The Southern Ocean has a profound impact on the Earth's climate system. Its strong winds, intense currents, and fierce waves are critical components of the air–sea interface and contribute to absorbing, storing, and releasing heat, moisture, gases, and momentum. Owing to its remoteness and harsh environment, this region is significantly undersampled, hampering the validation of prediction models and large-scale observations from satellite sensors. Here, an unprecedented data set of simultaneous observations of winds, surface currents, and ocean waves is presented, to address the scarcity of in situ observations in the region – https://doi.org/10.26179/5ed0a30aaf764 (Alberello et al., 2020c) and https://doi.org/10.26179/5e9d038c396f2 (Derkani et al., 2020). Records were acquired underway during the Antarctic Circumnavigation Expedition (ACE), which went around the Southern Ocean from December 2016 to March 2017 (Austral summer). Observations were obtained with the wave and surface current monitoring system WaMoS-II, which scanned the ocean surface around the vessel using marine radars. Measurements were assessed for quality control and compared against available satellite observations. The data set is the most extensive and comprehensive collection of observations of surface processes for the Southern Ocean and is intended to underpin improvements of wave prediction models around Antarctica and research of air–sea interaction processes, including gas exchange and dynamics of sea spray aerosol particles. The data set has further potentials to support theoretical and numerical research on lower atmosphere, air–sea interface, and upper-ocean processes.

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 composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question.

A data set of concurrent measurements of sea spray aerosol concentration, wind speed, sea state, and air and water temperature was acquired across all sectors of the Southern Ocean during the Antarctic Circumnavigation Expedition (Austral summer 2016/2017). In addition to the established dependence on wind speed, our observations demonstrate that sea spray aerosol concentrations depend on sea state and the stability of the marine boundary layer. Besides driving sea spray emissions, wind speed and sea state strongly influence the deposition onto the ocean surface and thus aerosol lifetime even for smaller particles. Stable atmospheric conditions allow a typical lifetime of up to 4 days, while tropospheric air entrainment in unstable conditions reduces the residence time of sea spray aerosol in the marine boundary layer to less than 2 days.

Sea state conditions can be estimated from the motion of a moving ship by converting its response to incident waves through the response amplitude operator. The method is applied herein to ship motion data from the icebreaker R/V Akademik Tryoshnikov and recorded during the Antarctic Circumnavigation Expedition across the Southern Ocean during the Austral summer 2016–17. The response amplitude operator of the vessel was estimated using two boundary element method models, namely NEMOH and HydroSTAR. An inter-comparison of model performance is discussed. The accuracy of the reconstructed sea states is assessed against concurrent measurements of the wave energy spectrum, which were acquired during the expedition with the marine radar WaMoS-II. Results show good agreement between reconstructed sea states (wave spectrum as well as integrated parameters) and direct observations. Model performances are consistent. Nevertheless, NEMOH produces slightly more accurate wave parameters when quantitatively compared against HydroSTAR.