Note - these data should be used with caution. The chief investigator for the dataset has indicated that a better quality dataset exists, but the AADC have been unable to attain it for archive. Matlab file containing raw data snowfall data collected aboard the RV Aurora Australis using Campbell Scientific dataloggers. Two Wenglor brand YHO3NCT8 photoelectric sensors were mounted on the forward railing of the ship's "monkey deck". The beam heights of the sensors were 18cm above the upper railing, oriented parallel to the railing (perpendicular to the long-axis of the ship), approximately 6m apart. The port sensor was purchased in 2012, from a batch of these sensors manufactured in a new Eastern European factory while the starboard sensor came from a lot purchased in 2007, manufactured in Wenglor's German factory and extensively tested for use in snow. Pulse counts measured by the port sensor were consistently lower in magnitude than those recorded during the same interval by the starboard sensor. It is not currently clear whether this was due to the ship's tendency to be oriented with the wind to starboard, or whether this is due to differences in instrument characteristics. Data recorded between 17/9/2012 and 26/10/2012 was logged by a CR10x datalogger. Data recorded after 26/10/2012 was logged by a CR1000 datalogger. Information on converting the pulse-count data into a mass flux of snow is contained in Leonard, K.C. and R.I. Cullather (2008) Snowfall measurements in the Amundsen and Bellingshausen Seas, Antarctica. Proceedings of the Eastern Snow Conference, 65, 87 - 98. These two datasets are identical, but have been separated into two matlab structures contained in the same "shipsnow.mat" file: "snow" and "snow2". Data contained in these structures includes the following variables, with units: Datenm: matlab 'datenumber'. Change to conventional format using the "datevec()" command Port: beam interruptions per 10s interval, port-side sensor Stbd: beam interruptions per 10s interval, starboard-side sensor Ptemp: temperature of a thermistor mounted beneath the datalogger's wiring panel. The datalogger was contained in a fiberglass box, strapped into the starboard side observation shelter on the monkey deck. Volt: voltage received and transmitted by the datalogger. Power came from a 12v 1Ah converter plugged into the ship's power supply. The data have also been reformatted into two csv files.

Some ecosystems can undergo abrupt transformation in response to relatively small environmental change. Identifying imminent "tipping points" is crucial for biodiversity conservation, particularly in the face of climate change. Here we describe a tipping point mechanism likely to induce widespread regime shifts in polar ecosystems. Seasonal snow and ice cover periodically block sunlight reaching polar ecosystems, but the effect of this on annual light depends critically on the timing of cover within the annual solar cycle. At high latitudes sunlight is strongly seasonal, and ice-free days around the summer solstice receive orders of magnitude more light than those in winter. Early melt that brings the date of ice-loss closer to midsummer will cause an exponential increase in the amount of sunlight reaching some areas per year. This is likely to drive ecological tipping points in which primary producers (plants and algae) flourish and out-compete dark-adapted communities. We demonstrate this principle on Antarctic shallow seabed ecosystems, which our data suggest are sensitive to small changes in the timing of sea-ice loss. Algae respond to light thresholds that are easily exceeded by a slight reduction in sea-ice duration. Earlier sea-ice loss is likely to cause extensive regime-shifts in which endemic shallow-water invertebrate communities are replaced by algae, reducing coastal biodiversity and fundamentally changing ecosystem functioning. Modeling shows that recent changes in ice and snow cover have already transformed annual light budgets in large areas of the Arctic and Antarctic, and both aquatic and terrestrial ecosystems are likely to experience further significant change in light. The interaction between ice loss and solar irradiance renders polar ecosystems acutely vulnerable to abrupt ecosystem change, as light-driven tipping points are readily breached by relatively slight shifts in the timing of snow and ice loss. This archive contains data and statistical code for the article: Graeme F. Clark, Jonathan S. Stark, Emma L. Johnston, John W. Runcie, Paul M. Goldsworthy, Ben Raymond and Martin J. Riddle (2013) Light-driven tipping points in polar ecosystems. Global Change Biology Data and code are organised into folders according to figures in the article. See the article for a full description of methods. Statistical code was written in R v. 2.15.0. In data files, rows are samples and columns are variables. Details for numerical variables in each data file are listed below. Figures 7 and 8 were made in MATLAB and code is not provided. Figure 1: rad_data.csv Solar irradiance data derived from: Suri M, Hofierja J (2004) A new GIS-based solar radiation model and its application to photovoltaic assessments. Transactions in GIS 8: 175-190. Figure 2: Fig. 2c.1.csv Light: Measured light at the seabed per day (mol photons m-2 d-1). Figure 2: Fig. 2c.2.csv Light: Measured light at the seabed per day (mol photons m-2 d-1). Light.mod.p: Light at the seabed per day (mol photons m-2 d-1) predicted from modeled seasonal variation. Figure 2: Fig. 2d.csv Light: Measured light at the seabed per day (mol photons m-2 d-1). Figure 3: Fig. 3a.csv Irradiance: Mean irradiance (micro mol photons m-2 s-1). P/R: Productivity/respiration ratios (micro mol photons O2-1 gFW-1 h-1). Figure 3: Fig. 3b.csv Light: Mean irradiance (micro mol photons m-2 s-1) in experimental treatments. Growth: Thallus growth (mm) of Palmaria decipiens under experimental treatments. Figure 3: Fig. 3c.csv Des, Him, Irr, Pal: Ice-free days required for minimum annual light budget Figure 3: Fig. 3c.bars.csv Prop: relative cover (sums to 1 per site) of algae and invertebrates, excluding Inversiula nutrix and Spirorbis nordenskjoldi. Figure 4: Fig. 4.csv Time: months after deployment Length: length of thalli (mm) Figure 5: Fig. 5c and d.csv Axis 1 and Axis 1: Values from first two axes of principal coordinate analysis IceCover: proportion of days that each site is free of sea-ice per year. Beta: Beta-diversity. Calculated as Jaccard similarity between the most ice-covered site (OB1) and each other site. Figure 5: Fig. 5e and f.csv IceCover: proportion of days that each site is free of sea-ice per year. Value: number of species per boulder (for Metric=Diversity), or percent cover per boulder (for Metric=Cover). Figure 6: Fig. 6a.csv Sites.lost: number of sites removed from dataset due to sea-ice loss. Ice: maximum ice-free days within the region (d yr-1). S: Total species richness across each subset of sites. Effort: relative sampling effort (number of sites sampled).

This document describes the deployment of five Ice Mass Balance Buoys (IMBs) and two automatic weather stations. These were primarily deployed on floes 2012103 and 20121029, as well as on helicopter flights (refer to buoy metadata for these). IMBs are labelled WHOI-1 to WHOI-6. WHOI-1 was not deployed and WHOI-3 and WHOI-5 failed and were recovered. TAS-2 was exchanged for WHOI-1 Deployments (successful): TAS-2 deployed on helo flight 20 km from ship WHOI-4 deployed on helo flight 20 km from ship WHOI-6 Deployed next to AWS-1 on ice station 1013 on 11/04 WHOI-2 Deployed next to AWS-2 on ice station 1029 on 11/01 Each AWS record air temp, relative humidity, wind speed and direction, total incident short wave, snow depth, GPS position and snow particles near ground level and at about 1m height. AWS-1 deployed on 1013 AWS-2 deployed on 1029 IMBs record GPS position and temperature in air,snow,ice, and ocean. Sensors also have a heating mode that permit determination of media they are embedded in so that snow and ice thickness can be determined. REFER TO MAKSYM LOGBOOK SCANS FOR MORE DETAILS