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  • 1
    Publication Date: 2022-05-26
    Description: Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 46(10), (2019): 5369-5377, doi: 10.1029/2019GL082078.
    Description: Seasonal evolution of the barrier layer (BL) and temperature inversion in the northern Bay of Bengal and their role on the mixed layer temperature (MLT) is examined using observations from a single Argo during December 2013 to July 2017. During fall, low salinity at surface generates BL in this region. It thickens to almost 80 m in winter enhanced by deepening of isothermal layer depth due to remote forcing. During winter, surface cooling lowers near‐surface temperature, and thus, the subsurface BL experiences a significant temperature inversion (~2.5 °C). This temperature inversion diffuses to distribute heat within ML and surface heating begins deep penetration of shortwave radiation through ML during spring. Hence, the ML becomes thermally well stratified, resulting in the warmest MLT. The Monin‐Obukhov length attains its highest value during summer indicating wind dominance in the ML. During spring and fall, upper ocean gains heat allowing buoyancy to dominate over wind mixing.
    Description: A. S. and S. S. thank financial support from Space Application Centre (SAC), Indian Space Research Organization (ISRO), Government of India (Grant: SAC/EPSA/4.19/2016). This study was also supported by the first phase of Ministry of Earth Sciences (MoES), Government of India grant to establish a Bay of Bengal Coastal Observatory (BOBCO) at IITBBS (Grant: RP088). Authors acknowledged NCPOR Contribution number J ‐ 03/2019‐20 for this work. The authors are grateful to the reviewers and the Editor for constructive suggestions. The figures are generated using Matlab. The data source and availability are given in the Text S1.
    Description: 2019-10-24
    Keywords: Argo ; Bay of Bengal ; mixed layer ; temperature inversion ; barrier layer ; Monin‐Obukhov length
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-10-26
    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.
    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.
    Description: AS 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).
    Keywords: Subseasonal ; Seasonal ; Predictions ; Air-sea interaction ; Satellite ; Argo ; Gliders ; Drifters
    Repository Name: Woods Hole Open Access Server
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  • 3
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Roemmich, D., Alford, M. H., Claustre, H., Johnson, K., King, B., Moum, J., Oke, P., Owens, W. B., Pouliquen, S., Purkey, S., Scanderbeg, M., Suga, T., Wijffels, S., Zilberman, N., Bakker, D., Baringer, M., Belbeoch, M., Bittig, H. C., Boss, E., Calil, P., Carse, F., Carval, T., Chai, F., Conchubhair, D. O., d'Ortenzio, F., Dall'Olmo, G., Desbruyeres, D., Fennel, K., Fer, I., Ferrari, R., Forget, G., Freeland, H., Fujiki, T., Gehlen, M., Greenan, B., Hallberg, R., Hibiya, T., Hosoda, S., Jayne, S., Jochum, M., Johnson, G. C., Kang, K., Kolodziejczyk, N., Kortzinger, A., Le Traon, P., Lenn, Y., Maze, G., Mork, K. A., Morris, T., Nagai, T., Nash, J., Garabato, A. N., Olsen, A., Pattabhi, R. R., Prakash, S., Riser, S., Schmechtig, C., Schmid, C., Shroyer, E., Sterl, A., Sutton, P., Talley, L., Tanhua, T., Thierry, V., Thomalla, S., Toole, J., Troisi, A., Trull, T. W., Turton, J., Velez-Belchi, P. J., Walczowski, W., Wang, H., Wanninkhof, R., Waterhouse, A. F., Waterman, S., Watson, A., Wilson, C., Wong, A. P. S., Xu, J., & Yasuda, I. On the future of Argo: A global, full-depth, multi-disciplinary array. Frontiers in Marine Science, 6, (2019): 439, doi:10.3389/fmars.2019.00439.
    Description: The Argo Program has been implemented and sustained for almost two decades, as a global array of about 4000 profiling floats. Argo provides continuous observations of ocean temperature and salinity versus pressure, from the sea surface to 2000 dbar. The successful installation of the Argo array and its innovative data management system arose opportunistically from the combination of great scientific need and technological innovation. Through the data system, Argo provides fundamental physical observations with broad societally-valuable applications, built on the cost-efficient and robust technologies of autonomous profiling floats. Following recent advances in platform and sensor technologies, even greater opportunity exists now than 20 years ago to (i) improve Argo’s global coverage and value beyond the original design, (ii) extend Argo to span the full ocean depth, (iii) add biogeochemical sensors for improved understanding of oceanic cycles of carbon, nutrients, and ecosystems, and (iv) consider experimental sensors that might be included in the future, for example to document the spatial and temporal patterns of ocean mixing. For Core Argo and each of these enhancements, the past, present, and future progression along a path from experimental deployments to regional pilot arrays to global implementation is described. The objective is to create a fully global, top-to-bottom, dynamically complete, and multidisciplinary Argo Program that will integrate seamlessly with satellite and with other in situ elements of the Global Ocean Observing System (Legler et al., 2015). The integrated system will deliver operational reanalysis and forecasting capability, and assessment of the state and variability of the climate system with respect to physical, biogeochemical, and ecosystems parameters. It will enable basic research of unprecedented breadth and magnitude, and a wealth of ocean-education and outreach opportunities.
    Description: DR, MS, and NZ were supported by the US Argo Program through the NOAA Grant NA15OAR4320071 (CIMEC). WO, SJ, and SWi were supported by the US Argo Program through the NOAA Grant NA14OAR4320158 (CINAR). EuroArgo scientists were supported by the two grants: (1) AtlantOS funding by the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement No. 633211 and (2) Monitoring the Oceans and Climate Change with Argo (MOCCA) Co-funded by the European Maritime and Fisheries Fund (EMFF) Project No. SI2.709624. This manuscript represents a contribution to the following research projects for HC, CaS, and FD: remOcean (funded by the European Research Council, grant 246777), NAOS (funded by the Agence Nationale de la Recherche in the frame of the French “Equipement d’avenir” program, grant ANR J11R107-F), AtlantOS (funded by the European Union’s Horizon 2020 Research and Innovation Programme, grant 2014-633211), and the BGC-Argo project funded by the CNES. DB was funded by the EU RINGO project (730944 H2020-INFRADEV-2016-1). RF was supported by the AGS-1835576. GCJ was supported by the Global Ocean Monitoring and Observing Program, National Oceanic and Atmospheric Administration (NOAA), U.S., and the Department of Commerce and NOAA Research. LT was funded under the SOCCOM Grant No. NSF PLR-1425989. VT’s contribution was supported by the French National Research Agency (ANR) through the EQUIPEX NAOS (Novel Argo Observing System) under the reference ANR-10-EQPX-40 and by the European H2020 Research and Innovation Programme through the AtlantOS project under the reference 633211. WW was supported by the Argo Poland program through the Ministry of Sciences and Higher Education Grant No. DIR/WK/2016/12. AmW was funded by the NSF-OCE1434722. K-RK is funded by the National Institute of Meteorological Sciences’ Research and Development Program “Development of Marine Meteorology Monitoring and Next-generation Ocean Forecasting System” under the grant KMA2018-00421. CSchmid is funded by NOAA/AOML and the US Argo Program through NOAA/OOMD. MBa is funded by NOAA/AOML.
    Keywords: Argo ; Floats ; Global ; Ocean ; Warming ; Circulation ; Temperature ; Salinity
    Repository Name: Woods Hole Open Access Server
    Type: Article
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