Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition
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Date | 2021-11 | ||||||||||||||||||||
Language | English | ||||||||||||||||||||
Author(s) | Landwehr Sebastian1, Volpi Michele2, Haumann F. Alexander3, 4, Robinson Charlotte M.5, Thurnherr Iris6, 7, Ferracci Valerio8, Baccarini Andrea1, 13, Thomas Jenny9, Gorodetskaya Irina10, 11, Tatzelt Christian12, Henning Silvia12, Modini Rob L.13, Forrer Heather J.14, 15, Lin Yajuan16, 17, 18, Cassar Nicolas16, 17, Simó Rafel19, Hassler Christel9, 20, Moallemi Alireza13, Fawcett Sarah E.14, Harris Neil8, Airs Ruth21, Derkani Marzieh H.22, Alberello Alberto23, Toffoli Alessandro22, Chen Gang13, Rodríguez-Ros Pablo19, Zamanillo Marina19, Cortés-Greus Pau19, Xue Lei24, Bolas Conor G.25, Leonard Katherine C.11, 26, Perez-Cruz Fernando2, 27, Walton David4, Schmale Julia1 | ||||||||||||||||||||
Affiliation(s) | 1 : Extreme Environments Research Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 2 : Swiss Data Science Center, ETH Zurich, Switzerland 3 : Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ 08540-6654, USA 4 : British Antarctic Survey, Cambridge CB3 0ET, UK 5 : Remote Sensing and Satellite Research Group, School of Earth and Planetary Sciences, Curtin University, Kent Street, Bentley, WA 6102, Australia 6 : Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland 7 : Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway 8 : Centre for Environmental and Agricultural Informatics, School of Water, Energy & Environment Cranfield University, College Road, Cranfield MK43 0AL, Bedfordshire, UK 9 : Swiss Polar Institute, Lausanne, Switzerland 10 : Centre for Environmental and Marine Studies, Department of Physics, University of Aveiro, Aveiro, Portugal 11 : Laboratory of Cryospheric Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 12 : Leibniz Institute for Tropospheric Research, Leipzig, Germany 13 : Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland 14 : Department of Oceanography, University of Cape Town, 7701, Cape Town, South Africa 15 : Earth, Ocean and Atmospheric Science Department, Florida State University, Tallahassee, FL 32306, USA 16 : Division of Earth and Climate Sciences, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA 17 : Laboratoir des sciences de l'environnement marin, University of Brest, Brest, France 18 : Duke Kunshan University, Kunshan, Suzhou, Jiangsu, China 19 : Institut de Ciències del Mar (ICM-CSIC), Barcelona, Catalonia, Spain 20 : Department F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva, Geneva, Switzerland 21 : Plymouth Marine Laboratory, Plymouth PL1 3DH, UK 22 : Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC 3010, Australia 23 : Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan 24 : Department of Chemistry, College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA 25 : ITOPF Ltd., London EC1Y 1DT, UK 26 : Cooperative Institute for Research in Environmental Sciences at the University of Colorado, Boulder, CO 80309, USA 27 : Department of Computer Science at ETH Zurich, Zurich, Switzerland |
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Source | Earth System Dynamics (2190-4979) (Copernicus GmbH), 2021-11 , Vol. 12 , N. 4 , P. 1295-1369 | ||||||||||||||||||||
DOI | 10.5194/esd-12-1295-2021 | ||||||||||||||||||||
WOS© Times Cited | 10 | ||||||||||||||||||||
Abstract | 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. |
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