The oceans have a very important role in climate regulation due to their massive heat storage capacity. Thus, for the past decades, oceans have been observed by satellites to better understand their dynamics. Satellites retrieve several data with various spatial resolutions. For instance, sea surface height (SSH) is a low-resolution data field where sea surface temperature (SST) can be retrieved in a much higher one. These two physical parameters are linked by a physical link that can be learned by a super-resolution machine-learning algorithm. In this work, we present a subpixel convolutional deep learning model that takes advantage of the higher resolution SST field to guide the downscaling of the SSH one. The data fields that we use are simulated by a physic-based ocean model at a higher sampling rate than the satellites provide. We compared our approach with a convolutional neural network model. Our architecture generalized well with validation performances of 3.94 cm root mean squared error (RMSE) and training performances of 2.65 cm RMSE.

King penguins (Aptenodytes patagonicus Miller) are major consumers in the Southern Ocean. The colony at Ile aux Cochons, Iles Crozet, in the southern Indian Ocean was known in the 1980s as the largest king penguin colony and the second largest penguin colony in the world. However, there have not been any recent estimates of this colony. Aerial photographs taken from a helicopter, and satellite images were used to report on changes in the colony and population sizes over the past 50 years. The colony has declined by 88% over the past 35 years, from c. 500 000 pairs to 60 000 pairs. The possible causes of this decline were explored but no plausible explanation for such an unprecedented decrease in penguin populations was found. The study highlights the use of satellite imagery as a non-invasive technique for population monitoring, and stresses the need for further research on the causes of this alarming trend in this colony.

To improve the understanding of the mechanism driving recent changes in outlet glaciers in East Antarctica, we measured changes in the terminus position, ice flow velocity and surface elevation of the Langhovde Glacier located on the Sôya Coast. From satellite images from 2000–12 and field measurements taken in 2012 the glacier terminus position and flow velocity showed little change between 2003 and 2007. After this quiescent period, the glacier progressively advanced by 380 m and the flow velocity increased near the calving front by 10 m a-1 from 2007–10. No significant change was observed in surface elevation during the study period. The changes in the terminus position and flow velocity imply a reduction in the calving rate from 93 m a-1 (2003–07) to 16 m a-1 (2007–10). This suggests that calving was inhibited by stable sea ice conditions in the ocean. Theses results indicate that the Langhovde Glacier was in a relatively stable condition during the study period, and its terminus position was controlled by the rate of calving under the influence of sea ice conditions.