Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions

Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the sta...

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Published in:	Earth and space science (Hoboken, N.J.) Vol. 6; no. 5; pp. 784 - 794
Main Authors:	Sonnewald, Maike, Wunsch, Carl, Heimbach, Patrick
Format:	Journal Article
Language:	English
Published:	United States John Wiley & Sons, Inc 01.05.2019 John Wiley and Sons Inc American Geophysical Union (AGU)
Subjects:	Algorithms Artificial intelligence big data Budgets Computational Geophysics Consortia Data Analysis: Algorithms and Implementation General Circulation General circulation models Geodesy and Gravity Geography global patterns Informatics Machine Learning Mass Balance Natural Hazards Neural Networks, Fuzzy Logic, Machine Learning ocean dynamics ocean modeling Ocean Monitoring with Geodetic Techniques Oceanography: Physical physical oceanography Physics Statistical Analysis Statistical methods: Descriptive Topography ocean dynamics ocean modeling physical oceanography big data machine learning global patterns
ISSN:	2333-5084, 2333-5084
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Abstract	Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a “Quasi‐Sverdrupian” regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual “dominantly nonlinear” regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries. Plain Language Summary A geography of the global ocean is presented of dynamical regions found using unsupervised machine learning techniques. A bottom‐up approach is used to identify emergent patterns in the modern global ocean. Their existence demonstrates commonalities that lead to methods for understanding the global circulation. Five areas vary from a depth coherent flow, quasi‐Sverdrupian, interior, interior with a strong vertical component, and an interior flow specific to the Southern Ocean. In some regions nonlinear terms are important, which will be the subject of future work at higher resolution. Key Points Machine learning is applied to create a geography of global dynamical regions, emergent dynamics are discussed A barotropic vorticity framework is sufficient for 93% of the ocean, with nonlinear terms have a small extent but could impact circulation Robust and novel application of machine learning techniques are demonstrated to offer insight into large-scale ocean physical regimes
AbstractList	Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K ‐means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a “Quasi‐Sverdrupian” regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual “dominantly nonlinear” regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries. A geography of the global ocean is presented of dynamical regions found using unsupervised machine learning techniques. A bottom‐up approach is used to identify emergent patterns in the modern global ocean. Their existence demonstrates commonalities that lead to methods for understanding the global circulation. Five areas vary from a depth coherent flow, quasi‐Sverdrupian, interior, interior with a strong vertical component, and an interior flow specific to the Southern Ocean. In some regions nonlinear terms are important, which will be the subject of future work at higher resolution. Machine learning is applied to create a geography of global dynamical regions, emergent dynamics are discussed A barotropic vorticity framework is sufficient for 93% of the ocean, with nonlinear terms have a small extent but could impact circulation Robust and novel application of machine learning techniques are demonstrated to offer insight into large-scale ocean physical regimes Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a “Quasi‐Sverdrupian” regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual “dominantly nonlinear” regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries. Plain Language Summary A geography of the global ocean is presented of dynamical regions found using unsupervised machine learning techniques. A bottom‐up approach is used to identify emergent patterns in the modern global ocean. Their existence demonstrates commonalities that lead to methods for understanding the global circulation. Five areas vary from a depth coherent flow, quasi‐Sverdrupian, interior, interior with a strong vertical component, and an interior flow specific to the Southern Ocean. In some regions nonlinear terms are important, which will be the subject of future work at higher resolution. Key Points Machine learning is applied to create a geography of global dynamical regions, emergent dynamics are discussed A barotropic vorticity framework is sufficient for 93% of the ocean, with nonlinear terms have a small extent but could impact circulation Robust and novel application of machine learning techniques are demonstrated to offer insight into large-scale ocean physical regimes Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20-year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K-means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a "Quasi-Sverdrupian" regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual "dominantly nonlinear" regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries. Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20-year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K-means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a "Quasi-Sverdrupian" regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual "dominantly nonlinear" regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries.Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20-year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K-means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a "Quasi-Sverdrupian" regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual "dominantly nonlinear" regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries. Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a “Quasi‐Sverdrupian” regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual “dominantly nonlinear” regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries. Machine learning is applied to create a geography of global dynamical regions, emergent dynamics are discussedA barotropic vorticity framework is sufficient for 93% of the ocean, with nonlinear terms have a small extent but could impact circulationRobust and novel application of machine learning techniques are demonstrated to offer insight into large-scale ocean physical regimes Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the standardized BV equation, identifying five unambiguous regimes. Cluster 1 covers 43 ± 3.3% of the ocean area. Surface and bottom stress torque are balanced by the bottom pressure torque and the nonlinear torque. Cluster 2 covers 24.8 ± 1.2%, where the beta effect balances the bottom pressure torque. Cluster 3 covers 14.6 ± 1.0%, characterized by a “Quasi‐Sverdrupian” regime where the beta effect is balanced by the wind and bottom stress term. The small region of Cluster 4 has baroclinic dynamics covering 6.9 ± 2.9% of the ocean. Cluster 5 occurs primarily in the Southern Ocean. Residual “dominantly nonlinear” regions highlight where the BV approach is inadequate, found in areas of rough topography in the Southern Ocean and along western boundaries.
Author	Wunsch, Carl Heimbach, Patrick Sonnewald, Maike
AuthorAffiliation	1 Department of Earth, Atmospheric and Planetary Scences Massachusetts Institute of Technology Cambridge MA USA 2 Department of Earth and Planetary Sciences Harvard University Cambridge MA USA 3 Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin TX USA
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References	2013; 26 2012 2017; 44 1997; 48 2013; 103 1973 2006 2004 2015; 8 2007; 34 2007; 37 2015; 45 1940; 3 2017b 2013; 2013 2017a 1967; 1 1999; 39 2010; 115 2017; 98 2007; 230 1948; 29 2017 2015 2005; 92 1981 2013 1950; 7 2011; 28 2018; 99 1996; 26 2018; 31 2016; 8 2016; 9 2016; 46 2012; 42 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 Church J. A. (e_1_2_7_7_1) 2013 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_27_1 e_1_2_7_28_1 Munk W. (e_1_2_7_20_1) 1950; 7 e_1_2_7_29_1 Munk W. H. (e_1_2_7_21_1) 1940; 3 Akaike H. (e_1_2_7_3_1) 1973 e_1_2_7_30_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_32_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_22_1 e_1_2_7_34_1 e_1_2_7_35_1 e_1_2_7_36_1 e_1_2_7_37_1 Priestley M. B. (e_1_2_7_24_1) 1981
References_xml	– volume: 1 start-page: 281 year: 1967 end-page: 297 – year: 1981 – volume: 45 start-page: 1258 year: 2015 end-page: 1284 article-title: Topographic coupling of the Atlantic overturning and gyre circulations publication-title: Journal of Physical Oceanography – volume: 42 start-page: 2229 year: 2012 end-page: 2233 article-title: The effects of altimeter instrument noise on the estimation of the wavenumber spectrum of sea surface height publication-title: Journal of Physical Oceanography – volume: 44 start-page: 5016 year: 2017 end-page: 5024 article-title: Delineating environmental control of phytoplankton biomass and phenology in the Southern Ocean publication-title: Geophysical Research Letters – start-page: 1 year: 2012 end-page: 14 – volume: 7 start-page: 80 year: 1950 end-page: 93 article-title: On the wind‐driven ocean circulation publication-title: Journal of Atmospheric Sciences – year: 2017b – volume: 31 start-page: 2599 year: 2018 end-page: 2611 article-title: Linear predictability: A sea surface height case study publication-title: Journal of Climate – volume: 8 start-page: 491 year: 2016 end-page: 518 article-title: Ocean data assimilation in support of climate applications: Status and perspectives publication-title: Annual Review of Marine Science – volume: 46 start-page: 289 issue: 1 year: 2016 end-page: 303 article-title: North Atlantic barotropic vorticity balances in numerical models publication-title: Journal of Physical Oceanography – volume: 34 year: 2007 article-title: Atlantic meridional overturning circulation and the Southern Hemisphere supergyre publication-title: Geophysical Research Letters – volume: 26 start-page: 671 year: 1996 end-page: 683 article-title: JEBAR, bottom pressure torque, and gulf stream separation publication-title: Journal of Physical Oceanography – start-page: 139 year: 2004 end-page: 150 – volume: 29 start-page: 202 issue: 2 year: 1948 end-page: 206 article-title: The westward intensification of wind‐driven ocean currents publication-title: Eos, Transactions American Geophysical Union – volume: 3 start-page: 53 year: 1940 end-page: 55 article-title: Note on the dynamics of the Antarctic Circumpolar Current publication-title: Tellus – volume: 8 start-page: 3071 year: 2015 end-page: 3104 article-title: ECCO version 4: An integrated framework for non‐linear inverse modeling and global ocean state estimation publication-title: Geoscientific Model Development – start-page: 1137 year: 2013 end-page: 1216 – volume: 48 start-page: 243 issue: 3 year: 1997 end-page: 250 article-title: Three clusters of male alcoholics publication-title: Drug Alcohol Depend – volume: 37 start-page: 2550 year: 2007 end-page: 256 article-title: Global ocean meridional overturning publication-title: Journal of Physical Oceanography – volume: 26 start-page: 2514 year: 2013 end-page: 2533 article-title: Objective determination of feature resolution in two sea surface temperature analyses publication-title: Journal of Climate – volume: 39 start-page: 181 issue: 3 year: 1999 end-page: 9 article-title: Use of clusters analysis to validate IHS diagnostic criteria for migraine and tension‐type headache publication-title: Headache – year: 2006 – volume: 230 start-page: 197 year: 2007 end-page: 208 article-title: Practical global oceanic state estimation publication-title: Physica D – volume: 99 start-page: 2107 year: 2018 end-page: 2128 article-title: A dynamically‐consistent ocean climatology publication-title: Bulletin of the American Meteorological Society – year: 2017a – volume: 103 start-page: 553 year: 2013 end-page: 579 – start-page: 267 year: 1973 end-page: 281 – volume: 28 start-page: 1504 year: 2011 end-page: 1521 article-title: Geostrophic velocity measurement techniques for the meridional overturning circulation and meridional heat transport in the South Atlantic publication-title: Journal of Atmospheric and Oceanic Technology – volume: 98 start-page: 95 year: 2017 end-page: 105 article-title: CMIP5 scientific gaps and recommendations for CMIP6 publication-title: Bulletin of the American Meteorological Society – volume: 92 start-page: 937 year: 2005 end-page: 950 article-title: “Can the strengths of AIC and BIC be shared?” publication-title: Biometrika – volume: 115 year: 2010 article-title: The color of sea level: Importance of spatial variations in spectral shape for assessing the significance of trends publication-title: Journal of Geophysical Research – year: 2017 – volume: 2013 start-page: 13 year: 2013 article-title: “Using ensemble of neural networks to learn stochastic convection parameterizations for climate and numerical weather prediction models from data simulated by a cloud resolving model” publication-title: Advances in Artificial Neural Systems – volume: 9 start-page: 1937 year: 2016 end-page: 1958 article-title: Overview of the coupled model intercomparison project Phase 6 (CMIP6) experimental design and organization publication-title: Geoscientific Model Development – year: 2015 – start-page: 267 volume-title: Second International Symposium on Information Theory year: 1973 ident: e_1_2_7_3_1 – ident: e_1_2_7_16_1 doi: 10.1007/978-3-319-20010-1 – volume: 3 start-page: 53 year: 1940 ident: e_1_2_7_21_1 article-title: Note on the dynamics of the Antarctic Circumpolar Current publication-title: Tellus doi: 10.3402/tellusa.v3i1.8609 – ident: e_1_2_7_22_1 doi: 10.1175/1520-0485(1996)026<0671:JBPTAG>2.0.CO;2 – ident: e_1_2_7_14_1 doi: 10.1029/2010JC006102 – ident: e_1_2_7_36_1 doi: 10.1093/biomet/92.4.937 – ident: e_1_2_7_12_1 doi: 10.1175/BAMS-D-17-0213.1 – ident: e_1_2_7_13_1 doi: 10.1016/S0376-8716(97)00130-0 – ident: e_1_2_7_37_1 doi: 10.1175/JPO-D-14-0100.1 – ident: e_1_2_7_11_1 doi: 10.5194/gmd-8-3071-2015 – ident: e_1_2_7_23_1 doi: 10.1175/JTECH-D-11-00058.1 – ident: e_1_2_7_17_1 – ident: e_1_2_7_33_1 doi: 10.1016/j.physd.2006.09.040 – ident: e_1_2_7_18_1 doi: 10.1175/JPO3130.1 – volume-title: Spectral analysis and time series year: 1981 ident: e_1_2_7_24_1 – ident: e_1_2_7_26_1 doi: 10.1002/2017GL076101 – ident: e_1_2_7_34_1 doi: 10.1016/B978-0-12-391851-2.00021-0 – ident: e_1_2_7_15_1 doi: 10.1155/2013/485913 – ident: e_1_2_7_10_1 doi: 10.5194/gmd-9-1937-2016 – ident: e_1_2_7_8_1 – ident: e_1_2_7_2_1 – ident: e_1_2_7_25_1 doi: 10.1175/JCLI-D-12-00787.1 – volume: 7 start-page: 80 year: 1950 ident: e_1_2_7_20_1 article-title: On the wind‐driven ocean circulation publication-title: Journal of Atmospheric Sciences – ident: e_1_2_7_31_1 doi: 10.1029/TR029i002p00202 – start-page: 1137 volume-title: Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change year: 2013 ident: e_1_2_7_7_1 – ident: e_1_2_7_30_1 doi: 10.1146/annurev-marine-122414-034113 – ident: e_1_2_7_19_1 – ident: e_1_2_7_29_1 doi: 10.1029/2007GL031583 – ident: e_1_2_7_6_1 doi: 10.1046/j.1526-4610.1999.3903181.x – ident: e_1_2_7_32_1 doi: 10.1175/BAMS-D-15-00013.1 – ident: e_1_2_7_5_1 doi: 10.1007/978-0-387-45528-0 – ident: e_1_2_7_35_1 doi: 10.1175/JPO-D-12-0106.1 – ident: e_1_2_7_4_1 doi: 10.1002/2016GL072428 – ident: e_1_2_7_9_1 – ident: e_1_2_7_27_1 doi: 10.1175/JPO-D-15-0133.1 – ident: e_1_2_7_28_1 doi: 10.1175/JCLI-D-17-0142.1
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SubjectTerms	Algorithms Artificial intelligence big data Budgets Computational Geophysics Consortia Data Analysis: Algorithms and Implementation General Circulation General circulation models Geodesy and Gravity Geography global patterns Informatics Machine Learning Mass Balance Natural Hazards Neural Networks, Fuzzy Logic, Machine Learning ocean dynamics ocean modeling Ocean Monitoring with Geodetic Techniques Oceanography: Physical physical oceanography Physics Statistical Analysis Statistical methods: Descriptive Topography
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Title	Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions
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