Melissa Fedrigo

Identifying the full suite of processes that drive the melting of Antarctic sea ice each summer is crucial to improving the currently-poor ability of contemporary models to accurately simulate the climatological retreat phase of the annual sea-ice cycle. This is critical to (1) understanding and attributing observed trends and recent abrupt changes in sea-ice coverage and (2) the more robust prediction of future sea-ice conditions and impacts. This paper identifies wave-driven processes that can accelerate the seasonal melting of sea ice both in the marginal ice zone (MIZ) and in open-water areas within the interior sea-ice zone (SIZ). It builds on the long-held view that seasonal Antarctic sea-ice ablation is primarily driven by ice-floe lateral and basal melting enhanced in the MIZ by wave breakup of ice floes, by demonstrating that ocean waves play important additional roles in generating surface and interior melting (termed “wave melting”) via three sets of processes: “wave flooding”, “wave pulverisation”, and “wave greening” (involving algal proliferation in wave-modified ice). Based on existing observations and simple one-dimensional modelling, these wave processes are estimated to reduce ice albedo by 0.38–0.64 compared to snow-covered ice, resulting in vertical melt-rate enhancements of 0.9–5.2 cm d⁻¹ amplified by wave greening to 1.1–6.1 cm d⁻¹. The study also identifies five positive feedback and sub-feedback mechanisms that likely accelerate the ice melting further. It addresses a gap in current climate and Earth system models, which account for wave effects on floe-size distributions but overlook these coupled wave-driven dynamic, thermodynamic and biological processes that may contribute to explaining why and how Antarctic sea ice can melt back so rapidly each summer. An intention of this foundational study is to stimulate further targeted investigation aimed at quantifying the role of wave melting in the annual sea-ice cycle – as well as the contribution of wave greening to primary production in the sea-ice zone and its role in key biogeochemical processes that feed back to climate. The work has implications for planetary albedo, global climate feedbacks, marine ecosystems, and the accuracy of future sea-ice and climate projections in an increasingly-stormy Southern Ocean, as well as in a changing Arctic.

Identifying the processes that drive the rapid climatological retreat phase of Antarctica’s annual sea-ice cycle is crucial to understanding, modelling and attributing observed trends and recent high variability in sea-ice extent, and to projecting future sea-ice conditions and impacts accurately. To date, the rapid annual retreat of Antarctic sea ice each spring–summer has been largely attributed to lateral and basal melting of ice floes, enhanced by wave-induced breakup of large floes. Here, based on observations and modelling, we propose that waves play important additional roles in generating previously-neglected surface and interior melting, by removing snow from small floes, flooding them, and pulverising them into slush. Results here show a resultant estimated reduction in albedo by 0.38–0.54, that increases melting by 0.9–5.2 cm day^-1 at 60–70^o S compared to a snow-covered floe of first-year ice, and depending on surface type, wave-flooding coverage, latitude and ice density. Rapid proliferation of algae within and on the high-light and high-nutrient environment of the wave-modified ice reduces the albedo by a further 0.1 to increase the melt-rate enhancement to 1.1–6.1 cm day^-1. Melting is further accelerated by a wave-induced ice–albedo feedback mechanism, similar to that associated with Arctic melt ponds but involving seawater rather than freshwater. This positive feedback is strengthened by ice-algal greening. Floe thinning and weakening by wave-melting initiate additional dynamic–thermodynamic feedbacks by increasing the likelihood of both wave-flooding and flexural breakup, leading to further floe melting. Wave melting and the associated physical–biological feedbacks will likely increase in importance in a predicted stormier and warmer Southern Ocean, and will also become more prevalent in a changing Arctic. There are implications for global weather and climate, the health of the ocean and its ecosystems, fisheries, ice-shelf stability and sea-level rise, atmospheric and oceanic biogeochemistry, and human activities.

Identifying the processes that drive the rapid climatological retreat phase of Antarctica’s annual sea-ice cycle is crucial to understanding, modelling and attributing observed trends and recent high variability in sea-ice extent, and to projecting future sea-ice conditions and impacts accurately. To date, the rapid annual retreat of Antarctic sea ice each spring–summer has been largely attributed to lateral and basal melting of ice floes, enhanced by wave-induced breakup of large floes. Here, based on observations and modelling, we propose that waves play important additional roles in generating previously-neglected surface and interior melting, by removing snow from small floes, flooding them, and pulverising them into slush. Results here show a resultant estimated reduction in albedo by 0.38–0.54, that increases melting by 0.9–5.2 cm day^-1 at 60–70^o S compared to a snow-covered floe of first-year ice, and depending on surface type, wave-flooding coverage, latitude and ice density. Rapid proliferation of algae within and on the high-light and high-nutrient environment of the wave-modified ice reduces the albedo by a further 0.1 to increase the melt-rate enhancement to 1.1–6.1 cm day^-1. Melting is further accelerated by a wave-induced ice–albedo feedback mechanism, similar to that associated with Arctic melt ponds but involving seawater rather than freshwater. This positive feedback is strengthened by ice-algal greening. Floe thinning and weakening by wave-melting initiate additional dynamic–thermodynamic feedbacks by increasing the likelihood of both wave-flooding and flexural breakup, leading to further floe melting. Wave melting and the associated physical–biological feedbacks will likely increase in importance in a predicted stormier and warmer Southern Ocean, and will also become more prevalent in a changing Arctic. There are implications for global weather and climate, the health of the ocean and its ecosystems, fisheries, ice-shelf stability and sea-level rise, atmospheric and oceanic biogeochemistry, and human activities.

Identifying the processes that drive the rapid climatological retreat phase of Antarctica’s annual sea-ice cycle is crucial to understanding, modelling and attributing observed trends and recent high variability in sea-ice extent, and to projecting future sea-ice conditions and impacts accurately. To date, the rapid annual retreat of Antarctic sea ice each spring–summer has been largely attributed to lateral and basal melting of ice floes, enhanced by wave-induced breakup of large floes. Here, based on observations and modelling, we propose that waves play important additional roles in generating previously-neglected surface and interior melting, by removing snow from small floes, flooding them, and pulverising them into slush. Results here show a resultant estimated reduction in albedo by 0.38–0.54, that increases melting by 0.9–5.2 cm day^-1 at 60–70^o S compared to a snow-covered floe of first-year ice, and depending on surface type, wave-flooding coverage, latitude and ice density. Rapid proliferation of algae within and on the high-light and high-nutrient environment of the wave-modified ice reduces the albedo by a further 0.1 to increase the melt-rate enhancement to 1.1–6.1 cm day^-1. Melting is further accelerated by a wave-induced ice–albedo feedback mechanism, similar to that associated with Arctic melt ponds but involving seawater rather than freshwater. This positive feedback is strengthened by ice-algal greening. Floe thinning and weakening by wave-melting initiate additional dynamic–thermodynamic feedbacks by increasing the likelihood of both wave-flooding and flexural breakup, leading to further floe melting. Wave melting and the associated physical–biological feedbacks will likely increase in importance in a predicted stormier and warmer Southern Ocean, and will also become more prevalent in a changing Arctic. There are implications for global weather and climate, the health of the ocean and its ecosystems, fisheries, ice-shelf stability and sea-level rise, atmospheric and oceanic biogeochemistry, and human activities.

Identifying the processes that drive the rapid climatological retreat phase of Antarctica’s annual sea-ice cycle is crucial to understanding, modelling and attributing observed trends and recent high variability in sea-ice extent, and to projecting future sea-ice conditions and impacts accurately. To date, the rapid annual retreat of Antarctic sea ice each spring–summer has been largely attributed to lateral and basal melting of ice floes, enhanced by wave-induced breakup of large floes. Here, based on observations and modelling, we propose that waves play important additional roles in generating previously-neglected surface and interior melting, by removing snow from small floes, flooding them, and pulverising them into slush. Results here show a resultant estimated reduction in albedo by 0.38–0.54, that increases melting by 0.9–5.2 cm day^-1 at 60–70^o S compared to a snow-covered floe of first-year ice, and depending on surface type, wave-flooding coverage, latitude and ice density. Rapid proliferation of algae within and on the high-light and high-nutrient environment of the wave-modified ice reduces the albedo by a further 0.1 to increase the melt-rate enhancement to 1.1–6.1 cm day^-1. Melting is further accelerated by a wave-induced ice–albedo feedback mechanism, similar to that associated with Arctic melt ponds but involving seawater rather than freshwater. This positive feedback is strengthened by ice-algal greening. Floe thinning and weakening by wave-melting initiate additional dynamic–thermodynamic feedbacks by increasing the likelihood of both wave-flooding and flexural breakup, leading to further floe melting. Wave melting and the associated physical–biological feedbacks will likely increase in importance in a predicted stormier and warmer Southern Ocean, and will also become more prevalent in a changing Arctic. There are implications for global weather and climate, the health of the ocean and its ecosystems, fisheries, ice-shelf stability and sea-level rise, atmospheric and oceanic biogeochemistry, and human activities.

Identifying the processes that drive the rapid climatological retreat phase of Antarctica’s annual sea-ice cycle is crucial to understanding, modelling and attributing observed trends and recent high variability in sea-ice extent, and to projecting future sea-ice conditions and impacts accurately. To date, the rapid annual retreat of Antarctic sea ice each spring–summer has been largely attributed to lateral and basal melting of ice floes, enhanced by wave-induced breakup of large floes. Here, based on observations and modelling, we propose that waves play important additional roles in generating previously-neglected surface and interior melting, by removing snow from small floes, flooding them, and pulverising them into slush. Results here show a resultant estimated reduction in albedo by 0.38–0.54, that increases melting by 0.9–5.2 cm day^-1 at 60–70^o S compared to a snow-covered floe of first-year ice, and depending on surface type, wave-flooding coverage, latitude and ice density. Rapid proliferation of algae within and on the high-light and high-nutrient environment of the wave-modified ice reduces the albedo by a further 0.1 to increase the melt-rate enhancement to 1.1–6.1 cm day^-1. Melting is further accelerated by a wave-induced ice–albedo feedback mechanism, similar to that associated with Arctic melt ponds but involving seawater rather than freshwater. This positive feedback is strengthened by ice-algal greening. Floe thinning and weakening by wave-melting initiate additional dynamic–thermodynamic feedbacks by increasing the likelihood of both wave-flooding and flexural breakup, leading to further floe melting. Wave melting and the associated physical–biological feedbacks will likely increase in importance in a predicted stormier and warmer Southern Ocean, and will also become more prevalent in a changing Arctic. There are implications for global weather and climate, the health of the ocean and its ecosystems, fisheries, ice-shelf stability and sea-level rise, atmospheric and oceanic biogeochemistry, and human activities.

Deer populations and their impacts on forest ecosystems are increasing globally. Given the imperative and expense to mitigate impacts of invasive deer, we aimed to elucidate critical drivers of (i) deer density, (ii) deer impacts, and (iii) the relationship between them, to facilitate targeted management. We used quantile regression forests to model deer density (faecal pellet counts at 1948 locations) and impacts (browsing and other impacts on > 23,000 woody plants at 343 locations) across a mosaic of agricultural and forested ecosystems in Victoria, Australia (12,775 km²). Climate, topography, vegetation cover, and distance to water features were included as model covariates. Modelled deer density (r² = 0.71, MAE = 0.56 pellets/m²) was most influenced by distance to waterbodies (> 10 ha, 31.2%), elevation (14.3%) and woody vegetation cover (12.9%). Modelled deer impact (r² = 0.32, MAE = 6.9%) was most influenced by deer density (21.0%), mean annual precipitation (12.8%) and elevation (12.2%). Deer density was typically highest near large waterbodies, at low elevation, and with intermediate tree cover (40–70%). Impacts increased steadily with deer density up to ~ 2 pellets/m². Our study demonstrates the importance of forest water and forest agricultural interfaces for both deer density and impacts. Deer are likely to be most abundant near waterbodies due to the availability of high-quality forage and water, and prefer lowland locations that have access to both open and forested habitats. Spatial models can be used to predict deer density and associated impacts to facilitate targeted invasive deer management.

Predictive vegetation mapping is an essential tool for managing and conserving high conservation-value forests. Cool temperate rainforests (Rainforest) and cool temperate mixed forests (Mixed Forest, i.e., rainforest spp. overtopped by large remnant Eucalyptus trees) are threatened forest types in the Central Highlands of Victoria. Logging of these forest types is prohibited; however, the surrounding native Eucalyptus forests can be logged in some areas of the landscape. This requires accurate mapping and delineation of these vegetation types. In this study, we combine niche modelling, multispectral imagery, and LiDAR data to improve predictive vegetation mapping of these two threatened ecosystems in southeast Australia. We used a dataset of 1586 plots partitioned into four distinct forest types that occur in close proximity in the Central Highlands: Eucalyptus, Tree fern, Mixed Forest, and Rainforest. We calibrated our model on a training dataset and validated it on a spatially distinct testing dataset. To avoid overfitting, we used Bayesian regularized multinomial regression to relate predictors to our four forest types. We found that multispectral predictors were able to distinguish Rainforest from Eucalyptus forests due to differences in their spectral signatures. LiDAR-derived predictors were effective at discriminating Mixed Forest from Rainforest based on forest structure, particularly LiDAR predictors based on existing domain knowledge of the system. For example, the best predictor of Mixed Forest was the presence of Rainforest-type understorey overtopped by large Eucalyptus crowns, which is effectively aligned with the regulatory definition of Mixed Forest. Environmental predictors improved model performance marginally, but helped discriminate riparian forests from Rainforest. However, the best model for classifying forest types was the model that included all three classes of predictors (i.e., spectral, structural, and environmental). Using multiple data sources with differing strengths improved classification accuracy and successfully predicted the identity of 88% of the plots. Our study demonstrated that multi-source methods are important for capturing different properties of the data that discriminate ecosystems. In addition, the multi-source approach facilitated adding custom metrics based on domain knowledge which in turn improved the mapping of high conservation-value forest.