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This article gives an overview of the DACAPO-PESO field experiment, which has taken place in Punta Arenas, Chile, from November 2018 to November 2021, and showcases first exciting research results that have already emerged from it., In diesem Artikel wird ein Überblick über das DACAPO-PESO Experiment gegeben, welches von November 2018 bis November 2021 in Punta Arenas, Chile, stattgefunden hat. Außerdem werden erste spannende Forschungsergebnisse vorgestellt, die bereits daraus gewonnen wurden.

This article gives an overview of the DACAPO-PESO field experiment, which has taken place in Punta Arenas, Chile, from November 2018 to November 2021, and showcases first exciting research results that have already emerged from it., In diesem Artikel wird ein Überblick über das DACAPO-PESO Experiment gegeben, welches von November 2018 bis November 2021 in Punta Arenas, Chile, stattgefunden hat. Außerdem werden erste spannende Forschungsergebnisse vorgestellt, die bereits daraus gewonnen wurden.

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 avail

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 nutr

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 avail

The first results from ACE-SPACE highlight that the Southern Ocean is a region with highly heterogeneous aerosol properties. The areas around the strong westerly wind belt are characterized by significant sea spray contributions to the total particle and CCN number concentrations in the MBL. Future work will link detailed wave and wind observations to sea spray production. In the Ross and Amundsen Sea polynyas (leg 2), biogenic emissions appear to play an important role for CCN abundance. There are a number of open questions associated with this observation. First, even though this particular region was probed during a phytoplankton bloom period, it was not the only region with microbial activity but showed the clearest link to high CCN concentrations. Hence, either DMS production from dimethylsulfoniopropionate in the water and/or DMS fluxes into the atmosphere were enhanced. Second, the major pathway of how MSA is added to the particle phase remains to be identified. There are two possibilities: It can condense from the gas into the particle phase, or it can be added during cloud processing. The latter process would be consistent with the reduced efficiency of wet removal because of droplet evaporation or snowflake sublimation in the cold and dry airmasses from Antarctica. Our results also indicate that the absence of MSA-related processes in the aerosol model could explain the underestimation of CCN concentration, particularly in high aerosol-MSA regions. Given that the number of CCN influence Nd, this is an important issue to solve, especially close to the coast of Antarctica where clouds could impact the surface snow mass balance by influencing both the surface energy budget and precipitation. Further studies are planned that more closely investigate the linkages between CCN number concentrations and model simulations that take DMS emissions fluxes and particle phase MSA into account. A comparison of satellite-retrieved Nd90 and ship-based measurements of CCN s