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Mini Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Mar. Sci. | doi: 10.3389/fmars.2019.00427

Ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability

  • 1University of Colorado Boulder, United States
  • 2Scripps Institution of Oceanography, University of California, San Diego, United States
  • 3European Centre for Medium-Range Weather Forecasts, United Kingdom
  • 4Indian Institute of Tropical Meteorology (IITM), India
  • 5Colorado State University, United States
  • 6Physical Sciences Division, Earth System Research Laboratory (NOAA), United States
  • 7Bureau of Meteorology (Australia), Australia
  • 8King Abdullah University of Science and Technology, Saudi Arabia
  • 9United States Naval Research Laboratory, United States
  • 10Meteorological Research Institute (MRI), Japan
  • 11National Oceanic and Atmospheric Administration (NOAA), United States
  • 12Korea Institute of Ocean Science and Technology, South Korea
  • 13University of Tsukuba, Japan
  • 14Woods Hole Oceanographic Institution, United States
  • 15Seoul National University, South Korea
  • 16University of Maryland, College Park, United States
  • 17University of Massachusetts Dartmouth, United States
  • 18Institute of Oceanology, Chinese Academy of Sciences, China
  • 19Pacific Marine Environmental Laboratory (NOAA), United States

Subseasonal-to-seasonal (S2S) forecasts have the potential to provide advance information about weather and climate events. The high heat capacity of water means that the subsurface ocean stores and re-releases heat (and other properties) and is an important source of information for S2S forecasts. However, the subsurface ocean is challenging to observe, because it cannot be measured by satellite. Subsurface ocean observing systems relevant for understanding, modeling, and forecasting on S2S timescales will continue to evolve with the improvement in technological capabilities. The community must focus on designing and implementing low-cost, high-value surface and subsurface ocean observations, and developing forecasting system capable to extract their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatiotemporal resolution data in regions that are sources of predictability on these timescales (coastal, tropical, and polar regions). While ENSO has been a driving force for the design of the current observing system, the subseasonal time scales present new observational requirements. Advanced observation technologies such as autonomous surface and subsurface profiling devices as well as satellites that observe the ocean-atmosphere interface simultaneously can lead to breakthroughs in coupled data assimilation (CDA) and coupled initialization for S2S forecasts. These observational platforms should also be tested and evaluated in ocean observation sensitivity experiments with current and future generation CDA and S2S prediction systems. Investments in the new ocean observations, as well as model and DA system developments, can lead to substantial returns on cost savings from disaster mitigation as well as socio-economic decisions that use S2S forecast information.

Keywords: Subseasonal, seasonal, Predicability, air-sea interaction, Satellite, Gliders, Argo and Argos observations, Drifter, assimilation

Received: 01 Nov 2018; Accepted: 05 Jul 2019.

Edited by:

AMOS T. KABO-BAH, University of Energy and Natural Resources, Ghana

Reviewed by:

Richard J. Matear, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Eric Hackert, National Aeronautics and Space Administration (NASA), United States  

Copyright: © 2019 Subramanian, Balmaseda, Chattopadhyay, Centurioni, Cornuelle, DeMott, Hamill, Hendon, Hoteit, Flatau, Fujii, Gille, Kumar, Lee, Lucas, Matsueda, Mahadevan, Nam, Paturi, Penny, Rydbeck, Sun, Tandon, Takaya, Todd, Vitart, Yuan and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Aneesh Subramanian, University of Colorado Boulder, Boulder, United States, acsubram@ucsd.edu