IOCAS-IR
Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability
Subramanian, Aneesh C.1; Balmaseda, Magdalena A.2; Centurioni, Luca3; Chattopadhyay, Rajib4; Cornuelle, Bruce D.3; DeMott, Charlotte5; Flatau, Maria6; Fujii, Yosuke7; Giglio, Donata1; Gille, Sarah T.3; Hamill, Thomas M.8; Hendon, Harry9; Hoteit, Ibrahim10; Kumar, Arun11; Lee, Jae-Hak12; Lucas, Andrew J.3; Mahadevan, Amala13; Matsueda, Mio14; Nam, SungHyun15; Paturi, Shastri16; Penny, Stephen G.17; Rydbeck, Adam18; Sun, Rui3; Takaya, Yuhei7; Tandon, Amit19; Todd, Robert E.13; Vitart, Frederic2; Yuan, Dongliang20; Zhang, Chidong21
2019-08-08
Source PublicationFRONTIERS IN MARINE SCIENCE
Volume6Pages:8
Corresponding AuthorSubramanian, Aneesh C.(aneeshcs@colorado.edu)
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.
Keywordsubseasonal seasonal predictions air-sea interaction satellite Argo gliders drifters
DOI10.3389/fmars.2019.00427
Indexed BySCI
Language英语
Funding ProjectNOAA Climate Variability and Prediction Program[NA14OAR4310276] ; NSF Earth System Modeling Program[OCE1419306] ; NASA[NNX14AO78G] ; NASA[80NSSC19K0059] ; NSFC[91858204] ; NSFC[41720104008] ; NSFC[41421005] ; [NA16OAR4310094]
WOS Research AreaEnvironmental Sciences & Ecology ; Marine & Freshwater Biology
WOS SubjectEnvironmental Sciences ; Marine & Freshwater Biology
WOS IDWOS:000479256900001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/162340
Collection中国科学院海洋研究所
Corresponding AuthorSubramanian, Aneesh C.
Affiliation1.Univ Colorado, Atmospher & Ocean Sci, Boulder, CO 80309 USA
2.ECMWF, Reading, Berks, England
3.Univ Calif San Diego, Scripps Inst Oceanog, San Diego, CA 92103 USA
4.Indian Inst Trop Meteorol, Pune, Maharashtra, India
5.Colorado State Univ, Dept Atmospher Sci, Ft Collins, CO 80523 USA
6.US Naval Res Lab, Monterey, CA USA
7.Japan Meteorol Agcy, Meteorol Res Inst, Tsukuba, Ibaraki, Japan
8.NOAA, Earth Syst Res Lab, Div Phys Sci, Boulder, CO USA
9.Bur Meteorol, Melbourne, Vic, Australia
10.King Abdullah Univ Sci & Technol, Earth Sci & Engn, Thuwal, Saudi Arabia
11.Climate Predict Ctr, Natl Ctr Environm Predict, College Pk, MD USA
12.Korea Inst Ocean Sci & Technol, Busan, South Korea
13.Woods Hole Oceanog Inst, Woods Hole, MA 02543 USA
14.Univ Tsukuba, Ctr Computat Sci, Tsukuba, Ibaraki, Japan
15.Seoul Natl Univ, Res Inst Oceanog, Sch Earth & Environm Sci, Seoul, South Korea
16.NOAA, IMSG, Environm Modeling Ctr, College Pk, MD USA
17.Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA
18.US Naval Res Lab, Stennis Space Ctr, Hancock, MS USA
19.Univ Massachusetts, Mech Engn, Dartmouth, MA USA
20.Chinese Acad Sci, Inst Oceanol, Qingdao, Shandong, Peoples R China
21.NOAA, Pacific Marine Environm Lab, 7600 Sand Point Way Ne, Seattle, WA 98115 USA
Recommended Citation
GB/T 7714
Subramanian, Aneesh C.,Balmaseda, Magdalena A.,Centurioni, Luca,et al. Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability[J]. FRONTIERS IN MARINE SCIENCE,2019,6:8.
APA Subramanian, Aneesh C..,Balmaseda, Magdalena A..,Centurioni, Luca.,Chattopadhyay, Rajib.,Cornuelle, Bruce D..,...&Zhang, Chidong.(2019).Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability.FRONTIERS IN MARINE SCIENCE,6,8.
MLA Subramanian, Aneesh C.,et al."Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability".FRONTIERS IN MARINE SCIENCE 6(2019):8.
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