Ocean temperature and salinity components of the Madden-Julian oscillation observed by Argo floats

New diagnostics of the Madden-Julian Oscillation (MJO) cycle in ocean temperature and, for the first time, salinity are presented. The MJO composites are based on 4 years of gridded Argo float data from 2003 to 2006, and extend from the surface to 1,400 m depth in the tropical Indian and Pacific Oceans. The MJO surface salinity anomalies are consistent with precipitation minus evaporation fluxes in the Indian Ocean, and with anomalous zonal advection in the Pacific. The Argo sea surface temperature and thermocline depth anomalies are consistent with previous studies using other data sets. The near-surface density changes due to salinity are comparable to, and partially offset, those due to temperature, emphasising the importance of including salinity as well as temperature changes in mixed-layer modelling of tropical intraseasonal processes. The MJO-forced equatorial Kelvin wave that propagates along the thermocline in the Pacific extends down into the deep ocean, to at least 1,400 m. Coherent, statistically significant, MJO temperature and salinity anomalies are also present in the deep Indian Ocean

Nonlinear shallow water tropical instability waves on the equatorial β ‐plane: Genesis of two distinct types of waves

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95089/1/grl26480.pd

Development and Application of Social Studies Digital Contents for Inquiry Based Learning: A Case of “NONTA’s Classroom” and “Higashi-Hiroshima City Library Collaboration Seminar”

The purpose of this study is to develop digital contents that support inquiry based learning and to propose application examples of “local community studies” conducted in Japanese primary social studies education. For this reason, we set up a development project team by researchers and university students majoring in social studies education and developed digital contents over a year. In this project, we formulated five “design principles” for development. The contents were envisioned in consideration of the format, cooperation with social studies textbooks and supplementary readers for community studies, correspondence with the course of study, and the fairness of the areas handled. Finally, the digital contents which name is “NONTA’s Classroom” was developed and 10 keywords were released on the website. Along with the release, in cooperation with Higashi-Hiroshima City Library, we invited local elementary school students to use the digital contents and learn and verified the application and improvement of digital content. As a result of this research, 1)we conceived the design and concrete contents and structure of digital content that supports inquiry based learning and developed it. Also, 2)we specified an example of how to apply and confirmed its effectiveness

The Optimal Exponent Base for emPAI Is 6.5

Exponentially Modified Protein Abundance Index (emPAI) is an established method of estimating protein abundances from peptide counts in a single LC-MS/MS experiment. EmPAI is defined as 10PAI minus one, where PAI (Protein Abundance Index) denotes the ratio of observed to observable peptides. EmPAI was first proposed by Ishihama et al [1] who found that PAI is approximately proportional to the logarithm of absolute protein concentration. I define emPAI65 = 6.5PAI-1 and show that it performs significantly better than emPAI, while it is equally easy to compute. The higher accuracy of emPAI65 is demonstrated by analyzing three data sets, including the one used in the original study [1]. I conclude that emPAI65 ought to be used instead of the original emPAI for protein quantitation

Sea Surface Salinity And Barrier Layer Variability In The Equatorial Pacific As Seen From Aquarius And Argo

ISI Document Delivery No.: AB6DZ Times Cited: 0 Cited Reference Count: 52 Cited References: Alory G, 2012, J GEOPHYS RES-OCEANS, V117, DOI 10.1029/2011JC007802 Ando K, 1997, J GEOPHYS RES-OCEANS, V102, P23063, DOI 10.1029/97JC01443 Argo Steering Team, 1998, 21 ARG STEER TEAM IN, V21 BINGHAM FM, 1995, DEEP-SEA RES PT I, V42, P1545, DOI 10.1016/0967-0637(95)00064-D Bosc C, 2009, J GEOPHYS RES-OCEANS, V114, DOI 10.1029/2008JC005187 Boutin J, 2013, OCEAN SCI, V9, P183, DOI 10.5194/os-9-183-2013 Boyer TP, 2002, J GEOPHYS RES-OCEANS, V107, DOI 10.1029/2001JC000829 Chen D, 2004, J TROP OCEANOGR, V23, P1 Cronin MF, 2002, J GEOPHYS RES-OCEANS, V107, DOI 10.1029/2001JC001171 de Boyer Montegut C., 2004, J GEOPHYS RES, V109, DOI 10.1029/2004JC002378 Delcroix T, 2002, J GEOPHYS RES-OCEANS, V107, DOI 10.1029/2001JC000862 DELCROIX T, 1992, J GEOPHYS RES-OCEANS, V97, P5423, DOI 10.1029/92JC00127 Fujii Y, 2003, J GEOPHYS RES-OCEANS, V108, DOI 10.1029/2002JC001745 GODFREY JS, 1989, J GEOPHYS RES-OCEANS, V94, P8007, DOI 10.1029/JC094iC06p08007 Hasegawa T, 2013, J CLIMATE, V26, P8126, DOI 10.1175/JCLI-D-12-00187.1 Henocq C, 2010, J ATMOS OCEAN TECH, V27, P192, DOI 10.1175/2009JTECHO670.1 Johnson ES, 2002, J GEOPHYS RES-OCEANS, V107, DOI 10.1029/2001JC001122 Juza M, 2012, J OPER OCEANOGR, V5, P45 Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)0772.0.CO;2 Kessler WS, 1998, J CLIMATE, V11, P777, DOI 10.1175/1520-0442(1998)0112.0.CO;2 KESSLER WS, 1990, J GEOPHYS RES-OCEANS, V95, P5183, DOI 10.1029/JC095iC04p05183 Lagerloef G., 2013, AQ014OPS0016 Lagerloef G, 2008, OCEANOGRAPHY, V21, P68 Lee T, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL052232 Levitus S., 1982, 13 NOAA LINDSTROM E, 1987, NATURE, V330, P533, DOI 10.1038/330533a0 LUKAS R, 1991, J GEOPHYS RES-OCEANS, V96, P3343 Maes C, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL019867 Maes C, 2008, J GEOPHYS RES-OCEANS, V113, DOI 10.1029/2007JC004297 Maes C, 2011, SOLA, V7, P97, DOI 10.2151/sola.2011-025 Maes C, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL024772 Maes C, 2000, GEOPHYS RES LETT, V27, P1659, DOI 10.1029/1999GL011261 Maes C, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL016029 Maes C, 2005, J CLIMATE, V18, P104, DOI 10.1175/JCLI-3214.1 Lukas R, 1996, J GEOPHYS RES-OCEANS, V101, P12209, DOI 10.1029/96JC01204 MCPHADEN MJ, 1992, J GEOPHYS RES-OCEANS, V97, P14289, DOI 10.1029/92JC01197 MCPHADEN MJ, 1990, SCIENCE, V250, P1385, DOI 10.1126/science.250.4986.1385 PALMER TN, 1984, NATURE, V310, P483, DOI 10.1038/310483a0 Picaut J, 2001, J GEOPHYS RES-OCEANS, V106, P2363, DOI 10.1029/2000JC900141 Picaut J, 1997, SCIENCE, V277, P663, DOI 10.1126/science.277.5326.663 Qu TD, 1999, J PHYS OCEANOGR, V29, P1488, DOI 10.1175/1520-0485(1999)0292.0.CO;2 Qu TD, 2013, J PHYS OCEANOGR, V43, P1551, DOI 10.1175/JPO-D-12-0180.1 Qu TD, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL035058 Reverdin G., 2013, OCEANOGRAPHY, V26, P4857, DOI 10.5670/oceanog.2013.04 Riser SC, 2008, OCEANOGRAPHY, V21, P56 Rodier M., 2000, J OCEANOGR, V56, P463, DOI 10.1023/A:1011136608053 SHINODA T, 1995, J GEOPHYS RES-OCEANS, V100, P2523, DOI 10.1029/94JC02486 Singh A, 2011, J GEOPHYS RES-OCEANS, V116, DOI 10.1029/2010JC006862 Song Y. T., 2013, J GEOPHYS R IN PRESS SPRINTALL J, 1992, J GEOPHYS RES-OCEANS, V97, P7305, DOI 10.1029/92JC00407 Takahashi K, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047364 Yu JY, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007654 Qu, Tangdong Song, Y. Tony Maes, Christophe NSF [OCE11-30050]; NASA [NNX12AG02G]; Jet Propulsion Laboratory, California Institute of Technology, under NASA; IRD T. Qu was supported by NSF through grant OCE11-30050 and by NASA as part of the Aquarius Science Team investigation through grant NNX12AG02G. Y. T. Song was supported by the Jet Propulsion Laboratory, California Institute of Technology, under contracts with NASA. C. Maes is supported by IRD. The authors are grateful to N. Schneider and I. Fukumori for useful discussion on the topic, to K. Yu for assistance in processing the Aquarius data, and to two anonymous reviewers for valuable comments on this manuscript. School of Ocean and Earth Science and Technology contribution number 9054 and International Pacific Research Center contribution IPRC-1033. 0 AMER GEOPHYSICAL UNION WASHINGTON J GEOPHYS RES-OCEANSThis study investigates the sea surface salinity (SSS) and barrier layer variability in the equatorial Pacific using recently available Aquarius and Argo data. Comparison between the two data sets indicates that Aquarius is able to capture most of the SSS features identified by Argo. Despite some discrepancies in the mean value, the SSS from the two data sets shows essentially the same seasonal cycle in both magnitude and phase. For the period of observation between August 2011 and July 2013 Aquarius nicely resolved the zonal displacement of the SSS front along the equator, showing its observing capacity of the western Pacific warm pool. Analysis of the Argo data provides further information on surface stratification. A thick barrier layer is present on the western side of the SSS front during all the period of observation, moving back and forth along the equator with its correlation with the Southern Oscillation Index exceeding 0.80. Generally, the thick barrier layer moves eastward during El Nino and westward during La Nina. The mechanisms responsible for this zonal displacement are discussed. Key Points Aquarius nicely resolved the SSS front along the equator in the western Pacific A thick barrier layer is always present on the western side of the SSS front Both the SSS front and thick barrier layer are highly correlated with ENS

Nanoemulsion stability: experimental evaluation of the flocculation rate from turbidity measurements

The coalescence of liquid drops induces a higher level of complexity compared to the classical studies about the aggregation of solid spheres. Yet, it is commonly believed that most findings on solid dispersions are directly applicable to liquid mixtures. Here, the state of the art in the evaluation of the flocculation rate of these two systems is reviewed. Special emphasis is made on the differences between suspensions and emulsions. In the case of suspensions, the stability ratio is commonly evaluated from the initial slope of the absorbance as a function of time under diffusive and reactive conditions. Puertas and de las Nieves (1997) developed a theoretical approach that allows the determination of the flocculation rate from the variation of the turbidity of a sample as a function of time. Here, suitable modifications of the experimental procedure and the referred theoretical approach are implemented in order to calculate the values of the stability ratio and the flocculation rate corresponding to a dodecane-in-water nanoemulsion stabilized with sodium dodecyl sulfate. Four analytical expressions of the turbidity are tested, basically differing in the optical cross section of the aggregates formed. The first two models consider the processes of: a) aggregation (as described by Smoluchowski) and b) the instantaneous coalescence upon flocculation. The other two models account for the simultaneous occurrence of flocculation and coalescence. The latter reproduce the temporal variation of the turbidity in all cases studied (380 \leq [NaCl] \leq 600 mM), providing a method of appraisal of the flocculation rate in nanoemulsions

Review of phase change emulsions (PCMEs) and their applications in HVAC systems

Phase change material emulsions (PCMEs) are multifunctional fluids consisting of phase change materials (PCMs) and carrier fluids. PCMEs could be potential candidates as heat transfer media in heating, ventilation and air conditioning (HVAC) systems. This is mainly because PCME could take advantage of its high heat capacity to reduce flow rate and thus saving pumping power whilst delivering the same amount of cooling effect. PCME can also simultaneously act as cold storage to shift peak-load to off-peak time and improve the COP of systems. However, the optimum design of integrated system requires a good understanding of flow behaviour and heat transfer characteristics of PCMEs. In this paper, comprehensive reviews of their thermo-physical properties and potential applications as thermal energy storage and as alternative heat transfer fluids in air conditioning systems have been carried out to establish their limitations for future research

A road map to IndOOS-2 better observations of the rapidly warming Indian Ocean

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(11), (2020): E1891-E1913, https://doi.org/10.1175/BAMS-D-19-0209.1The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.We thank the World Climate Research Programme (WCRP) and its core project on Climate and Ocean: Variability, Predictability and Change (CLIVAR), the Indian Ocean Global Ocean Observing System (IOGOOS), the Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO), the Integrated Marine Biosphere Research (IMBeR) project, the U.S. National Oceanic and Atmospheric Administration (NOAA), and the International Union of Geodesy and Geophysics (IUGG) for providing the financial support to bring international scientists together to conduct this review. We thank the members of the independent review board that provided detailed feedbacks on the review report that is summarized in this article: P. E. Dexter, M. Krug, J. McCreary, R. Matear, C. Moloney, and S. Wijffels. PMEL Contribution 5041. C. Ummenhofer acknowledges support from The Andrew W. Mellon Foundation Award for Innovative Research.2021-05-0

A sustained ocean observing system in the Indian Ocean for climate related scientific knowledge and societal needs

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hermes, J. C., Masumoto, Y., Beal, L. M., Roxy, M. K., Vialard, J., Andres, M., Annamalai, H., Behera, S., D'Adamo, N., Doi, T., Peng, M., Han, W., Hardman-Mountford, N., Hendon, H., Hood, R., Kido, S., Lee, C., Lees, T., Lengaigne, M., Li, J., Lumpkin, R., Navaneeth, K. N., Milligan, B., McPhaden, M. J., Ravichandran, M., Shinoda, T., Singh, A., Sloyan, B., Strutton, P. G., Subramanian, A. C., Thurston, S., Tozuka, T., Ummenhofer, C. C., Unnikrishnan, A. S., Venkatesan, R., Wang, D., Wiggert, J., Yu, L., & Yu, W. (2019). A sustained ocean observing system in the Indian Ocean for climate related scientific knowledge and societal needs. Frontiers in Marine Science, 6, (2019): 355, doi: 10.3389/fmars.2019.00355.The Indian Ocean is warming faster than any of the global oceans and its climate is uniquely driven by the presence of a landmass at low latitudes, which causes monsoonal winds and reversing currents. The food, water, and energy security in the Indian Ocean rim countries and islands are intrinsically tied to its climate, with marine environmental goods and services, as well as trade within the basin, underpinning their economies. Hence, there are a range of societal needs for Indian Ocean observation arising from the influence of regional phenomena and climate change on, for instance, marine ecosystems, monsoon rains, and sea-level. The Indian Ocean Observing System (IndOOS), is a sustained observing system that monitors basin-scale ocean-atmosphere conditions, while providing flexibility in terms of emerging technologies and scientificand societal needs, and a framework for more regional and coastal monitoring. This paper reviews the societal and scientific motivations, current status, and future directions of IndOOS, while also discussing the need for enhanced coastal, shelf, and regional observations. The challenges of sustainability and implementation are also addressed, including capacity building, best practices, and integration of resources. The utility of IndOOS ultimately depends on the identification of, and engagement with, end-users and decision-makers and on the practical accessibility and transparency of data for a range of products and for decision-making processes. Therefore we highlight current progress, issues and challenges related to end user engagement with IndOOS, as well as the needs of the data assimilation and modeling communities. Knowledge of the status of the Indian Ocean climate and ecosystems and predictability of its future, depends on a wide range of socio-economic and environmental data, a significant part of which is provided by IndOOS.This work was supported by the PMEL contribution no. 4934