Estimating global ocean heat content from tidal magnetic satellite observations

Ocean tides generate electromagnetic (EM) signals that are emitted into space and can be recorded with low-Earth-orbiting satellites. Observations of oceanic EM signals contain aggregated information about global transports of water, heat, and salinity. We utilize an artificial neural network (ANN) as a non-linear inversion scheme and demonstrate how to infer ocean heat content (OHC) estimates from magnetic signals of the lunar semi-diurnal (M2) tide. The ANN is trained using monthly OHC estimates based on oceanographic in-situ data from 1990–2015 and the corresponding computed tidal magnetic fields at satellite altitude. We show that the ANN can closely recover inter-annual and decadal OHC variations from simulated tidal magnetic signals. Using the trained ANN, we present the first OHC estimates from recently extracted tidal magnetic satellite observations. Such space-borne OHC estimates can complement the already existing in-situ measurements of upper ocean temperature and can also allow insights into abyssal OHC, where in-situ data are still very scarce.

[1]  Masson-Delmotte,et al.  The Physical Science Basis , 2007 .

[2]  Alexei Kuvshinov,et al.  3-D Global Induction in the Oceans and Solid Earth: Recent Progress in Modeling Magnetic and Electric Fields from Sources of Magnetospheric, Ionospheric and Oceanic Origin , 2008 .

[3]  Hermann Lühr,et al.  Satellite Observations of Magnetic Fields Due to Ocean Tidal Flow , 2003, Science.

[4]  Brian Hamilton,et al.  International Geomagnetic Reference Field: the 12th generation , 2015, Earth, Planets and Space.

[5]  M. Nair,et al.  A Comparison of Model‐Based Ionospheric and Ocean Tidal Magnetic Signals With Observatory Data , 2018, Geophysical Research Letters.

[6]  R. Ponte.  An assessment of deep steric height variability over the global ocean , 2012 .

[7]  R. Cabeza,et al.  Present and Future , 2008 .

[8]  Nick Rayner,et al.  EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates , 2013 .

[9]  N. Olsen,et al.  A new model of Earth's radial conductivity structure derived from over 10 yr of satellite and observatory magnetic data , 2015 .

[10]  Jan Saynisch,et al.  Impact of oceanic warming on electromagnetic oceanic tidal signals: A CMIP5 climate model‐based sensitivity study , 2017 .

[11]  Cecile Cabanes,et al.  The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements , 2013 .

[12]  M. Ishii,et al.  Accuracy of Global Upper Ocean Heat Content Estimation Expected from Present Observational Data Sets , 2017 .

[13]  Matt A. King,et al.  Ocean tides in the Weddell Sea: new observations on the Filchner-Ronne and Larsen C ice shelves and model validation , 2011 .

[14]  Takeshi Kawano,et al.  Deep ocean heat content changes estimated from observation and reanalysis product and their influence on sea level change , 2011 .

[15]  John Abraham,et al.  Improved estimates of ocean heat content from 1960 to 2015 , 2017, Science Advances.

[16]  J. Abraham,et al.  Global Upper Ocean Heat Content Estimation: Recent Progress and the Remaining Challenges , 2015 .

[17]  Nils Olsen,et al.  A comprehensive model of Earth’s magnetic field determined from 4 years of Swarm satellite observations , 2018, Earth, Planets and Space.

[18]  N. Olsen,et al.  CM5, a Pre-Swarm Comprehensive Geomagnetic Field Model Derived from Over 12 Yr of CHAMP, Orsted, SAC-C and Observatory Data , 2015 .

[19]  Maik Thomas,et al.  Impact of variable seawater conductivity on motional induction simulated with an ocean general circulation model , 2016 .

[20]  K. Katsumata,et al.  Heat Content Change in the Pacific Ocean between the 1990s and 2000s , 2010 .

[21]  Maik Thomas,et al.  Impact of climate variability on the tidal oceanic magnetic signal—A model-based sensitivity study , 2016 .

[22]  Dean Roemmich,et al.  Unabated planetary warming and its ocean structure since 2006 , 2015 .

[23]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[24]  S. Maus,et al.  Tidal signals in ocean-bottom magnetic measurements of the Northwestern Pacific: observation versus prediction , 2012 .

[25]  G. Hulot,et al.  Swarm: A constellation to study the Earth’s magnetic field , 2006 .

[26]  Alexey Kuvshinov,et al.  Global 3-D imaging of mantle electrical conductivity based on inversion of observatory C-responses - I. An approach and its verification , 2012 .

[27]  Mark E. Everett,et al.  Effects of near-surface conductance on global satellite induction responses , 2003 .

[28]  Stephen C. Riser,et al.  The Argo Program : present and future , 2017 .

[29]  Joanna Staneva,et al.  Data assimilation of ocean wind waves using Neural Networks. A case study for the German Bight , 2015 .

[30]  Amir Hossein Alavi,et al.  Machine learning in geosciences and remote sensing , 2016 .

[31]  P. Voosen.  Billionaire's gift pushes ocean sensors deeper. , 2017, Science.

[32]  Tong Lee,et al.  Ocean heat content variability and change in an ensemble of ocean reanalyses , 2017, Climate Dynamics.

[33]  S. Riser,et al.  The Argo Program : observing the global ocean with profiling floats , 2009 .

[34]  Gregory C. Johnson,et al.  Informing Deep Argo Array Design Using Argo and Full-Depth Hydrographic Section Data* , 2015 .

[35]  R. Tyler,et al.  Predictability of Ocean Heat Content From Electrical Conductance , 2019, Journal of Geophysical Research: Oceans.

[36]  Paola Malanotte-Rizzoli,et al.  Principles of Ocean Physics , 1989 .

[37]  Jürgen Sündermann,et al.  Consideration of ocean tides in an OGCM and impacts on subseasonal to decadal polar motion excitation , 2001 .

[38]  William W. Hsieh,et al.  Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography. , 1998 .

[39]  Timothy P. Boyer,et al.  Sensitivity of Global Upper-Ocean Heat Content Estimates to Mapping Methods, XBT Bias Corrections, and Baseline Climatologies* , 2016 .

[40]  S. Maus,et al.  Ocean tidal signals in observatory and satellite magnetic measurements , 2004 .

[41]  Son V. Nghiem,et al.  The extreme melt across the Greenland ice sheet in 2012 , 2012 .

[42]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[43]  D. Stammer,et al.  Inferring deep ocean tidal energy dissipation from the global high‐resolution data‐assimilative HAMTIDE model , 2014 .

[44]  N. Olsen,et al.  Extracting ocean‐generated tidal magnetic signals from Swarm data through satellite gradiometry , 2016 .

[45]  J. Larsen.  Electric and Magnetic Fields Induced by Deep Sea Tides , 1968 .

[46]  S. Levitus,et al.  World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010 , 2012 .

[47]  John Abraham,et al.  A review of global ocean temperature observations: Implications for ocean heat content estimates and climate change , 2013, Reviews of Geophysics.

[48]  Peter J. Gleckler,et al.  Improved estimates of upper-ocean warming and multi-decadal sea-level rise , 2008, Nature.

[49]  Stephen J. Wright,et al.  Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent , 2011, NIPS.

[50]  Yoshua Bengio,et al.  Maxout Networks , 2013, ICML.

[51]  Thomas B. Sanford,et al.  Motionally Induced Electric and Magnetic Fields in the Sea , 1971 .