Long-term mean Antarctic landfast sea ice persistence map

Full description

This dataset provides the dataset underlying Figure 2 from the following publication:
Fraser, A. D., Massom, R. A., Handcock, M. S., Reid, P., Ohshima, K. I., Raphael, M. N., Cartwright, J., Klekociuk, A. R., Wang, Z., and Porter-Smith, R.: Eighteen-year record of circum-Antarctic landfast-sea-ice distribution allows detailed baseline characterisation and reveals trends and variability, The Cryosphere, 15, 5061–5077, https://doi.org/10.5194/tc-15-5061-2021, 2021.

It gives a map of the long-term (2000-2018) mean persistence of Antarctic landfast ice by simply averaging across the time dimension.

From the referenced paper:

Around Antarctica, landfast or fast ice is a stationary and consolidated form of sea ice which is attached to, and held in place by, the coastline or floating ice shelf fronts (World Meteorological Organization, 1970) and icebergs grounded in waters shallower than approximately 400 m (Giles et al., 2008; Fraser et al., 2012). As such, Antarctic fast ice forms only on the continental shelf, typically in narrow (50 to 250 km wide) bands adjacent to the coast and/or upstream of protrusions into the westward Antarctic Coastal Current that intercept encroaching (drifting) pack ice (Fraser et al., 2012; Nihashi and Ohshima, 2015). Depending on location, Antarctic fast ice can range in persistence from annual through perennial to multi-decadal (e.g. Massom et al., 2010), with certain regions being highly variable and breaking out and reforming several times per year (e.g. Massom et al., 2009; Fraser et al., 2012).

Antarctic fast ice is not only a sensitive bellwether of climate change and variability (given its intimate linkage and interaction with the high-latitude ocean and atmosphere, Massom et al., 2009; Fraser, 2011; Aoki, 2017), but its distribution also influences the size of adjacent coastal polynyas (Massom et al., 1998, 2001; Nihashi and Ohshima, 2015; Fraser et al., 2019), affecting regional rates of sea-ice production, water mass modification and the formation of globally important Antarctic bottom water in certain key locations (e.g. Kusahara et al., 2017; Ohshima et al., 2013). Moreover, recent work has shown the importance of fast ice in mechanically bonding and stabilising vulnerable outer margins of floating glacier tongues and ice shelves (Massom et al., 2018, 2015, 2010) and also in controlling the seasonal dynamics and discharge rate of certain outlet glaciers (Greene et al., 2018). Fast ice is also of major ecological importance as a key breeding habitat for emperor penguins and Weddell seals (Kooyman and Burns, 1999; Massom et al., 2009), plays a role in structuring shallow coastal benthic ecosystems (Clark et al., 2017), and is a region of high primary productivity (concentrated ice algal growth, Meiners et al., 2018). Coastal fast ice also constitutes a reservoir of nutrients (de Jong et al., 2013) which can substantially enhance primary production in the coastal zone when released into the water column upon fast-ice breakout/melt (particularly for thick, multi-year fast ice, e.g. Shadwick et al., 2013). Finally, fast ice can either facilitate or impede aviation and station resupply activities, depending on its location, extent and thickness (COMNAP, 2015). It follows that change and/or variability in Antarctic fast-ice distribution and seasonality have wide-ranging ramifications, and characterisation of where and how fast ice is changing is a high priority.

Accurate, consistent, long-term and year-round time-series mapping of Antarctic fast ice at a high spatio-temporal resolution and on a circumpolar scale requires satellite observation but is technically challenging (Fraser et al., 2009, 2010; Nihashi and Ohshima, 2015; Fraser et al., 2020; Kim et al., 2018, 2020; Li et al., 2020). Knowledge of its distribution and trends has been identified as a major gap (Vaughan et al., 2013; Meredith et al., 2019). This has severely limited our understanding of the important coastal icescape and key interactive physical, biological and biogeochemical processes therein. Fraser et al. (2012) released an 8.8-year dataset of East Antarctic fast-ice extent from 2000 to 2008, but this dataset has not been updated. Using passive microwave satellite data, Nihashi and Ohshima (2015) subsequently produced a dataset of circum-Antarctic fast-ice extent from 2003 to 2011, but at a relatively coarse resolution of 6.25 km per pixel, and this technique does not detect and include young fast ice (Fraser et al., 2019). Li et al. (2020) recently released a high-spatial-resolution circum-Antarctic dataset of fast ice covering November only in the years 2006–2011 and 2016–2017 using synthetic aperture radar (SAR) image analysis. However, since November is a month characterised by regionally variable fast-ice retreat (Fraser et al., 2012), it is inadequate for analysis of long-term trends in extent.

As such, detailed circumpolar characterisation of fast ice has not been possible due to the lack of a suitable underlying dataset. This gap has recently been filled by the publication of a new time series of fast-ice extent from March 2000 to March 2018 (Fraser et al., 2020). This dataset contains 432 contiguous maps of fast-ice extent at a 1 km and 15 d resolution, generated by compositing cloud-free visible and thermal infrared imagery from NASA Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra and Aqua satellites (Fraser et al., 2009, 2010). The process of generating the cloud-free composite imagery relies upon the MOD35 cloud mask product (Ackerman et al., 2006), which performs brightness temperature and reflectance-based spectral tests to determine the probability of cloud contamination. This product has limitations, especially during polar night (Fraser et al., 2010), and the procedure for composite generation may be improved using machine-learning-based techniques such as those demonstrated by Paul and Huntemann (2021). Such improvements may be implemented in a future fast-ice product.

Here, we use the newly released fast-ice dataset to perform a first detailed characterisation of circum-Antarctic fast-ice distribution, change and variability. We first identify eight distinct regions in terms of fast-ice co-variability, which form the basis of the new analysis of fast-ice trends around Antarctica. These regions differ from the sectors more traditionally used in Antarctic sea-ice analyses (Zwally et al., 1983). We then present the overall extent time series and annual climatology, spatial characterisation of mean fast-ice persistence, age and timing of minimum/maximum extent across the 18-year dataset. We also analyse fast-ice persistence in concert with bathymetric depth, and interpret this regionally, to more widely assess and determine the linkages between fast ice and grounded icebergs, which act both as stable anchor points for fast-ice formation (e.g. Massom et al., 2009; Li et al., 2020) and to intercept and retain encroaching pack ice, thus encouraging fast-ice formation upstream (Massom et al., 2001; Massom, 2003).

Lineage

Progress Code: completed

Statement: Quality of the underlying product described in detail in the associated dataset paper: Fraser, A. D., Massom, R. A., Ohshima, K. I., Willmes, S., Kappes, P. J., Cartwright, J., and Porter-Smith, R.: High-resolution mapping of circum-Antarctic landfast sea ice distribution, 2000–2018, Earth Syst. Sci. Data, 12, 2987–2999, https://doi.org/10.5194/essd-12-2987-2020, 2020.

Notes

Purpose
The purpose of releasing this processed/derived product is to facilitate wider distribution of this commonly-requested product. This product is also a candidate for Quantarctica Version 4, so a DOI will facilitate the incorporation of this dataset into that collection.