This is a scanned copy of a document detailing data on the extent of sea ice in Antarctic from 1980 to 1988. The scanned pages consist of latitude and distance of the south pole of the northern edge of Antarctic sea ice each 10 degrees of longitude. These data were originally extracted from the U.S. navy - NOAA joint ice centre weekly maps of sea ice extent, and compiled by Jo Jacka.

Maps of East Antarctic landfast sea-ice extent, generated from approx. 250,000 1 km visible/thermal infrared cloud-free MODIS composite imagery (augmented with AMSR-E 6.25-km sea-ice concentration composite imagery when required). Because of imperfections in the MODIS composite images (typically caused by inaccurate cloud masking, persistent cloud in a given region, and/or a highly dynamic fast-ice edge), automation of the fast-ice extent retrieval process was not possible. Each image was thus classified manually. A study of errors/biases of this process revealed that most images were able to be classified with a 2-sigma accuracy of +/- ~3%. More details are provided in Fraser et al., (2010). *Version 1.2 with extra QC around the Mawson coast and Lutzow-Holm Bay The directory named "pngs" contains browsable maps of fast-ice extent, in the form of Portable Network Graphics (PNG) images. Each of the 159 consecutive images (20-day intervals from Day Of Year (DOY) 61-80, 2000 to DOY 341-366, 2008) contains a map of fast-ice extent along the East Antarctic coast, generated from MODIS and AMSR-E imagery. The colour scale is as follows: Dark blue: Fast ice, as classified from a single 20-day MODIS composite image Red: Fast ice, as classified using the previous or next 20-day MODIS composite images Yellow: Fast ice, as classified using a single 20-day AMSR-E composite image White: Antarctic continent (including ice shelves), as defined using the Mosaic of Antarctica product. Light blue: Southern ocean/pack ice/icebergs These maps are also provided as unformatted binary fast ice images, in the directory named "imgs". These .img files are all flat binary images of dimension 4300 * 425 pixels. The data type is 8-bit byte. Within the .img files, the value for each pixel indicates its cover: 0: Southern Ocean, pack ice or icebergs, corresponding to light blue in the PNG files. 1: Antarctic continent (including ice shelves), as defined using the Mosaic of Antarctica product, corresponding to white in the PNG files. 2: Fast ice, as classified from a single 20-day MODIS composite image, corresponding to dark blue in the PNG files 3: Fast ice, as classified using a single 20-day AMSR-E composite image, corresponding to yellow in the PNG files 4: Fast ice, as classified using the previous or next 20-day MODIS composite images, corresponding to red in the PNG files To assist in georeferencing these data, files containing information on the latitude and longitude of each pixel are provided in the directory named "geo". These files are summarised as follows: lats.img: File containing the latitude of the centre of each pixel. File format is unformatted 32-bit floating point, 4300 * 425 pixels. lons.img: File containing the longitude of the centre of each pixel. File format is unformatted 32-bit floating point, 4300 * 425 pixels. The .gpd Grid Point Descriptor file used to build the projection is also included. It contains parameters which you can use for matching your projection. To refer to the time series, climatology, or maps of average persistence, please reference this paper: Fraser, A. D., R. A. Massom, K. J. Michael, B. K. Galton-Fenzi, and J. L. Lieser, East Antarctic landfast sea ice distribution and variability, 2000-08, Journal of Climate 25, 4, pp. 1137-1156, 2012 In addition, please cite the following reference when describing the process of generating these maps: Fraser, A. D., R. A. Massom, and K. J. Michael, Generation of high-resolution East Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery, Elsevier Remote Sensing of Environment, 114 (12), 2888-2896, doi:10.1016/j.rse.2010.07.006, 2010. To reference the techniques for generating the MODIS composite images, please use the following reference: Fraser, A. D., R. A. Massom, and K. J. Michael, A method for compositing polar MODIS satellite images to remove cloud cover for landfast sea-ice detection, IEEE Transactions on Geoscience and Remote Sensing, 47 (9), pp. 3272-3282, doi:10.1109/TGRS.2009.2019726, 2009. Please contact Alex Fraser (adfraser@utas.edu.au) for further information.

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.

These data give the maximum extent of sea ice in the southern hemisphere by day and by winter season and the mean maximum extent by month. Data cover the 1979/1980 to 2007/2008 seasons. The data are available in csv files and, in the case of the mean monthly data, as point and line shapefiles.

This dataset comprises high spatial- and temporal-resolution maps of coastal landfast sea ice (fast ice) distribution in the vicinity of the Cape Darnley Polynya in East Antarctica, in the June-November (winter-spring) periods of 2008 and 2009. The maps were derived from cross-correlation of pairs of spatially-overlapping Envisat Advanced Synthetic Aperture Radar (ASAR) images, using a modified version of the IMCORR algorithm to determine vectors of sea-ice motion (as described in Giles et al., 2011). Fast ice is then distinguished from moving pack ice by the fact that it is stationary. The raw ASAR WSM data (swath width 500 km) were processed using ENVI image processing software to produce geo-referenced images with a 75m pixel size. Use of SAR data ensures coverage uninterrupted by cloud cover or polar darkness. Image pairs were chosen with a time separation between 2 and 21 days. IMCORR processing of the image pairs for mapping fast ice follows Giles et al (2011) – using a reference tile size of 32x32 pixels and a search tile size of 64 x 64 pixels. A land mask was applied to avoid contamination from matches on stationary features over the continental ice sheet. The grid spacing was set to 16 x 16 pixels, so the images were over-sampled by a factor of 2 to provide a more dense set of results. Stationary fast ice vectors were chosen from the IMCORR results using a combination of the cluster search technique and a variation of the z-axis threshold technique as detailed in Giles et al (2011). The cluster search technique was applied to the IMCORR results from each image pair to derive the initial set of valid vectors – this set could contain both stationary fast ice vectors and non-stationary pack ice vectors. Due to registration errors in the image pairs, the stationary vectors will not necessarily be centred around zero, so using a simple window around the zero offset mark to differentiate the fast ice vectors was not possible. To select the stationary vectors, a 2D histogram was constructed from the X-Y vector displacements, and a 2D Gaussian was fitted to this histogram. The fast ice vectors will dominate because of the large image pair time separation and small search tile size, so the Gaussian peak should correspond to the centre of the stationary fast ice vectors. All vectors that are within 5 standard deviations of the Gaussian peak are tagged as valid fast ice vectors. This is a minor modification to the method of Giles et al (2011), who used a simple threshold cut on the z-axis of the 2D histogram to define the fast ice vectors. Data format – one fully annotated (self-describing) netCDF file per image pair containing latitude/longitude coordinates of the stationary fast ice vectors. This technique and dataset complement a lower resolution but longer-term dataset (2000-2014) derived from satellite MODIS visible and thermal infrared data. (AAS_4116_Fraser_fastice_mawson_capedarnley).

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.

Metadata record for data from AAS (ASAC) Project 3024. Public The proposed research will derive improved estimates of East Antarctic fast-ice extent and thickness, and their variability, from satellite data. These will be used to explicitly test relationships between fast ice/other environmental parameters and Emperor penguin population dynamics. We shall also combine observations with a wave-ice shelf-sea ice interaction model to test the hypothesis that catastrophic ice shelf break-up events on the E. Antarctic Peninsula are linked to increased ocean wave energy associated with sea-ice extent anomalies (driven by atmospheric anomalies), and/or long-period swell from far-remote storms. This work will aid comprehension of processes responsible for recent rapid ice-shelf demise. Project objectives: 1. To measure and monitor East Antarctic fast ice areal extent and thickness, and their spatio-temporal variability, using satellite remote sensing. 2) To analyse the impact of fast ice variability on the breeding success of Emperor penguins (Aptenodytes forsteri). 3) To investigate the potential impact of sea ice on recent ice shelf break-up breakup on the Antarctic Peninsula. Taken from the 2008-2009 Progress Report: This project has shown a strong correlation between interannual fast ice variability and Emperor penguin breeding success at Dumont d'Urville, and has produced satellite-based maps of East Antarctic fast ice (radar snapshot mosaics from November 1997/98 and 20-day composite images for 2005-2008, extending back to 2000). Secondly, significant progress was made towards implicating an atmospherically-driven anomalous lack of sea ice in recent Antarctic ice-shelf disintegrations. Finally, new research highlights a previously-overlooked mechanical coupling between the floating Mertz Glacier tongue and very thick (greater than 25m) and old (greater than 20yrs) fast ice attached to it, with important implications for ice-sheet margin stability. Taken from the 2009-2010 Progress Report: Progress against objectives: 1) To measure and monitor East Antarctic fast ice areal extent and thickness, and their spatio-temporal variability, using satellite remote sensing. Considerable progress has been made against this objective, building on last year's publication of the first detailed "snapshot" maps of landfast sea ice (fast ice) extent around the East Antarctic coast from 75 degrees E-170 degrees E for the Novembers of 1997 and 1999 using RADARSAT satellite ScanSAR images (see Giles et al., 2008). The main achievements are: * The development of an improved semi-automated method to successfully derive fast ice extent (and pack ice motion) from time series of Envisat Advanced SAR images (Giles et al., in prep.), via a project with the European Space Agency and the International Space Science Institute (Berne, Switzerland). Fast ice is identified as regions of zero motion in the cross-correlation analysis of carefully co-registered pairs of satellite SAR images. * Significant progress in the PhD project (Alex Fraser) aimed at deriving longer and near-continuous time series of fast ice extent from time series of NASA MODIS visible and thermal IR imagery at 1 km resolution. A major challenge has been to address the problem of effectively 'removing' persistent cloud cover from the images. This has been achieved by compositing many thousands of MODIS images to create 20-day composite images of the entire East Antarctic coastal zone from 10W to 170E. This technique was showcased at the prestigious International Geoscience and Remote Sensing 2009 conference in South Africa in July 2009 (Fraser et al., 2009a), with subsequent publication by Fraser et al. (2009b). During the year, this work resulted in an important new time series of fast ice extent that runs from 2000 to 2008 inclusive (Fraser et al., in prep.), with techniques being described in Fraser et al. (in press). This unique dataset represents by far the most detailed estimate of East Antarctic fast ice and its spatio-temporal variability to date. It furthermore represents an important new baseline against which to gauge change, given that Antarctic fast ice is a key yet poorly understood component of the global cryosphere (and ocean freshwater budget), is of immense ecological significance (see 2 below), and is a sensitive indicator of climate change/variability. This baseline is directly comparable to the more familiar overall sea ice (pack ice) extent product. Work is underway to determine why large regional differences occur in fast ice distribution and behaviour, including analysis of the role of bathymetry, grounded icebergs and changes in wind patterns. This work also provides crucial regional-scale fast ice information in support of detailed localised fast ice measurements carried out within the Antarctic Fast Ice network at Casey and Davis (AAS 3032). * A collaborative project has been established with Drs Fricker (USA) and Legresy (France) to estimate the thickness of a large region of perennial fast ice adjacent and attached to the Mertz Glacier Tongue. This has been achieved by combining satellite imagery with surface elevation data from the NASA's ICESat laser altimeter satellite, although current unknowns include the thickness and density of the overlying snowcover. The results suggest that this fast ice is extraordinarily thick i.e. greater than 25 m, and may be at least 20 years old (Massom et al., subm., a). Work examining the glaciological significance of this extremely thick fast ice is described in 3 (below). Work is also underway to evaluate the impact on this and regional fast ice of the major calving of the Mertz Glacier in February 2010. 2) To analyse the impact of fast ice variability on the breeding success of Emperor penguins The first element of this multi-disciplinary, international study was completed last year i.e. a case study showing strong links between Emperor penguin breeding success at Dumont d'Urville and fast ice distribution along the Adelie Land coast of East Antarctica and its variability due to variability in the regional wind field. Results were published in Marine Ecology Progress Series (Massom et al., 2009a), and were also presented in a keynote address to the Xth SCAR International Biology Symposium in September 2009. Work is underway to extend this study both temporally and to other species and regions, using the new MODIS-derived time series of 20-day composite maps of fast ice extent (see 1 above). This work will include a comparison of the fast ice information with new data from French penguin scientists (Drs Barbraud, Ancel and LeMayo) on Emperor penguin mortality and other demographic parameters, with a view to discovering links between the penguin demographics and fast ice variability due to changing weather patterns. Further work is in its initial stages to study the impact of fast ice variability on i) Weddell seal foraging behaviour (with Dr Hindell's group at the Univ. of Tasmania), ii) Adelie penguin breeding success and foraging behaviour (with Drs Southwell and Emmerson, AAD), and iii) other Emperor penguin colonies in East Antarctica (with Dr Wienecke, AAD). Ongoing/future work will also evaluate the impact of abrupt change on the seals and penguins at Dumont d'Urville following the Mertz Glacier calving in February 2010.

Abrupt Mid-Twentieth Century Decline in Antarctic Sea-Ice Extent from Whaling Records. Format is a WinZip'ed Microsoft Access 2000 database. The northernmost edge of the sea ice was derived from the southernmost positions of whale catches. A supplemental word document is also included with the dataset. This dataset was originally compiled by Bill de la Mare. The fields in this dataset are: record ID latitude mean latitude longitude longitude Interval date season decade id catch Record species ID

In situ measurements of ice and snow thickness, and freeboard along an irregular transect on the fast, complementing the repeat ROV (Remotely Operated Vehicle) transects. During our deployment at Davis in 2015 logistics and environmental conditions permitted measurements along 4 transects. The location of the reference grid (ROV box) had its origin (x=0, y=0) at (-68.568904 degrees N,+77.945439 degrees E). Transects 1 – 4 started at x=60, x=70, x=80 and x=90 m and were sampled at y-positions of 0m, 0.5m, 1m, 2m, 4m, 8m, 16m, 32m, 64m, 128m, (256m, and 512m), respectively. Depending on working conditions the overall transect lengths varied from 128 – 512 m. Sampling dates for in situ ice physcis: Transect ID Date of sampling Zice and FB measured at Ice core taken at Snowpit measured at T1 19/11/2015 0, 0.5, 1, 2, 4, 8, … 64m. 0m, 128m, 512m 0m, 128m, 512m T2 23/11/2015 0, 0.5, 1, 2, 4, 8, … 64m. 0m, 128m, 512m 0m, 128m T3 29/11/2015 0, 0.5, 1, 2, 4, 8, … 64m. 0m, 128m 0m, 128m T4 02/12/2015 0, 0.5, 1, 2, 4, 8, … 64m. 0m, 128m 0m, 128m Ice cores and snow pits were collected at the 0m, 50m and 100m mark along the transect, where possible. Additionally, ice cores for density analysis were taken at a few of the ice-core sites for independent verification of ice density.

Imagery of Aurora Australis and sea ice captured by a 'quadcopter' (Inspire) drone launched from the ship