Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

• Published in: Earth System Dynamics
• Main Authors: Landwehr, Sebastian, Volpi, Michele, Haumann, F. Alexander, Robinson, Charlotte M., Thurnherr, Iris, Ferracci, Valerio, Baccarini, Andrea, Thomas, Jenny, Gorodetskaya, Irina, Tatzelt, Christian, Henning, Silvia, Modini, Rob L., Forrer, Heather J., Lin, Yajuan, Cassar, Nicolas, Simó, Rafel, Hassler, Christel, Moallemi, Alireza, Fawcett, Sarah E., Harris, Neil R. P., Airs, Ruth, Derkani, Marzieh H., Alberello, Alberto, Toffoli, Alessandro, Chen, Gang, Rodríguez-Ros, Pablo, Zamanillo, Marina, Cortés-Greus, Pau, Xue, Lei, Bolas, Conor G., Leonard, Katherine C., Perez-Cruz, Fernando, Walton, David, Schmale, Julia
• Format: Article in Journal/Newspaper
• Language: English
• Published: EGU: European Geophysical Union 2021
• Subjects: Antarc*; Antarctic; Southern Ocean
• Online Access: https://doi.org/10.5194/esd-12-1295-2021; http://dspace.lib.cranfield.ac.uk/handle/1826/17322

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community ...