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dc.contributor.authorSubramanian, Aneesh C.  Concept link
dc.contributor.authorBalmaseda, Magdalena A.  Concept link
dc.contributor.authorCenturioni, Luca R.  Concept link
dc.contributor.authorChattopadhyay, Rajib  Concept link
dc.contributor.authorCornuelle, Bruce D.  Concept link
dc.contributor.authorDeMott, Charlotte  Concept link
dc.contributor.authorFlatau, Maria  Concept link
dc.contributor.authorFujii, Yosuke  Concept link
dc.contributor.authorGiglio, Donata  Concept link
dc.contributor.authorGille, Sarah T.  Concept link
dc.contributor.authorHamill, Thomas M.  Concept link
dc.contributor.authorHendon, Harry  Concept link
dc.contributor.authorHoteit, Ibrahim  Concept link
dc.contributor.authorKumar, Arun  Concept link
dc.contributor.authorLee, Jae-Hak  Concept link
dc.contributor.authorLucas, Andrew J.  Concept link
dc.contributor.authorMahadevan, Amala  Concept link
dc.contributor.authorMatsueda, Mio  Concept link
dc.contributor.authorNam, SungHyun  Concept link
dc.contributor.authorPaturi, Shastri  Concept link
dc.contributor.authorPenny, Stephen G.  Concept link
dc.contributor.authorRydbeck, Adam  Concept link
dc.contributor.authorSun, Rui  Concept link
dc.contributor.authorTakaya, Yuhei  Concept link
dc.contributor.authorTandon, Amit  Concept link
dc.contributor.authorTodd, Robert E.  Concept link
dc.contributor.authorVitart, Frederic  Concept link
dc.contributor.authorYuan, Dongliang  Concept link
dc.contributor.authorZhang, Chidong  Concept link
dc.date.accessioned2019-10-10T18:17:14Z
dc.date.available2019-10-10T18:17:14Z
dc.date.issued2019-08-08
dc.identifier.citationSubramanian, A. C., Balmaseda, M. A., Centurioni, L., Chattopadhyay, R., Cornuelle, B. D., DeMott, C., Flatau, M., Fujii, Y., Giglio, D., Gille, S. T., Hamill, T. M., Hendon, H., Hoteit, I., Kumar, A., Lee, J., Lucas, A. J., Mahadevan, A., Matsueda, M., Nam, S., Paturi, S., Penny, S. G., Rydbeck, A., Sun, R., Takaya, Y., Tandon, A., Todd, R. E., Vitart, F., Yuan, D., & Zhang, C. (2019). Ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability. Frontiers in Marine Science, 6, 427.en_US
dc.identifier.urihttps://hdl.handle.net/1912/24685
dc.description© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Subramanian, A. C., Balmaseda, M. A., Centurioni, L., Chattopadhyay, R., Cornuelle, B. D., DeMott, C., Flatau, M., Fujii, Y., Giglio, D., Gille, S. T., Hamill, T. M., Hendon, H., Hoteit, I., Kumar, A., Lee, J., Lucas, A. J., Mahadevan, A., Matsueda, M., Nam, S., Paturi, S., Penny, S. G., Rydbeck, A., Sun, R., Takaya, Y., Tandon, A., Todd, R. E., Vitart, F., Yuan, D., & Zhang, C. Ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability. Frontiers in Marine Science, 6, (2019): 427, doi:10.3389/fmars.2019.00427.en_US
dc.description.abstractSubseasonal-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.en_US
dc.description.sponsorshipAS was funded by NOAA Climate Variability and Prediction Program (NA14OAR4310276) and the NSF Earth System Modeling Program (OCE1419306). CD was funded by NA16OAR4310094. SG and DG were funded by NASA awards NNX14AO78G and 80NSSC19K0059. DY was supported by NSFC (91858204, 41720104008, and 41421005).en_US
dc.publisherFrontiers Mediaen_US
dc.relation.urihttps://doi.org/10.3389/fmars.2019.00427
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectsubseasonalen_US
dc.subjectseasonalen_US
dc.subjectpredictionsen_US
dc.subjectair–sea interactionen_US
dc.subjectsatelliteen_US
dc.subjectArgoen_US
dc.subjectglidersen_US
dc.subjectdriftersen_US
dc.titleOcean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variabilityen_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fmars.2019.00427


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International