sea ice extent averaged over the period 1987 - 1998. The data referenced by this metadata record has been sourced from another metadata record in this catalogue. For more information on the dataset see: Antarctic CRC and Australian Antarctic Division Climate Data Set - Northern extent of Antarctic sea ice [climate_sea_ice].

Data repository for the paper: "The roles of sea-ice, light and sedimentation in structuring shallow Antarctic benthic communities" Graeme F. Clark, Jonathan S. Stark, Anne S. Palmer, Martin J. Riddle, Emma L. Johnston. PLoS ONE Data are boulder communities (epifauna), annual light budgets, and sediment traps. See the paper for more details. ABSTRACT On polar coasts, seasonal sea-ice duration strongly influences shallow marine environments by affecting environmental conditions, such as light, sedimentation, and physical disturbance. Sea-ice dynamics are changing in response to climate, but there is limited understanding of how this might affect shallow marine environments and benthos. Here we present a unique set of physical and biological data from a single region of Antarctic coast, and use it to gain insights into factors shaping polar benthic communities. At sites encompassing a gradient of sea-ice duration, we measured temporal and spatial variation in light and sedimentation and hard-substrate communities at different depths and substrate orientations. Biological trends were highly correlated with sea-ice duration, and appear to be driven by opposing gradients in light and sedimentation. As sea-ice duration decreased, there was increased light and reduced sedimentation, and concurrent shifts in community structure from invertebrate to algal dominance. Trends were strongest on shallower, horizontal surfaces, which are most exposed to light and sedimentation. Depth and substrate orientation appear to mediate exposure of benthos to these factors, thereby tempering effects of sea-ice and increasing biological heterogeneity. However, while light and sedimentation both varied spatially with sea-ice, their dynamics differed temporally. Light was sensitive to the site-specific date of sea-ice breakout, whereas sedimentation fluctuated at a regional scale coincident with the summer phytoplankton bloom. Sea-ice duration is clearly the overarching force structuring these shallow Antarctic benthic communities, but direct effects are imposed via light and sedimentation, and mediated by habitat characteristics. Data files: Boulder_community_data.csv - Percentage cover data for sessile organisms (invertebrates and algae) growing on boulder surfaces. - Columns 1 to 5 are sample attributes, columns 6 to 57 are measured variables (species or bare space). Light_budget_data.csv - Annual light budgets at each site, recorded by light metres. - Columns are site name and annual light budget (mol photons m-2 year-1) Sediment_trap_data.csv - Total sediment collected in sediment traps - Columns are site label, position in bay, replicate, dates deployed and retrieved, and the calculated sediment flux (g m-2 d-1)

An image correlation technique has been applied to RADARSAT ScanSAR images from November in 1997, and November 1999, to create the first detailed maps of fast ice around East Antarctica (75E-170E). This method is based upon searching for, and distinguishing, correlated regions of the ice-covered ocean which remain stationary, in contrast to adjacent moving pack ice. Within the overlapping longitudinal range of ~86E-150.6E, the total fast-ice area is 141,450 km2 in 1997 and 152,216 km2 in 1999. Calibrated radar backscatter data are also used to determine the distribution of two fast-ice classes based on their surface roughness characteristics. The outer boundaries of the determined fast-ice area for November in 1997 and 1999 are contained in the data files for this record. This work has been allocated to ASAC project 3024.

Taken from the "Supporting Information" for the main paper. See the referenced papers for more information. Our results are based on numerical simulation of Southern Ocean sea ice, conducted using the Los Alamos numerical sea-ice model CICE version 4.0 [CICE4; Bailey et al., 2010] configured in stand-alone mode on a 0.25 degree x 0.25 degree grid, extending to 45 degrees S, with 3-hourly output [Stevens, 2013]. The atmospheric forcing for CICE4 came from the hemispheric forecasting model Polar Limited Area Prediction Systems [Polar- LAPS; Adams, 2006] and ocean forcing from the global ocean general circulation model Australian Climate Ocean Model [AusCOM; Bi and Marsland, 2010]. The model is well-constrained in its representation of processes of sea ice formation and melt, and comparison with observed areal ice extent shows minimal deviations over the 1998-2003 period, particularly during winter [Stevens 2013]. Stevens [2013] evaluates the sensitivity of the model to the number of ice thickness categories. Sea ice thickness sensitivities in the CICE model are considered in detail in Hunke [2010, 2014]. For the warm climate scenario, changes were implemented that are consistent with the A1B scenario from the Fourth Assessment from the IPCC [Meehl et al., 2007]. This is a mid-range scenario that assumes rapid economic growth before introduction of new and more efficient technologies mid century. Specifically, the following changes were applied uniformly to the current climate forcing field for a single year: a 2 degrees C increase in air temperature, a 0.2 mm/day increase in rain, a 1.5% increase in cloud fraction, a -2.3 hPa change in surface air pressure, a 25% increase in wind, a 12 Wm-2 increase in long wave downward radiation and a 20% increase in humidity. Outputs and forcings from CICE4 that are relevant for consideration of under-ice habitats for larval krill include: snow depth, ice thickness, ice concentration, movement, ridging rate, day length (dependent on day-of-year and latitude), radiation above the ice (influenced by cloud cover), and radiation below the ice (influenced by ice and snow depth). Table 1 in the main text describes how these were used in the following two filters and one overlay for evaluating the location and suitability of potential larval krill habitat during winter. Taken from the abstract of the main paper: Over-wintering of larvae underneath Antarctic pack ice is a critical stage in the life cycle of Antarctic krill. However, there are no circumpolar assessments of available habitat for larval krill, making it difficult to evaluate how climate change may impact this life stage. We use outputs from a circumpolar sea-ice model, together with a set of simple assumptions regarding key habitat features, to identify possible regions of larval krill habitat around Antarctica during winter. In particular we assume that the location and suitability of habitat is determined by both food availability and three dimensional complexity of the sea ice. We then compare the combined area of these regions under current conditions to that under a warm climate scenario. Results indicate that, while total areal sea-ice extent decreases, there is a consistently larger area of potential larval krill habitat under warm conditions. These findings highlight that decreases in sea-ice extent may not necessarily be detrimental for krill populations and underline the complexity of predicting future trajectories for this key species in the Antarctic ecosystem.

Metadata record for data from ASAC Project 1329 See the link below for public details on this project. ---- Public Summary from Project---- The Antarctic Circumpolar Wave is a mode of high latitude variability involving the atmosphere, ocean and sea ice. Some research indicates it has a period of about 5 years but the robustness and persistence has yet to be fully established. This project will examine the nature of the ACW in a long data series, and will determine whether the wave is related to Australian rainfall. In this project, sea ice data were sourced from the National Snow and Ice Data Center (CIRES, University of Colorado, Boulder, CO 80309-0449, USA). The NCEP reanalysis data set was sourced from: NOAA/ National Weather Service, National Centers for Environmental Prediction (5200 Auth Road, Camp Springs, Maryland, 20746 USA). Australian rainfall data were taken from Jones and Weymouth (1997: An Australian Monthly Rainfall Dataset. Technical Report No. 70, Bureau of Meteorology, 19 pp.) compilation and provided digitally by the Bureau of Meteorology. The sea ice concentration data used were for the Antarctic only (the entire Antarctic sea ice domain). Data started in 1978. All data were collected by satellite. A link to a metadata record for these data are available from the URL given below. Two NCEP reanalysis data sets were used in this study. The first was NCEP/NCAR, with 6-hourly data available from 1958 (see the URL provided below for further information). The second was the NCEP/DOE set, with 6-hourly data available from 1979 (see the URL provided below for further information). In this project the following model/analysis was applied: Application of The University of Melbourne cyclone tracking scheme (Simmonds et al., 2003, Monthly Weather Review, 131, 272-288) and a broad range of statistical tests. Brief details are provided in the Summary. See the link for the pdf document for more detailed information. These complex statistical analyses were run over the entire length of the project (2001/02 - 2003/04). They were run on the Sun Workstation cluster in the School of Earth Sciences, The University of Melbourne.

ASPeCt is an expert group on multi-disciplinary Antarctic sea ice zone research within the SCAR Physical Sciences program. Established in 1996, ASPeCt has the key objective of improving our understanding of the Antarctic sea ice zone through focussed and ongoing field programs, remote sensing and numerical modelling. The program is designed to complement, and contribute to, other international science programs in Antarctica as well as existing and proposed research programs within national Antarctic programs. ASPeCt also includes a component of data rescue of valuable historical sea ice zone information. The overall aim of ASPeCt is to understand and model the role of Antarctic sea ice in the coupled atmosphere-ice-ocean system. This requires an understanding of key processes, and the determination of physical, chemical, and biological properties of the sea ice zone. These are addressed by objectives which are: 1) To establish the distribution of the basic physical properties of sea ice that are important to air-sea interaction and to biological processes within the Antarctic sea-ice zone (ice and snow cover thickness distributions; structural, chemical and thermal properties of the snow and ice; upper ocean hydrography; floe size and lead distribution). These data are required to derive forcing and validation fields for climate models and to determine factors controlling the biology and ecology of the sea ice-associated biota. 2) To understand the key sea-ice zone processes necessary for improved parameterization of these processes in coupled models. These ASPeCt measurements were taken onboard the Aurora Australis during the SIPEX voyage in the 2007-2008 summer season.