Ocean Observations to Improve Our Understanding, Modeling, and Forecasting of Subseasonal-to-Seasonal Variability

doi:10.3389/fmars.2019.00427

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, Luca 3; Chattopadhyay, Rajib 4; Cornuelle, Bruce D.3; DeMott, Charlotte 5; Flatau, Maria 6; Fujii, Yosuke 7; Giglio, Donata 1; Gille, Sarah T.3; Hamill, Thomas M.8; Hendon, Harry 9; Hoteit, Ibrahim 10; Kumar, Arun 11; Lee, Jae-Hak 12; Lucas, Andrew J.3; Mahadevan, Amala 13; Matsueda, Mio 14; Nam, SungHyun 15; Paturi, Shastri 16; Penny, Stephen G.17; Rydbeck, Adam 18; Sun, Rui 3; Takaya, Yuhei 7; Tandon, Amit 19; Todd, Robert E.13; Vitart, Frederic 2; Yuan, Dongliang20 ; Zhang, Chidong 21
	2019-08-08
发表期刊	FRONTIERS IN MARINE SCIENCE
卷号	6 页码:8
通讯作者	Subramanian, Aneesh C.([email protected])
摘要	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.
关键词	subseasonal seasonal predictions air-sea interaction satellite Argo gliders drifters
DOI	10.3389/fmars.2019.00427
收录类别	SCI
语种	英语
资助项目	NOAA Climate Variability and Prediction Program[NA14OAR4310276]###2996; NSF Earth System Modeling Program[OCE1419306]###2997; NASA[NNX14AO78G]###2998; NASA[80NSSC19K0059]###2999; NSFC[91858204]###2909; NSFC[41720104008]###815; NSFC[41421005]###748; [NA16OAR4310094]###3000; NOAA Climate Variability and Prediction Program[NA14OAR4310276]; NSF Earth System Modeling Program[OCE1419306]; NASA[NNX14AO78G]; NASA[80NSSC19K0059]; NSFC[91858204]; NSFC[41720104008]; NSFC[41421005]; [NA16OAR4310094]
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology
WOS类目	Environmental Sciences ; Marine & Freshwater Biology
WOS记录号	WOS:000479256900001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/162340
专题	中国科学院海洋研究所
通讯作者	Subramanian, Aneesh C.
作者单位	1.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
推荐引用方式 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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