Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability
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....
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-08-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fmars.2019.00427/full |
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Article |
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DOAJ |
| language |
English |
| format |
Article |
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DOAJ |
| author |
Aneesh C. Subramanian Magdalena A. Balmaseda Luca Centurioni Rajib Chattopadhyay Bruce D. Cornuelle Charlotte DeMott Maria Flatau Yosuke Fujii Donata Giglio Sarah T. Gille Thomas M. Hamill Harry Hendon Ibrahim Hoteit Arun Kumar Jae-Hak Lee Andrew J. Lucas Amala Mahadevan Mio Matsueda SungHyun Nam Shastri Paturi Stephen G. Penny Adam Rydbeck Rui Sun Yuhei Takaya Amit Tandon Robert E. Todd Frederic Vitart Dongliang Yuan Chidong Zhang |
| spellingShingle |
Aneesh C. Subramanian Magdalena A. Balmaseda Luca Centurioni Rajib Chattopadhyay Bruce D. Cornuelle Charlotte DeMott Maria Flatau Yosuke Fujii Donata Giglio Sarah T. Gille Thomas M. Hamill Harry Hendon Ibrahim Hoteit Arun Kumar Jae-Hak Lee Andrew J. Lucas Amala Mahadevan Mio Matsueda SungHyun Nam Shastri Paturi Stephen G. Penny Adam Rydbeck Rui Sun Yuhei Takaya Amit Tandon Robert E. Todd Frederic Vitart Dongliang Yuan Chidong Zhang Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability Frontiers in Marine Science subseasonal seasonal predictions air–sea interaction satellite Argo |
| author_facet |
Aneesh C. Subramanian Magdalena A. Balmaseda Luca Centurioni Rajib Chattopadhyay Bruce D. Cornuelle Charlotte DeMott Maria Flatau Yosuke Fujii Donata Giglio Sarah T. Gille Thomas M. Hamill Harry Hendon Ibrahim Hoteit Arun Kumar Jae-Hak Lee Andrew J. Lucas Amala Mahadevan Mio Matsueda SungHyun Nam Shastri Paturi Stephen G. Penny Adam Rydbeck Rui Sun Yuhei Takaya Amit Tandon Robert E. Todd Frederic Vitart Dongliang Yuan Chidong Zhang |
| author_sort |
Aneesh C. Subramanian |
| title |
Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability |
| title_short |
Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability |
| title_full |
Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability |
| title_fullStr |
Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability |
| title_full_unstemmed |
Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability |
| title_sort |
ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability |
| publisher |
Frontiers Media S.A. |
| series |
Frontiers in Marine Science |
| issn |
2296-7745 |
| publishDate |
2019-08-01 |
| description |
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 of extracting their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatio-temporal 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. |
| topic |
subseasonal seasonal predictions air–sea interaction satellite Argo |
| url |
https://www.frontiersin.org/article/10.3389/fmars.2019.00427/full |
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doaj-d0ad05b7393147e59407b91fa874ef202020-11-25T01:22:15ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452019-08-01610.3389/fmars.2019.00427434400Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal VariabilityAneesh C. Subramanian0Magdalena A. Balmaseda1Luca Centurioni2Rajib Chattopadhyay3Bruce D. Cornuelle4Charlotte DeMott5Maria Flatau6Yosuke Fujii7Donata Giglio8Sarah T. Gille9Thomas M. Hamill10Harry Hendon11Ibrahim Hoteit12Arun Kumar13Jae-Hak Lee14Andrew J. Lucas15Amala Mahadevan16Mio Matsueda17SungHyun Nam18Shastri Paturi19Stephen G. Penny20Adam Rydbeck21Rui Sun22Yuhei Takaya23Amit Tandon24Robert E. Todd25Frederic Vitart26Dongliang Yuan27Chidong Zhang28Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, United StatesECMWF, Reading, United KingdomScripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United StatesIndian Institute of Tropical Meteorology, Pune, IndiaScripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United StatesDepartment of Atmospheric Science, Colorado State University, Fort Collins, CO, United StatesUnited States Naval Research Laboratory, Monterey, CA, United StatesMeteorological Research Institute, Japan Meteorological Agency, Tsukuba, JapanAtmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, United StatesScripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United StatesNational Oceanic and Atmospheric Administration, Earth System Research Laboratory, Physical Sciences Division, Boulder, CO, United StatesBureau of Meteorology, Melbourne, VIC, Australia0Earth Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia1National Centers for Environmental Prediction, Climate Prediction Center, College Park, MD, United States2Korea Institute of Ocean Science and Technology, Busan, South KoreaScripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United States3Woods Hole Oceanographic Institution, Woods Hole, MA, United States4Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan5School of Earth and Environmental Sciences/Research Institute of Oceanography, Seoul National University, Seoul, South Korea6I.M. Systems Group (IMSG), National Oceanic and Atmospheric Administration, Environmental Modeling Center, College Park, MD, United States7Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, MA, United States8United States Naval Research Laboratory, Stennis Space Center, Hancock, MS, United StatesScripps Institution of Oceanography, University of California, San Diego, San Diego, CA, United StatesMeteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan9Mechanical Engineering, University of Massachusetts Dartmouth, Dartmouth, MA, United States3Woods Hole Oceanographic Institution, Woods Hole, MA, United StatesECMWF, Reading, United Kingdom0Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China1National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory, Seattle, WA, United StatesSubseasonal-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 of extracting their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatio-temporal 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.https://www.frontiersin.org/article/10.3389/fmars.2019.00427/fullsubseasonalseasonalpredictionsair–sea interactionsatelliteArgo |