A SPECIES DISTRIBUTION MODEL OF THE ANTARCTIC MINKE WHALE (BALAENOPTERA BONAERENSIS)

The Antarctic minke whale (Balaenoptera bonaerensis) is regarded a Southern Hemisphere endemic found throughout the Southern Hemisphere, generally south of 60°S in austral summer. Here they have been routinely observed in highest densities adjacent to and inside the sea ice edge, and where they feed predominantly on krill. Detecting abundance trends regarding this species by employing visual monitoring is problematic. Partly this is because the whales are frequently sighted within sea ice where navigational safety concerns prevent ships from surveying. In this respect species-habitat models are increasingly recognized as valuable tools to predict the probability of cetacean presence, relative abundance or density throughout an area of interest and to gain insight into the ecological processes affecting these patterns. The objective of this study was to provide this background information for the above research needs and in a broader context use species distribution models (SDMs) to establish a current habitat suitability description for the species and to identify the main environmental covariates related to its distribution. We used filtered 464 occurrences to generate the SDMs. We selected eight predictor variables with reduced collinearity for constructing the models: mean annuals of the surface temperature (ºC), salinity (PSS), current velocity (m/s), sea ice concentration (fraction, %), chlorophyll-a concentration (mg/m³), primary productivity (g/m3/day), cloud cover (%), and bathymetry (m). Six modeling algorithms were test and the Bayesian additive regression trees (BART) model demonstrated the best preformance. Based on variable importance, those that best explained the environmental requirements of the species, were: sea ice concentration, chlorophyll-a concentration and topography of the sea floor (bathymetry), explaining in sum around 62% of the variance. Using the BART model, habitat preferences have been interpreted from patterns in partial dependence plots. Areas where the AMW have particularly high likelihood of occurrence are East Antarctica, NE of the Weddell Sea, areas around the northern tip of the Antarctica Peninsula, areas bordering the Scotia–Weddell Confluence. Given the association of AMWs with sea ice the pagophilic character of their biology makes them particularly vulnerable to climate change and a perfect biological indicator for tracking these changes.

A SPECIES DISTRIBUTION MODEL OF THE ANTARCTIC MINKE WHALE (BALAENOPTERA BONAERENSIS)

The Antarctic minke whale (Balaenoptera bonaerensis) is regarded a Southern Hemisphere endemic found throughout the Southern Hemisphere, generally south of 60 degrees S in austral summer. Here they have been routinely observed in highest densities adjacent to and inside the sea ice edge, and where they feed predominantly on krill. Detecting abundance trends regarding this species by employing visual monitoring is problematic. Partly this is because the whales are frequently sighted within sea ice where navigational safety concerns prevent ships from surveying. In this respect species-habitat models are increasingly recognized as valuable tools to predict the probability of cetacean presence, relative abundance or density throughout an area of interest and to gain insight into the ecological processes affecting these patterns. The objective of this study was to provide this background information for the above research needs and in a broader context use species distribution models (SDMs) to establish a current habitat suitability description for the species and to identify the main environmental covariates related to its distribution. We used filtered 464 occurrences to generate the SDMs. We selected eight predictor variables with reduced collinearity for constructing the models: mean annuals of the surface temperature (degrees C), salinity (PSS), current velocity (m/s), sea ice concentration (fraction, %), chlorophyll-a concentration (mg/cub. m), primary productivity (g/cub.m/day), cloud cover (%), and bathymetry (m). Six modeling algorithms were test and the Bayesian additive regression trees (BART) model demonstrated the best preformance. Based on variable importance, those that best explained the environmental requirements of the species, were: sea ice concentration, chlorophyll-a concentration and topography of the sea floor (bathymetry), explaining in sum around 62% of the variance. Using the BART model, habitat preferences have been interpreted from patterns in partial dependence plots. Areas where the AMW have particularly high likelihood of occurrence are East Antarctica, NE of the Weddell Sea, areas around the northern tip of the Antarctica Peninsula, areas bordering the Scotia-Weddell Confluence. Given the association of AMWs with sea ice, the pagophilic character of their biology makes them particularly vulnerable to climate change and a perfect biological indicator for tracking these changes.

Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations

The indirect effect of winter Arctic Oscillation (AO) events on the proceeding summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer time scales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in northern-hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the Adjusted Rand Index – a method for comparing spatial patterns of variability, and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability of the AO relatively well, although over-estimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean, and under-estimate the variability over the north Africa and southern Europe. They also under-estimate the importance of regions such as the Beaufort, East Siberian and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.

Eddies in the Marginal Ice Zone of Fram Strait and Svalbard from Spaceborne SAR Observations in Winter

Here we investigate the intensity of eddy generation and their properties in the marginal ice zone (MIZ) regions of Fram Strait and around Svalbard using spaceborne synthetic aperture radar (SAR) data from Envisat ASAR and Sentinel-1 in winter 2007 and 2018. Analysis of 2039 SAR images allowed identifying 4619 eddy signatures. The number of eddies detected per image per kilometer of MIZ length is similar for both years. Submesoscale and small mesoscale eddies dominate with cyclones detected twice more frequently than anticyclones. Eddy diameters range from 1 to 68 km with mean values of 6 km and 12 km over shallow and deep water, respectively. Mean eddy size grows with increasing ice concentration in the MIZ, yet most eddies are detected at the ice edge and where the ice concentration is below 20%. The fraction of sea ice trapped in cyclones (53%) is slightly higher than that in anticyclones (48%). The amount of sea ice trapped by a single ‘mean’ eddy is about 40 km2, while the average horizontal retreat of the ice edge due to eddy-induced ice melt is about 0.2–0.5 km·d–1 ± 0.02 km·d–1. Relation of eddy occurrence to background currents and winds is also discussed.

Parameterization of submesoscale mixed layer restratification under sea ice

Commonly used parameterization of mixed layer instabilities in general circulation models (Fox-Kemper and Ferrari 2008a) was developed for temperate oceans and does not take into account the presence of sea ice in any way. However, the ice-ocean drag provides a strong mechanical coupling between the sea ice and the surface ocean currents and hence may affect mixed layer restratification processes. Here we use idealized simulations of mixed layer instabilities to demonstrate that the sea ice dramatically suppresses the eddy-driven overturning in the mixed layer by dissipating the eddy kinetic energy generated during instabilities. Considering the commonly-used viscous-plastic sea ice rheology, we developed an improvement to the existing mixed layer overturning parameterization, making it explicitly dependent on sea ice concentration. Below the critical sea ice concentration of about 0.68, the internal sea ice stresses are very weak and the conventional parameterization holds. At higher concentrations, the sea ice cover starts acting as a nearly-immobile surface lid, inducing strong dissipation of submesoscale eddies and reducing the intensity of the restratification streamfunction up to a factor of 4 for a fully ice-covered ocean. Our findings suggest that climate projection models might be exaggerating the restratification processes under sea ice, which could contribute to biases in mixed layer depth, salinity, ice-ocean heat fluxes, and sea ice cover.

Assessment of sea ice melting rates in the Antarctic from SSM/I observations

Sea ice governs the fluxes of heat, moisture and momentum across the ocean-atmosphere interface. Because it is thin, sea ice is vulnerable to small perturbations within the ocean and the atmosphere, which considerably change the extent and thickness of the polar ice cover. Thus, sea ice is a climate change indicator. The DMSP SSM/I monthly ice concentration data over the Antarctic region have used to calculate the monthly sea ice extents (August to February) for each year during 1988-2006. Melting rates based on seasonal cycle of solar irradiance as well as the SSM/I data have been calculated. Compared to the melting rates based on seasonal cycle of solar irradiance, the SSM/I estimated melting rate, is less in the beginning of September and increases to its peak value by the end of December. The observed melting rate behaviour indicates that apart from the seasonal cycle of solar irradiance, it is controlled by other mechanisms also. The present study estimates the feedback impact factor, response time, accelerating and decelerating melting rate duration for the period 1988-2006.