This dataset contains records of ice thickness and snow thickness from Mawson, Antarctica. Measurements were attempted on a weekly basis and have been recorded since 1954 and are ongoing, although this record only contains data up until the end of 1989. The observations are not continuous however. The dataset is available via the provided URL. These data were also collected as part of ASAC projects 189 and 741. Logbooks(s): Glaciology Sea Ice Log, Mawson 1969 Glaciology Mawson Sea Ice Logs, 1995-2000

This dataset contains records of ice thickness and snow thickness from Davis Antarctica. Measurements were attempted on a weekly basis and have been recorded since 1957 and are ongoing, although data have only been archived here until 2002. The observations are not continuous however. The dataset is available via the provided URL. This data were also collected as part of ASAC projects 189 and 741. Logbook(s): Glaciology Davis Sea Ice Logs 1992-1999

This dataset contains records of ice thickness and snow thickness from Casey, Antarctica. Measurements were attempted on a weekly basis and were recorded between 1979 and 1992. The observations are not continuous however. The dataset is available via the provided URL. This data were also collected as part of ASAC projects 189 and 741. The Casey fast ice thickness data are no longer being collected.

This dataset (provided as a series of CF-compatible netcdf file) consists of 432 consecutive maps of Antarctic landfast sea ice, derived from NASA MODIS imagery. There are 24 maps per year, spanning the 18 year period from March 2000 to Feb 2018. The data are provided in a polar stereographic projection with a latitude of true scale at 70 S (i.e., to maintain compatibility with the NSIDC polar stereographic projection).

This data describe a set of sea-ice and seawater physical and biochemical parameters obtained from seawater samples and ice cores drilled from land fast sea ice in the vicinity of Davis Station, East Antarctica at six different dates (stations 1-6) during late Spring 2016. Stations 1: 16 Nov. 2016 Stations 2: 21 Nov. 2016 Stations 3: 23 Nov. 2016 Stations 4: 26 Nov. 2016 Stations 5: 29 Nov. 2016 Stations 6: 02 Dec. 2016 Parameters measured: - Temperature, salinity; - Iron: Dissolved (less than 0.2um), soluble (less than 0.02um) colloidal (between 0.02 and 0.2um) and Particulate fractions (greater than 0.2um); - Macronutrients: Nitrate (NO3), nitrite (NO2), silicate (Si), phosfate (PO4) and ammonium (NH4); - Chlorophyll-a (Chla); - Particulate Organic Matter: Particulate Organic Carbon (POC) and Particulate Organic Nitrogen (PON) SW0: seawater collected at the surface SW3: seawater collected at 3m depth SW10: seawater collected at 10m depth

The Antarctic Fast Ice Algae Chlorophyll-a (AFIAC) dataset is a compilation of currently available sea ice chlorophyll-a data from land-fast sea ice (i.e., excluding pack ice (see ASPeCt-Bio, Meiners et al. 2012)) cores collected at circum-Antarctic locations during the period 1970 to 2015. Data come from peer-reviewed publications, field-reports, data repositories and direct contributions by field-research teams. During all campaigns the chlorophyll-a concentration (in micrograms per litre) was measured from melted ice-core sections, using standard procedures, e.g., by melting the ice at less than 5 degrees C in the dark; filtering samples onto glassfibre filters; and fluorometric analysis according to standard protocols [Holm-Hansen et al., 1965; Evans et al., 1987]. Ice samples were melted either directly or in filtered sea water, which does not yield significant differences in chlorophyll-a concentration [Dieckmann et al., 1998]. The dataset consists of 888 geo-referenced ice cores, consisting of 5718 individual ice core sections, and including 404 full vertical profiles with a minimum of three sections. Samples/sections from the remaining cores represent: i) bottom 0.05 m only (n= 32), ii) bottom 0.1 m only (n = 301), complete cores (n = 66), as well as intermittent profiles (n = 85) with at least 3 sections but gaps in-between them. For questions about this dataset please contact: Klaus Meiners and Martin Vancoppenolle This data compilation was carried out under the auspices of the Scientific Committee on Antarctic Research - ASPeCt program and the Scientific Committee on Ocean Research (SCOR) working group on Biogeochemical Exchange Processes at the Sea-Ice Interfaces (WG-140). It also contributes to SCOR WG-152 on Measuring Essential Climate Variables in Sea Ice (ECV-Ice). An update to this dataset was submitted in September, 2018.

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).

This indicator is no longer maintained, and is considered OBSOLETE. INDICATOR DEFINITION Regular measurements of the thickness of the fast ice, and of the snow cover that forms on it, are made through drilled holes at several sites near both Mawson and Davis. 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 Each season around the end of March, the ocean surface around Antarctica freezes to form sea ice. Close to the coast in some regions (e.g. near Mawson and Davis stations) this ice remains fastened to the land throughout the winter and is called fast ice. The thickness and growth rate of fast ice are determined purely by energy exchanges at the air-ice and ice-water interfaces. This contrasts with moving pack ice where deformational processes of rafting and ridging also determine the ice thickness. The maximum thickness that the fast ice reaches, and the date on which it reaches that maximum, represent an integration of the atmospheric and oceanic conditions. Changes in ice thickness represent changes in either oceanic or atmospheric heat transfer. Thicker fast ice reflects either a decrease in air temperature or decreasing oceanic heat flux. These effects can be extrapolated to encompass large-scale ocean-atmosphere processes and potentially, global climate change. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: At sites near Australian Antarctic continental stations: Davis; Mawson. Frequency: at least weekly, reported annually Measurement Technique: Tape measurements through freshly drilled 5 cm diameter holes in the ice at marked sites. RESEARCH ISSUES To more effectively analyse the changes in Antarctic fast ice a detailed long-term dataset of sea ice conditions needs to be established. This would provide a baseline for future comparisons and contribute important data for climate modelling and aid the detection of changes that may occur due to climate or environmental change. LINKS TO OTHER INDICATORS SOE Indicator 1 - Monthly mean air temperatures at Australian Antarctic stations SOE Indicator 40 - Average sea surface temperatures in latitude bands 40-50oS, 50-60oS, 60oS-continent SOE Indicator 41 - Average sea surface salinity in latitude bands: 40-50oS, 50-60oS, 60oS-continent SOE Indicator 42 - Antarctic sea ice extent and concentration The fast ice data are also available as a direct download via the url given below. The data are in word documents, and are divided up by year and site (there are three sites (a,b,c) at each station). Snow thickness data have also been included. A pdf document detailing how the observations are collected is also available for download.

From the abstract of one of the papers: Oxygen microelectrodes were used to measure the photosynthetic rates of Antarctic fast ice algal mats. Using the oxygen flux across the diffusive boundary layer below the fast ice at Davis, a productivity range of 0-1.78mg C per square metre per hour was measured. This is at the lower end of fast ice productivity estimates and suggests that conventional carbon 14 techniques may overestimate sea ice algal mat productivity. Photosynthetic capacity (P max) approached 0.05 mg per C.(mg chlorophyl a) per hr. Onset of photosynthesis saturation, E k, was found at about 14 micromol photons per square metre per second. The irradiance of photoinhibition onset, E inh, was about 20 micromol photons per square metre per second and the irradiance at the compensation point, E c, was 4 micromol photons per square metre per second.

Field-based sampling: As part of Australian Antarctic Science project # 4298, a total number of 44 sea ice sites were sampled for bio-optical measurements along 4 transects on land-fast sea ice off Davis Station (Antarctica) during November – December 2015. Measurements included simultaneous hyperspectral down-welling (ice surface) irradiance (triplicate) and under-ice radiance (triplicate) measurements (320 – 900 nm, 3.3 nm resolution) with a TriOS ACC and Trios ARC radiometer, respectively. The radiance measurements were conducted with the TriOS ARC radiometer mounted onto an L-shaped arm (for deployment details see Melbourne-Thomas et al. 2015). Subsequently, snow thickness was measured with a ruler and an ice core was collected directly above the radiometer location. Sea-ice freeboard (tape measure) and ice thickness (ice core length) were also recorded. Ice cores (9 cm internal diameter) were cut into sections, and these were melted in the dark at +4 degrees C, filtered onto GFF filters and then used to measure ice algal pigment content (using High Performance Liquid Chromatography (HPLC) and spectral ice algal absorption coefficients (ap, ad, aph) for entire vertical profiles or for the lower-most 0.1 m of ice cores. The location of the sampling grid had its origin (x=0, y=0) at GPS position: -68.568904, 77.945439. Transects (128m – 512 m in length) 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) on 19/11/2015, 23/11/2015, 29/11/2015 and 02/12/2015, respectively. Analysis of ice algal chlorophyll a concentration: For pigment analysis, 0.25 to 1.0 litres of melted ice core subsamples were passed through 25 mm diameter glass-fiber (Whatman GF/F) filters. The filters were then frozen and stored below −80 degrees C prior to analysis using HPLC. Samples were extracted over 15 to 18 hours in acetone before analysis by HPLC using a modified C8 column and binary gradient system with an elevated column temperature [Van Heukelem and Thomas, 2001]. Pigments were identified by retention time and absorption spectra from a photo-diode array (PDA) detector, and concentrations were determined from commercial and international standards (Sigma; DHI, Denmark). Analysis of particulate (algal and non-algal) absorption: The optical density (OD) spectra of the particulate material on these filters (see section above) were measured over the 350 to 750 nm spectral range in 0.9 nm increments, using a Cintra 404 UV/VIS dual-beam spectrophotometer equipped with an integrating sphere. The pigments on the sample filter were then extracted using the method of Kishino et al. [1985]'s method to determine the OD of the non-algal particles in a second scan. The OD due to ice algae was then obtained by calculating the difference between the optical density of the total particulate and non-algal fractions. The OD measurements were converted to absorption spectra using blank filter measurements, and by first normalizing the scans to zero at 750 nm and then correcting for the path length amplification using the coefficients of Mitchell [1990]. A detailed description of the method is given in Clementson et al. [2001], and followed SeaWiFS protocols [Muller et al., 2003]. An exponential function was fitted to all spectra of non-algal particulate material: ad(λ) = ad(350 nm) exp[−S(λ − 350 nm)] + b, (1) where ad(λ) is the residual absorption coefficient over the wavelength (λ) range 350 to 750 nm of the particles after methanol extraction, also referred to as absorption of detritus [m−1] although this may include absorption of non-extractable pigments and heterotrophic protists. A non-linear least-squares technique was used to fit Equation 1 to the untransformed data, where S and b are empirically-determined constants. The inclusion of an offset b allows for any baseline correction. In some samples, pigment extraction was incomplete, leaving small residual peaks in detritus spectra at the principal chlorophyll absorption bands. To avoid distorting the fitted detritus spectra, data at these wavelengths were omitted when all spectra were fitted. Total particulate spectra were smoothed using a running box-car filter with 10 nm width, and the fitted detritus spectra were subtracted to yield the ice algae spectra. Subtracting fitted detritus spectra minimized any artifacts due to incomplete extraction of pigments. The resulting ice algae spectra were base-corrected by subtracting absorption at 750 nm to obtain aph(λ). The following parameters were then determined: ap(λ) = absorption coefficient of particles [m−1]; aph(λ) = absorption coefficient of ice algae [m−1] calculated as the difference between ap(λ) and ad(λ). Literature cited: Clementson, L. A., J. S. Parslow, A. R. Turnbull, D. C. McKenzie, and C. E. Rathbone (2001), Optical properties of waters in the Australasian sector of the Southern Ocean, Journal of Geophysical Research: Oceans, 106(C12), 31,611–31,625, doi:10.1029/2000jc000359. Kishino, M., M. Takahashi, N. Okami, and S. Ichimura (1985), Estimation of the spectral absorption-coefficients of phytoplankton in the sea, Bulletin of Marine Science, 37(2), 634–642.Melbourne-Thomas, J., K. Meiners, C. Mundy, C. Schallenberg, K. Tattersall, and
G. Dieckmann (2015), Algorithms to estimate Antarctic sea ice algal biomass from under-ice irradiance spectra at regional scales, Marine Ecology Progress Series, 536, 107–121, doi:10.3354/meps11396. Mitchell, B. G. (1990), Algorithms for determining the absorption coefficient for aquatic particulates using the quantitative filter technique, Orlando’90, 1302, 137–148, doi:10.1117/12.21440. Müller, J. L., R. R. Bidigare, C. Trees, W. M. Balch, and J. Dore (2003), Ocean Optics Protocols for Satellite Ocean Colour Sensor Validation, Revision 5, Volume V: Biogeochemical and Bio-Optical Measurements and Data, NASA Tech. Memo. Van Heukelem, L., and C. S. Thomas (2001), Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments, Journal of Chromatography A, 910(1), 31–49, doi:10.1016/s0378-4347(00)00603-4.