This dataset contains iceberg observations collected routinely on Australian National Antarctic Research Expeditions (ANARE) by Antarctic expeditioners on a volunteer basis. The observations were made each austral summer from the 1978/1979 season until the 2000/2001 season. Data included voyage number, date, time, latitude, longitude, sea ice concentration, water temperature, total icebergs, number of icebergs in each width category, the width to height ratio of selected larger tabular icebergs. It was been compiled and presented on the web by the Glaciology program of the Antarctic CRC (now ACE CRC).

This is a simple index which looks at the 360x1-degree longitudinal wedges around the Antarctic continent to see if there is any sea ice (where sea ice concentration is greater than 15%) to the north of the continent in each of these wedges. The index goes from 0 (sea ice to the north off the continent in every longitude wedge) to 360 (no sea ice around the continent at all. Notes about the spreadsheet: "-" means no data. Satellite data was not available for those years. Otherwise the index goes from 0 through to 360. - Zero means that there is no longitude around the continent where there is coastal exposure. - 18 (for example) means that there are 18 longitudinal wedges around the continent with coastal exposure. This project used the following NASA data to develop the coastal exposure index: Cavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated yearly. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. [1979-2015]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/8GQ8LZQVL0VL. [2016-05-30]

Between 1954 and 1975, iceberg observations were collected on Australian National Antarctic Research Expeditions (ANARE) by Antarctic expeditioners on a volunteer basis as they travelled to and from Antarctica. No fixed format for data collection had been determined, and many of the observations recorded are in diary format. The data have not been converted to electronic form, and are available only in the original logbooks held at the National Archives Office.

We estimate population size in terms of the number of occupied nests for the Adélie penguin metapopulation in western Mac. Robertson Land, East Antarctica in 2009/10 and 2019/20. We also assessed demographic data from a single breeding site in the central part of this area (Béchervaise Island: 67°35'S, 62°49'E) including reproductive success, resight data, and fledgling mass from 1991/92 to 2019/20. We collated environmental covariates of potential drivers in this area over the same time period from sources described below. These are presented in the file “Time series demography and environmental covariates.xls”. Environmental covariates: Sea-ice concentration: Summer sea-ice concentration (SIC) was obtained for the area bounded by longitudes 60 - 65°E, to the south by the Antarctic coastline and the north by latitude 66.75°S. This approximately 250 km stretch of coastline incorporates the location of all Adélie penguin breeding sites across the metapopulation. The area defines the most northerly limit of fast-ice during chick rearing and encompasses the longitudinal range of the birds’ summer foraging activities. The sea-ice contained within this ‘near-shore’ region is predominantly composed of fast-ice (ice that is attached to land but covers seawater). Summer SIC was calculated as an average over the three-week period 25th December to 15th January when adults are guarding chicks for each breeding season. Winter SIC was determined in the following three areas of the penguins’ winter migratory route as defined previously. Each area was defined between specific longitudes and from 50°S south to the Antarctic coastline. The sea-ice contained within this area is composed of fast-ice near the coastline and pack-ice (all sea-ice that is not fast-ice) beyond the fast-ice edge. Two sectors defined the outward journey as they travelled westward towards their winter foraging grounds (50 - 65°E during March, and 30 - 50°E during April), a winter area (15 - 30°E during May-Jul) was considered as the sea ice became more extensive with both 15-100% SIC and 15-80% SIC which is considered more in line with suitable winter foraging ice conditions. The final area was associated with their eastwards journey towards the colony (30 - 50°E during Aug-Sep). For each area and time period, an average SIC was determined for each year in each of these areas. SIC values reflect the total area (km2) covered in sea-ice between either 15-100% or 15-80% SIC in each year and time period using 25x25km pixels. Sea-ice data were obtained from the National Snow and Ice Data Center (NSIDC) (Cavalieri et al. 1996) using Raadtools (Sumner 2017). Broad-scale climatic indices and local weather conditions: We determined the weather conditions during periods reflecting the end of the austral summer when the penguins were leaving their colonies (Feb-Mar) and the inter-breeding winter period (Apr-Sep). The Southern Oscillation Index (SOI) and the Southern Annular Mode (SAM) were included as broad indicators of climatic conditions, and local weather conditions included air and windchill temperatures. SOI was obtained from the Australian Bureau of Meteorology (www.bom.gov.au) and SAM from the NOAA Climate Prediction Centre (http://www.cpc.noaa.gov/products/precip/CWlink/daily_ao_index/aao/aao_index.html). Mawson Station local weather: Local weather data recorded at Mawson Station were obtained from the Australian Bureau of Meteorology. We considered two covariates: windchill and air temperatures both reported in °C. Windchill temperatures were determined from the ambient air temperature, wind speed and the relative humidity: AT= Ta +0.33e-0.7ws-4.0, where Ta is the dry bulb temperature (°C), e is the water vapour pressure (hPa), and ws is the windspeed (ms-1) at 10 m elevation. Water vapour pressure was determined from: (see the actual equation in the download file - "Emmerson_AADC Metadata Records_GCB_2022.docx" - unable to be reproduced here), where rh is the relative humidity (%). This formula follows the Australian Bureau of Meteorology calculation (www.bom.gov.au/info/thermal_stress/). Seabird population parameters: Pre-fledging mass "adjusted.fledge.mass.5_Feb": We determined pre-fledging mass (g) of chicks on the 5th February by either measuring their mass on that date, or by standardising to that date from measurements made between the 3rd and 14th February. Total chick productivity "tot.chick.prod.past.5.yrs": Cumulative five-year total chick productivity (total chicks) was calculated for each year using total counts across Béchervaise Island from the preceding five years. This represents the cumulative pool of pre-breeders on the basis that Adélie penguins typically recruit into the breeding population between the ages of one and five years. Breeding success "bs.3.yr.ave": at Béchervaise Island was measured as the number of chicks crèched (end-January) in relation to the number of nests occupied at the start of incubation (late November and beginning of December). Units of measurement are chicks per occupied nest. Nest and chick counts were obtained annually from on-ground island-wide surveys. Because reproductive performance fluctuates dramatically across years, we calculated three-year rolling averages centred on the year of interest. Resight data: Age of first return to the colony or recruitment into the breeding population “Age first.nesting.all.6.years” were based on resights of birds in their natal colony. Marked birds were resighted via colony-wide detection from a tag reader when they were on nests. Files for each year contain data from resighting with hand-held tag readers across Béchervaise Island including date of resight and the tag number with each file named as “2003_04 resights.xls” for the resights in 2003/04 split-season for example. For resight data outside the years available in this data repository, please contact Data Custodians. Population growth rates: Circum-Antarctic population growth rates: To allow a circum-Antarctic comparison of this populations growth rate with other sites or regions, we performed a literature review of published data or growth rates for estimating a consistent metric of growth rate. Data from this search are included in this dataset along with estimates of population growth rate in this study in file “Circum-Antarctic estimates of population growth rates for Adelie penguins Figure 2.pdf”. Occupied nest counts Mac. Robertson Land: Adult counts were adjusted for phenology-related variable attendance and potential methodology bias to a standard metric (the number of occupied nests at the beginning of the incubation period). The adjustment process is described in detail in Southwell et al. (2013) and propagates the uncertainties from accounting for these biases through to the final estimate of occupied nests. Data include 1000 bootstrap estimates of occupied nests from this procedure for the Mac. Robertson Land area to standardise raw counts to the metric of occupied nests labelled as “O.N.bootstrap.estimates.2009_10” for 2009/10 and 2019/20 which we summarised with the median and 95 percentile limits. Please see manuscript for further details on the standardisation process. Data presented in file “W Mac. Robertson Land Adelie penguin population estimates.xls”. Any data use from this repository in any publication, report or presentation, should include the following acknowledgement in each data file based on the following “Data from Béchervaise Island or Mac. Robertson Land were derived from Australian Antarctic Science projects 2205, 2552, 4088, 4086 and 4518. All procedures were approved through Australian Antarctic Division animal ethics and ATEP approvals.” Please refer to the Seabird Conservation Team Data Sharing Policy for use, acknowledgement and availability of data prior to downloading data.

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.

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 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.

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.

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)

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.